Impacts of Unconventional Oil and Gas Booms on Public Education

A Mixed-Methods Analysis of Six Producing States

Nathan Ratledge and Laura Zachary

This report was produced as part of The Community Impacts of Shale Gas and Oil Development, an RFF initiative.

AUGUST 2017 Impacts of Unconventional Oil and Gas Booms on Public Education A Mixed-Methods Analysis of Six Producing States

Nathan Ratledge and Laura Zachary ∗ Abstract Unconventional oil and gas development has revolutionized the global energy marketplace, particularly in the United States. Rapid industry expansion has also had significant and widespread impacts at the community level. This study provides an in-depth look at pre-K–12 educational impacts across six oil- and gas-producing states—Pennsylvania, Ohio, West Virginia, North Dakota, Montana, and Colorado—to understand the benefits and challenges of the recent resource booms on student enrollment, teachers, public education finances, and student achievement metrics. Understanding the effects of such booms on public education is significant in the short and long term, notably because of their potential influence on educational achievement, career-based decisionmaking, and subsequently, the economic health of a community. A mixed-methods design, coupling difference-in-difference statistical analysis with extensive interviews, reveals a series of key insights across and within states. Broadly, we find divergent trends in student enrollment, student-teacher ratios, and per pupil revenue and expenses between school districts in the eastern versus western United States. In contrast to much of the existing literature, interviews across all regions reported minimal concern with increased dropout rates. Stress from financial uncertainty was also acute and common across all boom districts. Taken together, this analysis underscores the importance of the mixed-methods approach and cautions against overgeneralization of effects across disparate boom regions.

Key Words: shale oil, shale gas, Bakken, Marcellus, fracking, hydraulic fracturing, public education, oil and gas, unconventional oil and gas

∗ Nathan Ratledge is co–principal investigator of Resources for the Future’s (RFF’s) Shale Schools Project. Before teaming with RFF, Nathan was the executive director of the Community Office for Resource Efficiency. He graduated summa cum laude as a foundation fellow from the University of Georgia and holds an MPA from Princeton’s Woodrow Wilson School of Public and International Affairs. He is currently pursuing a PhD in Stanford’s E-IPER program.; Laura Zachary is an independent energy and climate consultant. Laura has worked on energy, climate, and environmental policy issues in the US and abroad. She received a BA from Tufts University and an MPA from Princeton’s Woodrow Wilson School of Public and International Affairs. ACKNOWLEDGMENTS: This work was made possible by the generous support of the Smith Richardson Foundation, for which we are very grateful. We thank Dr. Kai Schafft, Dr. Julia Haggerty, Dr. Lucija Muehlenbacks, Dr. Sean Reardon, Dr. Casey Wickman, Brandon Cunningham, and Alexander Egorenkov, for their help designing elements of the study or reviewing drafts of this work. Most importantly, we appreciate Dr. Alan Krupnick’s oversight throughout the project. We also express our sincere gratitude to all the teachers, staff, and administrators from across the country who took time out of their busy schedules to meet with us. Your participation in this study was fundamental in understanding the specific impacts of rapid development. Thank you. © 2017 Resources for the Future (RFF). All rights reserved. No portion of this report may be reproduced without permission of the authors. Unless otherwise stated, interpretations and conclusions in RFF publications are those of the authors. RFF does not take institutional positions. Resources for the Future (RFF) is an independent, nonpartisan organization that conducts rigorous economic research and analysis to help leaders make better decisions and craft smarter policies about natural resources and the environment.

Key Points • An evaluation of the effect of energy resource booms on preK–12 public schools reveals divergent trends in student enrollment, student-teacher ratios, and per pupil revenue and expenses that split largely between school districts in the eastern versus western United States. • Student enrollment, particularly in the younger grades, was statistically higher in boom districts than in nonboom districts in North Dakota. Conversely, Marcellus boom districts experienced a statistically significant decline in student enrollment compared with nonboom districts, despite striking increases in natural gas production. • Notwithstanding the clear challenges of a spiking student population in rural areas, North Dakota interviews revealed that the greater challenge was exceptionally high levels of student mobility—a trend that was commonly referred to as a ‘revolving door’. Not knowing when a student might arrive or leave created distinct challenges for budgeting and curriculum planning, with several teachers citing physical and emotional fatigue from constantly working to integrate and connect with new students. • Financial effects were also divergent between eastern and western districts. North Dakota boom districts experienced a statistically significant decline in per pupil funding, whereas Marcellus boom districts had a statistically positive increase in per pupil revenue. • From an expense perspective, less money was spent on educational services within North Dakota, while statistically more money was spent on capital projects. Increased capital spending is not overly surprising, given the growth in student numbers in the Bakken; however, the decrease in per pupil educational spending raises red flags for long-term effects. • Education professionals across all regions expressed several similar themes, including: o In contrast to what has been written in some of the historic literature, there was little concern about high school students leaving school early to work in the industry. o Despite the disparate regional impacts, nearly all districts reported heightened stress from financial volatility of oil and gas markets.

Contents

1. Introduction ...... 1

2. Background and Literature Review ...... 4 2.1. Historic Resource Booms ...... 4 2.2. Unconventional Oil and Gas Boom ...... 6 2.3. Public Education Finance ...... 7 2.4. Unconventional Oil and Gas and School Impacts ...... 9

3. Data and Statistical Methods ...... 11 3.1. Sample Choice ...... 12 3.2. Data Sources ...... 12 3.3. Empirical Strategy and Difference-in-Difference Estimation ...... 13 3.4. Qualitative Approach ...... 15

4. Statistical Results and Interview Responses ...... 16 4.1. Student Population and Teachers ...... 17 4.2. Education Finance ...... 20 4.3. Academic Performance and Dropouts ...... 24

5. Discussion and Conclusions ...... 25

References ...... 28

Appendix ...... 32

Resources for the Future | Ratledge and Zachary

MAP OF US SHALE AND TIGHT ROCK OIL AND GAS BASINS (STUDY REGIONS HIGHLIGHTED IN BOXES)

Source: Energy Information Administration. stabilized domestic natural gas prices, made 1. Introduction natural gas exports increasingly cost-effective, Since its initial expansion in the early placed downward pressure on US coal 2000s, unconventional oil and gas have demand, and caused it to become a driver of fundamentally shifted the international oil and climate and renewable energy dialogues. This gas marketplace.1 Unconventional oil— unexpected energy boom, driven by primarily from North Dakota’s Bakken field technological advances rather than new and multiple Texas plays—has helped make resource discoveries, has also been credited the United States one of the most dominant with contributing to the revitalization of the players in the oil market. The abundance of American economy postrecession (Feyrer et unconventional natural gas has lowered and al. 2016). The boom’s benefits have not come

1 We use unconventional oil and gas to refer to the marriage of hydraulic fracturing, horizontal drilling, and 3D seismic surveys to access previously uneconomic geologic formations to produce oil and gas. Often this is referred to as tight oil, shale oil, shale gas, or fracked gas. We have attempted to use the term shale only where the formation is actually a shale play.

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without costs. At the national level, concerns In the broadest sense, this research seeks about methane leaks and climate change to answer the following question: Did public effects are paramount. school districts in regions with high levels of oil and gas production during the recent Our concern is with local benefits and unconventional energy booms fare better or costs. A community may see revenues rise worse in terms of financial and educational from severance taxes or other state or local performance outcomes than comparable taxes on the sectors causing the boom. These school districts in regions that did not revenues can be spent to increase the quantity experience a boom? and quality of public services or to reduce tax rates. At the same time, the economic boom To answer this question, we use a mixed increases demand for public services such as methods research approach, relying on a water, sewage, roads, and schools (Raimi and quasi-experiment combined with Newell 2014). Residential housing prices can semistructured interviews. The quasi- increase generally with the boom, as can experiment uses a difference-in-difference rental housing prices (Price et al. 2014; (DID) design that is employed widely in the Jacquet 2009; Headwaters Economics 2008), resource economics literature. To complement but for homes that are near wells and rely on the statistical analysis and probe deeper into groundwater for drinking, housing prices can related issues, we conducted extended in- be heavily discounted (Muehlenbachs et al. person interviews with numerous sources in 2014). each of the states included in the study. Importantly, detailed quantitative data analysis Oil and gas booms also can have at the district level and complementary significant short- and long-term impacts on interview responses allow the statistical public education. In the short term, outcomes to be reinforced and better educational outcomes can serve as a proxy for explained, providing greater confidence in the community-level economic health. In the long results. term, resource booms can influence career- based decisionmaking and educational This mixed methods research extends the attainment—two drivers of a diverse and existing body of literature related to US robust local economy (Marchand and Weber commodity booms and public education. 2015; Measham and Fleming 2014; Rickman Notably, it is the first study that we are aware et al. 2017). Therefore, it is in the public of that uses extensive interviews in interest to ensure that the net effect of a conjunction with DID analysis to analyze the resource boom on education is at worst neutral effects of unconventional oil and gas on public and ideally a positive factor for individuals education. and communities. Amid a series of compelling and complex A limited number of studies focus on the results, several important themes rise to the effect of localized resource booms on public surface. These include a distinct difference in school metrics, including budgets, student student population trends between North population, and student-teacher ratios, or on Dakota and the Marcellus, a difference in per student performance metrics, such as pupil school funding among regions, and a standardized test scores or graduation or common perception of low potential impacts dropout rates (Marchand and Weber 2015; on dropouts and educational achievement Cascio and Narayan 2015; Rickman et al. across all regions. Generally, interviews did 2017). not contradict the statistical results. Although this paper finds little evidence that the recent

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boom affected student learning outcomes in Another insight revealed through the short run, we encourage future interviews concerned teacher retention. On examination of long-term impacts on student one hand, conversations indicated a negligible performance. As discussed in section 4, issues concern over teachers or staff leaving the such as classroom congestion and high rates of educational sector for higher-paying industry teacher turnover in western rural districts jobs in any of the regions. On the other hand, suggest a potential for lower educational most western districts reported challenges achievement in the long run. with teacher acquisition and retention because of being so remote. This situation was Regarding student population, North exacerbated during the boom years. Dakota had a statistically significant increase during the boom, whereas Pennsylvania and As with student population estimates, the larger Marcellus regions experienced the there were parallel though somewhat less opposite effect across most grade levels. pronounced east-west divergent trends in per Similarly, student-teacher ratios (STR) rose in pupil total revenue. North Dakota boom North Dakota boom districts and dropped in districts experienced a weakly statistically the Marcellus. Importantly, the trend in STR significant (at a 90 percent confidence level) implies that despite budget challenges across decrease compared with nonboom districts. Pennsylvania, on average teachers were Marcellus districts experienced the opposite retained in Marcellus boom area public effect and saw per pupil revenue rise schools. compared with nonboom districts. Statistical results in Colorado were insignificant. Interviews with education professionals revealed important insights that were not On the spending side of the ledger, total apparent when looking only at the official per pupil expenditures show a complementary population data. Annual student enrollment scenario; however, the analysis did uncover totals underestimate the real increase in new interesting nuances. One notable example is Bakken students because of high levels of that capital spending increased significantly in student mobility throughout the school year, North Dakota boom districts versus Marcellus which is not captured in the official population boom districts, which did not exhibit a data recorded in October each year. For statistical difference from nonboom districts. example, consider a school that reported 20 A common interview theme reported new students in one year, a significant amount across all regions was stress related to for many rural districts. It would be uncertainty about the future of school funding. reasonable to find that in actuality, 40 new Educational professionals were particularly students arrived and 20 students left— vocal about this concern in western oil resulting in the net reported increase of 20. districts, likely because of the salience of the Addressing the needs of 40 new students recent downturn in global oil prices. Several requires twice as many resources as 20 new administrators suggested it was safer to spend students, and yet this is lost in the official money on onetime investments such as new numbers. Teachers and staff throughout North computers than on recurring costs such as Dakota and Montana boom districts reported teacher pay raises. this revolving door phenomenon as a key challenge for the schools. Education A final theme was the limited concern professionals also reported the revolving door expressed toward student dropout rates or effect in Colorado but to a lesser degree than negative effects on educational attainment in in the Bakken. boom districts. Without the retracted

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quantitative data, interviews provided and per capita income; however, research on valuable, if potentially biased, insight.2 Across the localized socioeconomic consequences almost all districts interviewees did not feel remains relatively limited. Fortunately, a that the boom increased the rate of student larger body of studies examining the regional dropouts, commonly suggesting that this or local consequences of historic upticks in boom had higher technological demands than conventional energy extraction and mining previous oil and gas development periods. industries extending back to the 1970s provides a foundation of material for the Analysis of third- and eighth-grade unconventional oil and gas domain. standardized test data, as well as SAT and ACT scores, in Marcellus states did not reveal In addition to our review of energy any overarching conclusions, as results were extraction literature, Fleming et al. (2015) largely mixed. (DID analysis in other states offer extensive insight on prevailing statistical was precluded by the limited time series of methods and a comparison of results across available data.) Interviews reinforced the quantitative papers that analyze statistical outcomes, suggesting there was socioeconomic impacts of unconventional little impact on student performance in the fossil fuel development. Marcellus. However, a lack of measurable short-term impacts does not preclude impacts 2.1. Historic Resource Booms on long-term educational attainment. The oil boom of the 1970s and subsequent The remainder of the paper is structured as bust of the early 1980s gave rise to a series of follows. Section 2 reviews the existing local impact studies (e.g., Davenport and literature on resource booms, noting relevant Davenport 1979; Jones 1982). Work during statistical designs, and provides an overview this period tended to focus on broad social of public education funding sources as well as challenges that boomtowns experienced. Few variations in tax policies among the six states early studies used quantitative methods, nor included in this study. Section 3 discusses the highlighted specific educational impacts of paper’s sample choice, data sources, and resource booms (Deller and Schreiber 2012). methodology. Section 4 reviews the primary Two studies that comment on education as econometric results in parallel with interview part of broader social analyses are Cortese and findings. Section 5 includes a short discussion Jones (1977) and Freudenburg (1984). and conclusions. Freudenburg compares differences in perceptions between adolescents and adults 2. Background and Literature Review across communities in western Colorado that The literature reviewed for this study experienced rapid industry growth and their encompasses several bodies of research, peers in neighboring nonboom districts. including resource economics, education, and Highlighting the potential for the booming public finance. The rapid expansion of industry to pull students out of school, unconventional oil and gas starting in the early Freudenburg finds that adolescents in boom 2000s spurred a new series of research regions felt significantly more negative about projects assessing the effect on employment their school, teachers, administrators, and

2 The dropout data source identified for this study was retracted by the National Center for Education Statistics (NCES) during the course of our analysis.

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studying than nonboomtown youths. that resource booms did not overtly lead to Evaluating a variety of metrics in the northern increased local economic development. plains and Rocky Mountain regions affected A seminal study in the resource economics by energy development, Cortese and Jones literature by Black et al. (2005) analyzes (1977) provide wide-ranging insights on Appalachian coal communities from the 1970s school impacts from the 1970s and early to the 1980s. The authors observe positive 1980s. The authors find that, on the whole, effects during the boom period. Using a DID costs related to the boom outweighed the analysis, they estimate a net loss to boom aggregate benefits for schools. Cortese and communities as a result of the negative Jones note that teachers were burdened by impacts of the bust outweighing gains from increased housing prices in boom the boom period, particularly in relation to communities and that faculty experienced few jobs. increases in salaries to make up for the difference in living costs. They also find rapid Because of the rapid growth of changes in student enrollment as new families unconventional oil and gas, several moved to the districts. These unexpected contemporary studies have revisited these arrivals combined with uncertainty about how historic boom and bust periods. Jacobsen and long the new students would remain made it a Parker (2016) explore oil boom and bust struggle for school administrators to plan counties during the 1970s and 1980s by ahead. Cortese and Jones also report that regressing actual wells drilled on an estimated classrooms became overcrowded and that counterfactual for the number of wells drilled. boom schools often experienced high levels of Similarly to Black and colleagues, Jacobsen student transiency during the school year. As a and Parker conclude that even though positive result, Cortes and Jones conclude that students employment and income impacts occurred experiencing numerous disruptions in their during the growth period, negative income education often struggled more than their impacts were greater during the bust. peers and required more attention from Conclusions from an analysis by Haggerty et teachers to meet their academic needs. Many al. (2014) are consistent with that of Jacobsen of their findings were echoed in our study’s and Parker. Using generalized estimating interview component, particularly in the equations to regress 11 metrics on the long- Bakken. term impacts of oil and gas specialization in six Rocky Mountain states involved in the Freudenburg and Wilson (2002) provide a 1970s and 1980s energy boom, Haggerty and comprehensive review of the literature colleagues find that economic specialization in available at that time that looked at mining oil and gas over the long term was associated effects in nonmetropolitan areas. Synthesizing with worse outcomes for communities 301 studies, they conclude that roughly half regarding crime, income, and education. found negative outcomes in mining communities, one-quarter found favorable Broadly, the results from the more recent outcomes, and one-quarter uncovered neutral retrospective work support Freudenberg and or indeterminate impacts. The authors note Wilson’s consensus that resource booms do that most of the positive results come from not necessarily lead to long-term local studies conducted before 1982 and that dealt economic gains. Taken together, these studies with large western coal regions, such as the note that communities experience positive Powder River Basin. Overall, their review of economic growth during the boom period, but the available literature as of 2002 determines that ultimately the long-term negative impacts are larger. The studies analyzing historic boom

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and busts also suggest that community outcomes and Weber (2015) take a look at this effect in are dependent on how local institutions plan for Texas. Their paper is discussed in depth below. the boom, including long-term development Paredes et al. (2015) use a propensity strategies (Freudenberg and Wilson 2002). score matching approach to analyze the effects of natural gas development on income and 2.2. Unconventional Oil and Gas Boom employment in the Marcellus region. The A growing catalog of literature is authors find a generally negligible trend assessing the current and future economic regarding income effect but a more substantial effects of shale oil and gas at the national and impact on employment. To explain the latter, state levels (EMF 2013; Haggerty et al. 2014; the authors hypothesize that many of the CBO 2014; Hausman and Kellogg 2015). A higher-paying industry jobs are temporary and separate body of work is beginning to look taken by outsiders. The interview component even more narrowly at the local economic of our study further supports these points. impacts (Weber 2012, 2014; Raimi and As part of the Shale Public Finance Newell 2014, 2016; Weinstein 2014; Paredes project, Daniel Raimi and Richard Newell et al. 2015). (2016) have compiled an extensive body of Weber (2012) is one of the earliest papers research from across US oil and gas plays, to undertake a quantitative analysis of particularly focusing on the flow of energy- economic effects. Via a DID analysis, Weber related dollars at the state and local levels. looks at counties in Colorado, Texas, and Raimi and Newell generally conclude that Wyoming and finds modest growth in local governments have received a net employment and income, specifically that increase in income from development each $1 million of gas created 2.35 local jobs. activity—whether from local or state Weber (2014) looks for “conditions taxation—and that the majority of regional symptomatic for the resource curse” in governments had neutral to positive net Arkansas, Oklahoma, Louisiana, and Texas financial impacts. Looking specifically at via a combined DID and two-stage least- conclusions drawn for regions covered in our squares design (2014, 4). Weber does not find study, Raimi and Newell find that local strong evidence of an emerging resource governments in the Marcellus have “uniformly curse, citing a lack of noteworthy increases in net positive” effects and the Bakken had average earnings per job and no crowding out “mixed positive/negative” financial impacts. of manufacturing. In addition, Weber notes In Colorado, the Denver-Julesburg is that “human capital is often cited [as] a cause described as positive, while the Piceance is for long-term growth, [and thus] a decline in defined as a mix of positive and negative. education attainment is another sign of the In light of Raimi and Newell’s findings making of resource curse.” Weber concludes regarding positive impacts on local revenues, that increased resource extraction did not lead observations in Marchand and Weber’s (2015) to a less educated workforce in the study states review of education literature are particularly (2014, 9, 4). Although Weber (2014) is assessing noteworthy. Not only do greater revenues not the education level of adults, his point also always translate to increased school spending, holds for younger generations. Assuming but also “additional revenue from one source some percentage of students stay in the local may [or may not] crowd out revenue from area, their level of education (i.e., human other sources,” such as transfers from the state capital) could significantly affect the economic or local tax collections (Marchand and Weber health of the community. In fact, Marchand

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2015, 7).3 In fact, the growth and subsequent school revenue and note important differences shrinking of local tax dollars can create even among the six states included in the study. greater volatility for boomtown schools, as Figure 1 compares sources of educational discussed in Section 5. funding in the 2013–14 school year in the six The income and labor studies discussed study states against the national average. The here lay an important foundation for average state’s education revenue is composed community impacts. However, the trends do of 10 percent federal, 45 percent state, and 45 not necessarily correlate with net public percent local funding. Figure 1 shows that education funding or education outcomes. Ohio, Pennsylvania, and Colorado have higher local revenue sources. One key differentiating 2.3. Public Education Finance factor for communities is whether and how Public school finance is a complicated local governments generate revenue from oil system of state-based algorithms and local and gas production or whether those revenues funding mechanisms. In general, local funding are collected and redistributed by the state. is primarily drawn from property values; state- Table 1 provides a broad description of how local governments levy and receive revenue based funding to districts is more reliant on 4 student enrollment numbers. Understanding from the oil and gas industry. It is notable public school funding becomes even more that the two largest oil and gas producing complex when multiple states are discussed. states discussed herein do not have the We do not aim to explain the fine nuances of authority to levy local taxes on fuel state-specific educational funding in this production. However, comparing the six states paper. Rather, this section is meant to provide in our study, there does not appear to be a an introductory look at the basic sources of clear pattern between local taxing authority and the local percentage of school funding.

3 Gordon (2004) found that Tile I federal funds did in 4 See Raimi and Newell (2016) for further discussion fact crowd out local and state revenues. On the other on local funding. hand, a study by Dahlberg et al. (2008) concluded that federal grants did not result in lower local tax rates while also spurring local government spending.

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FIGURE 1. PUBLIC EDUCATION FUNDING SOURCES ACROSS SIX STATES, 2013–14 Federal Revenue State Revenue Local Revenue 9% US Average 46% 45% 7% Pennsylvania 37% 56%

10% West Virginia 58% 32%

8% Ohio 44% 47% 10% North Dakota 59% 28%

12% Montana 48% 31%

7% Colorado 44% 49% 0% 10% 20% 30% 40% 50% 60% 70%

Source: Department of Education (2017)

TABLE 1. OVERVIEW OF LOCAL AND STATE ENERGY PRODUCTION REVENUES

Local Production State Production Taxes/Fees Dates Tax Rate or Fee Dates Impact fee per well Implemented Pennsylvania No N/A (dependent on $ of gas) 2013

Average effective tax Effective tax rate 0.5– Ohio Preboom Preboom rate 1–2.6% 0.8%

Average effective tax 5% on wellhead value of West Virginia Preboom Preboom rate around 2% oil and gas produced

Total rate 10–11.5% 2015: oil rate North Dakota No N/A (annually adjusted per change 6.5–5% mcf) Preboom, local Montana No N/A 0.5–14.8% distribution modified 2–5% (severance tax can be reduced to credit Colorado 4–15% Preboom Preboom 87.5% of ad valorem taxes) Note: Stated rates are before deductions, etc. Many states have exemptions or alternative rates for stripper wells.

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2.4. Unconventional Oil and Gas and Looking at Pennsylvania, Schafft et al. School Impacts (2014b) find little evidence that an increase in unconventional oil and gas development is The current literature specifically associated with changes in student addressing the influence of unconventional oil demographics or other outcomes, such as and gas development on public education is changes in the number of English language limited. Some contemporary research studies learner students or the share of students with on local impacts included schooling as a special education accommodations. However, tangential component to a community-wide the study did find that rates of students analysis, but they do little to discuss or qualifying for free or reduced-price lunch quantify the effects (BBC Research & programs increased at a lesser rate in top Consulting 2008; Ferrell and Sanders 2013; Oyakawa et al. 2012; Christopherson and producing counties compared with counties in Rightor 2012; Perry 2012; Bartik et al. 2016). the rest of Pennsylvania, although it is important to note that these counties still had An exception comes from a cadre of Penn higher poverty rates among students compared State University researchers who have with the state averages. Schafft et al. (2014b) published several survey-based analyses over find negligible change in dropout rates overall the recent boom, polling educators and school in the four top producing counties in administrators in the Marcellus shale region of Pennsylvania (the same as our findings). In Pennsylvania regarding both the opportunities terms of financial impacts, Kelsey et al. and challenges facing their districts as a result (2012) conduct a survey-based study of shale energy development (Schafft et al. examining whether local school districts in the 2014a, 2014b; Schafft and Biddle 2014; Marcellus would see any benefit from Kelsey et al. 2012). increased tax revenues from oil and gas Despite concerns that the boom would development. The authors note several bring a rapid influx of students to districts potential upsides, but they highlight that most overlying the Marcellus play, enrollments in interviewees felt that little of the financial these typically rural districts actually benefit had been directed to public schools in 5 continued on the previous trajectory of a Pennsylvania. steady, long-term decline (Schafft et al. 2.4.1. School Funding 2014b). In fact, Kelsey et al. (2012) and Schafft et al. (2014b) find a negative Ample evidence from the education association between oil and gas development literature shows that increased funding can in Pennsylvania and slightly larger decreases lead to increased student outcomes. In a in student enrollments. Schafft et al. (2014b) comprehensive review of peer-reviewed report that between 2005–6 and 2010–11, the quantitative analyses on the impact of money four top producing counties in Pennsylvania in education, Bruce D. Baker (2016) experienced a nearly 8 percent decline in concludes that per pupil spending is positively student enrollment compared with a state associated with improved or higher student average decline of around 2 percent. outcomes. Baker also finds that on average, “sustained improvements to the level and distribution of funding across local public

5 The 2012 study was conducted before the new impact fee was implemented.

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school districts can lead to improvements in tax may crowd out revenue from federal or the level and distribution of student outcomes” state sources (Gordon 2004). Finally, research (2016, i). In a study using nationally from Davis et al. (2016) suggests the representative data to assess the effect of importance of the predictability of funding to school spending on long-run adult outcomes, student achievement by enabling schools to Jackson et al. find that a “10 percent increase better use funds in ways that improve student in per-pupil spending each year for all twelve outcomes. years of public school leads to 0.27 more 2.4.2. Educational Attainment completed years of education, 7.25 percent higher wages, and a 3.67 percentage-point A review of the literature revealed only a reduction in the annual incidence of adult handful of attempts to quantitatively measure poverty” (2015, abstract). The authors note the effect of oil and gas development on that effects are far more pronounced for educational attainment. Three studies relied on children from low-income families. Further, the American Community Survey (ACS) to they assert that exogenous school spending measure dropouts or completions on an increases were associated with sizable individual basis (Weber 2014; Rickman et al. improvements in measurements of school 2017; Cascio and Narayan 2015). We question quality, including reductions to student- the robustness of using ACS data because of teacher ratios, increases in teacher salaries, concerns about insufficient coverage for low and longer school years. population areas. Many of the communities affected by the recent oil and gas boom have Weber et al. (2016) report that increased small populations, where the ACS historically shale development caused a substantial relied on three- or five-year averages. increase in the property tax base and Furthermore, the three studies have competing subsequently increased per student revenues results. A fourth paper by Marchand and and expenditures in the Barnett shale. Weber (2015) focuses solely on Texas and However, this finding applies to only one play therefore was able to use district-level data in Texas, which falls in the Dallas–Fort Worth from the Snapshot School District Profiles of vicinity—a highly populated urban center. the Texas Education Agency. Local governments can treat tax revenue Weber (2014) was the first to use ACS differently. As Marchand and Weber (2015) data and did not find that increased shale gas explain, with substantial revenue coming in production in nonmetropolitan counties across relatively rapidly because of resource booms, Arkansas, Louisiana, Texas, and Oklahoma school districts may find themselves contributed to lower educational attainment of unprepared to use the money in ways targeted a population. In contrast, Weber reports that to improve educational outcomes in the long shale gas production increased the portion of run. Essentially, even if school spending the population with a high school degree and increases it may not affect student some college education, and “furthermore, achievement because of how schools spend greater extraction did not erode the human additional funds (Baker 2016; Sander 1993, capital stock; it may have even improved it by 1999; Chaudhary 2009; Cobb-Clark and Jha increasing the semi-skilled population (high 2013). school and some college)” (2014, 24). It Furthermore, additional revenue from one should be noted that Weber’s study captures source does not mean greater overall school the entire population, rather than those born in revenue. For example, increased funds for the region as attempted by Rickman et al. school districts coming from a local severance (2017).

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A 2015 National Bureau of Economic variation to find that resource development in Resources (NBER) working paper from Texas school districts slightly decreased Elizabeth Cascio and Ayushi Narayan (2015) student performance on state exams. They relies primarily on a DID approach to assess note that resource-rich districts often the effect on educational outcomes in all shale responded to expanded tax bases by lowering reserve regions covering 30 of the Lower 48 tax rates or allocating additional funds to states. Their study focuses on dropout rates capital projects rather than to teacher pay. and finds that highly developed shale plays Importantly, their study finds that the effect saw an increase in dropout rates among 17- from the labor market pulled teachers out of and 18-year-olds. In addition to the ACS schools. This pull effect increased teacher concern, this paper does not differentiate turnover and inexperience, resulting in a between oil and gas or offer conclusions decline in teacher quality. among plays. The potential for Marchand and Weber conclude that overgeneralization may be problematic for “unless funding is allocated disproportionately interpreting the results. to districts where the production occurs, and is Most recently, Rickman et al. (2017) used to improve teacher quality, rising wages report significant reductions in both high may reduce student performance as districts school and college attainment among native- struggle to retain teachers” (2015, 41). Our born residents in Montana, North Dakota, and work came to dissimilar conclusions regarding West Virginia. By including only those born teacher attrition, but it does not directly within the specified states in their study, the challenge Marchand and Weber’s results, as researchers explain that they “omit in- our study does not analyze Texas plays and migration effects on educational attainment notes heterogeneity across plays. outcomes” (2017, 7). Using a synthetic control Overall, the limited body of existing work method instead of a DID approach, Rickman and concerns about some data accuracy and colleagues examine educational illustrate the need for further data-based and attainment data for persons aged 18–24 from mixed methods research into school financing the pooled 2006–13 ACS. The researchers and educational attainment. By working with a associate educational attainment of an large sample across states and conducting individual with the state in which they were interviews, we have forwarded this cumulative born and the year they turned age 18, which is body of existing education and resource computed as the survey year minus age at the economics literature. time of the survey plus 18. Thus an individual is assumed as treated by the fracking boom if he or she turned 18 during the treatment year 3. Data and Statistical Methods or later. The researchers therefore consider all This section describes the sample individuals who turned 18 before the selection, lists data sources, and discusses the treatment year as never having been treated. study’s methodology. The maximum time Joseph Marchand and Jeremy Weber frame of the quantitative data analysis (based (2015) employ an instrumental variable on available data) spans school years approach to investigate how the labor market beginning in 2000–2013; not all metrics cover and finance channels of an energy boom could the entirety of the time series. The 2000–2013 impact teacher quality and student period captures the production boom of both achievement. The authors use shale depth oil and gas in each of the six sample states and variation and annual oil and gas price the gas price collapse in the late 2000s, but it does not catch the 2014 global oil price

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collapse. We were able to examine the oil 2014; Paredes et al. 2015; Maniloff and price drop and ensuing production decline via Mastromonaco 2014; Cascio and Narayan interviews with educators and staff. 2015), we chose to evaluate impacts on a more detailed district-specific basis, as districts are 3.1. Sample Choice smaller than counties in most US states The quantitative data analysis covers (Marchand and Weber 2015). Data analysis 1,496 nonmetropolitan school districts in six and interviews suggest that differences among states that overlie major oil and gas districts within the same county or region can formations: Colorado, Montana, North be large. Furthermore, using districts has the Dakota, Ohio, Pennsylvania, and West benefit of increasing the sample size. Virginia. Together, the six states cover several Using nonmetropolitan districts precludes major plays, including the Marcellus-Utica districts located in cities (with populations (Pennsylvania, Ohio, and West Virginia), the over 250,000) from biasing estimates. In Bakken (North Dakota and Montana), and the addition, nonmetropolitan districts are more Denver-Julesburg, Piceance, and San Juan representative of the core population of Basin (Colorado). Extensive interviews were interest, as the majority of unconventional oil conducted in major development regions in and gas production occurs in rural regions. each of the study states. This six-state sample was chosen because 3.2. Data Sources it includes important energy development The National Center for Education regions and because each of the regions is Statistics (NCES), within the US Department dissimilar from the others, allowing for intra of Education, collects extensive data on and interplay comparisons. The Bakken is an students, teachers, and school finances. We oil play, whereas the Marcellus-Utica region is use their data on student enrollment by grade, primarily a natural gas play. Colorado student-teacher ratios, student demographics, produces meaningful quantities of both natural and participation in additional programs such gas and oil. Therefore, this study can compare as free and reduced lunches, as well as school impacts within the Bakken against general revenues and expenditures. Unfortunately, effects in the Marcellus, for example. NCES retracted dropout and high school Although Montana did not ultimately completion data during the research phase of experience a large increase in oil production, this project because of poor data quality and we included it in the analysis because of its inconsistency across states. We attempted to proximity to North Dakota and to explore circumvent this problem by collecting a spillover effects in neighboring districts. separate data series on dropouts and completions from state resources but found In contrast to several studies that analyze the data to be similarly inadequate for the local economic or public school effects and difference-in-difference (DID) analysis aggregate to the county level or commuting zone (Jacobsen and Parker 2016 Weber 2012,

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employed in this study.6 However, interview 3.3. Empirical Strategy and Difference- results are reported for each region. in-Difference Estimation The Stanford Education Data Archive This study uses a mixed methods approach (SEDA) aggregates and standardizes a (statistical analysis and interviews) to explore nationwide database of district-level test the effects of the resource boom on school scores by means and standard deviations to finance and education metrics. The statistical allow for comparisons on a subject and grade- analysis relies on a DID regression design that level basis. This paper uses SEDA data to uses binary indicators of boom time periods analyze English and math test scores for third and boom districts to designate participation through eighth grades. However, we could not in the treatment (Black et al. 2005; Marchand include these data for North Dakota’s or 2012; Weber 2012, 2014; Jacobsen and Parker Colorado’s DID analyses because both states 2016; Bartik et al. 2016). The DID approach lack preboom data within the dataset. simulates a natural experiment by examining Although SEDA was not applicable for all the differential effect (such as participation in study states, interview responses for each state a resource boom) on a treatment group are reported in the results section. compared with a control group. It does so by estimating an unobserved counterfactual for Additional data from state agencies that the treatment group and subtracting that catalog education data contributed to estimate from the observed outcome in the supplemental and supporting analysis where control group. Equation 1 describes the appropriate.7 When data availability permits, mathematical formulation for DID: the study includes subanalysis of data regarding high school educational attainment Yi = α + β + λt + δDt + xit + εi (1) (dropouts, graduation, and completion rates) Yi is the dependent variable for district i, and college entrance exams such as the ACT including, for example, student-teacher ratio and SAT. or per pupil revenue. α represents a constant, Local employment, personal income, and β is the treatment group effect, λt is the time total population data come from the Regional trend, xit represents a vector of control Economic Accounts of the US Bureau of variables, and εI is the district-specific error Economic Analysis (BEA). Total land area of term. δDt is an interaction between group and each county comes from the US Census time variables and represents the treatment Bureau, and unemployment statistics come effect. from the US Bureau of Labor Statistics. Oil and gas production were accessed through To account for unobserved variation in Drilling Info.8 local characteristics and across years, this study employs a combination of regional fixed effects (Marchand and Weber 2015; Maniloff and Mastromonaco 2014; Cascio and Narayan

6 Several studies (Rickman et al. 2016; Weber 2014; 7 Supplemental data come from the Pennsylvania Cascio and Narayan 2015) use the American Department of Education, Ohio Department of Community Survey (ACS) to measure dropouts or Education, West Virginia Department of Education, completions. Because of large gaps in the time series Colorado Department of Education, Montana Office of and the use of multiyear averages for populations under Public Instruction, and North Dakota Department of 65,000, we question the robustness of using ACS data Public Instruction. on dropouts. 8 See https://info.drillinginfo.com/.

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2015) and year fixed effects (Black et al. In light of varying procedures in the 2005; Marchand and Weber 2015; Maniloff literature, we evaluated a number of methods and Mastromonaco 2014; Cascio and Narayan to classify areas as boom or nonboom. 2015; Paredes et al. 2015; DeLeire et al. Ultimately, our study defines a boom district 2014). When adding year and state fixed based on a district meeting three production effects in addition to clustering standard errors metrics: (1) top average producing district by district for each of the runs, the results during the boom (measured at the top 10 show generally the same trends but with percent and top 20 percent over a four-year higher significance. average); (2) exceeding national average Several recent studies defined their boom percentage change in production from treatment as an interaction between geologic preboom to boom; and (3) having a positive change in the number of wells averaged over endowments and a time indicator. For the 9 geographic variable, the basic approach was to peak boom years. measure the percentage of an area, such as a A producing district in the top 10 percent county, over an oil or gas play (Weber 2012, is defined as being in the core treatment 2014; Maniloff and Mastromonaco 2014; group. Total treatment districts include those Michaels 2011; Fetzer 2014; Cascio and that are in the top 20 percent of production Narayan 2015). This endowment method has during the boom. Both top 10 percent and top the primary benefit of alleviating endogeneity 20 percent thresholds are used to define concerns, yet it also represents a problem treatment groups in the existing literature. because it uses somewhat arbitrary cutoffs, Weber (2012) defines a boom county as one in such as the top tercile or quintile, to define the top 20 percent for the change in gas boom areas (Weber 2014; Bartik et al. 2016). production during the boom period. Jacobsen A series of other papers use production and Parker (2016) define boom counties as the output such as change in British Thermal set for which the total number of “extra” oil Units (Btus) (Weber 2014), number of wells and gas wells drilled in a given county (Jacobsen and Parker 2016), a lagged well exceeds 200. This cutoff corresponds to 10 count (Paredes et al. 2015; DeLeire et al. percent of counties, and it is robust when the 2014), or a change in the monetary value of authors use other ways to measure boom production (Weber 2012). A third group of counties. In our study, we chose to explore the papers define treatment based on the share of effects in both the top 10 percent and 20 population deriving a majority of income from percent to infer whether impacts were the resource (Haggerty et al. 2014; Black et al. hyperlocalized or more widespread across a 2005; Weinstein 2014; Tsvetkova and region. Using variation in the size of the Partridge 2015). For example, Black et al.’s treatment has been used by other studies, (2005) seminal study on coal in Appalachia including Weber (2012, 2014), Maniloff and defined their treatment by counties that Mastromonaco (2014), Fetzer (2014), and derived at least 10 percent of income from the Black et al. (2005). coal industry.

9 Average boom production is estimated over a four- year period: the peak production year in each state and the three preceding years. This average is used to identify the top 10 percent and 20 percent of districts based on production.

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Exceeding national average percentage variables used in the literature, including change (2000–2003 preboom and the four- population, population density, per capita year boom average) was chosen to guard income, total employment, and unemployment against legacy production regions that had rate.10 Many of these controls are reported on high production rates but did not necessarily a county basis. Thus for any district that grow or benefit from the unconventional oil straddles two or more counties, we attributed and gas boom. Examples of this scenario are controls based on which county contains the several counties in West Virginia that had largest percentage of the district by land mass. high historic natural gas production from As the general narrative does not change when coalbed methane. Similar to the second alterations are made to the parameters (i.e., criteria, increasing well numbers over the dropping neighbors), section 4 reports the boom period is meant to winnow away estimates for regressions for the core districts that saw production increases during treatment (top 10 percent) and drops the boom but not from new well development. neighboring districts, unless otherwise noted. To increase confidence in the statistical Montana was ultimately not included in results, this study ran DID regressions under the statistical analysis because of irregularities four primary scenarios. with data aggregation across primary and 1. Core treatment, with neighboring districts. secondary school districts. Counties in western Colorado’s Piceance Basin were also 2. Core treatment, dropping neighboring districts. dropped because of increasing levels of natural gas production immediately prior to 3. Total treatment, with neighboring district. the introduction of unconventional gas, as 4. Total treatment, dropping neighboring defined herein. However, both regions are districts. included in the qualitative analysis and DID regressions with and without contribute important insights into the effects neighboring districts are meant to explore the of rapid oil and gas development on public potential for spillover effects. Although schools. specific estimates change within the two treatment levels and when dropping 3.4. Qualitative Approach neighboring districts, each of the regressions Realizing that a quantitative approach presents the same general conclusions for the alone can miss nuanced parts of any story, we metrics analyzed. conducted over 70 interviews across the In addition, this study ran each regression regions involved in the study between with varying controls and found similar trends February and August 2016. The interviewees throughout most runs. For the results shown in involved teachers, school and district section 4, we selected a set of common control administrators, district staff including chief financial officers, school board members, and

10 Publicly available BEA data suppress observations where the number of establishments creates confidentiality problems, such as for individual economic sectors. As a result, we chose to use total unemployment in primary regression analysis rather than control for the share of total earnings in the mining, construction, manufacturing, agricultural, and retail sectors.

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state government, nonprofit, and community them, and integrating them into the school college staff. Some meetings were with single community. Being able to illustrate these types of individuals; others were held in a group situations in detail helps confirm or contradict setting. We used a semistructured interview statistical results and aids in understanding the approach to explore the primary metrics of true magnitude of costs and benefits stemming interest but also allowed for natural, from the unconventional oil and gas boom. unstructured conversation. In total, the qualitative component was The broad goals of these meetings were to fundamental in developing a more complete better understand nuances in the data and to and nuanced understanding of local impacts. It incorporate the on-the-ground experience also distinguishes this study from similar analyses. before the boom, during the boom, and during the recent oil price decline (post-2014). The 4. Statistical Results and Interview personal perspectives deepened our Responses understanding and also helped triangulate results. For example, some stories, such as Table 2 provides a summary analysis of heightened student mobility in the Bakken, DID regression results across eight key were not apparent in the standard student metrics. It lists estimated effects from pooling population data. In this case, student five states, the Marcellus as a region and state population growth numbers likely specific outputs. Regression scenarios for each underestimate new arrivals, as many students location can be found in Appendix Tables A1–A7. were moving out of the district as well. Some Results from the five-state pooled model teachers described this as a revolving door suggest that on average, boom districts in the scenario, where in many cases they were not sample states experienced a decline in student aware a student was leaving or arriving until population compared with nonboom districts. the day it occurred. As another example, the The results also show that per pupil local statistical analysis of revenue and revenue increased over the boom period, while expenditures fails to capture the uncertainty of per pupil state revenue decreased. Differences future income, which many administrators in four other primary metrics were not noted as a paramount concern for planning statistically significant. and investment. Several of the pooled model results were Furthermore, the interviews helped build unexpected. While the pooled results for deeper insight into the practices being used outcomes such as student population contradicted among school districts to cope with the general findings in western districts, other resource booms and why their efforts are or outcomes such as state revenue per pupil are not working in each situation. One appeared to be in sync with findings in western illustration of this point is the challenge of districts. A simple comparison between the acquiring and retaining new teachers. five-state pooled results and the state-specific Administrators in many western communities outputs reveal that many trends had opposite were forced into nationwide searches, offered effects between eastern states and North housing bonuses, and in some cases even Dakota. Because of this heterogeneity in became landlords to reduce the cost of statistical results across states and the mismatch housing. Despite these efforts, many young with interview responses, the remainder of this teachers in western districts often left after just section focuses on a comparison between the a few years. This common scenario increases Marcellus and the Bakken. the soft costs of finding teachers, training

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TABLE 2. SUMMARY OF DID REGRESSIONS (FOR CORE TREATMENT WITH NEIGHBORING DISTRICTS DROPPED)

Student Student- Total Local Property State Education Capital Enrollment Teacher Revenue Revenue Tax Rev. Revenue Spending Spending #s Ratio /pupil /pupil /pupil /pupil /pupil /pupil 5 State  –0.0428 –127.6 310.5* 95.3 –367*** –201.5 218.9 (0.148) (223.9) (181.1) (144.3) (119.6) (151.5) (190.6) Marcellus  –0.325** 374.8** 138.2 62.56 205.7* 240.3** 127.5 (0.135) (176.1) (117.7) (109.9) (123.0) (112.0) (208.6) PA  –0.292* 161.3 –2.188 –29.00 103.7 64.84 90.21 (0.151) (162.4) (130.2) (117.5) (93.21) (122.5) (267.6) OH − –0.0843 525.5 428.4*** 420.9*** 75.42 199.9 424.3 (0.393) (469.6) (149.8) (158.3) (403.5) (178.6) (605.6) WV  –0.612*** 559.3 963.6** 875.4** –449.2*** 532.9** –182.5 (0.099) (474.7) (461.9) (410.7) (123.9) (211.9) (198.4) ND  1.120*** –1,498* –390.4 –1,195*** –1,212*** –1,451*** 872.6* (0.365) (807.2) (470.2) (376.1) (250.7) (548.4) (466.4) CO (D-J) − 1.391 –1,759 –310.2 88.85 –1,341 –1,112 340.1 (1.189) (1,657) (1,065) (753.7) (1,033) (1,062) (1,085)

Note: For student enrollment numbers, arrows imply statistical significance and direction. Minus signs imply a negative trend direction but no statistical significance. As discussed herein, this report focuses on an analysis of regression results for the Marcellus, North Dakota, and Colorado. This main generalization about differences 4.1. Student Population and Teachers between eastern and western outcomes begs Student population is a prominent example for some explanation. Simply, the Bakken— of the differing impacts among the Bakken, and to a slightly less extent, parts of western Marcellus, and Colorado regions. North Colorado—are truly much more remote than Dakota experienced statistically significant rural regions in the Marcellus. This positive changes in student population for remoteness likely accounts for much of the grades 1–10. Only preK and kindergarten and growth in total population and the magnitude the 11th- and 12th-grade groupings in North of industrial expansion, including support Dakota did not see significant increases over services. nonboom districts. Conversely, and despite Within the Bakken, statistical analysis was striking growth in natural gas production, possible only for North Dakota; however, Marcellus districts experienced a statistically interviews are reported from both North significant decline in student population Dakota and nearby districts in Montana. across all grades in boom districts, except Statistical output and interviews are also preK and kindergarten. Boom districts included for Colorado; generally, Colorado’s concentrated in the Denver-Julesburg area of experience, especially in the western part of Colorado had a weakly negative but not the state, is more analogous to the Bakken significant outcome. than to the Marcellus.

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Interviewees in each region expressed children. In their case, the flood never came. similar expectations on the front side of the Interviewees reported that most new gas boom, anticipating large influxes of workers workers were temporary workers who never and families. The outcomes were clearly much took up full-time residence in the area, different. Bakken teachers and staff reported choosing instead to base primarily out of an initial heavy influx of young men. Texas or Louisiana. Colorado interviewees Eventually, families and children followed. revealed an increase in student populations, Interviewees said that most of the new but conversations suggested that not all new students were clustered in the lower grades. arrivals—especially in western Colorado— Several interviewees noted either that most came as a result of employment in the oil and men coming to work the fields were not old gas sector. Interviewees also cited the enough to have children in higher grades or booming tourism industry surrounding the that high-school-age children were often left nearby ski resorts as one cause for new back home. As a result, some schools reported students in the Piceance region. Denver- a “bubble” of students moving through lower Julesburg interviewees were also mixed, grade levels and into the upper grades over time. reporting that some new students had parents tied to the oil and gas sector, but staff also stated Marcellus staff and teachers also had that school choice in the region complicated expected a surge of gas workers and began to an analysis of the movement of students.11 prepare to accept an associated inflow of

TABLE 3. EFFECT OF BEING A TOP-PRODUCING DISTRICT COMPARED WITH CONTROL DISTRICTS DURING THE BOOM ON PUBLIC SCHOOL POPULATIONS

Note: As discussed herein, this report focuses on an analysis of regression results for the Marcellus, North Dakota, and Colorado.

11 Colorado law allows students to enroll in schools even in districts outside their original zone. C.R.S. 22- 36-101 is referred to as the Public Schools of Choice law or open enrollment. See https://www.cde.state.co.us/choice/openenrollment.

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Despite concerns that the boom would some cases the same student had reappeared cause rapid influxes of students to the top later in the year after drilling had resumed producing districts in the Marcellus, following the winter. This heightened level of enrollments actually continued on a steady student mobility was reported to have caused long-term declining trajectory. In fact, similar disruption in the classroom and contributed to to the findings of Kelsey et al. (2012), our teacher fatigue. Several teachers commented analysis shows that unconventional energy that it became more difficult to want to get to development was associated with “slightly know the new students. Elevated student faster declines in student enrollments” in the mobility was not referenced as a problem in Marcellus. Looking at Pennsylvania, Kelsey the Marcellus. The mobility concern was and colleagues hypothesize that a rise of non- reported in interviews in western Colorado, native workers without children, the but the phenomenon did not seem entirely increasing demand for housing, and the related to gas development. subsequent increase in the cost of rent could 4.1.2. Student-Teacher Ratios have driven lower-income households with children out of these communities. In contrast, Student-teacher ratio (STR) trends in many oil workers in North Dakota eventually North Dakota and the Marcellus were also brought along their families. divergent. Across all grades, the coefficient on the STR was 1.12 (at a 99 percent confidence An additional explanation for these level) in the Bakken. The coefficient on core diverging trends beyond the differing Marcellus districts was –0.325, significant at remoteness of the regions is the relative value the 95 percent level. Essentially, classrooms in of oil versus gas and the potential premium for North Dakota became relatively more pay in an area like the Bakken. A third congested compared with nonboom districts, potential factor is that the level of secondary and districts in the core boom of the Marcellus development—for roads, facilities, and had more teachers per student compared with support activities—was simply that much similar districts. This contradictory trend is larger in the Bakken. When we visited the important to remember when considering both Williston region, the development of short- and long-term student achievement. industrial parks, corporate headquarters, and Colorado statistical results, mainly reflecting supporting businesses was abundantly effects within the Denver-Julesburg, were obvious, which was not the case in the insignificant. Marcellus. Interviewees in Montana and North 4.1.1. Student Mobility Dakota referenced significantly higher levels In addition to a net increase in student of classroom congestion—supporting the populations, Bakken teacher and staff empirical evidence—whereas Marcellus frequently stated that student turnover (student teachers did not cite increased classroom size. mobility) was much larger than the commonly Educators in several western Colorado reported population statistics made it appear. districts also noted increasing classroom Several interviewees suggested that the actual congestion. As with student population, number of new students could be twice as Colorado teachers and staff felt that gas large as the net gain, meaning that a activity and volatile local revenue were part of significant number of students were leaving the problem but did not account for all of the the school on a regular basis. A handful of changes to classroom atmosphere. interviewees mentioned that students would Interviewees also heavily referenced declining come and go without any notice and that in state funding in the postboom period due in

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large part to the “negative factor”—a state- Pennsylvania districts reported the imposed reduction in school funding opposite experience—in most cases, teachers stemming from the national recession, came from the local area and stayed for their discussed later in this section. entire career. In fact, the only real threat to retention was teachers leaving for nearby 4.1.3. Teacher Recruitment and Attainment districts that offered higher pay. This up- Bakken interviews revealed an additional trading occurred across state lines in the problem related to teachers: the critical Marcellus. West Virginia educators in challenge of hiring and retaining qualified particular stressed that school districts face candidates. In the most extreme case, one competition from districts in southeast North Dakota district reported having to hire Pennsylvania and Ohio—neighboring states 12 full-time teachers within two weeks of the that pay higher wages and are only a few school year commencing. miles away. Administrators in West Virginia Finding qualified teachers was noted that any increase in teacher pay over the problematic given the small population within last three years came exclusively from the Bakken region, and administrators individual districts and not the state. reported conducting nationwide searches for School staff in the Denver-Julesburg and new talent. Finding candidates was just the Piceance Basin reported both a challenging first hurdle. New teachers and administrators hiring environment and the loss of good then faced the much-publicized issue of highly teachers to wealthier districts. Western elevated housing prices, which most new Colorado administrators also noted that the instructors simply could not afford. To difference in facilities and funding from overcome this obstacle, superintendents often school foundations made it extremely difficult offered housing bonuses and in some cases for Piceance districts to compete with the began purchasing district-owned housing, thus Aspen or Denver-Boulder districts. Housing becoming landlords themselves. In one dire prices were also referenced as a limiting factor; case, a superintendent reported striking a deal although in general, these problems appeared with local homebuilders to house teachers less severe than those experienced in the Bakken. until the new homes were sold. Significantly, none of the regions reported Once hired, trained, and living in the a concern over teachers leaving for higher- Bakken, schools were then faced with the paying oil and gas industry jobs. The only challenge of retaining new teachers. With the commonly reported threat to school workforce remote geography and lack of social outlets, poaching across all regions was related to bus numerous interviewees recounted situations in drivers, who could earn significantly more which new teachers left after two to three with their commercial driver’s licenses. years. This cycle of finding, hiring, training, and refinding represents significant soft costs 4.2. Education Finance to Bakken districts. Given the consistent This study found that financial effects of influx of largely first-year teachers, this energy booms also had diverging trends among situation represents a potential threat to the school districts in the East versus the West. quality of classroom education.

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surprising, given that Pennsylvania counties, 4.2.1. Per Pupil Revenue where many of the boom districts occur, are Revenue trends were opposite in the not able to tax oil and gas production as local Marcellus and the Bakken. As shown in Table property, as is commonly the case. 4 Marcellus core districts, with a relatively Conversely, North Dakota boom districts had shrinking student body, saw a statistically a statistically significant decline in per pupil significant increase in total per pupil revenue revenue at a 90 percent level of confidence, at a 95 percent confidence level. When which is also not unexpected given the revenue is broken down into local, state, and striking growth in student enrollment. federal income, state income is statistically Colorado core districts experienced a weakly higher, but local and federal revenue are not. negative statistical effect. The significance of state funding is not overly

TABLE 4. EFFECT OF BEING A TOP PRODUCING DISTRICT COMPARED WITH CONTROL DISTRICTS DURING THE BOOM ON PUBLIC SCHOOL FINANCE

Note: As discussed herein, this report focuses on an analysis of regression results for the Marcellus, North Dakota, and Colorado.

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While not statistically significant, jockeying had instigated an annual net Colorado school revenue changes provide an reduction in education spending, commonly important insight into how booms and busts referred to as the “negative factor.” The net can affect locally funded public institutions. effect was estimated to be a 10–15 percent Colorado is unique with respect to taxation reduction in what districts would have because of the Taxpayer’s Bill of Rights expected to receive previously. In total, boom (TABOR). TABOR principally requires that districts were not only suffering the all tax increases must be approved by the consequences of a local industry decline but voter, and that the voter must also approve any also facing a relatively starker financial revenue exceeding the approved amount plus picture than nonboom districts. inflation or it is required to be returned to the While perhaps extreme, Colorado’s public. Thus TABOR effectively limits situation illustrates the challenging economic revenue and reduces expenditure flexibility. situation faced by a public school system that Severance tax revenue began to grow in is unable to capture local revenue, save excess western Colorado with increasing gas funds, and mitigate downturns. Without this production in the mid-2000s. As a result of the ability to smooth spending over time, the increasing revenue per millage, boom district western Colorado natural gas boom translated tax rates were automatically ratcheted down to into a desperate bust for many local schools. compensate for the higher payments. As a As a result, several districts have taken the result, many schools in boom districts were notable step of moving to a four-day school unable to capture all the income that would week. have been generated without the declining tax 4.2.2. Per Pupil Expenditures rates and were also limited from saving all excess funds, unlike a county’s rainy day Statistical analysis of per pupil spending in fund, for example.12 When gas prices the three regions tells a similarly disparate declined, production slowed and revenue story between Bakken and Marcellus public decreased. Schools then faced lower tax rates, schools. Total Marcellus per pupil which do not automatically ratchet back up, expenditures saw a weakly positive increase in on lower production—ultimately leaving a boom areas. Educational spending, a significant hole in their budget. This funding component of total per pupil spending, was shortage left the local governments with two significantly positive at a 95 percent options: go back to the voter to reinstate confidence interval (CI). This relative increase higher tax rates at a time of local economic in educational spending means that more hardship or request more funds from the state money per student was being spent on teacher of Colorado. salaries or educational materials than in nonboom counties. North Dakota counties saw The state is normally expected to backfill a distinctly negative effect on per pupil funds to a foundational level on a per pupil educational spending, at the 99 percent level. basis. Unfortunately for western Colorado Conversely, capital spending was statistically boom districts, the recession and political positive at a 90 percent CI. Together, the

12 For comparison, in 2010, as gas prices and production declined, Garfield County sat on a $100 million reserve fund that it had amassed during the boom years. https://garfield-county.com/news/finance- 2011-budget.aspx.

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North Dakota results imply that while When we visited the Bakken in spring expenditures decreased per pupil, available 2016, oil prices had been near record lows for funds were commonly allocated to capital over a year, new well completions were vastly improvements, including school expansions or diminished, and industry layoffs had begun. constructing new buildings. Many teachers and staff wondered aloud what would happen if the drilling did not return. Near Williston, North Dakota— While the acuteness of the global oil glut was “Boomtown, USA”—high levels of capital clearly felt in the Bakken oil fields, the spending were commonly reported in common refrain of financial uncertainty was a interviews. Given the significant rise in point echoed across the Marcellus and Colorado. student population, expansions were essential in many cases. However, the largest capital As noted above, Colorado schools had project was a new high school near Watford compounding reasons to be concerned with City, which opened in 2016 with a price tag of long-term school financing other than simply $50 million. Pictured in Figure 2, the state-of- the oil and gas busts. Pennsylvania districts the-art school is an example of a distinctly faced similar uncertainty due to a bitter and positive impact of the oil boom. prolonged state budget battle that left some schools within weeks of closing their doors. In Not all Bakken districts were as successful both cases, as well as in eastern Ohio districts, as Watford City in gaining voter approval for declining production-based revenue new bonds to support school construction. exacerbated the common stress of financial One district reported being unsuccessful on security. In a broad sense, this uncertainty the ballot three times. While new classrooms refrain calls into question the design of school are a positive impact, their financing through funding mechanisms and to what extent they taxpayer-backed bonds also represents a risk restrict administrators from optimizing to taxpayers over the long term if oil financial strategies. development slows down over long stretches.

FIGURE 2. A NEW $50 MILLION HIGH SCHOOL NEAR WATFORD CITY, NORTH DAKOTA

Photo: Nathan Ratledge, 2016.

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limited time series from the Stanford 4.3. Academic Performance and Education Data Archive, DID analysis was Dropouts possible only in the Marcellus, where third- The third chief takeaway from interviews through eighth-grade standardized test scores was the lack of reported concern about were analyzed. Results from English and math dropout rates and academic performance. scores were mixed, with a weakly negative These two trends held true across the Bakken, trend across the five grade levels. Additional Marcellus, and Colorado. Unfortunately for DID analysis of SAT and ACT scores in the DID analysis, NCES retracted its dropout Pennsylvania and Ohio did not return any and high school completion data during the statistically meaningful difference when research phase of this project. Efforts to compared with nonboom counties. collect dropout data from state agencies did The exception to the Bakken and not prove fruitful either, as the data was Marcellus sentiments came from western inconsistent over time in most states. Colorado, where several interviewees Fortunately, interviews in each region expressed appreciable concern with student provided valuable insight. performance related to oil and gas 4.3.1. Dropout Rates development and the ensuing industry Across the Bakken, Colorado, and the retraction. Unfortunately, reliable data were Marcellus, there was a consensus that the oil not ultimately available for comparative and gas boom had not meaningfully increased analysis. dropout rates. When probed on the subject, 4.3.3. Academic Attainment several interviewees in different regions Interestingly, when discussing academic responded that the oil and gas development attainment and potential educational effects was technically advanced compared with prior stemming from the oil and gas boom, boom periods and that it was not conducive to interviews in each section of the country high school dropouts looking for employment. referenced the positive impact of community The sole exception was western Colorado’s colleges, suggesting that area community Piceance Basin, which was developed before colleges were providing training to prepare most other unconventional oil and gas regions. local candidates for higher-paying work in the In the Piceance, there was a reported marginal oil and gas sector. One specific example is a increase in dropouts in the mid-2000s, but it scholarship fund in North Dakota, stemming was short-lived. In general, the consensus of from local oil revenue, that provides tuition- interviewees across regions runs counter to free community college to any high school some previous studies (e.g., Cascio and graduate in the five-county region. While the Narayan 2015; Rickman et al. 2017) and increasing role of community colleges was a suggests that students were not incentivized to constant story, it is unclear whether the drop out for work in the oil and gas industry community college route is diverting students during the recent unconventional oil and gas away from a four-year degree or increasing boom. educational attainment among students that 4.3.2. Student Performance would not have otherwise gone to college. Interviews across the Bakken and the From the available data and extensive Marcellus also reported limited concern about interviews, it is not obvious that the oil and impacts on academic performance. Because of gas boom in the Bakken or Marcellus has had poor data quality from state agencies, a distinctly negative effect on educational changing test structures over time, and a

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outcomes in the short term. However, it is population from industry-driven in-migration important to note that interview responses and the size of industrial and infrastructure could have been influenced by several expansion in a locality. These two hypotheses common cognitive biases, including the account for the divergent trends but do not availability heuristic or optimism bias. limit some common effects. For example, it is Furthermore, a lack of evidence in the short not surprising to see greater student term does not preclude long-term impacts, enrollment in the Bakken, given the which are not currently measurable (Weber remoteness of the region and substantially 2014; Haggerty et al. 2014). For example, larger buildout in support services. classroom congestion, higher STRs, and Conversely, a heightened sense of financial teacher turnover in the Bakken are reasonable uncertainty could be common across energy red flags for negative long-term effects on regions experiencing volatility in new educational performance. resource-based income. The statistical results and interview Taken together, the divergent trends responses illustrate a general bifurcation captured in statistical analysis and interviews between the Bakken and the Marcellus in between the Bakken and the Marcellus warn regard to student populations, teacher against overgeneralization with respect to the demands, and revenue and expenditures. Yet effects of natural resource booms. Just as neither region reported concerns with some international analyses of the resource increased dropout rates or effects on academic curse are not wholly applicable to the United achievement. Colorado districts generally fell States, researchers and policymakers should between the two, although they shared more be equally wary of applying broad statements common characteristics with the Bakken and conclusions across all unconventional oil despite having robust oil (Denver-Julesburg) and gas development areas within the United and natural gas (Piceance) driven States. Consider, for example, the stark development. This divergence of trends across contrast between the extremely remote and regions gives us pause about overgeneralizing sparsely populated Bakken region and the impacts of resource booms. relatively populated development regions near Pittsburgh (Marcellus), Denver-Boulder 5. Discussion and Conclusions (Denver-Julesburg), or at perhaps the farthest extreme, the Barnett Shale, located near the Unconventional oil and gas booms can heavily developed and highly populated Fort have diverse impacts on public schools in Worth region in Texas. It would be entirely boom regions. As shown, student enrollment unsurprising to find that some impacts on and mobility, student-teacher ratios, and schools would be markedly different among financial trends can vary across development regions. regions. At the same time, other trends, as assessed in this study, can be common across While the differing trends observed herein diverse development areas. Examples include provide vital insight for school administrators a common low concern with dropout rates, and policymakers in boom states, the infrequent reporting of teachers and school commonalities are equally, if not more, staff leaving for higher-paying industry jobs, insightful when fully understood. The and a heightened sense of unease regarding interview results reporting minimal effects of financial volatility. increased high school dropouts across all states contradict the historic resource The primary drivers of the variation across economics literature, which found increased boom regions appear to be growth in

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rates of student dropouts in mining boom Montana, North Dakota, and western regions (Black et al. 2005; Emery et al. 2012; Colorado. Interestingly, the increase in Marchand and Weber 2015). Several housing and rental costs in parts of interviewees explained that while they worried Pennsylvania may have contributed to the about decreases in graduation rates as energy more rapid student enrollment declines in development began to increase in their boom regions by pushing out lower-income districts, those fears never materialized, families. concluding that the recent oil and gas booms A final commonality across regions may required a higher level of training than what be the most important for policymakers— most potential high school dropouts possess. increased concern with financial volatility. In We find this explanation plausible given the each region we visited, including those in the highly technical nature of unconventional oil Marcellus that did not experience outsize and gas development, similar responses across student enrollment increases, many staff regions, and conversations with community expressed concern about future funding and college administrators, who cited an increased suggested that a decline in fossil fuel–based enrollment in technical training programs revenue would be detrimental to school related to the oil and gas industry. budgets. Notably, interviews stated that this However, neither the lack of concern uncertainty was influencing near-term about high school dropouts nor the lack of decisions as well as long-term financial evidence of an impact on student achievement commitments. Several interviewees noted that in the short run alleviates long-term concerns the financial stress was limiting increases in over student educational attainment in some teacher pay and causing reductions in states. In fact, observations of reduced per nonessential education programs. Instead, pupil educational spending, rapid teacher some administrators referenced committing turnover, and low levels of experience for only to short-term expenditures. With the many new teachers in the Bakken raises red overall growth in revenue generation from flags regarding long-term student success fuel production, which remains relatively high areas. With most of the new boom-related in most regions compared with preboom households arriving in the Bakken in the late levels, it is a distinctly negative sign for public 2000s, and a majority of these young families schools that financial stress from uncertainty having students enrolling in grade school, it has grown with increased production. may take several more years for potential From more than 70 interviews, only a impacts of the boom on student achievement handful of interviewees suggested their to become measurable. districts were strictly better off because of the Interviews across all states also reported influence of unconventional oil and gas. infrequent teacher attrition directly to the oil Although oil and gas revenue has certainly and gas industry. Despite this conclusion, it created a variety of new upsides and does appear that increasing industry wages are opportunities for local economies in the short indirectly affecting teacher quality via term, this common response from school staff challenges with acquisition and retention of is discouraging for the health of public new teachers. In particular, the high housing education in many areas. prices—driven by increasing demand and the Therefore, we encourage local and state high ability of industry workers to pay—made policymakers to consider revisions to it challenging to hire and retain new teachers education funding procedures that would that were not already living in rural parts of allow for greater savings opportunities in

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times of excess revenue generation, allow for quality data, researchers may need to wait for boom districts to capture larger net revenues the data quality and standardization of metrics from local resource extraction, and provide on a national scale to improve before tackling long-term, clearly stated commitments on the dropout question via statistical analysis state-based funding. Each of these updates across a number of producing states. would allow for consistency in revenue and Recognizing the important role that recent expenditure smoothing over time, thereby papers have had in evaluating the effects of providing districts a better opportunity to historic booms, we also recommend that allocate resources based on their changing effects on standardized test scores would be needs. good to revisit at some point in the future. As a whole, the conclusions reinforce In addition, we encourage future several of the prevailing long-term concerns researchers exploring similar questions to associated with resource-based economic consider employing a mixed method analysis specialization. Simultaneously, the nuanced that includes an interview- or survey-based and divergent effects noted herein add component. The interviewees’ contributions to significant insight into a resource boom’s this analysis were essential to disentangling effects on public education and local details in the data and unearthing hidden economic health. However, substantial further stories. Without the interview component, we research is needed to address several likely would have misinterpreted the remaining questions. magnitude or nuances of several important We suggest three important lines of storylines. inquiry for future analysis. First, the varying To conclude, we return to the central designations or measurements for defining a question guiding this paper: Did public school boom create ambiguity in interpreting results districts in regions with high levels of oil and across studies. Resource economists could gas production during the recent work to more clearly define what technically unconventional energy booms fare better or constitutes a resource boom, although worse in terms of financial and educational consensus seems unlikely. Another approach performance outcomes than comparable would be to retroactively compare results of school districts that did not experience a published papers if a different definition for boom? Although some distinct local benefits boom were employed. As an example, a paper from the unconventional oil and gas boom with a boom defined such as ours would benefit have occurred within public education, the net from being reevaluated based on a geographic impact has not been strictly positive. Despite information system derived boom treatment. dedicated and sustained efforts by many Despite the relative consensus stemming district staff and communities to mitigate the from our inquiries, we also believe further challenges, it appears that most regions in this statistical analysis is needed regarding the study struggled to manage the impacts of effect of boom development on dropout rates. uncertainty and volatility to school districts in On one hand, our study covered only six the short term. Furthermore, with rapidly states. Moreover, the retraction of NCES data changing student numbers in the West and in August 2016, poor-quality state data, and prevalent revenue uncertainty across all high- potentially insufficient specificity from ACS producing regions, students enrolled in boom all raise concerns regarding the validity of past school districts remain at risk of long-term analyses. Notwithstanding states with high- negative impacts.

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Appendix

Table A1. Marcellus Regional Regression Results

TABLE A1A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN THE MARCELLUS REGION DURING THE SHALE BOOM ON EDUCATION

TABLE A1B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN THE MARCELLUS REGION DURING THE SHALE BOOM ON PUBLIC EDUCATION FINANCE

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TABLE A1C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN THE MARCELLUS REGION DURING THE SHALE BOOM ON STUDENT PERFORMANCE

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Table A2. Pennsylvania Regression Results

TABLE A2A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN PENNSYLVANIA DURING THE SHALE BOOM ON EDUCATION

TABLE A2B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM IN PENNYSLVANIA ON PUBLIC EDUCATION FINANCE

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TABLE A2C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN PENNSYLVANIA DURING THE SHALE BOOM ON STUDENT PERFORMANCE

TABLE A2D. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN PENNSYLVANIA DURING THE SHALE BOOM ON ADDITIONAL AVAILABLE EDUCATION INDICATORS

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Table A3. Ohio Regression Results

TABLE A3A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN OHIO DURING THE SHALE BOOM ON EDUCATION

TABLE A3B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN OHIO DURING THE SHALE BOOM ON PUBLIC EDUCATON FINANCE

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TABLE A3C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN OHIO DURING THE SHALE BOOM ON STUDENT PERFORMANCE

TABLE A3D. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN OHIO DURING THE SHALE BOOM ON ADDITIONAL AVAILABLE EDUCATION INDICATORS

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Table A4. West Virginia Regression Results

TABLE A4A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN WEST VIRGINIA DURING THE SHALE BOOM ON EDUCATION

TABLE A4B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN WEST VIRGINIA DURING THE SHALE BOOM ON PUBLIC EDUCATION FINANCE

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TABLE A4C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN WEST VIRGINIA DURING THE SHALE BOOM ON STUDENT PERFORMANCE

TABLE A4C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN WEST VIRGINIA DURING THE SHALE BOOM ON ADDITIONAL AVAILABLE EDUCATION INDICATORS

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Table A5. North Dakota Regression Results

TABLE A5A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN NORTH DAKOTA DURING THE SHALE BOOM ON EDUCATION

TABLE A5B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM IN NORTH DAKOTA ON PUBLIC EDUCATION FINANCE

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Table A6. Colorado Regression Results

TABLE A6A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT IN COLORADO DURING THE SHALE BOOM ON EDUCATION

TABLE A6B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM IN COLORADO ON PUBLIC EDUCATION FINANCE

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Table A7. Five-State Pooled Regression Results

TABLE A7A. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM ON EDUCATION

TABLE A7B. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM ON PUBLIC EDUCATION FINANCE

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TABLE A7C. EFFECT OF BEING A TOP ENERGY PRODUCING DISTRICT DURING THE SHALE BOOM ON STUDENT PERFORMANCE

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