Research Topics

Structural change has long been at the core of economic development debates. However, the gender implications of structural change are still largely unexplored. This paper helps to fill this gap by analyzing the role of structural change in the gender distribution of sectoral employment in sub-Saharan African countries. I employ aggregate and disaggregate measures of gender sectoral segregation in employment on a panel database consisting of 10 sectors and 11 countries during 1960–2010. Fixed effects and instrumental variables’ regression models show a significant, non-linear link between labor productivity and gender segregation. Increasing labor productivity depresses gender segregation at initial phases of structural change. However, further productivity gains beyond a certain threshold of sectoral development increases gender segregation. Country-industry panel data models complement the analysis by considering relative labor productivity as a determinant of sectoral feminization. The estimates suggest that manufacturing, utilities, construction, business, and government services are key to correcting gender biases in employment along the process of structural change.

Is the Curse More Difficult to Dispel in Oil States than in Mineral States?

The hypothesis of the natural resource curse has captivated the economics profession, and since the mid-1990s has generated a large body of policymaking initiatives aimed at dispelling the curse. In this paper, we evaluate how the effect of resource abundance on economic growth has changed since these policies were first introduced by comparing the periods 1970–89 and 1996–2008. We disaggregate resources into oil, gas, coal, and nonfuel mineral resources, and find that disaggregation unmasks diverse effects of resources on concurrent economic and institutional outcomes, as well as on the ability of countries to transform their economic and institutional infrastructure. We consider resource dependence and institutional quality as two channels linking resource abundance to economic growth in the context of an instrumental variables (IV) model. In addition to exploring these channels, the IV framework enables us to test for the endogeneity of the measures of resource dependence and institutional quality in the growth regressions, paying particular attention to the weakness of the instruments.

 

The economic returns to education in transition countries have been extensively evaluated in the literature. The present study contributes to this literature by estimating the returns to education in Georgia during the last transition period 2000–04. We find very low returns to education in Georgia and little evidence of an increasing trend in the returns. This picture contrasts with somewhat higher rates of return to education in the mid-1990s in Georgia and the recent estimates from other transition countries. A further analysis of the shifts in the supply and demand for education sheds light on possible causes. In particular, on the supply side, the decline in the quality of education in the 1990s has negated the improvements in the provision of skills needed by market economies during this period. On the demand side, the expansion of the Georgian economy has taken place in the direction of fields such as public administration and education that employ a highly educated workforce but do not remunerate well. Yet it would be a mistake to conclude that education is not a valuable asset in Georgia. The role of education is largely manifested in its impact on the employability of individuals, an issue that has been overlooked in the transition literature. Once this impact is taken into account, education is shown to play an increasingly important role in influencing the earnings of the working population in Georgia. The paper uses the ordinary least squares approach, instrumental variables approach, and sample selection correction, taking into account conditional and unconditional marginal effects of education on earnings.

 

Self-reported home values are widely used as a measure of housing wealth by researchers; the accuracy of this measure, however, is an open empirical question, and requires some type of market assessment of the values reported. In this study, the authors examine the predictive power of self-reported housing wealth when estimating housing prices, utilizing the portion of the University of Michigan’s Health and Retirement Study covering 1992–2006. They find that homeowners, on average, overestimate the value of their properties by 5–10 percent. More importantly, the authors establish a strong correlation between accuracy and the economic conditions at the time of the property’s purchase. While most individuals overestimate the value of their property, those who buy during more difficult economic times tend to be more accurate; in some cases, they even underestimate the property's value. The authors find a surprisingly strong, likely permanent, and in many cases long-lived effect of the initial conditions surrounding the purchase of properties, and on how individuals value them. This cyclicality of the overestimation of house prices provides some explanation for the difficulties currently faced by many homeowners, who were expecting large appreciations in home value to rescue them in case of interest rate increases—which could jeopardize their ability to live up to their financial commitments.