Foreign Aid in the Aftermath of Large Natural Disasters

This paper examines Official Development Assistance (ODA) in the aftermath of large natural disasters between 1970 and 2008. Using an event‐study approach, the paper finds that while the median increase in ODA is 18% compared with pre‐disaster flows, the typical surge is small in relation to the size of the affected economies. Moreover, aid surges typically cover only 3% of the total estimated economic damages caused by the disasters. The main determinants of post‐disaster aid surges are found to be the intensity of the event itself and the recipient country's characteristics such as the level of development, country size and the stock of foreign reserves. The paper does not find evidence that political considerations or strategic behavior on the part of donors determine the size of post‐disaster aid surges.     

Monetary,fiscal, and structural drivers of inflation in Ethiopia: new empirical evidence from time series analysis

This study empirically investigates the drivers of inflation in Ethiopia using monthly data over the period July 1998 to September 2020. It explores short-run and long-run effects of domestic and external determinants of inflation—including demand-side, supply-side, and structural factors—using the cointegration and vector error-correction methodology. Four measures of inflation are considered: cereals, food, nonfood, and all-items Consummer Price Index (CPI) inflation. A key contribution to the existing literature is the investigation of the role of the fiscal sector in modeling inflation, a topic that has been neglected in the existing studies on inflation in Ethiopia. The empirical results show that disequilibria in the monetary sector, grains sector, and food markets have long-run effects on inflation. In the short run, inflation is driven by structural factors (notably, cereal output gaps and imported inflation) as well as demand-side factors (notably, money growth and public sector borrowing). The results hold when analysis is limited to the high growth period from 2005 onward, following the end of the International Monetary Fund (IMF) program in the country. The evidence provides valuable insights in the context of ongoing macroeconomic policy reforms in Ethiopia.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.     

“When Life Gives You Lemons”: Using Cross-Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data

At the core of poverty eradication is the need to eliminate that poverty that is persistent over time (chronic poverty). Unfortunately, traditional approaches to identifying chronic poverty require longitudinal data that is rarely available. In its absence, this paper proposes an alternative approach that only requires 1 year of cross-sectional data on monetary and non-monetary poverty. It puts forth two conjectures and contends that the combined profile of a household as both income poor and multidimensionally poor can be used as a proxy of that household being chronically income poor. To explore the viability of this approach, we use a probit model and longitudinal data for three Latin American countries to estimate households’ probabilities of remaining in income poverty based on their past income and multidimensional poverty statuses. We find empirical support for the approach that is significant, consistent across countries, and robust to various controls and periods of analysis.     

A balance-sheet approach to fiscal sustainability

This paper proposes an alternative methodology to assess fiscal sustainability. Our balance-sheet approach (BSA) relies on estimating separately all of a government's assets and liabilities as opposed to focusing only on the burden of explicit liabilities. In our approach, assets are primarily the present discounted value of taxes, and liabilities include explicit liabilities but also the present discounted value of expenditures. Using the value of assets and liabilities, we compute the government's balance sheet, and therefore net worth. We then evaluate the response of net worth to growth, commodity prices or real exchange rate shocks. By computing a value for the government's net worth, our methodology allows an assessment of fiscal sustainability that is less reliant on the analyst's assumptions than traditional debt sustainability analysis (DSA).     

Green technology co-patenting networks: international dynamics

The present study examined how the multi-country green technology co-patenting network structure evolved from 1997 to 2016. For that purpose, we used Social Network Analysis tools, which allowed us to assess the network structure from a visual and quantitative perspective. The results indicate that the network expanded as the number of participating countries and ties increased. In all periods, the network grew significantly centralized around a small group of countries, in which the U.S., Great Britain, Germany, France, and Canada had paramount weight. Emerging countries like India and China also stood out due to their growth over time, as they eventually managed to gain central positions in the network. Other developing countries remained marginal, such as Brazil.

     

Investment–cash flow sensitivity and investor protection

We examine the role of country-level legal investor protection (i.e., shareholder and creditor protection) on firm investment–cash flow sensitivity (ICFS). Using underexplored research data on investor protection across 21 countries and working with a conservative empirical design, we extend prior literature on the relation between investor protection and ICFS and provide new evidence on how these country-level attributes interact to explain a firm's ICFS. We find that either the strong legal protection of minority shareholders or the strong legal protection of creditors reduces the sensitivity of investment to internal cash flow. However, in countries with strong levels of both minority shareholder and creditor protection, ICFS increases. Our results remain robust after controlling for several alternative explanations. The results support the argument that overregulation arises when policymakers increase investor protection at levels that lead firms to avoid external sources of finance, hampering firm investment. Our findings suggest that countries face a regulatory trade-off such that increasing investor protection (either shareholder or creditors protection) enhances financial markets efficiency, but excessive regulation can indeed lead to financial markets inefficiencies.