The Unassuming Economist

An interesting new piece in VoxEu by Paolo Acciari, Alberto Polo and Gianluca Violante on inter-generational mobility in Italy:

“In a new paper (Acciari et al. 2019), we add to this recent wave of studies and introduce a new dataset that allows us to develop the first systematic investigation of intergenerational income mobility for the Italian economy. Our starting point is the administrative electronic database on individual tax returns maintained by the Italian Ministry of Economy and Finance. From this data source, we extracted a sample of two cohorts of Italians born between 1942-1963 and 1972-1983. We matched parents and children through their social security numbers. Our final dataset contains nearly 650,000 parent–child pairs with detailed income information for three years in each cohort.”

Table 1 National quintile transition matrix (%)

“We follow up on this last finding by exploring the geographical differences in inter-generational upward mobility across the 110 Italian provinces. We document a staggering amount of variation, with a steep south-north gradient, as depicted in Figure 2. Relative to the south of Italy, provinces in the north (especially the regions in the northeast), are both more egalitarian – i.e. they display higher relative mobility – and more upward-mobile – i.e. they display higher absolute mobility. In the north, children from parents with unequal background are more similar in their economic outcomes when adults, and children from poor parents fare better when adults. The level of upward mobility in northern Italy exceeds that of Scandinavian countries”

Figure 2: Estimated transition probability from bottom to top 20% of income distribution across provinces

A new IMF paper explains unemployment in ECCU countries:

“Unemployment rates in Grenada (GRD), St. Lucia (LCA), and St. Vincent and Grenadines (VCT) have been above 20 percent in recent years. Unemployment in Dominica (DMA) has also been high by international standards even before the natural disasters that recently hit the country. While it is likely that weak employment this decade was partly related to the impact of the global economic downturn, unemployment was already high prior to the global crisis in most of these countries, thus suggesting there are structural factors behind it. This paper evaluates factors that could explain high unemployment in ECCU countries, cyclical and structural.1 Our analysis systemically reviews demand, supply, and institutional factors that could foster unemployment. Within this framework, we analyze the potential impact of the global financial crisis, the downfall of the banana/sugar industries, the hurricanes that frequently hit these countries, the relatively rigid wage setting process, as well as factors that could increase reservation wages.”

From latest IMF report on Germany:

“Does the large current account surplus in Germany reflect export-driven income gains that are evenly shared among the population? The evidence strongly suggests this is not the case and underscores the important role of German business wealth concentration in this context. As high corporate savings and underlying profits largely reflect capital income accruing to wealthy households and increasingly retained in closely-held firms, the buildup of external imbalance has been accompanied by widening top income inequality, rising private savings and compressed consumption rates.”

A new paper by Xiang Li and Dan Su highlights the possible relationship between capital account liberalization and inequality:

From a new Vox piece on robots and firms:

“Frey and Osborne (2017) predict that almost 47% of total US employment could be automated in the nearest future. Focusing on the period from 1993 to 2007 and covering 17 different countries, Graetz and Michaels (2018) find that the growing intensity of robot use accounted for 15% of aggregate economy-wide productivity growth, contributed to significant growth in wages, and had virtually no aggregate employment effects. At the same time, Acemoglu and Restrepo (2017) investigate the US labour market between 1990 and 2007 and show that one additional robot per thousand workers reduces the employment to population ratio by about 0.2 percentage points and wages by 0.37 percent within commuting zones. Dauth et al. (2018) study Germany between 1994 and 2014 and find no effects on total employment, but identify a substantial shift in the composition of jobs away from manufacturing and towards business service”