Work, Health, and Mortality:The Case of WLEMMAs in the Shale Boom and Bust

Joseph Marchand ∗

University of AlbertaKevin Milligan †

University of British Columbia

Preliminary Draft:July, 2020

Abstract

White, lower-educated, males in middle-age (WLEMMAs) have seen broad-based de-clines in socio-economic outcomes in recent decades, sparking a debate about the fac-tors driving the decline. But not all WLEMMAs live in areas undergoing economicdecline—WLEMMAs also comprise the majority of those working in the energy ex-traction sector, an industry that has undergone strong positive and negative economicshocks over this same era. Using the timing of the boom and bust across the UnitedStates, instruments are formed to predict the strength of the local labor demand shock.The shale oil boom and bust allows for the testing of symmetric effects, which bolstersthe case for economic forces as an important factor driving the outcomes of WLEM-MAs. We find that the boom led to sharply better labor market outcomes and im-provements in health. We also find starkly different effects on mortality across ages,with younger males showing an increase and older ages a decrease in mortality duringboom years.

JEL codes: H15; J11; J21; J61; Q33; R23.

Keywords: boom; bust; health; local labor markets; middle-aged; mortality; shale.

∗Marchand: First author. Associate Professor, Department of Economics, University of Alberta, 7-29HM Tory, Edmonton, AB, T6G 2H4, Canada. E-mail: joseph.marchand@ualberta.ca.†Milligan: Corresponding author. Professor, Vancouver School of Economics, University of British

Columbia, 6000 Iona Drive, Vancouver, BC, V6T 1L4, Canada. E-mail: kevin.milligan@ubc.ca.

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1 Introduction and Background

The socio-economic outcomes of white, lower-educated, males in middle-age (WLEMMAs)have deteriorated in recent decades. Lower-educated, middle-aged (25-54) men saw declinesin real earnings and labor force participation over the last 50 years in the US, while those sameoutcomes for college-educated men increased (Binder and Bound, 2019).1 Factors include aninward labor demand shift contemporaneous with rising disability insurance uptake (Milliganand Schirle, 2019), and chnges to incarceration and family structure. Working class men havealso recently shown a general detachment from institutions, such as religion and marriage, aswell as from the workplace (Edin et al., 2019). Whites tend to feel more negative about theireconomic situation relative to their parents, (i.e. parents had more opportunities), whereasblacks and Hispanics feel more positive (they had more opportunities). Even increasing videogame quality has been argued to contribute to these trends among younger males, througha rising utility of leisure causing a reduction in their work hours (Aguiar et al., 2017).

Perhaps most worrying is that the morbidity and mortality of WLEMMAs has beenon the rise in the US, going against the declines for every other US demographic groupand also compared to the same demographic group in other developed countries, seeminglyattributable to increases in “deaths of despair” (drug overdoses, liver damage, suicides)(Case and Deaton, 2015). Coile and Duggan (2019) document slowing mortality gains,with rising morbidity and disability program enrollment that are stronger for less-educatedmales and in states with lower levels of education. There were larger declines in mortalityand incarceration for blacks and Hispanics since 2000, with no increases in suicides or theoverdose deaths that whites had. They attribute this to blacks and Hispanics having rapideducational gains relative to whites during this time. A decline in military service may alsobe related.

Opioids and other drug use has also been linked to the decline in WLEMMA outcomes.This could come from the supply side of the drug market or the demand side. Deaths andemergency department visits tend to increase during economic weakness, with the effectlargely driven by the death rates of whites (Hollingsworth et al., 2017) This runs counterto the evidence on pro-cyclical mortality first postulated by Ruhm (2000) (although Ruhm2015 shows that pro-cyclical mortality hasn’t held up in recent years). Stevens et al. (2015)had found that health care quality was the main driver for pro-cyclical mortality and thatemployment did not matter as much. Ruhm (2018) finds that changes in economic conditionsonly account for a tenth or less of the explanation behind deaths of despair, and Currie et

1Although our focus remains on lower educated males, there is also recent evidence of higher-educatedmales being displaced in the labor market, due to a rise in the demand for social skills more attributed tocollege-educated women (Cortes et al., 2019).

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al. (2019) find no relationship between opioids and the employment rates of men. Of course,poor economic conditions could have sparked the uptake in opioids, with subsequent growthno longer tied to economic conditions.

Underlying structural economic explanations for the decline in outcomes amongWLEMMAalso play a role. The declining outcomes could stem from cumulative disadvantage in thelabor market (Case and Deaton, 2017). For example, the decline of US manufacturing due toincreased exposure to international trade, mainly with China, led to long-term decreases inWLEMMA income and employment (Autor et al., 2013, 2016), as well as worsening familyoutcomes, including fertility, living circumstances, and marriage (Autor et al., 2019). Thisanalysis of US manufacturing decline has been influential in shaping views about the plightof WLEMMAs in the 21st Century. However, the extent to which these WLEMMA impactsare driven by economics versus other factors, as well as the symmetry between positive andnegative conditions, requires new evidence.

In our current study, we explore an alternative set of economic shocks in another industrypredominantly comprising WLEMMAs: the shale oil boom (and bust). We identify effectsthrough instrument sets built from novel measures of energy resource geology, energy-sectoremployment, and energy prices. Our analysis covers time intervals from the mid-to-late 2000sconventional boom, to the early-to-mid 2010s shale boom, and to the shale bust of 2015 andits wake. The focus of our paper spans labor market outcomes (employment, earnings,hours worked), public assistance outcomes (disability insurance, and other public benefits),and health-related outcomes (ranging from self-assessed health difficulties to disability tomortality). We study the magnitude of these labor demand shocks on males who are white(relative to being another race or ethnicity), less educated (relative to more educated),male (relative to female), and in middle age (relative to younger and older). The data forthese outcomes come from the American Community Survey of the Census Bureau and theWONDER data from the Centers for Disease Control and Prevention.

Previous literature has tied the boom and bust of energy resources to similar outcomes,such as research on the impact of the coal boom and bust on disability (Black et al., 2002),welfare (Black et al., 2003), education (Black et al., 2005a), and its overall effects (Black etal., 2005b). More recently, Kearney and Wilson (2017) found that the fracking boom liftedwages for less-educated men, but there was no corresponding increase in marriage. Marchand(2020) offers further hope for WLEMMAs (and others that make up the middle class) withevidence form the Canadian 1990s to 2000s energy boom, finding increased demand for theroutine manual tasks in the lower middle of the occupational distribution.

We find that white, lower-educated, middle-aged males, particularly those aged 45-54,tended to benefit the most from the energy boom, symmetric to what has been found for the

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manufacturing bust. This finding extends over a range of outcomes, beginning with employ-ment and extending all the way to their mortality. The employment gains for WLEMMAsduring booms seem to draw from those otherwise not in the labor force, rather than thoseconsidered unemployed. We also find strong effects on individuals reporting a health dif-ficulty, with a substantial decline led by WLEMMAs. Finally, for mortality, we uncoveran intriguing result: at younger ages, the oil and gas boom led to higher mortality, whileat older ages mortality declined. Our evidence suggests an important role for “deaths ofdespair” in explaining these patterns.

2 Local Economic Shocks and Instruments

Our approach uses labor demand changes generated by movements in energy prices, whichare themselves a construct of the supply and demand for energy products. There are severalways to think about and measure the abundance (or lack) of energy resources and how theyare extracted. Marchand and Weber (2018) differentiate between “dependence, such as theshare of earnings accounted for by the oil and gas sector; endowments, such as oil and gasreserves; and extraction, such as the number of wells drilled.” Each measure has advantagesand disadvantages.

In our case, for example, industry employment shares could fit as a measure of depen-dence, resource geology could represent an endowment measure, and our timing variables(wells, production, prices, earnings trends) could fit extraction. A dependence measure thathas previously been used is the total earnings in a local area coming from the industry ofenergy extraction (ex. Marchand, 2012, 2015, 2020). However, dependence and extractionmeasures may reflect local economic conditions, limiting how well they serve as exogenoussources of variation. For this reason, endowment-based measures may be preferable, as thecase for exogeneity is easier to make, with the analysis proceeding to establish a link betweenendowment measures and dependence measures, like Black et al. (2005b).

In this section, we provide background on the geographical aggregation, the resourcegeology, and the timing of production measures that comprise our instruments. We aim tobuild instruments combining a fixed resource endowment with national time-series variationin the style of Bartik (1991), as well as building upon the work of Feyrer et al. (2017) andBartik et al. (2019). These instruments allow us to characterize the local economic shocksof interest, namely the shale oil and gas boom and bust. In this section, we discuss ourdevelopment of the fixed regional measures based on resource geology, and the options weconsider for the national trend. First, though, we describe the geographical aggregations.

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Geographic Aggregation

Our data sources are linked by first aggregating each data set up to a common geographiclevel to represent local labor markets across the United States. The particular geographiclevel for the analysis will ultimately be decided by whatever best balances sufficient repre-sentation within a cell, made up of individuals by gender, age, and with or without resources,with the overall count of cells, i.e. the observations needed for the regression analysis.

For the United States, our best options include counties (based on the Federal Infor-mation Processing Standards or FIPS codes), groupings of counties or portions of countiesknown as Public Use Microdata Areas (PUMAs) or Consistent Public Use Microdata Areas(ConsPUMAs or CPUMAs), and of course, states.2 PUMAs are aggregations of countieswhen counties are small, but they can also be portions of counties for larger-populationareas, often with larger cities, in an attempt for each PUMA to represent about 100,000individuals. We use the available crosswalk files to compare across the types of geographicdefinitions, namely the FIPS to PUMA to CPUMA matches.

Given that border definitions can change over time, along with the population, thesechanges need to be harmonized relative to a given base year (or, in some cases, multiplebase years). For example, during our time period of analysis of 2005 to 2018, the PUMAdefinitions include both the 2000 boundaries, as well as the 2010 (labeled 2012) boundaries.The 2010 CPUMAs are a built-in remedy to harmonize the 2000 and 2010 boundaries. Intotal, for the lower 48 states, there are 1,066 CPUMAs, as compared with 2,336 PUMAs and3,109 FIPS.3 Some states with energy resources do not have much geographical variation.North Dakota, for example, has only 2 CPUMAs, 5 PUMAs, and 53 FIPS.

Resource Geology

Energy resource geological data for the US is available from both publicly-available andprivately-available sources. The publicly-available data comes courtesy of the US Energy In-formation Administration (EIA), providing the geographic locations of all shale plays withinthe continental US, as well as the depth and thickness of those shale plays. The selectedshale plays account for approximately 68 percent of tight oil production and 70 percent oftight/shale gas production, as of May 2019. However, there are several major shale plays forwhich the EIA has not yet published data.4

2There is also something known as a common commuting zone in the US, which is based on the CurrentPopulation Survey (CPS), but we are not using that data for this study.

3Data for the US has been omitted for Alaska, Hawaii, and Puerto Rico. With those areas included, thetotals are 1,085 CPUMAs, 2,378 PUMAs, and 3,141 FIPS.

4Examples are the Austin Chalk, Barnett, Fayetteville, Haynesville, Mississippian, Spraberry, and Wood-ford. With data for these plays, approximately 98 percent of all tight oil and gas production would be

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There are also several private resource data sources, with possible providers includingAdvanced Resources International (ARI), Enverus (formerly known as DrillingInfo and acompany that the EIA regularly cites as a provider for their data), IHS Energy, and RystadEnergy. The current study uses the Shale Well Cube data from Rystad Energy from 2020.We chose the path of purchasing a private data subscription for several reasons. First, theprivate data is more up-to-date, contains more resource measures, and has more coveragethan the publicly-available data. Second, several recent notable publications have also usedprivate data, with Feyrer et al. (2017) using data from DrillingInfo (now Enverus) and Bartiket al. (2019) also using data from Rystad Energy (from 2014).

Our study currently uses four types of resource measures from the Rystad data: coverage,depth, thickness, and the prospectivity index. The first measure of resource geology, thepercentage of coverage over a shale oil or gas play, is the simplest measure to produce, aswe are basically just seeing where two geographical boundaries, of the deposit and of thegeographical aggregation, overlap. The downside to this measure is that not all potentialplays are actually producing, or have ever produced, oil or gas.5 In addition, this measure isdependent on the other measures, in that, in order for it be non-zero, some other measureof resources must be non-zero.

The depth of a shale play within a geography is created by using sets of concentric circles.We currently use depth in two forms: the weighted average depth and the maximum (deepest)depth. Average depth is defined as the average distance in meters from the surface to the topof the formation. Depth was made relative to the surface, regardless of elevation, by dividingboth elevation and depth into 1 km square grids and then differencing each of those at thekm. We then took the median of the difference within the geographic boundary. Areas withmultiple plays are averaged. Depth is calculated as a negative, so any interpretation of aregression coefficient on depth will have to take this into account.

Shale depth provides a continuous measure of shale richness and proxies for the district’sresource endowment. Because deeper shale tends to have greater pressure, it generally hasmore productive and profitable wells (EIA/ARI, 2013). But, it is also used an indicator ofthe ratio between oil and gas, as the deeper it is, the more likely it is to be gas, and thedeeper the gas is, the higher the quality of gas, i.e. the sweeter the gas. Across the majorshale formations in the U.S., Brown et al. (2016) found that a ten percent increase in averagedepth is associated with a seven percent increase in the ultimate recovery of a typical county

accounted for.5For example, New York state has a moratorium on shale drilling, so despite being over a shale play, no

production is currently taking place there. We also do not separate out which plays are primarily producingoil and which are primarily producing natural gas. This could be done by comparing the plays that residewithin a state to production data, typically available publicly at the state level.

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well. Depth was first used in the literature as an instrumental variable in Marchand andWeber (2020), in the form of normalized shale depth, but that study only focused on Texasand only used two (oil) of the four main (oil and gas) plays in that state in most of itsspecifications.

The thickness of a shale play is also created by using sets of concentric circles. This isan indicator of the total amount of hydrocarbons (i.e. oil and gas), with thicker meaningmore. Thickness itself is relative to the top and bottom of a play (i.e. elevation is irrelevantin this case, so also no differencing is needed). Then, we took median thickness in metersacross 1 km grids within geographic boundaries. Areas with multiple plays are summed.One major advantage is that thickness can be summed across multiple plays, unlike depth.Whereas depth is calculated as a negative value, thickness is recorded as a positive value,so the regression coefficients will need to be interpreted with this in mind. This measure ofthickness was used as an alternative instrument in Marchand and Weber (2020).

The set of shale plays in the lower 48 states as of 2016 is displayed in Figure 1a. We showhow we use this to derive our coverage measure in Figure 1b, which shows the proportionof each county that is covered by a shale play. Figures 1a and 1b show the universe of allshale plays, our data currently only cover a subset of those plays, as described above. Atthe moment, we show our depth and thickness measures for seven shale plays in Figures2b through 2f, with both FIPS county and PUMA boundaries, for the Bakken (Figure 2b),Delaware and Wolfcamp (Figure 2c), Eagle Ford (Figure 2d), Nibrara (Figure 2e), and Uticaand Marcellus (Figure 2f).

The depth and thickness data are provided as contour lines, similar to how elevation isdisplayed on a standard map. This means our data is only as precise as the intervals theychoose to display (i.e. if depth is measured in 40ft intervals, we cannot know the variationbetween 40ft and 80ft, for example). So, for both depth and thickness, the raw data is inisopochs (i.e. concentric shapes), with each boundary reflecting a value in meters. Betweenthe isopoch lines, a value is interpolated using the nearest neighbor approach (i.e. under agravity model). Additionally, there is no standard interval for depth or thickness, meaningthat the data for some shale plays will be more or less precise when compared to others.

Our final measure of resource geology is something known as a prospectivity index orp-index. This prospectivity index is a novel measure, only available from Rystad Energy, ofwhat can eventually be produced from drilling a well into those resources. The 2014 versionof the prospectivity was previously used by Bartik et al. (2019). It is a weighted index ofdepth, thickness, thermal maturity, porosity, permeability, clay content, and total organiccontent. Thermal maturity, for example, is a measure of how much pressure a rock wasunder and for how long, which will be correlated with total organic content (the greater it

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is, the more oil there will be).This prospectivity index does have its weaknesses, however. For example, the p-index is

not available for all shale plays and, for those shale plays with missing measures, the weightsare automatically reallocated toward other measures, causing the weights to be differentacross plays.6 Most shale plays rely on a weighted mixture of depth, thickness, and thermalmaturity. It is also a categorical variable from 0 to 5, which introduces its own problems, butgiven that we are calculating at various geographical levels, it will appear to be a continuousvariable with a value between 0 and 5.

Timing of Production

To form our Bartik-style instrument, we need to combine a fixed regional measure with anational time trend. Here, we discuss the time trends we consider: sectoral earnings, resourceprices, and period indicators. The first variable we use is earnings among workers in the oiland gas sector.7 To calculate this trend, we include all earnings of males employed in thisindustry. We do this on a “leave out” basis separately for each state, calculating the nationaltrend excluding observations for that state. This therefore captures national trends in oiland gas earnings without being contaminated by the realization of earnings within one’s ownstate. There are currently 251 CPUMAs in our data with a positive share of oil and gasworkers.

We then focus primarily on energy prices. Figure 3a shows the oil price movements sincethe late 1990s using the West Texas Intermediate (WTI) price per 42-gallon barrel, and 3bshows the price for natural gas from the Henry Hub in USD per MMBtu, which differ fromoil especially from the Great Recession in 2008 to the shale bust of 2014. In general, boomsin energy prices tend to be sustained over time, while busts come quick. In principle, onecould use an index of energy prices but for now we proceed with oil prices as our main trend.

Lastly, we follow the spirit of Black et al. (2005b), as we also use annual variation, bydefining each of our years into three periods: boom, peak, bust. This is based on the changesin the energy prices from year to year. However, where we differ is that we go beyond thebinary treatment that they use for resources and instead use a set of continuous measure,already described. While they also produce a three estimate set for each tercile in the resourcedistribution, we do not currently follow this. We also go beyond the boom-peak-bust set of

6Because of this, it might be better to only use the prospectivity index for plays which have the sameweighting scheme, i.e. no missing values for any of the measures. In addition, it would be beneficial to knowwhich of the singular measures may be driving the results, and how correlated the measures may be witheach other.

7In the American Community Survey, we use industry 042 “oil and gas extraction” for the IND1990industry variable.

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binaries to represent the two boom-peak-bust periods, based on primary conventional andthen shale sources, as unique from one another.

Forming the Bartik-style instruments

We proceed by forming Bartik-style instruments based on the following fixed and trendcomponents. For the fixed components, we take our thickness and depth measures from ourresource geology data. In addition to the geology fixed measures, we also take an approachmore akin to the original Bartik (1991) analysis, forming a share of people in each regionemployed in the oil and gas sector in the first year of our data, 2005. For the time trendcomponent, we use oil prices and oil and gas sector earnings trends as described above, alongwith binary period dummies. For geographical aggregation, we use CPUMA for the mostpart, but bring in county measures in places.

We use these instruments to predict a measure of local labor market earnings, formed asthe regional average of earnings, among those employed and with earnings and between theages of 25 and 59. These take the form:

ln(Earnings)ct = β0 + βk ·Resourcec × Timingt + β2 ·Xct + αc + γt + ect (1)

Here, Earningsct is earnings at the CPUMA level at time t, Resourcec is a fixed measure ofresource exposure, Timingt is one of our time trend variables, Xct is a vector of time-varyingcharacteristics (like race and education; to be described below), αc and γt are sets of fixedeffects for CPUMA and year, and ect is the error term. Our resource and timing variablesare drawn from the sets:

Resourcec =

Coveragec

Depthc

Thicknessc

P − Indexc

(2)

Timingt =

OGEarnt

OilPricet

Periodst

(3)

Identification for this type of instrumental variables strategy has been subject to recentdebate. Goldsmith-Pinkham et al. (2019) analyze the use of an initial industry share, empha-sizing the possibility it is correlated with unobservables or pre-trends. Jaeger et al. (2018)note the bluntness of the timing assumptions in some applications, which requires very par-

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ticular assumptions about the speed of response expected from the endogenous variable tothe instruments.

While our oil and gas industry share variable may be subject to the Goldsmith-Pinkhamet al. (2019) critique, we have formulated our variable in a way that is maximally transparentabout the industry that drives the variation, rather than the composite fixed shares approachon which they focus. As for the timing, the labor market outcomes on which we focus shouldadjust speedily to changes in the resource-sector environment, but the health measures weexplore may be slower moving.

Our measures based on fixed shale geology present the strongest case for avoiding theGoldsmith-Pinkham et al. (2019) critique, but are not beyond reproach. These measuresare clearly external (in the language of Deaton, 2010), as they were determined long agoand do not change over time. However, to be exogenous one must also build the case forexcludability, which could fail if the presence or absence of propitious shale geology werecorrelated with economic outcomes through channels other than oil industry developmentsuch as long-run physical or human capital investment. So, to make the case for excludabilityof geology, we rely on the time series elements of identification which allow us to includegeographical fixed effects and identify the impact from short-run changes in the value of theexisting oil resources rather than levels.

3 Data

We employ several sources of data for our analysis. The EIA energy data was describedabove in the discussion of our geology-based instruments. The other two main sources weuse are the American Community Survey for income, labor market, and disability measuresand mortality data from the Centers for Disease Control. We describe each in turn.

American Community Survey Data

For US labor market, income, and disability information we use data from the AmericanCommunity Survey (ACS) of the Census Bureau. It is available annually since 2000 but withadequate geographic identifiers only since 2005. The ACS covers 1% of the US population.The advantage of the ACS over other possibilities (such as BEA aggregate data or theCurrent Population Survey) is the level of geography available, the large sample sizes, andthe relative breadth and depth of information about each individual. We use the iPUMSversion for our analysis.

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Geography in the ACS is reported for PUMAs, CPUMAs, and some counties. ThePUMAs for 2005-2011 are based on the 2000 Census, while those for 2012 onward are basedon the 2010 Census. A consistent PUMA across all years of the ACS is available, and weuse this CPUMA for most of our analysis. We also make some use of county-level analysis.In the ACS, county is only reported for counties with sufficient population. When county isnot reported, we impute county based on a PUMA-county crosswalk file.8

Our main sample is 45-54 year old males, from which we form a sub-sample of those withat least some college education and a sample of WLEMMAs—white non-Hispanic males withhigh school or less education. For some of our analysis we expand to other age ranges.

We form several sets of variables in the ACS. For the labor market, we form variablesindicating employment, unemployment, and being out of the labor force. We also form avariable for being currently in school. We combine wages and salaries with self-employment toobtain total earnings. We also observe income from various sources such as Social Securityand welfare programs, along with an indicator for whether the person lived with incomeunder the federal poverty line. The ACS reports demographic information, from which wederive dummies for race (white, black, other) and Hispanic status, along with education andmarital status. Finally, the ACS provides five indicators of disability, showing cognitive,ambulatory, self-care, independent living, or vision/hearing difficulties. The survey questiondoes not require these difficulties to be work related. From these five responses, we forma binary variable for whether any of these five responses are positive, and another variableproviding the sum of ‘1’s across the five disability categories.

We report means and standard deviations for these data in Table 1 for the whole sample,and for sub-samples of those with high-education, WLEMMAs, and those from a CPUMAcovered at least in part by a shale play. Total earnings are slightly lower for oil and gasCPUMAs than the full sample, with the higher education group having more than doublethe total earnings as WLEMMAs. The means for the overall sample and the oil and gassample are very similar for the labor market outcomes employed, not in the labor force,unemployed, in school, hours worked, under the poverty line, any Social Security income,any welfare income, any difficulties, and married. The higher-educated group had greateremployment, lower not in the labor force, much less unemployment, and lower poverty rates.Social Security income at these ages for males is mostly disability insurance income, asregular retirement benefits are not available at these ages and there are few widowers withsurvivor benefits. SS income is much higher in the WLEMMA sample than other samples.

8We use the crosswalk provided by he Missouri Census Data Center(http://mcdc.missouri.edu/applications/geocorr2014.html). When there are several counties in a PUMA ora PUMA spans county lines, we probabilistically impute a county to each observation based on populationproportions.

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The percentage white and black were virtually the same between the highly educated andthe oil and gas areas, while the percentage Hispanic was the same for oil and gas areas as itwas in the full sample. By definition, WLEMMAs are 100 percent high school or less, withthe highly educated have 100 percent some college or more. The shale coverage measure is ofcourse much higher in the oil and gas areas than the overall sample or by demography, whiledepth and thickness (both in meters) are virtually the same across all four columns. Noteagain that depth is measured as a negative value and thickness is measured as a positivevalue.

WONDER data

For US mortality rates, we use the Wide-ranging Online Data for Epidemiologic Research(WONDER) data from the Centers for Disease Control and Prevention (CDC). Full deathand population counts are available by year, sex, age, race, and Hispanic status. For geog-raphy, we can obtain data at the county level. We use overall mortality counts here, butdisaggregated cause of death data are available.To form CPUMA-level analysis to align withour ACS data, we merge the WONDER data death rates onto the imputed county for eachindividual in the ACS, and then average the death rates within each CPUMA.

A limitation of the WONDER data is that the public-use data are suppressed when arequest yields fewer than ten deaths for a given cell. This limits the analysis of smallercounties, smaller age groups, and smaller race and ethnicity categories. For this reason,we aggregate our data into four 3-year groupings from 2006-08 to 2016-18. This increasesour non-suppressed county count by tripling the potential size of each cell compared to theannual data.

4 Establishing the Impact of Shale Oil Boom and Bust

In this section we present two sets of analysis with the aim of establishing the impact of theshale oil boom and bust on local labor markets. We do this first through a basic difference-in-differences approach which compares a set of treated regions to a set of comparison regions;before, during, and after the boom. We then provide estimates of the first stage of our in-strumental variables analysis which links together resource geology with local labor markets.

We begin by analyzing a figure that provides the first indication of the magnitude of theshale oil boom. We sort all CPUMAs in 2005 by the share of workers in the oil and gasextraction industry. There are 251 with any oil and gas workers, or just under 25 percentof the total number of CPUMAs. We take the average earnings for males age 45-54 usingdifferent groupings of CPUMAs based on these rankings. Figure 4 shows the top 10, ranks

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11-100, ranks 101-251, and the rest of the CPUMAs over the time period 2005-2018, witheach average set to an index of 100 in 2005. Earnings for the top 10 oil and gas CPUMAsshow a strong cyclicality, with earnings reaching 16 percent higher than 2005 levels by 2013.Earnings for those not in the top 10 but still in the top 100 (#11-100) show fairly steadyearnings, while those out of the top 100 suffer an earnings drop through the years of thefinancial crisis and recovery.

Figure 4 also graphs the annual oil price. There is a clear relationship visible betweenearnings for the top 10 CPUMAs and the oil price, with a one-to-two year lag. So, thispreliminary evidence suggests a strong relationship between oil prices and the earnings ofworkers in heavily oil-dependent CPUMAs.

Difference in Differences

We continue with the analysis of the shale oil boom and bust by explicitly creating dummyvariables for different time periods and investigating how labor market and health outcomesdiffered across oil-and-gas dependent CPUMAs and others. We compare a pre period (2006-07) to a boom period (2008-15) and a bust period (2016-18). We run difference-in-differencesmodels where treatment is assigned to the ‘top 25’ CPUMAs as ranked by their 2005 oil andgas worker share. The analysis is aggregated to CPUMA cells, with population counts usedas weights. The goal of this analysis is to provide some indication of how the shale oilboom relates to our outcomes of interest in the simplest-possible empirical framework. Wewill expand and deepen the analysis using the instrumental variables approach in the nextsection.

The results are reported in Table 2, with the upper panel showing all males age 45-54and the lower panel restricted to WLEMMAs. Earnings for middle-age males in the top 25oil-and-gas CPUMAs was $4,791 higher in the boom than before it, and then fell back topre-boom levels in the bust of 2016-18. Employment rates fell sharply in the bust by 3.2percentage points, split almost evenly between higher unemployment and being out of thelabor force. During the boom period, poverty rates dropped, as did uptake of public welfarebenefits. Finally, the indicator for having any reported health difficulties dropped in theboom period by 1.7 percentage points. Earnings and employment showed a larger increasefor WLEMMAs, but the health indicators in this analysis do not show signficant changesfrom zero.

These difference-in-differences findings give a strong indication that the outcome variableswe are using here are responsive to the oil boom and bust in CPUMAs where we would expectto see it. This sets the stage for the full instrumental variables analysis.

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Instrumental variables

We described our approach to identifying a labor demand shock driven by geology, initialemployment patterns, and the price of oil in Section 2. Here, we investigate the first-stage relationship that underlies our empirical strategy by showing results for regressionsof a measure of local earnings on different sets of instrumental variables. The dependentvariable here is the average earnings of males aged 25-59 for each CPUMA. We use CPUMA-aggregated cells here, with average values for race, Hispanic status, and education included.

Each column of Table 3 shows one of the various combinations of instruments that wehave at our disposal. The first four columns compare using lagged oil price for the time-series component of the instrument across three fixed components: the 2005 oil and gasshare of employment, shale deposit thickness, and deposit depth. We use a one-year lag ofthe oil price because of the lagged cyclicality evident in Figure 4a. All three combinationsof instrument choices in the first three columns show significant coefficients and F-statisticsover 45. In the fourth column when both thickness and depth are included, thickness losesstatistical significance. As discussed earlier and can be seen in Figure 2g and 2h, these twomeasures are correlated so the results here in column (4) may reflect this collinearity.

The fifth column tries using the trend in oil-and-gas worker earnings in place of the laggedoil price as the time-series component. The result is statistically significant coefficients, butan F-statistic that falls to 22.9. Finally, in the last column we try depth and oil-and-gasshare along with both of the time-series measures. Here, everything is significant and theF-statistic is 41.0.

The first stage results here look sufficiently strong. Based on this analysis, we use theinstrument set from column (3) for the rest of the paper as our default instruments.

5 Labor, Health, and Mortality

The transition away from manufacturing in the first 15 years of the 21st century affectedparticular cohorts of workers who were earlier exposed to employment opportunities andthen suffered when the manufacturing decline set in. (See Autor et al. (2013, 2016, 2019).)In this section we provide similar analysis of the shale oil boom and bust to see if the effectsare comparable.

How do resource booms and their subsequent busts play a role on the well-being ofmiddle age and older male workers, particularly for their labor market outcomes, health,and mortality? Are increased employment opportunities and higher earnings from an energyprice boom a blessing (or maybe even a curse) for the health of these male age groups? Booms

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could be associated with several negatives, such as increased risky behaviors (like drug use),increased pollution, and overburdened or understaffed medical facilities. In that regard,there would be positives to come from a bust, which would be the opposite of everythingjust mentioned. Could it be that large economic changes (good or bad) are both bad forhealth and that small economic changes (normal) are good?

The framework we use for this analysis employs individual ACS data on 45-54 year-oldmales to examine the impact of changes in the labor market induced by the shale oil boomand bust on outcomes. The regressions take the form:

Outcomecti = θ0 + θ1 · ln(Earnings)ct + θ2 ·Xcti + πc + δt + µcti (4)

where ln(Earnings)ct is instrumented using two instrumental variables: oil-and-gas 2005share and shale oil depth, both interacted with lagged oil price. The local earnings measureand the instruments vary at the geography-year level, which for most of this analysis isCPUMA-annual. The outcomes we include are presented in three sets here: labor marketoutcomes (earnings, employed, unemployed, out-of-labor-force, hours, under poverty, any SSincome, and any welfare income), disability indicators, and mortality. The control variablesinclude dummies for race and Hispanic status, along with dummies for education. We presentthe results first for all men age 45-54, followed by sub-samples of those with some college ormore and the WLEMMA sample. Furthermore, we also show results for other age groups.

Labor Market Outcomes

The main labor market results appear in Table 4. The coefficient of interest is θ1, whichmeasures in log terms the impact of a change in local earnings. To calibrate the presentationof our results to the magnitude of the local-earnings gains we saw in Figure 4, we present theresults by scaling the estimated coefficients by 0.1 so that the interpretation of the estimatesis the impact of a 10 percent change in local earnings.

In the first panel of Table 4 are the results for all men age 45-54. There is a large increasein all measures of labor market activity, including a 5.1 point boost in the employment ratecoming a bit more than half out of unemployment and the rest from not in the labor force.Indicators for presence of poverty, Social Security income, and welfare income all drop. Theseresults are all strongly significant.

The next two panels of Table 4 contrast the results for higher-educated and WLEMMAsamples. For the higher-educated sample, the results are smaller than for all men, with somelosing statistical significance. In contrast, the labor market boost of a ten-percent localearnings boom is substantially larger for WLEMMAs, with a 7.9 percentage point increase

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in the employment rate, and substantial changes in hours, poverty, and Social Securityincome. We graph these results in Figure 5a, showing 95 percent confidence intervals aroundeach point estimate.

The labor market results across different age groups for WLEMMAs are shown in Table5. There is a strong employment boost associated with a 10 percent change in local earningsat all ages under 65. At younger ages, this comes out of unemployment while at older agesit comes out of those not in the labor force. The impact on Social Security is only evidentat ages 45-54, while rising out of poverty can be seen across the age groups under age 65.Figure 7a-7h graph these outcomes for the high-educated sample WLEMMAs across ages.

The evidence presented here suggests a strong relationship between oil-driven changes inthe local labor market and employment and income outcomes. The impact is much strongeron WLEMMAs.

Health Outcomes

We now turn to the disability measures available in the ACS, which come from questionsasking respondents about difficulties they face in their life. Table 6 presents these resultsfor men age 45-54, split across all men and then sub-samples of higher-educated men andWLEMMA. The reported coefficients are again scaled so that they can be interpreted as theimpact of a 10 percent change in local labor market earnings driven by the shale oil boomand bust.

The first column of Table 6 shows the coefficient on an indicator for having any reporteddifficulty across all the categories, and the second column shows the sum. Both coefficientsare significantly negative in the sample of all men and for WLEMMAs. Compared to themean, the incidence of having any difficulties drops by 2.3 percentage points with the notional10 percent increase in local labor market earnings. Two of the particular difficulties aredriving this result: ambulatory difficulties and hearing or vision difficulties.

The bottom row of Table 5 reports the results by age for the ‘any difficulties’ measure.There is a distinct and interesting pattern by age. At ages 25-34 there is a 2.2 percentagepoint increase, significant at the 5 percent level. In contrast, the impact at ages 45-54 isnegative. This suggests that there may be opposite impacts on health of a booming locallabor market across ages.

Mortality

The mortality analysis uses the same empirical approach, but with one main difference—weuse counties here instead of CPUMAs. The mortality data in WONDER are reported at

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the county level and we can form our geological and employment share instruments at thecounty level as well. However, because of small-population counties, we tend to lose manycounties because of the small-sample suppression (when deaths are fewer than 10) especiallyat younger ages. We present results for annual data and then for 3-year groupings with theaim of improving the number of observations that we keep because of the N<10 suppressionrule.

The results are presented in Table 7. The dependent variable is the log of the mortalityrate and we use the same set of two instrumental variables as for the previous analysis. Weshow results for a sample of all men, and for white non-Hispanic men, both for the annualsamples and the 3-year aggregated samples. We cannot split our sample by education inthe WONDER data source. The results in the top of Table 7 are for annual deaths for allmen, and they show a large 15.3 percent increase in the mortality rate at ages 25-34 and10.9 percent at 35-44. In contrast, by ages 55-64 there is a drop in the mortality rate of 3.3percent. This same pattern holds in the 2nd panel, which is the sample of white non-Hispanicmales, with a stronger decline in mortality at ages 55-64.

In the bottom two panels are the results with the three-year groupings. Here, the samplesize of counties included drops less at younger ages, as the suppression rule binds less withthe three-year age groups. The effects seen above are reinforced with the evidence here, withnegative coefficients at ages 55-64 and positive at ages 25-34..

We continue the analysis of mortality in Table 8, where we look into the details of thecauses of death. We use the “deaths of despair” definition provided by Case and Deaton(2017) and also take deaths caused by accidents. The first panel of the table repeats theresults for white non-Hispanic males using all deaths. The second panel has deaths of despair,followed by accidents in the third panel. The results indicate that deaths of despair decreasefor the age 55-64 group and increase for the age 25-34 group. The accident results areimprecisely estimated, leading them to be insignificant at all age groups.

6 Robustness and addressing mobility

We performed several robustness checks which we report in this section.

Tighter comparison groups

One might be concerned that the inclusion of all CPUMAs with no oil and gas geology oractivity as a comparison group for our treated CPUMAs is too broad. Instead, we couldnarrow the comparison group to those most like the CPUMAs with oil and gas. We coulddo this by systematically choosing comparison CPUMAs based on observables, like in a

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propensity score matched sample approach. For now, we perform a simpler test: using onlyCPUMAs in states that have oil and gas, leaving out from the analysis all CPUMAs fromthe other states.

The results from this robustness check appear in Table 8. The first panel repeats thecore results shown before. The second panel shows the results just for those men who livein states with oil and gas, as revealed by our ‘coverage’ geology measure. The sample dropsby about 30 percent. However, the results are nearly identical, suggesting that our broadcontrol group was not a key determinant of our results.

Addressing Mobility Concerns

A second concern is the potential for mobility to affect our estimates. If changes in theeconomic prospects of a region attract in-migrnats, our results may be affected. We performsensitivity tests to assess the importance of these concerns to our estimates.

In general, employment changes (economic) are not necessarily the same thing as pop-ulation changes (demographic) (see Partridge and Rickman, 2003). From Isserman et al.(1987): “Although the existence of these economic-demographic interrelationships may seemobvious in that they constitute a considerable simplification of the functioning of labor mar-kets, demographers and economists very rarely model population and the economy as if theywere interconnected ... Without more meaningful representations of regional labor markets,the contributions of such regional economic models to labor policy analysis will and oughtto remain very limited.” With regard to resource cycles, Carson et al. (2016) lay out a niceway to think about possible population movements and local natural resources. They alsohighlight the work of Clapp (1998), who describes the resource cycle with regard to fishingand forestry.

For some examples, deaths would definitely cause population changes, but would onlycause employment changes if those that died had been working. On the other hand, an influxof workers would definitely change employment as well as the population; the latter evenmore so if others are tied to their move (such as a partner or spouse, children, or other familymember). In addition, employment changes could be caused by the local population takingjobs in booms and losing them in busts (without any geographic movement taking place).Employment changes may (or may not) reflect movements of temporary workers (whetherforeign or domestic) which may (or may not) change the population counts.

As an ancillary finding to the local employment and earnings effects of energy boom-bust-boom in Western Canada, Marchand (2012) showed that cohorts of older individualswere differentially reduced in the local areas containing energy resources during the bust ofthe 1980s, as compared to similar cohorts in the non-energy areas. More specifically, while

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the youngest cohort of 15-24 year olds was not differentially altered by this bust, the 25-34 and 35-44 age groups each experienced differential declines of 7.3 percent. In addition,the oldest cohort of 45-54 year olds differentially declined by 13.9 percent during this time,almost twice as much the two younger cohorts.

Differential population declines among working age individuals might be expected duringan energy bust, due to migration away from the resource-dependent local areas. But thesepopulation declines did not coincide with significant employment losses, as local employmentremained unchanged or even increased during this time. However, these differential employ-ment changes were not previously examined by age and skill groups. And even if there wereno employment changes across age and skill groups, it is the youngest individuals who aremost likely to be mobile, not the oldest.

These population losses could also be concentrated among those who are already un-employed or detached from the labor force in some other way, in which case employmentwould not be differentially altered. This could imply and be related to greater retirementtransitions, or perhaps disability transitions, away from energy areas than from non-energyareas. But, this was also not previously examined (beyond Black et al., 2002). Althoughthere may not be enough of these particular individuals to change the aggregate level of localemployment, there might be important differential transitions among certain skill groups.The evidence on disability take-up and mortality of Milligan and Schirle (2019, forthcoming)likely has a role to play here too, as that was also for greater North America.

In addressing whether mobility may be a concern, we conduct the robustness analysisdisplayed in Table 8. We form an indicator for whether each person observed in the ACSstill lives in their birth state, and use this variable to limit our sample.9 This results inthe loss of about half our sample. However, the estimated coefficients are almost identical,although there are now smaller coefficients for being under poverty and having indications ofSocial Security or welfare income. This evidence is in line with Bound and Holzer (2000) andDiamond (2016), who both show that the lower-educated tend to be less or unresponsive interms of geographic mobility when faced with local labor demand shocks. That said, severalspatial worries remain, such as commuting over a geographic boundary. This could be dealtwith by either using the location of work rather than the location of residence, using othermobility variables available in the data, using commuting zones, or by dropping neighboringareas to the treatment areas.

9In addition to whether an individual is still living in their state of birth, we also have whether theindividual is living in their current house for the past 10 years of more, which has not yet been used.

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7 Preliminary Conclusion

Over the past two decades, one demographic group seems to be moving in a downwarddirection in terms of employment and health outcomes: white males who are older and haveless of an education. Perhaps most worrying is that the mortality and morbidity of thisgroup has been on the rise, going against the declines for virtually every other demographicgroup in the US and other developed countries, seemingly attributable to increases in deathsof despair (Case and Deaton, 2015).

One underlying explanation for their decline stems from their cumulative disadvantagein the labor market. This disadvantage is tied to the decline of US manufacturing throughincreased exposure to international trade, mainly with China, leading to long-term decreasesin their income and employment (Autor et al., 2013). This analysis of the US manufacturingdecline has been insightful and very influential for our understanding of the social phenomenashaping the 21st Century.

However, the years from 2000 to 2015 also saw a sustained resource industry boom (andthen crash) in many parts of the United States and Canada. In this research, we study theimpact of resource booms on the same older, lower-educated, white males that are the focusof the ‘decline’ literature. Recent work by Marchand (2020) shows that the routine manualtasks that these workers are best at performing were in demand during an energy boom inWestern Canada.

Are increased employment opportunities and higher earnings from an energy price booma blessing (or maybe even a curse) for the these male age groups? How do resource boomsand their subsequent busts play a role on the well-being of middle-age and older male work-ers, particularly for their morbidity and mortality? Booms could be anecdotally associatedwith several health negatives, such as increased drug use, other risky behaviors, increasedpollution, and overburdened or understaffed medical facilities. In that regard, there wouldbe positives to come from a bust, which would be the opposite of everything just mentioned.

This evidence helps to partially answer the call of Marchand and Weber (2018) to furtherexamine the long-term effects of natural resources and their booms and busts. An additionalcontribution to the literature is to bridge the gaps that exist between demography andeconomics as they relate to natural resource abundance. For example, natural resourcesmay cause employment changes that could be caused by the local population taking jobs inbooms and losing them in busts or by the movements of temporary workers that alter thepopulation. Our evidence suggests that mobility has little influence on our estiamtes.

The evidence presented in this study provides an important complement to the influentialevidence of the impact of manufacturing decline on the social and economic outcomes on

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older workers (i.e. serve a twist in the ‘decline’ literature), with our similar focus on olderworkers in order to explicitly mirror the findings of previous analyses. While the declineof one male-dominated sector of manufacturing might be hurting WLEMMAs, the rise ofanother male-dominated sector of energy extraction might (partially) be helping them. Thisevidence might suggest that if a shale bust were large enough, it would result in an additionaldecline of WLEMMAs from the labor market.

Acknowledgments

The authors would like to thank Kristian Blais from the University of Alberta, Marc-AntoineLaflamme from the University of British Columbia, and Samantha Mackie from LodestoneAnalytics for their research assistance with coding, data cleaning, and mapping. The au-thors would also like the thank the participants of the Labor Demand and Older Workersworkshop at the National Bureau of Economic Research and seminar participants at Mc-Master University and the University of Waterloo. This research was supported under theLonger Working Lives project through funds made available by the Sloan Foundation andthe National Bureau of Economic Research. As a part of the University of Alberta’s FutureEnergy Systems research initiative, this research was made possible thanks to funding fromthe Canada First Research Excellence Fund. This research was supported by funds to theCanadian Research Data Centre Network from the Social Sciences and Humanities ResearchCouncil, the Canadian Institute for Health Research, the Canadian Foundation for Innova-tion, and Statistics Canada. Although the research and analysis are based on data fromStatistics Canada, the opinions expressed do not represent the views of Statistics Canada.

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Figure 1a: Lower 48 US Shale Plays in 2016

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Figure 1b: Example of Coverage Measure

Figure 2a: Specific Play - Bakken

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Figure 2b: Specific Play - Delaware and Wolfcamp

Figure 2c: Specific Play - Eagle Ford

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Figure 2d: Specific Play - Nibrara

Figure 2e: Specific Play - Utica and Marcellus

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Figure 2f: Example of Depth with Well Locations - Marcellus

Figure 2g: Example of Density with Well Locations - Marcellus

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Figure 3a: Oil Price Movements

Figure 3b: Natural Gas Price Movements

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Figure 4a: Earnings Changes with CPUMAs Ranked by Share of Workers in Oil and Gas

Figure 4b: Year to Year Changes in the Oil Price

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Figure 5a: Impact of a 10% local earnings change on those with high education and WLEM-MAs

Figure 5b: Impact of a 10% local earnings change on those with high education and WLEM-MAs: Difficulties

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Figure 6: Robustness analysis with different samples

Figure 7a: Results by age group: High education and WLEMMAs

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Figure 7b: Results by age group: High education and WLEMMAs

Figure 7c: Results by age group: High education and WLEMMAs

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Figure 7d: Results by age group: High education and WLEMMAs

Figure 7e: Results by age group: High education and WLEMMAs

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Figure 7f: Results by age group: High education and WLEMMAs

Figure 7g: Results by age group: High education and WLEMMAs

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Figure 7h: Results by age group: High education and WLEMMAs

Figure 8: Mortality for men

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Table 1: Descriptive Statistics

All High education WLEMMA Oil/Gas CPUMAObservations 3,034,640 1,735,829 861,708 1,014,736

Total earnings 65,883 88,080 42,441 60,870[82,095] [97,099] [46,908] [74,388]

Employed 0.808 0.871 0.741 0.798[0.394] [0.335] [0.438] [0.401]

Not in labor force 0.144 0.091 0.202 0.156[0.351] [0.287] [0.401] [0.363]

Unemployed 0.048 0.038 0.057 0.046[0.214] [0.191] [0.233] [0.208]

In school 0.023 0.036 0.005 0.022[0.151] [0.186] [0.0696] [0.146]

Hours 37.7 40.8 34.9 37.5[18.36] [16.32] [20.07] [18.81]

Under Poverty line 0.093 0.052 0.119 0.098[0.290] [0.222] [0.324] [0.298]

Any SS income 0.041 0.025 0.069 0.046[0.199] [0.155] [0.253] [0.210]

Any welfare income 0.012 0.008 0.016 0.012[0.110] [0.0899] [0.125] [0.107]

Any difficulties 0.130 0.089 0.193 0.145[0.336] [0.284] [0.394] [0.352]

Married 0.648 0.703 0.586 0.652[0.477] [0.457] [0.493] [0.476]

White 0.770 0.801 1.000 0.799[0.421] [0.399] [0] [0.400]

Black 0.115 0.096 0.000 0.096[0.319] [0.294] [0] [0.295]

Hispanic 0.135 0.083 0.000 0.135[0.341] [0.276] [0] [0.342]

High school or less 0.437 0.000 1.000 0.471[0.496] [0] [0] [0.499]

College 0.563 1.000 0.000 0.529[0.496] [0] [0] [0.499]

Coverage 0.170 0.161 0.212 0.516[0.376] [0.368] [0.409] [0.500]

Depth, if <0 -5706.3 -5557.6 -5555.4 -5712.4[3332.5] [3211.1] [3178.7] [3290.8]

Thickness, if >0 405.3 438.1 326.3 300.1[618.2] [664.3] [536.1] [545.1]

Note: Shown are means and standard deviations from the American Community Survey(2005-2018) and geological sources. The samples here are for men age 45-54. The columnsshow different subsamples as described in the text.

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Table 2: Basic Difference in DifferencesTotal Under Any SS Any welfare Any health

Earnings Employed NLF Unemployed Hours Poverty income income difficultiesAll Males Age 45-54

Mean 65,883 0.808 0.144 0.048 37.7 0.093 0.041 0.012 0.130std dev [82,095] [0.394] [0.351] [0.214] [18.4] [0.290] [0.199] [0.110] [0.336]

Boom 4,791*** 0.008 0.002 -0.010*** 0.501 -0.015*** -0.004 -0.003** -0.017**(2008-15) [1,083] [0.007] [0.007] [0.003] [0.422] [0.005] [0.004] [0.001] [0.007]Bust -723 -0.032*** 0.017* 0.015*** -1.403** -0.002 -0.001 -0.002 -0.004(2016-18) [1611] [0.011] [0.010] [0.004] [0.631] [0.008] [0.005] [0.002] [0.012]R-squared 0.920 0.748 0.753 0.348 0.770 0.678 0.530 0.320 0.691

WLEMMAMean 42,441 0.741 0.202 0.057 34.9 0.119 0.069 0.016 0.193std dev [46,908] [0.438] [0.401] [0.233] [20.07] [0.324] [0.253] [0.125] [0.394]

Boom 5,716*** 0.025** -0.015 -0.010** 1.170* -0.010 -0.010 -0.002 -0.014(2008-15) [1545] [0.011] [0.009] [0.005] [0.633] [0.012] [0.007] [0.003] [0.010]Bust 605 -0.027** 0.011 0.015** -1.847** -0.003 -0.006 -0.005 0.006(2016-18) [1596] [0.013] [0.012] [0.006] [0.862] [0.014] [0.009] [0.003] [0.017]R-squared 0.356 0.331 0.314 0.123 0.378 0.267 0.126 0.067 0.210

Note: Each column shows the results of a regression using annual CPUMA cell data for age 45-54 males with 13,900observations. Regressions include controls for race, Hispanic status, education, fixed effects for year, age, and CPUMA.Significance is indicated by 3 stars for 1%, 2 stars for 5%, and 1 star for 10%. Reported coefficients are for indicator variablesfor the Boom (2008-2015) and Bust (2016-18) periods, relative to 2006-07, for the top 25 CPUMAs, as ranked by their oil andgas worker share in 2005.

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Table 3: First Stage

Dependent variable: log local earnings(1) (2) (3) (4) (5) (6)

OilGasShare X 1.286*** 1.260*** 1.065*** 1.079*** 1.009***lag oil price [0.114] [0.122] [0.156] [0.155] [0.177]

Thickness X 0.492 -0.558lag oil price [0.688] [0.631]

Depth X -0.367*** -0.394*** -0.297**lag oil price [0.114] [0.125] [0.126]

OilGasShare X 0.022** 0.016**OilGasEarnings [0.010] [0.007]

Depth X -0.273*** -0.236***OilGasEarnings [0.064] [0.063]

R-squared 0.9532 0.9532 0.9532 0.9532 0.9530 0.9534F-test 127.0 60.5 46.3 32.2 22.9 41.0

Note: First stage regressions on individual data (2,820,954 observations) from the ACS,2006-2018. Regressions include controls for race, Hispanic status, education, fixed effectsfor year, age, and CPUMA. Instruments organized at the CPUMA level. Reportedcoefficients are scaled; standard errors are clustered at the CPUMA level.

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Table 4: Main instrumental variables results

Total Under Any SS Any welfareEarnings Employed NLF Unemployed Hours Poverty income income

All men10% local 9,062*** 0.051*** -0.022*** -0.029*** 2.643*** -0.022*** -0.006** -0.004***earnings change [1,036] [0.008] [0.005] [0.005] [0.404] [0.005] [0.003] [0.001]

Some college or more10% local 10,488*** 0.035*** -0.01 -0.025*** 2.021*** -0.014*** -0.006* -0.004***earnings change [1,641] [0.008] [0.006] [0.005] [0.417] [0.005] [0.003] [0.001]

WLEMMA10% local 8,844*** 0.079*** -0.042*** -0.037*** 3.889*** -0.036*** -0.014** -0.004earnings change [1,047] [0.015] [0.010] [0.008] [0.596] [0.007] [0.006] [0.003]

Note: Instrumental variable regressions on individual data (2,820,954 observations). The endogenous variable is the log of localearnings, measured at the CPUMA level. Instruments are the national oil price interacted separately with the 2005 CPUMAshare of oil and gas workers and the CPUMA depth of deposits. Regressions include controls for race, Hispanic status,education level, fixed effects for year, age, and CPUMA. Reported coefficients are for a 10% change in earnings, standarderrors are clustered at the CPUMA level. Significance is indicated by 3 stars for 1%, 2 stars for 5%, and 1 star for 10%.

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Table 5: Results by age

18-24 25-34 35-44 45-54 55-64 65-74 75-84Observations 504,562 486,791 553,179 797,239 760,629 584,570 393,038

Earnings 4,111*** 10,397*** 6,626*** 8,844*** 6,667*** 1,688** 409[995] [1,539] [1,535] [1,047] [1,280] [894] [793]

Employed 0.076*** 0.073*** 0.053*** 0.079*** 0.058*** 0.028** 0.003[0.023] [0.012] [0.013] [0.015] [0.013] [0.012] [0.009]

Not in Labor Force -0.004 -0.019* -0.006 -0.042*** -0.024** -0.022* -0.001[0.015] [0.010] [0.009] [0.010] [0.011] [0.012] [0.009]

Unemployed -0.073*** -0.054*** -0.047*** -0.037*** -0.034*** -0.007*** -0.002[0.014] [0.007] [0.008] [0.008] [0.005] [0.003] [0.002]

In school 0.001 -0.007 -0.001 0.000 -0.002* 0.001 0.002[0.011] [0.005] [0.002] [0.001] [0.001] [0.001] [0.002]

Hours 4.273*** 4.594*** 2.780*** 3.889*** 2.824*** 1.177* 0.097[0.977] [0.697] [0.537] [0.596] [0.645] [0.607] [0.350]

Under poverty -0.043*** -0.048*** -0.033*** -0.036*** -0.021** -0.001 0.005[0.015] [0.011] [0.009] [0.007] [0.009] [0.006] [0.008]

Any SS income 0.000 -0.002 0.009 -0.014** -0.019* -0.011 0.006[0.004] [0.004] [0.007] [0.006] [0.010] [0.011] [0.009]

Any welfare -0.002 -0.006*** -0.008* -0.004 -0.005* 0.003 -0.003[0.002] [0.002] [0.004] [0.003] [0.003] [0.002] [0.003]

Any difficulties 0.012 0.022** -0.015 -0.035*** 0.005 -0.024* -0.012[0.010] [0.010] [0.014] [0.012] [0.011] [0.014] [0.017]

Note: Instrumental variable regressions on individual data. The endogenous variable is the log of localearnings, measured at the CPUMA level. Instruments are the national oil price interacted separately withthe 2005 CPUMA share of oil and gas workers and the CPUMA depth of deposits. Regressions includecontrols for race, Hispanic status, education level, fixed effects for year, age, and CPUMA. Reportedcoefficients are for a 10% change in earnings, standard errors are clustered at the CPUMA level.Significance is indicated by 3 stars for 1%, 2 stars for 5%, and 1 star for 10%.

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Table 6: Disability indicators

Caring for HearingAny Sum Cognitive Ambulatory Independent self or vision

All men10% local -0.023*** -0.040*** -0.010** -0.013*** -0.008** -0.003* -0.007*earnings change [0.004] [0.010] [0.004] [0.003] [0.003] [0.002] [0.003]

Some college or more10% local -0.012** -0.022** -0.005 -0.008** -0.007* 0.000 -0.001earnings change [0.006] [0.011] [0.004] [0.004] [0.004] [0.002] [0.005]

WLEMMA10% local -0.035*** -0.064** -0.009 -0.021*** -0.010 -0.005 -0.019**earnings change [0.012] [0.027] [0.007] [0.008] [0.006] [0.006] [0.008]

Note: Instrumental variable regressions on individual data (2,820,954 observations). The endogenous variable is the log of localearnings, measured at the CPUMA level. Instruments are the national oil price interacted separately with the 2005 CPUMAshare of oil and gas workers and the CPUMA depth of deposits. Regressions include controls for race, Hispanic status,education level, fixed effects for year, age, and CPUMA. Reported coefficients are for a 10% change in earnings, standarderrors are clustered at the CPUMA level. Significance is indicated by 3 stars for 1%, 2 stars for 5%, and 1 star for 10%.

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Table 7: Impact on mortality

Dependent variable: log mortality

Age groups 15-24 25-34 35-44 45-54 55-64 65-74 75-84Annual: all deaths

Observations 6,184 8,548 11,901 22,235 30,112 32,949 34,577Pop coverage 73.1% 81.6% 85.8% 94.3% 97.7% 98.6% 99.1%

10% local 0.068 0.153*** 0.109*** 0.020 -0.033*** 0.000 -0.007earnings change [0.072] [0.045] [0.036] [0.022] [0.013] [0.010] [0.011]

Annual: White Non-HispanicObservations 4,079 6,472 9,795 19,545 27,786 31,587 33,503Pop coverage 61.4% 75.3% 81.8% 92.4% 96.7% 98.0% 98.7%

10% local 0.196 0.204** 0.098** -0.005 -0.049*** 0.005 -0.021earnings change [0.146] [0.085] [0.038] [0.025] [0.017] [0.013] [0.014]

3yr groups: all deathsObservations 4,812 6,048 7,586 10,317 11,488 11,876 10,770Pop coverage 90.7% 94.4% 96.4% 99.1% 99.7% 99.9% 99.9%

10% earnings 0.038** 0.133*** 0.157*** 0.026 -0.041** 0.007 -0.009change [0.019] [0.036] [0.059] [0.019] [0.018] [0.014] [0.013]

3yr groups: White Non-HispanicObservations 3,822 5,006 6,559 9,636 11,148 11,667 10,661Pop coverage 86.4% 91.7% 94.5% 98.5% 99.5% 99.7% 99.8%

10% local 0.030 0.137*** 0.082 -0.005 -0.064*** 0.016 -0.019earnings change [0.030] [0.049] [0.063] [0.023] [0.022] [0.022] [0.019]

Note: Instrumental variable regressions on aggregated data. The endogenous variable isthe log of local earnings, measured at the county level. Instruments are the national oilprice interacted separately with the 2005 county share of oil and gas workers and thecounty depth of deposits. Regressions include controls for race, Hispanic status, educationlevel, fixed effects for year, age, and county. Reported coefficients are for a 10% change inearnings, standard errors are clustered at the county level. Significance is indicated by 3stars for 1%, 2 stars for 5%, and 1 star for 10%.

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Table 8: Impact on mortality

Dependent variable: log mortality

Age groups 15-24 25-34 35-44 45-54 55-64 65-74 75-843yr groups: White Non-Hispanic

Observations 3,822 5,006 6,559 9,636 11,148 11,667 10,661Pop coverage 86.4% 91.7% 94.5% 98.5% 99.5% 99.7% 99.8%

10% local 0.030 0.137*** 0.082 -0.005 -0.064*** 0.016 -0.019earnings change [0.030] [0.049] [0.063] [0.023] [0.022] [0.022] [0.019]

3yr groups: White Non-Hispanic despair deathsObservations 1,614 2,669 3,055 4,291 4,177 2,636 1,513Pop coverage 69.7% 82.1% 83.2% 87.9% 86.9% 77.1% 65.6%

10% local 0.089 0.189* -0.018 0.013 -0.121*** 0.015 -0.208earnings change [0.074] [0.099] [0.114] [0.051] [0.046] [0.034] [0.372]

3yr groups: White Non-Hispanic accident deathsObservations 1,324 1,246 1,209 2,011 2,224 2,299 3,198Pop coverage 64.1% 65.7% 64.2% 73.6% 74.7% 73.4% 80.3%

10% local 0.010 -0.087 -0.102 -0.111 -0.031 -0.001 0.069earnings change [0.073] [0.076] [0.174] [0.130] [0.045] [0.053] [0.066]

Note: Instrumental variable regressions on aggregated data. The endogenous variable isthe log of local earnings, measured at the county level. Instruments are the national oilprice interacted separately with the 2005 county share of oil and gas workers and thecounty depth of deposits. Regressions include controls for race, Hispanic status, educationlevel, fixed effects for year, age, and county. Reported coefficients are for a 10% change inearnings, standard errors are clustered at the county level. Significance is indicated by 3stars for 1%, 2 stars for 5%, and 1 star for 10%.

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Table 9: Robustness analysis

Total Under Any SS Any welfare Any healthEarnings Employed NLF Unemployed Hours Poverty income income difficulties

All menObservations 2,820,95410% local 9,062*** 0.051*** -0.022*** -0.029*** 2.6*** -0.022*** -0.006** -0.004*** -0.023***earnings change [1,036] [0.008] [0.005] [0.005] [0.4] [0.005] [0.003] [0.001] [0.004]

Just men in states with oil and gasObservations 1,057,56910% local 8,927*** 0.051*** -0.027*** -0.024*** 2.5*** -0.023*** -0.008* -0.005*** -0.028***earnings change [1,478] [0.011] [0.007] [0.008] [0.6] [0.007] [0.004] [0.002] [0.006]

Just men who live in their birth stateObservations 1,446,98510% local 9,975*** 0.054*** -0.024*** -0.031*** 2.5*** -0.024*** -0.007 -0.004** -0.022***earnings change [1,289] [0.009] [0.006] [0.007] [0.4] [0.006] [0.005] [0.002] [0.006]

Note: Instrumental variable regressions on individual data. The endogenous variable is the log of local earnings, measured at the CPUMA level.Instruments are the national oil price interacted separately with the 2005 CPUMA share of oil and gas workers and the CPUMA depth of deposits.Regressions include controls for race, Hispanic status, education level, fixed effects for year, age, and CPUMA. Reported coefficients are for a 10%change in earnings, standard errors are clustered at the CPUMA level. Significance is indicated by 3 stars for 1%, 2 stars for 5%, and 1 star for 10%.

43

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