shale gas and state level outcomes by mouhcine guettabi assistant professor of economics institute...

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.