shale gas and state level outcomes by mouhcine guettabi assistant professor of economics institute...
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Shale Gas and State Level Outcomes
By Mouhcine Guettabi
Assistant Professor of EconomicsInstitute of Social and Economic Research
University of Alaska Anchorage
• According to the 2011 IHS report, shale gas production supported more than 600,000 jobs in 2010.
• The same report states that the shale industry’s multiplier exceeds three and is larger than that of the construction and financial industries.
Previous Analysis
• Weber(2012) used a difference in difference approach to analyze initial employment effects in Colorado, Texas, and Wyoming.
• He finds that each million dollars in gas production created 2.35jobs in the county of production, which led to an annualized increase in employment that was 1.5% of the pre-boom level for the average gas boom county
Input output
• the projections based on IO models hinge on assumptions about multipliers between economic sectors and a lack of supply constraints.
• Existence of county spillovers/mobility and transitory nature of workers.
Data• The state Occupational Employment and Wage Estimates
are calculated from data collected in a national survey of employers. Data on occupational employment and wages are collected from employers of every size, in every state, in metropolitan and nonmetropolitan areas, in all industry sectors.
• These estimates are cross-industry estimates; each occupation's employment and wage estimates are calculated from data collected from employers in all industry sectors. Self-employed persons are not included in the survey or estimates. The 2012 OES estimates are the first based on the full 2010 Standard Occupational Classification (SOC) system.
• The Occupational Employment Statistics (OES) survey is a semiannual mail survey measuring occupational employment and wage rates for wage and salary workers in nonfarm establishments in the United States.
• OES data available from BLS include cross-industry occupational employment and wage estimates for the nation; over 500 areas, including states and the District of Columbia, metropolitan statistical areas (MSAs), metropolitan divisions, nonmetropolitan areas, and territories
Analysis
• Use both difference in difference and synthetic control methods to evaluate the effect of shale gas development on state level employment and earnings.
Effect of shale on overall earnings and Economic Profile
VARIABLESIncome per Capita
Per Capita Dividends
Earnings by Place
Wages and Salary F.P wages F.P emp
Avg. Wage and salary
boomperiod 0.00706* 0.0340*** 0.00724* 0.00778** 0.00389** 0.00401* 0.00376*Unemp Y Y Y Y Y Y YPoverty rate Y Y Y Y Y Y YPopulation Y Y Y Y Y Y YState FE Y Y Y Y Y Y YYear FE Y Y Y Y Y Y Y
Selected Results
Occ- code Total employment 10th percentile(wages) 25th percentile Median 75th percentile90th percentile
C& EX Boom period 0.0416*** -0.00248 -0.00309 -0.00362 -0.00146 0.00133
(0.00914) (0.00537) (0.00408) (0.00374) (0.00380) (0.00389)
F&S boomperiod 0.00126 0.0121 0.0101** 0.0111*** 0.00421 -0.00249
(0.00465) (0.00775) (0.00485) (0.00398) (0.00366) (0.00406)
A,D& E boomperiod -0.00761 0.0219** 0.00567 -5.92E-05 -0.00702 0.00389
(0.0125) (0.00918) (0.00922) (0.00739) (0.00738) (0.00906)
Health boomperiod -0.00893* 0.00123 -0.00309 -0.00433 -0.00261 0.0168
(0.00489) (0.00491) (0.00416) (0.00337) (0.00417) (0.0122)
Case Study of specific occupations Donor pool (set of control states)
29 states that did not have shale gas development
Step in Synthetic Control Method (SCM) Pre-intervention matching: choose some characteristics to match each
control state with the same characteristic of the treated state
obtain the optimal weights for each state
Use these weights to generate the outcome variable for pre and post intervention. This is the synthetic (or counterfactual Florida)
Compare the outcome of the synthetic Florida with actual Florida outcome
Post-intervention comparison: The gap between the synthetic and actual outcome is the effect of the intervention
Advantages of SCM
Weighting the control (non-intervention) states In most matching estimates: either subjective or ad hoc weighting Diff-in-diff: assigns every state in the control set the same weight Synthetic control: Assigns ‘optimal’ weight on each control state Only pre-intervention matching: researcher honesty [Rubin 2001]
Potentially restrictive assumption Unlike Diff-in-diff, Synth control method does not assume away time-
varying unobservables [Abadie, Diamond & Hainmueller 2010]
Synthetic Control Method
Abadie & Gardeazabal (2003), Abadie, Diamond & Hainmueller (2010)
1,...,1 Ji , 1=shale gas state of interest
Tt ,...,1 , Intervention at ),1(0 TT
itY = observed outcome for state i at time t
NitY = outcome for state i at time t in absence of the intervention
We want to estimate, Nttt YY 111 , },...,1{ 0 TTt
NtY1 not observed, need to estimate it
Synthetic Control Method
itittttNitY μλZθ ,
t = unknown common factor constant across states,
tZ = )1( r vector of observed covariates
tλ = )1( F vector of unobserved time-varying common factors
[Unlike traditional diff-in-diff, differencing does not eliminate
unobserved confounders]
it = unobserved transitory shocks at the state level with zero mean
),( it μθ = unknown parameters
Under standard conditions,
0),...,(1
2112
jt
J
j jNtJ YwYwwW
So, we can use,
},...,1{,ˆ 0
1
211 TTtYwY jt
J
j jtt
Obtaining W*
is
T
s si YkY 0
0
~K : certain combination of pre-intervention outcome
)~
,...,~
,( 11111 MYY KKZX : )1( k pre-intervention characteristics of exposed state
)~
,...,~
,( 10 M
jjj YY KKZX : )( Jk same characteristics for the unexposed states
Obtain ),...,( 12
JwwW by solving
1and}1,...,2|0{
)()(min
1
2
0101
J
j jj wJjw
WXXVWXXW
V = a )( kk symmetric positive semidefinite matrix
Choose V such that the mean squared prediction error (MSPE) of the outcome
variable is minimized for the pre-intervention periods [Abadie and Gardeazabal (2003); Abadie, Diamond and Hainmueller (2010)]
Architecture and Engineering occupations (Texas)
2001 2002 2003 2004 2005 2006 2007 2008 2009 20100
200
400
600
800
1000
1200
1400
1600
TreatedSynthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010-20
0
20
40
60
80
100
120
140
160
Treatment Minus Synthetic
Treatment Minus Synthetic
20012002
20032004
20052006
20072008
20092010
-200
-100
0
100
200
300
400 All control states
Treated minus Synthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-200
-100
0
100
200
300
400 Excluding states with 10 times pre-intervention RMSPE
Treated minus Synthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-200
-100
0
100
200
300
400excluding states with 5 times pre-intervention RMSPE
Treated minus Synthetic
Inference
Question: How often would we obtain results of this magnitude if we had chosen a state at random?
Answer: Apply placebo studies by implementing the synthetic control method on states that did not have shale gas development
Significant: If the gap estimate for Florida is unusually large compared to the gaps estimates for the states that did not have SYGL
0
10
20
30
40
50
60
Texas
Post-Pre RMSPE
Construction and extraction occupations
20012002
20032004
20052006
20072008
20092010
0
500
1000
1500
2000
2500
3000
3500
New Mexico
TreatedSynthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010-200
0
200
400
600
800
1000
New Mexico: Difference betwen actual and synthetic
Employment Gaps for Actual and Synthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-1500
-1000
-500
0
500
1000
1500
2000Employment Gaps for Texas and gaps of All control states
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-600
-400
-200
0
200
400
600
800
1000
1200Employment gaps for Texas
excluding states with RMSPE 10 or more
0
50
100
150
200
250
300
350
400
New Mexico
Post/Pre ratio shale gas dev. New Mexico and donor pool
Food and Service Related Occupations
20012002
20032004
20052006
20072008
20092010
3000
3100
3200
3300
3400
3500
3600
3700
3800
3900
Pennsylvania
TreatedSynthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 20103000
3200
3400
3600
3800
4000
4200 New Mexico
TreatedSynthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 20100
2000
4000
6000
8000
10000
12000
14000
16000
18000
Texas
TreatedSynthetic
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-500
-400
-300
-200
-100
0
100
200
300
400
500
Pennsylvania
0
10
20
30
40
50
60
Pennsylvania
Post/Pre MSPE shale gas for Penn-sylvania and donor pool
Conclusions
• Heterogeneity of effects across states. • Wage effects do not seem to be pronounced
in non-oil gas occupation. • Significant effect on construction and
extraction related occupations.• Food and Service related occupations are
largely unaffected.