OCS Study 

MMS 2006-063 

 

Coastal Marine Institute 

Economic Effects of Petroleum Prices 

and Production in the Gulf of Mexico OCS 

on the U.S. Gulf Coast Economy 

Trends in Unemployment Rates in the U.S. Coastal Gulf States

0

2

4

6

8

10

12

14

16

18

1976.1 1978.1 1980.1 1982.1 1984.1 1986.1 1988.1 1990.1 1992.1 1994.1 1996.1 1998.1 2000.1

Unemployment Rates, %

ALQUR LAQUR MSQUR TXQUR

Cooperative Agreement

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2006-063 

Coastal Marine Institute 

Economic Effects of Petroleum Prices 

and Production in the Gulf of Mexico OCS 

on the U.S. Gulf Coast Economy 

Authors 

Omowumi O. Iledare 

Williams O. Olatubi 

October 2006 

Prepared under MMS Contract 

1435-01-99-CA-30951-17805 

by 

Louisiana State University 

Center for Energy Studies 

Baton Rouge, Louisiana 70803 

Published by 
iii

DISCLAIMER

This report was prepared under contract between the Minerals Management Service (MMS) and 

Louisiana State University’s Center for Energy Studies. This draft report has not been technically 

reviewed by MMS. 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. It is, however, exempt from review and compliance 

with MMS editorial standards. 

REPORT AVAILABILITY

Extra copies of the report may be obtained from the Public Information Office (Mail Stop 5034) 

at the following address: 

 

 U.S. Department of the Interior 

 Minerals Management Service 

 Gulf of Mexico OCS Region 

 Public Information Office (MS 5034) 

 1201 Elmwood Park Boulevard 

 New Orleans, Louisiana 70123-2394 

 Telephone Number: 1-800-200-GULF or 

 504-736-2519 

CITATION

Suggested Citation: 

Iledare, O.O. and W.O. Olatubi. 2006. Economic Effects of Petroleum Prices and Production in 

the Gulf of Mexico OCS on the U.S. Gulf Coast Economy. U.S. Dept. of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study 

MMS 2006-063. 59 pp. 

ACKNOWLEDGMENTS

This report is based primarily on OCS leasing records made available to the Center for Energy 

Studies by the Minerals Management Service, New Orleans. Barbara Kavanaugh, Versa Stickle, 

Ric Pincomb, Yan Zhang, and Amar Dave were very helpful in obtaining and processing these 

data. 
v

ABSTRACT 

The purpose of this study is to analyze the dynamic interaction between changes in crude oil 

prices, oil and gas industry activity in the OCS (measured in terms of petroleum production) and 

selected indicators of the Gulf Coast economies. The scope of the study is expanded to include 

E&P activity in the deepwater. A vector auto-regression (VAR) model framework showing the 

interaction between crude petroleum price, oil and gas production, the U.S. interest rates, the 

U.S. gross domestic product, and selected indicators of the state of the Gulf Coast economy—

personal income, unemployment rate and revenue—was developed and estimated. The model 

framework enables us to establish the direction, symmetry, causation, duration, responsiveness, 

and correlation between industry activity and state economic activity indicators and oil price 

changes over time. 

The empirical results show that changes in crude oil prices have significant effects on oil and gas 

production in the Gulf of Mexico OCS and on measures of the Gulf Coast economy. The effects 

of oil prices on the state of the economy in the Gulf Coast are two-pronged. There is an 

established direct effect on the macroeconomic aggregates and there is also an indirect effect 

through production activity. As expected, the results show that the magnitude and duration of a 

crude oil price shock on the state of the economies in the Gulf States, as well as oil and gas 

production, differ significantly by state. 

In a broad sense, the study shows that while the national economy may have become less 

sensitive to oil price shocks in the aggregate, the Gulf Coast economies are still prone to oil price 

shocks, albeit with variations across the states in the Gulf Coast. Thus, the study reaffirms the 

need to be cautious about policy responses that tend to focus only on the national response to 

policy issues with regional implications. The assumption that such national response is 

applicable or appropriate across regions may be erroneous. This demonstrates that understanding 

the dynamic of oil prices and their impacts on macroeconomic aggregates in and within the 

regions/states are as important as ever, even as mitigating national policies and response 

strategies evolve. 
vii

TABLE OF CONTENTS 

 Page 

LIST OF FIGURES ....................................................................................................................... ix 

LIST OF TABLES......................................................................................................................... xi 

EXECUTIVE SUMMARY .............................................................................................................1

1. INTRODUCTION ...............................................................................................................7 

 1.1. Background...................................................................................................................7 

 1.2. Study Objectives ...........................................................................................................9 

 1.3. Regional Scope of the Study.........................................................................................9 

2. DATA SOURCES AND DESCRIPTIVE ANALYSIS ....................................................11 

 2.1. Sources of Data...........................................................................................................11 

 2.2. Key Indicators of Economic Performance..................................................................12 

3. VAR MODELING OF THE ECONOMIC EFFECTS OF PETROLEUM 

 PRODUCTION AND PRICES ..................................................................................21 

 3.1. VAR Model Specification...........................................................................................21 

 3.2. Empirical VAR Model Representation.......................................................................21 

 3.3. VAR Model Estimation and Analysis.........................................................................22 

4. ESTIMATED VAR MODEL RESULTS: VARIANCE DECOMPOSITION 

 ANALYSIS.................................................................................................................25 

 4.1. VAR Results from OCS Aggregate Production System Equations............................25 

 4.1.1. OCS Petroleum Production and the Louisiana Economy.................................25 

 4.1.2. OCS Petroleum Production and the Alabama Economy ..................................25 

 4.1.3. OCS Petroleum Production and the Mississippi Economy ..............................27 

 4.1.4. OCS Petroleum Production and the Texas Economy .......................................27 

 4.2. VAR Results from OCS Deepwater Production System Equations ...........................27 

 4.2.1. OCS Deepwater and the Louisiana Economy ..................................................27 

 4.2.2. OCS Deepwater and the Alabama Economy....................................................29 

 4.2.3. OCS Deepwater and the Mississippi Economy................................................29 

 4.2.4. OCS Deepwater and the Texas Economy.........................................................29 

5. ESTIMATED VAR MODEL RESULTS: IMPULSE RESPONSE FUNCTION 

 APPROACH...............................................................................................................31 

 5.1. IRF Results from OCS Aggregate Production System Equations..............................31 

 5.1.1. Price Shock, Gulf OCS Production, and the Louisiana Economy....................31 

 5.1.2. Price Shock, Gulf OCS Production, and the Alabama Economy .....................31 

 5.1.3. Price Shock, Gulf OCS Production, and the Mississippi Economy .................35 

 5.1.4. Price Shock, Gulf OCS Production, and the Texas Economy..........................35 
viii

TABLE OF CONTENTS 

(Continued) 

 Page 

 5.2. IRF Results from OCS Deepwater Production System Equations .............................38 

 5.2.1. Price Shock, OCS Deepwater Production, and the Louisiana Economy..........38 

 5.2.2. Price Shock, OCS Deepwater Production, and the Alabama Economy ...........38 

 5.2.3. Price Shock, OCS Deepwater Production, and the Mississippi Economy .......38 

 5.2.4. Price Shock, OCS Deepwater Production, and the Texas Economy................43 

6. ECONOMIC INTERPRETATIONS OF THE VAR MODEL RESULTS .......................45 

 

7. SUMMARY AND CONCLUSIONS ................................................................................51 

REFERENCES ..............................................................................................................................53 

APPENDIX A–AN OUTLINE OF THE VAR PROCEDURE.....................................................55 

 
ix

LIST OF FIGURES

Figure Description Page

 

1. Trends in Annual Revenue of the U.S. Gulf States ...........................................................16 

2. Trends in Quarterly Personal Income of the U.S. Gulf States...........................................17 

3. Trends in Unemployment Rates in the U.S. Coastal Gulf States.......................................17 

4. Trends in Crude Petroleum Price Index, 1976-2000 .........................................................18 

5. Gulf of Mexico OCS Petroleum Production by Water Depth Category............................19 

6. Louisiana Personal Income and OCS Production Dynamic Paths.....................................32 

7. Louisiana Unemployment and OCS Production Dynamic Paths ......................................32 

8. Dynamic Paths of Louisiana Revenue and OCS Production.............................................33 

9. Responses of Gulf Production & AL Unemployment Rate to Price .................................33 

10. Responses of Gulf Production & AL Personal Income to Price........................................34 

11. Responses of Gulf Production & AL Revenue to Price.....................................................34 

12. Responses of Gulf Production & MS Unemployment Rate to Price.................................36 

13. Responses of Gulf Production & MS Personal Income to Price .......................................36 

14. Responses of Gulf Production & MS Revenue to Price ....................................................37 

15. Responses of Gulf Production & TX Unemployment Rate to Price .................................37 

16. Responses of Gulf Production & TX Personal Income to Price........................................39 

17. Responses of Gulf Production & TX Revenue to Price.....................................................39 

18. Responses of Deepwater Production & LA Unemployment to Price................................40 

19. Responses of Deepwater Production & LA Personal Income to Price..............................40 

20. Responses of Deepwater Production & AL Unemployment Rate to Price .......................41 

21. Responses of Deepwater Production & AL Personal Income to Price..............................41 

22. Responses of Deepwater Production & MS Unemployment Rate to Price.......................42 

23. Responses of Deepwater Production & MS Personal Income to Price .............................42 

24. Responses of Deepwater Production & TX Unemployment Rate to Price .......................43 

25. Responses of Deepwater Production & TX Personal Income to Price..............................44 
xi

LIST OF TABLES

Table Description Page

1. Variable Names, Descriptions, and Transformation Method ............................................13 

2. Correlation Matrix of Model Variables .............................................................................15 

3a. Quarterly Summary Statistics of Model Variables, 1976:1-1999:1...................................15 

3b. Annual Summary Statistics of Model Variables, 1954-1999 ............................................16 

4. Decomposition of the Variance of Macroeconomic Variables Due to Changes in 

 Petroleum Prices and OCS Gross Petroleum Production ......................................26 

5. Decomposition of the Variance of Macroeconomic Variables Due to Changes in 

 Petroleum Prices and OCS Deepwater Petroleum Production ..............................28 

6. Estimated Range of the Impact of Changes in Price and OCS Production on 

 Macroeconomic Variables Using the Impulse Response Function 

 Technique (%)........................................................................................................46 

7. Estimated Range of the Impact of Changes in Price and Deep OCS Production on 

 Macroeconomic Variables Using the Impulse Response Function 

 Technique (%)........................................................................................................47 

8. Price Elasticity of Macroeconomic Variables and the Quantity Equivalence 

 Conditional on the Dynamics of OCS Petroleum Production and the Gulf 

 Coast Economy ......................................................................................................48 

9. Estimated Adjustment Paths to Equilibrium Following a Price Shock Impact on 

 Aggregate OCS Petroleum Production and the Economy .....................................50 
1 

EXECUTIVE SUMMARY

There is a general consensus that declining oil prices stimulate economic growth while 

increasing oil prices tends to dampen economic performance; the effects are not generally 

conclusive, however, for sub-national economies. While the effects of changes in oil price 

structure on the U.S. national economy are generally understood, the impacts of such changes on 

the state or sub-regional economies are less fully examined. Very few studies have studied the 

impact of changes in crude oil price on state economic performance, and such studies tend to 

conclude that a rising oil price more often than not stimulates economic growth in oil exporting 

states and hinders growth in oil importing states. The converse is true for declining oil prices. 

For effective policy and regulatory guidance within the context of the overall national energy 

policy, agencies such as the MMS need reliable information at the regional levels, where most 

relevant oil and gas activities take place. This is because each state or region often possesses 

unique characteristics that are at variance with national outlooks. Therefore, such unique 

situations require a different policy or regulatory framework. Accordingly, this study is proposed 

to fill these gaps by extending previous national studies to sub-national economies, especially to 

areas where MMS has jurisdictional mandates. 

This study analyzes the interactions between crude oil prices, oil and gas industry activity in the 

Outer Continental Shelf (OCS), and selected economic indicators of the Gulf Coast States. Total 

revenue, personal income, and the unemployment rate of four states in the U.S. Gulf Coast are 

used as proxies for measuring the strength of the U.S. Gulf Coast economy. The states were 

selected on the basis of some unique structural and economic characteristics as specified below: 

 Louisiana: Represents net oil exporter with limited diversified economy; 

Mississippi: Represents net oil importer with limited diversified economy; 

 Texas: Represents net oil exporter with relatively diversified economy; 

Alabama: Represents net oil exporter with limited diversified economy. 

Three key indicators for measuring E&P industry activity and performance that are highly 

correlated with crude oil price movements both in the short run and long run have been identified 

as drilling rig counts, production, and capital expenditures. However, due to data limitations, oil 

and gas production was used as a proxy for measuring the trends in E&P industry activity. The 

scope of the study was also expanded to cover deepwater operations. 

Data and Methods: The data used for this study are basically secondary in nature from various 

sources. The data source for the oil and gas production series is from the MMS oil and gas 

database. The oil price data is the crude oil producer price index deflated by the all commodities 

price index series, which is available from the U.S. Bureau of Labor Statistics. Data for 

unemployment rates are available from the U.S. Bureau of Labor Statistics. The Bureau of 

Economic Analysis (BEA) is the source of the following data: the quarterly personal income and 

the annual revenue series for the states, U.S. real GDP, GDP implicit deflator and interest rates. 

A vector auto-regression (VAR) model framework showing the interactions between crude 

petroleum price, oil and gas production, the U.S. interest rates, the U.S. gross domestic product, 
2 

and selected indicators of the Gulf Coast States’ economies—personal income, unemployment 

rate and revenue—was developed and estimated. The VAR approach has been used generally for 

forecasting systems of interrelated time series and for analyzing the impact of a random 

disturbance on a system of variables. In this formulation, every endogenous variable is modeled 

to depend on its own lag(s), lags of other endogenous variables, and any exogenous variables 

that may also be included. 

Variance decomposition and impulse response functions represent two complementary ways to 

characterize the dynamic effects of an unexpected shock to a given economic system that is 

represented by a VAR model. The variance decomposition procedure provides a way to 

decompose the effects of a shock on the system to their component parts. The percentage share 

of the effect of each particular shock provides an indication of its relative potency in explaining 

the observed variations in each variable experiencing the shock. The impulse response functions, 

on the other hand, provide a way to examine the paths of the effects of an exogenous shock of 

one variable on other variables and to further characterize the stability and its duration for the 

variables. The persistence of such shocks reveals the pace and pattern of the adjustment process 

of the system to its long-run equilibrium. The faster it takes a shock to dampen, the faster the 

adjustment process back to equilibrium (Brown and Yucel, 1995). 

Economic Effects of Oil Price and E&P Activity in the Gulf OCS: 

On Louisiana Economy: The dynamic VAR analysis of the interactions between changes in 

crude oil prices, oil and gas production in the Gulf of Mexico (GOM) OCS and Louisiana 

unemployment rate shows that price is significant and it explains, on average, approximately 11 

percent of the observed variation in unemployment over time. Crude oil price interacting with oil 

and gas production in the Gulf of Mexico OCS also explains at most 14 percent of the expected 

variation in personal income and between 11 to 16 percent of the variation in revenue. To our 

surprise, however, autonomous oil and gas production has no direct significant effect on 

unemployment according to our VAR results. The impulse response results present the 

adjustment paths associated with price shocks. The results show that it can take more than 10 

years for unemployment, about 3 years for personal income, and up to 20 years for revenue to be 

restored to initial equilibrium. 

On Alabama Economy: The model results describing the interactions between oil price, oil and 

gas production in the Gulf of Mexico OCS and Alabama unemployment rate shows that price 

explains up to 30 percent of the expected variation in Alabama unemployment. The results also 

show that a price shock conditional on OCS oil and gas production profile explains up to 11 

percent of the observed variation in personal income in Alabama. Further, a price shock exposed 

to oil and gas production path in the Gulf also has a potential impact of at most 29 percent in the 

long-term on Alabama revenue. The ensuing impulse response functions reveal the adjustment 

paths for the interactions between a price shock and gross oil and gas production in the Gulf. The 

response paths show that it takes approximately 6 years for unemployment, 2 years for personal 

income, and 12 years for revenue to be restored to their initial equilibriums subsequent to the 

shock. The autonomous direct impact of oil and gas production in the Gulf OCS on Alabama 

unemployment is also not significant, according to the VAR model results. 
3

On Mississippi Economy: The model results, which describe the interactions between oil price, 

oil and gas production in the Gulf OCS, and Mississippi economic variables show that the 

percentage of the variation in the state’s unemployment accounted for by price is less than 10 

percent on the average, but significant. Similarly, the empirical results indicate that the effect of 

price on personal income subject to OCS production path can be up to 15.5 percent. The price 

impact on revenue according to the VAR model results is as high as 16.7 percent. Further 

analysis of the impulse response results and subsequent adjustment paths to a price shock 

indicate that unemployment rate takes more than 8 years, personal income takes about 2 years, 

and revenue takes 5 years to adjust to their initial equilibrium levels. Just as is the case with 

Louisiana and Alabama, oil and gas production in the Gulf has no direct significant impact on 

the state unemployment rate. 

On Texas Economy: The estimated model results of the effect of oil price interactions with Gulf 

oil and gas production and state economic variables with respect to the Texas economy show that 

the impact of a price shock on the Texas unemployment rate is relatively small, although 

significant. As much as 19 and 18 percent of the variations in personal income and revenue in the 

state are explained by price shocks, respectively. With regard to the adjustment paths over time, 

unemployment rate takes less than 10 years, personal income takes approximately 4 years, and 

revenue takes about 7 years for initial equilibrium to be restored. The effect of OCS production 

on Texas unemployment rate, unlike in the other Gulf States, is significant, but small. 

Economic Effects of Oil Prices and Deepwater E&P Activity in the Gulf OCS: 

 

On Louisiana Economy: The estimated model results for the interactions between oil prices and 

deepwater oil and gas production show that the effect of a price shock on Louisiana 

unemployment is relatively small (2 percent). However, deepwater production shows no 

significant and direct impact on the state unemployment. The model results further show that a 

price shock, conditioned on OCS deepwater production path, explains as high as 16.5 percent of 

the variation in Louisiana personal income. The paths of adjustment to price changes if 

deepwater production is restricted show a lag of 18 quarters for unemployment and 6 quarters for 

personal income. 

On Alabama Economy: The model results explaining the dynamic interactions between price and 

deepwater oil and gas production show that price explains up to 33 percent of the variability of 

Alabama unemployment. Also, within the same context, but unlike the estimated effect on 

Louisiana, OCS deepwater production has a highly significant effect on Alabama 

unemployment. OCS deepwater production explains up to 22 percent of the observed variation in 

Alabama unemployment. The effect of price shocks on personal income in Alabama is also 

found to be significant with respect to deepwater production. Under that scenario, a price shock 

explains up to 5.9 percent of the variation in personal income as well. 

On Mississippi Economy: The VAR results describing the effect of a price shock and oil and gas 

production from OCS deepwater on Mississippi economic variables show that the changes in 

price explain a relatively small proportion of the observed variation in unemployment (roughly 4 

percent). A shock to deepwater production has a significant effect on unemployment. Price 

shocks explain up to 5.3 percent of the observed variation in personal income. The paths of 
4

adjustment to price changes subject to deepwater production profile show a lag of 13 quarters for 

unemployment and 6 quarters for personal income. 

On Texas Economy: According to the VAR model results, the response of Texas unemployment 

to changes in oil price subject to the interactions between oil and gas production from OCS 

deepwater and price is not statistically significant. However, deepwater production has a direct 

and significant impact on unemployment. The results further suggest that price shocks explain up 

to 16.3 percent of the observed personal income variation. 

The paths of adjustment to price changes interacting with deepwater production from the Gulf 

OCS show a lag that is more than 24 quarters for unemployment and 10 quarters for personal 

income. 

In an overall sense, this study suggests that petroleum production in the Gulf of Mexico OCS 

responds positively to a positive price shock in the economy. 

Further, the study shows that: 

• Unemployment rates in coastal Gulf States in the U.S. tend to decline in response to 

increases in petroleum prices. It is interesting to note, however, that the responsiveness of 

unemployment rates to changes in prices differ significantly across the Gulf States. 

Texas has the least unemployment responsiveness to a price shock and Alabama has the 

highest among the four Gulf States. 

• According to the VAR model results, personal income tends to increase following a 

positive shock to petroleum prices in the presence of rising petroleum production. The 

degree of income responsiveness to price shocks varies across the U.S. Gulf States. In 

general, personal income responsiveness in Texas is greater than that of Mississippi, 

Louisiana, and Alabama, in that order. The empirical results also suggest that the Texas 

economy, because of its size, tends to experience a more lingering path to adjustment for 

personal income than Louisiana, Mississippi and Alabama. Similarly, personal income in 

Louisiana tends to experience more lingering effects than Mississippi and Alabama 

following a petroleum price shock. 

• Positive changes in petroleum prices lead to increases in annual revenue in Louisiana, 

Texas, and Alabama. The responsiveness of revenue to price changes, however, varies 

across Gulf States just as changes in unemployment and personal income vary across the 

Gulf States. 

• Surprisingly, unemployment rates in the Gulf States appear to be relatively less sensitive 

to production activities in the Gulf States than expected. In many instances in the U.S. 

Coastal Gulf States, the direct impacts of changes in production on unemployment rates 

are insignificant. 
5 

Finally, there is statistical evidence suggesting significant differences in the duration of the 

lingering effects of a price shock on the economic performance of the Coastal Gulf States we 

investigated in this study. 
7 

1. INTRODUCTION 

1.1. Background 

The Minerals Management Service, a federal agency in the U.S. Department of the Interior, 

manages more than one billion offshore acres and has collected about 4-5 billion dollars in 

mineral revenues annually over the past five years (USDOI, MMS, 2003). The Gulf of Mexico 

OCS region accounts for about 25 percent of the oil and gas produced in the U.S. (USDOE, EIA, 

2002). Thus, the oil and gas industry in the Gulf Coast is important to the nation’s economy, 

especially to the states in the U.S. Gulf Region. Hence, whatever happens in the oil market 

portends a certain trend for the national or regional economies, either in the short or long run. 

Perhaps the most important variable in the oil market is crude oil prices. Thus, a few economic 

impact studies supported by the MMS have focused on the effect of oil prices on the economies 

of Gulf of Mexico (GOM) communities. This is because oil prices, in addition to affecting the 

revenue base of adjacent states and communities, also have profound effects on the profits of oil 

companies operating in the region, and consequently, the levels of industry activities in the 

GOM. 

 

Over the past three decades, policy makers have become overtly concerned with the effects of oil 

prices on the economic performance of nations or regions. The very high oil prices in the 1970s 

and the very low prices in the mid-1980s and the early 1990s amplify these concerns. Most 

studies of national economies have concluded that changes in oil price significantly affect 

variations in macroeconomic aggregates and hence, the growth of economies. 

It is generally agreed that a declining oil price stimulates economic growth while an increasing 

oil price tends to dampen economic performance. These effects are often exacerbated depending 

on whether the nation is net oil importing or net oil exporting. The seminal work by Hamilton 

(1983) laid the foundation for the observed linkage between crude oil price movements and the 

level of economic activity in the U.S. economy. Other studies have since been revealed to 

debunk or support Hamilton’s claim that a sharp increase in oil prices produced the recessions 

between the end of World War II and early 1980. For example, Considine (1988) shows that 

gains in output and employment in the U.S. economy were relatively small following the 1986 

drop in oil price. Tatom (1988) also shows that changes in oil prices affect real GNP, 

productivity, and terms of trade and that these effects are asymmetric. A review of several 

econometric models by Hickman (1984) supports Tatom’s conclusions. On the other hand, 

Prescott (1986) maintains that oil price shocks have little or no effect on national production 

possibilities. These studies suggest that a sustained decline or increase in oil price and its effects 

on national economies can be predicted to some degree. 

Although the effects of oil price on the national economy are generally understood, the impacts 

of such movements on state or sub-regional economies are less fully examined. Few studies 

(Brown and Hill, 1988; Brown and Yucel, 1995; Yucel and Guo, 1994) have studied the effects 

of crude oil price on state economic performance. Most of these studies, unlike national studies, 

tend to show that a rising oil price stimulates economic growth in oil exporting states and hinder 

growth in energy importing states. The reverse is the case for declining oil prices. These studies 
8 

also imply sustained declines or increases and their effects can be ascertained and policy 

responses designed appropriately. 

For effective policy and regulatory guidance, keeping in perspective the overall national energy 

policy objectives, agencies such as the MMS desire reliable information at the regional levels, 

where oil and gas activities take place. This is because each state or region possesses unique 

operating environments that are at variance with the national outlook. Hence, a different policy 

or regulatory framework is required. The purpose of this study is to fill these information gaps by 

extending previous national studies to sub-national economies, especially to areas where MMS 

has jurisdictional mandates in the Gulf of Mexico OCS region. 

Analyses of microeconomic data at the level of individual industries, firms, or workers have 

established that there is significant correlation between oil price shocks and output, employment, 

or real wages (Keane and Prasard, 1996; Davis et. al., 1996; Lee and Ni, 1999). For example, an 

increase in the price of oil leads to an upward shift in firm’s cost curve, reducing profit levels, 

and hence, lowering employment and subsequently output. Such an increase may lead to 

substitution away from oil to other inputs with potential for further pressure on the cost curves as 

the prices of those inputs rise. A price decline in the oil market may have the opposite effect, 

although not likely of a similar magnitude. At the general economic level, because of linkages in 

the economy among sectors, an increase in oil price may induce inflation, hence, the notion that 

increases in oil price have preceded most recessions in the U.S. (Carruth et al., 1998). 

Changes in crude oil prices do affect revenue and the personal incomes of communities in many 

nations where the oil and gas industry looms large in the economy. For most oil producing 

regions, tax revenue from oil is a major source of general fiscal revenue, hence, a decline or 

increase in the levels of firm’s profits can influence this tax base significantly. Besides, an 

increase in oil price will generally induce cost-cutting measures by firms. The first casualty in 

this situation is often labor inputs. To get to equilibrium following an energy price increase as 

firms cut output and employment, wages are often cut, thus income of households will become 

negatively affected. In oil importing nations, oil price increases may be inflationary and lead to a 

dramatic fall in real wages and income. 

In a general sense, the above theoretical description may be true for national or cross-national 

economies, but the reality may be different and more complex in some states or regions. For 

example, an exporter of oil may receive some benefits from an oil price increase, but the non-oil 

firms located in that region may face increases in input costs. The converse may be true in an oil 

importing state. On the other hand, a decrease in oil price may also produce a depressed demand 

in some sectors of the state economy, and unemployed labor is not immediately shifted 

elsewhere1

. This effect may be quite pronounced because states within nations may possess 

economies with rigidities that are substantially different from their national economies as a 

whole. The overall net effect in each case is therefore subject to empirical verification and 

therefore the relevance of our focus on state-level analyses. 

 

1

 Potential structural rigidities and the degree of sectoral dependencies in a particular region’s economies will 

largely influence this situation. A region with a high concentration of oil dependent sectors will be especially 

complex to analyze. 
9 

1.2. Study Objectives 

This study develops economic and econometric models that examine the effects of changes in 

crude oil prices on both the E&P oil industries and the relevant regional economies in the Gulf of 

Mexico. The research uses recent econometric tools to provide quantitative estimates of the 

responsiveness and correlation between past and current activities of the oil industries and Gulf 

States’ economic growth and oil price changes and volatility. 

Specifically, the following objectives are addressed: 

• examine the changes in some specific economic indicators of E&P activities of the OCS 

oil industries as a result of oil price changes and price volatility over time; 

• examine the type of relationships that exist between oil price changes and the level of 

economic activities of the Gulf Region; 

• forecast potential impacts of future changes in oil prices on industry activities and state 

aggregate economic variables; and 

• identify possible policy responses to these changes by the industry and the relevant 

government in the Gulf. 

In order to meet the above challenges, recent developments in time series econometric modeling 

tools are employed. These tools enable us to establish the direction, causation, duration, 

responsiveness, and correlation between industry and states’ economic activity indicators and oil 

price changes over time. 

1.3. Regional Scope of Study

This study covers selected representative states in the GOM Region. Specifically, we selected the 

following states based on their unique structural and economic characteristics specified in each 

case. 

 Louisiana: Represents net oil exporter with limited diversified economy; 

Mississippi: Represents net oil importer with limited diversified economy; 

Texas: Represents net oil exporter with relatively diversified economy; 

Alabama: Represents net oil exporter with limited diversified economy. 

In terms of industry-level, the project focuses on two levels of activities. First, at the aggregate 

level the study examines oil price impact on industry and state-level macro-aggregates using 

industry activities for the entire OCS in the GOM. It is hoped that the results of such analysis 

will provide a broad picture of the potential impact of oil price driven policy variables of OCS 

activities on the individual state. Second, because MMS policy is often applied by planning area 

or water depth, the study repeats the same exercise at a more disaggregated level of industry 

activity in the deepwater. 
11 

2. DATA SOURCES AND DESCRIPTIVE ANALYSIS 

2.1 Sources of Data 

Most of the previous research on the economic effects of oil price shocks on macroeconomic 

variables have relied on national data, which are easily available from a variety of sources. One 

of the reasons for paucity in regional/state-level analyses is because reliable sources of statelevel information in the preferred format are limited. The data collection efforts in this study 

were very focused on finding accurate sources of data that are both comprehensive and tenable. 

In order to establish the robustness of our model, both from statistical and economic theory 

perspectives, we also used other U.S. macroeconomic aggregate data in the estimation 

procedures. The national-level aggregate economic variables used in the model include quarterly 

and annual data on real gross domestic product, crude oil producer price index, all commodities 

price index, interest rates (the 3-month treasury bill rates), and implicit gross domestic product 

deflator series. These national-level aggregate data are important inputs into the decision making 

process of the oil and gas industry for making exploration and production investment decisions. 

For example, given an oil price, the choice of the level of investment and hence, potential 

industry output, may be driven by the prevailing interest rates. With regards to the states, it is 

also expected that states’ economic variables at the state-level will to a large extent correlate 

with important national aggregates such as the overall GDP, which measures national economic 

output in the U.S. 

The data on oil and gas production came from the MMS oil and gas database. The oil price is the 

crude oil producer price index deflated by the all commodities price index. Both series are 

available from the U.S. Bureau of Labor Statistics. The data on unemployment rates for the 

states also came from the U.S. Bureau of Labor Statistics. The Bureau of Economic Analysis 

(BEA) is the source for the following economic variables: quarterly personal income and annual 

revenue data for the states; U.S. real GDP, GDP implicit deflator and interest rates. Table 1 

describes the nature of the data used in this study. 

Data for the following state macroeconomic aggregates: personal income, unemployment rate, 

and OCS oil and gas production are reported quarterly. However, for any aspects of the analyses 

which require the use of state revenue data, we have used annual series of the relevant variables. 

This was because quarterly data on revenue at the state level was not available. Table 1 shows 

the data that were available for different time spans for each of the variables. The modeling 

framework applied is thus restricted by the most time-limiting series. Crude oil equivalence as a 

measure of oil and gas activities in the OCS was used. 
12 

2.2. Key Indicators of Economic Performance 

The following macro-aggregates2

 or indicators are used as proxies for gauging the economic 

strength at the state-level: 

Revenue: Many GOMR States derived a large percentage of their budgetary revenue from 

the oil and gas industry located in their areas and some have industry sectors that are 

highly energy-dependent;

Unemployment: A lot of people in most of the states in the GOMR are employed directly 

or indirectly in the oil and gas sector, hence, any unusual developments in the sector will 

reflect on states’ welfare; unemployment level is one such closely watched variable; 

Personal Income: Apart from the substantial number of jobs produced by the oil and 

related sectors, wages in the oil sectors are often higher than other sectors, thus overall 

personal income levels in the states may be affected by downturns or booms in the oil 

sector. 

There are several potential indicators of industry activities in the oil and gas industry. Three key 

indicators of the level of economic activities in the oil and gas industry that may indirectly affect 

state economic performance and directly mirror the potential performance of the industry itself 

are drilling rig counts, exploration and development drilling, and production. These indicators 

are highly tied to the price of oil in the short-run as well as in the long run depending on current 

and expected profit margins in the industry. However, due to data limitations, especially at the 

more disaggregated levels of water depths, oil and gas production is used as a proxy for industry 

activity in our model formulation and estimation. 

The modeling approach developed and estimated in this section is premised on our desires to 

answer the following questions: 

a. What have been the general trends in oil prices, industry indicators, and 

macroeconomic variables over time? 

b. Are oil price movements correlated with identified macroeconomic and industry 

indicators and to what degree? 

c. Is there strong causality between oil price movements and identified performance 

variables in the state?3

d. How long does the effect of an oil price change persist before equilibrium is restored 

in these relevant aggregates? 

e. What is the degree of responsiveness (i.e. elasticity), if any, of these economic 

indicators to changes in oil price? 

 

2

 Output was originally proposed as one of the indicators but could not be used because of the length of the series at 

the state level. GSP is only available from 1977 and only on an annual basis. 

3

 Causality is defined in the Granger-sense here, not in the commonly understood sense. See appendix for details. 
13

Table 1 

 Variable Names, Descriptions, and Transformation Method 

* Year:Quarter-Year:Quarter. 

Table 2 presents the basic correlation coefficients among macroeconomic aggregates and 

selected exogenous variables. In general, the crude petroleum price index is shown to be 

negatively correlated with personal income, but positively correlated with unemployment ratesexcept in Louisiana. The correlation coefficients between price and unemployment rates are, 

however, relatively small in value. Personal income is highly and positively correlated with the 

overall OCS oil production. The correlation coefficients between unemployment rates and OCS 

production, in general, are similar in magnitude to those between production and personal 

income, but the signs of the correlation coefficients are reversed. State revenue shows a positive 

correlation with both price and crude petroleum production in the OCS. It should be noted that 

these results are only indicative of the potential relationships among the variables; correlation is 

not causation. Therefore, a more robust tool of analysis such as a VAR is often required for an 

in-depth examination of such relationships among variables. 

Descriptive statistics of all the variables discussed in the estimation process are shown in Tables 

3a and 3b. Average personal income is highest in Texas, followed by Louisiana, Alabama and 

Mississippi, respectively. However, the range in average personal income between the states is 

relatively large. Over the period, unemployment rates in these states are quite high, ranging from 

a mean value of 6.2 percent in Texas to 8.08 percent in Louisiana. The Gulf OCS gross oil and 

Variable Description Period* Length Seasonally Transformation Deflated by

 Adjusted 

ALQPI AL Quarterly Personal Income 1969:1-2000:2 126 No Log Difference GDPI 

LAQPI LA Quarterly Personal Income 1969:1-2000:2 126 No Log Difference GDPI 

MSQPI MS Quarterly Personal Income 1969:1-2000:2 126 No Log Difference GDPI 

TXQPI TX Quarterly Personal Income 1969:1-2000:2 126 No Log Difference GDPI 

ALQUR AL Quarterly Unemployment Rates 1976:1-2000:4 100 Yes Non Differenced 

LAQUR LA Quarterly Unemployment Rates 1976:1-2000:4 100 Yes Non Differenced 

MSQUR MS Quarterly Unemployment Rates 1976:1-2000:4 100 Yes Non Differenced 

TXQUR TX Quarterly Unemployment Rates 1976:1-2000:4 100 Yes Non Differenced 

QCPPI Quarterly Crude oil PPI 1947:1-2000:4 216 No Log Level QAPPI 

CPPIV Quarterly Crude oil PPI Volatility 1947:1-2000:4 216 No Non Differenced 

QAPPI Quarterly All Commodities PPI 1947:1-2000:4 216 No Non Differenced 

RGDP Real GDP in 1996 Dollars 1947:1-2000:4 216 Yes Log Difference 

GDPI Implicit GDP Deflator 1947:1-2000:4 216 Yes Log Difference 

TRBR Three Month Treasury Bill Rate 1947:1-2000:4 216 No Non Differenced 

GOSHA Gulf: Oil & Gas Production Shallow. Waters 1948:1-2000:4 212 No Log Difference 

GODEP Gulf: Oil & Gas Production Deep Waters 1979:3-2000:4 86 No Log Difference 

GOTOT Gulf: Oil & Gas Production Total 1948:1-2000:4 212 No Log Difference 

ALARV AL Annual Revenue 1950-2000 51 No Log Difference GDPI 

LAARV LA Annual Revenue 1950-2000 51 No Log Difference GDPI 

MSARV MS Annual Revenue 1950-2000 51 No Log Difference GDPI 

TXARV TX Annual Revenue 1950-2000 51 No Log Difference GDPI 

 
14

gas production averaged about 287.7 MMB annually. The average distribution of annual revenue 

in the states also shows a similar pattern to the distribution of quarterly personal income. Texas 

is considerably ahead of the others in state revenue on both an absolute and per capita basis. The 

trends in unemployment rates, personal income and annual revenue, macroeconomic indicators 

of the strength of the U.S. Gulf Coast economy, are discussed briefly below and depicted in 

Figures 1 through 3. 

The trends in annual state revenue as depicted in Figure 1 also show similar patterns to the trends 

in personal income of the four states (see Figure 2). Louisiana has had the lowest growth rate in 

revenue, especially since the early 1990s. Prior to the late 1980s, revenue derived from the oil 

and gas sector accounted for more than one third of the state government aggregate revenue. 

Presently, however, the oil and gas sector of the economy accounts for less than 12.5 percent of 

government revenue (Iledare and Olatubi, 2004). Figure 2 shows the trends in quarterly per 

capita personal income in the four Gulf States over time. It shows that the growth rate in Texas 

personal income is much higher than the growth in the other three Gulf States. Personal income 

in Alabama and Mississippi has grown in tandem over this period and the growth is better than 

the growth in Louisiana. 

Figure 3 presents the trends in another important macroeconomic variable--unemployment rates 

in the Gulf States. Employment levels provide an important indication of the level of economic 

activity in a state. Unlike personal income and revenue trends discussed earlier, the trends in 

unemployment rates follow similar patterns in all of the states. Generally, there were low 

unemployment rates until the early 1980s, when it increased dramatically. It is interesting to note 

that the net-petroleum importing states—Alabama and Mississippi—experienced the highest 

reported unemployment rates in the early 1980s. Many people in the Gulf States are employed 

directly or indirectly in the oil and gas sector, so any unusual developments in the petroleum 

sector will reflect on the state’s welfare. 

The trend in quarterly crude petroleum producer price index (QCPPI), a measure of composite 

oil prices, is presented in Figure 4. In general, oil price was stable until the mid-1970s. From the 

mid-1970s, the crude oil price index rose sharply to its historical high in the early 1980s. 

Although the price fell in the mid to late 1980s relative to the previous decade, it was relatively 

more volatile in the 1990s. In fact, the 1990s witnessed at least two spikes in oil prices. 
15

Table 2 

Correlation Matrix of Model Variables 

ANNUAL SERIES: 1954-1999 

Price Index Production Real GDP Treasury Bill 

Revenue, AL 0.577 0.792 0.976 0.164 

Revenue, LA 0.607 0.805 0.979 0.188 

Revenue, MS 0.554 0.788 0.971 0.153 

Revenue, TX 0.550 0.766 0.965 0.126 

Production 0.726 1.000 0.896 0.586 

QUARTERLY SERIES: 1976:1-1999:4 

Price Index Production Real GDP Treasury Bill 

Income, AL -0.149 0.833 0.981 -0.451 

Income, LA -0.168 0.835 0.979 -0.467 

Income, MS -0.184 0.854 0.990 -0.433 

Income, TX -0.188 0.871 0.994 -0.422 

Unemp., AL 0.103 -0.823 -0.907 0.124 

Unemp., LA -0.156 -0.622 -0.797 0.154 

Unemp., MS 0.095 -0.713 -0.890 0.271 

Unemp., TX 0.053 -0.796 -0.913 0.032 

Production -0.109 1.000 0.881 -0.245 

Table 3a 

 Quarterly Summary Statistics of Model Variables, 1976:1-1999:1 

Mean Median Max Min Std. 

Dev. Obs. 

ALQPI* 56,564 53,748 102,073 19,221 24,644 96 

LAQPI 59,655 54,557 101,460 21,017 22,686 96 

MSQPI 31,506 29,110 58,531 11,141 13,547 96 

TXQPI 273,960 246,886 551,782 78,828 129,791 96 

ALQUR** 7.78 7.15 15.55 4.09 2.56 96 

LAQUR 8.09 7.15 13.38 4.22 2.29 96 

MSQUR 7.89 7.45 13.49 4.82 2.13 96 

TXQUR 6.22 6.12 9.27 4.15 1.29 96 

Price Index 61.35 56.50 114.90 26.20 21.70 96 

Real GDP 6,261 6,255 9,084 4,266 1,311 96 

Treasury Bill 6.85 5.86 16.30 2.93 2.84 96 

OCS Total Prod. 287.70 286.13 356.59 229.23 29.18 96 

* XQPI represents quarterly personal income measured in millions of real dollars for state X. 

** XQUR is unemployment rates in percent for state X. Production is measured in Millions of barrels of oil 

equivalent and the real GDP is in trillion dollars. 
16

Table 3b 

 Annual Summary Statistics of Model Variables, 1954-1999 

Mean* Median Max Min Std. 

Dev. Obs. 

Revenue in AL 4,132.23 2,649.99 13,675.00 313.85 3,919.64 45 

Revenue in LA 4,963.84 3,216.15 14,498.00 556.95 4,366.19 45 

Revenue in MS 2,644.05 1,759.38 8,399.93 217.10 2,505.38 45 

Revenue in TX 14,273.62 8,090.17 47,970.04 855.65 14,584.57 45 

Price Index 39.95 35.70 109.60 12.60 27.79 45 

Real GDP 4,759.42 4,511.80 8,875.80 2,099.50 1,947.78 45 

Treasury Bill 5.61 5.06 14.03 1.73 2.66 45 

OCS Production 805.13 962.08 1,406.15 19.57 452.30 45 

* Annual revenue is reported in million dollars. 

 

0

20

40

60

80

100

120

19501952195419561958

196019621964

1966

1968

1970

19721974

197619781980

1982

1984

1986

1988199019921994199619982000

Revenue Index (1950=100)

ALARV LAARV MSARV TXARV

 Figure 1: Trends in Annual Revenue of the U.S. Gulf States. 
17

0

200

400

600

800

1000

1200

1400

1600

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

1970Q1=100

ALQPI LAQPI MSQPI TXQPI

 Figure 2: Trends in Quarterly Personal Income of the U.S. Gulf States. 

0

2

4

6

8

10

12

14

16

18

1976.1 1978.1 1980.1 1982.1 1984.1 1986.1 1988.1 1990.1 1992.1 1994.1 1996.1 1998.1 2000.1

Unemployment Rates, %

ALQUR LAQUR MSQUR TXQUR

 Figure 3: Trends in Unemployment Rates in the U.S. Coastal Gulf States. 
18

Figure 5 shows that oil and gas production from the OCS has increased significantly since the 

late 1950s. There was a rapid growth in oil and gas production from 1959 to the late 1970s. 

However, from the mid-1970s to the late 1990s, the rate of growth in production moderately 

declined. Since the late 1990s, there appears to be a sharper decline in production rate than any 

other time in history. In terms of water depth, most of the production activities in the GOM have 

historically occurred in the shallow waters. However, since the early 1990s, production has 

declined in the shallow waters while the production in the deep waters has been rising. 

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

1976.1 1978.1 1980.1 1982.1 1984.1 1986.1 1988.1 1990.1 1992.1 1994.1 1996.1 1998.1 2000.1

Index(1982=100)

 Figure 4: Trends in Crude Petroleum Price Index, 1976-2000. 
19

0

50

100

150

200

250

300

350

400

1959.11961.11963.11965.11967.11969.1

1971.11973.1

1975.11977.1

1979.1

1981.1

1983.11985.1

1987.11989.11991.11993.11995.1

1997.11999.1

Oil-Equivalent(MMB)

GODeep

GOShall

Note: For this report, a lease is considered to be located in deepwater if the average water depth is at least 200 m. 

Figure 5: Gulf of Mexico OCS Petroleum Production by Water Depth Category. 
21

3. VAR MODELING OF THE ECONOMIC EFFECTS OF PETROLEUM 

PRODUCTION AND PRICES 

3.1. VAR Model Specification 

Recent developments in time series analysis, especially in the theory of co-integration, errorcorrection and Granger-causality, have extended the opportunities available to analyze, in-depth, 

economic equilibrium relationships. In this study, as in most studies of macroeconomic impact of 

oil price change, a VAR modeling approach is adopted. VAR modeling is a multi-stage 

process—involving unit roots tests, co-integration examination, and Granger-causality 

exploration4

. The VAR approach is commonly used for forecasting systems of interrelated time 

series and for analyzing the dynamic impact of random disturbance on the system of variables. In 

this formulation, every endogenous variable is modeled as being dependent on its own lag(s) and 

the lags of other endogenous variables. Exogenous variables may also be included in the 

specification of the systems. 

The general mathematical formulation usually takes the form: 

t t p t p Bxt t y = A y + + A y + + ε − − ..... 1 1 (1) 

where yt is a vector of k dependent variables, xt is a vector of m independent variables, A1, ...., Ap

and B are matrices of coefficients to be estimated. The term εt is disturbance term that may be 

correlated with each other but may not be correlated with their immediate past values (εt-1) or 

other variables on the right-hand-side. 

3.2. Empirical VAR Model Representation 

The VAR model, which describes the interactions among the Gulf Coast economic indicators, oil 

and gas production in the Gulf OCS, and changes in crude petroleum price index is represented 

by the following system of equations (2): 

t i t i i t i i t i i t i t i t

t i t i i t i i t i i t i i t i t

t i t i i t i i t i i t i t

D

p

i

X X

p

i

y

p

i

y

p

i

y

p

i

y

D

p

i

X X

p

i

y

p

i

y

p

i

p

i

y y

D

p

i

X X

p

i

p

i

y y

p

i

y

3 30 3 1 3 2 3 3 3 1 2 3 1 3

2 20 2 1 2 2 2 3 2 1 1 2 2 1 2

1 10 1 1 1 2 1 1 1 2 1 1 1

1 1 1 1 1

1 1 1 1 1

1 1 1 1

α β γ λ ω φ δ μ

α β γ λ ω φ δ μ

α β γ ω φ δ μ

∑ + +

=

∑ +

=

∑ +

=

∑ +

=

∑ +

=

= +

∑ + +

=

∑ +

=

∑ +

=

∑ +

=

∑ +

=

= +

∑ + +

=

∑ +

=

∑ +

=

∑ +

=

= +

− − − − −

− − − − −

− − − −

 (2) 

where: 

yit (i=1,2,3): 1= natural log of crude petroleum price index; 2= natural log of oil and gas 

production; and 3= quarterly unemployment rates or natural log of annual state revenue or 

natural log of quarterly personal income; 

 

4

 A brief outline of a typical VAR procedure is given in Appendix A. 
22

X1t = the U.S. three-month treasury bill rate in levels (a proxy for interest rates); 

X2t = natural log of real U.S. gross domestic product; 

D1 = a deterministic dummy which equals 1 for the period 1979 to 1986 and 0 otherwise; 

p = the number of past values (lags) of the dependent variables in the system equations included 

as independent variables. 

The dummy variable D1 is included in each equation of the system to capture the period when oil 

prices declined and crashed. In addition, the proxy for economic indicator, y3t, does not appear in 

the price equation because the included measures of the economy in the Gulf States are not 

expected to have a direct influence on the crude petroleum price index because most economic 

activities in the Gulf States are price takers in the overall global petroleum economy. The 

number of past values of the dependent variables (length of lags) in each system of equations is 

determined statistically using a combination of Schwartz Bayesian Criteria (SBC) and Akaike 

Information Criteria (Iledare and Olatubi, 2004). 

Further, the general formulations represented in the above system of equations (2) are indeed a 

standard format of VAR model representation. In the primitive forms, the current levels of the 

other variables are included in the right-hand-side of the equation defining the evolution of that 

variable. From a statistical perspective, the primitive system of these equations suffers an 

‘identification’ problem. In addition, not all of the parameters of the primitive forms can be 

recovered from estimating the standard form. 

To identify the primitive system, restrictions have been imposed on some of the parameters. 

Such restrictions are based on economic theory or the intuition of the researcher. A common 

type of restriction is to ‘order’ the variables (and hence, the error terms) according to the effects 

that are believed to be ‘a priori’. For example, in this study, we order the variables as follows: 

[oil price → OCS activity → economic indicators]. This ordering implies that the shocks on 

economic variables flow from the shock to oil price and OCS activity in that order. By 

implication, oil price is not directly affected by either OCS activity or economic variables. A 

different ordering may produce a different response path, hence, we chose carefully the 

appropriate ordering based on economic theory or alternative plausible results from different 

orderings. 

3.3. VAR Model Estimation and Analysis 

Generally, a VAR model such as the type we specified in equation (1) can be estimated using 

ordinary least squares (OLS), if each equation in the system contains the same number of 

variables and has similar lags on the right-hand-side. OLS in this case provides estimates that are 

both consistent and asymptotically efficient. The system formulation in equation (2) does not 

fully meet this criteria; hence, the specification in this paper can be described as near-VAR 

models. The near-VAR model in each of the cases formulated is estimated using seemingly 

unrelated regression (SUR) techniques. 

A dynamic formulation of the VAR-type has been found to perform better in macroeconomic 

forecasting than theoretically based large structural models of the past. Hence, VAR has become 

a popular means of studying the structural path of dynamic series. Its usefulness for economic 
23

analysis also lies in the flexibility offered to test various hypotheses of causation (in the Granger 

sense) among the variables. In addition, the structure of the VAR can be exploited through what 

is generally referred to as innovation accounting. Two processes in innovation accounting—

impulse response function (IRF) and variance decompositions—are adopted to study effects of 

shocks (i.e. unexpected policy changes) on the system represented in equation (1). 

To estimate the system of equations which involve personal income or unemployment rates, 

quarterly data for all model variables for Alabama, Louisiana, Mississippi, and Texas were 

collected, processed and organized into a regression format. However, all estimation procedures 

involving state revenue data utilized annual data for the estimation procedure because of a lack 

of quarterly data for state revenue. As mentioned earlier, Louisiana (LA) represents a petroleum 

producing and net petroleum exporting state with a limited diversified economy. Mississippi 

(MS), on the other hand, represents a net petroleum importing state with a limited diversified 

economy and Texas (TX) is a relatively more diversified economy than Louisiana. The 

economic base of Texas is also considerably larger than Louisiana. Texas is also a net exporter 

of natural gas but a net oil importer. Lastly, Alabama (AL) is a borderline net petroleum importer 

(high net oil importer and low net gas exporter) and its economy is less dependent on petroleum 

than Texas or Louisiana. 

The long-run impact of a policy change affecting one of the variables in the system can be 

investigated using the impulse response function and the proportion of these “changes” that are 

attributable to each variable in the system can be evaluated using variance decomposition 

analysis. Accordingly, the central focus of VAR analysis is the finding and understanding of the 

interrelationship among variables over time and not necessarily on the assessment of point 

estimates. Thus, the VAR results are discussed generally in terms of the variance decomposition 

and impulse response functions generated from estimating the VAR model represented by the 

system of equations in (2). 

The empirical results reported in this report have been derived from estimating the system of 

equations in (2) individually for employment, real personal income, and state revenue in 

combination with OCS petroleum production by planning area and water depth—one at a time.5

 

Variance decomposition and impulse response function analyses for each of the Gulf States have 

been applied to the VAR model results. The variance decomposition procedure provides a way to 

decompose the impact of a shock on the economic system into its component parts. The relative 

proportion of the decompositions indicates the relative potency of the effect of a particular shock 

in explaining the observed variations in each variable experiencing the shock. 

On the other hand, the impulse response function technique characterizes the dynamic effects of 

an unexpected shock in a given economic system. It shows the dynamic paths of the effects of an 

independent shock of one variable on another variable and it is also useful for characterizing the 

 

5

 This implies estimating several different models/systems for each state: (1) price, OCS production, and 

employment, (2) price, OCS production, and personal income, (3) price, OCS production, and revenue, (4) price, 

OCS deepwater production, and employment, (5) price, OCS deepwater production, and personal income, and (6) 

price, OCS deepwater production and revenue. Interest rate, time dummies, and GDP appear in each model/system 

as exogenous variables. 
24

stability and duration of such effects. The persistence of such a shock reveals how fast the 

system will return to its original equilibrium. The faster it takes a shock to dampen, the shorter 

the adjustment period (Brown and Yucel, 1995). 
25

4. ESTIMATED VAR MODEL RESULTS: VARIANCE 

DECOMPOSITION ANALYSIS 

The empirical results reported in this report have been derived from estimating the system of 

equations in (2) individually for employment, real personal income, and state revenue in 

combination with OCS petroleum production in the OCS and deepwater—one at a time.6

 

Variance decomposition and impulse response function analyses for each of the Gulf States have 

been applied to the VAR model results. The variance decomposition procedure provides a way to 

decompose the impact of a shock on the economic system into its component parts. The relative 

proportion of the decompositions indicates the relative potency of the effect of a standard 

deviation price or production shock in explaining the observed variations in each variable 

experiencing the shock. 

 

4.1. VAR Results from OCS Aggregate Production System Equations 

4.1.1. OCS Petroleum Production and the Louisiana Economy: According to the results 

reported in Table 4, the dynamic VAR analysis of the interactions between changes in crude 

petroleum prices and oil and gas production in the Gulf of Mexico OCS, and Louisiana 

unemployment rates shows a significant price effect on unemployment rates. Price explains 

about 0.45-11.43 percent of the observed variation in unemployment over time. Crude oil price 

interacting with oil and gas production in the Gulf of Mexico OCS also explains about 5.91-

14.61 percent of the expected variation in personal income and between 11.45 to 16.81 percent 

of the variation in revenue. The autonomous oil and gas production shows no significant direct 

effects on unemployment according to the VAR results. Nonetheless, a relatively significant 

variation in personal income and state annual revenue is explained by changes in autonomous 

production. In an overall sense, both oil prices and Gulf oil production have more impact on 

revenue than they have on Louisiana unemployment rates and personal income. 

4.1.2. OCS Petroleum Production and the Alabama Economy: The model results describing 

the interactions among oil prices and oil and gas production in the Gulf of Mexico OCS and 

Alabama unemployment rates indicate that petroleum price variation explains up to 30 percent of 

the expected variation in Alabama unemployment. The results also show that a price shock 

conditional on the OCS oil and gas production profile explains up to 11 percent of the observed 

variation in personal income in Alabama. Further, a price shock interacting with oil and gas 

production also has a potential impact of at most 29 percent in the long-term on Alabama 

revenue. The autonomous direct impact of oil and gas production in the Gulf OCS on Alabama 

unemployment is also not significant, according to the VAR model results. 

 

6

 This implies estimating several different models/systems for each state: (1) price, OCS production, and 

employment, (2) price, OCS production, and personal income, (3) price, OCS production, and revenue, (4) price, 

OCS deepwater production, and employment, (5) price, OCS deepwater production, and personal income, and (6) 

price, OCS deepwater production and revenue. Interest rate, time dummies, and GDP appear in each model/system 

as exogenous variables. 
26

Table 4 

 Decomposition of the Variance of Macroeconomic Variables 

Due to Changes in Petroleum Prices and OCS Gross Petroleum Production 

Period 

States/Variables 1 4 12 18 24 

A LA Unemployment 

 OCS Production 0.012 1.273 1.487 1.571 1.600 

 Price Index 0.450 1.606 11.393 11.212 11.434 

LA Personal Income 

 OCS Production 2.653 3.141 3.312 3.331 3.335 

 Price Index 5.910 14.218 14.609 14.606 14.605 

LA Revenue 

 OCS Production 6.934 10.981 12.601 12.594 12.613 

 Price Index 11.456 12.784 16.584 16.789 16.807 

B AL Unemployment 

 OCS Production 0.043 0.282 0.524 0.524 0.524 

 Price Index 0.052 9.158 29.844 29.873 29.895 

AL Personal Income 

 OCS Production 1.303 3.308 3.993 4.107 4.138 

 Price Index 4.296 7.378 10.804 10.837 10.847 

AL Revenue 

 OCS Production 1.111 2.012 2.621 2.632 2.632 

 Price Index 14.244 20.785 28.905 28.950 28.953 

C MS Unemployment 

 OCS Production 0.780 0.558 0.343 0.321 0.314 

 Price Index 1.255 0.947 8.346 9.210 9.448 

MS Personal Income 

 OCS Production 3.376 4.911 5.315 5.404 5.438 

 Price Index 9.868 13.949 15.535 15.576 15.583 

MS Revenue 

 OCS Production 41.119 40.89 40.101 40.100 40.100 

 Price Index 11.958 15.139 16.747 16.749 16.749 

D TX Unemployment 

 OCS Production 1.199 0.956 1.159 1.186 1.190 

 Price Index 1.472 1.531 2.285 2.605 2.667 

TX Personal Income 

 OCS Production 0.171 0.908 3.167 3.305 3.331 

 Price Index 10.066 18.791 18.607 18.632 18.635 

TX Revenue 

 OCS Production 0.036 1.949 2.143 2.413 2.143 

 Price Index 0.133 18.023 18.032 18.033 18.033 

 
27

4.1.3. OCS Petroleum Production and the Mississippi Economy: The model results, which 

describe the interactions between oil price and oil and gas production in the Gulf OCS and 

Mississippi economic variables demonstrate that the variation in the state’s unemployment 

accounted for by petroleum prices is less than 10 percent on average, but significant. Similarly, 

the empirical results indicate that the effects of petroleum prices on personal income interacting 

with OCS production may be about 15.5 percent. The price impact on revenue, according to the 

VAR model results, reaches as high as 16.7 percent. The impact of a change in oil and gas 

production in the Gulf, as is the case with Louisiana and Alabama, has no direct significant 

impact on the state unemployment rate. However, the impact of production on revenue and 

personal income is statistically significant as evident in Table 4. 

4.1.4. OCS Petroleum Production and the Texas Economy: The estimated model results 

reported in Table 4 show that the impact of a price shock on Texas unemployment rates is 

relatively small, although significant. The variations in personal income and revenue in Texas 

explained by price shocks are 19 and 18 percent, respectively. The effects of OCS production on 

Texas unemployment rates, unlike in the other Gulf States, is significant, but small. Production 

effect on Texas revenue ranges from 0.04 percent in the short-run to 2.14 percent in the long-run. 

This is a significant departure from the trends observed for Louisiana, Alabama and Mississippi. 

4.2. VAR Results from OCS Deepwater Production System Equations 

The empirical results reported in Table 5 have been derived from estimating the system of 

equations in (2) for employment, real personal income, and state revenue in combination with 

OCS deepwater petroleum production and by using the variance decomposition procedure for 

each of the Gulf States. The relative importance of changes in petroleum prices and production in 

explaining volatility in economic activity in these states is discussed briefly as follows. 

4.2.1. OCS Deepwater and the Louisiana Economy: The deepwater model results indicate 

that variation in price and deepwater production has little or no influence on the observed 

variation on Louisiana unemployment rates over time. This is contrary to expectation in 

comparison to the other Gulf States. On average, however, price and deepwater production 

explains about 16 and 2.6 percent of the observed variation in Louisiana personal income, 

respectively. We did not estimate the deepwater system of equations for revenue because of data 

limitations. 
28

Table 5 

Decomposition of the Variance of Macroeconomic Variables 

Due to Changes in Petroleum Prices and OCS Deepwater Petroleum Production 

Period 

States/Variables 1 4 12 18 24 

A LA Unemployment 

 OCS Production 0.006 1.282 1.412 1.432 1.434 

 Price Index 0.636 1.637 2.196 2.078 2.012 

LA Personal Income 

 OCS Production 2.350 2.533 2.610 2.612 2.612 

 Price Index 5.895 15.357 16.497 16.494 16.494 

LA Revenue 

 OCS Production - - - - - 

 Price Index - - - - - 

B AL Unemployment 

 OCS Production 13.005 19.826 22.570 22.320 22.311 

 Price Index 0.020 10.523 32.025 33.071 33.119 

AL Personal Income 

 OCS Production 0.813 4.630 6.912 6.973 6.991 

 Price Index 1.952 3.922 5.660 5.843 5.852 

AL Revenue 

 OCS Production - - - - - 

 Price Index - - - - - 

C MS Unemployment 

 OCS Production 0.073 5.168 7.848 7.899 7.923 

 Price Index 2.426 5.462 4.225 4.066 3.997 

MS Personal Income 

 OCS Production 1.494 2.158 2.476 2.483 2.483 

 Price Index 3.337 5.167 5.366 5.367 5.368 

MS Revenue 

 OCS Production - - - - - 

 Price Index - - - - - 

D TX Unemployment 

 OCS Production 2.618 1.903 7.564 7.792 7.827 

 Price Index 0.958 0.302 0.973 1.083 1.099 

TX Personal Income 

 OCS Production 3.180 5.095 5.826 5.811 5.866 

 Price Index 7.705 14.644 16.359 16.360 16.361 

TX Revenue 

 OCS Production - - - - - 

 Price Index - - - - - 

 
29

4.2.2. OCS Deepwater and the Alabama Economy: The model results describing the 

interactions among oil prices and deepwater production in the Gulf of Mexico OCS and Alabama 

unemployment rates indicate that petroleum price variation explains up to 33 percent of the 

expected variation in Alabama unemployment. The autonomous direct impact of deepwater 

production in the Gulf OCS on Alabama unemployment is significant, according to the VAR 

model results, explaining between 13-25 percent of the observed variation in Alabama 

unemployment. The results also show that a price shock conditional on the OCS deepwater 

production profile explains 1.95-5.85 percent of the observed variation in personal income in 

Alabama. The variation in Alabama personal income explained by changes in deepwater 

production ranges from 0.80 to 7.00 percent.

4.2.3. OCS Deepwater and the Mississippi Economy: The VAR results describing the effect 

of a price shock and oil and gas production from OCS deepwater on Mississippi economic 

variables show that the changes in price explain a relatively small proportion of the observed 

variation in unemployment (roughly 4 percent on average). A shock to deepwater production 

also has a significant effect on unemployment. The results show that approximately 8 percent of 

the observed variation in unemployment is explained as a result of production shocks. Price 

shocks also explained up to 5.368 percent of the observed variation in personal income over the 

period. Production impact, on the other hand, explained less than 2.5 percent of the variation in 

personal income over the period. 

4.2.4. OCS Deepwater and the Texas Economy: According to the VAR model results, the 

impact of changes in oil prices on Texas unemployment subject to variation in OCS deepwater 

oil and gas production is not statistically significant. However, deepwater production has a 

direct and significant impact on Texas unemployment. The results further suggest that price 

shocks explained up to 16.3 percent of the observed personal income variation, and deepwater 

production explained a little less than 6 percent of the observed variation in Texas personal 

income. 
31

5. ESTIMATED VAR MODEL RESULTS: IMPULSE RESPONSE 

FUNCTION APPROACH 

To further quantify the responsiveness of the economic performance indicators to price shocks 

and OCS production in the Gulf States, the impulse response function technique for 

characterizing the dynamic effects of an unexpected shock in a given economic system is applied 

separately to data from Alabama, Louisiana, Mississippi, and Texas. Generally, the impulse 

response function (IRF) shows the dynamic paths of the effects of an independent shock of one 

variable on another variable and it is also useful for characterizing the stability and duration of 

such effects. 

5.1. IRF Results from OCS Aggregate Production System Equations 

5.1.1. Price Shock, Gulf OCS Production, and the Louisiana Economy: The impulse 

response of Gulf oil production and Louisiana unemployment rate to a one-time positive shock to 

crude oil price is presented in Figure 7. Unemployment rate falls and oil production increases in 

response to the shock. Unemployment rate reaches its highest level within 10 quarters after the 

shock. This corresponds to about 0.6 percent above its initial equilibrium. The minimum level of 

unemployment rate (0.26 percent in below equilibrium) was attained within three quarters 

subsequent to the shock. Unemployment rate gradually moves towards equilibrium after reaching 

its maximum. 

Gulf aggregate production, on the other hand, rises within five quarters to a maximum of 0.35 

percent above the initial equilibrium and falls to a minimum of 0.26 percent below its initial 

level within three quarters. Oil production fluctuates around its equilibrium level over the time 

horizon. It is also noted that both oil and gas production and the unemployment rate return to 

their original equilibrium levels, although the dynamic paths to equilibrium are different; oil 

production fluctuates much more than unemployment rate. 

The dynamic response of Louisiana personal income and Gulf OCS production to price is 

depicted in Figure 6. A positive shock to price initially leads to a positive response from both oil 

production and personal income. The affected variables return to the initial equilibrium levels 

quickly. Figure 8 shows that the dynamic paths of production and revenue rose following a price 

shock. Revenue rose to a maximum 0.35 percent of its initial level before the shock. However, 

all variables fluctuated widely, albeit towards equilibrium restoration, and movements in 

production and revenue were much more in tandem during the period. 

5.1.2. Price Shock, Gulf OCS Production, and the Alabama Economy: The impulse 

responses of aggregate OCS petroleum oil production and Alabama unemployment rate, personal 

income and gross revenue to a one-time positive shock to crude oil price are presented in Figures 

9 through 11. Figure 9 presents the response of Gulf oil production and Alabama unemployment 

rate to a one-time positive 1-standard deviation shock to crude oil price. The immediate effect is 

a decrease in unemployment rate and an increase in oil production. The highest level of 

unemployment reached is about 0.85 percent (in 6 quarters) above its initial equilibrium level 

while the minimum reached is 0.15 percent (in 16 quarters) below equilibrium. 
32

Figure 6: Louisiana Personal Income and OCS Production Dynamic Paths. 

Figure 7: Louisiana Unemployment and OCS Production Dynamic Paths. 

0 10 20 30 40 50 60 70 80 90

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Income

0 10 20 30 40 50 60 70 80 90

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Unemployment

Production
33

Figure 8: Dynamic Paths of Louisiana Revenue and OCS Production. 

 Figure 9: Responses of Gulf Production & AL Unemployment Rate to Price. 

0 5 10 15 20 25 30 35

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

 Production 

 Revenue 

0 10 20 30 40 50 60 70 80 90

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Unemployment 
34

Figure 10: Responses of Gulf Production & AL Personal Income to Price. 

Figure 11: Responses of Gulf Production & AL Revenue to Price. 

0 10 20 30 40 50 60 70 80 90

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Income 

0 5 10 15 20 25 30 35

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Revenue 
35

Petroleum production also rises to a maximum of 0.27 percent (in 1 quarter) and falls to a 

minimum of 0.24 percent (in 3 quarters) below its initial level. Alabama unemployment and OCS 

aggregate oil production returns to initial equilibrium level at about the 24th period. It is noted 

that while all variables return to their original equilibrium, the dynamic paths are not the same. 

Oil production fluctuates much more than unemployment rate. 

The response of Alabama personal income to price in the context of all Gulf oil production is 

shown in Figure 10. A positive shock to price leads to positive response from both oil production 

and personal income. In this case the affected variables fluctuate around their base-levels, 

although this pattern is more pronounced for oil production. The dynamic paths for production 

and revenue are depicted in Figure 11. Alabama’s state revenue responds positively to positive 

price shock. The response is within 0.38 percent of its initial levels before a shock. However, the 

movements in revenue and oil production are not in tandem. 

5.1.3. Price Shock, Gulf OCS Production, and the Mississippi Economy: The response of 

Gulf oil production and Mississippi unemployment rate, MSQUR, to a one-time positive shock 

to crude oil price is shown in Figure 12. The immediate effect is a decrease in unemployment 

rate and an increase in oil production. Unemployment rate reaches its highest level of about 0.29 

percent above its initial equilibrium in 6 quarters and its minimum level of 0.13 percent in the 

first quarter. Aggregate OCS production fluctuates around its equilibrium throughout, rises to a 

maximum of 0.25 percent in the first quarter and falls to a minimum of 0.24 percent below its 

initial level in 3 quarters. Production returns fairly quickly to its initial level after the price shock 

while unemployment takes longer to return to full equilibrium. 

Figure 13 shows the time path of Mississippi personal income to price in association with 

aggregate petroleum production in the Gulf OCS. A positive response to price shock by 

production and personal income is evident in Figure 13. Both variables fluctuate, although the 

pattern is more persistent and pronounced for oil production. The dynamic paths of production 

and revenue in response to a positive price shock are depicted in Figure 14. The Figure shows 

that gross revenue in Mississippi responds negatively to a positive price shock. Revenue initially 

falls to 0.35 percent of its initial levels before the shock before rising towards its initial 

equilibrium level. But, as it is the case with Alabama, all variables fluctuate widely. 

5.1.4. Price Shock, Gulf OCS Production, and the Texas Economy: According to Figure 15, 

the immediate effect of a price shock to the interaction among Texas economy and OCS 

aggregate petroleum production is a decrease in unemployment rate and an increase in oil 

production. The overall effect is quite small for both variables. Although production and 

unemployment return to their original equilibrium, the dynamic paths are not the same; 

unemployment rate path is slightly different—longer and less cyclical. Oil production fluctuates 

in a much more cyclical trend but around its equilibrium level. 
36

Figure 12: Responses of Gulf Production & MS Unemployment Rate to Price. 

Figure 13: Responses of Gulf Production & MS Personal Income to Price. 

0 10 20 30 40 50 60 70 80 90

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Unemployment

0 10 20 30 40 50 60 70 80 90

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Income 
37

Figure 14: Responses of Gulf Production & MS Revenue to Price. 

Figure 15: Responses of Gulf Production & TX Unemployment Rate to Price. 

0 5 10 15 20 25 30 35

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Revenue 

0 10 20 30 40 50 60 70 80 90

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Unemployment 
38

Figure 16 depicts the response of Texas personal income to a price shock. A positive price shock 

leads to positive response from both oil production and personal income. Both variables 

fluctuate, but the pattern is more pronounced for oil production than for income. The former is 

more cyclical. Texas revenue increases initially in response to a positive price shock in the 

context of Gulf oil and gas production. However, all variables quickly trend toward equilibrium 

although the path to equilibrium is faster for revenue than production (see Figure 17). 

5.2. IRF Results from OCS Deepwater Production System Equations

5.2.1. Price Shock, OCS Deepwater Production, and the Louisiana Economy: The impulse 

response of OCS deepwater production and Louisiana unemployment rate to a one-time positive 

shock to crude oil price is presented in Figure 18. Louisiana unemployment and deepwater 

production decrease following a positive price shock. The negative production response is 

contrary to our expectation. However, this response is small and probably transitory, reflecting a 

lagged responsiveness. The response path for unemployment is also relatively short. Further, 

the response paths for deep OCS petroleum production and Louisiana quarterly personal income 

to a positive shock to crude petroleum prices are depicted in Figure 19. The figure shows that the 

impact of price on personal income is positive and it reaches a maximum of 0.33 percent within a 

year (3 quarters). The restoration to its original equilibrium is also in less than 12 quarters. 

5.2.2. Price Shock, OCS Deepwater Production, and the Alabama Economy: In Figure 20 

response of unemployment to a price shock is negative initially and deepwater production 

response is also unexpectedly negative. The response path for unemployment is striking. The 

unemployment response at its maximum is much larger (1.24 percent) than the initial shock to 

price. The subsequent hike in unemployment rate follows the ensuing sharp decline in price after 

the initial shock. The restoration to initial equilibrium takes much longer for unemployment and 

production in comparison to other scenarios. The response to positive price shock by Alabama 

personal income and OCS deepwater production are presented in Figure 21. The Figure shows 

that personal income fluctuates within 0.13 percent of its original equilibrium, whereas the 

variation in deepwater production nearly doubled the fluctuations in personal income following a 

price shock. 

5.2.3. Price Shock, OCS Deepwater Production, and the Mississippi Economy: As evident in 

Figure 22, the response of deepwater production or Mississippi unemployment rate to price 

shock is negative. The negative responsiveness is unexpected with respect to deepwater 

production. The response, however, shows minimal effects over time as deviations from 

equilibrium levels appear insignificant. The response to positive price shock by deepwater 

production and Mississippi personal income is presented in Figure 23. The figure shows that 

changes in personal income are never above or below 0.2 percent and similarly production 

deviations are less than 0.25 percent. Hence, impacts of oil price shock on Mississippi economy 

are very small in magnitude. 
39

Figure 16: Responses of Gulf Production & TX Personal Income to Price. 

Figure 17: Responses of Gulf Production & TX Revenue to Price. 

0 10 20 30 40 50 60 70 80 90

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Income 

0 5 10 15 20 25 30 35

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Revenue
40

 Figure 18: Responses of Deepwater Production & LA Unemployment to Price. 

 

 Figure 19: Responses of Deepwater Production & LA Personal Income to Price. 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Unemployment 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Income
41

Figure 20: Responses of Deepwater Production & AL Unemployment Rate to Price. 

Figure 21: Responses of Deepwater Production & AL Personal Income to Price. 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Income

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

Production 

Unemployment
42

Figure 22: Responses of Deepwater Production & MS Unemployment Rate to Price. 

 Figure 23: Responses of Deepwater Production & MS Personal Income to Price. 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Unemployment 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Production 

Income 
43

5.2.4. Price Shock, OCS Deepwater Production, and the Texas Economy: The impact of a 

positive price shock on Texas unemployment rate and deepwater production is presented in 

Figure 24. The figure shows that unemployment and production fall initially in response to a 

price shock. Unemployment rate rises to a maximum of about 0.26 percent and production 

declines at a similar magnitude in the opposite direction. The restoration to equilibrium takes at 

least 24 quarters for unemployment rate in Texas. The dynamic paths for OCS deepwater 

production interacting with Texas quarterly personal incomes are depicted in Figure 25. The 

Figure shows that personal income rises to about 0.3 percent of its initial state and a positive 

deviation from deepwater production equilibrium is at a slightly smaller level. 

Figure 24: Responses of Deepwater Production & TX Unemployment Rate to Price. 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Unemployment
44

Figure 25: Responses of Deepwater Production & TX Personal Income to Price. 

0 6 12 18 24 30 36 42 48 54 60 66 72 78

-0.25

0.00

0.25

0.50

0.75

1.00

Production 

Income 
45

6. ECONOMIC INTERPRETATIONS OF THE VAR MODEL RESULTS

The impulse response function results and the corresponding graphical representations have been 

used in quantifying the price responsiveness of state macroeconomic variables. The results are 

shown in Tables 6 and 7. Each elasticity reported in Table 7 is estimated by normalizing the 

variable response to oil price shock at the corresponding maximum. Hence, the implicit 

assumption of a constant-elasticity has been invoked (Brown and Yucel, 1995). 

With regard to state macroeconomic variables—unemployment, revenue, and personal income— 

the differences among states do not appear to be large. The respective means of each variable is 

included to give a sense of what each elasticity may mean in quantifiable terms. In general, all 

three macroeconomic variables are more elastic to price changes than oil and gas production. 

The highest oil price elasticity of unemployment is in Alabama (2.575) while Texas shows the 

least response (1.917). These represent a change of 0.2 and 0.119 in unemployment rates for 

Alabama and Texas, respectively. In the case of personal income response, each elasticity is 

similar in magnitude. In quantitative terms, and unlike in the case of unemployment rates, Texas 

is more responsive while Alabama is the least responsive. The responsiveness of revenue to 

price in all Gulf States is elastic, except for Mississippi (0.738). 

Two major inferences can be made from the empirical analysis as reported in Tables 6 and 7. 

First, the effects of production on macroeconomic variables in the Gulf Coast States are 

generally less than those of changes in prices. In other words, unemployment rates, personal 

income, and annual revenue are more directly affected following changes in petroleum prices 

than they are affected consequential to the changes in petroleum production, which ensue from 

changes in prices. This pattern indicates that the direct effects of price shocks on the economy in 

general should be of greater interest to policy makers in the Gulf Coast States than the direct 

effects of prices on oil and gas production. The only exception to this pattern is the price effects 

on the annual state revenue in Mississippi. 

The second inference is the lack of symmetry in the effects of a price shock and changes in 

petroleum production on economic performance indicators across the Gulf States. The empirical 

results show significant differences in the responsiveness of economic performance indicators to 

changes in prices across the states in the Gulf Coast. These differences are even more noticeable 

when the effects are translated into quantifiable terms (quantity equivalence of unemployment, 

state revenue and personal income with respect to the mean of the variable) than what the 

elasticity measures tend to portray (see Table 8). 
46

Table 6 

Estimated Range of the Impact of Changes in Price and OCS Production on 

Macroeconomic Variables Using the Impulse Response Function Technique (%) 

 Price Effect Production Effect

 Variables/VAR system High Low High Low

 

A Louisiana (LA) 

 Unemployment 11.40 0.45 1.60 0.01

 Personal Income 14.60 5.90 3.30 2.65

 Revenue 16.80 10.90 12.61 6.90

 

B Alabama (AL) 

 Unemployment 29.90 0.05 0.52 0.04

 Personal Income 10.85 4.30 4.14 1.30

 Revenue 29.95 14.24 2.63 1.02

 

C Mississippi (MS) 

 Unemployment 9.45 0.84 0.78 0.31

 Personal Income 15.58 9.87 5.44 3.33

 Revenue 16.75 11.96 42.50 40.10

 

D Texas (TX) 

 Unemployment 2.67 1.47 1.20 0.83

 Personal Income 18.64 10.07 3.33 0.17

 Revenue 18.03 0.13 2.14 0.04

 
47

Table 7 

Estimated Range of the Impact of Changes in Price and Deep OCS Production on 

Macroeconomic Variables Using the Impulse Response Function Technique (%) 

 Price Effect Production Effect

 Variables/VAR system High Low High Low

 

A Louisiana (LA) 

 Unemployment 2.20 0.64 1.43 0.06

 Personal Income 16.50 5.90 2.60 2.00

 Revenue 

 

B Alabama (AL) 

 Unemployment 33.12 0.02 22.32 13.00

 Personal Income 5.85 1.95 6.99 0.81

 Revenue\ 

 

C Mississippi (MS) 

 Unemployment 5.46 2.43 7.92 0.07

 Personal Income 5.37 3.34 2.48 1.49

 Revenue 

 

D Texas (TX) 

 Unemployment 1.10 0.29 2.83 1.36

 Personal Income 16.36 9.71 5.89 2.47

 Revenue 

 
48

Table 8 

 Price Elasticity of Macroeconomic Variables and the Quantity Equivalence Conditional on 

the Dynamics of OCS Petroleum Production and the Gulf Coast Economy 

Quarterly 

Unemployment 

Quarterly 

Personal Income 

Annual 

Revenue 

 

Louisiana 

Mean 8.09% $59,650 Million $4,963 Million 

Elasticity 2.499 1.131 1.504 

Quantity Equivalent* 0.20% $675 Million $74.666 Million 

 

Alabama 

Mean 7.78% $56,560 Million $4,132 Million 

Elasticity 2.575 1.066 1.554 

Quantity Equivalent* 0.20% $603 Million $64.202 Million 

 

Mississippi 

Mean 7.89% $31,510 Million $2,644 Million 

Elasticity 2.358 1.172 0.738 

Quantity Equivalent* 0.19% $369 Million $19.510 Million 

 

Texas 

Mean 6.22% $273,960 Million $14,273 Million 

Elasticity 1.917 1.211 1.135 

Quantity Equivalent* 0.12% $3,318 Million $161.991 Million 

 

 

* The corresponding average change in macroeconomic variables due to a percent change in price. 
49

Although the unemployment rates across the states tend to decline following an increase in 

petroleum prices, the highest oil price elasticity of unemployment rates occurs in Alabama 

(2.575), while Texas shows the least responsiveness of unemployment rates to price shocks 

(1.917). These represent a quantity equivalence of 0.200 and 0.119 percent change with respect 

to the mean value of unemployment rates in Alabama and Texas, respectively. Table 8 presents 

price elasticity of macroeconomic variables and the corresponding quantity equivalence. The 

elasticity estimates are conditional upon the interactions among OCS petroleum production, 

changes in petroleum prices, and the economy. 

Further analysis of the impulse response functions also reveals different adjustment paths to 

equilibrium for the Gulf States following a price shock (see Table 9). The empirical results 

indicate that it may take unemployment rates, personal income and government revenue more 

than ten years, about 3 years, and up to 20 years, respectively to be restored to initial equilibrium 

in Louisiana. For the Alabama economy, the response paths show that it may take approximately 

6, 2, and 12 years, respectively, to restore unemployment, personal income, and revenue to their 

initial equilibrium subsequent to any price shock. 

The adjustment paths to a price shock to the Mississippi economy indicate that unemployment 

rates take more than 8 years, personal income takes about 2 years, and revenue takes 5 years to 

adjust to their initial equilibrium levels. The adjustment paths over time for unemployment rate 

take less than 10 years, personal income takes more than 4 years, and revenue takes about 7 

years for initial equilibrium to be restored in response to a price shock to the Texas economy. 

The fact that it takes longer for the employment levels in Texas and Louisiana than Alabama and 

Mississippi to return to initial equilibrium after a price shock is most likely due to the fact that 

oil and gas production and oil and gas related businesses are more prevalent in Texas and 

Louisiana than Alabama and Mississippi. However, because Texas has a larger and more 

diversified economic base than Louisiana, it is more able to dampen the likely destabilizing 

effects of a price shock on employment levels than Louisiana. On the other hand, the economic 

size of Texas seems to cause the effects of changes in crude petroleum prices on personal income 

to linger longer than in Louisiana, Alabama and Mississippi, in that order. The gross annual 

revenue in Louisiana seems to be the most susceptible to an unexpected price shock and 

Mississippi annual revenue is more resilient than Louisiana, Alabama and Texas in this regard. 

The decline in petroleum revenue in Louisiana as a result of declining oil prices has tended to 

push Louisiana to the brink of a budget deficit in the more recent time than Texas (Brown and 

Yucel, 1995). The results for Alabama and Mississippi are also consistent with the declining 

relative exposure to the petroleum industry vagaries over time (Scott, 2002). 
50

Table 9 

 Estimated Adjustment Paths to Equilibrium Following a Price Shock 

Impact on Aggregate OCS Petroleum Production and the Economy 

Indicators Alabama Louisiana Mississippi Texas 

Unemployment 

(Quarters) 25 45 35 38 

 

Personal Income 

(Quarters) 8 12 8 18 

State Revenue 

(Years) 12 20 5 7 
51

7. SUMMARY AND CONCLUSIONS 

This study examines the interactions between oil price changes, oil and gas production and 

selected macroeconomic variables of each economy of the Gulf States. Rather than focus on 

point estimates from regression analyses, we employed a VAR approach to understand both the 

composition of potential effects of a price change and the adjustment paths of the economic 

variables and oil and gas production over time. By decomposing and examining the impulse 

responses of forecast errors, we are able to predict the relative magnitude and the dynamic 

adjustments of the selected variables to oil price shock. 

Specifically, the study shows that: 

• Oil and gas production in the Gulf as a whole responds positively to a positive shock in 

crude oil price. This is an expected result given that firms operating in the Gulf OCS 

desire to maximize return on investment, hence, an increase in the price of output is a 

signal from the market of a higher demand for oil and gas products. Likewise, a decrease 

in price will have the opposite effect. 

• Unemployment rates across all the states tend to decline following an increase in price of 

crude oil. This result is consistent with the fact that an increase in price of oil and gas 

industry output will spur the industry to expand output, and ceteris paribus, more 

workers are needed to meet the new desired levels of output. Thus, employment levels in 

the states will rise (means unemployment rates decline) to meet industry needs. It is noted 

that this is a net effect, because an increase in price of crude oil should also increase 

production cost in industries where oil and gas are the primary production input, e.g. 

chemical and allied products. This final result may therefore imply that price-elasticity of 

employment in the oil and gas producing industry is greater than price-elasticity of 

income for oil and gas consuming industry in these states. 

• Unemployment rates in the Gulf States appear to be relatively less sensitive to oil and gas 

production activities. That is, Gulf production role in states unemployment variation is 

relatively diminutive over time. In many instances, the impact of production shocks on 

unemployment is insignificant. 

• Personal income increases following a crude oil price positive shock. This result is also 

consistent with the fact that all the states considered are oil producers although in varying 

magnitude. In general, the oil and gas industry pays relatively higher wages than most 

other industries in these states. It follows, therefore, that personal income in these states 

rises following an oil price increase. Of course, the degree of this increase in income is 

asymmetrical across states. In general, Louisiana and Texas have higher responsiveness 

to price change than Mississippi and Alabama. 

• Revenue increases in each of the Gulf States following an unexpected increase in the 

price of crude oil and gas, except for Mississippi. However, as with employment, this 

must be regarded as a net-result. This is because other oil and gas using industries may 

decrease their production capacity and output leading to a decline in income tax base, 
52

another significant source of state revenue. It is also noted that because the impact of a 

price increase is not necessarily uniform across all states with positive response, the 

revenue effects are not uniform either. In fact, as noted, Mississippi’s revenue decline 

may be an indication that the positive effect of production in the Gulf is not enough to 

override the negative effect of income tax base erosion. 

Further, the response lags in the impulse response analysis give an indication of the length of the 

effects of a price shock on a state economy. At one extreme, a persistent effect will indicate a 

long-lasting impact that may in fact change the structure of the state economy. Thus our study 

suggests that: 

• The impact of a price change takes about 45 quarters to return to equilibrium with respect 

to employment in Louisiana. Similar measures for Alabama, Mississippi, and Texas are 

25, 35, and 38 quarters, respectively. In other words, such a price change may have more 

destabilizing effects on Louisiana, Texas, Mississippi, and Alabama, in that order. This 

change may be destabilizing because the change is a “shock” i.e. unexpected, which 

means firms and policy makers may find it difficult to respond adequately. This pattern 

among states may be explained by the fact that oil and gas production and oil and gas 

dependent industries are more prevalent in Texas and Louisiana than in the other states. 

However, Texas has a larger and more diversified economy than Louisiana and is more 

able to dampen the destabilizing effect of its exposure to the price shocks than Louisiana. 

• Personal income can take about 18 quarters in Texas, 12 quarters in Louisiana and 8 

quarters in Mississippi and Alabama to restore initial equilibrium after a price shock. In 

this case, Texas economic size seems to prolong the income effect of the change in price 

more than any other state in the Gulf States. However, the net oil exporting states are still 

far more exposed to such a shock than their net oil importing counterparts. 

In an overall sense, two major observations are evident in this study. Contrary to our initial 

hypothesis, the effects of oil and gas price shock on coastal Gulf States are more direct than 

indirect (through oil and gas production). In other words, employment, personal income, and 

revenue are impacted more directly following a price change rather than through changes in oil 

and gas production following a price shock. Further, according to our empirical results, there is a 

strong statistical evidence to suggest an asymmetric response of each of the three 

macroeconomic variables to price in the four coastal Gulf States. 
53

REFERENCES 

Brown, S.P.A. and J.K. Hill. 1988. “Lower oil prices and state employment.” Contemporary 

Policy Issues 6:60-68. 

Brown, S.P.A. and M.K. Yucel. 1995. “Energy prices and state economic performance.” 

Economic Review, Federal Reserve Bank of Dallas (Second Quarter). Pp. 13-23. 

Carruth, A.A., A.M. Hooker, and A.J. Oswald. 1998. “Unemployment equilibriums and input 

prices: Theory and evidence from the United States.” Review of Economics and Statistics

80:621-628. 

Considine, T.J. 1988. “Oil price volatility and U.S. macroeconomic performance.” 

Contemporary Policy Issues 6:83-96. 

Davis, S.J., P. Loungani, and R. Mahidhara. 1996. Regional labor fluctuations: Oil shocks, 

military spending, and other driving forces. Working paper, University of Chicago, IL. 

Hamilton, J.D. 1983. “Oil and the macro economy since World War II.” Journal of Political 

Economy 91(April):228-248. 

Hickman, B.G. 1984. Macroeconomic effects of energy shocks and policy response: A 

structural comparison of fourteen models. Energy Forum Working Paper 7.4, Stanford 

University, in Hickman, B.G., H.G. Huntington, and J.L. Sweeney, eds. 1987, The 

Macroeconomic Impacts of Energy Shocks, Amsterdam: Elsevier Science Publishers. 

Iledare, O. O. and W.O. Olatubi. 2004. “Impact of changes in crude oil prices and offshore oil 

production on economic performance of U.S. coastal Gulf States.” The Energy Journal

25(2): 97-113. 

Keane, M.P. and E. Prasad. 1996. “Employment and wage effects of oil price changes: A 

sectoral analysis.” Review of Economics and Statistics 78:389-400. 

Lee, K. and S. Ni. 1999. On the dynamic effects of oil price shocks: A study using industry 

level data. Working paper, University of Missouri, Colombia. 

Prescott, E.C. 1986. “Theory ahead of business cycle measurement.” Quarterly Review, 

Federal Reserve Bank of Minneapolis. (Fall):9-22. 

Scott, L. 2002. The ‘energy’ sector: Still a giant economic engine for the Louisiana economy. 

Louisiana Mid-Continent Oil and Gas Association, Baton Rouge, LA. 

Tatom, J.A. 1988. “Macroeconomic effects of the 1986 oil price decline.” Contemporary 

Policy Issues 6(July):69-82. 
54

U.S. Dept. of Energy. Energy Information Administration. 2002. U.S. Crude Oil, Natural Gas, 

and Natural Gas Liquids Reserves Annual Report - Historical. Internet website: 

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/crude_oil_natural_gas_res

erves/reserves_historical.html. 

U.S. Dept. of the Interior. Minerals Management Service. 2003. MRM Statistics Home. 

Internet website: http://www.mrm.mms.gov/MRMWebStats/Home.aspx. 

Yucel, M.K. and S. Guo. 1994. “Fuel taxes and co-integration of energy prices.” 

Contemporary Economic Policy 12(July): 33-41. 
55

APPENDIX A 

AN OUTLINE OF THE VAR PROCEDURE 

Step 1: Model Formulation 

A VAR analysis begins with the selection of a suitable model informed by economic theory. 

Usually, each variable in the system is treated symmetrically. Consider a two-variable case 

consisting of y1 and y2, each affecting the time-path of the other such that: 

1(t) 10 12 2(t) 11 1(t 1) 12 2(t 2) 1(t) y + v + v y + a y + a y + e − − (A1) 

 2(t) 20 21 1(t) 21 1(t 1) 22 2(t 2) 2(t) y + v + v y + a y + a y + e − − (A2) 

In a general matrix form with m variables and p lags, 

yt = v + A0yt + A1yt-1 + A2yt-2 + A3yt-3 + . . . . .+ Apyt-p + et (A3) 

Where yt, v and et are m x 1 column vectors and A0 , A1 , A2 , A3, . . . . . Ap are m x m matrices of 

coefficients. The m-element vector et are white noise residuals that are iid satisfying E{etet`}= D, 

where D is a diagonal matrix. Note also that e1(t) and e2(t) are uncorrelated and are pure 

innovations (or shocks) in y1(t) and y2(t), respectively. 

Equations (A1) and (A2) are referred to as primitive or structural form of a VAR. Often this 

primitive form is either over-identified or under-identified and the presence of the current levels 

of the other variable in its own equation implies correlation of the regressed with the error terms. 

Hence, consistent estimation of these forms cannot be obtained. To estimate each of these 

equations by OLS, one must obtained reduced forms. The system of equations is solved 

simultaneously to extract the reduced or standard VAR form: 

(I – A0) yt = v + A0yt + A1yt-1 + A2yt-2 + A3yt-3 + . . . . .+ Apyt-p + et (A4) 

Which reduces to 

yt = (I – A0)

-1 v + (I – A0)-1A1yt-1 + (I – A0)-1 A2yt-2 + (I – A0)-1 A3yt-3 + . . . . .+ 

(I – A0)

-1 Apyt-p + (I – A0)-1 et . (A5) 

In general matrix form, equation A5 becomes: 

yt = b + B1yt-1 + B2yt-2 + B3yt-3 + . . . . .+ Bpyt-p + ut (A6) 

Where 

 b = (I – A0)

-1 v , B1 = (I – A0)-1A1, B2 = (I – A0)-1A2 B3 = (I – A0)-1A3 . . . . . etc., and ut = (I – 

A0)

-1 et . 

The variance-covariance matrix of residuals of the vector ut equals 
56

 [((I – A0)

-1] D [(I – A0)-1]′. 

Each of the describing equations of A6 can be estimated by OLS. However, OLS can only be 

used if the system contains the same number of variables and lags in the right-hand sides. In this 

study, as may be observed in equation A4, the right-hand variables in each equation are not the 

same thus SUR is utilized. 

Step 2: Unit Root Tests 

Having formulated an appropriate theoretical model, the next step is to test for unit roots (or 

stationary) in all the variables. It has been shown that an OLS or SUR regression of the long-run 

relations implied by each describing equation of A6 is valid (non-spurious). Non-spuriousness of 

long-run relations means that the variables are co-integrated. To be co-integrated there must be 

unit roots in at least two or more of the variables. A common method to test for a unit root in a 

variable is by the Augmented Dickey Fuller (ADF) Test. Equation (A7) is estimated to perform 

the ADF test: 

 ∆yt = μ + γyt-1 + δ1∆yt-1 + δ2∆yt-2 + ....+ δp∆yt-p + εt (A7) 

Where ∆yt = (yt – yt-i), γ = ρ-1, while the null and alternative hypotheses are 

 Unit root: H0: γ = 0 

 No Unit Root: H1: γ < 0 

There is no consensus as to what should be done to the variable(s) subsequent to VAR estimation 

if a unit root is confirmed. Some suggest that the variable be differenced to remove the unit 

root(s). Others argue otherwise. Those who argue for non-differencing believe that since the goal 

of a VAR analysis is not to determine parameter estimates, but uncover dynamic 

interrelationships among variables, differencing “throws away” valuable information. However, 

the majority view is for differencing because a VAR should mimic the true data generating 

process. In this study, we adopt the majority view. 

Step 3: Exogeneity and Exclusion Tests 

Although in theory we have formulated A6 such that every endogenous variable is present in 

each equation and the lag length is also equal across equations, in reality, it may be that a 

variable or some of its lags does not really add to the forecasting performance of another variable 

and may therefore be excluded from the determination of that variable. The procedure to 

determine if a variable is a causal factor in predicting another is often the Granger causality and 

exclusion tests. If y1 does not improve the forecasting performance of y2, then y1 does not 

Granger-cause y2 and therefore nothing is gained by including it in the equation determining y2. 

The common F-test can be used to evaluate Granger-causality for a single equation. A test for 

exogeneity is technically different and more restrictive than Granger-causality, however. A 

necessary condition for the exogeneity of y1 is that the current and past values of y2 does not 

affect y1. A multivariate approach to carrying out the exogeneity and exclusion test is to use the 

so-called “block causality” test. 
57

To perform the test, run the system of equations with all the lags and variables (unrestricted 

form, U), and obtain the variance-covariance matrix, Σu. Then regress the system again excluding 

all the lags of the variable from the equations where it is theorized to be exogenous, and obtain 

the restricted Σr. The results are evaluated using the likelihood-ratio test (T-c)(log|Σr - log|Σu|), 

which is distributed as a chi-square with the degrees equal to the number of restrictions. T is the 

number of observations and c is the number of parameters estimated in the unrestricted form. 

This logic may be extended to the question of the inclusion of dummy variables as well. 

Step 4: Lag-Length Selection 

The selection of the appropriate lag-length in the system of equations is an important 

consideration. As in the selection of the appropriate variable(s) in the right-hand sides, the 

likelihood-ratio test is often used to select the appropriate lag length. The goal here is to ensure a 

parsimonious system with errors that are white noise as the theoretical model presumed. This test 

may also use the Akaike Information Criteria (AIC) and or the Schwartz-Bayesian Criteria 

(SBC). In the case of the AIC and SBC, we look for the model with the lowest value of the AIC 

or SBC estimates. 

Step 5: Estimation 

With steps 4 and 5 completed, the system of equations may still be symmetric. In this case, OLS 

is still the appropriate choice estimator applicable to each of the equations. However, it is 

possible that the resulting system after the previous two-steps produces a non-symmetric system 

such that either the right-hand variables are not the same across equations, or the lag-lengths 

differ across equations. In the non-symmetric situation, OLS is no longer an appropriate 

estimator as pointed out previously, we have to use another estimator such as a SUR. 

Step 6: Innovation Accounting 

Because of the restrictions implied in the reduced system in A6, not all of the parameters of the 

primitive forms can be recovered without even further restrictions. In addition, further restriction 

may be necessary to obtain consistent estimates of A6. Thus the main focus of a VAR is not on 

parameter estimates, rather it is to understand the time-path and dynamic interrelationships 

among included (endogenous) variables. One approach to obtain useful information from a VAR 

is to focus on the error terms in A6 since by design these are contemporaneously related across 

equations. In essence, we want to see what happens to a variable and to the other variables to 

which it is related if there is an innovation (or shock) to it. 

One method to accomplish this is to use a moving average representation of the system. For 

example, the system given by A6 is transformed such that: 

yt = C0ut + C1ut-1 + C2ut-2 + C3ut-3 + . . . . .+ Csut-s + y0 (A8) 

Where y0 equals initial value of yt. 
58

Equation A8 does not give a proper indication of how the system responds to shocks to the 

individual structural equations. This is because the shocks to the equations contained in the 

vector ut are correlated with each other. It is therefore not possible to determine the effects on the 

m variables of a shock to an individual structural equation would be as the observed ut represents 

the combined shocks to a number of equations. It is noted that ut = (I – A0)

-1

et..

To obtain unencumbered individual shocks in the structural system, it is necessary to solve the 

system for A0 and thus obtain (I – A0)

-1, which will enable us to transform the ut-j’s in into et-j’s. 

The transformation is done by selecting an appropriate matrix to orthogonalized the errors so that 

A0 is identified. Then 

 yt = Z0et + Z1et-1 + Z2et-2 + Z3et-3 + . . . . .+ Zset-s + y0 (A9) 

Where 

 Zj = CjG ; et-j = G-1

ut-j and G = (I – A0)

-1. 

The standard approach to identify the elements of A0 and hence decompose the matrix of 

reduced form residual in a VAR analysis is by the so-called Choleski Decomposition: 

 utu`t = Ω =Get. (Get.)` = Get. e`t.G` = GDG` 

Where D = I. 

The Choleski Decomposition of the matrix Ω is obtained such that 

 

I − A = G −1 ~

0 ( )

Which implies 1 ~

0

− A = I − G and ~

A0 is a representation of A0 after scaling of the variables in 

order to obtain D = I. With this G matrix the matrices Zj in equation A9 with the errors, et, of unit 

variance. 

The Zj matrices are called impulse-response functions. In this particular method of 

decomposition, a particular ordering of the variable is imposed on Ω. A different form of 

ordering will produce a different impulse response. Hence, the analyst must choose a plausible 

ordering guided by economic theory. (In this study we use the ordering: oil price, oil production, 

and state economic variable. This ordering implies that oil price is not affected by the other 

variables and the flow of causal relation is from price to production and then state economic 

variable). 

A plausible way to determine the importance of different exogenous shocks in explaining the 

dependent variables is by calculating the fractions of the forecast error variance of these 

variables attributable to such shocks. That is, the fractions of these forecast errors that are due to 

individual shocks can be obtained from equation A9. In the two-variable case considered here, 

the variance decomposition may be estimated as described below. 
59

Let 0

ij z be the ij-th element of Z0, we can express the current-period forecast error thus: 

 

t t t

t t t

y z e z e

y z e z e

2

0

1 22

0

1 21

2

0

1 12

0

1 11

= +

= + 

Then, 

 0 2

22

0 2

1 21

0 2

12

0 2

1 11

{ } ( ) ( )

{ } ( ) ( )

Var y z z

Var y z z

t

t

= +

= +

For e1 and e2 are independent shocks with unit variance. The standard deviations of these 

estimates are their respective square roots and the fraction of the error variance attributable to the 

shock to the first and second equations are 

 0 2

12

0 2

11

0 2

11

( ) ( )

( )

z z

z

+

 and . ( ) ( )

( )

0 2

12

0 2

11

0 2

12

z z

z

+ 
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.