Abstract
Limited access to safe transportation is one of the greatest challenges to labor force participation faced by women in developing countries. This paper quantifies the causal impacts of improved urban transport systems in women’s employment outcomes, looking at Bus Rapid Transit (BRT) and elevated light rail investments in the metropolitan region of Lima, Perú. We find large gains in employment and earnings per hour among women, and not for men, due to these investments. Most of the gains arise on the extensive margin, with more women being employed, but employment does not appear to be of higher quality than that for comparison groups. We find also evidence of an increase in the use of public transport. Results are robust to alternative specifications and we do not find evidence that they are driven by neighborhood composition changes or reorganization of economic activity. Overall, these findings suggest that infrastructure investments that make it faster and safer for women to use public transport can generate important labor market impacts for women who reside in the area of influence of the improved infrastructure.
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Notes
According to the Atlantic Council (2014), Latin American countries have undergone unprecedented urbanization in the past 60 years. From 1950 to 2014, the share of the population in Latin America living in urban areas increased from 40% to around 80% and it is expected to increase to 90% by 2050.
To the best of our knowledge, the only other studies we identified looking at a related topic are Asahi (2016), who finds evidence of a positive effect of proximity to a new subway line in Santiago (Chile) on employment outcomes for women, and Abu-Qarn and Lichtman-Sadot (2019), who find evidence of a trade-off between investment in education and time allocated to work by women after the introduction of bus services in Arab towns in Israel.
Sexual harassment issues experienced by women in transit include staring, unwanted comments on physical appearance, men touching or rubbing against women, and groping (Gomez, 2000). While in developed countries sexual harassment in public transport has been reported to be more verbal than physical, subtle groping and unwanted touching are common in rush hours (Hsu 2011; Gekoski et al. 2015). In developing countries, this pattern is more pronounced (Zermeno et al. 2009).
This strategy has been implemented in cities such as Mexico City, Rio de Janeiro, Tehran, and Tokyo.
There are no reliable figures on the share of trips of the system using the BRT or Line 1. An opinion survey on living conditions in the metropolitan area suggest that the two lines are used daily by around 10% of the population of Lima and 6.2% of the population of Callao (Lima Como Vamos 2017, p. 43).
To assign jobs to the top or bottom 25% of the earning distribution, we take the 341 occupations that appear in the ENAHO, rank them in the period 2005 to 2009 based on hourly earnings, and identify them as appearing in the bottom or top quartile of the earning distribution. We then classify all occupations in the period 2010–2017 based on the pre-intervention classification.
A conglomerate is a geographic area with approximately 140 private dwellings, defined by INEI to be the primary sampling unit in its surveys. It could comprise one or several city blocks, depending on the density of the area.
We exclude the feeders from our analysis as they run in non-segregated roadways, do not have dedicated stations, and do not provide information on headways, while both BRT and Line 1 stations do. This can have important implications on the safety of women traveling in these systems. Moreover, Line 1 does not have an established system of feeders, only the BRT does. As we are pooling both systems together, we focus only on the areas of influence around the BRT trunk line and around Line 1.
The survey indicates that 90% of passengers walk no more than 12 min (91% walk no more than 15 min) and 99% walk 20 min or less to reach public transportation.
Based on our empirical strategy, 32,229 observations in the ENAHO fall within the treatment and control areas between 2007 and 2017. We eliminate 615 observations due to missing information in key variables of interest, and 4947 observations due to improper geocoding. Our final estimation sample is 26,668 observations.
The covariates included in Xit are as follows: age (and its square), an indicator variable for married or cohabitating status, a dummy variable for indigenous language as mother tongue by the individual, a dummy for whether the individual is currently enrolled in school, a dummy for single-parent household, an indicator for female-headed household, number of household members, number of children under the age of 6 in the household, and the household dependency rate, defined as 1 minus the ratio of income earners over total members of the household. A possible concern of including time-varying covariates is that they could be endogenous to the treatment. Thus, we run regressions without covariates, with results that are in line with the ones we obtain. The results from these regressions are available upon request.
In all regressions, we cluster the standard errors at the district level × female level to allow for arbitrary correlation within districts by gender.
The propensity score is estimated by a logit regression with the following conglomerate-level covariates: percentage of households who use gas as cooking fuel; are connected to a public source of electricity; have a toilet inside the premises; have a water connection; have mud, wood, or other low-quality material walls; have dirt of bare concrete floor; live in an apartment; live in rental housing; the average number of rooms in the premises, members of the household, years of education of the working age population (18–64), years of education of household head, age of household head; percentage of indigenous population, female-headed households; first principal component of household assets and services; establishments per inhabitants (in logs); value added per employed individuals (in logs); conglomerate strata according to the poverty map; average income per capita according to the poverty map; road density; and share of high-skilled activities and non-tradable activities.
We drop those conglomerates for which the propensity score is lower than an optimal cutoff value q or higher than (1-q). We obtain the value of q following Crump et al. (2009), 0.104—close to the rule of thumb suggested by Crump et al. (2009), 0.10. This implies that our estimation sample decreases by 5330 observations to 21,338 observations.
The IHS transformation of yi is equal to \( \log \left({y}_i+{\left({y}_i^2+1\right)}^{1/2}\right) \) (see Burbidge, Magee, and Robb (1988) for details).
This index groups six dummy indicators associated with formal employment and type of occupation: firm keeps accounting books, firm is registered, firm has more than five employees, employee contributes to social security, employee has a contract, and occupation is in the top 25% occupation rank according to average earnings per hour.
Although infrastructure investments may bring some anticipated effects, our data do not lend well to study anticipated shocks. The ENAHO started providing geo-localization of households only in 2007, and this is the key variable that allows us to differentiate between households in treated and control areas. Moreover, the announcement of both infrastructure projects is much earlier. Line 1 was originally designed in the early 1980s. Its construction started in 1986 but was stalled due to the start of an economic and political crisis that lasted until the early 1990s. Construction was only resumed in 2010. The BRT was planned since 1996 and its construction started in 2006. Thus, our data unfortunately cannot capture anticipation effects. If these effects existed, we would probably be potentially underestimating the impacts of these projects if anticipation impacts are positive.
The full results for all regressions are available upon request.
Results before overlap are consistent with those after imposing overlap. For the sake of space, the full set of results before imposing overlap is available upon request.
When regression models have log transformed outcomes, the impact of a one-unit change in a covariate (X) is calculated by exponentiating the coefficient. In this case, the interpretation of impacts should be done as exp. (\( \hat{\beta} \)) − 1. For example, for a coefficient of 0.23, the effect is calculated as exp. (0.23) − 1 = 0.26. When the estimated coefficient is less than 0.10, the simple interpretation that a unit increase in X is associated with an average of 100*\( \hat{\beta} \) percent increase in Y works well. When the coefficient is above 0.10, the simple interpretation will underestimate effects. For simplicity, we report percentage changes using the simple interpretation throughout the text.
This includes small firms registered in special categories with small tax burden.
We are only able to conduct this analysis with the 2015 data only, as the National Economic Surveys do not repot the geolocation for firms for prior years. The hypothesis of equality of distributions cannot be rejected by the Wilcoxon rank-sum test (z = 1.36).
These results should be taken with caution, given that the two systems run parallel and very close to each other and there could be some ambiguity in treatment assignment (i.e., it is not clear which of the two systems would a person relatively close to both take for their work trips).
The 2004 Lima and Callao Master Transport Plan provides an individual-level trip diary survey for a sample of 35,000 households. This means that for each person in the sample, we know the purpose of each trip (e.g., work, leisure, shopping) travel time and mode of transport taken in the 24 h prior to the survey. The Master Transport Plan defined 427 traffic analysis zones (TAZ) around the city to be used as origin or destination for all trips in the survey. Thus, we identify the cluster of TAZ that receives the largest number of work-related trips using Anselin’s local indicators of spatial association and designated that as the “job city center.” We then calculate the average travel time to this cluster from every other TAZ using the reported travel times from the survey.
It is possible that feeders to the BRT act as a faster (when combined with the trunk) but not safer mode of transport (when compared to the trunk). We test this hypothesis by selecting individuals close to the BRT feeders and add them to our estimations as an additional treatment group. None of the “feeder” interactions are statistically significant. Although this might suggest that safety is playing a role in the results obtained, we cannot unequivocally conclude that gains in speed are not as important. The reason is that gains in speed for populations in the feeder might come only from those combining a feeder and a trunk trip, and we are not able to distinguish this in the ENAHO data.
We explore whether women living in a household with a high dependency ratio (above the 75th percentile of an index defined as the proportion of non-income earners over total members of the household) react differently after the intervention and find no difference in results.
Among the dimensions monitored by the LCM survey, there is a categorical question on the time required to travel to a main destination compared to the previous year. In the answer, individuals choose between three categories: (i) it takes longer, (ii) it is the same, and (iii) it takes less time. We re-classify this variable to take the value of one for those who responded it takes less time (iii) and zero otherwise. We compare mean differences for this new variable between users and non-users of BRT/Metro for men and women, over time, in Figure A2 in the online appendix.
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Acknowledgments
The authors would like to thank four anonymous reviewers for the useful comments on earlier drafts of the paper and Rafael Capristan for his support with the meetings and interviews with relevant government authorities and in procuring access to key data for this research.
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The Inter-American Development Bank (IDB) provided funding for this project through the ESW RG-E1502.
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Martinez, D., Mitnik, O.A., Salgado, E. et al. Connecting to Economic Opportunity: the Role of Public Transport in Promoting Women’s Employment in Lima. J Econ Race Policy 3, 1–23 (2020). https://doi.org/10.1007/s41996-019-00039-9
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DOI: https://doi.org/10.1007/s41996-019-00039-9