Evans School Policy Analysis & Research Group (EPAR)

Common aid allocation formulas incorporate measures of income per capita but not measures of poverty, likely based on the assumption that rising average incomes are associated with reduced poverty. If declining poverty is the outcome of interest, however, the case of Nigeria illustrates that such aid allocation formulas could lead to poorly targeted or inefficient aid disbursements. Using data from the World Bank and the Nigerian National Bureau of Statistics, we find that while the relationship between economic growth and poverty in Nigeria varies depending on the time period studied, overall from 1992-2009 Nigeria’s poverty rate has only declined by 6% despite a 70% increase in per capita gross domestic product (GDP). A review of the literature indicates that income inequality, the prominence of the oil sector, unemployment, corruption, and poor education and health in Nigeria may help to explain the pattern of high ongoing poverty rates in the country even in the presence of economic growth. Our analysis is limited by substantial gaps in the availability of quality data on measures of poverty and economic growth in Nigeria, an issue also raised in the literature we reviewed, but our findings support arguments that economic growth should not be assumed to lead to poverty reduction and that the relationship between these outcomes likely depends on contextual factors.

We review the current body of literature exploring the theories behind holistic human development measurements and the tradeoffs of different methodologies for the construction of human development indices. Through a systematic review of published and grey literature in the fields of human, international, and economic development we identify 22 current indices that aggregate measures from multiple dimensions of human development. We then analyze these indices to identify tradeoffs related to their unique characteristics and construction methodologies, considering ease of calculation, coverage of different measures of human development, ease of interpretation, comparability, and novelty. The report is accompanied by an appendix of summary tables for each index with further details regarding background information, methodology, index components, and evaluation criteria addressed within the report.

Consumer attitudes are a key component in private sector market segmentation. Knowledge about consumers’ tastes can lead to better product design and more effective communication with target markets. Similarly, evidence suggests that farmers’ attitudes influence whether they adopt productivity-increasing technologies. Using consumer insights from the private sector, agricultural intervention programs can use market research, product development, and communication strategies to better understand farmers as consumers and best target interventions. This brief provides an overview of how farmers' attitudes affect their willingness to adopt new technology, and how knowledge of farmer attitudes can improve program design and implementation.

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.