Evans School Policy Analysis & Research Group (EPAR)

EPAR has developed Stata do.files for the construction of a set of agricultural development indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). We are sharing our code and documenting our construction decisions both to facilitate analyses of these rich datasets and to make estimates of relevant indicators available to a broader audience of potential users. 

This report reviews approaches to results measurement used by multilateral and bilateral donor organizations and highlights trends and gaps in how donors measure and report on their performance. Our review consists of assessing donor organizations in terms of their institutional design and levels of evaluation for results measurement, their organizational processes for measuring types of results including coordination and alignment with recipients, outputs and implementation, outcomes and impacts, and costs and effectiveness, and their processes for reporting and using results information. We collect evidence on 12 bilateral organizations and 10 multilateral organizations. The evidence review includes multi-country reviews of aid effectiveness, peer reviews by other donor organizations, donor evaluation plans and frameworks, and donor results and reporting documents. The report is based on an accompanying spreadsheet that contains the coded information from the 22 donor organizations. We find that donors report several types of results, but that there are challenges to measuring certain results at the aggregate donor level, due to challenges with funding and coordination for results measurement at the project, country, portfolio, and donor levels. Approaches to results measurement vary across donor organizations. We identify some trends and differences among groups of donors, notably between bilateral and multilateral donors, but overall there are no clear delineations in how donors approach results measurement. 

Aid results information is often not comparable, since monitoring and evaluation frameworks, information gathering processes, and definitions of “results” differ across donors and governments. This report reviews approaches to results monitoring and evaluation used by governments in developing countries, and highlights trends and gaps in national monitoring and evaluation (M&E) systems. We collect evidence on 42 separate government M&E systems in 23 developing countries, including 17 general national M&E systems and 25 sector-specific national M&E systems, with 14 focused on HIV/AIDS, 8 on health, and 3 on agriculture. The evidence review includes external case studies and evaluations of M&E systems, government M&E assessments, M&E plans, strategic plans with an M&E component, and multi-country reviews of M&E, accountability, and aid effectiveness. We evaluate harmonization of government and development partner M&E systems, coordination and institutionalization of government M&E, challenges in data collection and monitoring, and analysis and use of results information. We also report on key characteristics of M&E systems in different sectors. 

This brief explores how two datasets – The Tanzania National Panel Survey (TZNPS) and the TNS-Research International Farmer Focus (FF) – predict the determinants of inorganic fertilizer use among smallholder farmers in Tanzania by using regression analysis. The (TZNPS) was implemented by the Tanzania National Bureau of Statistics, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team and includes extensive information on crop productivity and input use. The FF survey was funded by the Bill and Melinda Gates Foundation and implemented by TNS Research International and focuses on the on the behaviors and attitudes of smallholder farmers in Tanzania. The two datasets produce relatively comparable results for the primary predictors of inorganic fertilizer use: agricultural extension and whether or not a household grows cash crops. However, other factors influencing input use produce results that vary in magnitude and direction of the effect across the two datasets. Distinct survey instrument designs make it difficult to test the robustness of the models on input use other than inorganic fertilizer. This brief uses data inorganic fertilizer use, rather than adoption per se. The TZNPS did not ask households how recently they began using a certain product and although the FF survey asked respondents how many new inputs were tried in the past four planting seasons, they did not ask specifically about inorganic fertilizer.