By Amanda Weinstein Nov. 26, 2012 Local Labor Market Restructuring in Shale Booms

Outline Introduction to Shale Regional Shocks and Natural Resource Booms Methodology Results Conclusions

Motivation Commenting on shale energy development, Aubrey McClendon CEO of Chesapeake Energy of Oklahoma was quoted in the Columbus Dispatch saying, “This will be the biggest thing in the state of Ohio since the plow.” Various impact studies have estimated large employment effects for Ohio, Pennsylvania, and other areas. We are concerned that job numbers may be overinflated by the industry (or any industry) Policy makers often use these job numbers to justify supporting the industry through tax breaks and other measures We need to create a counterfactual to estimate what would have happened if there was no shale development. The difference between what did happen and the counterfactual is the shale development effect

Shale Booms Innovations in oil and gas extraction along with rising oil and gas prices have led to shale development across the U.S. Hydraulic fracturing and micro-seismic technology Impact on local employment and earnings The nature of local adjustments to economic shocks Natural resource curse Restructuring in the local labor market due to displacement effects (including “Dutch disease”) and other spillovers

Hydraulic Fracturing

Drilling Tower and Capped Well Marcellus Shale horizontal drilling tower in Lycoming County, PA.

U.S. Shale Plays

Shale Gas Production

Tight Oil production

Actual and Projected Production (EIA)

Actual and Projected Production

The Employment Boom The boom in employment generally preceded the boom in production as many areas have a significant construction period before drilling began

North Dakota North Dakota oil and gas employment has shot up from holding steady at about 1,800 in 2004 to11,700 in 2011.

Regional Shocks The shale boom may be viewed more as a transitory shock whether it is or not Wages and prices adjust more in booms than busts and are more flexible in a transitory shock than a permanent one (Blanchard and Katz, 1992; Topel, 1986) After a shock, states return to the same growth rate on a different growth path (Blanchard and Katz,1992) Long run impacts are often negligible Military base closings (Dardia et al., 1996; Hooker and Knetter, 1999; Popper and Herzog, 2003) Large plant openings (Greenstone and Moretti, 2004; Edmiston, 2004)

Previous Natural Resource Shocks

Natural Resource Shocks Evidence of a “resource curse” has been found across nearly all levels of geography (Papyrakis and Gerlagh, 2007; Kilkenny and Partridge, 2009; James and Aadland, 2011) Reasons for the poor performance have generally been focused on their institutions (Mehlum et al., 2006; Rodriguez and Sachs, 1999) U.S. counties may be affected through mechanisms other than local institutions Specialization in natural resource extraction may lead to a less diverse and more volatile economy Natural resources have been found to affect agglomeration at the state level (but not lower levels of geography), natural resources used for energy are found to have no significant effect (Rosenthal and Strange, 2001)

Natural Resource Shocks Black et al. (2005) analyze the impact of the coal boom in the 1970s and the subsequent coal bust in the 1980s. Little evidence of crowding out Less than 2 jobs created for every 10 coal jobs created during the boom but 3.5 jobs lost for every 10 coal jobs lost during the bust. Marchand (2012) found that for every 10 energy extraction jobs created in Western Canada, there were 3 construction jobs, 2 retail jobs, and 4.5 service jobs created Recent analysis of the impact of mountaintop mining find that it may reduce poverty rates in the short term but not long term (Partridge et al., forthcoming; Deaton and Niman, 2012) Weber (2012) finds that $1 million in shale gas production results in 2.35 jobs within counties in Texas, Colorado, and Wyoming

Data: Setting up the Counterfactual Economic Modeling Specialists Intl. (EMSI) data from covers the general boom period from about 2005 onward and the years leading up to the shale boom EMSI data provides detailed employment and earnings data at the county level Counties in the lower 48 states (3,060 counties) Controls – population and education (U.S. Census Bureau), industry composition, county fixed effects, economic trends Define the boom counties and boom period

Data: Setting up the Counterfactual Measuring the boom Both Black et al. (2005) and Marchand (2012) measure the boom using proportion of earnings derived from natural resource extraction which misses development in new counties such as in Pennsylvania and many other areas Weber (2012) uses data on gas production and earnings from production which may miss the benefits of initial construction as well as the tapering off when the drilling period ends EMSI data allows us to measure the boom using employment Measuring the boom period General boom period for U.S approx Define boom period by state

Boom Periods by State

Change in Oil and Gas Employment Direct oil and gas employment is measured as the sum of industry codes 2111 (oil and gas extraction) and 2131 (support activities for mining).

Shale Boom Counties In a shale booming state (defined by oil and gas production and employment) At least 10% increase in oil and gas employment growth and at least 20 additional oil and gas workers during the boom period.

Descriptive Statistics

Boom vs. Non-boom Counties: Employment

Boom vs. Non-boom Counties: Earnings Boom counties seem to be benefitting in terms of employment and earnings though pre-boom trends varied between the two

Methodology At best, a well done impact study should tell you how many jobs are ‘supported’ by an industry, not how many jobs it ‘created.’ – Not a counterfactual The goal of the difference-in-difference methodology is to set up this counterfactual The difference-in-difference approach Y it is Δ ln(employment or earnings) Parameter of interestCounty Fixed Effect

Difference-in-Difference Results Boom counties were associated with an annual increase in employment of 1.59% and Increase in earnings of 3.08%

Difference-in Difference with Trends From Greenstone et al (large plant openings) When β 2 = β 3 = β 5 = β 7 = 0, reduces to equation 1 As shown in the previous employment and earnings growth graphs the economic trends leading up to the boom period are different for boom counties and non-boom counties

Results with Trends The effect on earnings is again nearly double that of the effect on employment The impact of shale development decreases over time

Endogeneity The OLS methodology assumes that a change in oil and gas production (and employment) is driven by an exogenous shock Concerns that shale development may occur in pro-business counties biasing our results Shale development may be occurring in struggling communities trying to attract economic development of any kind Or in communities that have done well in the past because of their pro- business policies. Instrument for boom counties using the percent of the county covering shale resources which we would expect to be endogenous Although there may be endogeneity in the location choice of drilling firms, we expect that county fixed effects (and various other controls are sufficient)

Instrument: Percent Shale

Instrumental Variables Results Significant first stage results using percent shale Instrumental variables coefficient estimates similar in sign to previous estimates though not significance Hausman tests suggest that our identification of shale boom counties is not endogenous

The Size of the Boom The binary variable used for boom counties may miss some of the variability in the size of the boom and the impact of shale development We would also like to estimate the employment multiplier associated with shale employment growth Equation 3 below incorporates the size of the boom measured by Δ ln(oil and gas employment) Direct oil and gas employment is measured as the sum of industry codes 2111 (oil and gas extraction) and 2131 (support activities for mining).

Finding the Employment Multiplier There is on average 1 oil and gas worker for every 87 non-oil and gas workers in shale boom counties in additional oil and gas worker is associated with 0.46 additional jobs (or a multiplier of 1.46) Calculations similar to Moretti (2010)

Impact on the Traded and Nontraded Sectors The impact on tradable sectors other than oil and gas is 1.08 The employment multiplier for nontradable sectors is 1.42

Conclusions Labor Market Restructuring Although we find little evidence of crowding out, the multiplier effect is only significant for the nontradable goods sector With an employment multiplier less than 2, the local labor market is restructuring by shifting the share of employment toward oil and gas extraction jobs The average percent of mining employment in boom counties increased from approximately 4% before the boom period to 6.8% in The average percent of mining in non-boom counties remained steady before and during the boom at about 0.89% of total employment.

Conclusions The Economic Impact Boom counties experienced an increase in both employment growth and earnings growth As Blanchard and Katz (1992) found, growth rates seem to be returning to their original levels after the initial increase in growth The Employment Multiplier At 1.46, the employment multiplier is lower than previously expected and than impact studies suggest Policy implications Importance of realistic expectations Future Research: multipliers by region or state, border counties and spatial spillovers 37

Amanda Weinstein Graduate Research Assistant The Swank Program in Rural-Urban Policy Dept. Agricultural, Environmental & Development Economics The Ohio State University Thank You 38

Extra Slides

Difference-in-Difference Methodology [E(Y b0 )-E(Y n0 )]-[E(Y b0 )-E(Y n0 )] The difference-in-difference approach Y it is Δ ln(employment or earnings) Through asymptotics it can be shown that the probability limit of b3 is

U.S. Shale Plays

Prices - Booms and Busts

Oil Prices

Major Holders of Utica Shale Right in Ohio (April 2012)