The Economic Impact of the Coal Boom and Bust

Abstract

In this paper, we examine the impact of the coal boom in the 1970s and the subsequent coal bust in the 1980s on local labour markets in Kentucky, Ohio, Pennsylvania, and West Virginia. We address two main questions in our analysis. How were non-mining sectors affected by the shocks to the mining sector? How did these effects differ between sectors producing local goods and those producing traded goods? We find evidence of modest employment spillovers into sectors with locally traded goods but not into sectors with nationally traded goods.Assumptions about the effects of shocks on local labour markets strongly influence local economic policies. Communities often bitterly oppose plant closures believing that closures will create devastating ripple effects throughout the local economy. Similarly, local and state governments often provide a variety of incentives, such as tax breaks and loans, to encourage businesses to locate in their area, hoping that in addition to the direct economic benefits of a new facility, existing local businesses will also benefit from the additional economic activity generated by the new employment. Indeed, the business press trumpets these ÔspilloverÕ effects as an important benefit of a firm's location decision. Despite these widespread beliefs and government actions, relatively little is known about the indirect impact of local economic shocks. It is difficult to quantify the effect of a shock to the local labour market because the counterfactual (what would have happened in the absence of the shock) is missing.In this paper we take advantage of an economic shock that induced a substantial exogenous shift in the demand for labour in certain local labour markets. We examine the impact of the coal boom in the 1970s and the subsequent coal bust in the 1980s on local economies in the four-state region of Kentucky, Ohio, Pennsylvania, and West Virginia. During the 1970s, regulatory changes and the Organisation of Petroleum Exporting Countries (OPEC) oil embargo drove up the price of coal and generated an enormous boom in the coal economy. There was a tremendous long-term infusion of mining jobs into areas with coal reserves as new mines were opened and existing ones were expanded. The coal boom lasted for more than a decade. By 1983, however, oil prices had declined, alternative mines had opened in the western US, and improvements in mining technology had reduced the demand for coal workers. The coal boom collapsed into a bust.The coal boom and bust primarily affected counties that had large coal industries. By comparing counties in this region that have large coal industries to counties that have no coal to mine, we measure the effect of the coal boom and

Unconventional Gas and Oil Development in the United States: Economic Experience and Policy Issues

“…Another possible reason for a natural resource curse is that high wages in the resources sector for lessskilled workers reduces the incentives for further education and training (Black et al, 2005b;Freudenburg 1981Freudenburg , 1984, which puts those less-skilled workers (and the region as a whole) in a vulnerable position when the energy economy inevitably turns down. The employment which does occur in booms largely is in the extraction sectors, and in non-tradable sectors like construction, services, and the retail sector (Carrington, 1996;Black et al, 2005a;Marchand, 2012), which have little sustaining power once the boom ends. may be susceptible to weak institutions, bad governance, rent-seeking, and corruption-e.g., Nigeria or stereotypes of say Louisiana (Acemoglu et al, 2004) or possibly modern North Dakota (Sontag and McDonald, 2014).…”

The local employment impacts of mining: an econometric analysis of job multipliers in northern Sweden

“…They find evidence of significant indirect employment impacts for some local services (e.g., transport, rental, and accommodation services), but insignificant impacts in the tradable goods sectors (e.g., manufacturing, agriculture, etc.). Additional studies include Black et al (2005) and Fleming and Measham (2015) (see further BMethodological approaches in previous empirical researchŝ ection). The purpose of this paper is to apply a novel econometric approach to assess mining-induced job multipliers in the empirical context of northern Sweden.…”

Estimating the Effects of Nuclear Power Facilities on Local Income Levels: A Quasi-Experimental Approach

“…This study will contribute to the following two research strands in economics. First, this study is closely related to recent literature on the effects of specific economic shocks on local and regional economies, such as the effects of pipeline construction on the local labor market (Carrington, 1996), the effects of terrorist conflict on regional per capita GDP (Abadie and Gardeazabal, 2003), the effects of military base closures on local employment (Dardia et al, 1996, Hooker and Knetter, 2001, and Poppert and Herzog Jr., 2003, the effects of coal boom and bust on local employment and earning (Black et al, 2005), and the effects of large plant openings on total factor productivity of incumbent plants (Greenstone et al, 2010) 3 . 1 This passage is quoted from an interview with Toshitsuna Watanabe in Japanese at Diamond Online http://diamond.jp/articles/-/16605.…”

“…For example, Abadie and Gardeazabal (2003), using the Synthetic Control Method (SCM), estimate that terrorist conflict in the Basque Country causes 10% loss in per capita GDP . The baseline estimation in Black et al(2005), whose research design is similar to a conventional difference-in-differences (DID) approach, find that earnings per worker grew around 3 % faster during the coal boom and 2.8% slower during the bust. Greenstone et al (2010), also exploiting several DID approaches, estimate that a large plant opening in a "winning" county leads to 12 % higher total factor productivity in incumbent plants five years after the opening.…”

“…As in Black et al (2005) and Greenstone et al (2010), estimation strategies based on DID approaches may be applicable, but there are several challenges when I apply DID estimation in this study.…”