metadata: authors: 0: author: Bussolo,Maurizio
1: author: Torre,Ivan
2: author: Winkler-Seales,Hernan Jorge
count: Europe
docna: 0: docna: Does Job Polarization Explain the Rise in
            Earnings Inequality ? Evidence from Europe
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Gender and Development,Labor & Employment Law,Poverty Reduction Strategies
teratopic: Social Protections and Labor,Education,Gender,Law and Development,Poverty Reduction
abstracts: cdata!: Earnings inequality and job polarization
            have increased significantly in several countries since the
            early 1990s. Using data from European countries covering a
            20-year period, this paper provides new evidence that the
            decline of middle-skilled occupations and the simultaneous
            increase of high- and low-skilled occupations are important
            factors accounting for the rise of inequality, especially at
            the bottom of the distribution. Job polarization accounts
            for a large share of the increasing inequality between the
            10th and the 50th percentiles, but it explains little or
            none of the increasing inequality between the 50th and 90th
            percentiles. Other important developments during this
            period, such as changing wage returns, higher educational
            attainment, and increased female labor force participation,
            account for a small portion of the changes in inequality.
repnme: repnme: Does Job Polarization Explain the Rise in
            Earnings Inequality ? Evidence from Europe
display_title: Does Job Polarization Explain the
            Rise in Earnings Inequality Evidence from Europe
listing_relative_url: /research/2018/11/30639318/job-polarization-explain-rise-earnings-inequality-evidence-europe
topicv3: Education m1327048 470,Gender m1327621 615,Income Inequality m1503891 2668,Jobs m1503931 2670,Household Surveys m1504071 2761
publishtoextweb_dt: 2018-11-26T00:00:00Z
historic_topic: Social Protections and Labor,Education,Gender,Law and Development,Poverty Reduction
no_of_pages: 54
author_keys: 0: author_key: 669228
1: author_key: 1521219
2: author_key: 776733
model_extractions: WPS8652 Policy Research Working Paper 8652 Does Job Polarization Explain the Rise in Earnings Inequality?

Evidence from Europe _Maurizio Bussolo Ivan Torre Hernan Winkler_ Europe and Central Asia Region Office of the Chief Economist November 2018
Policy Research Working Paper 8652

# **Abstract**

Earnings inequality and job polarization have increased significantly in several countries since the early 1990s. Using data from European countries covering a 20-year period, this paper provides new evidence that the decline of middle-skilled occupations and the simultaneous increase of high- and low-skilled occupations are important factors accounting for the rise of inequality, especially at the bottom of the distribution. Job polarization accounts for a large share of the increasing inequality between the 10th and the 50th percentiles, but it explains little or none of the increasing inequality between the 50th and 90th percentiles. Other important developments during this period, such as changing wage returns, higher educational attainment, and increased female labor force participation, account for a small portion of the changes in inequality.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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 mbussolo@worldbank.org, itorre@worldbank.org, and hwinkler@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
# **Does Job Polarization Explain the Rise in Earnings Inequality?**

# **Evidence from Europe**<sup>*****</sup>

Maurizio Bussolo<sup></sup> Ivan Torre<sup>¥</sup> Hernan Winkler<sup>§</sup> JEL Codes: D31, J21, J24, J31 Keywords: Inequality, Polarization, Decomposition

> * 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.  The World Bank, e‐mail: mbussolo@worldbank.org

> ¥ The World Bank, e‐mail: itorre@worldbank.org

> § The World Bank, e‐mail: hwinkler@worldbank.org
# **1. INTRODUCTION**

Income inequality rose steadily in Europe over the past decades. While the share of total income accruing to the richest 10 percent was less than 30 percent in 1970, it reached 35 percent in 2010 (Piketty and Saez, 2014). Accordingly, the Gini coefficient experienced a significant increase in most European economies since the 1980s (OECD, 2017; Cingano, 2014). This was also particularly true for labor income inequality, whose Gini index rose on average by 5 points across Europe between 1990 and 2015 (Figure 1). There is a large body of empirical literature documenting the negative impacts of rising income inequality on a host of socio‐economic outcomes such as health, safety, social mobility, cohesion and economic growth.<sup>1</sup> Several arguments have been put forward as potential drivers of the rise in inequality, including skill‐ biased technology adoption (Acemoglu, 1998), institutional change (Piketty and Saez, 2006) and globalization (Jaumotte and Papageorgiou, 2013; Meschi and Vivarelli, 2009). The rise in labor income inequality coincided with another important development: the increase in job polarization (Figure 2). This process, which is characterized by a decline in middle‐skill jobs and a simultaneous rise in low‐ and high‐ skill jobs, has been pervasive in developed and developing countries.<sup>2</sup> Goos, Manning and Salomons (2014) find that job polarization in Europe was driven by routine‐biased technological change and offshoring, as both forces disproportionately lowered the demand for middle‐skilled workers, who tend to be in occupations intensive in routine tasks. At the same time, this process increased the relative demand of high‐skilled and low‐skilled workers, who tend to be in occupations intensive in non‐routine cognitive and manual tasks, respectively. The displacement of workers from middle skill jobs to high‐ and low‐skill jobs may have an impact on the wage distribution, since both the volume of individuals with low earnings and the volume of individuals with high earnings will increase, leading to an overall higher wage inequality. In fact, Goos and Manning (2007) find that job polarization can explain most of the increase in wage inequality experienced by the United Kingdom between the 1970s and 1990s. However, the empirical link between income inequality and job polarization has not been systematically investigated

> 1 See, for example, Pickett and Wilkinson (2015); Enamorado et al. (2016); Aaberge et al. (2002); Winkler (2016); Cingano (2014).

> 2 See Autor, Katz and Kearney (2006) for the US, Goos, Manning and Salomons (2009) for Europe and World Bank (2016) for developing countries.

2
<!-- Start of picture text --> Evolution of labor income inequality, Europe<br>Wage employees only<br>~<br>2<br>E<br>ionos .° ° 8 8 é i<br>me2 °e e ° : e e<br>80 e °<br>£% e ° :<br>= e e So<br>2 $ ee e 8 *<br>= e e e<br>e88e<br>=<br>oS 8<br>= ° e e e°®<br>a7 °e e . ° e °<br>3° e<br>=<br>)<br>N<br>1990 1995 2000 2005 2010 2015<br>Year<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 2<br>Evolution of occupational structure, EU-15<br>1995 2000 2005 2010 2013<br>Occupation by task intensity<br>Routine (ISCO 88) Routine (ISCO 08)<br>Non-Routine Manual (ISCO 88) Non-Routine Manual (ISCO 08)<br>Non-Routine Cognitive (ISCO 88) Non-Routine Cognitive (ISCO 08)<br><!-- End of picture text --> Note: this figure shows the evolution of the occupational structure of wage employment of the aggregated EU‐15 countries (excluding Finland, Germany and Sweden) from 1995 to 2013. Occupations are classified according to their task intensity – See Appendix 2 for a detailed description of the classification. The red line indicates the share of wage employment in occupations intensive in routine tasks; the blue line indicates the share of wage employment in occupations intensive in non‐routine, manual tasks and the green line indicates the share of wage employment in occupations intensive in non‐routine, cognitive tasks. The source is the annual EU Labor Force Survey. Because of a change in the classification of occupations used by the survey (From ISCO 88 to ISCO 08) there is a break in the data after 2011.

Our decomposition method follows Bourguignon and Ferreira (2005). We first estimate an occupational choice model linking individual characteristics to occupations, and a set of Mincer equations for each type of occupation and worker, for the initial and final year of a period running from the early 1990s to 2013. We then carry out a set of counterfactual simulations, whereby we alternatively switch parameters and variables’ values in the final year with those of the initial year to assess the importance of each factor. Each counterfactual creates a simulated distribution of wages, which is “in between” the initial and the final distribution. In this way, the full change between the initial and final distribution, in terms of inequality or other indicators, can be decomposed in various parts.

More specifically, the decomposition technique assumes that the earnings distribution changes because of the shifts in the structure of occupations and because of the changes in the returns paid to specific occupations and workers. In addition, the structure of occupation changes because of variations in the characteristics of individual workers (education, age, gender amongst other) and the “utility” returns paid to them. Thus, to isolate the inequality impact of the polarization of the occupations, we proceed in stages.

4
In a first stage, we assume that the occupation polarization is due only to changes of the parameters (“utility” returns) linking individual characteristics to specific occupations. To do that individual characteristics are kept at the levels they had in the initial year, but the parameters are changed from those of the initial year to those of the final year. This simulation assumes, for example, that college graduates at the end of the period have a different probability than college graduates at the beginning of the period to be in high‐skill occupations, but that the share of college graduates in the pool of workers is constant in the two periods. This simulated occupation structure is then used to calculate a simulated (“intermediate‐parameters”) earnings distribution. This is done by recalculating the earnings for each worker who has switched occupation with the returns to the occupations kept equal to those of the initial year.

In a second stage, we assume that the occupational structure changes only because the characteristics of the labor force are shifting. This exercise produces another simulated (“intermediate‐characteristics”) earnings distribution and accounts for the share of occupational changes and earnings inequality driven by factors such as changing age and gender composition of the labor force or educational upgrading, under the assumption that the probability of having a certain occupation conditional on individuals’ characteristics is the same as in the initial year. In a final stage, the model allows to simulate changes in earnings driven directly by changes in returns to specific occupations and workers (“intermediate‐ returns”).

Comparing the position of these three simulated intermediate earnings distributions – the “parameters”, the “characteristics”, and the “returns” one – with respect to the initial and the final distribution allows to decompose the total distributional change into parts that are associated to each of these specific factors.

Using repeated cross‐sections of household surveys for Germany, Poland and Spain from the early 1990s to 2013, this paper shows that this period was characterized by a significant increase in earnings inequality and polarization of occupations. We find that holding everything else constant, the occupational _parameters_ account for at least 44 percent of the increase in the P90/10 earnings ratio over the considered period and group of countries. In terms of the P50/10 earnings ratio, the rise of low‐skill occupations and the decline of mid‐skill ones account for more than its full observed rise; so the model over‐estimates the role of occupational change for the increase of inequality at the bottom part of the distribution. In contrast, the decomposition model shows that simulated occupational shifts explain none or very little of the inequality increase at the top of the income distribution. These findings confirm those 5
of Goos and Manning (2007) for the United Kingdom. Other important labor market developments that took place during this period such as changes in wage returns to education, changing occupation wage premia, higher female labor force participation, a rapidly aging labor force and human capital upgrading account for a lesser fraction of the rise in inequality.

An interesting feature of the decomposition model is that it allows to generate synthetic‐panels and makes possible to follow displaced routine workers. Therefore, we also investigate the profiles of workers who are more likely to lose their routine jobs. We find that many of them move to unemployment or to low‐skilled jobs.<sup>5</sup> Only a minority of them move to a non‐routine cognitive occupation. This is consistent with the predictions of Autor (2010) for the US. Educational attainment is an important factor at explaining the direction of the transition, as more educated workers are more likely to move to non‐routine cognitive occupations, while the least ones tend to move to non‐routine manual occupations or unemployment. Workers in routine occupations who change status are, in general, younger than those who do not.

This paper focuses on Europe, and more specifically on Germany, Poland and Spain, for three reasons. First, given the long‐standing emphasis on wage and employment protection policies in European economies, this paper shows that inequality may still increase because of changes in the employment structure, even as wages and employment rates are not shifting. Second, the countries selected represent three broad EU regions, namely Northern, Eastern and Southern Europe. These regions have different levels of economic development, trajectories, labor market institutions and policies, thereby any common patterns among them would add robustness and external validity to our results. Third, they have reliable and comparable household surveys over a time span long enough to capture changes in inequality and job polarization. Appendix 4 extends the analysis to four non‐EU countries which have witnessed a different evolution of their labor market: Georgia, the Kyrgyz Republic, the Russian Federation and Turkey.

The rest of this paper is structured as follows. Section 2 describes the methodology. Section 3 describes the data sources and provides descriptive statistics. Section 4 presents the results, and Section 5 concludes.

> 5 Since the model does not distinguish between unemployment and inactivity, we use the term “unemployed” to group both.

6
|"W) = Seay TIX)<br>(xX) dx<br>a<br>OD) =Seg “OLX "(ax<br>YO) =egy TIX)<br>“(XDax<br>a<br>OSHO)andKO)=fo“)<br>(4)|
|---|
()-fO =i &) FO +O) — f°"I "O)—fO=Lh ) — FO + FO) — FeO]

|y=G[0,W,«; 2,]<br>0=H(|W,n;®,]|
|---|
<!-- Start of picture text --> °W) = Ioweasey “4 * [Snavney-0 TM an] YW) do aw<br>DIY, 1,1.) = Socowe,any ME) de X ucwmerao Hn) dn| ¥(W) do dw<br><!-- End of picture text -->

<!-- Start of picture text --> “y)- fo) =f" O)- Dy ee oe ltt<br>(DIY, we ge oe tl DM oe er ere<br>(DIY. gt er te tl] - DIM oe oe oer oe +<br>{D[", t’ ¢t' ot tl-f'O)}<br><!-- End of picture text -->
<!-- Start of picture text --> "(0) = Sew “COlW) (Ww) aw<br>0 0) = Sloan “COlW) ‘(wy aw<br>ww (0) = Seay *COlW) (Ww) aw<br><!-- End of picture text --> k <!-- Start of picture text --> 4 4<br>k<br><!-- End of picture text -->
<!-- Start of picture text --> f=1ifW, *+ek> Max(0,W, TM+e!"),k =1,..,K, Vm #k<br>k=0 k=1,..,K ; *+ek<0 k =1,...,K<br>U. kK<br>k<br>U<br>i “te<br><!-- End of picture text --> In(y) =X; *+ef i
variable assumed to be distributed identically and independently across individuals according to the standard normal distribution. We estimate equation (14) by ordinary least squares.

# **c. Decomposition approach**

We estimate models (13) and (14) for the first and final year, and simulate the impact of occupational changes by substituting the estimated parameters for one year with the parameters of the other year. We then use this hypothetical income to calculate a series of distributional statistics and compare them against those estimated using the actual income data.

 **C.1** Accounting for the impact of occupational changes To carry out this simulation, we assign the estimated coefficients of equation (13) in year t′ to the household survey in year t . To allow individuals to change occupations in the simulation, we need the � residual terms ε� of the multinomial logit in equation (13), which are unobserved. Following Inchauste et al. (2014) and Train and Wilson (2008), we draw the residuals from an extreme value distribution in a way that is consistent with observed choices. The simulated earnings for individual i are given by: The difference between this simulated distribution and the earnings distribution observed in year _t_ is accounted by the change in the occupational structure, which in turn is due to changes in structural parameters ( Φ ) of the occupation model. This is the main focus of the decomposition in this paper. Note that the occupation structure can also shift because the exogenous characteristics change. However, while the impact of shifting characteristics can be used to decompose the distribution of occupations, one cannot separate the _direct_ impact of shifting characteristics on earnings from the _indirect_ impact of shifting characteristics on occupations and from these on earnings. This simultaneous direct and indirect impacts of shifting characteristics are thus jointly accounted in step C.2 below as follows.

 **C.2** Accounting for the impact of changes in relevant exogenous characteristics In order to study the role played by changes in individual and household characteristics which are _exogenous_ to the occupational choice model (such as education, gender and age structure of the population) we perform a reweighting exercise as the one proposed by Bourguignon et al. (2008). First of all, we split exogenous characteristics ( Z� for the occupational choice model and X� for the earnings equation) into a group of relevant, common characteristics ( W� : education, gender and age ), which are 12
<!-- Start of picture text --> Z<br>i<br>X<br>i<br>it it<br>i<br>4<br>l _ TieC Rit tT it) peRie it) 2 it)<br>ow =) Ke pZ wa pkt ky KkepX ma okt k<br>k=1<br>4<br>~tot'<br>= Tit it Ait , Et)<br>l oO _ > k K Kt! 2k<br>k=1<br><!-- End of picture text -->
<!-- Start of picture text --> occ ~tot'<br>=Vit~Nn<br>4<br>occ =_ Vit _ > whek KkLt( Zit kt! ,€ ok)<br>k=1<br>exc_ gtot'r — <tot’ Tw<br>= —N<br>4 4<br>exc=)_ yfk &(Zit k,t!ek)ok) _ > 4eRZo.it Tkt! »€it)ok “KeitCRitxX ——itaokt it)k<br>k=1 k=1<br>wor ~tot'T,W ~tot' I,W,0.<br>pr= M nN<br>4<br>wpr =— > TkT(r,Z weTEk,t! ete)ok “KeTe(RieX ——ueOMokt 7)k<br>k=1<br>4<br>_ > Tk(Re,Z—ith Pet’T et)ok \ te(Riek(pX ——utcCOERs OFS e7,) -k<br>k=1<br>Z xX k Kk<br>i i i i<br>unx— stot Tw,<br>=uNU — Vit!<br><!-- End of picture text -->
Note that, in a repeated cross‐section setting, y�,�� is unobserved because individuals are not followed across years. Thus, the unexplained part of changes in earnings will only be possible to estimate for aggregate, anonymous quantiles of the distribution.

# **3. DATA AND DESCRIPTIVE STATISTICS**

# **3.1 DATA SOURCES**

Most of the empirical work done on labor markets in Europe uses the harmonized EU‐LFS quarterly labor force survey. This survey represents an invaluable data source for labor economists. However, public access microdata do not include information on earnings of workers. We thus use household surveys harmonized by the Luxembourg Income Study (LIS) center. These surveys include information on both employment characteristics and earnings of individuals, allowing to carry out the decomposition analysis detailed before. LIS harmonizes different household surveys to a common standard to assure comparability. In this work we use the German Socio‐Economic Panel editions of 1994 and 2013, the Household Budget Survey of 1992 and the EU‐SILC (Statistics and Income Living Conditions) edition of 2013 for Poland, and the Household Budget Survey of 1990 and the EU‐SILC edition of 2013 for Spain<sup>6</sup> .

The main variables of interest of our analysis are occupations and labor related earnings. With respect to occupations, we classify them into three categories based on their most intensive task, using O*NET task content information: 1) routine task intensive jobs; 2) non‐routine, manual task intensive jobs; 3) non‐ routine, cognitive task intensive jobs. In Appendix 2 we provide a detailed description of how we construct this classification. With respect to labor related earnings, we use annual earnings coming from wage employment. Due to the limitations that household surveys usually have in correctly capturing self‐ employed income, we exclude self‐employed from our analysis. Self‐employed represent between 9% and 15% of the total employment in the countries included in our work. Moreover, we restrict our sample to individuals aged between 18 and 64.

Appendix 1 provides more details on the surveys used and the construction of the variables relevant to our analysis.

> 6 The analysis is also done for Georgia, the Kyrgyz Republic, the Russian Federation and Turkey. See Appendix 4 for data sources and analysis.

15
<!-- Start of picture text --> Labor income, wage employees Per capita household income<br>< <<br>Os Germany xe<br>< r=<br>sg<br>SS<br>z Spain z<br>[o) (0)<br>se Poland co)<br>s 8<br>Ca. 1992 2000 2007 2013 Ca. 1992 2000 2007 2013<br>Year Year<br><!-- End of picture text -->
– <u>Figure 4 Change in wages by quantiles of wage distribution</u> These set of figures plot the log change in wages for the different ventiles of the wage distribution from the initial period to the final period. Changes are expressed with respect to the median, whose change is normalized to zero. Only the wage of wage workers is considered in this analysis.

# _Occupational changes_

Job polarization has been defined as the clustering of jobs at the extremes of the distribution of occupations and, correspondingly, a hollowing of the middle portion of this distribution. This definition entails establishing an ordering of the occupations. This ordering can be obtained according to: (a) the wage or skill level of a specific occupation, or (b) the intensity of certain types of tasks.

Since the correlation between wages and years of schooling (a proxy of skill level) is typically stable over time, some authors (Goos, Manning and Salomons, 2014) have used mean wages as the ranking variable of occupations. Following this approach, we order occupations at the 2‐digit level of the ISCO 88 classification according to their mean wage in the initial year, and then plot the change in their shares of total employment in the following 20 or so years. Poland is excluded as the microdata do not allow such level of disaggregation for the initial and final years. The results are shown in Figure 5, which highlights evidence of job polarization for Germany and, to a lesser extent, for Spain.

17
<u>Figure 5</u> This figure plots the percentage point change in employment shares from the initial year (1990 for Spain and 1994 for Germany) to the final year (2013) by occupations ranked according to their mean wage in the initial year. The changes are plotted by a locally weighted smoothing regression. Occupations are aggregated to the 2‐digit level of the ISCO 88 classification.

This ordering of occupations is country‐specific: an occupation may be paid highly in a country but lowly in another, and vice versa. To determine a ranking of the occupations that is common across countries, we use the approach based on the task content.

According to Acemoglu and Autor’s (2011) conceptual framework, occupations can be classified into three categories: occupations relatively intensive in routine tasks, occupations relatively intensive in non‐ routine cognitive tasks and occupations relatively intensive in non‐routine manual tasks. Note that any occupation implies carrying out both routine and non‐routine tasks and both cognitive and manual tasks since these are not mutually exclusive; nonetheless, an ordinal index based on the relative intensity of these tasks can be constructed.<sup>7</sup> Occupations intensive in routine tasks tend to be mid‐skill occupations, while those intensive in non‐routine tasks tend to be on either ends of the skill distribution, cognitive non‐ routine task intensive ones at the upper end and manual ones at the lower end. Within this framework, job polarization is thus represented by a decrease of the employment share of the routine‐intensive occupations and an increase of the non‐routine ones. Changes in the employment share of each of the three occupation categories for each country are shown in Figure 6, which confirms that job polarization

> 7 For more details of how the index is calculated for the various occupations see Appendix section 2 .

18
is present in Germany, Poland and up to a certain degree also in Spain. In these three countries de‐ routinization seems to be a common trend.

<u>Figure 6: Change in employment shares, by country and occupation</u> <!-- Start of picture text --> Germany: 1994‐2013 Poland: 1992‐2013 Spain: 1990‐2013<br>15.6% 14.7%<br>12.0%<br>6.9%<br>1.6%<br>‐4.2%<br>‐8.5%<br>‐10.5%<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br>percent of wage employees<br><!-- End of picture text --> Source: Authors calculations using LIS harmonized household surveys. Note: This figure shows the change, in percentage points, of the share of total employment (wage employees, excluding self‐employed) over a period of about twenty years of three occupations categories: non‐routine manual task intensive occupations, routine ones, and non‐routine cognitive tasks intensive ones. The initial and final years depends on data availability. For more details on the construction of the occupation categories please see the appendix.

The two approaches presented above do not produce the exact same ranking of occupations, however the two rankings are not far apart. This is because, as illustrated in Figure 5, there is a correlation between the intensity of task contents of occupations and wages paid in these occupations. Using the normalized task index of occupations, Figure 7 shows that, for Germany and Spain, the intensity of non‐routine cognitive tasks is higher for jobs at the top of the wage distribution while routine task intensity is higher in the middle of the distribution. Low pay jobs are more intensive in non‐routine manual occupations.

In sum, no matter what the criteria for ordering of occupations is used – either the average wage paid in each occupation, or the intensity of their task content – there is evidence of a hollowing of the middle of the distribution of occupations, or of its polarization. For the three countries under study, jobs entailing a high degree of routine tasks and paid around the middle of the wage scale are declining.

19
<u>Figure 7</u> This figure plots three task content indices of occupations ranked by their mean wage in the initial period as in Figure 3 XXX. The three task content indices (intensity in non‐ routine, cognitive, analytical tasks; intensity in non‐routine, manual, physical tasks; routine task intensity) are normalized to their economy‐wide means. The indices are plotted by a locally weighted smoothing regression. Occupations are aggregated to the 2‐digit level of the ISCO 88 classification

# **4. RESULTS**

# **4.1 ACCOUNTING FOR THE INCREASE IN EARNINGS INEQUALITY**

In all three countries, changes in the occupational structure seem to be a major factor – vis‐à‐vis changes of assets and of returns – behind the increasing inequality observed during the period, particularly at the bottom of the wage distribution. This main finding of the decomposition exercise is in line with what Goos and Manning (2007) found for the United Kingdom, while it differs slightly from the result of Acemoglu and Autor (2011) who showed that, for the US, polarization in occupations was related to increased wages both at the bottom and at the top of the distribution.

A scenario where the characteristics/assets of individuals and their relative returns remain the same but where the occupational structure is shifting would register an increase of the P90/10 ratio (the ratio between the average earnings of the 90<sup>th</sup> and 10<sup>th</sup> ) equivalent to 44 percent of the total increase in Germany, 91 percent in Poland, and 312 percent in Spain, as shown in column (1) of Table 1. So, indeed, occupational changes have a significant distributional impact on earnings. More in detail, the hollowing of the middle jobs seems to generate more inequality pressure at the bottom half of the earning range. This scenario accounts for 211, 184, and 772 percent of the total change of the P50/10 ratio in Germany, 20
Poland and Spain, respectively. Conversely, occupational shifts at the top half of the distribution account for much smaller shares (see the P90/50 ratios in column (1) of table 1).

There are two reasons for these differential polarization impact on the top and bottom part of the earnings distribution. The first is technical and has to do with the fact that the occupational change simulated with a change of the parameters is not equivalent to the full change, and the second is because most of the displaced middle skilled workers tend to move down in the distribution. A more detailed explanation is provided below.

<u>Table 1 – Decomposition results</u>

|||<br>|<br> <br>Percen|<br> <br>tage explain|<br> <br>ed by:|
|---|---|---|---|---|---|
|||Change|**Occup.**|**Charact.**|<br>**Returns**|
|Country|Inequality measure,<br>labor income|1990s‐<br>|<br>(C1,<br>|(C2,<br>|<br>(C3,<br>|
|||2013|section 2.2)|section 2.2)|section 2.2)|
||||(1)|(2)|(3)|
||Gini coefficient|0.058|‐1|20|30|
|Germany|P90/10|3.148|44|14|185|
||P90/50|0.404|‐6|13|21|
||P50/10|0.311|211|23|818|
||Gini coefficient|0.078|42|‐4|27|
|Poland|P90/10|0.938|91|‐10|59|
||P90/50|0.303|26|‐9|30|
||P50/10|0.208|184|‐4|79|
||Gini coefficient|0.068|92|‐21|51|
|Sain|P90/10|2.672|312|‐10|158|
|p|P90/50|0.466|39|‐19|60|
||P50/10|0.442|772|86|114|

Source: Authors’ calculations based on LIS harmonized surveys. Note: the percentages shown in columns (1), (2) and (3) are derived using the distributions of earnings simulated according to the methods described in the respective sub‐sections of section 2.2. The change in the inequality indicators corresponds to the ones estimated in the working sample and may differ from those of the whole sample of wage workers. Observations with missing data in some of the relevant variables are excluded. The change in the Gini coefficient of labor income for the whole set of wage workers (including those with missing information) was 0.065 in Germany, 0.060 in Poland, and 0.071 in Spain The education, age and gender structure of the labor force underwent significant transformations between the early 1990s and 2013. For example, in our sample, the share of individuals with tertiary education expanded substantially in Poland, where it went from 7% to 20%, and in Spain where it went from 12% to 32%; but not so much in Germany where it increased from around 22% to 27%. Another scenario, where only assets/characteristics of individuals are changing, is therefore simulated to account for the distributional impact on earnings of these transformations. Note that, when characteristics are varying, there are two effects on earnings. One effect is direct; for example, a larger number of well‐ 21
educated workers translates into a larger group of people earning higher wages<sup>8</sup> . The second effect is indirect and operates through occupational shift. A large share of the increased pool of well‐educated people, as in the previous example, will find jobs in non‐routine occupations and benefit from both an education and an occupation premium. These two effects cannot be disentangled and their joint impact is shown in column (2) of table 1. In short, changes in individuals’ characteristics, in isolation, account for a minor share of the overall changes in inequality.

A final scenario is simulated to assess the importance of changes in the returns of occupations (and individual characteristics). Changes of inequality driven by the returns account for a non‐trivial fraction of the overall inequality increase, but this fraction is smaller than that accounted for the polarization of occupations. According to column (3) of Table 1, changes in returns to characteristics explain between 30 to 50 percent of the increase in the Gini coefficient. Comparing the results of the P90/50 with those of P50/10 illustrates that changes in inequality were driven by both a strong increase in relative earnings at the top, and a stronger, disproportionate decline in relative earnings at the bottom of the distribution of earnings. To a large extent, this was driven by the increase in the returns to education among non‐routine, cognitive task intensive occupations, more prevalent at the top of the wage distribution.<sup>9</sup> The summary results of table 1 can be extended to the full distribution by plotting a series of growth incidence curves (GIC), as done in Figure 8. For each percentile – ordered from the poorest 1 percent to the richest 1 percent – these curves illustrate the change of earnings from the initial year to the final year, i.e. the full observed change, as well as the change that is due to a single component of the decomposition. In all three countries, the ‘full’ GIC (the yellow line in the figure) is upward sloped indicating that the earnings distribution underwent a regressive adjustment. In addition, changes in occupations, individual characteristics and wage returns over‐account for the relative decline of earnings at the bottom of the distribution. This means that factors not included in the model (like, for instance, within‐firm or within‐ sector changes in returns to individual characteristics) partially offset the role of these changes in driving inequality at the bottom of the distribution. In particular, the results highlight the magnitude of occupational changes as a driver of earnings inequality at the bottom of the earnings distribution. In contrast, the simulations account for almost all the increase in relative earnings at the top of the distribution in Poland and Spain. In Germany, the simulations account for only a small share of the increase

> 8 In this scenario, returns are unchanged.

> 9 According to the estimates of the Mincer equations in Tables A.2 and A.3 in the appendix, the returns to college education in those occupations increased by between 8 and 23 log points in Spain and Germany over the period under analysis, while they remained stable or slightly decreased in Poland.

22
<!-- Start of picture text --> Incidence ofGermany changes 1994-2013 - Poland<br>03 Incidence of changes 1992-2013<br>c 03<br>&<br>so B02<br>3E<br>201 £ 01<br>a3g __ -s 3y 0<br>e ° | oo 45 50 55 60 65 70 75 80 85 90 95<br>3 50 55 60 65 70 75 80 85 90 95 $ -01<br>ri:wD0.1 £% -0.2<br>3 02 So -0.3<br>c)<br>8<br>-0.3 Quantiles, of wage distributionae Quantilesof wage distribution<br>mmm Occup. StructuralParam. mmmmiCharacteristics mmm Returns =Total Mmmm Occup.StructuralParam. mmlmliCharacteristics MlmmReturns =Total<br>Spain<br>Incidence of changes 1990-2013<br>04<br>03<br>&zsc 02<br><!-- End of picture text -->

<!-- Start of picture text --> E 01<br>2<br>50¥ 50<=55 60 65 70 75 80 |85 90TM95<br>% -0.1<br>3<br>£ 0.2<br>=@oD&%0 -03<br>ge -04<br>-0.5<br>-0.6<br>Quantiles of wage distribution<br>mmm Occup. StructuralParam. mmm Characteristics mammReturns =Total<br><!-- End of picture text -->
occupational changes, this section aims at clarifying the drivers of occupational change and thus link these drivers to the inequality of earnings.

The first partial occupational shift is depicted in Figure 9 alongside the observed full shift. In Germany, the simulation and the observed data look very similar. This means that changes in structural parameters account for the bulk of the occupational change. In the case of Poland and Spain, the decline in routine intensive occupations is closely approximated, but the growth in non‐routine, manual task intensive jobs is overestimated. Interestingly, the shift in parameters plays a negligible role in accounting for the observed growth of jobs intensive in non‐routine cognitive tasks. In these two countries, the impact of the parameters‐related polarization of jobs on earnings must be taken with a grain of salt, given that the polarization simulated with just the shift in parameters (that reported in column (1) of table 1) does not correspond so closely with the observed polarization.

– <u>Figure 9 Simulation of occupational change, holding constant all variables except occupation structural parameters</u> <!-- Start of picture text --> Germany Poland Spain<br>19.8%<br>15.6% 14.7%<br>12.0% 11.4%<br>6.9% 5.8%<br>1.6% 2.5% 1.6%<br>‐1.2%<br>‐4.2%<br>‐8.5% ‐8.3%<br>‐10.5%<br>‐13.0%<br>Actual Simulation ‐18.5%<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br><!-- End of picture text --> Note: this figure compares the observed change in occupational structure (dark colors) with the one in the counterfactual scenario (light colors) where the occupation structural parameters in 2013 are the same as in the 1990s.

Figure 10 illustrates the second partial occupational change, the one accounted for by changes in individual characteristics. As expected, in the case of Germany, the simulated occupational change is very small. In the case of Poland and Spain, this second simulation, by underestimating the growth in non‐ routine, manual task intensive skills, offsets the overestimation of the first (parameters‐related) simulation. At the same time, this second simulation matches quite well the increase in the share of non‐ routine, cognitive task intensive jobs.

24
# – <u>Figure 10 Simulation of occupational change, holding constant all variables except individual characteristics (education, age, gender structure)</u>

<!-- Start of picture text --> Germany Poland Spain<br>15.6% 15.9% 14.7%16.7%<br>12.0%<br>6.9%<br>1.6% 2.5%<br>‐1.5% ‐1.1% ‐1.7%<br>‐4.2%<br>‐6.3%<br>‐8.5% ‐9.6% ‐10.5%<br>‐15.0%<br>Actual Simulation<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br>percent of wage employees<br><!-- End of picture text --> Note: this figure compares the observed change in occupational structure (dark colors) with the one in the counterfactual scenario (light colors) where the average individual characteristics (education, age, gender structure) in 2013 are the same as in the 1990s.

In sum, these simulations highlight that the shift of the parameters linking given characteristics to given occupations is a prominent driver for the reduction of routine workers for all the countries. In other words, the simulations suggest that no matter how much the characteristics of the working population had changed, if these parameters had remained unchanged across time, there would have been less hollowing out of the middle occupations.

Conversely, the expansion of the bottom and the top portions of the distribution of occupation seems driven by different factors in the different countries. In Germany, shifts of parameters are still playing the main role, but in Poland and Spain education upgrading and changes in other characteristics mattered for growth of non‐routine cognitive jobs at the top; while a combination of both explains the increase, at the bottom, for non‐routine manual jobs.

Given these results, and specifically that the occupational parameters are the main driver of the drop of the middle routine intensive occupations, we can finally use the model to generate a pseudo‐panel and thus, ultimately, to tell us where the displaced routine workers go and what happens to their earnings.

A brief explanation is helpful here. As shown above, for all the three countries, a shift of the parameters of the occupation model generates a counterfactual where there is no de‐routinization or, equivalently, a counterfactual where more individuals are employed in middle routine jobs. But in the real world, where the parameters did change, these counterfactual routine workers end up in different occupations. This procedure allows ‘following’ these individuals, from their counterfactual routine occupations to their observed non‐routine occupations.

25
Two main findings are highlighted by this exercise and illustrated in tables 2 and 3 below. Firstly, most displaced routine workers end up either not employed or in non‐routine manual jobs and only a few can move up to non‐routine cognitive jobs. In addition, disproportionally more women, and younger and less educated people fall down in the distribution of occupations when de‐routinization hits. Both these shifts translate into large losses in terms of earnings, as shown in figure 11, and ultimately explain the rising inequality of the distribution of earnings.

More in details, Table 2 describes for the three countries, where each individual of a group of 100 counterfactual routine workers actually ends up in the 2013 observed data. Consider, for example, the case of Germany: 15 workers are now not employed, 8 of them are in non‐routine manual jobs, and only 6 are in non‐routine cognitive jobs; and up to 71 remain in routine jobs. For all the countries the movement into non‐routine manual intensive jobs is larger than the movement into non‐routine cognitive task intensive jobs, which is consistent with the predictions of Autor (2010) for the US.

<u>Table 2 – Flows out of routine‐task intensive occupations</u>

|||Observed o|ccupations||Counter‐<br>factual|
|---|---|---|---|---|---|
||Not employed|Non‐routine,<br>manual|Non‐routine,<br>cognitive|Routine|All in routine|
|||Movers||Stayers||
|Germany|15|8|6|71|100|
|Poland|10|15|9|66|100|
|Spain|24|11|3|62|100|

Source: Authors’ calculations. Note: This table presents the observed occupations of 100 individuals who would be, in the counterfactual scenario, employed in routine task intensive occupations. For example, the first column shows individuals who are ‘not employed’ but that in the counterfactual simulation (obtained by shifting the parameters of the occupational model from their current 2013 values to the values they had at the beginning of the 1990s) would have been employed in routine task intensive occupations. The second column shows individuals who are employed in ‘non‐routine, manual task intensive occupations’ but would have been employed in routine task intensive occupations in the counterfactual scenario, and so on.

The characteristics of displaced routine workers who end up jobless or in lower occupations are quite different from the characteristics of those who remain in routine jobs or of those who move up to better paid cognitive jobs. In Germany, as shown in table 3, the share of female workers is higher among the displaced routine workers who move into non‐routine manual and cognitive occupations, while the share of males is higher among those who become unemployed. In contrast, in Poland and Spain displaced workers are more likely to be female regardless of their new status when compared to those who kept their routine occupation in the simulation. In Germany, Poland and Spain, routine workers who move into 26
non‐routine cognitive occupations have more years of education, while those who move into non‐routine manual occupations have roughly the same level of education than those who stay in routine jobs. Those who move from routine occupations into unemployment have a substantially lower educational attainment. For instance, a routine worker who does not change occupations in Germany has 12 years of education, while one that moves into unemployment has only 8.9 years of education. In Germany and Spain, routine workers who move into any other status including unemployment are younger than those who stay in routine jobs. The same holds in Poland, although the share of routine workers who move into non‐routine cognitive positions tends to be older than the rest.

<u>Table 3 – Flows out of routine‐task intensive occupations, individual characteristics</u>

|||<br>|<br> <br>Observed o|<br> <br>ccupations||
|---|---|---|---|---|---|
|Country|Characteristic|Not<br>employed|Non‐routine,<br>manual|Non‐routine,<br>cognitive|Routine|
||||Movers||Stayers|
||Percentage of women|37.8|83.6|56.6|48.6|
|Germany|Years of schooling|8.9|11.1|13.3|12.0|
||Age|39.3|42.1|40.5|43.9|
||Percentage of women|64.7|49.1|41.0|25.5|
|Poland|Years of schooling|9.8|12.2|14.2|11.8|
||Age|35.4|39.0|39.0|40.2|
||Percentage of women|38.0|45.1|33.3|37.8|
|Spain|Years of schooling|11.8|12.0|14.4|12.1|
||Age|38.9|39.3|41.9|39.9|

Note: this table shows a set of descriptive characteristics of workers grouped according to their observed occupation in contrast with their simulated routine occupation. So, for example, the first column shows individuals who in the simulation would have been employed in routine occupations but that, because of the change in the parameters of the occupation model, are now not employed. The other columns are interpreted in the same way.

Occupational changes have significant implications for earnings. Figure 11 shows the simulated change in the wage of workers who in the simulation would have been in routine jobs but then move into non‐ routine manual or cognitive occupations. As expected, the change in earnings is significant. For example, a displaced German routine worker moving to a non‐routine manual occupation is expected to experience a close to 50 percent decline in wages; in Poland and Spain the corresponding losses would be of about 20 and 30 percent, respectively. Clearly, gains in wages are shown for the fewer displaced workers who move up in the occupation distribution.

27
<!-- Start of picture text --> Figure 11<br>0.4<br>0.3 0.27 0.28<br>0.20<br>0.2<br>0.1<br>0<br>‐0.1<br>‐0.2<br>‐0.21<br>‐0.3 ‐0.27<br>‐0.4<br>From (Sim) Routine to Non‐Routine Manual<br>‐0.5<br>‐0.51 From (Sim) Routine to Non‐Routine Cognitive<br>‐0.6<br>Germany Poland Spain<br>Log change in wage<br><!-- End of picture text --> Note: this figure shows the difference between the actual wages in the observed occupations of individuals who, in the simulation, would have earned wages in routine occupations. The blue bars show the loss in terms of lower wages (log change in wage can be interpreted as a percentage difference) for the “movers” in non‐routine, manual task intensive. The orange bars show the gains for the “movers” in non‐ routine, cognitive task intensive jobs. Note that, as shown in table 2, fewer workers move to these higher paid occupations.

# **5. CONCLUSIONS**

This paper provides new evidence that the rise in job polarization accounts for a significant share of the increase in earnings inequality observed in a group of European countries over a 20‐year time span. Using decomposition techniques, it shows that changes in occupation structural parameters ‐which explain a great deal of the polarization in occupations‐ account for a part of the observed increase in earnings inequality that is larger than that related to changes in the returns or individual characteristics, such as educational attainment. While these countries experienced a rise in inequality both at the top and the bottom of the earnings distribution, changes in occupation structural parameters mostly explain the latter.

The fact that the match between individual characteristics and occupations, a correlation which is captured by parameters of the occupational model, has changed considerably in the last twenty years may be due to different reasons – technological change being a leading explanation. De‐routinization and new technologies may have changed the types of tasks performed by middle and high paying occupations, 28
and thus may have made the skills of workers holding a high school diploma or less no longer sufficient to be employed in these occupations.

These results show that labor market inequalities can emerge even when wage and employment protection legislation limit the extent of wage flexibility or job losses. Given that the process of job polarization appears to be ongoing, these results raise concerns about the future path of inequality in Europe.

Finally, it is important to mention some caveats of the methodology used in this paper.<sup>11</sup> First, decomposition techniques do not allow to show general equilibrium effects as they just generate statistical counterfactuals. For example, if the occupational structure changed, this would likely change the wage returns as well, something that our model cannot capture. Second, while our results help uncover a robust connection between job polarization and earnings inequality, they do not address what are the causes behind it. Thereby, understanding the role of technological change as a causal factor of job polarization and earnings inequality in a general equilibrium setting remains an important area of future research.

> 11 See Fortin, Lemieux and Firpo (2010) for a review of decomposition methods in economics.

29