Literature review: The China trade shock and U.S. labor market outcomes
“Trade almost necessarily grows the size of the economic pie, but it also changes the size of different slices.”
D. Autor
Mainstream economists have long argued that international trade improves welfare. Although trade may redistribute income, theory assures us that under standard conditions the gains to winners are more than sufficient to offset any losses incurred by those suffering adverse effects from foreign competition. In recent decades a consensus had emerged that trade had not been a major contributor to declining manufacturing employment or rising wage inequality in developed economies. Workers employed in regions specializing in import-competing sectors could readily reallocate to other regions if displaced by trade.
However, the advance of China made much clearer that trade not only has benefits but also significant cost including distributional and adjustment costs. In a series of recent papers, famous labor economist David Autor from Massachusetts Institute of Technology (MIT) and co-authors (‘ADH’) challenged the long held view studying these impacts and adjustment frictions. The work was featured in lively social media discussions and print media articles (Washington Post, Economist) lately and we provide a literature review.
Industry adjustment to import competition
Between 1991 and 2011, the mean annual industry-level change in US exposure to Chinese imports was 0.5 percentage points. In the same time period, the mean annual change in employment in manufacturing industries was minus 2.7 percentage points. Chinese import penetration accelerated to 0.8 percentage points per anno during 1999 to 2007 (the period of China’s WTO accession). The decline in manufacturing employment accelerated over time: The average industry contracted by 0.3 percentage points between 1991 and 1999, by 3.6 percentage points between 1999 and 2007 and by 5.7 percentage points in the Great Recession period of 2007 to 2011. More generally, a one percentage-point rise in industry import penetration reduces domestic industry employment by 1.3 percentage points in the authors’ regressions.
The challenge is, however, how to measure the distributional consequences and the net economic costs and benefits of these labor market impacts. In particular, how do industry shocks translate into localized employment shocks and are they offset or amplified by local labor market mechanisms? To what extend are trade-induced employment contractions offset by employment gains elsewhere in the economy, potentially outside of trade-impacted regions? Do trade adjustments occur on the employment margin, the wage margin, or both? Are the costs of trade adjustment borne disproportionately by workers employed at trade-impacted firms and residing in trade-impacted local labor markets? Or do these shocks diffuse nationally, thus moderating their concentrated effects.
Regional employment impacts
Over the period between 1990 and 2007, commuting zones (CZ) – a spatial measure of local labor markets – that were more exposed to increased import competition from China experienced substantially larger reductions in manufacturing employment. A 1.000 USD increases in a CZ’s per-worker import exposure reduces the fraction of the working age population employed in manufacturing and non-manufacturing, respectively, by -0.60 and -0.18 percentage points, and raises the fraction of unemployed and out of the labor force by 0.22 and 0.55 percentage points. This shows that industry-level impacts of Chinese import competition are equally visible within local labor markets in the United States. Trade-induced manufacturing declines in CZs are not, over the course of a decade, largely offset by sectoral reallocation or labor mobility. Instead, overall CZ employment-to-population rates fall at least one-for-one with the decline in manufacturing employment. Labor market impacts of trade shocks are likely to be amplified by slow and incomplete adjustment: Rather than modestly reducing wage levels among low-skill workers nationally, these shocks catalyse significant falls in employment rates within trade-impacted local labor markets.
National impacts versus regional impacts
Acemoglu et al. (2016) assess whether the seemingly adverse industry- and region-level impacts are offset by employment responses elsewhere in the economy. They estimate that had import penetration from China not grown after 1999, there would have been 560,000 fewer manufacturing jobs lost through the year 2011. Actual US manufacturing employment declined by 5.8 million workers from 1999 to 2011, making the counterfactual job loss from direct import competition amount to 10 % of the realized job decline.
Negative shocks to one industry are transmitted to other industries via economic linkages between sectors. One source of such linkages is buyer-supplier relationships. Rising import competition in apparel and furniture – two sectors in which China is strong – will cause these “downstream” industries to reduce purchases from “upstream” sectors that supply them with fabric, lumber, and textile and woodworking machinery. Because buyers and suppliers often locate near one another, much of the impact of increased trade exposure in downstream industries is likely to transmit to suppliers in the same regional or national market. Estimates indicate negative employment effects in industries that sell outputs to directly trade-exposed industries, from which trade exposure propagates upstream in the supply chain. Applying the direct plus the indirect exposure increases estimates of trade-induced job losses from 1999 to 2011 to 985,000 workers in manufacturing, and to 2.0 million workers in the entire economy. Interindustry linkages thus magnify the employment effects of trade shocks, almost doubling the size of the impact within manufacturing and producing an equally large employment effect outside of manufacturing.
Additional sources of linkages between sectors operate through changes in aggregate demand and the broader reallocation of labor. When manufacturing contracts, workers who have lost their jobs or suffered declines in their earnings reduce their spending on goods and services. Helping offset these negative aggregate demand effects, workers who exit manufacturing may take up jobs in the service sector or elsewhere in the economy, replacing some of the earnings lost in trade-exposed industries. Aggregate demand and reallocation effects work in opposed directions. Estimates of the net impact imply that import growth from China between 1999 and 2011 led to an employment reduction of 2.4 million workers. There is little evidence for substantial offsetting employment gains in local industries that are not exposed to the trade shock.
Trade and technology
Many economists view technology and trade as two of the paramount forces shaping labor markets in the US and other advanced economies. Are these two forces distinct shocks or, rather, are they varied facets of a common phenomenon? There is an obvious link between them, as rapid technical progress (e. g. the computer revolution) and growth in emerging economies (e. g. the rise of China) are roughly contemporaneous events. Have technology and trade had quantitatively similar impacts on overall employment and is the timing of these effects in fact coincident?
A further ADH paper reveals that there is a surprising degree of divergence between the labor market consequences of these two phenomena – both across industrial, occupational, geographic and demographic groups, and over time as the trajectory of these forces has evolved.
Technology and trade have distinct effects on labor market aggregates. Whereas import competition leads to sharp declines in local manufacturing employment and corresponding growth in local unemployment and non-employment, exposure to routine task specialisation has largely neutral overall employment effects. The authors provide estimates of the impact of technology and trade exposure on the employment-to-population ratio. The regressions do not detect a robust relationship between technology exposure and changes in the employment-to-population ratio. The point estimate -0.05 on routine-share measure is statistically insignificant and small in magnitude. However, the coefficient of on the import exposure variable is -0.70 and highly significant. It indicates that a 1,000 USD rise in a CZ’s import exposure per worker over a 10-year period reduces the CZ’s employment-to-population rate by seven-tenths of a percentage point. In addition, areas with high trade exposure have somewhat lower exposure to routine-task displacement and vice versa.
Effects on demographic groups
Employment effects differ with respect to demographic groups. In contrast to insignificant relationship between routinisation and aggregate employment, unemployment and non-participation, CZs that were initially specialised in routine-intensive occupations saw significant falls in the employment-to-population rate of females. The implied effect is economically meaningful. Comparing a CZ at the 75th percentile and 25th percentile of exposure to task-replacing technical change, the more exposed CZ would see a relative decline in the female employment-to-population rate of 1.8 percentage points per decade. Furthermore, the effects of exposure to routinisation also appear larger for older versus younger workers, though the difference is less precisely estimated.
Workers with less than a college education are those most affected by trade but show only small employment declines from technological change. A 1,000 USD increase in per-worker import exposure is estimated to reduce the non-college employment rate by 1.21 percentage points and the college employment rate by 0.53 percentage points. More surprising is that the effects of trade shocks on employment are otherwise uniformly large and significant for both males and females and for both younger and older workers.
Hence, the negative employment impacts of routinisation are concentrated among females and to some extent among older workers. By contrast, trade shocks appear to reduce employment among all groups of workers that we considered, with a disproportionately large effect among non-college workers.
Effects on occupations and tasks
While technology affects the labor at the occupation level by shifting occupational composition within sectors, trade competition has a broad sectoral impact and depresses employment across all occupation groups in manufacturing, with a notable negative employment effect for higher skilled managerial, professional and technical jobs.
The estimated effect of routinisation on employment is negative, significant and large for only one occupational category: routine task-intensive occupations. The point estimate of -0.36 implies a substantial 1.8 percentage point per decade differential decline in the share of working-age adults employed in this broad occupational category in the 75th percentile CZ relative to the 25th percentile CZ. The pattern of results is consistent with the well-known finding that computerisation is associated with occupational polarisation – that is, gains in the share of employment in relatively high-education, abstract-task-intensive occupations and relatively low-education, manual-task-intensive occupations to the employment in middle-skill, routine task-intensive jobs.
By contrast, increases in trade exposure reduce overall employment across all three broad task categories (managerial, professional and technical – production, clerical and administrative – mechanics, craft and repair, agricultural and service occupations). The largest impact is found in employment in routine task-intensive (i. e. production, clerical and administrative service) occupations with -0.48 percentage points for a 1,000 USD rise in trade exposure.
Technology-induced losses in routine employment among men are offset by corresponding gains in occupations with abstract tasks, such offsetting employment gains are absent for women, thus generating a negative overall impact of technology exposure on female employment. Among young and among college-educated workers, all offsetting employment gains occur in abstract task-intensive jobs, while any offsetting gain among older and less educated workers is in the manual task-intensive jobs that include many low-wage occupations.
Trade shocks uniformly have the greatest (negative) impact on employment in routine task-intensive occupations across all demographic groups with the largest impacts found on females and non-college adults. Trade shocks also substantially reduce employment in manual-task-intensive occupations among males, non-college workers and younger workers and reduce employment in abstract-task-intensive occupations among females, non-college adults and older adults.
Timing of trade and technology effects
The timing of the sectoral impacts of technology and from trade strongly diverge. With the rapid growth of US imports from china, the effect of trade competition on manufacturing has increased over time. Conversely, the effect of technological change on employment composition inside of manufacturing has decelerated, with the largest impacts detected in the 1980s and the smallest impacts found in the 2000s. Outside of manufacturing, however, the impact of automation accelerates during the three decades, suggesting that computerisation of information processing knowledge-intensive industries continues to intensify. The impacts of technology and trade appear to have little overlap either across pace or across time.
Summary
Competition from Chinese imports cost millions of American workers their jobs. Regions more exposed to import competition faced larger reductions in manufacturing employment and higher fractions of unemployed and out the labor force. Local labor markets adjust incomplete over time. Trade-induced local manufacturing declines are not offset largely by sectoral reallocation or labor mobility. Negative industry-specific shocks transmit to other industries via sectoral linkages. Therefore, during a decade, trade-induced total estimated job losses might have added up to 2.4 million workers in the entire economy. There is no evidence for substantial offsetting employment gains in non-exposed local industries or sectors.
There is a surprising divergence of labor market consequences of trade and technological change. Overall, exposure to routine task specialisation has largely neutral employment effects. On a spatial level, routine-intensive occupations saw significant falls in the employment-to-population rate of females due to routinisation and effects are also larger for older versus younger workers. Non-college workers are those most affected by trade but show only small declines in employment due to technology. However, technology is significantly and negatively effecting routine task-intensive occupations. Females and non-college adults are those demographic groups in routine task-intensive occupation particularly hard hit by trade shocks. Overall, the effects of technology and trade appear to have little overlap across time and space. The impact of automation continues to intensify.
Author
Martin Ertl
Chief Economist
UNIQA Capital Markets GmbH
Disclaimer
This publication is neither a marketing document nor a financial analysis. It merely contains information on general economic data. Despite thorough research and the use of reliable data sources, we cannot be held responsible for the completeness, correctness, currentness or accuracy of the data provided in this publication.
Our analyses are based on public Information, which we consider to be reliable. However, we cannot provide a guarantee that the information is complete or accurate. We reserve the right to change our stated opinion at any time and without prior notice. The provided information in the present publication is not to be understood or used as a recommendation to purchase or sell a financial instrument or alternatively as an invitation to propose an offer. This publication should only be used for information purposes. It cannot replace a bespoke advisory service to an investor based on his / her individual circumstances such as risk appetite, knowledge and experience with financial instruments, investment targets and financial status. The present publication contains short-term market forecasts. Past performance is not a reliable indication for future performance.
Imprint
Information required pursuant to the Austrian Media Act
Publisher of this publication:
UNIQA Insurance Group AG
Untere Donaustraße 21, A-1029 Vienna, Austria
Media owner of this publication:
UNIQA Capital Markets GmbH
Untere Donaustraße 21, A-1029 Vienna, Austria
Management Board of UNIQA Capital Markets GmbH:
Dr. Andreas Bertl, Mr. Franz Hagmann
UNIQA Capital Markets GmbH is constituted as a limited liability company; the media owner is licenced as an investment firm and regulated by the Austrian Financial Market Authority (FMA-Finanzmarktaufsicht).
Ownership structure of the media owner:
UNIQA Insurance Group AG is 100% owner of UNIQA Capital Markets GmbH.
Basic tendency of the content of this publication:
Information on general economic data.