Periodic Reporting for period 4 - AUTOMATION (AUTOMATION AND INCOME DISTRIBUTION: A QUANTITATIVE ASSESSMENT)
Période du rapport: 2023-05-01 au 2025-04-30
Since the invention of the spinning frame, automation has been a key driver of economic growth. Yet, workers, economists and the general public have been concerned that automation, by enabling the replacement of some workers with machines, may destroy jobs or create inequality. This concern is particularly prevalent today with the sustained rise in economic inequality and fast technological progress in AI or robotics. The empirical literature has shown that automation contributes to rising inequality and sometimes a decrease in employment. What has been largely missing from the analysis is the feedback effect of labor market conditions on automation innovations: higher labor costs should incentivize firms to undertake more automation innovations. This feedback is key to assessing the long-term effect of policies: An increase in the minimum wage may have more negative effects on employment than previously thought if it incentivizes the development of new automation technologies. My project aims to provide the first quantitative account of the two-way relationship between automation and income distribution.
The project has three parts. Part 1 uses patent data to study empirically the causal effect of labor costs on automation innovations. We develop a new classification of patents in machinery as automation or not. Then, we analyze how innovation incentives for machinery producers depend on the wages faced by their potential customers: that is, if the clients of a machine-tool producer were to face a higher low-skill wage, would that machine-tool producer innovate more in automation? We exploit variation in innovating firm’s exposure across labor markets to build firm-specific variations in labor costs. We find that automation innovations react strongly to changes in labor conditions.
Part 2 uses the same classification of machinery patents to study the effect of automation on employment in the United States. We find that manufacturing industries that are more exposed to automation experienced a decline in employment, while the effect of overall machinery innovation switches from positive to negative. In addition, commuting zones more exposed to automation experienced a decline in both manufacturing and total employment.
Finally, Part 3 analyzes the evolution of the labor share at the firm level in the Danish economy using micro data from 1995 onwards. The decline in the aggregate labor share is one of the most salient features of the global economy in the last 40 years, contributing to a rise in income inequality. Our analysis emphasizes that composition effects drive the decline in the labor share, and while we do not rule out automation as a plausible driver, we find that trade plays a major role.
The project has three parts. Part 1 uses patent data to study empirically the causal effect of labor costs on automation innovations. We develop a new classification of patents in machinery as automation or not. Then, we analyze how innovation incentives for machinery producers depend on the wages faced by their potential customers: that is, if the clients of a machine-tool producer were to face a higher low-skill wage, would that machine-tool producer innovate more in automation? We exploit variation in innovating firm’s exposure across labor markets to build firm-specific variations in labor costs. We find that automation innovations react strongly to changes in labor conditions.
Part 2 uses the same classification of machinery patents to study the effect of automation on employment in the United States. We find that manufacturing industries that are more exposed to automation experienced a decline in employment, while the effect of overall machinery innovation switches from positive to negative. In addition, commuting zones more exposed to automation experienced a decline in both manufacturing and total employment.
Finally, Part 3 analyzes the evolution of the labor share at the firm level in the Danish economy using micro data from 1995 onwards. The decline in the aggregate labor share is one of the most salient features of the global economy in the last 40 years, contributing to a rise in income inequality. Our analysis emphasizes that composition effects drive the decline in the labor share, and while we do not rule out automation as a plausible driver, we find that trade plays a major role.
Part 1 makes four major contributions. First, we develop a new measure of automation in machinery. We classify technology codes (CPC/IPC codes) using the text of some patents. As IPC codes are universal, we can then classify all patents. This classification is publicly available, and several researchers have used it for their own projects. Second, we map patents to their industry of use, using data on the inventing industry and a capital flow table (an input/output table for capital goods). We find that industries exposed to a higher share of automation patents in machinery experience a decline in routine tasks, a decline in the labor share, and an increase in the ratio of high- to low-skill workers. Third, we use global firm-level data to analyze the causal effect of an increase in labor costs on automation innovations. We measure innovators’ exposure to each country using patent data and compute the low- and high-skill labor costs faced by their customers as a firm-specific weighted average of country-level labor costs. We control for (main) country-year fixed effects, ensuring that we compare how firms from the same country react to wage-induced foreign demand shocks. We find a positive effect of low-skill wages on automation innovation with an elasticity of 2-5 and a negative effect of high-skill wages. There is no effect on non-automation innovation performed by the same set of firms. An increase in minimum wages also induces automation innovation. Fourth, we look at the German Hartz reforms, which reduced low-skill labor costs. We show in an event-study that these reforms reduced automation innovations of non-German firms highly exposed to Germany. We presented this paper in many venues, and it is now published at the Journal of Political Economy.
Part 2 leverages the classification of Part 1. We use more detailed disaggregated US sectoral data from 1980 to 2010 and compute decadal change in employment. We find that overall machinery innovations used to increase employment but now decrease it. Decomposing these into automation and non-automation innovations, the former decrease employment while the latter increase it. An industry which is 1 standard deviation more exposed than another one to automation experiences a decrease in employment of 16% per decade, which is equal to the average employment decrease in manufacturing per decade. We then move to the commuting zone (CZ) level. We measure local automation exposure using a shift-share strategy. The CZ results mirror the industry level results but allow us to go further: we show that the non-manufacturing sector does not compensate for manufacturing employment losses from automation, and that routine occupations are particularly affected. Finally, we show that automation innovations do not generate compensating employment gains for the producing industries or the CZ where innovations take place. We have derived the empirical results and are about to write the draft.
In Part 3, we find that the decline in the aggregate labor share is mostly a within industry phenomenon: a larger share of the industry value added is produced by low labor share firms, while the labor share of the median firm increases. This reallocation is driven by firms that at the same time become low labor share and increase their size. While we do not rule out a role for automation, our analysis emphasizes the role of trade: the decline in the labor share is more pronounced in export-oriented sectors, and low labor share firms grow by expanding their sales abroad. R&D investment predicts declines in the labor share, suggesting that a plausible channel is that successful innovators, who can charge a large mark-up, are now able to capture a larger market.
This part is still ongoing: We have established the stylized facts, we did some preliminary work on the model, but we are not done yet.
Part 2 leverages the classification of Part 1. We use more detailed disaggregated US sectoral data from 1980 to 2010 and compute decadal change in employment. We find that overall machinery innovations used to increase employment but now decrease it. Decomposing these into automation and non-automation innovations, the former decrease employment while the latter increase it. An industry which is 1 standard deviation more exposed than another one to automation experiences a decrease in employment of 16% per decade, which is equal to the average employment decrease in manufacturing per decade. We then move to the commuting zone (CZ) level. We measure local automation exposure using a shift-share strategy. The CZ results mirror the industry level results but allow us to go further: we show that the non-manufacturing sector does not compensate for manufacturing employment losses from automation, and that routine occupations are particularly affected. Finally, we show that automation innovations do not generate compensating employment gains for the producing industries or the CZ where innovations take place. We have derived the empirical results and are about to write the draft.
In Part 3, we find that the decline in the aggregate labor share is mostly a within industry phenomenon: a larger share of the industry value added is produced by low labor share firms, while the labor share of the median firm increases. This reallocation is driven by firms that at the same time become low labor share and increase their size. While we do not rule out a role for automation, our analysis emphasizes the role of trade: the decline in the labor share is more pronounced in export-oriented sectors, and low labor share firms grow by expanding their sales abroad. R&D investment predicts declines in the labor share, suggesting that a plausible channel is that successful innovators, who can charge a large mark-up, are now able to capture a larger market.
This part is still ongoing: We have established the stylized facts, we did some preliminary work on the model, but we are not done yet.
Part 1 makes several contributions to the literature including a new classification of automation technologies and the first causal evidence of induced automation innovation at the firm-level. From a methodological standpoint, we show how to assess the robustness of shift-share instruments in a non-linear setting. Part 2makes a key contribution by showing how machinery innovations have progressively shifted from encouraging to discouraging employment. Part 3 belongs to a growing literature that documents dramatic changes in the firm-level distribution of labor shares. Our approach presents several advantages: thanks to the richness of the Danish firm data, we can properly compute the labor share outside of manufacturing, and we can empirically investigate several channels. We are still working on this part.