Rural Roads, Poverty, and Resilience: Evidence from Ethiopia

Abstract

This study analyses the linkage between the recent rural road development and household welfare, resilience, and economic conditions in Ethiopia. The empirical approach relies on a difference-in-differences matching method, taking advantage of a nationally representative household survey and an original road database, both of which are panel data spanning the period 2012–2016. The results of the econometric analysis suggest that Ethiopia’s rural road development was associated with a significant increase in household welfare or significant smaller losses in household consumption during the severe droughts. In addition, rural roads in very remote areas were associated with farmers’ sales of a larger share of their harvests and higher chance of fertiliser use. Rural road development was also associated with a higher likelihood of earning income from wage employment, particularly for women and youth. Taken together, the results suggest that, by connecting remote communities to markets and the main road network, rural roads have substantially supported the welfare and resilience of rural households in shock-prone Ethiopia.

Acknowledgements

The authors would like to thank Ruth Hill and two anonymous referees for useful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organisations, or those of the Executive Directors of the World Bank or the governments they represent.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplementary Materials

Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2020.1736282

Notes

1. Moreover, it is often hard to keep track of the rural road development across time and space if the program covers the entire area of a country. It is becoming common to rely on satellite imageries in economic studies (Donaldson & Storeygard, 2017), though rural roads – which are community roads – and their changes in quality are hard to identify with such an approach because of their narrow and short paths.

2. There are some other studies investigating the impacts of rural road access in the developing world. Jalan and Ravallion (2002) find positive impacts of rural roads on consumption growth in China. A study of Vietnam by Mu and van de Walle (2011) found positive impacts of rural road rehabilitation on local market development. Damania et al. (2016) found that transport cost reduction encourages farmers to adopt modern technologies in Nigeria.

3. Aggarwal (2018) finds lower prices, increased availability of non-local goods, increased use of agricultural technologies, and increased school enrolment among younger children in districts where the PMGSY program was implemented. Another study of the PMGSY by Shamdasani (2016) finds crop diversification among farmers in connected villages.

4. A ‘kebele’ is a subdivision of woreda and the smallest administrative unit in Ethiopia. It is similar to a ward, a neighbourhood, or parish in other countries. We refer to it as community in the remaining part of this paper. Woreda is an administrative unit of one level higher than kebele.

5. In addition to the URRAP, the Productive Safety Net Program (PSNP) – a large social protection project providing transfers to food insecure areas – also has a component of rural road construction. Those roads are generally earth roads for very low traffic levels, connecting watersheds to kebeles and other service centres. Some feeder roads were constructed under the Agricultural Growth Program (AGP), which is a program focusing on increasing sustainable agriculture growth in potentially rich, but underdeveloped woredas of the country. See World Bank (2018b) for a comprehensive review of Ethiopia’s rural road programs.

6. The identification of URRAP and other rural roads is based on the Ethiopia road database. The total length of those roads does not necessarily correspond to the official records. Somali region and a part of Amhara and Oromia regions are not included for this study due to data availability.

7. The RAI was measured by the share of population in each community who lives within 2 km from any all-weather road. The calculated RAI differs from World Bank (2016) due to the definitions of all-weather roads, as well as the coverages of roads and geographic areas.

8. Market accessibility index (MAI) was calculated for each community as the sum of the population in the other communities within a certain travel time, as in Donaldson and Hornbeck (2016) and Berg, Blankespoor, and Selod (2016).

9. Development of non-rural roads, such as regional corridors, is not considered as the treatment. Iimi, Mengesha, Markland, Asrat, and Kassahun (2018) is an example that analyses the impacts of road access by distinguishing rural roads and regional corridors in Ethiopia.

10. No spatial spillover is a key assumption for the identification of the impacts of rural roads (stable unit treatment value assumption, or SUTVA).

11. In the ESS data, a lot of households reported that they had been affected by drought during the last 12 months. In the study area, 29 per cent of the households were affected by drought in 2015/16. However, such measure of drought exposure reported by households – and even by communities – can be susceptible to measurement errors, and the errors can be correlated with the respondents’ income levels if lower-income households are more likely to overstate the drought impact and/or higher-income households tend to understate the impact.

12. NDVI was calculated based on the MODIS Terra satellite data.

13. Agroecological zones include tropic-warm/arid zone, tropic-warm/semi-arid zone, tropic-warm/subhumid zone, tropic-cool/subhumid zone, tropic-cool/semi-arid zone, and tropic-cool/humid zone (HarvestChoice and IFPRI, 2016).

14. Panel (A) of Figure A1 in the Appendix shows the distribution of propensity scores for both treated and control groups. It is visually clear that communities in the treated group are concentrated in higher scores, whereas many communities in the control group are in lower scores. Panel (B) of Figure A1 shows propensity scores for the matched households.

15. The Epanechnikov kernel function with a bandwidth of 0.06 is specified for the analysis. Robustness against the choice of bandwidth is examined in Section 5.3.

16. Among the covariates are the controls for assistance, including the dummy indicator about whether the household received a cash transfer from the PSNP during the last 12 months; the dummy indicator whether any member of the household participated in the PSNP labour work during the last 12 months; and the natural logarithm of the amount of transfer received by the household during the last 12 months. All these variables are based on self-reports in the ESS.

17. Travel time to the nearest town is calculated by assuming different travel speeds for various road types in the Ethiopia road database. Foot-based speed is used for the path from the centroid of the community to the nearest road.

18. The results based on different sets of specifications are reported in Table A3 in Appendix. The coefficient estimate for the interaction term (β₃ in Equation (3)(3) $y_{ijt}=\alpha +{\beta }_1\mathrm{R}\mathrm{O}\mathrm{A}{\mathrm{D}}_j+{\beta }_2\mathrm{P}\mathrm{O}\mathrm{S}{\mathrm{T}}_t+{\beta }_3{\left(\mathrm{R}\mathrm{O}\mathrm{A}\mathrm{D}\times \mathrm{P}\mathrm{O}\mathrm{S}\mathrm{T}\right)}_{jt}+{\beta }_4{X}_{ijt}+{\epsilon }_{it}$(3) )(3) $y_{ijt}=\alpha +{\beta }_1\mathrm{R}\mathrm{O}\mathrm{A}{\mathrm{D}}_j+{\beta }_2\mathrm{P}\mathrm{O}\mathrm{S}{\mathrm{T}}_t+{\beta }_3{\left(\mathrm{R}\mathrm{O}\mathrm{A}\mathrm{D}\times \mathrm{P}\mathrm{O}\mathrm{S}\mathrm{T}\right)}_{jt}+{\beta }_4{X}_{ijt}+{\epsilon }_{it}$(3) between the dummy variable about the rural road indicator (ROAD) and the year dummy (POST) indicates the association between rural road development and household consumption changes between 2012 and 2016. The coefficient estimate in the baseline result with no control variable in column 1 (0.135) indicates that the mean consumption in the treated communities is 14.5 per cent higher than that in the control communities. The coefficient estimate remains at the similar level when additional control variables are added, such as household characteristics (column 2), drought shocks (column 3), and assistance (column 4). Compared to the results based on the unmatched sample above, the results for the matched sample show slightly stronger association.

19. A recent case study of Ethiopia road development by Iimi et al. (2018) finds that farmers’ access to input markets (including fertilisers) was improved mainly by main corridor improvement instead of feeder road development.

20. Results are available upon request.