We examine the relationship between the rapid pace of trade and financial globalization and the rise
in income inequality observed in most countries over the past two decades. We find that technological
progress has had a greater impact than globalization on inequality within countries. The limited overall
impact of globalization reflects two offsetting tendencies: whereas trade globalization is associated with
a reduction in inequality, financial globalization—and foreign direct investment in particular—is associated
with an increase in inequality. We conclude that policies aimed at reducing barriers to trade and broadening
access to education and credit can allow the benefits of globalization to be shared more equally. A key finding
is that both globalization and technological changes increase the returns on human capital, underscoring the
importance of education and training in both developed and developing countries in addressing rising inequality.
This paper sheds new light on the impact of AIDS on cross-country income levels. We consider new UNAIDS/WHO
data on officially reported AIDS cases for a panel of 89 countries over a 15 year period from 1986-2000 during
which AIDS has spread across the world. These data are used to estimate cross-country level regressions employing
panel data techniques. Our findings are as follows: First, when using the entire sample of countries we find that
AIDS has a negative albeit marginally significant effect on the level of income. Second, when we control for regional
effects we show that this negative effect is primarily driven by the sub-Sahara Africa and Latin America subsamples.
Third, using AIDS data by age group, we find that the disease has a significantly negative impact on income only via
infected people between the ages 16 and 34. Finally, while the economic impact of AIDS is negative and statistically
significant, its economic significance measured by the estimated size of the AIDS coefficient is quite small.
We construct a cross-country dataset on female human capital inequality. Unlike the existing literature that primarily
focuses on the average years of women's education, we use this dataset to examine the relationship between female human
capital inequality and infant mortality. We show that higher education inequality among women, measured by the Gini
coefficient, leads to substantially higher infant mortality. This finding is robust to various alternative specifications
and subsamples considered. We also consider whether this channel is important in explaining growth. Growth regressions
show favorable but weak evidence that education inequality among women is associated negatively with growth via its effect
on infant mortality. Our main results have implications related to the policy question on the optimal allocation of
educational subsidies. If infant mortality reduction is a priority for policy makers, then educating the least educated
women first seems to be an effective (and also simple) policy recommendation.
We construct a cross-country dataset on female human capital inequality. Unlike the existing literature that primarily
focuses on the average years of women's education, we use this dataset to examine the relationship between female human
capital inequality and infant mortality. We show that higher education inequality among women, measured by the Gini
coefficient, leads to substantially higher infant mortality. This finding is robust to various alternative specifications
and subsamples considered. We also consider whether this channel is important in explaining growth. Growth regressions
show favorable but weak evidence that education inequality among women is associated negatively with growth via its effect
on infant mortality. Our main results have implications related to the policy question on the optimal allocation of
educational subsidies. If infant mortality reduction is a priority for policy makers, then educating the least educated
women first seems to be an effective (and also simple) policy recommendation.
Economic growth has been a showcase of model uncertainty, given the many competing
theories and candidate regressors that have been proposed to explain growth. Bayesian
Model Averaging (BMA) addresses model uncertainty as part of the empirical strategy,
but its implementation is subject to the choice of priors: the priors for the
parameters in each model, and the prior over the model space. For a well-known
growth dataset, we show that model choice can be sensitive to the prior specification,
but that economic significance (model-averaged inference about regression coefficients)
is quite robust to the choice of prior. We provide a procedure to assess priors in
terms of their predictive performance. The Unit Information Prior, combined with a
uniform model prior outperformed other popular priors in the growth dataset and in
simulated data. It also identified the richest set of growth determinants, supporting
several new growth theories. We also show that there is a tradeoff between model and
parameter priors, so that the results of reducing prior expected model size and
increasing prior parameter variance are similar. Our branch-and-bound algorithm
for implementing BMA was faster than the alternative coin flip importance sampling
and MC3 algorithms, and was also more successful in identifying the best model.
We propose an economic theory of infectious disease transmission and rational behavior.
Diseases are costly due to mortality (premature death) and morbidity
(lower productivity and quality of life). The theory offers three main insights.
First, higher disease prevalence implies lower saving-investment propensity.
Preventive behavior can partially offset this when the prevalence rate and negative
disease externality are relatively low. Secondly, infectious diseases can generate a
low-growth trap where income alone cannot push an economy out of underdevelopment.
This is distinctly different from development traps in the existing literature. Since
income per se does not cause health in this equilibrium, successful interventions have
to be health specific. Thirdly, a more favorable disease ecology propels the economy
to a higher growth path where health and income co-evolve and infectious diseases
disappear. Even so, diseases significantly slow down convergence. These results
suggest the empirical relationship between health and income at the aggregate level
may be more nuanced than realized.
Trade theories covering Preferential Trade Agreements (PTAs) are as diverse as the literature in search of their
empirical support. To account for the model uncertainty that surrounds the validity of the competing PTA theories,
we introduce Bayesian Model Averaging (BMA) to the PTA literature. BMA minimizes the sum of Type I and Type
II error, the mean squared error, and generates predictive distributions with optimal predictive performance. Once
model uncertainty is addressed as part of the empirical strategy, we report clear evidence of Trade Creation, Trade
Diversion, and Open Bloc effects. After controlling for natural trading partner effects, Trade Creation is weaker –
except for the EU. To calculate the actual effects of PTAs on trade flows we show that the analysis must be
comprehensive: it must control for Trade Creation and Diversion as well as all possible PTAs. Several prominent
control variables are also shown to be robustly related to Trade Creation; they relate to factor endowments and
economic policy.
We investigate the heterogeneous effects of initial conditions on post-World War II growth in sub-
Saharan Africa. Our empirical strategy is based on Bayesian Model Averaging (BMA) that allows us to
consider both model uncertainty (about the preferred theory and model) and parameter heterogeneity
(that countries are not homogeneous objects) into an internally coherent estimation procedure. Our main
fnding is that the impact of initial conditions on subsequent growth in sub-Saharan Africa is distinct.
How and why these initial conditions have a differential effect on this region is examined.