The Australian National University
Centre for Economic Policy Research
DISCUSSION PAPER
Why Has Wage Inequality Risen Most Where Wage
Shares Have Fallen Least?
Declan Trott
DISCUSSION PAPER NO. 685
October 2013
Keywords: wage inequality, wage share, labour share, bottom 99%
JEL codes: D33, E25, J31
ISSN: 1442-8636
ISBN: 978-1-921693-69-4
WHY HAS WAGE INEQUALITY RISEN MOST WHERE WAGE SHARES HAVE
FALLEN LEAST?
Declan Trott 1
Most developed countries have experienced rising wage inequality and falling wage shares,
which are often blamed on the same forces – globalisation, technical change, and weakening
labour market institutions. This paper shows, however, that wage inequality has risen the
most in those countries where wage shares have fallen the least, and vice-versa. It is difficult
to plausibly account for this pattern using a range of proxies for the above and other
suggested explanations. Excluding the top 1% of incomes from the wage share makes little
difference to the results.
Keywords: wage inequality, wage share, labour share, bottom 99%
JEL codes: D33, E25, J31
1 Department of Employment, 50 Marcus Clarke Street, Canberra, ACT 2601, Australia. Email:
[email protected]
Thanks for helpful comments especially to Paul Chen and Andrew Leigh; also Heather Anderson, Robert
Breunig, Greg Connolly, Tanuja Doss, Steve Dowrick, Shane Evans, Dana Hanna, Andrew Leigh and Thomas
Lemieux, and seminar participants at the Australian National University and the Australian Conference of
Economists. The opinions expressed in this paper are solely the author’s and are not those of the Department of
Employment or the Australian government.
INTRODUCTION
In the last few decades, wage inequality has risen considerably in many developed countries,
particularly the United States. The causes of this rise have been the subject of much debate:
international trade was initially the main suspect, followed by skill biased technical change.
More recently, the role of labour market institutions has received attention, as has immigration.
All of these interpretations still have their defenders and detractors – see Gordon and DewBecker (2008) for a survey, or Machin (2008) for a more international perspective.
Over the same period, the share of labour compensation in gross domestic product, a.k.a.
the wage share, has been falling in many countries, particularly in continental Europe.
Interestingly, the most common explanations fall into the same categories as those proposed for
increasing wage inequality: globalisation, technology, and institutions (e.g. Blanchard and
Giavazzi 2003, Jaumotte and Tytell 2007).
It would seem natural to analyse these two trends together rather than separately. After
all, they are both outcomes of the same structural process: the interaction between labour supply,
labour demand, and institutions that determines employment and wages. Moreover, exactly the
same theories have been advanced to explain both trends. Yet they have rarely been considered
jointly. Checchi and Garcia-Penalosa (2008, 2010) are an exception, integrating wage inequality
and the wage share in their analysis of overall income inequality. They do not, however, make
any comparison between the trends in wage inequality and wage shares across countries.
Such a comparison is at the heart of this paper. In a sample of twelve OECD countries
from 1975-2009, wage inequality has risen in most, and the wage share has fallen in all.
Surprisingly, however, wage inequality has risen the most in precisely those countries where
wage shares have fallen the least, while wage shares have fallen the most where inequality is
stable or even falling.
Individually, these differences have not gone unnoticed in the literature. While the
2
emphasis has been on rising wage inequality in the United States and falling wage shares in
Europe, there has been some discussion of the dogs that did not bark – stable European wage
inequality and the muted fall in the wage share in the US. Since these have been treated as
separate subjects, however, their full significance has possibly not been appreciated. Hornstein
et. al. (2005) mention the contrasting trends in the two variables, but only for the United States
compared to continental Europe as a whole. It is shown here that the relationship holds for a
larger sample which includes several continental countries individually, as well as other Englishspeaking and Asian economies.
The most common explanations for rising wage inequality and falling wage shares,
however, sit oddly with the inverse correlation between the strength of the two trends. Whether
one blames the effects of globalisation, technological change, or weakening labour market
institutions, it seems logical that, if both trends are being driven by the same forces, bigger rises
in wage inequality should be correlated with bigger, not smaller, falls in the wage share. A more
complex story involving additional variables, or perhaps interactions between shocks and
institutions, seems to be required.
To explore these possibilities, I estimate panel regressions with the wage share and wage
inequality as dependent variables, using a range of proxies for globalisation, technical change
and institutions, as well as education, capital accumulation and the output gap. The results should
be seen as analogous to a growth accounting or inequality decomposition exercise, rather than an
attempt to identify the exogenous ‘deep determinants’ of wage inequality and the wage share.
These models, however, do not even provide a convincing proximate explanation of the puzzle.
Finally, extending the work of Glyn (2009) for the United States, I calculate bottom 99%
wage shares using data from the World Top Incomes Database. These give a better picture of
what is happening to typical workers than wage shares inclusive of the top 1%, whose incomes
have expanded dramatically in some countries. The adjustment does not, however, substantially
3
change the results: it increases the size of the falls in the wage share, but the relationship with
changes in wage inequality (and its resistance to plausible accounting) remains unchanged.
The paper is organised as follows. Section I documents the puzzle that the rest of the
paper seeks to explain. Section II discusses popular explanations for rising wage inequality and
falling wage shares. Section III describes the explanatory variables and estimation technique for
the panel regressions. Section IV presents the results. Section V details the bottom 99%
adjustment to the wage share and its consequences. Section VI concludes.
I. THE PUZZLE
Wage inequality has risen most where the wage share has fallen least. To ensure the robustness
of this key result, two different measures of wage inequality and three of the wage share are
used. For wage inequality, the ratio between the 9th and 1st deciles of full-time earnings, for all
persons and for men only, is taken from the OECD. For the wage share, one series is also from
the OECD, and two alternatives (market price and factor cost) from AMECO. (A more detailed
description of these and all subsequent variables is given in the Appendix.) Selecting only those
countries for which both variables are available prior to the 1990s gives an unbalanced panel
covering twelve countries from 1975-2009. The endpoint is chosen to match the availability of
additional variables used later in the paper.
The evolution of wage inequality over this period is shown in Figure 1. It is rising or
stable over time in most countries, although France shows a perceptible decline. (South) Korea
exhibits a striking U-shaped pattern, and is suspiciously uniform in the first decade. The both
sexes and men only series track each other fairly closely, with the men showing, if anything, a
slightly greater rise in inequality.
<Figure 1 about here>
The three measures of the wage share are shown in Figure 2. All display similar declines
4
over time in all countries. New Zealand has an unusually low, and Korea an unusually high,
wage share. The level of the AMECO market price share is lower, as expected (see Appendix).
<Figure 2 about here>
The focus of this paper is on how the trends in these two variables have related to each
other over time. I therefore calculate the change in the three-year centred moving averages of
both from 1981-2004 and 1987-2007. These time periods were chosen as having the maximum
number of available countries out of all periods of at least two decades, for which the three-year
averages do not overlap. Table 1 shows the correlations between these changes for each pair of
wage inequality and wage share measures, which are all positive – for all combinations of
variables and time periods, wage inequality has risen most in those countries where wage shares
have fallen least – and mostly highly significant. The significance levels are even more
surprising on reflection, since even the null of zero correlation that is being rejected would be
odd if rising wage inequality and falling wage shares were being driven by the same forces.
Furthermore, use of a one- rather than two-sided alternative would halve the p-values again. The
correlations are higher for the 1981-2004 period than for 1987-2007. Among other gaps, all
correlations using the AMECO wage share exclude Korea, and 1981-2004 Italy and New
Zealand, because of missing data.
<Table 1 about here>
As a final check, I calculate OLS time trends for the OECD wage share and all persons
decile ratio for each country for 1980-2008. The correlation between the wage share and
inequality coefficients is 0.80, based on 12 observations.
Figure 3 illustrates the pattern for the 1981-2004 time period, all persons decile ratio, and
OECD wage share, which are used in the rest of the analysis. 1981-2004 is longer than 19872007, closer in its results to that of the time trends, and allows greater availability of the other
data used in the regressions. Since the correlation between changes in wage shares and inequality
5
is higher in the earlier period, it is also more of a challenge to explain. The decile ratio for all
persons is preferred to that for men, and the OECD wage share to the AMECO versions, both for
their greater availability and the desirability of using a common data source.
<Figure 3 about here>
Visually, the pattern described in Table 1 is clearly evident. Where wage inequality has
risen the most, in Denmark, the United Kingdom, and the United States, the wage share has
fallen the least, by only a few percentage points. By contrast, wage inequality has actually fallen
in Finland, France, Japan, and Korea, which have experienced much larger, often double-digit
falls in the wage share. While there is clearly a general trend towards falling wage shares and
rising wage inequality, the variation between countries is much more striking than these overall
trends. One may speak roughly, as do Hornstein et. al. (2005), of a contrast between the US and
continental Europe, but the pattern is wider. Not only do the additional English-speaking and
Asia-Pacific economies fit, but there is almost as much variation within Europe as there is in the
whole sample.
Why is this surprising, and potentially important? If rising wage inequality and falling
wage shares were being driven by the same forces, such as a common shock from globalisation
or technological change, and these forces were similar in their impact across countries, one
would expect all countries to have a similar experience: Figure 3 should be a single dot, or tight
ball. If the same forces were at work, but operating with different strengths or timing across
countries – some countries may be more or less open to the world economy, adopt new
technology at a different pace, or have different institutions – Figure 3 should slope in the
opposite direction, with large rises in wage inequality correlated with larger falls in the wage
share. Even if the trends were driven by totally independent forces, or if the data was of such low
quality as to be meaningless, one would expect merely a lack of any discernible pattern. The
result may imply either that the causes of declining wage shares and rising inequality are distinct
6
and negatively correlated, or that given shocks have very different effects in different countries.
II. STORIES
A variety of explanations have been advanced for rising wage inequality and falling wage shares.
While the wage inequality literature is vast, its main themes are fairly few. The following
discussion relies on the surveys of Gordon and Dew-Becker (2008) and Machin (2008). Since
the wage share literature is smaller, relevant papers are cited individually.
Globalisation incorporates capital mobility, international trade, and immigration, since
the logic is similar in all cases – they tend to help the relatively abundant factor(s) in an
economy, and hurt the relatively scarce factor. This may be through factor mobility directly
changing relative factor supplies, or through trade replicating these effects as in the HeckscherOhlin theorem. As Jaumotte and Tytell (2007:5) put it: ‘. . . the effective global labor supply
quadrupled between 1980 and 2005 . . . Advanced economies can access this increased pool of
global labor both through imports of goods and services and through immigration.’ In the case of
a developed country, capital is abundant relative to labour, and skilled labour relative to
unskilled. Therefore, it is argued, globalisation will reduce the wage share and increase wage
inequality. Making these assertions simultaneously would logically require consideration of at
least a three-factor production function, but this is rarely done explicitly. Another caveat is that
factor mobility would not necessarily lead to a change in the wage share, as opposed to wage
rates, depending on the elasticity of substitution between capital and labour. These
complications are explored below in the discussion of capital accumulation.
Technology has often been cited as a driver for wage inequality – skill biased technical
change has raised the demand for skilled workers relative to unskilled, while the supply has not
kept up. Hence, wages for skilled workers have increased relative to those for unskilled workers.
It is also possible that technological change affects the wage share, although the effect is
7
theoretically indeterminate, depending on whether capital or labour is being augmented/saved,
and, again, the relevant elasticities of substitution. Bentolia and St Paul (2003) and Guscina
(2006) both argue that technical change has been biased in favour of capital in the relevant time
period.
Institutions, like globalisation, is something of a catch-all term. With respect to the labour
market, it usually refers to various laws and regulations (e.g. concerning minimum wages,
unions, employment protection, and unemployment benefits), as well as social norms regarding
fair pay levels. In general, these institutions are considered to alter the relative power of different
parties within some kind of implicit or explicit bargaining framework. A stronger bargaining
position for either party leads almost tautologically to higher income. Strong institutions are
generally considered to favour lower paid workers, so that their weakening is seen as a cause of
rising wage inequality. Similar arguments have been advanced for labour as a whole vs. capital,
so that weaker institutions would lower the wage share (e.g. Blanchard and Giavazzi 2003, and
Bental and Demougin 2006).
Education is almost universally cited as a factor affecting wage inequality. If an increase in
the demand for skilled labour tends to increase wage inequality, an increase in the supply of
skilled labour should logically reduce it. Daudey and Decreause (2006) argue that higher levels
of education will also increase the labour share by making workers more mobile. While
education levels are almost universally rising, it is suggested that it is ‘losing the race’ with the
other factors mentioned above, hence contributing by omission to higher wage inequality (and
possibly lower shares).
These explanations have simple, appealing theoretical arguments behind them, and are
given plausibility by the general direction of change in the world economy. Yet it is precisely
because they can explain both rising wage inequality and falling wage shares that they seem
unpromising as reasons why the strength of the two trends is inversely correlated across
8
countries. If we are interested in explaining this correlation, we must look elsewhere, or argue
that these forces are not uniform in their effects. For example, shocks from globalisation or
technical change may affect countries differently depending on their institutions. We now turn to
several alternative possibilities.
Product market competition may be an important factor. If firms have monopoly power,
they can increase their profits by restricting output (and thus employment and wage income)
relative to the competitive level. Conversely, more competitive product markets should increase
the wage share, although the effect is less clear if monopoly rents are shared with unions (Ripatti
and Vilmunen 2001). The effect on wage inequality is less obvious, but Lindsey (2009) argues
that more competitive product markets may undermine institutions such as unions that support
wage compression.
Capital accumulation could affect the wage share depending on the elasticity of
substitution between labour and capital. A value of one for this elasticity (as in a Cobb-Douglas
production function) implies constant factor shares, irrespective of the capital-labour ratio. If the
value is below one, the wage share rises with the capital-labour ratio; above one, and it falls.
This conclusion is, however, conditional on accumulation being exogenous with respect to
technical progress. If it is instead a response to labour augmenting technical progress, factor
shares will remain constant whatever the elasticity of substitution (Arpaia, Perez and Pichelmann
2009). Andersen, Klau and Yndggard (1999), Chirinko (2008), and Checchi and Garcia-Penelosa
(2010) all argue that the literature supports an elasticity of less than one, although Bentolia and
Saint-Paul (2003) present a range of estimates on either side. Regarding wage inequality, capital
and skilled labour are generally considered to be complements (Hamermesh 1993), so that
capital accumulation should increase the relative demand for, and hence the wages of, skilled
labour, all else being equal.
A combination of these two mechanisms – an elasticity of substitution between capital
9
and labour of less than one, and capital skill complementarity – might seem to imply that capital
accumulation can increase both wage inequality and the wage share, thus providing a partial
solution to our puzzle (although we would still require some other reason why wage shares are
generally falling). However, insights from a two-factor setting do not always transfer smoothly
when three factors are considered.
For example, a popular way of modelling capital-skill complementarity is to aggregate
capital K and skilled labour S, and then combine this aggregate with unskilled labour U i.e.
Y  gU , f K , S  , where f and g are CES functions (Krusell et. al. 2000). This function displays
capital-skill complementarity as long as the elasticity of substitution between K and S in f is less
than the elasticity of substitution between U and f K , S  in g. Unfortunately, the effect of
capital accumulation on the wage share in this setting is ambiguous (Arpaia, Perez and
Pichelmann 2009). By contrast, the more conventional approach of aggregating skilled and
unskilled labour i.e. Y  gK , f S ,U  makes the labour share simple to compute, but eliminates
capital-skill complementarity.
A more complex interaction between capital accumulation and institutions is presented
by Hornstein et. al. (2005), who incorporate vintage capital in a search model, and show that the
effects of an increase in the rate of capital-embodied technical progress depend on the level of
regulation of the labour market. Qualitatively, they find that the more regulated ‘European’
economy should experience a lower wage share, higher unemployment and lower wage
inequality relative to the less regulated ‘US’ economy. Quantitatively, their simulations generate
a large fraction of the observed divergence in the labour share and unemployment, but not in
wage inequality. Although not capable of accounting for the puzzle, it is an interesting example
of the possible interactions between shocks and institutions mentioned above.
Top incomes are a potential complicating factor. The wage share can include some very
high incomes that might more properly be classed separately from the income of the mass of
10
workers. We may broadly say that the UK and US have seen greater rises in wage inequality and
smaller falls in the wage share when compared with most of Europe and Japan. It is well known
that these are also the countries where the trend towards increasing CEO pay has been the
strongest (Gabaix and Landier 2008). More generally, many refer to a distinct Anglo-Saxon form
of capitalism, with features such as a reliance on financial markets over banking, more dispersed
shareholding (Morck 2009), and more lawyers (Karabel 2010 Fig. 11). Perhaps, in marginal
product terms, there should have been a shift from labour to capital everywhere, but in Englishspeaking countries a large portion has been siphoned off by rent-seeking executives, financiers,
and lawyers. Alternatively, the earnings of ‘superstars’ should have been rising everywhere at
the expense of the average worker, but have been repressed in Europe and Japan by some
combination of social norms, regulation, higher taxes, and smaller native language labour
markets, so the benefits have flowed to the owners of capital instead.
III. DATA AND METHODS
Explanatory variables were selected based on a mix of theoretical considerations, empirical
support from previous studies, and data availability. More details, and descriptive statistics, are
given in the Appendix. Because of data availability and quality issues, Korea was not included in
the regressions.
The preferred measure of globalisation is the constant price trade share of GDP, from the
Penn World Table. It is commonly used in the literature e.g. by Guscina (2006) and Bassanini
and Manfredi (2012). While there has been criticism of the use of this variable in growth
regressions, it seems more reasonable in this context, since reverse causation from wage shares
or inequality to trade seems less likely than from growth, and the use of country fixed effects and
logarithms controls for the tendency of smaller economies to have larger trade shares. Price
rather than quantity-based measures may also be used, as in Jaumotte and Tytell (2007) and Ellis
11
and Smith (2010). I follow the latter in using the BIS real exchange rate index as an alternative
to the trade share. Finally, the sum of foreign assets and liabilities (as a share of GDP) is used as
an indicator of financial or capital market globalisation, following the ILO (2013). Immigration
was not included, since its effects should be captured by the capital-output ratio and education
measures.
Institutions are proxied by a range of variables: union density, bargaining coverage, and
wage coordination from the ICTWSS database, and the unemployment benefit replacement rate,
from Van Vliet and Caminda (2012). As argued by Booth (1995:5) and Checchi and Visser
(2009), bargaining coverage gives a better indication of the strength of the institutional wage
floor than the more commonly used union density. In France, for example, collectively bargained
wages and conditions cover a large and rising majority of the workforce despite low and falling
union membership. As well as being included in levels, the institutional variables are also
interacted with a time trend, to capture the possibility that a common shock might affect
countries differently depending on their institutions. Minimum wages were not used, since
Denmark, Finland, Sweden, and the UK lacked a national minimum wage for most or all of our
period.
Education is measured by the average years of education and the share with some tertiary
education in the prime working age population, from Barro and Lee (2010). The International
Institute for Applied Systems Analysis (IIASA) provides an alternative source for the latter
variable.
Technology and capital accumulation are proxied by total or multifactor productivity (TFP
and MFP, from AMECO and the OECD respectively) and the capital-output ratio (K/Y, from
the OECD), following Bentolia and Saint-Paul (2003) and Bassanini and Manfredi (2012).
(Theoretically, we are really interested in factor-specific technical change, but this is difficult to
calculate and not widely available. See e.g. Ripatti and Vilmunen 2001.)
12
The OECD’s output gap is also included, since it is generally believed that the wage share
is anticyclical.
Some measure of product market competition would seem desirable, but product market
regulation was found to be insignificant by Jaumotte and Tytell (2007) and Ellis and Smith
(2010). As for other measures, Przybyla and Roma (2005) conclude unhelpfully that ‘amongst
the proxies used mark-up, measured as the inverse of the labour income share, performs best.’
Also not included is any variable for the sectoral makeup of each economy. Azmat,
Manning and van Reenen (2011) argue that privatization has reduced the wage share within
network industries, and speculate that the shift out of manufacturing may be important.
Empirically, however, Lawless and Whelan (2007), Glyn (2009), and Bassanini and Manfredi
(2012) all find that the fall in the wage share is largely a within sector phenomenon.
Finally, the contribution of top incomes is controlled for by the calculation of bottom 99%
wage shares, following the work of Glyn (2009) for the United States. Data from the World Top
Incomes Database are used to calculate the earnings of the top 1%, which are subtracted from the
total economy wage share.
Estimation
In estimating regression models for wage inequality and the wage share, the interest is not
simply in the significance and size of individual coefficients (particularly as the models are
probably overfitted – e.g. the capital-output ratio may well be affected by other variables), but in
whether the whole set of variables can explain why wage inequality has risen most in those
countries where wage shares have fallen least.
The starting point for our accounting is a model with year and country fixed effects (FE)
as the only explanatory variables. This captures the differences between countries that do not
13
change over time, and changes over time which affect all countries equally, making it a natural
baseline when trying to explain differences between countries that do change over time.
The correlation between the residuals from the wage share and wage inequality
regressions is a simple indication of whether there is an unexplained relationship between the
two variables. Given the results in Section I, one would expect a strongly positive correlation in
the FE only model. The hope is that the addition of explanatory variables will reduce this
correlation.
I also calculate an R2-like measure of the models’ ability to explain the long-run changes
illustrated in Figure 3. Any observed value y it of wage inequality or the wage share for country i
and year t may be expressed as the sum of a model prediction ŷ it and residual û it . Omitting time
subscripts, the change from any year to any other in country i is therefore y i  yˆ i  uˆ i . The
fraction of (between-country) variance in the (within-country) changes explained by the model is
(1)
' R2 ' 1
Var uˆ i 
Varyi  .
This is zero for the FE only model, since the predicted change is simply the change in the year
fixed effect, which does not vary across countries.
The seemingly unrelated regression (SUR) estimator is used, with year and country. SUR
is convenient for computing residual correlation, and more sophisticated methods were not
considered to offer either a plausible identification strategy or any advantage for the more
modest accounting objectives of this paper. Bassanini and Manfredi (2012), for example, use a
range of dynamic and GMM estimators, but put most emphasis on their static FE estimates. The
fixed effects not only control for time trends and unobserved heterogeneity, but also provide
useful diagnostic information. One would hope that inclusion of additional explanatory variables
would shrink the year and country FE (relative to the FE only baseline), as some of the
differences across time and between countries were explained.
14
IV. RESULTS
Column 1 of Table 2 shows the baseline model with year and country FE as the only explanatory
variables. The correlation between the residuals from the wage inequality and wage share
equations is positive and highly significant, indicating that, after the fixed effects are removed,
wage inequality tends to be high when wage shares are high.
<Table 2 about here>
The remaining columns of Table 2 show the estimation results from a range of models
using four different proxies for labour market institutions. Table 3 shows the results using
alternative proxies for globalisation, technical change, and education. Columns 5 and 6 reestimate the models from columns 1 and 2 of Table 2 on the smaller data set for which the
OECD MFP measure is available. They also show that using the changes from 1987-2007 rather
than 1981-2004 lowers the ‘R2’s, but does not change the overall pattern of results. Some results
seem promising: the residual correlations are much lowered from the FE only model, often to
statistical insignificance, and the ‘R2’s are fairly high, especially for wage inequality.
<Table 3 about here>
Consideration of individual regression coefficients, however, belies this promise. We are
particularly interested in variables which are significant in one equation but not the other, or are
significant in both with the same sign, as only these can help explain why wage inequality has
risen the most where wage shares have fallen the least. While there are several such variables –
technology, education, and the institutional measures, as well as the capital-output ratio – many
are not plausible as causal mechanisms. One would not wish to argue, for example, that
increasing educational attainment would reduce the wage share, that doubling TFP or the capitaloutput ratio would diminish the 9/1 decile ratio by one, or that going from zero to complete
bargaining coverage in 1980 would have increased the decile ratio by nearly 0.6. The signs of the
15
estimated coefficients do not change when different proxies for institutions, globalisation,
technology, and education are used. Even their sizes (where comparable) are not wildly different,
indicating what may be termed genuine spurious correlations and not just artefacts of particular
data sources.
Examination of the fixed effects reinforces this scepticism. In the FE only model, the
year FE rise steadily for wage inequality and fall steadily for the wage share, in line with the
overall trends in these variables (the 2007 values are shown in Tables 2 and 3). In all models
with additional explanatory variables, however, the increase in the wage inequality year FE is
much larger than in column 1 – around 1, rather than 0.4, in 2007 – and they actually rise rather
than fall for the wage share – positive, sometimes double-digit values in 2007 rather than -8. In
other words, the non-FE explanatory variables predict a counterfactual overall fall in wage
inequality and rise in the wage share over the sample period! The country FE (not shown) are not
generally reduced in size by the inclusion of the explanatory variables either.
Table 5 shows some illustrative examples of specification searching, starting from the
preferred model in column 2 of Table 2 and dropping the variables with the most egregiously
implausible coefficients. The results are not encouraging. By column 2, the ‘R2’ for the wage
share has been halved. Even the remaining explanatory power depends on dismissing the
negative correlation of the capital-output ratio with wage inequality as contrary to capital-skill
complementarity and hence spurious, but accepting the negative correlation with the wage share
as a genuine causal mechanism (via a capital-labour substitution elasticity of greater than unity,
or embodied labour-saving technical progress). When the ratio is removed from the wage share
regression in column 3, the ‘R2’ goes to zero. The wage inequality equation stands up better, but
cannot explain the puzzle on its own. Other specifications not shown here, such as a ‘kitchen
sink’ model with multiple institutional and globalisation proxies, and a ‘plausibly exogenous’
model including only education and institutions, were equally unrewarding.
16
V. BOTTOM 99% WAGE SHARES
Can the removal of top earnings from the wage share alter this picture? Two different estimates
of the labour component of the top 1% income share are used. Measure A uses World Top
Income Database data on ‘Top 1% income composition-Wages, salaries and pensions’. This
method probably understates the desired adjustment by missing self-employment income.
Method B assumes that the capital-labour split within the top 1% is the same as that for the
whole economy, which probably overstates the adjustment. In both cases, the bottom 99% wage
share is calculated by subtracting the resulting number from the wage share. I.e.
share99 _A  share  wsp.top1
,
share99 _B  share( 1  top1 )
(2)
where share99_A and _B are measures A and B of the bottom 99% wage share, share is the
OECD total economy wage share, top1 is the top 1% income share, and wsp is the wages,
salaries and pensions composition. For example, if the wage share is 60% of GDP, the top 1%
income share is 10%, and 60% of this is attributed to labour via wsp or share, then the bottom
99% wage share is 54%. The method is not strictly identical to that of Glyn (2009), since it
subtracts the wage incomes of the top 1% of income earners rather than the top 1% of wage
earners.
<Figure 4 about here>
Figure 4 shows the two measures of the bottom 99% wage share along with the unadjusted
OECD wage share. The adjustment clearly makes a bigger difference in some countries than
others, and produces a noticeably bigger fall than the unadjusted OECD measure in the UK and
US. As expected, measure B usually produces a lower wage share, especially in Italy.
<Figure 5 about here>
Figure 5 recreates Figure 3 with the changes in the bottom 99% wage share (measure A)
superimposed. While the bottom 99% wage shares have generally fallen more over time, the
17
differences between countries, and the correlation between larger rises in wage inequality and
smaller falls in the wage share, are little reduced. The last point is confirmed in Table 5 for both
time periods and measures of the bottom 99% wage share.
<Table 5 about here>
Table 6 shows the results of using measure A of the bottom 99% wage share in the FE only
and preferred models from Table 2. While the residual correlations and ‘R2’ for the wage share
are slightly lowered, the broad outline of the results remain unchanged. Only the FE only and
coverage specifications are shown, but the results for the other models (and using measure B of
the 99% wage share) are similar. Although only six observations are lost by using the bottom
99% wage share, models using the total wage share are also estimated on the smaller sample for
comparison. Again, this does not significantly change the results.
<Table 6 about here>
VI. CONCLUSION
We have examined the evolution of wage inequality and wage shares since 1975 for a sample of
twelve OECD countries. There is a trend towards higher wage inequality in most countries, and
lower wage shares in all. Considering these trends jointly rather than individually, however,
brings out an underappreciated point. They are inversely related at a country level: wage
inequality has risen most in those countries where wage shares have fallen least, and vice-versa.
This finding is robust to different measures of wage inequality and wage shares, and different
time periods of two decades and more. It is surprising, since the same forces, such as
globalisation, technology, and institutions, are often blamed for both rising wage inequality and
falling wage shares. A wide range of variables representing these forces and others proves unable
to give a convincing explanation within a panel regression framework. Removing the incomes of
the top 1% from the wage share does not improve the situation.
18
If the relationship between changes in wage inequality and changes in wage shares is still a
mystery, this paper has hopefully succeeded in the more modest aim of highlighting the
existence of this previously ignored puzzle, and the challenge it poses to popular explanations of
rising wage inequality and falling wage shares. Given that they have received so much attention
as individual phenomena (the x co-ordinate of the US data point in Figure 3 must have inspired
dozens if not hundreds of papers), the fact that the two trends have such a strong and counterintuitive relationship should excite considerable curiosity.
The puzzle may reflect shocks that have not yet been identified, interactions between
shocks and institutions that cause a common shock to have different effects in different
countries, or simply the need for better data. Product market competition, particularly, seems
potentially important, and it would seem desirable to have a better proxy for it than the wage
share itself. The effects of endogenous technological change, possibly over a longer period than
that of our data set, also invite further consideration. Wage shares in many countries were rising
before 1975 (when, unfortunately, wage inequality data is much less available), and theories of
endogenous technological change might explain the later falls in terms of induced labour-saving
invention, although a preliminary investigation showed little correlation between the size of the
initial rises and subsequent falls.
19
REFERENCES
Andersen, P., Klau, M. and Yndgaard, E. (1999). ‘Higher Profits and Lower Factor Prices: Is
Capital Allocation Optimal?’ Bank for International Settlements Working Paper No. 65.
Arpaia, A., Perez, E. and Pichelmann, K. (2009). ‘Understanding Labour Share Dynamics in
Europe’, MPRA Paper No. 15649. University Library of Munich.
Azmat, G., Manning, A. and Van Reenen, J. (2011). ‘Privatization and the Decline of Labours
Share: International Evidence from Network Industries’, Economica, vol. 79, pp. 470-492.
Barro, R. and Lee, J. (2010). ‘A New Data Set of Educational Attainment in the World, 19502010’, National Bureau of Economic Research Working Paper No. 15902.
Bassanini, A. and T. Manfredi (2012). ‘Capital's Grabbing Hand? A Cross-Country/CrossIndustry Analysis of the Decline of the Labour Share’, OECD Social, Employment and
Migration Working Papers, No. 133.
Bental, B. and Demougin, D. (2010). ‘Declining Labor Shares and Bargaining
Power: An Institutional Explanation’, Journal of Macroeconomics, vol.32(1), pp. 443-456.
Bentolia, S. and St. Paul, G. (2003). ‘Explaining Movements in the Labor Share’, Contributions
to Macroeconomics, vol. 3(1), Article 9. Berkley Electronic Press.
Blanchard, O. and Giavazzi, F. (2003). ‘Macroeconomic Effects of Regulation and Deregulation
in Goods and Labor Markets’, Quarterly Journal of Economics, vol. 118(3), pp. 879-907.
Booth, A. (1995). The Economics of the Trade Union. Cambridge: Cambridge University Press.
Checchi, D. and Garcia-Penalosa, C. (2008). ‘Labour Market Institutions and Income
Inequality’, Economic Policy, vol. 23(56), pp. 601-649.
Checchi, D. and Garcia-Penalosa, C. (2010). ‘Labour Market Institutions and the Personal
20
Distribution of Income in the OECD’, Economica, vol. 77(307), pp. 413-450.
Chirinko, R. (2008). ‘Sigma: The long and short of it’, Journal of Macroeconomics, vol. 30(2),
pp. 671-686.
Daudey, E. and Decreuse, B. (2006). ‘Higher Education, Employers’ Monopsony Power and the
Labour Share in OECD Countries’, MPRA Paper No. 3631.
Durlauf, S. and Blume, L. (2008). ‘Labour Market Institutions’, The New Palgrave Dictionary of
Economics Online.
Ellis, L. and Smith, K. (2010). ‘The Global Upward Trend in the Profit Share’, Applied
Economics Quarterly, vol. 56(3), pp. 231-55.
Gabaix, X. and Landier, A. (2008). ‘Why has CEO Pay Increased So Much?’ Quarterly Journal
of Economics, vol. 123(1), pp. 49-100.
Glyn, A. (2009). ‘Functional Distribution and Inequality’, in Oxford Handbook of Economic
Inequality, eds. Wiemer Salverda, Brian Nolan and Timothy Smeeding. New York: Oxford
University Press.
Gordon, R. and Dew-Becker, I. (2008). ‘Controversies About the Rise of American Inequality: A
Survey’, National Bureau of Economic Research Working Paper No. 13982.
Guscina, A. (2006). ‘Effects of Globalization on Labor’s Share in National Income’,
International Monetary Fund Working Paper 06/294.
Hamermesh, D. (1993). Labour Demand, Princeton: Princeton University Press.
Hornstein, A., Krusell, P. and Violante, G. (2005). ‘The Effects of Technical Change on Labour
Market Inequalities’, in Handbook of Economic Growth, eds. Philippe Aghion and Steven
Durlauf, North Holland.
21
International Labour Organisation (2013). Global Wage Report 2012/13: Wages and equitable
growth. Geneva, International Labour Office.
Jaumotte, F. and Tytell, I. (2007). ‘How Has The Globalization of Labor Affected the Labor
Income Share in Advanced Countries?’ International Monetary Fund Working Paper 07/298.
Karabel, J. (2010). ‘American Exceptionalism and the Quality of Life in the United States: Some
Preliminary Statistical Observations.’ Working Paper Series, Institute for Research on Labor and
Employment, UC Berkeley.
Krusell, P., Ohanian, L., Rios-Rull, J.-V., and Violante, G. (2000). ‘Capital Skill
Complementarity and Inequality: A Macroeconomic Analysis’, Econometrica, vol. 68(5), pp.
1029–1054.
Lindsey, B. (2009). ‘Paul Krugman’s Nostalgianomincs: Economic Policies, Social Norms, and
Income Inequality’, Cato Institute White Paper.
Lawless, M. and Whelan, K. (2007). ‘Understanding the Dynamics of Labor Shares and
Inflation’, Working Paper Series 784, European Central Bank.
Machin, S. (2008). ‘An Appraisal of Economic Research on Changes in Wage Inequality’,
Labour vol. 22(special issue), pp. 7-26.
Morck, R. (2009). ‘The Riddle of the Great Pyramids.’ National Bureau of Economic Research
Working Paper No. 14858.
Przybyla, M. and Roma, M. (2005). ‘Does Product Market Competition Reduce Inflation?
Evidence from EU Countries and Sectors’, Working Paper Series 453, European Central Bank.
Ripatti, A. and Vilmunen, J. (2001). ‘Declining labour share – Evidence of a change in
underlying production technology?’ Research Discussion Papers 10/2001, Bank of Finland.
Van Vliet, O. and Caminada, K. (2012). ‘Unemployment Replacement Rates Dataset Among 34
Welfare States, 1971-2009: An Update, Extension and Modification of the Scruggs Welfare
22
State Entitlements Data Set’, NEUJOBS Special Report No. 2.
Visser, J. and Checchi, D. (2009). ‘Inequality and the Labor Market: Unions’, in Oxford
Handbook of Economic Inequality, eds. Wiemer Salverda, Brian Nolan and Timothy Smeeding.
New York: Oxford University Press.
23
APPENDIX
Data sources
9/1 decile ratio for all persons and for men only, downloaded 10 September 2013 from
stats.oecd.org/ (Labour: Earnings: Decile Ratios of Gross Earnings). Some one and two year
gaps were filled using linear interpolation.
Wage shares OECD ‘Total economy’ downloaded 10 September 2013 from stats.oecd.org/
(Labour: Labour Costs: Unit Labour Costs: Annual Indicators: Labour Income Share Ratios);
AMECO ‘Market prices’ and ‘Factor cost’ downloaded 10 September 2013 from
ec.europa.eu/economy_finance/ameco/user/serie/SelectSerie.cfm Table 7.6: Adjusted wage
share. Both include nonwage compensation, and are adjusted for self-employment on the
assumption that the self-employed have the same average wage as employees. Since GDP at
market prices equals GDP at factor cost plus taxes less subsidies on products, the wage share at
market prices has a larger denominator (i.e. is smaller) than the wage share at factor cost.
Trade share of GDP at constant 2005 prices, downloaded 9 October 2012 from
pwt.econ.upenn.edu/php_site/pwt_index.php.
Real
exchange
rate
(2005=1),
downloaded
8
November
2012
from
www.bis.org/statistics/eer/. Converted from monthly to annual using Stata’s ‘collapse’
command.
Foreign assets and liabilities as a share of GDP, downloaded 24 June 2013 from
http://www.imf.org/external/pubs/ft/wp/2006/data/update/wp0669.zip.
Capital-output ratio and total factor productivity, downloaded 12 September 2013 from
ec.europa.eu/economy_finance/ameco/user/serie/SelectSerie.cfm (Table 8.1: Net capital stock at
constant prices: total economy; Table 8.2: Factor productivity: total economy).
Multi-factor
productivity
downloaded
23
May
2013
from
http://stats.oecd.org/
(Productivity: Multi-factor Productivity). Growth rates converted to levels with 1984=100.
24
Union density, bargaining coverage, and wage coordination from the Institutional
Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts Database
Version 4, downloaded 12 September 2013 from www.uva-aias.net/208 (variables UD, AdjCov
and WCoord). AdjCov required extensive linear interpolation.
Unemployment benefit replacement rates from Van Vliet & Caminada (2012), downloaded
24
May
2012
from
www.law.leidenuniv.nl/org/fisceco/economie/hervormingsz/datasetreplacementrates.html.
The
‘Net Unemployment Replacement Rate for an Average Production Worker’ was averaged across
single persons and one earner couples with two children. Some one year gaps were filled with
linear interpolation.
Average years of education and share with some tertiary education for the population aged
25-64 (both sexes) calculated from the Barro and Lee (2010) dataset, downloaded 6 February
2012 from www.barrolee.com/data/dataexp.htm. The alternative IIASA data for the latter
variable
(ages
20-64)
downloaded
28
May
2013
from
http://www.iiasa.ac.at/Research/POP/Edu07FP/proportion%20by%20education%20age%20sex
%201970_2050%2015Mar2010.zip. Linear interpolation was used to generate annual series
from the quinquennial data.
Output gap, downloaded 9 October 2012 from stats.oecd.org/ (OECD Economic
Projections: OECD Economic Outlook: OECD Economic Outlook No. 81 to 90: Economic
Outlook No 90: December 2011).
Top 1% income shares, downloaded 9 October 2012 from the World Top Incomes
Database g-mond.parisschoolofeconomics.eu/topincomes/. Data for Finland and the UK were
spliced to match the ‘tax data’ and ‘adults’ definitions using the ratio where they overlapped/met.
Where ‘Top 1% income composition-Wages, salaries and pensions’ is not available, the
composition from another similar country is used to calculate share99_A: the US for the UK,
25
Australia for New Zealand, and an average of France, Italy and the Netherlands for Denmark,
Finland, and Sweden.
Descriptive statistics are given in Table A.1. They include some observations generated by linear
interpolation, as described above. Some variables have been multipled or divided by 100 for
consistency, or to give handier regression coefficients. For the explanatory variables (all
variables other than the decile ratios and wage share), only those observations actually used in
the regressions are included.
TABLE A.1
DESCRIPTIVE STATISTICS
Variable
9/1 earnings decile ratio (all persons)
9/1 earnings decile ratio (men)
Wage share (OECD total economy)
Wage share (AMECO @ market prices)
Wage share (AMECO @ factor cost)
Bottom 99% wage share A
Bottom 99% wage share B
Trade share
Real exchange rate
Foreign assets & liabilities/GDP
Total factor productivity
Multi-factor productivity
Capital-output ratio
Union bargaining coverage
Coordination of wage bargaining
Unemployment benefit replacement rate
Union density
Average years of education
Some tertiary (Barro & Lee)
Some tertiary (IIASA)
Output gap
Observations
382
363
419
393
393
374
374
337
337
325
337
265
337
337
337
337
337
337
337
337
337
26
Mean
3.02
3.01
69.0
61.1
68.6
63.6
62.6
45.4
1.03
1.98
86.7
115.1
2.79
0.64
2.92
0.59
0.40
10.41
0.24
0.21
-0.20
Std. dev.
0.73
0.70
8.0
5.8
5.8
7.8
7.1
24.1
0.16
1.64
11.0
12.5
0.31
0.28
1.45
0.16
0.24
1.68
0.13
0.07
2.14
Min.
1.88
1.95
45.8
44.1
50.1
39.8
41.0
10.4
0.64
0.21
57.8
96.5
2.19
0.13
1.00
0.09
0.08
5.83
0.06
0.07
-8.39
Max.
4.98
5.14
93.7
75.8
84.7
79.6
76.7
131.3
1.64
9.33
105.6
165.2
3.63
0.95
5.00
0.92
0.87
13.45
0.57
0.41
7.33
TABLES AND FIGURES
TABLE 1
CORRELATIONS BETWEEN CHANGES IN WAGE INEQUALITY AND WAGE SHARES
Change from:
Wage share:
OECD, total economy
AMECO, market prices
AMECO, factor cost
1981-2004
9/1 decile ratio:
All persons
Men
0.87***
0.83***
(n=10)
(n=8)
0.80***
0.84**
(n=9)
(n=7)
0.87***
0.88***
(n=9)
(n=7)
1987-2007
9/1 decile ratio:
All persons
0.69**
(n=11)
0.58*
(n=10)
0.50
(n=10)
Notes
Changes in 3-year centred moving averages.
Number of observations (countries) in brackets below the Pearson correlation coefficient.
***/**/* indicate significance at 1/5/10% level.
27
Men
0.64**
(n=10)
0.65*
(n=9)
0.47
(n=9)
TABLE 2
SUR: ALTERNATIVE MEASURES OF LABOUR MARKET INSTITUTIONS
Dependent variable:
ln(Trade share)
(1) FE only
d91
Share
ln(TFP)
Union coverage
Union coverage.time
(2) Coverage
d91
Share
-0.037
-9.08
(0.29)
(3.86)
-1.49
-22.0
(7.76)
(6.19)
0.589
8.46
(5.53)
(4.33)
-0.023
-0.291
(8.67)
(5.99)
Coordination of wage bargaining
(3) Coordination
d91
Share
0.264
-4.33
(2.29)
(2.12)
-0.473
-9.18
(2.17)
(2.33)
0.040
(3.40)
-0.0025
(5.09)
Coordination.time
(4) Benefits
d91
Share
0.254
-4.25
(2.11)
(1.94)
-1.12
-16.8
(5.75)
(4.75)
0.305
(1.76)
-0.030
(3.35)
Unemployment benefit
replacement rate
Unemployment benefit.time
-0.407
(5.56)
-0.005
(0.84)
-3.83
(2.89)
-0.006
(0.06)
Union density
Union density.time
Average years of education
ln(K/Y)
Output gap
2007 year FE
0.36
-8.4
(5.06)
(8.32)
0.48 (p=0.00)
0
0
-0.189
-1.66
(11.0)
(5.21)
-1.56
-26.3
(9.42)
(8.63)
0.004
-0.285
(1.18)
(4.29)
1.56
13.1
(10.3)
(4.67)
0.02 (p=0.72)
0.82
0.65
(5) Density
d91
Share
0.16
-6.98
(1.35)
(3.26)
-0.830
-15.1
(3.89)
(4.01)
-0.228
-2.01
(7.65)
(15.3)
-0.468
-12.7
(3.05)
(4.58)
0.011
-0.207
(2.87)
(3.12)
0.93
3.73
(8.30)
(1.85)
0.11 (p=0.05)
0.68
0.56
-0.208
-1.79
(12.1)
(5.72)
-0.697
-15.3
(4.82)
(5.82)
0.013
-0.174
(3.62)
(2.61)
0.80
3.11
(6.55)
(1.41)
0.11 (p=0.04)
0.72
0.53
0.643
12.9
(3.84)
(4.37)
-0.015
-0.190
(4.35)
(3.24)
-0.251
-2.50
(16.3)
(9.19)
-0.918
-18.8
(6.10)
(7.08)
0.010
-0.194
(2.54)
(2.84)
1.17
9.4
(8.48)
(3.90)
0.10 (p=0.06)
0.71
0.52
Residual correlation
Changes, ‘R2’
Notes
All regressions on 337 observations from 11 countries (excluding Korea) spanning 1975-2009, and include country and year FE.
Z-statistics for regression coefficients in brackets. ***/**/* markers of significance omitted due to implausibility of coefficients (see text).
1980 base year for FE and time interactions. Changes for ‘R2’ measured from 1981-2004 (3-year centred moving averages). ‘R2’ defined in equation (1).
28
TABLE 3
SUR: ALTERNATIVE MEASURES OF GLOBALISATION, EDUCATION AND TECHNOLOGY
(1) Real exchange
rate
d91
Share
(2) Financial
globalisation
d91
Share
ln(Trade share)
ln(Real exchange rate)
ln(Foreign Assets
+ Liabilities / GDP)
ln(TFP)
0.040
(0.74)
-1.50
(8.00)
(3) Some tertiary
(Barro & Lee)
d91
Share
0.19
-6.73
(1.35)
(2.77)
(4) Some tertiary
(IIASA)
d91
Share
0.255
-8.16
(2.07)
(3.31)
-1.51
(7.06)
-0.559
(2.71)
(5) FE only
d91
Share
(6) TFP (AMECO)
d91
-0.229
(1.84)
Share
-5.32
(1.95)
-1.60
(6.55)
-29.7
(5.54)
Union coverage.time
Average years of
education
Some tertiary
(Barro & Lee)
Some tertiary (IIASA)
ln(K/Y)
Output gap
2007 year FE
0.567
(6.28)
-0.022
(8.60)
-0.189
(11.0)
d91
-0.18
(1.50)
Share
-4.65
(1.72)
-1.27
(8.68)
0.626
(6.50)
-0.015
(5.27)
-0.196
(12.2)
-20.8
(6.28)
5.36
(2.45)
-0.20
(3.11)
-1.69
(4.64)
6.24
(6.56)
-21.5
(6.46)
0.131
(3.28)
-1.73
(8.75)
-4.91
(6.88)
-9.95
(2.80)
-21.6
(5.90)
-22.3
(5.38)
ln(MFP)
Union coverage
(7) MFP (OECD)
3.57
(2.22)
-0.172
(3.75)
-1.61
(5.27)
-1.56
-24.7
(10.2)
(9.09)
0.004
-0.290
(1.15)
(4.56)
1.53
4.5
(17.2)
(2.86)
0.01 (p=0.87)
0.82
0.54
0.538
(5.84)
-0.026
(8.83)
-0.180
(10.2)
5.27
(3.20)
-0.092
(1.74)
-1.79
(5.67)
-1.70
-14.4
(10.7)
(5.07)
0.004
-0.206
(1.18)
(3.17)
1.39
9.4
(14.3)
(5.40)
0.10 (p=0.06)
0.80
0.73
0.787
(5.83)
-0.046
(12.9)
11.1
(4.81)
-0.525
(8.54)
-1.95
(6.56)
-20.6
(4.07)
-2.23
-32.1
(13.4)
(11.3)
-0.006
-0.380
(1.53)
(5.69)
1.67
14.6
(9.63)
(4.94)
0.09 (p=0.10)
0.77
0.73
-0.087
(0.55)
-0.030
(12.3)
5.11
(2.49)
-0.362
(7.48)
-5.25
-10.9
(12.8)
(1.33)
-0.876
-29.8
(4.82)
(8.19)
-0.003
-0.348
(0.87)
(5.14)
1.46
11.3
(10.1)
(3.91)
0.14 (p=0.01)
0.91
0.67
0.692
(6.57)
-0.013
(4.27)
-0.221
(13.5)
0.31
-5.2
(5.68) (6.00)
0.31 (p=0.00)
0
0
6.93
(3.01)
-0.174
(2.68)
-2.07
(5.79)
-1.52
-22.0
(7.70)
(5.07)
0.003
-0.236
(0.95)
(3.26)
1.41
12.5
(4.62)
(11,4)
-0.17 (p=0.01)
0.89
0.44
-1.05
-12.2
(7.84)
(3.99)
0.003
-0.227
(1.07)
(3.19)
1.28
9.5
(12.1)
(3.96)
-0.24 (p=0.00)
0.92
0.58
Residual correlation
Changes, ‘R2’
Notes
As for Table 2, except column 2 is on 325 observations (1975-2007), and columns 4-6 are on 265 observations (1984-2009), use 1984 as the base year, and measure changes
from 1987-2007.
29
TABLE 4
SUR: PARSIMONIOUS SPECIFICATIONS
(1)
Dependent variable:
ln(Trade share)
ln(TFP)
Union coverage
Average years of
education
ln(K/Y)
Output gap
2007 year FE
Residual correlation
Changes, ‘R2’
Notes
As for Table 2.
d91
0.432
(3.16)
(2)
Share
-2.6
(1.04)
-15.6
(4.12)
-5.54
(3.97)
d91
0.498
(3.74)
-0.120
(1.29)
-0.224
(11.9)
-0.16
-20.6
(2.57)
(7.66)
0.011
-0.238
(2.79)
(3.32)
0.45
-2.7
(3.56)
(1.09)
0.13 (p=0.02)
0.64
0.42
(3)
Share
-7.31
(3.26)
-20.7
(5.58)
d91
0.484
(3.65)
Share
-1.92
(0.78)
0.6
(0.19)
-0.07
(0.80))
-0.245
(13.7)
-0.07
(0.82)
-0.23
(13.0)
-24.2
(9.44)
0.015
-0.219
(4.06)
(3.00)
0.46
2.6
(3.62)
(1.17))
0.09 (p=0.10)
0.64
0.34
0.015
-0.03
(4.16)
(0.42)
0.43
-7.1
(3.46)
(3.20)
0.19 (p=0.00)
0.65
-0.03
TABLE 5
CORRELATIONS BETWEEN CHANGES IN WAGE INEQUALITY AND BOTTOM 99%
WAGE SHARES
Change from:
Wage share:
OECD total economy
Bottom 99%, measure A
Bottom 99%, measure B
1981-2004
9/1 decile ratio:
All persons
Men
0.86***
0.86***
(n=8)
(n=7)
0.75**
0.78**
(n=8)
(n=7)
0.67*
0.72*
(n=8)
(n=7)
Notes
As for Table 1. Measures A and B defined in equation (2).
1
1987-2007
9/1 decile ratio:
All persons
Men
0.69***
0.79**
(n=10)
(n=9)
0.67**
0.74**
(n=10)
(n=9)
0.59*
0.66**
(n=10)
(n=9)
TABLE 6
SUR: BOTTOM 99% WAGE SHARES
Dependent variable:
ln(Trade share)
d91
(1) FE only
Share99
ln(TFP)
Union coverage
Union coverage.time
Average years of education
ln(K/Y)
Output gap
2007 year FE
0.36
-10.5
(4.99)
(10.7)
0.33 (p=0.00)
0
0
(2) Coverage
d91
Share99
-0.035
-15.6
(0.27)
(6.50)
-1.47
-23.7
(7.48)
(6.44)
0.575
9.37
(5.34)
(4.64)
-0.023
-0.179
(8.63)
(3.57)
-0.187
-1.74
(10.6)
(5.28)
-1.54
-24.2
(9.24)
(7.72)
0.0042
-0.246
(1.14)
(3.59)
1.6
14.9
(10.1)
(5.15)
-0.06 (p=0.29)
0.82
0.60
Residual correlation
Changes, ‘R2’
Notes
As for Table 2, except all regressions on 331 observations. ‘Share99’ is the bottom 99% wage share, measure A.
2
Australia
Denmark
Finland
France
Italy
Japan
Korea
NZ
Netherlands
Sweden
UK
US
5.0
4.0
3.0
9/1 decile ratio
2.0
5.0
4.0
3.0
2.0
5.0
4.0
3.0
2.0
1975
1985
1995
2005
1975
1985
1995
2005
All persons
1975
1985
1995
2005
Men
FIGURE 1. Wage inequality, 1975-2009.
3
1975
1985
1995
2005
Australia
Denmark
Finland
France
Italy
Japan
Korea
NZ
Netherlands
Sweden
UK
US
100
80
60
Wage share (% of GDP)
40
100
80
60
40
100
80
60
40
1975
1985
1995
OECD
2005
1975
1985
1995
2005
1975
1985
1995
AMECO market prices
FIGURE 2. Wage shares, 1975-2009.
4
2005
1975
1985
1995
2005
AMECO factor cost
0
Change in wage share (% points)
UK
US
Denmark
-5
Netherlands
Sweden
Finland
Australia
-10
Japan
France
-15
Korea
-.5
0
.5
Change in 9/1 decile ratio
FIGURE 3. Changes in wage inequality and wage shares, 1981-2004.
Note. Changes are in 3-year centred moving averages.
5
1
Australia
Denmark
Finland
France
Italy
Japan
NZ
Netherlands
80
70
60
50
Wage share (% of GDP)
40
80
70
60
50
40
1975
Sweden
UK
1985
1995
2005
US
80
70
60
50
40
1975
1985
1995
2005
OECD
1975
1985
1995
2005
1975
1985
Bottom 99% Measure A
1995
2005
Bottom 99% Measure B
FIGURE 4. Bottom 99% wage shares, 1975-2008.
6
Change in wage share (% points)
0
Total
Bottom 99%
Denmark
UK
-5
US
Sweden
-10
Australia
Finland
France
Japan
-15
-.5
0
.5
Change in 9/1 decile ratio
FIGURE 5. Changes in wage inequality and bottom 99% wage shares, 1981-2004.
Note: Changes are in 3-year centred moving averages.
7
1