Poverty dynamics in Costa Rica with panel data from cross-sections

Includes bibliography

Ex-post evaluation of the employment effects of a preferential trade agreement: methodological issues, illustrated with a reference to Chile

Includes bibliography

Estimating the effects of United States food safety and agricultural health standards on agro-food exports from Latin America and the Caribbean

Includes bibliography

Peru: integration, sectoral specialization and synchronization with international gross domestic product cycles

Includes bibliography

Infrastructure and economic growth in Peru

Includes bibliography

Una revisión crítica de los métodos de series de tiempo y de datos de panel aplicados al caso de la hipótesis de export-led growth

Incluye Bibliografía

The impact of climate change on the tourism sector in the Caribbean

The main aim of this study is to estimate the economic impact of climate change on nine countries in the Caribbean basin: Aruba, Barbados, Dominican Republic, Guyana, Jamaica, Montserrat, Netherlands Antilles, Saint Lucia and Trinidad and Tobago. A typical tourism demand function, with tourist arrivals as the dependent variable, is used in the analysis. To establish the baseline, the period under analysis is 1989-2007 and the independent variables are destination country GDP per capita and consumer price index, source country GDP, oil prices to proxy transportation costs between source and destination countries. At this preliminary stage the climate variables are used separately to augment the tourism demand function to establish a relationship, if any, among the variables. Various econometric models (single OLS models for each country, pooled regression, GMM estimation and random effects panel models) were considered in an attempt to find the best way to model the data. The best fit for the data (1989-2007) is the random effects panel data model augmented by both climate variables, i.e. temperature and precipitation. Projections of all variables in the model for the 2008-2100 period were done using forecasting techniques. Projections for the climate variables were undertaken by INSMET. The cost of climate change to the tourism sector was estimated under three scenarios: A2, B2 and BAU (the mid-point of the A2 and B2 scenarios). The estimated costs to tourism for the Caribbean subregion under the three scenarios are all very high and ranges from US$43.9 billion under the B2 scenario to US$46.3 billion under the BAU scenario.

Agricultural incentives, growth and poverty in Latin America and the Caribbean: cross-country evidence for the period 1960-2005. Did trade liberalization increase the incomes of the poorest?

Includes bibliography

Risk-adjusted poverty in Argentina: measurement and determinants

Includes bibliography

Bank consolidation and credit concentration in Brazil (1995-2004)

Includes bibliography

Tourism competitiveness in the Caribbean

Includes bibliography

Inequality and academic achievement in Chile

Includes bibliography

The impact of aid for trade on the cost and time to trade The case of Latin America and the Caribbean

Includes bibliography.

Foreign direct investment and welfare

Includes bibliography.

Mexico: Combining monthly inflation predictions from surveys

We examine the problem of combining Mexican inflation predictions or projections provided by a biweekly survey of professional forecasters. Consumer price inflation in Mexico is measured twice a month. We consider several combining methods and advocate the use of dimension reduction techniques whose performance is compared with different benchmark methods, including the simplest average prediction. Missing values in the database are imputed by two different databased methods. The results obtained are basically robust to the choice of the imputation method. A preliminary analysis of the data was based on its panel data structure and showed the potential usefulness of using dimension reduction techniques to combine the experts' predictions. The main findings are: the first monthly predictions are best combined by way of the first principal component of the predictions available; the best second monthly prediction is obtained by calculating the median prediction and is more accurate than the first one.