# WPS4249

goods relative to the one in the US. All these variables are measured in levels at the beginning of the period.

This choice of regressors is motivated by three main considerations. First, we need a parsimonious model as the number of available observations dramatically falls once we estimate the equations for inequality, a variable for which data availability is quite limited. Second, this choice ensures comparability with existing work in the growth literature Third, the selected controls are relevant as explanatory variables both for our growth and inequality regressions. In this respect, even though Perotti (1996) justifies the selected regressors in a growth context, their relevance can also be defended as determinants of inequality. Indeed, education is usually seen as one of the main determinants of inequality and discriminating between female and male educational levels can also capture gender-related inequalities. Finally, the price of capital can capture incentives to substitute capital for labor.<sup>13</sup> Clearly, omitted variable bias could be problematic if there is strong correlation between the dependent variable in question, the remittances variable and a third omitted variable. Moreover, given the large potential number of variables that could be included in a growth regression it is almost impossible to infer how omitted variables could affect the estimates of remittances, growth and inequality. Here we have to admit that there is little that we can do other than, as stressed above, relying on a control set that has been extensively used in the literature, and on the fixed effects strategy to somewhat correct the problem, particularly when the omitted variables are time invariant or have low variability.

## **3. CROSS-COUNTRY DATA AND RESULTS**

## _(a) Cross-Country Data_

We use data for 59 industrial and developing countries spanning the years 1970-2000. All the regressions are performed using non-overlapping five year periods. The education variables come from the Barro and Lee (2001) database, income and the proxy for market distortions are from the PWT6.1 database, and the Gini indices are from Dollar and Kraay (2002). Remittances data are as in Aggarwal, Demirguc-Kunt, and Martinez Peria (2005) and the data come from the World Economic Outlook (2005). With some exceptions, these data are constructed as the sum of three items in the _Balance of Payment Statistics Yearbook_ (IMF): _workers’ remittances_ (current transfers made by migrants who are employed and resident in another economy); _compensation of employees (_ wages, salaries and other benefits earned by nonresident workers for work performed for resident of other countries _)_ ; and _migrant transfers_ (financial items that arise from the migration or change of residence of individuals from one economy to another). Once we bring all the variables together we end up with a sample of 221 observations in the growth regressions and a sample of 85 observations in the specifications for the Gini coefficient. To a large extent the difference in the size of these samples is driven by the different coverage of the income and inequality variables.<sup>14</sup>

> 13 Clearly, as in the case of the growth equation, there are many other variables that one could potentially consider as determinants of inequality, including the age profile of the population, the country’s fertility rate, etc.

> 14 In fact, whereas the PWT6.1 allows for the construction of more than 700 5-year observations for the growth variable between 1970 and 2000, the Dollar and Kraay (2002) database only allows for 274 observations for the

9