The goal of this paper is to measure the correlation between intergenerational mobility and a series of significant macroeconomic variables such as growth, income, crime or the degree of inequality. Our knowledge of these correlations at the empirical level is in-existent as measurements of intergenerational mobility obtained from traditional methods (based on panel data) are scarce, difficult to compare across countries and almost impossible to get across time. For example, we do not know how intergenerational mobility (IM) correlates with income. This is, we do not know if it is larger in richer or in poorer countries. Similarly, we do not know how IM correlates with growth, independently of their level of income. We do not know how it correlates with
crime, corruption, etc. These are obviously relevant questions for our understanding of how economies work as well as for the design of social policies. In this paper we apply a novel measure of IM, developed by Guell, Rodriguez Mora and Telmer(2007), to a very rich set of Italian data and we are able to to produce comparable measures of IM at the province level (there are about 100 provinces in Italy). We then exploit the large and significant differences across Italian provinces to explore how IM correlates with in a large array of socio-economic variables. The measure of IM proposed by Guell, Rodriguez Mora and Telmer (2007) is based on the idea that surnames are informative about family links. Since the distribution of surnames is necessarily very skewed, with many relatively infrequent surnames, it can be exploited to extract longitudinal information from a cross section of data. Surnames are largely inherited from parents to children together with other characteristics that matter for the children's well-being (such as having a certain occupation or belonging to a certain socioeconomic group). Hence, the more surnames are informative about the outcomes of their holders, the more important the characteristics inherited along with surnames must be in determining such outcomes and the less mobility is there. Our main data consists of the complete Italian tax records for the year 2005, where we observe each and every person who submitted a tax form for personal income taxation in Italy, together with their names and surnames (recoded with numerical ids for anonymity), their taxable incomes and their province of residence (plus a few other characteristics). Furthermore, we combine these tax records with data the complete registries of lawyers and politicians, whose actual names and surnames are publicly
available. We are thus able to compute our measure of IM based on different outcomes: not only income, but also the probability of begin a lawyer or a politician. We explore the correlation between our measures of IM and several socio-economic outcomes of the province: per-capita income, growth, employment and crime. Further, we also look at some occupation specific outcomes, like the effiency of the legal system (measured as the average duration of trials) and
of the political system (measured as the ability of the local administration to spend pre-allocated funds). Our preliminary results suggest that more social mobility is associated with more value added per capita, more exports, lower unemployment, more voters turnout and quicker trials.