Samuel W. Malone

Natural Resource Booms and Inequality: Theory and Evidence

Surprisingly little is known about the impact of natural resource booms on income inequality in resource rich countries (Ross, 2007). This paper develops a theory, in the context of a two sector growth model in which learning-by-doing drives growth, to explain the time path of inequality following a resource boom. Under the condition that the nontraded sector uses unskilled labor more intensively than the traded sector, we find that income inequality will fall in the short run immediately after a boom, and will then increase steadily over time as the economy grows, until the initial impact of the boom on inequality disappears. Using dynamic panel data estimation for 90 countries between 1965 and 1999, and exploiting variation in world commodity prices to identify resource booms, we find evidence in support of the theory, especially for oil and mineral booms. We also find that uncertainty about future commodity export prices significantly increases long-run inequality.

Balance sheet effects, external volatility, and emerging market spreads

This paper studies the determinants of emerging market spreads, and thus of the cost of borrowing for emerging market sovereigns, using recent data from JP Morgans EMBI+ index for a panel of 19 countries. Controlling for traditional spread determinants, we focus on three additional factors whose importance is suggested by recent work: external shocks, the balance sheet effect of real devaluations, and the degree of current account leverage. We find clear and strong evidence that the variables in the foregoing categories have an economically and statistically significant relationship with spreads. In particular, we find a major role for the terms-of-trade volatility and the level of current account leverage in explaining spread variation. The result on current account leverage establishes an important link between a factor shown to make countries more vulnerable to sudden stops of capital flows, and the premium required by international investors on their foreign debt.

The Black Market for Dollars in Venezuela

In February 2003, the Venezuelan government imposed a strict capital controls policy to stem the outflow of dollars. We describe the mechanics and structure of the resulting black market and analyze the comparative performance of alternative models in explaining and forecasting the black market premium. Robustly significant determinants of the premium include the lagged premium, the official real exchange rate, the implied returns from arbitrage, and the oil price. Our preferred model exhibits outstanding out-of-sample forecasting performance, with an average prediction error of -0.9 percent, and an error standard deviation of 7.8 percent, during the ten-month period until July 2009. We provide evidence that the exogenous change of the black market swap vehicle to government bonds in 2007 induced a significant shift in the relative importance of the determinants of the premium, causing shocks to become significantly more persistent, the coefficient on the implied returns from arbitrage to double, and rendering the beneficial effect of oil price increases insignificant.

Economic Growth, Political Institutions, and Leadership Transitions

Leaders matter for growth, but does growth matter for leaders? We introduce a set of strong instruments for growth, based on hill-shaped relationships of agricultural output with temperature and precipitation, to test the causal effect of growth on national leadership turnover, after controlling for educational, demographic, and a range of institutional and policy factors. We find that: (i) growth significantly reduces the probability of leadership transitions, (ii) transitions to democracy robustly accompany leadership transitions after controlling for growth, policies, and institutions, and (iii) during times of major institutional change, growth has a less precise but substantially positive effect on leadership transitions.

Timing Foreign Exchange Markets

To improve short-horizon exchange rate forecasts, we employ foreign exchange market risk factors as fundamentals, and Bayesian treed Gaussian process (BTGP) models to handle non-linear, time-varying relationships between these fundamentals and exchange rates. Forecasts from the BTGP model conditional on the carry and dollar factors dominate random walk forecasts on accuracy and economic criteria in the Meese-Rogoff setting. Superior market timing ability for large moves, more than directional accuracy, drives the BTGP’s success. We explain how, through a model averaging Monte Carlo scheme, the BTGP is able to simultaneously exploit smoothness and rough breaks in between-variable dynamics. Either feature in isolation is unable to consistently outperform benchmarks throughout the full span of time in our forecasting exercises. Trading strategies based on ex ante BTGP forecasts deliver the highest out-of-sample risk-adjusted returns for the median currency, as well as for both predictable, traded risk factors.