Transcript Rising Inequality in an Era of Austerity: The Case of the USA

Local Labor Market Restructuring in
Shale Booms
By
Amanda Weinstein
[email protected]
Nov. 26, 2012
Outline
 Introduction to Shale
 Regional Shocks and Natural Resource Booms
 Methodology
 Results
 Conclusions
Motivation
 Commenting on shale energy development, Aubrey McClendon CEO
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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
350
300
250
200
Employment Growth
Dallas, TX
Houston, TX
Tulsa, OK
Casper, WY
Williams, ND
US
150
50
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
100
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
2001-2011 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 2005 - 2011
 Define boom period by state
Boom Periods by State
State
Arkansas
Colorado
Indiana
Kansas
Kentucky
Louisiana
Mississippi
Montana
New Mexico
New York
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
West Virginia
Wyoming
Boom Period
2005
2003
2004
2004
2005
2005
2005
2002
2004
2004
2003
2011
2004
2004
2005
2004
2004
2004
2003
2002
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
N
2000 Population
2000 Percent College
2000 Unemployment Rate (%)
Percent Poverty
2001 Employment
2001 Earnings (million dollars)
2001-2005 Employment Growth
2005-2011 Employment Growth
2001-2005 Earnings Growth
2005-2011 Earnings Growth
Boom
Counties
Non-Boom
Counties
P-Value
455
103,369
0.1510
5.7839
0.1597
51,170
1,784
0.0265
0.0613
0.1880
0.3304
2,605
86,524
0.1487
5.2581
0.1385
39,393
1,389
0.0138
-0.0274
0.1626
0.1394
0.2130
0.5199
0.0001
0.0001
0.1270
0.2185
0.0078
0.0001
0.0009
0.0001
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
 Yit is Δln(employment or earnings)
Parameter of interest
County 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%
Variables ΔLN(Employment) ΔLN(Earnings)
0.0006
-0.0037**
Boom Period
(0.0009)
(0.0015)
-0.1711***
-0.3756***
Boom County
(0.0401)
(0.0710)
0.0159***
0.03084***
Period*County
(0.0016)
(0.0028)
0.0379**
0.0543*
Ln(Popn)
(0.0158)
(0.0280)
4.3709***
8.4158***
College
(0.6891)
(1.2186)
Industry Controls
Y
Y
County FE
Y
Y
R2
0.1699
0.1047
R2-Adj
N
0.0773
30,600
0.0047
30,600
Difference-in Difference with Trends
 From Greenstone et al. 2010 (large plant openings)
 When β2 = β3 = β5 = β7 = 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
Variables ΔLN(Employment) ΔLN(Earnings)
0.0156***
0.0419***
Boom Period
(0.0018)
(0.0032)
-0.1704***
-0.3628***
Boom County
(0.0398)
(0.0698)
0.0260***
0.0595***
Period*County
(0.0038)
(0.0067)
-0.0023***
-0.0049***
Trend
(0.0001)
(0.0002)
-0.0010***
-0.0041***
Trend*Period
(0.0002)
(0.0004)
0.0068***
0.0163***
Trend*County
(0.0010)
(0.0018)
-0.0062***
-0.0156***
Trend*Period*County
(0.0011)
(0.0019)
0.0350**
0.0489*
Ln(Popn)
(0.0157)
(0.0274)
4.1234***
7.6203***
College
(0.6829)
(1.1966)
Industry Controls
Y
Y
County FE
Y
Y
R2
0.1871
0.139
R -Adj
N
0.0963
30,600
0.0427
30,600
2
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 probusiness 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
Variables
First Stage
ΔLN(Earnings)
ΔLN(Earnings)
0.3298***
(0.0133)
0.3199***
(0.0245)
0.2139***
(0.0275)
0.3298***
(0.0133)
0.3199***
(0.0245)
0.2139***
(0.0275)
-0.3885
(1.0954)
1.1497
(3.4842)
-0.8491
(2.3270)
2.5242
(7.4018)
0.6707
1.4373
Industry Controls
County FE
(1.9302)
-0.6360
(1.8668)
Y
Y
(4.1005)
-1.3718
(3.9659)
Y
Y
R2
0.0024
0.00112
R -Adj
N
Hausman P-Value
-0.1091
30,600
>0.9999
-0.11052
30,600
>0.9999
using percent shale
Percent Shale
 Instrumental variables
Percent Shale*Period
coefficient estimates similar in
Trend*Period*Percent Shale
sign to previous estimates
Parameter Estimates
though not significance
Boom County
 Hausman tests suggest that
Period*County
our identification of shale
Trend*County
boom counties is not
Trend*Period*County
endogenous
2
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
Δln(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 2005
 1 additional oil and gas
worker is associated
with 0.46 additional
jobs (or a multiplier of
1.46)
 Calculations similar to
Moretti (2010)
Variables
ΔLn(Non-Oil & Gas Employment)
Boom Period
Boom County
ΔLN(Oil & Gas Emp)
Period*County*ΔLN(Oil & Gas Emp)
Ln(Popn)
College
Industry Controls
County FE
R2
R2-Adj
N
0.0036***
(0.0007)
-0.0564
(0.0396)
0.0013***
(0.0005)
0.0040***
(0.0014)
0.0354**
(0.0156)
2.1040***
(0.6803)
Y
Y
0.1618
0.0683
30,600
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
Variables
Boom Period
Boom County
ΔLN(Oil & Gas Emp)
Period*County*ΔLN(Oil & Gas Emp)
Ln(Popn)
College
Industry Controls
County FE
R2
R2-Adj
N
ΔLn(Trade Employment)
-0.0005
(0.0022)
0.0443
(0.1193)
0.0016
(0.0014)
0.0077*
(0.0042)
-0.0334
(0.0471)
1.0039
(2.0515)
Y
Y
0.1512
0.0564
30,600
ΔLn(Nontrade Employment)
0.0061***
(0.0007)
-0.0731*
(0.0383)
0.0009**
(0.0004)
0.0045***
(0.0013)
0.0538***
(0.0151)
2.1476***
(0.6579)
Y
Y
0.1765
0.0845
30,600
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 2011.
 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
Thank You
Amanda Weinstein
Graduate Research Assistant
The Swank Program in Rural-Urban Policy
Dept. Agricultural, Environmental & Development Economics
The Ohio State University
([email protected])
38
Extra Slides
Difference-in-Difference Methodology
 [E(Yb0)-E(Yn0)]-[E(Yb0)-E(Yn0)]
 The difference-in-difference approach
 Yit is Δln(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)
 Major Holders of Utica Shale Right in Ohio (April 2012)