fracking growth - csic · london school of economics october 4, 2013 thiemo fetzer (lse) fracking...

Fracking Growth

Thiemo Fetzer

London School of Economics

October 4, 2013

Thiemo Fetzer (LSE) Fracking Growth October 4, 2013 1 / 78

Introduction Question

Research Question

What are the economic impacts of fracking on the US?Tracing out the effect of recent boom from micro (resource extractionstage) to the resource consumption stage.

1. Causal impact of resource extraction boom on local economicstructure?

I Rising global demand for energy induces exploration in remote areas(scramble for arctic) or of new resources (tar sands).

I Resource booms in areas without previous resource extraction, whichare economically not well integrated.

2. What is the causal effect of lower energy prices on economic activity?

I Energy is a key factor of productionI Energy prices elasticities have previously mostly been studied in context

of rising prices.


Introduction Natural Experiments

Two Natural Experiments

Production Stage: Geographical shift in resource extraction activity

I Shift of resource extraction from South to Midwest and Northeast.

I Exploitation of shale gas and shale oil due to improvements inproduction technology and relaxation of regulatory environment.

I Resource booms in areas that had not experienced significant resourceextraction before.

Consumption Stage: Physical infrastructure access constraints

I No global market for Natural gas due to transportation costs.

I Market access constraints: not all places are equally exposed to theboom in shale gas production.

I Access to pipeline network is key factor that determines whether onecan profit from increased production.


Introduction The Bigger Picture

Back in 2005...

Natural Gas Production:

I US Domestic Natural Gas production was contracting until 2006.

U.S. natural gas consumption is projected to grow by 41 %between 2003 and 2025. Most of the additional supply isexpected to come from Alaska and imports of liquefiednatural gas (LNG). (EIA Energy Outlook 2005)

I until 2007-2008, 12 liquified natural gas import terminals wereconstructed in the Gulf of Mexico.

Oil Production:

I Oil production was in decline since 1986, dependence on foreign oilpeaked in 2005.

I EIA expected import share of oil to increase from 54% in 2003 to68% in 2025.



What has changed? Fracking and Horizontal Drilling

1. Horizontal drilling allows exploitation of thin layered oil and gasdeposits in so-called shale formations.

2. Hydraulic fracturing (fracking) creates mini-fissures through whichgas and oil is released. Environmentally very controversial.

Figure: Conventional versus Unconventional Deposits, picture from the EIA.



Stylised Fact (1): Fracking Changes Energy ProductionGeography

Conventional Deposit

Figure: Shale Deposits versus Conventional Deposits

Non-parametric.




Conventional Deposit Shale Deposit

Figure: Shale Deposits versus Conventional Deposits

Non-parametric.




Figure: Share of Shale Gas Production in Total Natural Gas Production



Stylised Fact (2): Lack of Infrastructure Constrains Growth

Figure: Gas Flaring in the Bakken Shale - Courtesy of NASA

“I’ll tell you why people flare: It’s cheap. Pipelines are expensive:You have to maintain them. You need permits to build them.They are a pain.”

Troy Anderson, North Dakota Gas Producers



Context (3): Divergence of Natural Gas Prices

Figure: Price Divergence of Natural Gas Prices between Producing States andOthers



Context (5): Loss of Link between Oil- and Gas Prices

25

50

75

100

125

2000 2002 2004 2006 2008 2010 2012 2014Date

Pric

e in

per

Bar

rel o

f Oil

Oil Price Natural Gas (energy equivalent)

Figure: Divergence of Oil- and Natural Gas Prices


Introduction Preview

Preview of Results

Causal impact of resource extraction boom on local economicstructure?

I No systematic evidence of Dutch disease style contraction inmanufacturing sector due to labour mobility, but adverse effect on(land intensive) agriculture.

I Significant expansion in local sectors (1) supplying to mining industry,(2) providing locally consumed services and (3) the public sector.

I Employment gains directly in resource extraction activity of around80,000 jobs, while boom in local labour markets creates another300,000 - 400,000 jobs.

I No evidence (yet) of localised up-stream linkages in energy intensivesectors.



Preview of Results

What is the causal effect of lower energy prices on economicactivity?

I Explain (natural gas) price-divergence through existing pipelineinfrastructure.

I Availability of infrastructure and connectivity to the grid aredetermining factor in whether areas benefit from available natural gas.

I Estimate an elasticity between natural gas prices and employment ofaround -0.244.

I Significant amount of heterogeneity across sectors and even withinmanufacturing.

I Sectors that were historically more natural gas intensive within the USin their production processes experienced significantly stronger growthcompared to their peers.



Related Literature

I Local Impacts of Demand Shocks: Moretti (2010b), Greenstone,Michael and Moretti (2010) , Moretti (2010a), Aragon and Rud(2013)

I Local Effects of Resource Shocks: Black et al. (2005), Mehlum et al.(2006),

I Sectoral Spillovers: Voigtlander (2013), Marden (2013)

I Energy Prices and Firms: Kahn and Mansur (2013), Deschenes (2010)


Data

Table of Contents

Introduction

Data

What are the local economic effects of fracking?

What are the effects of lower energy prices?

Conclusion


Data

Data (1)

Main Dependent Variable:

I US Bureau of Labour Statistics: Quarterly Workforce Indicators atcounty level, coverage of 96% of the jobs. Data disaggregated up to4-digit Sector Industry Classification.

Well Construction and Shale Formation Data:

I US Geological Survey: maps of shale formations, basins andconventional deposits.

I Oil- and Gas well construction compiled from various state-levelsources. A total number of roughly 20,000 newly constructed(unconventional) oil- and gas wells from 2000 to 2012, thoughunbalanced panel across states.


Data

Data (2)

Energy Information Administration:

I Physical pipeline capacity and direction between neighbouring states.

I Location of Gas Processing Plants, quarterly production data at statelevel.

I Some Natural Gas Price Data for municipality owned utilitycompanies.

Historical Property Rights Data:

I 1,191,416 homestead land titles issued between 1900-1938 in the MidWest from General Land Record Office.

Recovery and Reinvestment Act:

I Project Level Data at zip- code level from the American Recovery andReinvestment Act.


From Macro Facts to Micro Fracturing

Table of Contents

Introduction

Data



Conclusion


From Macro Facts to Micro Fracturing Two Empirical Approaches

Two Empirical Approaches

1. Intention-to-Treat: Explore path of economic divergence betweencounties on shale formations with control group counties.

I This approach highlights how shale deposits were becomingeconomically relevant only from 2005 onwards.

I Choice of control group addresses local spatial spill-overs andreallocation.

2. Local Average Effect: Explore economic divergence of counties fromone another inside shale deposits.

I Historical property rights and distance to infrastructure determineintensity of treatment.


From Macro Facts to Micro Fracturing Intention-to-Treat

An Intention-to-Treat Approach

I Empirical Strategy: Estimate path of economic divergence ofcounties that lie on Shale-Deposits relative to counties outside theshale deposits.

I Define control counties to address:

I Spatial Spillovers: Counties that lie close to shale deposits mayreceive indirect treatment due to proximity.

I Reallocation Effects: Counties that are above conventional deposits,but not on shale deposits may suffer from a reallocation of resourceextraction activity.

I Energy Price Effects: Spatially close enough to not receivedifferential energy price effect.

I This allows me to cleanly estimate the local effects due to resourceextraction.




Figure: Treatment Counties (blue) and Control Counties (red)



An Intention-to-Treat Approach (2)

Specification:

log(Employmentisct) = αic + βsct +∑t

γt × Sc + εicst (1)

with

Sc =

{1 county c lies on shale formation

0 not

i indicates sector, αic sector-county fixed effects, βsct state-time fixedeffects.



An Intention-to-Treat Approach (2) - Results-.0

20

.02

.04

.06

2002q1 2004q3 2007q1 2009q3 2012q1

Overall Economy

-.10

.1.2

.3

2002q1 2004q3 2007q1 2009q3 2012q1

Mining Sector

Figure: Sectoral Employment Growth and the Extent of Spillovers.



An Intention-to-Treat Approach (2) - Results

-.02

0.0

2.0

4.0

6

2002q1 2004q3 2007q1 2009q3 2012q1

Overall Non-Mining Sector Overall Economy

Figure: Comparing Overall-Effect and Non-Mining Employment




Table: Intention to Treat Results

Panel A: GDP Panel A: Employment Panel B: Wages

(1) (2) (3) (4) (5) (6) (7)GDP per Capita Overall Mining Non-Mining Overall Mining Non-Mining

Shale × 2012 0.044*** 0.271*** 0.032** 0.055*** 0.089** 0.044***(0.015) (0.086) (0.014) (0.012) (0.037) (0.011)

Shale × 2011 0.029*** 0.036** 0.207*** 0.026* 0.051*** 0.050* 0.041***(0.008) (0.015) (0.072) (0.014) (0.010) (0.030) (0.009)

Shale × 2010 0.025*** 0.029** 0.204*** 0.021 0.046*** 0.044 0.037***(0.008) (0.014) (0.079) (0.014) (0.009) (0.031) (0.008)

Shale × 2009 0.023*** 0.024* 0.174** 0.016 0.040*** 0.055* 0.033***(0.007) (0.014) (0.072) (0.014) (0.009) (0.030) (0.008)

Shale × 2008 0.016** 0.015 0.124** 0.008 0.037*** 0.032 0.030***(0.007) (0.015) (0.063) (0.015) (0.008) (0.025) (0.007)

Shale × 2006 0.006 0.010 0.052 0.005 0.025*** 0.025 0.021***(0.004) (0.012) (0.051) (0.013) (0.008) (0.026) (0.007)

Shale × 2004 -0.002 0.008 0.016 0.007 0.008 0.011 0.009**(0.005) (0.008) (0.048) (0.008) (0.005) (0.028) (0.004)

Shale × 2002 0.001 0.004 0.021 0.005 0.002 0.028** 0.002(0.002) (0.005) (0.026) (0.005) (0.002) (0.013) (0.002)

County FE Yes Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes Yes YesObservations 11903 49476 49476 49476 48600 48585 48585States 34 34 34 34 34 34 34

Notes: The dependent variable is the log of the variable given in the column head. Significance levels are indicated as * 0.10 **0.05 *** 0.01. Standard errors in the parentheses are clustered at the state level.



Intention-to-Treat Results

I Strong positive employment effects in mining sector and innon-mining sector.

I Strong positive effect on local earnings, strongest in mining sector.

I Caveat: intention-to-treat approach may underestimate the trueeffects, since not all shale deposits are currently actively explored.

I Exploit intensive-margin variation in resource extraction intensitythrough actual well construction.


From Macro Facts to Micro Fracturing An Instrumental Variables Approach

An Instrumental Variables ApproachI Exploit variation of resource extraction intensity within shale.I Use geocoded data on location of drilled unconventional oil and gas

wells.

Figure: Location of New Unconventional Oil-/ Gas Wells



Where does drilling take place?

Two of many relevant variables...

1. How far away is the location from the nearest processing facility?I Natural gas needs to be chemically processed before it can be sold.I Crude Oil needs to be refined.

2. Who actually owns the oil and gas beneath the surface?I On public lands, drilling for natural gas is legally very easy - though

very costly due to remoteness of unsettled land.I Drilling on (formerly public) lands that were settled, but where the

mineral rights were retained owned by the federal government isattractive.

I Owner of the mineral estate has the right to freely use the surfaceestate as well.

I Homestead Act Legislation in early 20th century generated a lot ofsuch “split estates” in the Midwest.



Historical Property Rights and Present Day Drilling

I Exploit variation of resource extraction intensity within shale.

I Share of Land where mineral rights were retained by FederalGovernment under early 20th century Homestead Acts.

I Most places in mid-west: oil and gas resources were not discovereduntil the 1950s.

Mineral Rights Ownership and Drilling Activity in Bakken Shale

Mineral Rights Owned by State Per Township Drilling Intensity per Township (9 x 9 km cells)



Instrumental Variables Specification

Estimate first stage:

Wellicst = αic + βt +∑t

δt × dcg +∑t

γt ×mc + εicst

where Wellicst measures the number of newly constructed wells (spudded)in county c at time t. dcg measures distance to nearest gas- or oilprocessing plant, while Hc measures the share of shale land granted ashomestead in the early 20th century with mineral rights retained by federalgovernment.Well-construction is a good proxy for actual production .Second Stage:

log(Employmentist) = aic + bst + Wellscst + νicst



Where does drilling take place?

First Stage: Predicting Well Construction

010

2030

2002q1 2004q3 2007q1 2009q3 2012q1

-4-3

-2-1

0

2002q1 2004q3 2007q1 2009q3 2012q1

Mineral Rights Distance to Processing Plant

Reduced Form: Effect on Mining Employment

-.002

0.002

.004

2002q1 2004q3 2007q1 2009q3 2012q1

-.3-.2

-.10

.1.2

2002q1 2004q3 2007q1 2009q3 2012q1

Mineral Rights Distance to Processing Plant



Instrumental Variables Results

Table: Estimating the Effect of Well Construction

Panel A: Employment Panel B: Wages

(1) (2) (3) (4) (5) (6)Overall Mining Non-Mining Overall Mining Non-Mining

Instrumental Variables:

Newy Constructed 0.027*** 0.055*** 0.022*** 0.027*** 0.020** 0.023***Wells (0.008) (0.020) (0.008) (0.009) (0.009) (0.008)F-Stat [15.98] [7.56] [15.83] [135.7] [26.7] [147.9]J-test pvalue [.59] [.27] [.55] [.93] [.34] [.68]

OLS:Newy Constructed Wells 0.004*** 0.016*** 0.003** 0.003*** 0.004*** 0.002***

(0.001) (0.003) (0.001) (0.001) (0.001) (0.001)Reduced Form:Distance Processing Plant x Post 2005 -0.026*** -0.079** -0.022*** -0.020*** -0.045*** -0.015***

(0.006) (0.033) (0.006) (0.004) (0.012) (0.003)Mineral Rights x Post 2005 0.466*** 0.802* 0.369*** 0.471*** 0.212 0.422***

(0.112) (0.470) (0.080) (0.138) (0.164) (0.099)County FE Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes YesObservations 11568 11568 11568 11241 11223 11237Counties 340 340 340 340 340 340

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient ”Common Trends” istesting whether the coefficient for the year 2000 is statistically different from the coefficient for 2004. Significance levels are indicatedas * 0.10 ** 0.05 *** 0.01. Standard errors in the parentheses are clustered at the state level.



Preliminary Summary

1. Significant Employment Gains and higher nominal wages.

2. Findings from Intention-to-Treat exercise confirmed using thewell-construction and IV estimation method.

3. Estimate imply that there are between 2-5 non-mining jobs created,for every mining sector job.

4. Explore now the micro-mechanisms that explain local employmentdynamics.

I Downstream linkages: mining sector input demand linkagesI Output linkages: upstream sectors benefiting from energy produce?I Tradeability versus non-tradability: Dutch disease mechanismsI Local Demand Effect: Local income shock generates demand shock.



Some Robustness

1. Common trends appears to hold

2. Robustness to treatment- / control group assignment

3. Some other unobservable may drive growth in some sectors, e.g. (1)exposure to the financial crisis and (2) the stimulus programs in theaftermath.

4. Other Mining activity may drive results.

5. Robustness to Employment Data non-disclosure constraints.

6. Matching-estimators yield similar results


From Macro Facts to Micro Fracturing An Anatomy of the Sector Specific Effects

An Anatomy of the Sector Specific Effects

Table: Three Digit Sector Employment Effects

Agriculture Mining and Utilities114 Fishing, Hunting and Trapping 0.046 922 Support Activities for Mining 0.303***112 Animal Production 0.006 923 Oil and Gas Extraction 0.141***111 Crop Production -0.112*** 926 Mining (except Oil and Gas) 0.032113 Forestry and Logging -0.097*** Utilities -0.023*

Construction Transportation237 Heavy and Civil Construction 0.105*** 484 Truck Transportation 0.089***236 Construction of Buildings 0.087*** 486 Pipeline Transportation 0.048238 Specialty Trade Contractors 0.084*** 488 Support Activities for Transportation -0.033

491 Postal Service -0.165**

Manufacturing Retail and Wholesale Trade332 Fabricated Metal Product 0.042* 444 Building Material and Supplies Dealers 0.069***339 Miscellaneous Manufacturing -0.079** 447 Gasoline Stations 0.02*336 Transportation Equipment -0.088* 445 Food and Beverage Stores -0.052***

454 Nonstore Retailers -0.061**

Financial, Insurance, Real Estate Education, Social Service, Health Care532 Rental and Leasing Services 0.122*** 621 Ambulatory Health Care Services 0.03**

515 Broadcasting (except Internet) -0.052* 611 Educational Services -0.014.524 Insurance Carriers and Related -0.032** 623 Nursing and Residential Care Facilities -0.021.

Public Sector Hospitality, Recreation and Other922 Justice, Public Order, and Safety 0.12*** 721 Accommodation 0.079***923 Admin of Human Resource Programs 0.116*** 722 Food Services and Drinking Places 0.029***926 Admin of Economic Programs 0.086*** 813 Religious, Grantmaking, Civic and similar -0.025.

814 Private Households -0.122***

Notes: The coefficients are obtained from separate regressions of the log of employment in a given sector on a set of treatment

dummies interacted with time. The standard errors were clustered at state level, significance levels are indicated as . 0.20 * 0.10 **

0.05 *** 0.01.


From Macro Facts to Micro Fracturing An Anatomy of the Sector Specific Effects

An Anatomy of the Mechanisms

I Tradability

I Energy Intensity

I Labour Intensity

I Labour Mobility and Supply of Housing

I Land Intensity: Dutch disease in agriculture?


From Macro Facts to Micro Fracturing Local Production Creates Distinct Local Shocks

Summary

I Employment gains concentrated in three broad categories: (1) Sectorssupplying inputs to mining, (2) Local Service sectors and (3) Publicsector.

I Overall effects imply direct (resource extraction) employment gainsbetween 60,000 - 80,000 and indirect gains between 300,000 -400,000.

I No systematic evidence suggesting Dutch disease style contraction

I There is no evidence of disproportionate gains to energy intensivesectors at the location of production.


From Resource Extraction to Energy Consumption

Table of Contents

Introduction

Data



Conclusion


From Resource Extraction to Energy Consumption Disturbing a Historical Equilibrium

Where do the Resources Flow?

I Almost no evidence of localised up-stream linkages: energy is verymuch tradable.

I General expectation is that the price of energy be equalised acrosslocations due to Law of One Price.

I But why did natural gas prices diverge?

I Who is affected by the energy supply shock?



Measuring Supply Competition (1)

Figure: Stylised Representation of US Pipeline Network Capacity in 2002

Transition - Matrix.




Examples:

1. Binding Pipeline Constraint: Production in Wyoming in 2000:QWY ,2000 = 3568.868 mil cubic feet per dayMaximal feasible flow from Wyoming to North Dakota:

ΦWY ,ND,t = min{min(362, 2485) + min(27, 53), 3568.868} = 389

2. Binding Production Constraint: Production in North Dakota in2000: QND,2000 = 152.6703 mil cubic feet per dayMaximal feasible flow from North Dakota to Montana:

ΦND,MT ,t = min{268 + min(2429, 85, 362), 152.6703} = 152.6703




Denote Fsj the maximal feasible flow from state j to state s and Qjt theproduction in state j .Then the overall potential supply in state s is:

Φst =∑j

min{Fsj ,Qjt}

This is a reduced form measure of the extent of supply that can reachstate s. Use this to predict prices in state s:

∆Pst = αs + βt + η ×∆Φst + εst

where Pst are natural gas prices, η measures the elasticity of prices withrespect to predicted inflow.




Exploit heterogeneity across counties in exposure to state-level measureΦst by interacting with a measure Gcs that varies across counties within astate.

∆Pcst = αs + βt + η × Gcs ×∆Φst + ξ ×∆Φst + εcst

as measure Gcs use the share of households which are connected to pipedutility gas ( Common Trends ).



Predicting Prices

Table: Explaining Natural Gas Price Differences

State Level County Level

(1) (2) (3) (4) (5) (6) (7)Base Placebo Base Pipeline Placebo

Gas Connectivity ×∆Φ -0.615*** -0.655** 0.038 -0.089(0.199) (0.278) (0.232) (0.280)

∆Φ -0.802*** -0.061 -0.109 -0.301*** -0.005(0.205) (0.135) (0.115) (0.092) (0.114)

Pipeline Density ×∆Φ -0.343(0.219)

State FE Yes Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes . Yes Yes .State Time FE . . No Yes No No YesObservations 1803 1803 27528 27528 27528 26290 26389Number of Groups 46 46 691 691 691 690 690

Notes: The dependent variable is the proportional change in natural gas prices. Significance levels are indicated as * 0.10** 0.05 *** 0.01. Standard errors in the parentheses are clustered at the county level.



Reduced Form Specification

Reduced form estimation:

∆Employmenticst = aic + bst + ξ × Gcs ×∆Φst + νicst

where ξ measures the elasticity of employment in sector i with respect tothe reduced form measure of supply competition.



Natural Gas Price Elasticity

Table: Effects of Natural Gas Prices

Panel A: Overall Employment Panel B: Sector Specific

(1) (2) (3) (4)Overall Non-Shale Counties Manufacturing Financial Services

Instrumental Variables:

∆ Natural Gas -0.357*** -0.341*** -0.630** 0.103Price (0.121) (0.107) (0.291) (0.148)

OLS:∆ Natural Gas Price 0.002 0.002 0.015 -0.004

(0.003) (0.004) (0.013) (0.009)Reduced Form:Gas Connectivity ×∆Φ 0.242*** 0.187* 0.068 0.068

(0.085) (0.101) (0.235) (0.218)∆Φ 0.020 0.020 -0.090 -0.090

(0.073) (0.065) (0.098) (0.114)County FE Yes Yes Yes YesTime FE Yes Yes Yes YesObservations 27395 21168 27335 27335States 43 42 43 691

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient”Common Trends” is testing whether the coefficient for the year 2000 is statistically different from thecoefficient for 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors in theparentheses are clustered at the state level.



Instrumental Variables and Reduced Form Results

Table: Baseline Reduced Form Results

(1) (1)

Sector IV Reduced Form

Overall -0.316*** 0.178***(0.094) (0.060)

Agriculture -0.159 0.108(0.217) (0.212)

Mining -0.22 -0.005(0.314) (0.326)

Utilities -0.335* 0.372*(0.195) (0.192)

Construction -0.584*** 0.47***(0.212) (0.179)

Manufacturing -0.544*** 0.288**(0.202) (0.139)

Wholesale Trade -0.04 -0.083(0.175) (0.186)

Retail Trade -0.208** 0.097(0.100) (0.117)

Transportation -0.229 0.378**(0.230) (0.176)

Educational Services 0.014 0.012(0.096) (0.123)

Health Care -0.017 -0.182*(0.109) (0.109)

Recreation, Sports, Arts and Related Activities -0.122 0.387*(0.220) (0.208)

Accomodation and Food Services -0.145 -0.155**(0.138) (0.092)

Other Services -0.152 -0.005(0.159) (0.176)

Public Administration -0.059 0.02(0.119) (0.122)

Notes: Column (1) presents results from instrumenting natural gas prices with thestate-level predicted inflow Φst and the interaction Gcs × Φst . Column (2) is thecoefficient on the the interaction Gcs × Φst from a reduced form. All regressioninclude time fixed effects, significance levels are indicated as * 0.10 ** 0.05 *** 0.01.Standard errors in the parentheses are clustered at the state level.



Results By Sector and Energy Cost Share

Crop Production

Animal Production

Forestry and Logging

Fishing, Hunting and Trapping

Support Activities for Agriculture and Forestry

Oil and Gas ExtractionMining (except Oil and Gas)Support Activities for Mining

Utilities

Food Manufacturing

Beverage and Tobacco Product ManufacturingTextile Mills

Textile Product Mills

Apparel Manufacturing

Leather and Allied Product Manufacturing

Wood Product Manufacturing

Paper Manufacturing

Printing and Related Support ActivitiesPetroleum and Coal Products Manufacturing

Chemical Manufacturing

Plastics and Rubber Products Manufacturing

Nonmetallic Mineral Product ManufacturingPrimary Metal Manufacturing

Fabricated Metal Product Manufacturing

Machinery Manufacturing

Computer and Electronic Product Manufacturing

Electrical Equipment, Appliance, and Component ManufacturingTransportation Equipment Manufacturing

Furniture and Related Product Manufacturing

Miscellaneous Manufacturing

Air Transportation

Rail Transportation

Truck TransportationTransit and Ground Passenger Transportation

Pipeline Transportation

Postal Service

Couriers and Messengers

Warehousing and Storage

Publishing Industries (except Internet)

Motion Picture and Sound Recording Industries

Broadcasting (except Internet)

Telecommunications

Data Processing, Hosting and Related Services

Other Information Services

Credit Intermediation and Related Activities

Securities, Commodity Contracts, and Other Financial Investments and Related ActivitiesInsurance Carriers and Related Activities

Funds, Trusts, and Other Financial Vehicles

Real Estate

Rental and Leasing Services

Lessors of Nonfinancial Intangible Assets (except Copyrighted Works)

Professional, Scientific, and Technical Services

Administrative and Support Services

Waste Management and Remediation Services

Educational ServicesAmbulatory Health Care Services

Hospitals

Nursing and Residential Care FacilitiesSocial AssistancePerforming Arts, Spectator Sports, and Related Industries

Museums, Historical Sites, and Similar Institutions

Amusement, Gambling, and Recreation Industries

Accommodation

Food Services and Drinking PlacesRepair and Maintenance

Personal and Laundry Services

Religious, Grantmaking, Civic, Professional, and Similar Organizations0.0

0.5

1.0

0.00 0.02 0.04 0.06Energy Cost Share

Est

imat

ed E

last

icity

significance

aaaa

*

**

***

Figure: Estimated Elasticity as a Function of Energy Cost Share



Robustness

I Common trends with respect to connectivity measure.

I Similar results using alternative identification strategy usingManufacturing Energy Consumption Survey

I Are results driven by lower electricity prices? (LINK)



Summary of Results

I There are strong-upstream linkages but they manifest themselves onlyin places that are strongly exposed to the energy boom.

I Concentration of effects in energy intensive manufacturing sector.

I Does fracking have the potential to revive the manufacturing sector?

I Strong positive complementarity of energy- and labour inputs.


Conclusion

Table of Contents

Introduction

Data



Conclusion


Conclusion

Conclusion

I First paper to evaluate the economic impact of the recent energyboom from the resource extraction stage to the resource consumptionstage.

I Highlights strong degree of link between industrial employment andavailability of energy inputs.

I Further work: evaluate consumption effects of lower energy prices.How do firms respond?

I Estimate the environmental impact: substitution away from coal/ oiltowards natural gas for electricity generation could yield possibly highreductions in CO2 emissions.

I Estimate value of amenity destruction through housing prices?


Regulatory Exemptions in 2005

Energy Policy Act of 2005:

SEC. 322. HYDRAULIC FRACTURING.Paragraph (1) of section 1421(d) of the Safe Drinking Water

Act (42 U.S.C. 300h(d)) is amended to read as follows:(1) UNDERGROUND INJECTION.The term underground

injection (A) means the subsurface emplacement of fluids by wellinjection; and (B) EXCLUDES

(i) the underground injection of natural gas for purposes ofstorage; and (ii) the underground injection of fluids or proppingagents (other than diesel fuels) pursuant to hydraulic fracturingoperations related to oil, gas, or geothermal production activities.

Back


Regulatory Exemptions in 2005

Changing definition of what a pollutant is considered to be:Clean Water Act: SEC. 502 [33 U.S.C. 1362]

The term ”pollutant” means dredged spoil, solid waste,incinerator residue, sewage, garbage, sewage sludge, munitions,chemical wastes, biological materials, radioactive materials, heat,wrecked or discarded equipment, rock, sand, cellar dirt andindustrial, municipal, and agricultural waste discharged intowater. This TERM DOES NOT MEAN (A) ”sewage fromvessels” within the meaning of section 312 of this Act; or (B)water, gas, or other material which is injected into a wellto facilitate production of oil or gas..

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Sector Specific Employment DynamicsFigure: Sector-Specific Employment Dynamics

Agriculture Mining Utilities Construction

-.2-.1

0.1

2002q1 2004q3 2007q1 2009q3 2012q1

-.10

.1.2

.3

2002q1 2004q3 2007q1 2009q3 2012q1

-.1-.05

0.05

2002q1 2004q3 2007q1 2009q3 2012q1

-.05

0.05

.1.15

.2

2002q1 2004q3 2007q1 2009q3 2012q1

Manufacturing Wholesale Trade Retail Trade Transportation

-.04

-.02

0.02

.04

.06

2002q1 2004q3 2007q1 2009q3 2012q1

-.05

0.05

.1

2002q1 2004q3 2007q1 2009q3 2012q1

-.02

0.02

.04

2002q1 2004q3 2007q1 2009q3 2012q1

-.05

0.05

.1.15

.2

2002q1 2004q3 2007q1 2009q3 2012q1

Finance & Insurance Real Estate Health Care Accommodation

-.04

-.02

0.02

.04

2002q1 2004q3 2007q1 2009q3 2012q1

-.05

0.05

.1

2002q1 2004q3 2007q1 2009q3 2012q1

-.04

-.02

0.02

.04

2002q1 2004q3 2007q1 2009q3 2012q1

-.05

0.05

.1

2002q1 2004q3 2007q1 2009q3 2012q1

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Treatment and Control Assignment


Reallocation Effects Spatial Spillovers


Shale × 2012 0.034** 0.198** 0.025* 0.044*** 0.307*** 0.031**(0.016) (0.093) (0.015) (0.014) (0.086) (0.013)

Shale × 2011 0.029* 0.131* 0.022 0.035*** 0.245*** 0.025*(0.017) (0.078) (0.017) (0.013) (0.076) (0.013)

Shale × 2010 0.026 0.178** 0.019 0.027** 0.232*** 0.018(0.016) (0.084) (0.017) (0.012) (0.079) (0.012)

Shale × 2009 0.022 0.149* 0.017 0.021* 0.204*** 0.013(0.017) (0.080) (0.017) (0.012) (0.073) (0.013)

Shale × 2008 0.016 0.112 0.012 0.015 0.159** 0.007(0.018) (0.074) (0.017) (0.013) (0.064) (0.013)

Shale × 2006 0.013 0.047 0.010 0.011 0.082 0.005(0.013) (0.063) (0.013) (0.010) (0.055) (0.011)

Shale × 2004 0.016* 0.020 0.014 0.009 0.021 0.008(0.009) (0.057) (0.010) (0.007) (0.048) (0.007)

Shale × 2002 0.006 0.022 0.006 0.007* 0.031 0.007*(0.004) (0.034) (0.005) (0.004) (0.023) (0.004)

County FE Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes YesObservations 36377.00 36377.00 36377.00 61270.00 61270.00 61270.00States 34 34 34 39 39 39

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient”Common Trends” is testing whether the coefficient for the year 2000 is statistically different from thecoefficient for 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors in theparentheses are clustered at the state level.

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Financial Crisis and Recovery


Convergence & Mining Crisis and Recovery Population Controls

(1) (2) (3) (4) (5) (6)Convergence Other Mining Financial Sector Recovery Share Employed Employment

Shale × 2012 0.062*** 0.054*** 0.053*** 0.053*** 0.014*** 0.071***(0.019) (0.020) (0.017) (0.020) (0.005) (0.021)

Intial GDP × 0.144***2012 (0.032)

Other Mining 0.001× 2012 (0.125)

Financial Crisis 1.570***× 2012 (0.327)

Stimulus Spending 0.001per Capita (0.001)

Shale × -0.001*Population Density × 2012 (0.000)

Common Trends -0.01 -0.01 -0.01 -0.01 -0.00 -0.02(0.01) (0.01) (0.01) (0.01) (0.00) (0.01)

County FE Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes YesObservations 129706 129706 125878 129074 129706 88584States 46 46 45 46 46 46

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient ”Common Trends” is testingwhether the coefficient for the year 2000 is statistically different from the coefficient for 2004. Significance levels are indicated as * 0.10 **0.05 *** 0.01. Standard errors in the parentheses are clustered at the state level.

BackThiemo Fetzer (LSE) Fracking Growth October 4, 2013 56 / 78

Homestead Act Mineral Reservations (1)Homestead Acts were several United States federal laws that gave anapplicant ownership of land, typically called a ”homestead”, at little or nocost.

1. Prior to 1916: various homestead acts gave surface rights as well asmineral-rights.

2. Post 1916: Stock-Raising Homestead Act separated Mineral Rightsfrom Surface rights to preserve coal.

3. Splitting of estates not endogenous to oil finds as shale deposits werediscovered only later, especially natural gas.

4. Strong evidence that resource extraction companies focus on theseareas since (1) they are already connected to some form ofinfrastructure and (2) mineral rights are considered the dominantestate, their interests take precedence over the rights of the surfaceowner.

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Homestead Act Mineral Reservations (2)

Figure: Location of each Homestead Granted between 1900 - 1938

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Figure: Location of each Homestead with Mineral Rights retained by Statebetween 1900 - 1938

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Well Construction Mining Employment Non Mining Employment

(1) (2) (3) (4) (5) (6)

Homestead Mineral 60.895*** 59.988*** 1.082** 1.060** 0.615*** 0.607***Reserves on Shale x 2012 (19.277) (18.702) (0.478) (0.459) (0.139) (0.124)

Distance to Gas -3.300** -0.231* -0.102***Processing Plant x 2012 (1.449) (0.123) (0.031)

County FE Yes Yes Yes Yes Yes YesTime FE Yes Yes Yes Yes Yes YesObservations 6138 6138 5586 5586 5664 5664States 153 153 149 149 149 149

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient”Common Trends” is testing whether the coefficient for the year 2000 is statistically different from the coefficientfor 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors in the parentheses areclustered at the state level.

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Non-Disclosure ConstraintsTwo hard-non disclosure conditions lead to missing data or data withsignificant amounts of noise infused:

1. At the geography x sector level, there be at least 3 different employersin a given sector.

2. At the geography x sector level, there be at least 10 employees.

Data generating process can be described as:

E ∗it = φ(f (Eit))

where f is the data filter function:

f (Eit) =

{1 if Eit < τ

missing if Eit ≥ τ

and φ() is the noise-infusion function. Estimate a two-stageHeckman-Selection Model to address this. Back


Non-Disclosure Constraints


Mahalonobis Matching yields similar results

Matching on natural gas connectivity, initial GDP per capita, initialpopulation density and population.


Panel A: Employment Effects Panel B: Wages


Treatment x 2012 0.042*** 0.331*** 0.026** 0.032*** 0.098*** 0.023***(0.012) (0.064) (0.012) (0.008) (0.026) (0.007)

Treatment x 2001 0.003 0.076** -0.000 0.001 0.028* -0.001(0.006) (0.035) (0.006) (0.004) (0.016) (0.004)

Common Trends -0.01 -0.05 -0.01 -0.01 -0.01 -0.01(0.01) (0.05) (0.01) (0.00) (0.02) (0.00)

County FE Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes YesObservations 89383 89383 89383 88296 88271 89383Counties 1906 1906 1906 1906 1906 1906

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient”Common Trends” is testing whether the coefficient for the year 2000 is statistically different from the coeffi-cient for 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors in the parenthesesare clustered at the state level.

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Well Construction and Production in North DakotaCounty level well-construction data correlated with county level oil and gasproduction.

0.0e+00

5.0e+06

1.0e+07

1.5e+07

2.0e+07

0 50 100 150Newly Constructed Wells

Oil

Pro

duct

ion

(Bar

rels

)

Figure: Correlation between Production and Well Construction

Back


Well Construction Across the USData on will-drilling activity between 2000-2012 obtained from variousstate-level sources. Unbalanced panel. Each dot indicates the location of asingle well.

Figure: Location of Newly Constructed Natural-Gas and Oil Wells mewl

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Limited Scope for Dutch disease: Labour Mobility


Employment Earnings


Shale x After 2012 x 0.077*** 0.349*** 0.051*** 0.085*** 0.157*** 0.050***Workers younger 24 (0.014) (0.070) (0.014) (0.011) (0.034) (0.010)

Shale x After 2012 x 0.071*** 0.219*** 0.040*** 0.040*** 0.060*** 0.030***Less than high school (0.013) (0.061) (0.013) (0.008) (0.023) (0.008)

Shale x After 2012 x 0.058*** 0.176*** 0.018 0.038*** 0.062*** 0.029***High school or equivalent, no college (0.011) (0.061) (0.011) (0.007) (0.023) (0.007)

Shale x After 2012 x 0.052*** 0.196*** 0.023** 0.028*** 0.033 0.021***Some college or Associate degree (0.011) (0.061) (0.011) (0.007) (0.024) (0.007)

Shale x After 2012 x 0.025** 0.218*** 0.003 0.014** 0.033 -0.001Bachelor’s degree or advanced degree (0.011) (0.060) (0.012) (0.007) (0.027) (0.008)County x Education Group FE Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes YesObservations 586247 234458 306147 571829 375433 306147States 2525 1677 2251 2525 2219 2251

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient ”Common Trends”is testing whether the coefficient for the year 2000 is statistically different from the coefficient for 2004. Significance levels areindicated as * 0.10 ** 0.05 *** 0.01. Standard errors in the parentheses are clustered at the state level.

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Energy-, Labour Intensity and Tradability

Table: Energy Intensity and Labour Costs Shares

Separate Interactions Putting together

(1) (2) (3) (4) (5) (6)Utility Intensity Labour Intensity Tradable Combined Post 2008 res22

Utility Cost Share x Shale x Post 2008 -0.036 -0.095 -0.245**(0.065) (0.067) (0.100)

Labour Cost Share x Shale x Post 2008 -0.123*** -0.082*** -0.107*** -0.109***(0.022) (0.024) (0.025) (0.024)

Tradable x Shale x Post 2008 0.015* 0.009 -0.014(0.008) (0.009) (0.018)

Utility Cost Share x Post 2008 0.040 0.284*** 0.518***(0.036) (0.037) (0.059)

Labour Cost Share x Post 2008 0.335*** 0.230*** 0.435*** 0.290***(0.012) (0.015) (0.015) (0.013)

Tradable x Post 2008 -0.127*** -0.098*** 0.045***(0.005) (0.005) (0.010)

Shale x Post 2008 0.011** 0.053*** 0.009** 0.038*** 0.045*** 0.047***(0.005) (0.009) (0.004) (0.010) (0.010) (0.010)

post08shaleutilitywage 1.242*(0.693)

post08shaletradablewage 0.090(0.068)

County x Industry FE Yes Yes Yes Yes Yes YesState x Time FE Yes Yes Yes Yes Yes YesObservations Yes Yes Yes Yes Yes YesNumber of Groups 6314065 6420784 8017873 6314065 6314065 6420784N g 2675 2675 2675 2675 2675 2675

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient ”Common Trends” is testing whether thecoefficient for the year 2000 is statistically different from the coefficient for 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standarderrors in the parentheses are clustered at the state level.

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Dutch disease in the Agricultural Sector?

I Mining activity directly hurts the agricultural sector due to (1)increased land use, (2) adverse effect of mining on land productivity(due to land fragmentation, environmental degradation), (3)increased factor prices.

I Some anecdotal evidence that mining sectors hurt dairy and cattleindustry in Pennsylvania and Wyoming especially due to methanecontamination of well-water.

I Find evidence that agricultural land rental rates are lower shale-playcounties.

I This could reflect... (1) general equilibrium downward adjustment infactor price due to higher labour cost (2) reflect reduced productivityof agricultural land.

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(1) (2) (3) (4) (5)All Rental Types Irrigated Non-irrigated Pasture Property Values

Shale x 2013 -0.047** -0.058 -0.049** -0.021(0.022) (0.039) (0.023) (0.033)

Shale x 2009 0.009 -0.056** 0.005 0.008(0.017) (0.023) (0.017) (0.030)

Shale x 2007 -337.111**(168.193)

Shale x 2002 -98.468(102.264)

County x Rental Type FE Yes Yes Yes Yes YesTime FE No No No No YesState x Time FE Yes Yes Yes Yes NoObservations 22681 2721 10819 9141 7931States 48 43 46 45 48

Notes: The dependent variable is log of employment in the sector given in the column head. The coefficient”Common Trends” is testing whether the coefficient for the year 2000 is statistically different from the coefficientfor 2004. Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors in the parentheses areclustered at the state level.

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I Mining activity directly hurts the agricultural sector due to (1)increased land use, (2) adverse effect of mining on land productivity(due to land fragmentation, environmental degradation), (3)increased factor prices.

I Some anecdotal evidence that mining sectors hurt dairy and cattleindustry in Pennsylvania and Wyoming especially due to methanecontamination of well-water.

I Find evidence that agricultural land rental rates are lower shale-playcounties.

I This could reflect... (1) general equilibrium downward adjustment infactor price due to higher labour cost (2) reflect reduced productivityof agricultural land.

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Limited Scope for Dutch disease due to factor mobility

I Classical Dutch-disease arguments posit that sectors with non-mobilefactors of production suffer. Two main factors: land and labour.

I Manufacturing sector seems not to suffer due to high labour mobilityacross locations.

I Further insurance to wage appreciation due to imperfect labourmobility across sectors.

I Some evidence that agricultural sector suffers potentially due to landbecoming less productive for large-scale farming operations.

Land Rental Rates .


Limited Scope for Dutch disease: Labour Mobility


Overall Population Age Cohort Effects

(1) (2) (3) (4)Overall Male Share Overall Male Share

Shale × 2002 0.002 0.000(0.001) (0.000)

Shale × 2012 0.012* 0.002***(0.006) (0.001)

Shale × 2012 0.009 0.000× Younger than 20 (0.007) (0.001)

Shale × 2012 0.028*** 0.003× 20 to 30 (0.010) (0.002)

Shale × 2012 0.028*** 0.004***× 30 to 40 (0.008) (0.001)

Shale × 2012 0.016** 0.001× 40 to 50 (0.008) (0.001)

Shale × 2012 0.003 0.002*× 50 to 60 (0.007) (0.001)

Shale × 2012 0.000 0.001× 60 to 70 (0.008) (0.001)

Shale × 2012 0.008 0.003***× Older than 70 (0.008) (0.001)

County x Age Group FE Yes Yes Yes YesState x Time FE Yes Yes Yes YesObservations 577515 577467 577515 577467States 2674 2674 2674 2674

Notes: The dependent variable is log of employment in the sector given in the columnhead. The coefficient ”Common Trends” is testing whether the coefficient for theyear 2000 is statistically different from the coefficient for 2004. Significance levels areindicated as * 0.10 ** 0.05 *** 0.01. Standard errors in the parentheses are clusteredat the state level.

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Transition Matrix

ALARAZCACOCTDCDEFLGAIAIDILINKSKYLAMAMDMEMIMNMOMSMTNCNDNENHNJNMNVNYOHOKORPARISCSDTNTXUTVAWAWIWVWY

AL

AR AZ

CA

CO CT

DCDE FL GA IA ID IL IN KSKY LA MA

MD

ME MI

MN

MOMS

MT

NCNDNE

NH NJ

NM NVNY

OHOK

OR PA RI

SCSD TN TXUTVA

WA WI

WV

WY

0

5000

10000

15000

20000value

Figure: Transition Matrix to Map Pipeline Capacity Across the US in 2002

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Common Trends with Respect to Connectivity Measure

-.1-.05

0.05

.1

2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 2012q1

Figure: Employment and Connectivity to the Natural Gas Grid Over Time

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Alternative Identification: Natural Gas ProductionIntensity (Reduced Form) (1)

Use data on Natural Gas Intensity in Manufacturing Sectors across UScensus regions.Core Idea: places that used relatively more natural gas to produce thesame goods in different locations in the US should benefit more for anygiven price drop.

I Define a benchmark natural gas intensity for production in sector i asb∗i = maxr bir .

I Construct a measure of “distance” to benchmark natural gas intensityas nir = bir − b∗

i .

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Alternative Identification: Natural Gas ProductionIntensity (Reduced Form) (2)Difference in Difference Plot:

-.8-.6

-.4-.2

0

2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 2012q1

Figure: Effect of Sector Production Technology Distance to Natural GasBenchmark on Employment.

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Alternative Identification: Natural Gas Production Intensity

∆Employmenticst = αic + βst + η ×∆Φstnir + νicst

Table: Exploiting Technological Path Dependence

(1) (2) (3)Base Industry Region and Industry

Distance to Benchmark x ∆Φ -0.768*** -0.277* -0.262*(0.153) (0.159) (0.158)

∆Φ 0.147*** 0.168*** 0.090**(0.037) (0.033) (0.036)

Industry x County FE Yes Yes YesRegion x Time FE Yes No YesIndustry x Time FE No Yes YesObservations 1218345 1218345 1218345Number of Groups 3044 3044 3044

Notes: Significance levels are indicated as * 0.10 ** 0.05 *** 0.01. Standard errors inthe parentheses are clustered at the county level.

Back


LiteratureAragon, F. and J. Rud (2013). Natural resources and local communities:

evidence from a Peruvian gold mine. American Economic Journal:Economic . . . , 1–31.

Black, D., T. McKinnish, and S. Sanders (2005). The Economic Impact ofthe Coal Boom and Bust. The Economic Journal (March 2004).

Deschenes, O. (2010). Climate Policy and Labor Markets.Greenstone, Michael, R. H. and E. Moretti (2010). Identifying

Agglomeration Spillovers: Evidence from Million-Dollar Plants. Journalof Political Economy 118(3), 536–598.

Kahn, M. E. and E. T. Mansur (2013, May). Do local energy prices andregulation affect the geographic concentration of employment?, Volume101.

Marden, S. H. (2013). You can grow your own way : Chinese agriculturalreforms and the role of agriculture in development.

Mehlum, H., K. Moene, and R. Torvik (2006). Institutions and theResource Curse. The Economic Journal 116(2001), 1–20.

Mian, A., A. Sufi, and F. Trebbi (2012). Foreclosures, House Prices, andthe Real Economy. mimeo.

Moretti, E. (2010a). Local Labor Markets. NBER Working Paper 15947.Moretti, E. (2010b, May). Local Multipliers. American Economic

Review 100(2), 373–377.Voigtlander, N. (2013). Skill Bias Magnified : Intersectoral Linkages and

White-Collar Labor Demand in U . S . Manufacturing. pp. 1–35.


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