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We would like to thank conference participants at the IMF’s 2004 Panel of Experts Conference organized by the Fiscal Affairs Department and in particular Dilip Mookherjee, Eduardo Ley, and Rodney Ramcharan for insightful discussions and useful references. Editorial assistance by Anne Robertson is gratefully acknowledged.
The institutional component of the questionnaire is divided into five sections covering (1) institutional arrangements between revenue administration and fiscal authority, (2) the macroeconomic forecast, (3) characterization of the revenue-forecasting process, (4) revenue-forecasting practices, and (5) data and forecasting methods. Questions referred to current institutional conditions as observed during the last three years. The data request inquired about budget forecast and outcomes for various revenue and macroeconomic parameters. Survey design and related data issues are discussed in a separate note available upon request.
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 the 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, and nongovernmental institutions and international organizations.
A complete description of all survey responses is discussed in a separate note and is available from the authors upon request.
Other areas of budget coverage are subnational governments (37 percent), non–social security funds (37 percent), public enterprises (20 percent), and social security funds (3 percent). Due to overlapping coverage, sample percentages add up to more than 50 percent.
For example, the revenue-forecasting process in Germany follows a well-established routine without having a fixed set of rules governing the process.
Based on F-test for group mean differences and t-test for Spearman correlation coefficients.
Data on revenue forecasts and outcomes were collected for the last five years. Submissions were however in many cases incomplete and error ridden. About 30 percent of the countries reported none or incomplete data, and about half of the countries had observations with excessively high forecast errors (exceeding 50 percent of actual outcomes). To augment the data set, we supplied missing data on GDP forecast or revenue outturns from past IMF staff reports. Observations with errors in excess of 50 percent were dropped from the sample. We then regressed percentage tax revenue forecast errors on percentage nominal GDP forecast errors and other control variables (per capita GDP, population size, reliance on natural resources, and data imputation and regional dummies). GDP forecast errors were positively correlated with revenue forecast and decline with the level of per capita income. The incidence of forecast interference had no significant effect when added to this specification.
Amemiya Generalized Least Squares (AGLS) estimators for probits with endogenous regressors.
The indicator is derived from questions on the relationship between the revenue administration and the main fiscal agency. The degree of autonomy measures whether the revenue administration (1) can set its salary scale, (2) can make firing and hiring decisions, and (3) has own resources to finance day-to-day operations.
The turning point for the transparency effect is calculated as -a/2b, where a is the parameter estimate on the transparency variable and b the estimate on the squared transparency variable.