Inequality is an urban matter, and it is due to new technologies

Mobile dating apps work best in dense urban areas. The same is true for information technology (IT), and this has profound implications for inequality and polarization in the labor market. Polarization has a marked geographic dimension (Autor 2019, Autor and Dorn 2013, Rossi-Hansberg et al. 2019), but little is known about the mechanism that links investment in IT and the displacement of jobs in space. . Much of the benefits of business investments in hardware and software come from the labor savings that these technologies bring (e.g. Atalay et al. 2020). This section discusses new evidence that these labor savings are greatest in dense urban areas – primarily large cities and metropolitan areas – and, therefore, urban businesses have the greatest incentive to invest in these. labor-saving technologies.

There are two reasons why companies in large cities invest more in labor-saving information technology. It’s a story of two prices: the wage cost of labor and the price of IT. Salaries are consistently higher in large cities. This is called the urban wage premium, which in the United States has an elasticity of around 4.5%. When the size of a city doubles, wages increase by 4.5%. This implies that average salaries in New York, NY are about 35% higher than in Janesville, WI, simply because New York with a population of 20 million is 125 times larger than Janesville with a population of 163,000. L The urban wage premium is the wage response to rising housing costs in large cities. And big cities are more expensive because they are more productive. All other things being equal, if wages were the same, workers would prefer to live in smaller, cheaper towns.

Instead, the price of computing is very similar all over New York and Janesville. These computer technologies are almost perfectly tradable, where software is increasingly installed online, hardware is shipped by courier, and technical support is provided remotely. As a result, the price of IT as a tradable good is independent of the location of the business.

So, with a comparable higher labor cost in big cities and the same cost of IT technologies, companies are disproportionately investing in IT in big cities. However, the adoption of new technologies varies according to the type of job, in particular if they are routine occupations. This affects the composition of skills (polarization of jobs) within and between cities, and has implications for wage inequalities (polarization of wages).

This economic mechanism driven by price differences is exactly what we corroborate in the data. In a new paper (Eeckhout et al. 2021), we use a new dataset, Aberdeen Group’s Ci technology database, with detailed information on hardware and software from over 200,000 facilities to test this hypothesis. and the evolution of polarization since 1990. Information on the total IT budget per worker as well as the spending and adoption of enterprise resource planning (ERP) software, we establish two robust stylized facts. First, IT investment is highest in companies located in cities with high housing costs. Figure 1 shows that cities like New York and San Francisco with the highest rent indexes spend more than 50% more on IT per worker than cities like Janesville and Springfield. We confirm the robustness of this result with different regression specifications.

Figure 1 Average IT per worker compared to the local price level

Second, the share of workers employed in office jobs (routine cognitive occupations) is declining faster in expensive cities. These are the occupations whose tasks are most likely to be displaced by technology. During the period 1990 to 2015, there was a decline in economy-wide routine cognitive employment, which has been widely documented (see, among others, Acemoglu and Autor 2011, Cortes et al. 2014, Goos et al. 2014). Our regressions show that this drop in routine cognitive occupations is greatest in the cities with the highest rent index. The most expensive cities recorded a 4.5% drop in the share of routine cognitive occupations compared to the cheapest locations, or a quarter of the average share of the economy in 2015.

These two new stylized facts are also essential for understanding the evolution of wage inequalities since 1980. In line with previous work (Baum-Snow and Pavan 2013, Eeckhout et al. 2014, Santamaria 2018) which document the variation in wage inequalities between and within cities, we find that wage inequality is greater in large cities, but this was not the case in 1980. Figure 2 shows that the variance in 2015 fell from 0.3 in the lowest cities. cheaper at nearly 0.5 in the more expensive. In 1980, the variance hovered around 0.21 to 0.22 in all cities. Thus, inequality is now much higher everywhere (the variance almost doubles for the city at median cost) and the inequality is increasing in cost. At the same time, the inequality between cities that can be measured by the urban wage premium has not changed over time. A decomposition of the variance of wages shows that 95% of the variance of hourly wages is within cities and 5% between the two, with no change over time.

Figure 2 Change in city wages by city housing cost

In order to assess the effect of policy interventions and calculate the impact on well-being, we propose a spatial equilibrium model that rationalizes these stylized facts. In the mechanism of the model, the substitution of routine workers by IT leads to greater adoption of IT in large cities due to higher cost of living and higher wages. We estimate the model to trace the effects of IT on the labor market between 1990 and 2015. We find that the fall in IT prices explains 50% of the growing wage gap between routine and non-routine cognitive jobs. . Falling information technology prices also explain 28% of the shift in employment from routine cognitive jobs to non-routine cognitive jobs. In addition, our estimates show that the impact of IT is uneven across space. Expensive locations have seen a larger shift from routine cognitive jobs and a larger widening of the pay gap between routine and non-routine cognitive jobs.

Polarization through the displacement of routine cognitive tasks is driven by the adoption of computing in the workplace. Krusell et al. (2000) show that differential adoption of technology is an important force behind the rise in inequalities between skilled and unskilled workers. Our results show that the adoption of technology also plays a key role in the polarization between cities with different living costs. Lower relative prices of new technology relative to the cost of labor lead to different investment choices by urban firms. This in turn leads to the polarization of professions across geography and explains the increase in wage inequalities within cities. In short, polarization and inequalities are an urban affair, driven by new technologies that thrive in dense and large cities. Just like mobile dating.

Author Note: The views expressed in this column are those of the authors and do not necessarily reflect those of the Federal Reserve System, the Federal Reserve Bank of Cleveland, or the Board of Governors.

The references

Acemoglu, D and DH Autor (2011), “Skills, tasks and technologies: implications for employment and income”, Manual of labor economics, 4: 1043–1171.

Atalay, E, P Phongthiengtham, S Sotelo and D Tannenbaum (2020), “The Changing Nature of Work in the United States,”, January 23.

Author, DH (2019), “Work from the past, work from the future”, Documents and acts of the AEA, 109: 1–32.

Autor, DH and D Dorn (2013), “The growth of low-skill service jobs and the polarization of the US labor market”, The American Economic Review, 103 (5): 1553-1597.

Baum-Snow, N and R Pavan (2013), “Inequality and city size”, Review of economics and statistics, 95 (5): 1535-1548.

Cortes, M, N Jaimovich, C Nekarda and H Siu (2014), “The Who’s and How’s of Routine Job Disappearance,”, October 2.

Eeckhout, J, R Pinheiro and K Schmidheiny (2014), “Tri spatial”, Journal of political economy, 122 (3): 554-620.

Eeckhout, J, C Hedtrich and R Pinheiro (2021), “IT and Urban Polarization”, CEPR Discussion Paper No. 16540.

Goos, M, A Manning and A Salomons (2014), “Explaining job polarization: Routine-biased technology change and offshoring”, American Economic Review, 104 (8): 2509–26.

Krusell, P, LE Ohanian, JV Rios-Rull and GL Violante (2000), “Capital-skills complementarity and inequalities: a macroeconomic analysis”, Econometrics, 68 (5): 1029-1053.

Rossi-Hansberg, E, PD Sarte and F Schwartzman (2019), “Optimal Policy Responses to the Growing Polarization of Occupations in Space,”, November 29.

Santamaria, C (2018), “Small teams in big cities: Inequality, city size and production organization”, Princeton, mimeo.

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