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The small-area estimation technique developed for producing poverty maps has been applied in a large number of developing countries. Opportunities to formally test the validity of this approach remain rare due to lack of appropriately detailed data. This paper compares a set of predicted welfare estimates based on this methodology against their true values, in a setting where these true values are known. A recent study draws on Monte Carlo evidence to warn that the small-area estimation methodology could significantly over-state the precision of local-level estimates of poverty, if underlying assumptions of spatial homogeneity do not hold. Despite these concerns, the findings in this paper for the state of Minas Gerais, Brazil, indicate that the small-area estimation approach is able to produce estimates of welfare that line up quite closely to their true values. Although the setting considered here would seem, a priori, unlikely to meet the homogeneity conditions that have been argued to be essential for the method, confidence intervals for the poverty estimates also appear to be appropriate. However, this latter conclusion holds only after carefully controlling for community-level factors that are correlated with household level welfare.

Confidence intervals --- Descriptive statistics --- Education --- Enumeration --- Geographical Information Systems --- Precision --- Predictions --- Reliability --- Sample design --- Sample surveys --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Small Area Estimation Poverty Mapping --- Standard errors --- Statistical and Mathematical Sciences --- Validity

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This paper uses firm level data from a cross-section of 57 countries to study how financial development affects innovation in small firms. The analysis finds that relative to large firms in the same industry, spending on research and development by small firms is more likely and sizable in countries at higher levels of financial development. The estimates imply that among firms doing research and development in a country like Romania, which is at the 20th percentile of financial development, a 1 standard deviation decrease in firm size is associated with a decrease of 0.7 standard deviations in research and development spending. In contrast, this decrease is only 0.2 standard deviations in a country like South Africa, which is at the 80th percentile of the distribution of financial development. Small firms also report producing more innovations per unit of research and development spending than large firms, and this gap is narrower in countries at higher levels of financial development. As a robustness check, the author shows that these patterns are stronger in industries inherently more reliant on external finance.

Access to Finance --- Debt Markets --- Education --- External finance --- Finance and Financial Sector Development --- Financial Development --- Financial market --- Financial systems --- Firm performance --- Informational asymmetries --- International Bank --- Lenders --- Market failures --- Microfinance --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Small loan

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It is assumed that added time to export adds cost to and lowers the volume of trade. Time delays may also affect the composition of trade and can disproportionately reduce trade in time-sensitive goods. This paper investigates the validity of these propositions using the World Bank Doing Business database and Enterprise Surveys for 64 developing countries. The authors find that in countries where there is longer time needed to export firms in time-sensitive industries are less likely to become exporters. Moreover, firms that do export have lower export intensities. Their findings imply that time to export is a significant determinant of comparative advantage. For example, consider two industries that have the same export probability and intensity - but differ in time-sensitivity by one standard deviation. Action taken to cut time to export by 50 percent for one industry opens a 6 percentage point difference between the export probabilities of the two industries. In addition, steps to cut time delays increase export intensities by 1.9 percentage points. This impact applies to industries with different productivity levels - and those in developing countries with different income levels.

Air --- Air transport --- Automotive sector --- Capital investments --- Comparative Advantage --- Cost-benefit analysis --- Economic Theory and Research --- Education --- Efficiency of infrastructure --- Free Trade --- Freight --- Infrastructure investment --- Inland transport --- Inspection --- International Economics & Trade --- International transport --- Science Education --- Scientific Research and Science Parks --- Shipping containers --- Transit --- Transport costs --- Transport Economics, Policy and Planning --- Transport modes --- Transport systems --- Transportation --- Transportation cost --- Transportation costs

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This paper uses firm level data from a cross-section of 57 countries to study how financial development affects innovation in small firms. The analysis finds that relative to large firms in the same industry, spending on research and development by small firms is more likely and sizable in countries at higher levels of financial development. The estimates imply that among firms doing research and development in a country like Romania, which is at the 20th percentile of financial development, a 1 standard deviation decrease in firm size is associated with a decrease of 0.7 standard deviations in research and development spending. In contrast, this decrease is only 0.2 standard deviations in a country like South Africa, which is at the 80th percentile of the distribution of financial development. Small firms also report producing more innovations per unit of research and development spending than large firms, and this gap is narrower in countries at higher levels of financial development. As a robustness check, the author shows that these patterns are stronger in industries inherently more reliant on external finance.

Access to Finance --- Debt Markets --- Education --- External finance --- Finance and Financial Sector Development --- Financial Development --- Financial market --- Financial systems --- Firm performance --- Informational asymmetries --- International Bank --- Lenders --- Market failures --- Microfinance --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Small loan

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April 1999 - The sensitivity of empirical results to potential data errors and model misspecification can yield misleading policy implications and investment signals. A widely disseminated study of the impact of the training and visit (T&V) system of management for extension services in Kenya is a striking example of how innocuous data errors and alternative specifications lead to strikingly different results. Gautam and Anderson revisit the widely disseminated results of a study (Bindlish and Evenson 1993, 1997) of the impact of the training and visit (T&V) system of management for public extension services in Kenya. T&V was introduced in Kenya by the World Bank and has since been supported through two successive projects. The impact of the projects continues to be the subject of much debate. Gautam and Anderson's paper suggests the need for greater vigilance in empirical analysis, especially about the quality of data used to support Bank policy and the need to validate potentially influential findings. Using household data from 1990, Bindlish and Evenson found the returns from extension to be very high. But Gautam and Anderson find that the returns estimated by Bindlish and Evenson suffer from data errors, and limitations imposed by cross-sectional data. After correcting for several data processing and measurement errors, the authors show the results to be less robust than reported by Bindlish and Evenson and highly sensitive to regional effects. When region-specific effects are included, a positive return to extension cannot be established, using Bindlish and Evenson's data set and cross-sectional model specifications. After testing the robustness of results using a number of tests, Gautam and Anderson could not definitively establish the factors underlying strong regional effects, largely because of the limitations imposed by the cross-sectional framework. Household panel data methods would have allowed greater control for regional effects and would have yielded better insight into the impact of extension. The impact on agricultural productivity in Kenya expected from T&V extension services is not discernible from the available data, and the impact may vary across districts. The hypothesis that T&V had no impact in Kenya between 1982 and 1990 cannot be rejected. The sample data fail to support a positive rate of return on the investment in T&V. This paper-a product of the Sector and Thematic Evaluation Division, Operations Evaluation Department-is part of a larger exploration by the department of the effects of the investment in agricultural extension in Kenya. The authors may be contacted at mgautam@worldbank.org or janderson@worldbank.org.

Agencies --- Agricultural --- Agricultural Extension --- Agricultural Production --- Agriculture --- Banks and Banking Reform --- Crops --- Crops and Crop Management Systems --- E-Business --- Econometrics --- Economic Theory and Research --- Education --- Extension --- Extension Services --- Family --- Farmers --- Farms --- Information --- Investment --- Labor Policies --- Land --- Livestock --- Macroeconomics and Economic Growth --- Management --- Private Sector Development --- Research --- Rural Development --- Rural Development Knowledge and Information Systems --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Social Protections and Labor --- Statistical and Mathematical Sciences --- Training