Working Papers
2020
March 13, 2020
Do FDI Firms Employ More Workers than Domestic Firms for Each Dollar of Assets?
Description: This paper studies whether FDI firms employ more workers than domestic firms for each dollar of assets. Using the Orbis database and its ownership structure information, we show that, in most economies, domestic firms tend to employ more workers per asset than FDI firms. The result remains robust across individual industries in the case study of the United Kingdom. The analysis of the switchers (ownership changes from domestic to foreign or vice versa) suggests that ownership changes do not have an immediate impact on the employment per asset. This result suggests that different patterns of employment per asset seem to come from technological differences rather than from different ownership structures.
March 6, 2020
Household Consumption Volatility and Poverty Risk: Case Studies from South Africa and Tanzania
Description: Economic volatility remains a fact of life in Sub Saharan Africa (SSA). Household-level shocks create large consumption fluctuations, raising the incidence of poverty. Drawing on micro-level data from South Africa and Tanzania, we examine the vulnerability to shocks across household types (e.g. by education, ethnic group, and economic activity) and we quantify the impact that reducing consumption volatility would have on aggregate poverty. We then discuss coverage of consumption insurance mechanisms, including financial access and transfers. Country characteristics crucially determine which household-level shocks are most prevalent and which consumption-smoothing mechanisms are available. In Tanzania, agricultural shocks are an important source of consumption risk as two thirds of households are involved in some level of agricultural production. For South Africa, we focus on labor market risk proxied by transitions from formal employment to informal work or unemployment. We find that access to credit, when available, and government transfers can effectively mitigate labor market shocks.
March 6, 2020
Operationalizing Inclusive Growth: Per-Percentile Diagnostics to Inform Redistribution Policies
Description: Inclusive growth, narrowly defined in this paper as growth that helps reduce inequality, is achieved if consumption of the poor increases faster than consumption of the rich. The paper presents a simple accounting framework for a per-percentile consumption diagnostics that could inform redistribution policies. The proposed framework is illustrated in application to Iraq and Tunisia.
February 28, 2020
Exploring Residual Profit Allocation
Description: Schemes of residual profit allocation (RPA) tax multinationals by allocating their ‘routine’ profits to countries in which their activities take place and sharing their remaining ‘residual’ profit across countries on some formulaic basis. They have recently and rapidly come to prominence in policy discussions, yet almost nothing is known about their impact on revenue, investment and efficiency. This paper explores these issues, conceptually and empirically. It finds residual profits to be substantial, but concentrated in a relatively few MNEs, headquartered in few countries. The impact on tax revenue of reallocating excess profits under RPA, while adverse for investment hubs, appears beneficial for lower income countries even when the formula allocates by destination-based sales. The impact on investment incentives is ambiguous and specific both to countries and MNE groups; only if the rate of tax on routine profits is low does aggregate efficiency seem likely to increase.
February 28, 2020
On the Capacity to Absorb Public Investment: How Much is Too Much?
Description: While expanding public investment can help filling infrastructure bottlenecks, scaling up too much and too fast often leads to inefficient outcomes. This paper rationalizes this outcome looking at the association between cost inflation and public investment in a large sample of road construction projects in developing countries. Consistent with the presence of absorptive capacity constraints, our results show a non-linear U-shaped relationship between public investment and project costs. Unit costs increase once public investment is close to 10% of GDP. This threshold is lower (about 7% of GDP) in countries with low investment efficiency and, in general, the effect of investment scaling up on costs is especially strong during investment booms.
February 28, 2020
Deus ex Machina? A Framework for Macro Forecasting with Machine Learning
Description: We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.
February 28, 2020
The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data
Description: We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.
February 28, 2020
Foreign Demand and Local House Prices: Evidence from the US
Description: We test whether foreign demand matters for local house prices in the US using an identification strategy based on the existence of “home bias abroad” in international real estate markets. Following an extreme political crisis event abroad, a proxy for a strong and exogenous shift in foreign demand, we show that house prices rise disproportionately more in neighbourhoods with a high concentration of population originating from the crisis country. This effect is strong, persistent, and robust to the exclusion of major cities. We also show that areas that were already expensive in the late 1990s have experienced the strongest foreign demand shocks and the biggest drop in affordability between 2000 and 2017. Our findings suggest a non-trivial causal effect of foreign demand shocks on local house prices over the last 20 years, especially in neighbourhoods that were already rather unaffordable for the median household.
February 28, 2020
Household Debt and House Prices-at-risk: A Tale of Two Countries
Description: To identify and quantify downside risks to housing markets, we apply the house price-at-risk methodology to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018. This paper finds that downside risks to housing markets in the United States have seemingly fallen over the past decade, while having increased in Canada. Supply-side drivers, valuation, household debt, and financial conditions jointly play a key role in forecasting house price risks. In addition, capital flows are found to be significantly associated with future downside risks to major housing markets, but the net effect depends on the type of flows and varies across cities and forecast horizons. Using micro-level data, we identify households vulnerable to potential housing shocks and assess the riskiness of household debt.
February 28, 2020
The Impact of Conflict and Political Instability on Banking Crises in Developing Countries
Description: While there is an extensive literature examining the economic impact of conflict and political instability, surprisingly there have been few studies on their impact on the probability of banking crises. This paper therefore investigates whether rising conflict and political instability globally over the past several decades led to increased occurrence of banking crises in developing countries. The paper provides strong evidence that conflicts and political instability are indeed associated with higher probability of systemic banking crises. Unsurprisingly, the duration of a conflict is positively associated with rising probability of a banking crisis. Interestingly, the paper also finds that conflicts and political instability in one country can have negative spillover effects on neighboring countries’ banking systems. The paper provides evidence that the primary channel of transmission is the occurrence of fiscal crises following a conflict or political instability.