Shale Gas and State Level Outcomes By Mouhcine Guettabi Assistant Professor of Economics Institute 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 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.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 VARIABLES Income per Capita Per Capita Dividends Earnings by Place Wages and SalaryF.P wagesF.P emp Avg. Wage and salary boomperiod *0.0340*** * ** ** * * UnempYYYYYYY Poverty rateYYYYYYY PopulationYYYYYYY State FEYYYYYYY Year FEYYYYYYY

Selected Results Occ- codeTotal employment10th percentile(wages)25th percentileMedian75th percentile 90th percentile C& EXBoom period0.0416*** ( )( )( )( )( )( ) F&Sboomperiod **0.0111*** ( )( )( )( )( )( ) A,D& Eboomperiod ** E (0.0125)( )( )( )( )( ) Healthboomperiod * ( )( )( )( )( )(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

Obtaining W*

Architecture and Engineering occupations (Texas)

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

Construction and extraction occupations

Employment Gaps for Actual and Synthetic

Food and Service Related Occupations

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.