Journal of Public Economics 101 (2013) 105–114 Contents lists available at SciVerse ScienceDirect Journal of Public Economics journal homepage: www.elsevier.com/locate/jpube Do local energy prices and regulation affect the geographic concentration of employment?☆ Matthew E. Kahn a, b,⁎, Erin T. Mansur b, c a b c UCLA Institute of the Environment, La Kretz Hall, Suite 300, 619 Charles E. Young Drive East, Box 951496, Los Angeles, CA 90095, United States National Bureau of Economic Research, United States Dartmouth College, Department of Economics, 6106 Rockefeller Hall, Hanover, NH 03755, United States a r t i c l e i n f o Article history: Received 9 September 2011 Received in revised form 18 January 2013 Accepted 11 March 2013 Available online 16 March 2013 Keywords: Manufacturing employment Electricity prices Regulation a b s t r a c t Manufacturing industries differ with respect to their energy intensity, labor-to-capital ratio and their pollution intensity. Across the United States, there is significant variation in electricity prices and labor and environmental regulation. This paper examines whether the basic logic of comparative advantage can explain the geographical clustering of U.S. manufacturing. We document that energy-intensive industries concentrate in low electricity price counties and labor-intensive industries avoid pro-union counties. We find mixed evidence that pollution-intensive industries locate in counties featuring relatively lax Clean Air Act regulation. © 2013 Elsevier B.V. All rights reserved. 1. Introduction Between 1998 and 2009, aggregate U.S. manufacturing jobs declined by 35 percent while the total production of this industry grew by 21 percent. 1 This loss of manufacturing jobs has important implications for the quality of life of the middle class. Manufacturing offers less educated workers employment in relatively well paying jobs (Neal, 1995). Despite public concerns about the international outsourcing of jobs, over eleven million people continue to work in the U.S. manufacturing sector. 2 The ability of local areas to attract and retain such manufacturing jobs continues to play an important ☆ We thank Severin Borenstein, Joseph Cullen, Lucas Davis, Meredith Fowlie, Jun Ishii, Enrico Moretti, Nina Pavcnik, Frank Wolak, Catherine Wolfram, and the seminar participants at the 2009 UCEI Summer Camp, UBC Environmental Economics and Climate Change Workshop 2010, the 2012 UC Berkeley Power Conference, ClaremontMcKenna College, Amherst College, the University of Alberta, the University of Michigan, and Yale University for their useful comments. We thank Wayne Gray for sharing data with us and Koichiro Ito and William Bishop for assisting with Fig. 1. We thank the two anonymous reviewers for their several useful comments. ⁎ Corresponding author. E-mail addresses: mkahn@ioe.ucla.edu (M.E. Kahn), erin.mansur@dartmouth.edu (E.T. Mansur). 1 The U.S. Bureau of Labor Statistics reports employment by sector. From 1998 to 2009, manufacturing employment fell from 17.6 million to 11.5 million (http://data.bls.gov/ timeseries/CES3000000001?data_tool=XGtable). The United Nations Statistics division reports gross value added by kind of economic activity at constant (2005) US dollars. From 1998 to 2009, manufacturing value went from $1348 billion to $1626 billion (http://data. un.org/Data.aspx?d=SNAAMA&f=grID%3a202%3bcurrID%3aUSD%3bpcFlag%3a0%3bitID% 3a12). 2 In March, 2011, 11.67 million people worked in manufacturing (NAICS 31–33) (source: http://www.bls.gov/iag/tgs/iag31-33.htm). 0047-2727/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpubeco.2013.03.002 role in determining the vibrancy of their local economy (Greenstone et al., 2010). Ongoing research examines the role that government regulations and local factor prices play in attracting or deflecting manufacturing employment. During a time when unemployment rates differ greatly across states, there remains an open question concerning the role that regulation plays in determining the geography of productive activity. A leading example of this research is Holmes' (1998) study that exploited sharp changes in labor regulation at adjacent state boundaries. He posited that counties that are located in Right-toWork states have a more “pro-business” environment than their nearby neighboring county located in a pro-union state. He used this border-pairs approach to establish that between 1952 and 1988 there has been an increasing concentration of manufacturing activity on the Right-to-Work side of the border. A recent Wall Street Journal piece claimed that, between the years 2000 and 2008, 4.8 million Americans moved from union states to Right-to-Work states. 3 In this paper, we build on Holmes' core research methodology along three dimensions. First, we focus on the modern period from 1998 to 2009. During this time period, the manufacturing sector experienced significant job destruction as intense international competition has taken place (Davis et al., 2006; Bernard et al., 2006). This time period covers the start of the recent deep downturn in the national economy and the earlier 2000 to 2001 recession. Past research has documented that industrial concentration is affected by energy prices 3 Arthur B. Laffer and Stephen Moore. “Boeing and the Union Berlin Wall.” http://online. wsj.com/article/SB10001424052748703730804576317140858893466.html. 106 M.E. Kahn, E.T. Mansur / Journal of Public Economics 101 (2013) 105–114 (Carlton, 1983), environmental regulation (Becker and Henderson, 2000; Greenstone, 2002; Walker, 2012), and labor regulation and general state level pro-business policies (Holmes, 1998; Chirinko and Wilson, 2008). Second, we use the border-pair methodology to study the relative importance of these three key determinants of the geographic concentration of manufacturing jobs in one unified framework. Third, we examine the heterogeneity of industries' response to these policies. We estimate a reduced form econometric model of equilibrium employment variation across counties that allow us to study how energy regulation, labor regulation and environmental regulation are associated with the spatial distribution of employment while holding constant the other policies of interest. Our identification strategy exploits within border-pair variation in energy prices and regulation to tease out the role that each of these factors play in influencing the geographical patterns of manufacturing employment. As we discuss below, county border pairs share many common attributes including local labor market conditions, spatial amenities, and proximity to markets. We compare our estimates of policy effects in regression results with different levels of geographic controls to see how robust our results are across different specifications. This paper studies where different industries cluster across different types of counties as a function of county regulation status. In the case of manufacturing, we disaggregate manufacturing into 21 three-digit NAICS industries. These industries differ along three dimensions; the industry's energy consumption per unit of output, the industry's labor-to-capital ratio, and the industry's pollution intensity. We model each county as embodying three key bundled attributes; its utility's average industrial electricity price, its state's labor regulation, and the county's Clean Air Act regulatory status. The basic logic of comparative advantage yields several testable hypotheses. In a similar spirit as Ellison and Glaeser (1999), we test for the role of geographical “natural advantages” by studying the sorting patterns of diverse industries. Energy-intensive industries should avoid high electricity price counties.4 Labor-intensive manufacturing should avoid pro-union counties. Pollution-intensive industries should avoid counties that face strict Clean Air Act regulation. We use a countyindustry level panel data set covering the years 1998 to 2009 to test all three of these claims. The paper also examines the relationship between energy prices and employment for specific industries. We recognize that manufacturing is just one sector of the economy and thus we examine how other major non-manufacturing industries are affected by energy, labor and environmental regulation. For 21 manufacturing industries and 15 major non-manufacturing industries, we estimate this relationship. We find that energy prices are not an important correlate of geographical concentration for most non-manufacturing industries. However, employment in expanding industries such as Credit Intermediation (NAICS 522), Professional, Scientific and Technical Services (NAICS 541), and Management of Companies and Enterprises (NAICS 551) is responsive to electricity prices with implied elasticities of approximately −.15. In comparison, the most electricity-intensive manufacturing industry, primary metals, has an elasticity of −1.17. 2. Empirical framework Our empirical work will focus on examining the correlates of the geographic clusters of employment and establishments by industry starting in 1998. Building on Holmes' (1998) approach, we rely heavily on estimating statistical models that include border-pair fixed effects. A border pair will consist of two adjacent counties. 4 Energy-intensive industries will also attempt to avoid high oil, coal, and natural gas prices, as well. However, our identification strategy examines differences between neighboring counties and while there are regional differences in coal and natural gas, these differences are likely to be small between neighboring counties. Comparing the geographic concentration of employment within a border pair controls for many relevant cost factors. Manufacturing firms face several tradeoffs in choosing where to locate, how much to produce, and which inputs to use. To reduce their cost of production, they would like to locate in areas featuring cheap land, low quality-adjusted wages, lax regulatory requirements and cheap energy. They would also like to be close to final consumers and input suppliers in order to conserve on transportation costs. Within a border pair, we posit that local wages are roughly constant as are location specific amenities and proximity to input suppliers and final consumers. Our unit of analysis will be a county/industry/year. First we study the geographic concentration of 21 manufacturing industries using the U.S. County Business Patterns (CBP) data over the years 1998 to 2009. 5 The CBP reports for each county and year the employment count, establishment count and establishment count by employment size. This last set of variables is important because the CBP suppresses the actual employment count and reports a “0” for many observations (Isserman and Westervelt, 2006). 6 Throughout this paper, we assume that each industry differs with respect to its production process (and hence in their firms' response to electricity prices and regulation) but any two firms within the same industry have the same production function. In general, energy inputs and the firm's environmental control technology may be either substitutes or complements with labor in a given industry (Berman and Bui, 2001). Our paper studies the effects of regulations on overall employment, combining both these substitution effects as well as scale effects. Our main econometric model is presented in Eq. (1). Estimates of Eq. (1) generate new finding about the equilibrium statistical relationship between regulation, electricity prices and manufacturing location choices between 1998 and 2009. The unit of analysis is by county i, county-pair j, industry k, and year t. County i is located in utility u and state s. In most of the specifications we report below, we will focus on counties that are located in metropolitan areas. 7 elec empijuskt ¼ β 1 P elec ut þ β 2 P ut ⋅ElecIndexkt þ β 3 Right s ⋅LabCapRatiokt þβ 4 Nonattainit þ β 5 Nonattainit ⋅PollIndexk þ β 6 NoMonitori þβ 7 NoMonitori ⋅PollIndexk þ θ1 ElecIndexkt þ θ2 Right s þ θ3 LabCapRatiokt þθ4 PollIndexk þ f ðPollit Þ þ δZi þ α j þ γkt þ π st þ εijuskt : ð1Þ In this regression, the dependent variable will be a measure of county/industry/year employment. The first term on the right side of Eq. (1) presents the log of the average electricity prices that the industry faces in a specific county. The second term allows this price effect to vary with the industry's electricity-intensity index. In the regressions, the electricity-intensity index is normalized to range from 0 to 1 for ease in interpreting the results. 8 Third is an interaction term between whether state s has Right-to-Work laws (Right) and the 5 County Business Patterns (http://www.census.gov/econ/cbp/download/index.htm). We use 1998 as our start date because this was the first year in which NAICS rather than SIC codes where used. All data use the 2002 NAICS definitions. 6 The CBP suppress employment counts to protect firms' privacy in certain cases. In 35 percent of our observations, employment equals zero despite there being a positive count of establishments in that county, industry and year. To address this issue, we impute the employment data using the establishment count data when suppression occurs. The CBP provides the counts of establishments by firm size category. We take the midpoint of employment for each of these categories and use the county/industry/year establishment count data across the employment size categories (1–4, 5–9, 10–19, 20–49, 50–99, 100–249, 250–499, 500–999, 1000–1499, 1500–2499, 2500–4999 and 5000+) to impute the employment count for observations that are suppressed. We top code the 5000+ employment observations at 6000. 7 MSA counties account for most of the population (78% of the 1995 US population), manufacturing establishments (78% in sample), and manufacturing workforce (74% in sample). 8 The NBER productivity data report electricity intensity in electricity usage (in kWh) per dollar value of shipments. We normalize this measure to range from zero to one to simplify the interpretation of the price coefficients. M.E. Kahn, E.T. Mansur / Journal of Public Economics 101 (2013) 105–114 industry's labor-to-capital ratio (LabCapRatio). Finally, we examine the effect of environmental policy. This includes the interaction of an indicator of nonattainment status (Nonattainment) and a continuous index of pollution from an industry (PollIndex). We also examine the interaction effect of an indicator of whether a county does not monitor the pollutant of interest (NoMonitor) and the PollIndex variable. In estimating these policy-relevant variables, we try to control for potentially confounding factors. There are several variables that we would estimate in a traditional difference-in-differences model, including the direct effects of ElecIndex, Right, LabCapRatio, and PollIndex: θ1 − θ4. However, all of these are perfectly collinear with the various fixed effects that we estimate. For example, the direct effect of Right-to-Work states cannot be separately identified given the inclusion of state-year fixed effects. We do control for a flexible function of pollution concentration levels, pollit.9 The Z vector has county variables: a county's population in 1970, its distance to the nearest metropolitan area's Central Business District (CBD), the county's land area, and the log of the 1990 housing values.10 In the core specifications we control for a county-pair fixed effect, industry-year fixed effects and state-year fixed effects. We rely heavily on these border-pair fixed effects to soak up spatial variation in local labor market conditions, climate amenities, and proximity to intermediate input providers and final customers. Past studies such as Dumais et al. (2002) have emphasized the importance of labor pooling as an explanation for why firms in the same industry locate close together. The industry-year fixed effects control for any macro level changes in demand due to shifting national consumption trends or world trade. 11 The state–year fixed effects control for local labor market conditions such as local wage trends and any state policy that affects a firm's propensity to locate within a state. For example, some states such as Missouri have low taxes while others such as California do not.12 We use several different dependent variables. We begin by examining the number of manufacturing employees. We also present results that focus on an industry's percentage of total county employment. In another specification, we report results for the natural log of employment, which is estimated only for observations with positive employment. As discussed below, 14 percent of our observations have no establishments and thus no employees. For each manufacturing industry, we can measure the electricity intensity and the labor–capital ratio. These data are from NBER Productivity Data Base and cover 1997 to 2009. 13 Below, we will also 9 Counties are more likely to be assigned to nonattainment status if their ambient air pollution levels in the recent past have been higher. If booming counties have high regulation levels, then a researcher could conclude that regulation raises employment levels when in fact reverse causality is generating this relationship. To sidestep this problem, we include a flexible function of the county's ambient pollution level. 10 Adjacent counties are unlikely to be “twins.” The classic monocentric model of urban economics predicts that counties closer to a major Central Business District will feature higher population densities and higher land prices than more suburban counties. We have also estimated specifications that included other county attributes such as a dummy indicating whether the county is the metropolitan area's center county and another dummy that indicates whether the county is adjacent to an Ocean or a Great Lake. The results are robust to controlling for these variables and are available on request. In Appendix Table A1, we present formal tests of whether our explanatory variables included in the Z vector are “balanced.” We find that these covariates vary by treatment for high electricity prices, labor regulation, and environmental regulation. In a regression reported in Table 5, we include linear trends for each covariate to test whether our results are robust. 11 Linn (2009) documents that linkages between manufacturing industries amplify the effect of macro energy price shocks. Given that energy-intensive industries are important input suppliers to other industries, there could be industry–year effects driven by such linkages. Including the industry–year fixed effects helps to address this issue. For more on the macroeconomics impacts of energy price changes see Killian (2008). 12 Recent empirical work has documented that minimum wage differences across states do not influence the locational choices of low skill jobs (Dube et al., 2010). 13 See http://www.nber.org/data/nbprod2005.html. We thank Wayne Gray for providing us with data that extends the sample through 2009. 107 present results for non-manufacturing industries but we cannot measure their electricity, labor, or pollution intensity. As such, our main results focus on manufacturing where we can test for the role of geographic regulations in attracting employment activity. The interaction terms presented in Eq. (1) allow us to test three hypotheses. The first hypothesis is that energy-intensive industries cluster on the low electricity price side of the border. The second hypothesis is that labor-intensive industries cluster on the Right-to-Work Side of the border. The third hypothesis is that high emission industries cluster in the low environmental re ...
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