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Do public transport investments promote urban economic development? Evidence from bus rapid transit in Bogotá, Colombia


In 2000, the city of Bogotá, Colombia embarked on a grand land use and transportation system experiment. The transformation of Bogotá included building the TransMilenio Bus Rapid Transit (BRT) system, a city-wide system that offers speed and convenience similar to that of an underground metro. TransMilenio is widely regarded as a success, and cities around the world are planning or building similar systems. In this paper, we use a repeated cross-section labor market dataset to assess whether access to the new BRT system affects the incomes of those who live in station area neighborhoods. Our results indicate that the opening of the TransMilenio system was associated with increased income for those living near—but not immediately adjacent to—trunk line stations. This relationship is strongest in the lower and middle-income range. There are at least two possible explanations for this result: 1. existing residents earn higher wages, or 2. higher income workers move to the neighborhood. Our data do not allow us to distinguish clearly between them, but available evidence suggests that much of the effect is likely due to relocation. Our results stand in contrast to prior work, which has largely suggested that improvements in public transit will tend to reduce wages in station areas.

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Fig. 1
Fig. 2


  1. 1.

    Eligible commute trips include those that are not walk trips and are less than 25 km.

  2. 2.

    Walking distance is characterized here as those transportation analysis zones (TAZs) that have some portion of their area within 1,000 m of a station.

  3. 3.

    About 56 % of the 209,055 observations in the original dataset were dropped due to their unemployment status. We leave for future work the inclusion of unemployed individuals in our analysis, which requires the specification of corner-solution or selection models. The size of our final sample (58,835) also reflects adjustments due to missing values for crucial variables such as age, education level, and type of occupation. Data from the first quarter of 2,000 were not considered reliable and therefore also not considered. Finally, individuals not reporting income and those in the bottom and top 0.5 % of the income distribution in each quarter were not considered in our estimations.

  4. 4.

    We categorized job categories as low or high status in the following way. Low status jobs included driver, construction or factory worker, farmer, security or police officer, server, hotelier, office worker, clergy, and low-level sales and management. High status jobs included professional, medical worker, teacher, creative, business owner, foreman, insurance, and high-level sales and management.

  5. 5.

    Note that a household could be located in tm1 and tm2 for one or more stations simultaneously. We implemented another set of regressions that included variables that take into account the number of stations to which the home is close to. The results are not different from the ones presented in Table 2. We also conducted a sensitivity analysis to explore the effect of different distance band boundaries. Reducing the tm1 upper boundary to 250 or 500 m results in no statistically significant findings for our estimated coefficients on postm2 for any tm2 upper boundary between 500 and 2,000 m in 250 m increments. Increasing the tm1 upper boundary to 1,000 m, however, does not qualitatively change our results. If the tm1 upper boundary is kept at 750 meters, varying the upper boundary of tm2 between 1000 and 1500 m also does not qualitatively change our results. We prefer our original catchment area boundaries since they yield a large number of households falling in each catchment area, increasing the precision of our estimate.

  6. 6.

    The number of observations in estrato 1 that are within the two distance bands before and after TransMilenio is very small. We discarded these data from the subsample analysis.


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This research was supported by grants from the Earth Institute at Columbia University and from the Sustainable Transportation Center at the University of California Davis, which receives funding from the U.S. Department of Transportation and Caltrans, the California Department of Transportation, through the University Transportation Centers program.

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Corresponding author

Correspondence to Deborah Salon.

Appendix: Summary statistics stratified by TransMilenio line

Appendix: Summary statistics stratified by TransMilenio line

See Tables 5 and 6.

Table 5 Selected pre-TransMilenio summary statistics by line
Table 6 Change in selected summary statistics by line between pre- and post-TransMilenio

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Heres, D.R., Jack, D. & Salon, D. Do public transport investments promote urban economic development? Evidence from bus rapid transit in Bogotá, Colombia. Transportation 41, 57–74 (2014).

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  • Income
  • Bus rapid transit
  • Spatial analysis
  • Economic development