OCS Study 

BOEM 2015-003 

U.S. Department of the Interior 

Bureau of Ocean Energy Management 

Gulf of Mexico OCS Region

Cooperative Agreement 

Coastal Marine Institute 

Louisiana State University

Coastal Marine Institute

Assessing Impacts of OCS Activities on

Public Infrastructure, Services, and 

Population in Coastal Communities 

Following Hurricanes Katrina and Rita
OCS Study

BOEM 2015-003

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Cooperative Agreement

Coastal Marine Institute

Louisiana State University

Coastal Marine Institute

Assessing Impacts of OCS Activities on

Public Infrastructure, Services, and 

Population in Coastal Communities 

Following Hurricanes Katrina and Rita

Authors

J. Matthew Fannin

Mark Schafer

Walter Keithly

January 2015

Prepared under BOEM Cooperative Agreement

M07AC12489

by

LSU AgCenter and LSU A&M

Department of Agricultural Economics and Agribusiness

Baton Rouge, LA 70803
i 

DISCLAIMER 

This report was prepared under contract between the Bureau of Ocean Energy Management 

(BOEM) and Louisiana State University. This report has been technically reviewed by BOEM 

and has been approved for publication. Approval does not necessarily signify that the contents 

reflect the view and policies of BOEM, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use. 

REPORT AVAILABILITY 

To download a PDF file of this Gulf of Mexico OCS Region report, go to the U.S. 

Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies 

Program Information System website and search on OCS Study BOEM 2015-003. 

This report can be viewed at select Federal Depository Libraries. It can also be obtained from 

the National Technical Information Service; the contact information is below. 

U.S. Department of Commerce 

National Technical Information Service 

5301 Shawnee Rd. 

Springfield, Virginia 22312 

Phone: (703) 605-6000, 1(800)553-6847 

Fax: (703) 605-6900 

Website: http://www.ntis.gov/

CITATION 

Fannin, J.M., M. Schafer, and W. Keithly. 2015. Assessing Impacts of OCS Activities on Public 

Infrastructure, Services, and Population in Coastal Communities Following Hurricanes Katrina 

and Rita. U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Gulf of Mexico OCS 

Region, New Orleans, LA. OCS Study 2015-003. 268 pp. 
iv 

ACKNOWLEDGMENTS 

Special thanks are given to the social scientists in the Gulf of Mexico Region who worked at 

one time on the cooperative agreement that made this project successful. This group includes 

Tim Holder, Kristen Strellec, Harry Luton, and Sindey Chaky. The authors also thank Kim 

Coffman and Sarah Peters in BOEM's Herndon Office for their assistance in working with 

MAG-PLAN as a part of this project. Finally, thanks to John Primo with BOEM for his 

assistance in some early feedback that improved the robustness of the results of this report. 

PROJECT PERSONNEL 

Chapter 1: Mark Schafer, Ashley Perry, and Don Asay (Louisiana State University) 

Chapter 2: Arun Adhikari and J. Matthew Fannin (Louisiana State University Agricultural 

Center) 

Chapter 3: Arun Adhikari and J. Matthew Fannin (Louisiana State University Agricultural 

Center) 

Chapter 4: Arun Adhikari and J. Matthew Fannin (Louisiana State University Agricultural 

Center) 

Chapter 5: Caroline Boen and Walter Keithly (Louisiana State University) and Arun Adhikari 

and J. Matthew Fannin (Louisiana State University Agricultural Center) 
v 

CONTENTS 

List of Figures ................................................................................................................................ ix

List of Tables ................................................................................................................................ ixi

Study Overview .............................................................................................................................. 1

Parish Profiles ........................................................................................................................... 1

Data ........................................................................................................................... 1

Fuzzy-Set Approach ....................................................................................................... 1

General Profile ............................................................................................................... 2

Parish Population Statistics ............................................................................... 2

Parish Demographic Information ...................................................................... 2

Parish Education and Income ............................................................................ 3

Parish Location and Geographic Factors .......................................................... 3

Labor Market Profile ...................................................................................................... 3

Labor Market Resources ................................................................................... 3

Labor Market Outcomes ................................................................................... 3

Labor Force Characteristics .............................................................................. 4

Education Profile ............................................................................................................ 4

Resources .......................................................................................................... 4

Educational Outcomes ...................................................................................... 4

Student Characteristics ...................................................................................... 5

District Processes .............................................................................................. 5

Analyzing Employment Change Following Hurricanes Katrina and Rita ................................ 5

Modeling the Local Labor Market ............................................................................................ 7

Modeling Local Government Expenditure ............................................................................... 8

Forecasting Local Government Expenditure Change From Gulf of Mexico Oil and Gas 

Activities ........................................................................................................................ 9

Chapter 1. Parish Profiles Using Fuzzy Set Approach ................................................................. 11

1.1. General Community Profile ............................................................................................. 12

1.1.1. Parish Population Statistics ................................................................................ 13

1.1.1.1. Large Population ................................................................................ 13

1.1.1.2. Small Population ................................................................................ 15

1.1.1.3. Parishes with Growing Populations ................................................... 17

1.1.1.4. Parishes with Declining or Stagnating Populations ........................... 19

1.1.2. Parish Demographic Information ....................................................................... 21

1.1.2.1. Low-Diversity Parish ......................................................................... 21

1.1.2.2. Old or Aging Parish ........................................................................... 22

1.1.2.3. Young Parish ...................................................................................... 24

1.1.3. Parish General Economic and Social Indicators Income ................................... 25

1.1.3.1. Wealthy Parish ................................................................................... 25

1.1.3.2. Poor Parish ......................................................................................... 27

1.1.3.3. Parish with High Adult Education Level ........................................... 29

1.1.3.4. High-Crime Parish ............................................................................. 30
vi 

1.1.4. Parish Location and Geographic Factors ............................................................ 32

1.1.4.1. Urban and Rural Parishes................................................................... 32

1.1.4.2. Parish with Noteworthy Natural Amenities ....................................... 33

1.1.4.3. Interstate Highway Parish .................................................................. 35

1.1.4.4. Coastal Parish..................................................................................... 36

1.1.4.5. Parish with Port .................................................................................. 38

1.2. Labor Market Profile Overview ....................................................................................... 39

1.2.1. Labor Market Resources .................................................................................... 40

1.2.1.1. Substantial Mining Employment ....................................................... 40

1.2.1.2. Small Business Intensive ................................................................... 42

1.2.1.3. Large Business Presence .................................................................... 44

1.2.2. Outcomes ............................................................................................................ 46

1.2.2.1. High Unemployment .......................................................................... 46

1.2.2.2. Job Growth ......................................................................................... 47

1.2.2.3. Competitive Per Capita Income ......................................................... 49

1.3. Education Profile Overview ............................................................................................. 51

1.3.1. Education Resources .......................................................................................... 52

1.3.1.1. Competitive Teacher Salary ............................................................... 52

1.3.1.2. Substantial Local Capacity ................................................................ 54

1.3.1.3. Onshore Oil and Gas Revenue Influence ........................................... 57

1.3.1.4. Focus on Instruction ........................................................................... 59

1.3.2. Educational Outcomes ........................................................................................ 61

1.3.2.1. Adequate School District Performance .............................................. 61

1.3.2.2. Few Low-Performing Schools ........................................................... 63

1.3.2.3. District Performance .......................................................................... 65

1.3.2.4. High College Attendance ................................................................... 67

1.3.3. Student Characteristics ....................................................................................... 69

1.3.3.1. High Percent of Students on Free or Reduced Lunch ........................ 69

1.3.3.2. Many African American Students ..................................................... 71

1.3.3.3. High Student Drop Outs Between Grades 8 and 12 .......................... 73

1.3.3.4. High Disciplinary Suspension Rate ................................................... 75

1.3.3.5. High Student Grade Repeat ............................................................... 77

1.3.4. District Structure ................................................................................................ 79

1.3.4.1. Small School District ......................................................................... 79

1.3.4.2. Large School District ......................................................................... 81

1.3.4.3. Many Students Enrolled in Private Schools....................................... 83

 
vii 

Chapter 2. Distinguishing Regional Compared with Local Comparative Advantage through Shift 

Share Analysis ........................................................................................................................ 85

2.1. Introduction ...................................................................................................................... 85

2.2. Spatial Shift Share Analysis ............................................................................................. 86

2.3. Economic Change Following the 2005 Hurricane Season .............................................. 88

2.4. Data and Methodology ..................................................................................................... 90

2.5. Results .............................................................................................................................. 91

2.6. Discussion ........................................................................................................................ 98

2.7. Conclusion ....................................................................................................................... 99

Chapter 3. Modeling the Labor Market and Estimating the Relative Performance of Estimators 

for Louisiana Labor Market: a Labor Force Module Approach ........................................... 101

3.1. Introduction and Background Information .................................................................... 101

3.2. Literature Review ........................................................................................................... 103

3.3. Conceptual Framework .................................................................................................. 106

3.4. Objectives of the Study .................................................................................................. 110

3.5. Data and Methodology ................................................................................................... 110

3.6. Results and Discussion .................................................................................................. 113

3.7. Conclusion and Limitations ........................................................................................... 119

Chapter 4. Modeling the Louisiana Local Government Fiscal Model in a Disequilibirum 

Environment: a Modified COMPAS Model Approach ........................................................ 121

4.1 Introduction and Background Information ..................................................................... 121

4.2. Literature on Local Fiscal Modeling .............................................................................. 122

4.2.2. COMPAS Modeling Framework ...................................................................... 124

4.2.3. Alternative Conceptual Frameworks for Public Service Delivery ................... 127

4.3. Empirical Specification of Fiscal Module ..................................................................... 128

4.4. Data and Methodology ................................................................................................... 129

4.5. Results and Discussion .................................................................................................. 131

4.6. Conclusion ..................................................................................................................... 144

Chapter 5. Measuring the Fiscal Effects Based on Changes in Deepwater OffShore Drilling 

Activities ............................................................................................................................... 147

5.1. Introduction .................................................................................................................... 147

5.2. Literature Review ........................................................................................................... 148

5.2.1. Fiscal Impact Modeling .................................................................................... 148

5.2.2. Lafayette Parish, Louisiana .............................................................................. 149

5.3. Objective and Method .................................................................................................... 149

5.4. Estimation Results ......................................................................................................... 150

5.5. The Policy Modeling Challenge .................................................................................... 151

5.6. Conclusion ..................................................................................................................... 152

 
viii 

References ................................................................................................................................... 154

Appendix A. Detailed Tables Of Fuzzy Set Membership .......................................................... 163

Appendix B. Lessons Learned from Meetings with Local Officials .......................................... 242

Appendix C. Oil and Gas Drilling Forecasting ........................................................................... 247
ix 

LIST OF FIGURES 

Figure 1.1. Large Population fuzzy set membership. .................................................................. 15

Figure 1.2. Small Population fuzzy set membership. .................................................................. 17

Figure 1.3. Population Growth fuzzy set membership (emerging parishes). ............................... 19

Figure 1.4. Declining/Stagnating Populations fuzzy set membership. ....................................... 20

Figure 1.5. Low Racial Diversity fuzzy set membership. ........................................................... 22

Figure 1.6. Old or Aging fuzzy set membership. ........................................................................ 23

Figure 1.7. Young Population fuzzy set membership. ................................................................ 25

Figure 1.8. Wealthy Population fuzzy set membership. ............................................................. 27

Figure 1.9. Poor Population fuzzy set membership. ................................................................... 28

Figure 1.10. Adult Education fuzzy set membership. ................................................................... 30

Figure 1.11. High Crime fuzzy set membership. .......................................................................... 31

Figure 1.12. Urban and Rural Parish fuzzy set membership. ........................................................ 33

Figure 1.13. Natural Amenities fuzzy set membership. ................................................................ 34

Figure 1.14. Interstate Highway fuzzy set membership. ............................................................... 36

Figure 1.15. Coastal fuzzy set membership. ................................................................................. 37

Figure 1.16. Major Port fuzzy set membership. ............................................................................ 39

Figure 1.17. Substantial Mining Employment fuzzy set membership. ......................................... 41

Figure 1.18. Small Business Intensive fuzzy set membership. ..................................................... 43

Figure 1.19. Large Business Presence fuzzy set membership. ..................................................... 45

Figure 1.20. High Unemployment fuzzy set membership. ........................................................... 47

Figure 1.21.Job Growth fuzzy set membership. .......................................................................... 49

Figure 1.22. Per Capita Income fuzzy set membership. ............................................................... 51

Figure 1.23. Competitive Teacher Salaries fuzzy set membership. .............................................. 54

Figure 1.24. Substantial Local Capacity fuzzy set membership. .................................................. 57

Figure 1.25. Oil and Gas Revenue Reliance fuzzy set membership. ............................................ 59

Figure 1.26. Instruction Expenditures fuzzy set membership. ...................................................... 61

Figure 1.27. Adequate School District Performance fuzzy set membership. ............................... 63

Figure 1.28. Few Low-Performing Schools fuzzy set membership. ............................................. 65

Figure 1.29. District Improvement fuzzy set membership. ........................................................... 67

Figure 1.30. First-time Freshmen fuzzy set membership. ............................................................ 69

Figure 1.31. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership. ........... 71
x 

Figure 1.32. High Percent of African American Students fuzzy set membership. ....................... 73

Figure 1.33. High Student Drop-out Rate fuzzy set membership. ................................................ 75

Figure 1.34. High Disciplinary Suspensions fuzzy set membership. ............................................ 77

Figure 1.35. High Percent of Students Retained fuzzy set membership. ...................................... 79

Figure 1.36. Small School District fuzzy set membership. ........................................................... 81

Figure 1.37. Large School District fuzzy set membership. ........................................................... 83

Figure 1.38. Private School Enrollments fuzzy set membership. ................................................. 84

Figure 2.1. Spatial shift share analysis of neighboring region and local effects for mining 

from 2001–2004. ....................................................................................................... 95

Figure 2.2. Spatial shift share analysis of neighboring region and local effects for mining 

from 2004–2006. ....................................................................................................... 96

Figure 3.1. Highlighting the labor market in the COMPAS modeling framework .................... 102

Figure 3.2. A conceptual labor market (Johnson 2006). ............................................................. 104

Figure 3.3. Result of labor demand change in employment and wages, assuming a constant 

supply. ..................................................................................................................... 107

Figure 3.4. Result of labor supply change in employment and wages, assuming a constant 

demand. ................................................................................................................... 108

Figure 3.5. Result of perfectly elastic labor supply and perfectly inelastic labor demand. ........ 109

Figure 3.6. Comparing off-years MAPE by OLS, 3sls, and panel data models. ........................ 117

Figure 4.1. Highlighting the fiscal module in the COMPAS modeling framework. .................. 125

Figure 4.2. Mean percent error of OLS, panel, and quantile estimators of COMPAS model. ... 130

 
xi 

LIST OF TABLES 

Table 1.1. Large Population fuzzy set membership for all Louisiana parishes (2009). ............... 14

Table 1.2. Large Population fuzzy set membership for BOEM-defined parishes (2009). ........... 14

Table 1.3. Small Population fuzzy set membership for all Louisiana parishes (2009). ............... 16

Table 1.4. Small Population fuzzy set membership for BOEM-defined parishes (2009). ........... 16

Table 1.5. Population Growth fuzzy set membership for all Louisiana parishes (2009). ............. 18

Table 1.6. Population Growth fuzzy set membership for BOEM-defined parishes (2009). ........ 18

Table 1.7. Declining or Stagnating Parish fuzzy set membership for all Louisiana parishes 

(2009).......................................................................................... . 20

Table 1.8. Declining or Stagnating Parish fuzzy set membership for BOEM-defined parishes 

(2009). ....................................................................................................................... 20

Table 1.9. Low Racial Diversity fuzzy set membership for all Louisiana parishes (2010). ......... 21

Table 1.10. Low Racial Diversity fuzzy set membership for BOEM-defined parishes (2010). ... 21

Table 1.11. Old or Aging fuzzy set membership for all Louisiana parishes (2009). .................... 23

Table 1.12. Old or Aging fuzzy set membership for BOEM-defined parishes (2009). ................ 23

Table 1.13. Young Population fuzzy set membership for all Louisiana parishes (2009). ............ 24

Table 1.14. Young Population fuzzy set membership for BOEM-defined parishes (2009). ........ 24

Table 1.15. Wealthy Population fuzzy set membership for all Louisiana parishes (2008). ......... 26

Table 1.16. Wealthy Population fuzzy set membership for BOEM-defined parishes (2008). ..... 26

Table 1.17. Poor Population fuzzy set membership for all Louisiana parishes (2008). ............... 28

Table 1.18. Poor Population fuzzy set membership for BOEM-defined parishes (2008). ........... 28

Table 1.19. Adult Education fuzzy set membership for all Louisiana parishes (2009). ............... 29

Table 1.20. Adult Education fuzzy set membership for BOEM-defined parishes (2009). ........... 29

Table 1.21. High Crime fuzzy set membership for all Louisiana parishes (2008). ...................... 31

Table 1.22. High Crime fuzzy set membership for BOEM-defined parishes (2008). .................. 31

Table 1.23. Urban and Rural Parishes fuzzy set membership (2008). .......................................... 32

Table 1.24. Urban and rural parishes. ........................................................................................... 32

Table 1.25. Natural Amenities fuzzy set membership for all Louisiana parishes (2005). ............ 34

Table 1.26. Natural Amenities fuzzy set membership for BOEM-defined parishes (2005). ........ 34

Table 1.27. Interstate Highway fuzzy set membership for all Louisiana parishes (2010). ........... 35

Table 1.28. Interstate Highway fuzzy set membership for BOEM-defined parishes (2010). ...... 35

Table 1.29. Coastal fuzzy set membership for all Louisiana parishes (2010). ............................. 37

Table 1.30. Coastal fuzzy set membership for BOEM-defined parishes (2010). ......................... 37
xii 

Table 1.31. Major Port fuzzy set membership for all Louisiana parishes (2010). ........................ 38

Table 1.32. Major Port fuzzy set membership for BOEM-defined parishes (2010). .................... 38

Table 1.33. Substantial Mining Employment fuzzy set membership for all Louisiana parishes 

(2006). ....................................................................................................................... 40

Table 1.34. Substantial Mining Employment fuzzy set membership for BOEM-defined 

parishes (2006). ......................................................................................................... 41

Table 1.35. Small Business Intensive fuzzy set membership for all Louisiana parishes (2006). . 42

Table 1.36. Small Business Intensive fuzzy set membership for BOEM-defined parishes 

(2006). ....................................................................................................................... 43

Table 1.37. Large Business Presence fuzzy set membership for all Louisiana parishes (2006). . 44

Table 1.38. Large Business Presence fuzzy set membership for BOEM-defined parishes 

(2006). ....................................................................................................................... 45

Table 1.39. High Unemployment fuzzy set membership for all Louisiana parishes (2010). ....... 46

Table 1.40. High Unemployment fuzzy set membership for BOEM-defined parishes (2010). ... 46

Table 1.41. Job Growth fuzzy set membership for all Louisiana parishes (2009–2010). ............ 48

Table 1.42. Job Growth fuzzy set membership for BOEM-defined parishes (2009–2010). ........ 48

Table 1.43. Per Capita Income fuzzy set membership for all Louisiana parishes (2006). ........... 50

Table 1.44. Per Capita Income fuzzy set membership for BOEM-defined parishes (2006). ....... 50

Table 1.45. Competitive Teacher Salaries fuzzy set membership for all Louisiana parishes 

(2007–2008). ............................................................................................................. 53

Table 1.46. Competitive Teacher Salaries fuzzy set membership for BOEM-defined parishes 

(2007–2008). ............................................................................................................. 54

Table 1.47. Substantial Local Capacity fuzzy set membership for all Louisiana parishes 

(2008–2009). ............................................................................................................. 56

Table 1.48. Substantial Local Capacity fuzzy set membership for BOEM-defined parishes 

(2008–2009). ............................................................................................................. 56

Table 1.49. Oil and Gas Revenue Reliance fuzzy set membership for all Louisiana parishes 

(2008–2009). ............................................................................................................. 58

Table 1.50. Oil and Gas Revenue Reliance fuzzy set membership for BOEM-defined parishes 

(2008–2009). ............................................................................................................. 58

Table 1.51. Instruction Expenditures fuzzy set membership for all Louisiana parishes (2005–

2006). ........................................................................................................................ 60

Table 1.52. Instruction Expenditures fuzzy set membership for BOEM-defined parishes 

(2005–2006). ............................................................................................................. 60

Table 1.53. Adequate School District Performance fuzzy set membership for all Louisiana 

parishes (2008). ......................................................................................................... 62
xiii 

Table 1.54. Adequate School District Performance fuzzy set membership for BOEM-defined 

parishes (2008). ......................................................................................................... 62

Table 1.55. Few Low-Performing Schools fuzzy set membership for all Louisiana parishes 

(2008). ....................................................................................................................... 64

Table 1.56. Few Low-Performing Schools fuzzy set membership for BOEM-defined parishes 

(2008). ....................................................................................................................... 64

Table 1.57. District Improvement fuzzy set membership for all Louisiana parishes (2009). ....... 66

Table 1.58. District Improvement fuzzy set membership for BOEM-defined parishes (2009). .. 66

Table 1.59. First-time Freshmen fuzzy set membership for all Louisiana parishes (2009). ......... 68

Table 1.60. First-time Freshmen fuzzy set membership for BOEM-defined parishes (2009). .... 68

Table 1.61. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership for all 

Louisiana parishes (2009). ........................................................................................ 70

Table 1.62. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership for 

BOEM-defined parishes (2009). ............................................................................... 70

Table 1.63. High Percent of African American Students fuzzy set membership for all 

Louisiana parishes (2009). ........................................................................................ 72

Table 1.64. High Percent of African American Students fuzzy set membership for BOEMdefined parishes (2009). ............................................................................................ 72

Table 1.65. High Student Dropout Rate fuzzy set membership for all Louisiana parishes 

(2007–2008; grades 8–12). ....................................................................................... 74

Table 1.66. High Student Drop-out Rate fuzzy set membership for BOEM-defined parishes 

(2007–2008; grades 8–12). ....................................................................................... 74

Table 1.67. High Disciplinary Suspensions fuzzy set membership for all Louisiana parishes 

(2008–2009). ............................................................................................................. 76

Table 1.68. High Disciplinary Suspensions fuzzy set membership for BOEM-defined 

parishes (2008–2009). ............................................................................................... 76

Table 1.69. High Percent of Students Retained fuzzy set membership for all Louisiana 

parishes (2008–2009). ............................................................................................... 78

Table 1.70. High Percent of Students Retained fuzzy set membership for BOEM-defined 

parishes (2008–2009). ............................................................................................... 78

Table 1.71. Small School District fuzzy set membership for all Louisiana parishes (2008). ....... 80

Table 1.72. Small School District fuzzy set membership for BOEM-defined parishes (2008). ... 80

Table 1.73. Large School District fuzzy set membership for all Louisiana parishes (2008). ....... 82

Table 1.74. Large School District fuzzy set membership for BOEM-defined parishes (2008). ... 82

Table 1.75. Private School Enrollments fuzzy set membership for all Louisiana parishes 

(2009–2010). ............................................................................................................. 84
xiv 

Table 1.76. Private School Enrollments fuzzy set membership for BOEM-defined parishes 

(2009–2010). ............................................................................................................. 84

Table 2.1. Comparing classical and augmented spatial shift share analysis on employment 

growth in the mining sector 2001–2004. .................................................................. 93

Table 2.2. Comparing classical and augmented spatial shift share analysis on employment 

growth in the mining sector 2004–2006. .................................................................. 94

Table 2.3. Correlation analysis testing the distinctness of neighboring region effect for the 

mining sector. ........................................................................................................... 98

Table 3.1. Expected signs for different variables for labor force module equations. ................. 112

Table 3.2. Variable description and summary statistics for analysis of Louisiana parishes. ...... 113

Table 3.3. Parameter estimates for OLS, 3sls, and panel regressions of Louisiana labor force 

module. ................................................................................................................... 114

Table 3.4. Average performance estimation measures for dependent variables in Louisiana 

labor force module. ................................................................................................. 116

Table 4.1. Summary of determinants of local public service expenditures in the 1960s and 

1970s. ...................................................................................................................... 123

Table 4.2. Variable description and summary statistics, Louisiana, 2007. ................................. 132

Table 4.3. Parameter estimates for panel, OLS, and quantile regressions, Louisiana. ............... 134

Table 4.4. Average performance estimation measures for different categories of expenditure. 137

Table 4.5. Parameter estimates for the naïve model, naïve plus model, and modified naïve 

model, for both OLS and panel data regressions. ................................................... 138

Table 4.6. Parameter estimates for the naïve model, naïve plus model, and modified naïve 

model, for quantile regressions. .............................................................................. 139

Table 4.7. Average performance estimation measures for different categories of expenditure. 141

Table 4.8. Mean absolute percent simulation error comparison test for general government 

expenditure, based on OLS model. ......................................................................... 143

Table 4.9. Mean absolute percent simulation error comparison test for public safety 

expenditure, based on OLS model. ......................................................................... 143

Table 4.10. Mean absolute percent simulation error comparison test for health and welfare 

expenditure, based on OLS model. ......................................................................... 143

Table 4.11. Mean absolute percent simulation error comparison test for public works 

expenditure, based on OLS model. ......................................................................... 143

Table 5.1. Per capita fiscal expenditure effects on Lafayette Parish from oil and gas wells 

drilled in the Gulf of Mexico. ................................................................................. 151

Table A1.1. Large Population fuzzy set scores for Louisiana parishes. ..................................... 163

Table A1.2. Small Population fuzzy set scores for Louisiana parishes. ..................................... 165

Table A1.3. Population Growth fuzzy set scores for Louisiana parishes. .................................. 167
xv 

Table A1.4. Population Decline fuzzy set scores for Louisiana parishes. .................................. 169

Table A1.5. Racial Diversity fuzzy set scores for Louisiana parishes. ....................................... 171

Table A1.6. Old and Aging Population fuzzy set scores for Louisiana parishes. ....................... 173

Table A1.7. Young Parish fuzzy set scores for Louisiana parishes. ........................................... 175

Table A1.8. Wealthy Parish fuzzy set scores for Louisiana parishes. ........................................ 177

Table A1.9. Poor Parish fuzzy set scores for Louisiana parishes. .............................................. 179

Table A1.10. Adult Education fuzzy set scores for Louisiana parishes. .................................... 181

Table A1.11. High Crime fuzzy set scores for Louisiana parishes. ............................................ 183

Table A1.12. List of urban and rural parishes. ........................................................................... 185

Table A1.13. Natural Amenities fuzzy set scores for Louisiana parishes. ................................. 187

Table A1.14. Interstate Highway fuzzy set scores for Louisiana parishes. ................................ 189

Table A1.15. Coastal fuzzy set scores for Louisiana parishes. ................................................... 191

Table A1.16. Major Port fuzzy set scores for Louisiana parishes. ............................................. 193

Table A1.17. Substantial Mining Employment fuzzy set scores for Louisiana parishes. .......... 195

Table A1.18. Small Business Intensive fuzzy set scores for Louisiana parishes. ...................... 197

Table A1.19. Large Business Presence fuzzy set scores for Louisiana parishes. ....................... 199

Table A1.20. High Unemployment fuzzy set scores for Louisiana parishes. ............................. 201

Table A1.21. Job Growth fuzzy set scores for Louisiana parishes. ............................................ 203

Table A1.22. Per Capita Personal Income fuzzy set scores for Louisiana parishes. .................. 205

Table A1.23. Competitive Teacher Salary fuzzy set scores for Louisiana parishes. .................. 207

Table A1.24. Substantial Local Capacity fuzzy set scores for Louisiana parishes. .................... 208

Table A1.25. Oil and Gas Revenue Influence fuzzy set scores for Louisiana parishes. ............ 211

Table A1.26. Expenditures on Instruction fuzzy set scores for Louisiana parishes. .................. 213

Table A1.27. Adequate School District Performance fuzzy set scores for Louisiana parishes. . 215

Table A1.28. Districts with Few Low-Performing Schools fuzzy set scores for Louisiana 

parishes. .................................................................................................................. 217

Table A1.29. District Performance fuzzy set scores for Louisiana parishes. ............................. 219

Table A1.30. First-time Freshmen fuzzy set scores for Louisiana parishes. .............................. 221

Table A1.31. At-risk Students fuzzy set scores for Louisiana parishes. ..................................... 223

Table A1.32. High Percent of African American Students fuzzy set scores for Louisiana 

parishes. .................................................................................................................. 225

Table A1.33. High Student Dropouts fuzzy set scores for Louisiana parishes........................... 227

Table A1.34. Disciplinary Action fuzzy set scores for Louisiana parishes. ............................... 229
xvi 

Table A1.35. Students Retained fuzzy set scores for Louisiana parishes. .................................. 231

Table A1.36. Large School District fuzzy set scores for Louisiana parishes. ............................ 233

Table A1.37. Small School District fuzzy set scores for Louisiana parishes. ............................ 235

Table A1.38. Non-Public School Enrollment fuzzy set scores for Louisiana parishes. ............. 237

Table A1.39. Data sources for fuzzy set analysis. ...................................................................... 239

Table AC.1. Coefficient estimates of model. .............................................................................. 250

Table AC.2. Variables and parameters for forecasting. .............................................................. 252

Table AC.3. Predicted number of wells drilled. ......................................................................... 253 
1 

STUDY OVERVIEW 

PARISH PROFILES

In Chapter One, our approach to analyzing community social, economic, and demographic 

capacity in Louisiana involves the development of three inter-related community profiles. These 

profiles use fuzzy set techniques to group similar parishes together and to differentiate among 

parishes that differ fundamentally on the specified profile element. In total, we have developed 

38 elements through which to compare and contrast Louisiana parishes. We do not claim these 

are comprehensive, but we do argue that these 38 profile elements allow us to get a very good 

sense of how the parishes in this state compare in relation to characteristics that may be relevant 

to gaining a better understanding of how the oil and gas industry affects these regions. The 

elements are grouped into three profiles. 

1. A General Community Profile that describes the general situation of each parish in the 

state, with a particular emphasis on those parishes within the U.S. Department of Interior 

Bureau of Ocean Energy Management (BOEM) identified impact areas. 

2. An Education Profile that compares parishes with respect to key education indicators. 

3. A Socio-economic and Demographic Profile that allows for comparisons in terms of their 

labor force characteristics. 

Data 

The profiles rely on secondary data that are available at the national or state level. Much of 

these data are gathered and published annually, allowing for regularly updating the profiles as 

they are constructed. The specific data sources for each profile element are detailed in the profile 

descriptions below. In addition, we include a spreadsheet in Appendix A with hyperlinks to the 

data for each profile element. 

Fuzzy-Set Approach 

We employ a fuzzy-set approach to developing the community profiles, in which we seek to 

group together parishes that are conceptually similar for a given profile element and differentiate 

among parishes that are clearly different. In this way, the fuzzy sets require us to think carefully 

about differences that matter “more” compared with differences that matter “less”. For example, 

in our general profile we define “large parishes” as those with large populations. We categorize 

all 14 parishes with populations greater than 100,000 as fully in the set of large parishes, and so 

we are less interested in population differences among this group; in fuzzy-set terms they are 

conceptually similar. 

Fuzzy-sets allow us to make key decisions about grouping that are grounded in the 

conceptualization of the profile element, rather than in the distribution of the data. Users are 

asked to think critically about the way each profile element is constructed and to recommend 

clarification and revisions if necessary. It is relatively easy to “reconstruct” a fuzzy set, so the 

critical element involves the ability to defend its conceptual construction. 

Each fuzzy set is constructed based on three critical boundaries or cut-off points. The first 

point defines the cut-off point where any case with that score or higher is definitely IN the set; all 
2 

parishes that are definitely in a set get a score of 1. The second cut-off point defines the line 

where any case with a score that is at or lower is definitely OUT of the set; all parishes that are 

definitely out of a set get a score of 0. All points between those two cut-off points are said to be 

PARTIALLY IN the set. The final cut-off point defines the score that distinguishes between 

partial set members that are MORE OUT THAN IN (score is > 0 and < 0.5) and partial set 

members that are MORE IN THAN OUT (score is > 0.5 and < 1). 

All of the fuzzy set parish profile element descriptions below define the three cut-off points 

and group parishes according to the four categories (or two, in the case of binary elements). In 

the main body of the report, we provide the following for each profile element: (1) a complete 

definition; (2) a summary discussion of the distribution of cases that are fully in, fully out, or 

partial members of each fuzzy set; (3) descriptive tables of each element for all Louisiana 

parishes and for the 32 parishes included in the BOEM impact areas; and (4) a map of Louisiana 

identifying which fuzzy set value each parish received for a given profile element. 

General Profile 

The General Profile is composed of 16 elements that provide a picture of the overall 

population, demographic, education, income, natural, and geographical environment of 

Louisiana parishes. The elements are organized into four categories. 

Parish Population Statistics 

This category includes four elements: Large Population, Small Population, Emerging or 

Growing Population, and Declining Population. Note that, in fuzzy-set terms, these elements are 

all conceptually distinct from one another. For example, Small Population is not simply the 

inverse of Large Population, and it is possible for a parish to be neither Large nor Small. For the 

state as a whole, only 10 of the 64 parishes were defined as definitely Large, while 27 were 

defined as definitely Small. Further, only seven parishes were defined as definitely 

Emerging/Growing, while 33 were defined as definitely Declining or Stagnating. 

Parish Demographic Information 

This category includes additional information about the racial/ethnic diversity and age 

structure across parishes. The three profile elements are Low Diversity, Old or Aging, and 

Young. Only 12 parishes in the state and only 4 in the BOEM-defined region met the standards 

to be considered Low Diversity. In terms of age, 33 parishes are Old or Aging, while 39 are 

Young. Note that several parishes met both thresholds: they are both Old and Young, having 

high percentages of older and younger demographics. 

 
3 

Parish Education and Income 

This category contains four elements: Wealthy, Poor, High Crime, and High Adult Education 

Level. Four parishes met the standard for being Wealthy, while another eight were “more in than 

out” of that set. Conversely, we used a fuzzy-set definition that classified 43 of the 64 parishes in 

Louisiana as being Poor, with a substantial percentage of the population in each of these parishes 

fitting the federal definition of poverty. Eight parishes met the standard to be considered High 

Crime parishes. Finally, four parishes were classified as Educated, based percentages of the adult 

population with at least a high school education. 

Parish Location and Geographic Factors 

The final five profile elements were all binary, and they were grouped together into a 

category that aims to describe locational and geographic features of parishes. First, the 

Urban/Rural classification included 29 parishes that were classified by the U.S. Census Bureau 

as urban; 35 parishes were classified as rural. The second element in this category, Parish with 

Noteworthy Natural Amenities, seeks to describe whether a parish is a desirable place to live. 

According to our classification system, 27 of the 64 parishes had Noteworthy Natural Amenities. 

The third element, also binary, simply assigned a 1 to any parish with an interstate highway 

traversing through it. There are 30 such parishes in Louisiana; 34 parishes do not contain an 

Interstate Highway. The next element, Coastal Parish, included the 13 parishes that lie along the 

Gulf Coast. Finally, 14 parishes had Major Ports. 

Labor Market Profile 

The Labor Market Profile included 11 elements that have been developed to provide a 

descriptive-comparative perspective on how Louisiana’s labor market varies across parishes. The 

elements have been grouped into four categories as described below. 

Labor Market Resources 

The fuzzy set elements in this category describe the contours of the labor market in some 

detail. The first Element, Substantial Mining Employment, seeks to compare parishes in 

connection with the numbers of employees in the mining sector. A total of 11 parishes have been 

classified as having substantial mining employment, with another six parishes in the More In 

Than Out category. The second element in this category, Small Business Intensive, serves to 

focus attention on the small business sector, and make comparisons based upon the firm-level 

characteristics. Thirty of the 64 parishes are classified as Small Business Intensive because more 

than three-quarters of the firms in each of these parishes have 10 or fewer employees. By 

contrast, the next element, Large Business Presence, makes comparisons across parishes based 

on the number of firms with 100 or more employees. There are 14 parishes in which 40 or more 

firms have 100+ employees; 10 of these parishes are located in the southern third of the state. 

 

Labor Market Outcomes 

We included two elements that focus attention on key labor market outcomes: High 

Unemployment and Job Growth. We found that 19 parishes are classified as having high 

unemployment (>9% in 2010), and only nine parishes realized job growth of 2% or higher from 

2009 to 2010. 
4 

Labor Force Characteristics 

This category of the labor market profile contains one element that focuses on the 

characteristics of the labor force. Competitive Per Capita Personal Income is an element that 

makes comparisons on the basis of where average personal income for each parish ranks in 

comparison to other parishes. All parishes with average incomes at or above the state level are 

fully in the set (12 parishes). 

Education Profile 

The education profile draws on data that are published annually by the Louisiana State 

Department of Education to provide a district-level comparative picture of the education 

situation in the state. For 63 of 66 parishes in Louisiana, the school district is coterminous with 

the parish. The remaining three parishes consist of two or more school districts as follows: 

 Washington Parish: Washington Parish and City of Bogalusa School Districts 

 Ouachita Parish: Ouachita Parish and City of Monroe School Districts 

 East Baton Rouge Parish: East Baton Rouge Parish, City of Zachary, City of Baker, 

and City of Central School Districts. 

In these cases, we have aggregated data to the parish level where possible. We divide the 16 

elements in the education profile into four categories as follows. 

Resources 

These profile elements attempt to compare Louisiana school districts according to the 

resources they have to invest in public schooling, as well as the focus of their educational 

investments. First, Competitive Teacher Salary compares parishes by average teacher salary, 

focusing on whether the average salary in the parish meets or exceeds the Southern Regional 

Education Board (SREB) average. No adjustments were made for cost of living differences 

across parishes. Second, Substantial Local Capacity compares parishes on their capacity to 

secure local educational investments through taxation. Third, Onshore Oil and Gas Industry 

influence compares the extent to which parishes can access onshore oil and gas revenues to 

support public education. These are sometimes referred to as Sixteenth Section land resources. 

Fourth, Focus on Instruction is an element that compares parishes by the proportion of their 

educational expenditures, whatever they may be, that are devoted to instructional categories in 

the expenditure budget. 

Educational Outcomes 

This category compares parishes/school districts according to various educational outcomes. 

Adequate School District Performance separates parishes that are performing up to national (No 

Child Left Behind) and state expectations. Note that district level performance scores are based 

partly on aggregated school-level performances and partly on district education office 

performance measures. Second, to get a better sense of within district school-level inequality, we 

included an element entitled, Few Low-Performing Schools which identifies those parishes that 

have been able to at least avoid having many low-performing schools (irrespective of how good 

the average and high-performing schools are doing). Third, District Performance is another 

measure of adequate performance that uses the state-defined “star” system as opposed to 

aggregated school performance. Fourth, High College Attendance utilizes more recently 
5 

available data to compare the proportion of recent high school graduates that went on to become 

first-time freshmen the following year. While the measure is imperfect because data is limited to 

those who enrolled in Louisiana colleges (and, therefore, those who enrolled out of state are 

scored equally as those who did not enroll at all, due to lack of data on out-of-state enrollments) 

we believe the measure works well for comparative purposes if the assumption is that out-ofstate enrollments are distributed relatively equally across school districts. 

Student Characteristics 

We included two student characteristics that have high relevance for the state of Louisiana. 

First, we compare parishes with high percentages of At Risk Students. These are defined as 

students who are eligible for free lunch or reduced cost lunch at public schools due to their 

household income status. Second, we compare parishes with high proportions of AfricanAmerican Students. These two measures exhibit significant overlap. 

District Processes 

This final category in the educational profile includes six elements that attempt to depict 

interesting differences in the way parishes and school districts “process” students. The first three 

elements focus on districts with High Dropout Rates, Many Disciplinary Cases, and Many Grade 

Repeaters. The next two categories simply define large and small school districts, with the 

assumption that district structure (large or small) may affect how schools operate and how 

students, therefore, are processed. Finally, we compare parishes according to whether or not 

there is Substantial Nonpublic School Enrollment. Large nonpublic school enrollments could 

reduce commitment to the public school system. 

 

In addition, we summarize lessons learned from our meetings with local state and educational 

officials. In total, we visited 33 officials from 16 parishes, beginning in the summer of 2008 and 

ending in the fall of 2009. We spoke by phone to officials in other parishes, and examined parish 

and school board web pages in order to get a good sense of the issues facing local parish school 

boards. A descriptive summary of the lessons we learned from these visits is detailed in 

Appendix A. 

ANALYZING EMPLOYMENT CHANGE FOLLOWING HURRICANES KATRINA AND RITA

The second chapter of this report focuses on one specific economic characteristic, 

employment, analyzing employment change during a period prior to the 2005 hurricane season 

(2001–2004) and a period that includes and immediately follows Hurricanes Katrina and Rita 

(2004–2006). In particular, employment change over these two time periods is evaluated for two 

major industry categories, Mining and Food Services, for the BOEM-defined onshore areas of 

South Louisiana. 

To address employment change, we apply a specific tool to decompose employment change 

called shift share analysis. This widely used method decomposes employment change in a given 

region (e.g., parish) into three elements: employment change due to the overall growth (positive 

or negative) of the national (U.S.) economy; change due to the growth (positive or negative) of 

the specific industry nationally; and, growth (positive or negative) due to the local (parish 
6 

specific) effects. The national effect is sometimes called “aggregate growth”. It assumes that an 

overall rising (expansionary) or receding (contractionary) tide of a national economy has a 

similar positive or negative effect on all industries in an economy. The growth of the specific 

national industry (sometimes called the industry mix or business mix) effect suggests that while 

the growth of the overall economy plays some role in an industry’s growth, specific industries 

nationally grow at differing rates and this differential should contribute to a specific industry’s 

growth locally. Finally, a region’s growth that is not accounted for by national growth and 

national industry growth is considered the local (or competitive) effect. This residual is 

sometimes called the competitive effect because it may be indicating some type of comparative 

advantage or disadvantage of the particular region due, for example, to natural site location, 

natural resource base, logistic advantage (e.g., transportation), or some type of pecuniary (price) 

advantage such as low cost labor. 

In this chapter, the authors extend this analysis by constructing an augmented shift share 

analysis that substitutes the industry’s regional growth (neighboring-region) effect for its 

national industry mix effect. This (and other similar variations) has been described as “spatial 

shift share analysis”. The regional growth effect is included because there may be regional 

factors at play that are beyond the control of the individual parish that are more influential than 

national industry trends. In fact all of its growth may be due to external regional effects. An 

example of such regional effects might be investments made by nearby parishes in such 

infrastructure as roads, education, and health care. The reference parish need not invest to 

receive positive spillover benefits. Hence the spatial shift share approach aims to disentangle 

regional comparative advantage from local comparative advantage. 

This distinction is important for both a regional and sectorial perspective. From a sectorial 

perspective, a spatial shift share analysis can identify how a sector may be linked spatially. That 

is, it can help to map out a supply chain in geographic space for a given industry. From a 

regional perspective, the approach can identify the key parish or parishes that drive employment 

activity for a given region, not in terms of just the direct jobs it provides from its own parish, but 

the jobs the parish may leverage or support in other parishes. 

Overall results are presented in Chapter Two; some of the key highlights are presented here. 

In Figure 2.1, we highlight BOEM onshore areas that are more or less influenced by their 

neighboring regions’ industry in the immediate period before the storm. We see that, during this 

period, some of the parishes with medium and large ports that directly and indirectly support 

deepwater oil and gas activities, including Plaquemines, Lafourche, St. Mary, and Iberia 

Parishes, generated positive local effects with negative neighboring effects. In the case of 

Lafourche, St. Mary, and Iberia Parishes, their positive local effects outweighed national and 

negative neighboring region growth effects that result in overall mining employment growth for 

each of these respective parishes between 2001 and 2004. In Figure 2.2, between 2004 and 2006, 

we see additional patterns emerging. In particular, there are a larger number of parishes 

experiencing both positive neighboring region effects and positive local effects. In particular, we 

see pockets of this in three key areas: (1) around the deepwater support hubs of Terrebonne and 

Lafourche Parishes, (2) the shallower water support hub of Cameron parish, and (3) collection of 

parishes along the Interstate 10 corridor encompassing measurable portions of the Baton Rouge 

and Lafayette Metropolitan Statistical Areas. These results suggest that a combination of 
7 

growing deepwater activity from the gradual shift of drilling from shallow water to deepwater in 

the Gulf combined with mining support activities for both offshore and onshore activities 

including repair and reconstruction following Hurricanes Katrina and Rita may be a contributing 

factor explaining these spatial patterns. 

In addition, since the incorporation of neighboring region effects through spatial shift share 

analysis, there has been no research testing whether a neighboring region effect is, in fact, a 

distinct effect. We performed a correlation analysis and tested whether correlations between the 

various effects were statistically significant. We found that for the Mining sector, the correlation 

between the competitive effect and industry mix effect was statistically significant suggesting 

that the traditional shift share model did not fully disentangle national industry growth effects 

from local growth effects. However, also for the Mining sector, no significance was found on the 

correlation between the neighboring region effect and local effect suggesting the neighboring 

region effect was truly a distinct effect. 

MODELING THE LOCAL LABOR MARKET

In Chapter Three, we present the first of two modules of the Louisiana Community Impact 

Model (LCIM), the labor force module. This module forecasts changes in labor force which is 

the third step in the LCIM impact assessment (see Figure 3.1). In particular, after an economic 

scenario is identified (e.g., number of wells drilled), and a model (such as MAGPLAN, 

IMPLAN, or other decision making tools) translates the economic scenario into a change in 

employment, this change in employment is applied to the variables in the labor force module. 

Six equations are estimated as a part of the labor force module system: wages, population, 

unemployment, in-commuters, out-commuters, and labor force. To maximize the amount of 

information available in the data, we performed a sensitivity analysis by estimating each of these 

equations in the labor force module using traditional ordinary least squares regressions using 

data from the year 2008 and a panel data and three-stage least squares regressions for the time 

period 2000–2008. In addition to estimating these three different models, we performed both insample and out-of-sample forecasts between 2001 and 2008 using model results. Basic forecasts 

such as the labor force variable in Figure 3.6 showed that for the ordinary least squares model, 

forecast accuracy declined as the time lapse between the forecast year and model year source 

data increased. The panel data model that was based on data from 2000 through 2008 showed 

less variability in forecasting performance. 

In addition, we performed a mean comparison test between the ordinary least squares, threestage least squares, and panel data models. Results from this test suggested that for most of the 

equations, the panel data model outperformed the ordinary least squares model. At the same 

time, there was a general disappointment that the labor force module did not perform as well as 

the authors would have hoped. Mean absolute percent errors exceeding 20% were common. The 

authors believed much of this error was due to large fluctuations in the labor force in these 

coastal parishes following the 2005 hurricane season. The large fluctuations forced the authors to 

move away from traditional equations to the use of lagged variables in the model that captured 
8 

most of the variation in the dependent variable in these equations leaving less variation to be 

captured by traditional labor market demand and supply conditions. 

MODELING LOCAL GOVERNMENT EXPENDITURE

In Chapter Four, we focus on modeling the fiscal sector, the fourth and final section of 

impact assessment from the Louisiana Community Impact Model. In particular, we focused on 

modeling the changes in expenditure demands driven by a changing labor market. We evaluated 

variables, such as commuting, on impact revenue capacity equations that drive changes in the 

level of public expenditures that occur in a given local parish government. 

Similar to the labor force module, we conduct sensitivity analysis to alternative model 

specifications. In addition to an ordinary least squares model based on 2007 data and a panel data 

model based on data from 2004–2007, we include a quantile regression model using 2007 data. 

In simple terms, quantile regression divides parishes up based on the level of expenditures one is 

trying to model. At a fundamental level, it assumes that parishes that have similar spending 

patterns for public services respond similarly to changes in demand for that service 

Using these three models, we estimate four categories of public service expenditure: general 

government, health and welfare, public safety, and public works. Our public expenditure data are 

derived from audited financial statement data provided to the Louisiana Legislative auditor. 

These data are seen as an improvement over the model constructed by Fannin et al. (2008) which 

used U.S. Census of Governments (COG) data. COG occurs every five years, so it limits the 

ability to estimate panel data models. Further, it does not receive 100 percent cooperation from 

all parish governments, so some data reported in COG are imputed estimates and thus have some 

level of imputation error included. 

Unfortunately, traditional models that include public service demand and supply causal 

factors did not generate reasonable forecasts for the in-sample year (2007). The ordinary least 

squares generated a mean absolute percent error that ranged from 34% to 68%, the panel data 

model from 37% to 88%, and the quantile regression model from 27% to 225%. 

The authors reviewed the model and some alternative conceptual frameworks to reconstruct 

the model were considered and incorporated. The authors re-estimated the fiscal module under 

an alternative conceptual framework, the bureaucratic model. In this model, local government 

elected officials are assumed to use the previous year’s budget either entirely, or as a starting 

point, in developing the budget for the present year. As a result, expenditure levels in a given 

year are driven in large part by the previous year’s spending levels in a given category. 

As a result, we developed three additional empirical models to incorporate the bureaucratic 

model conceptual framework. First, we include a simple naïve model that includes only the 

previous year’s expenditures in a given category to estimate expenditure in the current year. 

Second, we include a naïve plus model that incorporates the previous year’s expenditures plus 

revenue capacity variables as a proxy for the expected revenue the local government is to receive 
9 

in the current year. Finally, we add a modified naïve model which is basically our original model 

plus the previous year expenditure variable. 

Results of these models showed vast improvements in forecasting performance. When 

averaging over the entire 2004–2009 forecast period, the naïve model showed mean absolute 

percent errors that ranged from 16% to 37%, the naïve plus model errors between 12% and 19%, 

and modified naïve models between 12% and 17%. In addition, a mean comparison test on these 

models showed that all three models based on the bureaucratic model generated significantly 

improved forecasts as compared to the base ordinary least squares model. The naïve plus and 

modified naïve models also generated statistically lower forecast errors than the simple naive 

model in all but the public works expenditure equation. However, there was no statistical 

difference found between the means of the forecast errors of the naïve plus and modified naïve 

models. These results suggest at least over the forecast window analyzed (2004–2009), knowing 

last year’s expenditure levels and a forecast for the present year’s revenue stream generated 

reasonable forecasts of public sector expenditure for parish governments in Louisiana. 

FORECASTING LOCAL GOVERNMENT EXPENDITURE CHANGE FROM GULF OF 

MEXICO OIL AND GAS ACTIVITIES

In Chapter Five, we present a scenario where we evaluate the fiscal impact in terms of local 

government expenditure change from oil and gas operations in the Gulf of Mexico for a given 

year. First, the authors estimate the number of oil and gas wells that will be drilled in the Gulf of 

Mexico. Based on that forecast, the number of wells drilled is applied in an Exploration and 

Development (E&D) Scenario in the Gulf of Mexico MAG-PLAN model (Saha, Manik, and 

Phillips 2005). From this model an estimate of employment change is then applied to the labor 

force module. Finally changes in major labor force module variables are then applied to the 

revenue capacity equations which are finally applied to the fiscal module to estimate changes in 

key local government expenditure categories. 

From this table, we applied the 2011 forecasted number of wells drilled (119) to the E&D 

Scenario in MAG-PLAN for the South Central Louisiana BOEM region. We applied the 

estimated 3,600 jobs generated from MAGPLAN to the Labor Force and Fiscal Modules of 

LCIM for Lafayette Parish, Louisiana, dominate employer for the offshore oil and gas industry 

for the region. The 3,600 jobs were treated as lost jobs if the 119 wells were not drilled. 

As can be seen from Table 5.1, there was about 11 percent change in per capital health and 

welfare expenditure when moving from 2009 to 2010 and around 15 percent change when 

moving from 2010 to 2011. Thus, there was a difference of about four percent, which accounts 

for the spending effects as evaluated by the difference in the growth rates between years. 

Assuming the local government growth rate between 2009 and 2010 would have occurred 

without the wells drilled, the reduction in wells drilled would have resulted in loss in per capita 

spending in health and welfare of four percent. For other categories of expenditure, these effects 

are one, two, and five percent reductions for general government, public safety and public works 

expenditures per capita respectively. Overall, these per capita reductions would have resulted in 

approximately $41 million in reduced spending in the parish. 
10 

However, expenditures between 2010 and 2011 actually grew by $27 million. The poor 

performance of the model was explained by three key factors. First, the labor force and fiscal 

modules applied to this scenario were based on source data that had measurable volatility during 

Hurricanes Katrina and Rita creating forecasting challenges. Second, it appeared anecdotally that 

much of the oil and gas industry did not have the measurable decrease in payrolls due to the 

short-term government restrictions on drilling in the Gulf of Mexico during this period that the 

Louisiana COMPAS model would not have incorporated. Third, recent research suggests that 

during short-term periods of short-term revenue decline, local governments are less likely to 

reduce local government expenditures. Rather, they find alternative ways to finance these 

expenditures until revenues rebound. Each of these factors should be taken into account in future 

modeling and application of COMPAS type models in oil and gas-related scenarios. 
11 

CHAPTER 1. PARISH PROFILES USING FUZZY SET APPROACH 

The initial objective of this research project is to develop a socio-economic baseline and 

assessment of public sector infrastructure and services of coastal communities in Louisiana 

through an extensive collection of data on local socio-economic conditions. This objective is 

partially addressed in Chapter 1 in the organization and presentation of data through the fuzzy set 

approach. The remainder of the objective is addressed in Chapter 2 through the use of shift share 

analysis. 

Fuzzy-set analysis has its origins in the 1960s in the conceptualization of the fuzzy set in 

mathematical formulations as an extension classical set theory. Previously, sets were considered 

in binary terms, an element was either in a set or out of it, but fuzzy sets allowed for partial 

membership in sets (Zadeh 1965). Since then, fuzzy set theory has been applied in different 

academic disciplines. In social sciences, fuzzy set analysis was presented as an extension of 

binary comparative methods for analyzing similarities and differences across cases (Ragin 2001). 

Fuzzy-set and regression analyses address similar questions but with strongly contrasting 

approaches. Regression analysis seeks to isolate effect that each potential factor has on a certain 

dependent variable. Fuzzy-set analysis seeks to determine whether cases of “success” or 

“failure” are connected to particular causal conditions, or particular groupings of causal 

conditions. Fuzzy set analysis is particularly appropriate when theory suggests multiple, 

conjectural causation, sometimes referred to as pathways or configurations. 

Fuzzy-set procedures involve: (1) defining a desired outcome in fuzzy-set terms; (2) defining 

potential causal conditions in fuzzy-set terms; (3) using fuzzy-set analysis that isolates groups of 

causal conditions leading to specified outcomes; (4) employing a minimization process to 

“essentialize” groups of causal conditions by excluding all redundancies; and (5) organizing the 

reduced groupings into necessary and sufficient conditions. Necessary conditions are those that 

are almost always required for a successful outcome. Sufficient conditions are those that almost 

always lead to a successful outcome. Conditions, either on their own or in combination, may be 

both necessary and sufficient, neither necessary or sufficient, or necessary but not sufficient. The 

approach allows for the possibility that multiple combinations of conditions (sometimes called 

pathways) may lead to the same outcome. An alternative, related approach involves the 

assessment of the strength of the connections between conditions and outcomes in terms of 

consistency (the degree to which an outcome has been approximated by one or more subsets) and 

coverage (the relevance of a consistent subset), which is analogous to analyzing variance in 

regression analyses (Ragin 2006). 

 

Ragin and other practitioners argue that fuzzy set approaches are “at once” qualitative and 

quantitative when they are properly developed. First, fuzzy sets should be grounded in 

conceptualizations based on theory and not data. For example, an analysis of the impact of the oil 

and gas industry would first have to define, conceptually, the details of the term “impact” in 

specific dimensions. Second, researchers can pursue either qualitative/subjective or 

quantitative/objective approaches (or combine both) to assigning membership in developing 

fuzzy sets (Verkullen 2005). This allows researchers to achieve optimal precision in 

measurement, taking advantage of quantitative data when it exists without ignoring conceptually 

important characteristics due to data limitations. Third, fuzzy set approaches are increasingly 
12 

being integrated with traditional quantitative approaches. For example, Smithson (2005) 

developed statistical methods to assist in the evaluation of fuzzy set inclusion criteria (whether or 

not a case is in, out, or a partial member). In addition, fuzzy set techniques are increasingly being 

incorporated into statistical software. For example, STATA now has a Fuzzy command that can 

be used to perform fuzzy set analysis. 

The Fuzzy Set profiles represent the initial task of creating fuzzy sets based on concepts, 

using available data but transforming it in ways that are consistent with our conceptualization of 

key elements in the profiles. The approach involves the grouping of similar parishes into one of 

the four categories of fuzzy sets that are created by establishing three key markers: (1) the cut-off 

that marks the point beyond which all parishes are considered “in the set”; (2) the cut-off that 

marks the point beyond which all parishes are considered “out of the set”; and (3) the cut-off that 

distinguishes among the partial members of the set which are “more in than out” and which are 

“more out than in”. Fuzzy set approaches recognize that social analysis is a dynamic process that 

need not be constrained by initial conceptualizations. If, after reviewing the evidence for and 

against, these conceptualizations change in the midst of a study, the fuzzy sets can and should be 

reconfigured to reflect the latest conceptualization. What we have tried to do is to explain in 

detail how we developed all the fuzzy set profile elements, so that others can understand our 

rationale. However, we do not claim to have developed comprehensive profiles or even “the 

best” profiles. The profiles are probably best viewed as examples of how parishes can be 

categorized by transforming available data in ways that allow for conceptually meaningful 

comparisons across parish. 

1.1. GENERAL COMMUNITY PROFILE

The following profile elements are intended to present a broad description of a parish to 

provide initial context for subsequent analysis. The fuzzy set approach is generally qualitative 

and focuses on making meaningful distinctions across sets of units, in this case, among parishes 

in Louisiana and BOEM-defined impact areas. The approach can help to group quantitative data 

into meaningful categories of set membership. 

We organize our profile into four sections (see the outline below). Each section contains a 

range of fuzzy set profile elements. This report focuses on the presentation of the fuzzy set 

profile elements shown below. 

General Profile Outline 

 Parish Population Statistics 

o Large Population 

o Small Population 

o Emerging or Growing Parish 

o Declining or Stagnating Parish 

 Parish Demographic Information 

o Parish Diversity 

o Old or Aging Parish 
13 

o Young Parish 

 Parish General Social and Economic Indicators 

o Wealthy Parish 

o Poor Parish 

o Parish with High Adult Education Level 

o High Crime Parish 

 Parish Location and Geographic Factors 

o Urban or Rural Parish 

o Parish with Noteworthy Natural Amenities 

o Parish with Interstate Highway 

o Coastal Parish 

o Parish with Major Ports 

1.1.1. Parish Population Statistics 

This first set of measures group together similar parishes based on population characteristics. 

Note that, with fuzzy sets, each measure is independent of other members. For example, Large 

Population and Small Population fuzzy sets are independent of one another; meaning that Small 

Population is not simply the negation of Large Population. It is possible for a given parish to be 

in neither set (i.e., Neither Large Population nor Small Population). Taken together, the four 

measures provide a way of comparing both population levels and changes across Louisiana’s 

parishes. 

1.1.1.1. Large Population 

Definition. The tables below show detailed information about the population of Louisiana 

Parishes and define individual parishes as having a large population if there were more than 

100,000 inhabitants as of July 1, 2009. In addition to identifying large parishes, the data below 

identify non-large parishes and parishes that are partially large with respect to their inclusion in 

the fuzzy set. All parishes with a population below 50,000 were considered fully out of the set of 

parishes with a large population; parishes with a population between 50,000 and 100,000 were 

considered partial members of the set and assigned fuzzy set values respective to their 

population. Data were obtained from the U.S. Census Bureau’s 2009 population estimate. 

Discussion and Description. Among all Louisiana parishes, 14 parishes fit the definition of 

a large parish and are considered fully part of the fuzzy set. A further eight parishes are 

considered partially within the fuzzy set to varying degrees based on the size of their population 

and 42 parishes have a population that places them fully outside of the large population fuzzy 

set. 

Among parishes BOEM-defined impact areas, 10 fit the definition of a large parish and are 

considered fully within the fuzzy set. An additional seven can be considered partially within the 

set based on the size of their population and another 15 can be considered outside of the large 

population fuzzy set. Descriptive statistics and a layout of fuzzy set membership are included in 

Tables 1.1–1.2 and Figure 1.1 below. For Figure 1.1, as with all figures in the General 

Community Profile section, parishes shaded in dark blue are fully in a set, those in light blue are 

more in than out of the set, in pink are more out than in the set, and in red are fully out of the set. 
14 

Table 1.1. Large Population fuzzy set membership for all Louisiana parishes (2009). 

Parishes with a Large 

Population Verbal Level Fuzzy Set Membership Score Number 

100,00 or more parish 

inhabitants Fully in set 1 14 

Between 75,000 and 

100,000 inhabitants 

More in set than out 

of set 0.5-0.999 3 

Between 50,000 and 

75,000 inhabitants More out than in set 0.001-0.49 5 

Fewer than 50,000 

inhabitants Out of set 0 42 

Table 1.2. Large Population fuzzy set membership for BOEM-defined parishes (2009). 

Parishes with a Large 

Population Verbal Level Fuzzy Set 

Membership Score Number 

100,00 or more parish 

inhabitants Fully in set 1 10 

Between 75,000 and 100,000 

inhabitants 

More in set than out 

of set 0.5-0.999 2 

Between 50,000 and 75,000 

inhabitants More out than in set 0.001-0.49 5 

Fewer than 50,000 

inhabitants Out of set 0 15 
15 

Figure 1.1. Large Population fuzzy set membership. 

1.1.1.2. Small Population 

Definition. The definition of a large or small parish is somewhat problematic as there is no 

universally agreed upon measure for what constitutes a large or small one. For the purpose of 

this study, parishes are defined as fully within the set of parishes having a small population if 

they had 25,000 or fewer inhabitants. If they had between 25,000 and 50,000 the parish is 

considered partially in the set in relative proportion based on population size. If a parish had 

more than 50,000 inhabitants it is considered outside the small parish set. 

Discussion and Description. In the state of Louisiana, 27 parishes have fewer than 25,000 

inhabitants; these parishes are considered fully within the small population fuzzy set. There are 

15 parishes with between 25,000 and 50,000 inhabitants that are considered partially in the fuzzy 

set proportionally based on their population. There are 22 parishes with more than 50,000 

inhabitants that are considered outside of the small parish fuzzy set. 

Within the BOEM-defined parishes, only six are fully within the small parish fuzzy set, and 

an additional nine are partially within the set at varying degrees based on population size. The 

majority of the parishes in the BOEM-defined set (17) are considered fully outside of the small 

parish set. Descriptive statistics and the layout of fuzzy set membership are included in Tables 

1.3–1.4 and Figure 1.2. 
16 

Table 1.3. Small Population fuzzy set membership for all Louisiana parishes (2009). 

Parishes with a Small 

Population Verbal Level Fuzzy Set Membership Score Number 

25,000 or fewer parish 

inhabitants Fully in set 1 27 

Between 25,000 and 

37,500 inhabitants 

More in set than out 

of set 0.5-0.999 7 

Between 37,500 and 

50,000 inhabitants More out than in set 0.001-0.49 8 

More than 50,000 

inhabitants Out of set 0 22 

Table 1.4. Small Population fuzzy set membership for BOEM-defined parishes (2009). 

Parishes with a Small 

Population Verbal Level Fuzzy Set 

Membership Score Number 

25,000 or fewer parish 

inhabitants Fully in set 1 6 

Between 25,000 and 

37,500 inhabitants 

More in set than out 

of set 0.5-0.999 5 

Between 37,500 and 

50,000 inhabitants More out than in set 0.001-0.49 4 

More than 50,000 

inhabitants Out of set 0 17 

 
17 

Figure 1.2. Small Population fuzzy set membership. 

 1.1.1.3. Parishes with Growing Populations 

Definition. The U.S. Census 2009 measure of population growth rate is used to measure if a 

parish was emerging or growing in comparison to the decennial census from 2000. Within 

Louisiana, emerging or growing parishes have population growth rates above the national 

average of 9.1% to be considered fully within the fuzzy set definition of a growing parish. 

Parishes with a growth rate above 0.5% but below 9.1% are considered partially within the set at 

varying degrees based on their respective growth rates. Any parish with a growth rate below 

0.5% is considered fully out of the fuzzy set. 

Discussion and Description. In the state of Louisiana, seven parishes are defined as 

emerging with population growth exceeding the national average. Currently, 22 parishes are 

partially within the emerging growth category with growth rates exceeding the state average but 

below the national average for growth. The remaining 35 parishes had growth rates below 0.5% 

or were experiencing population decline. 

Among the BOEM-defined parishes, six experienced population growth above the national 

average of 9.1% and are considered to be fully within the fuzzy set for the purpose of this 

analysis. An additional 14 parishes are considered partially within the fuzzy set and 12 parishes 

fail to experience population growth and are considered fully outside of the fuzzy set. 
18 

Descriptive statistics and the layout of fuzzy set membership are included in Tables 1.5–1.6 and 

Figure 1.3. 

Table 1.5. Population Growth fuzzy set membership for all Louisiana parishes (2009). 

Parish with Population Growth Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish population growth rate 

greater than 9.1% Fully in set 1 7 

Parish population growth rate 

between 4.8% and 9.1% More in set than out of set 0.5-0.999 9 

Parish population growth rate 

between 0.5% and 4.8% More out than in set 0.001-0.49 13 

Parish population growth rate less 

than 0.5% Out of set 0 35 

Table 1.6. Population Growth fuzzy set membership for BOEM-defined parishes (2009). 

Parish with Population Growth Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish population growth rate 

greater than 9.1% Fully in set 1 6 

Parish population growth rate 

between 4.8% and 9.1% More in set than out of set 0.5-0.999 7 

Parish population growth rate 

between 0.5% and 4.8% More out than in set 0.001-0.49 7 

Parish population growth rate less 

than 0.5% Out of set 0 12 

 
19 

Figure 1.3. Population Growth fuzzy set membership (emerging parishes). 

1.1.1.4. Parishes with Declining or Stagnating Populations 

Definition. The U.S. Census 2009 estimate of population growth rate is used to determine if 

a parish was declining or stagnating in comparison to the decennial census from 2000. Within 

Louisiana, declining or stagnating parishes are defined as parishes that had had no population 

growth or a negative population growth rate. For the purpose of this analysis, parishes are 

divided into two categories reflecting their status as either declining or stagnating or as not 

declining or stagnating. 

Discussion and Description. In the state of Louisiana, 33 parishes are currently experiencing 

stagnation or declining population levels, and 31 parishes are experiencing growth ranging from 

0.2% to 36.8%. Within the BOEM-defined parishes, 12 parishes can be considered to be 

experiencing zero or negative growth and 20 parishes experiencing some level of growth over 

the past decade. Descriptive statistics and the layout of fuzzy set membership are included in 

Tables 1.7–1.8 and Figure 1.4. 

 
20 

Table 1.7. Declining or Stagnating Parish fuzzy set membership for all Louisiana parishes (2009). 

Parishes with Population Decline Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Zero or negative growth Fully in set 1 33 

Any growth Out of set 0 31 

Table 1.8. Declining or Stagnating Parish fuzzy set membership for BOEM-defined parishes (2009). 

Parishes with Population Decline Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Zero or negative growth Fully in set 1 12 

Any growth Out of set 0 20 

Figure 1.4. Declining/Stagnating Populations fuzzy set membership. 

 
21 

1.1.2. Parish Demographic Information 

This second set of fuzzy set profile elements distinguishes between different parishes and 

groups together similar parishes based on demographic data. 

1.1.2.1. Low-Diversity Parish 

Definition. Though there are several methods for measuring diversity, we draw upon the 

Theil index because it measures the uneven structure of the spatial distribution of the six major 

population subgroups within census tracts within counties. The measure ranges from 0 to 1 with 

a lower score indicating counties with low diversity. For the purpose of this report, the definition 

of a parish with low racial and ethnic diversity is defined as a parish with a Theil index score 

below .10 to be considered fully in the set, parishes with a score below .12 yet above .10 are 

considered to be partial members based on their distribution within this range. 

Discussion and Description. In the state of Louisiana, 12 parishes can be considered fully 

within the fuzzy set and considered to have low levels of racial diversity. An additional 10 

parishes are considered to be partially within the fuzzy set, having a Theil index score above 0.12 

but below the 0.10 cut-off point. A full 42 parishes are considered outside of this set. 

Within the BOEM-defined parishes, four parishes can be considered to have low levels of 

racial diversity and can be considered fully within the fuzzy set. An additional three parishes are 

considered partial members of the set to varying degrees based on their Theil index score. A total 

of 25 parishes can be considered fully outside of the fuzzy set of parishes with low racial 

diversity. Descriptive statistics and the layout of fuzzy set membership are included in Tables 

1.9–1.10 and Figure 1.5. 

Table 1.9. Low Racial Diversity fuzzy set membership for all Louisiana parishes (2010). 

Racial Diversity Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

0.10 or less Fully in set 1 12 

0.11 to 0.10 More in set than out of set 0.5-0.999 5 

0.12 to 0.11 More out than in set 0.001-0.49 5 

Greater than 0.12 Out of set 0 42 

Table 1.10. Low Racial Diversity fuzzy set membership for BOEM-defined parishes (2010). 

Racial Diversity Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

0.10 or less Fully in set 1 4 

0.11 to 0.10 More in set than out of set 0.5-0.999 3 

0.12 to 0.11 More out than in set 0.001-0.49 0 

Greater than 0.12 Out of set 0 25 
22 

Figure 1.5. Low Racial Diversity fuzzy set membership. 

1.1.2.2. Old or Aging Parish 

Definition. Using U.S. Census 2009 estimates of parish population, we were able to use the 

percentage of individuals within each parish over the age of 65 as an indicator of the parish 

having a large proportion of old or aging residents. For the purpose of this study, any parish with 

a proportion of old people higher than the nation average of 12.9% of their population was 

considered to have a large proportion of old or aging residents and as such was considered fully 

within the old or aging parish fuzzy set. Those parishes with a rate between the Louisiana state 

average of 12.3% and the national average of 12.9% were considered partially within the fuzzy 

set to the degree that their respective measures allow. Those parishes with less than 12.3% of 

their population over the age of 65 were not considered to be members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, 33 parishes have a sufficient 

proportion of their population above the age of 65 to be considered fully within the fuzzy set. 

Additionally, seven parishes can be considered partially in the fuzzy set to varying degrees, 

depending on the overall proportion of residents over the age of 65 in the parish. There are 25 

parishes fully outside of the fuzzy set. Descriptive statistics and the layout of fuzzy set 

membership are included in Tables 1.11–1.12 and Figure 1.6. 
23 

Table 1.11. Old or Aging fuzzy set membership for all Louisiana parishes (2009). 

Percent of Population 65 Years of 

Age and Older Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

 Greater than 12.9% Fully in set 1 33 

12.6 to 12.9% More in set than out of set 0.5-0.999 4 

12.4% to 12.6% More out than in set 0.001-0.49 3 

Less than 12.3% Out of set 0 25 

Table 1.12. Old or Aging fuzzy set membership for BOEM-defined parishes (2009). 

Percent of Population 65 Years of 

Age and Older Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

 Greater than 12.9% Fully in set 1 7 

12.6 to 12.9% More in set than out of set 0.5-0.999 3 

12.4% to 12.6% More out than in set 0.001-0.49 3 

Less than 12.3% Out of set 0 20 

Figure 1.6. Old or Aging fuzzy set membership. 
24 

1.1.2.3. Young Parish 

Definition. Using U.S. Census 2009 estimates of parish population, we were able to use the 

percentage of individuals within each parish aged 18 or below as an indicator of the parish 

having a large proportion of young residents. For the purpose of this study, any parish with a 

proportion of young people higher than the national average of 24.9% of their population was 

considered to have a large proportion of old or aging residents and as such was considered fully 

within the old or aging parish fuzzy set. Those parishes with a rate between the Louisiana state 

average of 24.4% and the national average of 24.9% were considered partially within the fuzzy 

set to the degree that their respective measures allow. Those parishes with less than 24.4% of 

their population aged 18 years or younger were not considered to be members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, 39 parishes have a sufficient 

proportion of their population aged 18 years or younger to be considered fully within the fuzzy 

set. Additionally, four parishes can be considered partially in the fuzzy set to varying degrees, 

depending on the overall proportion of residents aged 18 years or younger. There are 21 parishes 

fully outside of the fuzzy set. 

Within the BOEM-defined parishes, 24 parishes have a sufficient proportion of their 

population aged 18 years or younger to be considered fully within the fuzzy set. Additionally, 

there is one parish that can be considered partially in the fuzzy set. There are seven parishes fully 

outside of the fuzzy set. Descriptive statistics and the layout of fuzzy set membership are 

included in Tables 1.13-1.14 and Figure 1.7. 

Table 1.13. Young Population fuzzy set membership for all Louisiana parishes (2009). 

Percent of Population 18 Years of 

Age and Younger Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 24.9% Fully in set 1 39 

Between 24.7% and 24.9% More in set than out of set 0.5-0.999 1 

Between 24.4% and 24.7% More out than in set 0.001-0.49 3 

Less than 24.4% Out of set 0 21 

Table 1.14. Young Population fuzzy set membership for BOEM-defined parishes (2009). 

Percent of Population 18 Years of 

Age and Younger Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 24.9% Fully in set 1 24 

Between 24.7% and 24.9% More in set than out of set 0.5-0.999 0 

Between 24.4% and 24.7% More out than in set 0.001-0.49 1 

Less than 24.4% Out of set 0 7 
25 

Figure 1.7. Young Population fuzzy set membership. 

1.1.3. Parish General Economic and Social Indicators Income 

The following four indicators provide a way of grouping similar parishes together based 

upon general measures of income, education, and crime. 

1.1.3.1. Wealthy Parish 

Definition. Using U.S. Census 2008, estimates of parish median household income were 

used as an indicator of the overall wealth of a parish. For the purpose of this study, any parish 

with a median household income above the national median household income of $52,000 was 

considered to be a wealthy parish and considered fully within wealthy parish fuzzy set. Those 

parishes with a median household income between the Louisiana median of $44,000 and the 

national median of $52,000 were considered partially within the fuzzy set to the degree that their 

respective measures allow. Those parishes with a median income of less than $44,000 were not 

considered to be members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, four parishes have a median 

household income sufficient to be considered fully within the fuzzy set. Additionally, 13 parishes 

can be considered partially in the fuzzy set to varying degrees depending on the overall median 

household income. There are 47 parishes fully outside of the fuzzy set. 
26 

Within the BOEM-defined parishes, four parishes have median household income sufficient 

to be considered fully within the fuzzy set. Additionally, 10 parishes can be considered partially 

in the fuzzy set. There are 18 parishes fully outside of the fuzzy set. Descriptive statistics and the 

layout of fuzzy set membership are included in Tables 1.15-1.16 and Figure 1.8. 

Table 1.15. Wealthy Population fuzzy set membership for all Louisiana parishes (2008). 

Median Income Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than $52,000 Fully in set 1 4 

$48,000–$52,000 More in set than out of set 0.5-0.999 8 

$44,000–$48,000 More out than in set 0.001-0.49 5 

Less than $44,000 Out of set 0 47 

Table 1.16. Wealthy Population fuzzy set membership for BOEM-defined parishes (2008). 

Median Income Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than $52,000 Fully in set 1 4 

$48,000–$52,000 More in set than out of set 0.5-0.999 6 

$44,000–$48,000 More out than in set 0.001-0.49 4 

Less than $44,000 Out of set 0 18 

 
27 

Figure 1.8. Wealthy Population fuzzy set membership. 

1.1.3.2. Poor Parish 

Definition. We used U.S. Census 2008 estimates of the percent of the total population within 

a parish below the federally defined poverty line to define poor parishes. For the purpose of this 

study, any parish with more than 17.8% of the total population fitting the federal definition of 

poverty was considered to be a poor parish and considered fully within wealthy parish fuzzy set. 

Those parishes with between the Louisiana average of 17.8% and the national average of 13.5% 

were considered partially within the fuzzy set to the degree that their respective measures allow. 

Those parishes with a less than 13.5% of the population fitting the federal poverty definition 

were not considered to be members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, 43 parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, 17 parishes can be considered partially in the 

fuzzy set to varying degrees depending on the overall median household income. There are six 

parishes fully outside of the fuzzy set. 

Within the BOEM-defined parishes, 15 parishes have median household income sufficient to 

be considered fully within the fuzzy set. Fourteen can be considered partially in the fuzzy set. 

Five parishes are fully outside of the fuzzy set. Descriptive statistics and the layout of fuzzy set 

membership are included in Tables 1.17–1.18 and Figure 1.9. 
28 

Table 1.17. Poor Population fuzzy set membership for all Louisiana parishes (2008). 

Percent of Population Living 

Below Federal Poverty Line Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Great than 17.8% Fully in set 1 43 

15.65% – 17.8% More in set than out of set 0.5-0.999 10 

13.5% – 15.65% More out than in set 0.001-0.49 7 

Less than 13.5% Out of set 0 6 

Table 1.18. Poor Population fuzzy set membership for BOEM-defined parishes (2008). 

Percent of Population Living 

Below Federal Poverty Line Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Great than 17.8% Fully in set 1 15 

15.65% – 17.8% More in set than out of set 0.5-0.999 8 

13.5% – 15.65% More out than in set 0.001-0.49 6 

Less than 13.5% Out of set 0 5 

Figure 1.9. Poor Population fuzzy set membership. 
29 

1.1.3.3. Parish with High Adult Education Level 

Definition. Using U.S. Census 2009 estimates, parishes with high adult education levels 

were defined using the percent of the total population within a parish with a high school 

education or above. For the purpose of this study, any parish with more than the national average 

of 80.3% of the total population with a high school diploma or above is considered fully within 

the fuzzy set. Those parishes with between the Louisiana average of 75% and the national 

average of 80.3% were considered partially within the fuzzy set to the degree that their 

respective measures allow. Those parishes with a less than 75% of the population fitting the 

federal poverty definition were not considered to be members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, four parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, 13 parishes can be considered partially in the 

fuzzy set to varying degrees, depending on the overall median household income. There are 49 

parishes fully outside of the fuzzy set. 

Within the BOEM-defined parishes, two parishes have median household income sufficient 

to be considered fully within the fuzzy set. Additionally, eight parishes can be considered 

partially in the fuzzy set. There are 22 parishes fully outside of the fuzzy set. 

Table 1.19. Adult Education fuzzy set membership for all Louisiana parishes (2009). 

Percent of Adult Population That 

Graduated from High School Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 80.3% Fully in set 1 4 

77.65% – 80.3% More in set than out of set 0.5-0.999 7 

75% – 77.65% More out than in set 0.001-0.49 4 

Less than 75% Out of set 0 49 

Table 1.20. Adult Education fuzzy set membership for BOEM-defined parishes (2009). 

Percent of Adult Population That 

Graduated from High School Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 80.3% Fully in set 1 2 

77.65% – 80.3% More in set than out of set 0.5-0.999 4 

75% – 77.65% More out than in set 0.001-0.49 4 

Less than 75% Out of set 0 22 
30 

Figure 1.10. Adult Education fuzzy set membership. 

1.1.3.4. High-Crime Parish 

Definition. To evaluate the county crime rate, we draw upon USA Counties data from 2008. 

A significant source of variation among counties is non-participation and the use of differing 

definitions regarding what constitutes violent crime. For the purpose of this report, parishes with 

more than 1,000 violent crimes are considered fully within the fuzzy set. Parishes with between 

500 and 1,000 reported violent crimes are considered partially within the fuzzy set. Parishes with 

fewer than 500 violent crimes are considered outside of the fuzzy set. 

Discussion and Description. In the state of Louisiana, eight parishes can be considered fully 

within the fuzzy set and considered to have high levels of violent crime. An additional four 

parishes are considered to be partially within the fuzzy set because they have between 500 and 

1000 violent crimes. A full 52 parishes are considered outside of this set. 

Within the BOEM-defined parishes, four parishes can be considered to have high levels of 

violent crime. An additional three parishes are considered partial members of the set to varying 

degrees based on the number of violent crimes within the parish. A total of 25 parishes can be 

considered fully outside of the fuzzy set of parishes high violent crime rates. Descriptive 

statistics as well as the layout of fuzzy set membership are included in Tables 1.21–1.22 and 

Figure 1.11. 
31 

Table 1.21. High Crime fuzzy set membership for all Louisiana parishes (2008). 

Number Of Violent Crimes Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 1000 Fully in set 1 8 

Between 750 and 1000 More in set than out of set 0.5-0.999 2 

Between 500 and 750 More out than in set 0.001-0.49 2 

Fewer than 500 Out of set 0 52 

Table 1.22. High Crime fuzzy set membership for BOEM-defined parishes (2008). 

Number Of Violent Crimes Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 1000 Fully in set 1 4 

Between 750 and 1000 More in set than out of set 0.5-0.999 1 

Between 500 and 750 More out than in set 0.001-0.49 2 

Fewer than 500 Out of set 0 25 

Figure 1.11. High Crime fuzzy set membership. 
32 

1.1.4. Parish Location and Geographic Factors 

1.1.4.1. Urban and Rural Parishes 

Definition. For the purpose of identifying urban and rural parishes, we looked to the U.S. 

Census Bureau’s standard definition of Metropolitan Statistical Areas (MSAs). Any parish 

within an MSA is defined as urban and any parish outside of a MSA as rural. The U.S. Census 

Bureau defines a metropolitan statistical area MSA as a geographically defined area which can 

encompass multiple counties surrounding a metropolitan city. To be considered a part of an 

MSA, a county or parish needs to have at least one urbanized area with a minimum of 50,000 

people which is defined as the central county of the MSA. Outlier counties are defined as 

counties from which over 25% of employed residents from the outlier county commute to the 

central county. 

Discussion and Description. Louisiana has 29 parishes within metropolitan statistical areas, 

as defined by the U.S. Census Bureau, and can be considered urban for the purpose of this 

analysis, and 35 can be considered rural according to the criteria set forth above. Among the 

coastal parishes, 18 fit the definition of being considered urban for the purpose of this study, and 

14 fit the characteristics required to be listed as rural. Descriptive statistics and the layout of 

fuzzy set membership are included in Tables 1.23 and 1.24 and Figure 1.12. 

Table 1.23. Urban and Rural Parishes fuzzy set membership (2008). 

Parish Designation Verbal Level 

Fuzzy Set 

Membership 

Score 

Examples 

County part of a Census defined 

MSA 

Fully in urban 

set 1 Orleans Parish (Pop 354,850), Plaquemines (Pop 20,942) 

County not part of a Census 

defined MSA 

Fully out of 

urban set 0 West Carroll (Pop 11,392), St. Mary (pop 50,815) 

Counties outside of CBSA or 

members of a MCA 

Fully in rural 

set 1 Tensas (pop 5,609), 

Evangeline (Pop 35,330) 

Counties outside of CBSA or 

members of a MCA 

Fully out of 

rural set 0 East Baton Rouge (Pop 434,633) 

Table 1.24. Urban and rural parishes. 

Urban or Rural Parish 

Fuzzy Set 

Count 

(Louisiana) 

Fuzzy Set 

Count (BOEM 

Parishes) 

Urban parishes 29 18 

Rural parishes 35 14 
33 

Figure 1.12. Urban and Rural Parish fuzzy set membership. 

1.1.4.2. Parish with Noteworthy Natural Amenities 

Definition. Natural amenity scores are calculated on a national level based on a method 

developed by the USDA Economic Research Service and range from 1 to 7, indicating the 

degree to which a county or parish can be considered a desirable place to live. The scale is 

constructed using six measures of climate, topography, and water area that reflect environmental 

qualities that people prefer. Other measures include a warm winter, winter sun, temperate 

summers, low humidity, topographic variation and water area. Though natural amenity scores for 

the country ranged from 1 to 7, within the state of Louisiana these scores ranged from 3 to 4, 

indicating homogeneity within the state in regard to the distribution of natural amenities. 

Discussion and Description. In the state of Louisiana, 27 parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, there are 37 parishes fully outside of the 

fuzzy set. Within the BOEM-defined parishes, 14 parishes have natural amenity scores of four 

and are fully in the set. Additionally, there are 18 parishes fully outside of the fuzzy set. 

Descriptive statistics and the layout of fuzzy set membership are included in Tables 1.25-1.26 

and Figure 1.13. 
34 

Table 1.25. Natural Amenities fuzzy set membership for all Louisiana parishes (2005). 

Natural Amenity Score Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

4 Fully in set 1 27 

3 Out of set 0 37 

Table 1.26. Natural Amenities fuzzy set membership for BOEM-defined parishes (2005). 

Natural Amenity Score Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

4 Fully in set 1 14 

3 Out of set 0 18 

Figure 1.13. Natural Amenities fuzzy set membership. 
35 

1.1.4.3. Interstate Highway Parish 

Definition. Using maps of interstate highways and parish boundaries, interstate highway 

parishes were defined based on having a major interstate highway within the parish borders. The 

interstate parishes examined within Louisiana are: I-10, I-12, I-49, I-20, and I-55. Those parishes 

with the previously mentioned interstate highways within their borders are considered to be full 

members of the fuzzy set. Those parishes without an interstate highway within their borders are 

considered to be outside the fuzzy set. 

 

Discussion and Description. In the state of Louisiana, 30 parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, 34 parishes can be considered fully outside of 

the fuzzy set. Within the BOEM-defined parishes, 17 parishes have interstate highways to be 

considered fully within the fuzzy set. Additionally, there are 15 parishes fully outside of the 

fuzzy set. 

Table 1.27. Interstate Highway fuzzy set membership for all Louisiana parishes (2010). 

Interstate Highway Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Interstate highway runs through 

parish Fully in set 1 30 

No interstate highway runs through 

parish Out of set 0 34 

Table 1.28. Interstate Highway fuzzy set membership for BOEM-defined parishes (2010). 

Interstate Highway Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Interstate highway runs through 

parish Fully in set 1 17 

No interstate highway runs through 

parish Out of set 0 15 

 
36 

Figure 1.14. Interstate Highway fuzzy set membership. 

1.1.4.4. Coastal Parish 

Definition. Using maps of coastal and parish boundaries, coastal parishes were defined based 

on the presence of a border along the Gulf of Mexico or Lake Pontchartrain. 

 

Discussion and Description. In the state of Louisiana, 14 parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, 50 parishes can be considered fully outside of 

the fuzzy set. Because all coastal parishes are BOEM-defined parishes, 14 of the parishes are 

fully in the BOEM-defined set and 18 lie out of the fuzzy set. Descriptive statistics and the 

layout of fuzzy set membership are included in Tables 1.29–1.30 and Figure 1.15. 

 
37 

Table 1.29. Coastal fuzzy set membership for all Louisiana parishes (2010). 

Coastal Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish borders Gulf of Mexico or 

Lake Pontchartrain Fully in set 1 14 

Parish does not border Gulf of 

Mexico or Lake Pontchartrain Out of set 0 50 

Table 1.30. Coastal fuzzy set membership for BOEM-defined parishes (2010). 

Coastal Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish borders Gulf of Mexico or 

Lake Pontchartrain Fully in set 1 14 

Parish does not border Gulf of 

Mexico or Lake Pontchartrain Out of set 0 18 

Figure 1.15. Coastal fuzzy set membership. 
38 

1.1.4.5. Parish with Port 

Definition. Using data from the American Association of Port Authorities Seaports of 

America Directory, we identified parishes with a sizable cargo port. Many large-scale ports cross 

parish boundaries and all parishes with a major port within their boundaries are counted as full 

members of the fuzzy set. 

Discussion and Description. In the state of Louisiana, 14 parishes fit the measure to be 

considered fully within the fuzzy set. Additionally, 50 parishes can be considered fully outside of 

the fuzzy set. Within the BOEM-defined parishes, 13 parishes lie fully within the fuzzy set of 

parishes with a major port. Additionally, 19 parishes were fully outside of the fuzzy set. 

Descriptive statistics the layout of fuzzy set membership are included in Tables 1.3–1.32 and 

Figure 1.16. 

Table 1.31. Major Port fuzzy set membership for all Louisiana parishes (2010). 

Major Port Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish has major commercial port Fully in set 1 14 

No major port Out of set 0 50 

Table 1.32. Major Port fuzzy set membership for BOEM-defined parishes (2010). 

Port Status Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Parish has major commercial port Fully in set 1 13 

No major port Out of set 0 19 
39 

Figure 1.16. Major Port fuzzy set membership. 

1.2. LABOR MARKET PROFILE OVERVIEW

This profile uses fuzzy set theory to develop profile elements using available secondary 

economic and employment data. The fuzzy set approach is generally qualitative and focuses on 

making meaningful distinctions across sets of units, in this case, parishes in Louisiana and the 

BOEM-defined impact area. The approach can help to group or organize quantitative data into 

meaningful categories of set membership. We organize our profile into two sections (see the 

outline below). Each section contains a range of fuzzy set profile elements. This report focuses 

on the presentation of the fuzzy set profile elements. 

Labor Market Outline 

 Labor Market Resources 

o Substantial Mining Employment 

o Small Business Intensive 

o Large Business Presence 

 
40 

 Labor Market Outcomes 

o Low Unemployment 

o Job Growth 

o Competitive per Capita Personal Income 

1.2.1. Labor Market Resources 

Different levels of availability and access to resources can be an important advantage or 

disadvantage for parish labor markets. In this section, we developed three fuzzy set elements that 

focus on these resources and how they contribute to and define each parish’s potential for 

economic functioning or development. 

1.2.1.1. Substantial Mining Employment 

Definition. Using data from the Bureau of Economic Analysis (BEA) 2006 Regional 

Economic Information System (REIS), this fuzzy set illustrates the extent to which oil and gas or 

mining employment is a substantial contribution to the labor market. The REIS data provide 

information on the level of employment in each industry per parish. This set considers the 

number of individuals employed in the Mining industry in each parish. To be considered fully in 

the set of parishes with substantial mining employment, there must be 1,000 people or more 

employed in this industry. Those parishes that employ fewer than 100 people in this industry are 

out of the set. 

Discussion and Description. Table 1.33 indicates that only 11 Louisiana parishes are fully 

in the set of those with substantial employment in the Mining industry. Further, only six 

additional parishes are more in the set than out with 550–999 employees in this category. Eleven 

parishes are out of the set because they have fewer than 100 employees in this industry. Due to 

missing data, not all 64 parishes are represented in this table. 

Table 1.34 represents the fuzzy set scores for the 32 BOEM-defined parishes. Nine of the 

original 11 parishes that were fully in the set in Table 1.34 are within the BOEM-defined impact 

area. The remaining 17 parishes were non-members of the set, only four of which were out of the 

set with fewer than 100 employees. A breakdown of specific parish membership in this set is 

presented in Figure 1.17. 

Table 1.33. Substantial Mining Employment fuzzy set membership for all Louisiana parishes (2006). 

People Employed 

in Mining 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(REIS) 

Examples 

More than 1000 Fully in the set 1.00 11 Lafayette* at 16,385, 1.00 

Terrebonne at 5,780, 1.00 

550–999 More in than 

out of the set 

0.50 -0.99 6 St. Landry at 623, 0.58 

E. Baton Rouge at 849, 0.83 

99–550 More out of 

than in the set 

0.01-0.49 16 Iberville at 121, 0.02 

St. Martin at 489, 0.43 

Fewer than 100 Out of the set 0.00 11 Avoyelles* at 29, 0.00 

Vernon at 73, 0.00 

*highest and lowest values in range 
41 

Table 1.34. Substantial Mining Employment fuzzy set membership for BOEM-defined parishes (2006). 

People Employed 

in Mining 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(REIS) 

Examples 

More than 1000 Fully in the set 1.00 9 Lafayette at 16,385, 1.00 

Terrebonne at 5,780, 1.00 

550–999 More in than 

out of the set 

0.50 -0.99 5 St. Landry at 623, 0.58 

E. Baton Rouge at 849, 0.83 

99–550 More out of 

than in the set 

0.01-0.49 8 Iberville at 121, 0.02 

St. Martin at 489, 0.43 

Fewer than 100 Out of the set 0.00 4 Vernon at 73, 0.00 

Evangeline at 42, 0.00 

Figure 1.17. Substantial Mining Employment fuzzy set membership. 

 
42 

1.2.1.2. Small Business Intensive 

Definition. This table examines parish labor market size based on the percentage of 

businesses with fewer than 10 employees. If three-fourths or more of a parish’s businesses 

employ fewer than 10 people, it is considered fully in the set of parishes that are Small Business 

Intensive. Parishes with two-thirds or fewer businesses employing 10 or fewer people are out of 

the set of small business intensive parishes. 

Discussion and Description. Table 1.35 illustrates the many small labor market structures 

present in Louisiana. A total of 30 parishes are full members of the set with three-fourths or more 

of their businesses employing fewer than 10 people. By comparison, only two parishes lie fully 

outside the set. This leaves 32 parishes in Louisiana in the middle categories with partial 

membership in this set. 

Table 1.36 illustrates that, still, only two parishes within the BOEM-defined parishes are out 

of this set. Ten are fully in the set with three-fourths or more small business, and 20 occupy the 

middle two categories and are partial members. A breakdown of specific parish membership in 

this set is presented in Figure 1.18. 

Table 1.35. Small Business Intensive fuzzy set membership for all Louisiana parishes (2006). 

Percent of 

Businesses with 

Fewer than 10 

Employees* 

Verbal Label Fuzzy Set 

Membership 

Score 

Number Examples 

Greater than 75% Fully in the set 1.00 30 Allen at 80.65%, 1.00 

St. Bernard at 82.99%, 1.00 

70.83% – 75% More in than 

out 

0.50-0.99 14 Acadia at 74.93%, 0.99 

Jefferson at 71.76%, 0.61 

66.67% – 70.83% More out than 

in 

0.01-0.49 18 Ascension at 68.46%, 0.22 

Iberia at 66.86%, 0.02 

Less than 66.67% Out of the set 0.00 2 St. John at 66.25%, 0.00 

West Baton Rouge at 

61.18%, 0.00 

*Note that in almost all parishes the majority of businesses are considered small 

 
43 

Table 1.36. Small Business Intensive fuzzy set membership for BOEM-defined parishes (2006). 

Percent of 

Businesses with 

Fewer than 10 

Employees* 

Verbal Label Fuzzy Set 

Membership 

Score 

Number Examples 

Greater than 75% Fully in the set 1.00 10 Allen at 80.65%, 1.00 

St. Bernard at 82.99%, 1.00 

70.83% – 75% More in than 

out 

0.50-0.99 9 Acadia at 74.93%, 0.99 

Jefferson at 71.76%, 0.61 

66.67% – 70.83% More out than 

in 

0.01-0.49 11 Ascension at 68.46%, 0.22 

Iberia at 66.86%, 0.02 

Less than 66.67% Out of the set 0.00 2 St. John at 66.25%, 0.00 

West Baton Rouge at 

61.18%, 0.00 

*Note that in almost all parishes the majority of businesses are considered small 

Figure 1.18. Small Business Intensive fuzzy set membership. 
44 

1.2.1.3. Large Business Presence 

Definition. We define Large Business Presence by drawing on data that indicates the number 

of employees at each business. In this fuzzy set, it is estimated that 100 or more employees 

qualifies as a large business. In this table, parishes with 40 or more businesses that employ 100 

or more people are considered fully in the set. 

Discussion and Description. When the definition business size is altered to measure large 

parishes, Table 1.37 indicates that 14 parishes in Louisiana are fully in the set of parishes with a 

large business presence. On the other hand, as in tables in the previous section, almost half of the 

parishes (33) are in the smaller category, this time defined as 10 or fewer businesses with 100 or 

more employees. Further, when we add in the 12 parishes that are more out than in this set, it 

appears that the majority of parish economies in Louisiana (45) are composed mostly of small 

businesses. 

Interestingly, of the 14 parishes with a large business presence from Table 1.37, 10 are 

BOEM-defined parishes, as seen in 1.38. This time, though, the majority of parishes are not out 

of the set with 10 or fewer large businesses. The parish economies in the BOEM-defined impact 

area seem to be reasonably equally dispersed across the categories with the smallest number 

(four parishes) being in the “more in than out” category of 25–40 businesses with 100 or more 

employees. A breakdown of specific parish membership in this set is presented in Figure 1.19. 

Table 1.37. Large Business Presence fuzzy set membership for all Louisiana parishes (2006). 

Number of 

Businesses with 100 

or More Employees 

Verbal Label Fuzzy Set 

Membership 

Score 

Number Examples 

40 or more Fully in the 

set 

1.00 14 E Baton Rouge with 363, 1.00 

St. Tammany with 80, 1.00 

25–40 More in than 

out 

0.50-0.99 5 St. Charles with 35, 0.83 

St. Landry with 28, 0.60 

10–25 More out 

than in 

0.01-0.49 12 Vernon with 12, 0.07 

St. John with 19, 0.30 

10 or fewer Out of the set 0.00 33 Vermillion with 8, 0.00 

Cameron with 1, 0.00 
45 

Table 1.38. Large Business Presence fuzzy set membership for BOEM-defined parishes (2006). 

Number of 

Businesses with 100 

or More Employees 

Verbal Label Fuzzy Set 

Membership 

Score 

Number Examples 

40 or more Fully in the 

set 

1.00 10 East Baton Rouge with 363, 

1.00 

St. Tammany with 80, 1.00 

25–40 More in than 

out 

0.50-0.99 4 St. Charles with 35, 0.83 

St. Landry with 28, 0.60 

10–25 More out 

than in 

0.01-0.49 10 Vernon with 12, 0.07 

St. John with 19, 0.30 

10 or fewer Out of the set 0.00 8 Vermillion with 8, 0.00 

Cameron with 1, 0.00 

Figure 1.19. Large Business Presence fuzzy set membership. 
46 

1.2.2. Outcomes 

1.2.2.1. High Unemployment 

Definition. To define High Unemployment parishes, we draw upon annual estimates of 

employment data from the 2010 Bureau of Economic Analysis, Regional Economic Information 

System. High unemployment is defined as being higher than 9%; parishes that experience 

unemployment at or above the 9% level are considered fully within the high unemployment 

fuzzy set. Full employment is considered an unemployment rate below 6%, parishes with 6% or 

lower unemployment are considered outside the fuzzy set. Those parishes with between 6% and 

9% unemployment rates are considered partially within the fuzzy set to the degree that their 

respective unemployment rates warrant. 

Discussion and Description. In the state of Louisiana, 19 parishes can be considered fully 

within the high unemployment fuzzy set because they exhibit a high unemployment rate. An 

additional 39 parishes within Louisiana have experienced unemployment between 6% and 9% 

and are considered partially within the fuzzy set. Only seven parishes within Louisiana have less 

than a 6% unemployment rate. 

Within the BOEM-defined parishes, eight had unemployment rates sufficient to classify them 

as fully within the high unemployment fuzzy set. There were an additional 20 parishes partially 

within the set to varying degrees based on their level of unemployment. Five parishes are 

considered outside of the high unemployment fuzzy set because they have low levels of relative 

unemployment. Descriptive statistics and the layout of fuzzy set membership are included in 

Tables 1.39–1.40 and Figure 1.20. 

Table 1.39. High Unemployment fuzzy set membership for all Louisiana parishes (2010). 

Unemployment Rate Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 9% Fully in set 1 19 

7.5% – 9% More in set than out of set 0.5-0.999 21 

6% – 7.5% More out than in set 0.001-0.49 18 

Lsss than 6% Out of set 0 7 

Table 1.40. High Unemployment fuzzy set membership for BOEM-defined parishes (2010). 

Unemployment Rate Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 9% Fully in set 1 8 

7.5% – 9% More in set than out of set 0.5-0.999 6 

6% – 7.5% More out than in set 0.001-0.49 14 

Less than 6% Out of set 0 5 
47 

Figure 1.20. High Unemployment fuzzy set membership. 

1.2.2.2. Job Growth 

Definition. A measure of job growth was created using unemployment and job creation data 

from 2009 and 2010. The number of new jobs created between 2009 and 2010 is represented as a 

proportion of the total jobs in the parish in 2010. For the purpose of this fuzzy set, parishes with 

a job growth rate x above 2.0 are considered fully within the job growth fuzzy set. Parishes that 

have lost jobs or not created any jobs and as such score 0 or below are considered outside of the 

fuzzy set. Parishes with index scores between 0 and 2 are considered partially within the set. 

Discussion and Description. In the state of Louisiana, nine parishes can be considered fully 

within the job growth fuzzy set by exhibiting a large amount of job growth. An additional 24 

parishes within Louisiana have experienced some job growth and are considered partially within 

the fuzzy set. There are 27 parishes within Louisiana that had no job growth or lost jobs between 

2009 and 2010. 

Within the BOEM-defined parishes, three had job growth proportions sufficient to classify 

them as fully within the job growth fuzzy set. An additional 12 parishes were partially within the 

set to varying degrees based on their level of job growth. A full 17 parishes are considered 

outside of the job growth fuzzy set because they experienced no job growth or lost jobs over the 

past year. 
48 

Table 1.41. Job Growth fuzzy set membership for all Louisiana parishes (2009–2010). 

Job Creation Rate Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 2.0 Fully in set 1 9 

1.0 – 2.0 More in set than out of set 0.5-0.999 3 

0.0 – 1.0 More out than in set 0.001-0.49 21 

No Job Creation Or Net Job 

Loss Out of set 0 27 

Table 1.42. Job Growth fuzzy set membership for BOEM-defined parishes (2009–2010). 

Job Creation Rate Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 2.0 Fully in set 1 3 

1.0 – 2.0 More in set than out of set 0.5-0.999 1 

0.0 – 1.0 More out than in set 0.001-0.49 11 

No Job Creation Or Net Job 

Loss Out of set 0 17 
49 

Figure 1.21. Job Growth fuzzy set membership. 

1.2.2.3. Competitive Per Capita Income 

Definition. Tables 1.43 and 1.44 present fuzzy sets of parishes that offer competitive per 

capita personal income according to 2006 REIS data. Table 1.43 presents the fuzzy set for all 

parishes in Louisiana, while Table 1.44 presents the fuzzy set for the 32 parishes in the BOEMdefined impact region. 

The fuzzy set compares the per capita personal income in each parish to the per capita 

income for Louisiana. We defined Louisiana parishes as fully in the set of parishes offering a 

competitive income (FS=1) if the parish’s per capita income is at or above that of Louisiana. We 

defined the Louisiana parishes as being fully out of the set (FS=0) if the personal income of the 

parish is below the average per capita personal income of the bottom quartile of parishes. 

Discussion and Description. Table 1.43 shows that only 12 Louisiana parishes are fully in 

the set, meaning they offer competitive per capita personal income as compared to that of the 

Louisiana average ($38,821). Conversely, six Louisiana parishes are out of the set of parishes 

offering this competitive income, because theirs fall below the average of the bottom quartile 

($22,054). A majority of the parishes, 46, are partial members of the set offering competitive 

incomes. A total of 13 of these are more in than out because their averages are closer to the 

Louisiana average than to the bottom quartile average, and 33 are more out then in because their 

average per capita incomes are closer to the bottom quartile average. 
50 

Table 1.44 shows that of the original 12 parishes that are full members of this set, all but two 

are within the BOEM-defined impact area. Another eight are more in than out of the set, and 

only two of the impact area parishes are fully out of the set. 

Table 1.43. Per Capita Income fuzzy set membership for all Louisiana parishes (2006). 

Per Capita Income Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(REIS) 

Examples 

Greater than or 

equal to per capita 

income for LA 

($31,821) 

Fully in the 

set 

1.00 12 St. Tammany at $34,760, 1.00 

St. Bernard* at $61,201, 1.00 

$26,938–$31,820 More in than 

out of the set 

0.50 -0.99 13 Calcasieu at $30,488, 0.86 

St. John the Baptist at $27,257, 0.53 

$26,937-$22,053 More out of 

than in the 

set 

0.01-0.49 33 St. Landry at $24,731, 0.27 

Vermillion at $23,358, 0.13 

Less than average 

per capita income of 

bottom quartile in US 

($22,054) 

Out of the set 0.00 6 Evangeline at $20,216, 0.00 

Allen* at $19,386, 0.00 

*largest and smallest values in the range 

Table 1.44. Per Capita Income fuzzy set membership for BOEM-defined parishes (2006). 

Per Capita Income Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(REIS) 

Examples 

Greater than or 

equal to per capita 

income for LA 

($31,821) 

Fully in the 

set 

1.00 10 St. Tammany at $34,760, 1.00 

St. Bernard* at $61,201, 1.00 

$26,938–$31,820 More in than 

out of the set 

0.50 -0.99 8 Calcasieu at $30,488, 0.86 

St. John the Baptist at $27,257, 0.53 

$26,937-$22,053 More out of 

than in the 

set 

0.01-0.49 12 St. Landry at $24,731, 0.27 

Vermillion at $23,358, 0.13 

Less than average 

per capita income of 

bottom quartile in US 

($22,054) 

Out of the set 0.00 2 Evangeline at $20,216, 0.00 

Allen* at $19,386, 0.00 

*largest and smallest values in the range 
51 

. Figure 1.22. Per Capita Income fuzzy set membership. 

1.3. EDUCATION PROFILE OVERVIEW

The Education Profile provides a way of grouping similar parishes together on the basis of 

education indicators. We organize our profile into four sections, each containing a range of fuzzy 

set profile elements. The four sections—Resources, Outcomes, Student Characteristics, and 

District Process—allow us to compare and contrast parish/school districts across a range of 

educational indicators. 

Education Profile Outline 

 Education Resources 

o Competitive teacher salary 

o Substantial local capacity 

o Onshore oil and gas industry influence 

o Focus on instruction 

 
52 

 Education Outcomes 

o Adequate school performance 

o Few low-performing schools 

o District improvement 

o First-time freshmen 

 Student Characteristics 

o Districts with high percentage of at-risk students (free/reduced lunch) 

o Districts with high percentage of African American students 

 District Processes 

o District with high drop-out rate 

o Many disciplinary cases 

o Student retention (grade repeat) 

o Small district 

o Large district 

o Large non-public school enrollment 

1.3.1. Education Resources 

Education resources enable comparisons of school district capacity to hire qualified teaching 

staff, to spur local investment in schools, to secure resources from the oil and gas industry for 

education, and to channel resources toward instruction. Therefore, we developed four fuzzy set 

elements that focus on the capacity of local school districts to obtain and use the necessary 

resources for maintaining high quality schools. 

1.3.1.1. Competitive Teacher Salary 

Definition. Tables 1.45 and 1.46 present fuzzy sets of school districts offering competitive 

teacher salaries for the 2007–2008 school year. Table 1.45 presents the fuzzy set for all school 

districts in Louisiana, and Table 1.46 presents the fuzzy set for all school districts within the 32-

parish BOEM-defined impact region. 

The fuzzy set compares the average teacher’s salary in the school district with the Southern 

Regional Educational Board (SREB) average salary. The SREB consists of 16 states in the 

southern part of the United States. We defined Louisiana school districts as fully in the set of 

districts offering a competitive teacher’s salary (FS=1) if the parish’s average teacher salary 

matches or exceeds the SREB average. We defined the Louisiana school districts as being fully 

out of the set (FS=0) if the average salary of the parish is less than the average of the four SREB 

states in the bottom quartile of teacher salaries. 

Discussion and Description. Table 1.45 shows that 36 Louisiana school districts are fully in 

the set, meaning they clearly offer competitive teacher salaries higher than the SREB average 

($45,662 in 2007–2008). Conversely, 15 Louisiana school districts are out of the set of districts 

offering competitive teacher salaries, because their average salaries fall below the average of the 

bottom quartile average ($34,227 in 2007–2008). A total of 18 districts are partial members of 

the set of districts offering competitive teacher salaries. Further, 10 of these are more in than out 

because their average teacher salaries are closer to the overall SREB average than to the bottom 

quartile SREB average, and eight are more out then in because their average salaries are closer to 

the bottom quartile average. 
53 

Table 1.46 shows that 19 of the 32 school districts in the BOEM-defined impact areas have 

full membership in the set of those offering competitive teacher salaries, and another seven 

partial members are more in than out of the set. Only four of the 15 nonmembers of the set of 

districts offering competitive teacher salaries are in the BOEM-defined impact area. 

Figure 1.23 shows that most of the parishes that do not offer a competitive teacher salary are 

clustered in the Northeast region of Louisiana with a few exceptions. The six parishes in the 

BOEM-defined region not in the set of those offering competitive salaries are rural parishes with 

small populations. 

 Table 1.45. Competitive Teacher Salaries fuzzy set membership for all Louisiana parishes (2007–2008). 

Teacher Salary Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(LDE 2007-08)

Examples 

Greater than or 

equal to SREB 

average ($45,662) 

Fully in the 

set 

1.00 36 Natchitoches** at $52,389, 1.00 

Calcasieu at $46,630, 1.00 

$44,445–$45,661 More in than 

out of the set 

0.50 -0.99 10 Vermillion at $45,445, 0.91 

Terrebonne at $44,717, 0.61 

$43,228–$44,444 More out of 

than in the 

set 

0.01-0.49 8 Lafourche at $43,453, 0.09 

Washington at $43,373, 0.06 

Less than average 

of SREB’s lowest 

quartile* ($43,227) 

Out of the set 0.00 15 Cameron at $40,579, 0.00 

St. Helena** at $34,670, 0.00 

*Arkansas, Mississippi, Oklahoma, West Virginia 

**lowest and highest values in the range 
54 

Table 1.46. Competitive Teacher Salaries fuzzy set membership for BOEM-defined parishes (2007–

2008). 

Teacher Salary Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(LDE 2007-

08) 

Examples 

Greater than or 

equal to SREB 

average ($45,662) 

Fully in the 

set 

1.00 19 Calcasieu at $46,630, 1.00 

Vernon at $45,933, 1.00 

$44,445–$45,661 More in than 

out of the set 

0.50 -0.99 7 Vermillion at $45,445, 0.91 

Terrebonne at $44,717, 0.61 

$43,228–$44,444 More out of 

than in the 

set 

0.01-0.49 2 Lafourche at $43,453, 0.09 

Washington at $43,373, 0.06 

Less than average 

of SREB’s lowest 

quartile* ($43,227) 

Out of the set 0.00 4 Cameron at $40,579, 0.00 

Assumption at $42,959, 0.00 

*Arkansas, Mississippi, Oklahoma, West Virginia 

Figure 1.23. Competitive Teacher Salaries fuzzy set membership. 

 

1.3.1.2. Substantial Local Capacity 

Definition. Resources for public schooling come primarily from state and local sources of 

funding. Louisiana has put in place a system that balances the redistribution of state dollars for 
55 

education to low income areas with an incentive structure to encourage local communities to 

contribute to public schooling. The Minimum Foundation Program (MFP) collects data on the 

amount of local revenue collected and details on the percentage of local revenue contributed to 

schooling. 

Using Louisiana’s Minimum Foundation Program formula, in this table we compare actual 

local contribution to spending on education as compared to the amount of revenue the parish 

produces that is eligible for contribution. If the parish is contributing 75% or more of its eligible 

revenues toward their share of school spending, it is fully in the set of parishes with a substantial 

local capacity for supporting the school system. If it uses less than 35%, however, it is fully out 

of that set. 

Discussion and Description. Table 1.47 shows that 15 Louisiana school districts are fully in 

the set, meaning they are able to substantially contribute to their share of school spending. 

Conversely, 21 Louisiana school districts are out of the set because they contributed only 35% or 

less of their eligible revenue toward school spending. A total of 35 districts are partial members 

of this set. Further, 23 are more in than out because their percent of eligible revenues that went 

toward school spending was closer to the full membership goal of 75% than to the minimum of 

35%, and 12 are more out then in because the percentage of their contributions falls closer to 

35%. 

Table 1.48 shows that 11 of the 32 school districts in the BOEM-defined impact areas have 

full membership in the set of those contributing 75% or more of eligible revenues toward 

education, while eight are more in than out of the set. Only six districts of the 32 BOEM-defined 

impact area parishes are out of the set of parishes that are substantially able to contribute to 

education spending. 

Figure 1.24 shows that most of the parishes that are not in the set of parishes that 

substantially contribute to their share of spending on education are clustered in the Northeast 

region of Louisiana. It is also apparent that several of the southern members of the 32 BOEMdefined impact areas are either full or partial members of the set. 
56 

Table 1.47. Substantial Local Capacity fuzzy set membership for all Louisiana parishes (2008–2009). 

Use of Local 

Revenues for 

Education 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(MFP 

2008-09) 

Examples 

Used great than 75% 

of eligible revenues 

Fully in the 

set 

1.00 15 Plaquemines* at 129%, 1.00 

Cameron at 82.90%, 1.00 

Used 55–75% of 

eligible revenues 

More in than 

out of the set 

0.50 -0.99 23 Vermilion at 55.28%, 0.50 

Calcasieu at 66.49%, 0.78 

Used 35–55% of 

eligible revenues 

More out of 

than in the 

set 

0.01-0.49 12 Jefferson-Davis at 35.93%, 0.02 

Natchitoches at 46.31%, 0.28 

Used less than 35% 

of eligible revenues 

Out of the set 0.00 21 Grant* at 18.83%, 0.00 

Livingston at 24.80%, 0.00 

*Highest and lowest values in the range 

Table 1.48. Substantial Local Capacity fuzzy set membership for BOEM-defined parishes (2008–2009). 

Use of Local 

Revenues for 

Education 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(MFP 

2008-09) 

Examples 

Used great than 75% 

of eligible revenues 

Fully in the 

set 

1.00 11 Plaquemines at 129%, 1.00 

Cameron at 82.90%, 1.00 

Used 55–75% of 

eligible revenues 

More in than 

out of the set 

0.50 -0.99 8 Vermilion at 55.28%, 0.50 

Calcasieu at 66.49%, 0.78 

Used 35–55% of 

eligible revenues 

More out of 

than in the 

set 

0.01-0.49 7 Jefferson-Davis at 35.93%, 0.02 

Iberia at 45.99%, 0.27 

Used less than 35% 

of eligible revenues 

Out of the set 0.00 6 Livingston at 24.80%, 0.00 

Assumption at 31.44%, 0.00 
57 

Figure 1.24. Substantial Local Capacity fuzzy set membership. 

1.3.1.3. Onshore Oil and Gas Revenue Influence 

Definition. The Minimum Foundation Program also provides data on the sources of revenue 

for each parish. In the “Other” category, revenues that come from oil and gas royalties and 

forestry are represented. Though many parishes receive oil and gas revenues, only some allocate 

substantial percentages of those revenues toward education. To be considered fully in the set of 

districts with an oil and gas influence on education, the Other revenue category must comprise 

5% or more of the total revenue for that parish. Those with Other revenues below 2% of the total 

are out of the set. 

Discussion and Description. Table 1.49 indicates that only five Louisiana schools districts 

are fully in the set of those that exhibit an oil and gas revenue influence. Further, only one 

district is more in the set than out, because, at 3.92%, East Feliciana is closer to being above 5% 

than to the minimum of below 2%. The vast majority, 63 districts, are either out of the set or 

more out than in, and 43 districts out of the set of parishes with an oil and gas influence and 20 

with percentages closer to the low of 2% and therefore more out than in. 

Table 1.50 represents the fuzzy set scores for the 32 BOEM-defined impact areas. Only two 

districts had Other revenues above 5%, Cameron and Vermillion Parishes, and none of the 

BOEM impact districts were more in than out of the set. The remaining 30 districts were nonmembers of the set. Further, 24 districts received less than 2% revenues that were classified in 
58 

the Other category and so are out of the set of districts considered to have oil and gas influence. 

The remaining six districts had between 3.5% and the 2% minimum and so are more out of the 

set than in. 

Figure 1.25 maps the fuzzy set scores of all the LA parishes and the percent in their Other 

category. As only six parishes are full or partial members of the set, most of the map is colored 

red or pink to indicate the districts that are out of the set or more out than in. Many of the 

districts that are out of the set are clustered around Southeast and North- and Southwest regions 

of the state. 

Table 1.49. Oil and Gas Revenue Reliance fuzzy set membership for all Louisiana parishes (2008–2009). 

Percent ‘Other’ 

Revenue 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(MFP 2008-

09) 

Examples 

Greater than 5% Fully in the set 1.00 5 Cameron at 10.65%, 1.00 

Vermilion at 11.91%, 1.00 

3.5% – 5% More in than out 0.50-0.99 1 East Feliciana at 3.92%, 0.64 

2% – 3.5% More out than in 0.01-0.49 20 Natchitoches at 3.36%, 0.45 

Terrebonne at 2.90%, 0.30 

Less than 2% Out of the set 0.00 43 Ascension* at 0.25%, 0.00 

Plaquemines at 0.69%, 0.00 

*Lowest value in range 

Table 1.50. Oil and Gas Revenue Reliance fuzzy set membership for BOEM-defined parishes (2008–

2009). 

Percent ‘Other’ 

Revenue 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(MFP 2008-

09) 

Examples 

Greater than 5% Fully in the set 1.00 2 Cameron at 10.65%, 1.00 

Vermilion at 11.91%, 1.00 

3.5% – 5% More in than out 0.50-0.99 0 None 

2% – 3.5% More out than in 0.01-0.49 6 Terrebonne at 2.90%, 0.30 

St. Landry at 2.08%, 0.03 

Less than 2% Out of the set 0.00 24 Ascension* at 0.25%, 0.00 

Plaquemines at 0.69%, 0.00 

*Lowest value in range 
59 

Figure 1.25. Oil and Gas Revenue Reliance fuzzy set membership. 

1.3.1.4. Focus on Instruction 

Definition. The Louisiana Department of Education recommends that districts devote a 

minimum of 70% of education expenditures to instruction. The Minimum Foundation Program 

outlines what percentage of spending is devoted to instruction in each district, and these tables 

examine which districts do and do not meet these expectations, along with which exceed the 70% 

minimum. 

Compare percent of expenditures on instruction for each district to the overall goal of 70%. 

To be fully in the set of parishes that exceeded this goal, the percent of expenditures that went 

toward instruction had to be 5% or more above the 70% minimum according to the 2005–2006 

Minimum Foundation Program report. Conversely, districts that were not even able to meet the 

70% minimum requirement on instructional spending were out of the set. 

Discussion and Description. Table 1.51 illustrates the number of school districts that did or 

did not meet the 70% minimum requirement for instructional spending, and those that exceeded 

this expectation. Seven districts are fully in the set of those that exceeded the 70% requirement 

by 5% or more, with the highest spending on instruction being Rapides Parish at 77.5%. Fifteen 

districts were unable to meet the minimum spending and therefore out of the set, leaving 46 

parishes with partial membership. Of these 46, 13 districts exceeded the minimum by 2.5–5% 

and therefore are considered more in the set than out. The remaining 33 districts made up the 
60 

group that only barely met the minimum with percentages at 2.5% or less above 70, and these 

districts are subsequently labeled more out of the set than in. 

Table 1.52 represents the same data, but for the BOEM-defined impact area. In this table, 

only four districts exceeded the 70% minimum by 5% or more and were fully in the set. Also, 

however, only six BOEM districts were unable to meet this goal. Similar to above, a majority of 

the districts comprised the two partial membership categories. A breakdown of specific district 

membership in this set is presented in Figure 1.26. 

Table 1.51. Instruction Expenditures fuzzy set membership for all Louisiana parishes (2005–2006). 

Spending on 

instruction 

Verbal Label Fuzzy Set 

Membership Score 

Number 

(MFP 

2005-06) 

Examples 

Greater than 

75% 

Fully in the set 1.00 7 Rapides* at 77.50%, 1.00 

Tangipahoa at 75.69%, 1.00 

72.5–75% More in than 

out 

0.50-0.99 13 Washington at 72.70%, 0.54 

Terrebonne at 74.99%, 0.99 

70–72.5% More out than 

in 

0.01-0.49 33 Vermilion at 72.03%, 0.40 

Acadia at 70.83%, 0.16 

Less than 

70% 

Out of the set 0.00 15 Plaquemines* at 60.22%, 0.00 

Cameron at 68.91%, 0.00 

*highest and lowest values in range 

Table 1.52. Instruction Expenditures fuzzy set membership for BOEM-defined parishes (2005–2006). 

Spending on 

instruction 

Verbal Label Fuzzy Set 

Membership Score 

Number 

(LDE 

2006-07) 

Examples 

Greater than 

75% 

Fully in the set 1.00 4 St. James at 76.32%, 1.00 

Tangipahoa at 75.69%, 1.00 

72.5–75% More in than 

out 

0.50-0.99 9 Washington at 72.70%, 0.54 

Terrebonne at 74.99%, 0.99 

70–72.5% More out than 

in 

0.01-0.49 13 Vermilion at 72.03%, 0.40 

Acadia at 70.83%, 0.16 

Less than 

70% 

Out of the set 0.00 6 Plaquemines* at 60.22%, 0.00 

Cameron at 68.91%, 0.00 

*lowest value in range 
61 

Figure 1.26. Instruction Expenditures fuzzy set membership. 

1.3.2. Educational Outcomes 

1.3.2.1. Adequate School District Performance 

Definition. These tables are based on the Department of Education’s goal that by the year 

2010, each district’s performance score is to reach or exceed 100. In these tables, we consider a 

score of 95 or above to be fully in the set of parishes that are on their way to exhibiting this level 

of performance by that deadline, and so their performances can be considered adequate. 

We use the Fall 2008 District Performance Scores from the Louisiana Department of 

Education to determine whether or not each parish exhibits adequate school district performance. 

If the performance score for that district is at or above 95, it is fully in the set of parishes with 

adequate school district performance. A score of 85 or below signifies inadequate performance 

and that parish or district is therefore out of the set. 

Discussion and Description. In Table 1.53, 17 districts have been given a performance score 

of 95 or above and so are full members of the set. A large number of the districts, 32, received 

scores of 85 or below and are therefore considered out of the set of districts with adequate school 

performance. The other 20 districts were partial members with nine being more in than out at 

scores of 90–95, and 11 were more out than in with scores between our 85 minimum and 90. 
62 

Table 1.54 again repeats this information only for districts in the BOEM-defined parishes. In 

this table, 12 of the 32 districts are full members of the set of those with adequate school district 

performance. Almost the same number, however, are out of the set with scores of 85 or below, 

and eight more are more out of the set than in. 

Figure 1.27 contains a map of these scores for all the Louisiana parishes. There seems to be a 

small cluster of parishes in the Southeast of the state that are full members of the set, and another 

cluster in North Louisiana that are out of the set. However, there are still some exceptions and 

overall the map seems reasonably diversified. 

Table 1.53. Adequate School District Performance fuzzy set membership for all Louisiana parishes 

(2008). 

District 

Performance 

Score 

Verbal Label Fuzzy Set 

Membership Score 

Number 

(LDE Fall 

2008) 

Examples 

Greater than 95 Fully in the set 1.00 17 Jefferson Davis at 102.9, 1.00 

Beauregard at 100.5, 1.00 

90–95 More in than 

out 

0.50 -0.99 9 Rapides at 92.8, 0.78 

Lafayette at 91.5, 0.65 

85–90 More out than 

in 

0.01-0.49 11 Cameron at 87.4, 0.24 

Sabine at 89.8, 0.48 

Less than 85 Out of the set 0.00 32 Terrebonne at 84.4, 0.00 

Evangeline at 80.9, 0.00 

Table 1.54. Adequate School District Performance fuzzy set membership for BOEM-defined parishes 

(2008). 

District 

Performance 

Score 

Verbal Label Fuzzy Set 

Membership Score 

Number 

(LDE Fall 

2008) 

Examples 

Greater than 95 Fully in the set 1.00 12 Jefferson Davis at 102.9, 1.00 

Beauregard at 100.5, 1.00 

90–95 More in than 

out 

0.50 -0.99 2 Lafayette at 91.5, 0.65 

St. James at 91.9, 0.69 

85–90 More out than 

in 

0.01-0.49 8 Cameron at 87.4, 0.24 

St. Mary at 86.4, 0.14 

Less than 85 Out of the set 0.00 10 Terrebonne at 84.4, 0.00 

Evangeline at 80.9, 0.00 
63 

Figure 1.27. Adequate School District Performance fuzzy set membership. 

1.3.2.2. Few Low-Performing Schools 

Definition. If a school district has many low-performing schools, this may indicate unequal 

allocation of resources within the school district. Table 1.55 examines the range of school 

performance within districts based on the percentage of schools in that district that received a 

performance score of 1 star or lower out of a possible five stars. Schools with less than one star 

are those labeled Academically Unacceptable by the Department of Education. 

 

Using the School Performance Scores from the 2008 Louisiana Department of Education 

website, these tables illustrate what percentage of schools in each district is low-performing; the 

goal is less than 25%. A district with 25% or fewer of its schools receiving a score of one star or 

fewer (out of five stars) is considered fully in this set. Those with 50% or more low-performing 

schools are out of the set. 

Discussion and Description. In Table 1.55, we see that 24 school districts are fully in the set 

with 25% or fewer of their schools receiving a score of one star or fewer. Likewise, however, 21 

districts had half or more than half of their schools rated with only one star or Academically 

Unacceptable, which means they are out of the set of districts with few low-performing schools. 

Table 1.56 indicates that 15 BOEM districts are fully in the set, with fewer than 25% of their 

schools exhibiting low performance scores. Further, 11 more districts are more in than out of the 
64 

set, with percentages of low-performing schools between only 37.5% and 25%. This leaves only 

five districts with 37.5% or more of their schools deemed low-performing. 

This is also indicated by Figure 1.28, which illustrates that much of South Louisiana is made 

up of districts that are either in the set or more in than out, with only a few exceptions. It also 

appears from the map that many of the districts that are out of the set and have 50% or more of 

their schools not performing well are clustered in North Louisiana. 

Table 1.55. Few Low-Performing Schools fuzzy set membership for all Louisiana parishes (2008). 

Percent of 

Schools with 

One Star or 

Fewer 

Verbal 

Label 

Fuzzy Set 

Membership 

Score 

Number 

(LDE 2008) 

Examples 

25% or fewer Fully in the 

set 

1.00 24 St. Bernard at 0.00%, 1.00 

Vermillion at 11.11%, 1.00 

37.5–25% More in 

than out 

0.50 -0.99 14 Plaquemines at 28.57%, 0.86 

Orleans at 35.29%,0.59 

50– 37.5% More out 

than in 

0.01-0.49 6 Concordia at 40%, 0.40 

St. Martin at 43.75%, 0.25 

Greater than 

50% 

Out of the 

set 

0.00 22 East Baton Rouge at 70.51%, 0.00 

St. Helena at 100%, 0.00 

Table 1.56. Few Low-Performing Schools fuzzy set membership for BOEM-defined parishes (2008). 

Percent of 

Schools with 

One Star or 

Fewer 

Verbal 

Label 

Fuzzy Set 

Membership 

Score 

Number 

(LDE 2008) 

Examples 

25% or fewer Fully in the 

set 

1.00 15 St. Bernard at 0.00%, 1.00 

Vermillion at 11.11%, 1.00 

37.5–25% More in 

than out 

0.50 -0.99 11 Plaquemines at 28.57%, 0.86 

Orleans at 35.29%,0.59 

50– 37.5% More out 

than in 

0.01-0.49 1 St. Martin at 43.75%, 0.25 

Greater than 

50% 

Out of the 

set 

0.00 4 East Baton Rouge at 70.51%, 0.00 

Tangipahoa at 51.52%, 0.00 
65 

Figure 1.28. Few Low-Performing Schools fuzzy set membership. 

1.3.2.3. District Performance 

Definition. District performance was measured at the parish level by examining the 

percentage of schools that were performing as 3, 4, or 5 star schools out of a possible 5 stars. 

Districts where more than 50% of the schools are performing at the 3-, 4-, or 5-star levels can be 

considered to be performing adequately and can be considered fully within the fuzzy set. 

Districts with less than 20% of their schools performing at the 3-, 4-, or 5-star levels were 

considered fully outside of the fuzzy set. Those districts with between 20% and 50% of their 

districts performing at the 3-, 4-, or 5-star level were considered partially within the fuzzy set to 

the extent that the proportion of their performance warrants. 

Discussion and Description. The state of Louisiana has 13 districts in which more than 50% 

of the schools within the district are meeting adequate measures of district improvement and 

have been ranked as 3, 4, or 5 star schools and as such can be considered fully within the fuzzy 

set. There are 36 districts with less than 20% of their schools ranked at the 3, 4, or 5 star levels 

and as such considered fully outside the fuzzy set. There are 20 districts that have between 20% 

and 50% of their schools ranked at the 3, 4, or 5 star level; these schools are considered partially 

within to fuzzy set to varying extents. 

Within the BOEM-defined parishes, nine can be considered fully within the district 

performance fuzzy set, an additional 11 can be considered partially within the set, to varying 
66 

degrees. A total of 12 BOEM-defined parishes can be considered fully outside the adequate 

district performance fuzzy set. Descriptive statistics and the layout of fuzzy set membership are 

included in Tables 1.57–1.58 and Figure 1.29. 

Table 1.57. District Improvement fuzzy set membership for all Louisiana parishes (2009). 

District Improvement Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 50% of schools 

performing at 3-, 4-, or 5-star level Fully in set 1 13 

35–50% of schools at 3-, 4-, or 5-star 

level More in set than out of set 0.5-0.999 8 

20–35% of schools at 3-, 4-, or 5-star 

level More out than in set 0.001-0.49 12 

Less than 20% of schools performing 

at 3-, 4-, or 5-star level Out of set 0 36 

Table 1.58. District Improvement fuzzy set membership for BOEM-defined parishes (2009). 

District Improvement Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 50% of schools 

performing at 3-, 4-, or 5-star level Fully in set 1 9 

35–50% of schools at 3-, 4-, or 5-star 

level More in set than out of set 0.5-0.999 5 

20–35% of schools at 3-, 4-, or 5-star 

level More out than in set 0.001-0.49 6 

Less than 20% of schools performing 

at 3-, 4-, or 5-star level Out of set 0 12 
67 

 Figure 1.29. District Improvement fuzzy set membership. 

1.3.2.4. High College Attendance 

Definition. Districts that sent more than 50% of their high school graduates on to become 

first time freshman at Louisiana colleges and universities were considered to be fully within the 

fuzzy set and to fit the definition of having a large proportion of first time freshman. Ideally, this 

measure would include those who enroll as first time freshman in out of state colleges and 

universities, but data is lacking on out of state enrollments. Districts that send less than 35% of 

their graduating seniors to a Louisiana college or university were considered to be fully outside 

of the fuzzy set. Districts that send between 50% and 35% of their graduating seniors to a 

Louisiana college or university are considered partially within the set to various levels. 

Discussion and Description. In the state of Louisiana, in 18 districts more than 50% of the 

students that graduate go on to become first time freshmen at Louisiana colleges and universities 

and can be considered fully within the high college attendance fuzzy set. There are 44 districts 

with between 35% and 50% of the student population going on to attend a Louisiana college or 

university; these districts can be considered partially in the fuzzy set proportionally based on 

their proportions. There are eight districts where less than 35% of the population attending public 

schools go on to attend a Louisiana college or university; these districts are considered fully 

outside the fuzzy set. 
68 

Within the BOEM-defined area, only six can be considered fully within the high percent of 

first time freshmen fuzzy set; an additional 25 can be considered partially within the set to 

varying degrees. Only two can be considered fully outside the high percent of first time freshmen 

fuzzy set. Descriptive statistics as well as the layout of fuzzy set membership are included in 

Tables 1.59–1.60 and Figure 1.30 below. 

Table 1.59. First-time Freshmen fuzzy set membership for all Louisiana parishes (2009). 

Percent of High School Graduates 

Enrolling in College or University Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 50% Fully in set 1 18 

42.5–50% More in set than out of set 0.5-0.999 23 

35–42.5% More out than in set 0.001-0.49 21 

Less than 35% Out of set 0 8 

Table 1.60. First-time Freshmen fuzzy set membership for BOEM-defined parishes (2009). 

Percent of High School Graduates 

Enrolling in College or University Verbal Level 

Fuzzy Set 

Membership 

Score 

Number

Greater than 50% Fully in set 1 6 

42.5–50% More in set than out of set 0.5-0.999 14 

35–42.5% More out than in set 0.001-0.49 11 

Less than 35% Out of set 0 2 
69 

Figure 1.30. First-time Freshmen fuzzy set membership. 

1.3.3. Student Characteristics 

1.3.3.1. High Percent of Students on Free or Reduced Lunch 

Definition. Districts that have more than 65% of their student population receiving a 

federally subsidized free or reduced lunch are considered to be fully within the fuzzy set. 

Districts that have less than 50% of their students receiving free or reduced lunch are considered 

fully outside of the fuzzy set. Districts with between 50% and 65% of their students are 

considered partially within the set to the extent that the proportion warrants. 

Discussion and Description. In the state of Louisiana, in 42 districts more than 65% of the 

student population attending public schools receive free or reduced lunch; these districts are 

considered fully within the at risk student fuzzy set. There are 19 districts with between 50% and 

65% of the student population reported as receiving a free or reduced lunch; these districts can be 

considered partially in the fuzzy set proportionally based on the percentage for each category. 

There are eight districts where less than 50% of the population attending public schools receive a 

free or reduced lunch and are considered fully outside the fuzzy set. 

Within the BOEM-defined parishes, 17 districts can be considered fully within the many at 

risk students fuzzy set, an additional 12 can be considered partially within the set to varying 

degrees. A total of three can be considered fully outside the fuzzy set. Descriptive statistics as 

well as the layout of fuzzy set membership are included in Tables 1.61–1.62 and Figure 1.31. 
70 

Table 1.61. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership for all Louisiana 

parishes (2009). 

Percent of Students Receiving 

Free or Reduced Lunch Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 65% Fully in set 1 42 

57.5–65% More in set than out of set 0.5-0.999 14 

50–57.5% More out than in set 0.001-0.49 5 

Less than 50% Out of set 0 8 

Table 1.62. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership for BOEM-defined 

parishes (2009). 

Percent of Students Receiving 

Free or Reduced Lunch Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 65% Fully in set 1 17 

57.5–65% More in set than out of set 0.5-0.999 8 

50–57.5% More out than in set 0.001-0.49 4 

Less than 50% Out of set 0 3 
71 

Figure 1.31. At-risk Students Receiving Free or Reduced Lunch fuzzy set membership. 

1.3.3.2. Many African American Students 

Definition. A school district was defined as having many African American students if 

greater than 70% of the pupils attending within the district are African American. A district was 

considered fully outside the fuzzy set if less than 30% of the pupils within the district are African 

American. Those districts with between 30% and 70% of their pupils classified as African 

American were deemed to be partially within the fuzzy set. 

Discussion and Description. In the state of Louisiana, in 11 districts more than 70% of the 

student population attending public schools are reported as being African American; these 

districts are considered fully within the high proportion of African American students fuzzy set. 

There are 39 districts with between 30% and 70% of the student population reported as being 

African American; these districts can be considered partially in the fuzzy set proportionally 

based on their proportions. There are 19 districts where less than 30% of the population attending 

public schools is reported as being African American; these districts are considered fully outside 

the fuzzy set. 

Within the BOEM-defined parishes, only four districts can be considered fully within the 

large proportion of African American students fuzzy set; an additional 16 can be considered 

partially within the set to varying degrees. A total of 12 can be considered fully outside the large 

proportion of African American students fuzzy set. 
72 

Table 1.63. High Percent of African American Students fuzzy set membership for all Louisiana parishes 

(2009). 

Percent of Students Who Are 

African American Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 70% Fully in set 1 11 

50–70% More in set than out of set 0.5-0.999 15 

30–50% More out than in set 0.001-0.49 24 

Less than 30% Out of set 0 19 

Table 1.64. High Percent of African American Students fuzzy set membership for BOEM-defined parishes 

(2009). 

Percent of Students Who Are 

African American Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 70% Fully in set 1 4 

50–70% More in set than out of set 0.5-0.999 3 

30–50% More out than in set 0.001-0.49 13 

Less than 30% Out of set 0 12 
73 

Figure 1.32. High Percent of African American Students fuzzy set membership. 

1.3.3.3. High Student Drop Outs Between Grades 8 and 12 

Definition. Districts that have more than 10% of the students drop out between the grades 8 

and 12 are considered fully within the high student drop out fuzzy set. Districts that have less 

than 5% of their students drop out between grades 8 and 12 are considered fully outside the 

fuzzy set. Districts that have between 5% and 10% of their students drop out between grades 8 

and 12 are considered to be partially within the fuzzy set. 

Discussion and Description. The state of Louisiana has school districts where more than 

10% of the student population attending grades 8–12 dropped out within the 2008/2009 school 

year and are considered fully within the high percent of student drop outs fuzzy set. There are 34 

districts with between 5% and 10% of the student population between grades 8 and 12 dropping 

out; these districts can be considered partially in the fuzzy set proportionally based on their 

proportions. There are 30 districts where less than 5% of the population attending public schools 

dropped out over the 2008/2009 school year. 

Within the BOEM-defined parishes, only one can be considered fully within the high percent 

of student dropouts fuzzy set; an additional 15 can be considered partially within the set to 

varying degrees. A total of 16 districts can be considered fully outside fuzzy set. Descriptive 

statistics as well as the layout of fuzzy set membership are included in Tables 1.65-1.66 and 

Figure 1.33. 
74 

Table 1.65. High Student Dropout Rate fuzzy set membership for all Louisiana parishes (2007–2008; 

grades 8–12). 

Dropout Rate, Grades 8–12 Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 10% Fully in set 1 5 

7.5–10% More in set than out of set 0.5-0.999 12 

5–7.5% More out than in set 0.001-0.49 22 

Less than 5% Out of set 0 30 

Table 1.66. High Student Drop-out Rate fuzzy set membership for BOEM-defined parishes (2007–2008; 

grades 8–12). 

Dropout Rate, Grades 8–12 Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 10% Fully in set 1 1 

7.5–10% More in set than out of set 0.5-0.999 4 

5–7.5% More out than in set 0.001-0.49 11 

Less than 5% Out of set 0 16 
75 

Figure 1.33. High Student Drop-out Rate fuzzy set membership. 

1.3.3.4. High Disciplinary Suspension Rate 

Definition. Districts with more than 10% of the student body receiving an out of school 

suspension in the 2008-2009 school year are considered to be fully within the high out of school 

suspension fuzzy set. Districts that suspended less than 2% of the student body over the 

2008/2009 school year are considered fully outside the fuzzy set. Districts that have suspended 

between 2% and 10% of the student body are considered to be partially within the fuzzy set to 

various degrees. 

Discussion and Description. The state of Louisiana has 34 districts where more than 10% of 

the student population has received an out of school suspension during the 2008/2009 school 

year; these districts are considered fully within the high disciplinary action fuzzy set. There are 

24 districts with between 2% and 100% of the student population reported having been 

suspended outside of school; these districts can be considered partially in the fuzzy set 

proportionally based on their respective data. In 11 districts, less than 2% of the population 

attending public schools is reported as having been suspended during the 2008/2009 school year; 

these districts are considered fully outside the fuzzy set. 

Within the BOEM-defined parishes, 16 districts can be considered fully within the high 

disciplinary suspension rate fuzzy set, an additional 11 districts can be considered partially 

within the set to varying degrees. A total of five districts can be considered fully outside the 
76 

fuzzy set. Descriptive statistics as well as the layout of fuzzy set membership are included in 

Tables 1.67–1.68 and Figure 1.34. 

Table 1.67. High Disciplinary Suspensions fuzzy set membership for all Louisiana parishes (2008–2009). 

Percent of Students Receiving 

Out-of-School Suspensions Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 10% Fully in set 1 34 

6–10% More in set than out of set 0.5-0.999 19 

2–6% More out than in set 0.001-0.49 5 

Less than 2% Out of set 0 11 

Table 1.68. High Disciplinary Suspensions fuzzy set membership for BOEM-defined parishes (2008–

2009). 

Percent of Students Receiving 

Out-of-School Suspensions Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 10% Fully in set 1 16 

6–10% More in set than out of set 0.5-0.999 10 

2–6% More out than in set 0.001-0.49 1 

Less than 2% Out of set 0 5 
77 

Figure 1.34. High Disciplinary Suspensions fuzzy set membership. 

1.3.3.5. High Student Grade Repeat 

Definition. Districts where more than 9% of students are asked to repeat a grade are 

considered to be fully within the fuzzy set. Districts where less than 3% of the students were held 

back a grade are considered to be fully outside the fuzzy set. Districts where between 3% and 9% 

of the students are held back are considered to be partially within the fuzzy set to varying 

degrees. 

Discussion and Description. The state of Louisiana has 15 districts where more than 9% of 

the student population attending public schools are retained or ask to repeat a grade within 

school; these districts are considered fully within the high student retention fuzzy set. There are 

42 districts with between 3% and 9% of the student population reported as being retained; these 

districts can be considered partially in the fuzzy set proportionally based on their relative scores. 

There are two districts where less than 3% of the population attending public schools are 

reported as being retained, these districts are considered fully outside the fuzzy set. 

Within the BOEM-defined parishes, only six districts can be considered fully within the high 

percent of students retained fuzzy set; an additional 25 can be considered partially within the set 

to varying degrees. Only one district can be considered fully outside the large percent of students 

retained. Descriptive statistics as well as the layout of fuzzy set membership are included in 

Tables 1.69–1.70 and Figure 1.35. 
78 

Table 1.69. High Percent of Students Retained fuzzy set membership for all Louisiana parishes (2008–

2009). 

Percent of Students Retained to 

Repeat a Grade Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 9% Fully in set 1 15 

6–9% More in set than out of set 0.5-0.999 27 

3–6% More out than in set 0.001-0.49 25 

Less than 3% Out of set 0 2 

Table 1.70. High Percent of Students Retained fuzzy set membership for BOEM-defined parishes (2008–

2009). 

Percent of Students Retained to 

Repeat a Grade Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

Greater than 9% Fully in set 1 6 

6–9% More in set than out of set 0.5-0.999 13 

3–6% More out than in set 0.001-0.49 12 

Less than 3% Out of set 0 1 
79 

Figure 1.35. High Percent of Students Retained fuzzy set membership. 

1.3.4. District Structure 

1.3.4.1. Small School District 

Definition. Table 1.71 shows district size based on the number of schools in each district, 

with a range of two schools in St. Bernard Parish to 80 schools in East Baton Rouge Parish. 

Fifteen or fewer schools are considered fully in the set of small districts, and districts with 25 or 

more schools are out of the set of small school districts. 

Discussion and Description. Table 1.71 illustrates the many small school districts present in 

Louisiana. A total of 43 school districts are full members of the set, and have only 15 or fewer 

schools. By comparison, only 17 districts are members of the bottom category, and have 25 or 

more schools, and are out of the set. This leaves just eight districts in Louisiana with partial 

membership in this group. Seven districts have between 16 and 20 schools and are more in than 

out, and only one district has 21–24 schools making it more out than in. 

Table 1.72 illustrates that an equal number of districts are in compared to out of the set of 

small school districts in the BOEM-defined impact area. A total of 13 districts make up both the 

fully in and fully out categories and only six parishes exhibit partial membership (Note: this 

includes the Orleans Parish School District because only 17 schools remained under the authority 

of this school district by 2008. All the others were under control of the state or charters). Fuzzy 

set membership for each parish is defined in Figure 1.36. 
80 

Table 1.71. Small School District fuzzy set membership for all Louisiana parishes (2008). 

Number of 

Schools in 

Parish 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(LDE Fall 

2008) 

Examples 

15 or fewer 

schools 

Fully in the set 1.00 43 St. Bernard* at 2, 1.00 

Cameron at 4, 1.00 

16–20 schools More in than 

out 

0.50 -0.99 7 Orleans at 17, 0.80 

Vermilion at 18, 0.70 

21–24 schools More out than 

in 

0.01-0.49 1 Ascension at 21, 0.40 

25 or more 

schools 

Out of the set 0.00 17 E. Baton Rouge* at 80, 0.00 

Jefferson at 77, 0.00 

*Highest and lowest values in range 

Table 1.72. Small School District fuzzy set membership for BOEM-defined parishes (2008). 

Number of 

Schools in 

Parish 

Verbal Label Fuzzy Set 

Membership 

Score 

Number 

(LDE Fall 

2008) 

Examples 

15 or fewer 

schools 

Fully in the set 1.00 13 St. Bernard* at 2, 1.00 

Cameron at 4, 1.00 

16–20 schools More in than 

out 

0.50 -0.99 5 Orleans at 17, 0.80 

Vermilion at 18, 0.70 

21–24 schools More out than 

in 

0.01-0.49 1 Ascension at 21, 0.40 

25 or more 

schools 

Out of the set 0.00 13 E. Baton Rouge* at 80, 0.00 

Jefferson at 77, 0.00 

*Highest and lowest values in range 
81 

Figure 1.36. Small School District fuzzy set membership. 

1.3.4.2. Large School District 

Definition. This table also examines district size based on the number of schools in each 

district, but from the perspective of large school districts. In this table, fully in the set are districts 

with 50 or more schools and those with fewer than 30 are out of the set. 

Discussion and Description. When the definition of district size was altered to favor large 

school districts, Table 1.73 indicates that only four parishes in Louisiana are fully in the set of 

large districts with 50 or more schools. On the other hand, a majority of the districts are in the 

smaller category; this time with fewer than 30 schools and out of the set. 

Interestingly, of the four large districts from Table 1.73, three are BOEM districts, as seen in 

Table 1.74. Only one BOEM district is in the 18–24 schools range and therefore more in than out 

of the set. Again, a majority of the districts are small, with 23 districts out of the set at 10 or 

fewer schools and 5 districts more out of the set than in. The layout of fuzzy set membership by 

parish is included in Figure 1.37 below. 

 
82 

Table 1.73. Large School District fuzzy set membership for all Louisiana parishes (2008). 

Number of 

Schools in 

Parish 

Verbal 

Label 

Fuzzy Set 

Membership Score 

Number 

(LDE Fall 2008) 

Examples 

50 or more 

schools 

Fully in the 

set 

1.00 4 E. Baton Rouge* at 80, 1.00 

Jefferson at 77, 1.00 

40–49 schools More in 

than out 

0.50 -0.99 2 Rapides at 48, 0.90 

St. Tammany at 49, 0.95 

30–39 schools More out 

than in 

0.01-0.49 7 Lafayette at 38, 0.40 

Tangipahoa at 33, 0.15 

Fewer than 30 

schools 

Out of the 

set 

0.00 55 Vermilion at 18, 0.00 

St. Bernard* at 2, 0.00 

*Highest and lowest values in range 

Table 1.74. Large School District fuzzy set membership for BOEM-defined parishes (2008). 

Number of 

Schools in 

Parish 

Verbal 

Label 

Fuzzy Set 

Membership Score 

Number 

(LDE Fall 2008) 

Examples 

50 or more 

schools 

Fully in the 

set 

1.00 3 E. Baton Rouge* at 80, 1.00 

Jefferson at 77, 1.00 

40–49 schools More in 

than out 

0.50 -0.99 1 St. Tammany at 49, 0.95 

30–39 schools More out 

than in 

0.01-0.49 5 Lafayette at 38, 0.40 

Tangipahoa at 33, 0.15 

Fewer than 30 

schools 

Out of the 

set 

0.00 23 Vermilion at 18, 0.00 

St. Bernard* at 2, 0.00 

*Highest and lowest values in range 
83 

Figure 1.37. Large School District fuzzy set membership. 

 1.3.4.3. Many Students Enrolled in Private Schools 

Definition. Districts that have more than 2,000 students enrolled in private institutions are 

considered fully within the high private institution enrollment fuzzy set. Districts with no 

enrollment in private institutions are considered fully outside of the fuzzy set. Districts with 

some enrollment within private institutions but less than 2,000 students enrolled are considered 

partially within the fuzzy set to varying degrees. 

 

Discussion and Description. In the state of Louisiana, in15 districts more than 2,000 

students within the district are attending private schools; these districts are considered fully 

within the many students enrolled in private schools fuzzy set. There are 41 districts with 

between 1 and 2,000 students reported as attending private schools; these districts can be 

considered partially in the fuzzy set proportionally based on their overall numbers. There are 13 

districts where there is no enrollment in private schools; these districts are considered fully 

outside the fuzzy set. 

Within the BOEM-defined impact area, 13 parishes can be considered inside the high nonpublic school enrollment fuzzy set. A further 17 parishes can be considered partially within the 

set as they have some level of non-public school enrollment. Only two parishes in the BOEMdefined set have no private school enrollment. Descriptive statistics as well as the layout of fuzzy 

set membership are included in Tables 1.75–1.76 and Figure 1.38. 
84 

Table 1.75. Private School Enrollments fuzzy set membership for all Louisiana parishes (2009–2010). 

Students Enrolled in Non-Public 

Schools Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 2,000 Fully in set 1 15 

1,000–2,000 More in set than out of set 0.5-0.999 7 

1–1,000 More out than in set 0.001-0.49 34 

No private school enrollment Out of set 0 13 

Table 1.76. Private School Enrollments fuzzy set membership for BOEM-defined parishes (2009–2010). 

Students Enrolled in Non-Public 

Schools Verbal Level 

Fuzzy Set 

Membership 

Score 

Number 

More than 2,000 Fully in set 1 13 

1,000–2,000 More in set than out of set 0.5-0.999 3 

1–1,000 More out than in set 0.001-0.49 14 

No private school enrollment Out of set 0 2 

Figure 1.38. Private School Enrollments fuzzy set membership. 
85 

CHAPTER 2. DISTINGUISHING REGIONAL COMPARED TO LOCAL 

COMPARATIVE ADVANTAGE THROUGH SHIFT SHARE ANALYSIS 

2.1. INTRODUCTION

In the previous chapter, we attempted to generate a socio-economic baseline across general, 

labor market, and education categories. The overall strategy was to identify key variables that 

would identify socio-economic condition so that future change could be analyzed. Though some 

of the data presented in these profiles represented secondary data from sources that are updated 

annually, others may only be updated once every ten years (such as the decennial U.S. Census) 

or on an ad hoc basis from other federal and state administrative data sources.1

In this chapter, we narrow the focus of our socio-economic analysis to focus strictly on the 

employment variable in the BOEM-defined region of Louisiana. In particular, we focus on 

comparing changes in a period before Hurricanes Katrina and Rita and a period from just before 

to just after these two storms. 

Local governments formulate new policies and adjust the old ones for any fiscal year 

depending on the growth pattern of a region. Any exogenous shock, for example, natural 

disasters- hurricanes, floods, could affect employment levels of any given industry in a region 

and consequently create volatility in functioning of local governments. Further, changes in the 

local community may also be affected by changes in neighboring/contiguous regions. 

Unfortunately, in many cases, in their research and policy implementation, policymakers and 

regional scientists fail to take into account the concept of spatial interaction.2

 In most cases, 

policies set by local governments for resiliency after any natural disasters are meant to be applied 

uniformly across all regions. However, in the aftermath of such disasters, some regions are more 

severely affected than others. 

In the past, researchers have used shift share analysis to decompose a region’s sectoral 

growth within a given period of time. The classical shift share analysis at its core is a simple 

variance decomposition technique and many regional scientists over several decades have 

applied the technique to analyze growth in a region and/or county for a specific industry. 

However, the classical shift share analysis has several shortcomings. For example, the classical 

shift share analysis does not take into account the spatial interaction or the neighboring region’s 

growth when evaluating a region’s growth. Similarly, any externality (positive or negative) that 

is present during the study period is not accounted for. In this study, we propose to use spatial 

shift share analysis to address these shortcomings. The spatial shift share analysis attempts to 

overcome some of the potential shortcomings of classical shift share analysis; for example, the 

classical shift share analysis does not delineate the effects of growth in the neighboring region’s 

industry growth. Further, our results suggest that the spatial shift share model does provide a 

 1 This manuscript is the authors’ accepted version of Adhikari, Arun, J. Matthew Fannin, and Ashok K. Mishra. 

“Decomposing Changes in Employment Rate after Natural Disasters.” Journal of Policy Modeling. (Forthcoming). 

2 Policymakers are often interested in “one-size-fits-all” policies. 
86 

more “distinct” effect between the neighboring region and local effects than the uniqueness 

between the industry mix and competitive effects in the classical shift share model. 

The purpose of this chapter is to identify the interpretable distinctiveness of spatial shift share 

through an application of employment change in the mining sector following natural disasters 

like hurricanes. We address this issue by incorporating neighboring region effects through a 

spatial weight matrix to the classical shift share analysis. Further, we show that by using the 

spatial shift share approach, additional information could be provided for the policymakers, such 

as growth decompositions in any industry, by comparing neighboring regional industry effects to 

sub-regional local effects. 

The remainder addresses how these spatial shift share techniques can be applied to 

understanding these differing employment patterns over time and how they provide a more 

precise identification of comparative advantage from trade theory. We first present an alternative 

regionalized decomposition approach using spatial weight matrices as presented by Nazara and 

Hewings (2004). We then conclude with an empirical analysis of these novel shift share 

techniques to local employment data from parishes (counties) impacted by Hurricanes Katrina 

and Rita to test the distinctness of the regionalized effect. 

2.2. SPATIAL SHIFT SHARE ANALYSIS

The economic growth of any spatial unit is not independent of the growth of its neighboring 

units. Any spatial unit may be affected (positively or negatively) by the spatial spillovers 

transmitted from its neighboring regions (Isard 1960). Based on this idea, Nazara and Hewings 

(2004) incorporated a spatial structure within shift share analysis and developed an extensive 

taxonomy of regional growth decompositions. The general formula for their model replaces Gi

with gi



, which is a spatial lag variable that denotes the growth rate of sector i in the 

neighborhood regions. 

(E2.1) (growth)i = G + ( i g

 – G) + (gi - i g ) (Spatial shift share) 

The spatial lag variable, gi



 is a weighted average of neighboring regions, and is acquired by 

multiplying a square spatial weight matrix (R X R)3

, denoted as W, times the conformable 

column vector of neighboring values. W is therefore a spatial weight matrix whose elements wjk

describe the level of interdependence between spatial units j and k (Evans, 2008). The first part 

of the right hand side (G) refers to overall national growth. The second part ( gi



 – G) refers to 

the difference between the growth rate of ith sector in the neighboring region and overall national 

growth. This effect due to the growth in a neighboring region for any particular sector is termed 

as the neighboring region effect. A positive number reflects the growth of ith sector in the 

neighboring region grows faster than the total national growth. The third part (gi - gi



) is the 

difference in the growth of the ith sector in any specific local area and its neighboring region. 

 

3 R is the number of regions (parishes) in the system 
87 

This effect is termed as the local effect and the positive number implies that the growth of ith

sector in any specific region is faster than the growth of the same sector in its neighboring 

region. 

 

Nazara and Hewings (2004) calculated the all sector employment growth rate for the 

contiguous region k of a particular region j, denoted by g



. Mathematically, the formula is 

written as 

 

(E2.2) 



 



 

 

 J

k

t

jk k

J

k

J

k

t

jk k

t

jk k

w e

w e w e

g

1

1 1

1

~

) ~ ~ ( 

 

where wjk  ~ element of a square spatial row standardized weight matrix indicating the 

intensity of j’s interaction with region k. Similarly, the formula can be modified slightly to obtain 

the growth rate for region k’s neighbor for a specific sector i. 

(E2.3) 



 



 

 

 J

k

t

jk ik

J

k

J

k

t

jk ik

t

jk ik

i

w e

w e w e

g

1

1 1

1

~

) ~ ~ ( 

t1

ik e = total employment in region k for sector i for time period t+1 

t

ik e = total employment in region k for sector i for time period t 

In addition to the spatial augmentation of the classical shift share analysis, Nazara and 

Hewings (2004) present alternative shift share decompositions: 

(E2.4) Growth (gi) = Gi + ( g i

 – Gi) + (gi - g i

 ) (Augmented spatial shift share). 

In Equation E2.4, we see an approach to augmenting the spatial shift share decomposition. In 

the spatial shift share version of Equation E2.4, the decomposition creates a neighboring 

region/industry effect that compares the difference between the overall national growth of an 

industry against the neighboring region employment growth in that industry ( i g



 – Gi). Hence, 

this neighboring-region effect is unique to each region and represents how much faster or slower 

a neighboring region’s employment in a given industry contributes relative to the employment 

growth of the industry nationally. It creates a third “industry-based effect” and thereby better 

distinguishes where potential comparative advantage is geographically focused (nation/state, 

larger neighboring region, or localized area). 

Since the original formulation by Nazara and Hewings (2004), there have been a number of 

applications. Applications have been numerous for many geographic regions, including China 

(Chunyun et al. 2007), Australia (Mitchell, Myers, and Juniper 2005), Spain (Marquez, Ramajo, 

and Hewings 2009), Greece (Fotopoulas, Kallioras, and Petrakos 2009), and Texas (Tu and Sui 

2010). However, Nazara and Hewings’ (NH; 2004) approach has been criticized for the potential 
88 

in creating neighboring regions that are not sufficiently large to take into account sectors driven 

by larger geographic region effects (Fotopoulas, Kallioras, and Petrakos 2009); it has been used 

to evaluate tradeoffs between sectoral and regional classification (Marques, Ramajo, and 

Hewings 2009). The NH approach has been modified to include an exponential distance function 

for the spatial weight matrix (Mitchell, Myers, and Juniper 2005) and extensions of the NH 

approach to incorporate homothetic effects in the tradition of the Esteban-Marquillas (EM) 

approach (Pautelli et al. 2006). 

Unfortunately, to our knowledge, no study has attempted to test whether the spatial shift 

share model improves on the distinctiveness of localized effect. Loveridge and Selting (1998) 

tested a number of variations of classical shift share model and found that augmented models 

used in EM approach eliminate the proposed problem that the traditional competitive effect was 

actually measuring part of the industry mix effect. Loveridge and Selting used a correlation 

analysis to show that the EM family of models did reduce the correlation of the industry mix and 

competitive effect from the classical shift share model, but the solution, the breakup of the 

competitive effect into a traditional competitive effect component and homothetic (industry 

proportion) competitive effect component, resulted in almost perfect correlation of these 

components rendering their separate interpretative value meaningless. 

Because many of the aforementioned applications of the spatial shift share approach assume 

that the neighboring region effect is truly differentiable from the localized effect, their 

interpretations of these results would be invalid if the neighboring region effect created by the 

spatial weight matrix was not truly distinct. Further, the practical interpretative value of the 

neighboring region effect suggests that validation of its distinctness is important before the 

approach is extended from the academic realm to the economic development practitioner. 

2.3. ECONOMIC CHANGE FOLLOWING THE 2005 HURRICANE SEASON

Katrina and Rita, two of the most deadly hurricanes in the history of the United Sates, made a 

landfall less than a month apart in 2005. The U.S. Department of Commerce (2006) reported that 

Hurricane Katrina was the costliest hurricane in the history of the U.S., and responsible for $81.2 

billion4

 in damages. Hurricane Rita was recorded as the ninth costliest storm in U.S. history and 

responsible for approximately $10 billion in damages (Knabb, Brown, and Rhome 2007). These 

hurricanes had a strong impact on economies and employment in the affected areas. There were 

many incidences of mass layoffs and increase in unemployment rates after these hurricanes 

(Kosanovich 2006). Beside the decline in population and employment in many of the hurricane 

affected areas, significant changes in expenditure, revenues, assets and liabilities of local 

governments were observed in its aftermath. These changes can be examined by shift share 

analysis by decomposing the changes into various effects. 

With natural disasters, such as Hurricanes Katrina and Rita, the comparative advantage a 

region has over another region may vary due to a number of factors. For example, one of the key 

 

4 The amount is specified in 2005 U.S. dollars. 
89 

industry sectors in New Orleans, the Food Services sector, is driven by a combination of local 

demand from local residents and export demand from tourists. One would expect the timing of 

growth in this industry to lag, given the slow re-population of the historical population base 

necessary to support minimum efficient scale of restaurants in the region. Further, its support 

establishments–those inter-connected sectors both upstream and downstream–are also highly 

dependent on the export base of tourist population. Hence, we might expect, in a worst case 

scenario, the loss of market share because of an exogenous natural disaster to be significant 

enough to move the regional economy beyond a “sustain point” as described by Fujita et al. 

(1998), such that the previous agglomeration effects in the tourism industry are no longer 

attainable. At best, we might see local establishments in the Food Services sector temporarily 

move to other neighboring regions where the local population base has relocated, until that 

population relocates back to the urban core; the population base reaches a level to sustain food 

service establishments at a historically viable scale. 

On the other hand, the oil and gas extraction sector’s economic base comes from a 

combination of a historically large supply and low cost to extract petroleum and natural gas 

minerals along the Gulf Coast with a support of infrastructure of suppliers and transportation 

networks (ships, barges, pipelines, ports) to move these raw minerals from their extraction source 

to further processing (e.g., petroleum refineries) and eventually to end consumers. Further, the 

skilled labor involved in the industry is accustomed to the mobile nature of the industry. Both 

onshore and offshore drilling and related activities change geographic locations regularly, which 

lead to measurable dichotomies in place of work and place of residence. Hence, a worst case 

scenario might be that low producing oil or gas wells are simply shut and removed from 

production after natural disasters like hurricanes. At best, the loss and damage to infrastructure in 

the industry are quickly repaired, given that the labor force supporting the industry is 

geographically spread over a much greater area. 

The importance of deepwater drilling and production as a proportion of total oil production in 

the U.S. increased during this period. After the year 2000, average daily deepwater offshore oil 

production exceeded shallow water oil production in the U.S.5

 (Nixon et al. 2009). Louisiana’s 

total offshore oil production is dominated by federal outer-continental shelf production (mostly 

deepwater), with 48 million barrels extracted annually, compared to only 6.3 million barrels in 

shallow-water. Further, the majority of the servicing of deepwater rigs occurs in Louisiana, 

particularly in Port Fourchon, in Lafourche Parish. Approximately 90% of all deepwater rigs in 

the entire Gulf of Mexico are serviced in this area (Scott and Associates 2008). 

 

5 Deepwater drilling is considered drilling of wells with a water depth of at least 1,000 ft. Ultra-deepwater 

drilling is defined as drilling of wells with a water depth of at least 5,000 ft. 
90 

2.4. DATA AND METHODOLOGY

The mining industry in Louisiana is a good case for this analysis in that two conditions hold 

for its measurement. The first is that many of the physical inputs in mining, particularly Oil and 

Gas Extraction, are bulky making it cost prohibitive to transport the inputs long distances. The 

second is an artifact of the data. A larger proportion of the major physical and service inputs in 

the aggregate mining sector from the North American Industrial Classification System (NAICS) 

also are classified as mining sector industries. Consequently, when mining is decomposed using 

spatial shift share analysis, the supply chain linkages can be captured. For a sector where a large 

proportion of its inputs come from an entirely unrelated industry sector altogether, an aggregated 

industry decomposition using spatial shift share analysis would fail to capture the supply chain 

linkages. 

The analysis is performed on the basis of the classical shift share analysis and the spatial 

decomposition based on the contiguity of parishes. We focus on the Coastal Louisiana Region 

(CLR) parishes that are measurably influenced by industries geographically concentrated in this 

region as well as tropical storms6

. CLR parish level employment data are drawn from Wholedata 

(Isserman and Westervelt 2006). Wholedata uses county business patterns (CBP) data that 

provides detailed employment by up to six digit NAICS sectors; however, because many sectors 

in small regions have only small numbers of establishments, their employment data are not 

disclosed to protect confidentiality. Wholedata imputes the undisclosed data so that employment 

estimates are available for all detailed NAICS sectors for each county. Summed national industry 

employment is used for the aggregate effect; hence total national employment in the given time t 

is denoted by Et. The total employment in the given time t for different sectors in the nation are 

denoted as Eit. Similarly, the overall employment for each CLR parish in time t is denoted by et 

and the employment for each sector of each CLR parish is denoted by eit. 

The spatial weight matrix is developed based on the contiguity of the parishes. The matrix is 

then row standardized for further application in shift share analysis. To check the employment 

changes for every CLR parish, we evaluate a wide range of models enveloped by Nazara and 

Hewings (2004). The primary basis for spatial decomposition in this paper is the physical 

contiguity of parishes. Similarly, we can proceed further by creating the weight matrix on the 

basis of some economic variables and treating the neighboring regions on the basis of the 

interdependence in those economic variables. The weight matrix as constructed makes the 

neighboring region and local competitive effects easily interpretable by general practitioners. 

 

Nazara and Hewings (2004) suggest that some of the local competitive effect explained in the 

classical shift share model is actually explained by neighboring region industry effects. How 

might this be tested to know if the neighboring region effect is truly a distinct effect from the 

localized effect? We apply a correlation analysis test used by Loveridge and Selting (1998) to 

test the distinctiveness of the neighboring region effect. We apply the same correlation analysis 

by taking the average of annual shift share decomposition effects between 2001 and 2006 and 

 

6 Coastal Louisiana Region includes 32 parishes chosen from U.S. Dept. of the Interior Bureau of Ocean 

Energy Management (BOEM) definition of parishes influenced by energy industry activities off the outercontinental shelf (Saha, Manik, and Phillips 2005). 
91 

evaluating their pair wise correlations. Our hypothesis is that if the spatial neighboring region 

industry effect is a distinct decomposition effect, we would see the correlation between the 

neighboring region effect and the new localized effect created from the spatial model weakly 

correlated suggesting their effects are distinct. 

 

2.5. RESULTS

In Tables 2.1 and 2.2, we compare the classical shift share analysis against the spatial shift 

share model (Equation E2.1) and the augmented spatial shift share model (Equation E2.4) for 

two time periods: a three year period (2001–2004) preceding the impacts of Hurricanes 

Katrina/Rita (Table 2.1) and a two year period7

 (2004–2006) during which Katrina and Rita 

occurred. For comparison, we report results of the classical shift share analysis. Columns 1-3 in 

Table 2.1 and 2.2 refer to the classical shift share case in 2001–04 and 2004–06 respectively, 

columns 4–6 report the spatial shift share model results, and columns 7–9 report the augmented 

spatial shift share results. 

During 2001–04, the overall national employment was essentially flat (Table 2.1, column 1), 

and contributed to the industry mix effect of -3.2% (Table 2.1, column 2). These results from the 

classical shift share model suggest that any overall positive employment growth in an individual 

parish’s mining sector was entirely due to localized in-parish positive competitive effects. On the 

other hand, the spatial shift share model was able to tease out more precise localized effects from 

the larger regional effects. Take the example of Calcasieu Parish, using the spatial shift share 

model (Table 2.1, columns 4–6), results show that the new localized effect (26.5%) is much 

greater than the competitive effect (5.6%) in the classical shift share model. This difference may 

be due to industry mix in the classical shift share model which treats a portion of a local parish’s 

employment growth in a given industry, as being driven by the overall growth in that industry 

nationally. However, in the spatial shift share model, that growth in employment is not only 

driven by the same industry growth, but also in the neighboring contiguous parishes. If one 

prefers to include both the national industry growth rate and neighboring region effects 

separately in the shift share decomposition, then using the augmented spatial shift share models 

is appropriate (columns 7–9, Tables 2.1 and 2.2). While generating the same localized effect as 

the spatial shift share model, it disentangles any neighboring region effects that may be driven by 

national industry growth. 

In Table 2.2, we see even greater contrasts and interpretation between the classical and the 

spatial shift share models. In the Calcasieu Parish case, between 2004 and 2006, the classical 

shift share model shows that 19% of the 37% growth rate in employment in the mining sector 

was attributable to local competitive effects (Table 2.2, column 3) whereas the spatial shift share 

model shows that approximately 41% of the same 37% growth rate in employment was due to 

local effects. The underlying differences are driven by differences between the industry mix and 

neighboring region effects. In the classical shift share model, national industry growth would 

have contributed approximately 14% (Table 2.2, column 2) to overall employment growth rate, 

 

7 Only two year period was selected for analysis because of data availability issue. 
92 

whereas the neighboring region effect would have reduced growth rates in employment in 

Calcasieu Parish by 8% (Table 2.2, column 5). In the Calcasieu case, when using the augmented 

spatial shift share model, the neighboring region effect reduces growth rate in employment by 

over 21% (Table 2.2, column 8). 

To better understand how the spatial shift share model interprets growth in employment, we 

present a breakdown of growth by sign (+/-) of the neighboring region effects and local effects 

using the spatial shift share model for mining in Figures 2.1 (2001–2004) and 2.2 (2004–2006). 
93 

Table 2.1. Comparing classical and augmented spatial shift share analysis on employment growth in the mining se

Area name 

Classical Shift Share 

 (01-04) 

 Spatial Shift Share 

(01-04) 

Augmented Spatial Shift Shar

(01-04) 

G Gi-G gi-Gi G gibar-G gi-gibar Gi gibar-Gi gi-giba

(1) (2) (3) (4) (5) (6) (7) (8) (9) 

Nat. 

Effect 

Industry 

Mix 

Comp. 

Effect 

Nat. 

Effect 

Neigh. 

Region 

Effect Local Effect 

Nat. 

Industry 

Effect 

Neigh. 

Region 

Effect 

Local. 

Effect 

Acadia 0.0001 -0.032 0.442 0.0001 -0.281 0.691 -0.031 -0.249 0.69Allen 0.0001 -0.032 -0.094 0.0001 0.673 -0.798 -0.031 0.705 -0.79

Ascension 0.0001 -0.032 -0.382 0.0001 -0.178 -0.235 -0.031 -0.147 -0.23Assumption 0.0001 -0.032 0.240 0.0001 -0.129 0.337 -0.031 -0.097 0.33

Beauregard 0.0001 -0.032 0.410 0.0001 0.529 -0.150 -0.031 0.560 -0.15

Calcasieu 0.0001 -0.032 0.056 0.0001 -0.241 0.265 -0.031 -0.209 0.26Cameron 0.0001 -0.032 -0.535 0.0001 -0.334 -0.233 -0.031 -0.302 -0.23E. Baton Rouge 0.0001 -0.032 -0.608 0.0001 -0.221 -0.418 -0.031 -0.189 -0.41Evangeline 0.0001 -0.032 0.395 0.0001 -0.227 0.591 -0.031 -0.195 0.59Iberia 0.0001 -0.032 0.046 0.0001 -0.101 0.115 -0.031 -0.069 0.11Iberville 0.0001 -0.032 -0.149 0.0001 -0.102 -0.079 -0.031 -0.070 -0.07Jefferson 0.0001 -0.032 -0.273 0.0001 -0.202 -0.103 -0.031 -0.171 -0.10

Jefferson Davis 0.0001 -0.032 -0.503 0.0001 -0.001 -0.534 -0.031 0.031 -0.53Lafayette 0.0001 -0.032 -0.208 0.0001 -0.126 -0.114 -0.031 -0.094 -0.11

Lafourche 0.0001 -0.032 0.107 0.0001 -0.183 0.258 -0.031 -0.152 0.25

Livingston 0.0001 -0.032 -0.380 0.0001 0.356 -0.768 -0.031 0.387 -0.76Orleans 0.0001 -0.032 -0.227 0.0001 -0.072 -0.187 -0.031 -0.040 -0.18Plaquemines 0.0001 -0.032 0.027 0.0001 -0.156 0.151 -0.031 -0.125 0.15St. Bernard 0.0001 -0.032 0.127 0.0001 -0.132 0.227 -0.031 -0.100 0.22St. Charles 0.0001 -0.032 -0.589 0.0001 0.003 -0.624 -0.031 0.035 -0.62St. James 0.0001 -0.032 -0.254 0.0001 0.028 -0.313 -0.031 0.059 -0.31St. John 0.0001 -0.032 0.272 0.0001 0.096 0.144 -0.031 0.128 0.14

St. Landry 0.0001 -0.032 -0.627 0.0001 0.157 -0.815 -0.031 0.189 -0.81St. Martin 0.0001 -0.032 0.127 0.0001 -0.142 0.238 -0.031 -0.111 0.23

St. Mary 0.0001 -0.032 0.034 0.0001 -0.005 0.007 -0.031 0.027 0.00

St. Tammany 0.0001 -0.032 0.100 0.0001 1.464 -1.396 -0.031 1.495 -1.39Tangipahoa 0.0001 -0.032 2.267 0.0001 0.147 2.088 -0.031 0.179 2.08

Terrebonne 0.0001 -0.032 -0.306 0.0001 0.095 -0.433 -0.031 0.127 -0.43

Vermilion 0.0001 -0.032 -0.459 0.0001 -0.183 -0.307 -0.031 -0.152 -0.30

Vernon 0.0001 -0.032 2.781 0.0001 0.127 2.623 -0.031 0.158 2.62

Washington 0.0001 -0.032 0.724 0.0001 1.152 -0.459 -0.031 1.183 -0.45

W. Baton Rouge 0.0001 -0.032 0.154 0.0001 -0.410 0.532 -0.031 -0.378 0.53

Average 0.0001 -0.032 0.085 0.0001 0.044 0.009 -0.031 0.075 0.00
94 

Table 2.2. Comparing classical and augmented spatial shift share analysis on employment growth in the mining se

Area name 

Classical Shift Share 

 (04-06) 

Spatial Shift Share 

(04-06) 

Augmented Spatial Shift Share 

(04-06)

G Gi-G gi-Gi G gibar-G gi-gibar Gi gibar-Gi gi-gibar 

(1) (2) (3) (4) (5) (6) (7) (8) (9) 

Nat. 

Effect 

Industry 

Mix 

Comp. 

Effect 

Nat. 

Effect 

Neigh. 

Region 

Effect 

Comp. 

Effect 

Nat. 

Industry 

Effect 

Neigh. 

Region 

Effect 

Local. 

Effect 

G

(

Acadia 0.042 0.137 0.084 0.042 0.009 0.211 0.179 -0.127 0.211 Allen 0.042 0.137 -0.179 0.042 0.008 -0.050 0.179 -0.129 -0.050 

Ascension 0.042 0.137 1.025 0.042 0.209 0.953 0.179 0.072 0.953 Assumption 0.042 0.137 -0.223 0.042 0.310 -0.396 0.179 0.173 -0.396 

Beauregard 0.042 0.137 -0.649 0.042 0.338 -0.850 0.179 0.201 -0.850 

Calcasieu 0.042 0.137 0.190 0.042 -0.081 0.408 0.179 -0.218 0.408 Cameron 0.042 0.137 0.024 0.042 0.057 0.104 0.179 -0.080 0.104 E. Baton Rouge 0.042 0.137 0.521 0.042 0.621 0.036 0.179 0.485 0.036 Evangeline 0.042 0.137 -0.872 0.042 0.260 -0.996 0.179 0.124 -0.996 

Iberia 0.042 0.137 -0.494 0.042 0.111 -0.469 0.179 -0.026 -0.469 

Iberville 0.042 0.137 0.233 0.042 0.317 0.053 0.179 0.181 0.053 Jefferson 0.042 0.137 -0.162 0.042 0.182 -0.207 0.179 0.045 -0.207 

Jefferson Davis 0.042 0.137 -0.029 0.042 -0.121 0.229 0.179 -0.258 0.229 Lafayette 0.042 0.137 0.099 0.042 0.125 0.111 0.179 -0.012 0.111 Lafourche 0.042 0.137 0.043 0.042 0.147 0.033 0.179 0.010 0.033 

Livingston 0.042 0.137 0.561 0.042 0.329 0.369 0.179 0.192 0.369 Orleans 0.042 0.137 -0.228 0.042 0.053 -0.144 0.179 -0.084 -0.144 

Plaquemines 0.042 0.137 0.280 0.042 -0.116 0.533 0.179 -0.253 0.533 

St. Bernard 0.042 0.137 -0.370 0.042 0.163 -0.396 0.179 0.026 -0.396 

St. Charles 0.042 0.137 0.087 0.042 0.070 0.153 0.179 -0.066 0.153 

St. James 0.042 0.137 -0.579 0.042 0.328 -0.770 0.179 0.191 -0.770 

St. John 0.042 0.137 -0.080 0.042 0.210 -0.153 0.179 0.073 -0.153 

St. Landry 0.042 0.137 0.619 0.042 -0.002 0.757 0.179 -0.139 0.757 St. Martin 0.042 0.137 0.134 0.042 0.176 0.094 0.179 0.040 0.094 

St. Mary 0.042 0.137 0.005 0.042 0.245 -0.103 0.179 0.108 -0.103 

St. Tammany 0.042 0.137 -0.123 0.042 -0.415 0.428 0.179 -0.551 0.428 

Tangipahoa 0.042 0.137 -0.697 0.042 0.125 -0.685 0.179 -0.012 -0.685 

Terrebonne 0.042 0.137 1.017 0.042 0.078 1.075 0.179 -0.058 1.075 Vermilion 0.042 0.137 -0.402 0.042 0.073 -0.339 0.179 -0.063 -0.339 

Vernon 0.042 0.137 0.821 0.042 -0.277 1.235 0.179 -0.414 1.235 

Washington 0.042 0.137 -0.406 0.042 -0.273 0.004 0.179 -0.410 0.004 

W. Baton Rouge 0.042 0.137 0.120 0.042 0.514 -0.257 0.179 0.377 -0.257 

Average 0.042 0.137 0.012 0.042 0.118 0.030 0.179 -0.019 0.030 
95 

Figure 2.1. Spatial shift share analysis of neighboring region and local effects for mining from 2001–2004.

 

Vernon

Cameron

Rapides

Terrebonne

Allen

Lafourche

Vermilion

Calcasieu

Plaquemines

Beauregard

Acadia

Iberia

St. Landry

Avoyelles

Iberville

St. Mary

Tangipahoa

St. Tammany

St. Bernard

Livingston

Evangeline

St. Martin

Washington

Jefferson Davis

St. Helena

Pointe Coupee

East Feliciana

Assumption

West Feliciana

Lafayette Ascension

St. James

Iberia

Jefferson

St. Charles

Orleans

East 

Baton

Rouge

St. John

 the Baptist

West 

Baton 

Rouge

Pos. N. Region Effect - Pos. Local Effect

Pos. N. Region Effect - Neg.Local Effect

Neg. N. Region Effect - Pos. Local Effect

Neg. N. Region Effect - Neg. Local Effect
96 

Figure 2.2. Spatial shift share analysis of neighboring region and local effects for mining from 2004–

2006.

In Figure 2.1, we identify parishes based on the signs of growth rates of employment from 

both the neighboring region effect and the local effect from the spatial shift share model for the 

period 2001–04. As can be seen from the figure, red parishes have both neighboring region 

effects that are positive as well as localized effects that are positive. These parishes are thriving 

from both localized and regional effects. Parishes in purple represent those parishes that have 

positive neighboring region effects, but a negative local effect. These parishes are potentially 

receiving spillover employment benefits from a larger region and are likely to indicate that the 

parish may have a weaker comparative advantage in the industry compared to what the 

competitive effect would suggest in classical shift share analysis. Finally, parishes in orange 

have negative neighboring region effects but a positive local effect. These parishes are likely to 

have a strong comparative advantage effect locally and may have a larger region that supports a 

parish’s specific industry. Parishes in green represent poor localized conditions for employment 

growth and a weak regional environment to maintain existing employment in the mining 

industry. 

Vernon

Cameron

Rapides

Terrebonne

Allen

Lafourche

Vermilion

Calcasieu

Plaquemines

Beauregard

Acadia

Iberia

St. Landry

Avoyelles

Iberville

St. Mary

Tangipahoa

St. Tammany

St. Bernard

Livingston

Evangeline

St. Martin

Washington

Jefferson Davis

St. Helena

Pointe Coupee

East Feliciana

Assumption

West Feliciana

Lafayette Ascension

St. James

Iberia

Jefferson

St. Charles

Orleans

East 

Baton

Rouge

St. John

 the Baptist

West 

Baton 

Rouge

Pos. N. Region Effect - Pos. Local Effect

Pos. N. Region Effect - Neg.Local Effect

Neg. N. Region Effect - Pos. Local Effect
97 

During 2001–2004, oil prices stayed in the range of $20 to $40 per barrel, which did not 

economically support re-investment in more mature shallow depth oil fields. Structurally, growth 

in the oil and gas industry was occurring in the deep waters of the Gulf of Mexico. The major 

service port for a majority of drilling activity is Port Fourchon, at the southern tip of Lafourche 

Parish (see Figure 2.1). This parish showed an overall positive employment growth rate (7.5%) 

during this period, despite its neighboring region effect contributing to approximately 18% 

reduction in employment growth rate (Table 2.1, column 5). Related to this port, support activity 

industry establishments for deepwater drilling are spread across multiple parishes along the 

coast. The positive spillover effect of Lafourche Parish’s employment growth rate (driven largely 

in part by its deepwater port activity) dampened potentially larger reductions in employment 

growth for support industry in Terrebonne and St. James parishes. Further, Figure 2.1 highlights 

few spatial spillovers in mining originating from the major metropolitan centers in South 

Louisiana to surrounding areas. Orleans (New Orleans), East Baton Rouge (Baton Rouge), 

Lafayette (Lafayette) and Calcasieu (Lake Charles) core metro parishes only had one contiguous 

parish each that were categorized as having positive neighboring region effects. 

However, during 2004–2006, we see a changing spatial spillover landscape. In particular, we 

see two spatial “corridors” of thriving employment growth: a corridor along the Interstate 10 and 

Interstate 12 corridor from Livingston Parish in the east to Acadia Parish in the west, and a 

corridor extending through Terrebonne, Lafourche, and St. Charles Parishes. Sandwiched 

between these two corridors is a large horizontal corridor of parishes that received positive 

neighboring spillover benefits driven by neighboring parishes to both the north and the south. 

During this period, Terrebonne and Lafourche (as well as Cameron parish in Southwest 

Louisiana) parishes were major ports involved in the repair and restoration of many of the 

drilling rigs, platforms, and pipeline systems within the Gulf of Mexico. The I-10 and I-12 

corridor highlights a region likely benefitting from onshore mining support industries helping in 

the restoration of the offshore industry infrastructure. Finally, increasing oil prices during this 

period helped in creating a mild increase in onshore activities, such as work-over rigs and other 

repair and activities, on existing land-based wells to extract additional hydrocarbons from many 

older oil producing wells. 

 Results from the spatial shift share in Table 2.3 suggest that the spatial shift share model, 

unlike the Esteban-Marquillas (EM) approaches, tends to disentangle neighboring region effects 

( i g



-G) from truly localized competitive effects ( i g



 –gi) without creating additional correlation 

issues, compared to the classical shift share model. For example, in the classical shift share 

model for the mining industry sector, the correlation between the industry mix, (Gi-G), and 

competitive effect, (gi-Gi), was significant (-0.297). In the spatial shift share model for mining 

industry sector, the correlation between the neighboring region effect, ( i g



-G), and the local 

effect, (gi- i g



), was negative and insignificant (-0.013). Therefore, these results may suggest that 

the regional structural influence that was argued for the restructuring of the competitive effect in 

the EM approach, and argued as unsuccessful by Loveridge and Selting (1998), appears to have 

been mitigated with the spatial shift share method. 
98 

Table 2.3. Correlation analysis testing the distinctness of neighboring region effect for the mining sector. 

Decompositions National 

(G) 

Industry-mix 

(Gi

-G)

Competitive 

(gi

-Gi) 

Neigh. 

Region 

(gibar-G) 

Local 

(gi-gibar) 

National 1.000 

(G) 

Business-mix 0.994*** 1.000 

(Gi-G) (0.000) 

Competitive -0.324* -0.297* 1.000 

(gi-Gi) (0.070) (0.097) 

Neigh. Region -0.997*** -0.994*** 0.325* 1.000 

(gibar-G) (0.000) (0.000) (0.069) 

Local 0.014 0.042 0.940*** -0.013 1.000 

(gi-gibar) (0.937) (0.815) (0.000) (0.941) 

Values in parenthesis indicate p-values. *, **, and *** indicate the significance levels of 10%, 5% and 

1%, respectively. 

2.6. DISCUSSION

The results presented above for the mining industry sector suggest that some parishes may 

have localized competitive advantages despite having little economic support from neighboring 

parishes (negative neighboring region effect, positive local effect), whereas other parishes tend to 

ride the coat-tails of their neighbors economically (positive neighboring region effect, negative 

local effect). What might be driving these varying patterns? In particular, though the 

interpretation of the competitive effect of classical shift share analysis holds for interpreting the 

localized effect in the spatial shift share model, what may be driving the neighboring region 

effect? 

We posit two explanations. The first is that the larger region has multiple establishments 

producing products that are connected together as part of a larger supply chain. There is 

increased demand for a product at one point along the supply chain which is located in one 

parish in the larger region. To the extent that establishments upstream in the supply chain are in 

neighboring parishes, the backward linkage effects spill over to the neighboring parish. 

The second explanation suggests that a common site advantage, such as a harbor or river, 

may be shared by multiple parishes in a larger region to produce a similar product. Hence, if 

demand increases for a product that needs to take advantage of a natural site advantage, 

economies of scale may indicate expansion of an existing facility up to an efficient scale 

threshold. Beyond that point, increased demand may need to be met by a new establishment. The 

new establishment may take advantage of the natural site advantages in a neighboring parish 

possibly resulting in neighboring region spillover effects. 

In both explanations, the local effect in the spatial shift share model is strictly dependent on 

the growth rate of the neighboring region effect. That is, after controlling for the overall national 

growth of the economy, all of the remaining employment growth in a parish, for a particular 

industry, is first attributed to the neighboring region growth with the residual being the local 

effect. The decomposition assumes that a local parish’s employment growth is dependent on its 
99 

neighboring region’s growth. Unfortunately, there are some limitations to this assumption. For 

example, let’s assume a local port that supplies the offshore oil and gas industry grows over a 

period of time and reaches capacity with no possibility for further growth. Industries that use the 

existing port recognize the economic value of supporting their offshore activities using the canal 

to move finished products. If a neighboring parish’s port along the same canal increases the 

depth of its access points to supply the larger offshore vessels, it may generate additional 

employment growth from related industries. 

The spatial shift share model with the neighboring region effect in the above example would 

assume that the first port’s growth in employment (the port that reached capacity) was dependent 

on the employment growth of the neighboring parish’s port. However, the example shows that 

causality cannot be clearly inferred from spatial shift share analysis. In most cases for aggregated 

industries, the classical shift share decomposition is mostly immune to this shortcoming, because 

most small regions analyzed with classical shift share model are too small to cause economic 

growth in the larger region. 

The choice between the classical and numerous variants of spatial shift share analysis should 

not be taken lightly. As mentioned previously, both regional industry structure and data structure 

should be considered. For industries that are very homogenous in their production process across 

space or typically have demand effects that evenly spread across geographic space, a classical 

shift share model with national industry growth may be appropriate. However, for aggregated 

industry classifications with heterogeneous production processes across space, a spatial shift 

share model may be a preferable alternative. Finally, industries that have multiple sections of a 

supply chain in the same industry classification (such as mining) may also benefit because it 

highlights spatial policy spillovers from supply chain effects. 

2.7. CONCLUSION

Shift share analysis has been applied over the decades to provide local policy makers and 

development officials a better perspective concerning what factors drive their local economic 

growth. It is a statistical tool that decomposes a region’s growth into different effects. Most of 

the previous studies dealt with classical shift share analysis that decomposes a region’s growth 

into three different effects. In this study, we move further by applying spatial shift share analysis 

that takes into account a region’s growth that is affected by its neighboring region’s growth in 

any specific industry or all industries. There have been debates in earlier studies whether these 

different effects are truly distinct effects. We apply a correlation analysis test to test the validity 

of decomposition. Finally, this research identified an alternative decomposition technique that 

increased distinctiveness between industry and local effects that were not achieved by EstebanMarquillas shift share formulations. 

This research used an augmented spatial shift share model to understand regionalized 

comparative advantage in core economic sectors of the state of Louisiana and regional economic 

shifts that occurred after natural disasters like hurricanes Katrina and Rita. Our results indicate 

that while overall employment declined in the three year period prior to the natural disasters, the 

spatial shift share model identified regions that witnessed employment growth. Further, the 
100 

employment growth was broken into individual parishes that had localized comparative 

advantage. One of the possible explanations for this comparative advantage was a re-focusing of 

particular parishes to deepwater oil and gas exploration and development. It was argued that 

neighboring parishes to these parishes showing positive local comparative advantage from 

deepwater operations also received spatial spillovers in terms of employment growth. The study 

found that the spatial neighboring region industry effect was a distinct effect from the sub-region 

localized effect. The results indicate the importance of considering the neighboring region effect 

when formulating local growth and development policies. 

 
101 

CHAPTER 3. MODELING THE LABOR MARKET AND ESTIMATING 

THE RELATIVE PERFORMANCE OF ESTIMATORS FOR LOUISIANA 

LABOR MARKET: A LABOR FORCE MODULE APPROACH 

3.1. INTRODUCTION AND BACKGROUND INFORMATION

In the previous two chapters, we developed a socio-economic baseline for BOEM-defined 

Louisiana parishes. In particular, we identified key socio-economic variables and through the use 

of fuzzy set analysis and identified key strengths and weaknesses of these parishes based on the 

variables presented. Further, we focused on employment change in a key economic sector in the 

BOEM on shore area region of Louisiana and evaluated how they were impacted by Hurricanes 

Katrina and Rita. 

In the next two chapters, we present the development of a Community Policy Analysis 

System (COMPAS) model for Louisiana. This model can be applied to measure changes in local 

government revenues and expenditures from changes in major economic activity, such as 

deepwater oil and gas operations, among other external economic changes. In this chapter 

(Chapter 3), we present the development of the labor force module of COMPAS. In Chapter 4, 

we present the development of the fiscal module of COMPAS. Finally, in Chapter 5, we apply 

the fully developed COMPAS model to evaluate changes in local government revenues and 

expenditures for a given BOEM on shore area parish from drilling activity in the Gulf of Mexico. 

The Community Policy Analysis System (COMPAS) model is an effective tool to measure 

the labor and fiscal impacts of different industries in a region. The model exhibits inter-sectoral 

linkages, since an exogenous shock in any sector of the economy leads to a series of changes in 

other sectors. Community Policy Models such as the Louisiana Community Impact Model 

(LCIM) (Fannin et al. 2008; Adhikari and Fannin 2010a) have been helpful in addressing 

economic impact questions to address the policy issues of a region. Other policy analysis models 

such as the Virginia Impact Projection (VIP) Model developed by Johnson (1991), the Iowa 

Economic/Fiscal Impact Modeling System developed by Swenson and Otto (2000), and the 

Integrated Economic Impact and Simulation Model for Wisconsin Counties (Shields 1998) 

demonstrate how such a model could be used to aid local decision makers. This paper focuses in 

extending the results from Adhikari and Fannin (2008) by using panel models and comparing to 

3SLS modeling to measure the forecasting performances of estimators. 

The COMPAS modeling framework can be applied across the country to address labor 

market and fiscal impacts from initial changes in economic activity (Johnson, Otto and Deller 

2006). At its foundation, COMPAS is an employment driven model. Employment demand is 

generated by changes in local product demand. The definition of employment demand may vary, 

but the exogenous shock that appears from the changes in employment demand is the basis of the 

modeling system in COMPAS based models. In many cases, this product is converted to 

employment demand through the use of input-output models. The input-output (I/O) model treats 

final demand as exogenous and the labor market supply as perfectly elastic to meet the labor 

demands generated by the product demands (Beaumont 1990). In this I/O framework, an 

exogenous change in demand for the product and services interact with the rest of the economy 
102 

through linkages of industrial material goods and services in an economy, its local labor market, 

and ultimately, its fiscal sector. See Figure 3.1 for an example of this structure. 

Figure 3.1. Highlighting the labor market in the COMPAS modeling framework 

The chapter comprises several sections. The next section is a literature review where we 

present the major ideas of several scholars who have conducted similar studies and lay out a 

foundation for the development of the remaining sections of the chapter. Next, we set out a 

conceptual framework that explains the foundation of the model. This is followed by the 

objectives of the study, and then by a data and methodology section where we set forth the 

theoretical and empirical model and describe the data and methods we will be using for 

accomplishment of the study objectives. These results will then be discussed and compared 

based on their relative performance of alternative labor market estimators. 

Change in demand for local industry

(Block 1) 

Multiplier effects in local economy, direct, indirect, 

and induced effects on employment 

(Block 2) 

Changes in labor market

(Block 3) 

Changes in local government

revenue and expenditures 

(Block 4) 
103 

3.2. LITERATURE REVIEW

The labor force module is a demand driven framework based on employment demand 

(Swenson 1996; Johnson, Otto, and Deller 2006; Fannin et al. 2008). The underlying assumption 

is that economic growth is largely due to the exogenous increase in employment in a region. 

A concept of modeling the spatial labor market and a foundation of COMPAS-type models 

was developed by Johnson (2006); his assumption that economic growth of a community is 

based on the labor market that distributes jobs between in-commuters, out-commuters, currently 

unemployed and new entrants to the local labor market (Figure 3.2). Commuting plays a vital 

role while analyzing the labor market of a specific region. A small region might have a smaller 

resident labor force, but more commuters because of shorter travelling distance to its neighboring 

region. Similarly, a large and developed region might have measurable commuters because of 

more opportunities and job placement in the region. A labor market is conceptualized and 

presented in the Figure 3.2 below. 

The linking of the labor force module with input-output models such as IMPLAN (impact 

modeling for planning) is highlighted by Swenson and Otto (1998). They constructed an Iowa 

economic/fiscal impact model (IE/FIM) to generate detailed information for local decision 

makers about economic, demographic and fiscal variables. The model presents an interrelationship of the labor force module and a fiscal module in the sense that the changes in 

employment demand and the population are major factors affecting local tax bases, local 

revenues and expenditures. Labor force, out-commuters, and in-commuters are the three 

dependent variables used in the model whereas population is assumed to be a function of labor 

force and other variables that affect labor force participation rate. 
104 

Figure 3.2. A conceptual labor market (Johnson 2006). 

A similar study was carried out recently by Fannin et al. (2008) to evaluate the deepwater 

energy impacts on economic growth and public service provision in Lafourche Parish, Louisiana. 

The authors created a Louisiana community impact model (LCIM) in a block recursive fashion 

based on the COMPAS modeling framework to enumerate the linkages among local economic 

activity and the demand for local government services. A conjoined input-output and 

econometric model was used to analyze the economic impacts of the region. In this study, we 

propose modifications from the 2008 study in variables and the estimation procedure by 

inclusion of a three stage least squares model, and panel regression methods that account for 

cross-equation correlation and multi-year variation respectively. 

An extension of earlier studies was proposed by Evans and Stallmann (2006), where they 

proposed the Small Area Fiscal Estimation Simulator (SAFESIM) for Texas counties using a 

two-stage least squares procedure. SAFESIM was constructed as a spreadsheet-based simulator, 

which consisted of several socio-economic variables with data from county and school districts. 

Data in the model were obtained from a number of sources, including the U.S. Census Bureau, 

Woods and Poole Economics, Inc., National Center for Education Statistics’ Core of Common 

Labor 

Labor force 

Community 

Employment 

External employment 

External labor force 

Out-commuting 

In-commuting 

Commuting 

shed 
105 

Data, and the Census of Governments. A labor force module and fiscal module were estimated 

using a 14-equation model. Civilian labor force was defined to be a function of employment 

status and results showed that that the labor force was positively affected by population and 

negatively by the level of unemployment. Total population was assumed to be a function of total 

number of jobs (positive relationship) and net commuting (negative relationship). Similarly, net 

commuting (In-commuters minus Out-commuters) was defined as a function of the place of 

work employment and the level of unemployment. Results indicated that there was a positive and 

significant relationship of place of work employment with net commuting. As the number of jobs 

in a region increase, the number of in-commuters increase and out-commuters decrease and thus 

the net commuting is positive. The effect was opposite in case of the increased levels of 

unemployment. 

Many of the earlier studies in other disciplines used different techniques for evaluating 

forecasting performance. Cicarelli (1982) proposed a new method of evaluating the accuracy of 

economic forecasts where the probability of correctly forecasting directional change was 

introduced. Values of this measure were computed for eleven well-known macro econometric 

forecasting models. An inequality-type index of relative directional accuracy based on this 

measure was presented and used to evaluate the models in terms of their relative accuracy. Hsu 

and Wu (2008) performed a similar study for interval data with different evaluation techniques. 

They defined a criterion which was more efficient to evaluate forecasting performance for 

interval data, where they presented evaluation techniques for interval time series forecasting. The 

forecast results were compared by the mean squared error of the interval and mean relative 

interval error. 

Amirkhalkhali et al. (1995) examined the relative forecasting performance of different 

estimators proposed for a structural equation in a large system using Monte Carlo experiments 

with antithetic varieties. The performance of the estimators was compared in terms of the 

accuracy of the within-sample as well as post-sample predictions for 10 structural equations by 

using the mean absolute percentage error (MAPE) of forecasts. It concluded that the ridge-type 

estimator performed consistently better than other estimators in both the within-sample 

predictions and ex-post forecasts. Though many forecast evaluation techniques are available, 

most are designed for the end user of the forecasts. Most statistical evaluation procedures rely on 

a particular loss function. Forecast evaluation procedures, such as mean squared error and mean 

absolute error, that have different underlying loss functions, may provide conflicting results. 

Diersen and Manfredo (1998) developed a new approach of evaluating forecasts, a likelihood 

scoring method that does not rely on a particular loss function. The method takes a Bayesian 

approach to forecast evaluation and uses information from forecast prediction intervals. 

 

Most of the earlier community policy models dealt with the modeling issues and estimating 

relationships of several variables with labor market variables. Few of them have attempted to 

evaluate the forecasting performance of community policy models, Kovalyova and Johnson 

(2006), being one of them. They suggested that forecasting performance could improve model 

accuracy and validation. They ran simulations with all satisfactory models and looked for the 

best model (in terms of minimum error) from a statistical point of view to generate realistic 

economic predictions. They used several indicators to validate the Missouri Show Me model 

developed by Johnson and Scott (2006), which was estimated on the basis of cross-sectional 
106 

data. They used several quantitative indicators for each equation and each county in the sample 

to analyze the forecasting accuracy. Results showed that the “best” model performed with about 

10 percent error, as indicated by root mean square percent error and mean absolute percent error 

and concluded that the model produced forecasts of acceptable quality. 

Traditionally, most of the COMPAS models were built on cross-sectional frameworks. Data 

availability is one of the biggest issues when constructing COMPAS type models in different 

states. Commuting data were historically added in the model based on the census journey to 

work data that are released once every decade. This results in two constraints. First, one is forced 

to model the commuting patterns only in a census year. Second, if one relies on census data, one 

might need to assume that the commuting relation holds to the years between census periods, and 

this incorporates some level of measurement error into the model. A major contribution of our 

study is the addition of newly available annual commuting data by county (parish) which allows 

increasing reliability of off-census year cross-sectional models. This also provides the 

opportunity to develop a panel data estimator as an alternative in COMPAS labor market module 

estimation. 

This chapter concentrates on modeling the Louisiana labor market based on earlier developed 

community policy analysis models, and then on comparing and contrasting performance of 

alternative estimators using several approaches. As suggested by many researchers, we will 

estimate the performance using several quantitative methods where we analyze different 

indicators like mean error, mean square error, root mean square error and Theil’s coefficients as 

a benchmark for comparison. This will be a novel study in terms of comparing performance of 

several estimators of the labor force module in COMPAS modeling. 

3.3. CONCEPTUAL FRAMEWORK 

Labor markets involve a structural system where employment supply and employment 

demand are constantly changing between regions, and create a constant change in the flow of the 

labor force to meet demand both within and between regions. Neoclassical economics suggests 

that equilibrium in the labor market is the result of interactions between profit-maximizing firms 

and utility maximizing laborers. This interaction determines the price (wage in case of the labor 

market) and the quantity (number employed in case of labor market). One of the most common 

approaches of labor supply and labor demand could be the cases in Figure 3.3 and Figure 3.4 

where a region faces an upward sloping (positively sloped) labor supply and downward sloping 

labor demand (negatively sloped). In such cases, wage is determined where labor supply 

intersects labor demand (Hamermesh 1993). 

 
107 

w1

Figure 3.3. Result of labor demand change in employment and wages, assuming a constant supply. 

In Figure 3.3, LS1 and LD1 determine labor supply and labor demand in an equilibrium 

condition. If labor demand increases (with constant labor supply), we see that the labor demand 

curve moves outward (LD2) and thus both the employment and wages increases from E1 to E2

and w to w1 respectively. If the labor demand decreases (again, labor supply being constant), 

labor demand curve shifts inwards (LD3) and hence the employment decreases to E3 and wages 

decreases to w2. In case of Figure 3.4, LS1 and LD determine labor supply and labor demand in 

an equilibrium condition. If there is an increase in labor supply (labor demand holding constant), 

labor supply curve moves to right (LS2) and thus the employment increases from E1 to E2 but 

wage decreases from w to w1. On contrary, if the labor supply decreases, the labor supply curve 

moves to the left (LS3), resulting in the increase in wages to w2 but the decrease in employment 

to E3 (Figure 3.4). The magnitude of change in the employment and wages depend on the shift of 

the curve. 

 

E

w2 

LD3 

E1 E2 

LS1 

LD1 

LD2 
108 

Figure 3.4. Result of labor supply change in employment and wages, assuming a constant demand. 

Another approach is demonstrated by Johnson (2006), where individual labor faces a 

perfectly elastic labor supply, perfectly inelastic labor demand and exogenous wage8

 (Figure 

3.5). This approach is particularly relevant in the context of the COMPAS modeling framework 

since the model is implemented in a small open economy region, for example, a county or a city. 

Such a region faces a perfectly elastic labor supply because of its residents, in-commuters and inmigrants (Bhandari 2003). 

In Figure 3.5, a case of a small economy, labor supply is displayed as LS (which is infinitely 

elastic, as shown by horizontal line) and labor demand is displayed as LD (which is completely 

inelastic, as shown by vertical line). Wages are exogenous and shown as w in the vertical axis. 

An increase in labor demand from LD to LD1 would not change the wage rate but changes the 

total employment from E1 to E2. 

 

 

8 Here, we consider a small region, say county, and thus the change is labor demand may not necessarily 

change the wage rate. Hence, wage in such a case is exogenously calculated. 

w

L

L

S1

E

L

S2 

L

S3 

w

2

E E
109 

Figure 3.5. Result of perfectly elastic labor supply and perfectly inelastic labor demand. 

Changes in labor markets and how these labor market changes are influenced by changes in 

employment demand are described hereafter. Estimation of the labor force module plays a key 

role in our model, as is also the case with other COMPAS- based models. The Louisiana labor 

force module estimates structural equations for labor force, in-commuters and out-commuters, 

which closely explains the relationship between employment demand and the supply of labor 

needed to meet that demand. In the COMPAS modeling framework, labor supply is a function of 

labor force, unemployment, out-commuters and in-commuters within a region. Similarly, labor 

demand is the function of the wage rate. As shown in Figure 3.1, the labor force module lies 

between exogenous changes in employment and the ultimate fiscal effects (local government 

revenue and expenditures that occur in the local economy) in the COMPAS framework (Block 

3). 

Local and regional labor markets play a vital role in COMPAS-based models. These models 

assume that economic growth is caused mostly by an exogenous increase in employment. 

Conceptually, the labor force module intersects labor force demand and labor force supply: 

(E3.1) LD = LS 

where LD is labor force demand and LS is labor force supply (Johnson 2006). The demand 

curve for the labor force is a function of the wage rate: 

(E3.2) LD = f(w) 

where w is the wage rate. We can invert the labor demand equation to obtain 

L

D

L

D1 

L

S

E E
110 

(E3.3) w = g(LD) 

We can also evaluate the supply as disaggregated into the following components: 

(E3.4) LS =LF-U-OC+IC 

where LF is the total labor force, U is the total unemployment, OC is the total number of outcommuters, and IC is the total number of in-commuters. We can then evaluate each component 

of the total labor supply as a function of employment as well as a vector of supply shifters 

(Johnson, Otto and Deller, 2006). 

(E3.5) LF = fL (w, ZLF ) = fL (g (LD ), ZLF )

(E3.6) OC = fL (w, ZOC ) = fL (g (LD ), ZOC ) 

(E3.7) IC = fL (w, ZIC ) = fL (g (LD ), ZIC ) 

where Z is a vector of supply shifters for labor force, out-commuters, and in-commuters. 

3.4. OBJECTIVES OF THE STUDY

This study aims to develop a model to forecast labor demand in terms of labor force, incommuters, and out-commuters for the labor force module of Louisiana Community Impact 

Model (LCIM) using alternative procedures that are capable of increasing the performance over 

traditional COMPAS estimators. The specific objective includes modeling the labor force 

module (labor force, in-commuters and out-commuters) for all parishes of Louisiana with crosssectional, three stage least squares (3sls), and panel approaches to compare the relative 

forecasting performance of the alternative estimators. 

3.5. DATA AND METHODOLOGY

Estimation is based on the COMPAS model for all parishes of Louisiana that includes all 64 

parishes9

, where the variables for the labor force module were selected on the basis of Fannin et 

al. (2008) and were modified depending upon the requirements of our model. Louisiana is a good 

candidate for such a test because of the heterogeneity of the local labor force within the state. 

Seven different equations are estimated by a cross-section Ordinary Least Square (OLS) model 

as a base control with three stage least squares and the panel data model also estimated. We 

estimate the model using data from the Bureau of Economic Analysis (BEA) regional economic 

data series. In-commuting and Out-commuting Data come from the U.S. Census Bureau’s new 

Local Employment Dynamics Project. All regressions are performed using STATA. The 

forecasting performance is evaluated based on the procedures outlined in Johnson, Otto, and 

Deller (2006) and Kovalyova and Johnson (2006). 

 

9 A few outliers were removed using the r-student procedure. 
111 

The labor market equations in this module are based on the conceptual labor market 

discussed earlier in the paper. A cross-sectional OLS model is used as a base control model using 

the sample year of 2008. A panel data method is applied to observe whether the model performs 

better with increased observations, and the three stage least squares method is used to both 

improve model specification by explicitly modeling endogeneity between equations in the 

model, and to correct for any correlation, present between each individual equation’s error terms. 

Following the work by Johnson (1996); Swenson (1996); Shields (1998); and Fannin et al. 

(2008), the Louisiana labor force module empirically specifies several equations for these 

variables. 

Equations for Louisiana labor force module are specified as: 

(E3.8) WAGE= β20+β21EMP+ β22UNEMP+ β23WAGLAG+ ε

(E3.9) POP= β30+β31EMP+ ε

(E3.10) UNEMP = β40+β41EMPOP+ β42WAGE+ β43UNEMPLAG +ε

(E3.11) INCOMM = β50+β51RELLOCWA+ β52RELLOCUN+ β53EMPOP +ε

(E3.12) OUTCOMM = β60+β61RELLOCWA+ β62RELLOCUN+ β63EMPOP +ε

(E3.13) LABFOR = β70+β71POP+ β72ELDPOP+ β73WAGE +ε; 

where, LABFOR (labor force), UNEMP (unemployment), WAGE (average wage per job), POP 

(population), OUTCOMM (out-commuters), INCOMM (in-commuters) are endogenous 

variables and EMP (place of work employment), WAGLAG (wage lag), EMPOP (employment 

opportunities), UNMPLAG (unemployment lag), RELLOCWA (relative local wage), 

RELLOCUN (relative local unemployment), and ELDPOP (percentage of elderly population) 

are exogenous variables. The expected signs based on previous studies (Shields 1998; Johnson, 

Otto, and Deller 2006; Fannin et al. 2008) for these variables can be seen in the Table 3.1 below. 

The labor market equations provide the information on all the components of labor supply 

and labor demand. Most employed (including self-employed) workers commute some distance. 

The data that we use are organized as if jobs and workers were located in discontinuous 

locations. When data are recorded, some workers are identified as residents of a different 

location than that of their jobs. These workers are defined as commuters. This definition, 

however, is very much dependent on the arbitrary boundary of data cells; especially the size of 

the data cells. In practice, these data cells are typically counties or census places. Functional 

forms for each of the equations were based on Fannin et al. (2008); however, we also tested the 

functional forms for each equation by the box-cox test and results suggested the log-log form to 

be the most appropriate functional form based on the data. The log-log form was incorporated in 

the remainder of the equations. 
112 

Table 3.1. Expected signs for different variables for labor force module equations. 

Dependent Variables Independent Variables 

WAGE EMP 

(+) 

UNEMP 

(-) 

WAGLAG 

(+) 

UNEMP EMPOP 

(+) 

WAGE 

(-) 

UNEMPLAG 

(+) 

POP EMP 

(+) 

 

INCOMM RELLOCWA 

(+) 

RELLOCUN 

(-) 

EMPOP 

(+) 

OUTCOMM RELLOCWA 

(-) 

RELLOCUN 

(+) 

EMPOP 

(-) 

LABFOR POP 

(+) 

ELDPOP 

(-) 

WAGE 

(+) 

As stated earlier, the primary purpose of this chapter is the performance measurement of 

alternative estimators based on newly available datasets and to check whether the uniqueness of 

cross-sectional units matter. This is performed by evaluating different estimators of the general 

labor force module of Louisiana. We are interested in choosing an optimal model that maximizes 

the forecasting performance for the labor force module equations of the Louisiana COMPAS 

model. A cross-sectional OLS, 3sls, and a panel approach will be applied in order to model the 

labor force. Based on the results, we evaluate if the model specification addressing endogeneity 

(as observed from 3sls) or additional time series data (panel data set that incorporates both 

spatial and temporal dimensions) is relatively more important for increasing the forecasting 

performance. 

We start with the OLS/GLS framework where we take a single year’s worth of data as 

performed by Johnson et al. (2006). The base year as a sample for estimation is 2008. Next, we 

take into account the three stage least squares model (2000–2008) and a panel model (2000–

2008) that takes into account the newly available annual data on commuting. 

Comparing the performance of different estimators is an important step in the model building 

process since it can suggest the best model to be selected and different ways in which the model 

can be improved. Because on data availability, we compare forecasted labor market estimates for 

2008 to actual labor market data reported in the same year. The performance of estimators is 

compared on the basis of quantitative evaluation methods. These methods include analysis of 

mean simulation error (ME), mean percent error (MPE), mean absolute error (MAE), mean 

absolute percent error (MAPE), mean square error (MSE), root mean square error (RMSE), root 

mean square percent error (RMSPE), and Theil’s coefficient U1 and U2 (Theil, 1970, 1975; 

Pindyck and Rubinfield 1991, 1998; Johnson, Otto, and Deller 2006). These performance metrics 

will be provided for both in-sample years (2008) and selected year’s out-of-sample (2000–2008). 
113 

3.6. RESULTS AND DISCUSSION

Results from Table 3.2 demonstrate the descriptive statistics of variables used in the labor 

market equations of the labor force module of Louisiana. As can be seen, there is measurable 

variability in the data. It should be noted that unlike COMPAS type models in other states that 

incorporate only a subset of counties in a state for analysis, this model incorporates all Louisiana 

parishes, large and small, resulting in greater variability. 

Table 3.2. Variable description and summary statistics for analysis of Louisiana parishes. 

 Variable Variable Description Mean Standard 

Deviation 

Min Max 

 

EMP (#) Place of work/employment 30,165 43,908 1,944 221,739 

WAGE ($) Average wage per job 30,072 7,156 17,653 55,730 

UNEMP (#) Unemployment 1,615 2,189 114 13,931 

POP (#) Total population 69,315 95,303 5,671 483,663 

INCOMM (#) Total in-commuters 10,754 19,890 272 118,882 

OUTCOMM (#) Total out-commuters 10,552 13,194 488 86,044 

LABFOR (#) Total labor force 31,555 40,133 2,196 236,340 

WAGLAG ($) Wage lag 29,222 6,615 17,653 51,685 

UNEMPLAG (#) Unemployment lag 1,640 2,224 114 13,931 

RELLOCWA ($) Relative local wage 

(avg local wage/avg continuous wage) 

1.009 0.147 0.718 1.507 

RELLOCUN (#) Relative local unemployment 

(local unemployment/contiguous 

unemployment) 

0.305 0.569 0.010 4.512 

EMPOP (#) Relative employment opportunities 

(local employment/contiguous 

employment) 

0.318 0.561 0.012 4.997 

ELDPOP (%) % population over 65 years of age 12.64 2.28 6.96 18.05 

 

Results shown in Table 3.3 demonstrate parameter estimates comparison of the OLS 

estimators, 3sls estimators, and panel estimators for all equations of the labor force module of 

Louisiana. Most of the signs in the parameter estimates are as expected; however, there are some 

counter-intuitive estimates. 
114 

Table 3.3. Parameter estimates for OLS, 3sls, and panel regressions of Louisiana labor force module. 

Labor Force Module Linear (OLS) 3SLS Panel 

Coeff. t-stat Coeff. z-stat Coeff. z-stat 

Wage 

Employment 0.008 0.62 -0.002 -0.21 0.024*** 4.73 

Unemployment -0.010 -0.72 0.001 0.11 -0.026*** -4.50 

Wage lag 1.00*** 59.86 1.008*** 94.18 0.990*** 127.03

Intercept 0.051 0.34 -0.025 -0.28 0.092 1.22 

 

Unemployment 

Employment opportunities 0.008 0.44 0.042** 3.14 0.036*** 4.11 

Wage 0.103 1.32 -0.072 -1.58 -0.066*** -3.20 

Unemployment lag 0.995*** 37.70 0.926*** 56.38 0.945*** 85.04 

Intercept -0.874 -1.29 1.316*** 2.86 1.112*** 4.45 

 

Population 

Employment 0.906*** 32.57 0.889*** 50.22 0.881*** 35.27 

Intercept 1.788*** 7.01 1.979*** 11.45 2.050*** 8.92 

 

In-commuters (Dep var) (log-log 

model) 

Relative local wage 1.534** 2.35 1.673*** 6.38 0.701 1.64 

Relative local unemployment -0.630*** -5.73 -0.443*** -6.27 -0.283** -2.46 

Relative employment opportunities 0.172 1.49 0.158** 2.26 0.202*** 4.05 

Intercept 10.286*** 51.21 9.536*** 122.48 9.400*** 35.16 

 

Out-commuters (Dep var) (loglog model) 

 

Relative local wage -0.242 -0.43 -0.257 -1.36 -0.481 -1.10 

Relative local unemployment 0.515*** 5.15 0.334*** 6.87 0.306*** 4.49 

Relative employment opportunities 0.110 1.24 0.055 1.14 0.126 1.04 

Intercept 10.336*** 78.51 9.531*** 158.27 9.714*** 63.07 

 

Labor force (Dep var) (log-log 

model) 

 

Population 1.024*** 22.56 0.888*** 49.65 0.858*** 12.87 

% population over 65 years of age 0.110 0.48 -0.139*** 3.19 -0.415** 2.02 

Wage -0.280 -1.04 0.676*** 10.32 0.128*** 2.57 

Intercept 1.695 0.59 7.022*** 10.31 -1.620** -2.22 

 

***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively. 

Parameter estimates for the labor force module are presented in the Table 3.3. Predictably, in 

the wage equation, the current wage rate is significantly related to its lagged value. Parameter 

estimates for lagged wages close to one suggest that almost all effects are captured by the lagged 

variable and that the lagged wages are considered to be important determinants of current wages. 

Similar interpretation could be made in the case of the unemployment equation that the current 

unemployment rate is significantly related to its lagged value and the parameter estimates for 

lagged unemployment close to one suggest relative year-to-year stability of labor markets. The 

negative sign (3sls and panel model) for wage is consistent with the theory suggesting that an 
115 

increase in wage would attract more people and that would be an incentive for a decrease in 

unemployment. 

Place of work employment is considered to be the primary variable that drives changes in 

variables from the labor force module in COMPAS, since it determines the changes in 

population in the regions of study. Results from the population equation suggest that economic 

opportunity, as measured by the number of local jobs, has an important influence on the number 

of local residents. This is consistent with the theory, since people tend to live close to their place 

of work. Hence, as new local jobs are created, people migrate into the region: here 100 new jobs 

result in about 90 additional local residents. 

 

In case of the in-commuters equation (for all models), we see that an increase in the relative 

local wage would attract more in-commuters. When region A has more jobs than contiguous 

regions B, C, and D, people will in-commute to region A from B, C, and D in search of 

employment opportunities. Similarly, a negative sign for the relative local unemployment is 

consistent with theory, as it depicts that an increase in unemployment in region A compared to 

regions B, C, and D would decrease the number of in-commuters into region A; the workers 

from B, C, and D would work in their place of residence rather than commuting to region A. 

Furthermore, a positive sign for employment opportunity depicts that an increase in employment 

in region A compared to regions B, C, and D would increase the in-commuters of region A. 

There would be increased supply of jobs in region A and thus people from regions B, C, and D 

would out-commute to region A to meet this newly available supply. 

In case of the out-commuters equation, the negative sign of the relative local wage variable 

indicates that an increase in the local wage of region A compared to regions B, C, and D would 

lead to a decrease in out-commuters from region A. This is also consistent with the theory 

because an increase of local wages in region A works as an incentive for the workers of region A 

to live and work in their own region which certainly would decrease the number of outcommuters. Similarly, a positive sign for the relative local unemployment is consistent with 

theory, as it depicts that an increase in unemployment in a region A compared to regions B, C, 

and D would increase the out-commuters of region A as they would explore for jobs in their 

contiguous regions. Though the signs on the coefficients for relative employment opportunities 

run counter to theory, they are not statistically significant. 

Not surprisingly, population is the greatest determinant of the local labor force, as evident 

from the labor force equation. As observed from the panel data model, 100 additional residents 

lead to an around 85 person increase in the local labor force. The negative sign on the percent 

population above 65 years of age depicts that an increase in elderly population leads to decreases 

in the labor force of a region since fewer at this age will continue to work. Similarly, results 

show that an increase in wages lead to an increase in the labor force, which is consistent with the 

theory, because an increase in wages would be an incentive for people to starting looking for 

jobs and hence join the labor force. 
116 

Table 3.4. Average performance estimation measures for dependent variables in Louisiana labor force 

module. 

Labor Force Module Linear (OLS) 3sls Panel 

 

Wage 

Mean Percent Error 0.002 -0.003 0.0005 

Mean Absolute Percent Error 0.026 0.020 0.019 

Root Mean Square Percent Error 0.004 0.002 0.001 

Theil’s Coeff (U1) 0.010 0.048 0.046 

Theil’s Coeff (U2) 0.021 0.276 0.275 

 

Unemployment 

Mean Percent Error 0.027 0.027 0.026 

Mean Absolute Percent Error 0.181 0.173 0.172 

Root Mean Square Percent Error 0.068 0.068 0.068 

Theil’s Coeff (U1) 0.141 0.140 0.140 

Theil’s Coeff (U2) 0.689 0.690 0.689 

 

Population 

Mean Percent Error 0.044 0.054 0.038 

Mean Absolute Percent Error 0.183 0.191 0.174 

Root Mean Square Percent Error 0.059 0.067 0.054 

Theil’s Coeff (U1) 0.077 0.081 0.075 

Theil’s Coeff (U2) 0.176 0.192 0.385 

 

In-commuters 

Mean Percent Error 0.300 0.434 0.422 

Mean Absolute Percent Error 0.711 0.892 0.855 

Root Mean Square Percent Error 1.698 1.688 1.685 

Theil’s Coeff (U1) 0.246 0.267 0.206 

Theil’s Coeff (U2) 0.745 0.707 0.798 

 

Out-commuters 

Mean Percent Error 0.291 0.317 0.257 

Mean Absolute Percent Error 0.632 0.651 0.617 

Root Mean Square Percent Error 0.845 0.915 0.815 

Theil’s Coeff (U1) 0.264 0.138 0.136 

Theil’s Coeff (U2) 0.475 0.634 0.626 

Labor force 

Mean Percent Error 0.051 0.097 0.042 

Mean Absolute Percent Error 0.233 0.281 0.215 

Root Mean Square Percent Error 0.128 0.143 0.107 

Theil’s Coeff (U1) 0.051 0.052 0.047 

Theil’s Coeff (U2) 0.281 0.282 0.264 

 

When testing the relative performance between the models, for most cases, the panel data 

model outperformed both the ordinary least squares and three stage least squares models in terms 

of mean error, root mean square percent error, and Theil’s coefficients (Table 3.4). Theil’s 
117 

coefficients are calculated based on root mean square error and zero value of the coefficient 

indicates perfect prediction and any value up to 10% is considered effective. 

Referring to Figure 3.6, a comparison is made on the off-years forecasting performance 

between these models for the labor force equations. Our OLS model is based on cross-sectional 

data for the year 2008. Our 3sls and panel data are based on years ranging from 2000 to 2008. 

Results display a similar pattern in most cases (2005 and 2006 display some unusual pattern 

which might have resulted from the effects of hurricanes Katrina and Rita); panel data model 

outperform both the OLS and 3sls model, measured in terms of average absolute mean percent 

error measures. 

Figure 3.6. Comparing off-years MAPE by OLS, 3sls, and panel data models. 

When we compare OLS and 3sls by same sets of error measures, 3sls seems to outperform 

OLS on all three equations. This might be consistent with the theory because 3sls procedure 

improved model specification (by incorporating endogenous regressors) increased forecasting 

performance. Further, as expected, inaccuracy of forecasts increased as we back-casted further 

from the cross-sectional date from which the parameter estimates were constructed (2008). 

Although error measures were suggested by Kovalyova and Johnson (2006) to evaluate what 

would be considered quality forecasting performance, we conducted a mean comparison test in 

STATA to compare the base OLS model with 3sls and panel data models for four different 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

2008 2007 2006 2005 2004 2003 2002 2001

Mean Absolute Percent Error

Labor Force Equation

OLS

3sls

Panel
118 

equations (wage, in-commuter, out-commuter, and labor force) of the labor force module. The 

test performs a comparison of means for all possible combinations of groups. For instance, we 

have three types of models (OLS, 3sls, and panel) and we would like to see if there are 

differences in means between groups, this test computes the t-test for all three possible 

combinations. The output is presented in a table of differences in means (as denoted by 

magnitude) and includes the value (as denoted by t-stat), and significance level of the t-test (as 

denoted by single, double, and triple asterisks for indicating statistical significance at 10%, 5%, 

and 1% respectively. These results are presented in Tables 3.5–3.8. 

Table 3.5. Mean comparison test for wages, based on MAPE. 

Wages 

 OLS 3sls Panel 

 Magnitude t-stat Magnitude t-stat 

OLS -127 -4.35*** -0.002 -1.74** 

3sls 127 4.35*** 

Panel 

***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively 

Table 3.6. Mean comparison test for in-commuter, based on absolute mean percent error. 

In-commuter 

 OLS 3sls Panel 

 Magnitude t-stat Magnitude t-stat 

OLS 0.106 2.22** 0.069 1.132 

3sls -0.036 -1.37 

Panel 

***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively 

Table 3.7. Mean comparison test for out-commuter, based on MAPE. 

Out-commuter 

 OLS 3sls Panel 

 Magnitude t-stat Magnitude t-stat 

OLS 0.020 0.521 0.035 0.860 

3sls 0.014 1.36 

Panel 

 

***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively 

 

 
119 

Table 3.8. Mean comparison test for labor force, based on MAPE. 

Labor Force 

 OLS 3sls Panel 

 Magnitude t-stat Magnitude t-stat 

OLS -0.088 -4.064*** -0.051 -2.338** 

3sls 0.036 1.236 

Panel 

***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively. 

Overall, results from Tables 3.5–3.8 suggest that although the panel data model is always 

lower in magnitude in terms of error measures as compared to the base OLS model and the 3sls 

model for all five labor force module equations (e.g., Figure 3.6), it is not always significantly 

lower (in terms of absolute mean percent error) than the OLS or 3sls model. In most equations, 

either the 3sls or panel data model maintained a significantly lower mean absolute percent error 

than the OLS model. However, the test statistics were inconclusive as to whether a panel model 

taking into account endogeneity (3sls) or without endogeneity (panel) generated statistically 

superior forecasts. 

 

Average error measures are not a perfect method for evaluating the performance of entire 

region. We can, therefore, take individual parish data and evaluate the performance of estimators 

in terms of quantitative measures like mean error, mean percent error and root mean square error 

to figure out how much the predicted value deviates from the actual value. For the labor force 

equation (in case of OLS), we could see that the average mean percent error, average absolute 

mean percent error, and average root mean square percent error are 0.051, 0.233, and 0.128 

respectively (Table 3.4). However, because of the heterogeneity in space, some parishes, like 

West Feliciana, Plaquemines, Assumption, East Baton Rouge, Iberville, and Orleans, are not 

performing as well on average, because their predicted values are measurably different than their 

actual values and thus are the reason for higher error values. On the contrary, parishes like 

Calcasieu, Bossier, Caldwell, Claiborne, Richland, St. Helena, Terrebonne, Union, and Madison 

are performing better than the average error measures as the difference between the predicted 

and actual values are close to zero. 

3.7. CONCLUSION AND LIMITATIONS

This research identified newly available data from which to evaluate alternative models for 

improving forecasting performance for labor market module estimators in Community Policy 

Analysis System-type models. In particular, we applied new labor market data on commuting 

from the U.S. Census Bureau to apply more time accurate commuting data for OLS and three 

stage least squares models as well as develop a panel dataset of commuting to apply a panel data 

estimator to estimate and forecast labor force, in-commuting, and out-commuting. 

 

Panel data and the three stage least squares, in most of the cases, have advantages over crosssectional OLS regressions in improving model forecasting performance. The case can be made 

that the sample year (2008) for the OLS cross-section equation might not be a good year for the 
120 

labor force module given this year falls within the most recent national recession that impacted 

the country. 

One limitation of this study is the exclusion of spatial econometric analysis to build the 

models that might take into account spatial behavior in terms of distance measures. These spatial 

estimators might also be used as alternatives to the COMPAS model to evaluate whether these 

estimators would increase the forecasting performance by including a space variable in the 

model. Their inclusion would be a future extension of this research. 

An additional limitation of this research is the unfortunate timing of the exogenous shock of 

hurricanes Katrina and Rita during the modeling period. The 2005 and 2006 years are likely 

outliers in terms of temporary labor market shifts that did not settle out until 2007. Including 

these two years in our panel dataset may have reduced the forecasting performance of the panel 

data estimator. Future research may investigate panel data windows that exclude this period. 

An evaluation of the alternative methodologies performed in this study are expected to give 

regional economic modelers better information from which to choose econometric models for 

labor force modeling in COMPAS-type models. Using the data from different sources, this study 

developed a model to forecast different sectors of the labor force module using cross-sectional 

linear, three stage least squares, and panel data regression. Future optimal applications of these 

estimators will improve forecasts and increase the demand and application of these models by 

local governments and other constituencies. 

 
121 

CHAPTER 4. MODELING THE LOUISIANA LOCAL GOVERNMENT 

FISCAL MODEL IN A DISEQUILIBIRUM ENVIRONMENT: 

A MODIFIED COMPAS MODEL APPROACH 

4.1 INTRODUCTION AND BACKGROUND INFORMATION

Most of the public service expenditure models under the community policy analysis system 

(COMPAS) are structured under an equilibrium condition assumption, i.e., supply equals 

demand (Johnson, Otto, and Deller 2006). Based on Inman (1978), the expenditure equations 

tend to describe the equilibrium of public expenditure demand and supply. First, we explain the 

demand side, which determines how revenue is raised to pay for goods and services and/or how 

the goods and services will be produced. Second, we explain the supply (production) side by the 

process of transforming inputs to outputs. These models have rarely been tested in an 

environment where the public sector may be argued to operate in a disequilibrium environment.10

The primary objective of this study is to assess whether the forecasting performance of public 

sector expenditure under a COMPAS fiscal module (an equilibrium model) fits reasonably well 

under a disequilibrium environment. Conceptually, the fiscal module under a COMPAS 

framework represents an equilibrium concept and this equilibrium is operationalized by demand 

shifters modeled empirically. These shifters, however, may not work well in a disequilibrium 

environment, where exogenous shocks push the public sector into an intermediate period (or 

long-term period) where local government public sector supply in less sensitive to traditional 

demand curve shifting conditions. In such cases, one should consider alternative models for 

forecasting local government revenues and expenditures during the period of supply-demand 

disequilibrium. This study is focused on evaluating the conceptual framework for modern day 

local government revenue and expenditure forecasting along with the strengths and weaknesses 

of such modeling in terms of empirical specification. We compare the traditional COMPAS 

model with a modified COMPAS model and analyze the forecasting performance of several 

indicators under disequilibrium conditions. The study evaluates forecasting performance during 

the time frame of proposed disequilibrium, where the data represents a period of time of major 

exogenous shocks (hurricanes Katrina, Rita, and Gustav)11 to local government. 

A traditional equilibrium public service model is tested as compared to a naïve model (that 

incorporates dynamics with inclusion of a lagged dependent variable) where we evaluate public 

service expenditure forecasting in a disequilibrium environment. The naïve model (lagged 

dependent variable) then is tested against the naïve plus model (an inclusion of revenue capacity 

variables in the naïve model) and a modified naïve model (a hybrid model that includes the naïve 

plus model as well as demand shifter co-variates from the traditional COMPAS empirical 

 

10 This chapter is a reprint from Adhikari, Arun, and J. Matthew Fannin. (2013). “Modeling the Louisiana 

Local Government Fiscal Module in a Disequilibrium Environment: A Modified COMPAS Model Approach.” 

Journal of Regional Analysis and Policy. 43(2): 138-156. Permission to reprint granted from the editor of the 

journal. 

11 Hurricanes Katrina and Rita made landfall in Louisiana in 2005 and Hurricane Gustav made 

landfall in Louisiana in 2008.
122 

specification). In addition, a comparatively newer approach (quantile regression) is also 

introduced to evaluate its performance among existing single year cross-sectional data-based 

COMPAS estimators. 

The remainder of the study begins with a section that presents a historical background of 

local fiscal modeling. We explain the theoretical and conceptual background of local public 

service modeling in terms of COMPAS frameworks and alternative frameworks in this section. 

This will be followed by the empirical specification of the fiscal module, where we set forth the 

empirical model with revenue capacity and expenditure equations. The succeeding section 

describes the data and methodology used for the analysis. We will then analyze the data and 

discuss the results and key findings of the regressions and the performance comparison of 

different estimators from various underlying models and compare them based on their relative 

forecasting performance. Finally, we conclude the study by pointing out some limitations of the 

study and the future opportunities for research. 

4.2. LITERATURE ON LOCAL FISCAL MODELING

Several studies have focused on the construction and evaluation of fiscal modules by local 

governments to determine the level of public services provided to its residents. In the 1960s and 

1970s, ad hoc expenditure models dominated the modeling issues of the local public sector. 

Other models developed during this period incorporated the concept of modeling public services, 

concentrating on empirical analysis but often lacking a conceptual framework. We present a 

snapshot of some of these studies built on the empirical frameworks used to model local public 

service delivery in Table 4.1. 

The introduction of IMPLAN (Alward et al. 1989) created a revolution in regional economics 

for studying impact analysis, starting in the 1980s. IMPLAN was a major modeling 

accomplishment because of its creation of local input-output models based on secondary data 

that could be updated annually as compared to other models dependent on primary data for 

construction that were for typically larger regions and costly to construct and update (Johnson, 

Otto, and Deller 2006). Unfortunately, despite IMPLAN’s success at generating contribution and 

impact projections for community-wide current account variables such as output, value-added, 

labor income, and employment, it was less effective in providing valuable information for a 

community’s public sector. 

 
123 

Table 4.1. Summary of determinants of local public service expenditures in the 1960s and 1970s. 

Author 

(Year) 

Model 

Used 

Objectives of 

the Study 

Dependent 

Variables 

Major 

Regressors 

Major Findings 

Fisher 

(1961) 

Simple 

linear 

regression 

To estimate per 

capita 

expenditure of 

state and local 

government 

Per capita 

expenditure of 

state and local 

government 

Population, 

population 

density, per 

capita 

income 

Income positive 

and significant, 

population density 

negative and 

significant 

Sacks and 

Harris 

(1964) 

Ordinary 

least 

squares 

To analyze total 

direct 

expenditures on 

several 

categories of 

local government 

Total direct 

expenditures, 

health and 

hospital, 

education, and 

other 

expenditures 

Population, 

federal and 

state aids, 

per capita 

income, % 

urban 

Income and 

federal and state 

aids significantly 

describe local 

government 

expenditures 

Barr and 

Davis 

(1966) 

Simple 

and 

multiple 

regression 

To analyze 

determinants of 

several 

expenditure 

categories of 

Pennsylvania 

counties 

General 

government 

expenditure, 

highways 

expenditure, 

judicial 

expenditure, and 

other 

expenditure 

Property 

holdings, 

median 

income, 

median 

education, 

voting 

preferences 

Differences in 

preferences for 

expenditures 

significantly 

explains several 

local government 

expenditure 

Bahl and 

Saunders 

(1966) 

Ordinary 

least 

squares, 

non-linear 

regression 

To analyze the 

temporal pattern 

of determinants 

of state and local 

government 

expenditures 

State and local 

government 

expenditures 

Per capita 

federal grant, 

per capita 

income, 

population, % 

urban 

Per capita federal 

grant, income, 

population density 

and % urban were 

all positive and 

significant 

MacMohan 

(1970) 

Ordinary 

least 

squares 

To analyze 

determinants of 

public primary 

and secondary 

education 

expenditures by 

cross-sectional 

and time series 

data 

Public primary 

and secondary 

education 

expenditures 

Pupil per 

teacher, 

assessed 

value, federal 

and state 

aids, 

personal 

income, 

population 

Federal and state 

aids, pupil 

enrollment over 

time significantly 

explain growth of 

public primary and 

secondary 

education 

expenditures 

Bergstrom 

and 

Goodman 

(1973) 

Ordinary 

least 

squares 

To estimate 

demand functions 

for municipal 

public services 

General 

expenditures, 

police 

expenditures, 

parks and 

recreation 

expenditures 

Tax share, 

population 

change, 

crowding 

parameter, 

income 

Expenditures on 

different 

categories depend 

on locality. Income 

plays major role in 

most localities 

Consequently, researchers then focused on building models that could cater to the 

customized needs of communities for public sector impacts and forecasting based on secondary 

data. In an effort to develop advanced fiscal models for local communities, the regional rural 

development centers and the Rural Policy Research Institute (RUPRI) supported several rural 
124 

studies that intended to generate an empirically tractable approach to local public sector 

modeling (RUPRI 1995). RUPRI then extended its help and support for conducting multistate 

interdisciplinary research by building an outreach network, known as the community policy 

analysis network (CPAN) (Scott and Johnson, 1998). The network was made up of a group of 

social scientists who attend periodic meetings to develop new models and support tools on 

emerging issues that were important to rural communities. Their efforts began by developing a 

stylized model that was originally intended as a true general equilibrium-type fiscal model where 

one could formally model separately local public sector demand and supply. In an effort to 

explore a model that accounts for both the empirical and the conceptual framework and could be 

customized based on the needs of local public supply and demand, they developed what is today 

known as the community policy analysis system (COMPAS) models (Johnson, Otto, and Deller 

2006). These models originated from mostly CPAN researchers from Midwestern states 

developing models for rural counties in their respective states where these regions were quite 

homogenous and equilibrium assumptions held during the slow steady growth of these rural 

regions in the 1990s. They were developed, as their name implies, to focus on evaluating local 

community policies on labor markets and local governments; however, as their development and 

use evolved, modelers began applying these empirical tools to assist local governments with 

general forecasting. 

4.2.2. COMPAS Modeling Framework 

The COMPAS model is an effective tool to estimate the fiscal impacts of different 

policy/development scenarios on a region (Scott and Johnson 1997). COMPAS models are 

regional economic models that combine two different approaches (typically input-output and 

parametric econometric modeling) to build an integrated, or conjoined, model of rural economic 

structure (Johnson, Otto, and Deller 2006). These models are mostly used to evaluate the impacts 

within a small city, region or a county. COMPAS models typically treat employment demand as 

an exogenous driver of changes in the labor market which ultimately impact the fiscal sector. 

The fiscal module in this research is an extension to the module used by Fannin et al. (2008) and 

Adhikari and Fannin (2010a). 

 

COMPAS models incorporate statistically estimated relationships to forecast changes in 

demographic, economic and fiscal conditions under exogenous changes in economic activity. 

The model includes a system of cross sectional econometrically estimated equations estimated 

for communities in respective states (Johnson, Otto, and Deller 2006). These estimates, though in 

some cases statistically significant, might not perform well in terms of forecasting performance. 

These equilibrium COMPAS estimators could be tested under disequilibrium conditions in order 

to compare the relative forecasting performance based on multiple quantitative evaluation 

methods. The block recursive diagram of the COMPAS model is displayed in Figure 4.1. 

 
125 

Figure 4.1. Highlighting the fiscal module in the COMPAS modeling framework. 

The median voter model was introduced to develop the conceptual framework of public 

sector demand and supply based on the early voter theory of Black (1958). This median voter 

theory was used extensively to model the local public sector since the service demands of 

median voters were addressed by the political parties in order to carry elections. As was stated 

earlier, the local government’s fiscal behavior is demand driven (for public goods and services). 

In situations of majority rule, a median voter model has been used in many instances to analyze 

the fiscal behavior of a region. This approach of the median voter12 was initially developed by 

Barr and Davis (1966), but was then was applied by several scholars to replace the then popular 

ad hoc expenditure model. Median income levels, population, tax prices of public goods, and 

consumer’s tastes and preferences at the local level are assumed to determine the level of 

demand for local public goods and services. Any elected official approving government spending 

far from the median will be driven out of office by an opposition that proposes an expenditure 

level closer to the demands of the median voter. 

 

 

12 See Shaffer et al. (2004) for detailed explanation for median voter model, where the author has 

compared similarity between median voter model and Hotelling model by using a beach vendor example. 

Change in demand for local industry 

(Block 1) 

Multiplier effects in local economy, direct, indirect, 

and induced effects on employment 

(Block 2) 

Changes in labor market 

(Block 3) 

Changes in local government 

revenue and expenditures 

(Block 4) 
126 

There are a few limitations which could hinder the effectiveness of the model. Some of the 

factors that limit the supply demand equilibrium in the traditional conceptual framework are, but 

are not limited to, downward sloping supply curves, the nature of private and public goods, and 

the non-excludability and non-rivalrous nature of public goods (Buchanan 1965). Hence, applied 

researchers interested in providing local stakeholders valuable research tools developed an 

alternative framework that simply attempts to forecast the movement of public expenditure 

between equilibrium points over time (Johnson, Otto, and Deller 2006). 

In particular, they described an equilibrium point where structural demand meets structural 

supply. We can estimate a set of equations that models this equilibrium point as proposed by 

Johnson, Otto, and Deller (2006). 

(E4.1) 

    

n

i

e N I i Z 5

1 2 3 4       ,

where e is the expenditure (spending) of local governments,  are regression coefficients to be 

estimated,  is the tax share of median voters, N is the population of local government 

jurisdictions, I is income and Z are vectors of exogenous variables in the model. 

 

A plethora of studies was then developed based on these empirical applications of modern 

COMPAS modeling built on the foundation of the conceptual foundations of the median voter 

model. A comprehensive fiscal impact model for Virginia counties was estimated by Swallow 

and Johnson (1987) in which they developed a model to forecast the economic, demographic and 

fiscal impacts of regional economic shocks. The entire analysis was carried out by estimating 

sets of local government revenue capacity and local government expenditure equations. An 

extension and a slight modification of this work was presented by Shields (1998) who estimated 

different sectors of the local economy using two revenue capacity equations, six expenditure 

equations, and two housing market equations. A seemingly unrelated regression (SUR) model 

was then used to estimate the local government expenditures on a per capita basis on the health 

sector, government administration, public safety, public works and other amenities. His findings 

showed that local government expenditures were significantly impacted by variables such as 

income, assessed property values, and property taxes. 

Johnson and Scott (2006) proposed the Show Me Community Policy Analysis model, in 

which they collected data from county and city governments of Missouri to estimate the labor 

market and the fiscal module coefficients. The model was actually a spreadsheet-based model 

that was used in conjunction with the IMPLAN model. They regressed police expenditure, jail 

expenditure, court expenditure, road expenditure, administrative expenditure, and other 

expenditure with several socio-economic variables that served as demand shifters. Major results 

showed that demands for public services were a function of income, wealth, age, education, and 

few other factors, such as input and other demand conditions. Based on this conceptualized 

framework and data for the model, they constructed and estimated a labor force module and 

fiscal module for all counties of Missouri using three stage least squares. Their fiscal module 

included two revenue base equations, three revenue equations and six expenditure equations. 

Swenson and Otto (2000) provided continuity from earlier research and estimated an 

economic/fiscal impact modeling system for Iowa counties, in which they introduced the concept 

of housing market equations. The fiscal module was quite similar to the one used by Swallow 
127 

and Johnson (1987), which included six revenue capacity equations and various sets of 

expenditure equations. An extension of earlier studies was proposed by Evans and Stallmann 

(2006), in which they proposed the Small Area Fiscal Estimation Simulator for Texas counties 

using a two-stage least squares procedure. A labor force module and fiscal module were 

estimated using a 14-equation model. 

Most of the empirical models rely on the median voter model assumption heavily for their 

empirical specification. Further, COMPAS modelers assume that local governments consider the 

demands and provide the desired level of services at the lowest possible cost. When tax bases 

and demand for expenditures are known, local governments are assumed to adjust tax rate to 

balance their budget. Public services may be subject to increasing or decreasing (or both) returns 

to scale. Unit costs of public services could be hypothesized as a function of the level and quality 

of services, input and output factors, input prices, and the rate of population growth. 

4.2.3. Alternative Conceptual Frameworks for Public Service Delivery 

The CPAN network acknowledges an alternative conceptual framework for modeling public 

service delivery (Deller 2006): the bureaucratic approach (Niskansen 1971; Poole and Rosenthal 

1996). The bureaucratic approach of the local budget allocation decision was set forth initially by 

Niskansen (1971) and concentrates more on political practices than economic approaches. 

Bureaucrats regulate the local level budget request and allocation process and present them to 

elected officials. It depends on the bureaucrats whether to adjust budget requests taking into 

account the behavior of elected officials who might cut-off some portions of the proposed 

request. A regional economic modeler must consider the political attributes in addition to the 

economic attributes when modeling the local public sector. The supply/demand equilibrium 

model that we described earlier focuses more on the economic backgrounds and thus the political 

aspect of decision making is ignored. 

 

In this study, we focused on evaluating a fiscal module that was built in the equilibrium 

COMPAS modeling tradition. We explored alternative empiricial formulations that are more 

consistent with a bureaucratic model, focusing on the disquilibrium period immediately before 

and after the 2005 hurricane season in Louisiana. We estimate traditional OLS regressions with 

the COMPAS equilibrium model and compare it with panel data and a quantile regression 

model. Local governments may make decisions about the total expenditures in the fiscal year 

under a bureaucratic model conceptual framework based on the spending that was made in the 

previous year plus the total revenues that would be projected available in the current fiscal year. 

Our contribution would be the addition of dynamics in the model by incorporating the lagged 

dependent variable for different expenditure categories. We estimate the forecasting performance 

by several quantitative methods incorporating different indicators such as mean simulation error, 

mean square simulation error, root mean square simulation error, and Theil’s coefficients as a 

benchmark for comparison. 
128 

4.3. EMPIRICAL SPECIFICATION OF FISCAL MODULE

The fiscal module in a COMPAS framework is composed of two components, local 

government revenue and local government expenditures that use outcomes from the labor force 

module as exogenous variables. The endogenous variables from the labor force module (incommuter earnings, out-commuter earnings) serve as exogenous variables in the fiscal module 

that determine the factors contributing to total revenue. Local government revenue is generated 

by different forms of tax revenues (typically property taxes and sales taxes which are dependent 

on assessed property value and retail sales) and self-generated revenue (fees) and 

intergovernmental transfers (block grants from the federal and state governments, etc). Two 

equations measure revenue capacity in our fiscal module: assessed property value and retail 

sales13. 

(E4.2) ASDVAL = f(LNDNSTY, OUTCERN, RESEMPERN) 

(E4.3) RETSALE = f(LNDNSTY, INCERN, OUTCERN, RESEMPERN) 

Expenditure equations are explained by factors that measure the quantity of public services, 

quality of public services, demand conditions related to public services, and input conditions 

(Johnson 1996). For this study, four expenditure equations are accounted for through regression 

analysis, where a total of seven explanatory variables are used. The expenditure equations are 

presented as: 

(E4.4) GG EXP = f(ASDVAL, RETSALE, TOTINC, LNDNSTY, LCLRDMLS, POP)

(E4.5) HW EXP = f(ASDVAL, RETSALE, TOTINC, PERAFAM, POPPLUS, 

LCLRDMLS, POP) 

(E4.6) PS EXP = f(ASDVAL, RETSALE, TOTINC, PERAFAM, POPPLUS, POP)

(E4.7) PW EXP = f(ASDVAL, RETSALE, TOTINC, PERURB, LNDNSTY, LCLRDMLS, 

POP)

Variable descriptions are provided in Table 4.3. 

 

13 Other non-tax revenue, such as intergovernmental transfers, also make up the total public sector 

revenue available for spending on public services. However, many of these transfers are based on 

formulas that include the demand shifter co-variates in the public service expenditure equations. As a 

result, other public revenue sources are not included as covariates in the expenditure variables. 
129 

4.4. DATA AND METHODOLOGY

An initial comparison is made by modeling each of the equations using Ordinary Least 

Squares (OLS) regression, panel regression, and the quantile regression approach. As an 

alternative approach for the COMPAS models, OLS, panel, and quantile regressions are useful in 

measuring forecasting performance. OLS (and, to a lesser extent, panel) regression has been 

historically applied in COMPAS fiscal modeling. The inclusion of quantile regression represents 

an additional iteration (or sensitivity analysis) in COMPAS regression modeling. 

 For a distribution function FY (y), one can determine the probability φ of occurrence for a 

given value of the dependent variable y. However, quantiles are meant to do exactly the opposite. 

That is, one wants to determine for a given probability φ of the sample data set the corresponding 

value y. In OLS, given some explanatory variable xi, E[Y | xi], we would determine the 

conditional mean of the random variable Y. Cross-sectional data are used in the analysis process. 

 

Quantile Regression goes beyond this and enables one to pose such a question at any quantile 

of the conditional distribution function. Hence, quantile regression overcomes various problems 

of OLS and panel models because it focuses on the interrelationship between a dependent 

variable and its explanatory variables for a given quantile. Frequently, error terms are not 

constant across a distribution, thereby violating the axiom of homoscedasticity. Also, by 

focusing on the mean as a measure of location, information about the tails of a distribution is 

lost. Also, OLS and panel regressions are sensitive to extreme outliers, which can distort the 

results significantly. As has been indicated in the small example of Boston Housing data (Besley, 

Kuh, and Welsch 1980), sometimes a policy based on OLS might not yield the desired result 

because a certain subsection of the population does not react as strongly to this policy or, even 

worse, responds in a negative way, which was not indicated by OLS. Finally, quantile regression 

addresses a specific issue of public service delivery; that is, it accounts for differences in the 

quantity and quality of public services based on quantiles being defined on per capita 

expenditure levels of the dependent variable. Historically, COMPAS models have included 

quantity and quality demand conditions as exogenous regressors explaining expenditure 

variation. However, there may be unknown demand conditions explaining public expenditure 

variation or factors than are not easily measurable. Quantile regression is an alternative in these 

situations. 

This section also develops and demonstrates a model evaluation process for community 

policy analysis models and highlights a number of key steps in this evaluation process. In 

particular, the study evaluates, through theoretical discussion and empirical investigation, the 

quality of forecasts generated by one particular module, the fiscal module of the Louisiana 

Community Impact Model (LCIM). The base year for estimation is 2007, which is a desired time 

period because many parishes had measurably recovered from the serious damages caused by 

hurricanes Katrina and Rita and were not impacted by another sizeable hurricane, Gustav, which 

made landfall in 2008. Although the base year for estimation of OLS and quantile regression 

estimators is 2007 data, the study also assesses multi-year data (from 2004 to 2009) for 

forecasting purposes to compare performance within and outside of the in-sample year (see 

Figure 4.2. for on- and off-sample year forecasting performance comparison for different sets of 

models for the general government expenditure category). 
130 

Figure 4.2. Mean percent error of OLS, panel, and quantile estimators of COMPAS model. 

The performance of estimators is compared on the basis of quantitative evaluation methods. 

These methods include analysis of mean simulation error (ME), mean percent simulation error 

(MPE), mean absolute simulation error (MAE), mean absolute percent simulation error (MAPE), 

mean square simulation error (MSE), root mean square simulation error (RMSE), root mean 

square percent simulation error (RMSPE), and Theil’s coefficient U1 and U214 (Theil 1970, 

1975; Pindyck and Rubinfield 1991; Kovalyova and Johnson 2006). 

Estimation is based on the COMPAS model for Louisiana that includes all 64 parishes, 

where the variables for the fiscal module were selected on the basis of Fannin et al. (2008) and 

were modified depending on the requirements of our model and applied geographically to all 

 

14 Theil (1958) recommended an accuracy measure in forecasting, widely known as U1. The value of 

U1 lies between 0 and 1, regardless of how data are defined. Theil’s U1 normalizes RMSE with sum of 

root squares of actual and predicted values. A value of 0 indicates perfect prediction and the value of 1 

corresponds to inequality or negative proportionality between actual and predicted values. 

 







 2 2

2

ˆ

) ˆ ( 1

1

t t

t t

Y Y

Y Y

n U

or, 

    2 2 ˆ 1

Yt Yt

RMSE U

To address the shortcomings of U1, Theil (1966) proposed another modified error measure (U2) that 

normalizes RMSE with the root mean square actual values. The statistics U2 is bounded below by 0, same 

as the case in U1 but the upper bound is lacking in this case and would thus it is constrained to take the 

values between 0 and  . The choice of using U1 or U2 depends on the researcher and the objectives of 

the study. Again, a value of 0 indicates perfect prediction (smaller the better). 

 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2004 2005 2006 2007 2008 2009

Mean Percent Error

General Government

Panel

OLS

Quant 0.33

Quant 0.66

Quant 0.99
131 

Louisiana parishes. Louisiana parish level fiscal module data are obtained from audited financial 

statements of parish governments. The data collected uses a common federal accounting standard 

(Government Accounting Standards Board Standard 34). It has been collected annually by the 

Louisiana Legislative Auditor since 2004 and allows for the creation of a panel dataset of 

common local government expenditure categories for modeling purposes. Within the fiscal 

module, different expenditure equation data on public safety, public works, general government, 

and health and welfare sectors are estimated by a cross-section Ordinary Least Square (OLS) 

model as a base control with quantile and panel data regressions also estimated. Other major data 

sources for the co-variates include the Louisiana Department of Education, U.S. Census Bureau, 

and Bureau of Economic Analysis. We apply OLS regression and quantile regression using 

STATA. The forecasting performance is evaluated based on the procedures outlined in Johnson, 

Otto, and Deller (2006) and Kovalyova and Johnson (2006). 

4.5. RESULTS AND DISCUSSION

Descriptions of variables used in the study are presented in Table 4.2. The average spending 

for a Louisiana parish is about $13 million for general government, $3 million for health and 

welfare, $12.5 million for public safety and $14.5 million for public works categories 

respectively in 2007. Assessed value and retail sales average $418 million and $901 million 

respectively. Total income of the 64 Louisiana parishes average $2 billion, with measurable 

variation, from a low of $163 million (Tensas) to $19 billion (Jefferson). Average parish 

population totaled just over 68,000. 
132 

Table 4.2. Variable description and summary statistics, Louisiana, 2007. 

Variables 

(Units)

Description 

(Expected Sign) 

Mean Standard 

Deviation 

Min Max 

GEN_GOV 

($) 

General govt. 

expenditure 

12,907,252 37,669,961 593,955 210,722,026 

HEL_WEL 

($) 

Health & welfare 

expenditure 3,357,312 

7,399,740 5,664 13,602,439 

PUB_SAF 

($) 

Public safety 

expenditure 12,561,498 

40,169,582 232,882 189,130,903 

PUB_WRK 

($) 

Public works 

expenditure 14,526,595 

31,200,493 847,070 65,739,927 

GG_LAG 

($) 

GEN_GOV lag 

(+) 

9,097,823 25,819,736 555,209 191,462,016 

HW_LAG 

($) 

HEL_WEL lag (+) 2,894,097 5,003,084 5,016 28,751,486 

PS_LAG 

($) 

PUB_SAF lag (+) 11,361,581 30,625,856 178,617 17,260,2185 

PW_LAG 

($) 

PUB_WRK lag 

(+) 

12,895,400 29,179,849 685,291 20,744,981 

ASDVAL 

($) 

Assessed value 

(+) 

418,151,563 553,860,439 36,056,864 3,466,560,930 

RETSALE 

($) 

Retail sales (+) 901,353,145 1,355,501,809 29,883,946 7,612,001,075 

LNDNSTY 

(sq. miles)

Arable land 

density (+) 

770 431 190 2,413 

LCLRD 

(miles)

Local road miles 

(+) 

1,513 717 284 3,635 

POP (#) Population (+) 68,376 90,951 5,788 440,339 

TOTINC 

(thousands $)

Total income (+/-) 2,447,161 3,864,120 163,901 18,996,431 

PERAFAM 

(%)

Percent African 

American (+) 

32 14 3 68 

PERURB 

(%)

Percent urban 

(+/-) 

48 28 0 99 

POPPLUS 

(#)

Population above 

65 years old (+/-) 

8,290 10,291 660 58,362 
133 

Results from Table 4.3 demonstrate parameter estimates of the panel estimator, OLS 

estimator and quantile estimators, divided in three quantiles (0.33, 0.66, and 0.99) for four 

different expenditure categories within 64 parishes of Louisiana. Quantiles were divided based 

on the per capita expenditure levels of each of the dependent variables. Varying per capita 

expenditures highlight the differences in the quantity or quality (or both) of public services 

consumed. We might expect to see differing factors drive expenditures based on the quantity or 

quality of public services consumed per capita. Only three quantiles were chosen so that we 

could have enough degrees of freedom in each quantile. Most of the signs in the parameter 

estimates are as expected. If one focuses on the general government category, it is as expected; 

an increase in assessed value leads to an increase in the expenditure of the general government. 

That is, general government is a normal good given that incomes and assessed value are 

positively correlated, consumption of the public service increases as the assessed value increases. 

We see that public safety is also a normal good. 

 Results are mixed in identifying a superior model for forecasting when comparing panel, 

OLS and quantile regression (Table 4.4) in our traditional COMPAS model. In the general 

government category, the lowest quantile (0.33) on the quantile regression performs better than 

OLS and panel models in terms of mean percent simulation error, mean absolute percent 

simulation error, mean square percent simulation error and Theil’s coefficient (U1 and U2). 

Higher quantiles are far higher in terms of error measures (which demonstrate poorer model fit). 

 
134 

Table 4.3. Parameter estimates for panel, OLS, and quantile regressions, Louisiana. 

Expenditure Category 

Panel OLS Quantile Regression 

0.33 0.66 0.99 

Coeff. pvalue Coeff. p- value Coeff. p- value Coeff. p- value Coeff. p- value 

GEN_GOV Constant 0.051 0.96 -2.049 0.28 -2.590 0.46 -2.768 0.29 0.637 0.78 ASDVAL 0.425*** 0.001 0.175 0.36 0.067 0.82 0.338 0.28 0.195 0.60 

RETSALE 0.252*** 0.009 0.415* 0.07 0.584 0.26 0.361 0.43 0.242 0.56 

TOTINC 0.213* 0.09 1.988*** 0.001 2.025*** 0.003 2.049*** 0.01 1.239* 0.07 

LNDNSTY 0.227** 0.06 0.120 0.28 0.103 0.72 0.061 0.69 0.234 0.30 

LCLRD -0.45*** 0.003 -0.309* 0.06 -0.359 0.32 -0.223 0.33 -0.437* 0.09 

POP 0.047 0.62 -1.98*** 0.001 -0.207** 0.03 -2.201*** 0.001 -0.884 0.29 

HEL_WEL Constant -0.488 0.84 -8.612** 0.04 -10.244 0.19 -6.966 0.18 -6.243 0.59 ASDVAL 0.494** 0.015 0.617*** 0.009 0.520 0.33 0.449 0.40 0.772 0.45 

RETSALE 0.410* 0.09 0.085 0.81 0.540 0.34 0.423 0.39 0.066 0.96 

LCLRD -0.580** 0.02 -0.120 0.70 -0.073 0.90 -0.260 0.61 0.209 0.79 

PERAFAM 0.0006 0.99 0.279 0.12 0.104 0.63 0.169 0.56 0.059 0.91 

POP 0.017 0.96 -1.946* 0.06 -1.776 0.24 -3.817** 0.02 1.230 0.68 

TOTINC -0.385 0.29 1.878** 0.02 1.647 0.26 2.311 0.13 -0.333 0.89 

POPPLUS 0.705** 0.02 0.363 0.63 -0.144 0.91 1.572 0.23 -0.583 0.82 

PUB_SAF Constant -8.40*** 0.001 -15.92*** 0.001 -12.62*** 0.003 -17.49*** 0.001 -17.52*** 0.008 ASDVAL 0.633*** 0.001 0.528** 0.02 0.765* 0.09 0.454 0.28 0.247 0.31 

RETSALE 0.012 0.92 0.316 0.19 -0.198 0.77 0.505 0.24 0.555 0.28 

TOTINC 0.791*** 0.001 3.795*** 0.001 0.018 0.95 0.045 0.77 0.171 0.68 

POPPLUS -0.621* 0.06 -1.018** 0.05 -0.009 0.99 -1.546** 0.04 -2.623** 0.02 

PERAFAM 0.126 0.46 0.152 0.23 3.705*** 0.001 3.993*** 0.001 3.289*** 0.001 

POP 0.406 0.25 -2.929*** 0.003 -3.369** 0.02 -2.782*** 0.005 -0.67* 0.09 

PUB_WRK Constant -0.373 0.77 -0.219 0.89 0.398 0.92 0.369 0.89 1.912 0.56 ASDVAL 0.459*** 0.003 0.304 0.11 0.251 0.54 0.555 0.11 0.113 0.71 

RETSALE 0.223* 0.06 0.258 0.25 0.372 0.42 -0.011 0.98 0.182 0.70 

PERURB -0.077* 0.09 -0.020 0.68 -0.014 0.86 -0.027 0.58 0.028 0.88 
135 

LCLRD -0.28*** 0.002 -0.064 0.60 -0.175 0.56 -0.226 0.20 0.035 0.90 POP -0.42** 0.05 -0.759 0.20 -1.143 0.32 0.192 0.86 -1.268 0.29 

TOTINC 0.625*** 0.009 0.870* 0.09 1.063 0.30 0.180 0.82 1.486 0.19 

LNDNSTY 0.110 0.35 0.194* 0.06 0.198 0.59 0.336*** 0.01 0.128 0.56 
136 

For the health and welfare category, again the mean percent simulation error, mean absolute 

percent simulation error, mean square percent simulation error, and Theil’s coefficient (U1 and 

U2) are least in the lowest quantile (0.33), as compared to other higher quantiles and OLS and 

panel models. Public works and public safety categories follow a similar pattern as other 

categories described earlier. However, OLS has the advantage over panel regression in the cases 

of both public works and public safety. 

Although the lower quantiles displayed superior forecasting performance relative to other 

quantiles and the other two models in all four categories of expenditure, a more robust model is 

preferable to estimate and forecast public sector expenditure. As suggested by Johnson, Otto, and 

Deller (2006), the best way to validate model performance is by comparing the forecasts with 

those of naïve extrapolation. We applied a naïve model (cross-sectional) where all four 

categories of expenditures were regressed with its one year lagged value. This approach makes 

for a reasonable baseline because it suggests that any model estimated should forecast at least as 

well as simply using the information from last year’s expenditure. In addition, this approach 

forms the basis for a bureaucratic model conceptual framework to public sector expenditure 

given that local governments often make decisions on their spending for the fiscal year based on 

the spending that was made last year plus some adjustment for the current year. More 

specifically, bureaucrats use the previous year’s budget as a baseline to inflate budgets 

constrained by the level of expected growth in revenue collections. We also add revenue capacity 

variables in the naïve model to develop a new model (Naïve plus) for comparing the forecasting 

performance that incorporates expected revenues that can be spent in the current year. We further 

introduce a modified naïve model which includes the original COMPAS covariates to compare 

with the naïve and naïve plus model. The expenditure equations in the new models are now 

expressed as: 

NAÏVE MODEL 

(E4.8) GEN_GOV=f(GG_LAG) 

(E4.9) HEL_WEL=f(HW_LAG) 

(E4.10) PUB_SAF=f(PS_LAG) 

(E4.11) PUB_WRK=f(PW_LAG) 

 

NAÏVE PLUS MODEL 

(E4.12) GEN_GOV=f(GG_LAG,ASDVAL,RETSALE) 

(E4.13) HEL_WEL=f(HW_LAG,ASDVAL,RETSALE) 

(E4.14) PUB_SAF=f(PS_LAG,ASDVAL,RETSALE) 

(E4.15) PUB_WRK=f(PW_LAG,ASDVAL,RETSALE) 

 

MODIFIED NAÏVE MODEL

(E4.16) GEN_GOV = f(GG_LAG, ASDVAL, RETSALE, TOTINC, LNDNSTY, LCLRD, 

POP)

(E4.17) HEL_WEL = f(HW_LAG, ASDVAL, RETSALE, TOTINC, PERAFAM, POPPLUS, 

LCLRD, POP)

(E4.18) PUB_SAF = f(PS_LAG, ASDVAL, RETSALE, TOTINC, PERAFAM, POPPLUS, 

POP)
137 

(E4.19) PUB_WRK = f(PW_LAG, ASDVAL, RETSALE, TOTINC, PERURB, LNDNSTY, 

LCLRD, POP) 

Table 4.4. Average performance estimation measures for different categories of expenditure. 

Expenditure Category Panel OLS Quantile Regression 

0.33 0.66 0.99 

 

 

GEN_GOV 

Mean Percent Simulation Error 0.054 0.084 0.047 0.201 0.581 

Mean Absolute Percent Simulation Error 0.365 0.341 0.323 0.319 0.790 

Mean Square Percent Simulation Error 0.211 0.201 0.148 0.211 1.321 

Theil’s Coeff (U1) 0.285 0.246 0.183 0.206 0.583 

Theil’s Coeff (U2) 0.322 0.297 0.238 0.255 0.718 

 

HEL_WEL 

Mean Percent Simulation Error 0.443 0.276 0.271 0.524 2.097 

Mean Absolute Percent Simulation Error 0.888 0.682 0.562 0.749 2.097 

Mean Square Percent Simulation Error 2.354 0.846 0.645 1.305 10.934 

Theil’s Coeff (U1) 0.278 0.261 0.260 0.401 0.469 

Theil’s Coeff (U2) 0.337 0.304 0.296 0.514 0.608 

 

PUB_SAF 

Mean Percent Simulation Error 0.281 0.414 0.264 0.387 0.422 

Mean Absolute Percent Simulation Error 0.188 0.130 -0.063 0.512 2.254 

Mean Square Percent Simulation Error 0.570 0.439 0.306 0.624 2.254 

Theil’s Coeff (U1) 0.678 0.337 0.176 0.876 12.051 

Theil’s Coeff (U2) 0.209 0.372 0.200 0.343 0.347 

 

PUB_WRK 

Mean Percent Simulation Error 0.132 0.089 0.077 0.478 0.446 

Mean Absolute Percent Simulation Error 0.441 0.365 0.274 0.575 0.547 

Mean Square Percent Simulation Error 1.322 0.236 0.135 0.978 0.641 

Theil’s Coeff (U1) 0.184 0.194 0.174 0.326 0.325 

Theil’s Coeff (U2) 0.237 0.279 0.196 0.382 0.361 

 

Results from Table 4.5 demonstrate a parameter estimates comparison of the OLS and panel 

estimators of the naïve, naïve plus, and modified naïve model for four different expenditure 

categories within the Louisiana parishes. Similarly, results from Table 4.6 display parameter 

estimates for the naïve model, naïve plus model and modified naïve model based on three 

quantiles (0.33, 0.66, and 0.99) through quantile regression. The results are quite similar to 

earlier models. However, results are superior compared to earlier COMPAS equilibrium models 

because we observe the forecasting performance increases with inclusion of the lagged 

dependent variable (naïve model) in our earlier model. The lagged variable is highly significant 

for all models and for all categories of expenditure and suggests that previous year’s expenditure 

plays an important role in determining the future year’s expenditure. Except for the public works 

category, assessed value is positive which indicates an increase in assessed value leads to an 

increase in the expenditure of general government, public safety and health and welfare. 
138 

Table 4.5. Parameter estimates for the naïve model, naïve plus model, and modified naïve model, for 

both OLS and panel data regressions. 

 OLS Panel 

 Naïve Naïve Plus Modified 

Naive 

Naïve Naïve 

Plus 

Modified Naive 

 

GEN_GOV 

Constant -0.23 -0.45 -0.04 0.15* -0.54*** -0.49 

ASDVAL 0.08 0.11*** 0.11** 0.07 

RETSALE -0.03 -0.11** -0.03 -0.01 

TOTINC 0.06 0.29*** 

LNDNSTY -0.01 0.04 

LCLRD -0.03 -0.09 

GG_LAG 1.02*** 0.97*** 0.95*** 0.99*** 0.93*** 0.88*** 

POP 0.04 -0.24** 

 

HEL_WEL 

Constant 0.004 -1.33** -4.71*** 0.40* -1.32*** -1.06 

ASDVAL 0.25*** 0.22*** 0.12** 0.12** 

RETSALE -0.14*** -0.16 0.03 0.06 

TOTINC 0.78** -0.007 

LCLRD -0.16** -0.05 

POPPLUS -0.16 0.09 

PERAFAM 0.19** -0.002 

HW_LAG 0.99*** 0.94*** 0.91*** 0.97*** 0.87*** 0.86*** 

POP -0.64 -0.10 

 

PUB_SAF 

Constant 0.23 -1.10** -2.84*** 0.70*** -1.58*** -1.89*** 

ASDVAL 0.13* 0.08 0.22*** 0.20** 

RETSALE -0.05 -0.01 -0.004 -0.07 

TOTINC 0.64** 0.19 

POPPLUS -0.16 -0.28* 

PERAFAM 0.12*** 0.02 

PS_LAG 0.98*** 0.97*** 0.90*** 0.95*** 0.82*** 0.79*** 

POP -0.46 0.20 

 

PUB_WRK 

Constant 0.47 0.05 -1.63** 1.06** -0.49 -0.61 

ASDVAL 0.01 -0.003 -0.23*** -0.16*** 

RETSALE 0.05 0.15** 0.06 0.004 

TOTINC 0.27* 0.35* 

PERURB 0.01 -0.006 

LNDNSTY 0.07* 0.07* 

LCLRD 0.13** -0.03 

PW_LAG 0.98*** 0.92*** 0.89*** 0.93*** 0.66*** 0.66*** 

POP -0.46*** -0.24 
139 

Table 4.6. Parameter estimates for the naïve model, naïve plus model, and modified naïve model, for 

quantile regressions. 

 Naïve Naïve Plus Modified Naive 

 

 0.33 0.66 0.99 0.33 0.66 0.99 0.33 0.66 0.99 

 

GEN_GOV 

Constant -0.52** -0.19 -4.77* -0.87*** -0.54 2.04 -0.69 -0.36 -0.94 

ASDVAL 0.11*** 0.14 0.22 0.10 0.12 0.17 

RETSALE 0.16 -0.09 0.03 -0.05 -0.16** -0.19 

TOTINC 0.05 0.29 0.14 

LNDNSTY -0.002 -0.01 -0.10 

LCLRD -0.01 -0.02 0.14 

GG_LAG 1.03*** 1.02*** 1.38*** 0.96*** 0.99*** 1.13*** 0.95*** 0.95*** 0.99*** 

POP -0.01 -0.17 -0.04 

 

HEL_WEL 

Constant 0.02 -0.11 -1.01 -1.22 -1.12 -1.52 -4.08** -5.66*** -2.04 

ASDVAL 0.25*** 0.19 0.09 0.20* 0.15 0.33 

RETSALE -0.14* -0.11 0.06 -0.09 -0.07 -0.18 

TOTINC 0.26 0.94*** -0.29 

LCLRD 0.25* 0.20 0.07 

POPPLUS -0.71 -0.42 0.24 

PERAFAM 0.19 0.17* 0.04 

HW_LAG 0.99*** 1.01*** 1.12*** 0.95*** 0.97*** 0.92*** 0.94*** 0.92*** 0.89*** 

POP 0.24 -0.59 0.23 

 

PUB_SAF 

Constant -0.01 -0.11 1.86*** -1.78 -0.31 -1.34 -1.32 -3.01** -2.12 

ASDVAL 0.20 0.02 0.24 0.004 0.04 -0.01 

RETSALE -0.09 -0.005 -0.09 0.04 0.01 0.06 

TOTINC 0.31 0.79* 0.78* 

POPPLUS 0.14 -0.19 -0.42 

PERAFAM 0.11 0.10 0.11 

PS_LAG 1.00*** 1.01*** 0.92*** 0.96*** 1.00*** 0.93*** 0.97*** 0.93*** 0.85*** 

POP -0.48 -0.64 -0.14 

 

PUB_WRK 

Constant 0.47 0.41 1.17*** 0.03 -0.08 1.27 -1.13 -2.33** 0.123 

ASDVAL 0.05 -0.06 -0.03 0.01 -0.06 -0.04 

RETSALE -0.0006 0.08 0.02 0.07 0.22** 0.08 

TOTINC 0.53 0.29 0.02 

PERURB 0.02 0.002 0.07* 

LNDNSTY 0.04 0.066 0.08 

LCLRD 0.03 0.16* 0.19 

PW_LAG 0.97*** 0.98*** 0.95*** 0.93*** 0.99*** 0.96*** 0.86*** 0.95*** 0.89*** 

POP -0.60 -0.54** -0.12 

 
140 

There is again a mixed result in performance between OLS and quantile regression models 

(Table 4.7). All models including the lagged dependent variable outperform the baseline 

COMPAS models; however, performance varies in the quantile regression with lagged 

dependent variables. In most of the models, lower quantiles (0.33) perform better as compared to 

the middle (0.66) and higher quantiles (0.99). The OLS model outperforms the panel model 

(except in case of public works category) in most of the expenditure categories for naïve, naïve 

plus, and modified naïve, as measured in terms of the aforementioned error measures. Although 

the naïve model is superior when compared to our earlier model, the naïve plus model displays 

better forecasting performance than the naïve model measured in terms of mean percent 

simulation error, absolute mean percent simulation error, mean square percent simulation error 

and Theil’s coefficient. 

To get a better understanding of the relative performance of these estimators, we performed a 

mean absolute percent simulation error comparison test in STATA, where we compared the base 

OLS cross-section model with the cross-section models of each of the equations that 

incorporated the lagged dependent variable (naïve, naïve plus, and modified naïve). These results 

are presented in Tables 4.8–4.11. 

In considering only the lowest magnitudes (highest forecasting performance), we found the 

modified naïve model displayed superior results as compared to the naïve and naïve plus model, 

if measured in terms of absolute mean percent error. Overall, results from Tables 4.8–4.11 

suggested that lagged models are significantly lower in terms of error measures as compared to 

the base OLS model in all four categories of expenditure. However, the modified naïve model is 

not always significantly lower (in terms of absolute mean percent error) than the naïve and naïve 

plus model and thus one should not infer that modified naïve model outperforms the other lagged 

dependent variable models. In Table 4.8, one can observe that the modified naïve model displays 

better forecasting performance as compared to the base OLS model and naïve model, but there is 

no significant difference between the naïve plus and the modified naïve model. Also, the naïve 

plus model displays significantly better performance compared to the base OLS and naïve model. 

For public safety and health and welfare category of expenditure, test results show a similar 

pattern (Table 4.9 and 4.10). In the case of public works (Table 4.11), the modified naïve model 

performs significantly better than the base OLS and naïve models but not the naïve plus model. 

These results suggest that during this period, Louisiana parish governments were driven more by 

bureaucratic forces than equilibrium based supply and demand factors. 
141 

Table 4.7. Average performance estimation measures for different categories of expenditure. 

Error Measures Panel OLS Quantile Regressi

Naive Naïve 

Plus 

Mod. 

Naive 

Naive Naïve 

Plus 

Mod. 

Naive 

Naive Naïve Plus 

0.33 0.66 0.99 0.33 0.66 0.9

GEN_GOV 

Mean Percent Simulation Error 

0.17 0.13 0.11 0.12 0.11 0.05 -0.09 0.08 0.41 -0.06 0.06 0.3

Mean Absolute Percent Simulation Error 

0.16 0.12 0.12 0.10 0.10 0.08 0.10 0.16 0.43 0.08 0.13 0.3

Mean Square Percent Simulation Error 

0.14 0.10 0.09 0.13 0.09 0.06 0.03 0.09 0.19 0.02 0.03 0.1

Theil’s Coeff (U1) 0.11 0.09 0.07 0.06 0.04 0.04 0.05 0.08 0.31 0.03 0.07 0.2

Theil’s Coeff (U2) 0.16 0.12 0.09 0.09 0.07 0.07 0.08 0.10 0.42 0.06 0.11 0.2

HEL_WEL Mean Percent Simulation Error 

0.13 0.13 0.09 0.10 0.05 0.03 0.07 0.17 0.61 0.02 0.10 0.6

Mean Absolute Percent Simulation Error 

0.37 0.26 0.17 0.33 0.20 0.18 0.25 0.29 0.58 0.12 0.27 0.6

Mean Square Percent Simulation Error 

0.83 0.14 0.14 0.91 0.10 0.07 0.23 0.24 0.77 0.04 0.20 0.7

Theil’s Coeff (U1) 0.26 0.19 0.16 0.23 0.17 0.12 0.18 0.29 0.23 0.06 0.18 0.1

Theil’s Coeff (U2) 0.33 0.24 0.21 0.27 0.19 0.16 0.22 0.35 0.30 0.10 0.22 0.2Mean Percent Simulation Error

Mean Absolute Percent Simulation Error 

0.12 0.14 0.11 0.08 0.08 0.06 -0.07 0.14 0.36 -0.05 0.13 0.3

Mean Square Percent Simulation Error 

0.33 0.19 0.15 0.25 0.15 0.14 0.25 0.15 0.31 0.24 0.16 0.3

Theil’s Coeff (U1) 0.39 0.09 0.06 0.32 0.09 0.08 0.12 0.09 0.21 0.07 0.05 0.1

Theil’s Coeff (U2) 0.28 0.16 0.11 0.21 0.11 0.08 0.14 0.15 0.17 0.11 0.07 0.0

Mean Percent Simulation Error 

0.37 0.23 0.19 0.29 0.19 0.13 0.17 0.20 0.24 0.16 0.10 0.0

PUB_WRK 
142 

Mean Percent Simulation Error 

0.08 0.03 0.03 0.05 0.04 0.04 -0.05 0.13 0.33 -0.03 0.10 0.2

Mean Absolute Percent Simulation Error 

0.21 0.11 0.09 0.16 0.14 0.12 0.12 0.17 0.31 0.10 0.18 0.2

Mean Square Percent Simulation Error 

0.09 0.03 0.02 0.04 0.03 0.02 0.04 0.07 0.17 0.02 0.04 0.1

Theil’s Coeff (U1) 0.16 0.05 0.04 0.10 0.07 0.06 0.08 0.14 0.18 0.07 0.10 0.1

Theil’s Coeff (U2) 0.22 0.12 0.10 0.15 0.11 0.09 0.10 0.18 0.27 0.10 0.16 0.2
143 

Table 4.8. Mean absolute percent simulation error comparison test for general government expenditure, 

based on OLS model. 

 Base Naïve Naïve Plus Modified Naive 

 Magnitude t-stat Magnitude t-stat Magnitude t-stat 

Base 0.209 6.01*** 0.223 6.55*** 0.261 6.92*** 

Naive 0.014 1.38* 0.052 1.75** 

Naïve Plus 0.038 0.69 

Modified Naive 

*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively. 

Table 4.9. Mean absolute percent simulation error comparison test for public safety expenditure, based 

on OLS model. 

 Base Naïve Naïve Plus Modified Naive 

 Magnitude t-stat Magnitude t-stat Magnitude t-stat 

Base 0.189 2.32** 0.289 5.50*** 0.299 6.19*** 

Naive 0.102 1.54* 0.112 1.79** 

Naïve Plus 0.010 1.04 

Modified Naive 

*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively. 

Table 4.10. Mean absolute percent simulation error comparison test for health and welfare expenditure, 

based on OLS model. 

 Base Naïve Naïve Plus Modified Naive 

 Magnitude t-stat Magnitude t-stat Magnitude t-stat 

Base 0.352 1.38* 0.482 5.54*** 0.502 5.84*** 

Naive 0.130 1.53* 0.150 1.68** 

Naïve Plus 0.021 1.17 

Modified Naive 

*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively. 

Table 4.11. Mean absolute percent simulation error comparison test for public works expenditure, based 

on OLS model. 

 Base Naïve Naïve Plus Modified Naive 

 Magnitude t-stat Magnitude t-stat Magnitude t-stat 

Base 0.205 4.81*** 0.225 5.12*** 0.245 5.38*** 

Naive 0.023 1.21 0.041 1.57* 

Naïve Plus 0.020 0.90 

Modified Naive 

*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively. 
144 

4.6. CONCLUSION 

In this study, we evaluated whether the forecasting performance of public sector expenditure 

models under traditional COMPAS supply/demand equilibrium assumptions fit reasonably well 

in a disequilibrium environment. This study focused on evaluating the conceptual framework for 

modern day local government revenue and expenditure forecasting and the strengths and 

weaknesses of such modeling in terms of empirical specification. We compared the traditional 

COMPAS model with a modified COMPAS model and analyzed the forecasting performance of 

several indicators under disequilibrium conditions. The study evaluated forecasting performance 

during a time frame of supply demand disequilibrium, during a period of major exogenous 

shocks (Hurricanes Katrina, Rita and Gustav) to local government operations. Different models 

were compared parametrically using the cross-sectional OLS, panel data, and quantile regression. 

 

Most of the original COMPAS models were developed in Midwestern states where there was 

measurable homogeneity in economic and fiscal structure of rural regions (the focus of many of 

these models) during the period of their original creation. Our results showed that newer 

alternative methods, such as Quantile regression, have potential statistical advantages over 

traditional COMPAS model OLS and panel regression in improving model performance (as 

evidenced by our original model particularly in the lowest quantile). Consequently, Quantile 

regressions are proposed as another COMPAS estimator alternative because they apply varying 

parameter estimates in forecasting, depending on a county’s relative position within the 

distribution of a given public expenditure category among all counties in a state. Though early 

COMPAS models may have segmented based on rural or urban regions, these results suggest 

that segmentation may also occur on spending levels; such spending levels may or may not be 

related to population size. 

Further, results indicated that a bureaucratic model may have been a more appropriate 

conceptual framework during this public service delivery period of Louisiana local government 

history. However, these results are limited in that one cannot infer the bureaucratic model 

superior in all disequilibrium environments. In particular, due to data limitations, one cannot 

evaluate the pre-Katrina and Rita forecasting performance between traditional COMPAS models 

and the bureaucratic model. The panel dataset starts from the year 2004, the first year in which 

there were quality comparable public sector data across all parish jurisdictions. That is, 

Louisiana parish public sector spending may have followed a more bureaucratic model prior to 

the disequilibrium period brought about by the storms. 

There are some additional limitations. The greatest is the tradeoff of forecasting performance 

for potential reduced policy analysis. From a modeling perspective, the magnitude of the 

parameters that serve as demand shifters in the public service equation are measurably reduced in 

the modified naïve model (with lagged dependent variable) in Table 6 as compared to the base 

models in Table 3. Because the demand shift variables are typically the variables through which 

proposed policies are incorporated into COMPAS, reducing their influence on expenditure 

projections through the addition of a lagged dependent variable may be problematic for those 

interested in using COMPAS models for policy analysis. Because most COMPAS policy 

analysis includes evaluating the policy effect through the difference between baseline 

expenditure and policy enacted expenditure projections, much of the forecasting error is likely to 
145 

drop out in the difference between the two. The second limitation is that states with small 

numbers of counties will be limited in using the quantile regression approach because an 

insufficient number of counties would exist for generating statistically reliable results for each 

quantile subset. 

An evaluation of the alternative methods performed in this study are expected to give 

regional economic modelers better information from which to construct models projecting local 

public sector expenditures. Using data from different sources, this study developed a model to 

forecast different categories of expenditure in the fiscal module using OLS, panel, and quantile 

regression. Future research should focus on a further narrowing of the confidence interval around 

these forecasts. As increased quantity and quality of public sector data become available due to 

compulsory reporting requirements, researchers should be able to construct models with 

increasing forecast reliability that can be used by analyst-deficient local governments for more 

informed public sector decision making. 

147 

CHAPTER 5. MEASURING THE FISCAL EFFECTS BASED ON 

CHANGES IN DEEPWATER OFFSHORE DRILLING ACTIVITIES 

5.1. INTRODUCTION 

The Deepwater Horizon oil spill has brought to the forefront negative physical externalities 

related to offshore drilling. These costs have included damages to marine habitat, the oiling of 

beaches and wetlands, and the direct and indirect negative economic impacts these physical 

changes have had on service based sectors, such as tourism. 

However, the deepwater offshore oil industry has brought positive economic benefits to areas 

that have supplied its labor and served its onshore infrastructure (Fannin et al. 2008). Benefits in 

terms of jobs, income and value-added are created in many coastal communities around ports 

and fabrication facilities that supply the inputs for this industry. At the same time this industry 

provides these benefits, there are both benefits and costs to local governments from their 

operations. They receive sales taxes and property taxes from the deepwater support businesses 

and also income taxes and sales taxes from employees who earn and spend their wages and 

salaries. Conversely, the industry puts pressures on critical local infrastructure (e.g., roads, 

schools, water, sewer, and communications). Understanding the net fiscal effects in both local 

fiscal revenue received and incurred expenditures is important in evaluating how much better or 

worse off local governments are due to the existence and expansion or contraction of this 

industry. 

In terms of the policy perspective, regional economists and policy makers are interested in 

forecasting economic changes that are likely to take place at local and state levels after 

exogenous shocks to an economy. If changes in deepwater offshore drilling activities are termed 

as an example of an exogenous shock, then the impacts of such shocks could be observed at the 

level of employment, unemployment, commuting patterns, revenue streams like property and 

sales taxes, and local-level government expenditures. This paper focuses on determining fiscal 

impacts that would be created by an alteration of drilling activities in deepwater offshore areas of 

the Gulf of Mexico on an individual community, Lafayette Parish, Louisiana. This paper’s 

objective is accomplished by applying a Community Policy Analysis System (COMPAS) model 

for Louisiana based on Adhikari and Fannin (2009). In that model, wells drilled are treated as 

final demand in an input-output model framework to estimate exogenous changes in employment 

demand. This demand is then applied to a block recursive labor force module that measures 

changes in key labor market variables. These variables then serve as exogenous variables in 

revenue capacity equations. These revenue capacity variables are finally applied to local 

government expenditure demand equations. Per capita demand changes for key local government 

variables are then estimated. The results from this paper are intended to inform local and national 

policymakers of the strengths and weaknesses that COMPAS type models have in identifying 

policy impacts on a historically cyclical industry such as oil and gas extraction. 
148 

5.2. LITERATURE REVIEW

5.2.1. Fiscal Impact Modeling 

The Community Policy Analysis System (COMPAS) modeling framework has become a 

very efficient tool applied across the country to address labor market and fiscal impacts from 

initial changes in economic activity (Johnson, Otto, and Deller 2006). At its foundation, 

COMPAS is an employment driven model. Employment demand is generated by changes in 

local product demand. The definition of employment demand may vary but the exogenous shock 

that appears from the changes in employment demand is the basis of the modeling system in 

COMPAS based models (Adhikari and Fannin 2010b). In many cases, this product is converted 

to employment demand through the use of input-output models. The input-output (I/O) model is 

a case where the final demand is exogenous and the labor market supply is perfectly elastic to 

meet the labor demands generated by the product demands (Beaumont 1990). In this I/O 

framework, an exogenous change in demand for the product and services interacts with the rest 

of the economy through linkages of industrial material goods and services in an economy, its 

local labor market, and ultimately, its fiscal sector. 

There have been several studies regarding the construction of the fiscal module in COMPAS 

models and the use of spatial and non-spatial estimators for different purposes. These estimators 

are used in different fields of study, in which the heterogeneity issue needs to be accounted in a 

more sophisticated manner. A comprehensive fiscal impact model for Virginia counties was 

estimated by Swallow and Johnson (1987), in which they explained the model to forecast the 

economic, demographic and fiscal impacts of regional economic shocks. The entire analysis was 

carried out by estimating sets of local government revenue capacity and local government 

expenditure equations. An extension and a slight modification of this work was presented by 

Shields (1998), who estimated different sectors of local economy using two revenue capacity 

equations, six expenditure equations and two housing market equations. 

 

Using a three stage least squares approach, Johnson and Scott (2006) constructed and 

estimated a labor force module and fiscal module for all counties of Missouri. Their fiscal 

module included two revenue base equations, three revenue equations and six expenditure 

equations. Swenson and Otto (1999) provided continuity from earlier research and estimated an 

economic/fiscal impact modeling system for Iowa counties, where they introduced the concept of 

housing market equations. The fiscal module was quite similar to the one used by Swallow and 

Johnson (1987), which included six revenue capacity equations and various sets of expenditure 

equations. An extension of earlier studies was proposed by Evans and Stallmann (2006), which 

proposed the Small Area Fiscal Estimation Simulator for Texas counties using a two-stage least 

squares procedure. A labor force module and fiscal module were estimated using a 14-equation 

model. 
149 

5.2.2. Lafayette Parish, Louisiana 

Lafeyette Parish is located in South Central Louisiana and is the center of the Acadiana 

cultural region. It is the central parish of the Lafayette Metropolitan Statistical Area (MSA). The 

parish population, as of the 2010 census, totaled 221,578. Population grew by 31,075, or 16.31% 

from the 2000 census estimate of 190,503. Lafayette Parish has been a hub for employment 

growth with the larger Lafayette Parish, Louisiana, MSA, having the largest percentage growth 

rate in non-farm payrolls of any MSA in the United States growing at a rate of 10.1% between 

November 2011 and November 2012 (BLS 2013)15. 

At the same time, the parish has faced challenges with public service delivery. The rapid 

employment growth has not resulted in similar population growth for the Lafayette Parish. The 

growth in population over approximately the same one-year period for the parish was only 0.7%. 

Though some of the employment growth might have been met by an increased number of 

unemployed moving to employed status, a greater number is likely to have been driven by nonresidents commuting into the parish. This is supported by recent U.S. Census Bureau data 

showing that the Lafayette MSA had the 10th highest percentage of mega-commuters–hose 

commuters who travel more than 90 minutes and 50 miles to work (Rapino and Fields 2013). 

Such non-resident demand can have increased impacts on public services, such as road 

infrastructure. Further, increases in business establishments have other demands on public 

services, such as further increased demand on roads, water, sewer, and public safety, among 

others. Also, Lafayette Parish is one of the few public sector entities to own and operate its own 

fiber optic system providing extremely fast broadband Internet speed through fiber to the home 

and fiber to business services. The consequence of such an investment and keeping up demand 

for public services has resulted in measurable investments in public assets. 

5.3. OBJECTIVE AND METHOD

The objective of this study is to examine the potential fiscal impacts from a loss of oil and 

gas activities of the Gulf of Mexico region using a revised version of the Louisiana Community 

Impact Model (LCIM). An early iteration of similar study was carried out by Fannin et al. 

(2008), using the original LCIM model. They applied the model to address fiscal impacts of the 

newly developing the deepwater energy industry (DEI) on the local economy of Lafourche 

parish, the parish from which Port Fourchon, the major service port for a majority of the 

deepwater oil and gas industry, was located. Their results showed that the expansion of DEI led 

to the growth in both local government revenues and expenditures. 

In applying this revised version of LCIM, following the block recursive nature of the 

COMPAS model, demand for the final product, oil and gas, generates an employment demand. 

In this case, final demand is the number of wells to be drilled. Given that employment drives the 

COMPAS model, employment generated to drill the provided number of wells was derived by 

 

15 The Lafayette MSA consists of Lafayette Parish and St. Martin Parish. According to the 2010 

census, Lafayette Parish represents approximately 81% of the MSA’s population. 
150 

the MAGPLAN model (Saha and Phillips 2005) that offers direct, indirect, and induced 

employment impacts. (MAGPLAN is an IMPLAN-based input output model with algorithms 

that convert various Gulf of Mexico oil and gas operations, such as wells drilled, into 

employment final demand by major IMPLAN sector before applying to a standard multiplier 

matrix from IMPLAN). This total employment impact number is then inserted into the labor 

force module (Adhikari 2012) which ultimately feeds into the fiscal module of the revised LCIM 

(Adhikari and Fannin, forthcoming) for measuring local government revenue and expenditure 

impacts. 

The estimation method used to determine the number of wells drilled is presented in the 

Appendix. Results indicate that the total number of wells (119) to be drilled in 2011 would 

generate around 3,643 jobs from MAGPLAN in the LA-2 region as defined by the Bureau of 

Ocean Energy Management (BOEM, formerly MMS).16 Lafayette Parish makes up more than 

half of the total Mining jobs in the LA-2 region. We assumed the job losses for Lafayette Parish 

were equivalent to their proportion of total LA-2 region mining jobs. The fiscal impacts were 

generated based on two revenue capacity equations and four expenditure equations (Adhikari and 

Fannin, forthcoming). Estimates of assessed value and retail sales make up the revenue capacity 

equations. Local government revenue is commonly generated by different tax revenues and 

transfer revenues and these tax revenues are based upon assessed value and retail sales 

respectively. On the other hand, expenditure equations are built up in such a way that the 

expenditures are explained by factors that measure the quantity of public services, quality of 

public services, demand conditions related to the public services, and input conditions related to 

public services (Johnson 1996). 

5.4. ESTIMATION RESULTS

What would be the impact in terms of expenditure if those 119 wells were not drilled? This 

would be a fundamental question that needs to be addressed in order to evaluate the marginal 

effects of losing the jobs in that region. In the immediate short run, the demand for these public 

expenditure categories would not change significantly; however, as time passes, the demand for 

these categories might increase or decrease, depending on the preference and necessities of the 

people in the region. People who are no longer employed after losing their jobs may be interested 

in maintaining these public services. On the other hand, because of the loss of their jobs, people 

might not be able to afford these public services and the expenditure in any of these categories 

might decrease. 

As can be seen from Table 5.1, there is about 11% percent change in the health and welfare 

expenditure when moving from 2009 to 2010 and around 15% change when moving from 2010 

to 2011. Thus, there is a difference of about 4%, which accounts for the spending effects as 

evaluated by the difference in the growth rates between years. For other categories of 

expenditure, these effects are 1%, 2%, and 5% for general government, public safety and public 

works respectively. If we think of the wells drilled as wells that will not be drilled because of the 

 

16 LA-2 region is described by MMS as few southern parishes of Louisiana that includes seven parishes, 

namely, Acadia, Evangeline, Iberia, Lafayette, St. Landry, St. Martin, and Vermillion. 
151 

Deepwater Horizon spill, then the additional Health and Welfare spending effects above baseline 

growth would reduce Health and Welfare spending per capita back about halfway to 2009 levels. 

Similar effects occur in the other expenditure categories. It should also be noted that the per 

capita expenditure change is three to six times inflation. 

Table 5.1. Per capita fiscal expenditure effects on Lafayette Parish from oil and gas wells drilled in the 

Gulf of Mexico. 

Year General 

Government 

% 

Change 

Health and 

Welfare 

% 

Change 

Public 

Safety 

% 

Change 

Public 

Works 

% 

Change

2009 $149 $76 $90 $483 

2010 $175 17% $85 11% $107 19% $561 16% 

2011 $206 18% $98 15% $130 21% $680 21% 

5.5. THE POLICY MODELING CHALLENGE

 

The COMPAS model built for Louisiana and applied to Lafayette, Louisiana, seems to have 

performed rather poorly. Though the model predicts a loss of 3,643 direct jobs, there was an 

actual increase in jobs according to the County Business Patterns of 6,275 jobs between 2010 

and 2011 (County Business Patterns). Further, local government expenditure actually increased 

by $27 million rather than declining $41 million. Is this a function of a poor model, a poor 

scenario, or both? 

The answer to the first question is partially addressed by the source data for developing the 

COMPAS model (Adhikari and Fannin, forthcoming). A panel regression model was applied to 

each of the public service expenditure between 2004 and 2009. There was measurable volatility 

in both revenue and expenditure estimates during this period. Some of this volatility was driven 

by large amounts federal reimbursement dollars to cover emergency operations, debris removal, 

and re-construction expenses for those parishes closest to the coast. For those parishes further 

inland, there were less direct federal transfer payments, but for a larger parish like Lafayette, it 

became the temporary (and later permanent) home for some of the thousands of evacuees. While 

Lafayette witnessed a population increase of over 7,000 between 2005 and 2006, its per capita 

expenditure actually declined 3.64%. They declined then again another 2.68% between 2006 and 

2007 and then increased 17.86% from 2007 to 2008. Some of the parishes along the coast saw 

the opposite–larger increases immediately after the storms and reductions in the growth of 

spending as time further passed. In each of these cases, the increased volatility in such a 

disequilibrium environment makes it difficult for even panel data models to identify meaningful 

statistical relationships that improve local government revenue and expenditure forecasting. 

 

The second issue, and one that is more difficult to model, is the pattern of behavior of the oil 

and gas industry after the moratorium. The assumption incorporated here would have been that it 

would have followed other recent historical downturns in economic activity of the sector. The 

most recent glaring example of the downturn in this region was in the middle 1980s when a crash 

in oil prices occurred. At that time, there was a massive de-investment in both onshore and 

offshore drilling. The great number of layoffs resulted in a massive out-migration of residents 

from the state. The model predicting the number of jobs losses (the MAGPLAN model) is a 

linear model and assumes that impacts on jobs are the same in both directions; that is, the 
152 

increase in jobs created from new wells drilled would be similar to the number of jobs losses had 

the wells not been drilled. 

This assumption of the model and its application to this moratorium policy is one of the 

problem dimensions in the lack of performance of the COMPAS modeling system. In fact, 

though there were some reductions in workforce due to the moratorium, much of the larger oil 

and gas companies used the time to invest in repair and maintenance of rigs and performed other 

down-time procedures that are typically more difficult to accomplish at full drilling capacity. As 

a result, there was not a great number of job losses. Further, the oil spill itself created short-term 

employment opportunities for individuals to obtain jobs. 

The second assumption that is made is that expenditures on public services will be reduced as 

revenue availability declines. In our COMPAS model, revenue capacity (as measured by 

assessed valuation and retail sales) typically has a positive and significant effect on public sector 

expenditure. As a result, in the Louisiana COMPAS model, we might expect that as revenue 

capacity of the region increases or decreases, the expenditures would also increase or decrease, 

respectively. 

However, given that public expenditures actually increased, what might explain this? First, 

given that employment actually increased, we saw revenue capacity increases that resulted in 

increased public service expenditures. Second, even if we had seen declining revenue capacity, 

we may not have seen the symmetric reduction in public service expenditures. Deller and Maher 

(2007) showed that for Wisconsin municipalities, reductions in intergovernmental aid did not 

result in similar reductions in public service expenditures. This held especially for wealthier 

Wisconsin municipalities. Local governments were more likely to substitute other local revenue 

for lost intergovernmental aid. As a result, a larger parish government, such as Lafayette’s, may 

be more likely to tap savings from their balance sheets to mitigate the reduction in public 

services and soften the blow rather than proportionately reduce their expenditures by the 

reduction in revenues that are generated to cover expenditure demands. 

5.6. CONCLUSION

This paper attempts to estimate how the oil and gas industry activities impact a region’s 

onshore economies that service offshore drilling. In particular, an econometric model was 

developed to forecast the number of wells that would not be drilled in 2011 as a result of the U.S. 

Deepwater Drilling Moratorium, following the Deepwater Horizon oil spill. The reduced number 

of wells drilled was applied to an input-output model developed by a federal agency and applied 

by the researchers to evaluate total projected employment losses on Lafayette Parish, Louisiana. 

Further, these results were applied to a community policy analysis modeling framework which 

projected changes in local government expenditure demands on the parish. 

Impacts from the model showed that, though revenues would decline, expenditure demands 

would see greater declines. However, local government revenues and expenditures both 

increased in the parish between 2010 and 2011. It was hypothesized that many of the declines 
153 

did not materialize because the offshore oil and gas industry used the period to focus on 

maintenance and repair and expected the moratorium to be lifted at some point in the future. 

A few limitations should be noted. The COMPAS modeling framework, in many cases, has 

been used to look at policy impacts that have added employment to a locality. Results by Deller 

and Maher (2007) suggest that the COMPAS model may have challenges in forecasting 

expenditure declines when there are corresponding reductions in local revenue capacity. Local 

government officials are likely to try to find other methods, such as raising new revenue or 

tapping reserves, to maintain expenditure levels. 

Second, one might expect that COMPAS-type models are limited to how representative the 

data used to construct them can project about the future. Historical single-year cross-section 

COMPAS fiscal modules were vulnerable to the base year of data chosen not being a 

representative of a normal year of a local government’s activities. Though this study used a panel 

model containing six years of data, the panel covered a very disruptive period of time in local 

government financing and public service demands through both the 2005 and 2008 hurricane 

seasons in the state of Louisiana. Future research should consider how to properly incorporate 

major economic shocks, such as natural disasters or economic recessions, into their choice of 

data or economic model construction. 

This research took a commonly used policy projection tool and applied it to a policy shock 

that resulted from a technological disaster in a region with an economy in transition between 

recovering from multiple natural disasters and entering a period of national economic downturn. 

The lessons learned from the application of this tool suggest that policy modelers should be very 

cautious in using source data during periods of major economic upheaval to project policy 

impacts during less stressful economic periods. Further, counter-factual scenarios in policy 

analysis should be closely scrutinized to address unintended consequences. Adopting these best 

practices should help community policy modelers avoid these pitfalls and provide higher quality 

decision support information to local stakeholders. 
154 

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163 

APPENDIX A. DETAILED TABLES OF FUZZY SET MEMBERSHIP 

This appendix contains quantitative source data and the membership set of each parish or 

school district in each of the Fuzzy Sets presented in Figures 1.1–1.38. For example, source data 

for Figure 1.1 is located in Table A1.1, Figure 1.2 data is located in Table A1.2, etc. 

Table A1.1. Large Population fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Resident 

population estimate 

(July, 2009) 

Large Population 

Set Score 

Jefferson 443,342 1 

East Baton Rouge 434,633 1 

Orleans 354,850 1 

Caddo 253,623 1 

St. Tammany 231,495 1 

Lafayette 210,954 1 

Calcasieu 187,554 1 

Ouachita 151,502 1 

Rapides 133,937 1 

Livingston 123,326 1 

Tangipahoa 118,688 1 

Bossier 111,492 1 

Terrebonne 109,291 1 

Ascension 104,822 1 

Lafourche 93,682 0.874 

St. Landry 92,326 0.847 

Iberia 75,101 0.502 

Acadia 60,095 0.202 

Vermilion 56,141 0.123 

St. Martin 52,217 0.044 

St. Charles 51,611 0.032 

St. Mary 50,815 0.016 

St. John the Baptist 47,086 0 

Vernon 46,616 0 

Washington 45,669 0 

Lincoln 43,286 0 

Avoyelles 42,511 0 

St. Bernard 40,655 0 

Webster 40,544 0 

Natchitoches 39,255 0 

Beauregard 35,419 0 

Evangeline 35,330 0 
164 

Iberville 32,505 0 

Jefferson Davis 31,097 0 

Morehouse 28,223 0 

De Soto 26,401 0 

Allen 25,636 0 

Sabine 23,733 0 

Assumption 22,874 0 

West Baton Rouge 22,638 0 

Union 22,584 0 

Pointe Coupee 22,447 0 

St. James 21,054 0 

East Feliciana 20,970 0 

Plaquemines 20,942 0 

Richland 20,422 0 

Grant 20,164 0 

Franklin 19,807 0 

Concordia 18,989 0 

Claiborne 16,118 0 

Winn 15,331 0 

Jackson 15,063 0 

West Feliciana 15,055 0 

Bienville 14,729 0 

La Salle 13,964 0 

Madison 11,385 0 

West Carroll 11,329 0 

St. Helena 10,551 0 

Catahoula 10,460 0 

Caldwell 10,439 0 

Red River 9,003 0 

East Carroll 8,102 0 

Cameron 6,584 0 

Tensas 5,609 0 
165 

Table A1.2. Small Population fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Resident 

population estimate 

(July, 2009) 

Small Population 

Set Score 

Tensas 5,609 1 

Cameron 6,584 1 

East Carroll 8,102 1 

Red River 9,003 1 

Caldwell 10,439 1 

Catahoula 10,460 1 

St. Helena 10,551 1 

West Carroll 11,329 1 

Madison 11,385 1 

La Salle 13,964 1 

Bienville 14,729 1 

West Feliciana 15,055 1 

Jackson 15,063 1 

Winn 15,331 1 

Claiborne 16,118 1 

Concordia 18,989 1 

Franklin 19,807 1 

Grant 20,164 1 

Richland 20,422 1 

Plaquemines 20,942 1 

East Feliciana 20,970 1 

St. James 21,054 1 

Pointe Coupee 22,447 1 

Union 22,584 1 

West Baton Rouge 22,638 1 

Assumption 22,874 1 

Sabine 23,733 1 

Allen 25,636 0.975 

De Soto 26,401 0.944 

Morehouse 28,223 0.871 

Jefferson Davis 31,097 0.756 

Iberville 32,505 0.700 

Evangeline 35,330 0.587 

Beauregard 35,419 0.583 

Natchitoches 39,255 0.430 

Webster 40,544 0.378 

St. Bernard 40,655 0.374 

Avoyelles 42,511 0.300 

Lincoln 43,286 0.269 
166 

Washington 45,669 0.173 

Vernon 46,616 0.135 

St. John the Baptist 47,086 0.117 

St. Mary 50,815 0 

St. Charles 51,611 0 

St. Martin 52,217 0 

Vermilion 56,141 0 

Acadia 60,095 0 

Iberia 75,101 0 

St. Landry 92,326 0 

Lafourche 93,682 0 

Ascension 104,822 0 

Terrebonne 109,291 0 

Bossier 111,492 0 

Tangipahoa 118,688 0 

Livingston 123,326 0 

Rapides 133,937 0 

Ouachita 151,502 0 

Calcasieu 187,554 0 

Lafayette 210,954 0 

St. Tammany 231,495 0 

Caddo 253,623 0 

Orleans 354,850 0 

East Baton Rouge 434,633 0 

Jefferson 443,342 0 
167 

Table A1.3. Population Growth fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Percent Population 

Change 

Population Growth 

Set Score 

Ascension 36.8 1 

Livingston 34.3 1 

St. Tammany 21 1 

Tangipahoa 18 1 

Bossier 13.3 1 

Lafayette 10.8 1 

St. John the Baptist 9.4 1 

Grant 7.9 0.92 

St. Martin 7.5 0.87 

Beauregard 7.4 0.86 

St. Charles 7.4 0.86 

Rapides 6 0.70 

East Baton Rouge 5.3 0.62 

St. Landry 5.3 0.62 

West Baton Rouge 4.8 0.56 

Terrebonne 4.6 0.53 

Lafourche 4.1 0.48 

Vermilion 4 0.47 

Washington 4 0.47 

De Soto 3.6 0.42 

Ouachita 2.9 0.34 

Avoyelles 2.5 0.29 

Iberia 2.5 0.29 

Calcasieu 2.2 0.26 

Acadia 2.1 0.24 

Lincoln 1.8 0.21 

Sabine 1.2 0.14 

Allen 0.8 0.09 

Caddo 0.6 0.07 

Natchitoches 0.4 0 

St. Helena 0.2 0 

Evangeline -0.3 0 

West Feliciana -0.4 0 

St. James -0.7 0 

Union -1 0 

Caldwell -1.1 0 
168 

Jefferson Davis -1.1 0 

Pointe Coupee -1.4 0 

East Feliciana -1.8 0 

Assumption -2.2 0 

Jackson -2.2 0 

La Salle -2.2 0 

Iberville -2.4 0 

Jefferson -2.7 0 

Richland -2.7 0 

Webster -3.1 0 

Catahoula -4.2 0 

Claiborne -4.3 0 

St. Mary -5 0 

Concordia -6.2 0 

Red River -6.4 0 

Bienville -6.5 0 

Franklin -6.8 0 

West Carroll -8 0 

Morehouse -9 0 

Winn -9.3 0 

Vernon -11.2 0 

East Carroll -14 0 

Tensas -15.2 0 

Madison -17.1 0 

Plaquemines -21.7 0 

Orleans -26.8 0 

Cameron -34.1 0 

St. Bernard -39.5 0 
169 

Table A1.4. Population Decline fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Percent Population 

Change 

Population Decline 

Set Scores 

St. Bernard -39.5 1 

Cameron -34.1 1 

Orleans -26.8 1 

Plaquemines -21.7 1 

Madison -17.1 1 

Tensas -15.2 1 

East Carroll -14 1 

Vernon -11.2 1 

Winn -9.3 1 

Morehouse -9 1 

West Carroll -8 1 

Franklin -6.8 1 

Bienville -6.5 1 

Red River -6.4 1 

Concordia -6.2 1 

St. Mary -5 1 

Claiborne -4.3 1 

Catahoula -4.2 1 

Webster -3.1 1 

Jefferson -2.7 1 

Richland -2.7 1 

Iberville -2.4 1 

Assumption -2.2 1 

Jackson -2.2 1 

La Salle -2.2 1 

East Feliciana -1.8 1 

Pointe Coupee -1.4 1 

Caldwell -1.1 1 

Jefferson Davis -1.1 1 

Union -1 1 

St. James -0.7 1 

West Feliciana -0.4 1 

Evangeline -0.3 1 

St. Helena 0.2 0 

Natchitoches 0.4 0 

Caddo 0.6 0 
170 

Allen 0.8 0 

Sabine 1.2 0 

Lincoln 1.8 0 

Acadia 2.1 0 

Calcasieu 2.2 0 

Avoyelles 2.5 0 

Iberia 2.5 0 

Ouachita 2.9 0 

De Soto 3.6 0 

Vermilion 4 0 

Washington 4 0 

Lafourche 4.1 0 

Terrebonne 4.6 0 

West Baton Rouge 4.8 0 

East Baton Rouge 5.3 0 

St. Landry 5.3 0 

Rapides 6 0 

Beauregard 7.4 0 

St. Charles 7.4 0 

St. Martin 7.5 0 

Grant 7.9 0 

St. John the Baptist 9.4 0 

Lafayette 10.8 0 

Bossier 13.3 0 

Tangipahoa 18 0 

St. Tammany 21 0 

Livingston 34.3 0 

Ascension 36.8 0 
171 

Table A1.5. Racial Diversity fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Theil 

Multigroup 

Entropy Index 

Racial Diversity 

Set Score 

St. Helena 0.0103 1.000 

East Feliciana 0.0123 1.000 

Claiborne 0.0236 1.000 

Cameron 0.0298 1.000 

Caldwell 0.0373 1.000 

West Baton Rouge 0.0434 1.000 

Tensas 0.0475 1.000 

Iberville 0.0602 1.000 

West Carroll 0.0755 1.000 

Bienville 0.0786 1.000 

West Feliciana 0.0848 1.000 

Jefferson Davis 0.0965 1.000 

Franklin 0.1007 0.963 

Bossier 0.1012 0.942 

Jackson 0.1025 0.874 

Livingston 0.1050 0.749 

Allen 0.1091 0.544 

Winn 0.1110 0.448 

Avoyelles 0.1113 0.435 

La Salle 0.1122 0.391 

De Soto 0.1145 0.277 

Richland 0.1189 0.053 

St. Tammany 0.1208 0.000 

Red River 0.1226 0.000 

St. Martin 0.1256 0.000 

Concordia 0.1262 0.000 

Evangeline 0.1276 0.000 

Tangipahoa 0.1289 0.000 

Vernon 0.1359 0.000 

Catahoula 0.1445 0.000 

Terrebonne 0.1485 0.000 

Natchitoches 0.1512 0.000 

St. Landry 0.1581 0.000 

Webster 0.1618 0.000 

Plaquemines 0.1641 0.000 

St. John the Baptist 0.1658 0.000 

Iberia 0.1659 0.000 
172 

Pointe Coupee 0.1668 0.000 

Beauregard 0.1697 0.000 

Union 0.1734 0.000 

St. Mary 0.1767 0.000 

Vermilion 0.1781 0.000 

Sabine 0.1785 0.000 

St. Bernard 0.1797 0.000 

St. Charles 0.1822 0.000 

East Carroll 0.1860 0.000 

St. James 0.1869 0.000 

Acadia 0.1895 0.000 

Washington 0.1923 0.000 

Lafourche 0.1968 0.000 

Morehouse 0.2131 0.000 

Ascension 0.2278 0.000 

Assumption 0.2285 0.000 

Lafayette 0.2390 0.000 

Jefferson 0.2535 0.000 

Lincoln 0.2801 0.000 

Rapides 0.3211 0.000 

Calcasieu 0.3311 0.000 

Caddo 0.3442 0.000 

East Baton Rouge 0.3472 0.000 

Grant 0.3574 0.000 

Orleans 0.3740 0.000 

Madison 0.4397 0.000 

Ouachita 0.4613 0.000 
173 

 

Table A1.6. Old and Aging Population fuzzy set scores for Louisiana parishes. 

Louisiana 

Parish 

Percent of 

Population Age 65 

and Older 

Old and Aging Set 

Score 

Ascension 8.5 0 

Cameron 9.1 0 

St. Bernard 9.2 0 

St. John the Baptist 9.5 0 

Livingston 9.9 0 

St. Charles 9.9 0 

West Feliciana 10 0 

Vernon 10.5 0 

Lafayette 10.6 0 

West Baton Rouge 10.8 0 

East Baton Rouge 10.9 0 

St. Martin 10.9 0 

Plaquemines 11 0 

Tangipahoa 11.3 0 

Terrebonne 11.5 0 

Orleans 11.7 0 

Madison 11.9 0 

Bossier 12 0 

Lincoln 12 0 

Iberia 12.2 0 

St. Tammany 12.2 0 

Evangeline 12.3 0 

Iberville 12.3 0 

Lafourche 12.3 0 

Ouachita 12.3 0 

Assumption 12.4 0.17 

Acadia 12.5 0.33 

St. James 12.6 0.50 

Calcasieu 12.7 0.67 

East Feliciana 12.7 0.67 

Allen 12.8 0.83 

Natchitoches 12.9 1 

Vermilion 13 1 

Beauregard 13.1 1 

East Carroll 13.1 1 

Grant 13.1 1 

St. Mary 13.3 1 
174 

Avoyelles 13.4 1 

St. Helena 13.5 1 

Caddo 13.6 1 

Jefferson 13.7 1 

Jefferson Davis 13.7 1 

Rapides 13.8 1 

St. Landry 13.9 1 

De Soto 14 1 

Washington 14.3 1 

Red River 14.5 1 

Richland 14.5 1 

Winn 14.5 1 

Caldwell 15 1 

La Salle 15 1 

Tensas 15 1 

Catahoula 15.2 1 

Morehouse 15.3 1 

Concordia 15.4 1 

Pointe Coupee 15.5 1 

Claiborne 16.4 1 

Sabine 16.4 1 

Franklin 16.6 1 

West Carroll 16.8 1 

Union 16.9 1 

Webster 17 1 

Jackson 17.2 1 

Bienville 18 1 
175 

Table A1.7. Young Parish fuzzy set scores for Louisiana parishes. 

Louisiana Parish Percent of Population 18 

Years Old or Younger 

Young Parish Set 

Score 

West Feliciana 16.8 0 

Claiborne 21 0 

Lincoln 21.1 0 

East Feliciana 21.4 0 

Orleans 21.5 0 

Winn 22.4 0 

Cameron 22.8 0 

Jackson 23 0 

Jefferson 23 0 

Iberville 23.1 0 

Caldwell 23.2 0 

Bienville 23.2 0 

Union 23.3 0 

Allen 23.4 0 

Assumption 23.8 0 

Webster 23.9 0 

St. Helena 24 0 

West Carroll 24 0 

East Baton Rouge 24 0 

Pointe Coupee 24.2 0 

Catahoula 24.3 0 

Tensas 24.4 0.14 

Lafourche 24.5 0.29 

Natchitoches 24.6 0.43 

Morehouse 24.9 0.86 

Lafayette 25 1 

Caddo 25 1 

West Baton Rouge 25.1 1 

Franklin 25.2 1 

Sabine 25.2 1 

La Salle 25.3 1 

De Soto 25.3 1 

Avoyelles 25.5 1 

Washington 25.6 1 

Tangipahoa 25.6 1 

Bossier 25.7 1 

Concordia 25.8 1 

Grant 25.8 1 
176 

Calcasieu 25.8 1 

St. Tammany 25.8 1 

Richland 26 1 

Beauregard 26 1 

Rapides 26.1 1 

East Carroll 26.4 1 

Vermilion 26.4 1 

Ouachita 26.5 1 

St. James 26.7 1 

St. Martin 26.7 1 

Terrebonne 26.7 1 

St. Bernard 26.8 1 

St. Charles 26.9 1 

Vernon 27.1 1 

St. Mary 27.1 1 

Red River 27.2 1 

Plaquemines 27.2 1 

Madison 27.3 1 

Livingston 27.5 1 

Jefferson Davis 27.6 1 

Evangeline 27.6 1 

Acadia 27.6 1 

St. Landry 27.6 1 

St. John the Baptist 27.7 1 

Iberia 27.7 1 

Ascension 28.6 1 
177 

Table A1.8. Wealthy Parish fuzzy set scores for Louisiana parishes. 

Louisiana 

Parishes 

Median Household 

Income in 2008 ($) 

Wealthy Parish Set 

Score 

East Carroll 25100 0 

Madison 25788 0 

Tensas 26135 0 

Evangeline 29733 0 

Concordia 29807 0 

Franklin 29904 0 

Richland 30504 0 

Washington 30725 0 

West Carroll 30922 0 

Natchitoches 31000 0 

Winn 31108 0 

Catahoula 31236 0 

Claiborne 31386 0 

Bienville 31440 0 

Red River 31495 0 

Morehouse 32168 0 

St. Landry 32373 0 

Avoyelles 32744 0 

St. Helena 33075 0 

Caldwell 34298 0 

Sabine 34786 0 

Union 35624 0 

Webster 35981 0 

Jackson 36073 0 

Caddo 36527 0 

Lincoln 36720 0 

Orleans 37047 0 

De Soto 37490 0 

St. Martin 37779 0 

Acadia 37862 0 

Jefferson Davis 38577 0 

Iberville 38672 0 

Grant 38896 0 

La Salle 38926 0 

Ouachita 39056 0 

St. Bernard 39106 0 

East Feliciana 39244 0 
178 

Pointe Coupee 39379 0 

Tangipahoa 39604 0 

St. Mary 39856 0 

Allen 40131 0 

Iberia 40610 0 

Rapides 41200 0 

Vernon 41284 0 

Assumption 41319 0 

Vermilion 41414 0 

Beauregard 43398 0 

Calcasieu 44826 0.14 

West Baton Rouge 45177 0.18 

St. James 45303 0.20 

East Baton Rouge 46563 0.35 

Jefferson 47065 0.41 

Lafayette 48119 0.53 

Lafourche 49182 0.66 

Bossier 49331 0.68 

St. John the Baptist 49525 0.70 

Terrebonne 49786 0.73 

Cameron 49984 0.76 

West Feliciana 50095 0.77 

Plaquemines 50948 0.87 

Livingston 53237 1 

St. Charles 56886 1 

St. Tammany 57129 1 

Ascension 61345 1 
179 

Table A1.9. Poor Parish fuzzy set scores for Louisiana parishes. 

Louisiana 

Parishes 

Percent of Population 

Living Under Federal 

Poverty Line in 2008 

Poor Parish Set 

Score 

East Carroll 43.7 1 

Madison 34.6 1 

Tensas 32.4 1 

Natchitoches 31.7 1 

Claiborne 28.7 1 

Franklin 26.1 1 

St. Landry 25.6 1 

Concordia 25.2 1 

Morehouse 25.2 1 

Richland 24.2 1 

Washington 24.1 1 

Winn 23.9 1 

West Carroll 23.7 1 

Lincoln 23.6 1 

Red River 23.4 1 

Orleans 22.9 1 

Evangeline 22.7 1 

Tangipahoa 22.2 1 

Avoyelles 21.9 1 

Catahoula 21.9 1 

Ouachita 21.9 1 

West Feliciana 21.9 1 

Iberville 21.6 1 

Caldwell 21.5 1 

East Feliciana 21.2 1 

Bienville 21 1 

Pointe Coupee 21 1 

Sabine 20.9 1 

Allen 20.7 1 

St. Bernard 20.3 1 

Caddo 19.8 1 

St. Helena 19.8 1 

Acadia 19.7 1 

St. Martin 19.5 1 

Assumption 19.3 1 

Iberia 18.8 1 

St. Mary 18.5 1 

Jackson 18.4 1 
180 

Union 18.3 1 

De Soto 18.1 1 

Rapides 18 1 

Vermilion 17.9 1 

Webster 17.9 1 

Grant 17.3 0.93 

East Baton Rouge 17.2 0.91 

Jefferson Davis 16.7 0.80 

La Salle 16.2 0.68 

West Baton Rouge 16.1 0.66 

Lafourche 15.9 0.61 

St. James 15.8 0.59 

Terrebonne 15.8 0.59 

Calcasieu 15.4 0.50 

Vernon 15.4 0.50 

Plaquemines 15.1 0.43 

Lafayette 14.7 0.34 

St. John the Baptist 14.7 0.34 

Beauregard 14 0.18 

Bossier 13.6 0.09 

Jefferson 12.9 0 

Cameron 12.7 0 

St. Charles 11.6 0 

Livingston 11.4 0 

St. Tammany 10 0 

Ascension 9.9 0 
181 

Table A1.10. Adult Education fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Percent of People 25 

Years and Older With 

High School Diploma 

Adult Education Set 

Score 

St. Tammany 83.9 1 

East Baton Rouge 83.9 1 

Bossier 83 1 

Lincoln 80.4 1 

Vernon 80.1 0.95 

St. Charles 80 0.93 

Lafayette 79.8 0.89 

Ascension 79.6 0.86 

Jefferson 79.3 0.80 

Caddo 78.7 0.70 

Ouachita 78.6 0.68 

Livingston 77.2 0.43 

Calcasieu 77 0.39 

St. John the Baptist 76.9 0.38 

Beauregard 75 0.04 

Orleans 74.7 0 

Rapides 74.6 0 

St. James 73.9 0 

Jackson 73.6 0 

West Baton Rouge 73.4 0 

St. Bernard 73.1 0 

Grant 73.1 0 

Natchitoches 72.7 0 

Bienville 71.9 0 

Union 71.7 0 

Tangipahoa 71.5 0 

Sabine 70.8 0 

Webster 70.8 0 

East Feliciana 70.7 0 

De Soto 70.3 0 

Jefferson Davis 69.4 0 

Pointe Coupee 69.1 0 

Plaquemines 68.7 0 

La Salle 68.5 0 

Washington 68.2 0 

Cameron 68.1 0 

St. Helena 67.5 0 
182 

Red River 67.4 0 

Terrebonne 67.1 0 

Iberia 66.9 0 

Morehouse 66.6 0 

Lafourche 66.3 0 

St. Mary 65.9 0 

Iberville 65.7 0 

Claiborne 65.7 0 

Vermilion 65.6 0 

Caldwell 65.4 0 

Winn 65.4 0 

Acadia 64.7 0 

Concordia 64.6 0 

Madison 63.4 0 

Allen 63.2 0 

Tensas 63.2 0 

St. Martin 62.9 0 

St. Landry 62 0 

Richland 61.9 0 

Catahoula 61.4 0 

Franklin 61.4 0 

Avoyelles 59.8 0 

West Carroll 59.5 0 

Assumption 59.4 0 

East Carroll 57.9 0 

Evangeline 55.5 0 

West Feliciana 53.3 0 

 
183 

Table A1.11. High Crime fuzzy set scores for Louisiana parishes. 

Louisiana Parish Number of Violent 

Crimes Reported High Crime Set Score 

East Baton Rouge 3562 1 

Orleans 2893 1 

Jefferson 2865 1 

Caddo 2064 1 

Lafayette 1701 1 

Tangipahoa 1668 1 

Bossier 1493 1 

Rapides 1016 1 

Calcasieu 871 0.74 

Ouachita 758 0.52 

Terrebonne 645 0.29 

St. Tammany 623 0.25 

De Soto 502 0 

St. Mary 438 0 

Ascension 406 0 

Iberia 400 0 

Washington 336 0 

Natchitoches 334 0 

Vermilion 299 0 

Acadia 288 0 

Lafourche 277 0 

St. Martin 257 0 

Livingston 256 0 

Vernon 239 0 

Iberville 229 0 

Lincoln 217 0 

St. James 204 0 

Concordia 172 0 

St. Charles 172 0 

St. Landry 168 0 

St. John the Baptist 161 0 

Assumption 151 0 

Avoyelles 150 0 

Catahoula 150 0 

Morehouse 147 0 

Madison 140 0 

Jefferson Davis 139 0 
184 

St. Bernard 139 0 

Claiborne 131 0 

Evangeline 121 0 

Beauregard 110 0 

West Baton Rouge 105 0 

Webster 99 0 

Union 91 0 

St. Helena 86 0 

Winn 71 0 

Caldwell 62 0 

East Carroll 61 0 

Pointe Coupee 56 0 

Plaquemines 55 0 

West Feliciana 51 0 

Cameron 49 0 

East Feliciana 45 0 

Allen 43 0 

Bienville 43 0 

Red River 43 0 

Sabine 40 0 

La Salle 29 0 

West Carroll 28 0 

Grant 23 0 

Franklin 11 0 

Jackson 10 0 

Richland 9 0 

Tensas 0 0 
185 

Table A1.12. List of urban and rural parishes. 

Louisiana Parishes Urban Parish Rural Parish 

Acadia 0 1 

Allen 0 1 

Ascension 1 0 

Assumption 0 1 

Avoyelles 0 1 

Beauregard 0 1 

Bienville 0 1 

Bossier 1 0 

Caddo 1 0 

Calcasieu 1 0 

Caldwell 0 1 

Cameron 1 0 

Catahoula 0 1 

Claiborne 0 1 

Concordia 0 1 

De Soto 1 0 

East Baton Rouge 1 0 

East Carroll 0 1 

East Feliciana 1 0 

Evangeline 0 1 

Franklin 0 1 

Grant 1 0 

Iberia 0 1 

Iberville 1 0 

Jackson 0 1 

Jefferson Davis 0 1 

Jefferson 1 0 

La Salle 0 1 

Lafayette 1 0 

Lafourche 1 0 

Lincoln 0 1 

Livingston 1 0 

Madison 0 1 

Morehouse 0 1 

Natchitoches 0 1 

Orleans 1 0 

Ouachita 1 0 

Plaquemines 1 0 

Pointe Coupee 1 0 
186 

Louisiana Parishes Urban Parish Rural Parish 

Rapides 1 0 

Red River 0 1 

Richland 0 1 

Sabine 0 1 

St. Bernard 1 0 

St. Charles 1 0 

St. Helena 1 0 

St. James 0 1 

St. John the Baptist 1 0 

St. Landry 0 1 

St. Martin 1 0 

St. Mary 0 1 

St. Tammany 1 0 

Tangipahoa 0 1 

Tensas 0 1 

Terrebonne 1 0 

Union 1 0 

Vermilion 0 1 

Vernon 0 1 

Washington 0 1 

Webster 0 1 

West Baton Rouge 1 0 

West Carroll 0 1 

West Feliciana 1 0 

Winn 0 1 
187 

Table A1.13. Natural Amenities fuzzy set scores for Louisiana parishes. 

Louisiana Parishes Natural Amenity Rank Natural Amenities Set 

Score 

Acadia 3 0 

Allen 2 0 

Ascension 3 0 

Assumption 3 0 

Avoyelles 3 0 

Beauregard 3 0 

Bienville 3 0 

Bossier 4 1 

Caddo 4 1 

Calcasieu 3 0 

Caldwell 4 1 

Cameron 4 1 

Catahoula 4 1 

Claiborne 4 1 

Concordia 3 0 

De Soto 4 1 

East Baton Rouge 3 0 

East Carroll 3 0 

East Feliciana 3 0 

Evangeline 3 0 

Franklin 3 0 

Grant 3 0 

Iberia 4 1 

Iberville 3 0 

Jackson 4 1 

Jefferson Davis 4 1 

Jefferson 3 0 

La Salle 3 0 

Lafayette 2 0 

Lafourche 4 1 

Lincoln 3 0 

Livingston 4 1 

Madison 3 0 

Morehouse 3 0 

Natchitoches 4 1 

Orleans 4 1 

Ouachita 4 1 

Plaquemines 4 1 
188 

Pointe Coupee 4 1 

Rapides 3 0 

Red River 4 1 

Richland 3 0 

Sabine 4 1 

St. Bernard 3 0 

St. Charles 4 1 

St. Helena 3 0 

St. James 3 0 

St. John the Baptist 3 0 

St. Landry 3 0 

St. Martin 3 0 

St. Mary 4 1 

St. Tammany 4 1 

Tangipahoa 3 0 

Tensas 3 0 

Terrebonne 4 1 

Union 4 1 

Vermilion 4 1 

Vernon 4 1 

Washington 3 0 

Webster 4 1 

West Baton Rouge 3 0 

West Carroll 2 0 

West Feliciana 3 0 

Winn 3 0 
189 

Table A1.14. Interstate Highway fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Presence of Interstate Highway Interstate 

Highway Set 

Score I-10 I-12 I-49 I-20 I-55 

Caddo 0 0 1 1 0 1 

East Baton Rouge 1 1 0 0 0 1 

Lafayette 1 0 1 0 0 1 

St. John the Baptist 1 0 0 0 1 1 

St. Tammany 1 1 0 0 0 1 

Tangipahoa 0 1 0 0 1 1 

Acadia 1 0 0 0 0 1 

Ascension 1 0 0 0 0 1 

Avoyelles 0 0 1 0 0 1 

Bienville 0 0 0 1 0 1 

Bossier 0 0 0 1 0 1 

Calcasieu 1 0 0 0 0 1 

De Soto 0 0 1 0 0 1 

Iberville 1 0 0 0 0 1 

Jefferson Davis 1 0 0 0 0 1 

Jefferson 1 0 0 0 0 1 

Lincoln 0 0 0 1 0 1 

Livingston 0 1 0 0 0 1 

Madison 0 0 0 1 0 1 

Natchitoches 0 0 1 0 0 1 

Orleans 1 0 0 0 0 1 

Ouachita 0 0 0 1 0 1 

Rapides 0 0 1 0 0 1 

Richland 0 0 0 1 0 1 

St. Charles 1 0 0 0 0 1 

St. James 1 0 0 0 0 1 

St. Landry 0 0 1 0 0 1 

St. Martin 1 0 0 0 0 1 

Webster 0 0 0 1 0 1 

West Baton Rouge 1 0 0 0 0 1 

Allen 0 0 0 0 0 0 

Assumption 0 0 0 0 0 0 

Beauregard 0 0 0 0 0 0 

Caldwell 0 0 0 0 0 0 

Cameron 0 0 0 0 0 0 

Catahoula 0 0 0 0 0 0 
190 

Claiborne 0 0 0 0 0 0 

Concordia 0 0 0 0 0 0 

East Carroll 0 0 0 0 0 0 

East Feliciana 0 0 0 0 0 0 

Evangeline 0 0 0 0 0 0 

Franklin 0 0 0 0 0 0 

Grant 0 0 0 0 0 0 

Iberia 0 0 0 0 0 0 

Jackson 0 0 0 0 0 0 

La Salle 0 0 0 0 0 0 

Lafourche 0 0 0 0 0 0 

Morehouse 0 0 0 0 0 0 

Plaquemines 0 0 0 0 0 0 

Pointe Coupee 0 0 0 0 0 0 

Red River 0 0 0 0 0 0 

Sabine 0 0 0 0 0 0 

St. Bernard 0 0 0 0 0 0 

St. Helena 0 0 0 0 0 0 

St. Mary 0 0 0 0 0 0 

Tensas 0 0 0 0 0 0 

Terrebonne 0 0 0 0 0 0 

Union 0 0 0 0 0 0 

Vermilion 0 0 0 0 0 0 

Vernon 0 0 0 0 0 0 

Washington 0 0 0 0 0 0 

West Carroll 0 0 0 0 0 0 

West Feliciana 0 0 0 0 0 0 

Winn 0 0 0 0 0 0 
191 

Table A1.15. Coastal fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Has Coastal 

Lands on Gulf of 

Mexico or Lake 

Pontchartrain 

Cameron 1 

Iberia 1 

Jefferson 1 

Lafourche 1 

Orleans 1 

Plaquemines 1 

St. Bernard 1 

St. Charles 1 

St. John the Baptist 1 

St. Mary 1 

St. Tammany 1 

Tangipahoa 1 

Terrebonne 1 

Vermilion 1 

Acadia 0 

Allen 0 

Ascension 0 

Assumption 0 

Avoyelles 0 

Beauregard 0 

Bienville 0 

Bossier 0 

Caddo 0 

Calcasieu 0 

Caldwell 0 

Catahoula 0 

Claiborne 0 

Concordia 0 

De Soto 0 

East Baton Rouge 0 

East Carroll 0 

East Feliciana 0 

Evangeline 0 

Franklin 0 

Grant 0 

Iberville 0 
192 

Jackson 0 

Jefferson Davis 0 

La Salle 0 

Lafayette 0 

Lincoln 0 

Livingston 0 

Madison 0 

Morehouse 0 

Natchitoches 0 

Ouachita 0 

Pointe Coupee 0 

Rapides 0 

Red River 0 

Richland 0 

Sabine 0 

St. Helena 0 

St. James 0 

St. Landry 0 

St. Martin 0 

Tensas 0 

Union 0 

Vernon 0 

Washington 0 

Webster 0 

West Baton Rouge 0 

West Carroll 0 

West Feliciana 0 

Winn 0 
193 

Table A1.16. Major Port fuzzy set scores for Louisiana parishes. 

Louisiana Parish Parish Has A 

Major Port 

Ascension 1 

Bossier 1 

Calcasieu 1 

East Baton Rouge 1 

Iberia 1 

Lafourche 1 

Orleans 1 

Plaquemines 1 

St. Bernard 1 

St. Charles 1 

St. James 1 

St. John the Baptist 1 

Vermilion 1 

West Baton Rouge 1 

Acadia 0 

Allen 0 

Assumption 0 

Avoyelles 0 

Beauregard 0 

Bienville 0 

Caddo 0 

Caldwell 0 

Cameron 0 

Catahoula 0 

Claiborne 0 

Concordia 0 

De Soto 0 

East Carroll 0 

East Feliciana 0 

Evangeline 0 

Franklin 0 

Grant 0 

Iberville 0 

Jackson 0 

Jefferson Davis 0 

Jefferson 0 
194 

La Salle 0 

Lafayette 0 

Lincoln 0 

Livingston 0 

Madison 0 

Morehouse 0 

Natchitoches 0 

Ouachita 0 

Pointe Coupee 0 

Rapides 0 

Red River 0 

Richland 0 

Sabine 0 

St. Helena 0 

St. Landry 0 

St. Martin 0 

St. Mary 0 

St. Tammany 0 

Tangipahoa 0 

Tensas 0 

Terrebonne 0 

Union 0 

Vernon 0 

Washington 0 

Webster 0 

West Carroll 0 

West Feliciana 0 

Winn 0 
195 

Table A1.17. Substantial Mining Employment fuzzy set scores for Louisiana parishes. 

Louisiana Parish Mining 

Employment

Total 

Employment

Mining 

Employment 

Set Score 

Acadia 708 25143 0.68 

Allen . 12813 . 

Ascension . 65484 . 

Assumption 50 7019 0.00 

Avoyelles 29 16763 0.00 

Beauregard 82 13268 0.00 

Bienville 183 6720 0.09 

Bossier 1531 59793 1.00 

Caddo 5169 161883 1.00 

Calcasieu 1207 107064 1.00 

Caldwell 35 4031 0.00 

Cameron 184 4056 0.09 

Catahoula 81 4352 0.00 

Claiborne 485 6425 0.43 

Concordia 253 7917 0.17 

De Soto 520 9325 0.47 

East Baton Rouge 849 298175 0.83 

East Carroll . 3258 . 

East Feliciana . 7867 . 

Evangeline 42 11379 0.00 

Franklin . 9208 . 

Grant . 5310 . 

Iberia 3467 43628 1.00 

Iberville 121 17385 0.02 

Jackson . 5520 . 

Jefferson 2287 253727 1.00 

Jefferson Davis 303 12283 0.23 

La Salle 431 5698 0.37 

Lafayette 16385 165459 1.00 

Lafourche 910 56844 0.90 

Lincoln 298 23038 0.22 

Livingston 186 34980 0.10 

Madison (L) 4848 . 

Morehouse . 10899 . 

Natchitoches 44 19955 0.00 

Orleans 4282 217865 1.00 

Ouachita 486 91311 0.43 
196 

Plaquemines 1900 18838 1.00 

Pointe Coupee . 8812 . 

Rapides 162 76113 0.07 

Red River 81 3191 0.00 

Richland . 8365 . 

Sabine 91 8587 0.00 

St. Bernard . 14368 . 

St. Charles . 28764 . 

St. Helena . 4272 . 

St. James . 8702 . 

St. John the Baptist 422 19290 0.36 

St. Landry 623 33899 0.58 

St. Martin 489 15660 0.43 

St. Mary 1336 33858 1.00 

St. Tammany 593 105664 0.55 

Tangipahoa 160 54342 0.07 

Tensas . 2534 . 

Terrebonne 5780 61253 1.00 

Union . 8119 . 

Vermilion 1220 20656 1.00 

Vernon 73 27123 0.00 

Washington 116 16817 0.02 

Webster 571 18260 0.52 

West Baton Rouge . 12613 . 

West Carroll . 4761 . 

West Feliciana . 6710 . 

Winn 62 6764 0.00 
197 

Table A1.18. Small Business Intensive fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Total 

Number of 

Businesses

Number of 

Businesses 

with Fewer 

than 10 

Employees 

Percent 

with Fewer 

than 10 

Employees

Small 

Business 

Intensive 

Set Score 

Acadia 1057 792 74.93% 0.99 

Allen 341 275 80.65% 1.00 

Ascension 1823 1248 68.46% 0.22 

Assumption 256 199 77.73% 1.00 

Avoyelles 709 543 76.59% 1.00 

Beauregard 629 479 76.15% 1.00 

Bienville 248 192 77.42% 1.00 

Bossier 2244 1561 69.56% 0.35 

Caddo 6336 4370 68.97% 0.28 

Calcasieu 4250 2899 68.21% 0.19 

Caldwell 203 159 78.33% 1.00 

Cameron 146 106 72.60% 0.71 

Catahoula 186 143 76.88% 1.00 

Claiborne 246 181 73.58% 0.83 

Concordia 353 266 75.35% 1.00 

De Soto 376 286 76.06% 1.00 

East Baton Rouge 12036 8310 69.04% 0.29 

East Carroll 134 103 76.87% 1.00 

East Feliciana 254 193 75.98% 1.00 

Evangeline 538 417 77.51% 1.00 

Franklin 424 329 77.59% 1.00 

Grant 218 182 83.49% 1.00 

Iberia 1681 1124 66.86% 0.02 

Iberville 522 354 67.82% 0.14 

Jackson 278 217 78.06% 1.00 

Jefferson 12142 8713 71.76% 0.61 

Jefferson Davis 597 443 74.20% 0.90 

Lafayette 7459 5226 70.06% 0.41 

Lafourche 1878 1367 72.79% 0.73 

La Salle 302 235 77.81% 1.00 

Lincoln 1000 697 69.70% 0.36 

Livingston 1542 1181 76.59% 1.00 

Madison 205 143 69.76% 0.37 

Morehouse 516 391 75.78% 1.00 

Natchitoches 812 581 71.55% 0.59 

Orleans 7888 5931 75.19% 1.00 
198 

Ouachita 4278 3021 70.62% 0.47 

Plaquemines 674 462 68.55% 0.23 

Pointe Coupee 370 280 75.68% 1.00 

Rapides 3251 2290 70.44% 0.45 

Red River 142 102 71.83% 0.62 

Richland 412 320 77.67% 1.00 

Sabine 500 399 79.80% 1.00 

St. Bernard 629 522 82.99% 1.00 

St. Charles 980 658 67.14% 0.06 

St. Helena 103 69 66.99% 0.04 

St. James 313 214 68.37% 0.20 

St. John the 

Baptist 717 475 66.25% 0.00 

St. Landry 1626 1205 74.11% 0.89 

St. Martin 794 605 76.20% 1.00 

St. Mary 1344 932 69.35% 0.32 

St. Tammany 5659 4354 76.94% 1.00 

Tangipahoa 2242 1609 71.77% 0.61 

Tensas 102 83 81.37% 1.00 

Terrebonne 2841 1988 69.98% 0.40 

Union 331 260 78.55% 1.00 

Vermillion 994 756 76.06% 1.00 

Vernon 649 481 74.11% 0.89 

Washington 688 499 72.53% 0.70 

Webster 812 595 73.28% 0.79 

West Baton Rouge 474 290 61.18% 0.00 

West Carroll 201 164 81.59% 1.00 

West Feliciana 202 156 77.23% 1.00 

Winn 336 239 71.13% 0.54 
199 

Table A1.19. Large Business Presence fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Businesses 

with More 

than 100 

Employees 

Large Business 

Presence Set 

Score 

Acadia 13 0.10 

Allen 7 0.00 

Ascension 42 1.00 

Assumption 4 0.00 

Avoyelles 12 0.07 

Beauregard 10 0.00 

Bienville 4 0.00 

Bossier 42 1.00 

Caddo 171 1.00 

Calcasieu 103 1.00 

Caldwell 2 0.00 

Cameron 1 0.00 

Catahoula 1 0.00 

Claiborne 2 0.00 

Concordia 6 0.00 

De Soto 8 0.00 

East Baton Rouge 363 1.00 

East Carroll 1 0.00 

East Feliciana 6 0.00 

Evangeline 10 0.00 

Franklin 6 0.00 

Grant 3 0.00 

Iberia 50 1.00 

Iberville 17 0.23 

Jackson 4 0.00 

Jefferson 272 1.00 

Jefferson Davis 9 0.00 

Lafayette 211 1.00 

Lafourche 38 0.93 

La Salle 5 0.00 

Lincoln 26 0.53 

Livingston 14 0.13 

Madison 5 0.00 

Morehouse 9 0.00 

Natchitoches 9 0.00 

Orleans 216 1.00 

Ouachita 92 1.00 
200 

Plaquemines 19 0.30 

Pointe Coupee 6 0.00 

Rapides 75 1.00 

Red River 5 0.00 

Richland 7 0.00 

Sabine 6 0.00 

St. Bernard 9 0.00 

St. Charles 35 0.83 

St. Helena 1 0.00 

St. James 11 0.03 

St. John the Baptist 19 0.30 

St. Landry 28 0.60 

St. Martin 11 0.03 

St. Mary 36 0.87 

St. Tammany 80 1.00 

Tangipahoa 46 1.00 

Tensas 0 0.00 

Terrebonne 91 1.00 

Union 5 0.00 

Vermillion 8 0.00 

Vernon 12 0.07 

Washington 11 0.03 

Webster 21 0.37 

West Baton Rouge 16 0.20 

West Carroll 3 0.00 

West Feliciana 5 0.00 

Winn 10 0.00 
201 

Table A1.20. High Unemployment fuzzy set scores for Louisiana parishes. 

Louisiana Parish Unemployment 

Rate (%) 

High 

Unemployment 

Set Score 

Acadia 5.9 0.00 

Allen 10.6 1.00 

Ascension 7.1 0.37 

Assumption 10.4 1.00 

Avoyelles 8.2 0.73 

Beauregard 7.5 0.50 

Bienville 8.9 0.97 

Bossier 5.8 0.00 

Caddo 7.4 0.47 

Calcasieu 7 0.33 

Caldwell 9.8 1.00 

Cameron 6.2 0.07 

Catahoula 10.1 1.00 

Claiborne 9 1.00 

Concordia 10.9 1.00 

De Soto 8.1 0.70 

East Baton Rouge 7.2 0.40 

East Carroll 15.2 1.00 

East Feliciana 8.5 0.83 

Evangeline 8.6 0.87 

Franklin 11.2 1.00 

Grant 7.8 0.60 

Iberia 7.8 0.60 

Iberville 10.4 1.00 

Jackson 7.3 0.43 

Jefferson 6.9 0.30 

Jefferson Davis 6.3 0.10 

Lafayette 5.7 0.00 

Lafourche 5.2 0.00 

La Salle 6 0.00 

Lincoln 8.3 0.77 

Livingston 7.2 0.40 

Madison 10.6 1.00 

Morehouse 14.2 1.00 

Natchitoches 8.1 0.70 

Orleans 8.8 0.93 

Ouachita 7.7 0.57 

Plaquemines 6.6 0.20 
202 

Pointe Coupee 8.5 0.83 

Rapides 7 0.33 

Red River 8.8 0.93 

Richland 10.1 1.00 

Sabine 6.9 0.30 

St. Bernard 7 0.33 

St. Charles 7.2 0.40 

St. Helena 13 1.00 

St. James 11.4 1.00 

St. John the Baptist 10.1 1.00 

St. Landry 8.3 0.77 

St. Martin 7.3 0.43 

St. Mary 9.3 1.00 

St. Tammany 5.8 0.00 

Tangipahoa 8.9 0.97 

Tensas 15.1 1.00 

Terrebonne 5.5 0.00 

Union 8.6 0.87 

Vermilion 7.1 0.37 

Vernon 6.9 0.30 

Washington 9.8 1.00 

Webster 8 0.67 

West Baton Rouge 8.2 0.73 

West Carroll 16.1 1.00 

West Feliciana 8.3 0.77 

Winn 8.2 0.73 
203 

Table A1.21. Job Growth fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

New Jobs as 

a Percent of 

Total Jobs 

Job Growth 

Set Scale 

Acadia 2.31 1.00 

Allen -3.21 0.00 

Ascension -0.76 0.00 

Assumption -2.65 0.00 

Avoyelles 1.24 0.62 

Beauregard 1.96 0.98 

Bienville 4.09 1.00 

Bossier 0.45 0.22 

Caddo 0.44 0.22 

Calcasieu -0.56 0.00 

Caldwell -0.83 0.00 

Cameron -0.58 0.00 

Catahoula 1.55 0.78 

Claiborne 0.85 0.42 

Concordia -0.90 0.00 

De Soto 0.43 0.21 

East Baton Rouge -0.75 0.00 

East Carroll -1.89 0.00 

East Feliciana -0.74 0.00 

Evangeline -0.24 0.00 

Franklin 0.58 0.29 

Grant -0.83 0.00 

Iberia -3.19 1.00 

Iberville -0.73 0.00 

Jackson 0.83 0.42 

Jefferson 0.88 0.44 

Jefferson Davis -0.42 0.00 

Lafayette -0.06 -0.03 

Lafourche 0.08 0.04 

La Salle 7.06 1.00 

Lincoln 0.83 0.42 

Livingston -0.76 0.00 

Madison -2.08 0.00 

Morehouse -0.56 0.00 

Natchitoches 0.15 0.08 

Orleans 0.86 0.43 

Ouachita 0.14 0.07 

Plaquemines 0.88 0.44 
204 

Pointe Coupee -0.75 0.00 

Rapides -0.83 0.00 

Red River 5.53 1.00 

Richland 2.78 1.00 

Sabine 2.55 1.00 

St. Bernard 0.88 0.44 

St. Charles 0.88 0.44 

St. Helena -0.71 0.00 

St. James -0.04 -0.02 

St. John the Baptist 0.86 0.43 

St. Landry 0.83 0.42 

St. Martin -0.06 -0.03 

St. Mary -1.50 0.00 

St. Tammany 0.89 0.44 

Tangipahoa 0.61 0.31 

Tensas -5.19 0.00 

Terrebonne 0.08 0.04 

Union 0.13 0.06 

Vermilion -1.53 0.00 

Vernon 2.10 1.00 

Washington -0.70 0.00 

Webster 3.41 1.71 

West Baton Rouge -0.75 0.00 

West Carroll -1.46 0.00 

West Feliciana -0.74 0.00 

Winn 2.37 1.00 
205 

Table A1.22. Per Capita Personal Income fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Mean Per 

Capita 

Income 

Per 

Capita 

Income 

Set Score 

Allen $19,386 0.00 

Madison $20,184 0.00 

Evangeline $20,216 0.00 

West Feliciana $21,591 0.00 

Winn $21,613 0.00 

East Carroll $22,021 0.00 

West Carroll $22,159 0.01 

Avoyelles $22,286 0.02 

Morehouse $22,748 0.07 

Franklin $22,828 0.08 

Sabine $22,856 0.08 

Red River $22,869 0.08 

Richland $22,937 0.09 

Catahoula $22,972 0.09 

Grant $23,034 0.10 

Caldwell $23,166 0.11 

Vermillion $23,358 0.13 

Washington $23,365 0.13 

Concordia $23,393 0.14 

Jefferson Davis $23,472 0.15 

St Martin $23,626 0.16 

Bienville $23,723 0.17 

Beauregard $23,727 0.17 

Cameron $23,961 0.20 

Claiborne $24,250 0.22 

LaSalle $24,497 0.25 

St Landry $24,731 0.27 

Tangipahoa $24,836 0.28 

Acadia $25,037 0.31 

St James $25,094 0.31 

Jackson $25,116 0.31 

Union $25,354 0.34 

Natchitoches $25,482 0.35 

Livingston $25,919 0.40 

Tensas $25,932 0.40 

DeSoto $25,983 0.40 

Lincoln $26,080 0.41 
206 

St Helena $26,094 0.41 

Iberville $26,481 0.45 

East Feliciana $26,905 0.50 

Pointe Coupee $27,006 0.51 

Webster $27,143 0.52 

St John the Baptist $27,257 0.53 

Ouachita $28,993 0.71 

Assumption $29,329 0.74 

West Baton Rouge $29,378 0.75 

Iberia $29,507 0.76 

Bossier $29,622 0.77 

St Charles $29,640 0.78 

Calcasieu $30,488 0.86 

Terrebonne $30,848 0.90 

Ascension $31,196 0.94 

Rapides $31,977 1.00 

St Mary $32,202 1.00 

Lafourche $32,395 1.00 

Vernon $32,636 1.00 

Plaquemines $32,836 1.00 

Caddo $33,539 1.00 

East Baton Rouge $34,367 1.00 

St Tammany $34,760 1.00 

Jefferson $35,968 1.00 

Lafayette $36,925 1.00 

Orleans $59,449 1.00 

St Bernard $61,201 1.00 
207 

Table A1.23. Competitive Teacher Salary fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

LDE Actual 

Salary 

Fuzzy set 

score 

Acadia $42,543 0.00 

Allen $45,347 0.87 

Ascension $47,873 1.00 

Assumption $42,959 0.00 

Avoyelles $41,426 0.00 

Beauregard $45,348 0.87 

Bienville $52,251 1.00 

Bossier $47,959 1.00 

Caddo $47,701 1.00 

Calcasieu $46,630 1.00 

Caldwell $44,132 0.37 

Cameron $40,579 0.00 

Catahoula $35,491 0.00 

Claiborne $43,125 0.00 

Concordia $42,166 0.00 

DeSoto $49,677 1.00 

East Baton Rouge $50,014 1.00 

East Carroll $41,449 0.00 

East Feliciana $41,462 0.00 

Evangeline $46,395 1.00 

Franklin $40,837 0.00 

Grant $40,131 0.00 

Iberia $47,171 1.00 

Iberville $45,054 0.75 

Jackson $49,377 1.00 

Jefferson $51,416 1.00 

Jefferson Davis $46,723 1.00 

Lafayette $46,371 1.00 

Lafourche $43,453 0.09 

LaSalle $43,433 0.08 

Lincoln $47,393 1.00 

Livingston $47,416 1.00 

Madison $43,906 0.28 

Morehouse $46,454 1.00 

Natchitoches $52,389 1.00 

Orleans $48,224 1.00 

Ouachita $47,612 1.00 

Plaquemines $45,218 0.82 
208 

Pointe Coupee $43,878 0.27 

Rapides $44,665 0.59 

Red River $45,890 1.00 

Richland $45,499 0.93 

Sabine $43,428 0.08 

St. Bernard $45,596 0.97 

St. Charles $51,737 1.00 

St. Helena $34,670 0.00 

St. James $48,907 1.00 

St. John the Baptist $49,703 1.00 

St. Landry $42,833 0.00 

St. Martin $46,465 1.00 

St. Mary $45,761 1.00 

St. Tammany $50,060 1.00 

Tangipahoa $46,611 1.00 

Tensas $38,797 0.00 

Terrebonne $44,717 0.61 

Union $47,357 1.00 

Vermilion $45,445 0.91 

Vernon $45,933 1.00 

Washington $43,373 0.06 

Webster $50,762 1.00 

West Baton Rouge $46,047 1.00 

West Carroll $44,033 0.33 

West Feliciana $49,498 1.00 

Winn $45,248 0.83 

City of Monroe $51,469 1.00 

City of Bogalusa $52,045 1.00 

Zachary Community $49,298 1.00 

City of Baker $43,008 0.00 

Central Community $46,002 1.00 

Note: SREB average = $45,662; Bottom Quarter = $43,227
209 

Table A1.24. Substantial Local Capacity fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Eligible Revenue 

AC Local Share AD 

Percent of 

Eligible Revenue 

Substantial 

Local 

Capacity Set 

Score 

Acadia $5,791,101 $2,246,136 38.79% 0.09 

Allen $7,502,448 $2,478,899 33.04% 0.00 

Ascension $31,557,724 $18,037,006 57.16% 0.55 

Assumption $6,853,635 $2,154,893 31.44% 0.00 

Avoyelles $1,826,520 $527,791 28.90% 0.00 

Beauregard $8,669,346 $3,328,197 38.39% 0.08 

Bienville $4,292,543 $3,675,344 85.62% 1.00 

Bossier $32,717,693 $19,212,091 58.72% 0.59 

Caddo $73,944,451 $39,490,774 53.41% 0.46 

Calcasieu $56,757,242 $37,740,842 66.50% 0.79 

Caldwell $1,815,619 $554,308 30.53% 0.00 

Cameron $3,064,417 $2,540,524 82.90% 1.00 

Catahoula $1,160,739 $315,043 27.14% 0.00 

Claiborne $3,509,608 $1,290,609 36.77% 0.04 

Concordia $4,227,986 $1,468,971 34.74% 0.00 

DeSoto $8,873,439 $4,989,251 56.23% 0.53 

East Baton Rouge $78,835,984 $73,290,661 92.97% 1.00 

East Carroll $732,348 $175,090 23.91% 0.00 

East Feliciana $1,206,690 $449,970 37.29% 0.06 

Evangeline $6,429,855 $2,024,967 31.49% 0.00 

Franklin $830,798 $252,071 30.34% 0.00 

Grant $1,792,969 $337,688 18.83% 0.00 

Iberia $19,063,877 $8,768,011 45.99% 0.27 

Iberville $8,043,766 $7,999,557 99.45% 1.00 

Jackson $4,187,687 $3,188,689 76.14% 1.00 

Jefferson $76,751,971 $83,115,630 108.29% 1.00 

Jefferson Davis $9,180,697 $3,298,698 35.93% 0.02 

Lafayette $50,944,288 $40,569,993 79.64% 1.00 

Lafourche $24,637,228 $14,166,307 57.50% 0.56 

LaSalle $3,204,006 $1,094,463 34.16% 0.00 

Lincoln $11,603,627 $7,087,171 61.08% 0.65 

Livingston $24,083,073 $5,973,180 24.80% 0.00 

Madison $1,784,826 $465,397 26.08% 0.00 

Morehouse $5,750,054 $2,153,073 37.44% 0.06 

Natchitoches $10,078,818 $4,668,468 46.32% 0.28 

Orleans $57,564,633 $50,406,586 87.57% 1.00 

Ouachita $31,596,652 $11,162,718 35.33% 0.01 
210 

Plaquemines $6,674,084 $8,609,569 129.00% 1.00 

Pointe Coupee $2,869,930 $2,377,312 82.84% 1.00 

Rapides $27,653,687 $13,341,798 48.25% 0.33 

Red River $2,848,151 $824,471 28.95% 0.00 

Richland $4,249,539 $1,360,974 32.03% 0.00 

Sabine $4,214,346 $1,372,174 32.56% 0.00 

St. Bernard $7,651,867 $6,012,041 78.57% 1.00 

St. Charles $16,206,806 $18,227,924 112.47% 1.00 

St. Helena $626,346 $208,568 33.30% 0.00 

St. James $7,486,718 $6,076,730 81.17% 1.00 

St. John the Baptist $12,261,599 $7,368,828 60.10% 0.63 

St. Landry $13,265,908 $5,382,616 40.57% 0.14 

St. Martin $8,855,997 $3,204,879 36.19% 0.03 

St. Mary $14,194,049 $7,875,880 55.49% 0.51 

St. Tammany $64,479,887 $37,375,121 57.96% 0.57 

Tangipahoa $14,767,760 $5,776,084 39.11% 0.10 

Tensas $721,460 $325,739 45.15% 0.25 

Terrebonne $21,641,216 $13,370,463 61.78% 0.67 

Union $2,460,509 $1,038,975 42.23% 0.18 

Vermilion $4,787,721 $2,646,690 55.28% 0.51 

Vernon $8,841,484 $2,357,140 26.66% 0.00 

Washington $4,592,405 $959,721 20.90% 0.00 

Webster $12,548,372 $5,456,233 43.48% 0.21 

West Baton Rouge $6,636,074 $5,644,246 85.05% 1.00 

West Carroll $1,466,182 $382,814 26.11% 0.00 

West Feliciana $4,112,829 $4,264,247 103.68% 1.00 

Winn $3,488,356 $1,305,594 37.43% 0.06 

City of Monroe $16,253,991 $10,629,200 65.39% 0.76 

City of Bogalusa $2,606,439 $1,197,878 45.96% 0.27 

Zachary Community $7,535,214 $3,650,992 48.45% 0.34 

City of Baker $2,469,625 $844,878 34.21% 0.00 

Central Community $4,458,753 $1,897,324 42.55% 0.19 
211 

Table A1.25. Oil and Gas Revenue Influence fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

‘Other’ 

Revenue 

Total 

Revenue 

Percent 

‘Other’ 

Oil and Gas 

Revenue Set 

Score 

Acadia $324,736 $16,677,568 1.95% 0.00 

Allen $138,337 $11,790,818 1.17% 0.00 

Ascension $178,198 $72,374,448 0.25% 0.00 

Assumption $196,069 $10,945,966 1.79% 0.00 

Avoyelles $82,536 $7,210,848 1.14% 0.00 

Beauregard $293,404 $16,015,453 1.83% 0.00 

Bienville $145,526 $17,068,925 0.85% 0.00 

Bossier $590,934 $71,072,569 0.83% 0.00 

Caddo $2,839,941 $158,391,291 1.79% 0.00 

Calcasieu $1,044,943 $129,570,437 0.81% 0.00 

Caldwell $124,102 $3,600,473 3.45% 0.48 

Cameron $1,091,662 $10,244,460 10.66% 1.00 

Catahoula $87,486 $2,714,430 3.22% 0.41 

Claiborne $189,228 $6,644,067 2.85% 0.28 

Concordia $235,828 $8,688,892 2.71% 0.24 

DeSoto $413,614 $26,712,199 1.55% 0.00 

East Baton Rouge $4,044,494 $259,981,233 1.56% 0.00 

East Carroll $155,689 $1,902,308 8.18% 1.00 

East Feliciana $160,743 $4,096,260 3.92% 0.64 

Evangeline $237,519 $12,529,634 1.90% 0.00 

Franklin $76,448 $4,013,611 1.90% 0.00 

Grant $571,386 $3,960,214 14.43% 1.00 

Iberia $536,743 $38,681,850 1.39% 0.00 

Iberville $150,313 $33,064,574 0.45% 0.00 

Jackson $184,328 $13,158,093 1.40% 0.00 

Jefferson $2,247,769 $250,714,207 0.90% 0.00 

Jefferson Davis $313,728 $15,685,392 2.00% 0.00 

Lafayette $2,134,132 $133,769,048 1.60% 0.00 

Lafourche $1,533,741 $49,465,608 3.10% 0.37 

LaSalle $84,249 $5,919,050 1.42% 0.00 

Lincoln $285,724 $28,960,630 0.99% 0.00 

Livingston $739,471 $41,571,334 1.78% 0.00 

Madison $30,746 $3,591,484 0.86% 0.00 

Morehouse $386,919 $11,724,805 3.30% 0.43 

Natchitoches $659,914 $19,608,554 3.37% 0.46 

Orleans $2,420,236 $175,910,825 1.38% 0.00 

Ouachita $787,144 $51,543,541 1.53% 0.00 

Plaquemines $206,206 $29,833,590 0.69% 0.00 
212 

Pointe Coupee $209,427 $11,766,788 1.78% 0.00 

Rapides $1,254,990 $61,222,008 2.05% 0.02 

Red River $61,175 $4,275,738 1.43% 0.00 

Richland $224,877 $7,797,179 2.88% 0.29 

Sabine $159,381 $8,470,493 1.88% 0.00 

St. Bernard $347,404 $21,930,185 1.58% 0.00 

St. Charles $260,807 $91,821,507 0.28% 0.00 

St. Helena $72,353 $2,117,197 3.42% 0.47 

St. James $84,655 $25,802,874 0.33% 0.00 

St. John the Baptist $206,191 $28,840,726 0.71% 0.00 

St. Landry $672,058 $32,351,131 2.08% 0.03 

St. Martin $600,052 $17,956,990 3.34% 0.45 

St. Mary $681,145 $30,895,974 2.20% 0.07 

St. Tammany $1,846,208 $163,682,236 1.13% 0.00 

Tangipahoa $135,405 $37,547,168 0.36% 0.00 

Tensas $67,846 $1,996,714 3.40% 0.47 

Terrebonne $1,656,949 $57,070,583 2.90% 0.30 

Union $179,511 $6,461,152 2.78% 0.26 

Vermilion $2,338,372 $19,630,242 11.91% 1.00 

Vernon $562,268 $16,186,611 3.47% 0.49 

Washington $148,830 $8,226,859 1.81% 0.00 

Webster $372,037 $22,047,744 1.69% 0.00 

West Baton Rouge $177,385 $16,831,477 1.05% 0.00 

West Carroll $108,254 $3,434,218 3.15% 0.38 

West Feliciana $52,640 $11,646,635 0.45% 0.00 

Winn $450,845 $6,746,562 6.68% 1.00 

City of Monroe $326,807 $37,511,416 0.87% 0.00 

City of Bogalusa $217,880 $6,548,494 3.33% 0.44 

Zachary Community $58,733 $17,561,196 0.33% 0.00 

City of Baker $55,747 $4,692,142 1.19% 0.00 

Central Community $0 $8,424,713 0.00% 0.00 
213 

Table A1.26. Expenditures on Instruction fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Instruction 

Expenditure 

Expenditures 

on Instruction 

Set Score 

Acadia 70.83% 0.1660 

Allen 73.77% 0.7540 

Ascension 74.03% 0.8060 

Assumption 70.02% 0.0040 

Avoyelles 71.82% 0.3640 

Beauregard 71.16% 0.2320 

Bienville 71.28% 0.2560 

Bossier 71.60% 0.3200 

Caddo 71.78% 0.3560 

Calcasieu 72.49% 0.4980 

Caldwell 70.30% 0.0600 

Cameron 68.91% 0.0000 

Catahoula 69.25% 0.0000 

Claiborne 74.88% 0.9760 

Concordia 74.63% 0.9260 

DeSoto 72.16% 0.4320 

East Baton Rouge 67.87% 0.0000 

East Carroll 66.70% 0.0000 

East Feliciana 70.03% 0.0060 

Evangeline 73.83% 0.7660 

Franklin 71.39% 0.2780 

Grant 70.02% 0.0040 

Iberia 74.68% 0.9360 

Iberville 66.67% 0.0000 

Jackson 67.58% 0.0000 

Jefferson 71.72% 0.3440 

Jefferson Davis 73.27% 0.6540 

Lafayette 72.47% 0.4940 

Lafourche 75.47% 1.0000 

LaSalle 71.89% 0.3780 

Lincoln 76.21% 1.0000 

Livingston 76.51% 1.0000 

Madison 71.64% 0.3280 

Morehouse 72.42% 0.4840 

Natchitoches 71.26% 0.2520 

Orleans 67.94% 0.0000 

Ouachita 70.21% 0.0420 

Plaquemines 60.22% 0.0000 
214 

Pointe Coupee 66.81% 0.0000 

Rapides 77.50% 1.0000 

Red River 70.59% 0.1180 

Richland 70.53% 0.1060 

Sabine 71.33% 0.2660 

St. Bernard 73.21% 0.6420 

St. Charles 71.95% 0.3900 

St. Helena 62.51% 0.0000 

St. James 76.32% 1.0000 

St. John the Baptist 72.18% 0.4360 

St. Landry 71.26% 0.2520 

St. Martin 70.59% 0.1180 

St. Mary 72.13% 0.4260 

St. Tammany 73.80% 0.7600 

Tangipahoa 75.69% 1.0000 

Tensas 66.73% 0.0000 

Terrebonne 74.99% 0.9980 

Union 71.04% 0.2080 

Vermilion 72.03% 0.4060 

Vernon 71.29% 0.2580 

Washington 72.70% 0.5400 

Webster 76.32% 1.0000 

West Baton Rouge 69.95% 0.0000 

West Carroll 71.20% 0.2400 

West Feliciana 70.15% 0.0300 

Winn 67.58% 0.0000 

City of Monroe 73.12% 0.6240 

City of Bogalusa 74.71% 0.9420 

Zachary Community 68.00% 0.0000 

City of Baker 63.99% 0.0000 
215 

Table A1.27. Adequate School District Performance fuzzy set scores for Louisiana parishes. 

Louisiana Parish DPS 

Adequate 

School 

District 

Performance 

Set Score 

Acadia 87.00 0.20 

Allen 97.80 1.00 

Ascension 99.80 1.00 

Assumption 82.40 0.00 

Avoyelles 80.80 0.00 

Beauregard 100.50 1.00 

Bienville 81.70 0.00 

Bossier 97.10 1.00 

Caddo 82.30 0.00 

Calcasieu 96.80 1.00 

Caldwell 92.20 0.72 

Cameron 87.40 0.24 

Catahoula 94.50 0.95 

Claiborne 79.40 0.00 

Concordia 86.70 0.17 

DeSoto 81.20 0.00 

East Baton Rouge 74.10 0.00 

East Carroll 72.00 0.00 

East Feliciana 72.10 0.00 

Evangeline 80.90 0.00 

Franklin 71.50 0.00 

Grant 92.00 0.70 

Iberia 87.50 0.25 

Iberville 72.10 0.00 

Jackson 89.40 0.44 

Jefferson 73.50 0.00 

Jefferson Davis 102.90 1.00 

Lafayette 91.50 0.65 

Lafourche 87.80 0.28 

LaSalle 97.80 1.00 

Lincoln 91.90 0.69 

Livingston 101.50 1.00 

Madison 61.10 0.00 

Morehouse 82.30 0.00 

Natchitoches 76.60 0.00 

Orleans 96.10 1.00 
216 

Ouachita 101.10 1.00 

Plaquemines 98.30 1.00 

Pointe Coupee 71.80 0.00 

Rapides 92.80 0.78 

Red River 77.50 0.00 

Richland 79.00 0.00 

Sabine 89.80 0.48 

St. Bernard 87.60 0.26 

St. Charles 102.10 1.00 

St. Helena 57.40 0.00 

St. James 91.90 0.69 

St. John the Baptist 77.80 0.00 

St. Landry 87.90 0.29 

St. Martin 83.20 0.00 

St. Mary 86.40 0.14 

St. Tammany 105.70 1.00 

Tangipahoa 82.10 0.00 

Tensas 69.60 0.00 

Terrebonne 84.40 0.00 

Union 72.90 0.00 

Vermilion 98.90 1.00 

Vernon 103.40 1.00 

Washington 83.20 0.00 

Webster 84.10 0.00 

West Baton Rouge 87.50 0.25 

West Carroll 91.10 0.61 

West Feliciana 105.90 1.00 

Winn 91.80 0.68 

City of Monroe School District 80.30 0.00 

City of Bogalusa School District 66.80 0.00 

Zachary Community School District 112.60 1.00 

City of Baker School District 65.90 0.00 
217 

Table A1.28. Districts with Few Low-Performing Schools fuzzy set scores for Louisiana parishes.

Louisiana Parish 

# Schools 

with One 

Star or 

Fewer 

Total # of 

Schools 

Percent 

One Star 

Districts 

with Few 

LowPerforming 

Schools 

Set Score 

Acadia 7 26 26.92% 0.92 

Allen 0 11 0.00% 1.00 

Ascension 5 21 23.81% 1.00 

Assumption 3 9 33.33% 0.67 

Avoyelles 10 13 76.92% 0.00 

Beauregard 0 12 0.00% 1.00 

Bienville 4 8 50.00% 0.00 

Bossier 8 29 27.59% 0.90 

Caddo 39 67 58.21% 0.00 

Calcasieu 12 56 21.43% 1.00 

Caldwell 0 6 0.00% 1.00 

Cameron 1 4 25.00% 1.00 

Catahoula 0 9 0.00% 1.00 

Claiborne 6 8 75.00% 0.00 

Concordia 4 10 40.00% 0.40 

DeSoto 5 12 41.67% 0.33 

East Baton Rouge 55 78 70.51% 0.00 

East Carroll 4 5 80.00% 0.00 

East Feliciana 6 7 85.71% 0.00 

Evangeline 4 11 36.36% 0.55 

Franklin 4 6 66.67% 0.00 

Grant 1 8 12.50% 1.00 

Iberia 9 26 34.62% 0.62 

Iberville 7 8 87.50% 1.00 

Jackson 2 5 40.00% 0.40 

Jefferson . . . . 

Jefferson Davis 0 13 0.00% 1.00 

Lafayette 7 38 18.42% 1.00 

Lafourche 7 28 25.00% 1.00 

LaSalle 0 9 0.00% 1.00 

Lincoln 2 12 16.67% 1.00 

Livingston 1 39 2.56% 1.00 

Madison 4 4 100.00% 0.00 

Morehouse 7 12 58.33% 0.00 

Natchitoches . . . . 

Orleans 6 17 35.29% 0.59 

Ouachita 2 34 5.88% 1.00 

Plaquemines 2 7 28.57% 0.86 

Pointe Coupee 3 6 50.00% 0.00 

Rapides 13 48 27.08% 0.92 

Red River 2 3 66.67% 0.00 

Richland 6 11 54.55% 0.00 

Sabine 3 10 30.00% 0.80 

St. Bernard 0 2 0.00% 1.00 

St. Charles 2 17 11.76% 1.00 

St. Helena 3 3 100.00% 0.00 
218 

St. James 3 10 30.00% 0.80 

St. John the Baptist 6 9 66.67% 0.00 

St. Landry 12 37 32.43% 0.70 

St. Martin 7 16 43.75% 0.25 

St. Mary 7 26 26.92% 0.92 

St. Tammany 2 49 4.08% 1.00 

Tangipahoa 17 33 51.52% 0.00 

Tensas 3 3 100.00% 0.00 

Terrebonne 13 36 36.11% 0.56 

Union 6 7 85.71% 0.00 

Vermilion 2 18 11.11% 1.00 

Vernon 0 18 0.00% 1.00 

Washington 4 12 33.33% 0.67 

Webster 8 17 47.06% 0.12 

West Baton Rouge 5 10 50.00% 0.00 

West Carroll 1 6 16.67% 1.00 

West Feliciana 0 5 0.00% 1.00 

Winn 0 8 0.00% 1.00 

City of Monroe 8 18 44.44% 0.22 

City of Bogalusa 4 6 66.67% 0.00 

Zachary Community 0 5 0.00% 1.00 

City of Baker 5 5 100.00% 0.00 

Central Community . . . . 
219 

Table A1.29. District Performance fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of 

Schools with 3, 4, 

or 5 Stars 

District 

Performance Set 

Score 

Acadia 42.30 0.74 

Allen 54.50 1.00 

Ascension 52.20 1.00 

Assumption 11.10 0.00 

Avoyelles 0.00 0.00 

Beauregard 81.80 1.00 

Bienville 25.00 0.17 

Bossier 45.20 0.84 

Caddo 23.80 0.13 

Calcasieu 35.70 0.52 

Caldwell 0.00 0.00 

Cameron 0.00 0.00 

Catahoula 44.40 0.81 

Claiborne 0.00 0.00 

Concordia 10.00 0.00 

DeSoto 0.00 0.00 

East Baton Rouge 14.00 0.00 

East Carroll 0.00 0.00 

East Feliciana 14.30 0.00 

Evangeline 27.30 0.24 

Franklin 0.00 0.00 

Grant 12.50 0.00 

Iberia 19.20 0.00 

Iberville 0.00 0.00 

Jackson 20.00 0.00 

Jefferson 13.90 0.00 

Jefferson Davis 69.20 1.00 

Lafayette 36.90 0.56 

Lafourche 17.90 0.00 

LaSalle 44.40 0.81 

Lincoln 16.70 0.00 

Livingston 61.50 1.00 

Madison 0.00 0.00 

Morehouse 8.30 0.00 

Natchitoches 28.50 0.28 

Orleans 41.20 0.71 

Ouachita 57.10 1.00 
220 

Plaquemines 42.90 0.76 

Pointe Coupee 0.00 0.00 

Rapides 30.60 0.35 

Red River 0.00 0.00 

Richland 9.10 0.00 

Sabine 20.00 0.00 

St. Bernard 28.60 0.29 

St. Charles 58.80 1.00 

St. Helena 0.00 0.00 

St. James 30.00 0.33 

St. John the Baptist 11.10 0.00 

St. Landry 22.90 0.10 

St. Martin 18.80 0.00 

St. Mary 22.70 0.09 

St. Tammany 56.00 1.00 

Tangipahoa 9.10 0.00 

Tensas 0.00 0.00 

Terrebonne 22.20 0.07 

Union 0.00 0.00 

Vermilion 55.60 1.00 

Vernon 72.20 1.00 

Washington 8.30 0.00 

Webster 0.00 0.00 

West Baton Rouge 10.00 0.00 

West Carroll 33.30 0.44 

West Feliciana 80.00 1.00 

Winn 12.50 0.00 

Monroe City 27.80 0.26 

Bogalusa City 0.00 0.00 

Zachary Community 100.00 1.00 

City of Baker 0.00 0.00 

Central Community 60.00 1.00 
221 

Table A1.30. First-time Freshmen fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of High 

School 

Graduates That 

Go to College 

First-time 

Freshmen Set 

Score 

Acadia 39.1 0.27 

Allen 48.9 0.93 

Ascension 50.0 1.00 

Assumption 51.8 1.00 

Avoyelles 46.5 0.77 

Beauregard 48.8 0.92 

Bienville 49.3 0.95 

Bossier 53.6 1.00 

Caddo 51.2 1.00 

Calcasieu 50.5 1.00 

Caldwell 42.2 0.48 

Cameron 44.9 0.66 

Catahoula 47.4 0.83 

Claiborne 37.5 0.17 

Concordia 35.7 0.05 

DeSoto 44.6 0.64 

East Baton Rouge 45.0 0.67 

East Carroll 29.3 0.00 

East Feliciana 33.6 0.00 

Evangeline 41.1 0.41 

Franklin 36.9 0.13 

Grant 39.2 0.28 

Iberia 39.4 0.29 

Iberville 49.4 0.96 

Jackson 53.3 1.00 

Jefferson 41.0 0.40 

Jefferson Davis 54.0 1.00 

Lafayette 49.4 0.96 

Lafourche 41.0 0.40 

LaSalle 42.9 0.53 

Lincoln 52.8 1.00 

Livingston 49.5 0.97 

Madison 44.4 0.63 

Morehouse 59.6 1.00 

Natchitoches 50.2 1.00 

Orleans 44.5 0.63 

Ouachita 54.5 1.00 
222 

Plaquemines 47.3 0.82 

Pointe Coupee 33.0 0.00 

Rapides 47.6 0.84 

Red River 36.7 0.11 

Richland 36.9 0.13 

Sabine 34.8 0.00 

St. Bernard 46.8 0.79 

St. Charles 56.4 1.00 

St. Helena 31.3 0.00 

St. James 44.1 0.61 

St. John the Baptist 32.8 0.00 

St. Landry 42.2 0.48 

St. Martin 35.3 0.02 

St. Mary 42.8 0.52 

St. Tammany 57.8 1.00 

Tangipahoa 42.5 0.50 

Tensas 28.6 0.00 

Terrebonne 41.5 0.43 

Union 38.8 0.25 

Vermilion 40.3 0.35 

Vernon 38.5 0.23 

Washington 30.9 0.00 

Webster 43.4 0.56 

West Baton Rouge 48.0 0.87 

West Carroll 41.0 0.40 

West Feliciana 52.6 1.00 

Winn 48.3 0.89 

Monroe City 53.2 1.00 

Bogalusa City 35.2 0.01 

Zachary Community 58.3 1.00 

City of Baker 56.1 1.00 

Central Community 61.0 1.00 
223 

Table A1.31. At-risk Students fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of 

Students 

Receiving Free or 

Reduced Lunch 

At-risk Students 

Set Score 

Acadia 62.8 0.85 

Allen 61.3 0.75 

Ascension 43.7 0.00 

Assumption 65.9 1.00 

Avoyelles 83.6 1.00 

Beauregard 51.2 0.08 

Bienville 72.5 1.00 

Bossier 42.9 0.00 

Caddo 63.1 0.87 

Calcasieu 58.1 0.54 

Caldwell 64.8 0.99 

Cameron 60.9 0.73 

Catahoula 72.1 1.00 

Claiborne 72.4 1.00 

Concordia 73.4 1.00 

DeSoto 66.0 1.00 

East Baton Rouge 82.5 1.00 

East Carroll 91.5 1.00 

East Feliciana 85.1 1.00 

Evangeline 76.5 1.00 

Franklin 81.3 1.00 

Grant 62.5 0.83 

Iberia 65.8 1.00 

Iberville 83.7 1.00 

Jackson 58.3 0.55 

Jefferson 74.8 1.00 

Jefferson Davis 53.3 0.22 

Lafayette 60.3 0.69 

Lafourche 61.0 0.73 

LaSalle 48.9 0.00 

Lincoln 60.3 0.69 

Livingston 50.8 0.05 

Madison 90.6 1.00 

Morehouse 76.9 1.00 

Natchitoches 69.7 1.00 

Orleans 68.6 1.00 

Ouachita 51.0 0.07 
224 

Plaquemines 67.3 1.00 

Pointe Coupee 76.6 1.00 

Rapides 66.3 1.00 

Red River 85.3 1.00 

Richland 76.2 1.00 

Sabine 67.0 1.00 

St. Bernard 68.7 1.00 

St. Charles 47.8 0.00 

St. Helena 91.5 1.00 

St. James 71.6 1.00 

St. John the Baptist 85.5 1.00 

St. Landry 74.4 1.00 

St. Martin 67.1 1.00 

St. Mary 72.1 1.00 

St. Tammany 43.4 0.00 

Tangipahoa 73.8 1.00 

Tensas 93.6 1.00 

Terrebonne 59.8 0.65 

Union 74.7 1.00 

Vermilion 60.6 0.71 

Vernon 57.3 0.49 

Washington 84.5 1.00 

Webster 62.3 0.82 

West Baton Rouge 65.6 1.00 

West Carroll 75.5 1.00 

West Feliciana 46.3 0.00 

Winn 68.2 1.00 

Monroe City 80.0 1.00 

Bogalusa City 93.4 1.00 

Zachary Community 41.8 0.00 

City of Baker 84.6 1.00 

Central Community 42.7 0.00 
225 

Table A1.32. High Percent of African American Students fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of 

Students Who 

Are African 

American 

African American 

Students Set 

Score 

Acadia 28.8 0.00 

Allen 24.1 0.00 

Ascension 30.6 0.02 

Assumption 43.2 0.33 

Avoyelles 45.7 0.39 

Beauregard 16.5 0.00 

Bienville 58.0 0.70 

Bossier 29.9 0.00 

Caddo 64.5 0.86 

Calcasieu 35.9 0.15 

Caldwell 18.3 0.00 

Cameron 2.6 0.00 

Catahoula 41.6 0.29 

Claiborne 67.9 0.95 

Concordia 51.5 0.54 

DeSoto 50.7 0.52 

East Baton Rouge 83.0 1.00 

East Carroll 95.8 1.00 

East Feliciana 75.1 1.00 

Evangeline 40.8 0.27 

Franklin 51.6 0.54 

Grant 12.9 0.00 

Iberia 44.5 0.36 

Iberville 74.9 1.00 

Jackson 37.0 0.18 

Jefferson 49.5 0.49 

Jefferson Davis 23.3 0.00 

Lafayette 43.2 0.33 

Lafourche 22.3 0.00 

LaSalle 10.6 0.00 

Lincoln 48.5 0.46 

Livingston 6.3 0.00 

Madison 92.4 1.00 

Morehouse 64.5 0.86 

Natchitoches 57.8 0.70 

Orleans 76.5 1.00 

Ouachita 31.8 0.05 
226 

Plaquemines 32.3 0.06 

Pointe Coupee 58.8 0.72 

Rapides 43.0 0.33 

Red River 65.1 0.88 

Richland 52.0 0.55 

Sabine 24.3 0.00 

St. Bernard 21.9 0.00 

St. Charles 36.1 0.15 

St. Helena 94.4 1.00 

St. James 67.1 0.93 

St. John the Baptist 79.3 1.00 

St. Landry 57.0 0.68 

St. Martin 47.1 0.43 

St. Mary 45.5 0.39 

St. Tammany 19.1 0.00 

Tangipahoa 47.2 0.43 

Tensas 91.4 1.00 

Terrebonne 28.8 0.00 

Union 42.8 0.32 

Vermilion 22.5 0.00 

Vernon 20.2 0.00 

Washington 34.1 0.10 

Webster 43.6 0.34 

West Baton Rouge 53.0 0.58 

West Carroll 18.3 0.00 

West Feliciana 42.5 0.31 

Winn 35.0 0.13 

Monroe City 87.2 1.00 

Bogalusa City 67.7 0.94 

Zachary Community 42.2 0.31 

City of Baker 92.7 1.00 

Central Community 17.5 0.00 

 
227 

Table A1.33. High Student Dropouts fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of 

Students in 

Grades 8–12 Who 

Dropped Out 

High Student 

Dropouts Set 

Score 

Acadia 5.2 0.04 

Allen 4.4 0.00 

Ascension 4.1 0.00 

Assumption 8.3 0.66 

Avoyelles 7.8 0.56 

Beauregard 0.8 0.00 

Bienville 3.9 0.00 

Bossier 4.1 0.00 

Caddo 9.5 0.90 

Calcasieu 4.0 0.00 

Caldwell 1.9 0.00 

Cameron 1.2 0.00 

Catahoula 4.5 0.00 

Claiborne 5.5 0.10 

Concordia 6.1 0.22 

DeSoto 7.7 0.54 

East Baton Rouge 10.2 1.00 

East Carroll 4.7 0.00 

East Feliciana 6.0 0.20 

Evangeline 6.6 0.32 

Franklin 7.2 0.44 

Grant 4.9 0.00 

Iberia 7.2 0.44 

Iberville 8.4 0.68 

Jackson 5.8 0.16 

Jefferson 8.7 0.74 

Jefferson Davis 0.5 0.00 

Lafayette 6.2 0.24 

Lafourche 5.1 0.02 

LaSalle 3.8 0.00 

Lincoln 5.2 0.04 

Livingston 3.6 0.00 

Madison 10.3 1.00 

Morehouse 13.4 1.00 

Natchitoches 8.5 0.70 

Orleans 3.9 0.00 

Ouachita 6.8 0.36 
228 

Plaquemines 3.6 0.00 

Pointe Coupee 8.6 0.72 

Rapides 7.0 0.40 

Red River 13.3 1.00 

Richland 7.0 0.40 

Sabine 3.0 0.00 

St. Bernard 5.9 0.18 

St. Charles 3.7 0.00 

St. Helena 5.9 0.18 

St. James 4.4 0.00 

St. John the Baptist 9.3 0.86 

St. Landry 7.0 0.40 

St. Martin 6.3 0.26 

St. Mary 5.8 0.16 

St. Tammany 4.1 0.00 

Tangipahoa 7.4 0.48 

Tensas 4.6 0.00 

Terrebonne 5.7 0.14 

Union 9.6 0.92 

Vermilion 4.2 0.00 

Vernon 3.1 0.00 

Washington 3.4 0.00 

Webster 4.7 0.00 

West Baton Rouge 4.4 0.00 

West Carroll 5.5 0.10 

West Feliciana 4.3 0.00 

Winn 3.3 0.00 

Monroe City 7.9 0.58 

Bogalusa City 7.8 0.56 

Zachary Community 1.5 0.00 

City of Baker 10.6 1.00 

Central Community 1.2 0.00 
229 

Table A1.34. Disciplinary Action fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of Students 

in Grades 9-12 

Receiving Out of 

School Suspension 

Disciplinary 

Action Set 

Score 

Acadia 14.9 1.00 

Allen 11.8 1.00 

Ascension 6.7 0.59 

Assumption 16.5 1.00 

Avoyelles 21.9 1.00 

Beauregard 7.5 0.69 

Bienville 12.6 1.00 

Bossier 7.4 0.68 

Caddo 14.1 1.00 

Calcasieu 12.7 1.00 

Caldwell 0.6 0.00 

Cameron 1.0 0.00 

Catahoula 11.3 1.00 

Claiborne 14.7 1.00 

Concordia 16.1 1.00 

DeSoto 13.9 1.00 

East Baton Rouge 0.7 0.00 

East Carroll 20.9 1.00 

East Feliciana 13.3 1.00 

Evangeline 17.1 1.00 

Franklin 16.7 1.00 

Grant 4.2 0.28 

Iberia 6.8 0.60 

Iberville 15.8 1.00 

Jackson 1.2 0.00 

Jefferson 14.6 1.00 

Jefferson Davis 9.1 0.89 

Lafayette 14.3 1.00 

Lafourche 9.9 0.99 

LaSalle 0.8 0.00 

Lincoln 14.7 1.00 

Livingston 12.0 1.00 

Madison 20.9 1.00 

Morehouse 15.8 1.00 

Natchitoches 15.3 1.00 

Orleans 11.2 1.00 

Ouachita 9.6 0.95 
230 

Plaquemines 9.3 0.91 

Pointe Coupee 9.7 0.96 

Rapides 10.2 1.00 

Red River 5.4 0.43 

Richland 14.1 1.00 

Sabine 6.5 0.56 

St. Bernard 9.4 0.93 

St. Charles 3.6 0.20 

St. Helena 8.7 0.84 

St. James 15.8 1.00 

St. John the Baptist 12.6 1.00 

St. Landry 11.0 1.00 

St. Martin 13.7 1.00 

St. Mary 2.0 0.00 

St. Tammany 7.7 0.71 

Tangipahoa 16.8 1.00 

Tensas 3.8 0.23 

Terrebonne 17.6 1.00 

Union 19.2 1.00 

Vermilion 9.8 0.98 

Vernon 9.7 0.96 

Washington 1.2 0.00 

Webster 9.6 0.95 

West Baton Rouge 1.9 0.00 

West Carroll 6.4 0.55 

West Feliciana 7.1 0.64 

Winn 2.0 0.00 

Monroe City 12.9 1.00 

Bogalusa City 1.3 0.00 

Zachary Community 1.4 0.00 

City of Baker 8.2 0.78 

Central Community 2.1 0.01 
231 

Table A1.35. Students Retained fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Percent of 

Students 

Retained or Held 

Back 

Students 

Retained Set 

Score 

Acadia 8.3 0.88 

Allen 4.5 0.25 

Ascension 5.5 0.42 

Assumption 8.5 0.92 

Avoyelles 7.9 0.82 

Beauregard 4.4 0.23 

Bienville 5.9 0.48 

Bossier 4.5 0.25 

Caddo 9.0 1.00 

Calcasieu 5.2 0.37 

Caldwell 4.2 0.20 

Cameron 4.8 0.30 

Catahoula 10.5 1.00 

Claiborne 3.9 0.15 

Concordia 8.7 0.95 

DeSoto 5.9 0.48 

East Baton Rouge 10.6 1.00 

East Carroll 8.6 0.93 

East Feliciana 6.8 0.63 

Evangeline 7.1 0.68 

Franklin 9.4 1.00 

Grant 6.7 0.62 

Iberia 10.4 1.00 

Iberville 13.5 1.00 

Jackson 5.4 0.40 

Jefferson 11.0 1.00 

Jefferson Davis 5.8 0.47 

Lafayette 8.0 0.83 

Lafourche 6.3 0.55 

LaSalle 4.2 0.20 

Lincoln 6.7 0.62 

Livingston 5.4 0.40 

Madison 7.3 0.72 

Morehouse 8.8 0.97 

Natchitoches 8.8 0.97 

Orleans 2.6 0.00 

Ouachita 6.7 0.62 
232 

Plaquemines 6.6 0.60 

Pointe Coupee 9.8 1.00 

Rapides 5.8 0.47 

Red River 11.7 1.00 

Richland 7.2 0.70 

Sabine 5.6 0.43 

St. Bernard 6.1 0.52 

St. Charles 4.3 0.22 

St. Helena 6.7 0.62 

St. James 5.5 0.42 

St. John the Baptist 11.6 1.00 

St. Landry 11.6 1.00 

St. Martin 7.2 0.70 

St. Mary 6.8 0.63 

St. Tammany 5.7 0.45 

Tangipahoa 6.6 0.60 

Tensas 10.4 1.00 

Terrebonne 6.4 0.57 

Union 10.3 1.00 

Vermilion 6.7 0.62 

Vernon 5.2 0.37 

Washington 5.5 0.42 

Webster 5.8 0.47 

West Baton Rouge 6.3 0.55 

West Carroll 8.0 0.83 

West Feliciana 3.4 0.07 

Winn 4.6 0.27 

Monroe City 9.6 1.00 

Bogalusa City 14.5 1.00 

Zachary Community 2.5 0.00 

City of Baker 8.8 0.97 

Central Community 5.9 0.48 
233 

Table A1.36. Large School District fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Number 

of 

Schools 

Large School 

District Set 

Score 

Acadia 26 0.00 

Allen 11 0.00 

Ascension 21 0.00 

Assumption 9 0.00 

Avoyelles 13 0.00 

Beauregard 12 0.00 

Bienville 8 0.00 

Bossier 29 0.00 

Caddo 67 1.00 

Calcasieu 56 1.00 

Caldwell 6 0.00 

Cameron 4 0.00 

Catahoula 9 0.00 

Claiborne 8 0.00 

Concordia 10 0.00 

DeSoto 12 0.00 

East Baton Rouge 80 1.00 

East Carroll 5 0.00 

East Feliciana 7 0.00 

Evangeline 11 0.00 

Franklin 6 0.00 

Grant 8 0.00 

Iberia 30 0.00 

Iberville 8 0.00 

Jackson 5 0.00 

Jefferson 77 1.00 

Jefferson Davis 13 0.00 

Lafayette 38 0.40 

Lafourche 28 0.00 

LaSalle 9 0.00 

Lincoln 12 0.00 

Livingston 39 0.45 

Madison 4 0.00 

Morehouse 12 0.00 

Natchitoches 14 0.00 

Orleans 17 0.00 

Ouachita 34 0.20 

Plaquemines 7 0.00 

Pointe Coupee 6 0.00 

Rapides 48 0.90 

Red River 3 0.00 

Richland 11 0.00 

Sabine 10 0.00 

St. Bernard 2 0.00 

St. Charles 18 0.00 

St. Helena 3 0.00 

St. James 10 0.00 

St. John the Baptist 9 0.00 

St. Landry 37 0.35 
234 

St. Martin 16 0.00 

St. Mary 26 0.00 

St. Tammany 49 0.95 

Tangipahoa 33 0.15 

Tensas 3 0.00 

Terrebonne 36 0.30 

Union 7 0.00 

Vermilion 18 0.00 

Vernon 18 0.00 

Washington 12 0.00 

Webster 18 0.00 

West Baton Rouge 10 0.00 

West Carroll 6 0.00 

West Feliciana 5 0.00 

Winn 8 0.00 

City of Monroe 18 0.00 

City of Bogalusa 6 0.00 

Zachary Community 5 0.00 

City of Baker 5 0.00 
235 

Table A1.37. Small School District fuzzy set scores for Louisiana parishes. 

Louisiana Parish 

Number 

of 

Schools 

Small School 

District Set 

Score 

Acadia 26 0.00 

Allen 11 1.00 

Ascension 21 0.40 

Assumption 9 1.00 

Avoyelles 13 1.00 

Beauregard 12 1.00 

Bienville 8 1.00 

Bossier 29 0.00 

Caddo 67 0.00 

Calcasieu 56 0.00 

Caldwell 6 1.00 

Cameron 4 1.00 

Catahoula 9 1.00 

Claiborne 8 1.00 

Concordia 10 1.00 

DeSoto 12 1.00 

East Baton Rouge 80 0.00 

East Carroll 5 1.00 

East Feliciana 7 1.00 

Evangeline 11 1.00 

Franklin 6 1.00 

Grant 8 1.00 

Iberia 30 0.00 

Iberville 8 1.00 

Jackson 5 1.00 

Jefferson 77 0.00 

Jefferson Davis 13 1.00 

Lafayette 38 0.00 

Lafourche 28 0.00 

LaSalle 9 1.00 

Lincoln 12 1.00 

Livingston 39 0.00 

Madison 4 1.00 

Morehouse 12 1.00 

Natchitoches 14 1.00 

Orleans 17 0.80 

Ouachita 34 0.00 

Plaquemines 7 1.00 

Pointe Coupee 6 1.00 

Rapides 48 0.00 

Red River 3 1.00 

Richland 11 1.00 

Sabine 10 1.00 

St. Bernard 2 1.00 

St. Charles 18 0.70 

St. Helena 3 1.00 

St. James 10 1.00 

St. John the Baptist 9 1.00 

St. Landry 37 0.00 
236 

St. Martin 16 0.90 

St. Mary 26 0.00 

St. Tammany 49 0.00 

Tangipahoa 33 0.00 

Tensas 3 1.00 

Terrebonne 36 0.00 

Union 7 1.00 

Vermilion 18 0.70 

Vernon 18 0.70 

Washington 12 1.00 

Webster 18 0.70 

West Baton Rouge 10 1.00 

West Carroll 6 1.00 

West Feliciana 5 1.00 

Winn 8 1.00 

City of Monroe 18 0.70 

City of Bogalusa 6 1.00 

Zachary Community 5 1.00 

City of Baker 5 1.00 
237 

Table A1.38. Non-Public School Enrollment fuzzy set scores for Louisiana parishes. 

 Louisiana Parish 

Non-Public 

School 

Enrollment 

Non-Public 

School 

Enrollment Set 

Score 

Acadia 2111 1.00 

Allen 0 0.00 

Ascension 1781 0.89 

Assumption 221 0.11 

Avoyelles 1008 0.50 

Beauregard 63 0.03 

Bienville 0 0.00 

Bossier 140 0.07 

Caddo 4379 1.00 

Calcasieu 3177 1.00 

Caldwell 113 0.06 

Cameron 0 0.00 

Catahoula 0 0.00 

Claiborne 333 0.17 

Concordia 0 0.00 

DeSoto 145 0.07 

East Baton Rouge 17208 1.00 

East Carroll 176 0.09 

East Feliciana 424 0.21 

Evangeline 785 0.39 

Franklin 564 0.28 

Grant 0 0.00 

Iberia 2136 1.00 

Iberville 814 0.41 

Jackson 0 0.00 

Jefferson 19622 1.00 

Jefferson Davis 465 0.23 

Lafayette 7975 1.00 

Lafourche 2611 1.00 

LaSalle 0 0.00 

Lincoln 842 0.42 

Livingston 243 0.12 

Madison 287 0.14 

Morehouse 304 0.15 

Natchitoches 406 0.20 

Orleans 18483 1.00 

Ouachita 1494 0.75 
238 

Plaquemines 251 0.13 

Pointe Coupee 1258 0.63 

Rapides 2623 1.00 

Red River 316 0.16 

Richland 256 0.13 

Sabine 0 0.00 

St. Bernard 520 0.26 

St. Charles 611 0.31 

St. Helena 0 0.00 

St. James 241 0.12 

St. John the Baptist 2373 1.00 

St. Landry 2818 1.00 

St. Martin 1276 0.64 

St. Mary 1024 0.51 

St. Tammany 7325 1.00 

Tangipahoa 2624 1.00 

Tensas 194 0.10 

Terrebonne 3138 1.00 

Union 304 0.15 

Vermilion 841 0.42 

Vernon 131 0.07 

Washington 376 0.19 

Webster 574 0.29 

West Baton Rouge 412 0.21 

West Carroll 0 0.00 

West Feliciana 0 0.00 

Winn 0 0.00 

Monroe City 1025 0.51 

Bogalusa City 578 0.29 

Zachary Community 43 0.02 

City of Baker 232 0.12 

Central Community 693 0.35 
239 

Table A1.39. Data sources for fuzzy set analysis. 

 Source - Citation Link 

Louisiana Parish 

Population Statistics 

 

Large Population U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Small Population U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Emerging or Growing 

Parish 

U.S. Census Bureau - 

American Fact Finder 

http://factfinder2.census.gov/faces/nav/jsf/page

s/index.xhtml

Declining or Stagnating 

Parish 

U.S. Census Bureau - 

American Fact Finder 

http://factfinder2.census.gov/faces/nav/jsf/page

s/index.xhtml

Parish Demographic 

Information 

 

Parish Diversity Geo-Data Center For 

Geospatial Analysis and 

computation - ASU.edu 

http://geodacenter.asu.edu/%5Btermaliasraw%5D/diversity-and-s-0

Parish Crime Level U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Old or Aging Parish U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Young Parish U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Parish Education and 

Income 

 

Wealthy Parish U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Poor Parish U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Educated Parish U.S. Census Bureau - 

USA Counties 

http://censtats.census.gov/usa/usa.shtml

Parish Location and 

Geographic Factors 

 

Urban or Rural Parish U.S. Department of 

Agriculture - Economic 

Research Service 

http://www.ers.usda.gov/Data/UrbanInfluenceC

odes/

Parish with Noteworthy 

Natural Amenities 

U.S. Department of 

Agriculture - Economic 

Research Service 

http://www.ers.usda.gov/Data/NaturalAmenities

/

Parish with Interstate 

Highway 

Google Earth - Map http://www.google.com/earth/index.html

Coastal Parish Google Earth - Map http://www.google.com/earth/index.html

Parish with Major Ports American Association of 

Port Authorities - Seaports 

of the Americas Directory 

http://www.seaportsoftheamericas.com/

Labor Market Resources 

Substantial Mining 

Employment 

Regional Economic 

Information System - 

Bureau of Economic 

Analysis 

http://ciesin.org/datasets/reis/reis-home.html

Small Business Intensive Regional Economic 

Information System - 

Bureau of Economic 

http://ciesin.org/datasets/reis/reis-home.html
240 

Analysis 

Large Business Presence Regional Economic 

Information System - 

Bureau of Economic 

Analysis 

http://ciesin.org/datasets/reis/reis-home.html

Labor Market Outcomes 

Low Unemployment Regional Economic 

Information System - 

Bureau of Economic 

Analysis 

http://ciesin.org/datasets/reis/reis-home.html

Job Growth Regional Economic 

Information System - 

Bureau of Economic 

Analysis 

http://ciesin.org/datasets/reis/reis-home.html

Competitive Per Capita 

Personal Income 

Regional Economic 

Information System - 

Bureau of Economic 

Analysis 

http://ciesin.org/datasets/reis/reis-home.html

Education Resources 

Competitive Teacher 

Salary 

Southern Regional 

Educational Board 

http://www.sreb.org/

Substantial Local 

Capacity 

Louisiana Department of 

Education - Minimum 

Foundation Program 

http://www.doe.state.la.us/divisions/edfn/mfp_a

dmin.html

Oil and Gas Industry 

Influence 

Louisiana Department of 

Education - Minimum 

Foundation Program 

http://www.doe.state.la.us/divisions/edfn/mfp_a

dmin.html

Focus on Instruction Louisiana Department of 

Education - Minimum 

Foundation Program 

http://www.doe.state.la.us/divisions/edfn/mfp_a

dmin.html

Education Outcomes 

Adequate School 

Performance 

Louisiana Department of 

Education - District 

Performance Scores 

http://doe.louisiana.gov/data/district_accountabi

lity_reports.aspx

Few Low-Performing 

Schools 

Louisiana Department of 

Education - District 

Performance Scores 

http://doe.louisiana.gov/data/district_accountabi

lity_reports.aspx

District Improvement LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

First-Time Freshman LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Student Characteristics LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Percentage of At-Risk 

Students 

LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Percentage of African 

American Students 

LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html
241 

District Processes LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Drop-Out Rate LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Number of Disciplinary 

Cases 

LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Student Retention (Grade 

Repeat) 

LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Small District LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Large District LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

Non-public School 

Enrollment 

LA Department of 

Education - State 

Progress Reports 

http://doe.louisiana.gov/offices/infomanagemen

t/state_progress_reports.html

 
242 

APPENDIX B. LESSONS LEARNED FROM MEETINGS 

WITH LOCAL OFFICIALS 

A.B.1. CONTEXT

As part of the cooperative agreement, we traveled to parishes identified as being impacted by 

OCS activity to discuss how the oil and gas industry, broadly defined, and the 2005 hurricanes 

have affected local capacity to provide necessary services. More specifically, within each parish 

we attempted to meet with: 

1. The Superintendent or Chief Financial Officer of the parish school district (note, that 

in most cases the parish is coterminous with the school district, with the only two 

exceptions being East Baton Rouge and Washington Parishes) 

2. The Parish President or Chief Financial officer of the Police Jury or local 

administrative unit 

We used guided conversations with local officials, in that we began by explaining who we 

were and what our purposes were in meeting with them, including our desire to provide context 

for some of the quantitative analysis of financial statements and to learn more about the contexts 

surrounding financial and policy changes resulting from the 2005 hurricanes. Instead of asking 

specific questions, we allowed the conversations to evolve naturally, and attempted to cover a 

range of topics of interest. Here, we will summarize some of the lessons learned from these 

conversations. We do not intend for this summary to comprehensively report all the information 

we gleaned from our visits with local officials. 

The summaries will be presented in two sections, the first reporting on our key lessons 

learned from meeting with education officials and the second reporting our key lessons learned 

from meetings with local government officials. 

A.B.2. KEY LESSONS LEARNED FROM VISITS WITH EDUCATION OFFICIALS

One key topic of conversation revolved around the impact of the 2005 hurricanes. Of the 35 

school districts in southern Louisiana, 4 were heavily damaged. Hurricane Katrina devastated 

Orleans Parish School District, Plaquemines Parish School District, and St. Bernard Parish 

School District and Hurricane Rita devastated Cameron Parish School District. We did not get an 

opportunity to meet with education officials from these four parishes. However, we do know that 

the number of schools in operation declined substantially [include figures Here]. The remaining 

districts faced less drastic, but still significant challenges after the 2005 hurricanes. First, all 

districts accepted displaced students into their schools in the months following the hurricanes, 

although the numbers of students and their lengths of stays in the host schools varied widely. 

Second, school districts bordering the 4 most heavily damaged districts were additionally 

affected in at least four significant ways: (1) they tended to get many more displaced students; 

(2) more displaced students in these districts stayed for a longer term or permanently; (3) many 

existing students within these districts were displaced to other districts; and (4) many existing 
243 

students within these districts were displaced to other schools within the district. The last two 

trends were a result of the fact that, though not as devastating as in the worst cases, several of 

these border parishes districts suffered extensive damage to certain areas. Calcasieu, St. 

Tammany, Tangipahoa, Jefferson are examples of border parishes. Many local officials reported 

that the federal funding to assist with displaced students, along with increased economic activity 

related to recovery efforts, put many parishes on sound financial footing to deal with the ongoing 

challenges. 

Another discussion topic involved the influence of the Oil & Gas Industry, broadly defined, 

on students, their families, and the school financing. The majority of the education officials we 

spoke with reported that the oil and gas industry was an important employer in their school 

district. A large number of parents of students in southern Louisiana work either directly for the 

Oil & Gas Industry, on the rigs in the Gulf of Mexico, or in support industries or headquarters. In 

addition, the onshore oil and gas industry was particularly important in a small selection of 

school districts such as Vermillion Parish, which generated revenues from “sixteenth section” 

holdings. 

A third topic of discussion revolved school enrollment projects for the next several years. 

Most of the school districts in the western part of the state project stagnant or slightly declining 

enrollments, consistent with general population projects. A few districts project slight increases 

in enrollments, while several districts along the North Shore and between New Orleans and 

Baton Rouge project rapid population growth and school enrollment growth. Ascension and 

Tangipahoa are examples of districts projecting significant growth. The officials suggested that 

the trend could be seen even before 2005, but that the rate of increase accelerated after the 

hurricanes. 

A fourth area discussed involved the challenges facing all school districts in Louisiana, and 

the nation, in meeting the education standards incorporated into the No Child Left Behind act. 

We found that education officials in Louisiana have mixed opinions about NCLB and its local 

implications. Many officials emphasized the importance of hiring highly qualified teachers, 

which can be more difficult for smaller, more rural, and more isolated parishes. All districts 

reported that teachers and other school personnel had pursued and earned National Board 

Certification, and credited the governor’s office for providing financial incentives to all teachers 

in the state for pursuing the National Board Certification credential. Other positive reactions to 

the NCLB included comments that it refocused attention on insuring that all students receive a 

good education, that it encouraged administrators to find ways to support teachers, and it 

encouraged teachers to attend to the needs of all students, and that it encouraged a teachers to 

find ways to spend more time on task. At the same time, school officials in southern Louisiana 

also expressed several negative attitudes toward NCLB, including the opinion that it amounts to 

an unfunded mandate forcing schools to spend too much money on testing, instruction, and 

assessment—taking away from other important school objectives. Also, some officials expressed 

the view that NCLB was too narrowly focused on testing and fundamentals. Consistent with this 

view, several officials expressed a more holistic view of educational outcomes that included 

moral outcomes, civic outcomes, and a broader definition of successful students, schools, and 

districts. 
244 

A fifth topic covered in meeting with educational officials revolved around education 

financing, particularly the amount held in reserve in case of emergency, and insurance costs. 

Most districts held some cash reserves, so that they could continue to meet operating costs for a 

month, 2 months, 3 months, or longer in the case of a hurricane or similar disaster. At least two 

parishes tied the amount the held in reserve directly to the Standard and Poor’s bond rating 

criteria, which emphasized the reserves. Having a good bond rating is particularly important in 

districts projecting significant enrollment increases (such as Ascension Parish) because the rating 

will influence the interest rates they will be required to pay on the loans they will need for 

additional construction of schools and buildings. 

Sixth, we asked education officials about inequality within the parish/school district, and 

some noted that there was considerable intra-district inequality. In some cases, structural 

boundaries, such as the Mississippi River or marshlands, isolated certain towns and schools from 

the rest of the district exacerbating inequalities. In other cases, the school inequalities were tied 

directly to economic situations of families living in the communities served by the schools. 

Seventh, in addition to learning about the education finance and student outcomes challenges 

facing parish/school districts in Louisiana, we used our visits with local education officials to vet 

some of the fuzzy set descriptions we had developed for the parish education profiles. We briefly 

explained the purpose of the fuzzy sets as grouping like parishes together in ways that are 

consistent with the actual practices within the system. Several education officials, for example, 

mentioned that tying the notion of offering a competitive teacher salary to the Southern Regional 

Education Board averages was consistent with actual practices. 

A.B.3. KEY LESSONS LEARNED FROM VISITS WITH LOCAL PARISH GOVERNMENT 

OFFICIALS

When planning to visit and discuss with local government officials in their official capacity, 

our original goal was to gather contextual information on their financial situations in the wake of 

the 2005 hurricanes. In the initial meetings in 2007, we learned about available FEMA databases 

that kept this information in a form that we could access and merge with the audited financial 

statements compiled by the undergraduate student member of our research team. Gaining 

knowledge of this database saved us a significant amount of time. After learning of the database, 

we altered our approaches to our discussions with local officials from a focused discussion on 

the financial aspects of recovery to a more general, and wide-ranging conversation about the 

challenges facing local governments in southern Louisiana. 

Local governments in Louisiana’s coastal parishes provide important services to their 

populations. Drainage, road and bridge repair and maintenance, and corrections were all 

mentioned as being major financial challenges for local governments. We observed a wide 

variety of approaches to providing these services, from more centralized to more decentralized 

tax and structural approaches. 

Local officials are also responsible for planning for and providing needed services in the case 

of a hurricane, disaster, or other emergency. One key service in this respect is debris removal. 
245 

Although a few parishes maintain the equipment necessary to provide this service in-house, the 

majority tend to have contractual arrangements with firms in the private sector that kick into 

place once a disaster declaration is declared by the president or governor. Some officials 

expressed concern about whether the firms they contracted with would actually be able to honor 

the contracts, but most expressed confidence that their preparations would be adequate in all but 

the most extreme circumstances. For example, after the 2008 hurricanes, Gustav and Ike, one 

official learned that the firm they had contracted with for evacuation services could not provide 

the necessary number of emergency vehicles because they had also contracted with some 

neighboring parishes and were overextended. 

Nearly all local government officials we spoke with expressed concern over the number of 

people currently being held in the parish prison system and the costs incurred by the local 

government to maintain the number of prisoners. One cost that several officials complained 

about involved the health care cost of detainees (apparently, health costs for prisoners are 

reimbursed from state and federal funds, but local governments bare these costs for detainees). 

Further, if the prison population exceeds the number of beds in the parish jail (as it does in many 

cases) then the costs per prisoner increase because the cost of sending prisoners to another parish 

jail is far higher than the cost of holding them in the parish. 

Several officials in more rural and isolated parishes, such as Allen and Vernon, reported that 

it is sometimes difficult for them to retain good employees because parallel jobs in the private 

sector pay much more than they can afford. 

With regards to disaster planning, most parish officials reported that they had developed 

comprehensive plans before the 2005 hurricanes, as they had learned over the years from 

previous hurricanes dating back to the 1960s. However, the 2005 and 2008 hurricanes led many 

local officials to revisit their disaster planning and preparations. For example, many parishes 

increased the amount of cash they had in reserves so that they could afford to pay salaries and 

insurance deductibles on local government properties in need of repair after major hurricanes. 

Discussions regarding populations led us to observe a number of trends for the southern 

Louisiana region. First, parishes on the North Shore and along River Road between New Orleans 

and Baton Rouge are witnessing rapid population growth, while rural parishes in westernsouthern and central-southern regions of the state are declining. Rural parishes that are 

contiguous with the cities of Lake Charles, Lafayette, and Baton Rouge have witnessed an 

increase in inmigrants into “cottage communities” that commute to work in the city. The newly 

developed neighborhoods require services from the local parish government, but residents 

engage in most of their economic activities (work, shopping, dining out, etc.) in the larger urban 

city, thus contributing less in local sales taxes. Also, residents of these neighborhoods are less 

engaged in the parish planning and community activities. In connection with this movement, 

many southern Louisiana parishes are noticing a shift of the intra-parish population from older to 

emerging communities. In some cases, this shift was exacerbated by the 2005 and 2008 

hurricanes, especially in border parishes like St. Tammany and Vermillion, and also in the North 

Shore and River Road parishes. But, most local officials claim that the trend was not entirely 

caused by hurricanes but by the loss of locally based employment opportunities and the 

corresponding increase in opportunities to commute to work. 
246 

 
247 

APPENDIX C. OIL AND GAS DRILLING FORECASTING 

A hybrid approach somewhat similar to that of Walls (1994) is used to generate forecast for 

oil and gas wells drilled in the deepwater Gulf of Mexico region. Formulas (AC.2), (AC.3), 

(AC.4), (AC.5), and (AC.6) used in this study follow formulas (14), (12), (13), (2), and (3) 

respectively stated in Walls (1994). All prices are adjusted for inflation using the Producer Price 

Index (PPI) with 2007 as the base year (PPI = 100 for 2007). 

 

The total number of oil and gas wells drilled at period t (Wt) is as following: 

(AC.1) Wt = 0 + 1 Wt-1 + 2 Vt-1 + 3 lt + 4 Dtlt + t

 

where Vt-1 is the expected discounted present value of profits per well at period t-1. The 

argument for using a lag of expected discounted present value of profits is that expected 

discounted present value of profits in previous year (period t-1) affects drilling decision at period 

t. Wt is the summation of exploratory wells at time t and development wells at time t+1. Wt-1 is 

the lag value of Wt signifying that last period drilling activities might affect drilling activities at 

period t. Variable lt is the weighted average number of leased tracts in the Gulf of Mexico for 

five consecutive periods (period t-4, t-3, t-2, t-1 and t). The weights (summing to one) for each 

year are as following: .5000 for period t, .2600 for period t-1, .1352 for period t-2, .0703 for 

period t-3, and .0345 for period t-4. Walls’ study (1994) describes the weights as the impact of 

leasing on drilling activities that takes place over five-year period. The study mentions that half 

of the impact occurs in the first year. Dt is dummy variable that equals to zero prior to 1995 and 

equals to one otherwise. In 1995, the Deep Water Royalty Relief Act (DWRRA) was enacted to 

provide royalties relief to eligible leases for certain amounts of deepwater production. After its 

expiration in 2000, the DWRRA was then redefined and extended to promote deepwater 

exploration.17 Variable Dtlt is incorporated into the model to capture any influence from the 

DWRRA on the deepwater drilling. 

The expected present value profit per well (Vt) consists of four components: The after tax 

discounted present value of net operating profit for oil (in barrel) and gas (in thousand cubic 

feet/mcf), success ratio in finding oil or gas, expected size of new discoveries, and after tax 

drilling costs. The formula is given as following: 

(AC.2) Vt = t

o

 St

o

 at

o

 + t

g

 (St

o

 at

ag + Stg

 at

ng) – [Cdry (1 - t) + 

Cwet (1-t (exp + i (1 – exp)) 

where t

o and tg represent discounted present value net operating profit per barrel of oil and gas, 

respectively. St

o

 and St

g

 represent the success ratio of finding oil or gas, respectively. Cdry

represents exploratory and development drilling cost for dry hole per total well drilled, while Cwet

is for the successful wells drilled. Variable i shows the delays between drilling and production 

while variable exp is the proportion of successful well drilling costs. Variable at

o

 represents 

 

17 The U.S. Energy Information Administration website http://www.eia.doe.gov 
248 

additional oil discovered per successful well drilled, at

ag represents additional mcf associateddissolved gas discovered per successful oil well drilled, and at

ng represents additional mcf non 

associated gas discovered per successful gas well drilled. 

 

Associated-dissolved natural gas is natural gas that occurs in crude oil reservoirs either as 

free gas (associates) or as gas in solution with crude oil (dissolved gas). Non-associated natural 

gas is natural gas that is not in contact with significant quantities of crude oil in the reservoir.18

Variables at

o

, at

ag, and atng are defined as three-year moving averages. Additional oil discovered 

per successful well drilled (at

o

) is obtained by dividing three year moving average of total 

discoveries with three year moving average of successful well drilled lagged one period. The 

same procedure is applied to compute for at

ag and atng. 

The discounted present value net operating profit per barrel of oil (t

o

) is obtained as 

following: 

(AC.3) t

o

 = 

2

 b Pt

o

 / ( 1 -  e-b) 

The discounted present value net operating profit per mcf of gas (t

g

) is obtained as 

following: 

(AC.4) t

g

 = 

2

 c Pt

g

 / ( 1 -  e-c) 

where  is the discount factor ( = 1/(1 + r); r = discount rate). Variable b is the average crude 

oil production decline rate obtained by dividing total production (barrel) with total reserves 

(barrel). Variable c is the average gas production decline rate obtained by dividing total 

production (mcf) with total reserves (mcf). The lag between drilling and production is also 

shown through the square of . Pt

o

 is the net operating profit per barrel of oil. Pt

g is the net 

operating profit per mcf of gas. 

The net operating profit per barrel of oil (Pt

o

) is as following: 

(AC.5) Pt

o

 = t

o

 (1 - t (1 - t - t - t) - t - t) + t Pt

b

 – OCt

o

 (1 - t) 

where t

o

 is the wellhead oil price, t is the corporate income tax rate, t is the royalty rate, t is 

the depletion allowance rate, and t is the windfall profits tax rate. Pt

b

 is the spot market price of 

oil (West Texas Intermediate). OCt

o is the operating cost per barrel of oil. 

The net operating profit per mcf of gas (Pt

g) is as following: 

(AC.6) Pt

g

 = t

g

 (1 - t (1 - t - t) - t) – OCt

g (1 - t) 

where t

g

 is the wellhead gas price, t is the corporate income tax rate, t is the royalty rate, and 

t is the depletion allowance rate. OCt

g is the operating cost per mcf of gas. 

 

18 Definitions taken from the U.S. Energy Information Administration website http://www.eia.doe.gov
249 

An Autoregressive Distributed Lags (ADL) model is used to estimate model (1). Hill et al. 

(2008) describes that Autoregressive Distributed Lags model overcomes two problems found in 

finite distributed lag model. First is the problem of choosing how many lags to be put into the 

model and second is the auto correlated error problem. The inclusion of lagged values of the 

dependent variable eliminates this correlation. An ADL (1,1) is applied to model (1) where there 

are lag of both dependent and independent variables in the model. The coefficient estimates 

obtained shown on Table AC.1 are then used to generate forecast for the number of wells drilled. 

 

Results from the hybrid oil and gas model are used to evaluate labor and fiscal effects of an 

economy. After obtaining the total numbers of wells to be drilled for future years, those numbers 

are fed in MAGPLAN to generate the total number of employment to fulfill that final demand. 

Following the block recursive nature of the COMPAS model, demand for the final product, oil 

and gas, generates an employment demand. In our case, final demand is the number of wells to 

be drilled. Given that employment drives the COMPAS model, employment generated to drill 

the provided number of wells would offer direct, indirect and induced impacts. This employment 

number is then plugged into the labor force module which ultimately fed into the fiscal module 

in the Louisiana Community Impact Model19 for analyzing a specific parish (Lafayette—an 

example parish for this study) that are measurably impacted by deepwater oil and gas extraction. 

The values for royalty rate (t), corporate income tax rate (t), delays between drilling and 

production (i), proportion of successful well drilling costs (exp), windfall profit tax rate (t), and 

the success rate for oil and gas (St

o

 and St

g

) used in this study follow the values mentioned in the 

study by Walls (1994). The value for depletion rate (t) used in this study refers to a publication 

by the Independent Petroleum Association of America (2009). 

Operating costs (OCt

o

 and OCt

g

) for 1984 – 1989 and 1994 – 2007 are obtained from 

DOE/EIA-0185 publications (Costs and Indexes for Domestic Oil and Gas Field Equipment and 

Production Operations). Operating costs (OCt

o

 and OCt

g

) for 1990 – 1993 are obtained from 

DOE/EIA-TR-0568 publication (Cost and Indices for Domestic Oil and Gas Field Equipment 

and Production Operations 1990 through 1993). The amount of additional oil and gas 

discovered from new field discoveries, new reservoir discoveries in old fields, and extensions are 

obtained from DOE/EIA-0216 publications (U.S. Crude Oil, Natural Gas, and Natural Gas 

Liquids Reserves Annual Report). 

 

The discount rate (r) is the federal funds rate obtained from Federal Reserve Statistical 

Release. The PPI index is obtained from the U.S. Department of Labor, Bureau of Labor 

Statistics. The wellhead prices for oil and gas (t

o

 and t

g

), total oil and gas productions, total oil 

and gas reserves are obtained from the U.S. Energy Information Administration website. The 

number of tracts leased (lt) is obtained from the U.S. Department of the Interior, Bureau of 

Ocean Energy Management. The number of exploratory and development wells (Wt) as well as 

the number for operating oil and gas wells is obtained from API Basic Petroleum Data Book 

(2009). The drilling costs (Cwet and Cdry) are obtained from the yearly Joint Association Survey 

 
250 

(JAS) publication published by API. The market spot prices of crude oil (Pt

b

) based on West 

Texas Intermediate (WTI) are obtained through the LSU Center for Energy Studies website. 

The coefficient estimate for Wt-1 is positive implying that an increase in the number of wells 

drilled in the previous period (t-1) increases the number of wells drilled in period t. A positive 

coefficient on Vt-1 implies that an increase in the previous period expected discounted present 

value of profit per well increases the number of wells drilled at period t. Variable lt also has 

positive coefficient meaning that an increase in the number of leased tracts leads to an increase in 

wells drilling at period t. A positive coefficient on Dtlt implying that the 1995 DWRRA have a 

positive impact on the number of wells drilled. Wt-1 was significant at the 90 percent level. 

Table AC.1. Coefficient estimates of model. 

Number of obs = 23 

F(4,18) = 8.62 

Prob >F = 0.0005 

R-squared = 0.6188 

Root MSE = 40.68 

Wt Coef Robust Std. Err. t P > |t| 95% Conf. Interval 

Wt-1 0.60107 0.14468 4.15 0.001 0.2971058 0.9050445 

Vt-1 0.000001 0.000001 1.51 0.149 -0.000000078 0.000000474 

lt 0.04336 0.05813 0.75 0.465 -0.078767 0.1655044 

Dtlt 0.01600 0.03048 0.52 0.606 -0.0480443 0.0800452 

Constant 0.59378 38.1127 0.02 0.988 -79.47805 80.66562 

A joint significance test for all the right hand side (RHS) variables is conducted with the 

result that they are significant at 95 percent level. The Durbin-h test statistics is conducted to test 

for the null hypothesis of no serial correlation in the error term. The test fails to reject the null 

hypothesis at 95 percent level, implying that there is no serial correlation in the error term. 

Figure AC.1 shows the actual compared to the fitted values for the total wells drilled. 

Variables at

o

, at

ag, and atng are generated with a three-year moving average as following: 

(AC.7) at

o

 = [(at-1o + at-2o + at-3o)/ (at-2o + at-3o + at-4o)] at-1o

(AC.8) at

ag = [(at-1ag + at-2ag + at-3ag)/ (at-2ag + at-3ag + at-4ag)] at-1ag

(AC.9) at

ng = [(at-1ng + at-2ng + at-3ng)/ (at-2ng + at-3ng + at-4ng)] at-1ng 

The success ratio for oil and gas (St

o

 and St

g

) in the forecasting is much higher than the ones 

used in the model. A study published by MMS (2003) notes that due to advance technological 

progress in offshore drilling, this success rate has dramatically increased to about 50 percent. 

Forecasts for the crude oil and gas wellhead price (t

o

 and t

g

) as well as crude oil spot market 

price (Pt

b

) are obtained through the U.S. Energy Information Administration website 

(http://www.eia.doe.gov). 

The number of tracts leased (lt), exploratory and development drilling costs for dry hole as 

well as for successful well (Cdry and Cwet) are the average values from the sample period. The 
251 

operating cost for oil and gas (OCt

o and OCtg

) as well as the discount factor () are the values at 

the last year of sample period (in 2007). In 2005, the congress passed the Energy Policy Act that 

states the windfall profit tax rate (t) to be 25 percent for oil and gas production (Lazzari and 

Pirog, 2008). 

Figure AC.1. Comparison os actual compared to fitted values of total wells drilled. 

The variables and parameters used for the forecasting are shown on Table AC.2. The crude 

oil spot market price (Pt

b

) combined with the crude oil and gas wellhead price (t

o

 and t

g

) as 

well as operating cost for oil and gas (OCt

o and OCtg

) are applied to formula (5) and (6) yielding 

the net operating profit per barrel of oil (Pt

o

) and per mcf of gas (Pt

g), respectively. These results 

are then applied to formula (3) and (4) to obtain the discounted present value net operating profit 

per barrel of oil (t

o

) and per mcf of gas (t

g

), respectively. The prediction for expected present 

value profit per well (Vt) following formula (2) is then generated by combining the discounted 

present value net operating profit per barrel of oil and per mcf of gas (t

o and tg

) with the 

predicted three-year moving average of additional reserves (at

o

, at

ag, and atng) as well as the 

exploratory and development drilling costs for dry hole and for successful well (Cdry and Cwet) 

The predicted expected present value profit per well (Vt), the number of tracts leased (lt), and 

variable Dtlt to capture any influence from the DWRRA on the deepwater drilling are then 

combined with the coefficient estimates obtained from the regression (Table A1) to generate 
252 

prediction for the number of wells drilled (Wt). The predicted number of oil and gas wells drilled 

to the year 2020 is shown on Table AC.3. 

 

The discovery process components in the model (at

o

 and at

ag) are showing decreasing 

discovery rates, except for at

ng that increases during the forecast period. The decreasing 

discovery rates effects of at

o

 and at

ag are much less than the increasing discovery rates effect 

from at

ng. These discovery process components are built into the computation for Vt. Due to this, 

Vt increases over the forecast period as well. Since Vt and Wt are positively correlated, hence Wt

increases over the 2008–2020 period. 

Table AC.2. Variables and parameters for forecasting. 

Variables Values 

τt 0.34 

ρt 0.167 

i 0.921 

exp 0.3 

ωt 0.25 

δt 0.15 

o St 0.5 

g St 0.5 

o OCt $15.32/barrel 

g OCt $1.48/mcf 

β 0.9025 

o t $84.18/barrel 

g t $3.91/mcf 

lt 817 tracks 

Cwet $7,442,658/well 

Cdry $11,583,355/well 

b Pt $86.08/barrel 

Dt 1 

b 0.12 

c 0.18 

 
253 

Table AC.3. Predicted number of wells drilled. 

Year Number of Wells 

2008 101 

2009 110 

2010 115 

2011 119 

2012 120 

2013 121 

2014 122 

2015 123 

2016 124 

2017 124 

2018 125 

2019 126 

2020 127 
The Department of the Interior Mission

As the Nation’s principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural 

resources. This includes fostering the sound use of our land and water 

resources; protecting our fish, wildlife, and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The 

Department assesses our energy and mineral resources and works to ensure 

that their development is in the best interests of all our people by encouraging 

stewardship and citizen participation in their care. The Department also has a 

major responsibility for American Indian reservation communities and for people 

who live in island communities.

The Bureau of Ocean Energy Management Mission

The Bureau of Ocean Energy Management (BOEM) works to manage the 

exploration and development of the nation's offshore resources in a way that 

appropriately balances economic development, energy independence, and 

environmental protection through oil and gas leases, renewable energy 

development and environmental reviews and studies.