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The evaluation of policy performance against set targets is rarely adjusted to the heterogeneity in the initial distribution of characteristics. Building on previous literature, we propose a framework to account for differences in initial characteristics in evaluating policy performance. We apply the proposed framework to the appraisal of poverty reduction and show that initial characteristics can considerably affect performance. The framework advances by explicitly quantifying the importance of the non-linearity of the growth elasticity of poverty reduction. Whilst wealthier countries did perform better in reducing poverty during 1995–2008, after equalizing the mean of the initial distribution of income the situation reverses, with the poorest countries going from being the worse to being the best performers in poverty reduction.

The Millennium Development Goals (MDGs) target date of 2015 is rapidly approaching. Despite an increase in health and education funding, countries' inabilities to meet health and education goals (which make up more than half of the MDGs) put increased emphasis on the measurement of service delivery. The inabilities of countries in Sub-Saharan Africa to meet the target of reducing poverty by half has put emphasis on the importance of understanding how initial conditions impact a country's ability to meet this benchmark. This dissertation will add to this research. Chapter 2 introduces a new approach to the measurement of service delivery. This chapter introduces the Service Delivery Underperformance Index (SDUI) which measures the underperformance, or multiple inadequacies, in service delivery. Although there has been much work that has discussed how inadequacies in service delivery impact health and education outcomes no work has been done on the measurement of poor performance in the delivery of services. The measurement of the poor performance of service delivery can lead to identification of problems in the delivery of services and draw attention to them. The SDUI adapts the Alkire and Foster (2011) methodology used for poverty measurement to service delivery. The Alkire and Foster (2011) methodology is used because it allows the index to satisfy numerous properties that a measure of underperforming service delivery should have, including having a focus on underperformance, dimensional monotonicity, subgroup decomposability, and decomposability by dimensions and indicators. The possible dimensions and indicators of underperforming service delivery are discussed and it is shown how the Alkire and Foster (2011) methodology is applied to calculate the index using facilities as the unit of analysis. It is shown how statistical significance tests can be done to determine the statistical significance of the rankings determined by the SDUI. Chapter 3 uses the SDUI introduced in Chapter 2 to analyze underperforming healthcare delivery using data from Demographic and Health Surveys Service Provision Assessment. By applying the index, this chapter demonstrates how the SDUI can be used with a data source and how the inadequate delivery of services can be compared across countries and within a country. A cross-country comparison of healthcare delivery is done for Rwanda, Uganda, and Tanzania, where it is found that Rwanda has the best performing healthcare delivery despite being ranked below Tanzania and Uganda in terms of some health outcomes. A more extensive analysis of healthcare delivery in Rwanda shows that there are significant disparities in the performance of different types of facilities. The SDUI is used to evaluate facilities in Rwanda that did or did not participate in policies intended to improve healthcare delivery, and it is found that facilities that participated in community involvement performed better than facilities that did not. This observation calls for future work to be done using the SDUI as an impact evaluation tool to analyze how policies impact underperforming healthcare delivery. Chapter 4 applies the Service Delivery Underperformance Index introduced in Chapter 2 to the education sector of Papua New Guinea (PNG). By applying the SDUI to a country data source for the education sector, the index demonstrates its ability to make within-country comparisons of the delivery of education services and target facilities that are delivering the poorest services to populations for policy purposes. Results from this analysis show that there are significant differences in the delivery of education services across different managerial types. Because the education sector in PNG is highly decentralized, the SDUI is decomposed further to analyze rankings of different type of facilities and different managerial authorities within provinces. There are significant differences across managing authority and types of facilities depending on the province. The SDUI is used to evaluate facilities in PNG that did or did not participate in multigrade classroom use and that did or did not have community involvement. These are two policies that the government was interested in expanding at the time of the survey. It is found that facilities that participated in either policy performed better than facilities that did not. This observation calls for future work to be done using the SDUI as an impact evaluation tool to analyze how policies impact underperforming education delivery. Chapter 5 creates a framework to quantify the impact that poor initial conditions have on a country's ability to achieve inclusive growth, i.e. how much initial conditions affect a country's ability to reduce poverty given a level of growth. To do this, the framework calculates counterfactual poverty reduction when all countries are given the same initial conditions but still maintain their original growth rates. Results show that, due to initial conditions it is much more difficult for these countries to achieve inclusive growth. After equalizing initial conditions poorer countries are much more able to achieve high rates of poverty reduction given their original growth rates. This holds for different measures of poverty as well as different poverty lines.

Adopted on September 8 of 2000, the United Nations Millennium Declaration stated as its first goal that countries \"...[further] resolve to halve, by the year 2015, the proportion of the world's people whose income is less than one dollar a day and the proportion of people who suffer from hunger...\" Each country committed to achieve the stated goal, regardless of their initial conditions in terms of poverty and inequality levels. This paper presents a framework to quantify how much initial conditions affect poverty reduction, given a level of \"effort\" (growth). The framework used in the analysis allows for the growth elasticity of poverty to vary according to changes in the income distribution along the dynamic path of growth and redistribution, unlike previous examples in the literature where this is assumed to be constant. While wealthier countries did perform better in reducing poverty in the last decade and the half (1995-2008), assuming equal initial conditions, the situation reverses: we find a statistically significant negative relation between initial average income and poverty reduction performance, with the poorest countries in the sample going from the worse to the best performers in poverty reduction. The analysis also quantifies how much poorer countries would have scored better, had they had the same level of initial average income as wealthier countries. The results suggest a remarkable change in poverty reduction performance, in addition to the reversal of ranks from worse to best performers. The application of this framework goes beyond poverty targets and the Millennium Development Goals. Given the widespread use of targets to determine resource allocation, in education, health, or decentralized social expenditures, it constitutes a helpful tool to measure policy performance towards all kinds of goals. The proposed framework can be useful to evaluate the importance of initial conditions on outcomes, for a wide array of policies.