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There are large differences in mean income across US cities, but little is known about the levels of standard of living in each city���defined as the amount of market-based consumption that residents are able to afford. In this paper we provide estimates of standard of living by commuting zone for households in a given income or education group, and we study how they relate to local cost of living. Using a novel dataset, we observe all debit and credit card transactions, check and ACH payments, and cash withdraws of 5% of US households��� in 2014 and use it to measure mean consumption expenditures by commuting zone and income group. To measure local prices, we build income-specific consumer price indices by commuting zone. We uncover vast geographical differences in material standard of living for a given income level. Low income residents in the most expensive commuting zone enjoy a level of consumption that is about half that of low income residents in the most affordable commuting zone. In the second part of the analysis, we endogenize income and estimate the standard of living that low-skill and high-skill households can expect in each US commuting zone, once we account for geographical variation both in cost of living and also in expected income. We find that for college graduates, there is essentially no relationship between consumption and cost of living, suggesting that college graduates living in cities with high costs of living ��� including the most expensive coastal cities���enjoy a standard of living on average similar to college graduates with the same observable characteristics living in cities with low cost of living��� including the least expensive Rust Belt cities. For high school graduates and high school drop outs we find a significant negative relationship between consumption and cost of living, indicating that expensive cities offer lower standard of living than more affordable cities. The differences are quantitatively large: High school drop outs moving from the most to the least affordable commuting zone would experience a 23.5% decline in consumption.

Mit der Einwohnerzahl einer Stadt steigen im Schnitt auch die Löhne, denn die leistungsfähigsten und damit am besten bezahlten Arbeitskräfte tendieren eher dazu, in größeren Städten zu arbeiten. Mit der Größe einer Stadt steigt zugleich auch die Wahrscheinlichkeit, dass diese Arbeitskräfte bei Betrieben angestellt sind, die alle Beschäftigten überdurchschnittlich gut bezahlen. Dies kann die gesamtwirtschaftliche Produktivität steigern, vergrößert aber die regionalen Lohnunterschiede erheblich., IAB-Forum

In the US and many other OECD countries, expenditures for defense-related R&D represent a key policy channel through which governments shape innovation, and dwarf all other public subsidies for innovation. We examine the impact of government funding for R&D - and defense-related R&D in particular - on privately conducted R&D, and its ultimate effect on productivity growth. We estimate models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds we use changes in predicted defense R&D as an instrumental variable. In both datasets, we uncover evidence of “crowding in” rather than “crowding out,” as increases in government-funded R&D for an industry or a firm result in significant increases in private sector R&D in that industry or firm. A 10% increase in government-financed R&D generates 4.3% additional privately funded R&D. An analysis of wages and employment suggests that the increase in private R&D expenditure reflects actual increases in R&D employment, not just higher labor costs. Our estimates imply that some of the existing cross-country differences in private R&D investment are due to cross-country differences in defense R&D expenditures. We also find evidence of international spillovers, as increases in government-funded R&D in a particular industry and country raise private R&D in the same industry in other countries. Finally, we find that increases in private R&D induced by increases in defense R&D result in significant productivity gains.

In most countries, average wages tend to be higher in larger cities. In this paper, we focus on the role played by the matching of workers to firms in explaining geographical wage differences. Using rich administrative German data for 1985-2014, we show that wages in large cities are higher not only because large cities attract more high-quality workers, but also because high-quality workers are significantly more likely to be matched to high-quality plants. In particular, we find that assortative matching—measured by the correlation of worker fixed effects and plant fixed effects—is significantly stronger in large cities. The elasticity of assortative matching with respect to population has increased by around 75% in the last 30 years. We estimate that in a hypothetical scenario in which we keep the quality and location of German workers and plants unchanged, and equalize within-city assortative matching geographical wage inequality in Germany would decrease significantly. Overall, assortative matching magnifies wage differences caused by worker sorting and is a key factor in explaining the growth of wage disparities between communities over the last three decades. If high-quality workers and firms are complements in production, moreover, increased assortative matching will increase aggregate earnings. We estimate that the increase in within-city assortative matching observed between 1985 and 2014 increased aggregate labor earnings in Germany by 2.1%, or 31.32 billion euros. We conclude that assortative matching increases earnings inequality across communities, but it also generates important efficiency gains for the German economy as a whole.

In many European countries, wages are determined by collective bargaining agreements intended to improve wages and reduce inequality. We study the local and aggregate effects of collective bargaining in Italy and Germany. The two countries have similar geographical differences in firm productivity – with the North more productive than the South in Italy and the West more productive than the East in Germany – but have adopted different models of wage bargaining. Italy sets wages based on nationwide contracts that allow for limited local wage adjustments, while Germany has moved toward a more flexible system that allows for local bargaining. We find that, as a consequence, Italy exhibits limited geographical wage differences in nominal terms and almost no relationship between local productivity and local nominal wages, while Germany has larger geographic wage differences and a tighter link between local wages and local productivity. While the Italian system is successful at reducing nominal wage inequality, it also creates costly geographic imbalances. In Italy, low productivity provinces have significantly higher non-employment rates than high productivity provinces, because employers cannot lower wages, while in Germany the relationship between non-employment and productivity is significantly weaker. In Italy, the relationship between real wages and productivity is negative, with lower real wages in the North compared to the South, since the latter has low housing costs but similar nominal wages. Thus, conditional on having a job, Italian workers have higher purchasing power in the South, but the probability of having a job is higher in the North. We conclude that the Italian system has significant costs in terms of forgone aggregate earnings and employment because it generates a spatial equilibrium where workers queue for jobs in the South and remain unemployed while waiting. If Italy adopted the German system, aggregate employment and earnings would increase by 11.04% and 7.45%, respectively. Our findings are relevant for several other European countries with systems similar to Italy's.

In the US and many other OECD countries, expenditures for defense-related R&D represent a key policy channel through which governments shape innovation, and dwarf all other public subsidies for innovation. We examine the impact of government funding for R&D - and defense-related R&D in particular – on privately conducted R&D, and its ultimate effect on productivity growth. We estimate models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds we use changes in predicted defense R&D as an instrumental variable. In both datasets, we uncover evidence of "crowding in" rather than "crowding out," as increases in government-funded R&D for an industry or a firm result in significant increases in private sector R&D in that industry or firm. A 10% increase in government-financed R&D generates 4.3% additional privately funded R&D. An analysis of wages and employment suggests that the increase in private R&D expenditure reflects actual increases in R&D employment, not just higher labor costs. Our estimates imply that some of the existing cross-country differences in private R&D investment are due to cross-country differences in defense R&D expenditures. We also find evidence of international spillovers, as increases in government-funded R&D in a particular industry and country raise private R&D in the same industry in other countries. Finally, we find that increases in private R&D induced by increases in defense R&D result in significant productivity gains.

The high-tech sector is increasingly concentrated in a small number of expensive cities, with the top ten cities in "Computer Science", "Semiconductors" and "Biology and Chemistry", accounting for 70%, 79% and 59% of inventors, respectively. Why do inventors tend to locate near other inventors in the same field, despite the higher costs? I use longitudinal data on top inventors based on the universe of US patents 1971 - 2007 to quantify the productivity advantages of Silicon-Valley style clusters and their implications for the overall production of patents in the US. I relate the number of patents produced by an inventor in a year to the size of the local cluster, defined as a city x research field x year. I first study the experience of Rochester NY, whose high-tech cluster declined due to the demise of its main employer, Kodak. Due to the growth of digital photography, Kodak employment collapsed after 1996, resulting in a 49.2% decline in the size of the Rochester high-tech cluster. I test whether the change in cluster size affected the productivity of inventors outside Kodak and the photography sector. I find that between 1996 and 2007 the productivity of non-Kodak inventors in Rochester declined by 20.6% relative to inventors in other cities, conditional on inventor fixed effects. In the second part of the paper, I turn to estimates based on all the data in the sample. I find that when an inventor moves to a larger cluster she experiences significant increases in the number of patents produced and the number of citations received. Conditional on inventor, firm, and city _ year effects, the elasticity of number of patents produced with respect to cluster size is 0.0662 (0.0138). The productivity increase follows the move and there is no evidence of pre-trends. IV estimates based on the geographical structure of firms with laboratories in multiple cities are statistically similar to OLS estimates. In the final part of the paper, I use the estimated elasticity of productivity with respect to cluster size to quantify the aggregate effects of geographical agglomeration on the overall production of patents in the US. I find macroeconomic benefits of clustering for the US as a whole. In a counterfactual scenario where the quality of U.S. inventors is held constant but their geographical location is changed so that all cities have the same number of inventors in each field, inventor productivity would increase in small clusters and decline in large clusters. On net, the overall number of patents produced in the US in a year would be 11.07% smaller.