Taking the Sector Seriously: Data, Developments, and Drivers of Intrasectoral Earnings Inequality
Taking the Sector Seriously: Data, Developments, and Drivers of Intrasectoral Earnings Inequality
Stefan Thewissen 0 1 2
Olaf van Vliet 0 1 2
Chen Wang 0 1 2
Stefan Thewissen 0 1 2
Olaf van Vliet 0 1 2
0 School of Urban and Regional Science, Shanghai University of Finance and Economics , Shanghai , China
1 Department of Economics, Leiden University , Leiden , The Netherlands
2 Department of Social Policy and Intervention, and Nuffield College, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford , Oxford , UK
The widespread increase in earnings inequality within postindustrial countries has received a lot of attention in both the public debate and the academic literature. Remarkably, the developments in earnings inequality have mainly been studied at the country level, whereas there is substantial variation across sectors within countries. This study explores the developments and the drivers of earnings inequality at the sectoral level. From an empirical perspective, this study aims to contribute to the inequality literature by analyzing measures of sectoral earnings inequality. The study relies on newly assembled data based on Luxembourg Income Study micro data for eleven sectors across eight countries over the past few decades. As a theoretical contribution, the study examines the three key explanations for increasing earnings inequality that have been debated and analysed by economists, sociologists and political scientists-namely, globalisation, technological change and waning labour union power-but this time with sectoral earnings inequality data. Interestingly, the results provide only limited support for the argument that international trade leads to higher levels of earnings inequality. When we focus the analysis on trade with less developed countries we find a positive association between trade and earnings inequality. With regard to technological change, our findings provide mixed evidence for the hypothesis that skill-biased technological change increases earnings inequality. Our results bring back the waning country-wide labour union power as an
important driver of earnings inequality. This corresponds with the fact that our sectoral
data reveal a more general trend towards rising inequality across sectors over time.
A widely observed phenomenon in social sciences is the gradual and widespread increase
in earnings inequality within developed countries
(Atkinson 2003; Alderson et al. 2005;
Kenworthy and Pontusson 2005; Brandolini and Smeeding 2009; Immervoll and
Richardson 2011; Gornick and Ja¨ntti 2013; Iversen and Soskice 2013)
sociologists, and political scientists generally put forward three explanations for this
upsurge in inequality at the country level. The first two are increased international trade
and technological change, which are both arguably disadvantageous to the low-skilled. The
third line of explanations focuses on changes in labour market institutions, in particular
weakening employment protection legislation and union power
(e.g., Alderson and Nielsen
2002; Mahler 2004; Koeniger et al. 2007; Oliver 2008; OECD 2011a; Alderson and Doran
2013; Oesch 2013; Wren 2013; Swank 2015)
. Substantial attention has been paid to
inequality trends at the country level, but there is a remarkable knowledge gap with regard
to the developments within countries across different sectors. Some sectors are much more
exposed to international economic integration or technological change than other sectors.
Hence, it could be expected that sectors more exposed to these trends should have
witnessed increased levels of inequality. On the other hand, a common trend in inequality
levels across sectors within a country would provide evidence for country-level labour
market institutions as an explanation for the surge in inequality.
The aim of this paper is to explore the developments and the drivers of sectoral earnings
inequality across 11 sectors in 8 OECD countries. As such, this study seeks to make a
number of mainly empirical contributions. First, we use newly assembled data on sectoral
inequality based on Luxembourg Income Study (LIS) micro data
(LIS 2013; Wang et al.
.1 This dataset, based on harmonised sector and earnings information, is available
online. Second, compared to existing studies on earnings inequality in OECD countries—
by and large country-level studies—a sectoral approach increases the number of
observations substantially, which increases the leverage in pooled time-series cross-sectional
regression analyses. Our third contribution relates to the few studies that examine possible
determinants of rising inequality by means of a sectoral design in multiple countries over
(Mahler et al. 1999; OECD 2011a; Michaels et al. 2014)
. Whereas these three studies
examine only one or two determinants of earnings inequality, our study includes all three
variables that are highlighted in the debate on earnings inequality: international trade,
skillbiased technological change and labour market institutions. For international trade, we
make a distinction between trade in general and trade with less developed countries. A
1 See also Thewissen et al. (2013).
fourth and final contribution to these three studies is that these studies include two
moments per sector at maximum. Utilizing our newly assembled data, we include multiple
moments per sector which provide further insights in the developments in earnings
Our data show that there is substantial heterogeneity across sectors in levels of earnings
inequality. Furthermore, the levels of sectoral earnings inequality have generally increased
substantially over time. With regard to the drivers of these developments, the results
provide only limited support for the argument that international trade is associated with
higher levels of earnings inequality. When the analysis is focused on trade with less
developed countries, we find a positive association between trade and earnings inequality.
Furthermore, the results provide mixed evidence for the hypothesis that skill-biased
technological change is positively related to sectoral earnings inequality. Our results point
to waning trade union power at the country level as a driver of the general rise in sectoral
earnings inequality. When fewer employees are covered by bargaining agreements,
workers’ wages tend to be more unequal.
2 Literature and Hypotheses
Three explanations for the widespread trend of increasing earnings inequality at the
country-level are regularly put forward, namely, increasing international trade, skill-biased
technological change, and weaker labour market institutions. Starting with the first; the
amount of international trade increased substantially during the last decades, in particular
between developed and developing countries
(Harrison et al. 2011)
. Our theoretical
understanding of the distributive effects of international trade is based on two standard
models from international economics. In the Ricardo-Viner model, sectors are the central
unit of analysis as it is assumed that factor mobility is limited.2 Employees in sectors with
higher exports as a result of the reduction of trade restrictions benefit, whereas employees
in sectors with increased imports loose
(Samuelson 1971; Sirgy et al. 2007; Hays 2009;
Walter 2010; Thewissen and Van Vliet 2015)
. In contrast, the Stolper–Samuelson model
(1941) predicts that when countries engage into trade, the production factors that are
relatively abundant gain. In developed countries, where high-skilled workers are relatively
more abundant, engaging into trade will lead to a higher skill demand, whilst the
lowskilled will suffer from the increased competition with developing countries with a relative
abundance of low-skilled labour
. Hence, the hypothesis to be tested is that
sectors more exposed to international trade experience higher levels of earnings inequality.
This association can be expected to be stronger for trade with less developed countries.
A second prevalent explanation for increasing earnings inequality is skill-biased
(Goldin and Katz 2008; Oesch 2013; Wren 2013)
2 Limited mobility of employees between sectors can be a result of various labour market frictions, such as
search costs in looking for jobs
(Mortensen and Pissarides 1999)
, job and industry specific human capital
(Estevez-Abe et al. 2001)
, or institutions such as employment protection legislation
(Hellier and Chusseau
innovation complements the high-skilled, whilst it substitutes routine labour by capital.
The theory plays a central role in the wage literature, using skill demand or the skill wage
gap as dependent variable. The wage literature reports evidence for skill-biased
technological change leading to polarisation in the labour market, though the analyses are mainly
limited to the US
(e.g. Autor et al. 2003; Goldin and Katz 2008)
. In a study on the labour
market effects of information and communication technologies (ICT),
Michaels et al.
extend this empirical evidence to sectors in Japan and nine European countries. We
hypothesize that in sectors with more technological change, higher levels of earnings
inequality can be observed.
A third line of explanations highlights changes in labour market institutions as the main
cause of growing earnings dispersion in OECD countries. Wages and other working
conditions are set in negotiations between employers and employees
Christensen and Wibbels 2013; Western and Rosenfeld 2011)
. The outcomes of these
negotiations are largely determined by a country’s institutional setting of labour relations
and political power distributions
(Kenworthy 2001; Martin and Swank 2012; Huber and
Stephens 2014; Swank 2015)
. A first factor of relevance in this respect is the share of
employees covered by wage bargaining agreements
. When more
employees are covered by bargaining agreements, the variation in workers’ wages is
smaller. Hence, we hypothesize that bargaining coverage is negatively associated with
The level of wage coordination is a relevant factor in the wage setting as well. The
central hypothesis put forward in the earnings inequality literature is that countries with
centralised systems of wage bargaining have a more compressed wage distribution. The
underlying argument is that centralised wage bargaining creates fewer and smaller wage
differentials when more firms and industries are covered by the same wage settlements
(Wallerstein 1999; Rueda and Pontusson 2000; Mahler 2004)
. Interestingly, the existing
empirical evidence is based on country-level studies. Hence, it is an empirical question
whether and how coordination is associated with sectoral earnings inequality.
Another institution that may affect labour market outcomes is employment protection
legislation (EPL). As a result of EPL, the gap between employees with a permanent
contract (insiders) and employees without a permanent contract (outsiders) becomes larger.
Stricter EPL increases the costs of dismissal, which gives insiders bargaining power in
(Lindbeck and Snower 2001; Rueda 2007)
. However, this effect crucially
depends on the extent to which these costs can be shifted from employers onto workers
(OECD 2011a: 153)
. Hence, following the OECD (2011a), it is theoretically hard to
predict, and therefore an empirical question, how EPL is associated with earnings
Finally, the political colour of governments may have an impact on wage inequality. In
the literature on wage inequality, two effects can be distinguished. First, since governments
are widely involved in private-sector wage setting in many OECD countries, the political
colour of governments may have a direct effect on wage inequality. In this respect,
leftwing governments can be expected to pursue greater wage equality than conservative or
. A second argument, one that is more indirect, is
that governments may influence wages and employment through institutions and policies
such as minimum wage legislation, taxes, and other forms of income policies. Again, it
may be expected that left-wing governments opt for policies that lead to less earnings
inequality than conservative or liberal governments
(Rueda and Pontusson 2000;
Pontusson et al. 2002; Oliver 2008)
3 Data, Measures and Method
3.1 Sectoral Earnings Inequality
To analyse patterns of the level of intrasectoral earnings inequality, we calculate the Gini
coefficient. The Gini coefficient is the most frequently used inequality measure in the
literature, and for our purposes it has useful characteristics. As we are calculating
inequality within sectors, our sample becomes smaller. We can correct the Gini coefficient
for underestimation bias resulting from relatively small samples by multiplying it by n n 1,
called the first order correction
.3 We construct our dataset
(Wang et al. 2014)
on the basis of the Luxembourg Income Study (LIS) micro data
(see also Gornick and
, elaborating on
Mahler et al. (1999)
who calculate sectoral levels of earnings
inequality for two LIS waves. We follow their choice to restrict the sample to individuals
aged between 25 and 54, which are those people most dependent on earnings as source of
income. Since we are interested in labour earnings inequality, we only include income
from wages and salaries or self-employment, omitting income from other sources such as
interest and rent, and we do not adjust the wages for taxes or social contributions.4 We
follow standard LIS top- and bottom coding conventions. We base our calculations on
We standardise all sectoral information in LIS following the ISIC 3.0 classification. As
presented in Table 1, we distinguish between nine sectors at the two-digit level, and we
further break down the manufacturing into eleven subsectors using the three-digit level, as
Mahler et al. (1999)
, OECD (2011a), and
Michaels et al. (2014)
.5 Based on sectoral data
availability, we have to restrict our analysis to eight OECD countries of five periods of
around 5 years between 1984 and 2007. We can only use the LIS information up to and
including 2007 as our indicator for technological change, which we will discuss in
Sect. 3.2, is only available up to and including 2007. We do not include measures of
intersectoral earnings inequality; these are beyond the scope of this paper.
3.2 Independent Variables
To examine the sectoral exposure to international trade we use the OECD STAN database
(2011b) where we calculate trade values as the sum of sectoral imports and exports in
percentage of sectoral added value. This dataset contains information on the value of
traded goods in agriculture, mining, manufacturing and its sub industries, and the utilities
3 The first order corrected Gini is multiplied by 100 in the regressions to enhance readability of the
coefficients in the tables.
4 We refer to our income definition as ‘earnings’, which corresponds to ‘labour income’ in the LIS template.
5 No further breakdown in the community services sector is possible with LIS micro data for a sufficient
number of country-period observations. The community sector consists of people working in public
administration, education, health and social work, and other community and personal service activities. We
leave out total manufacturing; and manufacturing of chemical, rubber, plastics, and fuel products (23t25) in
our descriptives and regressions to avoid having sectoral overlap, as we include all constituent sectors
sector. We use the
EBOPS Trade in services dataset to obtain information on
the value of traded services for the services sectors.6
Unfortunately, with the OECD STAN (2011b) data no distinction is possible between
trade among developed and trade between developed and developing countries. To collect
data on trade with less developed countries, we use the
OECD STAN Bilateral (2016
database instead. Again, we complement this with the
EBOPS Trade in
Services dataset for the services sectors. Here, we calculate the sum of trade (imports and
exports) coming from the BRIICS countries (Brazil, Russia, Indonesia, India, China, South
Africa) and Mexico as a percentage of sectoral value added.7 For trade with developing
countries, substantially fewer (39%) observations are available.
To examine the association between skill-biased technological change and earnings
inequality, we follow
Michaels et al. (2014)
and include ICT capital compensation as a
share of sectoral value added from the EU-KLEMS dataset (2011).8 As already stated, for
this sectoral indicator information is only available up to and including 2007. Empirically,
skill-biased technological change is hard to measure
possibly affect our results—but we use the best indicator that is currently available for the
To account for general economic conditions at the sectoral level, we include the natural
logarithm of the volume of the gross sectoral value added from the EU-KLEMS dataset
Furthermore, the study examines a number of variables measuring labour market
variables that are highlighted in the comparative political economy literature on earnings
(Wallerstein 1999; Rueda and Pontusson 2000; Mahler 2004; Oliver 2008)
account for wage-setting institutions, we include measures of the bargaining coverage rate
6 The correspondence table for NACE and EBOPS looks as follows:
F. Construction 249 Construction services
GtH. Wholesale… 236 Travel
I Transp… 205 Transportation and 245 Communication services
JtK Finance… 253 Insurance services, 260 Financial services, 262,
Computer and information services, 266 Royalties
and license fees, 268 Other business services
LtQ Community… 287 Personal, cultural, and recreational services,
291 Government services, n.i.e. We linearly interpolate
the values of trade in services per country/sector to
maximize the number of observations. Furthermore,
for a number of observations we use data from the
previous years. Leaving out the supplemented
data does not affect any of our results, except that
trade with developing countries becomes significant
at the 1% instead of the 10%.
7 We linearly interpolate the data. Leaving out the interpolated data does not affect the significance of trade
from less developed countries in the regressions.
8 As Michaels et al. (2014) also note, since capital compensation is calculated as a residual, it could be
negative. We replace values by zeros if negative (3% of total observations). We calculate the indicator by
multiplying ICT capital compensation as a share of total capital compensation by capital compensation, and
divide this by value added, where we have placed capital compensation and value added in real dollars using
OECD information on exchange rates. We have to use the EU-KLEMS March 2008 version for Portugal.
We combine the 1994–1996 waves for Ireland where we recalculate the earnings information to 1995 levels
using information on inflation from the
World Bank (2013
and the level of wage coordination. The first one is measured as the proportion of
employees covered by wage bargaining agreements. For the level of wage coordination, we
use an index where a higher number indicates a more centralised level of wage
coordination.9 Both measures are taken from the ICTWSS database
.10 The study
accounts for a country’s strictness of employment protection legislation, relying on
). To examine the effect of left-wing governments, we use the percentage of total
cabinet posts held by left-wing parties from the Comparative Political Data Set
(Armingeon et al. 2012)
Unfortunately, there is no pooled time-series cross-sectional data available for our
labour market variables at the sectoral level. Arguably, labour market policies or
institutions are generally set at the country level, even though there can be sectoral differences for
instance in trade union influence. Evidence for labour market institutions affecting earnings
inequality would come in our framework from a significant conditional correlation
between a country-level labour market variable and a general trend in inequality levels
across sectors within that country.
Last, to account for more general economic conditions at the country level, we include
the unemployment rate. As low-skilled workers are more substitutable than high-skilled
workers, the bargaining position of low-skilled workers is more directly and more
9 For the level of wage coordination, there are five values, where the lowest (1) indicates fragmented wage
bargaining, and the highest (5) centralised (informal or formal) bargaining by peak associations or
monopolistic union confederations or influential large firms.
10 For Ireland there are only 3 observations available for bargaining coverage in the fourth version of
ICTWSS; the first observation is for 2000. We use the third ICTWSS version for this country and we
interpolate the data. The correlation between the linearly interpolated series from the third and fourth
version for the 9 overlapping observations is 0.89.
disadvantageously affected by unemployment
(Pontusson et al. 2002)
unemployment can be expected to be positively associated with earnings inequality.
Unemployment rates are taken from the
Labour Force Statistics.11
To explore the patterns in sectoral earnings inequality, the study relies on pooled time
series cross-section regression analysis. To account for unobserved heterogeneity
stemming from the fact that for a number of possibly relevant drivers of earnings inequality data
are not available, the model contains unit fixed effects. Given the structure of our data with
sectors in countries as our unit of observation, we include sector-by-country fixed effects.
In the sensitivity analysis presented below, we also examine specifications with separate
sector and country fixed effects and with random effects. Furthermore, we include wave
fixed effects. The estimated equation of the model is:
EIijt¼a0 þ b0xijtþb1zjt þ lij þ ht þ eijt
where i = sector, j = country and t = wave. EI denotes the level of earnings inequality.
a0 is the intercept. The vector of independent variables at the sectoral level is denoted by x,
including variables for international trade, technological progress, and sectoral value
added. The vector of independent variables at the country level is denoted by z. This vector
includes variables for the bargaining coverage rate, the level of wage coordination, the
strictness of employment protection legislation, the political colour of the government and
the unemployment rate. l represents the sector-by-country fixed effects and h are the wave
fixed effects. The error term e is assumed to follow an i.i.d. normal distribution with mean
zero. We estimate Eq. (1) using a fixed effects regression, with wave fixed effects and
standard errors clustered at the country level.
4 Empirical Analysis
4.1 Descriptive Statistics
We start by showing descriptive statistics for our main sectoral variables for around 1985,
1995, and 2005.12 The developments in sectoral earnings inequality are shown in Fig. 1.
The sectoral levels are pooled for all countries.13 We draw two general conclusions from
First, there is substantial variation in levels of inequality across sectors. The most
equally distributed sector, mining and quarrying, has a Gini coefficient of around 0.22
pooled across countries and years, whereas agriculture has a Gini coefficient of around
0.40. We find a similar large spread if we focus on a single country (results not shown
here). To put this in perspective, we also calculate the Gini coefficient using the same
11 Summary statistics for all variables are provided in ‘‘Appendix 1’’ of Table 5. We provide additional
summary statistics for the bargaining coverage across countries in Table 6.
12 We show information for around 2005 rather than 2007 to maximize data coverage—we do not have
information for Sweden and Czech Republic for 2007 (see Table 1).
13 Figure 1 barely changes if we restrict the sample to the four countries for which we have data for all
periods (Denmark, Germany, Finland, and the US). Inequality within the manufacturing of minerals in 1985
then becomes more pronounced.
income definition at the country level (results not shown here). The country with the
highest level of inequality pooled over time is the US, with a Gini of around 0.40. Earnings
are most equally distributed at the country level in Denmark with a Gini of around 0.26.
The fact that there is on average as much spread in sectoral levels of inequality within
countries, as there is in levels of country-level inequality, acknowledges the importance of
a sectoral perspective for our understanding of earnings inequality.
A second conclusion is that sectoral earnings in general have become more dispersed
over time. In 15 out of 19 sectors earnings were more dispersed in 2005 than around 1985
or 1995. In four sectors, in particular in agriculture which has the highest level of earnings
inequality on average, the level of dispersion decreased over time. This general trend in
rising levels of inequality across sectors seems to point to a common factor at the
countrylevel that has contributed to rising levels of earnings inequality. Particularly interesting
also is the comparison between the manufacturing sector, highly exposed to international
trade, and the community sector, sheltered against international trade. Contrary to what we
would expect from the literature, we see on average higher levels and a stronger increase of
inequality in the sheltered community sector than in the manufacturing industry.
Table 2 shows the degree to which sectors are exposed to trade with all countries, trade
with less developed countries, and technological change. With regard to the exposure to
trade with all countries, there is considerable variation across sectors. The highest levels
are found in the mining sector and the manufacturing sector. Within the manufacturing
ICT capital compensation (share in sectoral value added)
Source Import and export from OECD STAN, share of ICT in sectoral value added from EU-KLEMS
Information is pooled for countries for which data are available. 1985 data for trade with developing
countries is not available
sector, the textile, the machinery and the transport sectors are the most exposed to
international trade. Also the increase over time in international trade differs per sector. The
largest increase took place in the manufacturing of textile and manufacturing of transport.
In contrast, the amount of international trade barely rose in the utility sector.
With regard to trade with less developed countries, the highest sectoral exposure to this
type of trade can be found in the mining sector, which is the same as for trade with all
countries. The second-highest exposed sector is the textile sector. Most sectors became
more exposure to trade with less developed countries over time.
Also for the levels and developments of exposure to technological change we can see
differences between sectors. The starkest increases took place in the transport and
telecommunications sector. Within the manufacturing sector, technology changed the most
in the paper, metals, machinery and transport sectors. The ICT capital compensation
decreased in the wholesale sector and in the manufacturing of minerals sector.
4.2 Regression Results
The results of the regressions are presented in Table 3. The models indicate that the
sectoral exposure to international trade with all countries is not significantly associated
with sectoral earnings inequality. Hence, these results do not seem to provide evidence for
the hypothesis that sectors more exposed to international trade have a more dispersed
earnings distribution. Model 2 and Model 4 indicate a positive association between
technological progress and sectoral earnings inequality. This result is in line with the
hypothesis that skill-biased technological change leads to higher levels of earnings
inequality. However, as will be discussed below, this result is not robust.
Interestingly, the bargaining coverage rate is negatively associated with sectoral
earnings inequality. This corresponds to our hypothesis that bargaining coverage is negatively
associated with earnings inequality. When more employees are covered by bargaining
agreements, there is less variation in wages between workers. The unemployment rate is
negatively associated with earnings inequality. Yet, when we add the other independent
variables in Model 4, the coefficient for the unemployment rate is not significant anymore.
Finally, we do not find significant associations for the sectoral value added, the level of
wage coordination, the strictness of employment protection legislation, and for the political
ideology of governments.
Subsequently, we focus the analysis on trade with less developed countries. The results
are presented in Table 4. Model 1 indicates that trade with less developed countries is
positively associated with sectoral earnings inequality. Model 2 shows that this result holds
when the other independent variables are added to the model. These results suggest that
sectors more exposed to trade with less developed countries have a more dispersed
As discussed above, for trade with less developed countries, fewer observations are
available. This implies that the sample that we use in the analyses in Table 4 differs from
the sample that we use in Table 3. This difference in samples could possibly affect the
results. Therefore, we also run the regression specification of Model 4 in Table 3 with the
sample of Table 4. The results are presented in Model 3 of Table 4. The results for
international trade with all countries of Model 3 in Table 4 are highly comparable to the
results for international trade with all countries of Model 4 in Table 3. This indicates that
Estimated using fixed effects regression with wave fixed effects and standard errors clustered at the country
p values in parentheses * p \ 0.1; ** p \ 0.05; *** p \ 0.01
the results for trade with all countries are not affected by the different samples. In contrast,
the results show that the coefficient technological change is not significant anymore and
Model 3 of Table 4 suggests that this is the result of taking a different sample. The results
for the other variables in Model 3 in Table 4 are comparable to the results of Model 4 in
4.3 Sensitivity Analyses
To examine the robustness of our results, we perform a number of sensitivity analyses, the
results of which are shown in ‘‘Appendix 2’’. First, we use an alternative indicator for
international trade (Table 7). Instead of the sum of imports and exports, we include imports
and net imports (imports minus exports) separately in the regression
(as in Mahler 2004)
again expressed as a percentage of sectoral value added. The results presented above are
replicated, both for international trade with all countries and for trade with less developed
countries. For net imports the results show a positive and significant association with
Second, we examine the robustness of the results for technological change by including
the second technological change indicator available in the EU-KLEMS dataset at the
sectoral level, namely, the contribution of ICT capital to value added growth in percentage
points (Table 8). All the results presented above are replicated.
As in other recent inequality studies
(e.g. Michaels et al. 2014)
we do not account for
increased international capital mobility as another aspect of globalisation (Mahler 2004) in
our regressions because of limited data availability. As sensitivity tests, we include sectoral
data for total, inward, and outward foreign direct (FDI) investment positions (Table 9). We
again find estimates in line with our main results. Including FDI does not affect the
significance of trade with developing countries. In some models, we find positive and
significant coefficients for the FDI variable. However, as we lose up to half of the number
of observations, we do not prefer these models as our main estimations.14
Subsequently, we examine the dependence of the results on different specifications of
the empirical model (Table 10). First, estimations without wave dummies produce
comparable results for the main variables of interest. Second, instead of estimating a model
with unit-fixed effects (sector-by-country pairs), we run a pooled cross-section OLS
estimation with separate country and sector fixed effects (and wave effects). The results are in
line with the results for our main models. Third, random-effects models yield results,
which are in line with our main results.
Finally, we examine whether the results might be driven by developments in single
sectors (presented in Tables 11 and 12). When we run the regressions and drop the sectors
one by one, the results presented above are largely replicated.15
14 Moreover, FDI flows, which entail an even larger loss of data, are not significant (results available upon
15 When we omit the agriculture sector (AtB) and the manufacturing of minerals (26), technological change
becomes insignificant, whereas it becomes significant at the 1% without mining (C). Furthermore, trade with
less developed countries becomes (borderline) insignificant when we leave out agriculture (AtB),
manufacturing of food (15t16), chemicals (23t25), metals (27t28), transport (34t35), or construction (F), whilst it
becomes significant at the 5% when leaving out mining (C) or manufacturing other (36t37).
This study aims to contribute to our understanding of developments and drivers of earnings
inequality by using newly assembled sectoral data for eight countries between 1984 and
2007. The data show a spread in levels of earnings inequality across sectors comparable to
the spread in country-level earnings inequality across countries. This highlights the
importance of a sectoral perspective for our understanding of inequality in the
crosssectional dimension. Interestingly, our sectoral dataset also reveals that sectoral earnings
have generally become more unequal over time. This then suggests that a more common
factor is at work in pushing inequalities in the time series dimension.
With our sectoral data, we are able to examine drivers of earnings inequality at a more
detailed level based on longitudinal data across 8 OECD countries. The results provide
only limited support for the argument that international trade leads to higher levels of
earnings inequality. When we focus the analysis on trade with less developed countries—
rather than the sectoral exposure to international trade in general—we find a positive
association between trade and earnings inequality. This is in line with the argument that
when developed countries engage into trade with less developed countries, mainly the
lowskilled employees suffer from the increased competition
(e.g., Burgoon 2001; Thewissen
and Van Vliet 2015)
. As such, these findings are in line with and complement the findings
of other sectoral studies on earnings inequality that only include two time periods and
focus on particular determinants of earnings inequality
(Mahler et al. 1999; OECD 2011a;
Michaels et al. 2014)
With regard to technological change, our findings provide mixed evidence for the
hypothesis that skill-biased technological change increases earnings inequality. Only for
the larger sample for which we have data on trade with all countries, we find significant
positive associations between technological change and earnings inequality. The mixed
finding does not seem to be in line with the sectoral evidence reported by
Michaels et al.
, who find a consistently significant effect. However, Michaels et al. focus on
polarisation in skill demand rather than earnings inequality. Hence, a relevant question for
future research is in what way polarisation in skill demand seeps through to earnings
inequality at the sectoral level.
Interestingly, our results bring back labour market institutions at the country level as
important drivers of earnings inequality. This corresponds with the fact that our sectoral
data reveal a more general trend towards rising inequality at the sectoral level over time.
The bargaining coverage rate is consistently found to be negatively associated with sectoral
earnings inequality, which corresponds to the hypothesis that waning trade union power is
an explanation for rising inequality
(e.g., Koeniger et al. 2007)
. When fewer employees are
covered by bargaining agreements, there is more variation in wages between workers. To
obtain an even more fine-grained understanding of how the declining influence of trade
unions affects sectoral earnings inequality, sectoral data on bargaining coverage rates
would be needed. Unfortunately, such data are currently not available with sufficient detail
for our set of countries over time
(e.g. Pinto and Beckfield 2011; Kristal 2013)
generally, our results may be affected by the fact that for many variables, adequate
indicators at the sectoral level for analyses across countries and over time are not available.
Even though we used conventional—and the currently best available—measures for
technological change, these measures might only be limited proxies for the developments
of skill-biased technological change.
Acknowledgements We thank the editor, two anonymous reviewers, Ben Ansell, Jason, Beckfield, Koen
Caminada, Janet Gornick, Kees Goudswaard, Torben Iversen, and Brian Nolan for their helpful comments.
We gratefully acknowledge financial support from China Postdoctoral Science Foundation [grant number
2016M591645] and Shanghai Pujiang Program. The usual disclaimer applies.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
Appendix 1: Descriptive statistics
See Tables 5 and 6.
Source ICTWSS 4.0
, except for Ireland, which is taken from ICTWSS 3.0 and linearly
interpolated (see also footnote 10 in the main text)
The bargaining coverage rate is defined as the proportion of employees covered by wage bargaining
agreements. The data cover the following years
Wave 1: Denmark (1987), Finland (1987), Germany (1984), Sweden (1987), UK (1986) and US (1986)
Wave 2: Denmark (1992), Finland (1991), Germany (1989), Sweden (1992) and US (1991)
Wave 4: Denmark (2000), Finland (2000), Germany (2000), Sweden (2000), UK (1999) and US (2000)
Wave 6: Denmark (2007), Finland (2007), Germany (2007), Ireland (2007), UK (2007) and US (2007)
Appendix 2: Sensitivity Analyses
See Tables 7, 8, 9, 10, 11 and 12.
p values in parentheses * p \ 0.1; ** p \ 0.05; *** p \ 0.01
Technology (ICT capital compensation as share of sectoral value added)
Technology (contribution of ICT capital to value added growth in percentage
p values in parentheses * p \ 0.1; ** p \ 0.05; *** p \ 0.01
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* ** * *
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.()611 20 .3000 .(1203 .48428 .(4008 .3121 .(0309 .107- .(0001 .-0105 .(3803 .117- .(8300 .000- .(1301 .107- .(9004 .51631 .(0001 545 .1804
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3 5 6 6 0 1 1 0 1 7 4 5 0 1 1 6 2 0 8
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E 0 ( 4 ( 1 ( - ( - ( 0 ( - ( - ( 3 ( 5 0
* * * *
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1 to t3 00 .1 .4 .0 07 .3 .0 .0 .0 .8 .0 .8 .0 .3 .0 .0 .60 .00 58 .20
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