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Measuring The Economic Value of Shale Energy Development Presented for the BU/SRSA Shale Workshop Mark Partridge & Amanda Weinstein Presented at Bucknell.

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Presentation on theme: "Measuring The Economic Value of Shale Energy Development Presented for the BU/SRSA Shale Workshop Mark Partridge & Amanda Weinstein Presented at Bucknell."— Presentation transcript:

1 Measuring The Economic Value of Shale Energy Development Presented for the BU/SRSA Shale Workshop Mark Partridge & Amanda Weinstein Presented at Bucknell University July 31, 2012 Swank Chair in Rural-Urban Policy Department of Agricultural, Environmental & Development Economics Ohio State University Extension

2 Shale: Economic Development Game Changer?  I will follow Weinstein and Partridge (2011) and Farren et al. (2012).  I will focus on economic development. Of course, shale development also has major implications on world and US energy markets—see map.  For those interested in local/regional growth, long-term economic outcomes receive more weight than temporary booms revolving around construction. 2

3 Shale Energy is found all over US and the world.

4 Is Shale a Game Changer?  Commenting on Ohio’s shale energy development: “This will be the biggest thing in the state of Ohio since the plow…This is truly extraordinary.” Aubrey McClendon CEO of Chesapeake Energy of Oklahoma.  Quoted in the Columbus Dispatch “Realism on Renewable Energy.” September 22, 2011, Pp. B1-B2.  Economists have 150 years of evidence on natural resource booms and the evidence is often negative (e.g., Papyrakis, E. and R. Gerlagh, 2007; Kilkenny and Partridge, 2009; James and Aadland, 2011).  E.g., Natural Resources Curse & Dutch Disease  More cases like LA, WV, Venezuela, Nigeria vs Norway 4

5 Is shale a game changer?  In the latest year, PA has gained about 6,000 mining jobs (minus coal mining) and about 40,000 total jobs.  Since 2006, PA has gained about 18,500 mining jobs (minus coal mining).  PA’s total employment is over 5.7million  Ohio has gained about 500 mining jobs and 100,000 total jobs.  Source U.S. Bureau of Labor Statistics CES measured over June 2011-June

6 6 Figure 7: Total Employment and Previous Oil Booms in the U.S.: 1969=100

7 Shale: Game Changer? 1. Economists point out that ‘projects’ and policies should be judged on their net benefits and costs, and NOT net job creation.  E.g., CO2 content of coal vs natural gas.  E.g., lower energy costs (but energy security is not a large issue since NG replaces US coal). 2. The best source of an industry’s actual economic impact is NOT the industry itself, studies paid for by the industry, or sympathetic politicians and newspapers.  This is not a surprise.  In serious research, we use peer review to weed out poor studies. We create counterfactuals. 7

8 Shale: Game Changer?  A counterfactual is what would have happened if there was no shale industry. The difference between the number of jobs that happened and the counterfactual is the actual jobs created. 3. So-called ‘impact studies’ that estimate direct and indirect effects are over-estimates of new job creation and serious regional economists have not viewed them as anywhere near best practice for decades. NOT COUNTERFACTUALS!  At best, an impact study should tell how many jobs are ‘supported’ by an industry, not how many jobs it ‘created’ and explain the difference.  Energy is a capital intensive industry—fewer jobs. 8

9 Shale: Game Changer?--#3 cont.  Even in good impact studies, the “employment” effects are not continuous but in a piecemeal fashion. Construction, then drilling, then pipelines, and so on.  They are usually based on slightly dated national input-output estimates. Heavily weigh the Oil-Patch supply chain response, not actual PA/OH response. 9

10 10 Taken from: New drilling activity and its capital intensive nature in PA.

11 Shale: Game Changer?--#3 cont.  “Penn State (Considine) Impact Studies” funded by the shale industry is an example. It predicts 111,000 jobs in 2011 and 212,000 in 2020 using the IMPLAN software. {see Kelsey et al. (2011) for a different point of view}  Kleinhenz & Associates (2011) funded by the industry predicted over 200,000 jobs in Ohio by  Ohio Shale Coalition (2012) predicted 66,000 by

12 #3 Continued  Impact studies typically ignore displacement effects and do not compare development impact to the counterfactual.  Example of a coal counterfactual is Black et al in Economic Journal. Multiplier of  PA and OH studies estimate 95% of shale industry purchases are in PA and 90% in Ohio.  Examples of other problems:  No Price Effects or crowding out.  Entrepreneurs do other things.  Nationally, more natural gas means less coal needed for electricity and fewer coal jobs. [oil is new jobs] 12

13 Example of displacement or labor shortages elsewhere in the economy in North Dakota Bakken region.

14 What we do in our comparison Ohio to Pennsylvania?  (1) Assessment of impact analysis  (2) Statistical regressions on the entire state of Pennsylvania  (3) Compare PA to North Dakota’s Bakkan shale region which has had a similar employment change in ‘mining.’  (4) Examine the employment life cycle effects of natural gas and coal per kwh.  (5) Compare drilling counties with similar non-drilling counties in PA.  (6) Show the industry is too small to materially affect Ohio/PA employment. 14

15 Findings for OH based on PA  We conclude Ohio’s expected employment effects are near 20,000 workers (not counting displacement).  There are relatively “large” income effects in affected counties.  We do a difference in difference assessment of those with heavy mining vs similar counties w/o mining to get a handle on the actual income and job creation. 15

16 16 Estimates of the number of jobs required to produce a kWh by energy source Source: Weinstein et al. (2010) chart using data from Kammen et al. (2004) Total kWh from Coal 2009 Change in Jobs Change in Energy Costs (millions) Change in Emissions (lbs) Ohio 113,711,997, $491,804-23,822,663,372 Pennsylvania 105,474,534, $456,177-22,096,914,873 Source: EIA and Weinstein et al. (2010) Table 3: Effects of Displacing Coal with Natural Gas

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19 Source: U.S. Dept. of Labor QCEW and U.S. Bureau of Labor Statistics CES, Total Nonfarm Employment by state, Note: Oil and gas extraction Drilling Oil and Gas Wells Support Activities for Oil and Gas Operations Geophysical Surveying and Mapping Services Nonresidential Site Preparation Contractors Oil and Gas Field Machinery and Equipment Manufacturing Pump and Pumping Equipment Manufacturing for natural gas wells Pipeline Transportation of Natural Gas Oil and Gas Pipeline Construction 19

20 Population 2005 Per Capita Income 2005 Employment Growth Rate Employment Growth Rate Income Growth Rate Income Growth Rate Non- Drilling Counties 255,508$32,1875.3%-0.4%12.6%13.6% Drilling Counties 124,928$27,4501.4%-0.6%12.8%18.2% Table 1: Pennsylvania County Descriptive Statistics Source: BEA

21 PA Counties considered in our simple difference in difference counterfactual

22 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

23 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

24 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

25 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

26 Regression Approaches—the current gold standard  The need to establish a counterfactual in peer-reviewed research.  Many approaches. The three I consider are (1) matching, (2) 2SLS, and (3) difference in difference.  Matching uses trend and level attributes to identify otherwise equal locations when the treatment began—identify counter factual.  Regress the sample of matched and non- matched counties using an indicator for treatment (drilling). Complex methods can use trends as well (GHM, JPE, 2010). 26

27 Regression Approaches—the current gold standard  Drilling may be endogenous because places that accept drilling may be different than other places.  2SLS—find an instrument that is only related to the outcome through indirectly affecting the treatment.  E.g., geology only affects economic activity through indirectly affecting shale energy employment. Instrument is then used to predict where drilling takes place.  My results are OLS. 27

28 Regression Approaches—the current gold standard  Difference in Difference  Take the change in the economic outcome and regress it on the change in treatment.  E.g., difference in the percent change in employment growth regressed on the change in the number of wells in periods t, and 0. [period t is say 4 years after shale drilling and period 0 is say 4 years prior to drilling.  Need to pick initial periods carefully due to anticipation effects. 28

29 Regression Approaches—the current gold standard  You can include trend effects.  D in D’s key advantage is it differences out county fixed effects that influence long-term growth— e.g., culture, or demographics.  One can still condition on other variables that may affect near term economic growth. For instance, initial income or population. 29

30 The Model C is county fixed effect Period 0: Y i0 = β 0 + β 1 (Number of Wells) i0 + C i + ε i0 Period 1: Y i1 = β 0 + β 1 (Number of Wells) i1 + C i + ε i Difference the 2 equations and C falls out: Y i1 - Y i0 = β 0 + β 1 ( Δ Number of Wells) + ε i Add in other X variables to condition on. β 1 β 1 measures the positive indirect and induced effects net of any displacement 30

31 31 Change in Percent Employment Growth Compared to Parameter Estimatet-value Total Wells min Wells 1.769E Log Population Log Per Capita Income N67 R Adjusted-R Short-term Employment and Income Effects of Drilling Change in Percent income Growth Parameter Estimatet-value Total Wells min Wells 2.515E Log Population Log Employment N67 R Adjusted-R Source: BEA and PA DEP Date

32 Employment Growth Using National Data. 1. Y i0 = β 0 + β 1 (Oil and Gas Employment Growth) i0 + C i + ε i0 2. Y i1 = β 0 + β 1 (Oil and Gas Employment Growth) i1 + C i + ε i1 3. Y i1 - Y i0 = β 0 + β 1 ( Δ Oil and Gas Employment Growth) + ε i  Equation 3 estimates the impact of the difference in oil and gas employment growth (from the time period to the time period) on the difference in employment growth between period 1 and period 0 ( ).  The county fixed effect is differenced out and thus there should not be omitted variable bias.  β 1  β 1 measures the positive indirect and induced effects net of any displacement. 32

33 Change in Employment Growth Rate: minus Dependent Variable Variable Employment Growth Ratet-statistic Oil and Gas Employment Growth Log Population Log Average Wage Percent College Unemployment Rate Industry ControlsYes State Fixed EffectsYes R2R Adj-R N3065

34 Earnings Growth Rate: minus Dependent Variable VariableEarnings Growth Ratet-statistic Oil and Gas Employment Growth Log Population Log Average Wage Percent College Unemployment Rate Industry ControlsYes State Fixed EffectsYes R2R Adj-R N3065

35 Growth Rate of Establishments : minus Dependent Variable Variable Establishments Growth Ratet-statistic Oil and Gas Employment Growth Log Population Log Average Wage Percent College Unemployment Rate Industry ControlsYes State Fixed EffectsYes R2R Adj-R N3065

36 Tentative Estimated Impact on the Pennsylvania Marcellus Region Average county employment in the Pennsylvania Marcellus Shale region that increased the number of wells drilled (34 counties) was 56,885 in 2005 (total employment in the region was 1.9 million). Direct oil and gas employment [NAICS 2111 and 2131] in the Marcellus drilling region of PA went from 3,911 in 2001 to 4,922 in 2005 to 15,335 in 2011 (an increase in the percent growth of 186%) Expected percent growth in total employment is 0.038% (1.86* ) which amounts to 22 ( *56885) workers per county (735 total workers for the region) Expected percent growth in total earnings 0.049% or $985,821 ($33.5 million for the region) Expected percent growth in total establishments 0.011% or 0.35 establishments per county (12 for the region)

37 Top Counties by Percent Oil and Gas Employment Growth RankCountyState 2000 Population Shale Oil and Gas Growth 1WhiteAR67,16563%166,413%1,664 2BradfordPA62,761100%105,321%1,053 3LycomingPA120,04493%75,981%760 4FaulknerAR86,01459%36,452%1,059 5WyomingPA28,080100%27,209%272 6CleburneAR24,04680%23,308%233 7BarrowGA46,1440%12,069%121 8ConwayAR20,33683%11,827%118 9TiogaPA41,373100%11,598%116 10RobertsonTX16,0001%9,142%102

38 Top Counties by Oil and Gas Employment Growth RankCountyState 2000 Population Shale Oil and Gas Growth 1HarrisTX3,400,5780%30%20,054 2WilliamsND19,761100%404%5,663 3MidlandTX116,0090%56%5,183 4OklahomaOK660,4480%63%5,089 5EctorTX121,1230%109%4,206 6TarrantTX1,446,219100%105%4,145 7DenverCO554,636100%72%2,880 8KernCA661,64520%28%2,343 9DallasTX2,218,89932%34%2,188 10LafayetteLA89,9740%17%2,174

39 Conclusions  PA Shale natural gas is associated with significant income effects but modest employment effects.  The real question of shale investment is not job creation, but net benefits vs costs including pollution costs.  In this question, natural gas should be compared to coal, the true alternative.  Shale natural gas is lower cost, less carbon, and like coal has local pollution impacts.  States should consider higher severance tax for long-term needs.  Schools, infrastructure, environment. 39

40 Mark Partridge Swank Chair in Rural-Urban Policy Dept. Agricultural, Environmental & Development Economics The Ohio State University Google “Partridge Swank” and you will get my website (614)

41 Individual County Graphs Follow 41

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43 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7, Greene-Perry Matched Employment Pair

44 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7, Fayette-Franklin Matched Employment Pair

45 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7, Susquehanna-Carbon Matched Employment Pair

46 Tioga-Union Matched Employment Pair Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

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48 48

49 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

50 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

51 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

52 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,

53 Source: U.S. Bureau of Economic Analysis, REIS Data, Downloaded Oct. 7,


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