metadata: authors: 0: author: Bentaouet Kattan,Raja
1: author: Macdonald,Kevin Alan David
2: author: Patrinos,Harry Anthony
count: World
docna: 0: docna: Automation and labor market outcomes : the
            pivotal role of high-quality education
docty: Policy Research Working Paper
subtopic: Educational Sciences,Labor Markets,Education for Development (superceded),Education For All,Educational Populations
teratopic: Social Protections and Labor,Education
abstracts: cdata!: Automation will be a boon or a
            catastrophe depending on whom you listen to. This paper
            proposes an overlapping-generations model with endogenous
            school choice in which the quality of a country's
            education system determines how well skill supply can
            respond to increased demand from automation and subsequently
            whether automation will be beneficial or detrimental. In
            this sense, education quality in the model offers a bridge
            between the optimistic and pessimistic perspectives on
            automation. In testing the model's assumptions, the
            paper finds evidence that educational attainment, cognitive
            skills, and select noncognitive skills are associated with
            avoiding automation-prone occupations. Consistent with the
            model's predictions, census data indicate that
            countries have historically relied most on these types of
            occupations at middle-income status. The model and empirical
            findings suggest that it is middle-income countries that are
            most vulnerable to automation if their education systems are
            unable to affect cognitive and noncognitive skills
            sufficiently. As a result, automation may herald a much
            different growth model for developing countries: one in
            which developing these skills is central.
repnme: repnme: Automation and labor market outcomes : the
            pivotal role of high-quality education
display_title: Automation and labor market outcomes
            : the pivotal role of high-quality education
listing_relative_url: /research/2018/06/29993033/automation-labor-market-outcomes-pivotal-role-high-quality-education
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Quality of Education m1326344 491,Education m1327048 470,Jobs m1503931 2670
publishtoextweb_dt: 2018-06-14T00:00:00Z
historic_topic: Social Protections and Labor,Education
no_of_pages: 63
author_keys: 0: author_key: 500174
1: author_key: 962343
2: author_key: 95903
model_extractions: WPS8474 Policy Research Working Paper 8474 Automation and Labor Market Outcomes The Pivotal Role of High-Quality Education _Raja Bentaouet Kattan Kevin Macdonald Harry Anthony Patrinos_ Education Global Practice June 2018
Policy Research Working Paper 8474

# **Abstract**

Automation will be a boon or a catastrophe depending on whom you listen to. This paper proposes an overlapping-generations model with endogenous school choice in which the quality of a country’s education system determines how well skill supply can respond to increased demand from automation and subsequently whether automation will be beneficial or detrimental. In this sense, education quality in the model offers a bridge between the optimistic and pessimistic perspectives on automation. In testing the model’s assumptions, the paper finds evidence that educational attainment, cognitive skills, and select noncognitive skills are associated with avoiding automation-prone occupations. Consistent with the model’s predictions, census data indicate that countries have historically relied most on these types of occupations at middle-income status. The model and empirical findings suggest that it is middle-income countries that are most vulnerable to automation if their education systems are unable to affect cognitive and noncognitive skills sufficiently. As a result, automation may herald a much different growth model for developing countries: one in which developing these skills is central.

This paper is a product of the Education Global Practice. 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/research. The authors may be contacted at hpatrinos@ 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
# **Automation and Labor Market Outcomes: The Pivotal Role of High-Quality Education**

Raja Bentaouet Kattan Kevin Macdonald<sup>1</sup> Harry Anthony Patrinos JEL codes: E24, J24, J31, J62, O15, O33 Keywords: Automation, skill-biased technological change, overlapping-generations model

> 1 We acknowledge the support of the Korea-World Bank Partnership Facility.  We are grateful to Amer Hasan and Andrew Mason for their thoughtful comments and ideas.
# **INTRODUCTION**

Automation, robotics and artificial intelligence will solve all our problems – or end the human race, depending on whom you listen to (Ray Kurzweil (2005) or Stephen Hawking (Cellan-Jones 2014)). Sometime soon, machine intelligence is predicted to surpass human intelligence, a point in time known as “the singularity.” Whether the rise of the machines is an existential threat to humankind or not, there is a more mundane issue: robotics are being used to automate production. There are already more than 300,000 industrial robots in operation in Japan and another 200,000 in North America (Hagerty 2015). Brynjolfsson and McAfee (2014) suggest that as computers get more powerful, companies have less need for some kinds of workers. Freeman (2015) argues that robots can be a substitute for even highly skilled professionals. The idea that automation leads to a life of leisure took on new meaning when several jurisdictions started to experiment with basic guaranteed income programs. One of the issues raised is that automation will take so many jobs that too many people will be rendered unemployable. Rather than reduce productivity, the argument is to pay everyone a guaranteed minimum wage because of higher productivity from increased automation. Hawaii, Ontario and India are considering universal basic income programs following trials in Finland, Germany, Kenya, the Netherlands, Scotland, Uganda and the United States (Kentish 2017; Roberts 2017; McFarland 2017; Chapman 2017).

The range of tasks that computers are expected to be able accomplish in the near future has changed considerably since Autor, Levy and Murnane’s (2003) routine versus non-routine dichotomy. Computers approach problems in much different ways than humans, for example, by calculating all possible chess move permutations, or learning by using pattern detection in massive data sets (Susskind 2017). Given current trends in machine learning and artificial intelligence, researchers generally point to three types of skills that humans need in order to retain an advantage over and complement technology: (1) social intelligence; (2) creativity; and (3) perception or advanced pattern recognition (Frey and Osborne 2013; Brynjolfsson and McAfee 2011, 2014).

Based on the expected capabilities of machines, many studies have tried to estimate the percent of workers that may be substituted by automation, but these estimates vary considerably, 2
depending on methodology. Elliott (2017) estimates that 62 percent of workers in OECD countries use skills that computers are close to replicating. Frey and Osborne (2013) estimate that 47 percent of U.S. employment is at high risk of automation in the coming decades. Deloitte (2014) finds that 35 percent of workers are in occupations at high risk of automation in the U.K. Manyika (2017) finds that few occupations are fully automatable; about 60 percent of occupations have at least 30 percent of activities that could be automated. Arntz, Gregory and Zierahn (2016) find that only 9 percent of U.S. workers are at high risk of being displaced by automation. For developing countries, Chang and Huynh (2016) estimate the percent of workers in occupations at high risk of automation in East Asian developing countries ranges from 44 percent in Thailand to 70 percent in Vietnam; Asian Development Bank (2015) estimates that a substantially lower proportion are at risk. Hallward-Driemeier and Nayyar (2018:135) applying the method by Arntz, Gregory and Zierahn (2016) estimate 2 to 8 percent of workers are at high risk in low and middle-income countries.

The implications of technology more generally for employment have been studied extensively in the economics literature. This work is motivated by the reduction of the labor share in the U.S. and European labor markets (Karabarbounis and Neiman 2014; Oberfield and Raval 2014) as well as job polarization or the shift from mid-range wage employment to low and high range wage employment (Katz et al. 2006; Autor 2010; Autor and Dorn 2013; Beaudry et al. 2013; Goos et al. 2014). These outcomes are often attributed to skill-biased technological change (Katz and Murphy 1992; Acemoglu 2002); though, some attribute them to other factors including demographic change (Cortes, Jaimovich and Siu 2016) or institutions (Acemoglu and Robinson 2015; Berkowitz, Ma and Nishioka 2017). In developing countries, Maloney and Molina (2016) find little evidence of job polarization.

The “canonical model” of skill-biased technological change involves two labor inputs, low and high skill workers, with technology augmenting one of them (Acemoglu and Autor 2011). For example, if technology augments high-skill workers, then high-skill wages will rise relative to low-skill, and job polarization arises with the addition of a middle-skilled group. This canonical model motivates the pessimism about automation, with automation substituting low or middle skill workers and complementing high-skill workers (e.g., Hemous and Olsen 2014).

3
Of course, fear of technological change is not new. Frey and Osborne (2013) cite Queen Elizabeth, the first to be concerned that a frame knitting machine would displace knitters’ employment. Many would argue that we have historically benefited from technological change, and several models have been proposed in which automation is beneficial and does not perpetuate wage inequality or eliminate employment for low skill individuals (Autor, Levy and Murnane 2003; Acemoglu, Gancia and Zilibotti 2010; Acemoglu and Restrepo 2017).

A key difference between models in which automation and technology are beneficial and those in which they are detrimental is the endogenous shift of individuals from less technologycomplementing to more technology-complementing labor categories. In the canonical model, individuals’ skill types are innate, and there is generally no possibility for individuals to change their skill, but in Autor, Levy, and Murnane (2003), individuals are born with an innate advantage for task types but are able to shift between them. In Acemoglu, Grancia and Zilibotti (2010) and Acemoglu and Restrepo (2017), individuals’ skill categories are innate but as new tasks are endogenously created, older tasks are shifted to lower skill individuals whose skills complement older tasks.

We propose an approach to bridge the optimistic and pessimistic perspectives on automation. In our model, individuals are born with heterogeneous innate ability that determines their skill category in the labor market, but they can attend school and increase their ability and ultimately their skill category. If schooling has a large enough impact on their ability, in other words, is high quality, then those individuals with a low innate ability are able to achieve a high-skill category. In this scenario, individuals can adjust their skills to meet increased demand for highskill labor resulting in automation being beneficial. However, if schooling is low quality and has a low impact on ability, then it constrains the ability of individuals, especially with lower innate ability, to respond to increased demand for high skill labor. In this latter scenario, automation is detrimental.

We treat automation differently than skill-biased technological change. The difference with— and fear of—automation is that it is not only skill-biased, but it is also skill-replacing: there is 4
some segment of the labor force that automation will render redundant. To capture this, automation in our model’s production function reduces the marginal product of lesser-skilled labor while increasing the marginal product of high-skill labor, all things being equal. As a result, automation has a different effect on steady state outcomes than technological change in typical skill-biased technological change models.

The important role of education quality is motivated by education often being the prescribed remedy for automation (Brynjolfsson and McAfee 2011, 2014; Golden and Katz 2008). Given rapid adoption of computing technology in the past 40 years, global demand for education has remained strong with rates of return averaging 9-10 percent (Montenegro and Patrinos 2014; Psacharopoulos and Patrinos 2018), despite substantial increases educational attainment over this period (Barro and Lee 2013). Strong returns are attributed to both increased demand for skills but also restricted supply of quality education (Altinok, Angrist and Patrinos 2018).

Our paper offers several new theoretical and empirical contributions to the literature on the effects of automation on labor market outcomes. Theoretically, we show how, in a general equilibrium, overlapping-generations model, education quality can determine whether automation is beneficial or detrimental, helping to reconcile automation’s optimism and pessimism. Second, we provide a definition of automation that differs from skill-biased technological change in an aggregate production function. Third, we show how countries in the midst of development may be most at risk to automation if their education quality is poor. Empirically, we establish the link between skills, educational attainment and the likelihood of an occupation being automated globally. Second, we show that middle income countries tend to have the highest proportion of labor in automation-prone occupations, corroborating our model’s prediction. Coupled with evidence from international student assessments showing that low and middle-income countries generally have poor learning outcomes, our findings suggest that middle-income countries are most at-risk to automation. Finally, we show how vocational education programs that offer direct labor market entry while deemphasizing cognitive and noncognitive skills are especially problematic with automation.

Our paper is structured as follows. We first present an overlapping-generations model in which 5
schooling decisions are endogenous and describe the conditions which place economies at risk of being negatively affected by automation if education quality is poor. Our empirical application follows with a description of the data, methods and results. A discussion of the findings concludes.

# **THEORY AND MODEL**

We develop a general equilibrium, overlapping-generations model with individuals with heterogeneous ability and endogenous schooling choices, drawing on Heckman, Lochner and Taber (1998a; 1998b; 1998c) and Ábrahám (2008). In the initial period, individuals choose their level of schooling which incurs both a direct cost and the opportunity cost of lost wages, but then they reap the rewards with higher earnings in the future. Production in our model arises from a low-productive informal sector that employs low-skill labor and from a high-productive formal sector that employs middle skill labor and high skill labor such that capital affects high-skill labor productivity more than middle-skill labor. The informal sector is a perfect substitute to the formal sector and represents the informal agriculture sector that is a substantial sector of employment in many developing countries. Agents are born with heterogeneous ability that determines their skill type; however, they can attend basic education for one period or higher education for two periods, to increase their ability and become higher skilled workers. Unlike typical technological change, automation reduces the contribution of middle skill labor in the formal sector. If production were fully automated, then middle-skill workers’ marginal product is zero; they have no function in the economy as many fear. If schooling is high enough quality, then newly born labor can shift from low to medium to high skill as needed. In this case, automation has a positive effect on steady state output and wages because there is no constraint to skilled labor supply. If schooling is not of sufficient quality, then lower ability individuals who attend school will not graduate with high enough ability to enter the next level of skill category. In this case, the supply of skilled labor can be constrained, and automation can be detrimental to both overall output and low skill wages.

We use this model to study how automation can affect steady state outcomes for countries at different stages of development, derived from differing levels of exogenous capital stock. We define developed countries in our model as having virtually no informal employment while 6
developing countries have a range of employment in the informal sector. This reflects the large proportion of informal workers in developing countries, especially in agriculture.

The model predicts that when skills are constrained by poor education quality, automation will decrease middle skill labor and increase low-skill, informal sector labor. This is a result of automation lowering the marginal product and wages of middle-skill labor; because of the skills constraint, equilibrium cannot be regained by middle-skill labor shifting to high-skill labor, it can only shift to low-skill labor. In this sense, automation reverses the process of development when skills are constrained. Second, if automation would have a positive effect on high-skill labor in absence of a skills constraint, then, with a skills constraint, automation lowers middle-skill wages. By assuming a constant elasticity of substitution production function, the model further predicts that, in economies with a large proportion of middle skill labor, automation reduces total output with a skills constraint and middle-skill wages. Finally, we show that developed countries, defined as those countries with no informal sector, will be less affected than developing countries. This is a result of (1) the informal sector in developing countries presenting a viable alternative to middle-skill labor which is not the case in developed countries and (2) of developing countries, especially those in the middle of development, relying more on middle-skill labor. A simulation of the model using specific functional forms and parameterization is presented which demonstrates that automation can have positive effects on steady state outcomes in absence of a skills constraint, but, with a skills constraint, negative outcomes result and countries close to the threshold of developed-country status are most negatively affected.

# _Model_

Our model economy consists of four inputs to production at time, _t_ : unskilled labor, ��,�, middleskilled labor, ��,�, high-skilled labor, ��,�, and a fixed physical capital stock, �. A single final good, �� is produced either directly by low skill labor, representing a low-productive, informal sector or by a formal sector incorporating capital, middle and high skill labor in production function, �����,�, ��,�, ��,�, ��. We assume the formal sector exhibits diminishing returns to labor, �<sup>�</sup> �� �0 and some complementarity between inputs, �<sup>�</sup> �� �0, �<sup>�</sup> �� �0 for ��1,2 �<sup>�</sup> ��,� �����,� ���,����,� 7
<!-- Start of picture text --> J=12,t4] p<br>0~f (8)<br>F(6).<br>$1 1 +2<br>1 1 2<br>2 1 2 py p2<br>Wot W1,t Wot<br>C{(9) c2(@)<br>0<br>Wt<br>n-1<br>> Siw, ,:(8) if S=0<br>i=0<br>n-1<br>, MeCSIO) = 1Dma S!weyi(B+ $3) ~ €54(8) ifS=1<br>>, Siwesi(O +1 +2) —C2e(0) if S=2<br>i=2<br><!-- End of picture text -->
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<!-- Start of picture text --> 901, = max eu Wor + C1(9o1) = >n-1i=1 O' (Mr esi _ Wotsi) 17 of<br>9124 = max ie > OW, + C2(O42) — €,(42) = >n-1i=2 O' (W214 _ Wr e+i)<br>2-91 - 0<br>n-1<br>Gort = 902? Wit + OW e441 + C2(Oo2) = > 6! (Woes: _ W114i)<br>i=2<br>1-91 2-1-2<br>Bort S 92,4 SF F12,<br>Vor = F(6o12), U2t =1- F(@122)<br>Oo1t+1 P1241 9o1t-2n+5) +» Fort 91 2t-2n45) +1 912,<br><!-- End of picture text -->
Figure 1. Graphical depiction of schooling choice <!-- Start of picture text --> I H _._ Individualswith innate ability below these level<br>—— who attend 1-period or 2-period schooling will<br>A not acquire enough skillto be designated<br>innate = = middle-skillor high-skill (green and blue,<br> abt} Se * === ~respectively).<br>low skill middle skill high skill<br>oO; 02<br>Skill level after attending 1-period (green) or 2-period (blue) schooling<br><!-- End of picture text --> Panel b. schooling has lowvalue added and constrainsskill acquisition <!-- Start of picture text --> i wages of middle-(high-) skill labor, but<br>; schooling does notadd enough valueto<br>innate ability: dist ibutiorri. ~—>~ enable them to become middle (high) skill.<br>/, > we -><br>low skill middle skill high skill<br>©; ©<br>Skill level after attending 1-period (green) or 2-period (blue) schooling<br><!-- End of picture text -->
Oor 44 912,041 9o1t-n+3 = max eu Wot-n+3 + C1(891) = >n-1i=1 6! (Wr e-nt3ti _ Wo,t—-n+3+i) 1 of 912t-n+3 = max ie : OW1 t-n4+3 + €2(912) — €1(612) = >n-1i=2 6'(Wot_nt3ti _ W1t-n+3+i) 2-%1- |

K Ly Lo F(6o1) = 0 ~~Ly~~ L2 a a
Automation can be beneficial if education quality is sufficiently high and the labor stock is able to adjust to increased marginal product of high-skill labor. Figure 2 provides a graphical depiction of this scenario which is also demonstrated in the model’s simulation discussed below. Automation’s positive effect on high-skill wage and negative effect on middle-skill wage induces a shift from middle to high-skill labor by decreasing the ability threshold, ���, which determines whether an individual attends 2-period instead of 1-period schooling (and becomes high-skill instead of middle-skill). It also induces a shift from middle to low-skill labor by increasing the ability threshold, ���, which determines whether an individual attends 1-period instead of no schooling. The consequent reallocation of labor offsets these changes in wages, and in the simulation’s parameterization, both middle-skill and high-skill wages exhibit a net increase and a net decrease in low-skill labor and higher output.

When the value-added of education is low, individuals with lower ability cannot increase their skill level. This can constrain the supply of skills even when automation increases the demand for skilled labor. To study these effects, we consider the scenario when the value-added of education, �� and ��, are so low that ��� �Θ� ��� and ��� �Θ� ��� ��� in steady state. In this case, the supply of middle and high skill labor cannot increase because it would require increasing the skill level of individuals with lower ability which the education system cannot do.

Automation, coupled with this skills constraint, reduces middle skill labor and increases low skill, informal sector labor (Proposition 3 and Corollary 3.1) essentially reversing development. If, in absence of a skills constraint, automation increases high-skill labor, then the skills constraint is binding and high-skill labor does not change. In this case, automation with a skills constraint decreases middle skill wages (Proposition 4). In a developed country, the ability cutoff for entering the informal, low-skill sector is so low that automation does not affect the allocation of labor (Corollary 4.1) and equilibrium is restored through adjustments in wages alone.

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Figure 2. Graphical depiction of the effect of automation in absence of a <!-- Start of picture text --> skills constraint<br><!-- End of picture text --> <!-- Start of picture text --> 1 4<br>i<br>—|<br>low skill middle skill high skill<br>0; 02<br>Skill level after attending 1-period (green) or 2-period (blue) schooling<br><€— Automation reduces the marginal product of middle-skill laborand increasesthe<br>—> marginal product of high-skilllabor. This creates incentive for more of the ability<br>distribution to attend 2-period schooling, reducing 8;2 and reduces incentive for<br>some of the ability distribution to attend 1-period schooling, increasing 89;<br><!-- End of picture text -->

- “@— The reduction in §;; is offset by a shift from middle to high skill labor asincreased — > high-skill labor reduces high skill marginal product and consequent wages. The increase in 8p; is also offset because increased high-skill labor and lower middleskill labor increases middle-skill marginal product and wages. The net effect is an increase in wages anda decrease in low-skill labor.

Y Ye = Blot + ((1— alae? + a(KL ze)’ ) 1/p BO p € (0,1) a € (0,1)
����� alternative specification, for example, would be �<sup>�</sup> ��,� under which the findings are similar. �� �� ��� �� ��� �� ��� The effect of automation on total output is � �� � � ~~.~~ We define �� �� �� ��� �� ��� �� economies as being middle skill intensive if the ratio of middle-skill labor to capital augmented high-skill labor, �� �1. For these economies, �� �0 in a CES production function; in absence ��� �� of shifts in labor, automation reduces total output because it lowers the productivity of the dominant labor stock, middle skill labor. As a result, if automation has a positive effect on total output in absence of a skill constraint, then high-skill labor must increase. Output can only increase either as a result of an increase high-skill labor or by an increase in middle-skill labor. If middle-skill labor has an incentive to increase, then high skill labor must also increase in our model (Proposition 5). This suggests that if automation has a positive effect on output in absence of a skills constraint, then with a skill constraint, automation will lower the middle-skill wage (Proposition 4) in addition to the other negative effects described above. Figure 3 graphically represents how a skills constraint affects education choice with automation.

We define developing countries as being middle-skill intensive and discuss this in the empirical section below. Developed countries can also be middle-skill intensive, but our model predicts that the negative effects of automation are lower for developed countries. For developed countries in our model, little shift in labor results from automation because the informal sector is not a viable alternative. The negative effect of automation on output for these countries occurs �� through alone; however, under a CES production function, as the ratio of middle-skill to �� �� capital-augmented high-skill labor decreases, the negative effect of automation, ~~,~~ decreases in �� magnitude (Proposition 7). As developed countries become wealthier, their formal sector becomes more sophisticated and this ratio declines (Proposition 2). Hence, for developed countries, this negative effect of automation is maximized at the boundary between developing and developed country status, the negative effect is lower for developed countries. As a result, our model predicts that the country most adversely affected by automation will be a developing country. Note that the negative effect of automation depends on the size of the formal sector as well. The poorest developing countries may be least affected because of a very small formal 15
Figure 3. Graphical depiction of the effect of automation on skill supply with a skills constraint <!-- Start of picture text --> NI<br>_—<br>Sg, ig<br>lowskill middle skill high skill<br>oO; 02<br>Skill level after attending 1-period (green) or 2-period (blue) schooling<br>a Automation Automation reduces the the marginal product and wage of middle-skill laborand and wage of middle-skill laborand wage of middle-skill laborand of middle-skill laborand middle-skill laborand laborand<br>—> increases the marginal product and wage marginal product and wage and wage wage of high-skilllabor. high-skilllabor. This creates creates<br><!-- End of picture text -->

- a Automation Automation reduces the the marginal product and wage of middle-skill laborand and wage of middle-skill laborand wage of middle-skill laborand of middle-skill laborand middle-skill laborand laborand —> increases the marginal product and wage marginal product and wage and wage wage of high-skilllabor. high-skilllabor. This creates creates incentive for more of the ability distribution to attend 2-period schooling, reducing 8;2, and itreduces the incentive for some of the ability distribution to attend 1-period schooling, increasing 89;

- oo individualsin thisregion of the innate ability distribution would now profit from attending 2-period schooling but, because of low value-added schooling, they would not achieve high-skillif they did attend. This constrains additional high skill labor.

- €— Middle-skill labor declines as a result, and this increase middle-skill wages to establish the new equilibrium 893.

ci(0) =Cop — 04,9 =1,2
labor peaks at the threshold of developing country status. Automation, represented by a large increase in �, increases output and wages for all countries, decreases informal sector labor and middle-skill labor for most countries, when skill acquisition is unconstrained. When skills are constrained, output is negatively affected as are middle skill labor and wages; low-skill, informal agricultural employment increases. Countries just below the threshold for developed country status exhibit the largest negative effects.

The simulation also demonstrates how automation and skill-biased technological change differ in our model. In the CES production function, we explicitly modeled capital to be high-skill labor augmenting in part so that capital, �, is equivalent to skill-biased technological change, as in Ábrahám (2008). Doing so allows us to easily compare automation and a generalized treatment of technological change. First, how their effects on the production function differ becomes clear. Technological change, equivalent to �, increases both middle and high-skill labor marginal products, while automation, �, negatively affects middle-skill labor’s marginal product and positively affects high-skill labor’s marginal product. For this reason, technological change and automation have different effects on steady state outcomes. In absence of a skills constraint, the effects of automation and technological change are similar with increased output, increased highskill labor, decreased middle-skill labor and an increased wage differential. For developing countries, they differ as middle-skill labor increases with technological change but decreases with automation, for most countries. With a skills constraint, technological change increases the marginal products of middle and high-skill labor; however, the skills constraint prevents them from adjusting. Automation, however, leads to a decrease in output and, for developing countries, job polarization as the stock of middle-skill labor decreases and shifts to the low-skill sector. Note that in our model skill-biased technological change does not lead to job polarization. For developing countries, this is consistent with Maloney and Molina (2016) who find little job-polarization associated with technological change; job polarization is not possible in developed countries in our model because they have two labor sectors. Hence, in our model, a decrease in total output and job polarization for developing countries is a result of automation but not of skill-biased technological change. This difference in findings ultimately results from what many fear from automation: that there is a segment of the labor force whose marginal product will be reduced by automation.

17
In our model and simulation, the ability of education systems to add-value and increase the skills for a large proportion of a country’s ability distribution determines whether automation has a positive or negative effect. Countries in the middle of development are most at risk if education quality is low because of (1) their reliance on middle-skill labor in a relatively large formal sector and (2) because the informal sector still presents a viable alternative for middle-skill labor to exit. A developed country with poor education quality is less at risk because the informal sector is not a viable alternative to middle-skill labor.

# **EMPIRICAL APPROACH**

Until automation takes hold to the large extent that many expect in the coming decades, we cannot empirically identify how countries will be affected. We can, however, examine the employment characteristics and education quality of countries to identify which countries our model predicts are most likely to be negatively affected and the fears of automation realized.

Our empirical approach is as follows. First, we test the underlying assumption of the model that educational attainment and skills are positively associated with employment that is complementary to automation capital. In doing so, we identify how different types of education and different types of skills associate with this type of employment. Second, we test whether the model correctly predicts the relationship between economic development and the three types of employment in our model: that which is complimentary to automation capital, substitutable by automation capital and informal. Doing this identifies which countries exhibit the employment characteristics which our model predicts are risk factors for automation. Third, we study how international student assessment outcomes relate to these risk factors to identify which countries are most likely to be negatively affected by automation, according to our model. We also examine secondary-level vocational programs which are often viewed in developing countries as an important approach to preparing youth for the labor market.

_Identifying automation-substituting and automation-complementing labor sectors_ To identify employment that is automation-substituting and automation-complementing, we use the Frey and Osborne (2013) estimates of an occupation’s probability of being automated in the 18
coming decades. These authors combine data on the types of tasks involved in occupations in the United States with the opinions of machine-learning experts to determine the probability that an occupation can be automated. They use the Occupational Information Network data set (O*NET) to derive the types of tasks involved for the United States’ standard occupational classifications (SOC). For a subsample of 70 occupations, they examined the types of tasks and job descriptions and determined, with a team of machine learning experts, whether all the tasks of a job can “be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment.” If so, the occupation was deemed automatable. Then, by modeling the association between key O*NET task variables and whether these 70 occupations are automatable, they predicted the probability that the remaining six-digit occupation classifications are automatable. Their estimated probability that an occupation can be automated is therefore based on the tasks involved in the occupation derived from the O*NET data set and the association of these tasks with whether the original 70 occupations are automatable. They publish the estimated probabilities for 702, six-digit SOC occupational codes. These data are used to estimate the proportion of United States’ workers in occupations that are at low, medium and high risk of automation by probability cut-offs of 30 and 70 percent.

Their published data on the automation probabilities of the 702 SOC occupations are used by other researchers to estimate proportions of workers at high risk to automation. Several studies have used mappings between the United States’ SOC and International Standard Classification of Occupations (ISCO) to estimate the percent of workers in high risk occupations in other countries. Our methodology most closely resembles that of Chang and Huynh (2016), Asian Development Bank (2015) and Deloitte (2014). We use the publicly available mapping tables of the United States Bureau of Labor Statistics to map the six-digit SOC codes reported Frey and Osborne (2013) with the ISCO-08 coding system used in many census and survey data. We also use publicly available ILO mappings between ISCO-08 and ISCO-88 and the Hendrickx (2004) mapping between ISCO-88 and ISCO-68 to apply the automation probabilities to data sets using these earlier coding systems. In order to calculate the probability of automation for a particular ISCO-08 occupational classification, we take the average of the probabilities of the SOC occupations with a probability of automation that map to it. This same procedure is used to generate the probabilities of automation for the ISCO-88 occupation classifications from the 19
ISCO-08 occupation classifications and for the ISCO-68 classifications form the ISCO-88 classifications. With the Frey and Osborne estimated probabilities of automation, we classify occupations as being “formal sector, automation-prone” if they are more likely than not to be replaced by automation, in other words, having a probability of automation greater than or equal to 50 percent, and not in the informal agricultural sector defined below. This cut-off provides a broad definition of automation-prone because we are not interested in estimating precisely how many workers will be lose their jobs, but rather who are more likely employed in the formal, automation-substituting sector than not.

In our model, we define an informal sector that is a perfect substitute to the formal productive sector and is unaffected by automation. This captures the informal agricultural sector that comprises a large segment of employment in developing countries. Following Asian Development Bank (2015), we assume these occupations have zero risk of automation. In developing countries, these occupations are unlikely to have access to sufficient capital to adapt automation technology, and they represent default occupations. We define the informalagricultural sector by occupation classification in order not to require additional variables that some data sets may not have. For the ISCO-88 classification system, these are agriculture, fisheries, and related workers (921), subsistence agricultural and fishery workers (621), fishery workers, hunters and trappers (615), and market gardeners and crop growers (611). For the ISCO-68 classification system, these are mapped to the ISCO-88 occupations: general farmers (611), orchard, vineyard and related tree and shrub crop workers (623), nursery workers and gardeners (627), agricultural and animal husbandry workers not elsewhere classified (629), fishermen (641), and (649) fishermen, hunters and related workers not elsewhere classified.

One limitation with using mappings between SOC and ISCO classification systems is that they assume occupations in different countries involve the same tasks (Arntz, Gregory and Zierahn 2016). Our approach depends on the similarity of the task composition of jobs among mapped occupations between the different classification systems; it also relies on the similarity of the task composition of occupations across countries. For our purposes, these mappings are likely to be sufficient as we are not trying to estimate the exact proportion of workers at risk of losing their jobs to automation technology. Minor differences in the task compositions between 20
mapped occupations are unlikely to change the overall findings of our analysis.

# _Data_

Four data sets are used in our analysis. These are the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC), World Bank’s Skills Towards Employability and Productivity (STEP), the Integrated Public Use Microdata Series (IPUMS-I) by the Minnesota Population Center (2017), and the OECD’s Programme for International Student Assessment (PISA).

Both the PIAAC and STEP data are household surveys that collect data on individuals’ occupations (coded using ISCO-08) and measure individuals’ literacy achievement. STEP also measures non-cognitive skills including the “Big 5” personality traits of extraversion, conscientiousness, openness to experience, emotional stability and agreeableness. Both data sets also include data on educational attainment, gender and age. We apply the Frey and Osborne (2013) automation probabilities to the occupation coding as described above using the ISCO-08 occupation classifications included in the data set. These data are used to test the underlying assumption of the model that educational attainment and skills are positively associated with non-automation-prone occupations and to understand how educational attainment, cognitive skills (as measured by literacy achievement) and non-cognitive skills associate with being in a non-automation-prone occupation. For PIAAC data, post-secondary education is labeled as vocational if it is in the ISCED B track (whose graduates are normally destined for direct entry to the labor market). Also, those in ISCED levels 4A and 4B are grouped with general upper secondary while those in 4C are grouped with vocational upper secondary. For STEP data, a variable denoting ISCED level and orientation is used to define vocational and general programs for each level of education. STEP’s non-cognitive skill variables are standardized to mean 0 and standard deviation of 1. PIAAC data sets for 21 countries and STEP data sets for 7 countries have the necessary variables for our analysis. PIAAC surveys were conducted between 2008 and 2016 while STEP surveys were collected between 2011 and 2013.

From the IPUMS data collection, we select any country that has data available from 1990 onward with ISCO occupation classifications, and, of these countries, we apply the Frey and 21
Osborne (2013) estimate of an occupation’s probability of automation using the ISCO classification mapping described above. This provided us with data on 41 countries and 73 data sets, of which 57 of these data sets contained the ISCO-88 classification of occupations, and 16 contained the ISCO-68 classifications. For each data set, we estimate the proportion of workers in automation-prone occupations, non-automation-prone occupations, and the informal agricultural sector. For our analysis, we only compare proportions derived from the same ISCO coding system. Finally, estimates use sample weights when data are not self-weighted.

The fourth data set used is the OECD’s Programme for International Student Assessment (PISA). The 2015 data for 70 economies are used in this analysis; economies without GDP per capita data are not shown in the plots with GDP per capita. These data are representative of 15-yearold students in grades 7 or higher and include internationally comparable measures of student achievement on literacy, program of study (including vocational), grade, gender, an index of economic, social and cultural status (ESCS), and an index of a student’s value of cooperating with others. We use this latter measure as a measure of non-cognitive skills. We use the PISA data to compare education quality of countries and to compare the cognitive and non-cognitive skills across countries and to examine how being in a non-academic track (which we refer to as vocational) versus an academic track program associates with cognitive and non-cognitive skills.

# _Empirical models and estimation methods_

To test the model’s assumption of the association of being in a non-automation-prone occupation with educational attainment and skills in the STEP and PIAAC data, two logistic regression models are estimated. Let �� be a binary variable denoting whether individual _i_ is in an automation prone occupation, �� be a binary vector denoting his or her highest level of schooling excluding general post-secondary, �� denote his or her literacy achievement, �� be a binary variable denoting whether individual _i_ is female, and �� denote his or her age. The first model is For STEP countries, we add the “Big 5” non-cognitive skills, denoted by vector ��, 22
> in <u>(Frei</u> ~~P{a; = 1}~~ <u>Ty)</u> = Bb + Biss + 8h + BEF By + BRD,

li = Bo + Bivi + Brgi + B3fi t+ Bahi + Bar; + er; x; = Bo + Biv; + B29; + B3fi + Bahi + Bin; + e1,
PIAAC and STEP data, we test the underlying assumption that educational attainment and skills are positively associated with being in a non-automation-prone occupation. Our logit model estimates suggest that post-secondary education and literacy achievement are associated with being in non-automation-prone occupations in most countries. Two non-cognitive skills, openness and extraversion, are also found to be positively associated in some countries. These findings are consistent with the assumption underlying our model but also help us identify the types of skills and schooling to best complement automation-capital. Second, using the IPUMS data, we find that the relationship between GDP per capita and the proportions of workers in the three labor categories follows the patterns predicted by our model and presented in our simulation. These findings suggest that middle-income countries exhibit the employment characteristics that our model predicts are indicative of automation having a negative impact if education quality is too low.

Based on this first set of findings, we use PISA data to first show that developing countries including many middle-income countries have low levels of student achievement. Given the employment characteristics of middle income countries described above, we conclude that middle income countries are the most at risk of being negatively affected by automation. We then turn to secondary vocational programs. In the PIAAC and STEP data sets, vocational secondary programs generally do not offer any benefit for avoiding automation-prone occupations and in many countries increase the likelihood of being in such occupations. These programs generally focus on technical skills and deemphasize cognitive and non-cognitive skills. Using PISA data, we show that they tend to attract individuals with lower cognitive and noncognitive skills; the skills that the PIAAC and STEP data suggest are needed to mitigate the effect of automation.

# _Skills, educational attainment and automation-prone occupations_

In our model, automation replaces middle skill workers and complements high skill workers, and individuals who attend 2-period schooling versus 1-period schooling, in equilibrium, become high-skill rather than middle-skill. That education and skills determine whether an individual is replaceable by automation or not is motivated by the automation research looking at trends in machine learning and artificial intelligence which view education and skills as essential for 24
humans to be complementary to machines. Our assumption implies, empirically, that individuals employed in occupations that are complementary to automation should have better levels of educational attainment and skills; however, an unresolved question is what types of skills complement automation and, subsequently, what types of educational attainment provide these skills? For example, Frey and Osborne (2013) and Brynjolfsson and McAfee (2011, 2014) find that non-cognitive skills including creativity, social-intelligence as well as advanced perception will be humans’ comparative advantage over machines given current technological trends.

We test several hypotheses about which types of skills and educational attainment are positively associated with being in a non-automation-prone occupation. First, we test the hypothesis, more general than the automation literature, that cognitive skills and non-cognitive skills are positively associated with non-automation-prone occupations. In the PIAAC and STEP we use literacy achievement as a measure of cognitive skills. In the STEP data, we use the “Big 5” personality traits. Second, we test whether general education versus vocational or technical education is positively associated with non-automation-prone occupations. Finally, we test whether postsecondary general education is better than general secondary programs. In doing so we validate the underlying assumptions of the model but also better define what types of skills associate with employment that is complementary to automation and which types of education programs best promote these skills.

Estimates of logit model (8) applied to 21 PIAAC country data sets are presented in Table 1, and 7 STEP country data sets are presented in Table 2. In this model, the dependent variable is being in an automation-prone occupation; positive coefficients imply that a variable is positively associated with being in an automation-prone occupation. First, in all countries, there is a positive and statistically significant difference in the likelihood of being in an automation-prone occupation between those with general upper secondary education and those with general postsecondary education. Second, there is a negative and statistically significant difference between those in general upper secondary and those in vocational upper secondary in 9 countries and a positive association in 1 country, but in the remaining 15 countries (which have a vocational upper secondary track), there is no statistically significant association. Third, in half of the countries, literacy achievement has a statistically significant and negative association with being 25
in an automation-prone occupation; in the other half, no statistically significant association is found.

Table 1. Logit model (eq. 8) estimates PIAAC countries

|country|||||||
|---|---|---|---|---|---|---|
||Belgium|Chile|Cyprus|Czech Rep.|Denmark|Spain|
|literacy achievement|‐0.09|‐0.1|‐0.06|‐0.11|‐0.16***|‐0.16**|
||(0.06)|(0.1)|(0.06)|(0.09)|(0.05)|(0.07)|
|educational attainment (relative|<br>to general<br>|<br>post‐second<br>|<br>ary)||<br>|<br>|
|none|3***|4.34***|n.a.|n.a.|2.37|2.02***|
||(0.89)|(0.71)|||(1.66)|(0.36)|
|primary|2.26***|3.74***|1.88***|n.a.|2.97***|2.03***|
||(0.3)|(0.46)|(0.2)||(0.73)|(0.17)|
|lower secondary|2.23***|3.43***|1.49***|2.58***|2***|1.86***|
||(0.2)|(0.38)|(0.2)|(0.36)|(0.15)|(0.13)|
|vocational upper secondary|2.03***<br>(0.18)|n.a.|n.a.|2.2***<br>(0.2)|1.45***<br>(0.12)|1.58***<br>(0.29)|
|general upper secondary|1.73***|3.04***|1.49***|1.34***|1.3***|1.52***|
||(0.14)|(0.35)|(0.12)|(0.17)|(0.12)|(0.13)|
|technical post‐secondary|0.64***|1.89***|0.86***|0.55**|0.34***|1.43***|
||(0.12)|(0.33)|(0.14)|(0.27)|(0.12)|(0.12)|
|age|‐0.01**|‐0.02***|‐0.01**|‐0.01|‐0.02***|‐0.01**|
||(0)|(0.01)|(0)|(0.01)|(0)|(0)|
|female|0.28***|‐0.05|0.55***|0.43***|‐0.17**|0.27***|
||(0.1)|(0.15)|(0.1)|(0.11)|(0.07)|(0.1)|
|constant|‐0.97***|‐0.93*|‐0.78***|‐0.79***|‐0.18|‐0.52**|
||(0.19)|(0.52)|(0.21)|(0.26)|(0.13)|(0.2)|
|observations|3083|3428|2496|3419|4547|3157|
|gen ‐ voc upper secondary|‐0.301**|n.a.|n.a.|‐0.865***|‐0.153|‐0.064|
||(0.139)|||(0.16)|(0.111)|(0.269)|

Authors' calculations using OECD PIAAC data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

26
Table 1, cont'd

|country|France|UK|Greece|Israel|Italy|Japan|
|---|---|---|---|---|---|---|
|literacy achievement|‐0.22***|‐0.18***|‐0.09|‐0.28***|‐0.1|‐0.17***|
||(0.04)|(0.06)|(0.07)|(0.06)|(0.06)|(0.05)|
|educational attainment (relative|to general<br>|post‐seconda<br>|ry)<br>||||
|none|2.88***|1.65***|0.84|1.88***|3.12|n.a.|
||(0.65)|(0.22)|(1.88)|(0.37)|(3.15)||
|primary|2.12***<br>(0.2)|n.a.|2.12***<br>(0.26)|1.92***<br>(0.26)|2.45***<br>(0.38)|n.a.|
|lower secondary|1.86***|0.83|1.75***|1.82***|1.91***|1.3***|
||(0.12)|(0.6)|(0.2)|(0.18)|(0.16)|(0.19)|
|vocational upper secondary|1.72***|1.39***|1.5***|1.68***|1.42***|1.35***|
||(0.09)|(0.14)|(0.17)|(0.13)|(0.2)|(0.13)|
|general upper secondary|1.35***|0.84***|1.45***|1.6***|1.41***|1.16***|
||(0.1)|(0.13)|(0.17)|(0.13)|(0.12)|(0.11)|
|technical post‐secondary|1.04***|0.32**|0.75***|1.11***|0.79|0.51***|
||(0.1)|(0.15)|(0.19)|(0.14)|(0.48)|(0.11)|
|age|‐0.02***|‐0.02***|‐0.01**|‐0.01***|‐0.02***|‐0.02***|
||(0)|(0)|(0.01)|(0)|(0.01)|(0)|
|female|0.16***|‐0.38***|0.2*|0.17**|0.22*|0.54***|
||(0.06)|(0.1)|(0.12)|(0.08)|(0.11)|(0.09)|
|constant|‐0.35**|‐0.08|‐0.37|‐0.98***|‐0.15|0.35**|
||(0.14)|(0.21)|(0.27)|(0.18)|(0.27)|(0.15)|
|observations|4306|3884|2225|3211|2577|3640|
|gen ‐ voc upper secondary|‐0.37***|‐0.553***|‐0.054|‐0.076|‐0.013|‐0.19|
||(0.086)|(0.142)|(0.168)|(0.13)|(0.17)|(0.128)|

27
Table 1, cont'd

|country|Korea,<br>Rep.|Lithuania|Netherlands|New<br>Zealand|Poland|Russia|
|---|---|---|---|---|---|---|
|literacy achievement|‐0.16***<br>(0.06)|‐0.09<br>(0.07)|‐0.08<br>(0.06)|‐0.14***<br>(0.05)|‐0.17***<br>(0.06)|‐0.11<br>(0.07)|
|educational attainment (relativ|e to general<br>|post‐secon|dary)<br>||||
|none|2.52***|n.a.|2.53***|n.a.|n.a.|2.61|
||(0.6)||(0.44)|||(3.72)|
|primary|2.28***|2.05***|2.5***|1.68***|1.36***|1.34|
||(0.25)|(0.66)|(0.2)|(0.47)|(0.48)|(1)|
|lower secondary|1.58***|2.94***|2.02***|1.52***|2.14***|1.04**|
||(0.15)|(0.3)|(0.12)|(0.16)|(0.22)|(0.42)|
|vocational upper secondary|1.28***|2.32***|1.77***|1.3***|2.16***|n.a.|
||(0.13)|(0.53)|(0.15)|(0.11)|(0.15)||
|general upper secondary|1.34***|2.01***|1.49***|1.47***|1.48***|0.92***|
||(0.12)|(0.12)|(0.11)|(0.14)|(0.12)|(0.1)|
|technical post‐secondary|0.65***|0.89***|0.88***|0.58***|0***|0.64***|
||(0.12)|(0.19)|(0.21)|(0.12)|(0)|(0.17)|
|age|‐0.02***|‐0.01***|‐0.02***|0***|‐0.01***|0.01***|
||(0)|(0)|(0)|(0)|(0)|(0)|
|female|‐0.27***|0.32***|‐0.06|‐0.04|0.28***|‐0.65***|
||(0.07)|(0.11)|(0.08)|(0.07)|(0.09)|(0.11)|
|constant|0.8***|‐0.8***|‐0.65***|‐0.83***|‐0.37**|‐0.53***|
||(0.18)|(0.22)|(0.18)|(0.1)|(0.16)|(0.17)|
|observations|4236|2858|3737|3407|4879|1663|
|gen ‐ voc upper secondary|0.065|‐0.313|‐0.279*|0.162|‐0.68***|n.a.|
||(0.132)|(0.524)|(0.146)|(0.125)|(0.129)||

28
Table 1, cont'd

|country<br>Slovak<br>Rep.|Slovenia|Turkey|
|---|---|---|
|literacy achievement<br>‐0.07|‐0.22***|‐0.06|
|(0.06)|(0.06)|(0.09)|
|educational attainment (relative to general p<br>|ost‐secondary)||
|none<br>0***|n.a.|1.45***|
|(0)||(0.42)|
|primary<br>3.68***|3.14***|1.75***|
|(0.95)|(0.7)|(0.2)|
|lower secondary<br>2.41***|3.37***|1.55***|
|(0.26)|(0.25)|(0.17)|
|vocational upper secondary<br>2.08***|2.93***|1.43***|
|(0.12)|(0.15)|(0.18)|
|general upper secondary<br>1.36***|1.94***|1.43***|
|(0.11)|(0.13)|(0.19)|
|technical post‐secondary<br>0***|0.89***|1***|
|(0)|(0.15)|(0.2)|
|age<br>‐0.01***|‐0.01|‐0.02***|
|(0)|(0)|(0.01)|
|female<br>0.21***|0.41***|0.14|
|(0.07)|(0.09)|(0.15)|
|constant<br>‐0.74***|‐1.46***|‐0.2|
|(0.16)|(0.22)|(0.24)|
|observations<br>3130|2873|1984|
|gen ‐ voc upper secondary<br>‐0.725***|‐0.988***|‐0.004|
|(0.115)|(0.122)|(0.187)|

29
Table 2. Logit model (eq. 8) estimates STEP countries country

||Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|---|
|literacy achievement|0.04|‐0.29***|‐0.08|‐0.05|‐0.35***|‐0.16*|‐0.21***|
||(0.09)|(0.11)|(0.11)|(0.09)|(0.1)|(0.08)|(0.07)|
|educational attainment (r|elative to ge|neral post‐s|econdary)|||||
|none|1.56|2.28***|1.5***|0***|2.52***|1.45***|2.16***|
||(1.31)|(0.36)|(0.44)|(0)|(0.4)|(0.3)|(0.33)|
|primary|1.29|2.81***|2.15***|1.6|2.26***|1.39***|1.65***|
||(1.25)|(0.63)|(0.36)|(1.12)|(0.42)|(0.23)|(0.2)|
|lower secondary|1.83***|2.44***|1.69***|1.03**|1.93***|0.99***|1.31***|
||(0.45)|(0.28)|(0.41)|(0.44)|(0.36)|(0.22)|(0.15)|
|vocational upper sec.|1.41***|1.92***|1.74***|1.42***|1.97***|0.55**|0.45|
||(0.29)|(0.25)|(0.39)|(0.36)|(0.41)|(0.24)|(0.29)|
|general upper sec.|1.13***|2.06***|1.46***|0.88***|1.3***|0.82***|0.95***|
||(0.2)|(0.25)|(0.29)|(0.26)|(0.32)|(0.17)|(0.13)|
|technical post‐sec.|0.93***|1.28***|0.94***|1.28***|0.79**|‐0.92**|n.a.|
||(0.2)|(0.26)|(0.28)|(0.23)|(0.39)|(0.37)||
|age|0.01*|0|0|0|0|0.01**|‐0.02***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0)|
|female|‐0.26*|‐0.07|0.13|‐0.41**|0.27*|0.2*|0.18*|
||(0.15)|(0.13)|(0.17)|(0.19)|(0.14)|(0.11)|(0.11)|
|constant|‐0.95***|‐0.93***|‐0.63|‐0.42|‐0.94**|‐0.58***|0.19|
||(0.3)|(0.34)|(0.39)|(0.36)|(0.42)|(0.22)|(0.21)|
|observations|956|1760|1702|888|2094|2340|2305|
|gen ‐ voc upper|‐0.284|0.14|‐0.278|‐0.535|‐0.663*|0.272|0.503*|
|secondary|(0.297)|(0.26)|(0.335)|(0.391)|(0.356)|(0.194)|(0.287)|

Authors' calculations using World Bank STEP data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

Non-cognitive skill measures are available for the 7 STEP countries; estimates of model (9) are presented in Table 3. Openness to experience is found to have a negative, statistically significant association with being in an automation-prone occupation in three countries; extraversion is found to have a positive statistically significant association in one country and a negative statistically significant association in one country, and conscientiousness has a negative association in one country.
Table 3. Logit model (eq. 9) estimates STEP countries with non‐cognitive skill country

|country|Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|---|
|extraversion|0.03|0.01|‐0.02|0.07|0|0.1*|‐0.17***|
||(0.08)|(0.08)|(0.08)|(0.09)|(0.1)|(0.05)|(0.05)|
|conscientious|0.12|0.2**|‐0.09|0.05|‐0.03|‐0.04|‐0.11**|
||(0.09)|(0.09)|(0.1)|(0.09)|(0.11)|(0.06)|(0.05)|
|openness to exper.|‐0.14*|0.01|‐0.1|‐0.32***|0.02|0.06|‐0.12**|
||(0.08)|(0.09)|(0.09)|(0.1)|(0.09)|(0.06)|(0.06)|
|emot. stability|0.01|‐0.03|‐0.08|‐0.09|0.01|0.01|0.03|
||(0.08)|(0.09)|(0.08)|(0.08)|(0.08)|(0.06)|(0.05)|
|agreeableness|‐0.04|0.13|‐0.02|‐0.11|0.06|0.03|‐0.04|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.06)|(0.05)|
|literacy achievement|0.03|‐0.31**|‐0.06|0.03|‐0.39***|‐0.15*|‐0.18**|
||(0.09)|(0.12)|(0.11)|(0.11)|(0.12)|(0.08)|(0.08)|
|educational attainment (r|elative to ge|neral post‐s<br>|econdary)<br>|||||
|none|1.63|2.5***|1.39***|0***|2.08**|1.29***|2.1***|
||(1.43)|(0.39)|(0.46)|(0)|(0.84)|(0.31)|(0.35)|
|primary|1.05|3.04***|2.11***|1.1|2.7***|1.3***|1.58***|
||(1.37)|(0.66)|(0.37)|(1.13)|(0.52)|(0.24)|(0.2)|
|lower secondary|1.76***|2.74***|1.6***|0.9*|1.63***|0.89***|1.26***|
||(0.46)|(0.32)|(0.41)|(0.46)|(0.38)|(0.23)|(0.15)|
|vocational upper sec.|1.35***|2.11***|1.72***|1.17***|1.87***|0.49**|0.45|
||(0.3)|(0.31)|(0.4)|(0.38)|(0.46)|(0.24)|(0.3)|
|general upper sec.|1.07***<br>(0.2)|2.35***<br>(0.26)|1.44***<br>(0.29)|0.8***<br>(0.27)|1.27***<br>(0.33)|0.78***<br>(0.17)|0.98***<br>(0.14)|
|technical post‐sec.|0.92***|1.57***|0.91***|1.23***|0.71*|‐1.04***|0***|
||(0.2)|(0.26)|(0.27)|(0.24)|(0.4)|(0.38)|(0)|
|age|0.01*|0|0|0|0|0.01**|‐0.02***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0)|
|female|‐0.24|‐0.12|0.09|‐0.32|0.21|0.21*|0.18*|
||(0.15)|(0.14)|(0.19)|(0.2)|(0.19)|(0.11)|(0.11)|
|constant|‐0.92***|‐1.15***|‐0.59|‐0.4|‐0.9**|‐0.49**|0.18|
||(0.29)|(0.38)|(0.38)|(0.37)|(0.46)|(0.23)|(0.22)|
|observations|953|1641|1702|832|1019|2267|2303|

Authors' calculations using World Bank STEP data. Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted by ***, **, *, respectively.

That those with upper-secondary education are more likely than those with post-secondary 31
education to be in an automation-prone occupation is consistent with our model’s assumption. In all countries, post-secondary education appears to provide skills that enable individuals to be employed in non-automation-prone occupations. While the data are less unanimous on what these skills are, some important insights emerge. First, those with general rather than vocational upper secondary are more likely to be in a non-automation-prone occupation for those countries in which there is a statistically significant difference, with one exception. In the countries without a statistically significant difference, this at least suggests that vocational programs, which typically focus on technical skills and deemphasize cognitive and non-cognitive skills, offer no clear advantage to avoiding automation-prone occupations. Second, there is clear evidence that literacy achievement, as a measure of cognitive skills, tends to be positively associated with being in a non-automation-prone occupation. Third, we see some evidence that the non-cognitive skill of openness to experience is positively associated with being in a nonautomation-prone occupation. This is consistent with the automation literature’s finding that creativity is a determinant of being complementary to automation as openness to experience has been found to correlate with creativity in the psychology literature (Furnham 1999).

_Automation-prone occupations and stage of development_ Our model assumes that the proportion of labor in the automation-complementing sector (equivalent to high-skill labor) is higher for wealthier countries, and it predicts that wealthier countries will have lower proportions of informal sector (low-skill) labor. Our simulation predicts that the proportion of employment in the automation-substituting (middle-skill) labor peaks at the cusp of developed country status. Applying our definitions of automation-prone occupations to the IPUMS-I data shows that this pattern is generally correct.

Figure 4 presents estimates of the proportion of workers in informal agricultural occupations, automation-prone occupations and non-automation-prone occupations, plotted against real GDP per capita. The figure includes only countries’ latest data since 1990 and those data sets that use the ISCO-88 classification of occupations. Similar to the model’s simulation presented in Annex 1 Figure 1, the proportion of automation-prone occupations peaks just around the point where the informal sector disappears which corresponds closely to the high-income status threshold.

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Figure4. Percent of workers by sector by GDP per capita <!-- Start of picture text --> ' ° i<br>ie .° e* e = ® Non-automation-prone<br>' : } 9 (formal)<br>O9 @ e<br>50% eee i| © =—— Informal agriculture<br>{H * trend<br>= °° — Automation-prone<br>e * 8 (formal) trend<br>25% & $ e —— Non-automation-prone<br>e ' (formal) trend<br>; GS? |<br>a<br>0% °<br>Log GDP per capita (constant 2010 USS)<br><!-- End of picture text -->
<!-- Start of picture text --> Figure5. Proportion of workers in automation-prone, formal sector by<br>country, across time<br>non low income middle income high income<br>75% ~— ><br>~— — ii<br>ry 7<br>i YT)<br><!-- End of picture text --> <!-- Start of picture text --> 25%<br>eemel ><br>0% . > —= a,So -<br>Log GDP per capita (constant 2010 USS)<br>=—— Automation-prone sector<br>=—— Informal agriculture sector<br>(each line denotes a different country)<br><!-- End of picture text -->
that can be applied to a wide range of countries and GDP per capita. For our purposes, the magnitude of the proportion of laborers in automation-prone employment is less important than identifying which countries have the highest proportions. Different results than ours are found by Ahmed and Chen (2017) in Hallward-Driemeier and Nayyar (2018:135) who apply the Frey and Osborne (2013) probabilities to STEP countries following Arntz, Gregory and Zierahn (2016). In this study, they find that the high-income countries in PIAAC have a higher proportion of workers at high risk of automation than the middle-income countries in STEP. Similar to our results, however, lower middle-income countries have a lower proportion of workers at high risk. Even if high-income countries have a higher proportion of workers in automation-prone employment, our main findings to do not change. First, high income countries lack a viable informal sector which mitigates potential negative effects of automaton. Second, what matters in our model is the quality of education systems, and as discussed in the next section, middle income education outcomes tend to be worse than those in high income countries.

# _Learning outcomes of education systems_

Our model and simulation as well as our analysis of the IPUMS-I data suggest that automation can be beneficial to middle income countries, but if their education systems are not sufficiently high quality, then automation will be the most detrimental for them. Research on learning outcomes for low and middle-income countries finds, in general, quite poor learning outcomes. For example, Graham and Kelly (2018) find very low proportions of primary school students can read a simple sentence with ease and comprehension. Banerji et al. (2010) suggest that most students in middle income countries are functionally illiterate. The OECD PISA data provide an instructive comparison. It categorizes reading achievement into proficiency levels. In their assessment, minimum reading proficiency is defined at Level 2 in which students can infer information from a text, recognize the main idea of a text, and make comparisons. Students at the advanced level of reading proficiency, Level 5 or higher, can retrieve deeply imbedded information from and critically evaluate a text (OECD 2016b:162). Figure 6 presents the proportion of 15-year-old students attaining minimum proficiency and advanced proficiency in PISA, for the 67 participants that have GDP per capita data available. As shown, the proportion of students attaining minimum proficiency ranges between 20 to 60 percent with the exception of 35
<!-- Start of picture text --> Figure 6. Percent of15 year-olds attaining minimum and advanced<br>proficiency in PISA 2015 literacy<br>100<br>75 +|\¢* + gf +9io°.?<br>¢.¢ ¢ e<br>*<br>50 o . o¢ +4 +<br>+ % ¢ +<br>25 owen \ o¢ High income<br>middle | Upper middle<br>ea income oO we. as<br>0 a<br><!-- End of picture text -->

Source: OECD PISA 2015
Figure 7. Summary of regression model (eq. 10) estimates: coefficient for being ina vocational programme by country <!-- Start of picture text --> E @ statistically significant (10% level)<br>‘<br>B14 not statistically significant<br>S<br>o<br>4<br>;<br>1S) * ba &<br>=4<br>5<br>po || a<br>3& -2 ca 5 7 e<br>=<br>Ei + 5<br>E Upper midd +<br>= pper: micate High income<br>= income *<br>-4<br>GDP per capita (constant 2010 USS)<br><!-- End of picture text -->

Source: authors’ calculationsusing PISA 2015 data
<!-- Start of picture text --> Figure 8. Summary of regression model (eq. 11) estimates: coefficient for<br>being ina vocational programme by country<br>40%<br>@ statistically significant (10% level)<br>c 20% r not statistically significant<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>z 0% ee eeeee<br>=ix] 2 bs ¢ o ° o* %<br>ZS -20% A<br>8<br>“ v<br>S -40%<br>° %<br>Ss<br>€ -60% =<br>it)<br>13)<br>é -B0% Upper middle soa<br>income + High income<br><!-- End of picture text --> Source: authors’ calculations using PISA2015 data
We also find that cognitive skills and the non-cognitive skills of openness to experience are positively associated with non-automation-prone occupations. As an initial, empiricallygrounded benchmark, this is instructive. Vocational programs that are seen as an important piece of developing countries’ industrialization deemphasize these types of skills for the youth that lag furthest behind in them. In the labor force survey data, they rarely provide an advantage to general secondary education in avoiding automation-prone occupations.

Our model suggests countries will be negatively affected by automation unless their education systems are of high enough quality to allow even those with relatively low innate ability to acquire higher skills. For developing countries, this poses a significant challenge. Increasing educational attainment further, especially to higher education, is quite costly. While we see educational attainment associated with avoiding non-automation prone occupations, if they are of low quality and unable to increase the skills of lower innate ability students, then increasing attainment may not be effective.

As for non-cognitive skills, currently very little is known about how to promote them, especially social intelligence and creativity which the automation literature believes is crucial to future employment. For social intelligence, the evidence is limited but emerging. Social and behavioral skills tend to be most malleable in adolescence (Almlund et al. 2011). Several programs target social skills including youth programs in Latin America that tie socio-emotional learning with technical skills (Sanchez Puerta, Valerio and Bernal 2016). Creativity, on the other hand, is a much more amorphous concept. Much of the psychology literature on creativity focuses on definitions and theory; there is little consensus on whether creativity should be considered as a general skill or a skill specific to a field or domain (see Baer and Kaufman 2005). Several instruments have been developed to measure creativity, especially in terms of divergent thinking (Plucker and Makel 2010). Only a small body of empirical literature so far exists on how to stimulate or improve creativity (Grigorenko et al. 2008); the only consensus is that education systems, as they are designed now, do not integrate or promote much creativity and how to do so is also not well understood (Beghetto and Kaufman 2014).

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Finally, our model suggests that automation when skill acquisition is constrained by poor quality schooling will not yield the same output per capita for a given level of capital. This has important implications for industrialization and growth models in the future. The East Asian growth model involved substantial domestic and foreign investment but was coupled with high quality basic level education that enabled the supply of what or model perhaps would call middle-skills to respond to rapidly increased demand (World Bank 1993). Our model and findings suggest that, to achieve the same growth with automation, the same levels of capital investment would need to be coupled with much higher levels of cognitive skills as well as noncognitive skills. Automation will usher in a much different type of growth model where cognitive skills and non-cognitive skills including social intelligence and creativity are fundamental. Given what we know about producing these types of skills, this requires a significant shift in our approach to research on growth and development.

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