Alt, Robert, 1993, “Revenue Forecasting and Estimation—How It’s Done, State By State,” State Tax Notes, vol. 4 (May), pp. 1038–51.
Danninger, Stephan, Annette Kyobe, and Marco Cangiano, 2004, “The Political Economy of Revenue-Forecasting Experience from Low-Income Countries,” forthcoming IMF Working Paper (Washington: International Monetary Fund).
Golosov, Mikhail, and John King, 2002, “Tax Revenue Forecasts in IMF-Supported Programs,” IMF Working Paper 02/236 (Washington: International Monetary Fund).
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi, 2003,“Governance matters III: governance indicators for 1996–2002”, World Bank Policy Research Working Paper 3106 (Washington: World Bank).
Lienert, Ian C., and Feridoun Sarraf, 2001, “Systemic Weaknesses of Budget Management in Anglophone Africa,” IMF Working Paper 01/211 (Washington: International Monetary Fund).
OECD & World Bank 2003, “Results of the Survey on Budget Practices and Procedures,” World Bank Governance and Knowledge Sharing Program.
The authors wish to thank fiscal economists who filled in the data questionnaire, and Eduardo Ley for comments and suggestions.
Country classifications are based on the 2003 World Economic Outlook (WEO).
(i) Institutional arrangements between revenue administration and fiscal authority; (ii) macroeconomic forecast; (iii) characterization of revenue forecast; (iv) revenue forecasting practices; and (v) data and forecasting methods.
These were Venezuela, Djibouti, Tajikistan, and Nigeria.
The mean corruption perception index of countries in the sample, is 1/7 standard deviations higher than the overall sample average from 183 countries. The corruption index is taken from Kaufmann, Kraay, and Mastruzzi (2003) and constructed as an inverted average over the last two available years of their control of corruption index. The control of corruption index measures the perception of corruption, defined as exercise of public power for private gain. It is based on indicators from several sources using an unobserved components methodology, which optimally weights each individual source according to its precision and reliability. Sources are large private enterprises, citizen and expert surveys, as well as nongovernmental institutions and international organizations.
Breakdown does not add up to 50 percent due to multiple coverage.
Lebanon, Armenia, Bangladesh, and Ghana.
Based on F-test for group mean differences and t-test for spearman correlation coefficients.