Globalization, creative destruction, and labor share change: Evidence on thedeterminants and mechanisms from longitudinal plant-level data* / Petri Böckerman**& Mika Maliranta***

Abstract
We examine the sources and micro-level mechanisms of the changes in the labor share ofvalue added. We link the micro-level dynamics of the labor share change with that ofproductivity and wage growth. Using a useful variant of the decomposition method wemake a distinction between the change in the average plant and the micro-level
restructuring. With Finnish plant-level data covering three decades we show that microlevel restructuring is the link between the declining labor share and increasing productivity
in 12 manufacturing industries of four regions, and that increased international trade is afactor underlying those shifts.
JEL Classification: F16; J31
Key

words:

Globalization;

International trade;

Foreign ownership;

Micro-level

restructuring; Labor share* This study is funded by the Finnish Work Environment Fund (Työsuojelurahasto).
Financial support from the Yrjö Jahnsson Foundation is gratefully acknowledged (Grants
No. 5745 and 5765). Data construction and decomposition computations have been carriedout at Statistics Finland following the terms and conditions of confidentiality. This part of
the work has been done in the context of a joint project by the Helsinki School of
Economics (HSE), Statistics Finland and the Research Institute of the Finnish Economy(ETLA) funded by the Academy of Finland (Project No. 114827). To obtain access to thelongitudinal plant-level data, please contact the Research Laboratory of the Business
1Structures Unit, Statistics Finland, FI-00022, Finland. The industry-region panel andprograms that are used to produce the estimates of the paper are available for replication
purposes. We are grateful to Kristiina Huttunen, Niku Määttänen, Katariina Nilsson
Hakkala and Pekka Sauramo for comments. Earlier version was presented at the EALE
conference, Tallinn. We are grateful to the audience for comments. Paul A. Dillingham has
kindly checked the English language. The usual disclaimer applies.

** Labour Institute for Economic Research, Pitkänsillanranta 3A, FI-00530 Helsinki,
Finland. Fax: +358-9-25357332. Tel.: +358-9-25357330. E-mail:petri.bockerman@labour.fi*** Corresponding author. The Research Institute of the Finnish Economy, Lönnrotinkatu
4B, FI-00120 Helsinki, Finland. Fax: +358-9-601753. Tel.: +358-9-60990219. E-mail:mika.maliranta@etla.fi, and University of Jyväskylä, FIN-40014 Jyväskylä, Finland21. Introduction

The labor share of value added was regarded as “a magic constant” (Solow 1958). The
constancy of the labor share was also treated as one of the stylized facts of economic
growth (e.g. Gollin 2002). In contrast to this, there are several industrialized countries inwhich the labor share has declined substantially over the past few decades (e.g. Blanchard
1997, 2006). Secular decline in the labor share since the early 1980s has been much more
pronounced in Europe and Japan (roughly 10 percentage points) than in Anglo-Saxon
countries, including the United States (about 3-4 percentage points) (IMF 2007). Within
Europe, the strongest decline in the labor share has been experienced in Austria, Finland,
Ireland, and the Netherlands. It is important to know how these changes emerge: Are thelabor shares declining because of increasing productivity or falling wages, and in particular,
what are the micro-level mechanisms underlying those changes?

Modern growth theories emphasize the role of intra-industry micro-level restructuring as
one of the key mechanisms for explaining industry productivity growth (e.g. Aghion andHowitt 2006). Research in the field of international trade has also indicated that
globalization is an important stimulant of productivity-enhancing micro-restructuring(“creative destruction”) within industries (e.g. Bernard et al. 2007; Lileeva 2008). In
particular, Bernard and Jensen (2004) show that exporting does not increase plant
productivity growth but has positive aggregate productivity effects because it is associatedwith the reallocation of resources from less efficient to more efficient plants.

We contribute to the literature by distinguishing between two intrinsically different microlevel mechanisms underlying the industry labor share changes: 1) the labor share change of3

the average plant and 2) the micro-structural change. Furthermore, we both formally andempirically link the micro-level dynamics of the labor share change, productivity growth
and wage growth. These links emerge due to the fact that industry productivity and wage
growth together determine the change in the labor share. In this paper, we apply this novel
approach to analyze the potential contribution of globalization on the decline in the labor
share.1 In particular, we examine whether increased competition owing to the exposure tointernational trade and foreign ownership forces the plants with the highest share of labor
income to decrease their market shares and eventually forces them out of business. This
central hypothesis of the paper is closely related to the modern theoretical insights. Melitz(2003) argues that an increase in an industry’s exposure to international trade will lead tointer-firm reallocations towards more productive firms by increasing competitive pressures.

Our approach allows a coherent tracking of the roles of the micro-level dynamics ofproductivity and wage growth. We take advantage of longitudinal plant-level data from the
Finnish manufacturing sector over a period of 30 years. To preview, the results show that
globalization stimulates productivity-enhancing and the labor share curbing micro-level
restructuring within industries. Wage increases in the industries correspond closely to thoseof the plants, which implies that micro-level restructuring does not contribute much to theindustries’ wage increases.

The link between the labor share and globalization is not well understood. Existing
evidence about the effects of globalization on the labor share is based on cross-country
studies (e.g. European Commission 2007, 2008; Guscina 2006; Harrison 2002; Jaumotteand Tytell 2007; Jayadev 2007). The main problem with the cross-country studies is that
there are a large number of contributing factors across countries that are identified with a4relatively small number of observations, i.e. the curse of dimensionality plagues the crosscountry approach. Cross-country regressions are therefore likely to suffer from omitted
variable and parameter heterogeneity biases. The differences in the data characteristics
make it hard to conduct a reliable comparison of the plant-level dynamics of the labor share
across countries.2 This makes it particularly useful to provide a detailed analysis of one
country. To examine the effects of globalization, we construct a panel of industries andregions from the micro-level components of the labor share. While using the same plantlevel data in the analysis of industries and regions within the same country, datacomparability problems can be bypassed.

The role of labor market regulations and other institutional aspects has gained considerable
attention in the cross-country comparisons of the labor share dynamics (e.g. Azmat et al.
2007; Bentolila and Saint-Paul 2003; IMF 2007). In contrast to this research, we build on
the fact that there are large differences in the micro-level dynamics of the labor share acrossregions within the same country that share exactly the same institutions and regulations.
Hence, the focus on one country allows us to isolate the effects of globalization on the labor
share more clearly, because we are able to avoid, by construction, the problems that emerge
from the complex interactions between a variety of labor market institutions and openness
that Agell (1999) and Rodrik (2008) have pointed out.

The Finnish case provides an excellent opportunity to analyze the effects of globalization,
because the openness of the economy has greatly increased during the past few decades. At
the same time, there has been a considerable decline in the labor share. Figure 1 exhibits thetrends in the manufacturing sector in four Finnish regions. Plenty of variation in the trends

5

provides us with an interesting opportunity to identify the mechanisms underlying thesechanges.

=== FIGURE 1 HERE ===The structure of the paper is as follows. Section 2 introduces the decomposition method.
Section 3 describes the longitudinal plant-level data. Section 4 relates the micro-levelcomponents to the changes in international trade and foreign ownership. The last section
concludes.

2. Decomposition of the labor share into its micro-level components

The starting point of our analysis is the fact that the industry (or aggregate) labor sharedeclines when industry labor productivity growth exceeds industry wage growth (measured
in product prices). The existing studies in the literature assume that these industry or moreaggregate level changes represent changes in a representative firm, and, as a consequence,
the role of the micro-level restructuring is ignored. Our contribution is that we formally andempirically distinguish between the two intrinsically different mechanisms of the industry
change in the labor share: 1) the change in the average plant and 2) the change due to plantlevel restructuring.

The literature provides several different methods to decompose aggregate productivity
growth (e.g. Bartelsmans and Doms 2000; Foster et al. 2001; Kruger 2008). In this paper
we adopt a formula that has several useful properties that make the interpretation of thecomponents easy in this context. Our method has some important resemblances to those6

proposed by Maliranta (1997), Vainiomäki (1999) and, more recently, by Diewert and Fox(2009).3 The within component is defined as the weighted average of the changes in thelabor shares of the continuing plants. The between component is negative when there is asystematic structural change among continuing plants in terms of value added towards
those plants that have a lower labor share. It is also possible to separate the entry and exit
components. The total effect of entries and exits is the difference between the total
aggregate change in the labor share and the aggregate change in the labor share among thecontinuing units.

We denote the labor share of a production plant i in period t with fit = wit vit , where wit is
the wage sum (including social security payments) and vit is value added. Both of them aremeasured in product prices (i.e. deflated by a price index of the industry).4 We decompose
the change rate of the aggregate labor share from period t-1 to period t. Plants appearing inperiods t-1 and t are classified into three groups: those appearing in both t-1 and t, i.e.
continuing plants, are indicated by C, those appearing in t but not in t-1, i.e. entrants, areindicated by E, and those appearing in t-1 but not in t, i.e. disappearing plants, are indicated
by D.

The aggregate labor share change rate consists of two distinct main components. These are
the change rate within units and the effect of micro-structural change that is the structuralcomponent (denoted by STRtF ):5

f - f i,t -1
Ft - Ft -1= å si it+ STRtF
fi
F
iÎC

7

(1)

where Ft = å i wit å i vit is the aggregate labor share in period t; F = 0.5 ( Ft -1 + Ft ) is theaverage aggregate labor share in periods t-1 and t; fi = 0.5 ( f i,t -1 + f it ) is the average labor
share of plant i in periods t-1 and t, and sit = vit å jÎC v jt is the weight of unit i as measured
by its share of aggregate value added among continuing units.

It should be noted that

Ft - Ft -1
F@ log t and
Ft -1
F
fit - f i ,t -1
fi

@ log

f it
f i ,t -1.

Consequently, here the within component is similar to a Divisia index of the growth rate of
the total factor productivity but now applied to the continuing plants (where the change rate
is relevant) and used for the estimation of the growth rate of the labor share. It describes thechange rate of the labor share in a “representative” plant.

The structural component consists of four sub-components(F - F ) - S (F - F ) +STR = S
E

F
t

E
t

C

t

t

F

D
t -1

D
t -1

C
t -1

F

å F (s - s ) + å s
fi

it

iÎC

i , t -1iÎCifit - f i ,t -1 æ fi - F ö
ç
÷
fi
è F ø

(2)

where F X = å iÎX wi å iÎX vi is the aggregate labor share among the group X Î {E , C , D} ,and S X = å iÎX vi å i vi is the value added share of the group X Î {E , C , D} , and si is theaverage of st -1 and st .

8

The direct contribution of the entering plants to the aggregate growth rate of the labor shareis gauged by the first component of the Equation (2). It is positive when the average labor
share of the new plants (weighted by the value added share) in the period t is higher thanthat of those who appeared also in the period t-1. The magnitude of the contribution isdependent on the value added share of the new plants in the period t.

The second component, the exit component, is analogous to the entry component.
Therefore, one of the great advantages of this decomposition method is that entries andexits are treated symmetrically. The exit component is negative when the average labor
share of the disappearing plants (weighted by the value added share) is higher than that ofthose plants that appear also in the next period t. The magnitude of the contribution isdependent on the value added share of the disappearing plants before they leave, i.e. in the
period t-1 (see also Maliranta 1997, 2003; Diewert and Fox 2009).

The third component is the between component, which captures the effect of the value
added share changes among the continuing plants on the aggregate labor share change rate.
This component is negative when the low labor share plants (i.e. high profitability plants)
increase their market shares at the cost of the high labor share plants (i.e. low profitability
plants). The between component, like the within component, is defined among thecontinuing plants only. As a result, entering or disappearing plants do not have any direct
effect on those components. The fourth component on the right-hand side of the Equation(2) can be called the cross-component.

The Equations (1) and (2) can be applied to industry productivity and wage growth. Whenindustry labor productivity and industry wage growth are measured in nominal terms or9when both are deflated by the same price index (as in our paper), the following relationshipholds at the industry (or aggregate) level:

Ft - Ft -1 Wt - Wt -1 Pt - Pt -1»
FW
Pf - ft -1
w - wt -1
p - pt -1
Û t+ STRtF » t+ STRtW - t- STRtPfw
p(3)

where W is the wages and social security payments per labor input, P is the value added
per labor input (i.e. labor productivity), and STRtW and STRtP are the structural components
of industry wage and industry productivity growth, respectively.

Naturally, an analogous relationship holds at the plant level, i.e.

ft - ft -1 wt - wt -1 pt - pt -1
»
f
w
p

(4)

where f, w and p denote labor share, wages and social security payment per labor input andvalue added per labor input in a plant, respectively.

By inserting (4) into (3) we obtain

STRtF » STRtW - STRtP(5)which shows how the structural components of the labor share change, wage growth andproductivity growth are related to each other.

10

Panel A of Table 1 illustrates our approach to track the changes in the labor share. The
earlier literature has focused only on some specific aspects of these changes. There arevarious popular ways to decompose the industry-level aggregate productivity growth (seeFoster et al. 2001). However, the existing studies have not provided decompositions ofwage growth (‘dW’) that is the other important aggregate determinant of the observedchanges in the labor share. In this paper, we provide a complete and coherent picture of the
micro-level mechanisms (‘a’, …, ‘d’) that are behind the aggregate movements in the labor
share.

=== TABLE 1 HERE ===Analysis of the micro-level components of the labor share is particularly useful in the
Finnish context, because the wage bargaining system adopted in Finland has distinct
implications on the evolution of the micro-level components. The coverage of collective
agreements is roughly 95% of all employees in Finland, one of the highest rates among theOECD countries (Layard and Nickell 1999). Minimum increases in nominal wages are
determined by collective bargaining. The Finnish ‘wage increase formula’ defines the scopefor nominal wage cost increases as the sum of the core inflation target (e.g. 2% per annum)
and the average increase in productivity across the whole economy. Nominal wages are
therefore not encouraged to adjust to the changes in labor productivity that are considered
to be isolated to certain sectors or regions. Specifically, wage increases have not been tied
to plant-level (real or nominal) productivity advances.

11

Because of the attributes of the Finnish wage bargaining system, our expectation is thatwage growth takes place mainly through the within plant component and therefore intraindustry restructuring is not a significant source of industry wage growth. If industry
productivity growth equals industry wage growth (i.e. the aggregate labor share is stable)
and industry productivity growth exceeds plant productivity growth, the within componentof the labor share change has a positive contribution and the restructuring component has
the opposite (negative) contribution. This paper looks at how globalization drives wedges
between these balances.

Panel B of Table 1 provides an empirical description of the micro-level channels of theindustry labor share change in Finnish manufacturing in the period 1976-2007, based on thelongitudinal plant-level data. It shows the break-down of the industry labor share change
into the contributions of productivity and wage growth, and what are the micro-levelcomponents of productivity and wage growth. The industry labor share has declined 0.5
percent per annum because the labor productivity growth rate (4.2 percent) has exceededthat of wages (3.7 percent, measured in product prices) by a margin of 0.5 percentage
points. At the “average plant” the development has been, however, quite different as can beseen from the table; wage growth has exceeded productivity growth by a margin of 0.5
percentage points. The last column of Panel B reveals the ultimate source of the labor sharedecline; the micro-level restructuring has been an essential ingredient of labor productivity
but not wage growth.

12

3. Data

The micro-structural components of the labor share are calculated by the use of
longitudinal plant-level panel data that has been constructed especially for economic
research purposes by the Research Laboratory of Statistics Finland. Our data are basedon the Annual Industrial Statistics Surveys that basically cover all manufacturing plants
employing at least five persons up to 1994. Since 1995 it has included all the plants
owned by firms that have no fewer than 20 persons. Maliranta (2003) has examined indetail how sensitive the patterns of productivity components are to the change in the
cut-off limit from 5 to 20 in the period 1975-1994. The result was that the cut-off limit
made little difference. This is because larger plants account for a substantial share of thetotal input usage. Still, to make our decompositions as comparable as possible over all
the years we have harmonized the coverage of our data. We have included all the plantsthat have at least 5 persons and are owned by a firm that has no fewer than 20 persons.

Our data are exceptionally good when it comes to the coverage, the content and thelength of time series. However, as always with these kinds of data, our data are not
perfect, either. Data include outliers that might be influential in an economic analysis. A
transparent procedure is therefore needed to clean the data. We have adopted an
approach similar to that of Mairesse and Kremp (1993). Those observations are deemedas outliers whose log of the labor share differs by more than 4.4 standard deviations
from the input-weighted industry average in that year. We have performed thedecomposition computations for each pair of the consecutive years. If a plant isclassified as an outlier in either an initial (i.e. in year t-1) or an end year (i.e. in year t) it
is not included in this computation (but is possibly included in earlier and later periods).
13

This way we have avoided causing artificial entries or exits by removing outliers. In thecourse of our analysis we noted that a single plant might sometimes have an impact on
one of the components of our interest that is simply unbelievable. A more detailed
inspection of these cases revealed that the changes in value added or labor input are
sometimes erroneous beyond reasonable doubt. Since, on certain occasions, these errors
are very influential in our decomposition calculations, further cleaning may be
worthwhile. For this reason, the decompositions are made in two rounds. If the absolutevalue of the contribution of a single plant to one of the components is greater than four
percentage points, the plant is classified as an outlier in the first round. This is aconservative criterion since, as we will see below, usually the size of these components
is less than four percentage points at the level of industry and region. These outliers,
accounting for 9.6 percent of the total hours in the whole period, are removed in thesecond round, which generates our final decomposition results.

To examine the effects of globalization, we have computed the micro-level componentsof the labor share change rate, productivity growth rate and wage growth rate for 12
industries and four regions over the period 1976-2007. Our industry classification isclose to the two-digit standard industry classification, but we have combined someindustries. Our industries are the following: “Food” (NACE 15-16), “Textile” (17-19),“Wood” (20), “Paper” (21), “Printing” (22), “Chemicals” (23-25), “Minerals” (26),“Metal products” (27-28), “Machinery” (29 and 34-35), “Electrical equipment” (30-31),“Communications equipment” (32-33), and “Other” (36-37). This classification is
dictated by our need for a reliable measurement of the decompositions of the labor
shares by industries and regions.6

14

Finland is divided into five provinces (the so-called NUTS2 level in the European
Union). However, we exclude the province of Åland, because the small number ofplants in this island community means that the measures of the micro-level componentsof the labor shares would not be reliable. The use of these classifications gives us apanel data that has 4×12=48 observations per year. Since the components can becalculated for 31 pairs of years (1975-1976, 1976-1977,… , 2006-2007), in principal wehave 1488 observations. Since we use lagged explanatory variables, the number ofobservations is slightly smaller in our econometric analysis, however.

Productivity and wage growth rates are computed by using the industry-specificdeflators that are implicit price indexes of output obtained from the Finnish National
Accounts. We have used a “chain-index” procedure. For each pair of the consecutive
years, value added (and wages plus social security payments) of the end year (i.e. t) isconverted into the price level of the initial year (i.e. t-1). The use of price indexes is notnecessary in our analysis. (We obtained similar results with nominal measures.) The
main point here is that we use the same deflators for both productivity and wage growth(or no deflators at all) to obtain consistent results for productivity, wage and labor sharechange (see Feldstein 2008).

Globalization is measured by two variables, which capture the exposure to international
trade and foreign ownership.7 The exposure to international trade is measured by dividing
exports by the gross output. This is the measure that has been most frequently used in theliterature to describe the effects of globalization on the labor share. For example, it has beenused by Harrison (2002) and Guscina (2006), among others. The share of foreign-owned

15

plants in an industry and a region is defined on the basis of output share. A 50% thresholdis used in classifying a particular plant as foreign owned.

4. Results

Descriptive evidence shows out there have been considerable differences in the micro-level
dynamics of the labor share across regions within the same country that have shared exactly
the same institutions and regulations. (These patterns are documented in our working paper
version.) The variation has been neglected in the literature. It can be exploited when we
estimate the effect of globalization on the labor share change.

We use Prais-Winsten regressions with panel-corrected standard errors as our baseline
reduced-form specifications for the period 1978-2007.8 The estimator is based on theprinciple of estimating the amount of autocorrelation and then re-weighting the standards
errors to correct them. Therefore, it is a weighted least squares estimator. The estimator ispreferable in terms of efficiency to OLS in our context, because we have a considerable
number of repeated observations on fixed units (industry × regions) with a potential firstorder serial correlation. We assume that the structure of the AR (1) process is similar ineach panel of the data, as recommended by Beck and Katz (1995). A further advantage of
Prais-Winsten regressions is that we are able to incorporate cross-sectional correlation to
the model when the number of time-series observations is less than the number of crosssectional observations, whereas standard feasible generalized least squares cannot (Chen etal. 2005). The sample consists of 1440 observations (4 regions, 12 industries and 30 years).

16

If the focus were the within plant changes, we would use those approximately 150 000
plant-level observations that are available in our original panel data. However, in this paper
our main interest is to look at the role of plant-level restructuring. The industry-region
panel constructed by the decomposition computations is a useful way to capture it. The aim
is to identify an additional role of the exposure to globalization, i.e. to study whether thereis evidence of a structural change in terms of valued added towards those plants that have alower labor share because of globalization. With the data and the methods applied here wecan consistently analyze the role of plant-level changes (i.e. within plant changes) andmicro-structural components in industry development.

The dependent variables of the models are the industry growth rates of the labor share,
labor productivity and wage, and their micro-level components by regions. The explanatory
variables are the measures of globalization along with the control variables. The control
variables include a full set of the unreported fixed industry-region effects. Prais-Winsten
regressions do not contain separate year effects. However, it is assumed that disturbances
are heteroscedastic and contemporaneously correlated across panels (i.e. industry ×
regions).

The variables that capture the changes in the exposure to international trade and foreign
ownership are included in the models as lagged up to two years. There are two reasons forthis. First, and most importantly, it should take some time before the effects on the labor
share change appear. In particular, Maliranta (2005) shows that it is worthwhile taking into
account the lagged effects when examining the influences of international trade on
restructuring. Second, the contemporary correlation between the exposure to international
trade and the labor share change could be the reverse, because a decline in the labor share17

improves the competitiveness of domestic production that tends to increase export volume
in a small open economy.

The lagged effect is arguably closer to the causal effect. That being said, we cannot directly
address the possibility that the measures of globalization may be endogenous, because ourdata do not contain appropriate economic instruments for globalization that could beclaimed to be truly independent of the micro-level components of the labor share change.
Thus, we document essentially correlations between globalization and different micro-levelcomponents of the labor share change. The problems regarding the causal interpretation of
the estimates are even more apparent in the existing cross-county studies (e.g. European
Commission 2007, 2008; Guscina 2006; Harrison 2002; Jaumotte and Tytell 2007; Jayadev
2007). One advantage of our approach is that the labor market regulations and otherinstitutional aspects are similar for all units of observation (industry × region) in theestimations, because we focus on one country. This reduces the omitted variable bias. We
are also able to test the strict exogeneity of the regressors by including the lead values ofthe measures of globalization among the explanatory variables. Furthermore, to examine
the robustness of the baseline estimates we have estimated GMM specifications that allow
us to instrument potentially endogenous variables with their lagged levels in the dynamic
setting. This modeling framework offers the second-best solution dealing with endogeneity
problems, since our data do not contain appropriate economic instruments.

All models are estimated by taking advantage of unfiltered data. The use of filtered data toestimate models is not an appropriate strategy for two reasons. First, the Hodrick-Prescottfilter smoothes the data and leads to a complicated pattern of autocorrelation with future
and past components in the dependent variables. This most likely would lead to spurious

18regression results (Meyer and Winker 2005). Second, the pre-filtering of the dependent
variables would mean that they are measured with an error. This generates a systematic
pattern of heteroskedasticity (Saxonhouse 1977). It would be very difficult to construct an
econometric model to tackle both of these problems.

Our baseline estimates that consist of nine models are reported in Table 2. The first column
shows that increasing exports lowers the industry labor share. The negative effect of theindustry labor share comes from both the within component (-0.096) and the structuralcomponent (-0.125), as shown in Columns 2-3, respectively. Note that the Equation (1)holds: -0.223 ≈ -0.096 – 0.125. Therefore, a substantial part of the negative effect ofincreasing exports on the labor share change can be attributed to the micro-structuralcomponent.

=== TABLE 2 HERE ===The negative industry effect of exports on the labor share (-0. 223) reported in Column 1 isa consequence of the fact that exports increase industry labor productivity (0.170), as
shown in Column 4. The relationship between industry labor share change, industry wage
growth (Column 7) and industry productivity growth given in the Equation (3) holds; forexample, for d_EXPORT(t-1) we find that -0.223 ≈ -0.043 – 0.170.

Column 6 reveals that greater international trade involves intra-industry productivityenhancing restructuring towards high productivity plants. This pattern is in accordance withthe findings in the literature (e.g. Bernard and Jensen 2004; Bernard et al. 2007; Maliranta

19

2005). The industry productivity effect is a sum of the within component and micro-level
restructuring, i.e. 0.170 = 0.010 + 0.160.

Column 7 of Table 2 shows that exports have no effect on industry wage growth. This
explains why the productivity effect has such a large dominance in the determination of thelabor share change. Exporting has only a weakly significant positive effect on the microstructural component of industry wage growth (Column 9).

Overall, the estimates reveal that micro-level restructuring is an important channel throughwhich exporting reduces the industry labor share. The effect comes essentially from therestructuring component of labor productivity growth. The restructuring component ofindustry wage growth has a very minor role to play, which can be seen from the empiricalcounterpart of the Equation (5) for the subcomponents of the restructuring component oflabor share change: -0.125 ≈ 0.013 – 0.160.

The results also point out that the exposure to international trade is clearly a more important
determinant of the micro-level components of the labor share change than foreign
ownership. Foreign ownership does not have a statistically significant effect on laborproductivity or wage growth.

So far we have found that the micro-structural components have had an important role inthe determination of the labor share and productivity. We next take a closer look at the subcomponents of the structural component, as described earlier in the Equation (2). This
decomposition allows us to pinpoint the exact sources of the effects.

20

Table 3 reports the estimates for the labor share change. Column 1 in Table 3 is the same asColumn 3 in Table 2. It is approximately the sum of the estimates of Columns 2-5 in Table
3 so that one can read the contribution of each sub-component to the structural component.
We discover that the negative effect of the structural component on the labor share change
emerges largely through the exits of plants, i.e. those plants with a particularly high share oflabor income are eventually forced out of business as exports increase (Column 3). In
addition, the between component has a negative contribution to the structural component.

=== TABLE 4 HERE ===Table 4 reports the corresponding results for labor productivity growth. These estimatesshow that the productivity-enhancing restructuring effect of exports derives essentiallyfrom the exit component (Column 3).

=== TABLE 5 HERE ===To assess the direction of causality between the variables of interest, we have tested thestrict exogeneity of the regressors by including the lead values of the explanatory variables(export share and foreign ownership) in the model (Wooldridge 2002). The results revealthat the lead values of export share and foreign ownership are not statistically significant innone of the nine models that are estimated (Table 5). This supports the conclusion that thecausal loop runs from the measures of globalization on the micro-level components of labor
share change and not the other way around. This interpretation of our findings is also
consistent with the theoretical and empirical literature that underlines the importance of
exposure to international trade as a stimulus of restructuring.

21

=== TABLE 6 HERE ===To examine the robustness of the results, we have performed several checks. First, we haveestimated models that also include a variable that measures skill upgrading (i.e. the change
in the highly educated) among the explanatory variables to account for the changes in labor
supply. Because this variable is not available for the earlier years, these estimations cover
the period 1990-2007. In spite of the different period and the slightly different set of
explanatory variables the main findings are relatively similar to those made in Table 2 (notreported). We also have estimated OLS models. (The control variables include a full set ofthe unreported industry-region and year effects.) The central findings for the role of microlevel restructuring remain (Appendix: Table A1). However, the estimated standard errors
are much larger than the panel-corrected standard errors from the Prais-Winsten
regressions, because the structure of autocorrelation is not taken into account in the OLS
estimation (see Beck and Katz 1995). We also have estimated specifications by using
system GMM panel estimation methods to tackle the potential endogeneity of the measuresof globalization (see Blundell and Bond 1998). The parameter estimates from system GMM
panel estimations are relatively close to those reported in Table 2, but they are notstatistically significant at the conventional levels (not reported). This is not surprising,
because the method is not very efficient in our context. Finally, we have estimated themodels of Table 2 separately for each of 12 manufacturing industries. These results reveal
an interesting pattern. It turns out that the contribution of international trade to the decline
in the labor share has been particularly pronounced within the telecommunicationequipment industry that consists of “communications equipment” (NACE 32-33)(Appendix: Table A2). Therefore, our results point out that the high-tech industries can22constitute an important vehicle through which the effects of globalization on the labor sharedecline manifest themselves in a small open economy. 9

To summarize the picture that emerges, the estimates show that globalization squeezes thelabor share because of increasing labor productivity. Labor productivity growth, in turn,derives to an important degree from intra-industry restructuring. The export share has
increased more than 20 percentage points over the last three decades, though it varies tosome extent between regions and a great deal between industries. According to ourestimates this would lead to a decline in the labor share by 10 percent (or 5-6 percentage
points), of which 40 percent takes place through intra-industry restructuring.

5. Conclusions

The labor share is an important aggregate variable, subject to possible mistaken policy
interventions but mistaken interpretations as well. The labor share of the national income
has declined substantially in several industrialized countries during the past few decades.
Globalization is likely to have some role to play in directing the changes in the labor shares.
Despite the fact that the role of labor market regulations and other institutional aspects
affecting the determination of the labor share has gained a considerable amount of attention
in the cross-country comparisons (e.g. Azmat et al. 2007; Bentolila and Saint-Paul 2003;
IMF 2007), knowledge of the exact micro-level sources and mechanisms of industry wage
growth is absent. Therefore, the determinants of systematic movements in the labor sharesare not well understood.

23

This paper contributes to the literature by distinguishing between two intrinsically different
mechanisms underlying the industry labor share changes: 1) the labor share change of theaverage plant and 2) the micro-structural change. Specifically, we take advantage of a
useful variant of the decomposition of the labor share, labor productivity and wage growthin 12 manufacturing industries and four regions to distinguish between the within andmicro-restructuring components, through the use of Finnish longitudinal plant-level dataover the period 1975-2007. Regression analysis of the micro-level components allows us to
examine not only the effects of international trade and other factors on the labor sharechanges but also to look at the distinct micro-level mechanisms.

Our analysis points out the importance of looking at the micro-level mechanisms
underlying industry-level changes. The most important empirical finding is that we identify
an additional role of the exposure to international trade: there is evidence of a systematic
micro-structural change in terms of value added towards those plants that have a lower
labor share. Globalization squeezes the labor share because of increasing labor productivity.
The labor productivity growth effect of globalization, in turn, predominantly comes throughintra-industry restructuring. Furthermore, the negative effect of exporting on industry labor
share change emerges largely through the exits of plants, i.e. those plants with a particularlyhigh share of labor income are forced out of business as exports increase. In contrast, wage
formation has been largely insulated from the influences of increasing international tradeover a period of three decades. These micro-economic mechanisms are also relevant in theeconomic context of other advanced economies and the effects should generalize beyond
the Finnish case. Our results carry an important policy lesson. Taken that globalization
boosts labor productivity while wages do not fall, it will eventually also benefit employees
in the long run. However, this requires flexibility of labor markets so that employees from24the exiting plants move to more productive and profitable jobs. Greater wage flexibility
between plants would be one way to mitigate this pressure (see Moene and Wallerstein1997).

25

.7

.8

FIGURE 1 The labor share by region

South, H-P(6.25)
South
East, H-P(6.25)

.6

East
West, H-P(6.25)
West
North, H-P(6.25)

.4

.5

North

1975

1980

1985

1990
year

1995

2000

2005

NOTE: The labor shares are smoothed by using the Hodrick-Prescott filter with a parameter value of 6.25, as
proposed by Ravn and Uhlig (2002).

26

TABLE 1 Conceptual framework and empirical decomposition of the changes in the labor
share, annual weighted averages for the period 1975-2007
Panel A: Conceptual
framework
Industry-level
aggregates
Wage change (= a+b)
Labor productivity
change (= c+d)
The labor share change(= (dW - dP) = (a+b) (c+d) = (a-c) + (b-d))

Micro-level mechanisms
Change in
the average
plant

Plant-level
restructuring

dW

a

b

dP

c

d

dF

Panel B: Empiricaldecomposition
Industry-level
aggregates
Wage change = dWLabor productivity
change = dP
The labor share change =dF = (dW - dP)

Micro-level mechanisms
Change in
the average
Plant-level
plant
restructuring
3.7%

3.7%

0.0%

4.2%

3.3%

1.0%

-0.5%

NOTES: Averages are computed by using the valued-added shareweights. Figures may not add up due to rounding.

27

TABLE 2 Estimation results for the micro-level components of the labor share change, labor productivity growth and wage growth for the years 1978-2007
Labor share change
Labor productivity growth rate
(1)
(2)
(3)
(4)
(5)
(6)
total
within structural total
within structural
d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared-0.223** -0.096-0.125**0.170*(0.076)(0.066)(0.046)(0.078)-0.226** -0.180** -0.050
0.165*(0.078)(0.068)(0.047)(0.079)-0.011
0.009-0.0210.019(0.045)(0.040)(0.018)(0.050)0.005
0.020-0.0170.032(0.046)(0.041)(0.020)(0.052)
1440
0.057

1440
0.026

1440
0.087

1440
0.019

0.010
(0.069)0.138*(0.069)0.005(0.047)0.043(0.048)1440
0.036

Wage growth rate
(7)
(8)
(9)
total
within structural

0.160** -0.043(0.052)(0.055)0.028-0.059(0.053)(0.057)0.016
0.010(0.023)(0.039)-0.0090.044(0.025)(0.040)
1440
0.081

1440
0.097

-0.054(0.054)-0.057(0.056)0.010(0.038)0.053(0.039)0.013(0.009)-0.003(0.009)-0.001(0.007)-0.008(0.007)

1440
0.103

1440
0.033

NOTES: Coefficients are from Prais-Winsten regressions. Panel-corrected standard errors are reported inparentheses. Observations are weighted by the value added shares. Lagged values are used for the changes inexport and foreign ownership shares. All models include the fixed industry-region effects. Statisticalsignificance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

28

TABLE 3 Estimation results for the structural component of the labor share change for the years 1978-2007(1)
structural
d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared

(2)
entry

-0.125**0.017(0.046)(0.025)-0.0500.027(0.047)(0.023)-0.0210.004(0.018)(0.010)-0.017-0.023*(0.020)(0.011)
1440
0.087

1440
0.061

(3)
exit

(4)
between(5)
cross-component-0.115*(0.053)-0.060(0.052)-0.012(0.015)0.010(0.017)-0.074*(0.034)0.044(0.034)-0.003(0.024)0.000(0.025)0.046(0.032)-0.069*(0.032)-0.008(0.024)-0.005(0.025)

1440
0.092

1440
0.047

1440
0.033

NOTES: Coefficients are from Prais-Winsten regressions. Panel-corrected standard errors are reported inparentheses. Observations are weighted by the value added shares. Lagged values are used for the changes inexport and foreign ownership shares. All models include the fixed industry-region effects. Statisticalsignificance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

29

TABLE 4 Estimation results for the structural component of labor productivity growth for the years 1978-2007(1)
structural
d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared

(2)
entry

0.160**
0.007(0.052)(0.021)
0.028-0.008(0.053)(0.021)
0.016-0.006(0.023)(0.011)-0.0090.016(0.025)(0.012)
1440
0.081

1440
0.033

(3)
exit

(4)
between(5)
cross-component

0.110*(0.049)0.052(0.049)0.013(0.014)-0.006(0.016)0.004(0.016)0.014(0.016)0.017+(0.010)0.003(0.010)0.037(0.025)-0.016(0.025)-0.006(0.016)-0.020(0.016)

1440
0.084

1440
0.108

1440
0.014

NOTES: Coefficients are from Prais-Winsten regressions. Panel-corrected standard errors arereported in parentheses. Observations are weighted by the value added shares. Lagged values areused for the changes in export and foreign ownership shares. All models include the fixedindustry-region effects. Statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

30

TABLE 5 Estimation results for the micro-level components of the labor share change, labor productivity growth and wage growth for the years 1978-2007
Labor share change
Labor productivity growth rate
(1)
(2)
(3)
(4)
(5)
(6)
total
within structural total
within structural
d_EXPORT(t+1)
d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t+1)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared

0.049-0.013(0.077)(0.069)-0.229** -0.099(0.078)(0.068)-0.229** -0.180*(0.082)(0.070)0.017
0.000(0.042)(0.040)-0.0130.007(0.046)(0.040)-0.0010.024(0.047)(0.041)
1392
0.063

1392
0.027

0.068
-0.018(0.044)(0.080)-0.128**0.192*(0.047)(0.080)-0.0540.150+(0.049)(0.082)0.015
0.006(0.013)(0.046)-0.021
0.023(0.019)(0.050)-0.0250.033(0.021)(0.051)
1392
0.114

1392
0.024

0.000
(0.071)0.034(0.072)0.118+(0.072)0.021(0.044)0.009(0.048)0.037(0.049)1392
0.033

Wage growth rate
(7)
(8)
(9)
total
within structural-0.0190.039(0.050)(0.053)0.157** -0.029(0.052)(0.058)0.033-0.072(0.054)(0.058)-0.0150.016(0.017)(0.044)0.016
0.011(0.023)(0.039)-0.001
0.041(0.025)(0.040)
1392
0.106

1392
0.082

0.037
(0.052)-0.037(0.057)-0.068(0.056)0.023(0.042)0.010(0.038)0.049(0.039)0.003(0.008)0.009(0.008)-0.004(0.008)-0.008(0.006)0.001(0.007)-0.007(0.007)

1392
0.090

1392
0.042

NOTES: Coefficients are from Prais-Winsten regressions. Panel-corrected standard errors are reported inparentheses. Observations are weighted by the value added shares. Lagged values are used for the changes inexport and foreign ownership shares. All models include the fixed industry-region effects. Statisticalsignificance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

31

References

Agell, J. (1999) ‘On the benefits from rigid labor markets: Norms, market failures, andsocial insurance,’ The Economic Journal 109, F143-F164

Aghion, P., and P. Howitt (2006) ‘Appropriate growth policy: A unifying framework,’
Journal of the European Economic Association 2-3, 269-314

Azmat, G., A. Manning, and J. Van Reenen (2007) ‘Privatization, entry regulation and thedecline of labor’s share of GDP: A cross-country analysis of the network industries,’ Centre
for Economic Policy Research, Discussion Paper No. 6348

Bartelsmans, E.J., and M. Doms (2000) ‘Understanding productivity: Lessons fromlongitudinal microdata,’ Journal of Economic Literature 38, 596-594

Bartelsmans, E.J., J. Haltiwanger, and S. Scarpetta (2009) ‘Measuring and analyzing crosscountry differences in firm dynamics,’ in Producer Dynamics: New Evidence from MicroData, ed. Timothy Dunne, J. Bradford Jensen and Mark J. Roberts (Chicago: University of
Chicago Press)

Beck, N., and J.N. Katz (1995) ‘What to do (not to do) with time-series cross-section data,’
American Political Science Review 89, 634-647

Bentolila, S., and G. Saint-Paul (2003) ‘Explaining movements in the labor share,’Contributions to Macroeconomics 3, Article 932

Bernard, A.B., and J.B. Jensen (2004) ‘Exporting and productivity in the USA,’ Oxford
Review of Economic Policy 20, 343-357

Bernard, A.B., J.B. Jensen, S.J. Redding, and P.K. Scott (2007) ‘Firms in international
trade,’ Journal of Economic Perspectives 21, 105-130

Blanchard, O. (1997) ‘The medium run,’ Brookings Papers on Economic Activity 2, 89-158

Blanchard, O. (2006) ‘European unemployment: The evolution of facts and ideas,’
Economic Policy 45, 6-59

Blundell, R., and S. Bond (1998) ‘Initial conditions and moment restrictions in dynamic
panel data models,’ Journal of Econometrics 87, 115-143

Chen, X., S. Lin, and W.R. Reed (2005) ‘Another look at what to do with time-series crosssection data,’ Unpublished

Diewert, W.E., and K.A. Fox (2009) ‘On measuring the contribution of entering and exiting
firms to aggregate productivity growth,’ in Index Number Theory and the Measurement ofPrices and Productivity, ed. W. Erwin Diewert, Bert M. Balk, Dennis Fixler, Kevin J. Fox
and Alice Nakamura (Victoria: Trafford Publishing)

European Commission (2007) Employment in Europe (Brussels: European Commission)

33

European Commission (2008) Income Inequality and Wage Share: Patterns andDeterminants (Brussels: European Commission)

Feldstein, M.S. (2008) ‘Did wages reflect growth in productivity?’ National Bureau of
Economic Research, Working Paper No. 13953

Foster, L., J. Haltiwanger, and C.J. Krizan (2001) ‘Aggregate productivity growth: Lessons
from microeconomic evidence,’ in New Developments in Productivity Analysis, ed. Charles
R. Hulten, Edwin R. Dean and Michael J. Harper (Chicago: University of Chicago Press)

Gollin, D. (2002) ‘Getting income shares right,’ Journal of Political Economy 110, 458474

Guscina, A. (2006) ‘Effects of globalization on labor’s share in national income,’ IMF
Working Paper No. 294

Harrison, A.E. (2002) ‘Has globalization eroded labor’s share? Some cross-country
evidence,’ Berkeley, CA: University of California at Berkeley and NBER

Helpman, E., M.J. Melitz, and S.R. Yeaple (2004) ‘Export versus FDI with heterogeneousfirms,’ The American Economic Review 94, 300-316

IMF (2007) World Economic Outlook (Washington, CD: IMF)

34

Jaumotte, F., and I. Tytell (2007) ‘How has the globalization of labor affected the labor
share in advanced countries?’ IMF Working Paper No. 298

Jayadev, A. (2007) ‘Capital account openness and labour share of income,’ Cambridge
Journal of Economics 31, 423-443

Kruger, J.J. (2007) ‘Productivity and structural change: A review of the literature,’ Journalof Economic Surveys 22, 330-363

Kyyrä, T., and M. Maliranta (2008) ‘The micro-level dynamics of declining labour share:
Lessons from the Finnish great leap,’ Industrial and Corporate Change 17, 1147-1172

Layard, R., and S. Nickell (1999) ‘Labor market institutions and economic performance,’ inHandbook of Labor Economics, Volume 3C, Orley Ashenfelter and David Card
(Amsterdam: North-Holland)

Lileeva, A. (2008) ‘Trade liberalization and productivity dynamics: Evidence from
Canada,’ Canadian Journal of Economics 41, 360-390.

Mairesse, J., and E. Kremp (1993) ‘A look at productivity at the firm level in eight French
service industries,’ Journal of Productivity Analysis 4, 211-234

35

Maliranta, M. (1997) ‘Plant-level explanations for the catch-up process in Finnish
manufacturing: A decomposition of aggregate labour productivity growth,’ in The
Evolution of Firms and Industries. International Perspectives, Ed. Seppo Laaksonen(Helsinki: Statistics Finland)

Maliranta, M. (2003) Micro-level Dynamics of Productivity Growth. An Empirical Analysisof the Great Leap in the Finnish Manufacturing Productivity in 1975-2000 (Helsinki:
Helsinki School of Economics A-230)

Maliranta, M. (2005) ‘R&D, international trade and creative destruction – Empiricalfindings from Finnish manufacturing industries,’ Journal of Industry, Competition andTrade 5, 27-58

Melitz, M.J. (2003) ‘The impact of trade on intra-industry reallocations and aggregate
industry productivity,’ Econometrica 71, 1695-1725

Meyer, M., and P. Winker (2005) ‘Using HP filtered data for econometric analysis: Some
evidence from Monte Carlo simulations,’ AStA Advances in Statistical Analysis 89, 303320

Moene, K.O., and M. Wallerstein (1997) ‘Pay Inequality,’ Journal of Labor Economics 15,
403-430.

36

Prais, S.J., and C.B. Winsten, C (1954) ‘Trend estimators and serial correlation,’ Cowles
Foundation Discussion Paper No. 383

Ravn, M.O., and H. Uhlig (2002) ‘On adjusting the Hodrick-Prescott filter for thefrequency of observations,’ The Review of Economics and Statistics 84, 371-376

Ripatti, A., and J. Vilmunen (2001) ‘Declining labor share: Evidence of a change in theunderlying production technology?’ Bank of Finland, Discussion Paper No. 10/2001

Rodrik, D. (2008) One Economics, Many Recipes: Globalization, Institutions, andEconomic Growth (Princeton, NJ: Princeton University Press)

Saxonhouse, G.R. (1977) ‘Regressions from samples having different characteristics,’ The
Review of Economics and Statistics 59, 234-237

Sauramo, P. (2004) ‘Is the labour share too low in Finland?’ in Collective Bargaining andWage Formation. Performance and Challenges, ed. Hannu Piekkola and Kenneth Snellman(Heidelberg: Physica-Verlag)

Solow, R.M. (1958) ‘A skeptical note on the constancy of relative shares,’ The American
Economic Review 48, 618-631

37

Vainiomäki, J. (1999) ‘Technology and skill upgrading: Results from linked worker-plantdata for Finnish manufacturing,’ in The Creation and Analysis of Employer-Employee
Matched Data, ed. John C. Haltiwanger, Julia Lane, James R. Spletzer, Jules J. M.
Theuwes and Kenneth R. Troske (Amsterdam: North-Holland)

Wooldridge, J.M. (2002) Econometric Analysis of Cross Section and Panel Data
(Cambridge, MA: MIT Press)

38

APPENDIXDERIVATION OF THE EQUATIONS (1) AND (2)The aggregate labor share is

(A.1)

Ft =

åw = v w
åv v
åv
i

it

it

it

t

it

i

i it

In period t plants can be divided into two groups: plants that appeared also in period t-1,
(continuing plants, denoted by C) and plants that made an entry after t-1 (entering plants,
denoted by E). Then the aggregate labor share can be written as follows:v jt w jtv w
Ft = å iÎC it it + å jÎEvt vitvt v jt
Û Ft =

(A.2)

å

v

iÎC it

vt

å
å

iÎC

wit

iÎC

vit

+

å

jÎE

v jt å jÎE w jt

vt

å

jÎE

v jt

æ å jÎE v jt ö å wit å jÎE v jt å jÎE w jt
÷ iÎC
Û Ft = ç1 +ç
÷ å vitvt
vt
å jÎE v jt
i
Î
C
è
ø
å wit å v jt å iÎC wit + å jÎE v jt å jÎE w jt
Û Ft = iÎC - jÎEvtvt
å iÎC vitå iÎC vitå jÎE v jt
Û Ft =

å w + å v æç å
çå
v
å v
è
it

jÎE

iÎC it

t

iÎC

jt

jÎE

w jt

jÎE

v jt

-

å
å

wit ö
÷
v ÷
iÎC it øiÎCIn a similar manner we can define the aggregate labor share in period t-1:

(A.3)

Ft -1 =

åwåvv w
= å i i ,t -1 i ,t -1
vt -1 vi ,t -1
i i ,t -1

i

i ,t -1

In period t-1 plants can be divided into two groups: plants that survive until period t(continuing plants, denoted by C) and plants that disappear after period t-1.
39

w j ,t -1
v wv
Ft -1 = å iÎC i ,t -1 i ,t -1 + å kÎD k ,t -1
vt -1 vi ,t -1
vt -1 vk ,t -1

å w +å v å w
v
v
å v
å v
æ å v
öå w
å v å w
Û F = ç1 +
÷
ç
÷å v
v
v
å v
è
ø
å w -å v å w +å v å
ÛF =
v
v
å v
å v
å
å w + å v æç å w - å w ö÷
ÛF =
çå v
v
å v
å v ÷ø
èÛ Ft -1 =

(A.4)

å

v

iÎC i ,t -1

iÎCi ,t -1

k ÎD k ,t -1

t -1

iÎC

i ,t -1

t -1

k ÎD

t -1

k , t -1

iÎCi ,t -1

iÎCi ,t -1t -1

i ,t -1k ÎD

iÎC i ,t -1

t -1

iÎC

t -1

iÎC

t -1

i ,t -1

k ,t -1k ÎD

k ,t -1

k ÎD k ,t -1

k , t -1

k ÎD

t -1

iÎC

i , t -1

iÎCi , t -1

k ÎD

k ÎD

k ,t -1

k ÎD

k , t -1

k , t -1

t -1

k ÎD k ,t -1

kÎD

k ,t -1

iÎCi ,t -1

t -1

k ÎD

k ,t -1

iÎCi , t -1

iÎC i ,t -1

By using (A.3) and (A.4) we obtainFt - Ft -1 =(A.5)

å w + å v æç å w - å w ö÷ çå vv
å v
å v ÷ø
è
å w - å v æç å w - å w
çå v
v
å v
å v
è
it

jÎE

iÎC ittiÎC

i ,t -1

iÎCjt

k ,t -1k ÎD

iÎC i ,t -1

t -1

jÎE

jt

iÎC

it

jÎE

jt

iÎC

it

kÎD

k ,t -1

kÎDk ,t -1ö÷÷iÎC i ,t -1 øi ,t -1

iÎCLet us denote

å

jÎE

v jt

vt

å

k ÎD

å w =F ;å w =F ;å v
å v
å w =F ;å w =F
=S ;
å v
å v

= StE ;

jÎE

jt

jÎE

vk ,t -1

vt -1

D
t -1

E

iÎC

it

C

t

t

iÎC itjtk ÎD

k ,t -1

kÎDk ,t -1

D
t -1

iÎC

i ,t -1

Ct -1

iÎC i ,t -1

We then get(A.6)

Ft - Ft -1 = Ft C - Ft C-1 + StE ( Ft E - Ft C ) - StD-1 ( Ft -D1 - Ft C-1 )

40

k ÎD

wk ,t -1k ÎD

vk ,t -1

This shows that the aggregate labor share change, i.e. Ft - Ft -1 , is the change in theaggregate labor share change among the continuing plants, i.e. Ft C - Ft C-1 , plus the entry
effect, i.e. StE ( Ft E - Ft C ) , plus the exit effect, i.e. - StD-1 ( Ft -D1 - Ft -C1 ) .

The change in the aggregate labor share among the continuing plants can be further
decomposed as follows:

Ft C - Ft C-1 =(A.7)

å w -å
å v å
iÎC

it

iÎC

wi ,t -1

iÎC

it

iÎC

vi ,t -1

æ vit
vi ,t -1 ö æ wit wi ,t -1 öÛ Ft C - Ft C-1 = 0.5 ç+÷ç å vit å vi ,t -1 ÷ çç vit vi ,t -1 ÷÷
ø
è iÎC
iÎC
øè
æ w w ö æ vit
vi ,t -1 ö
+0.5 ç it + i ,t -1 ÷ ç
÷
çv
÷ç
÷
è it vi ,t -1 ø è å iÎC vit å iÎC vi ,t -1 ø

Let us use the following expressions

å w = f ;å w
å v
å v
iÎC

iÎC

it

i ,t -1

it

å

vit
v

iÎC it= fi ,t -1; 0.5 ( fit + f i ,t -1 ) = fi ;iÎC i ,t -1

iÎC it= sit ;

åvi ,t -1
iÎCvi ,t -1

= si ,t -1; 0.5

Then, inserting (A.7) into (A.6) we obtain(A.8)

Ft - Ft -1 = å iÎC si ( fit - f i,t -1 ) + å iÎC ( sit - si,t -1 ) fi + StE ( Ft E - Ft C ) - StD-1 ( Ft -D1 - Ft C-1 )This formula has been used in Kyyrä and Maliranta (2008). In this paper, we turn thisdecomposition into a rate form. We can do this by dividing all the terms of (A.8) by theaverage aggregate labor share, i.e. F = 0.5 ( Ft + Ft -1 ) . Then we have41(A.9)

E
E
C
D
D
C
Ft - Ft -1 å iÎC si ( fit - f i ,t -1 ) å iÎC ( sit - si ,t -1 ) fi St ( Ft - Ft ) St -1 ( Ft -1 - Ft -1 )
=
+
+
F
F
F
F
F

One of our main goals in this paper is to look at the growth rate of the labor share within
the plants. Therefore we would like to develop the first component of the right-hand side of(A.9).

ås ( fit - f i ,t -1 )

iÎC i

F

(A.10)Ûå=ås ( fit - fi ,t -1 )

iÎC i

F

s ( f it - fi ,t -1 )

iÎC i

F
=

å

+

å

s ( fit - fi ,t -1 )

iÎC i

fi

s ( fit - fi ,t -1 )

iÎC i

fi
+

å

-

å

s ( f it - fi ,t -1 )iÎC is ( f it - fi ,t -1 ) é fi ùê - 1ú
fi
ëF û

fi

iÎC i

Inserting (A.10) into (A.9) we get(A.11)( fit - fi,t -1 ) +( fit - fi ,t -1 ) é fi - 1ù +Ft - Ft -1fi= å iÎC sisåiêú å iÎC ( sit - si ,t -1 ) +
iÎC
F
fi
fi
F
ëF û
StE ( Ft E - Ft C )F-

StD-1 ( Ft -D1 - Ft -C1 )F

42

TABLE A1 OLS estimation results for the micro-level components of the labor share change, labor productivity growth and wage growth for theyears 1978-2007

d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared
Adj. R-squared

Labor share change
Labor productivity growth rate
Wage growth rate
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
total
within structural
total
within structural total
within-0.0610.014-0.074+0.052-0.0730.125*-0.009-0.027(0.077)(0.072)(0.042)(0.100)(0.092)(0.052)(0.045)(0.043)-0.089-0.066-0.023
0.097
0.074
0.023
0.008
0.011
(0.065)(0.066)(0.029)(0.079)(0.080)(0.038)(0.044)(0.043)-0.0060.006-0.012
0.054
0.048
0.006
0.048
0.051
(0.041)(0.037)(0.018)(0.052)(0.049)(0.020)(0.036)(0.036)-0.058-0.017-0.042
0.094
0.069
0.025
0.036
0.041
(0.056)(0.040)(0.035)(0.062)(0.043)(0.040)(0.029)(0.026)
1440
0.184
0.157

1440
0.196
0.169

1440
0.074
0.043

1440
0.137
0.108

1440
0.179
0.151

1440
0.079
0.048

1440
0.313
0.290

1440
0.317
0.294

(9)
structural
0.017*(0.008)-0.003(0.007)-0.003(0.005)-0.005(0.008)
1440
0.076
0.045

NOTES: The estimation method is ordinary least squares. Lagged values used for the changes in export and foreign
ownership shares. All models include a full set of the industry-region and year effects. Robust standard errors
are reported in parentheses. Weighted by the value added shares. Statistical significance: + p<0.1, * p<0.05, **p<0.01, *** p<0.001.

43

TABLE A2 Estimation results for the micro-level components of the labor share change, labor productivity growth and wage growth in thetelecommunication equipment industry (NACE 32-33) for the years 1978-2007

d_EXPORT(t-1)
d_EXPORT(t-2)
d_FOROWN(t-1)
d_FOROWN(t-2)
Observations
R-squared

Labor share change
Labor productivity growth rate
Wage growth rate
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
total
within structural
total
within structural total
within structural-0.683*** -0.148-0.510**0.625** -0.040
0.603*** -0.035-0.092
0.056*(0.173)(0.121)(0.157)(0.199)(0.147)(0.163)(0.075)(0.069)(0.023)-0.560** -0.382** -0.176
0.455*
0.422**
0.043-0.096-0.1090.023(0.199)(0.126)(0.187)(0.226)(0.154)(0.195)(0.082)(0.074)(0.027)-0.3900.009-0.4250.163-0.5190.645-0.188-0.301+0.111(0.432)(0.319)(0.374)(0.500)(0.379)(0.398)(0.194)(0.180)(0.067)0.840*
0.544*
0.299-0.866+-0.267-0.529-0.008-0.0070.015(0.412)(0.256)(0.356)(0.473)(0.303)(0.402)(0.155)(0.143)(0.058)
120
0.262

120
0.187

120
0.186

120
0.179

120
-0.102

120
0.193

120
0.182

120
0.158

120
0.081

NOTES: Coefficients are from Prais-Winsten regressions. Panel-corrected standard errors are reported inparentheses. Observations are weighted by the value added shares. Lagged values are used for the changes in export
and foreign ownership shares. All models include the fixed industry-region effects. Statistical significance: +p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

44

1

Ripatti and Vilmunen (2001), Sauramo (2004), and Kyyrä and Maliranta (2008) provide earlier evidence on

the development of the labor share in Finland.

2

Bartelsmans et al. (2009) note the same fact in the context of cross-country comparisons of productivity

dynamics.
3

Maliranta (2003) provides an illustration of the features of various methods applied in the literature.

4

To be more precise, we use a chain-index procedure. Value added and wages in year t are expressed in year

t-1 prices.
5

Derivation of the Equations 1-2 is presented in the Appendix.

6

The assignment of plants to industries is not particularly problematic for two reasons. First, a plant is definedin the Annual Industrial Statistics Survey as a local kind-of-activity unit. It is a specific physical location,
which is specialized in the production of certain types of products or services. Second, in this paper we use a
relatively aggregated industry classification.
7

Helpman et al. (2004) stress that multinational firms have the highest productivity. Therefore, they could be

especially important in productivity dynamics as well. It is unfortunate that we do not have this informationfor our analysis for a period long enough. Because we take advantage of a panel of industries and regions, weare not able to incorporate import penetration into the models by using OECD’s STAN database. One
problem with the inclusion of imports would be the possible positive correlation between the changes inimports and exports.
8

Prais and Winsten (1954) present the idea of the estimation method.

9

Since the mid-1990s this sector has been dominated by the manufacture of mobile phones (and Nokia).

45