Rwanda Rural Feeder Roads Impact Evaluation: Creating a Data Ecosystem Maria Jones 18 July 2017

1. Rwanda Rural Feeder Roads Impact Evaluation:Creating a Data EcosystemMaria Jones18 July 2017

2. national scope = unique IE opportunity

3. rehabilitation of rural feeder roads

4. research questions improved roads market efficiency faster development What is the impact of feeder road rehabilitation on … … market prices of village imports and exports? (trade economics) … HH adaptations to price changes in terms of goods produced and purchased? (consumer welfare functions) … market valuation of improved road access as measured by aggregate land value changes? (asset pricing approach) … Regional development as measured by total population? (welfare measure from urban economics)

5. design • Event study (at the segment level) • Exact timing of rehabilitation of any particular segment as good as random • idiosyncrasies of donor calendars, construction delays, permitting, and weather • Track exact start and end of construction for each segment • Key explanatory variable is road roughness before and after upgrading • Identification relies on high-frequency market information in catchment area of each road segment • sample restricted to segments located close to an existing market

6. Data

7. household sample • Sample frame: all villages within 1km of road segment • 2 sampled villages per segment • Class 1: close to a market (for identification) • Class 2: very remote, i.e. those whose transport costs are expected to change most with the road rehabilitation • 15 HHs randomly selected in each village

8. HH surveys necessary but not sufficient • IE design • Event study design requires higher frequency data than practical through HH surveys • Scope of research questions • interest in precisely measuring market-wide price changes, which will be difficult for any one household to report • Sample size / budget practicalities • Difficult to know ex-ante which households will benefit most; catchment areas are large and little pre-existing data • impact for any one HH in segment catchment likely small

9. feeder roads: data ecosystem

10. what makes this work? • Collaboration with project team started long before road construction (2012) • Coordinated monitoring data collection plan across donors so data can be easily merged • Rwandan government collects a lot of administrative data, relatively organized • Advantage of one national ID number, used for all government interactions

11. Early Results

12. market integration • At baseline, market integration is poor • Most market vendors report being professional traders (not producers), but the majority sell at only one market • Product availability and price varies within small geographic areas

13. trader occupation

14. trader movement across markets

15. product availability Source: 2017 Price market survey

16. product price variance Source: 2017 Price market survey

17. preliminary HH impacts • First follow-up survey conducted one year after baseline • Few segments actually completed; analysis focuses on initial short-term impacts • Follow-up surveys to be conducted every 1-2 years over the project lifetime

18. preliminary results on HH income • Suggestive evidence that investing in feeder roads allows relatively remote HHs to catch up to relatively more connected HHs • Being located in a remote village decreases HH income by $73 • Mean HH income is $316, so 23.1% decrease • But feeder road rehabilitation increases HH income by $74 (23.4% increase) • Results in a full catch-up to more connected villages • Effect driven by income from HH farm & related activities

19. Thank you!