Academic journal article The Journal of Developing Areas

Distributional Effects of Agricultural Infrastructure in Developing Countries: Large Irrigation Dams and Drought Mitigation in Nigeria

Academic journal article The Journal of Developing Areas

Distributional Effects of Agricultural Infrastructure in Developing Countries: Large Irrigation Dams and Drought Mitigation in Nigeria

Article excerpt


Irrigation is an important technology that enables both agricultural intensifications and rainfall risk mitigation. Dams provide one of the important sources of irrigation water. In 2000, approximately 30% of irrigated areas in the world (close to 40% in some countries like India) was irrigated by dams (Duflo and Pande 2007). There have been continued interests by the donors in the investments into the improved utilization of irrigation dams in SSA countries like Nigeria (FAO et al. 2014). The knowledge about the impacts of dams for these countries remains critical in better informing such efforts.

Potentially important aspects of the effects of large irrigation dams are spatial distributions of the benefits, and dimensions of benefits at household levels. Past studies assessed them separately. Using district level or land-pixel level statistics, Duflo and Pande (2007) and Strobi and Strobi (2011) show that there are distributional effects of large irrigation dams across districts or drainage basins, but the similar evidence is absent for household level outcomes. Other studies focusing on household level outcomes show multi-dimensional benefits of irrigation, ranging from agricultural incomes, household incomes, consumptions, or benefits through mitigations of weather-related risks (Dillon 2011; Takeshima and Yamauchi 2012), but do not assess how they are affected by the locations of large irrigation dams and drainage basins. Evidence is scarce regarding whether large irrigation dams lead to similar household level benefits, and whether such household level benefits are distributed at regional levels, particularly across drainage basins.

We partly fill this knowledge gap using Living Standard Measurement Study (LSMS) survey data in Nigeria, as well as various spatial data on dams, drainage basins, and drought occurrence that remains one of the major weather-related risks in SSA. Nigeria is an appropriate case. It has more than 200 dams among which 57 are "large irrigation dams" (typically defined as dams that are 15 m or more in heights and / or have reservoir capacities of 3 million m3 or more (Duflo and Pande 2007)), one of the highest in SSA (FAO 2015; FMWR 2007). Substantial shares of the population in Nigeria live in drainage basins that are associated with these large irrigation dams. It had also invested as much as 3 billion US dollars between 1970s and 1980s for the constructions of dams (Pradhan 1993, p.21). However, little rigorous impact assessment has been conducted in Nigeria. Lastly, LSMS allow comparisons of households in dam-related basins across wide geographical regions, which improve the external validity of the findings, providing more applicable implications to other regions in SSA.

Most large irrigation dams in Nigeria had already been built by early 1990s. This poses some challenges in identifying their impacts using recently collected data like LSMS. We address this issue by constructing suitable counterfactual samples with varying spatial relationships to dams, so that their comparisons can mimic the comparisons of the same households before and after the dam constructions. Following Strobi and Strobi (2011) we identify three types of drainage basins in Nigeria; dam-basins (basins that contain large irrigation dams or those located upstream of such basins), down-stream basins (those located immediately downstream of dam-basins), and non-dam basins which are neither dam-basins nor down-stream basins, and apply multiple-treatment inverse probability weighting method (MIPW) (Cattaneo 2010; Cattaneo, Drukker & Holland 2013) that jointly estimates the treatment effects for a household of switching from one type of basin to the other. We further improve sample matching quality by focusing on the first differences of outcome indicators by exploiting the panel structure of each of waves 1 and 2 of LSMS.

Our analyses suggest that in 2010 and 2012, households in downstream basins were relatively less affected by the drought and enjoyed relatively stable between-season growth rates of real per capita income and food consumption, compared to what they would have experienced had they been in other types of basins. …

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