Abstract
This paper evaluates the labor market effects of subway systems on low-skilled workers. A simple model of labor supply predicts that access to subway services can decrease transportation costs and improve labor force participation, but has ambiguous effects on the intensive margin of labor market outcomes. Empirical estimates from US cities show that a 10% expansion in subway miles increases the labor force participation of low-skilled individuals without a car by eight percentage points. However, subway expansions have no significant effect on the labor force participation of low-skilled individuals who own automobiles or on high-skilled workers. In contrast, expansions of light rails and buses have no significant effect on the labor market outcomes of low-skilled individuals. Improved subway services do not affect wages, hours worked, and commuting times, suggesting that the labor market benefits of subways mainly lie in the extensive margin of labor supply.
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Data availability
The datasets analyzed in this paper are available from the corresponding author on reasonable request.
Notes
As will be discussed in Section 4, the empirical results in Online Appendix Table A4 suggest the share of high-skilled individuals in a city (and its 3- and 5-year lags) does not predict the level of transit services. The results in Online Appendix Table A5 and A6 suggest the transit services (and their 3- and 5-year lags) do not affect city population, the share of low-skilled workers, and the share of high-skilled workers. Thus, it is not strict to assume that the supply of transit services is not affected by households’ behaviors. The supply of transit services is treated as determined by factors outside of the model.
It should be noted that \({w}{\prime}\left(u\right)>0\) does not need to hold for all possible values of \(u\). \({w}{\prime}\left(u\right)>0\) should hold for at least one value of \(u\). The ACS data shows that more than 60% of low-skilled workers earn wages that are higher than the minimum wage. Thus, low-skilled workers have the potential to earn higher wages if they can search more extensively in geography and get access to more job opportunities (Ihlanfeldt and Sjoquist 1989; Holzer et al. 1994; Raphael and Rice 2002; Baum 2009).
IPUMS uses the CONSPUMA variable to represent observations that are in the same set of consistently delineated Public Use Microdata Areas (PUMAs). These PUMAs’ boundaries do not change over time, and we thus are able to maintain a set of cities with fixed boundaries over time.
Los Angeles, CA, Oakland (San Francisco), CA, Washington, DC, Miami, FL, Chicago, IL, Boston, MA, NYC, NY, Baltimore, MD, Jersey City, NJ, Cleveland, OH, Philadelphia, PA, Atlanta, GA. These 12 subway cities are in 11 states.
Little Rock, AR, Phoenix, AZ, Los Angeles, CA, Sacramento, CA, San Francisco, CA, San Jose, CA, Stockton, CA, Denver, CO, Chicago, IL, New Orleans, LA, Boston, MD, Baltimore, MD, Minneapolis, MN, Charlotte, NC, Newark, NJ, Buffalo, NY, New York City, NY, Cleveland, OH, Portland, OR, Philadelphia, PA, Pittsburgh, PA, Memphis, TN, Nashville-Davidson, TN, Austin, TX, Houston, TX, Lewisville, TX, Salt Lake City, UT, Alexandria, VA, Hampton, VA, Seattle, WA.
For cities that do not have subways or light rails, we replace the values of \({{\text{ln}}(S}_{ct})\) and \({\text{ln}}({R}_{ct})\) in these cities by 0.
The labor demand shock variable is constructed as the weighted averages of national employment growth across industries using city industry employment shares as weights. It measures local labor demand in each city in each year. The employment growth rate in the past is measured by the annual growth rate of city employment during the previous 10 years. When the Census and ACS data are not available for certain years, we refer to the County Business Pattern to calculate cities’ employment levels.
In 2013, 42% of the capital funding comes from federal government (APTA 2015). Berechman (2010) mentions “…the proclivity of local decision makers to accept a project regardless of its actual benefits and risks increases with the proportion of funding obtained from higher levels…Our hypothesis states that local authorities, as recipients of federal and state money, tend to regard external funding as costless and as political benefits. They are therefore predisposed to promoting infrastructure projects containing a large external funding component…this tendency promotes the implementation of inefficient projects, selected without any regard for their social rate of return.”.
For example, the No.7 subway extension in New York City stretches 1 mile southwest from its previous terminus. This project was originally proposed in 2005, and construction started in 2007. The extension’s opening was pushed back multiple times from its original target of December 2013. The extension finally opened to the public in September 2015. This project of building 1-mile subway takes 10 years to finish.
More capable low-skilled individuals are defined as people with some years of high school education.
Eligibility rules for welfare are from Urban Institutes Welfare Rules Database. State average insurance premium is from the Auto Insurance Database Report of National Association of Insurance Commissioners. Gasoline tax information is from the Brookings/Urban Institute’s Tax Policy Center. The four state-level instruments are linked to individual-level data by year and state.
Although the two-step Heckman sample selection model assumes the distribution of error terms is jointly normal, the empirical results are not sensitive to this normal assumption. We follow Terza et al. (2008) and include the first stage predicted residuals instead of the inverse Mills ratio term in the second-stage regression. We also use a logit regression to model the second stage. The regression results not shown here are similar to the results obtained under the normal assumption.
The subway miles variable always takes the value of zero in non-subway cities, regardless of the changes in low-skilled LFP rates. Therefore, this irrelevance between subway miles and the low-skilled LFP decisions in non-subway cities brings noises to the estimation of subways’ effect on low-skilled LFP when we include all cities in the sample. This might explain why subways are found to have an insignificant positive effect on low-skilled LFP in panel C of Online Appendix Table A9. Nevertheless, the identified effect of subways on low-skilled LFP in this paper only applies to subway cities and may not be extrapolated to other non-subway cities.
As discussed in Callaway et al. (2021), a two-way fixed effect estimator with a continuous treatment variable needs a stronger parallel trend assumption than the case with a binary treatment variable. While the exercise here only applies to the DID estimation with a binary treatment variable and thus cannot rule out the existence of biases in the two-way fixed effect estimator with a continuous treatment variable, it is a strong indication that biases caused by potential heterogeneous treatment effects are not likely to drive the empirical estimates.
The centralization of employment is measured by the share of MSA employment that is concentrated in central cities. We first calculate the total employment of the MSA (in 1990 definitions) that a city is located in. The centralization of employment is measured by the ratio of city employment with respect to the corresponding MSA employment.
These speed numbers are from the following website pages:
http://www.nyctransitforums.com/forums/topic/17313-subway-system-average-speed-by-line/
http://www.wnyc.org/story/traffic-speeds-slow-nyc-wants-curb-car-service-growth/
The average subway city has a population of 1.5 million. We assume the subway systems are operating on both directions in a subway tunnel. Thus, the subway directional route miles are equal to the subway tunnel miles multiplied by two. The $12,558 annual income is the average wage income of low-skilled no-vehicle workers in 2014 ACS.
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Acknowledgements
We appreciate the guidance of Stuart Rosenthal. We thank editor Alfonso Flores-Lagunes and four anonymous reviewers, Hugo Jales, Matthew Kahn, Matt Turner, Krieg Tideman, and Michael Wasylenko, for helpful comments and suggestions. All errors are our own.
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This paper is supported by the National Natural Science Foundation of China (Grant Nos. 71903060 and 72373114).
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Pang, J., Shen, S. Do subways improve labor market outcomes for low-skilled workers?. J Popul Econ 37, 5 (2024). https://doi.org/10.1007/s00148-024-00995-z
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DOI: https://doi.org/10.1007/s00148-024-00995-z