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

Rwanda Rural Feeder Roads Impact Evaluation: Creating a Data Ecosystem Maria Jones 18 July 2017. national scope = unique IE opportunity. rehabilitation of rural feeder roads. research questions. improved roads. market efficiency. faster development.

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

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  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!

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