Catálogo de publicaciones - libros

A critical challenge for all policymakers wrestling with economic development and poverty reduction in Africa—as well as everywhere else in the world—is how to assess which programs and policies actually work. A corollary to this challenge is to identify, among the programs that do work, those that provide the best value for money. Methodological challenges include the development of adequate quantitative economic modelling tools for a comprehensive growth poverty analysis in an economywide framework. An even greater challenge, however, is for the knowledge and insights generated from economic modeling to find their way into the decision making process. In this context, the present volume contains a selection of tools and methodologies that can help to tackle the complexities of the analysis of policy processes and outcomes under the implementation of the CAADP agenda. The contributions go beyond the state-of-the-art methods and tools applied for quantitative policy impact analyses, as they also examine the process behind the choice of policies and the factors that determine the likelihood of their adoption and implementation.

The Common Agricultural Policy of ECOWAS (ECOWAP) was adopted in January 2005, following a close consultation among member states and regional professional organizations. The adoption came <2 years after the launch of the Comprehensive Africa Agriculture Development Program (CAADP) under the New Partnership for Africa’s Development (NEPAD), an initiative of the African Union. In March 2005, ECOWAS organized, in Bamako, Mali, the Regional Implementation Planning Meeting for CAADP in West Africa. The meeting reviewed the objectives, targets, and principles of CAADP and their alignment with ECOWAP, and confirmed the latter as the political as well as institutional framework for the implementation of the former in the West Africa region. In May 2005, ECOWAS and the NEPAD Secretariat developed a joint ECOWAP/CAADP action plan for the period 2005–2010 for the development of the agricultural sector.

With the recent discovery of crude oil reserves along the Albertine Rift, Uganda is set to establish itself as an oil producer in the coming decade. Total oil reserves are believed to be two billion barrels, with recoverable reserves estimated at 0.8–1.2 billion barrels. This is comparable to the level of oil reserves in African countries such as Chad (0.9 billion barrels), Republic of the Congo (1.9 billion barrels), and Equatorial Guinea (1.7 billion barrels) but far short of Angola (13.5 billion) and Nigeria (36.2 billion) (World Bank 2010). Using a conservative reserve scenario of 800 million barrels, peak production, likely to be reached by 2017, is estimated by the World Bank to range from 120,000 to 140,000 barrels per day, with a production period spanning 30 years. A more optimistic scenario in this study is based on 1.2 billion barrels and sets peak production at 210,000 barrels per day (see Wiebelt et al. 2011). Although final stipulations of the revenue sharing agreements with oil producers are not yet known, government revenue from oil will be substantial. One estimate, based on an average oil price of US$75 per barrel, puts revenues at approximately 10–15% of GDP at peak production (World Bank 2010). The discovery of crude oil therefore has the potential to provide significant stimulus to the Ugandan economy and to enable it to better address its development objectives, provided oil revenues are managed in an appropriate manner.

An important problem in causal inference and estimation of treatment effects is identifying a reliable comparison group (control observations) against which to compare those that have been exposed to the treatment (treated observations). It is common knowledge that the estimate obtained by the difference in the values of the indicator of interest associated with the two groups could be biased due to lack of overlap in the covariate distributions or common support between the treated and control observations (Dehejia and Wahba 2002; Imbens and Wooldridge 2009). This is especially problematic with non-experimental control observations (Dehejia and Wahba 2002) in which case combining propensity score matching and regression methods has been suggested to yield more consistent estimates of the treatment effect than using either method alone (Imbens and Wooldridge 2009). Matching removes self-selection bias due to any correlation between the observable (pre-treatment) covariates and the dependent variable, while regression isolates the effect of change in the covariates on change in the dependent variable over the period of the treatment. Using the combined approach, this paper discusses the effect of using different sets of control groups on estimates of treatment effects of the agricultural extension system in Uganda, the National Agricultural Advisory Services (NAADS) program.

While growth is necessary for poverty reduction, the extent to which poverty declines depends on its level and structure as well as characteristics of the poor. Agricultural growth in particular has been shown to be effective at reducing poverty in developing countries. For this reason there is also a perception that agricultural growth improves food and nutrition security outcomes, whether through home-production-for-own-consumption or household income channels. However, evidence in this regard is more mixed. Drawing on economywide modeling analyses, this paper explores methods for analyzing the complex linkages between (agricultural) growth, poverty, and food and nutrition security outcomes in developing country contexts. We find that the structure of growth and linkages between poor or malnourished households and the economy indeed strongly influence welfare outcomes. However, modeling analyses still fall short in accurately characterizing the “utilization” dimension of food security; for example, improvements in health or education associated with growth may improve the responsiveness of nutrition to higher agricultural productivity or household incomes. Some areas for model development and further research are identified.

Analyzing the poverty and distributional impact of macro events requires understanding how shocks or policy changes on the macro level affect household income and consumption. It is clear that this poses a formidable task, which of course raises the question of the appropriate methodology to address such questions. This paper presents one possible approach: A sequential methodology that combines a macroeconomic model with a behavioral micro-simulation. We discuss the merits and shortcomings of this approach with a focus on developing country applications with a short to medium run time horizon.

This chapter develops the eCGPE as a theoretical framework and an empirically applicable tool for defining, evaluating and designing efficient participatory and evidence-based policy processes. The eCGPE is a sequential dynamic political economy equilibrium model that incorporates five modules that model legislative decision making, the transformation of policies into socioeconomic outcomes, interest mediation via voting and lobbying, political belief formation and policy learning. In contrast to existing political economy models, which highlight the biased incentives of politicians as a main cause of persisting inefficient policies, the CGPE approach explicitly incorporates the lack of adequate political knowledge as another important source of inefficient policy choices.

This chapter proposes a network-based framework to analyze and evaluate participatory and evidence-based policy processes. Four network based performance indicators are derived by incorporating a network model of political belief formation into a political bargaining model of the Baron–Grossmann–Helpman type. The application of our approach to the CAADP reform in Malawi delivers the following results: (i) beyond incentive problems, i.e. the lack of governmental accountability and government capture, political performance is limited due to a lack of adequate political knowledge and lack of political ownership. (ii) Changing participation structures implies a trade-off between different aspects of political performance, for example, we found a trade-off between political ownership and the use of political knowledge, shifting constitutional power from the government to the parliament in Malawi. Analogously, increasing political influence of donors implies a more efficient use of political knowledge but results in a simultaneous decrease of political ownership. (iii) There is no blueprint model for designing effective participation structures, hence, the identified causal relationships between structure and performance depend on the specific social, political and economic framework conditions found in Malawi.

This chapter presents an empirical approach towards analyzing the determinants of elite communication networks. Such an analysis allows for the evaluation of real-world participatory policy processes in terms of their informing and distorting nature. A case in point is the participatory policy process leading to the launch of the Comprehensive Africa Agriculture Development Programme (CAADP) in Malawi. Network data were collected via face-to-face interviews with Malawi’s political elite. In this approach, we use an extended binary regression framework, which can deal with missing values inevitably occurring within survey data. In fact, model parameters are estimated using an adaptation of the Bayesian estimation scheme for binary probit models based on the Markov Chain Monte Carlo (MCMC) methodology. Empirical results suggest that (i) homophily in policy interests is not a significant basis for tie choice, (ii) knowledge is an important but not leading determinant of communication, and (iii) the structural embeddedness of organizations within the network mainly determines the formation of ties. Overall, we summarize that, although the policy network formation underlying participatory structures in Malawi clearly discriminates between types of non-governmental organizations, it is not distorted in favor of specific social groups.

This chapter integrates existing political economy approaches that explain the impact of voter behavior on government performance and advanced empirical voter studies that focus on voter behavior to derive theoretical hypotheses that will be empirically tested by estimating a probabilistic voter model for Malawi. In particular, we provide a theory that relates the relative importance of different voting motives for different social voter groups to induced electoral incentives for politicians and subsequently to government performance. Based on our theoretical model, we derive indices of government accountability and capture that are defined in political equilibrium and measure the government’s incentives to implement policies that serve pure self-interest or special interests at the expense of the general public. Based on the estimated model, we calculate theoretically derived indices of government accountability and capture. Moreover, we derive indices that measure the relative importance of different policy- and non-policy-oriented voting motives for a number of socioeconomic groups. Further, we evaluate the relationship between the relative importance of different voting motives and government performance (i.e., accountability and capture).