metadata: authors: 0: author: Kindberg-Hanlon,Gene
count: World
docna: 0: docna: The Technology-Employment Trade-Off :
            Automation, Industry, and Income Effects
docty: Policy Research Working Paper
subtopic: Labor Markets,Food & Beverage Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,International Trade and Trade Rules
teratopic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,International Economics and Trade
abstracts: cdata!: New technologies can both substitute for
            and complement labor. Evidence from structural vector
            autoregressions using a large global sample of economies
            suggests that the substitution effect dominates in the
            short-run for over three-quarters of economies. A typical 10
            percent technology-driven improvement in labor productivity
            reduces employment by 2 percent in advanced economies in the
            first year and 1 percent in emerging market and developing
            economies (EMDEs). Advanced economies have been more
            affected by employment-displacing technological change in
            recent decades but the disruption to the labor market in
            EMDEs has been more persistent. The negative employment
            effect is larger and more persistent in economies that have
            experienced a larger increase, or smaller fall, in
            industrial employment shares since 1990. In contrast,
            economies where workers have been better able to transition
            to other sectors have benefited more in the medium run from
            the positive ``income effect'' of new
            technologies. This corresponds with existing evidence that
            industrial jobs are most at risk of automation and
            reduced-form evidence that more industrially-focused
            economies have tended to create fewer jobs in recent
            decades. EMDEs are likely to face increasing challenges from
            automation as their share of global industry and production
            complexity increases.
repnme: repnme: The Technology-Employment Trade-Off :
            Automation, Industry, and Income Effects
display_title: The Technology-Employment Trade-Off
            : Automation, Industry, and Income Effects
listing_relative_url: /research/2021/01/32785870/technology-employment-trade-off-automation-industry-income-effects
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Jobs m1503931 2670,Labor Productivity m1504060 2756
publishtoextweb_dt: 2021-01-28T00:00:00Z
historic_topic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,International Economics and Trade
origu: EFI - Prospects Group (EPGDR)
no_of_pages: 62
author_keys: 0: author_key: 1676939
model_extractions: Policy Research Working Paper 9529 The Technology-Employment Trade-Off Automation, Industry, and Income Effects _Gene Kindberg-Hanlon_ Prospects Group January 2021
Policy Research Working Paper 9529

# **Abstract**

New technologies can both substitute for and complement labor. Evidence from structural vector autoregressions using a large global sample of economies suggests that the substitution effect dominates in the short-run for over three-quarters of economies. A typical 10 percent technology-driven improvement in labor productivity reduces employment by 2 percent in advanced economies in the first year and 1 percent in emerging market and developing economies (EMDEs). Advanced economies have been more affected by employment-displacing technological change in recent decades but the disruption to the labor market in EMDEs has been more persistent. The negative employment effect is larger and more persistent in economies that have experienced a larger increase, or smaller fall, in industrial employment shares since 1990. In contrast, economies where workers have been better able to transition to other sectors have benefited more in the medium run from the positive “income effect’’ of new technologies. This corresponds with existing evidence that industrial jobs are most at risk of automation and reduced-form evidence that more industrially-focused economies have tended to create fewer jobs in recent decades. EMDEs are likely to face increasing challenges from automation as their share of global industry and production complexity increases.

This paper is a product of the Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at gkindberghanlon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Technology-Employment Trade-Off: Automation, Industry, and Income effects<sup>∗</sup>

Gene Kindberg-Hanlon<sup>†</sup>

**Keywords** : Employment, Productivity, Technology shocks, SVARs. **JEL classification** : C11, C30, C33, E24

> ∗I would like to thank Alistair Dieppe, John Fernald, Neville Francis, Ayhan Kose, Franziska Ohnsorge, Temel Taksin, and Lei Ye for valuable comments and Aygul Evdokimova for excellent research assistance. I gratefully acknowledge financial support from the PHRD fund. The findings, interpretations, and conclusions expressed herein are those of the author and do not necessarily reflect the view of the World Bank.

> †World Bank gkindberghanlon@worldbank.org
# **1 Introduction**

Concerns are frequently raised about how the gains from new technologies are shared, particularly through their impact on employment. Currently, concerns are highest around the automation of manufacturing jobs but historically many major innovations have been accompanied by the threat of job losses (World Bank, 2019). New technologies can be both a substitute or a complement for labor, and therefore can boost job opportunities as well as reduce them (Autor, 2015). Certain segments of the labor market can be harmed by technological change even where they result in new labor tasks. Where the skills needed to accompany new technologies are unavailable, or demand for new labor tasks does not rise sufficiently, aggregate employment can be persistently lower.

A large literature has attempted to assess the impact of technological change on employment within affected sectors but so far the effects on aggregate employment have been under-explored, particularly in EMDEs. This paper sheds new light on the question of how typical technology shocks affect aggregate employment in a broad range of 30 advanced economies and 96 emerging and developing economies (EMDEs). The large coverage relative to existing studies is enabled by new methodologies developed in Dieppe et al. (2019, 2021).<sup>1</sup> A further key contribution of this paper is that the large sample size of economies under consideration allows for an exploration of country-characteristics which are important in determining the size and persistence of employment impacts following a technology shock.

“Technology shocks” in the sense discussed in this paper are derived from a structural vectorauto regression (SVAR). These are identified as innovations which account for the largest share of long-term (low-frequency) changes in productivity or TFP (Dieppe et al., 2019). The resulting shocks “look-through” temporary changes in labor productivity or TFP, which are often driven by non-technological factors such as changing factor utilization (Basu et al., 2006).<sup>2</sup> Furthermore, this methodology captures many types of technology shock; these can be neutral or complementary to labor, resulting in increased employment, and can also be labor-saving, such as automating technology developments (Acemoglu, 2003). The shocks captured by the SVAR approach will therefore reflect the “typical” technology introduced over the estimation horizon.

Ninety percent of advanced economies and 70 percent of EMDEs experience a fall in employment in year 1 following a positive technology shock, while half of advanced economies and one-third of EMDEs experience a statistically significant fall in employment. In many economies, this fall in employment is relatively persistent, lasting 10 years on average before the effect dissipates. The finding is robust across multiple estimation methodologies for identifying technology shocks and suggests that the type of technology shocks affecting most economies have been labor-substituting.

Economies with higher average productivity levels have suffered larger initial employment losses

> 1Gal ́ı (1999) and Rujin (2019) assess the impacts of technology shocks on hours and employment in the G7.

> 2This identification does not rule out that factors other than productivity-enhancing technologies can drive long-run productivity developments but it does assume that this is the dominant driver.

2
following technology shocks, reflecting a higher propensity to introduce labor-saving technologies in advanced economies relative to EMDEs. EMDEs have yet to be affected by labor-substituting technological change to the same degree as advanced economies. However, EMDEs have been affected by these technologies in large number and experienced more persistent employment losses on average than advanced economies. This finding corresponds with the finding that “premature deindustrialization” has been a widespread phenomenon in EMDEs. Higher trade openness and exposure to FDI inflows are associated with _lower_ initial employment losses following technology shocks, consistent with a large literature that has found FDI is associated with job gains in EMDEs; firms frequently invest in these economies to take advantage of lower labor costs, not to remove workers from the production process.

Those economies where employment shares have remained more concentrated in the industrial sector since 1990 have seen larger and more persistent employment losses on average following a technology shock. This may seem paradoxical at first: the industrial sector, which has had a higherthan-average potential for automation in recent decades, has proved more resilient as a share of employment in the face of labor-displacing technological innovation in some economies. A simple adaption of the framework of Acemoglu and Restrepo (2017, 2018) to incorporate differential income elasticities shows that the services sector will be the key beneficiary of increased demand due to the efficiency gains from automation that occur in the industrial sector. This is because the incomeelasticity and price-elasticity of demand for industrial goods is low, so that the displacement effect for workers is unlikely to be offset by rising demand. In a world where the majority of economies have experienced the direct effects of the loss of employment from labor-displacing technologies, those that have been best able to transition workers to less-affected sectors such as services will have stronger aggregate employment growth and a larger fall in the share of industrial employment. Those economies that have been less successful at redeploying workers to other sectors will have lower aggregate employment but higher industrial employment shares.

This paper proposes an additional perspective on the “premature deindustrialization” hypothesis (Rodrik, 2016), where lower employment shares in industry are presented as a loss of the “escalator of development”; instead, accelerated rotation away from industrial employment may be seen as a sign of success in many cases, resulting from a smooth transition of workers and demand to new sectors following the introduction of new labor-displacing technologies in industry.

A lack of transferable skills to other sectors will reduce the growth of aggregate employment following a labor-substituting technology shock. Policies to improve the skill base of workers can aid worker transitions to sectors less affected by labor-displacing technologies but may require a prolonged period to implement and reap returns. Policies to increase demand for industrial products may provide nearer-term relief; there are large scale requirements for infrastructure investment to meet poverty reduction targets in EMDEs and emission reduction targets in both advanced economies and EMDEs. Public sector investment and increased incentives for private sector invest3
ment will boost industrial output and could help reduced prolonged falls in employment as new technologies are introduced.

# **2 Literature**

Before turning to the new SVAR evidence on the effects of productivity-enhancing technology shocks presented in this paper, it is useful to review the evidence from the existing SVAR literature, alongside the theoretical and non-SVAR empirical evidence for the effects of automation and new technologies on employment.

## **2.1 Theory**

Productivity-improving technologies can generate opposing forces on total employment in an economy: first, a substitution effect, where new technologies can replace the need for workers; and second, an income effect, where increases in the profitability of production increase the demand for labor, in the affected or alternative sectors (Aghion and Howitt, 1994). The ability of the income effect to offset automation will depend crucially on the type of workers required to complement new technologies and capital assets, and the supply of workers with the appropriate skills for these tasks (Acemoglu and Autor, 2011; Acemoglu and Restrepo, 2018).

Search and matching models have been used to examine the employment displacement effects of introducing new technologies into the production process. Mortensen and Pissarides (1998) show that as the costs of updating skills and equipment for existing workers grow, “creative destruction”, or the replacement of current labor tasks with capital embodying new technologies is likely to occur (Schumpeter, 1942). In addition, they find that employment protection and unemployment benefit levels can exacerbate the degree and persistence of unemployment following a technology shock (Mortensen and Pissarides, 1999). Restrepo (2015) finds that employment in routine jobs is likely to have declined since the 1990s due to search-and-matching frictions, as new technologies frequently require increasingly novel skills.

This paper focuses on new technologies displacing labor as a driver of falling employment following a technology shock. However, a second mechanism exists for technology shocks to reduce employment (or labor input more generally). The bulk of the literature analyzing the impact of technology shocks using SVAR identification techniques have focused on the effects of sticky prices in the canonical New Keynesian model as the reason for falling employment. Here, the mechanism is that aggregate demand remains inflexible in the short run due to sticky prices and grows by less than productivity following a technology shock. This leads firms to cut labor input in response to new technologies. As prices adjust, demand expands and employment recovers (Gal ́ı, 1999; Basu 4
et al., 2006).<sup>3</sup> In contrast, this paper finds evidence of more persistent employment effects than might be driven by sticky prices. For most economies, investment does not fall in response to a technology shock. Investment, as well as employment, is expected to fall initially in standard New Keynesian models.

In some cases, the SVAR literature has assigned the fall in employment found in some advanced economies following an SVAR-identified technology shock to the displacement of labor for new technologies, rather than the sticky-price mechanism. Canova et al. (2013) and Michelacci and Lopez-Salido (2007) attribute falling employment after a technology shock to Schumpeterian ‘creative destruction’ effects. This paper builds on this tradition, using a significantly larger set of economies under consideration, and using new SVAR identification techniques. However, a range of alternative and complementary approaches have also been implemented to establish the effects of new technologies on employment.

# **2.2 Sectoral evidence**

A large body of evidence has shown that jobs have become increasingly polarized into low- and high-skill occupations in the U.S. and Europe in recent decades, as a combination of automation and offshoring has reduced demand for middle and low-skilled workers performing routine and codifiable jobs (Acemoglu, 1999; Autor, 2015; Goos et al., 2014). Many of these lost occupations were in the industrial sector, even as value-added produced by the sector remained resilient; in the United States, employment of machine operators, assemblers, and other production employees fell by over one-third every 10 years between 1980 and 2005 (Autor and Dorn, 2013). In a study of 16 European economies during 1993-2010, the share of employment accounted for by middle- and lowskilled industrial sector occupations fell by nearly 10 percentage points (Goos et al., 2014). In the United States and France, the increased use of robotics in industry is found to be inversely related to industrial employment levels since 1990 and 2010, respectively (Acemoglu et al., 2020; Acemoglu and Restrepo, 2020). Some service sector occupations are also found to have been negatively affected by this trend in both regions, notably middle-skilled jobs such as office clerks. However, codifiable middle- and low-skill jobs have been (at least partially) replaced by higher demand for both lowskill service sector jobs, which are less easy to automate, and higher-skill jobs that complement new technologies. SVAR analysis of sectoral manufacturing data for advanced economies has also found negative effects on total hours worked of developments that have driven persistent positive TFP growth (Chang and Hong, 2006; Park, 2012; Khan and Tsoukalas, 2013).

> 3A wide range of SVAR literature has tackled the response of labor inputs in response to technology to assess the presence of a sticky-price mechanism, including (to name just a few) Christiano et al. (2004), Dedola and Neri (2007), Collard and Dellas (2007), Francis and Ramey (2005), Francis et al. (2014), and Canova et al. (2010). The majority of research has found evidence of decreasing employment from technology shocks in the U.S. context.

5
## **2.3 General equilibrium impacts of technological progress on employment**

Several studies in the U.S. have found that technological change has caused _aggregate_ employment to fall. These studies have gone beyond an examination of the sectoral effects of technological job displacement. During the so-called ‘jobless recoveries’ in the US after the recessions of 1991, 2001, and 2008, when the employment rate fell or stagnated overall, middle-skilled and automatable roles declined, particularly in the manufacturing sector (Jaimovich and Siu, 2019; Charles et al., 2016). It has been further argued that even high-skilled workers have been substituted for newer technologies and pushed into lower-skilled positions, reducing overall employment (Beaudry et al., 2016). However, there remains controversy over the net impacts of technological change. In some cases, the fall in employment in the sector affected by technological change is found to be offset by employment gains in other sectors, particularly in “downstream” sectors which use inputs from the affected sector (Autor and Salomons, 2018).

## **2.4 EMDE evidence**

There is so far little evidence of the effects of technological change on employment in EMDEs. In part this is because EMDEs have been large beneficiaries of outsourcing from advanced economies; many manufacturing and codifiable service sector jobs have moved to EMDEs to take advantage of cheap labor costs (Maloney and Molina, 2016). What technology-influenced change does appear to be occurring has increased the share of routine semi-skilled jobs in many EMDEs, in contrast to the fall in these types of jobs in advanced economies (World Bank, 2019). That said, large increases in manufacturing productivity do appear to have resulted in “premature deindustrialization” in EMDEs, where employment falls in the sector at much lower levels of income per capita than has occurred historically (Rodrik, 2016). That could suggest that productivity-enhancing technology in the manufacturing sector has reduced employment relative to a counterfactual, which would have been otherwise higher still.

# **3 Estimating the effects of technology shocks on employment**

Much of the evidence on the impact of productivity-improving technology on employment has centered around the impacts of IT and manufacturing technologies in the US and Europe in recent decades. There has yet to be an assessment of the effects of general improvements in technology on employment in a broad range of countries. To assess the impact of productivity-enhancing technology changes on a range of countries, we turn to the SVAR literature, which has already 6
extensively estimated the relationship between “technology shocks” and total hours worked in the United States and some European economies, finding a negative impact on total hours worked in the first year following the shock (Gal ́ı, 1999; Francis and Ramey, 2005; Francis et al., 2014).<sup>4</sup> In this paper, innovations in technology (“technology shocks”) are identified as shocks that drive the largest proportion of long-run developments in labor productivity, while other shocks are assumed to have only transitory effects. Therefore, technology shocks represent any structural development that results in lasting changes in labor productivity. These innovations could boost labor productivity through higher technical efficiency, in the sense of higher TFP in a standard Cobb-Douglas framework. Equally, new technologies may allow for the replacement of tasks previously completed using manual labor with capital, or “robot” inputs, altering the ratio of labor and capital inputs, without necessarily boosting the overall efficiency with which capital and labor are combined (Acemoglu and Restrepo, 2018). In both cases, output per unit of labor input is increased. The identification may capture a mix between labor-augmenting and labor-displacing technologies.

Traditionally, long-run restrictions have been used to identify technology shocks. However, longrun restrictions are susceptible to significant bias from the presence of non-technology shocks which can also drive developments in productivity (Chari et al., 2009; Dieppe et al., 2019, 2021). Instead, the Spectral identification, detailed below, is more robust, particularly when applied to volatile emerging and developing market economy data.

# **3.1 Spectral identification of technology shocks**

The Spectral identification searches for the shock which maximizes the contribution to the variance of productivity at long-run frequencies. This abstracts from the shocks which may instead drive variation in labor productivity at business-cycle frequencies, such as demand and other short-run impact shocks. This approach effectively applies a band-pass filter to the reduced-form coefficients of a VAR containing macroeconomic variables, identifying the spectral density of the variables within a particular frequency band. The technology shock is then identified by maximizing the variance of labor productivity explained at the desired frequency.

We start by writing the Wold representation of the VAR (assuming it is invertible): Here, _B_ are the reduced-form auto-regressive coefficients, while _u_ are the reduced form errors in the VAR representation and _D_ reflects the sum of the MA-representation coefficients of the VAR. By post-multiplying _Yt_ by _Yt−τ_ and summing across its lags (of _τ_ periods), the series of auto and cross covariances ( _γ_ ) can be written as a function of _D_ and the variance-covariance matrix of errors:

> 4For a detailed review of the technology SVAR literature, see Ramey (2016).

7
The spectral density of _Y_ at frequency _ω_ can be written as a function of _D_ , _D_ � _e_<sup>_−iτω_�</sup> = � _I −_ � _B_ 1 _Le_<sup>_−iω_</sup> + _B_ 2 _L_<sup>2</sup> _e_<sup>_−i_2</sup><sup>_ω_</sup> + _. . . BpL_<sup>_p_</sup> _e_<sup>_−ipω_��</sup><sup>_−_1</sup> : Due to the limited time series available for estimation in many EMDEs, the “limited spectral” identification, where _D_ is truncated at the 10-year horizon due to the associated biases when estimating the long-run estimation on finite data (Dieppe et al., 2019, 2021).

To assess the spectral density within a frequency band, the spectral power can be integrated between _ω_ = [ _<u>ω</u> , ω_ ̄]. The shock that maximizes the variance of labor productivity over the desired frequency is the eigenvector associated with the largest eigenvalue of _SY Y_ ( _ω_ ), see (Uhlig, 2003). The frequency band of interest here is from 10- _∞_ years so that technology shocks are identified as the shock that dominates very long-run productivity developments, while excluding short-term developments such as those at business-cycle frequencies from the maximization problem.

# **3.2 Assessing the impact of technology shocks in a pooled panel environment**

Initially, in order to understand the typical impacts of technology shocks across countries, a simple pooled panel estimation with fixed effects is used. This is performed separately across advanced economies and EMDEs, particularly since the variance of productivity is three times larger in EMDEs than in advanced economies, given assumptions required on common parameters for panel estimations. For each economy, the VAR consists of the log-level of labor productivity, logemployment per capita (including self-employment), the log share of investment (gross-fixed capital formation), and separately consumption in GDP, and consumer price inflation.

Macroeconomic aggregates such as GDP and employment are from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database (TED) for employment. Employment and GDP data are extended where available with WDI and ILO employment estimates and Penn World Table 9.1 GDP data where possible. The majority of economies have full data coverage over the same period over which the growth accounting components are available, and the average sample length is 40 years for EMDEs and 45 years for advanced economies. Hence, here, annual data is used to estimate the VARs. This choice reduces the degrees of freedom in the VAR estimation, while at the same time significantly lengthening the period over which the VAR is estimated for many EMDEs. The span of the data is critical for identifying 8
technology shocks as those which drive long-term developments in productivity. The estimation is performed using the pooled panel VAR approach: Where _C_<sup>_n_</sup> varies across countries, _n_ , while _B_ and the variance-covariance matrix of residuals Σ _u_ are assumed to be common across countries. The estimated parameters, _B_ and Σ _u_ , can then be used to identify the effects of technology shocks using the spectral identification described above. Standard Normal-Inverse-Wishart priors are used in the Bayesian estimation. For several EMDEs, periods of high, or hyper-inflation, have occurred. Dummy variables are included for those economies during periods in which inflation has exceeded 20 percent.

In both EMDEs and advanced economies, the impulse responses of a range of macroeconomic variables behave in a way consistent with theoretical predictions. Labor productivity rises on a sustained basis over the 10-year horizon under consideration (Figure 1). The additional IRFs are scaled to the size of the effect on productivity for greater ease in comparing the impacts between advanced economies and EMDEs. The IRFs for employment, investment, consumption, and consumer price inflation can, therefore, be interpreted as the impact on those variables for 10 percent improvement in labor productivity resulting from the technology shock.

In advanced economies, employment falls in the first year following a technology shock by 2 percentage points for each 10 percentage point rise in labor productivity.<sup>5</sup> In EMDEs, employment falls by 1 percent, but more persistently, only becoming statistically insignificant from zero after 9 years. In both advanced economies and EMDEs, investment rises more than one-for-one with the impact on productivity for the majority of the IRF horizon, while consumption adjusts more slowly to the new steady state of higher output. Consumer prices fall in both regions as output can be produced with fewer inputs. In EMDEs, the adjustment of prices is slower, possibly reflecting less well-anchored inflation expectations.

While the initial impact on employment in EMDEs is smaller than in advanced economies, it is more persistent. One interpretation is that EMDEs have introduced fewer labor-displacing technologies with a smaller initial disruptive impact on employment than in advanced economies. However, EMDEs have coped less well at moving workers into new roles, or generating higher demand from these new technologies, even at long horizons. The smaller negative employment impact in EMDEs is consistent with the literature which has found that the replacement of low and middle-skilled workers has primarily been an advanced-economy phenomenon so far, with many new technologies implemented in EMDEs specifically designed to take advantage of lower-cost labor inputs.

> 5Rounding from 1.6 percent for advanced economies, and 1.1 percent for EMDEs.

9
<!-- Start of picture text --> Productivity<br>SSScOo<br>24<br>a<br>0Fo<br>2 4 6 8 10<br>Years<br>Employment, scaled<br>7) ee<br>5 0 ———— |<br>© -4<br>a<br>-2<br>2 4 6 8 10<br>Years<br>Investment, scaled<br>Oo°10<br>2 4 6 8 10<br>Years<br>Consumption, scaled<br>5<br>o 5<br>Oo<br>2 4 6 8 10<br>Years<br>CPI, scaled<br>0<br>5<br>o2<br>a<br>=<br>2 4 6 8 10<br><!-- End of picture text -->

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<!-- Start of picture text --> 100<br>HE Pane! estimation<br>[Fa individual estimation<br>80<br>ec 60<br>a<br>2<br>a<br>a 40<br>: -<br>0<br>EMDE: negative AE: negative EMDE: significant AE: significant<br><!-- End of picture text -->
<!-- Start of picture text --> 0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>-20 15 10 6 0 5 10<br>Employment impact<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>020 -15 -10 5 0 5 10 15 20<br>Employment impact<br><!-- End of picture text --> <!-- Start of picture text --> 0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>-20 -15 10 5 0 5 10<br>Employment impact<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>020 -15 -10 5 0 5 10 15 20<br>Employment impact<br><!-- End of picture text -->
advanced economies and EMDEs at modestly negative values. Importantly, the distribution does not display bi or multi-modal features. Therefore, the negative impact does not appear to be a categorical effect, i.e. countries that have a specific feature will see employment fall but rather the are a continuous distribution across economies.

A forecast error variance decomposition (FEVD) is one method of assessing the importance of technology shocks in driving the variation of employment relative to other shocks in the typical economy. For both advanced economies and EMDEs, technology shocks drive around 30-40% of the forecast error variance of employment at the 10-year horizon (Table 1).

**Table 1:** Forecast error variance contribution of technology shocks to employment

||**Year 1**|**Year **|**5**<br>**Year 10**|
|---|---|---|---|
|**Advanced economy**|13%|24%|31%|
|**EMDE**|19%|31%|42%|

Note: Average of individual EMDE and advanced economy country-by-country estimated forecast error variance decompositions

# **3.3 What country-specific factors are associated with falling employment following a technology shock**

The degree of labor market disruption in each economy can depend on multiple factors, including the types of technologies introduced over the sample period, and the degree to which they complement skilled or unskilled labor; the policies enacted by governments in each economy to engage workers in new roles following technology-driven labor market disruption; and measurement or data issues which may influence the results. To examine the underlying driver and correlates of employment disruption following technology shocks, the median scaled employment IRF for each country is treated as the dependent variable in cross-section regressions containing potential structural factors associated with the employment effect.

Proxies for the degree to which economies experiencing large scale technological change are rare, particularly on a consistent basis across countries. Sectoral employment shares, both in levels and how much they have changed in recent decades are one proxy for economies going under significant technological transformation. In particular, the literature has widely found that in advanced economies, manufacturing jobs have particularly been at risk of replacement, making industrial employment, which includes manufacturing and is widely available across economies, a useful covariate to test. Data on sectoral employment shares are taken from the World Bank’s World Development Indicators, as are data on foreign direct investment net inflows, which may also be a proxy for the implementation of new technologies. Educational attainment is measured 13
in average years of schooling, taken from the Barro and Lee (2015) dataset.

Variables are taken as averages during the period 1990-2018, primarily due to widespread sectoral data availability only starting in the 1990s. This is a shorter horizon than over which the VARs are estimated. However, results are robust to using longer averages including the 1980s for non-sectoral data and shorter averages since 2000. The overall explanatory power of the covariates is modest but material for the employment impact in year 1, with an adjusted-R<sup>2</sup> of 0.18 in the best-fitting specification, particularly given the wide range of factors that will influence this outcome across economies. There are many statistically significant determinants of the employment effect found despite the simple nature of the proxies for structural and technological change (Table 2).

**_Rising industrial employment_** . An increasing share of employment in the industrial sector since 1990 is strongly correlated with the negative employment effects of technology shocks at all horizons.<sup>6</sup> A 10 percentage point rise in the industrial employment share over this period is associated with a 0.1-0.2 percentage point larger negative impact on employment on average from a technology shock. The effect is statistically significant at the 1% or 5% level in each of the horizons. The average _level_ of industrial employment during this period is also separately a statistically significant determinant of the employment impact at the 5 and 10-year horizons but it loses significance when included in the same estimation as the _change_ in employment share. As will be discussed in the following section, all advanced economies in the sample lost employment share in the manufacturing sector since 1990. However, a smaller loss of employment share in the industrial sector, relative to a large loss, remains a significant determinant of the size of the negative employment impact. Changes in the share of employment in agriculture and services are not statistically-significant determinants of the employment impact.

An interpretation of this finding is that economies that have made productivity gains that are more centered around the industrial sector have experienced less job intensive (or even job-reducing) productivity growth. Economies with increasing employment shares in the industrial sector since the 1990s will have been at the highest risk from automation. In addition, those economies with increasing employment shares in this industry may have had the least success in increasing employment in other sectors following job losses in the industrial sector due to automation. This finding links directly to much of the existing literature which has found that routine manufacturing jobs (although to a lesser extent routine service sector jobs) have been at the highest risk of automation.

> 6While industrial production can include types of production other than manufacturing, such as mining and extraction industries, there is no relationship between the proportion of exports made up of commodities and the employment impact, suggesting that the manufacturing component of industry is the key driver of the employment dislocation following technology shocks.

14
**Table 2:** Determinants of employment IRF at multiple time horizons

|Observations<br>Adjusted R2<br>Residual Std. Error<br>_Note:_|Change in trade openness<br>_1990s to 2010-18_<br>Constant|Trade openness<br>_Exports & imports % GDP_|Productivity growth<br>_1990s to 2010-18_<br>Agriculture employment share<br>_%_<br>Commodity exports<br>_% total_<br>Industry employment share<br>_% total_|Productivity level _log_<br>Years schooling<br>∆industry employment share<br>_1990s to 2010-18_<br>FDI<br>_% GDP_||
|---|---|---|---|---|---|
|108<br>0.182<br>0.321|1.305_∗∗_<br>(0.562)||0.129<br>(0.113)<br>_−_0.300<br>(0.328)<br>0.003<br>(0.002)|_−_0.169_∗∗∗_<br>(0.055)<br>_−_0.003<br>(0.019)<br>_−_1.613_∗∗_<br>(0.798)<br>1.008_∗∗_<br>(0.446)|After 1 year<br>(1)|
|108<br>0.139<br>0.329|0.058<br>(0.107)<br>1.363_∗_<br>(0.689)|0.127_∗_<br>(0.070)|0.103<br>(0.116)<br>_−_0.543<br>(0.440)<br>_−_0.648<br>(0.720)|_−_0.156_∗∗∗_<br>(0.058)<br>0.001<br>(0.019)|After 1 year<br>(2)|
|107<br>0.091<br>0.282|0.127<br>(0.495)||0.257_∗∗_<br>(0.099)<br>_−_0.046<br>(0.289)<br>0.003<br>(0.002)|_−_0.036<br>(0.048)<br>_−_0.007<br>(0.016)<br>_−_1.920_∗∗∗_<br>(0.706)<br>0.503<br>(0.394)|_Depend_<br>Empl<br>After 5 years<br>(3)|
|107<br>0.035<br>0.291|0.082<br>(0.094)<br>0.628<br>(0.609)|0.011<br>(0.062)|0.250_∗∗_<br>(0.102)<br>_−_0.639<br>(0.389)<br>_−_1.321_∗∗_<br>(0.637)|_−_0.039<br>(0.051)<br>_−_0.001<br>(0.016)|_ent variable:_<br>oyment IRF<br>After 5 years<br>(4)|
|108<br>0.081<br>0.265<br>_∗_p_<_0.1; _∗∗_p_<_|_−_0.534<br>(0.465)||0.301_∗∗∗_<br>(0.093)<br>0.154<br>(0.271)<br>0.001<br>(0.002)|0.043<br>(0.045)<br>_−_0.018<br>(0.015)<br>_−_1.417_∗∗_<br>(0.661)<br>0.288<br>(0.369)|After 10 years<br>(5)|
|108<br>0.073<br>0.267<br>0.05; _∗∗∗_p_<_0.01|0.133<br>(0.087)<br>_−_0.029<br>(0.558)|_−_0.022<br>(0.056)|0.294_∗∗∗_<br>(0.094)<br>_−_0.368<br>(0.357)<br>_−_1.069_∗_<br>(0.584)|0.031<br>(0.047)<br>_−_0.014<br>(0.015)|After 10 years<br>(6)|

15
**_Education/Schooling levels_** . At each horizon, the employment impact following a technology shock is unrelated to the average years of schooling of the workforce. The insignificant effect possibly reflects opposing forces. Education levels may affect both the likelihood of domesticallygenerated innovation and adoption of new technologies, or cushion the impact of adopting new technologies, which more highly skilled workers able to complement new forms of production. The distribution of education may be broad, such that a highly-skilled workforce can incorporate skillbiased technologies, but have little success in reallocating labor to other tasks due to a large low-skilled segment of the workforce.

**_Productivity levels and productivity growth_** The log-level of productivity, measured in 2010 US dollars (at 2010 exchange rates) has a negative relationship with the employment impact in year 1, consistent with the larger negative impact experienced by advanced economies. In addition, the cumulative growth of productivity is positively correlated with the effect on employment: faster productivity growth since 1990 is associated with a more positive impact on employment. It is less clear how to interpret this finding. More frequent and larger positive technology shocks will result in greater cumulative productivity growth; it may be that a larger variety of introduced technologies is associated with smaller negative employment impacts, or it is plausible that the types of economies experiencing large cumulative productivity growth over this period have been better at ensuring displaced workers are successfully reintegrated into the workforce.

**_Foreign direct investment and trade_** The extent of net foreign direct investment and trade openness is positively related to the degree of employment variation following a technology shock. As FDI is one of the key vehicles for transferring technology to EMDEs, this is reassuring. A range of literature, including panel estimations and country-specific case studies, has found FDI to be associated with increased employment, particularly where it is export-focused (Hale and Xu, 2016; Waldkirch et al., 2009).

# **3.4 Robustness and alternative explanations for technology-driven fall in employment**

This paper has attributed the fall in employment following an SVAR-identified technology shock to the introduction of labor-displacing technology. This section briefly reviews other plausible candidate drivers for this result, finding each to be an unlikely source of the result.

**_Short sample biases_** The high persistence of EMDE employment effects following a shock may fuel concerns that some of this effect may be driven by short-sample issues and limited degrees of freedom in estimation. However, short-sample issues are found to be associated with _lower_ IRF persistence than the true IRF (Jarocinski and Marcet, 2010). Therefore, this effect should be in the opposite direction to the result found for emerging markets. Including the sample length for each economy under consideration as a regressor in Table 2 does not yield a statistically significant

16
coefficient at horizons, 1, 5, or 10 years.

**_Unrelated low-frequency trends in productivity and employment_** The second source of bias that could be present in advanced economies and EMDEs is the effects of unrelated longrun trends in productivity growth and employment Fernald (2007). In the US context, long-run trends in these variables are found to change the sign of the employment effect of technology shocks. Tests for the presence of structural breaks in unspecified time periods in the employment and productivity growth series suggests the presence of multiple breaks for all economies in the sample (Bai and Perron, 1998). The multitude of structural breaks could suggest the presence of low-frequency unrelated trends across both advanced economies and EMDEs. To ensure that these potentially unrelated trends are not driving the negative employment effect, an HP filter is applied to the log employment series, removing low-frequency trends a leaving only higher frequency movements in the employment data. The panel-estimation result continues to be a negative response of employment in both advanced economies and EMDEs (Figure 4). By construction, the removal of low-frequency variation in employment will reduce the size and persistence of the IRFs in both groups, with both IRFs now no longer significantly different from zero in the second year. The presence of low-frequency correlations between technology and employment cannot be ruled out as unrelated. However, the presence of a range of structural characteristics associated with the degree of persistence of the IRFs suggests that the relationship is unlikely to be spurious.

**_New Keynesian explanations of negative employment response to technology shocks_** The initial technology shock SVAR literature focused on determining whether the economy reacted to technology shocks consistent with an RBC or New Keynesian description of the economy. In the New Keynesian explanation, all factor inputs to production should contract initially following a technology shock, as prices are inflexible in the short run, restricting increases in production. And the same quantity of production could be achieved with fewer inputs. This framework predicts that both investment and employment or hours should fall in the short term. As documented above, it is clear that employment falls in the short-term for around three-quarters of advanced economies and EMDEs. However, the same exercise finds that investment falls in just one-third of advanced economies and less than 10% of EMDEs in the sample. Just 20% of the negative impacts are statistically significant in advanced economies, falling to 2% in EMDEs.

17
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**Table 3:** Determinants of employment effect: employment protection

||||_Depend_|_ent variable:_|||
|---|---|---|---|---|---|---|
||||Emplo|yment IRF|||
||After 1 year|After 1 year|After 5 years|After 5 years|After 10 years|After 10 years|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Productivity level _log_|_−_0.229<sup>_∗∗∗_</sup>|_−_0.115<sup>_∗∗∗_</sup>|_−_0.076|_−_0.038|0.023|_−_0.003|
||(0.070)|(0.038)|(0.059)|(0.032)|(0.049)|(0.030)|
|Years Schooling|0.039|0.019|0.017|0.021|_−_0.011|0.008|
||(0.031)|(0.018)|(0.026)|(0.015)|(0.022)|(0.014)|
|WEF: Hiring and firing|_−_0.013|0.021|_−_0.035|_−_0.026|_−_0.008|_−_0.009|
||(0.073)|(0.049)|(0.062)|(0.041)|(0.052)|(0.038)|
|OECD: Collective dismissal|_−_0.007||_−_0.045||_−_0.006||
||(0.109)||(0.092)||(0.077)||
|OECD: Individual dismissal|_−_0.015||_−_0.009||_−_0.022||
||(0.044)||(0.037)||(0.031)||
|Constant|1.800<sup>_∗∗_</sup>|0.628<sup>_∗_</sup>|0.796|0.218|_−_0.091|_−_0.056|
||(0.741)|(0.366)|(0.626)|(0.307)|(0.523)|(0.286)|
|Observations<br>|56|103|55|102|56|103|
|Adjusted R<sup>2</sup>|0.188|0.105|_−_0.045|_−_0.006|_−_0.073|_−_0.022|
|Residual Std. Error|0.357|0.333|0.301|0.279|0.252|0.260|

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 Note: Regression with the scaled employment response to technology shocks as the dependent variable, and a range of variables reflecting labor market frictions as the regressors. WEF variable refers to the World Economic Forum’s survey of firm’s ability to easily hire and fire workers, ranked from 1 (difficult) to 7 (easy). OECD indicators are the OECD’s ranking of the strictness of employment legislation to protect against individual and collective dismissal, with a higher index value indicating increased strictness

**_Labor market frictions and ease of hiring and firing_** Cross-country variations in the degree of labor market flexibility and employment protections provide an alternative explanation for the variation in the persistence of employment changes following a technology shock (Rujin, 2019). The OECD’s Indicators of Employment Protection cover 73 economies, 55 of which are included in the dataset of SVAR estimations. This database includes relative measures across economies of the strictness of protections against individual and collective dismissal. A separate database produced by the World Economic Forum produces a measure of labor market flexibility on a comparative scale across all economies where SVAR estimates of the employment impact are available. While labor market structures may play an important role in governing the persistence of the employment response in theory, these variables are found to be uncorrelated with the employment impact at all horizons (Table 3).

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# **4 Industrial employment and employment losses following technology shocks**

Several characteristics have been identified as being associated with the size and persistence of the impact of technology shocks on employment. Only the change in the share of industrial employment since 1990 is found to be a key determinant of the employment effect at all horizons. This section examines the SVAR and reduced-form evidence on the importance of the industrial employment share and its association with technology-driven employment losses. It then outlines a theoretical model to explain the empirical findings.

## **4.1 Pooled estimation of high and low industrial employment share economies**

This section examines the differences between economies that have experienced higher changes in industrial employment shares in recent decades, and how their employment impact has differed from economies experiencing falling industrial employment using separate panel VAR estimations.<sup>7</sup> All advanced economies in our sample have experienced a fall in the industrial share of employment since the 1990s (Figure 5). Those with a “high” employment share change can be regarded as having experienced the smallest fall in industrial employment shares in the advanced economy cohort. Those with a “high” employment share change include the United States, Australia, Finland, France, and Italy. Those that have experienced a “low” change in the industrial share of employment include Hong Kong, SAR, Singapore, Spain, Ireland, and Slovenia. These economies have more rapidly de-industrialized. In the EMDE sample, more than half of the 96 economies have increased in their share of industrial employment between 1990-99 and 2010-18. These include economies such as Vietnam, China, Thailand, Indonesia, and Oman. Those EMDEs with “low” or falling manufacturing shares include Bulgaria, the Russian Federation, South Africa, and Zimbabwe.

It is clear that the services sector has been the sector where the most new jobs have been created in both advanced economies and EMDEs. However, not all of this change has been generated by technological-displacement. There have been large flows in EMDEs from agriculture to services, and also a component of job flows due to changing income elasticities of demand for these products (Comin et al., 2015; Rodrik, 2016). This additional factor may be the reason that changes and the level of employment in agriculture and services are not found to be related to employment disruption changes following a technology shock; structural change has dominated employment flows in these sectors. This structural change has occurred regardless of whether increasing income has been driven by rising participation rates, employment-augmenting technologies, or employment displacing technologies.

> 7This approach has been used in different contexts to identify characteristic dependent fiscal multipliers (Ilzetzki et al., 2013).

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The change in the industrial employment share remains a statistically significant determinant of aggregate employment growth controlling for all of these additional factors (Table 4).

**Table 4:** Covariates of the change in employment and labor force to population ratio, 1990-99 to 2010-18

||_Dependent _|_variable:_|
|---|---|---|
||Employment|Labor force|
||(1)|(2)|
|Change in industry employment share|_−_0.148<sup>_∗_</sup>|_−_0.227<sup>_∗∗∗_</sup>|
|Change in share over 50 in working-age population|-0.03|-0.159|
|Change in female employment share|0.458<sup>_∗∗∗_</sup>|0.517<sup>_∗∗∗_</sup>|
|Change in log GDP per capita|0.835|0.311|
|Log GDP per capita|0.271|0.944<sup>_∗∗∗_</sup>|
|Constant|-3.70<sup>_∗∗∗_</sup>|-8.489<sup>_∗∗_</sup>|
|Observations|100|100|
|Adjusted R<sup>2</sup>|0.30|0.51|
|_Note:_|_∗_p_<_0.1; _∗∗_p_<_0|.05; _∗∗∗_p_<_0.01|

Note: Changes are calculated as the difference between the period averages for each economy during 1990-99 and 2010-18

# **4.3 Theoretical framework: industrial employment share and employment growth**

This section outlines a static theoretical model that explains the empirical findings concerning technology shocks, employment growth, and the industrial employment share. It uses a simplified version of the framework of Acemoglu and Restrepo (2018, 2020), and closely follows the notation of Acemoglu and Restrepo (2017), which assesses the overall impact of automation on aggregate job creation. In their framework, the job-displacing effect from automation can in part be offset by two factors: higher relative demand for the products in the affected industry as goods are produced 24
more cheaply due to automation; and secondly, higher overall income driving higher demand for all goods. Automation can increase aggregate employment if the latter effects are large enough.

This section shows that aggregate job gains can result from labor-substituting technologies, but likely alongside an even larger fall in employment shares in the sector most affected historically, industry. The finding applies where low price elasticities of substitution and low income elasticities of demand exist for those sectors subject to automation. Where there is little or no offsetting income or substitution effect from automation affecting the industrial sector, aggregate employment will rise by less or fall by more, but employment shares in industry will remain more robust. This framework purely considers demand-side mechanisms, while there are a range of supply-side factors that could contribute to this finding, complementing this approach.

This framework augments that of Acemoglu and Restrepo (2018, 2020) by also considering the effects of different income-elasticities of demand between sectors; it is well documented that the demand for industrial goods falls relative to that of services as income rises (Rodrik, 2016; Comin et al., 2015). This helps to explain why the industrial sector shrinks more in economies with large positive income and employment effects from automation, but less so in economies that have not experienced as large a positive effect.

In a simplification, a model is created of an industrial sector that is subject to labor-substituting technological change, and a services sector that is not. While it is true that certain jobs in the services sector have been automated, analysis of U.S. and European job markets has found that industrial middle and low-skilled jobs have been most affected in recent decades by automation (Goos et al., 2014; Autor, 2015).

**Firms** are split into sectors _k_ that produce industrial goods (I), and those that produce nonautomatable services (NAS). Industrial goods and services producers produce output _X_ ( _k_ ) by combining tasks _x_ , such that production is performed as

Tasks, _S_ , are divided into those that can only be performed by workers ( _w_ ), and those that can be performed by labor or capital ( _r_ ). Specifically, in the industrial sector tasks _S ∈_ [0 _, M_ ] are technologically automated and can be performed by capital. The output of capital in every task is set to 1, and to further simplify the model it is assumed that the productivity of labor in each task is constant as well and equal to _γ_ . Consequently, the production function for task _s_ in industry _k_ can be written as: There is therefore perfect substitution of labor for capital in the automatable tasks _s < M_ . It 25
is assumed that capital is always cheaper than employing labor, such that automation increases the cost-effectiveness of production. The savings from switching a unit of production from labor to capital is reflected as: _π_ = (1 _−_<sup>_<u>Qγ</u>_</sup> _W_<sup>),where</sup><sup>_Q_isthepriceofcapital,and</sup><sup>_W_reflectsthewagerate.</sup> Therefore, the demand for labor from industry is: In the services sector, no tasks are automated (M=0). All tasks require labor for production such that the process can be described as a single type of task.

In the services sector, total labor demand will be equal to: Given that prices are equivalent to marginal costs, it is assumed that: It is clear that an increase in _M_ , the proportion of tasks that can be produced by capital, will lower the price of goods in industry, and reduce the quantity of labor required to produce a given level of output. An increase in M is therefore an example of labor-productivity enhancing technological change considered in the empirical SVAR exercise. **Households** have the following consumption and labor preferences: Where _Y_ , income, is equivalent to total consumption, and _L_ is the total hours worked. The consumption bundle is defined over the variety of goods produced by the industrial and services sectors.

A constraint allows for a non-homothetic aggregate with different income elasticities of demand for the goods and services produced, _Y_ ( _I_ ) and _Y_ ( _NAS_ ), as in Comin et al. (2015): where _σ_ measures the degree of substitutability between industrial and non-automatable services and _αI_ + _αS_ = 1. The parameter _εk_ governs the degree of income elasticity for each sector, such 26
that consumption of a sector’s produce rises by more with respect to income for higher _εk_ . Maximisation is subject to the following sequence of period budget constraints: where _D_ is the nominal payoff of a portfolio held from the previous period, _Q_ is the stochastic discount factor for one-period ahead nominal payoffs for the domestic household, _W_ is the nominal wage for each sector.

Optimality results in the following demand functions for industrial and services sector products: Where _E_ is total expenditure, defined as: Defining _P_ = _Y_<sup>_<u>E</u>_andsettingthisaggregatepriceindextobeanumeraireallowsequation5tobe</sup> rewritten: The remaining optimality conditions concern labor supply and intertemporal allocations. Labour supply satisfies: Finally, automating capital, _R_ , is supplied at price _Q_ , and is assumed to have an upward sloping supply curve:

**Income and substitution effects of automation** . In response to a change in the proportion of production tasks that can be automated, 1 _<u>d−MM</u>_ , total labor demand in this economy depends � � on several effects (Annex A.2).

27
The first is direct displacement, where a share of the workers in industry ( _l_ ( _I_ )) are directly replaced by capital. The additional effects are a substitution and income effect, where the cost savings from automation ( _π_ ) reduces the price of goods in the industrial sector, increasing demand for them. This effect restores some workers in industry but crucially depends on the price elasticity of demand for these goods ( _σ_ ), and the proportion of costs that were accounted for by labor in industry ( _sL,I_ ). Finally, there is an income effect, where the gains from cheaper production are spent on goods in industry and the non-automatable services sector. This also crucially depends on the savings from the automation of industrial jobs ( _π_ ). Those economies where automation does not produce large cost savings, perhaps because the supply of automating capital is inelastic or expensive to install, or where the price elasticity of demand is low ( _σ_ ), will not see aggregate employment increase in response to automation.

**Labor substituting technological change and employment shares.** In this economy, because automation is specified to only occur in the industrial sector, the share of labor employment in industry falls in response to labor-substituting technological change for plausible parameter values. As a counter-example, in order for the share of industrial employment to remain constant, the price effect must offset both the direct displacement effect and the fact that industry has a lower income-elasticity than services. The change in demand for labor in industry with response to increased automation ( _M_ ) can be written:

For non-automatable services, the effect on labor demand is purely due to the income effect.

In order for the _share_ of employment in industry not to fall, the price effect must offset both the displacement effect and the difference in income elasticities between the two sectors.

28
First, in the simplified case where the income elasticities are equivalent, this requires: Even in cases where the income elasticity of demand in both sectors was equal, where the labor share was equivalent to 0.5 in industry ( _sL_ ), and the cost savings from switching to capital is 20 % ( _π_ ), the elasticity of substitution parameter, _σ_ , would have to reach 10 to ensure that the share of labor in industry did not fall. Consumers would have to be highly price elastic in response to changes in the prices of manufactured goods and services, which is at odds with the data. _σ_ is estimated to be well below unity in almost all economies (Comin et al., 2015). Therefore, even in cases where automation had positive aggregate employment effects, we should expect the most affected industries to lose employment shares. This is consistent with the SVAR results, where industrial employment falls in economies experiencing higher employment impacts from technology shocks.

**Why are economies with lower job creation following the introduction of new technologies associated with higher industrial shares?** The above demonstrates a mechanism through which economies will lose employment shares in industry if that sector experiences labordisplacing technology improvements, even where aggregate employment increases. However, it does not explain why economies with smaller employment losses from new technologies will experience relatively larger falls in their industrial employment share. This section outlines the conditions through which a larger positive income effect from a labor-substituting technology shock results in a larger reallocation of demand away from the industrial sector while boosting aggregate employment.

Differential income elasticities play a role in boosting consumption and employment by more in the service sector than industry following an improvement in income, further reducing the recovery of employment in the sector most affected by automation, industry. Where the income-elasticity differential between the two sectors outweighs the effect of lower prices in industry from the productivity improvement (low _σ_ ), aggregate employment will increase due to automation while shrinking the industrial employment share.

The income elasticity of services is found to be higher than manufacturing and industry across OECD and emerging market economies (Comin et al., 2015). Aggregate and household estimations find that the difference between the manufacturing and services income elasticity has ranged between _−_ 0 _._ 18 and _−_ 0 _._ 57, broadly equivalent to the parameters _εI −εNAS_ . _σ_ is found to be inelastic, ranging 29
from 0 _._ 2 _−_ 0 _._ 63.

The larger the rise in incomes due to the efficiency savings from the technology shock (driven by the savings, _π_ ), the larger the fall in the employment share of industry in cases where:

## _σsL,I ≤ sLl_ ( _I_ )( _εI − εNAS_ )

Therefore, for a sufficiently low price elasticity of substitution, or a sufficiently high income elasticity differential between the sectors, the more industrial employment shares will fall for a given saving from automation, _π_ . The function also depends on the difference in the labor share between the industrial sector ( _sL,I_ ) and the aggregate economy ( _sL_ ).

In economies where aggregate employment has fallen by more following a technology shock, industrial employment shares have remained robust since 1990. This plausibly reflects a lower efficiency improvement from automation in these economies, and thus a smaller reallocation of demand to the services sector. It could also point to a failure to accommodate rising services demand with domestic employment and production.

**Additional supply-side channels.** This simple model only considers demand-side drivers of this result. Several supply-side factors could have contributed to weaker industrial employment shares in those economies with larger aggregate employment growth due to new technologies and smaller aggregate employment growth in those with more robust industrial employment shares. Several studies have documented that rising market power has increased markups and reduced the labor share in those sectors most affected by technological change, including within manufacturing (Autor et al., 2020; De Loecker et al., 2020). In this case, savings produced by automation would not flow through to lower prices, producing a smaller increase in demand for goods in that sector.

Secondly, this simple framework does not account for skills mismatch. Where labor-substituting technology shocks result in income gains that increase demand for products and services requiring high- or niche-skills, supply constraints may prevent an increase in employment in these other sectors, and aggregate employment may not rise (Restrepo, 2015). This effect would be compounded by the demand channels described above. A low income elasticity for industrial goods would reduce demand for re-employment in roles requiring similar skill-sets. Economies that have the skill-base to meet the demand for workers in other sectors would see rising aggregate employment and a more rapid decline in industrial employment.

# **5 Summary, future risks, and policy options**

This paper has found that labor-substituting technological change has been widespread across advanced economies and EMDEs in recent decades. That is to say, while not all productivityenhancing technologies are labor displacing, they have accounted for a sizeable proportion of 30
productivity-enhancing technologies, such that employment falls in the majority of economies following an “average” technology shock. Other explanations for falling employment, such as a New Keynesian sticky-price mechanism or labor market regulations and frictions, are ruled out as drivers of this result.

Technology-driven employment losses are larger in economies with higher productivity levels. This plausibly reflects a higher propensity to invest in labor-substituting technologies in economies with more sophisticated production capabilities and those with a labor force more able to design and implement these production methods. However, job losses from new technologies have often been more persistent in lower-income EMDEs. Trade openness and FDI inflows appear to be associated with reduced employment losses from new technologies, consistent with a large literature that has found that export-focused FDI is often designed to take advantage of lower labor costs in EMDEs and is associated with higher employment levels.

Higher industrial employment share growth since the 1990s is a key correlate of larger and more persistent falls in aggregate employment from productivity-enhancing technologies. A model adapted from Acemoglu and Restrepo (2018) shows that where the income effects from automation are large, low-income elasticities of demand could reduce relative demand for employment in industry even where aggregate employment improves. Therefore, those economies with a successful reallocation of labor and large income effects from job-displacing technologies have seen a larger fall in industrial employment. The reallocation of demand to sectors other than industry, where jobdisplacing technologies have been more prevalent, could exacerbate difficulties in matching workers with new roles suitable for their skill-sets.

This analysis has examined historical relationships. However, several studies find a pronounced risk from new technologies to employment in the future. In advanced economies, a wide range of estimates have been provided for the proportion of jobs which are at risk of future automation: Arntz et al. (2016) find that 9 percent of jobs across 21 OECD economies are at high risk of automation in the future. A broader study of 32 economies, including several EMDEs, has found that on average 14 percent of jobs are at high risk of automation, with a further 32 percent at risk of significant change due to automation, primarily in the manufacturing sector, consistent with the finding that industrial concentration has been associated with lower job growth in this paper (Nedelkoska and Quintini, 2018; Organisation for Economic Co-operation and Development, 2019). Increasingly, many service sector roles will be at risk according to these studies. These include roles such as food preparation and some sales roles. This literature has not considered the effect of new roles that could be created by the introduction of technologies, or the macroeconomic implications stemming from higher demand, so are gross rather than net impacts on total jobs. So far, no studies have estimated the impact of anticipated technological change on a large sample of EMDE labor markets. As EMDEs acquire an increasing share of global industrial and manufacturing employment, it is likely that they will increasingly face challenges from automation.

31
Reforms aimed at increasing workforce skills to complement new technologies and service sector roles appear to be key in reducing lost employment due to automation but could require significant lengths of time to result in improved workforce capabilities. In the nearer term, policies aimed at restoring demand in industrial sectors most affected by job displacing technologies may be appropriate to sustain employment levels.

Policies that help enhance workforce skills and educational attainment could potentially reduce the fall in employment following the incorporation of production processes which remove the need for lower- or middle-skilled employment. Many EMDEs will need to make improvements at earlier levels of education in order to build a foundation for more advanced levels of education which will adequately complement new technologies (World Bank, 2018, 2019). EMDE universities and on-the-job training are also often under-provided in many EMDEs, but also display high returns in terms of wage premia where they exist, in addition to enabling better adaptation to changing production technologies.

The persistence of declining employment in EMDEs and large scale of displacement in advanced economies highlights the need for adequate social protection to ensure that those who are displaced from their employment can increase their opportunities to transition into new industries. Encouraging both private savings and social insurance for unemployment is needed in the formal and informal sectors will act as a safety net for displaced workers.

While supply-side policies to improve skill matching may take decades, policymakers may respond to job displacement by directly increasing demand for industrial goods or incentivizing private sector demand or investment in the sector. Substantial investment requirements have been identified in both advanced economies and EMDEs to meet climate and poverty reduction targets, particularly in infrastructure investment which would require a significant increase in industrial production.<sup>8</sup>

> 8For example, the European Commission has estimated that investment amounting to 2% of European Union GDP would be required each year to meet current climate and energy targets in 2030 (European Commission, 2020). In addition, Rozenberg and Fay (2019) find that EMDEs may need to invest between 2 and 8 percent of GDP each year, primarily in infrastructure for energy, sanitation, agriculture, and transportation to meet the United Nation’s Sustainable Development Goals by 2030.

32
# **A Appendix: Results using long-run and Max-Share technology identifications, and targeting TFP instead of labor productivity**

This appendix provides robustness checks on the estimations of the employment effect of technology in the main text. In the main text, a spectral identification is implemented, which is found to be more robust to the short-sample and volatile data used across countries. Here, the Max-Share and long-run identifications of technology shocks (Francis et al., 2014; Gal ́ı, 1999) are also implemented, finding similar results. These identifications, and those in the main text, are also applied to VARs estimated using the hierarchical-prior methodology of Jaroci ́nski (2010) to aid robustness given the short sample nature of the data for many economies.

## **A.0.1 Max-Share SVAR identification methodology**

The Max-Share identification is a similar approach to the Spectral identification which does not utilize the frequency domain. It is a more established methodology of which variants have also been used to identify technology “news” shocks (Francis et al., 2014; Barsky and Sims, 2011). It is therefore used as a robustness check on the results of the Spectral SVAR. Instead, it assumes that technology shocks are the _predominant_ driver of productivity around the 10-year horizon of the forecast error variance. In this identification, the technology shock is that which drives the largest proportion of the forecast error variance of labor productivity at this horizon, as in (Francis et al., 2014).

10 years is longer than the period over which the business cycle occurs (typically assumed to be 2-8 years) but short enough to reduce challenges related to estimation on a finite sample. Francis et al. (2014) imposes this restriction in a VAR containing productivity, hours, consumption and investment as a share of GDP. The forecast error at horizon _k_ can be written: By defining an orthonormal matrix _A_ 0 with columns _α_ , and _e_ as a selection vector (size 1 _×_ n), we find the shock _j_ which maximizes the contribution to the total forecast error variance of variable _i_ at horizon _k_ 33
|tLost<br>eeeeee<br>ee|
|---|
# **A.0.3 Heterogenous Panel VAR identification of technology shock and employment impact**

There are several limitations to a standalone identification procedure for individual economies: annual data use limits the degrees of freedom for estimation; and, measurement error, particularly in EMDEs, could limit estimation accuracy and inference. These limitations can be partially tackled using a panel structural VAR approach which allows for some heterogeneity across economies. Here, estimation priors for each economy are informed by the mean group estimator of Pesaran and Smith (1995), and implemented as in Jaroci ́nski (2010).

Each economy has a unique set of reduced-form coefficients _βc_ and variance-covariance matrix Σ _c_ . The VAR estimation has the standard likelihood function In addition, the country-specific parameters have a common mean across countries: The prior density is defined as: The _q × q_ matrix Σ _b_ further embed prior variances that are similar to the Minnesota prior through the matrix Ω _b_ : Here, the parameter _λ_ 1 governs the tightness of the application of the pooled-group priors to individual country parameters (lower _λ_ 1 lowers the variance of the priors, and therefore lowers the variation of country parameters more rigorously).

Additional priors within Ω _b_ replicate the Minnesota approach: _λ_ 3 guides the variance on coefficients on own lags: _λ_ 2 changes the variance applied to lags of other endogenous variables: 35
_λ_ 4 governs the tightness on exogenous variables including the constant.

Additionally, because some EMDE economies have experienced periods of high, or even hyper, inflation, additional dummies are included in countries experiencing above 20% annual inflation.

The priors are applied separately to the group of advanced economies and EMDEs given their characteristic _λ_ 2 is set to 0.5, _λ_ 3 is set to 1, and _λ_ 4 is set to 100. _λ_ 1 is set to an inverse-gamma distribution on each draw, with the variance dependent on the variation of parameters around the mean-group estimator.

# **A.1 Results of Long-run and Max-Share identifications**

Results for both the long-run (Figure 8) and Max-Share identifications (Figure 9) show a broadbased finding of negative employment impacts in the initial year following a technology shock. When estimated using hierarchical priors using the long-run identification, over 70% of economies experience a negative impact. Using the Max-Share identification, over 70% of advanced economies experience a negative initial impact, while nearly 60% of EMDEs do. However, for advanced economies, this rises to 80% when the VARs are estimated without hierarchical priors. The use of hierarchical priors substantially increases the proportion of economies where the negative impact is statistically significant in both identifications. Both identifications are shown to be more biased by non-technology shocks than the Spectral approach used in the main text (Dieppe et al., 2019).

36
<!-- Start of picture text --> 100<br>HE Pane! estimation<br>[Fa individual estimation<br>80<br>ec 60<br>a<br>2<br>a<br>a 40<br>20<br>a =<br>EMDE: negative AE: negative EMDE: significant AE: significant<br>100<br>HE Pane! estimation<br>[Fa individual estimation<br>80<br>ec 60<br>a<br>2<br>a<br>a 40<br>: ' -<br>0<br>EMDE: negative AE: negative EMDE: significant AE: significant<br><!-- End of picture text -->
<!-- Start of picture text --> TFP<br>ww 2<br>cao<br>24<br>o<br>2 4 6 8 10<br>Years<br>Employment, scaled<br>—<br>5 | aS<br>20<br>4 6 8 10<br>Years<br>Investment, scaled<br>= 15<br>o©2Oo 105<br>Oe<br>2 4 6 8 10<br>Years<br>Consumption, scaled<br>(yea<br>ao 5<br>2 4 6 8 10<br>Years<br>CPI, scaled<br>ee<< eeea SS)<br>Oo O ————_<br>g-1<br>1 J<br>o 3-3fo<br>2 4 6 8 10<br>Years<br><!-- End of picture text -->

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<!-- Start of picture text --> 100<br>[EEE Pane! estimation<br>80 [SS Aiindividual estimation<br>ec 60<br>a<br>2<br>a<br>a 40<br>: L :<br>0<br>EMDE: negative AE: negative EMDE: significant AE: significant<br><!-- End of picture text -->
# **B Theoretical framework: Sectors affected and unaffected by automation.**

## **Labor demand and supply**

To disentangle how changing automation ( _M_ ) in the industrial sector will affect the aggregate demand for labor, we uncover how automation will affect aggregate output and relative prices. The demand for each product _Y_ ( _k_ ) is assumed to be equal to output, such that _X_ ( _k_ ) = _Y_ ( _k_ ). Therefore, the demand for the products produced by the services and industrial sector’s respectively can be written: Following the concepts in Acemoglu and Restrepo (2018), and closely following the notation of Acemoglu and Restrepo (2017), the envelope theorem is first used to disentangle the influence of automation ( _M_ ) on aggregate income. Holding quantities _L_ ( _NAS_ ) _, L_ ( _I_ ), and _R_ fixed Since the price index and demand for services products does not directly feature _M_ , only the demand for industrial sector goods is affected to the first order.<sup>9</sup> The change in output is therefore a function of the cost savings from switching from paying wages to paying for capital, the elasticity of substitution between industrially-produced and services products, and the share of the product<sup><u>(</u></sup><sup>_I_</sup><sup><u>)</u></sup><sup>_X_</sup><sup><u>(</u></sup><sup>_I_</sup><sup><u>)</u></sup> in aggregate production _sYI_ =<sup>_P_</sup> _Y_ .

Substituting for the share of industry in total output and the equation for labor demand for a given _X_ ( _I_ ) yields:

> 9In Acemoglu and Restrepo (2020), _σ_ is eliminated from this equation using the idealized price index: 1 =

> <sup><u>(</u></sup><sup>_k_</sup><sup><u>)</u></sup><sup>_−σ_</sup> � _K_<sup>_αkYεkP_</sup> _X_<sup>1</sup><sup>_−_</sup> _k_<sup>_σ_.Differentiatingyields:0 = �</sup><sup>_αk_</sup><sup><u>(1</u></sup><sup>_−σ_</sup> 1<sup><u>)</u></sup><sup>_P_</sup> , which in turn leads to<sup>�</sup> _αP_<sup>_−σ_</sup> =<sup>�</sup> _ασP_<sup>_−σ_</sup>

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Where _sL_ = _<u>W LY</u>_<sup>,theincomeshareofalllabor,and</sup><sup>_lI_=</sup> _LL_ <u>(</u> _I_ <u>)</u> is the share of workers in the industrial sector. _π_ = (1 _−_<sup>_<u>Qγ</u>_</sup> _W_<sup>),orthecostsavingsfromchangingfromlabortocapital.</sup> Intuitively, this equation can be thought of reflecting the product of the cost savings from switching to capital/robot-driven production ( _π_ ), multiplied by the elasticity of substitution for consuming industrial products ( _σ_ ), multiplied by the income share of industrial workers ( _sI_ ), multiplied by the share of industrial workers actually employed in industry (and not the services sector), _l_ ( _I_ ).

This partial differentiation holds inputs of labor ( _L_ ) and capital ( _R_ ) constant. Using factor income shares, _Y_ = _WL_ + _QR_ , total differentiation yields: Multiplying by numerator and denominator by labor or capital inputs in each component of the equation (and using the fact that _d lnX_ =<sup>_<u>d</u>_</sup> _X_<sup>_<u>X</u>_)allowsittoberewrittenas:</sup> Where _s_ reflects the share of each factor of production in total output (i.e. _sL_ =<sup>_<u>W L</u>_</sup> _Y_<sup>).The</sup> share of output allocated to each factor is clearly dependent on the demand for that factor and wages in that sector.

**How do prices respond to automation?** Here we assess the changes in the price of industrial goods in response to an increase in automation. Starting with the fact that the price of industrial goods is equal to their marginal cost:

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Using the definition of the cost saving from automation: _π_ = (1 _−_<sup>_<u>Qγ</u>_</sup> _W_<sup>)</sup> Output in the industrial sector can be written as the sum of wages and payments to capital, _P_ ( _I_ ) _Y_ ( _I_ ) = _WLI_ + _QR_ . Given that _LI_ = <u>(1</u> _γ−_<sup>_I_</sup> _MA_ <u>)</u><sup>_Y_(</sup><sup>_I_)and</sup><sup>_R_=</sup> <u>(</u> _MA_ <u>)</u><sup>_P_(</sup><sup>_I_)</sup><sup>_Y_(</sup><sup>_I_),</sup><sup>_Y_(</sup><sup>_I_)=</sup> _Y_ ( _I_ ) � _W_<sup><u>(1</u></sup> _γ_<sup>_−IM_</sup> _A_<sup><u>)</u></sup> +<sup><u>(</u></sup><sup>_M_</sup> _A_<sup><u>)</u></sup><sup>_<u>Q</u>_</sup> �. Using the definition _sL,I_ = _W γAP_<sup><u>(1</u></sup><sup>_−M_</sup> ( _I_<sup><u>)</u></sup> )<sup>and</sup><sup>_sR,I_=</sup> _AP_<sup><u>(</u></sup><sup>_M_</sup> (<sup><u>)</u></sup> _I_<sup>_<u>Q</u>_</sup> )<sup>:</sup> Prices are therefore a function of the cost savings from switching from labor to capital for production, and the changes in the cost of wages and capital used in production. Prices in the non-automatable service sector do not depend on the substitution away from labor to machines, but simply wages: Now that the determinants of changing income and prices have been determined, demand for labor can therefore be written as a combination of the impacts on jobs immediately affected by automation and jobs that are not: The fact that the income elasticities do not enter this equation is due to the assumption that it is assumed that<sup>�</sup> _k_<sup>_l_(</sup><sup>_k_)(</sup><sup>_εk_+</sup><sup>_σ_)=1,orthattheincomeeffectacrossallsectorsaveragetounity,</sup> such that aggregate consumption rises in line with income.

Finally, this can be combined with the additional conditions: 42
Which comes from the partial differentiation of the identities _YI_ = _LI W_ + _RI Q_ , _Y_ = _LW_ + _RQ_ , reflecting changes in factor prices but while keeping factor quantities unchanged.

Secondly, the labor and capital supply schedules can be differentiated as The equation for _d lnL_<sup>_d_</sup> , after substituting in the income identity, becomes: _d lnR_ now needs to be replaced using an expression purely in terms of _M_ and _d lnL_ in order to solve for the effects of automation on labor demand. Substituting M in for the wage and capital supply equations From the differentiated income identify, _d lnQ_ = _s_<sup>_<u>s</u>_</sup> _R_<sup>_<u>L</u>πl_(</sup><sup>_I_)</sup> 1<sup>_<u>d</u>_</sup> _−_<sup>_<u>M</u>_</sup> _M_<sup>_−_</sup> _s_<sup>_<u>s</u>_</sup> _R_<sup>_<u>L</u>dlnW_</sup> 43
This can now be substituted into the _d lnR_ term in the labor demand equation.

An inelastic supply of capital ( _η_<sup><u>1</u>)magnifiestheemploymenteffect,whileaninelasticsupplyof</sup> labor reduces it ( _φ_<sup><u>1</u>).For example, a large fall in labor demand due to automation could significantly</sup> reduce wages and the supply of labor in other sectors if labor supply is highly elastic, but this effect is dulled if labor supply is inelastic.

44
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