i

iii

Upgrading the Outer Continental Shelf 

Economic Impact Models for the 

Gulf of Mexico and Alaska 

MAG-PLAN 

Study Report 

Version 2.0 

Authors 

Bansari Saha 

Julio Manik 

Michael Phillips 

Prepared Under MMS Contract 

1435-01-03-CT-72470 

by 

ICF Consulting, Inc. 

9300 Lee Highway 

Fairfax, VA 22031-1207 

Published by: 

U.S. Department of the Interior Herndon, VA

Minerals Management Service November 2005 

OCS Study 

MMS 2005-048

DISCLAIMER 

This report and the accompanying model were prepared under contract between the Minerals 

Management Service (MMS) and ICF Consulting, Inc. While the report has been technically 

reviewed by MMS, it is exempt from review and compliance with MMS editorial standards. 

Approval does not signify that the contents necessarily reflect the view and policies of the 

Service, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use. 

REPORT AVAILABILITY

Extra copies of this report may be obtained from: 

 U.S. Department of the Interior 

 Minerals Management Service 

 Environmental Studies Program 

 381 Elden Street 

 Herndon, VA 20170-4817 

SUGGESTED CITATION 

United States Department of the Interior, Minerals Management Service. Upgrading the Outer 

Continental Shelf Economic Impact Models for the Gulf of Mexico and Alaska (MAG-PLAN 

Study Report). OCS Report MMS 2005-048. Prepared by ICF Consulting, Inc., 2005. 

ACKNOWLEDGMENT 

The ICF Consulting team comprised ICF Consulting (prime contractor) and three subcontractors 

-- Applied Statistical Associates, Petrotechnical Resources of Alaska, and Dr. Arshad Khan. The 

principal investigators for this project were Dr. Bansari Saha, Dr. Julio Manik, and Michael 

Phillips. Technical assistance was also provided by Zeta Rosenberg, Robin Petrusak, and Don 

Robinson of ICF Consulting, Dr. Adam Rose, of Pennsylvania State University, and Scott 

Lindall, of the Minnesota IMPLAN Group (MIG). The authors would also like to thank Kim 

Coffman, Kristen Strellec, Tim Holder, Asha Luthra, Brian Lombard, and Marianna Feagans, all 

of MMS, for their helpful comments and assistance with preparing the report. 

ABOUT THE COVER 

The cover depicts some of the offshore oil and gas infrastructure and activities that affect 

regional economics, as estimated by the new models. The cover was designed by Dianna 

Bhiwandiwalla, an employee of the Minerals Management Service in Herndon, Virginia. 

v

TABLE OF CONTENTS 

MAG-PLAN Study Summary...................................................................................................... xiii 

1 Purpose and Need .................................................................................................................... xiii 

2 MAG-PLAN Model Overview ................................................................................................ xiv 

 MAG-PLAN Basic Structure..................................................................................................xv 

 Distinction between ABC-Override and ABC-Detailed...................................................... xvii 

 How to use MAG-PLAN..................................................................................................... xvii 

3 Data Used in MAG-PLAN..................................................................................................... xviii 

 Gulf of Mexico Offshore Modeling Region ....................................................................... xviii 

 Determining Total Cost/Expenditure and Labor Share of E&D Activities.......................... xix 

 Determining Economic Sector Allocations for GOM ............................................................xx 

 Determining Onshore Area Distributions for GOM Goods and Services Spending............ xxi 

 Gulf Coast Oil Directory (GCOD)................................................................................... xxi 

 County Business Patterns (CBP) ..................................................................................... xxi 

 Determining Onshore Area Labor Income Distributions ..................................................... xxi 

 Modeling Revenue Data ....................................................................................................... xxi 

 Alaska Offshore Modeling Region....................................................................................... xxi 

 Activity Function Equations and Parameters ...................................................................... xxii 

 ABC-Detailed Data – for Arctic Modeling Sub-Region ..................................................... xxii 

 Capital Assets................................................................................................................. xxiii 

 Labor.............................................................................................................................. xxiii 

 Material.......................................................................................................................... xxiii 

 Modeling Revenue Types for Alaska Modeling Region .................................................... xxiv 

 Onshore Areas .................................................................................................................... xxiv 

 ABC-Detailed Data – for South Alaska Modeling Sub-Region......................................... xxiv 

 Onshore Areas ......................................................................................................................xxv 

 ABC-Detailed Data – for Bering Sea Modeling Sub-Region............................................. xxvi 

 Onshore Areas ................................................................................................................... xxvii 

4 Output ................................................................................................................................... xxvii 

5 Built-in Flexibility for Future Uses....................................................................................... xxvii 

1 Introduction..................................................................................................................................1 

2 Gulf of Mexico.............................................................................................................................3 

 2.1 Default Settings in MAG-PLAN ...................................................................................3 

 2.2 Activity Functions..........................................................................................................3 

 2.3 Total Expenditures and Labor Percentages....................................................................6 

 Exploratory Drilling and Development Drilling.........................................................7 

 Drilling Plus Completion ............................................................................................9 

 Production Operations and Maintenance..................................................................10 

 Workovers.................................................................................................................10 

 Platform Fabrication and Installation........................................................................11 

 Offshore Gas Processing Construction .....................................................................12 

 Offshore Gas Processing Operations and Maintenance............................................13 
vi

 Onshore Gas Processing Construction......................................................................14 

 Onshore Gas Processing Operations and Maintenance ............................................14 

 Pipeline Construction................................................................................................14 

 Pipeline Operations and Maintenance ......................................................................15 

 Platform Decommissioning (with and without explosives)......................................16 

 Oil Spills (reaching and not reaching shore).............................................................17 

 2.4 Economic Sector Allocations by Activity Function ....................................................21 

 Change in direct proportion to the ratio of distance to shore....................................23 

 Change by a factor of the square of the distance ratio..............................................23 

 Change by a factor of the cube of the distance ratio.................................................23 

 Change by the (square or cube) root of the distance ratio ........................................24 

 No change .................................................................................................................24 

 Using IMPLAN Production Functions ........................................................................26 

 2.5 Onshore Area Distribution...........................................................................................29 

 Using GCOD for the Major IMPLAN Sectors .........................................................31 

 1. Extracted GCOD data for the above categories................................................32 

 2. Mapped GCOD data to relevant county codes .................................................32 

 3. Identified the number of companies, using “Area of Involvement” 

 as a filter........................................................................................................32 

 4. Determined “Other U.S.” using IMPLAN RPC’s from an 

 “All Gulf Model” ..........................................................................................32 

 Using County Business Patterns for Other IMPLAN sectors...................................33 

 1. Extracted CBP data for all the counties in the 5 Gulf states.............................33 

 2. Mapped CBP data to the relevant IMPLAN sectors and OSA codes...............33 

 3. Converted 2-digit NAICS to 3-digit NAICS in CBP........................................33 

 4. Determined “Other U.S.” using IMPLAN RPC’s from an 

 “All Gulf Model” ..........................................................................................34 

 5. Used other post-processing assumptions ..........................................................34 

 Using Assumptions to Change WGOM or CGOM OSA Distributions ...................34 

 1. Identify sectors that are most-likely “local” .....................................................34 

 2. Assume Texas provides locally to WGOM and Louisiana provides 

 locally to CGOM...........................................................................................34 

 3. Take small steps and keep the 100 percent total distribution ...........................35 

 Onshore Area Labor, Income, and Personal Consumption Expenditure 

 Distribution .........................................................................................................35 

 Onshore Area Labor Distribution.............................................................................35 

 Income Distribution..................................................................................................38 

 Personal Consumption Expenditures .......................................................................39 

 2.6 Government Revenue Data ......................................................................................39 

 Revenue Allocation.............................................................................................40 

 Onshore Area Distribution..................................................................................41 

3 Alaska – Arctic Modeling Areas................................................................................................42 

 3.1 Default Settings and Modeling Areas in MAG-PLAN ............................................42 

 3.2 Activity Functions....................................................................................................42 

 3.2.1 Activity Function Equations and Parameters.........................................................44 
vii

 3.3 ABC-Detailed Data ..................................................................................................45 

 3.3.1 Capital Assets.........................................................................................................45 

 3.3.2 Labor......................................................................................................................46 

 Production Labor ................................................................................................46 

 Non-production Labor ........................................................................................46 

 Overhead Expenses.............................................................................................47 

 Purchased Services Employee Compensation ....................................................47 

 Production and Non-production Labor to Onshore ............................................47 

 Use of PCE and Income Distribution..................................................................49 

 3.3.3 Material..................................................................................................................49 

 Purchased Services..............................................................................................49 

 Estimates of Fuel Used .......................................................................................51 

 Other Material Inputs..........................................................................................52 

 3.4 Revenue Function Modeling ....................................................................................54 

 3.5 Onshore Areas ..........................................................................................................56

 Beaufort and Beaufort Local Gas .......................................................................57 

 Chukchi-Hope.....................................................................................................57 

4 Alaska – South Alaska Modeling Areas ....................................................................................58 

 4.1 Default Settings and Modeling Areas in MAG-PLAN ............................................58 

 4.2 Activity Functions....................................................................................................58 

 4.3 Total Expenditures and Sector Allocations..............................................................60 

 4.3.1 Estimating Total Expenditures of Each Activity Function....................................61 

 4.3.2 Separating Expenditures on Assets and Materials.................................................62 

 4.3.3 Updating IMPLAN Sector Definitions ..................................................................62 

 4.4 Onshore Areas ..........................................................................................................63

 Cook Inlet OMA .................................................................................................64 

 Semi-Remote OMA ............................................................................................64 

 Remote OMA......................................................................................................64 

5 Alaska – Bering Sea Modeling Areas........................................................................................65

 5.1 Default Settings in MAG-PLAN..............................................................................65 

 5.2 Offshore Modeling Areas.........................................................................................65 

 Similarities and Differences Between the NAB and Cook Inlet 

 Planning Areas....................................................................................................66 

 Defining Bering Sea OMA’s ..............................................................................68 

 5.3 Activity Functions.....................................................................................................70 

 5.4 Total Expenditures and Sector Allocations...............................................................76 

 5.4.1 Total Cost Adjustments..........................................................................................76 

 Southeastern Bering OMA-1 ..............................................................................76 

 North Bering OMA-2..........................................................................................77 

 Western Bering OMA-3......................................................................................78 

 5.5 Onshore Areas ..........................................................................................................78 

 Southeastern Bering OMA..................................................................................78 

 North Bering OMA.............................................................................................78 

 Western Bering OMA.........................................................................................78 
viii 

6 Possible MAG-PLAN Enhancements........................................................................................80 

 6.1 Bering Sea Data........................................................................................................80 

 6.2 TCAF/LPCAF ..........................................................................................................80 

 6.3 Pacific and Atlantic OMA’s.....................................................................................81 

 6.4 2005 Energy Policy Act ...........................................................................................81 

Appendices ..........................................................................................................................83 

 Appendix A ..........................................................................................................................85 

 GOM Offshore Drilling Cost Estimates ............................................................................85 

 The Statistical Model of Drilling Cost.........................................................................85 

 Pooling and Averaging ................................................................................................86 

 Exploration Versus Development Costs ......................................................................86 

 Appendix B ..........................................................................................................................93 

 Additional Data for GOM Modeling Regions ...................................................................93 

 Labor Needs Survey PCE Data..........................................................................................93 

 Revenue Data.....................................................................................................................95 

 Federal Allocation of Revenue Received ..........................................................................98 

 Appendix C ........................................................................................................................101 

 Algorithm for Calculating Fuel Price in MAG-PLAN ....................................................101 

 Components of Retail Price of Refined Products .........................................................101 

 1. Crude Oil Cost ......................................................................................................101

 2. Refinery Margins and Marketing Costs................................................................101 

 3. Distribution and Marketing Costs and Profits ......................................................101 

 4. Federal and State Taxes ........................................................................................101 

 Prediction of Product Prices..........................................................................................101 

 1. Crude Oil Cost ......................................................................................................102

 2. Refinery Margins, Distribution and Marketing Costs and Profits ........................102 

 3. Regional Price Spread...........................................................................................102 

 4. Federal and State Taxes ........................................................................................102 

 MAG-PLAN Fuel Price Algorithm .................................................................................103 

 Appendix D ........................................................................................................................109 

 MAG-PLAN Technical Documentation..........................................................................109 

 Introduction ...................................................................................................................109 

 A. Direct Expenditures..............................................................................................109 

 A.1.a. Labor Expenditures (ABC-Override Approach).........................................110 

 1. Unit Cost .............................................................................................110 

 2. Activity Cost .......................................................................................110 

 3. Labor Cost...........................................................................................111 

 4. OSA Allocation of Labor Cost ...........................................................111 

 5. Disposable Income..............................................................................111 

 6. Sector Allocations by OSA.................................................................112 

 7. Labor Expenditure ..............................................................................112 
ix

 A.1.b. Non-Labor Expenditures (ABC-Override Approach).................................112 

 1. Non-Labor Costs.................................................................................112 

 2. Sector Allocation of Non-Labor Cost (Non-Labor 

 Expenditure by Sector) ...................................................................113 

 3. OSA Allocation of Non-Labor Expenditure.......................................113 

 A.2.a. Activity Function Unit (ABC-Detailed Approach) ....................................113 

 A.2.b. Labor Expenditures (ABC-Detailed Approach).........................................114 

 1. Task Force............................................................................................114 

 2. Average Task Force .............................................................................114 

 3. Man Week............................................................................................115 

 4. Production Workers Full Time Equivalent..........................................115 

 5. Production Workers Weekly Wage ....................................................115 

 6. Production Workers Total Wage .........................................................116 

 7. Production Workers Total Fringe Benefit............................................116 

 8. Production Workers Total Wage and Benefit......................................116 

 9. Non-Production Workers Full Time Equivalent..................................116 

 10. Non-Production Workers Total Wage .................................................117 

 11. Non-Production Workers Total Fringe Benefit ...................................117 

 12. Non-Production Workers Total Wage and Benefit..............................117 

 13. Production and Non-Production Workers Cost ...................................117 

 14. OSA Allocation of Production and Non-Production 

 Workers Cost ..................................................................................118 

 15. Disposable Income...............................................................................118 

 16. Sector Allocation of Production and Non-Production 

 Workers Cost ..................................................................................118 

 17. Production and Non-Production Workers Expenditure .......................119 

 18. Production Workers Total Days Worked for Overhead 

 Calculations.....................................................................................119 

 19. Overhead Cost for Work Clothes.........................................................119 

 20. Overhead Cost for Local Travel ..........................................................120 

 21. Overhead Cost for Hotel/Camp ...........................................................120 

 22. Overhead Cost for Food Service..........................................................121 

 23. Overhead Cost for Air Travel ..............................................................121 

 24. Sector Allocation of Overhead Cost ....................................................121 

 25. OSA Allocation of Overhead Cost ......................................................122 

 26. Labor Expenditure ...............................................................................122 

 A.2.c. Non-Labor Expenditures (ABC-Detailed Approach) ....................................123 

 Asset Expenditures......................................................................................123 

 1. New Asset Cost....................................................................................123 

 2. Used Asset Cost ...................................................................................124 

 3. Sector Allocation of New Asset Cost ..................................................124 

 4. Sector Allocation of Used Asset Cost..................................................124 

 5. OSA Allocation of New Asset Expenditure ........................................125 

 6. OSA Allocation of Used Asset Expenditure........................................125 
x 

 Material Expenditures....................................................................................125 

 1. Non-Fuel Material Cost .......................................................................125 

 2. Fuel Cost ..............................................................................................126 

 3. Sector Allocation of Non-Fuel Material Cost......................................126 

 4. Sector Allocation of Fuel Cost.............................................................126 

 5. Total Sector Allocation of Material Expenditure.................................127 

 6. OSA Allocation of Material Expenditure ............................................127 

 Total Non-Labor Expenditures ......................................................................127 

 A.3. Expenditure Source Revenue Expenditures......................................................128 

 1. Revenue Function to Expenditure Source.................................................128 

 2. OSA Allocation of Expenditure Source Revenues ...................................128 

 3. Recycled Revenues from Expenditure Sources ........................................129 

 4. Disbursement of Expenditure Source Revenue to OSA ...........................129 

 5. Disbursement of OSA Revenues to Sectors..............................................130 

 A.4. Total Expenditures ............................................................................................130 

 B. Multiplier Impacts....................................................................................................130 

 B.1. Direct Output.....................................................................................................130

 B.2. IMPLAN Regional Type 1 Multipliers and Indirect Output.............................131 

 1. IMPLAN Regional Type 1 Multipliers.....................................................131 

 2. Indirect Output..........................................................................................131 

 B.3. IMPLAN Regional Induced Multipliers and Induced Output...........................132 

 1. IMPLAN Regional Induced Multipliers ...................................................132 

 2. Induced Output..........................................................................................132 

 B.4. Total Output ......................................................................................................132

 B.5. IMPLAN Study Area Data................................................................................133 

 1. Employment Ratio ....................................................................................133 

 2. Employee Compensation Ratio.................................................................133 

 3. Proprietor’s Income Ratio.........................................................................133 

 4. Labor Income Ratio ..................................................................................134 

 5. Total Value Added Ratio ..........................................................................134 

 6. Indirect Business Taxes Ratio...................................................................134 

 7. Other Property Type Income Ratio...........................................................135 

 B.6. Direct, Indirect, Induced, and Total Study Area Impacts..................................135 

 1. Direct, Indirect, Induced, and Total Employment Impacts.......................135 

 2. Direct, Indirect, Induced, and Total Employee 

 Compensation Impacts..........................................................................136 

 3. Direct, Indirect, Induced, and Total Proprietor’s 

 Income Impacts.....................................................................................136 

 4. Direct, Indirect, Induced, and Total Labor 

 Income Impacts.....................................................................................137 

 5. Direct, Indirect, Induced, and Total Total Value 

 Added Impacts ......................................................................................137 

 6. Direct, Indirect, Induced, and Total Indirect Business 

 Taxes Impacts .......................................................................................138 

 7. Direct, Indirect, Induced, and Total Other Property 

 Type Income Impacts............................................................................138 

Bibliography ................................................................................................................................139 
xi

Exhibit 

Exhibit C-1: Excerpt from EIA’s AEO 2004, January 2004 ......................................................107 

Figures 

Figure 5-1: North Aleutian Basin Potential Area of Interest for Leasing, 

 Exploration and Production ................................................................................68 

Figure 5-2: Bering Sea; Southern Extent of Winter Sea Ice .......................................................69 

Figure C-1: Refinery Margins, Distribution and Marketing Costs and Profits for 

 Petroleum Products (in cents/gallon)................................................................105 

Tables 

Table 1: Activity Function Codes and E&D Scenario Mapping.......................................... xix 

Table 2-1: Activity Function Codes and E&D Scenario Mapping..............................................5 

Table 2-2: Exploratory Drilling – Expenditures per Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ..................................8 

Table 2-3: Development Drilling – Expenditure per Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ..................................8 

Table 2-4: Drilling Plus Completion – Expenditure per Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ..................................9 

Table 2-5: Production O&M – Expenditure per Production Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ................................10 

Table 2-6: Workover – Expenditure per Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ................................11 

Table 2-7: Platform F&I – Expenditure per Production Well (in thousands) and 

 Labor Share (percent of total expenditure, in parentheses) ................................12 

Table 2-8: Pipeline Construction – Expenditure ($ per mile) and 

 Labor Share (percent of total expenditure, in parentheses) ................................15 

Table 2-9: Pipeline O&M – Expenditure ($ per Bcf per year) and 

 Labor Share (percent of total expenditure, in parentheses) ................................15 

Table 2-10: Decommissioning Platform Without Explosives – Expenditure per 

 Platform and Labor Share (percent of total expenditure, in parentheses)...........17 

Table 2-11: Decommissioning Platform with Explosives – Expenditure per 

 Platform and Labor Share (percent of total expenditure, in parentheses)...........17 

Table 2-12: Oil-Type Modifiers for Oil Spill Cleanup Costs (in percentage).............................18 

Table 2-13: Location Modifiers for Oil Spill Cleanup Costs (in percentage) .............................19 

Table 2-14: Spill Size Modifiers for Oil Spill Cleanup Costs (in percentage) ............................19 

Table 2-15: Oil Spill Cleanup – Expenditure per Gallon (in thousands) and 

 Labor Share (percent of total expenditure) .........................................................20 

Table 2-16a: Rules to Extrapolate Sector Allocations by Water Depths (see Key at the 

 end of Table 2-16b for an explanation of the codes) ..........................................24 

Table 2-16b: Rules to Extrapolate Sector Allocations by Water Depths (see Key at the 

 end for explanation of the codes)........................................................................25 

Table 2-17: IMPLAN Production Function Example (in percent and actual dollars).................27 
xii 

Table 2-18: Reallocation Sectors.................................................................................................29 

Table 2-19: Mapping between IMPLAN Sectors and GCOD Industry Categories ....................31 

Table 2-20: Mapping IMPLAN and NAICS Codes ....................................................................33 

Table 2-21: Estimated Distribution of Home Zip Codes from LNS (in percent and 

 population total)..................................................................................................36 

Table 2-22: OSA Distribution for GOM Workers (in percent) ...................................................37 

Table 2-23: Personal Income from LNS (in percent and population total) .................................38 

Table 2-24: Household Income from LNS (in percent and population total)..............................39 

Table 2-25: Rental Rates (in %), Royalty Rates (in %), and Royalty Suspension 

 Values (boe) by Water Depth .............................................................................40 

Table 3-1: Activity Types and Functions for Arctic OMA’s.....................................................43 

Table 3-2: Production and Non-Production Labor Onshore Area Distribution (in %)..............48 

Table 3-3: Income Distribution for Alaska Modeling Areas (in percent) .................................49 

Table 3-4: Purchased Services IMPLAN Sectors......................................................................50 

Table 3-5: Material Category OSA Distribution for AF “Operate Production 

 Island” (in %)......................................................................................................53 

Table 3-6: Revenue Type to Function Mapping for Arctic OMA’s ..........................................55 

Table 4-1: Activity Types and Functions for South Alaska OMA’s .........................................59 

Table 4-2: Total Cost Changes for Selected Activity Functions (in $) .....................................61 

Table 4-3: Examples of Multiple IMPLAN Matches ................................................................63 

Table 5-1: Bering Sea OMA’s ...................................................................................................69 

Table 5-2: Sample North Aleutian Basin E&D Scenario ..........................................................72 

Table 5-3: Current Cook Inlet and Proposed Activity Functions for Modeling 

 Areas 1 and 3 ......................................................................................................73 

Table 5-4: Current Arctic and Proposed Activity Functions for Bering OMA 2 ......................74 

Table 5-5: Scaling Factors for Percentage Increases in Costs to Cook Inlet 

 Activity Functions (in %)....................................................................................76 

Table A-1: Regression Results for Oil Well Drilling Costs .......................................................88 

Table A-2: Regression Results for Gas Well Drilling Costs ......................................................89 

Table A-3: Regression Results for Dry Hole Drilling Costs ......................................................90 

Table A-4: Average Cost in 2002 Dollars .................................................................................91 

Table A-5: Estimated Exploratory and Development Drilling Expenditures.............................91 

Table B-1: Regression Estimates of Employee Expenditure Fractions (in %)...........................94 

Table B-2: Pre-2001 Royalty Suspension Regime (in BOE) .....................................................96 

Table B-3: Shallow Water Deep Gas Royalty Suspension Volume Provisions (in Bcf) ...........97 

Table C-1: Refinery Margins, Distribution and Marketing Costs and Profits for 

 Petroleum Products (in cents/gallon, real 2000 dollars) ...................................104 

Table C-2: Spread-between regional petroleum prices and the U.S. national average 

 product prices excluding taxes (in cents/gallon, real 2000 dollars)..................106 

Table C-3: Federal and state taxes on various petroleum products (in cents/gallon, 

 real 2000 dollars) ..............................................................................................106 
MAG-PLAN Study Summary 

xiii

1 Purpose and Need 

The Minerals Management Service (MMS) of the U.S. Department of the Interior has 

responsibility for managing minerals and certain other resources on Federal submerged lands. 

When the Secretary of the Interior considers proposals to lease rights to oil and gas resources in 

Federal waters, MMS conducts economic, environmental, and various other analyses as part of 

the decision making process. For example, MMS uses its Outer Continental Shelf (OCS) 

Economic Impact Models (EIMs) to provide projections of employment and labor income that 

may result from proposed actions when developing analyses pursuant to the requirements of the 

Outer Continental Shelf Lands Act (OCSLA), as amended, and of the National Environmental 

Policy Act (NEPA). 

The MMS has developed a set of OCS Economic Impact Models (EIMs) to estimate the regional 

economic impacts of activities in the Gulf of Mexico (GOM) and on the Alaska OCS using a 

theoretically consistent approach across these areas. When MMS prepares a new 5-Year OCS 

oil and gas program or an Environmental Impact Statement (EIS), at least one of the OCS EIMs 

is used to estimate the additional employment and, sometimes, the labor income and/or other 

impacts anticipated to result from the different alternatives formally put before MMS. Analyses 

based on OCS EIM estimates are used primarily in the following documents: 

• 5-Year Program Decision Documents 

• 5-Year Program EIS’s 

• Multi-Sale EIS’s 

• Lease Sale EIS’s 

The GOM and the Alaska OCS differ considerably in terms of operating conditions and 

infrastructure, and the OCS EIM data for these regions were developed by two groups working 

independently, so the existing models at the time this study was begun—while theoretically 

consistent—exhibited very different levels of detail and software structures. Because of rapid 

technological growth and the movement of exploration and operations into ever-deeper waters, 

the GOM models, based on data for 1994-2000, were somewhat outdated. On the other hand, the 

Alaska models were more recent, but there was room for improvement, particularly in respect to 

the treatment of activities in areas more remote than the Beaufort Sea and the Cook Inlet. 

Therefore, the study documented in this report has 4 main purposes: 

• to update the data underlying the basic OCS EIM for the Gulf of Mexico, 

• to confirm and, where necessary, update the data underlying the OCS EIM’s for the 

Alaska OCS (Arctic and Sub-Arctic models), 

• to improve and standardize the existing OCS EIM software structure, and 

• to document the data and software improvements in a user manual and a report. 
MAG-PLAN Study Summary 

xiv 

The model developed through this effort is called MAG-PLAN (MMS Alaska-GOM Model 

Using IMPLAN1

) and allows MMS the option of using the same modeling framework for any 

OCS planning area in either the GOM or the Alaska region. The model itself is not available to 

the public, in part because the IMPLAN data tables incorporated into MAG-PLAN are 

proprietary. 

2 MAG-PLAN Model Overview 

MAG-PLAN has been developed on the Microsoft Access platform with user-friendly forms to 

enter and manipulate data and create new runs. The Access database that houses data for all 

modeling regions does not require any user manipulation, thus making the model more userfriendly and relatively easy to learn. Users can work in the familiar Windows environment to 

add, delete, or otherwise edit data on the forms without having to worry about the relational 

structure in the database. More advanced users can, however, change the database or the queries 

that form the core of MAG-PLAN. 

Moreover, MAG-PLAN provides MMS an integrated framework for using IMPLAN multipliers2

to estimate the regional macro-level socioeconomic impacts in one model. Users of MAGPLAN can undertake both stages of the entire modeling exercise within the framework of one 

model. MAG-PLAN also uses the disaggregated full multiplier matrices from IMPLAN in this 

integrated framework to provide results that are more accurate and easily defensible.3

In the broadest terms, MAG-PLAN consists of two parts and requires an MMS-developed 

Exploration & Development (E&D) scenario to run. It retains the two-stage process of the older 

models: 1) estimating the level and allocation of expenditures resulting from proposed OCS oil 

and gas projects, and, 2) estimating the ripple effects of that spending on each selected onshore 

area. As in the older models, the first-stage process blends three basic sets of estimates: how 

much is spent for each kind of activity (e.g., drilling an exploratory well, constructing and laying 

a mile of pipeline, or operating and maintaining a production facility for a year), how much of 

that amount is spent in each of several hundred industry sectors, and in which user-designated 

Onshore Areas (OSA’s) the money is spent. The second-stage process uses IMPLAN multipliers 

 

1 IMPLAN is a commercial input-output software and data package widely used to estimate the effects of new 

spending on the economy. See www.implan.com/products.html for more information. 

2

 IMPLAN multipliers are the coefficients used in the input-output analysis based on inter-industry relationships to 

calculate the direct, indirect, and induced impacts. In simpler terms, they estimate the ripple effects that occur 

throughout the economy after any new initial spending. There are eight categories of IMPLAN multipliers: 

Employment, Employee Compensation, Proprietor’s Income, Other Property Type Income, Indirect Business Taxes, 

(total economic) Output, and the aggregates Labor Income (Employee Compensation plus Proprietor’s Income) and 

Total Value Added (Labor Income, Other Property Type Income, and Indirect Business Taxes). More information 

can be found in the IMPLAN Pro User’s Guide or at the web site shown in footnote 1. 

3

 Previous MMS models used the aggregated multiplier outputs from IMPLAN instead of the full multiplier 

matrices. This simplification masked the finer details of the inter-industry contributions because it only uses the 

aggregated sector impacts for those sectors that show changes in final demands. MAG-PLAN instead uses the 

disaggregated multiplier matrices, and thus the full, disaggregated sector impacts are estimated in MAG-PLAN, 

which accurately captures the inter-industry linkages. 
MAG-PLAN Study Summary 

xv

to estimate the economic output, employment, and other economic effects in each Onshore Area 

(OSA). MAG-PLAN also continues to use MMS-developed Exploration and Development 

(E&D) scenarios to drive the estimates. E&D scenarios, provided by MMS Resource Evaluation 

offices, are estimates of the oil-and-gas-related activities that could plausibly take place as the 

result of a proposed oil and gas project, lease sale, or 5-year program (a collection of lease sales). 

The E&D scenario provides MMS’s estimate of future exploration, development, and production 

activity levels; MAG-PLAN’s Stage I translates these activity levels into industry spending; and 

Stage II of MAG-PLAN uses IMPLAN multipliers to estimate the employment, income, and 

other economic effects selected by the user. For each of the economic effects, the model 

estimates direct, indirect, induced, and total effects.4 An alternative to using MAG-PLAN for 

Stage II is to use the Stage I outputs as inputs directly into IMPLAN Professional software. 

MAG-PLAN Basic Structure 

MAG-PLAN contains all the data needed to create estimates for these 11 offshore modeling 

areas (OMA’s): 

• Eastern Gulf of Mexico 

• Central Gulf of Mexico 

• Western Gulf of Mexico 

• South Alaska-SemiRemote 

• South Alaska-Remote 

• South Alaska-Cook Inlet 

• Southeastern Bering Sea 

• North Bering Sea 

• Western Bering Sea 

• Arctic-Chukchi/Hope 

• Arctic-Beaufort5

To run MAG-PLAN for one of these eleven pre-defined OMA’s6

, the basic requirement is an 

E&D scenario provided by the appropriate Resource Evaluation office in MMS. As indicated 

above, once this E&D scenario is imported, MAG-PLAN houses all the data that would be 

 

4 In standard usage, the direct effects would refer to the spending of the oil and gas industry as a result of the 

projects being analyzed, as well as to the employment, income, and other such effects caused by that spending. 

Indirect effects are those that arise from subsequent rounds of spending by contractors, vendors, and other 

businesses. Induced effects arise from the spending of worker households. However, while total effects remain the 

same, most “direct” MAG-PLAN estimates include the first round of indirect and induced effects. MAG-PLAN 

direct effects can be thought of as the effects of local payroll and non-payroll expenditures of oil and gas companies, 

as well as of their immediate suppliers. 

5

 The model also has the data needed to estimate the effects of producing Beaufort Sea natural gas for local 

consumption. 

6

 The flexibility of the model structure allows the user to create new OMA’s as needed. So, for example, if one has 

the industry spending and allocation data, as well as the multipliers, one could define Southern California and the 

Mid-Atlantic as new OMA’s and create a sub-model within MAG-PLAN for each. 
MAG-PLAN Study Summary 

xvi 

needed to create a run in a reasonable time, without the need to change any data except for 

periodic updates such as those to the IMPLAN data. 

In MAG-PLAN, all of the changeable model settings are referred to as inputs. An Input Class 

consists of the data settings or selections of a particular type, such as IMPLAN multipliers or the 

relationships between Activity Types (e.g., exploration wells) and Activity Functions (e.g., the 

cost function for drilling a well). There are eleven Input Class Types: 

• E&D Scenario 

• Activity Definitions 

• Activity Based Cost (ABC)-Capital (part of the ABC-Detailed model framework) 

• ABC-Labor (part of the ABC-Detailed model framework) 

• ABC-Materials (part of the ABC-Detailed model framework) 

• ABC-Override (used as an alternative when ABC-Detailed data are not available) 

• Income 

• Revenue Definitions 

• Revenue Parameters 

• Expenditures 

• Multipliers 

There is at least one default data set already in MAG-PLAN for each OMA-Input Class 

combination. 

The E&D scenario inputs are classified as Activity and Revenue Types under the Input Class 

Type “E&D Scenario.” The Activity Types are linked to units of Activity Functions under Input 

Class Type “Activity Definitions.” For example, Exploratory Wells Drilled is an Activity Type, 

and Exploratory Drilling is the corresponding Activity Function.7

 The input for an Activity (or 

Revenue) Type is a level of OCS activity specified in an E&D scenario, e.g., the number of 

Exploration Wells Drilled. Exploratory Drilling comprises the data and equations necessary to 

estimate the spending that would result from one exploratory well drilled in a given water-depth 

range within a given well-depth range, as well as the allocation of this spending to economic 

sectors and the distribution to OSA’s. Similarly, the Revenue Types are linked to Revenue 

Functions under the Input Class Type “Revenue Definitions.” Revenue Types are just like 

Activity Types except that, instead of driving industry and household spending that goes directly 

into the economy, they drive estimates of additional government revenue. For example, oil and 

gas lease holders must make rental payments to the Federal Government based on the number of 

acres they’ve leased, so Acres Leased is considered a Revenue Type, rather than an Activity 

Type. The Activity and Revenue Functions are then modeled with data on labor, capital, and 

materials (for Activity Functions) and revenue-specific parameters (for Revenue Functions) 

using the remaining Input Classes. These calculations produce the Stage I output with estimates 

of the direct expenditures for the given E&D scenario. Using the IMPLAN multipliers (under 

the Input Class Type “Multipliers”) on these Stage I direct expenditures then produces the (Stage 

II) economic output, employment, and/or other economic effects, depending on the multiplier 

categories selected by the user. 

 

7

 An Activity Function (or a Revenue Function) can be thought of as a production function. To avoid confusion 

with IMPLAN production functions, MMS uses Activity/Revenue Function, or the more generic “cost function,” 

when referring to the equivalent set of data and equations in MAG-PLAN. 
MAG-PLAN Study Summary 

xvii

Distinction between ABC-Override and ABC-Detailed 

MAG-PLAN uses an Activity-Based Cost (ABC) framework to model the effects of different oil 

and gas activities, allowing the use of either engineering cost data or average cost data. Under 

this framework, the person using the ABC-Detailed approach to create an Activity (or Revenue) 

Function provides detailed capital, labor, and material estimates that are then used by MAGPLAN to estimate the cost per unit of an activity as well as the dollar amount of goods and 

services provided by each IMPLAN sector in each OSA. However, if detailed data on cost 

components are not available, as is the case for the GOM modeling regions, the user can use the 

ABC-Override approach, by providing average-cost labor and non-labor cost/expenditure data 

for different activities and then allocating the costs/expenditures by IMPLAN sectors and 

onshore areas. 

Under the ABC-Detailed approach, users develop the cost per unit of an Activity Function by 

IMPLAN sector and onshore area (OSA) based on detailed capital (may be new or used), labor, 

and material categories. This framework allows MMS to use engineering cost data when there is 

a lack of historical OCS activity data. For the Alaska modeling areas, MMS uses a bottom-up 

approach: hypothetical cost functions are constructed based on engineering information on 

different activities. Each Activity Function in this approach is defined using several 

“Parameters” that allow the user to model complicated Activity Functions in several simple 

steps. For example, one user-adjustable parameter for Arctic activities is the number of 

helicopter trips per day during construction of a production island. Each Activity Function has a 

related set of parameters defined in MAG-PLAN. 

MAG-PLAN provides an alternative framework if detailed engineering cost data for the 

components of an activity are not available. This approach is consistent with the current GOM 

data and is termed ABC-Override in MAG-PLAN. ABC-Override allows the user to assign the 

labor and non-labor share in total expenditure per Activity Function unit. These correspond to 

the cost function values used by MMS in the previous GOM modeling framework. Specifying 

labor share separately allows the model to estimate different onshore area distributions for these 

expenditures based on the Personal Consumption Expenditure (PCE) vectors from IMPLAN for 

different income categories. If labor share is not specified, then the total expenditure per unit is 

assigned to the appropriate IMPLAN sectors assuming it as a non-labor expenditure. 

How to use MAG-PLAN 

The simplest way to use MAG-PLAN is to specify a run by choosing a default data setting (preexisting Input Class) for each Input Class Type (see “MAG-PLAN Basic Structure” above). In 

MAG-PLAN, a combination of these Input Classes Types defines a full run. If the user chooses 

to take advantage of the data already in the model, then he can create a run in this simple 

framework. However, in almost every application after initial instruction and testing, the user 

will have to import a new E&D scenario for each run. MAG-PLAN will allow the user to use 

this imported scenario with the existing economic data housed in the model to estimate the 

economic impacts of various offshore oil and gas activities. The user chooses the appropriate 

OSA’s where these impacts are likely to be felt as well as the appropriate IMPLAN multipliers 

(economic output, employment, labor income, etc.) to measure the macro-level regional 

economic impacts of these activities. 
MAG-PLAN Study Summary 

xviii 

The user can also update the IMPLAN multiplier files (and personal consumption expenditure 

vectors) whenever MMS buys new IMPLAN data (usually near the beginning of each calendar 

year), as well as the GDP deflator numbers, which are published by the Bureau of Economic 

Analysis. The user may also want to change the income distribution data in MAG-PLAN if new 

and improved data become available (usually from the decennial Census data). 

There may be a few other settings that should be changed for particular analyses. These include: 

• the base year for a new run 

• Activity Function parameters for Alaska modeling areas 

• Revenue Function parameters (both global and OSA specific) 

In most cases, the user would normally want to use the default settings for the other Input Class 

Types. However, if MMS has new data from studies, other research, or industry applications, 

then the user can create new Activity Types and the corresponding Activity Functions. The same 

is true for Revenue Types, which can also be updated using MAG-PLAN’s relatively easy 

framework for changing and creating new Activity and Revenue Functions. 

3 Data Used in MAG-PLAN 

The delivered version of MAG-PLAN covers two main modeling regions – the Gulf of Mexico 

and Alaska OCS. The model contains all the relevant data needed to estimate economic impacts 

in both regions. Data made available in the model has been obtained from various sources, 

including a significant effort expended under this contract to update major parts of the GOM 

data. Most of the existing GOM data in MMS repository were deemed to be somewhat outdated. 

Therefore, an effort was made to update major portions of the GOM data and this section 

summarizes those changes. Data for the Alaska OCS, on the other hand, were considered to be 

relatively new and were used in MAG-PLAN with the necessary modifications and changes 

explained below. 

Gulf of Mexico Offshore Modeling Region 

There were three major components of the data work: 

• Determining the total cost/expenditure per unit of an activity and the labor share 

• Determining how this spending should be allocated among economic sectors 

• Determining the onshore areas to which this spending should be distributed 
MAG-PLAN Study Summary 

xix

Determining Total Cost/Expenditure and Labor Share of E&D Activities 

Table 1 shows the Activities modeled in MAG-PLAN, along with the units for the total 

expenditures. 

Table 1: Activity Function Codes and E&D Scenario Mapping 

Activity Type in E&D Scenario Activity Function in MAG-PLAN Total Cost Unit 

Exploratory Wells Drilled Exploratory Drilling $/Well 

Non-Production Wells Drilled Development Drilling $/Well 

Production Wells Drilled Development Drilling and 

Completion $/Well 

Production Wells Drilled Production Operations and 

Maintenance $/Well 

Workovers Workover $/Well 

Platforms Added $/Platform (Average) 

Platforms Added $/Fixed Platform 

Platforms Added $/Compliant Tower 

Platforms Added $/Tension Leg 

Platform 

Platforms Added $/Spar 

Platforms Added 

Platform Fabrication and 

Installation 

$/FPSO 

New Gas Processing Facilities Onshore Gas Processing 

Construction 

$/Facility (per Bcf/day 

capacity) 

Gas Production (Bcf) Onshore Gas Processing O&M $/Facility per Bcf 

New Gas Processing Facilities Offshore Gas Processing 

Construction 

$/Facility (per Bcf/day 

capacity) 

Gas Production (Bcf) Offshore Gas Processing O&M $/Facility per Bcf 

Pipeline Miles Added Pipeline Construction $/Mile 

Pipeline Miles Added Pipeline Operations and 

Maintenance $/Mile 

Platforms Removed Decommission Platform $/Platform (Average) 

Platforms Removed with Explosives Decommission Platform with 

Explosives $/Platform (Average) 

Oil Spilled, Reaching Shore Onshore Oil Spill $/Gallon 

Oil Spilled, Not Reaching Shore Offshore Oil Spill $/Gallon 

Source: MMS E&D scenario data and MAG-PLAN assumptions.
MAG-PLAN Study Summary 

xx 

The Activity Types correspond to the definition of variables in the E&D scenario. Once they are 

imported into MAG-PLAN, they are then linked to Activity Function units using a user-defined 

mapping. Table 1 above shows the mappings used for the GOM defaults in the delivered version 

of MAG-PLAN. The last column presents the units for defining the total cost of an Activity 

Function. 

Determining Economic Sector Allocations for GOM 

Sector allocations were estimated for two broad types of sectors – major and non-major. The 

major sectors were deemed to be those that have a significant contribution to offshore oil and gas 

activities in the GOM. For example, some of the major sectors considered in MAG-PLAN were: 

• Oil and gas extraction (IMPLAN sector 19) 

• Support activities for oil and gas operations (IMPLAN sector 28) 

• Iron, steel pipe and tube manufacturing (IMPLAN sector 205) 

• Air transportation (IMPLAN sector 391) 

• Water transportation (IMPLAN sector 393) 

• Shipbuilding and repairing (IMPLAN sector 357). 

A total of 40 major sectors were identified using various data sources and the expert opinion of 

an ICF Consulting panel of oil and gas industry analysts. The previous vectors for sector 

allocations estimated in the Coastal Marine Institute (CMI) study by the Center for Energy 

Studies at Louisiana State University were used as the starting point. Then, using various other 

sources, the expert panel modified the allocations to reflect the current conditions and the needs 

of MAG-PLAN. This approach led to a re-evaluation of the original CMI allocations and 

provided a richer distribution of the major sectors than the previous work suggested. Typically, 

the sector allocation vector for each delineated Activity Function included approximately 10-12 

of the 40 major sectors. 

After identifying the major sectors, IMPLAN production function data were used to “expand” 

the allocations to the non-major sectors based on the input-output relationships of the major 

sectors. That is, the IMPLAN production functions from an appropriately chosen modeling 

region comprising 31 south Louisiana parishes were used for the first five major sectors for each 

Activity Function to allocate to other sectors that fed into these major sectors. This method 

ensured that the allocation vectors for the major sectors, which originally were restricted to a 

subset of 40 major sectors, would now include more sectors based on the input-output 

relationships. The final vectors used in MAG-PLAN typically include between 40 and 60 total 

sectors (of which about 10 are major and the rest non-major). 
MAG-PLAN Study Summary 

xxi

Determining Onshore Area Distributions for GOM Goods and Services Spending 

Two major data sources were used for the onshore area distributions. These were: 

Gulf Coast Oil Directory (GCOD) – an electronic database of companies that serve the oil and 

gas industry in the Gulf coast. The GCOD was used to determine the onshore area distributions 

of the major sectors discussed above using a detailed process of matching the GCOD sector 

definitions with the IMPLAN sector definitions. 

County Business Patterns (CBP) – for the non-major sectors identified as relevant for each 

delineated Activity Function, the Census Bureau’s CBP data for 2002 were used to estimate the 

onshore area distributions of the economic sectors that contribute to the offshore oil and gas 

activities in the Gulf. 

Determining Onshore Area Labor Income Distributions 

The onshore area distributions for the labor share of the total expenditures for each Activity 

Function were determined using zip code data from the Labor Needs Survey (LNS) on GOM 

worker residence and place of work. LNS also provided data on GOM worker income 

distribution that were used to allocate the total labor expenditures by income categories. 

Personal Consumption Expenditure (PCE) data from IMPLAN (by income category) were then 

used to allocate these labor shares to various IMPLAN sectors. 

Modeling Revenue Data

Three main revenue streams were modeled in MAG-PLAN based on the E&D scenario data. 

These were: 

• Total Royalty Revenue 

• Total Acres Leased (multiplied by rental rates) 

• Bonus Bid Amount 

All three revenue categories also included separate revenue streams generated from the 8(g) 

zone, for a total of six revenue streams. The allocation of the revenue streams to various 

expenditure sources was done based on various revenue parameters, such as rental and royalty 

rates, at various water depth categories. 

Alaska Offshore Modeling Region 

The Alaska modeling region in MAG-PLAN was divided into three sub-regions, each of which 

was further sub-divided into multiple offshore modeling areas (OMA’s): 

• Arctic Modeling Areas – consists of two OMA’s – Beaufort Sea8

 and Chukchi-Hope 

Basin. 

 

8

 Also includes a separate sub-model, or combination of data and model settings, for the Beaufort Local Gas 

impacts. 
MAG-PLAN Study Summary 

xxii 

• South Alaska Modeling Areas – consists of three OMA’s – Cook Inlet/Shelikof Strait, 

Semi-Remote planning areas, and Remote planning areas. 

• Bering Sea Modeling Areas – consists of three OMA’s – Southeastern Bering, North 

Bering and Western Bering. 

An OMA is an OCS planning area, or group of planning areas, for which the same data and 

settings are used in a model run. For the Alaska region, MMS made the decision that scarce 

research dollars should not be expanded to create a sub-model/OMA for each possible leasing 

area, given data scarcity and the uncertainty that any particular frontier area would be selected 

for in-depth analysis. MAG-PLAN provides the flexibility to use the same approximate data for 

initial runs for similar areas, then to utilize more accurate data for future runs, as industry 

becomes more interested in a particular frontier planning area and as more applicable data 

become available. 

MAG-PLAN provides the capability to import Alaska E&D scenarios directly into the model, 

using pre-determined templates. MAG-PLAN treats the E&D scenario variables as Activity 

Types (and Revenue Types) and maps them to units of Activity Functions (and Revenue 

Functions). Then, estimates of capital assets, labor, and materials needed for a unit of an 

Activity Function are used to calculate the direct expenditures needed for a particular run. 

Activity Function Equations and Parameters 

Because the Alaska OMA data were developed under the ABC-Detailed approach (see 

MAG-PLAN Model Overview), each Alaska OMA Activity Function comprises 

cost/expenditure data and equations, as opposed to the average costs/expenditures used for GOM 

OMA Activity Functions. 

MAG-PLAN provides an Activity Function Equation Editor where the user can define or modify 

detailed equations for an Activity Function with a user-friendly (point-and-click) editor window. 

These equations can be defined as functions of Activity Types, other Activity Functions, Activity 

Function Parameters, and other pre-defined functions. 

ABC-Detailed Data – for Arctic Modeling Sub-Region 

There are three basic default settings, with accompanying data, in MAG-PLAN for the Arctic 

sub-region: 

• Default Beaufort – covers OCS oil-related activities in the Beaufort Sea and the 

associated regional economic impacts in the North Slope Borough and other OSA’s. 

• Default Beaufort – Local Gas – covers activities related to natural gas resources 

developed for local use and the associated regional economic impacts in the local areas 

and other OSA’s. 

• Default Chukchi-Hope – covers OCS oil-related activities in the Chukchi Sea and Hope 

Basin Planning Areas and the associated regional economic impacts in the North Slope 

Borough, Northwest Arctic Borough, and other OSA’s. 
MAG-PLAN Study Summary 

xxiii

There are default data and settings for each OMA in the delivered version of MAG-PLAN. 

Data for the Arctic modeling areas primarily came from the Arctic IMPAK model 

documentation.9

 These included data on capital assets, materials and labor expenditures. 

Capital Assets 

Data on capital assets needed per unit of an Activity Function were estimated from the various 

tables in the IMPAK report. The original IMPAK data used the 2000-IMPLAN sector 

definitions (based on the Standard Industrial Classification), whereas MAG-PLAN uses 2001 

definitions (based on the North American Industrial Classification System). As a result, the 

sector definitions in the original data were updated using a pre-determined crosswalk table 

between the two sets of classification schemes. 

Labor 

Labor data used in MAG-PLAN came from the IMPAK model documentation and were broken 

down into four different labor categories: 

• Production labor – for the actual labor categories working on the fields 

• Non-production labor – for the office and headquarters personnel required for each 

Activity Function 

• Overhead costs for labor – for the overhead expenditures required for the non-local labor 

force 

• Purchased Service employee compensation – for the expenditures on employee 

compensation for the purchased service sector inputs (see Chapter 6 of the Arctic IMPAK 

report).

Material 

Data on materials needed for the different Activity Functions also came from the IMPAK report. 

The fuel use data required special treatment in MAG-PLAN, because fuel expenditures were not 

sensitive to huge differences in fuel price assumptions used in MMS sensitivity analyses. 

Therefore, the total expenditures on fuel use are calculated in MAG-PLAN using a pre-defined 

(but modifiable) algorithm based on the crude oil price and parameters entered into the model by 

the user. Because the product prices are calculated in MAG-PLAN as model-generated inputs, 

this ensures that a uniform method is used to estimate the onshore area impacts of the fuel used 

across all modeling areas. 

 

9 Arctic Economic Impact Model for Petroleum Activities in Alaska (Arctic IMPAK) OCS Study MMS 2002-066, 

conducted by Jack Faucett Associates under contract. 
MAG-PLAN Study Summary 

xxiv 

Modeling Revenue Types for Alaska Modeling Region 

Five main Revenue Types10 were modeled in the delivered version of MAG-PLAN, based on the 

current Alaska E&D scenarios. These were: 

• Total Oil Production 

• Total Gas Production 

• Acres Leased 

• Bonus Bid 

• TAPS Surcharge 

Of these, the first four also include separate revenue streams for the 8(g) zone, for a total of nine 

separate revenue types. MAG-PLAN provides a separate Revenue Definition Equation Editor to 

specify equations for the Revenue Functions, using various revenue-related parameters. Note 

that the Revenue Functions modeled for the three Alaska modeling regions are identical (except 

TAPS Surcharge, which is only used for the Arctic modeling region). 

Onshore Areas 

Following is a list of Arctic onshore areas for which impacts are estimated in the delivered 

version of MAG-PLAN: 

Beaufort Modeling Area North Slope Borough 

 Other – Alaska – Beaufort 

 Other – U.S. 

Chukchi/Hope Modeling Area North Slope Borough 

 Northwest Arctic Borough 

 Other – Alaska – Beaufort 

 Other – U.S. 

See Section 3.5 of the text for information on three other onshore areas common to all Alaska 

OMA’s: Alaska, undesignated U.S., and Foreign. 

ABC-Detailed Data – for South Alaska Modeling Sub-Region 

There are three basic default settings, with accompanying data, in MAG-PLAN for the South 

Alaska OMA’s: 

• Default Cook Inlet – covers exploration and development activities in the Cook 

Inlet/Shelikof Strait Planning Area and the associated regional economic impacts in the 

Kenai Peninsula Borough and other OSA’s. 

 

10 As explained above, Revenue Types are equivalent to Activity Types, except that they drive levels of new 

government revenue, as opposed to driving direct spending on goods and services. “Revenue” refers to what the 

activity produces in the model, not to the type of activity so labeled. 
MAG-PLAN Study Summary 

xxv

• Default Semi-Remote – used to model activities in other South Alaska planning areas that 

have nearby population centers that can supply labor, assets, and other materials and 

services. This delivered version of MAG-PLAN (and/or its associated data) is considered 

appropriate for the Gulf of Alaska Planning Area and the associated regional economic 

impacts in the Kenai Peninsula Borough, other local areas, and other OSA’s. 

• Default Remote – used to model activities in other South Alaska planning areas that have 

no nearby population centers of any significance for measuring economic impacts. This 

delivered version of MAG-PLAN (and/or its associated data) is considered appropriate 

for the other planning areas (Kodiak, Shumagin, and Aleutian Arc) and the associated 

regional economic impacts in the Kenai Peninsula Borough, other local areas, and other 

OSA’s. 

There is a sample run for each OMA in the delivered version of MAG-PLAN. Data for the 

South Alaska Modeling Sub-region were obtained partly from the Sub-Arctic IMPAK 

documentation11 and partly from the Microsoft Excel-based Sub-Arctic IMPAK model.12 

Because of the way data are presented in the Sub-Arctic IMPAK model and the needs for MAGPLAN, total expenditures per unit of each Activity Function were calculated using data from the 

input matrices in the model. This was done separately for the three OMA’s in South Alaska 

noted above. These total expenditure vectors were then updated to reflect current conditions 

based on the knowledge of industry analysts. These expenditures were then broken down in two 

steps – first the labor shares of the total expenditures were subtracted and modeled under ABCLabor with the same level of detail as the Arctic data discussed above. Second, the residual nonlabor shares were further broken down into expenditures on assets and materials, by IMPLAN 

sector, in order to conform to the modeling structure in MAG-PLAN. IMPLAN sector 

allocations were then updated to the 2001 definitions using the crosswalk table mentioned above. 

Onshore Areas 

Following is a list of South Alaska onshore areas for which impacts are estimated in the 

delivered version of MAG-PLAN: 

Cook Inlet/Shelikof Strait Modeling Area Kenai Peninsula Borough 

 Other – Alaska – Cook 

 Other – U.S. 

SemiRemote Modeling Area Local (user determined) 

Kenai Peninsula Borough 

 Other – Alaska – Cook 

 Other – U.S. 

 

11 Sub-Arctic Economic Impact Model for Petroleum Activities in Alaska (Sub-Arctic IMPAK) OCS Study MMS 

2002-060, conducted by Jack Faucett Associates under contract. 

12 Because the level of detail provided in the Sub-Arctic IMPAK model documentation was not the same as that in 

the Arctic IMPAK documentation, ICF supplemented this with data from the model where needed. 
MAG-PLAN Study Summary 

xxvi 

Remote Modeling Area Local (user determined) 

Kenai Peninsula Borough 

 Other – Alaska – Cook 

 Other – U.S. 

See Section 3.5 of the text for information on three other onshore areas common to all Alaska 

OMA’s: Alaska, undesignated U.S., and Foreign. 

ABC-Detailed Data – for Bering Sea Modeling Sub-Region 

In previous economic impact models used by MMS, the Bering Sea planning areas were part of 

the Sub-Arctic Modeling Sub-Region. However, MAG-PLAN provides an independent Bering 

Sea sub-region with its own modeling areas and cost functions. Because of this new treatment, a 

considerable amount of effort was spent on defining appropriate OMA’s and Activity Functions 

for leasing, exploration, development, production, and transportation for the Bering Sea 

modeling areas, with a focus on the North Aleutian Basin (NAB) Planning Area.13

Three OMA’s were created in MAG-PLAN for this modeling sub-region: 

• Southeastern Bering Sea – consisting of North Aleutian Basin and (shallow water) St. 

George Basin 

• North Bering Sea – consisting of Norton Basin, St. Matthew Hall, and (shallow water) 

Navarin Basin 

• Western Bering Deepwater – consisting of Aleutian Basin, Bowers Basin, (deep water) 

Navarin Basin, and (deep water) St. George Basin. 

Most of the Activity Functions modeled for these OMA’s were derived from the Cook Inlet 

OMA under South Alaska Modeling Sub-Region (for the first and third Bering Sea OMA’s), and 

the Beaufort OMA (for the second Bering Sea OMA). However, the cost functions were 

appropriately adjusted to account for the lack of E&D infrastructure and the relative remoteness 

of this region. This included adjusting the total expenditures as well as the IMPLAN sector 

allocations, where applicable. The rest of the data work involved steps similar to the work 

described for the South Alaska Modeling Sub-Region above. 

 

13 ICF Consulting was directed to identify one planning area in the Bering Sea sub-region from which to extrapolate 

industry activities and expenditures to similar activities in the other six Bering Sea planning areas. Although it is 

not now available for leasing, the North Aleutian Basin (NAB) was selected as the best Bering Sea prototype 

because of data availability and the assumed existence of both oil and gas in commercial quantities. Development of 

NAB data for MAG-PLAN is solely to provide the most “typical” and realistic industry production functions 

possible for Bering Sea activity, and does not imply any judgment as to whether the Federal government should, or 

will, consider NAB for leasing in the future. 
MAG-PLAN Study Summary 

xxvii

Onshore Areas 

Following is a list of Bering Sea onshore areas for which impacts are estimated in the delivered 

version of MAG-PLAN: 

Southeastern Bering Modeling Area Dillingham 

 Other – Alaska – NAB 

 Other – U.S. 

North Bering Modeling Area Nome 

Bethel 

 Other – Alaska – NAB 

 Other – U.S. 

Western Bering DW Modeling Areas Kenai Peninsula Borough 

Aleutians West 

 Other – Alaska – NAB 

 Other – U.S. 

See Section 3.5 of the text for information on three other onshore areas common to all Alaska 

OMA’s: Alaska, undesignated U.S., and Foreign. 

4 Output 

Output is obtained in two steps. First, before running the model, the user decides the output to 

be produced. MAG-PLAN can produce just the Step I results, which are spending totals for each 

OSA, by economic sector, or the (the usual) Step II results, based on the user-specified multiplier 

types (direct, indirect, induced, and total) and categories (total economic output, employment, 

etc.). This creates a temporary Access table with all the results within MAG-PLAN, as well as a 

comma separated value (CSV) output file that is saved outside MAG-PLAN (to conserve 

memory). Second, the user opens an Excel pivot table template and indicates the location of the 

output file with the results. The pivot table then creates summary data tables so that the user can 

view the results by OSA, by multiplier category, or by year of analysis. 

5 Built-in Flexibility for Future Uses 

MAG-PLAN is designed in such a way that it can be revised and/or expanded to incorporate new 

data and to be used in ways not currently foreseen. As explained above, the user can import new 

data that is revised periodically, such as IMPLAN data and GDP deflator data. However, the 

user can also create new Activity Types and Functions for other industries. For example, MMS 

reviews Coast Guard analyses of proposals for new offshore liquefied natural gas (LNG) 

terminals, and the recently enacted Energy Policy Act of 2005 gives MMS certain 

responsibilities to oversee other alternative energy developments offshore. With the appropriate 

cost function data, MAG-PLAN could be expanded for use in estimating onshore economic 

effects of such facilities as a new LNG terminal or a new wind farm. 
MAG-PLAN Study Summary 

xxviii 

Therefore, MAG-PLAN’s flexible structure allows MMS users to continue a low-maintenance 

usage pattern -- updating only data readily available from other sources, to create incremental or 

basic updates of Activity Types and cost function data, and to create new uses for the model, all 

without changing the software structure. 
MAG-PLAN Study Introduction 

1

1 Introduction 

The Minerals Management Service (MMS) of the U.S. Department of the Interior has regulatory 

oversight of all minerals developments in the federal offshore regions. Part of this responsibility 

is the evaluation of the environmental impacts of oil and gas activities in those regions, both on 

the offshore area itself and on the coastal regions. A component of MMS’ environmental 

analysis is the evaluation of the potential onshore socio-economic impacts. These 

responsibilities are in part pursuant to the amended Outer Continental Shelf Lands Act (OCSLA) 

and the National Environmental Policy Act (NEPA). To perform these activities, MMS requires, 

and has used, an array of existing OCS Economic Impact Models (EIM) to estimate the 

additional employment, personal income, and other impacts anticipated to result from the 

different alternatives formally considered by the decision makers. 

The purpose of this study was to develop a new, unified OCS EIM structure, along with new data 

and accompanying documentation. Like the existing OCS EIM, the new model, named MAGPLAN (MMS Alaska-GOM Model Using IMPLAN), allows MMS to include regional economic 

impact estimates in its various analyses, including: 

• 5-Year OCS Oil and Gas Leasing Program Decision Documents 

• 5-Year OCS Oil and Gas Leasing Program EIS’s 

• Multi-Sale EIS’s 

• Lease Sale EIS’s 

Prior to the development of MAG-PLAN, when MMS prepared a new 5-year OCS oil and gas 

program or an EIS, at least one of the OCS EIM was used to estimate the additional employment, 

personal income, etc., anticipated from these proposals. MMS had developed a set of models to 

estimate the economic impacts of activities in the Gulf of Mexico (GOM) and the Alaska (AK) 

OCS using a theoretically consistent approach across these areas. However, because of the 

differences in the two OCS regions and the fact that these models were developed independent of 

each other, there were differences in terms of detail and software needs for the models. Because 

of rapid technological growth and the movement of exploration and operations in even-deeper 

waters, the GOM models, based on data for 1994-2000, were outdated. On the other hand, the 

Alaska models were more recent but there was room for improvement, particularly in respect to 

the areas more remote than the Beaufort Sea and the Cook Inlet. 

For the first time, MMS has the option of using the same modeling framework for both the GOM 

and the AK OCS regions using MAG-PLAN.14 MAG-PLAN uses a uniform modeling structure 

for estimating these regional economic impacts irrespective of the planning area. Stage I of the 

model uses different E&D scenarios as the basic input to estimate the direct expenditures 

required for offshore oil and gas activities, then Stage II uses IMPLAN multipliers with the 

 

14 MAG-PLAN does not include any data, or default runs, for the Pacific OCS Region, the Atlantic OCS Region, or 

the Straits of Florida Planning Area in the GOM OCS Region. Unless current prohibitions are lifted, the Pacific and 

Atlantic regions cannot be considered for leasing, and MMS does not expect to consider any Straits of Florida 

leasing proposals in the foreseeable future. 
MAG-PLAN Study Introduction 

2 

direct expenditure estimates to get indirect and induced expenditures.15 MAG-PLAN has been 

developed on the Microsoft Access platform with user-friendly forms to enter and manipulate 

data and create new runs. The Access database that houses data for all modeling regions does 

not require any user manipulation, thus making the model more user-friendly and relatively easy 

to learn. Users can work in the familiar Windows environment to add, delete, or otherwise edit 

data on the forms without having to worry about the relational structure in the database. More 

advanced users can, however, change the database or the queries that form the core of 

MAG-PLAN. 

Moreover, MAG-PLAN now provides MMS an integrated framework for using the IMPLAN 

multipliers to estimate the regional macro-level socioeconomic impacts in one model. Users of 

MAG-PLAN can now undertake both stages of the entire modeling exercise within the framework of one model. This previously was not true for the Alaska OCS EIM. MAG-PLAN also 

uses the disaggregated full multiplier matrices from IMPLAN in this integrated framework to 

provide results that are more accurate and easily defensible.16

This study report discusses the data and methods used in MAG-PLAN for the different modeling 

areas. A separate User Manual, to be employed as a “living document” by MMS staff members, 

provides detailed instructions on how to use the model. This report begins with a summary of 

the work performed under this contract to provide a synopsis of the model as well as the data 

collected for the model. Chapter 1 then provides an introduction to this report. Discussion of the 

Gulf of Mexico data collection effort undertaken as part of this study is then discussed in chapter 

2. A major part of this work involved updating the GOM cost function data to better reflect the 

current technological and economic conditions. Chapter 3 explains the changes that were 

implemented for the Arctic modeling data in the Arctic IMPAK17 in order to make it compatible 

with this integrated framework. Chapter 4 focuses on the work performed for the OMA’s 

covering the South Alaska planning areas. The definition of this sub-region of the Alaska OCS 

is different from the previous MMS definition used for the Sub-Arctic IMPAK18. An additional 

feature in MAG-PLAN is the explicit modeling for the planning areas under the Bering Sea SubRegion in Alaska, and chapter 5 discusses the data and methods used for this sub-region. As is 

true with any modeling effort, MAG-PLAN Version 2.0 is the first complete version of this new 

model with possibly significant room for improvements and upgrades. Because of the flexibility 

of this new modeling framework, MMS can add several new elements to the runs with limited 

effort, and chapter 6 provides suggestions for some possible enhancements. Finally, this report 

ends with four appendices that provide the reader with more technical information used in 

developing MAG-PLAN. 

 

15 See discussion on page 3 and in footnote 3, above, for an explanation of direct, indirect, and induced spending 

estimates. 

16 Previous MMS models used the aggregated multiplier results from IMPLAN instead of the full multiplier 

matrices. This simplification masked the finer details of the inter-industry contributions because it only uses the 

aggregated sector impacts for those sectors that show changes in final demands. MAG-PLAN instead uses the 

disaggregated multiplier matrices and thus the full, disaggregated sector impacts are estimated in MAG-PLAN, 

which accurately captures the inter-industry linkages. 

17 Arctic Economic Impact Model for Petroleum Activities in Alaska (Arctic IMPAK) OCS Study MMS 2002-066. 

18 Sub-Arctic Economic Impact Model for Petroleum Activities in Alaska (Sub-Arctic IMPAK) OCS Study MMS 

2002-060. 
MAG-PLAN Study Gulf of Mexico 

3

2 Gulf of Mexico 

2.1 Default Settings in MAG-PLAN 

MAG-PLAN was delivered with data and settings for a default run for each OMA. There are 

three default GOM settings in MAG-PLAN: 

• Default CGOM – covers the Central GOM Planning Area. 

• Default WGOM – covers the Western GOM Planning Area. 

• Default EGOM – covers the Eastern GOM Planning Area. 

2.2 Activity Functions 

This chapter discusses the activity- and revenue-based data used in the Gulf of Mexico (GOM) 

portion of MAG-PLAN. The model is designed to capture the regional economic impacts of oil 

and gas activities on the Outer Continental Shelf (OCS), and its structure requires an Exploration 

and Development (E&D) scenario as input. The E&D scenario data can be divided into two 

broad categories: activities that result in exploration and development for oil and gas and the 

revenue generated by these activities (or measures used to calculate/assess such revenues, e.g., 

Acres Leased, from which rentals are calculated). Both the activity and revenue data 

incorporated into the model are described in this chapter. Below are the GOM E&D scenario 

variables important to both activity and revenue functions in MAG-PLAN. Oil Production and 

Gas Production are relevant to both, while the others are mutually exclusive. See chapter 3 for 

the list of variables applicable to the Alaska OMA’s. 

E&D Scenario Variables (Activity Types) Important to Activity Functions19: 

• Exploratory Wells Drilled 

• Production Wells Drilled 

• Non-Production Wells Drilled 

• Platforms Added 

• Platforms Removed 

• Platforms Removed with Explosives 

• Pipeline Miles Added 

• New Gas Processing Facilities 

• Workovers 

• Oil Spilled, Not Reaching Shore 

 

19 Activity Functions are the data and formulas used to estimate the expenditures associated with one unit of a 

specific Activity Type from the E&D scenario, along with the allocation of that spending among industry sectors in 

each applicable onshore area. E.g., the Activity Function for one 10,000-foot Exploratory Well Drilled in 60-200 

meters of water represents, on average, how much industry spends to drill the well and where the money goes (or, 

put another way, which onshore area sectors supply how much of the goods, services, and labor necessary to drill 

each such well). Activity Functions are the core units in MAG-PLAN. Conceptually, Revenue Functions are very 

similar, in that they are used to calculate all government revenue streams in MAG-PLAN. Revenue Functions are 

driven by the Revenue Type variables in the E&D scenario. 
MAG-PLAN Study Gulf of Mexico 

4 

• Oil Spilled, Reaching Shore 

• Oil Production 

• Gas Production 

E&D Scenario Variables (Revenue Types) Important to Revenue Functions: 

• Oil Production 

• Gas Production 

• Total Royalty Revenue 

• Total Royalty Revenue from 8(g) Zone Production 

• Oil Production from 8(g) Zone 

• Gas Production from 8(g) Zone 

• Acres Leased 

• Acres Leased in 8(g) Zone 

• Bonus Bid Amount 

• Bonus Bid Amount for 8(g) Zone 

To begin the modeling process, the E&D scenario will be populated by MMS and imported into 

MAG-PLAN. MAG-PLAN will take the populated E&D scenario and estimate a set of direct 

expenditures, referred to as Stage I outputs. These direct expenditures are the direct dollar 

impacts on different economic sectors in different onshore areas (OSA’s).20 Direct expenditures 

are determined by the location (or OMA) of the activity, the value of the associated variable in 

the E&D scenario (or the units of each activity), and by the activity’s distance from shore, water 

depth, and well depth (through its impact on the cost per unit of the activity). Four types of data 

are needed to create the first part of the direct expenditure matrix: 

1. The total cost of a particular activity, 

2. The labor percentage of the activity’s total cost, 

3. The allocation of each activity’s non-labor spending to IMPLAN sectors, and 

4. The distribution of each IMPLAN sector’s non-labor spending to OSAs. 

Other data needed in the MAG-PLAN direct expenditure matrix to capture the regional 

economic impacts are: 

5. Each activity’s labor cost to OSA distribution, 

6. Each OSA’s income distribution, 

7. Each OSA’s personal consumption expenditure distribution, and 

8. The revenue created from various taxes, bonus bids, and royalties. 

Note that although there are three offshore modeling areas (OMA’s) in MAG-PLAN for the 

GOM OCS region, the default settings assume no variations in total expenditures, labor shares, 

IMPLAN sector allocations, or onshore area distributions among the three runs. This 

simplifying assumption had to be used because of the lack of adequate data to make distinctions 

among the 

 

20 Onshore areas are groups of counties and parishes to which the onshore impacts are mapped in MAG-PLAN. 

Examples include FL-1 (Florida-1, encompassing seven coastal Florida counties), FL-2, LA-1, TX-1, AL-1, etc. 

See Section 2.5 for more details including a full list of the OSAs used in the default runs. 
MAG-PLAN Study Gulf of Mexico 

5

OMA’s. This chapter discusses the data that are applicable across all three OMA’s. Section 2.5 

below explains steps that can be used to change this assumption and customize the modeling for 

the three OMA’s in the future. 

Table 2-1 below displays the Activity Type E&D scenario variables and the Activity Functions 

they stimulate. 

Table 2-1: Activity Function Codes and E&D Scenario Mapping 

Activity Type in E&D Scenario Activity Function Total Cost Unit

Exploratory Wells Drilled Exploratory Drilling $/Well 

Non-Production Wells Drilled Development Drilling $/Well 

Production Wells Drilled Development Drilling and 

Completion $/Well 

Production Wells Drilled Production Operations and 

Maintenance $/Well 

Workovers Workover $/Well 

Platforms Added $/Platform (Average) 

Platforms Added $/Fixed Platform 

Platforms Added $/Compliant Tower 

Platforms Added $/Tension Leg Platform 

Platforms Added $/Spar 

Platforms Added 

Platform Fabrication and Installation 

$/FPSO 

New Gas Processing Facilities Onshore Gas Processing 

Construction 

$/Facility (per Bcf/day 

capacity) 

Gas Production (Bcf) Onshore Gas Processing O&M $/Facility per Bcf 

New Gas Processing Facilities Offshore Gas Processing 

Construction 

$/Facility (per Bcf/day 

capacity) 

Gas Production (Bcf) Offshore Gas Processing O&M $/Facility per Bcf 

Pipeline Miles Added Pipeline Construction $/Mile 

Pipeline Miles Added Pipeline Operations and 

Maintenance $/Mile 

Platforms Removed Decommission Platform $/Platform (Average) 

Platforms Removed with 

Explosives 

Decommission Platform with 

Explosives $/Platform (Average) 

Oil Spilled, Reaching Shore Onshore Oil Spill $/Gallon 

Oil Spilled, Not Reaching Shore Offshore Oil Spill $/Gallon 

Source: MMS E&D scenario data and MAG-PLAN assumptions. 
MAG-PLAN Study Gulf of Mexico 

6 

The following sections explain how the total expenditure, labor percentage, and economic sector 

allocation were derived for each activity used in the default runs. 

2.3 Total Expenditures and Labor Percentages 

This section describes the sources used, analyses performed, and major assumptions used in 

developing the activity-based total expenditure21 and labor percentage estimates. To begin the 

process, ICF Consulting performed an exhaustive literature search that included industry trade 

journals, MMS reports and analyses, academic research papers, and Internet-based research. 

Multiple sources were identified as crucial to the development of the final figures. The 

American Petroleum Institute’s (API) Joint Association Survey (JAS), the Energy Information 

Administration’s (EIA) Costs and Indices for Domestic Oil and Gas Field Equipment and 

Production Operations Database, and the Minerals Management Service’s Labor Needs Survey

(LNS) provided detailed data that were combined to populate the Activity Function data matrix. 

For example, EIA’s Costs and Indices data provided a detailed breakdown of annual operating 

costs for 18-slot oil and gas production platforms in the GOM at 100, 300, and 600 feet of water 

depth during the past 17 years. It provided the same information for 12-slot platforms in 100 and 

300 feet of water. It also provided annual labor and workover costs, along with a few other 

major accounting-level costs. 

The JAS data provided drilling costs by well depth and shallow-water depth categories. The JAS 

data did not accurately reflect the drilling categories required by MMS and MAG-PLAN, but a 

detailed analysis was performed on this data to make them fit using well-backed assumptions. 

Appendix A includes a detailed description of the analysis performed on these data. 

ICF Consulting contacted industry representatives to obtain data to supplement the data for some 

of the MAG-PLAN categories. Many of the data needed for MAG-PLAN are considered 

proprietary and the sources contacted were not willing to provide the figures outright. As a 

result, feedback from the industry was limited and it was agreed that a different approach was 

needed to supplement the initial industry feedback. A data-weighting method was developed to 

populate an initial set of values for industry analysts to view and offer their opinions. 

The weighting system was developed by ICF Consulting to serve three purposes: to allow the 

use of the Labor Needs Survey data with less than three point estimates, to help blind the limited 

proprietary data received from industry contacts, and to correctly estimate data points using a 

measure of confidence in the data as a factor. Each data set received an estimation of 

confidence. These weights were used to determine the initial data point. For example, when 

estimating the total cost data for production operations and maintenance, measures of confidence 

were assigned to LNS, Coastal Marine Institute (CMI)22, EIA Cost and Indices, and various 

 

21 Throughout this report, total expenditure and total cost will be used synonymously to express the dollar 

expenditures for a unit of an Activity Function. 

22 This MMS-sponsored study provided a logical starting point for several of the data elements needed for MAGPLAN. In particular, this study was helpful in providing the starting point for some of the total expenditure and 
MAG-PLAN Study Gulf of Mexico 

7

literature search data to estimate the initial data points by MAG-PLAN’s water and well depth 

categories. The weights will not be reported due to the sensitivity of the various data sources 

involved; namely, the LNS and the limited responses from industry analysts. 

After the initial total expenditure and labor percentage data were derived using the weighting 

system on the researched data from various sources and in-house analyses, some of the data 

points were either underrepresented or not represented at all. ICF Consulting formed a 

committee of team members who are experienced in the offshore oil and gas industry to identify 

alternative sources to populate the missing values. Some assumptions had to be made to fully 

populate the values, and some speculation had to be made in the ultra-deep water depth 

categories where activities are known to be nonexistent (such as pipeline and decommissioning 

costs in greater than 2,400 meters of water). One alternative source identified for various total 

costs was a Gulf of Mexico production model developed by ICF Consulting for the U.S. 

Department of Energy’s Energy Information Administration (EIA) in 2003. This proved to be a 

useful source of data for exploratory and development drilling, drilling completion, production 

O&M, various types of platform fabrication and installation, and pipeline construction. The data 

from the ICF’s Offshore Model were based on engineering cost data for deepwater obtained from 

MMS and were updated and revised using industry sources on deepwater costs available in 

industry trade publications. 

Exploratory Drilling and Development Drilling 

Special consideration was given to the Exploratory and Development Drilling Activity 

Functions’ total cost data due to the availability of the JAS data. Appendix A explains the 

regressions run on the JAS data and how the shallower water depth categories were populated. 

For reasons explained in Appendix A, the JAS data were used to estimate the total expenditures 

for the shallow water depth categories. These included depths of 0-60, 60-200, 200-400, and 

400-800 meters. The regression model provided the cost estimates in 2002 dollars. These 

estimates are given in table A-5 in appendix A. These were then scaled to 2004 dollars and used 

in MAG-PLAN (using 1.03 as the GDP deflator to scale costs up). Final estimates used in 

MAG-PLAN for the shallow water depth categories are given in table 2-2.23

 

IMPLAN sector allocations, as discussed below. For more details, see Dismukes, D.E, et al. Modeling the 

Economic Impacts of Offshore Oil and Gas Activities in the Gulf of Mexico: Methods and Applications. Prepared 

by the Center for Energy Studies, Louisiana State University, Baton Rouge, LA. U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study MMS 2003-018 

(henceforth referred to as the CMI Cost Function Study). 

23 Water depth categories used in table 2-2 and others below show the same cost estimates (and labor shares) for the 

200-400 meters and 400-800 meters categories. This is because originally these estimates were developed for 200-

800 meters water depth category. However, MMS water depth categories changed after the study was well 

underway and the 200-800 meters was broken down into two smaller categories: 200-400 meters and 400-800 

meters. ICF Consulting assumed the same costs and labor percentages for both these categories. 
MAG-PLAN Study Gulf of Mexico 

8 

Table 2-2: Exploratory Drilling – Expenditures per Well (in thousands) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $9,426 (40%) $17,702 (40%) $26,296 (40%) 

60-200 Meters $11,537 (40%) $21,437 (40%) $33,758 (40%) 

200-400 Meters $15,916 (40%) $26,008 (40%) $40,355 (40%) 

400-800 Meters $15,916 (40%) $26,008 (40%) $40,355 (40%) 

800-1,600 Meters $16,712 (35%) $27,309 (35%) $42,372 (35%) 

1,600-2,400 Meters $17,547 (35%) $28,674 (35%) $44,491 (35%) 

2,400+ Meters $18,425 (35%) $30,108 (35%) $46,716 (35%) 

Sources: Total Expenditure values based on Joint Association Survey and ICF calculations. 

Labor Share values estimated based on Schlumberger (2004). 

Deepwater total expenditures (i.e., for water depths beyond 800 meters) were increased by 5 

percent for each water depth category after the 400-800 meter water depth category. Data 

suggested that most well costs reached a maximum around $40 million in the deeper waters. 

Some of the data revealed very expensive exploratory wells in excess of $60 million, but ICF 

Consulting determined that these data were outliers due to the extreme well depths in which they 

were drilled. The EIA Offshore model was also consulted when determining this cost escalation 

factor. 

Total expenditure and labor share estimates used for Development Drilling Activity Function in 

MAG-PLAN are shown in table 2-3 below. Again, the regression model estimates from the JAS 

data were used for the shallower water depth categories. The actual model estimated costs are 

shown in table A-5 in appendix A. These were then scaled to 2004 dollars and are presented in 

table 2-3. 

Table 2-3: Development Drilling – Expenditure per Well (in thousands) and Labor Share 

(percent oftotal expenditure, in parentheses) 

Well Depth

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $9,395 (27%) $17,643 (27%) $26,209 (27%) 

60-200 Meters $9,877 (27%) $18,354 (27%) $28,903 (27%) 

200-400 Meters $11,642 (27%) $19,024 (27%) $29,519 (27%) 

400-800 Meters $11,642 (27%) $19,024 (27%) $29,519 (27%) 

800-1,600 Meters $12,224 (22%) $19,976 (22%) $30,994 (22%) 

1,600-2,400 Meters $12,835 (22%) $20,974 (22%) $32,544 (22%) 

2,400+ Meters $13,477 (22%) $22,023 (22%) $34,171 (22%) 

Sources: Total Expenditure values based on Joint Association Survey and ICF calculations. 

Labor Share values estimated based on Schlumberger (2004). 
MAG-PLAN Study Gulf of Mexico 

9

Many data sources were available to determine the initial labor percentage for exploratory and 

development drilling. The CMI Cost Function Study had labor percentage below ten percent for 

both exploratory and development drilling, and the data received from the MMS Labor Needs 

Survey averaged 46 percent over the three drilling projects identified with labor data. Using the 

AFE (Authorization For Expenditure)-type figures in the Schlumberger (2004) report24, we 

estimated that 40 percent of the total costs for exploratory drilling are attributed to labor as are 

27 percent of development drilling costs for wells in water no more than 800 meters deep. The 

estimated labor shares are shown in the tables above in parenthesis. 

Drilling Plus Completion 

An Activity Function was needed to distinguish the cost difference between non-producing and 

producing wells, and completion costs were determined to be the best method for capturing this 

cost difference. Development drilling costs were added to completion costs. The total cost data 

were originally taken from the EIA Offshore Model. However, the completion costs for deeper 

well depths were increased after discussing the expenditures with a representative of a major oil 

and gas exploration and development company. Table 2-4 shows the total expenditure and labor 

share estimates for this Activity Function used in MAG-PLAN for various water and well depth 

categories.25 

Table 2-4: Drilling Plus Completion – Expenditure per Well (in thousands) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $10,195 (27%) $19,843 (27%) $36,209 (20%) 

60-200 Meters $11,077 (27%) $20,854 (27%) $38,903 (20%) 

200-400 Meters $13,342 (27%) $21,924 (27%) $39,519 (20%) 

400-800 Meters $13,342 (27%) $21,924 (27%) $39,519 (20%) 

800-1,600 Meters $14,424 (22%) $23,176 (22%) $40,994 (20%) 

1,600-2,400 Meters $16,635 (22%) $24,774 (22%) $42,544 (20%) 

2,400+ Meters $17,277 (22%) $25,823 (22%) $44,171 (20%) 

Sources: Total Expenditure based on JAS, EIA Offshore Model, and ICF calculations. 

Labor Share based on development drilling values from Schlumberger (2004). 

ICF Consulting was unable to identify any sources for the labor percentage estimate for drilling 

completion. Default values are populated with the same labor percentage used for development 

drilling (except the ultra-deep wells in 18,000+ feet, where drilling completion was assumed to 

be more capital intensive), based on assumptions made by the ICF expert committee. 

 

24 This was a study sponsored by the U.S. Department of Energy’s National Energy Technology Laboratory 

(NETL), to provide current drilling technology and cost benchmarks. 

25 If readers are interested in estimating the completion cost only, they can do so by subtracting the drilling cost from 

table 2-3 from the costs in table 2-4, for corresponding water and well depth categories. 
MAG-PLAN Study Gulf of Mexico 

10 

Production Operations and Maintenance 

Data sources for production O&M total expenditures are prevalent. The EIA Costs and Indices 

data proved useful, as well as the CMI Cost Function Study data. Also, the EIA Offshore model 

had estimations for production O&M total costs. The EIA Costs and Indices production O&M 

cost data were reported on an annual basis by size of platform and water depth. These data also 

included workovers, which were subtracted from the total before comparing them to the CMI 

and EIA Offshore model data. All of the figures were in the same range, with the EIA Offshore 

model providing the highest degree of confidence for the deepwater estimates. Table 2-5 shows 

the estimates used in MAG-PLAN for various water and well depth categories for this Activity 

Function. 

Table 2-5: Production O&M – Expenditure per Production Well (in thousands) and Labor 

Share (percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $188 (33%) $200 (33%) $210 (33%) 

60-200 Meters $188 (36%) $200 (36%) $210 (36%) 

200-400 Meters $188 (30%) $200 (30%) $210 (30%) 

400-800 Meters $188 (30%) $200 (30%) $210 (30%) 

800-1,600 Meters $282 (22%) $301 (22%) $316 (22%) 

1,600-2,400 Meters $282 (15%) $301 (15%) $316 (15%) 

2,400+ Meters $282 (10%) $301 (10%) $316 (10%) 

Sources: Total Expenditure based on ICF calculations using data from the EIA Offshore 

Model and other sources. Labor Share values estimated based on EIA Costs and 

Indices and other sources. 

There were also many sources for labor data in the production O&M activity. The EIA Costs 

and Indices data, CMI Cost Function Study, and LNS were consulted to estimate the labor 

percentages. The CMI data were in the low 20s, whereas the LNS reported 38 percent for 0-60 

meters of water depth and 12 percent for water depths greater than 60 meters. The EIA Costs 

and Indices data, when available, provided the highest degree of confidence for the three 

shallowest water depths, and the LNS data were evaluated on a project-specific level to better 

estimate the deeper water depth categories. These labor shares are provided in parentheses in the 

table above. 

Workovers 

The workover costs were one of the most difficult to populate. EIA Costs and Indices provided 

workover costs on an annual basis, but MAG-PLAN required the data by single workover by 

well. Workovers are performed more frequently in shallow waters than they are on deepwater 

wells due to the age of the wells and the need for stimulation and other workover-type services. 
MAG-PLAN Study Gulf of Mexico 

11

Deepwater workovers are performed less frequently, but are much more expensive than shallow 

water workovers. Due to the differences in costs and labor intensity, the estimates changed 

dramatically as the continental shelf26 gives way to the continental slope, as shown in table 2-6. 

Table 2-6: Workover – Expenditure per Well (in thousands) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $50 (27%) $50 (27%) $50 (27%) 

60-200 Meters $100 (27%) $100 (27%) $100 (27%) 

200-400 Meters $130 (27%) $130 (27%) $130 (27%) 

400-800 Meters $130 (27%) $130 (27%) $130 (27%) 

800-1,600 Meters $5,000 (8%) $5,000 (8%) $5,000 (8%) 

1,600-2,400 Meters $8,500 (5%) $8,500 (5%) $8,500 (5%) 

2,400+ Meters $10,000 (5%) $10,000 (5%) $10,000 (5%) 

Sources: Total Expenditure based on EIA Costs and Indices data and ICF calculations. 

Labor Share values estimated based on CMI Cost Function Study and ICF 

calculations. 

Labor percentage data are also sparse for workover-type activities. The CMI Cost Function 

Study data for workover labor percentage are under 10 percent. Based on its industry 

experience, the expert committee assumed that workovers conducted in deepwater are likely to 

be more capital- and material-intensive due to the use of remote control and robotics, and that the 

CMI data are relevant for the deeper water categories. Shallower water categories require more 

labor and the labor share should be higher. The expert committee determined that development 

drilling is similar in labor intensity to workovers in the shallower waters and used 27 percent for 

categories up to 800 meters of water depth. 

Platform Fabrication and Installation 

Average Platform Fabrication and Installation costs were populated using the CMI data inflated 

to 2004 dollars. Costs for an “average” production platform were nonexistent in the research. 

As a result, the goal at the outset of the project was to offer MMS multiple options for different 

types of production facilities. The deepwater platform fabrication and installation expenditures 

in the EIA Offshore Model were applied because these were updated and calibrated to current 

publicly available literature and reports on deepwater expenditures. Costs were estimated for 

Fixed, Compliant Tower, and Tension Leg Platforms at applicable water depths, with Spars and 

Floating Production, Storage, and Offloading vessels (FPSOs) covering the deepwater 

categories. 

 

26 The continental shelf, not to be confused with the legally defined “Outer Continental Shelf,” is a gently sloping 

submerged plain that is an underwater extension of the (onshore) coastal plain. At the edge of the shelf, there is an 

abrupt change, and water depths increase fairly rapidly as activities move away from shore, and the continental slope 

begins its plunge downward to the depths of the ocean. 
MAG-PLAN Study Gulf of Mexico 

12 

The following are the platform types assumed for different water depths in MAG-PLAN and 

reflected in the total expenditure numbers in table 2-7 below. 

• 0-60 ------- Fixed 

• 60-200 ------- Fixed 

• 200-400 ------- Compliant Tower 

• 400-800 ------- Compliant Tower 

• 800-1,600 ------- TLP 

• 1,600-2,400 ------- Spar 

• 2,400+ ------- Spar 

Table 2-7: Platform F&I – Expenditure per ProductionWell (in thousands) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $10,450 (20%) $10,450 (20%) $10,450 (20%) 

60-200 Meters $47,050 (20%) $47,050 (20%) $47,050 (20%) 

200-400 Meters $190,550 (20%) $190,550 (20%) $190,550 (20%) 

400-800 Meters $190,550 (20%) $190,550 (20%) $190,550 (20%) 

800-1,600 Meters $298,010 (20%) $298,010 (20%) $298,010 (20%) 

1,600-2,400 Meters $370,190 (20%) $370,190 (20%) $370,190 (20%) 

2,400+ Meters $405,040 (20%) $405,040 (20%) $405,040 (20%) 

Sources: Total Expenditure based on CMI Cost Function Study, EIA Offshore Model, and 

ICF calculations. Labor Share values estimated based on Labor Needs Survey. 

Labor costs were assumed to be 20 percent for all platform fabrication and installation Activity 

Functions. The LNS data provided credence to this estimate. 

Offshore Gas Processing Construction 

Natural gas processing is a step required to remove impurities from produced gas as well as to 

extract more valuable hydrocarbon components for separate sale as products or as petrochemical 

feedstocks. Gas dehydration, fractionation, and removal of various other inert or corrosive 

species are performed to reach the required sales specifications for transmission pipelines. 

Offshore gas processing is expected to become more prevalent in the coming years due to the 

advanced technologies in large deepwater production and processing facilities. ICF, with 

assistance from MMS, estimated the amount of gas currently processed offshore as opposed to 

onshore in order to estimate the impact of offshore gas processing facilities in the future using 

MAG-PLAN.27 ICF Consulting estimated a lower bound estimate of about one percent processed 

 

27 Currently, this Activity Function is not used in MAG-PLAN; however, it could be used in the future to estimate 

the additional costs of newer, more advanced, large, deepwater-facilities with sales-quality offshore gas processing 

units. An example of this type of facility is Shell’s Na Kika. The costs for this facility are discussed below. 
MAG-PLAN Study Gulf of Mexico 

13

offshore based on the data analysis. That is, of the gas that is processed from production in the GOM, at 

least one percent is processed to sales specifications offshore. 

There are various levels of gas processing, the highest of which is processing to sales-quality 

gas. The GOADS 2000 database can be used to find a lower bound on the volume of offshore 

gas processed to a sales specification. Gas processing entails dehydrating gas to prevent water 

condensation in pipelines, removal of acid gases such as H2S and CO2 to keep the gas at the 

specified heat content, and removing higher-carbon gases which can be sold at higher prices. 

GOADS 2000 identified gas dehydrators and acid gas removal units, but fractionators and other 

separators that exclude ethane, propane, butane, etc., from natural gas are not explicitly identified 

in GOADS 2000. 

Gas dehydration alone is not indicative of sales-quality natural gas. Produced gas is commonly 

dehydrated offshore to prevent water from accumulating in gathering lines, but the gas is further 

processed onshore with acid gas removal units and fractionators. Roughly one-third of gas 

produced in the Gulf of Mexico federal waters is dehydrated offshore. 

 

Offshore acid gas removal, however, was assumed to be indicative of sales-quality natural gas. 

H2S and CO2 will not condense in gathering lines, so the reason to remove acid gas offshore is 

to create sales-quality gas. Therefore, the total amount of gas passing through an acid gas 

removal unit can be assumed to also have passed through dehydration and fractionation. 

 

Eight acid gas removal units operated in the Gulf of Mexico federal waters in 2000, processing a 

total of 0.047 Tcf of gas. This is 0.95 percent of the total gas production in the Gulf of Mexico 

federal waters. Six of the acid gas removal units were on platforms in 0-60 meters of water, and 

two units were on platforms in 60-200 meters. 

The total cost for a gas processing facility on a Bcf per day capacity was calculated by using the 

costs for Shell’s Na Kika facility, located in the GOM deepwater (6,000 feet of water depth). 

The total cost to fabricate and install the host facility is approximately $420 million (30 percent 

of the total facility cost) and is expected to produce 325 MMcf/day of gas. Dividing these 

figures gives approximately $1.3 billion per Bcf per day, which is the estimate used across all 

water depth categories in MAG-PLAN for this Activity Function. 

The ICF expert committee assumed that 20 percent of this cost was due to labor across all water 

depth categories, based on its industry expertise. 

Offshore Gas Processing Operations and Maintenance 

Operating costs can also be measured using the costs from the Na Kika facility. $25 million in 

annual operating costs for the gas processing facility, with a total of 325 MMcf/day of 

production over 365 days equals approximately 118 Bcf annually. Dividing these figures gave 

approximately $200,000 per Bcf, which is the estimate used across all water depth categories in 

MAG-PLAN for this Activity Function. This seemed reasonable given that one Bcf of gas at $6 

per Mcf (thousand cubic feet) equals $6 million. This places operating costs at just above 3 

percent the value of gas. 
MAG-PLAN Study Gulf of Mexico 

14 

Again, it is assumed that labor percentage was 20 percent across all water depth categories, 

based on assumptions made by the ICF expert committee. 

Onshore Gas Processing Construction 

ICF Consulting used the CMI Cost Function Study’s cost estimate of approximately $146 

million per facility for onshore gas processing construction. This figure seemed reasonable 

because it is about ten times less than the offshore gas processing construction estimate and a 

contact from a large oil and gas E&D company indicated that offshore processing is 

approximately ten times more expensive than onshore processing. 

The labor percentage is estimated at 20 percent across all water depth categories, based on the 

experience of the expert committee. 

Onshore Gas Processing Operations and Maintenance 

Using the offshore cost and the estimation factor from the E&D company representative 

mentioned above, ICF estimated that the cost of onshore gas processing is about $20,000/Bcf and 

this is the figure used in MAG-PLAN across all water depth categories. The CMI figure of 

$41/Bcf seemed low to the expert committee and ICF revised this cost estimate to the above 

figure.28

The CMI Cost Function Study labor percentage for 0-60 meters was 20 percent. CMI data 

showed an increase in labor percentage from 10 percent in the 60-200 meter water depth 

category to 47 percent in the deepest water depth category (900 meters and above). However, 

ICF used the 2002 Economic Census for Industrial Gas Manufacturing in Texas to estimate the 

labor percentage at ten percent across all water depth categories. 

Pipeline Construction 

Pipeline construction data were estimated using the EIA Offshore model and backed by Internet 

research. The CMI Cost Function Study data were similar in the water depth categories 

encompassing 60-900 meters, but much higher for the deepest water depth category. Table 2-8 

shows the total expenditure estimates used in MAG-PLAN. 

The 2002 Economic Census for Offshore Oil and Gas Support Services was used to estimate the 

labor percentage of 32 percent. 

 

28 It is not clear why CMI estimated the cost to be $41/Bcf. One possibility could be that it was a typographical 

error, but the number seems extremely low given ICF’s interaction with the industry. 
MAG-PLAN Study Gulf of Mexico 

15

Table 2-8: Pipeline Construction – Expenditure ($ per mile) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $960 (32%) $960 (32%) $960 (32%) 

60-200 Meters $1,070 (32%) $1,070 (32%) $1,070 (32%) 

200-400 Meters $1,470 (32%) $1,470 (32%) $1,470 (32%) 

400-800 Meters $1,470 (32%) $1,470 (32%) $1,470 (32%) 

800-1,600 Meters $2,000 (32%) $2,000 (32%) $2,000 (32%) 

1,600-2,400 Meters $2,230 (32%) $2,230 (32%) $2,230 (32%) 

2,400+ Meters $2,680 (32%) $2,680 (32%) $2,680 (32%) 

Sources: Total Expenditure based on EIA Offshore model, CMI Cost Function Study, and 

ICF calculations. Labor Share values estimated based on Economic Census (2002).

Pipeline Operations and Maintenance 

The pipeline O&M costs in the CMI report seemed very low on a per Bcf basis, but ICF 

Consulting did not identify any additional sources for these data. Table 2-9 shows the estimates 

used in MAG-PLAN based on the CMI Cost Function Study data. The labor percentage for 

pipeline O&M was estimated to be 32 percent for all water and well depths. This figure was an 

average offered by a pipeline company representative. 

Table 2-9: Pipeline O&M – Expenditure ($ per Bcf per year) and Labor Share 

(percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $59 (32%) $59 (32%) $59 (32%) 

60-200 Meters $73 (32%) $73 (32%) $73 (32%) 

200-400 Meters $75 (32%) $75 (32%) $75 (32%) 

400-800 Meters $75 (32%) $75 (32%) $75 (32%) 

800-1,600 Meters $87 (32%) $87 (32%) $87 (32%) 

1,600-2,400 Meters $87 (32%) $87 (32%) $87 (32%) 

2,400+ Meters $87 (32%) $87 (32%) $87 (32%) 

Sources: Total Expenditure based on CMI Cost Function Study data and ICF calculations. 

Labor Share values estimated based on the opinion of an industry analyst. 
MAG-PLAN Study Gulf of Mexico 

16 

Platform Decommissioning (with and without explosives) 

Total costs for decommissioning a platform by non-explosive removal were estimated at 

$885,000 for a four-pile structure and $1,344,000 by the Twachtman, Snyder & Byrd (2003), and 

CMI Cost Function studies performed for the MMS in August of 2003. They are nominal costs 

derived from regressions on data collected over a ten-year period from 1991 to 2001. The CMI 

study reported similar decommissioning costs in shallow water depths, but greatly increased 

costs in the 200-900 meter and 900 meter and above water depth categories. 

The shallow water decommissioning (non-explosive) cost data were derived from the CMI 

study’s mechanical cutting figures. Four-pile platform costs were used in the 0-60 and 60-200 

meter water depth categories, 90 percent of the costs in the 200-900 meter category were used in 

the 200-800 meter category, and 80 percent of the costs in the 900 meter and above category 

were used to populate the 800-1,600 meter water depth category. 

The shallow water decommissioning (with explosive) cost data were derived from the CMI Cost 

Function Study’s Bulk Explosives (standard practice) costs. Four-pile platform costs were used 

in the 0-60 and 60-200 meter water depth categories, 90 percent of the costs in the 200-900 meter 

category were used in the 200-800 meter category, and 80 percent of the costs in the 900 meter 

and above category were used to populate the 800-1,600 meter water depth category. 

Deepwater costs were determined for both methods of removal by using the Platform Deck and 

Jacket Decommissioning Cost calculations in the MMS Offshore Facility Decommissioning 

Costs Pacific OCS Region (2004) report. It was assumed that platforms in the 1,600-2,400 meter 

range had a 10 percent weather contingency factor with $250,000/day costs for 100 days. For 

platforms in the deepest water depth category, a 20 percent weather contingency factor was used 

at $290,000/day over a 100 day period. Table 2-10 and 2-11 below summarize the total 

expenditure estimates used in MAG-PLAN for decommissioning without explosives and 

decommissioning with explosives, respectively. 

The 2002 Economic Census for Offshore Oil and Gas Support Services was used to estimate the 

labor percentage of 32 percent. 
MAG-PLAN Study Gulf of Mexico 

17

Table 2-10: Decommissioning Platform Without Explosives – Expenditure per Platform 

(in thousands) and Labor Share (percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $1,160 (32%) $1,160 (32%) $1,160 (32%) 

60-200 Meters $2,581 (32%) $2,581 (32%) $2,581 (32%) 

200-400 Meters $14,510 (32%) $14,510 (32%) $14,510 (32%) 

400-800 Meters $14,510 (32%) $14,510 (32%) $14,510 (32%) 

800-1,600 Meters $20,753 (32%) $20,753 (32%) $20,753 (32%) 

1,600-2,400 Meters $27,500 (32%) $27,500 (32%) $27,500 (32%) 

2,400+ Meters $34,800 (32%) $34,800 (32%) $34,800 (32%) 

Sources: Total Expenditure based on various sources, including CMI Cost Function Study, 

Twachtman, Snyder & Byrd (2003), and MMS Pacific OCS Study (2004). Labor 

Share values estimated based on Economic Census (2002). 

Table 2-11: Decommissioning Platform with Explosives – Expenditure per Platform 

 (in thousands) and Labor Share (percent of total expenditure, in parentheses) 

Well Depth 

Water Depth 0-15,000 Feet 15,000-18,000 Feet 18,000+ Feet 

0-60 Meters $840 (32%) $840 (32%) $840 (32%) 

60-200 Meters $1,992 (32%) $1,992 (32%) $1,992 (32%) 

200-400 Meters $16,440 (32%) $16,440 (32%) $16,440 (32%) 

400-800 Meters $16,440 (32%) $16,440 (32%) $16,440 (32%) 

800-1,600 Meters $25,306 (32%) $25,306 (32%) $25,306 (32%) 

1,600-2,400 Meters $27,500 (32%) $27,500 (32%) $27,500 (32%) 

2,400+ Meters $34,800 (32%) $34,800 (32%) $34,800 (32%) 

Sources: Total Expenditure based on various sources, including CMI Cost Function Study, 

Twachtman, Snyder & Byrd (2003), and MMS Pacific OCS Study (2004). Labor 

Share values estimated based on Economic Census (2002). 

Oil Spills (reaching and not reaching shore) 

Total costs for oil spills were developed using a modified version of the latest marine oil spill 

cleanup cost model developed by Dr. Etkin of Environmental Research Consulting. The Etkin 

model is similar to an MMS model developed in 1992 that evaluates multiple factors that 

influence costs and uses scaling factors (in percentages) to estimate the final costs. Factors 

involving the oil type, spill size, geographical location, shore vicinity, cleanup method, and 

amount of shoreline oiling are all variables in the Etkin model. The percentages used to scale the 

costs were derived from analyses of over 200 oil spills. The modifications and assumptions 

made to develop the costs for MAG-PLAN are described below. 
MAG-PLAN Study Gulf of Mexico 

18 

First, the Etkin model uses the following algorithm and methodology to estimate cleanup costs: 

Cui = Cn ti oi mi si ri li

and Cei = Cui Ai

where: 

Cui = the response cost per gallon for scenario i

Cli = the cost per gallon spilled for scenario i

Cn = the general cost per gallon spilled in nation n

Cei = the estimated total response cost for scenario i

ti = the oil type modifier for scenario i

oi = the shoreline oiling modifier factor for scenario i

mi = the cleanup methodology modifier factor for scenario i

si = the spill size modifier for scenario i

ri = the regional location modifier factor for scenario i

li = the local location modifier for scenario i

Ai = the specified spill amount for scenario i

Cn for the United States is $95.34 per gallon, which is the average cost of cleaning up a marine 

oil spill irrespective of the petroleum type. Solving for Cui , the response cost per gallon, the 

general petroleum cleanup cost can be modified by using any of the modifiers in the formula. 

Each modifying factor used in this calculation is based on the percentage difference of the 

average factor cost relative to the median costs for the data in each cost factor category. To 

solve for the crude oil type, we can multiply the oil type modifier by $95.34 per gallon and, 

assuming all else is equal, (solving for the median values of oi , mi , si , ri , and li), estimate the 

average marine crude oil spill cleanup cost. For example, the crude oil type modifier is listed in 

Table 2-12 below as 0.55. This implies that crude oil spills cost on average 55 percent of the 

median costs as calculated for all petroleum types. The result of this simplified calculation was 

$52.44 per gallon. This was the base, or average crude oil cleanup cost per gallon in the United 

States given this modification factor and assuming the medians of the other factors. 

Table 2-12: Oil-Type Modifiers for Oil Spill Cleanup Costs (in percentage) 

Oil Type Percentage Modifier 

No. 2 fuel 0.18 

Light Crude 0.32 

No. 4 fuel 1.82 

No. 5 fuel 1.82 

Crude 0.55 

Heavy Crude 0.65 

No. 6 fuel 0.71 

Source: Etkin (2003). 
MAG-PLAN Study Gulf of Mexico 

19

The regional and local location modifiers can be used to increase or decrease this estimated cost. 

In this analysis, ICF assumed that there are no substantial modifiers that would increase or 

decrease costs based on regional variation. In other words, it can be assumed that the Gulf of 

Mexico’s marine oil spill cleanup costs are on par with the rest of the United States. The 

location of the spill, however, is one modifier that can be used to distinguish between spills 

nearshore and those further offshore. Table 2-13 shows the local location modifier data from the 

Etkin model. 

Table 2-13: Location Modifiers for Oil Spill Cleanup Costs (in percentage) 

Local Location Percentage Modifier 

Nearshore 1.46 

 Source: Etkin (2003). 

Using these modifiers and the median of the other modifiers, it was estimated that a crude oil 

spill nearshore costs approximately $76.56 per gallon and an offshore spill costs about $24.12 

per gallon. This assumes the median values for spill size, cleanup method, and the amount of 

shoreline oiling. These modifier data are listed in table 2-14. 

Table 2-14: Spill Size Modifiers for Oil Spill Cleanup Costs (in percentage) 

Spill Size Percentage Modifier 

< 34 tonnes 2.00 

34-340 tonnes 0.65 

340-1,700 tonnes 0.27 

1,700-3,400 tonnes 0.15 

3,400-34,000 tonnes 0.05 

>34,000 tonnes 0.01 

Primary Cleanup Method Percentage Modifier 

Dispersants 0.46 

In-Situ Burning 0.25 

Mechanical 0.92 

Manual 1.89 

Natural Cleansing 0.10 

Shoreline Oiling Percentage Modifier 

0-1 km 0.47 

2.5 km 0.54 

8-15 km 0.54 

20-90 km 0.61 

100 km 1.06 

500 km 1.53 

Source: Etkin (2003) 
MAG-PLAN Study Gulf of Mexico 

20 

By using the 0-1 km shoreline oiling modifier, the average marine crude oil spill with no (or 

limited) shore response can be estimated. The cost for a spill nearshore that has no shore contact 

is $76.56 x 0.47 ($35.98 per gallon) and the offshore cost is $24.12 x 0.47 ($11.34 per gallon). 

ICF assumed the “nearshore” costs to be associated with the 0-60 meter water depth category in 

MAG-PLAN; and “offshore” to represent the remaining higher water depth categories. 

Therefore, the following initial costs to populate MAG-PLAN (table 2-15) were developed. 

Table 2-15: Oil Spill Cleanup – Expenditure per Gallon (in thousands) and Labor Share 

(percent of total expenditure) 

Water Depth Offshore Oil Spill Cost per 

gallon (reaching shore) 

Offshore Oil Spill Cost per 

gallon (NOT reaching shore) 

0-60 $76.56 (20%) $35.98 (15%) 

All others $24.12 (20%) $11.34 (15%) 

Source: ICF analysis of Etkin (2003). 

Deviations in greater than 60 meters water depth categories were explored by researching the 

various cleanup methodologies and whether they could be used to distinguish any cost 

differences. However, no significant method could be identified due to the multitude of factors 

involved in determining what type of cleanup methodology to use. Water depth is not a good 

indicator of the type of cleanup methodology employed for these deeper water depth categories. 

There are too many variables of circumstance such as weather and water conditions that play an 

important role in cost structures. Spill size is another major factor that is left constant in this 

analysis, but there is no correlation between spill sizes and water depth either. Water 

transportation costs may increase in the deeper water depths, but deeper water depths also could 

offer the spill response team opportunities to employ less expensive cleanup methodologies 

depending on varying wind speeds, water temperatures, currents, waves, and other factors that 

are not modeled in MAG-PLAN.29 Thus, water depth is not a good indicator of unit costs when 

estimating oil spills other than using depth as an indicator for proximity to shore. Therefore, the 

initial (default) cost estimates used in MAG-PLAN were the same for all water depth categories 

deeper than 60 meters. 

Labor percentage was set at 15 percent for oil spills not reaching shore and 20 percent for those 

reaching shore. These estimates were based on costs developed by Etkin to estimate a spill in 

Galveston Bay with differing size. Equipment, labor, government, disposal and decontamination 

costs were evaluated in the study. The range in labor percentage by spill size was 15 to 23 

percent. ICF assumed that more labor (by percentage) is required for spills that reach the shore 

due to increased manual cleanup efforts. 

 

29 One way to model changes in water transportation costs by water depths in MAG-PLAN is by revising the 

IMPLAN sector allocations. Increasing the contribution of IMPLAN sector 393 for deeper water depth delineations 

would capture the effect (so long as shares for other sectors are appropriately reduced to sum to 100 percent). 
MAG-PLAN Study Gulf of Mexico 

21

2.4 Economic Sector Allocations by Activity Function 

Once the total expenditures and labor percentages are determined for all Activity Functions, the 

second part of the data work involved determining the shares to allocate to appropriate economic 

sectors, based on IMPLAN’s sector definition. The first step in this sector allocation involved 

identifying the major economic sectors impacted by offshore oil and gas activities. This was 

done by having the expert committee review the CMI sector allocation data as a starting point, 

along with other sources discussed below, to generate a list of major sectors. Forty major sectors 

were identified and these were: 

 19 Oil and gas extraction 

 25 Sand, gravel, clay, and refractory mining 

 27 Drilling oil and gas wells 

 28 Support activities for oil and gas operations 

 40 Water, sewer, and pipeline construction 

 43 Maintenance and Repair of Nonresidential Buildings 

142 Petroleum refineries 

145 Petroleum lubricating oil and grease manufacturing 

161 Paint and Coating Manufacturing 

191 Cement manufacturing 

205 Iron, steel pipe and tube from purchased steel 

206 Rolled Steel Shape Manufacturing 

207 Steel Wire Drawing 

248 Metal valve manufacturing 

252 Fabricated pipe and pipe fitting manufacturing 

259 Construction machinery manufacturing 

261 Oil and gas field machinery and equipment 

288 Pump and pumping equipment manufacturing 

289 Air and gas compressor manufacturing 

307 Broadcast and wireless communications equipment 

308 Other communications equipment manufacturing 

314 Search, detection, and navigation instruments 

344 Automobile and light truck manufacturing 

345 Heavy duty truck manufacturing 

350 Motor vehicle parts manufacturing 

352 Aircraft engine and engine parts manufacturing 

353 Other aircraft parts and equipment 

357 Ship building and repairing 

358 Boat building 

390 Wholesale trade 

391 Air transportation 

392 Rail transportation 

393 Water transportation 

394 Truck transportation 

428 Insurance agencies, brokerages, and related 
MAG-PLAN Study Gulf of Mexico 

22 

Forty major sectors (continued) 

431 Real estate 

437 Legal services 

439 Architectural and engineering services 

455 Business Support Services 

459 Other Support Services 

After identifying the sectors, each Activity Function’s non-labor expenditure on these sectors 

was ranked low (less than one percent), medium (one to ten percent), high (above ten percent), or 

zero. After the rankings were completed, the data sources available for populating the table with 

its initial economic sector allocation values were evaluated. Listed below are the sources 

evaluated during this process: 

• FERC Form 6 for pipeline construction and O&M (FERC) 

• LNS for production O&M and exploratory and development drilling (LNS, 2005 

 forthcoming) 

• Foster Wheeler technical paper for decommissioning (Hughes & Fish, 2005) 

• WEC Tech paper for pipeline construction (D’Adda, 2005) 

• Draft NETL report on benchmark drilling costs for exploratory and development drilling 

 (Schlumberger, 2004) 

• EIA Costs and Indices report for production O&M (EIA, 1996) 

• CMI data for various activities (MMS, 2003) 

For the most part, the sources concurred with the low, medium, and high rankings established by 

the industry experts. Thus, this approach provided a crosscheck on the validity of the major 

sectors. However, based on the expert committee’s opinion, it was determined that the CMI data 

overestimated the impact in their highest impacted categories. Based on the expert committee’s 

experience working with oil and gas industry data, including the work done on the GOADS data 

that provide detailed operations data on federal waters, as well as the data sources mentioned 

above, the CMI data for the major sectors were adjusted. During this process, the projectspecific LNS data and the draft NETL report on benchmark drilling costs provided valuable 

inputs. 

Since many of the sources evaluated (including the GOADS data) provided inputs only for the 

shallow water depths, the initial estimates, developed above, were used for the IMPLAN sector 

allocation for the 60-200 meters water depth category (and All Well Depths). Next, these shares 

were adjusted for other water depth categories based on certain assumptions for the average 

Euclidean distance in miles from shore using contour maps for the GOM offshore. This is 

because the expenditure shares for some sectors will change based on the distances from shore 

indicated by various water depths. For example, costs for steel pipes are expected to vary by 

water depths and correspondingly by the average distance in miles from shore as pipes not only 

need to be longer, but may also need to have a bigger diameter in deeper waters. All the major 

sectors were analyzed by activity function. Specific algebraic functions were used to estimate 

the changes in sector allocation by water depths based on engineering experience. 
MAG-PLAN Study Gulf of Mexico 

23

The average distance used for the different water depth categories were: 

 Average 

Water depths distance (miles) 

0-60 meters 20 miles 

60-200 meters 70 miles 

200-800 meters 80 miles 

800-1,600 meters 120 miles 

1,600-2,400 meters 160 miles 

2,400+ meters 180 miles 

The following rules were used to change the sector allocations by average distance as a proxy for 

water depths from the baseline water depth of 60-200 meters. The general conditions required 

for their applicability are also discussed under each rule: 

Change in direct proportion to the ratio of distance to shore – This rule was used for those 

sectors where the allocations were assumed to increase linearly in proportion to the increase in 

distance to shore. Thus, for these sectors, it was assumed that their contributions would increase 

in direct proportion to the change in the distance ratio as the activities move deeper and deeper 

into the offshore. For example, allocation for sector 142, “Petroleum Refineries” for the Activity 

Function “Development Drilling,” was assumed to change at this rate. Thus, the percent 

allocation would change from 10 percent in the baseline case (60-200 m, or 70 miles from shore) 

to approximately 17 percent at depths of 800-1,600 m (120 mile), as (=10*(120/70))≈17. 

Change by a factor of the square of the distance ratio – This rule was used for those sectors for 

which the allocations were expected to increase more than linearly with the increase in the 

distance to shore. In other words, their contributions were expected to increase at a rate greater 

than the proportional increase in distance to shore. For example, allocation for sector 28, 

“Support Activities for Oil and Gas Operations,” for the Activity Function “Pipeline O&M,” was 

assumed to change at this rate. Thus it went from 5 percent for the baseline (60-200 meters, or 70 

miles from shore) to 6.5 percent for 200-800 m, 80 miles from shore (=5*(80/70)^2

). 

Change by a factor of the cube of the distance ratio – This rule was used for those sectors 

where the allocation was also expected to increase more than linearly to the distance ratio. For 

these sectors, however, the allocations also depended heavily on the depth of the seafloor at 

different distance from shores. This meant that for these sectors, their total contribution would 

not only increase because of increasing distance to shore (by a factor larger than a linear 

proportion), but would also increase due to the increasing water depths. A combination of these 

two factors meant that the allocation for these sectors would not only increase by a square of the 

distance ratio, but a factor that was larger than that. In such cases, ICF assumed that it would 

increase by a cube of the distance ratio. For example, for the Activity Function “Pipeline 

Construction,” sector 25, “Sand, Gravel, Clay, and Refractory Material” allocations were 

assumed to increase at this rate. Thus it went from 3 percent for the baseline (60-200 m, or 70 

miles from shore) to 4.5 percent for the 200-800 m, 80 miles from shore (=3*(80/70)^3

). 
MAG-PLAN Study Gulf of Mexico 

24 

Change by the (square or cube) root of the distance ratio – This rule was used for those sectors 

for which the contribution could be assumed to increase, but at a decreasing rate (for reasons 

such as economies of scale) with increasing distance to shore. The choice of square or cube root 

was made based on the level of these scale economies for different sectors for different Activity 

Functions. For example, for “Exploratory Drilling,” going from a depth of 0-60 m (20 mile from 

shore) to a depth of 60-200m (70 miles from shore), ICF estimated that sector 27, “Drilling Oil 

and Gas Wells,” would change by a square root factor. In this case, the percent allocation of 

sector 27 went from 40 percent in the baseline to approximately 21.4 (=40*(20/70)^1/2) percent at 

this depth. 

No change – This rule was used for those sectors where the contributions of sectors were not 

expected to change as a result of increasing water depths or distance to shore. For example, for 

“Pipeline O&M,” the contribution of sector 437, “Legal Services” was not expected to be a 

function of the distance from shore and hence the contribution was left unchanged at different 

water depths. 

The following tables summarize the rules that were used for the top five representative sectors 

for each Activity Function. 

Table 2-16a: Rules to Extrapolate Sector Allocations by Water Depths 

(see Key at the end of Table 2-16b for an explanation of the codes) 

IMPLAN 

Sector 

Expl. 

Drilling 

Devel. 

Drilling 

Platform 

F&I 

Product. 

O&M 

Pipeline 

Const 

Pipeline 

O&M Workover Decommission 

19 F 

25 F F SQ 

27 SQRT SQRT 

28 SQRT SQ F 

40 SQ 

142 D D D SQ 

191 SQ 

205 F F SQRT CUBE F 

206 SQRT 

252 CUBE 

261 F F 

357 D 

391 F D D 

393 D D D F D D D D 

437 F 

439 SQRT CUBE SQRT 
MAG-PLAN Study Gulf of Mexico 

25

Table 2-16b: Rules to Extrapolate Sector Allocations by Water Depths 

(see Key at the end for explanation of the codes)

Sector 

Onshore 

Gas 

Const. 

Onshore 

Gas 

O&M 

Offshore 

Gas 

Const. 

Offshore 

Gas 

O&M 

Decommission 

w/Expl. 

Drilling 

Completion 

Offshore 

Oil Spill 

(Reaching 

shore) 

Offshore 

Oil Spill 

(Not 

reaching 

shore) 

19 0% F 0% F 

25 0% 0% SQ 

27 0% 0% 0% 0% F 

28 0% F 0% F CUBE D 

142 F SQRT SQRT CUBE D 

191 0% 0% SQ 

205 F 0% F 0% F 

206 F 0% F 0% 

261 0% 0% 0% CUBE F 

289 F F SQRT 

357 0% 0% 0% SQRT D 

391 0% 0% D D D 

393 0% 0% D D D 

428 0% 0% 0% 0% SQRT 

437 0% 0% SQRT 

439 F F F SQRT F 

455 0% F 0% 

Key: D = Change in Direct Proportion to Distance Ratio 

 SQ = Change in Proportion to Square of the Distance Ratio 

 CUBE = Change in Proportion to Cube of the Distance Ratio 

 SQRT = Change in Proportion to Square Root of the Distance Ratio 

 F = Fixed (No change) 

Source: ICF analysis. 

For brevity, the examples given above (both in the descriptions of the five rules and in the tables) 

are for representative sectors only. However, each major sector for each Activity Function was 

analyzed separately to determine the rule that would be most applicable. In most cases, 

estimation of the major sectors using existing data and ICF expertise as explained above added 

up to less than 100 percent of the total cost. In such instances, the residual cost was allocated to 

the “non-major” sectors identified in the original data. 
MAG-PLAN Study Gulf of Mexico 

26 

Using IMPLAN Production Functions 

After identifying the major economic sectors and their shares, the next step in sector allocation 

involved using IMPLAN production function data to “expand” the allocation based on inputoutput relationships of the major sectors. The first step in this process was to choose an 

appropriate modeling region in IMPLAN, which, according to ICF’s judgment best captured the 

offshore oil and gas industry. The modeling region ICF chose comprised 31 parishes in south 

Louisiana that were expected to have a significant contribution to offshore activities. These 

parishes were:30

Acadia Orleans 

Allen Plaquemines 

Ascension St. Bernard 

Assumption St. Charles 

Beauregard St. James 

Calcasieu St. Johns the Baptist 

Cameron St. Landry 

East Baton Rouge St. Martin 

Evangeline St. Mary 

Iberia St. Tammany 

Iberville Tangipahoa 

Jefferson Davis Terrebonne 

Jefferson Vernon 

Lafayette Washington 

Lafourche West Baton Rouge 

Livingston 

Production function data for this south Louisiana model were then used for the first five major 

sectors (i.e., the top five sectors that showed the highest percentage of the cost in the original 

data) to allocate to the sectors that “feed” into these sectors. That is, if Sector 19 were identified 

as one of the top five sectors for an Activity Function, then its production function from the 

south Louisiana model was used to determine the sectors that contribute to its output, based on 

the regional input-output data used by IMPLAN. The original share of Sector 19 was then 

distributed to all the sectors that contribute to its production function. Similar calculations were 

performed for the four other major sectors for each Activity Function. The final shares were 

then calculated based on the adjusted (by production function) data for the top five major sectors 

and the adjusted plus original shares for the other major sectors, if applicable. 

Conceptually, this allocation mechanism would not lead to double counting. That is, suppose the 

original data indicated that Sector 30 (power generation and supply) provided 5 percent of the 

total cost, and using Sector 19 production function data, another 2 percent was allocated to 

Sector 30. A pertinent question is whether adding these two shares (i.e., 5 percent of the total 

 

30 IMPLAN 2002 data were used to create the south Louisiana model. ICF compared the production function 

coefficients of this south Louisiana model with those of another IMPLAN model comprising parts of coastal 

Louisiana and Texas counties. No significant differences in the IMPLAN coefficients to warrant using a different 

modeling region were found. 
MAG-PLAN Study Gulf of Mexico 

27

cost of 100 percent, plus 2 percent of sector 19 share in total cost) is counting the same 

contribution twice. ICF, in consultation with MIG, determined that based on the definition of 

input-output accounts, this does not lead to double counting, because the 5 percent share is the 

contribution of Sector 30 directly to a unit of the Activity Function, whereas the fraction of 2 

percent contribution via the Sector 19 production function is its contribution indirectly through 

its contribution to Sector 19 output. Thus, the total contribution of each sector calculated this 

way captures its direct contribution through the cost functions plus the first round of its indirect 

contribution based on the input-output relationship. Using multipliers in MAG-PLAN will then 

capture the further indirect effects (i.e., second round onwards) due to the inter-linkages of the 

different industry sectors. 

To illustrate the method, consider the following hypothetical example: 

Table 2-17: IMPLAN Production Function Example (in percent and actual dollars) 

Original Data Major Sector Prodn Fn Final Data 

Sector Original $ Original 

Share 19 35 36 142 143 Calculated $ Total $ Final Share 

19 $600 28% 66% 8% 20% 9% 6% $510 $510 24% 

28 $200 9% 5% 5% 7% 5% 5% $93 $293 14% 

35 $400 19% 1% 60% 6% 5% 5% $285 $285 13% 

36 $220 10% 5.0% 5.0% 45.0% 8.0% 7.0% $186 $186 9% 

140 $200 9% 6.0% 6.0% 6.0% 6.0% 9.0% $110 $310 14% 

142 $250 12% 4.0% 4.0% 4.0% 55.0% 6.0% $201 $201 9% 

143 $240 11% 10.0% 10.0% 10.0% 10.0% 60.0% $291 $291 14% 

144 $10 0% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $10 0.5% 

145 $12 1% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $12 0.6% 

146 $5 0.23% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $5 0.25% 

147 $0 0.00% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $0 0.02% 

148 $0 0.00% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $0 0.01% 

149 $0 0.00% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $0 0.01% 

150 $0 0.00% 0.4% 0.4% 0.4% 0.4% 0.4% $6 $6 0.28% 

151 $0 0.00% 0.1% 0.1% 0.1% 0.1% 0.1% $2 $2 0.09% 

152 $0 0.00% 0.2% 0.2% 0.2% 0.2% 0.2% $3 $3 0.16% 

153 $0 0.00% 0.4% 0.4% 0.4% 0.4% 0.4% $7 $7 0.35% 

154 $0 0.00% 0.1% 0.1% 0.1% 0.1% 0.1% $2 $2 0.08% 

155 $0 0.00% 0.0% 0.0% 0.0% 0.0% 0.0% $0 $0 0.01% 

156 $0 0.00% 0.1% 0.1% 0.1% 0.1% 0.1% $2 $2 0.09% 

157 $0 0.00% 0.1% 0.1% 0.1% 0.1% 0.1% $3 $3 0.12% 

158 $0 0.00% 0.4% 0.4% 0.4% 0.4% 0.4% $7 $7 0.34% 

Total $2,137 100% 100% 100% 100% 100% 100% $1,711 $2,138 100% 

Source: ICF analysis of IMPLAN data. 

 Note: Recalculations using the rounded percentages shown in the table will lead to small 

differences in some results. 
MAG-PLAN Study Gulf of Mexico 

28 

In this example, the original sector allocation for a representative activity function shows Sectors 

19, 35, 36, 142, and 143 as the top five sectors with the highest shares (lightly shaded in table 2-

17 above). The corresponding production functions for these five sectors are given in the middle 

section of the table under “Major Sector Prodn Fn.” Note that the production function 

coefficients add up to 100 percent. Thus the production function for Sector 35 implies that 8 

percent of a unit of output from 35 is actually contributed by Sector 19, another 5 percent by 

Sector 28, and so on. The highest contribution comes from the same sector because of its value 

added component. Thus, the calculated share of Sector 19’s contribution comes from the value 

added contribution to its own production function (66 percent of $600) plus what it contributes to 

other sectors’ production functions (i.e., 8 percent of Sector 35’s $400 plus 20 percent of Sector 

36’s $220 plus 9 percent of Sector 142’s $250 and 6 percent of Sector 143’s $240). Using the 

production functions in this way, Sector 19’s contribution is estimated at $510 as opposed to 

$600 from the original estimation. The final estimated allocation for each sector is given by the 

calculated shares for the top five sectors, and the calculated (via production function 

contributions) plus the original shares for the others. 

This method of adjustment (using the input-output relationship) reduced the shares of the biggest 

sectors (for example, Sector 19’s share went down from 28 percent to 24 percent in the example 

above), but increased the contributions of smaller sectors (for example Sector 143 share went up 

from 11 to 14 percent). Moreover, this adjustment meant adding significantly more sectors in 

the allocation data given IMPLAN’s estimated production function relationships. In table 2-17 

above, these new sectors are shown towards the bottom of the table, starting with Sector 147 (in 

gray font). 

One downside of using the production functions in this manner is that it introduces a large 

numbers of sectors with extremely low allocation shares. Typically, each delineated Activity 

Function’s sector allocation included approximately 250 – 300 new sectors with less than 0.1 

percent allocation. These smaller sectors, while adding very little to the model’s results, added 

substantially more in terms of its computational requirements. Moreover, these sectors did not 

have an intuitive connection to offshore oil and gas activities. It was determined that the tradeoff 

in deleting these smaller sectors to make the model more efficient would not lead to any loss of 

MAG-PLAN’s actual explanatory power. Deleting these smaller sectors meant re-allocating 

approximately 2-3 percent of the total allocation back to the original major sectors.31

Moreover, in some instances, the production functions also introduced some sectors that were 

counter-intuitive. For example, the production functions allocated more than 0.1 percent share to 

sector 473 – “Independent Artists, writers, and performers.” ICF determined that this share 

should actually be allocated to the support activities (Sector 27) rather than 473 because this 

allocation seemed unrealistic. The table below shows the sectors that were deemed counterintuitive and the sectors to which we allocated their shares. 

 

31 We added this “residual” share for each delineated activity function to the major sector that showed the largest 

difference going from the original allocation to the adjusted allocation. 
MAG-PLAN Study Gulf of Mexico 

29

Table 2-18: Reallocation Sectors 

Production Fn “Counter-Intuitive Sectors Sectors to which Re-allocated 

397 - Scenic and sightseeing transportation 393 - Water transportation 

473 - Independent artists, writers, and performers 27 - Drilling oil and gas wells 

456 - Travel arrangement and reservation services 391 - Air transportation 

285 - Turbine and turbine generator set units manufacturing 357- Ship building and repairing 

 Source: ICF analysis of IMPLAN data. 

The final allocations used in MAG-PLAN thus included substantially more sectors than previous 

MMS data. The same methodology was used to allocate costs shares for all water depths. 

2.5 Onshore Area Distribution 

Determining the onshore area distributions for the different IMPLAN sectors was the next step in 

the GOM data analysis. The specific onshore areas used for this part are based on the spatial 

aggregation scheme provided by MMS and are listed below:32 

TX-1: Cameron, Hidalgo, Starr, Willacy, Aransas, Brooks, Duval, Jim Wells, Kenedy, 

Kleberg, Nueces, Refugio, and San Patricio Counties 

TX-2: Brazoria, Matagorda, Wharton, Calhoun, Colorado, Dewitt, Fayette, Goliad, 

Gonzales, Jackson, Lavaca, and Victoria Counties 

TX-3: Hardin, Jasper, Jefferson, Newton, Orange, Polk, Tyler, Austin, Chambers, 

Fort Band, Galveston, Harris, Liberty, Montgomery, San Jacinto, Waller, and 

Washington Counties 

LA-1: Allen, Beauregard, Calcasieu, Cameron, Jefferson Davis, and Vernon Parishes 

LA-2: Acadia, Evangeline, Iberia, Lafayette, St. Landry, St. Martin, and Vermillion 

Parishes 

LA-3: Ascension, East Baton Rouge, Iberville, Livingston, Tangipahoa, West Baton 

Rouge, Assumption, Lafourche, St. Mary, and Terrebonne Parishes 

LA-4: Jefferson, Orleans, Plaquemines, St. Bernard, St. Charles, St. James, St. John the 

Baptist, St. Tammany, and Washington Parishes 

MS-1: George, Greene, Hancock, Harrison, Jackson, Pearl River, and Stone Counties 

AL-1: Baldwin, Clarke, Conecuh, Escambia, Mobile, Monroe, Washington, and Wilcox 

Counties 

FL-1: Bay, Franklin, Gulf, Escambia, Okaloosa, Santa Rosa, and Walton Counties 

FL-2: Calhoun, Gadsden, Holmes, Jackson, Jefferson, Leon, Liberty, Wakulla, 

Washington, Columbia, Hamilton, Lafayette, Madison, Suwannee, and Taylor 

Counties 

 

32 Apart from these specific OSA’s, several state-level OSA’s are also used in MAG-PLAN, generally for mapping 

revenue streams. 
MAG-PLAN Study Gulf of Mexico 

30 

Spatial aggregation scheme continued. 

FL-3: Citrus, Marion, Alachua, Bradford, Dixie, Gilchrist, Levy, Union, Hernando, 

Hillsborough, Pasco, and Pinellas Counties 

FL-4: Collier, Lee, Broward, Miami-Dade, Monroe, Charlotte, DeSoto, Manatee, and 

Sarasota Counties 

Other GOM: Remaining counties and parishes in Texas, Louisiana, Mississippi, Alabama, and 

Florida 

Other U.S: Remaining 45 states and the District of Columbia 

In addition, for each delineated Activity Function, MAG-PLAN reports direct total industry 

output for an additional onshore area: Foreign. This represents estimated expenditures on 

imported goods and services. While such expenditures represent leakage from the domestic 

economy, the separate accounting allows the user to estimate the total expenditures for a given 

E&D scenario. 

Two major data sources were used for this distribution. One was the Gulf Coast Oil Directory 

(GCOD), which is an electronic database of companies that serve the Gulf coast of Texas and the 

states of Louisiana, Mississippi, Alabama, Florida, and Oklahoma. This database was used for 

most of the major economic sectors discussed in the previous section. Since GCOD was the 

most reliable (and with the widest coverage) data source available on the Gulf of Mexico oil and 

gas industry, it was determined that it provided the best representation for the different 

companies that are most likely to be involved with this industry in the GOM, and hence was used 

as the primary data source for this work. 

As explained in the previous section, once the major sectors that are impacted by the different 

Activity Functions were determined, IMPLAN production functions were used to expand the 

allocations to other sectors based on the input-output accounts. Since GCOD was only usable for 

the major sectors, the County Business Patterns (CBP) data for 2002 were used to determine the 

onshore area distributions for the non-major sectors. 
MAG-PLAN Study Gulf of Mexico 

31

Using GCOD for the Major IMPLAN Sectors 

Table 2-19: Mapping between IMPLAN Sectors and GCOD Industry Categories 

IMPLAN Sectors GCOD Industry Categories 

19 Oil and gas extraction 54, 302 

25 Sand, gravel, clay, and refractory mining 62 

27 Drilling oil and gas wells 1, 2, 10, 14, 15, 26, 30 

28 Support activities for oil and gas operations 12, 28, 40, 45, 60, 74, 78 

40 Water, sewer, and pipeline construction 8, 48, 50,52, 64 

43 Maintenance and Repair of Nonresidential Buildings 34 

142 Petroleum refineries 53 

145 Petroleum lubricating oil and grease manufacturing 62 

161 Paint and Coating Manufacturing 62 

191 Cement manufacturing 60 

205 Iron, steel pipe and tube from purchased steel 70 

206 Rolled Steel Shape Manufacturing 22, 62 

207 Steel Wire Drawing 62 

248 Metal valve manufacturing 72 

252 Fabricated pipe and pipe fitting manufacturing 70 

259 Construction machinery manufacturing 22, 62 

261 Oil and gas field machinery and equipment 22, 62 

288 Pump and pumping equipment manufacturing 62 

289 Air and gas compressor manufacturing 62 

308 Other communications equipment manufacturing 62 

314 Search, detection, and navigation instruments 62 

344 Automobile and light truck manufacturing 62 

345 Heavy duty truck manufacturing 62 

350 Motor vehicle parts manufacturing 62 

352 Aircraft engine and engine parts manufacturing 62 

353 Other aircraft parts and equipment 62 

357 Ship building and repairing 58 

358 Boat building 58 

391 Air transportation 68 

392 Rail transportation 68 

393 Water transportation 68 

394 Truck transportation 68 

428 Insurance agencies, brokerages, and related 29 

431 Real estate 36 

437 Legal services 32 

439 Architectural and engineering services 18, 46 

455 Business Support Services 20, 47 

Note: For definitions of the GCOD sectors, see the file named “gcodchap.doc” provided with the 

 original GCOD database. 

Source: ICF analysis of IMPLAN and GCOD data 
MAG-PLAN Study Gulf of Mexico 

32 

The following steps were performed on the GCOD data to estimate the OSA distribution for the 

important IMPLAN sectors: 

1. Extracted GCOD data for the above categories 

After identifying the relevant GCOD sectors based on the mapping above, data from 

individual Excel spreadsheets were extracted and appended to create one large database of all 

GCOD industry files. This database consisted of over 26,000 companies in the six Gulf 

states (henceforth referred to as the GCOD database). 

2. Mapped GCOD data to relevant county codes 

In order to match the GCOD companies to the relevant OSAs based on MMS definitions, the 

zip code information for these companies was then matched to the county FIPS codes using 

Census data on zip code-county mapping. This created a database of all the GCOD 

companies with their corresponding county and OSA information in the five Gulf states 

relevant for MAG-PLAN. 

3. Identified the number of companies, using “Area of Involvement” as a filter 

The GCOD survey collected data on the “area of involvement” for these companies. This 

variable provided information on whether the company actually participated in the offshore 

oil and gas industry (based on self-reporting). Out of the 26,000 companies in the database, 

with only 2 percent not responding, the following is the breakdown of their responses to this 

question: 

Only Offshore 9 percent 

Only Onshore 28 percent 

Both Offshore and Onshore 63 percent 

The onshore area distributions for all the OSAs were then created for those companies that 

explicitly responded to working in the offshore industry (63+9 percent) for the different 

IMPLAN sectors. Restricting the sample to the offshore companies only led to no impact on 

one OSA (FL-2). MAG-PLAN is populated using this distribution for the major sectors. 

4. Determined “Other U.S.” using IMPLAN RPCs from an “All Gulf Model” 

Since GCOD only conducted the survey on the Gulf state companies, it did not provide the 

information needed to estimate the distribution for “Other U.S.” (which, by definition, meant 

all non-Gulf states and other countries). To estimate the distribution of companies in this 

OSA, ICF used the Regional Purchase Coefficient (RPC) data from IMPLAN 2002. A 

model for the 5 Gulf States was created, and the RPC vector was extracted from it. Since the 

RPC vector indicates the share of the regional demand purchased from local (in this case, the 

5 Gulf states) producers for each IMPLAN sector, (1-RPC) is the share that is purchased 

from the non-local producers. Shares for the “Other US” OSA for each IMPLAN sector 

were then created using this data. 
MAG-PLAN Study Gulf of Mexico 

33

Using County Business Patterns for Other IMPLAN sectors 

The following steps were performed on the County Business Patterns (CBP) data to create the 

OSA distribution for the other IMPLAN sectors. 

1. Extracted CBP data for all the counties in the 5 Gulf states 

Data from the Census website were extracted for all NAICS categories for the 5 states. Most 

of the CBP data identified the 3-digit NAICS codes for each county-industry observation (see 

below, #3, for those that did not). 

2. Mapped CBP data to the relevant IMPLAN sectors and OSA codes 

CBP data were then mapped to the corresponding IMPLAN sector definitions based on the 

crosswalk between IMPLAN sector codes and NAICS. The county-level CBP data were 

then mapped to the different OSA definitions to determine their corresponding OSA. 

3. Converted 2-digit NAICS to 3-digit NAICS in CBP 

For three of the IMPLAN categories that were identified in the database, the CBP data were 

provided only at the 2-digit NAICS level. ICF individually mapped these to the 

corresponding 3-digit NAICS codes, based on their NAICS/IMPLAN descriptions. These 

three were: 

NAICS 23 -- Construction 

NAICS 42 -- Wholesale Trade 

NAICS 55 -- Management of Companies and Enterprises 

Since MIG mapped these to multiple IMPLAN codes, ICF first identified the relevant sectors 

(i.e., those that have a positive impact through the sector allocation work described above). 

They were then mapped to the appropriate 3-digit NAICS codes based on the description 

provided in NAICS. The resulting mapping is shown in the table below: 

Table 2-20: Mapping IMPLAN and NAICS Codes 

IMPLAN Sector Titles 2-Digit NAICS 3-Digit NAICS 

 41 Other new construction 23 233, 234 

 45 Other maintenance and repair construction 23 235 

390 Wholesale trade 42 421, 422 

451 Management of companies and enterprises 55 551 

 Source: ICF analysis. 
MAG-PLAN Study Gulf of Mexico 

34 

4. Determined “Other U.S.” using IMPLAN RPCs from an “All Gulf Model” 

IMPLAN RPC data were used to determine the “Other U.S.” shares for each sector, similar 

to the process described above. 

5. Used other post-processing assumptions 

In many instances, the onshore area distribution calculated using the CBP data listed only 

one company in the “Other GOM” OSA, with no other companies in the other OSAs. 

However, for many of these sectors, IMPLAN indicated a high RPC (greater than 95 percent) 

implying that a major portion of the regional demand for these sectors was met through local 

production. These two facts seem to contradict each other since a high RPC implies there is 

a high probability that there are more companies in all the relevant OSAs than is indicated by 

the CBP data, which only shows one company in “Other GOM” (and none in the other 

OSAs).33 In such cases, ICF analyzed the definitions for these sectors from IMPLAN and 

NAICS to determine whether any other sector distribution within the same 3-digit NAICS 

code was close enough (in terms of the types of activities performed). If such a category was 

identified, then the original CBP distribution was replaced (i.e., 1 under “Other-GOM”) with 

this new distribution. In some cases, a determination was made that no other 3-digit NAICS 

sector resembled the sector in question and thus the original CBP distribution was left intact. 

The final onshore area distribution data were thus created by combining the data from GCOD 

and CBP to create a distribution for 377 IMPLAN sectors. This final distribution is used to 

populate the GOM section of MAG-PLAN. 

Using Assumptions to Change WGOM or CGOM OSA Distributions 

The delivered version of MAG-PLAN has the same OSA distributions for WGOM and CGOM, 

which may not reflect actual industry purchasing patterns for activities in those two planning 

areas. However, there are no data to indicate how those patterns might differ. Moreover, most 

of the impacts in the GCOD database show up for TX-3, LA-3, and LA-4. Ideally, one should 

treat the default distribution in MAG-PLAN as being the “baseline” applicable for both CGOM 

and WGOM. Because of the lack of actual data to differentiate, it is up to the users of MAGPLAN to tweak these shares to better reflect the differences between CGOM and WGOM. If 

MMS were to change the initially populated OSA distribution data, ICF recommends the 

following procedure: 

1. Identify sectors that are most-likely “local” 

The sectors that will most-likely be provided by local vendors will be retail and service 

sectors. These are IMPLAN sectors 391 and above. 

2. Assume Texas provides locally to WGOM and Louisiana provides locally to CGOM 

The GCOD directory provides company listings for the entire Gulf of Mexico Oil and Gas 

business. If changing the OSA distributions for WGOM, the “local” (TX-1, TX-2, and TX3) distributions should be increased for the service and retail sectors selected and the 

Louisiana distributions for these sectors should be decreased (to sum to 100 percent). 

 

33 In these instances, ICF assumed this was a missing data problem. 
MAG-PLAN Study Gulf of Mexico 

35

3. Take small steps and keep the 100 percent total distribution 

Evaluating the local service and/or retail sector impacts with any assumptions should be done 

with caution. If changing the OSA distribution for WGOM, users should try taking just a 

portion of the LA-1, LA-2, LA-3, and LA-4 distribution figures from one sector and 

reallocating them to TX-1, TX-2, and TX-3. Then, run MAG-PLAN Stage I and analyze the 

impacts to see if it makes much difference. Users should weigh the option of having to do 

this for multiple sectors or leaving it as it was initially populated. Tutorial 6 in the MMS 

MAG-PLAN User Manual provides more details on how MMS staff members can create 

such sensitivity runs in MAG-PLAN. 

Onshore Area Labor, Income, and Personal Consumption Expenditure 

Distribution 

This section details the Onshore Area labor distribution, income distribution, and personal 

consumption expenditure data used in MAG-PLAN. 

Onshore Area Labor Distribution 

As the distribution of where GOM workers live would be useful to MMS, the employee survey 

forms from the Labor Needs Survey (LNS) included a field for home zip code. This was an item 

that almost all respondents completed; 281 observations were available from the five strata with 

employee data.34 

Some five-digit zip code areas have fewer than three responses. To preserve confidentiality, 

such areas were combined with other contiguous areas. In the coastal GOM it was found that 

grouping by three-digit zip codes provided the smallest area that the data could support. Less 

densely populated areas were grouped by two-digit zip codes. Table 2-21 shows the resulting 

distribution that was estimated using the percentage distribution of the 281 respondents and the 

total population for the entire area of 28,163. 

 

34 For more information on the Labor Needs Survey, which was a separate study conducted by ICF Consulting for 

MMS, refer to “Labor Needs Survey,” prepared for the U.S. Department of the Interior, Minerals Management 

Service, Gulf of Mexico OCS Region, New Orleans, LA. ICF Consulting (forthcoming). 
MAG-PLAN Study Gulf of Mexico 

36 

Table 2-21: Estimated Distribution of Home Zip Codes from LNS 

(in percent and population total) 

 Estimated 

Zip-Code or Other Area Population 

Percent 

Population 

Total 

Standard 

Error 

32 Northern Florida 1.94 547 231 

36 Mobile, Montgomery, AL 3.33 939 301 

39 Biloxi, Jackson, Southern MS 4.28 1,207 339 

Arkansas & Tennessee 0.82 232 151 

700 Metairie, Chalmette, LA 3.67 1,033 315 

701 New Orleans, LA 0.66 186 136 

703 Houma, Donaldsonville, LA 20.78 5,852 679 

704 Hammond, Ponchatoula, Bogalusa, LA 4.62 1,302 352 

705 Lafayette, New Iberia, Abbeville, LA 18.75 5,281 654 

706 Lake Charles, LA 1.40 394 197 

Other South LA 0.58 162 127 

71 Shreveport, LA 4.01 1,129 329 

75-76 Dallas & Fort Worth, TX 0.89 252 158 

770 Houston, TX 9.81 2,764 498 

773 Humble, Kingwood, Spring, TX 9.14 2,574 483 

774 Katy, Park Row, Sugar Land, TX 1.20 339 183 

775 Deer Park, Galveston, Pearland, TX 5.23 1,472 373 

Other Coastal TX 0.69 193 138 

78 Austin, Corpus Christi, San Antonio, TX 6.46 1,818 412 

9 West Coast 0.79 223 149 

Other Lower 48 0.94 266 162 

Total 100.00 28,163 

Source: Labor Needs Survey. 

The largest fraction of the employee population, 20.78 percent, lives in the HoumaDonaldsonville, Louisiana area, which is three-digit zip code 703. The second largest fraction, 

18.75 percent, lives in zip code 705, which includes Lafayette, New Iberia, and Abbeville, 

Louisiana. The fraction estimated to live in New Orleans proper is less than one percent. Two 

three-digit zip codes in the Houston area, 770 and 773, have nine to ten percent each of the 

employee population. Four other areas in Louisiana and three others in Texas have more than 

one percent each. Less than one percent of the employee population is estimated to live close to 

the coast and outside of these areas in each state. 

Only three zip code areas outside of Louisiana and Texas are estimated to have more than one 

percent each of the employee population. The code with Biloxi, Jackson, and other southern 

Mississippi area is estimated to have 4.28 percent. The Mobile and Montgomery, Alabama zip 

code area has 3.33 percent. Finally, northern Florida had 1.94 percent. The only other specific 

areas outside the GOM coastal region were Arkansas and Tennessee (combined) with 0.82 

percent and the U.S. West Coast with 0.79 percent. All other lower-48 areas contributed a 

combined total of only 0.94 percent. 
MAG-PLAN Study Gulf of Mexico 

37

According to these data, only a small percentage of employees commuted to work from outside 

the GOM coastal areas in 2000. However, given the low response, the assumption is that the 

results are biased. 

This table and all the categorical employee statistics below can be adjusted to any other 

exogenous population that the user would rather use. As the percent of the population in each 

area is provided, computing the population in each is straightforward. The formula for the 

estimated standard error is provided below35, but for small changes in the total population (less 

than a factor of 1.5), simply multiplying the standard errors in the table by the ratio of the new to 

the old population estimate is sufficiently accurate. 

Using the home zip code distribution from the Labor Needs Survey gives the following OSA 

distribution: 

Table 2-22: OSA Distribution for GOM Workers (in percent) 

Onshore Area Percent of Population 

TX-1 2.4 

TX-2 2.0 

TX-3 23.5 

LA-1 1.4 

LA-2 12.5 

LA-3 23.9 

LA-4 5.9 

MS-1 2.1 

AL-1 3.3 

FL-1 0.5 

FL-2 0.5 

FL-3 0.5 

FL-4 0.0 

Other-GOM 19.1 

Other-US 2.6

Total 100 

Source: Labor Needs Survey. 

A few assumptions were made to derive the distribution. If the zip code area spanned over 

multiple OSAs, the distribution was evenly distributed to each. For instance, zip code 32-

Northern Florida was distributed to FL-1, FL-2, FL-3, and Other-GOM. Each of the four OSA’s 

received 25 percent of the total for 32-Northern Florida in the OSA distribution. 

 

35 In all of the tables with statistics on categorical variables, the estimate of the standard error around the mean in the 

ith category is 

( ) [ ][ ] 1/ 2 SE = p q N N − n / n −1 i i i

where pi and qi are the fractions of the population in and not in the i’th category; 

N is the population total; and 

n is the sample size. 
MAG-PLAN Study Gulf of Mexico 

38 

Income Distribution 

Tables 2-23 and 2-24 below show employee personal and household income data from the Labor 

Needs Survey. The differences between the two questions on the survey are as follows: 

“Which of the following categories contains your personal income before taxes for work 

related to the U.S. Gulf of Mexico offshore in 2000? Please do NOT include the income 

from any other sources of income, such as your second job. 

Which of the following categories contains your total household income before taxes for 

2000? Please include income from any other jobs you may have, or that your spouse or 

partner contributes to the household.” 

Respondents appear to have followed these directions. Employees are well paid, with 74 percent 

falling into the ranges above $50,000 per annum. Approximately 40 percent fall into the ranges 

of $70,000 per annum and above. Household income is also high, with 77 percent above 

$50,000 per annum, and 62 percent above $70,000 per annum. 

The U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) 

program publishes employment and wages reported by employers covering up to 98 percent of 

U.S. jobs. State averages from total covered employment for 2000, in Louisiana and Texas are 

$27,888 and $34,943, respectively. Employees in the Louisiana and Texas mining sector 

average $53,299 and $78,302, respectively, according to the QCEW. 

Table 2-23: Personal Income from LNS (in percent and population total) 

 Population Estimates 

Personal Income Percent Total SE 

Under $20,000 4.2% 1,187 328 

$20,000 to $29,999 5.1% 1,440 359 

$30,000 to $39,999 7.2% 2,036 422 

$40,000 to $49,999 13.5% 3,789 557 

$50,000 to $69,999 34.4% 9,693 775 

$70,000 to $89,999 28.2% 7,946 734 

$90,000 to $109,999 5.1% 1,435 359 

$110,000 to $149,999 2.4% 669 248 

$150,000 or more 4.1% 1,155 324 

Total 100.0% 28,163 

Source: Labor Needs Survey. 
MAG-PLAN Study Gulf of Mexico 

39

Table 2-24: Household Income from LNS (in percent and population total) 

 Population Estimates 

Household Income Percent Total SE 

Under $20,000 2.4% 680 250 

$20,000 - $29,999 3.3% 937 293 

$30,000 - $39,999 3.5% 988 300 

$40,000 - $49,999 6.5% 1,820 401 

$50,000 - $69,999 24.7% 6,952 703 

$70,000 - $89,999 30.0% 8,456 748 

$90,000 - $109,999 16.0% 4,515 598 

$110,000 - $149,999 9.6% 2,713 481 

$150,000 or more 6.3% 1,782 397 

Total 100.0% 28,163 

Source: Labor Needs Survey. 

Personal Consumption Expenditures 

The IMPLAN Personal Consumption Expenditure (PCE) data were used in MAG-PLAN for 

stimulating spending on labor. Chapter 5 of the MAG-PLAN User Manual available to MMS 

staff members provides more details on how to extract PCE data from IMPLAN and use them in 

MAG-PLAN. In the future, an alternative source for the PCE vectors could be the Labor Needs 

Survey data, if MMS sponsors regular, full-sample rounds of the survey. Appendix B below 

describes the LNS data on personal consumption expenditures. 

MAG-PLAN was populated with the PCE data for the different onshore areas discussed in 

Section 2.5 above. The model provides the option to either use the “raw” PCE vectors from 

IMPLAN or vectors that have been adjusted by the Regional Purchase Coefficients (RPC) to 

account for the spending leakages from the relevant onshore areas. The MAG-PLAN defaults 

include the raw PCE vectors because it is assumed that the detailed cost function data used in 

MAG-PLAN already accounts for the leakages from the personal incomes for the workers and, 

therefore, do not need to be adjusted by the RPCs. The User Manual (chapter 5 and tutorial 5) 

provides more details for MMS staff members. 

2.6 Government Revenue Data 

This section describes the data used to calculate the government revenue portion of the direct 

expenditures matrix in MAG-PLAN. As mentioned earlier, direct expenditures are the dollar 

impacts of the initial round of spending on an economic sector in an onshore area (OSA). Direct 

Expenditures are determined by the location (or OMA) of the activity, the value of the associated 
MAG-PLAN Study Gulf of Mexico 

40 

variable in the E&D scenario, and frequently, by the activity’s distance from shore, water depth, 

and well depth. Where applicable, MAG-PLAN is populated with the data described below. 

Three revenue streams to government entities are produced by oil and gas operations in the OCS. 

They are bonus bid payments, rentals, and royalties. Bonus bids represent the cash amount 

successfully bid to win the rights to a lease. Rentals are annual payments, which are normally a 

fixed dollar amount per acre required to preserve rights to a lease on which there is not yet oil or 

gas production. Finally, royalties are due when production begins, and are payments that 

represent a stated share or percentage of the value of oil and/or gas produced. Bonus bid values 

can vary significantly based on the numerous variables that are used to compute the value of a 

lease, and thus, are populated by the values in the E&D scenario. Revenues from royalties can 

often be calculated from the value of the oil and gas produced and the applicable royalty rates. 

However, because effective royalty rates are not always straight calculations (e.g., deep-water 

royalty relief provisions suspend royalty obligations for fixed initial production volumes but also 

reinstate these obligations when prices rise above specified levels), MAG-PLAN allows for 

royalty revenues to be provided directly in the E&D scenario or to be calculated by the model. 

Rentals usually are straight calculations based on the acreage leased and the rental rates.36 The 

acreage, production volumes, and values of oil and gas are populated in the E&D Scenario. The 

default royalty and rental rates for each water depth category are shown in table 2-25 below. 

Table 2-25: Rental Rates (in %), Royalty Rates (in %), and 

Royalty Suspension Values (boe)37 by Water Depth 

Water Depth (m) Royalty Rate (%) Rental Rate ($/acre) Royalty Suspension Volume (boe) 

<200 16.67% $ 5.00 N/A 

200-400 16.67% $ 7.50 N/A 

400-800 12.50% $ 7.50 5,000,000 

800-1,600 12.50% $ 7.50 9,000,000 

>1,600 12.50% $ 7.50 12,000,000 

Source: OCS, Central GOM, Oil and Gas Lease Sale 190, FNOS 190, MMS.

Revenue Allocation 

The revenue collected by different institutions (federal, state, and local governments) is a 

function of the royalty rates, rental rates, and bonus bids received from drilling in federal waters. 

MAG-PLAN provides the capability to model these revenue streams separately for the different 

Expenditure Sources (or institutions). 

One variation in the allocation of federal revenues that ultimately affects the amount of money 

received by the U.S. Treasury is the proportion of revenues collected from leases that fall within 

 

36 MAG-PLAN does not allow the use of rental rates that escalate or otherwise change over time, but the User 

Manual discusses a way to approximate the effect of such rate schemes. 

37 Royalty Suspension data are provided for information purposes only and are not used in MAG-PLAN. See 

appendix B below for more detailed discussion on royalty suspension for future modeling purposes. 
MAG-PLAN Study Gulf of Mexico 

41

the 8(g) zone. The OCS Lands Act, as amended (and interpreted by the courts), requires that the 

federal government provide to adjacent states 27 percent of the royalty, rental, and bonus bid 

payments received from leases within 3 miles of the edge of state waters.38 This initial 3-mile 

strip of Federal waters is known as the 8(g) zone, after the section of legislation that created this 

requirement. 

The following is a list of the IMPLAN sectors used to allocate the revenue streams for the 

different institutions. 

• Federal Government --- 496 – Other Federal Govt. Enterprises 

• State Government (for onshore 

 area groups, such as full states) --- 503 – State and Local Education 

• State Government (for individual 

 onshore areas, such as LA-1, etc.) --- 499 – Other State and Local Govt. Ent. 

Onshore Area Distribution

As of the completion of this study, OCS-related government revenues flow directly to the 

Federal Government and to specific states (not localities) only. Therefore, the onshore areas for 

such revenues are: 

• Texas 

• Louisiana 

• Mississippi 

• Alabama 

• Florida 

• Undesignated U.S. 

To the extent the user has data on indirect revenue-flow patterns (e.g., through the Land and 

Water Conservation Fund) and/or direct-distribution patterns change (e.g., through the newly 

mandated, but not yet implemented, Coastal Impact Assistance Program), revenues can be reallocated accordingly outside the model. 

 

38 However, Royalty Revenues from 8(g) zone are modeled in MAG-PLAN such that all of the revenue is allocated 

to the respective state government. This is done to be consistent with the definition of 8(g) Royalty Revenue in the 

E&D scenario. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

42

3 Alaska – Arctic Modeling Areas 

3.1 Default Settings and Modeling Areas in MAG-PLAN 

There are three basic default settings, with accompanying data, in MAG-PLAN for the Arctic 

sub-region: 

• Default Beaufort – covers OCS oil-related activities in the Beaufort Sea and the 

associated regional economic impacts in the North Slope Borough and other OSAs. 

• Default Beaufort – Local Gas – covers activities related to natural gas resources 

developed for local use and the associated regional economic impacts in the local areas 

and other OSAs. 

• Default Chukchi-Hope – covers OCS oil-related activities in the Chukchi Sea and Hope 

Basin Planning Areas and the associated regional economic impacts in the North Slope 

Borough, Northwest Arctic Borough, and other OSAs. 

There is a sample run for each OMA in the delivered version of MAG-PLAN. 

3.2 Activity Functions 

The basic framework in MAG-PLAN allows the user to import any E&D scenario for the Arctic 

modeling areas and estimate the economic impacts for the various activities needed for the 

offshore oil and gas exploration. To do this, MAG-PLAN treats the E&D scenario variables as 

Activity Types (and Revenue Types) and allows the user to map them to units of Activity 

Functions. An Activity Function comprises the estimates of capital assets, labor, and materials 

needed to produce one unit of a specific Activity Type (e.g., drilling one exploration well), so the 

Activity Types stimulate the Activity Functions to calculate the direct expenditures needed for a 

particular run. Note, as explained in Section 3.3, below, that MAG-PLAN Activity Functions for 

Alaska OMA’s were developed with Activity-Based Costs, unlike the average costs used to 

construct the Activity Functions for Gulf of Mexico OMA’s. 

As indicated in Section 3.3, the Activity Function data for the Arctic modeling areas came 

largely from previous models. For an explanation of how IMPLAN sectors from those models 

were converted to the new sectioning scheme introduced with the 2001 IMPLAN data, see 

Section 4.3.3, (page 62). 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

43

Table 3-1 shows the Activity Type to Activity Function mapping used for the Arctic modeling 

areas. 

Table 3-1: Activity Types and Functions for Arctic OMA’s 

Activity Type Activity Functions 

Seismic Survey Geo Survey 

Helicopter Support 

Spill Contingency Exploration Wells 

Drill Exploration Wells 

Spill Contingency Delineation Wells (Number) 

Drill Exploration Wells 

Spill Contingency 

Exploration Ice Islands (Number) Ice Exploration Islands 

Ice Roads 

Spill Contingency 

Production Wells Drill Production Wells 

Well Workover 

Spill Contingency 

Place Gravel Island 

Protect Gravel Island 

Equip Production Island 

Operate Production Island 

Ice Roads 

Helicopter Support 

Platform Abandonment 

Gravel Production Islands 

Barge Support 

Construct Offshore Pipeline 

Spill Contingency 

Operate Rough Water Drill Ship 

Sea-floor Tie Back Pipeline 

Move Platforms 

Helicopter Support 

Sub-sea Production Well Completions 

Barge Support 

Spill Contingency 

Operate Calm Water Drill Ship 

Move Platforms 

Helicopter Support 

Drill Ships for Calm Water Exploration 

Barge Support 

 

 

 

 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

44

Table 3-1: Activity Types and Functions for Arctic OMA’s (continued) 

Activity Type Activity Functions 

Spill Contingency 

Operate Rough Water Drill Ship 

Move Platforms 

Helicopter Support 

Drill Ships for Rough Water Exploration 

Barge Support 

Spill Contingency 

Operate MBFS for Exploration 

Move Platforms 

Place Gravel Islands 

Helicopter Support 

Mobile Bottom Founded Structures (MBFS) 

for Exploration 

Barge Support 

Spill Contingency 

Equip Production Islands 

Move Platforms 

Purchase MBFS for Production 

Platform Abandonment 

Ice Roads 

Operate Production Island 

Place Gravel Islands 

Protect Gravel Islands 

Helicopter Support 

MBFS for Production 

Barge Support 

Spill Contingency 

Offshore Pipeline (Miles) Construct Offshore Pipeline 

Ice Roads 

Spill Contingency 

Onshore Pipeline (Miles) Construct Onshore Pipeline 

Ice Roads 

Landbase Operations Landbase Operations 

Note: In addition, two other secondary Activity Functions – General Personnel Transport and Camp 

Support are also mapped to all the Activity Types. 

3.2.1 Activity Function Equations and Parameters 

Units of each Activity Function are then calculated based on detailed equations in an Activity 

Function Equation Editor in MAG-PLAN. These equations are functions of Activity Types, 

other Activity Functions, Parameters and other pre-defined functions. MMS readers can refer to 

the User Manual for details about how to define or redefine these equations. 

MAG-PLAN includes sets of Parameters specific to each Activity Function. Parameters are predefined values specific to an Activity Function and are necessary to convert Activity Type counts 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

45

to Activity Function counts. For example, the Activity Type Drill Ships for Calm Water 

Exploration in the Arctic-Beaufort, the first Activity Function that is mapped to this Activity 

Type is Drill Ships for Calm Water Exploration. The parameters for this Activity Function could 

be the components that constitute this AF such as (with the appropriate units in parentheses): 

• Drill ships calm water operating days per site (days) 

• Drill ships calm water sites per year (number) 

Values for these Parameters are specified in MAG-PLAN in Form C-2. Default settings use 

values based on the IMPAK models but users can change these default values in C-2. 

3.3 ABC-Detailed Data 

The ABC-Detailed framework in MAG-PLAN involves developing cost per unit of an Activity 

Function by IMPLAN sector and OSA based on detailed capital (may be new or used), labor, and 

material categories. This framework was built in MAG-PLAN to allow MMS to model for the 

Alaska modeling areas based on the engineering data available for these areas. Because of the 

lack of historical data for Alaska, MMS uses a bottom-up approach for modeling in Alaska, 

where hypothetical cost functions are constructed based on engineering information on different 

activities. Most of the data used under the ABC-Detailed framework in MAG-PLAN came from 

existing MMS studies (discussed below). 

3.3.1 Capital Assets 

Expenditures on machines and equipment used in offshore exploration and drilling activities are 

modeled through the capital assets section in MAG-PLAN. Data on capital assets used under the 

ABC-Detailed framework were estimated using information from appendix A in the Arctic 

IMPAK documentation.39 These expenditures were estimated for the Activity Functions shown 

in table 18 above and then allocated to appropriate IMPLAN sectors and onshore areas. 

Appendix A-1 of the IMPAK documentation provided the data needed to estimate the total 

annualized cost of an asset per unit of an Activity Function. These expenditures were estimated 

separately for new and old assets. Expenditures were annualized based on the lifespan of the 

asset. MAG-PLAN Technical Documentation in appendix C below provides details about the 

equations used to estimate the expenditures for new and used assets. 

Note that since used assets are not manufactured for the activities themselves, the economic 

impacts associated with these assets are estimated based on the expenditures on retrofitting and 

margin activities.40 This is consistent with the IMPAK assumptions. Also, since the total 

expenditures are based on the delivered price of the assets, parts of these expenditures are 

allocated to appropriate margin sectors to separate margin contributions from the producer price. 

 

39 Arctic Economic Impact Model for Petroleum Activities in Alaska (Arctic IMPAK) OCS Study MMS 2002-066. 

For a discussion of the different activities and the assets used in them, refer to Chapter 7. Appendix A provided the 

data used in MAG-PLAN. 

40 Margin activities are those that are not necessary to produce an asset but that do contribute to the final price/cost, 

usually transportation and retailer activities. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

46

Data for margin adjustment for new and used assets came from IMPAK appendices A-2 and A-3. 

Also, IMPLAN sector definitions in the original IMPAK data were based on the 2000 sector 

definitions. These were updated to the 2001 IMPLAN sector definitions using the crosswalk 

table discussed above. 

Asset costs were then distributed to onshore areas separately for new and used assets using data 

from appendix A-4 in IMPAK. Asset categories in A-4 were first mapped to their corresponding 

IMPLAN sector codes using data from appendix A-5 and the old to new sector concordance. 

Note that the “Foreign” shares used here are based on the original data. They are considered as 

leakages and are not used in estimating the Direct Expenditures. 

3.3.2 Labor 

Labor data used in MAG-PLAN came from the IMPAK model documentation chapter 5. Labor 

categories are defined in MAG-PLAN for four types of labor inputs for each Activity Function: 

• Production Labor 

• Non-production Labor 

• Overhead Costs for Labor 

• Purchased Service Employee Compensation 

Production Labor 

Production labor categories are estimated for the actual labor used in the “construction and 

operation of the oil and gas facilities.” Detailed data on various categories of production labor 

were taken from exhibit 5-1. 

For some Activity Functions, data were missing for individual labor categories and an 

aggregated “Total” for the production labor category was used (see for example, labor data on 

Barge Support in form A-4 in MAG-PLAN). 

Non-production Labor 

Non-production labor categories were estimated for the office and headquarters personnel 

attributable to each Activity Function. Two types of non-production labor are modeled in MAGPLAN: 

• Alaska non-production labor – estimated as 35 percent of total production Full Time 

Equivalents (FTEs) for each Activity Function. 

• U.S. non-production labor – estimated as 5 percent of production FTEs for each Activity 

Function. 

These estimates are based on the Census data analysis explained in the IMPAK documentation as 

are their annual wages $61,902 (for Alaska non-production) and $45,188 (for U.S. nonproduction). 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

47

Refer to the Technical Documentation in appendix C below for the formulas used in MAGPLAN for labor categories. 

Overhead Expenses 

In addition to the labor categories estimated above, several overhead expenditure categories were 

also provided in the IMPAK data that are used in MAG-PLAN as additional overhead labor 

categories. As the IMPAK model documentation explains, “(w)hile these costs are theoretically 

not part of employee compensation, but rather part of overhead costs, their levels are dependent 

upon the numbers of employees and are therefore most accurately estimated along with 

employee compensation.” However, one difference in modeling these overhead expenditures in 

MAG-PLAN involves how they impact economic sectors. Labor expenditures are mapped to 

IMPLAN sectors using IMPLAN’s Personal Consumption Expenditure (PCE) vectors (discussed 

below). On the other hand, overhead expenditures in MAG-PLAN are mapped directly to 

IMPLAN sectors without the use of PCEs. 

However, IMPAK did not provide data on the specific IMPLAN sectors that could be used to 

map the overhead expenditure categories. In the absence of actual data, ICF chose the most 

appropriate IMPLAN sectors and assumed all 100 percent would go to that sector. The mapping 

used for the overhead categories are: 

• Work Clothes -- Sector 108 -- Accessories and other apparel manufacturing 

• Local Travel -- Sector 395 -- Transit and ground passenger transportation 

• Food Service -- Sector 481 -- Food services and drinking places 

• Air Travel -- Sector 391 -- Air Transportation 

• Hotel/Camp -- Sector 479 -- Hotels and motels, including casino hotels 

Default settings and the accompanying data assume all overhead expenditures come from the 

North Slope Borough Onshore Area. Users can change the default assumption in Form C-4 

Overhead Labor categories in MAG-PLAN. 

Purchased Services Employee Compensation 

The final labor category used in MAG-PLAN came from the employee compensation data for 

purchased service sector inputs discussed in chapter 6 of the IMPAK documentation. While the 

other categories of purchased services were modeled under the ABC-Material Input Class 

(discussed below), each Activity Function also included a line item for employee compensation 

that was modeled under ABC-Labor. 

Production and Non-production Labor to Onshore 

Because of the distinction between place of income and place of residence and the fact that 

workers tend to spend more money at their place of residence, the onshore area distribution of 

labor income is estimated according to the spending patterns of both local and non-local workers. 

Since most of the work performed in this area is by workforce that commutes to the area, 

estimates of workers’ wages by place of residence needed to be adjusted to capture their onshore 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

48

area spending patterns. This adjustment was done using the data from the IMPAK models (for 

more details refer to chapter 5 in the Arctic IMPAK documentation). 

To do that, the total disposable income was estimated by subtracting 20.5 percent from the wages 

by onshore area for each Activity Function. This variable is known as the Total Personal Income 

in IMPAK. The 20.5 percent includes 18 percent for total income taxes (includes local, state and 

federal income taxes) and 2 percent for savings. The estimated tax and savings rates are taken 

from IMPAK and used to calculate the total disposable income for both production and nonproduction workers. Then, using the spending by onshore area data from IMPAK (IMPAK 

Report, exhibit 5-6), the proportion of the disposable income spent in each onshore area was 

estimated. The resulting estimates are given in table 3-2 below. 

Table 3-2: Production and Non-Production Labor Onshore Area Distribution (in %) 

Activity Function NSB Other-AK Other-US 

Geological Survey 10.6% 72.8% 16.6% 

North Slope Support 10.3% 76.0% 13.7% 

General Personnel Transport 9.7% 66.4% 23.9% 

Construct Ice Road 16.6% 68.2% 15.1% 

Construct Ice Island 17.7% 67.6% 14.8% 

Move Drill Ship/Bottom Founded Platform 7.4% 74.7% 18.0% 

Operate Drill Ship in Calm Waters 7.4% 74.6% 17.9% 

Operate Drill Ship in Rough Seas 7.4% 74.7% 18.0% 

Operate Mobile Exploration Platform 7.4% 74.7% 18.0% 

Helicopter Support 6.9% 83.1% 10.0% 

Barge Support 6.5% 79.4% 14.1% 

Drill Exploration Well 7.5% 70.6% 21.9% 

Drill Production Well 7.5% 70.6% 21.9% 

Well Workover 7.5% 70.6% 21.9% 

Construct Gravel Island 7.8% 78.3% 13.9% 

Protect Gravel Island 12.0% 74.8% 13.2% 

Equip Production Island 10.0% 72.9% 17.1% 

Operate Production Island 8.1% 74.2% 17.7% 

Construct Gas Production Facility 7.4% 74.7% 18.0% 

Operate Gas Production Facility 9.0% 73.6% 17.4% 

Construct Offshore Pipeline 6.6% 75.2% 18.2% 

Construct Onshore Pipeline 6.5% 75.2% 18.2% 

Lay Sea Floor Pipeline 5.3% 76.0% 18.7% 

Land Base Operation 8.6% 73.6% 17.8% 

Spill Contingency 7.8% 74.4% 17.8% 

Abandon Production Island 8.9% 73.7% 17.4% 

Source: Arctic IMPAK and ICF analysis. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

49

Use of PCE and Income Distribution 

The economic impacts of the onshore area labor expenditures calculated above are then 

estimated using the Personal Consumption Expenditure (PCE) vectors from IMPLAN and 

income distribution data from Census. The approach is similar to the steps discussed above for 

the GOM data. However, the income distribution data for the Alaska modeling areas are 

different from those used for GOM which were based on the Labor Needs Survey. For Alaska, 

income distribution data from the Census were used and these are presented in table 3-3. 

Table 3-3: Income Distribution for Alaska Modeling Areas (in percent) 

Income Category Allocation 

Households LT10k 5.1% 

Households 10-15k 7.0% 

Households 15-25k 9.0% 

Households 25-35k 13.0% 

Households 35-50k 14.9% 

Households 50-75k 21.5% 

Households 75-100k 12.6% 

Households 100-150k 10.0% 

Households 150k+ 6.9% 

Source: Economic Census (2002). 

The PCE vectors were imported from IMPLAN 2002 data for various onshore area models. 

Refer to the User Manual for instructions on how to import or change the PCE data. Note that 

the MAG-PLAN default data sets use the raw PCE data, unadjusted by the Regional Purchase 

Coefficients (RPC). Users can change this assumption if needed and re-import the PCE vectors. 

3.3.3 Material 

The final components of the ABC-Detailed data in MAG-PLAN were for the material 

expenditures for the different Activity Functions. Two types of material expenditures were 

considered for the model. First, purchases from service sectors were estimated separately 

from capital expenditures discussed above, as they represent certain overhead expenditures that 

are not usually captured in the asset costs. Estimates for these expenditures were derived from 

the Purchased Services inputs data in chapter 6 of the IMPAK report. Second, other material 

inputs, including fuel consumption for the different assets, were then estimated using data from 

chapter 8. 

Purchased Services 

The purchased service inputs data came from a series of exhibits under exhibit 6-5 in chapter 6 

of the IMPAK report. Estimates for the dollar values for purchased services for each Activity 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

50

Function were used from these tables by IMPLAN sector and onshore area. Table 3-4 below 

shows the new IMPLAN sectors that were used for allocating purchased services data in MAGPLAN. 

Table 3-4: Purchased Services IMPLAN Sectors 

Sector Name IMPLAN ID 

Telecommunications 422 

Cable networks and program distribution 421 

Monetary authorities and depository credit intermediation 430 

Non-depository credit intermediation and related activities 425 

Insurance carriers 427 

Real estate 431 

Services to buildings and dwellings 458 

Employment services 454 

Data processing services 424 

Investigation and security services 457 

Machinery and equipment rental and leasing 434 

Other support services 459 

Management consulting services 444 

Scientific research and development services 446 

Advertising and related services 447 

Legal services 437 

Architectural and engineering services 439 

Accounting and bookkeeping services 438 

Food services and drinking places 481 

Automotive equipment rental and leasing 432 

Civic, social, professional and similar organizations 493 

Postal service 398 

Source: Arctic IMPAK and ICF analysis. 

Total purchased service inputs by IMPLAN sector were then distributed to three onshore areas 

based on their shares as given in the original data.41 These onshore areas were: 

• North Slope Borough 

• Other – Alaska – Beaufort 

• Other U.S. 

 

41 For most of the secondary activities, the IMPAK data on onshore area distributions for purchased service inputs 

did not add up to 100 percent. Because it wasn’t clear from the data whether this was due to typographical errors, or 

whether this was due to leakages to Foreign contributions, ICF assumed the latter. Since this “less than 100 percent” 

phenomenon occurred for only the secondary activities, ICF determined this was a definite pattern that was indeed a 

data-driven fact rather than an error. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

51

Estimates of Fuel Used 

Data on estimates of equipment fuel use came from chapter 8, exhibit 8-1. This exhibit provided 

fuel use data by equipment for each Activity Function. However, for some activities, estimated 

fuel use was provided as an aggregated number (e.g., see fuel use for Geological Survey). In 

such instances, MAG-PLAN includes the material category “Fuel-All Equipment.” Gallons per 

day of fuel use were multiplied by the total number of days used in a year to obtain the total 

annual fuel use per unit of equipment. This was then multiplied by the number of units of 

equipment needed for each activity function unit to obtain the total annual fuel used for each unit 

of an Activity Function. 

Note that data on fuel used for Camp Support activity function were obtained from exhibit 8-2 

instead. Because the data in this table did not provide the same variables to estimate the total 

annual fuel used, this had to be estimated based on the following calculations. 

Total cost of fuel used for truck transportation was estimated by aggregating all the fuel use 

categories under various Truck Transportation entries and was estimated to be $1,206,325. 

IMPAK used a constant $1.50 per gallon as the cost of fuel. This implied that the total annual 

fuel use for Camp Support was approximately 804,217 gallons (=$1,206,325/$1.50). Assuming 

truck transportation is needed all year round, this translated to 2,203 gallons per day 

(=804,217/365). These figures were then entered in MAG-PLAN to make the calculations 

compatible with other fuel categories. 

The next step in calculating the total cost of fuel used and their onshore impacts was to multiply 

the total annual fuel used by the price of fuel per gallon. Previous MMS models used several 

assumptions regarding the fuel price in different models. In an effort to make the calculations 

consistent across modeling areas, MAG-PLAN provides the flexibility to use a model-generated 

fuel price based on certain user-defined parameters. This ensures there is a uniform method used 

for the estimated fuel price for different modeling areas in MAG-PLAN and that there is a 

central location to change the fuel price assumption depending on the E&D scenario used. The 

details about the algorithm used in MAG-PLAN to estimate the fuel price is given in appendix B 

below. 

Total cost of the fuel was then allocated to two IMPLAN sectors, based on the assumptions in 

the Arctic IMPAK model. These were: 

• Petroleum refineries – Sector 142 – two-thirds of the total cost was allocated to this 

sector. 

• Truck transportation – Sector 394 – the remaining one-third was allocated to this sector. 

Finally, it was assumed that the truck transportation needed for the fuel would be local, and that 

the refineries supplying the fuel are based in Prudhoe Bay. Thus, the onshore area distributions 

were assumed to be the following: 

• Sector 394 – North Slope Borough (100 percent) 

• Sector 142 – Other-AK-Beaufort (100 percent) 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

52

Other Material Inputs 

Other material inputs were estimated from various exhibits in chapter 8. Unlike the capital asset 

and labor data for the Arctic modeling regions discussed above, material input data were unique 

in that they did not follow any specific pattern. Different exhibits had different levels of detail in 

the data and had to be treated individually to enter them in MAG-PLAN. As a result of these 

differences, users will notice that some data fields are populated with -999 to indicate that these 

fields are not required for the particular Activity Function’s material category. For example, the 

goal in the calculation is to estimate the total expenditure by material category based on per unit 

expenditure for a given Activity Function unit. In some cases, the total expenditure was a given 

field and hence MAG-PLAN includes a -999 for the per unit expenditure field. Moreover, 

because MAG-PLAN uses percentage shares for onshore area distributions and IMPAK provided 

data in absolute dollar figures, the onshore area distributions were estimated by taking the shares 

coming from each OSA as a proportion of the total expenditure. 

The following sections explain how these were entered into MAG-PLAN. Note that IMPLAN 

sector definitions were updated to conform to the 2001 definitions for all sector allocations. 

Data for Camp Support activity function came from exhibit 8-2. It includes data for a 300-

person hotel/camp and includes various material categories such as soaps, polishes, laundry 

supplies, oil, and antifreeze for generators; chlorine, iodine, and filters for water systems; and 

chemicals for sewage systems. 

Data for casing, tubing, and wellhead supplies, needed for well drilling purposes, were estimated 

from data in exhibit 8-5. We assume these material categories would be needed for the two 

Activity Functions – “Exploration Wells” and “Production Wells.” Consistent with the 

assumptions in IMPAK, both activities are assumed to use identical amounts of these materials 

and therefore have the same costs as well. 

Additionally, data on drilling fluids, muds, and cement for these types of wells were estimated 

using data from exhibits 8-5 and 8-6. Again, based on the IMPAK assumptions, data on these 

materials were also used for the “Well Workover” Activity Function. Exhibit 8-5 was used to 

allocate the manufacturing sector expenditures to IMPLAN sectors, along with the margin sector 

allocations that came from exhibit 8-6. 

Data from exhibits 8-7 and 8-8 were combined to populate the materials needed for two Activity 

Functions – “Place Gravel Islands” and “Protect Gravel Islands.” The following material 

categories were assumed for these two Activity Functions: 

 

Place Gravel Island Cement 

Sand, gravel, aggregates 

Cables and Shackles 

Protect Gravel Island Erosion Protection Fabric 

Sheet Pilings 

Welding Rods 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

53

Data from exhibits 8-9 and 8-10 were combined to estimate three material categories needed and 

the corresponding IMPLAN sector allocations for the Activity Function “Equip Production 

Island.” These are: 

• Piping and related services 

• Electrical systems 

• Structural shapes, siding, roofing, doors, and windows 

Material categories needed for the Activity Function “Operate Production Island”, along with 

their IMPLAN sector allocations and onshore area distributions were estimated from Exhibit 8-

11. “Scaled Estimate of Yearly Cost” was used for the total cost of each material category. 

Again, consistent with IMPAK assumptions, sector allocations between production and margin 

sectors were assumed to be the same for all material categories and these were: 

• Production – 77 percent 

• Wholesale Trade – 17 percent 

• Water Transportation – 2 percent 

• Rail Transportation – 1 percent 

• Truck Transportation – 3 percent 

Based on the original data, table 3-5 presents the onshore area distributions used in MAG-PLAN 

for these material categories. 

Table 3-5: Material Category OSA Distribution for AF “Operate Production Island” (in %) 

Sector & OSA Share 

Production Sector 

Other AK-Beaufort 95% 

Other U.S. 5% 

Wholesale Trade 

AK-NSB 5% 

Other AK-Beaufort 48% 

Other U.S. 47% 

Water Transportation 

Other AK-Beaufort 50% 

Other U.S. 50% 

Rail Transportation 

Other U.S. 100% 

Truck Transportation 

AK-NSB 14% 

Other AK-Beaufort 43% 

Other U.S. 43% 

Source: Arctic IMPAK and ICF analysis. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

54

Materials needed for both onshore and offshore pipeline construction and laying sea-floor tieback pipeline were estimated from Exhibit 8-12, 8-13, and 8-16 in IMPAK. The material 

categories used for these three Activity Functions are: 

Construct Onshore Pipeline Pipe 

Cathodic Protection 

Wrapping 

Construct Offshore Pipeline Pipe 

 Vertical Support Members (VSM) 

 Cathodic Protection 

 Wrapping 

 Insulation 

Sea Floor Tie-Back Pipeline Pipe 

 Welding Rods 

 Cathodic Protection 

 Wrapping 

 Concrete Structural Supports 

 Vessel Operations 

3.4 Revenue Function Modeling 

Apart from the Activity Types in the E&D scenario that were used to estimate direct 

expenditures through the ABC-Detailed framework explained above, several variables were also 

classified as Revenue Types that generate various revenue streams for federal, state, and local 

governments. As explained earlier in the report, Revenue Types are activities that result in 

payments to government entities, as opposed to those that require payments to other companies 

for goods and services or payments related to labor. So, for example, oil production is a 

Revenue Type, because it is used to calculate royalty and other such payments to government 

entities or to similar organizations (as in the case of Trans-Alaska Pipeline System charges). 

There are nine Revenue Types associated with the Arctic modeling regions, out of which the 

Revenue Types related to oil production (Total and 8(g)) are used for the Arctic-Beaufort and 

Chukchi-Hope data whereas gas production Revenue Types (Total and 8(g)) are used in the 

“Local Gas” data. The remaining Revenue Types are common to both. Table 3-6 below shows 

the Revenue Types and Functions used in the default data and settings. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

55

Table 3-6: Revenue Type to Function Mapping for Arctic OMA’s 

Revenue Types Revenue Functions 

Fed Tax Revenue 

Gravel Revenues to NSB 

Gravel Revenues to Other AK 

Other AK Revenue to NSB1

Other AK Tax Revenue 

Trans-Alaska Pipeline System (TAPS) Revenue 

Total Oil Production 

Total Royalty

AK (8g) GF Revenues to NSB 

AK (8g) Revenues to Permanent Fund (PF) 

AK 8(g) Royalty 

NSB Intergovernmental Revenues 

Other AK PF Dividend 

Other AK Revenue to Other AK-Beaufort (not going to NSB) 1

8(g) Oil Production 

Other U.S. 8(g) Revenues 

Other U.S. 8(g) Royalty

Total Gas Production Fed Tax Revenue 

Total Royalty

AK (8g) GF Revenues to NSB 

AK (8g) Revenues to PF 

AK 8(g) Royalty 

Other AK PF Dividend 

Other AK Revenue to NSB1

Other AK Revenue to Other AK-Beaufort (not going to NSB) 1

Other AK Tax Revenue 

Other U.S. 8(g) Revenues 

8(g) Gas Production 

Other U.S. 8(g) Royalty 

TAPS Revenue

Acres Leased Total Rental Revenue

AK (8g) GF Revenues to Other AK 

AK 8(g) Rental Revenue 

NSB PF Dividend 

NSB PF Expenditures 

NSB Tax Revenue 

Other U.S. 8(g) Rental Revenue 

8(g) Lease Acreage 

PF Balance

Total Bonus Bid Total Bonus Bid 

Total Revenue

AK (8g) Revenues to General Fund (GF) 

AK 8(g) Bonus Bid 

AK 8(g) Revenues 

Other AK PF Expenditures 8(g) Bonus Bid 

Other U.S. 8(g) Bonus Bid 

Other U.S. Total Revenues

TAPS Surcharge TAPS Revenue

1 These Revenue Functions are new and were created in MAG-PLAN to avoid double counting. 

 Source: MAG-PLAN assumptions. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

56

The difference in the modeling framework between MAG-PLAN and the IMPAK models meant 

that some of the Revenue Function equations needed to be defined differently in order to avoid 

double counting. The new Functions are noted in the Table above. 

Since MAG-PLAN allocates each delineated Revenue Function to onshore area and sector 

individually (whereas IMPAK allocated the final revenue streams to onshore areas), there was a 

potential for double counting in trying to model the revenues in the same way. As a result, the 

revenue streams were separated and mapped individually. This meant creating two new Revenue 

Functions in MAG-PLAN. These Functions and their equations are given below: 

Other AK Revenue to NSB = 

«GRP: Percent of AK Tax and 8g Revenues Distributed to NSB» * «RF: Other AK Tax 

Revenue» 

Other AK Revenue to Other AK-Beaufort (not going to NSB) = 

(1 - «GRP: Percent of AK Tax and 8g Revenues Distributed to NSB») * «RF: Other AK Tax 

Revenue» 

Where GRP indicates a Global Revenue Parameter (the internal MMS User Manual explains 

GRP values for staff members) and RF is the acronym for Revenue Function. 

3.5 Onshore Areas 

The onshore areas in the Alaska modeling regions are treated differently from those for the Gulf 

of Mexico. Recall that the definitions of GOM OSAs are fixed irrespective of which OMA’s are 

used within the GOM. However, for Alaska, MMS uses sub-state onshore areas that depend on 

the modeling sub-regions. For example, the definition of Other-AK is dependent upon the 

modeling sub-region used. When modeling in the Arctic, Other-AK contains all Alaska 

boroughs except the North Slope Borough (and the Northwest Arctic Borough, for the ChukchiHope modeling area). Similarly, Other-Alaska for the South Alaska OMA’s contains the North 

Slope Borough, along with all other boroughs except the Kenai Peninsula Borough. 

Three of these are special OSA’s. Because, as of the completion of this study, OCS-derived 

government revenues flowed directly to the Federal Government and to specific states (not to 

localities) only, the OSA’s for such revenues are Alaska and undesignated U.S.. As direct and 

indirect revenue flow patterns change (e.g., through the Land and Water Conservation Fund on 

the newly mandated Coastal Impact Assistance Program), the user can reallocate portions of the 

revenues accordingly. Expenditures on imported goods and services are allocated to Foreign, 

and “OSA” for which only direct Total Industry Output (i.e., first-round expenditures) is tallied. 

This allows the user to calculate total expenditures and/or to estimate expenditure leakage for a 

given E&D scenario. 
MAG-PLAN Study Alaska – Arctic Modeling Areas 

57

The onshore areas used for the three OMA’s are as follows: 

Beaufort and Beaufort Local Gas – Default settings assume three main onshore areas, in line 

with those used for the Cook Inlet data. These are: 

• North Slope Borough (NSB) 

• Other – Alaska – Beaufort 

• Other – U.S. 

All Alaska OMA’s use these four OSA’s: 

• Alaska 

• Other – U.S. 

• Undesignated U.S. 

• Foreign 

Note that there is a fourth Foreign onshore area in MAG-PLAN for each delineated Activity 

Function. This is used to show the proportion of the expenditure supplied by foreign sources and 

is considered a leakage in MAG-PLAN without any impact on the U.S. economy. However, 

payments to foreign suppliers are reported as part of the Stage I output in MAG-PLAN, should 

the user want to see the total expenditures for a given E&D scenario. 

Chukchi-Hope – Default settings assume four main onshore areas. These are: 

• North Slope Borough (NSB) 

• Northwest Arctic Borough (NwAB) 

• Other – Alaska – Beaufort 

• Other – U.S. 

 

For this OMA, it is assumed that five percent of local spending that would otherwise be allocated 

to the North Slope Borough will instead be spent in the Northwest Arctic Borough. For this 

modeling area also, a fifth onshore area called Foreign is used in MAG-PLAN for each 

delineated Activity Function. This is used to show the proportion of the expenditures allocated 

to foreign suppliers and is considered a leakage in MAG-PLAN without any impact on the U.S. 

economy. However, the Foreign totals are reported as part of the Stage I output in MAG-PLAN, 

should the user want to see the total expenditures for a given E&D scenario. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

58

4 Alaska – South Alaska Modeling Areas 

4.1 Default Settings and Modeling Areas in MAG-PLAN 

There are three basic default settings, with accompanying data, in MAG-PLAN for the South 

Alaska Modeling Areas: 

• Default Cook Inlet – covers exploration and development activities in the Cook 

Inlet/Shelikof Strait Planning Area and the associated regional economic impacts in the 

Kenai Peninsula Borough and other OSAs. 

• Default Semi-Remote – used to model activities in other South Alaska planning areas that 

have nearby population centers that can supply labor, assets, and other materials and 

services. This delivered version of MAG-PLAN (and/or its associated data) is considered 

appropriate for the Gulf of Alaska Planning Area and the associated regional economic 

impacts in the Kenai Peninsula Borough, other local areas, and other OSAs. 

• Default Remote – used to model activities in other South Alaska planning areas that have 

no nearby population centers of any significance for measuring economic impacts. This 

delivered version of MAG-PLAN (and/or its associated data) is considered appropriate 

for the other planning areas (Kodiak, Shumagin, and Aleutian Arc) and the associated 

regional economic impacts in the Kenai Peninsula Borough, other local areas, and other 

OSAs. 

There is a sample run for each OMA in the delivered version of MAG-PLAN. 

4.2 Activity Functions 

Table 4-1 shows the Activity Type E&D scenario variables and their corresponding Activity 

Functions for the South Alaska modeling areas. Note that the secondary support activities are 

given in italics. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

59

Table 4-1: Activity Types and Functions for South Alaska OMA’s 

Activity Types Activity Functions 

Seismic Survey 

Helicopter Support 

Small Work Boat 

Dive Boat 

GEO Survey 

Camp Support 

Spill Contingency 

Drill Exploration Wells 

Helicopter Support Exploration Wells 

Camp Support 

Spill Contingency 

Drill Delineation Wells 

Helicopter Support 

Delineation Wells 

Camp Support 

Spill Contingency 

Construct Exploration Shore Base 

Operate Exploration Shore Base 

Install One Exploration Platform 

Operate Exploration Platform 

Helicopter Support 

Large Work Boat 

Small Work Boat 

Exploration Platforms 

Camp Support 

Spill Contingency 

Drill Production Wells 

Well Workover 

Helicopter Support 

Large Work Boat 

Production Wells 

Camp Support 

Spill Contingency 

Construct Production Shore Base 

Operate Production Shore Base 

Install Production Platforms 

Operate Production Platform 

Major Platform Maintenance 

Helicopter Support 

Large Work Boat 

Small Work Boat 

Camp Support 

Production Platforms 

 Platform Abandonment 

 

 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

60

Table 4-1: Activity Types and Functions for South Alaska OMA’s (continued) 

Activity Types Activity Functions 

Construct Onshore Production Facility Onshore Production Facility Operate Onshore Production Facility 

Construct Marine Terminal 

Marine Terminal 

Operate Marine Terminal 

Spill Contingency 

Lay Offshore Pipeline 

Helicopter Support 

Large Work Boat 

Dive Boat 

Offshore Pipeline 

Camp Support 

Spill Contingency 

Lay Onshore Pipeline 

Helicopter Support Onshore Pipeline 

Camp Support 

Source: ICF Consulting, Inc. 

4.3 Total Expenditures and Sector Allocations 

Existing Sub-Arctic/South Alaska data lacked the level of detail needed under the ABC-Detailed 

framework explained above. Although the labor data had a level of detail similar to the Arctic 

IMPAK data and thus could be used in the same ABC-Detailed structure as above, the same 

were not true for the assets and material sections of the data. As a result, ICF needed to use an 

alternative strategy in order to obtain the capital and material data needed for MAG-PLAN. This 

required using the data that was in the Sub-Arctic IMPAK model and estimating the capital and 

material shares and their IMPLAN sector allocations in a format compatible with MAG-PLAN. 

Several updating procedures were used in order to use the data in MAG-PLAN. The major tasks 

that were required to update the new MAG-PLAN data included analyzing the total expenditures 

and IMPLAN sector allocations (shares) to ensure their validity and updating the IMPLAN 

sector definitions from the 2000 sector definitions (SIC-based) used in IMPAK to the 2001 

definitions (NAICS-based) to be used in MAG-PLAN. With respect to the first data update, ICF 

Consulting relied on the recommendations of local Alaska experts, who were part of the ICF 

team, to review the old total costs, non-labor costs,42 and sector allocations and to suggest 

changes based on their knowledge of the industry. IMPLAN sectors were then updated from the 

old 2000 definitions to the new 2001 definitions based on a crosswalk table to produce the new 

expenditures and sector allocations. 

The various steps involved in estimating the new expenditures from the existing IMPAK data to 

a format compatible with MAG-PLAN are described below. 

 

42 The non-labor cost for each activity function refers to the total cost less the labor cost. It includes expenditures 

on capital, materials, and purchased services. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

61

4.3.1 Estimating Total Expenditures of Each Activity Function 

The original data for the South Alaska modeling areas came from the Excel-based Sub-Arctic 

IMPAK, a model developed by Jack Faucett Associates for MMS.43 This model estimates the 

direct expenditures required for exploration and development activities for three separate 

scenarios, namely Cook Inlet, Remote and Semi-Remote (synonymous with the three default 

South Alaska OMA’s mentioned above). Data for the expenditures per unit of activity are same 

across all scenarios (the difference is in the onshore area distribution only). In order to use the 

existing IMPAK data in MAG-PLAN, data on each Activity Function was extracted from four 

input matrices for the “Cook Inlet Scenario” (note that “CookLocalMatrix” had all zeros since 

projects in Cook Inlet were estimated to have onshore impacts on only one local area –Kenai 

Peninsula Borough). The four matrices were then aggregated across sector allocations, along 

with the “Foreign” contribution for each sector (based on the RPC data discussed below) to 

accommodate for all leakages. This provided the estimated total expenditure for each Activity 

Function. Note that although the leakages due to foreign contribution will not have any 

economic impact on the national economy, it is important to include those in calculating total 

expenditures to accurately reflect the actual expenditures for each activity. Eventually, the 

Foreign contributions were considered leakages in MAG-PLAN when estimating the economic 

impacts on the U.S. economy, but are reported as part of the Stage I output, should the user want 

to see the total expenditures for a given E&D scenario. 

Estimated total expenditures were then evaluated in order to ensure that they reflected the current 

state-of-the-art in technologies and their associated costs. Most of the total cost estimates were 

judged to be appropriate. However, for five activity functions, total costs per unit were adjusted 

based on the ICF teams expert knowledge in Cook Inlet operations. Table 4-2 below shows the 

result of this review with both the old and new total cost numbers. Total expenditures were 

revised upward for four activity functions, and scaled down for the Abandonment function.44 

Table 4-2: Total Cost Changes for Selected Activity Functions (in $) 

 Total Cost Per Unit (1999$) 

Activity Function Unit Old New 

Geo Survey /Month $649,338 $1,250,000 

Install Exploration Platform /Platform $4,426,980 $5,000,000 

Drill Exploration Well /Well $2,433,784 $5,000,000 

Drill Production Well /Well $1,454,589 $3,000,000 

Abandonment /Abandonment $26,538,467 $15,000,000 

Source: Sub-Arctic IMPAK and ICF analysis. 

 

43 Sub-Arctic Economic Impact Model for Petroleum Activities in Alaska (Sub-Arctic IMPAK) OCS Study MMS 

2002-060. 

44 Abandonment in the South Alaska modeling areas is defined as Production Platform Abandonment, consistent 

with previous MMS modeling approach. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

62

4.3.2 Separating Expenditures on Assets and Materials 

Because the level of detail in the labor component of the total expenditure was compatible with 

the ABC-Detailed framework, the labor and non-labor expenditure shares were separated, so that 

the focus would be on the non-labor shares only. This was achieved by aggregating the Total 

Personal Income components from the four input matrices and subtracting this total from the 

total expenditures estimated above. The difference was the estimated total non-labor expenditure 

that was then split between assets and materials. 

Splitting the non-labor expenditures between assets and materials involved several steps. Those 

sectors that were similar to the Arctic IMPAK sector allocation tables were identified. Although 

the activity functions may be dissimilar between the Arctic and South Alaska Modeling Subregions, the individual IMPLAN sectors tended to follow a pattern. Some sectors contribute 

predominantly to asset expenditures whereas others contribute predominantly through material 

and purchased services. For many of the sectors this initial split was performed based on 

research on these sector definitions and by using the Arctic IMPAK model as a guide. 

Remaining sectors were split between capital and materials based on ICF team’s expert judgment 

in the Cook Inlet and North Slope operations. 

The final sector allocations for assets and materials were then reviewed to make adjustments 

where needed to capture the differences between Cook Inlet and Beaufort Sea operations. In 

most cases, the sector allocations in the original data were deemed appropriate. In some 

instances, however, the sector allocations needed to be scaled up or down were adjusted based on 

an understanding of the industries that contribute to the activities in Cook Inlet. In a few cases, 

some new sectors with specific shares were added that did not show any impact in the original 

IMPAK data to reflect the current state-of-the-art. After performing these changes where 

applicable, the sector allocations were normalized to ensure that they added up to 100 percent. 

4.3.3 Updating IMPLAN Sector Definitions 

Another update required to make the IMPAK data compatible with the needs of MAG-PLAN 

involved updating the IMPLAN sectors. The original IMPAK data were based on the 2000 

IMPLAN definitions, whereas MAG-PLAN is based on the most recent 2001 IMPLAN sector 

definitions. The main difference in the IMPLAN classification schemes lie on the original data 

source used. While the 2000 classification scheme is based on the Standard Industrial 

Classification (SIC), the 2001 definitions are based on the new North American Industrial 

Classification System (NAICS). The main data source used for this step was the crosswalk 

between the old (2000) and the new (2001) IMPLAN sector definitions provided by the 

Minnesota IMPLAN Group (MIG). In most cases, the crosswalk between the old and the new 

definitions provided a unique match. That is, one old sector matched to one new sector. For 

some sectors, although the original IMPLAN crosswalk did not provide a unique match, it was 

assumed that only one of the new sectors would be impacted by any activity in the oil and gas 

industry and thus used 100 percent allocation to that sector. For example, old IMPLAN Sector 

160 (Furnitures and Fixtures, NEC45) was matched to new IMPLAN Sectors 366 (Institutional 

Furniture Manufacturing) and 376 (Surgical Appliance and Supplies Manufacturing) in the 

 

45 NEC stands for “not elsewhere classified”. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

63

original crosswalk table. However, since most of the activities in the offshore oil and gas can be 

assumed to impact Sector 366, old 160 was uniquely mapped to new 366. 

However, this simplifying assumption was not applicable for all multiple matches. In some 

instances, it was difficult to determine if only one of the new sectors would be impacted by the 

offshore activities. For these, it was assumed that all of the new sectors would be impacted by 

the estimated proportions based on the data analysis on the original crosswalk table. Thus, the 

final sector allocation used in MAG-PLAN has more sectors with positive impacts than were 

estimated in the original IMPAK data. Table 4-3 below provides examples of three such 

multiple matches. 

Table 4-3: Examples of Multiple IMPLAN Matches 

Old (2000) 

IMPLAN 

Sector 

Old Definition 

New (2001) 

IMPLAN 

Sector 

New Definition Proportion 

 28 Support activities for oil and 

gas operations 60% 

 57 27 Drilling oil and gas wells 38% 

Maintenance 

and repair oil 

and gas wells 439 Architectural and engineering 

services 3% 

 73 Bread and bakery product, 

except frozen, manufacturing 89% 

 79 

Bread, cake, 

and related 

products 72 Frozen cakes and other 

pastries manufacturing 11% 

112 Sawmills 84% 

118 Cut stock, resawing lumber, 

and planing 15% 

123 Miscellaneous wood product 

manufacturing 1% 

134 

Sawmills and 

planing mills, 

general 

119 Other millwork, including 

flooring 0.1% 

Note: Totals may not add due to rounding. 

Source: Sub-Arctic IMPAK and ICF analysis. 

To illustrate this argument, assume that the original IMPAK data estimated 5 percent of the total 

cost of operating an exploration platform would come from Sector 79. Because the old sector 79 

is matched to two new sectors, in the updated data used in MAG-PLAN, assume that 4.5 percent 

(89% of 5%) would come from new Sector 73 and 0.5 percent (11% of 5%) would come from 

Sector 72. Thus one old sector is matched to two new sectors. 

4.4 Onshore Areas 

Data for the onshore area distributions for the South Alaska modeling areas came from work 

done by a previous MMS contractor.46 

 

46 Previously Regional Purchase Coefficient data were used to estimate the onshore area impacts in the Sub-Arctic 

modeling areas. These data were provided to ICF Consulting courtesy of Jack Faucett Associates, the contractor 

who performed this work. 
MAG-PLAN Study Alaska – South Alaska Modeling Areas 

64

The onshore areas used for the three modeling areas are as follows: 

Cook Inlet OMA – As delivered, MAG-PLAN assumes three main onshore areas, in line with 

those used for the Cook Inlet data. These are: 

• Kenai Peninsula Borough (KPB) 

• Other – Alaska – Cook 

• Other – U.S. 

Note that there is a fourth Foreign onshore area in MAG-PLAN for each delineated Activity 

Function. This is used to show the proportion of payments to foreign suppliers and is considered 

a leakage in MAG-PLAN without any impact on the U.S. economy. 

Semi-Remote OMA – Default settings assume four main onshore areas, in line with those used 

for the Semi-Remote input matrices used in IMPAK. These are: 

• Local 

• Kenai Peninsula Borough (KPB) 

• Other – Alaska – Cook 

• Other – U.S. 

For this modeling area also, a fifth onshore area called Foreign is used in MAG-PLAN for each 

delineated Activity Function. This is used to show the proportion of payments to foreign 

suppliers and is considered a leakage in MAG-PLAN without any impact on the U.S. economy. 

However, they are reported as part of the Stage I output in MAG-PLAN, should the user want to 

see the total expenditures for a given E&D scenario. 

Remote OMA – Default settings assume four main onshore areas, in line with those used for the 

Remote input matrices used in IMPAK. These are: 

• Local 

• Kenai Peninsula Borough (KPB) 

• Other – Alaska – Cook 

• Other – U.S. 

For this modeling area also, a fifth onshore area called Foreign is used in MAG-PLAN for each 

delineated Activity Function. This is used to show the proportion of payments to foreign 

suppliers and is considered a leakage in MAG-PLAN without any impact on the U.S. economy. 

However, they are reported as part of the Stage I output in MAG-PLAN, should the user want to 

see the total expenditures for a given E&D scenario. 

In order to estimate the Stage II impacts, MAG-PLAN uses the Kenai Peninsula Borough 

multipliers for the “undefined” Local onshore area. Users can change those to multipliers from 

another borough, if required. MMS staff members can look in the User Manual for instructions 

on how to update multipliers. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

65

5 Alaska – Bering Sea Modeling Areas 

5.1 Default Settings in MAG-PLAN 

There are three basic default settings, with accompanying data, in MAG-PLAN for the Bering 

Sea Modeling Areas: 

• Default Southeastern Bering – covers exploration and development activities in parts of 

North Aleutian Basin and St. George Basin Planning Areas and the associated regional 

economic impacts in the Dillingham Borough and other OSAs. 

• Default North Bering – covers exploration and development activities in Norton Basin, 

St. Mathew Hall, and the shallow water parts of Navarin Basin Planning Areas, and the 

associated regional economic impacts in Nome and Bethel Boroughs and other OSAs. 

• Default Western Deepwater Bering – covers exploration and development activities in 

Bowers Basin, Aleutian Arc, and the deep water parts of Navarin Basin, and St. George 

Basin Planning Areas, and the associated regional economic impacts in Kenai Peninsula 

Borough and Aleutians West and other OSAs. 

There is a sample run for each OMA in the delivered version of MAG-PLAN. 

5.2 Offshore Modeling Areas 

Previous economic impact models used by MMS treated the Bering Sea Planning Areas as part 

of the Sub-Arctic modeling areas. However, in MAG-PLAN, Bering Sea is treated as an 

independent sub-region with its own modeling areas and cost functions. Because of this new 

treatment, a considerable amount of effort was spent on defining appropriate OMA’s and 

Activity Functions for leasing, exploration, development, production, and transportation for the 

Bering Sea modeling areas, with focus on the North Aleutian Basin (NAB) Planning Area.47 

Critical factors that impact exploration and development (E&D) activities and expenditures in 

the Bering Sea include the expected size and distribution of oil and gas resources; the Bering 

Shelf gradient and water depth; winter ice conditions; weather patterns, tides and winds; marine 

and air access; sensitive areas for fishing and marine mammals; and the remoteness of the Bering 

Sea and lack of E&D infrastructure. Critical factors for onshore economic impacts include the 

small size and remote location of local population centers; skills and education; road and air 

access, other employment, income, and cultural norms. The original plan was to use the activity 

function data for Cook Inlet oil and gas operations from MMS’s current Sub-Arctic IMPAK 

model as the starting point for the activity functions to be used for all the planning areas within 

 

47 ICF Consulting was directed to identify one planning area in the Bering Sea sub-region from which to extrapolate 

industry activities and expenditures to similar activities in the other six Bering Sea Planning Areas. Although it is 

not now available for leasing, the North Aleutian Basin (NAB) was selected as the best Bering Sea prototype 

because of data availability and the assumed existence of both oil and gas in commercial quantities. Development of 

NAB data for MAG-PLAN is solely to provide the most “typical” and realistic industry production functions 

possible for Bering Sea activity, and does not imply any judgment as to whether the Federal government should, or 

will, consider NAB for leasing in the future. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

66

the Bering Sea sub-region, including the North Aleutian Basin.48 Since then, the research 

indicated that part of this area may have more in common with the Arctic cost functions used for 

the Arctic IMPAK model. Because of this, MAG-PLAN now uses both the Cook Inlet and 

North Slope cost functions for relevant parts of the Bering Sea modeling areas. The approach 

used to define the appropriate cost functions for different offshore areas within this area is 

presented below. 

ICF Consulting compared oil and gas activities in the Cook Inlet with potential oil and gas 

activities in the NAB starting with the MMS’s sample E&D scenario for the North Aleutian 

Basin Planning Area shown in table 5-2 below. The conclusion was that most of the Activity 

Function data in the Sub-Arctic IMPAK model would apply (with adjustments) in the 

Southeastern area of the Bering Sea Shelf that does not experience winter sea ice. Areas of the 

Bering Sea Shelf north of 57 degrees latitude (including the northern NAB planning area) are 

impacted by persistent winter sea ice. Consequently, oil and gas activity functions and 

expenditures for the Northern Bering Sea are best modeled using the Activity Function cost data 

from the Arctic IMPAK model.49 Since, water depth and winter ice conditions will directly 

impact Bering Sea oil and gas Activity Functions and corresponding costs, the Bering Sea 

OMA’s were defined on a combination of water depth and winter ice conditions. 

Similarities and Differences Between the NAB and Cook Inlet Planning Areas 

The North Aleutian Basin Planning Area is located on the southeastern part of the Bering Sea 

and is bounded on the south and east by the Alaska Peninsula and the Alaska mainland. Nearby 

populated onshore areas include the Dillingham Census Area, the Bristol Bay Borough, the Lake 

and Peninsula Borough, and the Aleutians East Borough. Mean undiscovered, recoverable oil 

and gas resources for NAB are estimated to be 6.791 trillion cubic feet (Tcf) of gas and 223 

million barrels of oil (MMb) in a single Tertiary-age geologic play (Oligocene-Miocene play). 

The estimated recoverable resources under the High Case scenario are 17.33 Tcf of gas and 575 

MMb of oil (with a 5-percent probability of occurrence). The single stratigraphic test well 

drilled in the NAB showed good to excellent reservoir quality at depths above 9,500 feet, and 

indications that the Basin is gas-prone.50 North Aleutian Basin Lease Sale 92 was held October 

1988 and more than 5.6 million acres were offered in 990 lease blocks. Twenty-three leases 

were issued for a total of 121,757 acres, but no commercial oil and gas production was 

established. Figure 5-1 shows the NAB Tertiary play boundary, the leased acreage from Sale 92, 

and the location of the stratigraphic test well. The Cook Inlet, in contrast, has experienced nearly 

five decades of oil and gas exploration and development activity in state waters and onshore. 

Proved reserves in the Cook Inlet are 80 million barrels of oil and 2.088 Tcf of gas. Total oil and 

natural gas liquids (NGL) production was 10.059 million barrels in 2003; total net gas 

 

48 Sub-Arctic Economic Impact Model for Petroleum Activities in Alaska (Sub-Arctic IMPAK) OCS Study MMS 

2002-060. 

49 Arctic Economic Impact Model for Petroleum Activities in Alaska (Arctic IMPAK) OCS Study MMS 2002-066. 

50 See Parker, J. and R. Newman, 1998, North Aleutian Basin Assessment Province in Undiscovered Oil and Gas 

Resources of Alaska Federal Offshore, U.S. Minerals Management Service OCS Monograph MMS 98-0054. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

67

production was 208 Bcf. Forest Oil’s Osprey platform was installed at Redoubt Shoal Field in 

June 2000 and oil production began in December 2002. Osprey is the first platform installed on 

a new Cook Inlet Field since 1986. 

The Bering Sea Shelf is an extremely wide, shallow continental shelf, ranging from 300 miles 

wide in the southeast along the Alaska Peninsula to more than 450 miles wide in the north. This 

area is very flat with a gentle shelf gradient and water depths less than 200 meters. The water 

depth across most of the North Aleutian Basin Planning Area is less than 50 meters. The wide, 

shallow Bering Shelf contributes to large tidal ranges, huge storm waves, localized areas of sea 

floor scouring and erosion, and coastal erosion during storms. 

Unlike most of the Bering Sea, Cook Inlet is relatively narrow. Cook Inlet is bounded by 

mudflats along the coast and water depth in the center of Cook Inlet ranges to 120 meters. Cook 

Inlet experiences very large tidal ranges (up to 30 feet and six knots) and substantial coastal 

erosion. Whereas much of the Bering Sea is ice-covered in winter, during warm years and 

minimal ice cover, this area is largely ice-free. During typical years, the maximum ice cover 

may extend only to the northern half of the NAB.51 Exploration wells and production facilities 

for both the North Aleutian Basin and Cook Inlet must be able to withstand rough marine 

conditions including high tidal ranges, storm scouring and coastal erosion, and the potential for 

severe winter ice. Winter ice conditions in the Cook Inlet are less severe than the North Aleutian 

Basin. 

 

51 See, for example, The Bering Sea Ecosystem, National Academy of Sciences, 1996. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

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Figure 5-1: North Aleutian Basin Potential Area of Interest for 

Leasing, Exploration and Production 

Source: Sherwood, K. W., Craig, J. D., and L. W. Cooke, 1996. 

Defining Bering Sea OMA’s 

The Bering Sea in general, and the North Aleutian Basin Planning Area, in particular, present 

unique challenges for MAG-PLAN because of the combined influence of water depth and winter 

ice conditions on drilling and production activity functions. The type of drilling and production 

technology used in Bering Sea oil and gas E&D activities, and the estimated cost of the 

corresponding activity functions, are largely determined by water depth and winter ice 

conditions. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

69

Figure 5-2: Bering Sea; Southern Extent of Winter Sea Ice 

Source: ICF analysis on Alaska OCS map, courtesy MMS. 

In the NAB Planning Area, for example, figure 5- 2 shows that the area south of latitude 57 

degrees is generally free of severe winter ice conditions. Modified costs and E&D activities 

from the Sub-Arctic IMPAK model could be applied in this area. In the northern portion of the 

NAB Planning Area, winter ice conditions are more severe, but the water depth is shallow, so the 

Arctic IMPAK Activity Functions and costs can be adapted. As a result, MAG-PLAN treats the 

Bering Sea area into three OMA’s as shown in table 5-1 below. 

Table 5-1: Bering Sea OMA’s 

Bering Sea OMA’s Southeastern Bering 

Sea (OMA-1) 

North Bering Sea 

(OMA-2) 

Western Bering Deep 

Water (OMA-3) 

Planning Areas: North Aleutian and 

St. George (Sea) Basins 

Norton, St. Matthew Hall 

and Navarin (Sea) Basins 

Aleutian, Bowers, 

Navarin (DW) and 

St. George (DW) Basins 

Boundary Southeast of 57 Deg N 

Latitude: 

North of 57 Deg N 

Latitude: 

Southeast of 57 Deg N 

Latitude: 

Water Depth, Meters 

Range Average 

0 – 200 Meters 

70 Meters 

0 – 200 Meters 

70 Meters 

200 – 4,000 Meters 

3,000 Meters 

Sea Ice Ice Free Annual Winter Sea Ice Ice Free 

Potential Supply Base 

Port/Airport Dutch Harbor Nome or St. Paul Adak 

Production Technology Sub-Arctic Arctic Sub-Arctic (Deep Water) 

Data Source (with 

adjustments) 

Cook Inlet (Sub-Arctic 

IMPAK) Arctic IMPAK South AK-Semi Remote (Sub-Arctic IMPAK) 

Local Onshore Area Dillingham Nome KPB 

Other Local Area Aleutians East Borough Bethel Aleutians West 

Source: ICF analysis. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

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5.3 Activity Functions 

Table 5-2 below contains a prototype E&D scenario52 that can be applied to the Southeastern 

Bering (OMA-1) and Western Bering (OMA-3).53 The primary Activity Functions assumed for 

this E&D scenario are the same as those modeled for the Cook Inlet Modeling Area in MAGPLAN including, for example, leasing acreage, seismic surveys, exploratory wells, delineation 

wells, production wells, production platform, and pipelines for transporting crude oil and natural 

gas. It is assumed that the main area of interest in the Bering Sea will be the reservoir rock 

identified in figure 5-1 as the “thick Tertiary age section, high interest” and, specifically, the 

vicinity of the stratigraphic test well and acreage leased during Sale 92. Estimated water depth is 

approximately 10 to 50 meters or about 33 to 164 feet. The estimated exploratory well depth for 

this modeling areas is 16,000 to 18,000 feet and the estimated well depth for production wells is 

10,000 to 15,000 feet. 

Possible onshore support locations for seismic and exploratory drilling are Unalaska (Dutch 

Harbor) because of its harbor and marine facilities, and Cold Bay, because of its airport. The 

approximate distance from Unalaska to the potential exploration area of interest is 250 miles; the 

approximate distance from the community of Cold Bay to the exploration area is less than 100 

miles. The proposed exploration area of interest is approximately 600 miles from Anchorage, 

more than 220 miles from Dillingham, and approximately 200 miles from King Salmon. 

Table 5-3 below lists the Activity Functions currently used for the Cook Inlet portion in MAGPLAN. Most activity functions are expected to be common to oil and gas E&D operations in 

both Cook Inlet and OMA’s 1 and 3 in the Bering Sea, including “secondary” activities that 

comprise the more complex Activity Functions. For these Activity Functions, the MAG-PLAN 

data for Cook Inlet are applied to these OMA’s, with appropriate adjustments as needed to 

accommodate the different conditions. For example, marine seismic surveys are conducted in 

both Cook Inlet and Bering Sea. Both areas require the construction of an exploration shore base 

and a production shore base. Both areas drill exploration wells, delineation wells and production 

wells. However, wells in the Bering Sea OMA’s are expected to be deeper than Cook Inlet 

wells, so costs are appropriately scaled to reflect that. Installation of production platforms, 

pipelines, and onshore production facilities operations are common to both areas. Both the Cook 

Inlet and Bering Sea can be expected to require oil spill contingencies, well workovers and 

platform maintenance as well as helicopter support, base camp support, and a host of support 

tasks by workboats, dive boats and other marine vessels. 

Construction and operation of an exploration shore base and an onshore production shore base 

are expected to be Activity Functions common to both Cook Inlet and Southeastern Bering Sea 

(OMA-1). Due to the lack of roads and the remoteness of the onshore areas that correspond to 

 

52 Note that this is an illustrative scenario and does not include all the elements that a full MMS E&D scenario 

comprises for this OMA, nor does it reflect actual MMS data (which have not been updated for the NAB since the 

area was made unavailable for leasing). MMS staff members can refer to the User Manual for the full E&D scenario 

template. 

53 Because the Southeastern Bering (OMA-1) is the main modeling area in MAG-PLAN, the following discussion 

focuses on the activities that can be used for this OMA. Table 5-4 below summarizes the Activity Functions used 

for North Bering (OMA-2). For this OMA, MAG-PLAN uses the Arctic E&D scenario along with the Arctic costs. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

71

the most promising areas of the Bering Sea for oil and gas production, greater use of helicopter 

support, camp support, supply boats and workboats are expected for this modeling area than for 

Cook Inlet, with corresponding higher unit costs. Onshore pipelines will be needed for this 

modeling area to transport oil and gas to sales points, which might include crude oil and 

condensate to a marine terminal on the Alaska Peninsula, natural gas to local markets or natural 

gas to an LNG plant or transmission pipeline. 

Finally, the Southeastern Bering Sea Activity Functions should include the final disposition of 

produced oil and gas including construction and operation of a crude marine terminal and 

construction and operation of onshore production facilities that will include gas and oil 

processing in preparation for shipping.54 The Sub-Arctic IMPAK model contains data for Cook 

Inlet for construction and operation of a marine terminal and an onshore production facility with 

oil and gas processing trains. These data are applied to the Bering Sea OMA’s with appropriate 

scaling factors. 

 

54 ICF recommends that in future, MMS consider construction and operation of an LNG plant as a possible option 

for the Bering Sea OMA’s in MAG-PLAN. However, this function is currently not modeled in MAG-PLAN. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

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Table 5-2: Sample North Aleutian Basin E&D Scenario (Unit of measure – number, unless otherwis

(Does not include all of the revenue elements such as 8 (g) Lease acreage, bonus bid; 

 gas and oil price, expected gas production and contains illustrative data only) 

Year 

Shore 

Base 

Distance 

(Miles) 

Seismic 

Survey 

(Months) 

Total 

Lease 

Acreage 

(‘000 

Acres) 

Exploration 

Wells 

Delineation 

Wells 

Exploration 

Platforms 

(Or Other 

Options) 

Production 

Platforms 

Production 

& Injection 

Wells 

Offshore 

Pipeline 

(Miles) 

Onsho

Pipelin

(Miles

1 25 4 121.8 0 0 0 0 0 0 0 2 25 0 121.8 1 0 0 0 0 0 0 3 25 0 121.8 1 0 0 0 0 0 0 4 25 0 121.8 1 2 0 0 0 0 0 5 25 0 121.8 1 1 0 0 0 0 0 6 25 0 121.8 1 2 1 1 4 0 0 7 25 0 121.8 1 2 0 0 12 0 0 8 30 0 121.8 1 3 1 1 4 0 0 9 30 0 100 1 2 0 0 13 0 0 

10 35 0 100 0 0 1 1 4 35 80 11 35 0 90 0 0 1 1 17 50 70 12 35 0 90 0 0 1 1 12 35 80 13 35 0 80 0 0 0 0 10 50 0 14 35 0 80 0 0 0 0 0 0 80 15 35 0 65 0 0 0 0 0 0 70 16 35 0 65 0 0 0 0 0 0 0 17 35 0 55 0 0 0 0 0 0 0 18 35 0 55 0 0 0 0 0 0 0 19 40 0 50 0 0 0 0 0 0 0 20 40 0 50 0 0 0 0 0 0 0 21 40 0 40 0 0 0 0 0 0 0 22 40 0 40 0 0 0 0 0 0 0 23 40 0 30 0 0 0 0 0 0 0 24 40 0 30 0 0 0 0 0 0 0 25 40 0 25 0 0 0 0 0 0 0 26 40 0 25 0 0 0 0 0 0 0 27 40 0 20 0 0 0 0 0 0 0 28 40 0 20 0 0 0 0 0 0 0 29 40 0 20 0 0 0 0 0 0 0 30 40 0 20 0 0 0 0 0 0 0 

 Source: Minerals Management Service 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

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Table 5-3: Current Cook Inlet and Proposed Activity Functions for Modeling Areas 1 and 3

Activity Type (Units) Activity Functions For OMA’s 1 and 3 

Seismic Survey 

Helicopter Support 

Small Work Boat 

Dive Boat 

GEO Survey (Months) 

Camp Support 

Spill Contingency 

Drill Exploration Wells 

Helicopter Support Exploration Wells (Number) 

Camp Support 

Spill Contingency 

Drill Delineation Wells 

Helicopter Support Delineation Wells (Number) 

Camp Support 

Spill Contingency 

Construct Exploration Shore Base 

Operate Exploration Shore Base 

Install One Exploration Platform 

Operate Exploration Platform 

Helicopter Support 

Large Work Boat 

Small Work Boat 

Exploration Platforms (Number) 

Camp Support 

Spill Contingency 

Drill Production Wells 

Well Workover 

Helicopter Support 

Large Work Boat 

Production Wells (Number) 

Camp Support 

Spill Contingency 

Construct Production Shore Base 

Operate Production Shore Base 

Install Production Platforms 

Operate Production Platform 

Major Platform Maintenance 

Helicopter Support 

Large Work Boat 

Small Work Boat 

Camp Support 

Production Platforms (Number) 

 Platform Abandonment 

Construct Onshore Production Facility Onshore Production Facility (Number) Operate Onshore Production Facility 

Construct Marine Terminal Marine Terminal (Number) Operate Marine Terminal 

 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

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Table 5-3: Current Cook Inlet and Proposed Activity Functions for Modeling Areas 1 and 3 

(continued)

Activity Type (Units) Activity Functions For OMA’s 1 and 3 

Spill Contingency 

Lay Offshore Pipeline 

Helicopter Support 

Large Work Boat 

Dive Boat 

Offshore Pipeline (Miles) 

Camp Support 

Spill Contingency 

Lay Onshore Pipeline 

Helicopter Support Onshore Pipeline (Miles) 

Camp Support 

Note: Secondary activity functions are given in italics in table 5-3 above. 

Table 5-4 below lists the Activity Functions that are used for the Northern Bering Sea Modeling 

Area (OMA-2). These Activity Functions are derived from the Arctic IMPAK model and are 

used in the delivered version of MAG-PLAN. 

Table 5-4: Current Arctic and Proposed Activity Functions for Bering OMA 2 

Activity Type (Units) Activity Functions For OMA-2 

Seismic Survey Geo Survey Helicopter Support 

Spill Contingency Exploration Wells Drill Exploration Wells 

Spill Contingency Delineation Wells (Number) Drill Exploration Wells 

Spill Contingency 

Exploration Ice Islands (Number) Ice Exploration Islands 

Ice Roads 

Spill Contingency 

Production Wells Drill Production Wells 

Well Workover 

Spill Contingency 

Place Gravel Island 

Protect Gravel Island 

Equip Production Island 

Operate Production Island 

Ice Roads 

Helicopter Support 

Platform Abandonment 

Gravel Production Islands 

Barge Support 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

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Table 5-4: Current Arctic and Proposed Activity Functions for Bering OMA 2 (continued) 

Activity Type (Units) Activity Functions For OMA-2 

Construct Offshore Pipeline 

Spill Contingency 

Operate Rough Water Drill Ship 

Sea-floor Tie Back Pipeline 

Move Platforms 

Helicopter Support 

Sub-sea Production Well Completions 

Barge Support 

Spill Contingency 

Operate Calm Water Drill Ship 

Move Platforms 

Helicopter Support 

Drill Ships for Calm Water Exploration 

Barge Support 

Spill Contingency 

Operate Rough Water Drill Ship 

Move Platforms 

Helicopter Support 

Drill Ships for Rough Water Exploration 

Barge Support 

Spill Contingency 

Operate MBFS for Exploration 

Move Platforms 

Place Gravel Islands 

Helicopter Support 

Mobile Bottom Founded Structures (MBFS) for 

Exploration 

Barge Support 

Spill Contingency 

Equip Production Islands 

Move Platforms 

Purchase MBFS for Production 

Platform Abandonment 

Ice Roads 

Operate Production Island 

Place Gravel Islands 

Protect Gravel Islands 

Helicopter Support 

MBFS for Production 

Barge Support 

Spill Contingency 

Offshore Pipeline (Miles) Construct Offshore Pipeline 

Ice Roads 

Spill Contingency 

Onshore Pipeline (Miles) Construct Onshore Pipeline 

Ice Roads 

Landbase Operations Landbase Operations 

Note: In addition, two other secondary activity functions – General Personnel Transport and Camp 

 Support are also mapped to all the Activity Types. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

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5.4 Total Expenditures and Sector Allocations 

5.4.1 Total Cost Adjustments 

Because the default data for the Bering Sea modeling areas in MAG-PLAN were based on the 

Cook Inlet data (and on the Beaufort Sea data for OMA-2), the problems associated with the 

Cook Inlet data discussed above were also applicable for the Bering Sea OMA’s. As a result, 

similar adjustments were needed to ensure that the data were compatible with the MAG-PLAN 

format. Out of the three modeling areas in MAG-PLAN for the Bering Sea, adjusted Cook Inlet 

(and South Alaska) cost functions are used for two (OMA-1 and OMA-3). Arctic cost functions 

are used for the third modeling area. This section describes the adjustments performed to the 

default cost functions. 

Southeastern Bering OMA-1 – Because of the lack of E&D infrastructure and the relative 

remoteness of this region compared to the other modeling sub-regions in MAG-PLAN, the 

default cost functions were adjusted to reflect the higher expenditures that would be needed to 

conduct exploration and development activities. As a result, most of the Cook Inlet costs were 

adjusted upwards before using them in MAG-PLAN as shown in table 5-5 below.55

Table 5-5: Scaling Factors for Percentage Increases in Costs to 

Cook Inlet Activity Functions (in %) 

Activity Function Scaling Factors 

1. Geological Survey 20% 

2. Spill Contingency Response 100% 

3. Construct Exploration Shore Base 100% 

4. Operate Exploration Shore Base 40% 

5. Install Exploration Platform 20% 

6. Operate Exploration Platform 20% 

7. Drill Exploration Well 30% 

8. Construct Production Shore Base 100% 

9. Operate Production Shore Base 30% 

10. Install Production Platform 30% 

11. Operate Production Platform 20% 

12. Drill Production Well 30% 

13. Lay Offshore Pipeline 30% 

14. Lay Onshore Pipeline 30% 

15. Construct Onshore Production Facility 30% 

16. Operate Production Facility 20% 

17. Construct Marine Terminal 30% 

18. Operate Marine Terminal 20% 

19. Major Platform Maintenance 20% 

20. Well Workover 30% 

 

55 Note that MAG-PLAN also provides a convenient way to use scaling factors on these costs. MMS staff members 

can refer to the user manual for more details. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

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Table 5-5: Scaling Factors for Percentage Increases in Costs to 

Cook Inlet Activity Functions (in %) (continued) 

Activity Function Scaling Factors 

21. Helicopter Support 20% 

22. Large Workboat 20% 

23. Small Workboat 20% 

24. Landing Craft 20% 

25. Dive Boat 30% 

26. Camp Support 20% 

27. Abandonment 20% 

Source: ICF analysis. 

Total costs for three Activity Functions in the Cook Inlet were thought to be significantly low to 

model the expenditures needed in the Southeastern Bering Sea. Hence, cost of “Spill 

Contingency Response,” and “Construction of an Exploration Shore Base,” and “Construction of 

a Production Shore Base” were doubled from the Cook Inlet default values. Total cost for “Spill 

Contingency Response” in the Cook Inlet was estimated to be $550,000, whereas in the 

Southeastern Bering Sea, it was estimated to cost approximately $1.1 million. Similarly, total 

cost for “Constructing an Exploration Shore Base” was estimated to be $3.7 million as opposed 

to approximately $1.7 million in the Cook Inlet and the corresponding cost estimates for 

“Constructing a Production Shore Base” were $4.6 million (Southeastern Bering) and $2.3 

million (Cook Inlet). 

The next highest increase in total cost was estimated to be for “Operating an Exploration Shore 

Base.” The Cook Inlet cost of $950,000 was increased by about 40 percent to $1.4 million. The 

remaining total cost numbers were increased by 20 percent to 30 percent to better reflect the 

Bering Sea conditions. 

Recall that because of the lack of detailed engineering estimates for these Activities in the Cook 

Inlet data and because MAG-PLAN’s framework was built on the assumption of using detailed 

estimates, it was necessary to break down the total cost to labor and non-labor costs. The nonlabor costs were then further broken down to capital and material expenditures (which included 

purchased services). Increasing the total costs also meant that the labor and non-labor costs were 

appropriately scaled up in MAG-PLAN. Moreover, IMPLAN sector allocations were also 

appropriately scaled up due to this. For example, for the “Construction of Exploration Shore 

Base” Activity Function, allocation for Sector 259 (Construction machinery manufacturing) 

increased from $105,000 in the Cook Inlet costs to $210,000 for the Bering Sea modeling areas. 

Similar proportionate increases were estimated for the other sectors for all the Activity 

Functions. 

North Bering OMA-2 – the default cost functions used for the North Bering OMA were based 

on the detailed engineering cost estimates from the Arctic IMPAK model. Users in MAG-PLAN 

can scale those costs up using a similar methodology when creating new cost functions. 
MAG-PLAN Study Alaska –Bering Sea Modeling Areas 

78

Western Bering OMA-3 – the default cost functions used for the Western Bering Deepwater 

OMA are from the South Alaska Semi-Remote cost functions in Sub-Arctic IMPAK. However, 

because there are no differences between the total cost estimates between the Cook Inlet and the 

Semi-Remote areas in IMPAK, the total costs used in MAG-PLAN are also the same as those 

discussed above (with some rounding error). 

5.5 Onshore Areas 

The onshore areas used for the three modeling areas are as follows: 

Southeastern Bering OMA – the delivered version of MAG-PLAN assumes three main onshore 

areas, in line with those used for the Cook Inlet data. These are: 

• Dillingham 

• Other – Alaska – NAB 

• Other – U.S. 

Note that there is a fourth Foreign onshore area in MAG-PLAN for each delineated Activity 

Function. This is used to show the proportion of payments to foreign suppliers and is considered 

a leakage in MAG-PLAN without any economic impact. However, they are reported as part of 

the Stage I output in MAG-PLAN, should the user want to see the total expenditures for a given 

E&D scenario. 

North Bering OMA – the delivered version of MAG-PLAN assumes four main onshore areas, in 

line with those used for the Arctic (North Slope) data. These are: 

• Nome 

• Bethel 

• Other – Alaska – NAB 

• Other – U.S. 

For this modeling area also, a fifth onshore area called Foreign is used in MAG-PLAN for each 

delineated Activity Function. This is used to show the proportion of payments to foreign 

suppliers and is considered a leakage in MAG-PLAN without any economic impact. However, 

they are reported as part of the Stage I output in MAG-PLAN, should the user want to see the 

total expenditures for a given E&D scenario. 

Western Bering OMA – the delivered version of MAG-PLAN assumes four main onshore areas, 

in line with those used for the South Alaska Semi Remote data. These are 

• Kenai Peninsula Borough 

• Aleutians West 

• Other – Alaska – NAB 

• Other – U.S. 
MAG-PLAN Study Alaska – Bering Sea Modeling Areas 

79 

For this modeling area also, a fifth onshore area called Foreign is used in MAG-PLAN for each 

delineated Activity Function. This is used to show the proportion of payments to foreign 

suppliers and is considered a leakage in MAG-PLAN without any economic impact. However, 

they are reported as part of the Stage I output in MAG-PLAN, should the user want to see the 

total expenditures for a given E&D scenario. 
MAG-PLAN Study Possible MAG-PLAN Enhancements 

80

6 Possible MAG-PLAN Enhancements 

MAG-PLAN is a powerful and user-friendly modeling framework. Version 2.0 is a workable, 

flexible model with scope for further enhancement. MMS’ need for a model that uses a uniform 

approach across the Gulf of Mexico and Alaska modeling regions will be met by MAG-PLAN. 

However, as is true with any new model, the framework in MAG-PLAN has areas with 

significant undeveloped potential that MMS can use to build on the current version. These 

would require more data, but the framework has been created, such that, if the data are available, 

MMS can expand its modeling capabilities with limited effort. The following illustrates a few of 

these areas that can be enhanced in future. 

6.1 Bering Sea Data 

The current Activity Functions used for the Bering Sea modeling areas in MAG-PLAN are based 

on the South Alaska and Arctic modeling areas. However, for the next version of MAG-PLAN, 

MMS should consider certain Activity Functions that may be unique for the Bering Sea. 

For example, several new Activity Functions for oil and gas production are possible for the 

Bering Sea, depending upon winter ice conditions and water depth. All new production 

platforms in this area would probably be equipped with injection wells for waste disposal, so 

injection wells could be treated as a new Activity Function. 

In addition, more than one Activity Function for drilling exploratory wells could be considered 

for the Bering Sea, such as Drill Ships and/or Jack-up Rigs. Recent Cook Inlet exploration wells 

are drilled as extended reach wells from an exploration or production platform. No jack-up rigs 

or drill ships are currently used in Cook Inlet, although these may be viable options for future 

exploratory drilling in the Bering Sea. Finally, MMS should consider construction and operation 

of an LNG plant as new Activity Functions for the Bering Sea. 

6.2 TCAF/LPCAF 

Because of limited resources, several new and useful features created in MAG-PLAN are not 

used optimally in Version 2.0. For example, Technological Change Adjustment Factors (TCAF) 

and Labor Productivity Change Adjustment Factors (LPCAF) are features that can be used to 

capture the impact of innovation and learning-by-doing on offshore activities. This is crucial not 

only because this industry is known for its innovative prowess, but also because MMS considers 

E&D scenarios that cover an extended time period. ICF recommends MMS consider improving 

these features in the model to better reflect the current and future state of the art. 

One possible approach to model the impact of technological change and innovation is to 

incorporate the effect of learning-by-doing through progress ratio parameters. Because unit 

costs of offshore activities tend to decrease with experience from repeated activities, progress 

ratios measure the rate at which this cost reduction takes place when output increases. For 

example, a progress ratio of 80 percent implies that costs decrease by 20 percent for every 

doubling of output. 
MAG-PLAN Study Possible MAG-PLAN Enhancements 

81 

While evaluating the presence of progress ratio data for offshore oil and gas activities was 

beyond the scope of this study, further research in this area could be undertaken to identify 

studies that estimate progress ratios using historical data. Some of this modeling enhancement

may have to rely on professional judgment, but performing a literature review to determine the 

scope of prior research in this area would be a logical first step. 

6.3 Pacific and Atlantic OMA’s 

MAG-PLAN Version 2.0 has placeholders for both the Pacific and Atlantic OMA’s. ICF 

Consulting understands that the Pacific OCS Regional Office may have access to data that can be 

used to create default settings in MAG-PLAN similar to the GOM and Alaska modeling areas. 

This could be a significant upgrade to MMS’s modeling capabilities. MAG-PLAN provides the 

user the necessary flexibility to populate with new data for new and existing modeling areas. 

Moreover, because of the option to use either ABC-Detailed or ABC-Override, depending on 

data availability, MMS could consider adding data for these OMA’s with limited effort. 

6.4 2005 Energy Policy Act 

The recently enacted 2005 Energy Policy Act grants MMS new authority to regulate alternative 

energy uses of the OCS, including wind, wave, and solar energy. With limited effort, MAGPLAN can be used to model the regional economic impacts arising from these new activities. 

While this has not been evaluated yet, MAG-PLAN now provides the flexibility to analyze the 

regional economic impacts of developing these new sources of energy, if the relevant data exist 

and can be used to populate the model. 

83 

APPENDICES 

MAG-PLAN Study Appendix A

85

Appendix A 

GOM Offshore Drilling Cost Estimates 

The costs of drilling offshore wells for the Gulf of Mexico (GOM) were estimated with a multistep procedure, as follows: 

• A statistical model was fit to drilling cost data to give a method of adjusting offshore 

drilling costs across time to account for general changes in cost. 

• Four years of offshore cost data (1999-2002) were pooled by adjusting all costs to 2002 

dollars. 

• The average cost of a well of any type (oil, gas, or dry) was found for all combinations of 

three water-depth and three well-depth categories – nine means in all. The well-depth 

categories were 0-14,999 feet, 15,000-17,999 feet, and greater than or equal to 18,000 

feet. The water-depth categories were 1-249 feet, 250-249 feet, and more than 500 feet. 

• The first set of average costs was manipulated using data from CMI (Coastal Marine 

Institute) to obtain differential rates for exploratory and development well costs. 

The Statistical Model of Drilling Cost 

The cost of drilling offshore wells was estimated using a statistical model of data from recent 

annual editions of the “Joint Association Survey on Drilling Costs” (JAS) by API. The model 

was fit to data from the six-year period, 1997-2002. While more years of data are available 

before 1997, they do not include water depth categories, which were wanted in the model. Using 

the water depth information, however, required the use of the data for all offshore U.S. wells 

instead of just the Gulf of Mexico. However, this group is completely dominated by drilling in 

the GOM, so it is doubtful that this biases the results to any significant degree. 

The regression specification is one that has been tested many times in similar studies and has 

consistently provided better fits than the alternatives that appear in the relevant literature. It 

takes this form: 

t = + WellDeptht + ∑ i

WaterDepthi + ∑ jYearj + t ln(Cost ) α βsqrt( ) ω γ ε

where ln(Costt ) is the natural log of the average well cost for observation t; 

sqrt(WellDepth) is the square root of well depth; 

WaterDepthi is a set of dummy variables for the water-depth categories; 

Yearj is a set of dummy variables for the years of the data; 

εt is the residual error; and 

α, β, ω, and γ are parameters to be estimated. 
MAG-PLAN Study Appendix A

86

This equation is fit to the data for each well type separately. In the literature one can find the log 

of cost regressed on the log of well depth, and the JAS regresses the log of cost on depth and 

depth squared. The consistent experience of this study has been that the square root of depth 

performs the best. 

As several years of data were used, each year of data needs a dummy variable to account for 

changes in the general level of drilling costs not accounted for by changes in average well depth. 

Because the dependent variable is in log form, the coefficients on these yearly dummy variables 

estimate the relative rate of change in the general cost of drilling from the base year. The base 

year is 2002, so the coefficients on the year dummies can be interpreted as the relative difference 

in past years’ prices to those in 2002. 

The model also includes dummy variables for water depth. The JAS provides three water depths 

– zero to 249 feet, 250 to 499 feet, and over 500 feet. Because water depth is not available in the 

JAS before 1997, only six years of data were available for the offshore models. 

The JAS reports the number of wells collected into each reported average cost within a well 

depth range, such as 2,500 to 3,749 feet. These counts were used as weights in the regression 

models so that observations based on more wells would have more influence on the results. 

Otherwise, the means on the very deep wells, which can sometimes be based on a single well, 

would carry the same weight as a mean from the middle of the depth range based on scores of 

wells. 

Figures A-1, A-2, and A-3 show the summary results of the regressions on oil well costs, gas 

well costs, and dry hole costs. Each regression is based on only the last four years of the data 

(1999-2002) because the models showed signs of significant, unexplained shifts between the last 

four years of the data and the earlier two years (1997 to 1998). A full, time-consuming 

investigation of this seemed beyond the scope of this project, so the final model was based on the 

period 1999 to 2002, which provided a sufficient number of observations. The regression results 

show that the models are quite adequate for the purpose of this study. 

Pooling and Averaging 

Using the annual adjustment factors from the regression models, all the JAS observations from 

1999 through 2001 were adjusted to the cost one could have expected if the wells were drilled in 

2002. The averages for the nine categories are shown in table A-4. 

Exploration Versus Development Costs 

In the JAS data, there is no table of well costs by all the factors at once. It is not possible to get 

the data categorized both by completion type and by whether it was exploratory or development. 

To differentiate the average costs between exploratory and development, the ratio of the costs of 

those two at various water depths were used to adjust the estimates. 
MAG-PLAN Study Appendix A

87

First, however, it was necessary to convert the averages from the JAS data from their water depth 

categories measured in feet to those used by CMI and MMS, which are in meters. As 0-60 

meters is entirely contained in the interval 1-250 feet (0-76.2 meters), the costs for that water 

depth were simply carried over unmodified. The same was done for the deepest metric category 

that applies here, 201-800 meters, which must all come from the JAS observations above 500 

feet. Of the middle metric category, 61-200 meters, 11.6 percent lies in the 0-250-foot category, 

54.4 percent in 250-499 feet, and 34 percent is in the above-500-feet category. Therefore, this 

set of weights was used to estimate the average costs in that metric category. Exhibit A.5 shows 

the results of that weighting. 

The final step was to split the cost estimates of figure A-5 into exploratory and development cost 

estimates (the two right-most columns). As the CMI data give the ratio of these two costs 

(shown in the “Ratio of Dev. To Exp. Drilling Cost” column) by metric water depth category, a 

little algebra is all that is needed. Let D, X, and A be development, exploratory, and average 

cost, and r be the ratio of D to X. CMI data give us 

 D = rX (1) 

and the formula for the average is 

 A = (X + D) / 2 (2) 

Algebraic manipulation yields 

 X = 2A / (1+r) (3) 

and that is the formula used to solve for X, and equation 1 yields D. D and X are shown in the 

last two columns on the right of exhibit A.5. 
MAG-PLAN Study Appendix A

88

Table A-1: Regression Results for Oil Well Drilling Costs 

JAS data from 1999-2002 

Weighted 

The REG Procedure 

Model: MODEL1 

Dependent Variable: LnCost 

Weight: W 

Analysis of Variance 

Source DF Sum of 

Squares Mean Square F Value Pr > F 

 

Model 6 18.98893 3.16482 161.21 < .0001 

Error 59 1.15825 0.01963 

Corrected Total 65 20.14718 

Root MSE 0.14011 R-Square 0.9425 

Dependent Mean 8.94180 Adj R-Sq 0.9367 

Coeff Var 1.56693 

Parameter Estimates 

Variable DF Parameter 

Estimates Standard Error t Value Pr > | t | 

 

Intercept 1 6.58690 0.13034 50.54 <.0001 

SqrtDep 1 0.02399 0.00118 20.31 <.0001 

Water 2 1 0.00733 0.05848 0.13 0.9006 

Water 3 1 0.20209 0.04631 4.36 <.0001 

Y1999 1 -0.45681 0.05095 -8.97 <.0001 

Y2000 1 -0.42828 0.05341 -8.02 <.0001 

Y2001 1 -0.31695 0.05057 -6.27 <.0001 

Test WATERDEPTH Results for Dependent Variable LnCost 

Source DF Mean Square F Value Pr > F 

 

Numerator 2 0.19247 9.80 0.0002 

Denominator 59 0.01963 
MAG-PLAN Study Appendix A

89

Table A-2: Regression Results for Gas Well Drilling Costs 

JAS data from 1999-2002 

Weighted 

The REG Procedure 

Model: MODEL1 

Dependent Variable: LnCost 

Weight: W 

Analysis of Variance 

Source DF Sum of 

Squares Mean Square F Value Pr > F 

 

Model 6 19.61711 3.26952 178.19 < .0001 

Error 84 1.54130 0.01835 

Corrected Total 90 21.15842 

Root MSE 0.13546 R-Square 0.9272 

Dependent Mean 8.88385 Adj R-Sq 0.9220 

Coeff Var 1.52477 

Parameter Estimates 

Variable DF Parameter 

Estimates Standard Error t Value Pr > | t | 

 

Intercept 1 7.02961 0.08835 79.57 <.0001 

SqrtDep 1 0.02072 0.00076999 20.31 <.0001 

Water 2 1 0.00733 0.04433 0.76 0.4507 

Water 3 1 0.16978 0.05420 3.13 0.0024 

Y1999 1 -0.57763 0.04009 -14.41 <.0001 

Y2000 1 -0.52888 0.04006 -13.20 <.0001 

Y2001 1 -0.29233 0.04144 -7.05 <.0001 

Test WATERDEPTH Results for Dependent Variable LnCost 

Source DF Mean Square F Value Pr > F 

 

Numerator 2 0.09180 5.00 0.0089 

Denominator 84 0.01835 
MAG-PLAN Study Appendix A

90

Table A-3: Regression Results for Dry Hole Drilling Costs 

JAS data from 1999-2002 

Weighted 

The REG Procedure 

Model: MODEL1 

Dependent Variable: LnCost 

Weight: W 

Analysis of Variance 

Source DF Sum of 

Squares Mean Square F Value Pr > F 

 

Model 6 40.87499 6.81250 192.35 < .0001 

Error 95 3.36462 0.03542 

Corrected Total 101 44.23961 

Root MSE 0.18819 R-Square 0.9239 

Dependent Mean 9.10028 Adj R-Sq 0.9191 

Coeff Var 2.06800 

Parameter Estimates 

Variable DF Parameter 

Estimates Standard Error t Value Pr > | t | 

 

Intercept 1 7.01383 0.09479 73.99 <.0001 

SqrtDep 1 0.02185 0.00080737 27.07 <.0001 

Water 2 1 -0.13019 0.05931 -2.19 0.0306 

Water 3 1 0.26893 0.04203 6.40 <.0001 

Y1999 1 -0.57013 0.05410 -10.54 <.0001 

Y2000 1 -0.54248 0.05365 -10.11 <.0001 

Y2001 1 -0.22276 0.05471 -4.07 <.0001 

Test WATERDEPTH Results for Dependent Variable LnCost 

Source DF Mean Square F Value Pr > F 

 

Numerator 2 0.82798 23.38 <.0001 

Denominator 95 0.03542 
MAG-PLAN Study Appendix A 

91

Table A-4: Average Cost in 2002 Dollars 

Water Depth (ft) Well Depth (ft) Avg Cost 

1-249 0-14,999 9,139 

1-249 15,000-17,999 17,163 

1-249 >=18,000 25,495 

250-499 0-14,999 8,802 

250-499 15,000-17,999 18,190 

250-499 >=18,000 29,287 

>=500 0-14,999 13,382 

>=500 15,000-17,999 21,867 

>=500 >=18,000 33,929 

 Source: ICF Consulting, Inc. 

Table A-5: Estimated Exploratory and Development Drilling Expenditures1

Well 

Depth (ft) 

Water 

Depth 

(ft) 

Water 

Depth (m) 

Avg 

Cost 

Water 

Depth 

Bridge 

MMS 

Water 

Depths 

(m) 

Ratio 

of 

Dev. to 

Exp. 

Drilling 

Cost 

Avg 

Cost 

Exp. 

Cost 

Dev. 

Cost 

0-14,999 1-249 < 76.2 9,139 11.57% 0-60 99.67% 9,139 9,154 9,124

15,000-17,999 1-249 < 76.2 17,163 0-60 99.67% 17,163 17,191 17,134

>=18,000 1-249 < 76.2 25,495 0-60 99.67% 25,495 25,537 25,453

0-14,999 250-499 76.2 - 152.4 8,802 54.43% 61-200 85.62% 10,398 11,204 9,592

15,000-17,999 250-499 76.2 - 152.4 18,190 61-200 85.62% 19,321 20,819 17,824

>=18,000 250-499 76.2 - 152.4 29,287 61-200 85.62% 30,427 32,784 28,069

0-14,999 >=500 > 152.4 13,382 34.00% 201-800 73.15% 13,382 15,457 11,306

15,000-17,999 >=500 > 152.4 21,867 201-800 73.15% 21,867 25,258 18,476

>=18,000 >=500 > 152.4 33,929 201-800 73.15% 33,929 39,191 28,667

Source: ICF Consulting, Inc. 

1

 Costs are expressed in 2002 dollars. 

MAG-PLAN Study Appendix B 

93

Appendix B

Additional Data for GOM Modeling Regions 

This appendix presents additional data for the GOM modeling regions that are not used in the 

default runs in MAG-PLAN. These can be used in the future as alternative data sources and 

more detailed modeling approaches. This appendix includes discussions on two types of data: 

an alternate source for Personal Consumption Expenditure vectors and more detailed data for 

revenue modeling than are currently implemented in MAG-PLAN. 

Labor Needs Survey PCE Data 

The current modeling in MAG-PLAN includes Personal Consumption Expenditure (PCE) 

vectors from IMPLAN to stimulate the labor expenditures. The Labor Needs Survey (LNS), 

sponsored by MMS and conducted by ICF Consulting, can provide an alternate source for this 

data 

In conducting the LNS, employees were asked to provide percentage-based personal 

expenditures related to the income they received from actual offshore work or onshore work 

related to the offshore. Seemingly, there was some confusion in the responses on personal and 

household expenditures. 

Because MMS needed data on how employees spend their income and because the employee 

expenditure data had problems, great attention was paid to computing this distribution. The 

problems with the data were what one would expect when people try to recall their expenditures 

and express them as a percentage of income. Many people had different names for the same 

item, and some split expenditures into smaller categories than others. The data was consolidated 

into eleven standard categories as follows: 

• Auto insurance 

• Auto payment 

• Education 

• Food and clothing 

• Other insurance 

• Miscellaneous 

• Mortgage or rent 

• Recreation and entertainment 

• Saving and investment 

• Taxes 

• Utilities 

Predictably, respondents usually recalled information on their large expenditures. Most people 

entered their percent spent on housing, food, and clothing. Also, those who had large but 

temporary expenditures, such as education, noted those as well. In many cases, however, the 

miscellaneous class was a large percent or the respondent only recorded the large classes of 

expenditures. 
MAG-PLAN Study Appendix B 

94

To adjust for this bias, ICF used a constrained regression to estimate the mean percentage in each 

category. First, the data were prepared with a series of steps. A program examined each 

respondent’s set of expenditure percentage entries and added records to account for missing 

income or missing categories as follows: 

• If a respondent’s total expenditure percent equaled 100 and a miscellaneous category was 

not entered, all categories not entered were assumed to have expenditure fractions of zero. 

• If a respondent’s total expenditure percent equaled 100 and a miscellaneous category was 

entered, all categories not entered were assumed to be part of the miscellaneous category. 

For example, if all categories except utilities were entered, then it was assumed that the 

miscellaneous category included utilities. 

• If a respondent’s total expenditure percent was less than 100, the sum of expenditures on all 

missing categories was assumed to total to the missing percent. For example, if all 

categories except miscellaneous and utilities were entered and accounted for 92 percent, 

then the sum of expenditures on miscellaneous and utilities was assumed to equal eight 

percent. 

The records representing these assumptions were added to the data and were used in a regression 

model that produced the estimates shown in table B-1 below. The categories with the largest 

fractions had magnitudes that seemed reasonable. The estimated mean fraction of income spent 

on mortgage or rent was 21.5 percent. This was followed closely by 20.9 percent spent on food 

and clothing. The fraction for savings and investment at 13.5 percent may seem a bit high, but it 

could reflect the relatively high wages paid by the petroleum industry for offshore work. 

Table B-1: Regression Estimates of Employee Expenditure Fractions (in %) 

Consolidated Expense Category Estimated Fraction of Income 

Auto insurance 4.2 

Auto payment 10.9 

Education 2.5 

Food & Clothing 20.9 

Other insurance 6.0 

Miscellaneous 5.0 

Mortgage/rent 21.5 

Recreation/entertainment 11.8 

Saving/Investments 13.6 

Taxes 3.1 

Utilities 0.5

Total 100.0 

Source: ICF Consulting, Inc. 
MAG-PLAN Study Appendix B 

95

The 3.1 percent for taxes was clearly low as a fraction of gross income. This probably meant 

that the respondents were thinking in terms of take-home pay. MMS will need to consider this in 

the application of these estimates to its regional modeling. The very low fraction for utilities was 

probably a result of the small fraction of respondents that made an explicit entry for that 

category. Even with the help of the regression model, it was hard to attribute much of the 

miscellaneous category, which has 5.0 percent, to utilities. 

The U.S. Bureau of Labor Statistics Consumer Expenditure Survey (CEX) program provides 

information on the buying habits of American consumers, including data on their expenditures, 

income, and consumer unit (families and single consumers) characteristics. Comparing these 

expenditure patterns with the 2000 CEX, it was discovered that the Transportation category 

accounted for 19.5 percent of spending compared to the 15.1 percent in this study (auto 

insurance plus auto payment). Also of note are: 

• Food (13.6 percent), 

• Apparel and Services (4.9 percent), 

• Personal Insurance and Pensions (8.8 percent), 

• Other Expenditures56 (10.5 percent), 

• Housing (32.4 percent), and 

• Entertainment (4.9 percent). 

The only category not captured in the list of consolidated expense categories that was included as 

a major expenditure in the CEX was Health Care (5.4 percent). 

These shares could be used in MAG-PLAN for modeling the PCE vectors as an alternative to 

using the more detailed IMPLAN data. 

Revenue Data 

The level of detail in the revenue data, used in MAG-PLAN for the GOM modeling regions, was 

driven by the variables available from the E&D scenarios. An important component of the 

revenue streams arising from offshore activities is based on royalty suspension regimes. 

However, because this is not part of the current E&D scenario, the default runs in MAG-PLAN 

does not include modeling royalty suspensions. This section presents a discussion on royalty 

suspension for information purposes only. The same is true for some of the more detailed 

discussion on revenue disbursement below. 

Royalty relief based on suspension volumes was initially created in 1995 by the Outer 

Continental Shelf Deep Water Royalty Relief Act (DWRRA) as an incentive to expand 

production to deeper waters. Royalty suspension offers the operator of a lease relief on royalties 

for the first x amount of production, depending on water depth. The volume of this relief (x) is 

larger in deeper water. For example, under the current MMS royalty relief policy, if a lease falls 

 

56 Includes expenditures for alcoholic beverages, personal care products and services, reading, education, and 

tobacco products and supplies, as well as cash contributions and miscellaneous expenditures. 
MAG-PLAN Study Appendix B 

96

within the 400-799 meter range, the operator will not be required to pay royalties on the first 

5,000,000 BOE produced, subject to price thresholds that may apply to the lease. 

If the oil or gas price exceeds the specified threshold, the operator is required to pay royalties on 

production that otherwise would fall under royalty suspension. If during any calendar year the 

arithmetic average of the closing prices for the nearby delivery month on the NYMEX for the 

applicable product exceeds the adjusted product price threshold, the lessee must pay royalty on 

production. A lessee would be notified in March when adjusted price thresholds for the previous 

year were exceeded. For 2004, the price threshold for light sweet crude oil was $39/bbl, and the 

price threshold for natural gas was $6.50/MMBtu. The adjusted price threshold in any 

subsequent calendar year is computed by changing the base price by the percentage by which the 

implicit price deflator for the gross domestic product has changed. To further complicate the 

estimation of future royalties, some companies assert that the price thresholds do not apply to 

four sales for which the thresholds were omitted from the Notices of Sale, and others assert that 

they violate the intent of the DWRRA itself, creating the possibility that these price thresholds 

will be negated by future court decisions. 

For any leases entered into after the year 2000, the royalty suspension volumes in Section 2.6, 

Table 2-25, and the corresponding explanation apply. Leases in the Central and Western Gulf of 

Mexico that were granted in 1996 through 2000 fall under the DWRRA royalty suspension 

volume regime. Table B-2 below illustrates the DWRRA (pre-2001) royalty suspension regime. 

Table B-2: Pre-2001 Royalty Suspension Regime (in BOE) 

Water Depth Royalty Suspension 

Volume 

200 - 400 m 17.5 million BOE 

400 - 800 m 52.5 million BOE 

> 800 m 87.5 million BOE 

 Source: Deepwater Gulf of Mexico, America’s 

Emerging Frontier, MMS OCS Report 

MMS 2000-022. 

There are a couple of major differences between the DWRRA royalty suspension regime and the 

current one. First, as one can see, the volumes are much greater in the old regime. This is in part 

because the intent behind the old regime volumes was that they were to be based on production 

per field, rather than per lease.57 Second, the water depth categories have changed significantly. 

The lease specific 200-to-less-than-400-meters category has been eliminated, but discretionary 

relief is still available for this category of lease. The 400-to-less-than-800-meters category 

 

57 The suspension volumes in the DWRRA were based on data in a report supplied by MMS. However, the law, as 

written, was interpreted by the courts in a subsequent lawsuit (Santa Fe Synder Corp., et al. v. Norton) to apply 

suspension volumes to individual leases. In 2001, upon expiration of the royalty suspension requirements of the 

DWRRA, MMS started using lower volumes because its analysis indicated that the DWRRA volumes were too high 

and that suspension volumes specified on a per lease basis would be easier to administer. 
MAG-PLAN Study Appendix B 

97

remains, but the greater-than-800-meters category has been broken into 800-to-less-than1,600-meters, 1,600-to-less-than-2,000-meters, and 2,000 meters or greater. As time passes, 

the number of fields that have not yet been drilled and fall under the DWRRA royalty regime 

diminishes because, once a lease has been awarded, the operator has to begin drilling before the 

primary lease term expires. Therefore, it is probably best to go forward focusing on the current 

royalty suspension volumes, even thugh there are still fields which may fall under the previous 

royalty suspension volumes. 

The final facet of royalty suspension deals with shallow water, deep gas production. One 

requirement for a lease to be eligible for shallow water, deep gas production royalty suspension 

is that the lease must be located wholly west of 87 degrees, 30 minutes west longitude and 

entirely in water depths less than 40058 meters. This line of longitude falls just shy of the 

boundary between the Eastern and Central GOM and is the same line used as for the limit for 

deepwater royalty relief eligibility (although a lease cannot be eligible for both deepwater and 

deep gas royalty relief). Table B-3 below is a basic illustration of the shallow water, deep gas 

royalty suspension volume provisions. Note that the regulations for deep gas royalty relief are 

complex and the royalty suspension volumes are contingent on numerous factors beyond water 

depth and well depth, including the existence of previously produced wells, wells in deeper 

zones, dates of spud wells, production dates, sunset dates, sharing across wells, and sidetracks. 

There also is a royalty supplement credit of 5 Bcf that applies in certain cases to unsuccessful 

wells. 

Table B-3: Shallow Water Deep Gas Royalty Suspension Volume Provisions (in Bcf) 

Water Depth Well Depth Production 

< 400 Meters 15,000 -18,000 Feet 

TVD SS 15 Bcf 

< 400 Meters 

< 400 Meters 

18,000 - 20,000 Feet 

TVD SS 

20,000 Feet or deeper 

TVD SS 

25 Bcf 

35 Bcf 

Source: Federal Register, Vol 69 No 16, 26 JN. 2004, MMS Rule 

30 CFR Part 203.41. 

As was the case before, this acts as a temporary incentive for expanding production parameters. 

Again, as in the previous application of royalty suspension volumes, there is also a price 

threshold mechanism which would require the lessee to pay royalties that would otherwise fall 

under royalty suspension. For 2004, the shallow water, deep gas production base level price 

threshold for natural gas was $9.34/MMBtu. 

 

58 Whereas the deep gas production royalty suspension was initially applied only to leases in less than 200 meters of 

water, the Energy Policy Act of 2005 extended eligibility to leases in water depths of 200-400 meters. 
MAG-PLAN Study Appendix B 

98

Federal Allocation of Revenue Received 

The revenue the federal government collects is the sum of the royalties, rentals, and bonus bids 

received from drilling in federal waters. The federal government then disperses that revenue to 

different sectors. First, the revenue is dispersed to two federal special-purpose accounts, the 

National Historic Preservation Fund (NHPF) and the Land and Water Conservation Fund 

(LWCF). The NHPF is allocated $150,000,000 annually as a result of OCS drilling activity. 

Similarly, the LWCF is allocated $900,000,000 annually. The balance of the federal royalty 

revenues are then directed to the General Fund of the U.S. Treasury. This discussion is also for 

information purposes only. 

One variation in the allocation of federal revenues that would ultimately affect the amount of 

money received by the U.S. Treasury General Fund is if the revenues collected were from leases 

that fall within the 8(g) zone. Revenue created by drilling in the 8(g) zone is then allocated to 

states by the federal government in annual disbursements of 27 percent of the royalty, rent, and 

bonus bids received within each affected state’s 8(g) zone.59 The following bullets illustrate how 

these affected states in turn allocate the 8(g) zone revenue they receive from the federal 

government. State revenue allocations were obtained from various government web sites and 

offices. 

Alabama 

100% to the Alabama Trust Fund 

• 28% for Statewide capital improvement projects, 

• 7% to cities and counties based on population and other factors, for capital 

 improvements, and 

• 65% is retained by the Alabama Trust Fund and invested. 

The earnings from the ATF are distributed as follows: 

• 70% to the general fund to be used for essential state services, 

• 10% to the Forever Wild Land Trust to be used to purchase land, 

• 10% to the cities, and 

• 10% to the counties. 

Louisiana 

• 50% Louisiana School Board, 

• 40% Police Juris Parish in which severance or production occurs, and 

• 10% Parish Royalty Road Fund: Construction of roads; operation and maintenance of 

automobile ferries in the parish where severance or production occurs. 

 

59 However, Royalty Revenues from 8(g) zone are modeled in MAG-PLAN such that all of the revenue is allocated 

to the respective state government. This is done to be consistent with the definition of 8(g) Royalty Revenue in the 

E&D scenario. 
MAG-PLAN Study Appendix B 

99

Mississippi 

33.33% to a special fund to be appropriated by the Legislature 

• $100,000 for the administration of the Mineral Lease Division of the Department of 

Environmental Quality 

• The remainder to the Education Trust Fund 

66.66% to the Gulf and Wildlife Protection Fund 

• 50% or no more than $1,000,000 to the Mississippi Department of Wildlife, Fisheries 

and Parks solely for the purpose of clean-up, remedial or abatement actions involving 

pollution as a result of the exploration or production of oil or gas 

• The remainder to the Education Trust Fund 

• 50% of such Gulf and Wildlife Protection Fund and no more than $1,000,000 to the 

Mississippi Commission on Wildlife, Fisheries and Parks for use first in the prudent 

management, preservation, protection and conservation of existing waters, lands and 

wildlife of this state and then, provided such purposes are accomplished, for the 

acquisition of additional waters and lands 

• The remainder to the Education Trust Fund 

Texas 

• 100% to the School Fund based on School District Attendance 

Florida 

• No data were identified for allocation purposes. 

MAG-PLAN Study Appendix C 

101

Appendix C 

Algorithm for Calculating Fuel Price in MAG-PLAN 

This appendix outlines the methodology used to estimate the future price of refined petroleum 

products in MAG-PLAN based on the crude oil price input. The refined products covered here 

are gasoline, diesel, residual fuel oil, No. 2 distillate, No. 2 fuel oil, aviation gasoline, No. 4 Fuel 

Oil (marine diesel) and kerosene-type jet fuel. Product prices are calculated in MAG-PLAN 

based on a functional relationship that uses the crude oil price (converted from $/bbl to 

cents/gallon) along with several “adders.” These adders capture the contribution of different 

parts of the petroleum industry that play a role in developing the finished (refined) products. 

Components of Retail Price of Refined Products 

Retail price of refined products can be broken down into the following components: 

1. Crude Oil Cost – Crude oil is the major raw material used in the manufacture of refined 

petroleum products listed above. The cost of this component can be tracked by the world 

oil market for crude oil. 

2. Refinery Margins and Marketing Costs – This component consists of costs to refineries 

other than crude oil cost, such as processing costs, costs of other raw materials, energy 

costs, and marketing costs. Refinery profits are also included in this component. 

3. Distribution and Marketing Costs and Profits – This component includes costs that are 

incurred after the product leaves the refinery. The costs of product distribution, dealer 

margins, etc. are included here. 

4. Federal and State Taxes – Federal and State taxes levied on various refined products at 

the point of sale are included here. 

Prediction of Product Prices 

Various methods to predict the retail prices of products in relation to the crude oil price were 

evaluated. All methods focused on predicting the trend in individual components of product 

prices listed above, and subsequently aggregating the components to arrive at a final price. The 

analysis included understanding the historical trends and estimating the impact of future 

regulations on each component. The annual historical data on crude oil prices and product prices 

were obtained from the U.S. Department of Energy’s, Energy Information Administration 

(EIA).60 The impact of future regulations on product specifications on refining margins is taken 

from EIA’s Annual Energy Outlook 2004 (AEO 2004). An excerpt from AEO 2004 is included 

in exhibit C-2. The federal and state tax information was obtained from various sources 

including the American Petroleum Institute (API), the Alaska State Tax Division, and the 

Louisiana Department of Revenue. The methodology used to estimate each component of the 

product prices is explained below: 

 

60 Available at the website: www.eia.doe.gov. 
MAG-PLAN Study Appendix C 

102

1. Crude Oil Cost: Crude oil price is assumed to be an input in MAG-PLAN. In most 

cases, the reference crude oil price entered in the model (under Global Revenue 

Parameters) is given in the E&D scenario. 

2. Refinery Margins, Distribution and Marketing Costs and Profits: The second and third 

components are combined into one since data were not available for individual 

components. This component is calculated for each historical year by subtracting the 

annual average refiner’s acquisition cost of composite crude oil from the product price to 

end-user (excluding taxes). Table C-1 shows the average crude oil prices since 1987 and 

the refinery margins and profits for each product annually. When analyzed in real 

dollars, the refinery margins display a very stable trend with relatively small standard 

deviation and do not seem to be greatly influenced by the price of crude oil. Figure C-1 

shows the graphical representation of these margins and costs in real 2000 dollars. Linear 

trend lines for gasoline and diesel show that these margins have remained stable since 

1987. The EIA estimates that the effect of tightening specifications on petroleum 

products will cause a 12 cents/gallon increase in refinery processing costs for gasoline 

and diesel between 2002 and 2025. During the same period, the refinery processing costs 

for residual fuel oil and heating oil is estimated to decrease by 2.7 cents/gallon. Most of 

these cost changes will be due to required refinery investments to meet the new product 

standards. Instead of pro-rating the incremental refining costs over the time horizon until 

2025, ICF added the full incremental costs to the historical averages from the current 

period. This assumption is valid because fuel specifications have already started 

tightening from 2000 and most of the new regulations will be phased-in by 2010. 

Consequently, refinery investments necessary to improve processes to meet new 

standards have already begun and should be completed before 2010. By adding the 

incremental refining costs to the historical averages, ICF arrived at a constant number for 

each refined product that represents the refinery margin, distribution, and marketing costs 

and profits, and is constant in real dollar terms over the period of projection from 2004 to 

2025. 

3. Regional Price Spread: The two regions that are considered in the model are Alaska and 

the Gulf of Mexico (GOM). Spread-between product prices in these regions and the U.S. 

national average was calculated using historical EIA data. This spread accounts for the 

market conditions prevalent in these regions. For example in Alaska, the average price of 

gasoline is 29.81 cents/gallon higher than the U.S. national average, before taxes are 

applied. For GOM, data for the state of Louisiana are used as a surrogate. Table C-2 

below shows these regional price spreads. 

4. Federal and State Taxes: Nationally, the federal and state taxes average 42.7 

cents/gallon on gasoline and 48.5 cents/gallon on diesel. Federal taxes applicable on 

other products are shown in table C-4. Since the two primary regions of interest in 

MAG-PLAN are Alaska and the GOM, the state taxes for Alaska and Louisiana are 

added (as representative of all the state taxes in the GOM region) to federal taxes to 

arrive at total taxes for the refined products. As taxes are governed by policy and 

regulations that are difficult to predict, it was assumed that taxes remain constant based 

on current regulations for the entire projection period. 
MAG-PLAN Study Appendix C 

103

MAG-PLAN Fuel Price Algorithm 

Using the above components, the price of a refined product is calculated in MAG-PLAN by the 

following equation: 

Product Price = a*x + b + c + d 

where: 

a = 2.381 represents the conversion factor from $/bbl to cents/gallon, 

x represents the national average refinery acquisition cost of composite crude oil, 

b represents the refinery margins, distribution and marketing costs and profits, 

c represents the spread between regional product price and the U.S. average product 

price, and 

d represents the federal and state taxes. 

The values for parameters b, c, and d are given below in tables B-1, B-3, and B-4, respectively. 
MAG-PLAN Study Appendix C

104

Year

GDP 

Deflator

Crude Oil 

(cents/gal)

Motor 

Gasoline

Aviation 

Gasoline Jet Fuel

No. 1 

Distillate

No. 2 

Diesel Fuel

No. 2 Fuel 

Oil No. 4 Fue

1987 0.7311 58.29 33.21 65.77 15.98 24.32 17.07 21.17 11.87 

1988 0.7541 46.32 42.93 71.84 21.71 28.47 19.99 25.82 14.81 

1989 0.7834 54.62 41.89 72.40 20.95 29.76 20.06 20.31 10.74 

1990 0.8125 65.11 43.56 72.73 29.16 35.69 24.12 25.22 11.44 

1991 0.8430 53.83 40.71 70.37 23.51 33.95 23.04 25.05 14.97 

1992 0.8642 50.78 40.29 68.06 19.81 26.29 20.85 21.78 10.09 

1993 0.8838 44.21 41.67 67.81 21.42 31.15 23.91 23.91 12.48 

1994 0.9028 41.12 40.63 64.89 18.03 29.78 20.25 22.24 14.38 

1995 0.9218 44.50 38.49 64.52 14.08 22.76 16.25 16.46 10.28 

1996 0.9395 52.48 37.67 66.30 16.81 24.79 20.00 19.15 11.70 

1997 0.9559 47.42 40.35 70.58 16.70 24.65 19.74 19.11 11.68 

1998 0.9675 30.81 38.75 69.96 15.91 26.14 20.25 19.01 13.43 

1999 0.9802 42.53 37.14 65.51 12.86 20.82 17.05 14.39 5.82 

2000 1.0000 67.29 43.31 63.31 22.61 31.51 26.21 25.41 9.61 

2001 1.0234 53.39 47.45 75.88 22.33 34.74 28.88 27.61 12.95 

2002 1.0415 55.09 35.83 68.57 14.13 24.41 18.07 15.67 7.99 

Average (1987-2002) 50.49 40.24 68.66 19.13 28.08 20.98 21.40 11.52 Std Dev (1987-2002) 9.16 3.42 3.52 4.32 4.49 3.46 3.94 2.46 Additional refining cost through (2002-2025) from AEO 20042002 dollars (cents/gal) 12 -2.7 12 -2.72000 dollars (cents/gal) 11.52 0.00 -2.59 0.00 11.52 -2.59 0.00 Total Future Refining and marketing cots (cents/gal) in 2000 dollars2000 dollars (cents/gal) 51.76 68.66 16.53 28.08 32.50 18.80 11.52 

Table C-1: Refinery Margins, Distribution and Marketing Costs and Profits for Petroleum Pro

(in cents/gallon, real 2000 dollars) 

Note: The refinery margins given here are relative to the composite refiner’s crude oil acquisition cost. If ano

 crude oil price is used, then the margins given in the table may change.  Sources: GDP Deflator: NASA, http://www1.jsc.nasa.gov/bu2/inflateGDP.html  Product Prices: EIA, Petroleum Marketing Monthly, May 2004, table 2. “U.S. Refiner Prices of Petroleum Pro

 to End Users”  Crude RAC Prices: EIA, Petroleum Marketing Monthly, May 2004, table 1. 
MAG

105

-20.00-10.00

0.00

10.0020.0030.0040.0050.0060.0070.0080.0090.00

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Product Price (excl taxes) - Crude Price, cents/gal, Real 2000 $

Crude Oil (cents/gal) Motor Gasoline Aviation Gasoline Jet FuelNo. 1 Distillate No. 2 Diesel Fuel No. 2 Fuel Oil No. 4 FuResidual Fuel Oil Linear (Motor Gasoline) Linear (No. 2 Diesel Fuel)

Figure C-1: Refinery Margins, Distribution and Marketing Costs and Profits for Petroleum Products 

Source: Table C-1 and ICF analysis. 
MAG-PLAN Study Appendix C 

106 

Table C-2: Spread-between regional petroleum prices and the U.S. national average product 

prices excluding taxes (in cents/gallon, real 2000 dollars) 

Product Alaska Louisiana 

Gasoline 29.81 -4.34 

Jet Fuel 4.54 -3.41 

No 2 Heating Oil 3.96 NA 

No 2 Diesel 19.81 NA 

Residual Fuel Oil NA -5.72 

Note: For products not listed above or information not available (NA), 

 a spread of zero is assumed. 

Sources: EIA, EIA's Petroleum Product Prices for Alaska, Louisiana and 

United States, http://www.eia.doe.gov/emeu/states/_states.html, taken in 

May 2004. 

Table C-3: Federal and state taxes on various petroleum products 

(in cents/gallon, real 2000dollars) 

Product 

Federal 

Tax 

Alaska 

State Tax 

Louisiana 

State Tax 

Alaska 

(Fed + State) 

Louisiana 

(Fed + State) 

Motor Gasoline 18.4 8.0 20.03 26.4 38.4 

Aviation Gasoline- noncommercial use 19.4 4.7 

Aviation Gasoline - Helicopter for 

Oil and Gas Extraction 0 4.7 

Aviation Fuel - 

Non-commercial 21.4 0.0 0 21.4 21.4 

Aviation Fuel - Commercial 

Airlines 4.4 0.0 

Aviation Fuel - Helicopter for Oil 

and Gas Extraction 0 0.0 

Kerosene Type - Jet Fuel 0 3.2 3.2 0.0 

No. 1 Distillate 0 8.0 8.0 0.0 

No. 2 Diesel Fuel 

(On-road Diesel) 24.4 8.0 20 32.4 44.4 

No. 2 Fuel Oil 0 8.0 8.0 0.0 

No. 4 Fuel 0 8.0 8.0 0.0 

Residual Fuel Oil 0 0.0 0.0 0.0 

Note: Aviation Fuel is defined by the Internal Revenue Service as “any liquid (other than gasoline of 

 diesel fuel) that is suitable for use in an aircraft.” 

 Kerosene-type Jet Fuel is Aviation Grade kerosene covered by ASTMspecification D1655 or 

 military grade JP-5 or grade JP-8 

 Aviation Gasoline is defined as “all special grades of gasoline suitable for use in reciprocating 

 engines and covered by ASTM specification D910 or military Specification MIL-G-5572.” 

Sources: Internal Revenue Service, Alaska Dept. of Revenue, Louisiana Dept. of Revenue.
MAG-PLAN Study Appendix C 

107 

Exhibit C-1: Excerpt from EIA’s AEO 2004, January 2004 

Refining Costs for Most Petroleum Products Rise in the Forecast

Refined product prices are determined by crude oil costs, refining process costs (including 

refiner profits), marketing costs, and taxes. In the AEO 2004 projection, crude oil continues as 

the largest part of product prices. Marketing costs remain stable, but the contributions of 

processing costs and taxes are projected to change considerably. 

Refining costs for gasoline and diesel fuel, including processing costs and profits, are expected to 

increase by 12 cents a gallon from 2002 to 2025 (2002 dollars), primarily due to growth in 

demand for gasoline and diesel fuels and new Federal requirements for low-sulfur gasoline (2004 

to 2007) and ultra-low-sulfur diesel fuel (2006 to 2010). Refining costs for heating oil and jet 

fuel fall by 2.6 to 2.8 cents a gallon from 2002 to 2025. Tighter gasoline and diesel 

specifications cause some refiners to shift production from gasoline and diesel to jet fuel and 

heating oil, which have less stringent specifications. 

Whereas processing costs tend to increase refined product prices in the forecast, the assumption 

that Federal motor fuel taxes remain at nominal 2002 levels tends to reduce prices. Although 

Federal motor fuel taxes have been raised occasionally in the past, the assumption of constant 

nominal Federal taxes is consistent with history. The net impact of the assumption is an 

expected decrease in Federal taxes (in 2002 dollars) from 2002 to 2025—9 cents per gallon for 

gasoline, 12 cents for diesel fuel, and 2 cents for jet fuel. State motor fuels taxes are assumed to 

keep up with inflation, as they have in the past. 

MAG-PLAN Study Appendix D 

109

Appendix D 

MAG-PLAN Technical Documentation 

Introduction 

The purpose of this Appendix is to describe the mathematical formulations and methodologies 

utilized in the MAG-PLAN for calculating onshore area (OSA)- and sector-level economic 

impacts. These economic impact formulations/methodologies calculate: 

A. Direct Expenditures: 

 1. ABC-Override Approach 

a. Labor Expenditures 

b. Non-Labor Expenditures 

 2. ABC-Detailed Approach 

a. Activity Function Unit 

b. Labor Expenditures 

c. Non-Labor Expenditures 

i. Asset Expenditures 

ii. Material Expenditures 

iii. Total Non-Labor Expenditures 

3. Expenditure Source Revenue Expenditures 

4. Total Expenditures 

B Multiplier Impacts: 

1. Direct Output 

2. IMPLAN Regional Type I Multipliers and Indirect Output 

3. IMPLAN Regional Induced Multipliers and Induced Output 

4. Total Output 

5. IMPLAN Study Area (Employment, Employee Compensation, Proprietor's Income, 

Labor Income, Total Value Added, Indirect Business Taxes, and Other Property Type 

Income) Ratios 

6. Direct, Indirect, Induced, and Total Study Area Impacts 

A. Direct Expenditures 

Direct expenditures formulations describe labor, non-labor, and expenditure source spending 

patterns by sector, OSA, and year that result from offshore E&D activities. Labor and non-labor 

expenditures can be calculated in two different ways. The ABC-Override approach should be 

utilized if detailed cost data are not available (as is the case for the Gulf of Mexico OCS). The 

ABC-Detailed approach requires detailed cost information, including separate asset and material 

cost data for calculating non-labor expenditures (which is available for the Alaska OCS). 
MAG-PLAN Study Appendix D

110

A.1.a. Labor Expenditures (ABC-Override Approach) 

The ABC-Override approach calculates labor expenditures based on the cost of the E&D 

activities, labor share of activity costs, OSA and sector allocations by income categories, and 

income taxes as described in the following procedure: 

1. Unit Cost 

UCi,a,y UCi,a ADi,a = / (1) 

where: 

UCi,a,y Unit cost of activity function i in modeling area a in year y ($) 

UCi,a Total unit cost of activity function i in modeling area a ($) 

ADi,a Duration of activity function i in modeling area a (Year) 

The unit cost of an activity can vary over time due to changes in technology. Two Technological 

Change Adjustment Factors are used to describe the changes in cost from the year for which data 

is available to the year in which the activity begins (TCAF1) and the learning-by-doing effect on 

cost improvements during installation and operation after the activity begins (TCAF2 ). Default 

values for TCAF1 and TCAF2 are 1.0. 

UCi a yb = UCi a yd TCAF1yb−yd * , , , , (2) 

UCi a y = UCi a yb TCAF2y−yb * , , , , (3) 

where: 

UCi,a,yb Unit cost of activity function i in modeling area a when activity begins (at 

year yb) ($) 

UCi,a,yd Unit cost of activity function i in modeling area a at data year (at year yd) 

($) 

2. Activity Cost 

ACi,a,y nAi,a,y UCi,a,y = * (4) 

where: 

ACi,a,y Total cost of activity function i in modeling area a in year y ($) 

nAi,a,y Number of activities of function i in modeling area a in year y 
MAG-PLAN Study Appendix D 

111 

3. Labor Cost 

Labor cost is calculated based on the labor share of an activity cost. Labor productivity 

improvement can reduce the labor cost and is modeled using the Labor Productivity Change 

Adjustment Factor (LPCAF). Default value for LPCAF is 1.0. Values greater than 1.0 represent 

improvement in labor productivity (i.e., lower labor costs). 

( ) y yb

i

LCa y = ∑ ACi a y LSa i LPCAF − * / , , , , (5) 

where: 

LCa,y Labor cost in modeling area a in year y ($) 

LSa,i Labor share of activity function i in modeling area a (Fraction) 

4. OSA Allocation of Labor Cost 

= ∑( ) aLCo,y LCa,y LOSAAlloca,o * (6) 

where: 

LCo,y Labor cost in OSA o in year y ($) 

LOSAAlloca,o Allocation of labor cost in modeling area a to OSA o (Fraction) 

5. Disposable Income 

DIo,y = LCo,y ( )( ) − ∑FedTaxy 1− ∑StTaxo,y * 1 * (7) 

where: 

DIo,y Labor disposable Income for OSA o in year y ($) 

FedTaxy Average federal level income taxes in year y (e.g. federal tax, social 

security tax) (Fraction) 

StTaxo,y Average state- and local-level income taxes in OSA o in year y (e.g. state 

tax, property tax, local tax) (Fraction) 
MAG-PLAN Study Appendix D

112

6. Sector Allocations by OSA 

= ∑( ) mLSAlloco,s MAlloco,m PCEo,m,s * (8) 

where: 

LSAlloco,s Allocation of labor spending in sector s in OSA o (Fraction) 

MAlloco,m Allocation of income category m in OSA o (Fraction) 

PCEo,m,s Personal Consumption Expenditure distribution of income category m in 

sector s in OSA o (Fraction) 

7. Labor Expenditure 

LEo,s,y DIo,y LSAlloco,s = * (9) 

where: 

LEo,s,y Labor expenditure in sector s in OSA o in year y ($) 

A.1.b. Non-Labor Expenditures (ABC-Override Approach) 

ABC-Override approach calculates non-labor expenditures based on the cost of the E&D 

activities, non-labor share of the activity costs, and sector and OSA allocations as described in 

the following procedure: 

1. Non-Labor Cost 

NLCi,a,y ACi,a,y NLSa,i = * (10) 

where: 

NLCi,a,y Non-labor cost of activity function i in modeling area a in year y ($) 

NLSa,i 0 LSa,i = 1. − Non-labor share of activity function i in modeling area a (Fraction) 
MAG-PLAN Study Appendix D 

113 

2. Sector Allocation of Non-Labor Cost (Non-Labor Expenditure by Sector) 

= ∑( )

i

NLEa,s,y NLCi,a,y NLSAlloci,a,s * (11) 

where: 

NLEa,s,y Non-labor expenditure for sector s in modeling area a in year y ($) 

NLSAlloci,a,s Allocation of non-labor expenditure in modeling area a to sector s for 

activity function i (Fraction) 

3. OSA Allocation of Non-Labor Expenditure 

= ∑( ) aNLEo,s,y NLEa,s,y NLOSAAlloca,s,o * (12) 

where: 

NLEo,s,y Non-labor expenditure in sector s in OSA o in year y ($) 

NLOSAAlloca,s,o Allocation of non-labor expenditure of sector s in modeling area a to OSA 

o (Fraction) 

A.2.a. Activity Function Unit (ABC-Detailed Approach) 

ABC-Detailed approach calculates labor and non-labor (asset and material) expenditures based 

on the number of Activity Function Units (AFU). The AFU is not a primary parameter as its 

value is calculated based on other parameters such as activity types, activity functions, activity 

function parameters, and revenue types. Users can define AFU formulas in the Activity Function 

Definition editor. 

AFUi,a,y = [User-defined Formula] (13) 

where: 

AFUi,a,y The number of activity function units for activity function i in modeling 

area a in year y (scalar) 
MAG-PLAN Study Appendix D

114

A.2.b. Labor Expenditures (ABC-Detailed Approach)

ABC-Detailed approach calculates labor expenditures based on the AFU and detailed labor 

related parameters such as crew size, number of crews, shifts, wages, work duration, type of 

labor, hotel/camp, travel, and food. The following procedure describes labor expenditure 

calculations and the distributions to OSA and sector. 

1. Task Force 

L i a L i a L i a CrewSizeL i a TaskForce Crews ShiftFactor , , , , , , , , = * * (14) 

where: 

TaskForceL,i,a Number of people for labor category L from activity function i in 

modeling area a 

CrewsL,i,a Number of crews for labor category L from activity function i in modeling 

area a 

ShitFactorL,i,a Number of shifts for labor category L from activity function i in modeling 

area a 

2. Average Task Force 

AvgTaskForceL,i,a TaskForceL,i,a RotationWeeksOnL,i,a RotationWeeksTotL,i,a = * / (15) 

where: 

AvgTaskForceL,i,a Average task work force for labor category L from activity function i in 

modeling area a (People) 

RotationWeeksOnL,i,a Number of weeks on for labor category L from activity function i in 

modeling area a (Weeks) 

RotationWeeksTotL,i,a Number of weeks total for labor category L from activity function i in 

modeling area a (Weeks) 
MAG-PLAN Study Appendix D 

115 

3. Man Week 

L i a L i a DurationL i a ManWeek , , AvgTaskForce , , , , = * (16) 

where: 

ManWeekL,i,a Number of man weeks for labor category L from activity function i in 

modeling area a 

DurationL,i,a Length of activity for labor category L from activity function i in 

modeling area a (Weeks) 

4. Production Workers Full Time Equivalent 

= ∑( )

L

ProdFTEi,a TaskForceL,i,a * DurationL,i,a / 52 (17) 

where: 

ProdFTEi,a Number of full-time production workers from activity function i in 

modeling area a (FTEs) 

5. Production Workers Weekly Wage 

L i a

L i a L i a L i a L i a

LaborScalingFac

ProdWeekWage ProdHrWage ProdHrsDay ProdDaysWeek

, ,

, , , , , , , , = * * *

 (18) 

where: 

ProdWeekWageL,i,a Total weekly wage of production workers for labor category L from 

activity function i in modeling area a ($) 

ProdHrWageL,i,a Hourly wage for production workers of labor category L from activity 

function i in modeling area a ($) 

ProdHrsDayL,i,a Total hours worked per day of production workers for labor category L 

from activity function i in modeling area a (Hrs) 

ProdDaysWeekL,i,a Total days worked per week of production workers for labor category L 

from activity function i in modeling area a (Days) 

LaborScalingFacL,i,a Labor cost scaling factor for labor category L from activity function i in 

modeling area a (Scalar) 
MAG-PLAN Study Appendix D

116

6. Production Workers Total Wage 

L i a y L i a i a y ProdWeekWageL i a ProdWageTot ManWeek AFU * , , , , , , , , , = * (19) 

where: 

L i a y ProdWageTot , , , Total wage of production workers for labor category L from activity 

function i in modeling area a in year y ($) 

7. Production Workers Total Fringe Benefit 

L i a y L i a L i a y ProdFringeTot FringeRate ProdWageTot , , , , , , , , = /100 * (20) 

where: 

ProdFringeTotL,i,a,y Total fringe benefit of production workers for labor category L from 

activity function i in modeling area a in year y ($) 

L i a FringeRate , , Fringe benefit rate for labor category L from activity function i in 

modeling area a (%) 

8. Production Workers Total Wage and Benefit 

L i a y L i a y ProdFringeTotL i a y ProdWB ProdWageTot , , , = , , , + , , , (21) 

where: 

ProdWBL,i,a,y Total wage and fringe benefit of production workers for labor category L 

from activity function i in modeling area a in year y ($) 

9. Non-Production Workers Full Time Equivalent 

NonProdFTEL,i,a,y NonProdRateL,i,a ProdFTEi,a AFUi,a,y = /100 * * (22) 

where: 

NonProdFTEL,i,a,y Number of full-time non-production workers for labor category L from 

activity function i in modeling area a in year y (FTEs) 

NonProdRateL,i,a Percentage of non-production workers for labor category L from activity 

function i in modeling area a 
MAG-PLAN Study Appendix D 

117 

10. Non-Production Workers Total Wage 

NonProdWageTotL,i,a,y NonProdFTEL,i,a,y NonProdAnnWageL,i,a LaborScalingFacL,i,a = * * (23) 

where: 

NonProdWageTotL,i,a,y Total wage of non-production workers for labor category L from activity 

function i in modeling area a in year y ($) 

NonProdAnnWageL,i,a Non-production worker annual wage for labor category L from activity 

function i in modeling area a ($) 

11. Non-Production Workers Total Fringe Benefit 

L i a y L i a NonProdWageTotL i a y NonProdFringeTot , , , FringeRate , , , , , = /100 * (24) 

where: 

NonProdFringeTotL,i,a,y Total fringe benefit of non-production workers for labor category L from 

activity function i in modeling area a in year y ($) 

12. Non-Production Workers Total Wage and Benefit 

NonProdWBL,i,a,y = NonProdWageTotL,i,a,y + NonProdFringeTotL,i,a,y (25) 

where: 

NonProdWBL,i,a,y Total wage and fringe benefit of non-production workers for labor 

category L from activity function i in modeling area a in year y ($) 

13. Production and Non-Production Workers Cost 

= ∑( ) +

L

PnPCosta,i,y ProdWBL,i,a,y NonProdWBL,i,a,y (26) 

where: 

PnPCosta,i,y Production and non-production workers cost from activity function i in 

modeling area a in year y ($) 
MAG-PLAN Study Appendix D

118

14. OSA Allocation of Production and Non-Production Workers Cost 

= ∑∑( )

a i

o y a i y a i o PnPCost PnPCost PnPOSAAlloc , , , , , * (27) 

where: 

PnPCosto,y Production and non-production workers cost in OSA o in year y ($) 

a i o PnPOSAAlloc , , Allocation of production and non-production workers cost from activity 

function I in modeling area a to OSA o (Fraction) 

15. Disposable Income 

= ( ) −∑ ( −∑ ) DIo,y PnPCosto,y FedTaxy 1 StTaxo,y * 1 * (28) 

where: 

DIo,y Labor disposable Income for OSA o in year y ($) 

FedTaxy Average federal level income taxes in year y (e.g. federal tax, social 

security tax) (Fraction) 

StTaxo,y Average state- and local-level income taxes in OSA o in year y (e.g. state 

tax, property tax, local tax) (Fraction) 

16. Sector Allocation of Production and Non-Production Workers Cost 

= ∑( ) mLSAlloco,s MAlloco,m PCEo,m,s * (29) 

where: 

LSAlloc o,s Allocation of labor spending in sector s in OSA o (Fraction) 

MAlloco,m Allocation of income category m in OSA o (Fraction) 

PCEo,m,s Personal Consumption Expenditure distribution of income category m in 

sector s in OSA o (Fraction) 
MAG-PLAN Study Appendix D 

119 

17. Production and Non-Production Workers Expenditure 

PnPEo,s,y DIo,y LSAlloco,s = * (30) 

where: 

PnPEo,s,y Production and non-production workers expenditure in sector s in OSA o 

in year y ($) 

18. Production Workers Total Days Worked for Overhead Calculations 

L i a y L i a ProdDaysWeekL i a AFUi a y OHProdDaysTot ManWeek * , , , , , , , , , = * (31) 

where: 

OHProdDaysTotL,i,a,y Total days worked of production workers for labor category L from 

activity function i in modeling area a in year y (Days) 

19. Overhead Cost for Work Clothes 

h Work Clothes

OHC ProdAssgnWC / * ProdCostWC * OHProdDaysTot h L i a y L i a L i a L i a y

=

, , , , = , , 100 , , , , , (32) 

where: 

OHCh,L,i,a,y Overhead cost for overhead type h for labor category L from activity 

function i in modeling area a in year y ($) 

ProdAssgnWCL,i,a Percentage of work clothes in production assignment cost for labor 

category L from activity function i in modeling area a (%) 

ProdCostWCL,i,a Production assignment cost for work clothes for labor category L from 

activity function i in modeling area a ($/Day) 
MAG-PLAN Study Appendix D

120

20. Overhead Cost for Local Travel 

h Local Travel

OHC ProdAssgnLT / * ProdCostLT * OHProdDaysTot h L i a y L i a L i a L i a y

=

, , , , = , , 100 , , , , , (33) 

where: 

ProdAssgnLTL,i,a Percentage of local travel in production assignment cost for labor category 

L from activity function i in modeling area a (%) 

ProdCostLTL,i,a Production assignment cost for local travel for labor category L from 

activity function i in modeling area a ($/Day) 

21. Overhead Cost for Hotel/Camp 

{

( )

}

h Hotel Camp

NonProdRate / * NonProdLdgRate * OHProdDaysTot

ProdAssgnHC / * - Supervisor * ProdNonSpvrLdgRate

OHC ProdAssgnHC / * Supervisor * ProdSpvrLdgRate

L i a L i a L i a y

L i a L i a L i a

h L i a y L i a L i a L i a

/

100

100 1 /100

100 /100

, , , , , , ,

, , , , , ,

, , , , , , , , , ,

=

+

= +

 (34) 

where: 

ProdAssgnHCL,i,a Percentage of hotel/camp in production worker assignment cost for 

labor category L from activity function i in modeling area a (%) 

SupervisorL,i,a Percentage of supervisor for labor category L from activity function i 

in modeling area a (%) 

ProdSpvrLdgRateL,i,a Lodging rate for production worker supervisors for labor category L 

from activity function i in modeling area a ($/Day) 

L i a ProdNonSpvrLdgRate , , Lodging rate for production worker non-supervisors for labor category 

L from activity function i in modeling area a ($/Day) 

NonProdLdgRateL,i,a Lodging rate for non-production worker for labor category L from 

activity function i in modeling area a ($/Day) 
MAG-PLAN Study Appendix D 

121 

22. Overhead Cost for Food Service 

( )

h Food Service

OHProdDaysTot

OHC ProdAssgnHC / NonProdRate ProdCostFS *

L i a y

h L i a y L i a L i a L i a

=

= +

, , ,

, , , , , , , , , , 100 /100 *

 (35) 

where: 

ProdCostFSL,i,a Production assignment cost for food services for labor category L from 

activity function i in modeling area a ($/Day) 

23. Overhead Cost for Air Travel 

( )

h Air Travel

ProdTravelDays ProdNonTravelDays

OHC NonProdRate ProdCostAT OHProdDaysTot *

L i a L i a

h L i a y L i a L i a L i a y

=

= +

, , , ,

, , , , , , , , , , ,

/

1 /100 * *

 (36) 

where: 

ProdCostATL,i,a Production assignment cost for air travel for labor category L from 

activity function i in modeling area a ($/Round Trip) 

ProdTravelDaysL,i,a Production assignment travel days for labor category L from activity 

function i in modeling area a (Days) 

L i a ProdNonTravelDays , , Production assignment non-travel days for labor category L from 

activity function i in modeling area a (Days) 

24. Sector Allocation of Overhead Cost 

= ∑∑( )

i L

OHCh,a,s,y OHCh,L,i,a,y OHSAlloch,L,i,a,s * (37) 

where: 

OHCh,a,s,y Overhead cost for overhead type h in modeling area in sector s in year y 

($) 

OHSAlloch,L,i,a,s Cost allocation of overhead type h for labor category L from activity 

function I in modeling area a to sector s (Fraction) 
MAG-PLAN Study Appendix D

122

25. OSA Allocation of Overhead Cost 

= ∑∑( )

a h

OHCo,s,y OHCh,a,s,y OHOSAAlloch,a,s,o * (38) 

where: 

OHCo,s,y Overhead expenditure cost in sector s in OSA o in year y ($) 

OHOSAAlloch,a,s,o Cost allocation of overhead type h in modeling area a in sector s in OSA o 

(Fraction) 

26. Labor Expenditure 

LEo,s,y = PnPEo,s,y + OHCo,s,y (39) 

where: 

LEo,s,y Labor expenditure in sector s in OSA o in year y ($) 
MAG-PLAN Study Appendix D 

123 

A.2.c. Non-Labor Expenditures (ABC-Detailed Approach) 

ABC-Detailed approach separates the non-labor expenditures into asset and material 

expenditures and calculates them individually. 

Asset Expenditures 

1. New Asset Cost 

i a y K i a

K i a

K i a

K i a K i a

K i a

K i a y AFU KScalingFac

KLfSpnNew

KUCNew

KUtil KPctNew

KQty

KCNew , , , ,

, ,

, ,

, , , ,

, ,

, , , * *

* 100 * 100 *

= (40) 

where: 

KCNewK,i,a,y New asset cost for asset category K from activity function i in modeling 

area a in year y ($) 

KQty K,i,a Quantity of asset category K from activity function i in modeling area a 

(Number of asset category units/Day) 

KUtilK,i,a Annual utilization rate of asset category K from activity function i in 

modeling area a (%) 

KPctNewK,i,a New asset percentage of asset category K from activity function i in 

modeling area a (%) 

KUCNewK,i,a New asset unit cost for asset category K from activity function i in 

modeling area a ($) 

KLfSpnNew K,i,a New asset life span of asset category K from activity function i in 

modeling area a (Years) 

KScalingFacK,i,a Asset cost scaling factor for asset category K from activity function i in 

modeling area a (Scalar) 
MAG-PLAN Study Appendix D

124

2. Used Asset Cost 

i a y K i a

K i a

K i a

K i a K i a

K i a

K i a y AFU KScalingFac

KLfSpnUsed

KUCUsed

KUtil KPctNew

KQty

KCUsed , , , ,

, ,

, ,

, , , ,

, ,

, , , * *

* 100 * 1

100 * ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ −

= (41) 

where: 

KCUsedK,i,a,y Used asset cost for asset category K from activity function i in modeling 

area a in year y ($) 

KUCUSedK,i,a Used asset unit cost for asset category K from activity function i in 

modeling area a ($) 

KLfSpnUsed K,i,a Used asset life span of asset category K from activity function i in 

modeling area a (Years) 

3. Sector Allocation of New Asset Cost 

K a s

i

KENewK a s y KCNewK i a y KNewSAlloc , , , , , , , , = ∑ * (42) 

where: 

KENewK,a,s,y New asset expenditure for asset category K in modeling area a in sector s 

in year y ($) 

KNewSAllocK,a,s Allocation of new asset cost from asset category K in modeling area a to 

sector s (Fraction) 

4. Sector Allocation of Used Asset Cost 

K a s

i

K a s y K i a y KEUsed KCUsed KUsedSAlloc , , , , , , , , = ∑ * (43) 

where: 

KEUsedK,a,s,y Used asset expenditure for asset category K in modeling area a in sector s 

in year y ($) 

KUsedSAllocK,a,s Allocation of used asset cost from asset category K in modeling area a to 

sector s (Fraction) 
MAG-PLAN Study Appendix D 

125 

5. OSA Allocation of New Asset Expenditure 

= ∑∑( )

a K

KENewo,s,y KENewK,a,s,y KNewOSAAllocK,a,s,o * (44) 

where: 

KENewo,s,y New asset expenditure in sector s in OSA o in year y ($) 

KNewOSAAllocK,a,s,o Allocation of new asset expenditure from asset category K in modeling 

area a in sector s to OSA o (Fraction) 

6. OSA Allocation of Used Asset Expenditure 

= ∑∑( )

a K

o s y K a s y K a s o KEUsed KEUsed KUsedOSAAlloc , , , , , , , , * (45) 

where: 

KEUsedo,s,y Used asset expenditure in sector s in OSA o in year y ($) 

K a s o KUsedOSAAlloc , , , Allocation of used asset expenditure from asset category K in modeling 

area a in sector s to OSA o (Fraction) 

Material Expenditures 

1. Non-Fuel Material Cost 

MCNFM,i,a,y MQtyK,i,a MDaysYrK,i,a MUsgRateK,i,a MUCK,i,a AFUi,a,y MScalingFacK,i,a = * * * * * (46) 

where: 

MCNFM,i,a,y Non-fuel material cost for material category M from activity function i in 

modeling area a in year y ($) 

MQty M,i,a Quantity of material category M from activity function i in modeling area 

a (Number of material category units/Day) 

MDaysYrM,i,a Number of days used in a year for material category M from activity 

function i in modeling area a (Days) 

MUsgRateM,i,a The amount of materials used for material category M from activity 

function i in modeling area a (material category unit, vary by material 

category) 

MUCM,i,a Unit cost of material category M from activity function i in modeling area 

a ($/material category unit) 

MScalingFacM,i,a Material cost scaling factor for material category M from activity function 

i in modeling area a (Scalar) 
MAG-PLAN Study Appendix D

126

2. Fuel Cost 

M i a y K i a K i a K i a y i a y K i a MCF MQty MDaysYr MGPD MFP AFU MScalingFac , , , , , , , , , , , , , = * * * * * (47) 

where: 

MCFM,i,a,y Fuel cost for material category M from activity function i in modeling area 

a in year y ($) 

MQty M,i,a Quantity of material category M from activity function i in modeling area 

a (Number of material category units/Day) 

MDaysYrM,i,a Number of days used in a year for material category M from activity 

function i in modeling area a (Days) 

MGPDM,i,a The amount of fuel used for material category M from activity function i 

in modeling area a (Gallon/Day) 

MFPy Fuel price in year y ($/Gallon) 

3. Sector Allocation of Non-Fuel Material Cost 

= ∑( )

i

MENFM,a,s,y MCNFM,ia,y MNFSAllocM,i,a,s * (48) 

where: 

MENFM,a,s,y Non-fuel expenditure for material category M in modeling area a in sector 

s in year y ($) 

MNFSAllocM,i,a,s Allocation of non-fuel expenditure for material category M from activity 

function i in modeling area a to sector s (Fraction) 

4. Sector Allocation of Fuel Cost 

= ∑( )

i

MEFM,a,s,y MCFM,ia,y MFSAllocM,i,a,s * (49) 

where: 

MEFM,a,s,y Fuel expenditure for material category M in modeling area a in sector s in year y 

($) 

MFSAllocM,i,a,s Allocation of fuel expenditure for material category M from activity 

function i in modeling area a to sector s (Fraction) 
MAG-PLAN Study Appendix D 

127 

5. Total Sector Allocation of Material Expenditure 

MEM,a,s,y = MENFM,a,s,y + MEFM,a,s,y (50) 

where: 

MEM,a,s,y Material expenditure for material category M in modeling area a in sector 

s in year y ($) 

6. OSA Allocation of Material Expenditure 

= ∑∑( )

a M

MEo,s,y MEM,a,s,y MOSAAllocM,a,s,o * (51) 

where: 

MEo,s,y Material expenditure in sector s in OSA o in year y ($) 

MOSAAllocM,a,s,o Allocation of material expenditure from material category M in modeling 

area a in sector s to OSA o (Fraction) 

Total Non-Labor Expenditures 

NLEo,s,y = KENewo,s,y + KEUsedo,s,y + MEo,s,y (52) 

where: 

NLEo,s,y Non-labor expenditure in sector s in OSA o in year y ($) 
MAG-PLAN Study Appendix D

128

A.3. Expenditure Source Revenue Expenditures 

Expenditure Source is an entity that collects revenues resulting from E&D activities. Examples 

of Expenditure Source are federal government, state/local governments, and private sectors. In 

MAG-PLAN, revenue streams are generated within the “Revenue Type” as well as “Activity 

Type” sections of the E&D scenario. “Activity Type” revenues are labor income tax revenues 

collected by Expenditure Sources federal and state/local governments. The following procedure 

calculates revenues collected by the Expenditure Source and disbursement of the revenues back 

to the economies. 

1. Revenue Function to Expenditure Source 

Revenues collected by the Expenditure Source are calculated based on user-defined formulas 

which are specified in Revenue Function Definition Editor. This revenue is a function of several 

parameters such as Revenue Types, Revenue Functions, Activity Types, Global Revenue 

Parameters, and OSA-specific Revenue Parameters. For the ABC-Detailed approach, it is also a 

function of Activity Functions and Activity Function Parameters (see Activity Function Unit in 

ABC-Detailed section). 

ESRev a,i,e,y = nRa,I,y * [User-defined Formula]

 (53) 

where: 

ESRev a,i,e,y Revenue from revenue function i in modeling area a collected by 

Expenditure Source e in year y ($) 

nRa,I,y Quantity of revenue type I (which corresponds to revenue function i) in 

modeling area a in year y (Units vary by revenue type: Mcf for gas 

production, bbl for oil production, $ for total bonus bid, etc.) 

 An example of a User-defined Formula is “Federal Share of Total Royalty 

Revenue” 

2. OSA Allocation of Expenditure Source Revenues 

ESRev a,oe,i,e,y RFOSAAlloc a,i,oe ESRev a,i,e,y = * (54) 

where: 

ESRev a,oe,i,e,y Expenditure Source revenue from revenue activity i in modeling area a in 

year y collected by Expenditure Source e in OSA oe ($) 

RFOSAAlloca,i,oe Allocation of revenue from revenue function i in modeling area a to OSA 

oe (Fraction) 
MAG-PLAN Study Appendix D 

129 

3. Recycled Revenues from Expenditure Sources 

Revenue recycling rate (RRR) controls the amount of Expenditure Source revenues to be 

recycled back to the economies in the same year and the remaining is saved in an interest-bearing 

fund to be spent in the following year (or lagging several years). 

RESRev a,oe,i,e,y ESRev a,oe,i,e,y RRRa,i = * (55) 

( ) ( )

n

a oe i e y n a oe i e y a i IESRev = ESRev − RRR + r , , , , + , , , , * 1 , * 1 (56) 

where: 

RESRev a,oe,i,e,y Revenues from revenue activity i in modeling area a in year y collected by 

Expenditure Source e in OSA oe to be recycled back to the economies at 

year y ($) 

a oe i e y n IESRev , , , , + Revenues from revenue activity i in modeling area a in year y collected by 

Expenditure Source e in OSA oe to be recycled back to the economies at 

year y+n ($) 

RRRa,i Revenue recycling rates for modeling area a and revenue function i 

(Fraction) 

r Annual interest rate (Fraction) 

n Lag years 

4. Disbursement of Expenditure Source Revenue to OSA 

∑ ∑∑( ) ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ = +

oe

e oe o

a i

EStoOSARev e o y RESRev a oe i e y IESRev a oe i e y EStoOSAAlloc , , , , , , , , , , , , * (57) 

where: 

EStoOSARev e,o,y Revenues collected by Expenditure Source e in OSA o in year y ($) 

EStoOSAAlloce,oe,o Allocation of revenues collected by Expenditure Source e in OSA oe to 

OSA o ($) 
MAG-PLAN Study Appendix D

130

5. Disbursement of OSA Revenues to Sectors 

= ∑( ) eESEo,s,y EStoOSARev e,o,y EStoSAlloce,o,s * (58) 

where: 

ESEo,s,y Expenditure Source expenditure for sector s in OSA o in year y ($) 

EStoSAlloce,o,s Allocation of expenditures from Expenditure Source e in OSA o to sector 

s (Fraction) 

A.4. Total Expenditures 

Eo,s,y = LEo,s,y + NLEo,s,y + ESEo,s,y (59) 

where: 

Eo,s,y Total expenditures for sector s in OSA o in year y ($) 

B. Multiplier Impacts 

Multiplier impacts provide the direct, indirect, induced, and total economic impacts from the 

expenditures by OSA and aggregated IMPLAN sector. 

B.1. Direct Output 

Direct output is a set of vectors of total expenditures calculated in A.4 (Eqn. 59). One direct 

output vector of size N (number of IMPLAN sectors) is assigned to each OSA for each analysis 

year. 

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

= =

o N y

o y

o y

o s y o s y

ODir

ODir

ODir

ODir E

, ,

,2,

,1,

, , , , M (60) 

where: 

ODiro,s,y Direct output for sector s in OSA o in year y ($) 
MAG-PLAN Study Appendix D 

131 

B.2. IMPLAN Regional Type I Multipliers and Indirect Output 

1. IMPLAN Regional Type I Multipliers 

The IMPLAN Regional Type I Multipliers are imported from the IMPLAN output Access table 

“Regional Multipliers Type I.” It is a three-column table containing the fields: “Industry Col 

Code,” “Industry Row Code,” and “Multiplier.” The table is converted to an N by N matrix, 

where N represents the number of IMPLAN sectors. 

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

=

N N N N

N

N

N N

MIdr MIdr MIdr

MIdr MIdr MIdr

MIdr MIdr MIdr

MIdr

,1 ,2 ,

2,1 2,2 2,

1,1 1,2 1,

,

L

M M O

L

L

 (61) 

where: 

MIdrsrow,scol IMPLAN Type I multiplier that represents the direct+indirect impact in 

sector srow for direct expenditure in sector scol (scalar) 

2. Indirect Output 

Indirect output is defined as the inter-industry effects beyond the direct effects when Type I 

multipliers are used. 

( )

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

−

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

=

= −

o N y

o y

o y

o N y

o y

o y

N N N N

N

N

o s y N N o s y o s y

ODir

ODir

ODir

ODir

ODir

ODir

x

MIdr MIdr MIdr

MIdr MIdr MIdr

MIdr MIdr MIdr

OIdr MIdr x ODir ODir

, ,

,2,

,1,

, ,

,2,

,1,

,1 ,2 ,

2,1 2,2 2,

1,1 1,2 1,

, , , , , , ,

M M

L

M M O

L

L

 (62) 

where: 

OIdro,s,y Indirect output for sector s in OSA o in year y ($) 
MAG-PLAN Study Appendix D

132

B.3. IMPLAN Regional Induced Multipliers and Induced Output 

1. IMPLAN Regional Induced Multipliers 

IMPLAN Regional Induced Multipliers are imported from IMPLAN output Access table 

“Regional Multipliers Induced,” a three-column table with fields: “Industry Col Code,” “Industry 

Row Code,” and “Multiplier.” The table is converted to an N by N matrix, where N represents 

the number of IMPLAN sectors. 

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

=

N N N N

N

N

N N

MIdc MIdc MIdc

MIdc MIdc MIdc

MIdc MIdc MIdc

MIdc

,1 ,2 ,

2,1 2,2 2,

1,1 1,2 1,

,

L

M M O

L

L

 (63) 

where: 

MIdcsrow,scol IMPLAN induced multiplier that represents the induced impact in sector 

srow for direct expenditure in sector scol (scalar) 

2. Induced Output 

Induced output effects are generated due to the household expenditures in I/O analysis. 

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

= =

o N y

o y

o y

N N N N

N

N

o s y N N o s y

ODir

ODir

ODir

x

MIdc MIdc MIdc

MIdc MIdc MIdc

MIdc MIdc MIdc

OIdc MIdc x ODir

, ,

,2,

,1,

,1 ,2 ,

2,1 2,2 2,

1,1 1,2 1,

, , , , , M

L

M M O

L

L

 (64) 

where: 

OIdco,s,y Induced output for sector s in OSA o in year y ($) 

B.4. Total Output 

OToto,s,y = ODiro,s,y + OIdro,s,y + OIdco,s,y (65) 

where: 

OToto,s,y Total output for sector s in OSA o in year y ($) 
MAG-PLAN Study Appendix D 

133 

B.5. IMPLAN Study Area Data 

Data for IMPLAN study area ratios are obtained from IMPLAN output Access table “SARatios,” 

an eight-column table with fields: “Sector,” “Output,” “Employment,” “Employee 

Compensation,” “Proprietor Income,” “Total Value Added,” “Indirect Business Taxes,” and 

“OPTI.” 

1. Employment Ratio 

Employment ratio, obtained from field “Employment” of table “SARatios,” represents the 

number of direct, indirect, and induced jobs to be generated for every million dollars of direct, 

indirect, and induced output expenditures. 

REMo,s (66) 

where: 

REMo,s Employment ratio for OSA o and sector s (Number of jobs/$ Million) 

2. Employee Compensation Ratio 

Employee compensation ratio, obtained from field “Employee Compensation” of table 

“SARatios,” represents direct, indirect, and induced employee compensation ($) for every dollar 

of direct, indirect, and induced output expenditures. 

RECo,s (67) 

where: 

RECo,s Employee compensation ratio for OSA o and sector s (scalar) 

3. Proprietor’s Income Ratio 

Proprietor's income ratio, obtained from field “Proprietor's Income” of table “SARatios,” 

represents direct, indirect, and induced proprietor’s income ($) for every dollar of direct, indirect, 

and induced output expenditures. 

RPIo,s (68) 

where: 

RPIo,s Proprietor’s income ratio for OSA o and sector s (scalar) 
MAG-PLAN Study Appendix D

134

4. Labor Income Ratio 

Labor income ratio is the sum of employee compensation and proprietor's income ratios. It 

represents direct, indirect, and induced labor income ($) for every dollar of direct, indirect, and 

induced output expenditures. 

RLIo,s = RECo,s + RPIo,s (69) 

where: 

RLIo,s Labor income ratio for OSA o and sector s (scalar) 

5. Total Value Added Ratio 

Total value added ratio is a ratio of total value added to industry output obtained from fields 

“Total Value Added” ($Million) and “Output” ($Million) of table “SARatios,” respectively. It 

represents direct, indirect, and induced total value added ($) for every dollar of direct, indirect, 

and induced output expenditures. 

o s

o s

o s FO

FTVA

RTVA

,

,

, = (70) 

where: 

RTVAo,s Total value added ratio for OSA o and sector s (scalar) 

FTVAo,s Total value added from field “Total Value Added” of table “SARatios” for 

OSA o and sector s ($Million) 

FOo,s Industry output from field “Output” of table “SARatios” for OSA o and 

sector s ($Million) 

6. Indirect Business Taxes Ratio 

Indirect business taxes ratio, obtained from field “Indirect Business Taxes” of table “SARatios,” 

is a vector of size N (number of IMPLAN sectors) that represents direct, indirect, and induced 

indirect business taxes ($) for every dollar of direct, indirect, and induced output expenditures. 

RIBTo,s (71) 

where: 

RIBTo,s Indirect business taxes ratio for OSA o and sector s (scalar) 
MAG-PLAN Study Appendix D 

135 

7. Other Property Type Income Ratio 

Other property type income ratio, obtained from field “OPTI” of table “SARatios,” is a vector of 

size N (number of IMPLAN sectors) that represents direct, indirect, and induced other property 

type income ($) for every dollar of direct, indirect, and induced output expenditures. 

ROPTIo,s (72) 

where: 

ROPTIo,s Other property type income ratio for OSA o and sector s (scalar) 

B.6. Direct, Indirect, Induced, and Total Study Area Impacts 

Direct, indirect, and induced impacts of the seven study areas are calculated based on the direct, 

indirect, and induced output and the study area ratios. 

1. Direct, Indirect, Induced, and Total Employment Impacts 

( 1,000,000) EMDiro,s,y = REMo,s × ODiro,s,y (73) 

( 1,000,000) EMIdro,s,y = REMo,s × OIdro,s,y (74) 

( 1,000,000) EMIdco,s,y = REMo,s × OIdco,s,y (75) 

EMTOto,s,y = EMDiro,s,y + EMIdro,s,y + EMIdco,s,y (76) 

where: 

EMDiro,s,y Direct employment impact for OSA o and sector s in year y (Number of 

jobs) 

EMIdro,s,y Indirect employment impact for OSA o and sector s in year y (Number of 

jobs) 

EMIdco,s,y Induced employment impact for OSA o and sector s in year y (Number of 

jobs) 

EMToto,s,y Total employment impact for OSA o and sector s in year y (Number of 

jobs) 
MAG-PLAN Study Appendix D

136

2. Direct, Indirect, Induced, and Total Employee Compensation Impacts 

ECDiro,s,y = RECo,s × ODiro,s,y (77) 

ECIdro,s,y = RECo,s × OIdro,s,y (78) 

ECIdco,s,y = RECo,s × OIdco,s,y (79) 

ECTOto,s,y = ECDiro,s,y + ECIdro,s,y + ECIdco,s,y (80) 

where: 

ECDiro,s,y Direct employee compensation impact for OSA o and sector s in year y ($) 

ECIdro,s,y Indirect employee compensation impact for OSA o and sector s in year y 

($) 

ECIdco,s,y Induced employee compensation impact for OSA o and sector s in year y 

($) 

ECToto,s,y Total employee compensation impact for OSA o and sector s in year y ($) 

3. Direct, Indirect, Induced, and Total Proprietor’s Income Impacts 

PIDiro,s,y = RPIo,s × ODiro,s,y (81) 

PIIdro,s,y = RPIo,s × OIdro,s,y (82) 

PIIdco,s,y = RPIo,s × OIdco,s,y (83) 

PITOto,s,y = PIDiro,s,y + PIIdro,s,y + PIIdco,s,y (84) 

where: 

PIDiro,s,y Direct proprietor’s income impact for OSA o and sector s in year y ($) 

PIIdro,s,y Indirect proprietor’s income impact for OSA o and sector s in year y ($) 

PIIdco,s,y Induced proprietor’s income impact for OSA o and sector s in year y ($) 

PIToto,s,y Total proprietor’s income impact for OSA o and sector s in year y ($) 
MAG-PLAN Study Appendix D 

137 

4. Direct, Indirect, Induced, and Total Labor Income Impacts 

LIDiro,s,y = RLIo,s × ODiro,s,y (85) 

LIIdro,s,y = RLIo,s × OIdro,s,y (86) 

LIIdco,s,y = RLIo,s × OIdco,s,y (87) 

LITOto,s,y = LIDiro,s,y + LIIdro,s,y + LIIdco,s,y (88) 

where: 

LIDiro,s,y Direct labor income impact for OSA o and sector s in year y ($) 

LIIdro,s,y Indirect labor income impact for OSA o and sector s in year y ($) 

LIIdco,s,y Induced labor income impact for OSA o and sector s in year y ($) 

LITot o,s,y Total labor income impact for OSA o and sector s in year y ($) 

5. Direct, Indirect, Induced, and Total Total Value Added Impacts 

TVADiro,s,y = RTVAo,s × ODiro,s,y (89) 

TVAIdro,s,y = RTVAo,s × OIdro,s,y (90) 

TVAIdco,s,y = RTVAo,s × OIdco,s,y (91) 

TVATOto,s,y = TVADiro,s,y +TVAIdro,s,y +TVAIdco,s,y (92) 

where: 

TVADiro,s,y Direct total value added impact for OSA o and sector s in year y ($) 

TVAIdro,s,y Indirect total value added impact for OSA o and sector s in year y ($) 

TVAIdco,s,y Induced total value added impact for OSA o and sector s in year y ($) 

TVATot o,s,y Total total value added impact for OSA o and sector s in year y ($) 
MAG-PLAN Study Appendix D

138

6. Direct, Indirect, Induced, and Total Indirect Business Taxes Impacts 

o s y RIBTo s ODiro s y IBTDir , , = , × , , (93) 

o s y RIBTo s OIdro s y IBTIdr , , = , × , , (94) 

o s y RIBTo s OIdco s y IBTIdc , , = , × , , (95) 

o s y o s y o s y o s y IBTTOt IBTDir IBTIdr IBTIdc , , = , , + , , + , , (96) 

where: 

o s y IBTDir , , Direct indirect business taxes impact for OSA o and sector s in year y ($) 

o s y IBTIdr , , Indirect indirect business taxes impact for OSA o and sector s in year y ($) 

o s y IBTIdc , , Induced indirect business taxes impact for OSA o and sector s in year y ($) 

o s y IBTTot , , Total indirect business taxes impact for OSA o and sector s in year y ($) 

7. Direct, Indirect, Induced, and Total Other Property Type Income Impacts 

OPTIDiro,s,y = ROPTIo,s × ODiro,s,y (97) 

OPTIIdro,s,y = ROPTIo,s × OIdro,s,y (98) 

OPTIIdco,s,y = ROPTIo,s × OIdco,s,y (99) 

OPTITOt o,s,y = OPTIDiro,s,y + OPTIIdro,s,y + OPTIIdco,s,y (100) 

where: 

OPTIDiro,s,y Direct other property type income impact for OSA o and sector s in year y 

($) 

OPTIIdro,s,y Indirect other property type income impact for OSA o and sector s in year 

y ($) 

OPTIIdco,s,y Induced other property type income impact for OSA o and sector s in year 

y ($) 

OPTITot o,s,y Total other property type income impact for OSA o and sector s in year y 

($) 
MAG-PLAN Study Bibliography 

139

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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 

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 territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.