Person:
Essama-Nssah, Boniface
Author Name Variants
Fields of Specialization
Poverty and growth,
Program evaluation,
Social impact of public policy
Degrees
Externally Hosted Work
Contact Information
Last updated
January 31, 2023
Biography
B. Essama-Nssah worked for 17 years for the World Bank in Washington, DC, before he retired as a senior economist in 2011. During his tenure at the Bank, he performed economic analyses, prepared policy research and technical papers, and conducted an annual training course on impact evaluation methodologies for staff from the World Bank and client countries. Before joining the World Bank, Essama-Nssah worked for two years as a senior research associate on the Food and Nutrition Program at Cornell University, and for six years as head of the Economics Department and vice dean of the Faculty of Law and Economics of the University of Yaoundé, Cameroon. He currently works as a consultant focusing on poverty and growth incidence analysis, program evaluation, and analysis of the social impact of public policy. He holds a PhD in economics from the University of Michigan in Ann Arbor.
Publication Search Results
1 - 5 of 5
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Publication
A Unified Framework for Pro-Poor Growth Analysis
(World Bank, Washington, D.C., 2004-09)Starting with a general impact indicator as an evaluation criterion, The author offers an integrative framework for a unified discussion of various concepts and measures of pro-poor growth emerging from the current literature. He shows that whether economic growth is considered pro-poor depends fundamentally on the choice of evaluative weights. In addition, the author's framework leads to a new indicator of the rate of pro-poor growth that can be interpreted as the equally distributed equivalent growth rate. This is a distribution-adjusted rate of growth that depends on the chosen level of inequality aversion. Illustrations based on data for Indonesia in the 1990s show a strong link between growth and poverty reduction in that country. A decomposition of the observed poverty outcomes reveals the extent to which changes in inequality have blunted the poverty impacts of both growth and contraction. Finally, the results also demonstrate that absolute and relative indicators of pro-poor growth can lead to conflicting conclusions from the same set of facts. -
Publication
Building and Running General Equilibrium Models in EViews
(World Bank, Washington, DC, 2004-01)A crucial step in policy analysis involves computing consequences of policy actions. The author shows how to implement numerically a general equilibrium model in EViews. Computable general equilibrium models are now commonly used in both industrial and developing countries to assess the impact of external shocks or economic policies on the structure of the economy or the distribution of welfare. The current version of EViews offers a set of tools for building and solving simulation models in general. The same tools make it possible to conduct policy analysis within a general equilibrium framework. Based on the generalized Salter-Swan framework and macroeconomic data for Indonesia, the author shows how to process a social accounting matrix, specify and calibrate the model, and run simulations. The results replicate welfare and structural effects of shocks and policies consistent with the underlying conceptual framework. They also reveal the key role played by structural parameters, such as the elasticity of export transformation and that of import substitution, in determining the extent of structural adjustment to shocks and the relevance of the policy response. -
Publication
A Poverty Analysis Macroeconomic Simulator (PAMS) Linking Household Surveys with Macro-Models
(World Bank, Washington, DC, 2002-09)The Poverty Analysis Macroeconomic Simulator (PAMS) is a model that links standard household surveys with macro frameworks. It allows users to assess the effect of macroeconomic policies-in particular, those associated with Poverty Reduction Strategies papers-on sectoral employment and income, the incidence of poverty, and income distribution. PAMS (in Excel) has three interconnected components: (1) A standard aggregate macro-framework that can be taken from any macro-consistency model (for example, RMSM-X, 123) to project GDP, national accounts, the national budget, the BoP, price levels, and so on, in aggregate consistent accounts. (2) A labor market model breaking down labor categories by skill level and economic sectors whose production total is consistent with that of the macro framework. Individuals from the household surveys are grouped in representative groups of households defined by the labor category of the head of the household. For each labor category, labor demand depends on sectoral output and real wages. Wage income levels by economic sector and labor category can thus be determined. In addition, different income tax rates and different levels of budgetary transfers across labor categories can be added to wage income. (3) A model that uses the labor model results for each labor category to simulate the income growth for each individual inside its own group, assumed to be the average of its group. After projecting individual incomes, PAMS calculates the incidence of poverty and the inter-group inequality. PAMS can produce historical or counterfactual simulations of: + Alternative growth scenarios with different assumptions for inflation, fiscal, and current account balances. These simulations allow test tradeoffs within a macro stabilization program. + Different combinations of sectoral growth (agricultural or industrial, tradable or non-tradable goods sectors), within a given aggregate GDP growth rate. + Tax and budgetary transfer policies. For example, PAMS will simulate a baseline macro-scenario for Burkina Faso corresponding to an existing IMF/World Bank-supported program and introduce changes in tax, fiscal, and sectoral growth policies to reduce poverty and inequality more effectively than the base scenario. So, the authors argue that there are several possible "equilibria" in terms of poverty and inequality within the same macro framework. -
Publication
Assessing the Distributional Impact of Public Policy
(World Bank, Washington, DC, 2002-09)Economic development necessarily changes the welfare of socioeconomic groups to various degrees, depending on differences in their social arrangements. The challenge for policymakers is to select the changes that will be most socially desirable. The author demonstrates the usefulness of distributional analysis for social evaluation and, more specifically, for welfare evaluation, using data from the 1994 Integrated Household Survey in Guinea. Because the international community has declared poverty eradication a fundamental objective of development, the author uses a poverty-focused approach to social evaluation based on the maximum principle. This principle offers a unifying framework for analyzing the socioeconomic impact of public policy by using a wide variety of evaluation functions, inequality indicators (like the extended Gini coefficient), and poverty indices (such as Sen's index and the members of the Foster-Greer-Thorbecke family). The author also examines, within the context of commodity taxation, how to identify socially desirable policy options using both the dominance criterion and abbreviated social welfare functions. He includes computer routines for calculating various welfare indices and for plotting the relevant concentration curves. -
Publication
The Poverty and Distributional Impact of Macroeconomic Shocks and Policies : A Review of Modeling Approaches
(World Bank, Washington, DC, 2005-08)The importance of distributional issues in policymaking creates a need for empirical tools to assess the social impact of economic shocks and policies. This paper reviews some of the modeling approaches that are currently in use at the World Bank and other international financial institutions. The specification of these models is dictated by the issues at stake, the knowledge about the nature of the process involved, and the availability and reliability of relevant data. Furthermore, shocks and policies have macroeconomic, structural, and distributional implications. This creates interdependence between such policy issues. Finally, the distributional impact of shocks and policies hinges on the heterogeneity of socioeconomic agents with respect to endowments and behavior. In the end, each modeling approach should be judged on how well it handles the interdependence between policy issues and the heterogeneity of the stakeholders, given other constraints.