Final Report on Tajikistan Water Users Association Impact Evaluation

From 2004 to 2014, with the support of the U.S. Government’s Feed the Future initiative, USAID funded key components of the creation and support of water users associations (WUAs) in southern Tajikistan, as well as the training of members. Following the draw down in these investments, USAID commissioned the International Water Management Institute (IWMI) to examine the impact of USAID-supported WUAs created on sustained increases in resource productivity, food security and equity in southern Tajikistan.
“This evaluation is one of the first to look at the impact of our investments upon the drawdown of Feed the Future support,” commented Tatiana Pulido, USAID’s Feed the Future Country Monitoring and Evaluation Team Leader and Advisor to USAID’s Feed the Future Tajikistan program. “We have an exciting opportunity to measure some of the initial impacts on the sustainability water goverance and productivity through this ex-post evaluation.”
The final report (see sidebar accompanying this blog) provides a synthesis of these findings and related recommendations, summarized below. Visit the evaluation project’s resource page for related journal articles, event recordings and affiliated reports in multiple languages. Visit the video links in the sidebar for further reflections on key findings by the IWMI research team.
How Water User Associations Work
WUAs represent a form of community-based water management organization where the typical member is a private farm (dehkan). WUAs have a number of mandated functions related to the allocation and conveyance of irrigation water through canals and other delivery works, including scheduling, maintenance activities, fee collection and dispute resolution.
Impact Evaluation: Four Key Research Questions
The impact evaluation examines intermediate and short-term outcomes, focusing on four principal research questions, outlined along with the findings below.
Methodology
The impact evaluation utilizes a difference-in-difference, mixed methods approach to answer four key questions. It relies upon the collection and analysis of primary cross-sectional data collected at two intervals from farms and WUAs, first in 2015 and then again in 2017. Other forms of data gathering and investigation, such as the use of cross-sectional data from kitchen gardens and local government officers, focus group discussions and interviews with key informants also play a role. A carefully designed sampling program for the implementation of face-to-face interview surveys gathered most of the data to support descriptive analysis and hypothesis testing.
Findings
1. What are the impacts of the USAID‐supported WUAs on land and water management outcomes?
As measured by changes between the years 2014 and 2016, USAID WUAs are associated with an increased rate in the collection of membership fees. USAID WUAs increased their collection of fees from existing members by 19 percent more than non-USAID WUAs. As compared to non-USAID WUAs, USAID WUAs were also more like to have a seasonal water delivery plan; hold board meetings to plan water delivery activities; conduct pre-season canal cleaning and maintenance; and collect irrigation fees on behalf of the district irrigation departments. Therefore, USAID WUAs experienced greater improvements in performance than non-USAID WUAs.
An analysis of WUA participation conducted on a sample of 1,855 dehkan farms demonstrated that farms served by USAID WUAs performed better: there was an eight percent higher increase in the likelihood of payment of membership fees; a 20 percent higher increase in signing a water contract; and a nine percent higher increase in sending a farm representative to a WUA meeting. Farms served by USAID WUAs also increased their contributions of labor towards maintaining and cleaning canals by seven person-days more than the increase in the contribution made by farms served by non-USAID WUAs.
Farms that received formal training in extension (e.g., those that belong to USAID-funded WUAs) experienced a greater increase in the number of high-value crops cultivated. Translated to the area under high-value crops, farms served by USAID WUAs experienced an increase of 0.14 hectares (ha) than farms served by non-USAID WUAs. There was also a larger increase in an index of crop diversity for dehkan farms that received training. In an environment where cotton produced for export markets is a large incumbent crop choice, a move toward greater diversity almost always implies a move to a larger range of cash crops (in this case, fruits and vegetables).
2. How does the distribution of benefits among members of USAID‐funded WUAs differ from that in non‐USAID project areas?
Farm operators with less than three ha of land in the USAID group perceived the greatest improvements in distribution of water delivery. From 2014-2016, these farm operator’s perception of fairness in water delivery increased by 36 percent in USAID-supported WUA areas, compared to a perception of fairness increase of 24% in non-USAID supported WUA areas.
Additionally, more operators of farms on tertiary canals in the USAID group perceived improvements in the distribution of water, as compared to operators of farms on tertiary canals in the non-USAID group. In 2014, 11 percent of farms in the USAID group perceived water distribution to be “rather fair;” this increased to 16 percent in 2016, a 31 percent increase. In comparison, the perception of water distribution to be “rather fair” increased by 18 percent from 2014-2016 for the non-USAID group.
Taken together, these results suggest that a higher number of smaller farms and spatially disadvantaged farms experienced improvements in water delivery and distribution when they were served by USAID WUAs.
3. How sustainable are the impacts and how do beneficiaries perform over time when active donor support is withdrawn?
Results demonstrate that USAID WUAs supported a larger increase in satisfied farmers than non-USAID WUAs, even after donor support is withdrawn. Two indicators of sustainability are whether the users felt that the water allocations were timely and fair. For instance, there was an increase from 25 percent to 39 percent in the number of farms served by USAID WUAs who perceived the distribution of water to be “rather fair” (third highest of four ratings for fairness). The corresponding increase for the non-USAID group was from 24 percent in 2014 to 32 percent in 2016.
Equally important, an analysis of remote sensing data demonstrated an increase in staple crop production in areas supported by any WUA (not just those funded by USAID) and those without support. From 2010 to 2017, these data demonstrated a sustained increase in the areas allocated to the production of wheat, the staple crop. In 2010, only nine percent of the irrigated crop area with WUAs and five percent of the irrigated crop area without WUAs was under wheat cultivation. By 2017, 21 percent of the irrigated crop area with WUAs was under wheat cultivation. In contrast, the irrigated crop area without WUAs under wheat had only increased to 12 percent.
4. What are the key factors, mechanisms and local specificities that help to understand and explain what did and did not work in the process of bringing about the desired change among the beneficiary groups?
The duration of training and the "packaging" of training in water management and agricultural extension are key to improving water governance and food security. Future food security programming should address:
- The changing role of women in agriculture and food production with careful consideration of women’s time allocation and opportunity costs when evaluating new agricultural practices and technologies.
- The exclusion of numerous household water uses and users from WUAs and from other well-regulated water governance mechanisms.
- The establishment of a solid knowledge base of baseline conditions to describe both the treatment and control groups if an ex-post impact evaluation is desired. The modified difference-in-difference methodology developed and implemented during this study may serve as an example for other programmers and evaluators.