Impact of Pedestrian Footbridges on Economic, Health and Educational Outcomes in Rural Communities

Isolation caused by lack of transportation infrastructure affects almost every facet of life for the rural poor. Without adequate transportation access, families cannot access schools, health care, employment, or local markets to sell and buy goods. The World Bank estimates that nearly a billion people worldwide lack access to an all-season road within two kilometers, illustrating the scope of the problem, and the challenge of addressing it at scale.

Bridges to Prosperity (B2P) is a non-profit organization that builds footbridges to connect rural communities facing isolation to road networks and critical destinations and services. B2P has constructed more than 280 footbridges in 20 countries, an infrastructure intervention that is cost-effective, durable, and relatively simple to scale. B2P's field program in Rwanda started in 2012 and has led to the completion of 37 footbridges that have created new safe access for an estimated 225,000 people. Over the next five years, B2P plans to construct approximately 350 footbridges in Rwanda. This rapid program growth presents an unprecedented opportunity for rigorous investigation of the effects of new footbridges on a number of key economic, health, agricultural and education outcomes for rural communities.

As such, the research team has been brought on to carry out an impact evaluation of B2P-constructed footbridges in rural Rwanda. This protocol is for the first phase of the study and will focus on 12 footbridge sites and 12 comparison sites over the course of one year, while the larger study will encompass approximately 350 sites over the planned five-year construction period. The results of this first phase will inform the design of the larger study.

The study will be a matched-cohort study, in which twelve bridge sites will be matched to twelve comparison sites. The bridge sites have been defined and identified by B2P staff in conjunction with government officials through a systematic needs assessment carried out in 2014. Of the twelve bridge sites that have been confirmed for the study, seven are located in the Southern Province, and five are located in the Western Province. Comparison sites were identified from a 2018 needs assessment carried out by B2P. As with the bridge sites, seven of the comparison sites are in the Southern Province and five are in the Western Province. The table of bridge and control sites with their respective districts and provinces can be found in Appendix A.

The minimum detectable effect analysis for outcomes of interest determined that, with these 24 sites, the investigators should survey representatives from 100 households in each treatment site (those receiving a bridge) and comparison site (those that will not). For both treatment sites and comparison sites, B2P will identify the impacted side - that which their assessment indicates would benefit the most from the bridge. Investigators will then obtain the administrative list of households from the umudugudu chief (village leader) or cell secretary for the village that is closest to the bridge on the impacted side. If the list consists of at least 100 households, investigators will randomly select the 100 households to visit from this list. If the village has fewer than 100 households, enumerators will visit all households in that village and then, from the list for the next closest village, randomly select the remaining number required to reach 100 households.

As part of the pilot study, investigators are testing an experimental method to determine which households to sample within a community. This method involves tracing satellite imagery of the relevant communities to derive a vector dataset of all the households in the area, from which investigators randomly select certain ones based upon an algorithm. This approach is intended to make the sampling more robust by reducing the opportunity to introduce human bias. The tracing of homesteads will be done using freely available Bing aerial imagery and the data generated will be contributed to OpenStreetMap (OSM), in accordance with the Bing imagery license for OpenStreetMap. While all the households traced will be uploaded to OSM and be publicly available, all subsequent analysis of which households are to be surveyed and survey results themselves will be kept in private on password-protected devices and will only be used for the purposes of the study.

The study will utilize a difference-in-difference empirical design. That is, it will compare changes over time in communities that receive a bridge against those that do not. This is recovered from a linear regression:

y_vt = a + β\*Bridge_vt + E_vt In this equation, y_vt is some outcome of interest in community v at time t and Bridge_vt is an indicator of community v has a bridge at time t. Under the identification procedure described above, the coefficient β estimated in the above regression identifies the causal effect of bridge construction.

Based on the findings of Brooks and Donovan 2018, investigators are interested in exploring not only economic and agricultural outcomes but also using this larger opportunity to explore health and educational outcomes as well.

To utilize the difference-in-difference method, data is required for at least two points in time from all households in the study. Therefore, baseline surveys will be conducted before any bridges are built, followed by a second round of data from the same households after bridges are built, and a third round of data collection a year after the baseline. This will take place in both treatment communities and comparison communities.

The baseline survey will be conducted in February-March 2019. This corresponds with the main harvest period in Rwanda, allowing for the collection of specific information on agricultural outcomes while that information is still fresh in the minds of community members surveyed. The completion of bridges will begin in late February 2019 and continue through July 2019. A midline survey will be carried out in June-July 2019. And end-line data will be collected in February-March 2020. The same survey will be used at all three time points, thus allowing the testing of any differences that occur over time.

The survey will be carried out by trained enumerators fluent in Kinyarwanda on tablet computers. Once completed, the surveys will be uploaded directly to a secure server in the United States for analysis. A detailed list of the questions can be found in the attached survey.

As noted in the list of outcomes above, mid-upper arm circumference (MUAC) is one of the health outcomes of interest. MUAC is an easy body measurement that helps to monitor nutrition, and the nutritional status of children under five is a proxy indicator of the community's nutrition. However, MUAC is not used for children under six months of age because there are not established nutrition cutoff levels for this age group. With consent from the primary caregiver, MUAC will be measured for every child in the household who is between the ages of six months and five years. The instructions for this process are included in the survey.

In June 2019, cameras will be installed at the entrance to bridge crossings that will take pictures as individuals cross the bridges. This data will be analyzed to estimate bridge uses, allowing us to investigate the potential correlation between bridge use and economic, agricultural, health and educational benefits. The data from the cameras will be stored on local digital storage devices. The cameras will be locked in place. The camera data will be collected by study staff periodically and uploaded to secure, password protected databases. The pictures will be analyzed by computer and by study team review to count the number of users of the bridge. No personally identifiable information will be published in any form.

Eligibility Criteria:
Inclusion Criteria:

* Random selection of first 100 households in a village from administrative data
* Head of household in intervention and comparison villages

Exclusion Criteria:

* None