In search of an ideal method foranalyzing micro-level dynamics of agreat productivity leap∗

Mika Maliranta
The Research Institute of the Finnish Economy (ETLA), FIN-00120 Helsinki,
Finland, E-mail: mika.maliranta@etla.fi
University of Jyväskylä, FIN-40014 Jyväskylä, Finland
September 7, 2009
Preliminary: Please do not quote without permission. Comments are welcome.

Abstract
This paper has two aims; 1) to understand the micro-level mechanisms of a great
productivity leap in Finnish manufacturing industries since 1980s and 2) find amethod best suited for such analysis. Four alterantives are analytically andempirically evaluated by a use of a plant-level panel covering years 1975-2007 .
The paper has three main contributions. First, as for measuring the static OlleyPakes decomposition, a regression approach is proposed, which gives a standard
error estimate for the covariance component and allows controlling for the otherfactors of the firm productivity. Second, as for a dynamic analysis, a preferable
method with several theoretical and empiral advantages is proposed. It gives anunbiased view on the role of entries and exits as well as the reallocation between
staying firms in industry productivity growth. The firm (or plant) component
indicates the rate at which the productivity of an average input increases over time
when it stays in the same production unit. The method is partly related to a
dynamic Olley-Pakes decompositioin recently proposed by Melitz and Polanec(2009). Third, the acceleration of productivity growth in Finnish manufacturingindustries is found to be almost totally attributed to the intensified micro-level
restructuring. For the purpose of evaluating the decomposition methods, a numberof robustness checks have been made with alternative data sets, size thresholds,
time-windows and output measures.
Keywords: Productivity, index numbers, productivity decomposition methods,
industry dynamics, creative destruction, catching-up
JEL classification: C43, D24, O47, L60, J24∗ This study is funded by the Finnish Funding Agency for Technology and Innovation, TEKES(project 441/31/08). I thank Petri Böckerman and Pekka Ilmakunnas for very usefulsuggestions. Data construction and decomposition computations have been performed at
Statistics Finland following the terms and conditions of confidentiality. To obtain access tothese data, please contact the Research Laboratory of the Business Structures Unit, Statistics
Finland, FI-00022.

I. Introduction
The Finnish manufacturing sector has witnessed a great leap to the international
productivity frontier over the past three decades. To give a flavor of thesignificance of the upsurge, the labor productivity level of the Finnish
manufacturing sector was 68.6 percent of that in the United States in 1980 but had
increased to 99.6 percent by the year 1993 according to the computations made in
the Groningen University 1 . 2 This paper has two aims; 1) to understand the microlevel mechanisms of this catch-up and 2) find a method best suited for suchanalysis. Ideally, the method should have strong theoretical justifications andintuitive economic interpretations. In addition, it should not be sensitive toimperfections of empirical micro-data.
The paper has three main contributions. First, as for measuring the popularstatic Olley-Pakes decomposition (Olley & Pakes, 1996), a regression approach is
proposed, which gives a standard error estimate for the covariance component andallows controlling for the other factors of the firm productivity.
Second, as for a dynamic analysis, a preferable method with several
theoretical and empirical advantages is proposed. It gives an unbiased view on therole of entries and exits as well as the reallocation between incumbent firms in

1

Groningen

GrowthandDevelopment

Centre,ICOP

Database

1987

Benchmark,

http://www.ggdc.net

2 According to the results by O’Mahoney and Timmer (2009, page 386) for the manufacturingsector, the multi-factor productivity (MFP) level in Finland was 7% higher than in the UnitedStates in 2005. Out of 18 countries included in the comparison, Finland was at the third place
after Belgium (that had 9% higher MFP than the United states) and Ireland (54%).

3

industry productivity growth. The firm (or plant) component indicates the rate at
which the productivity of an average input increases over time when it stays in thesame production unit. The restructuring component consists of the entry, exit andbetween components. The fifth component of the method is called the crosscomponent. It essentially captures the difference between the standard measure of
the industry productivity growth rate (based on the sum-aggregates of output andinput) and the industry productivity growth rate computed by aggregating logproductivity of the firms, as typically done in the micro-level productivity
decompositions. Further, while this method takes a dynamic approach to themicro-level allocation of resources it is compatible with the view emphasized by
the modern economic growth literature that incessant restructuring is an essential
element of growth. Comparisons of the results obtained from alternative microdata sources, size thresholds, time-windows and output measures suggest that themethod is reasonably robust. Finally, an analytical and empirical comparison withan alternative novel method by Melitz and Polanec (2009) shows that bothmethods have some important similarities and seem to be complementary tools in
the analysis of the micro-level dynamics of productivity growth. By a use of afurther break-down by cohorts, the preferred decomposition method is particularly
suitable for distinguishing empirically between the phases of experimentation,
selection, restructuring and learning-by-doing in the life cycle of plants.
The third contribution of the paper is purely an empirical one. With
suitable methods and with exceptional rich and long panel data, a number ofimportant aspects in the micro-level dynamics of rapid industry productivity
growth in 15 Finnish manufacturing industries are unveiled. Three periods can be
distinguished in the development; 1) the pre-transition period (from 1976 to
1983), 2) the transition period (from 1984 to 1993) and 3) the post-transition

4

period (after 1994). The average annual restructuring component of laborproductivity growth in these three periods was 0.3, 1.3 and 0.8 percentage points,
respectively. The corresponding numbers for multi-factor productivity (MFP)
growth were larger being 1.1, 1.7 and 1.9 percentage points. Both productivityenhancing restructuring between the staying plants (i.e. the between component)
and the exit component positively contributed to industry labor productivity
growth whereas for MFP growth of the industries also the entry component
appears to have had a positive role to play, although the latter finding should beinterpreted with some caution. Furthermore, the plants that made their entry in theyears 1986-1990 is found to have had a special role in the years to come. They not
only had high productivity growth rates but they also had an important
contribution to productivity-enhancing intra-industry restructuring (i.e. the socalled “creative destruction”).
Evidence suggests that the collapse of the trade to the former Soviet Union
after 1983 triggered “creative destruction” in the Finnish manufacturing industries
that paved the way for a climb to the international productivity frontier by themid-90s. On the other hand, the results are broadly consistent with the view statedin the modern economic growth literature that micro-level restructuring is more
important for such economies and industries that are on the international
technology frontier than those far away it (e.g. Acemoglu et al., 2006; Bernard,
Redding et al., 2007).
A number of robustness checks have been performed by using alternative
measures of output, different sample selection criteria, longer time-windows inaddition to annual changes, and more importantly, alternative decomposition
methods as well as alternative firm- or plant-level data sources. These analyses
confirmed the main findings of the study but also indicated the great value of5good data and suitable methods. They indicate that the harmonization of thecomputation procedures for the international comparisons, as done by Bartelsman
et al. (2009), is crucial but also challenging task to do.
Theoretical underpinnings of this paper comes from the mutually related
literatures on economic growth (e.g. Aghion & Howitt, 2005; Caballero, 2007),
economic geography (e.g. Baldwin & Okubo, 2006) as well as international trade(e.g. Melitz, 2003; Bernard, Jensen et al., 2007; Melitz & Ottaviano, 2008). Thesemodels provide elaborated explanations for a variety of fresh empirical findings
made ever since the comprehensive micro-level data become increasingly
accessible in many countries in the 90s.
Important contributions to the literature of micro-level decompositions of
productivity growth include, among others, Baily, Hulten and Campbell (1992),
Griliches and Regev (1995), Haltiwanger (1997), Bartelsman and Dhrymes(1998), and Aw, Chen and Roberts (2001). Olley and Pakes (1996) presents amethod for decomposing aggregate productivity level into an unweighted average
effect and allocation (covariance) effect. Discussion on the strengths andweaknesses of different methods is provided, among others, by Foster,Haltiwanger and Krizan (2001), Maliranta (2003), Diewert and Fox (2009), andBartelsman, Haltiwanger and Scarpetta (2009).
To provide some background and motivation for the empirical part of thestudy, some key aspects of the Finnish manufacturing sector are highlighted in thenext section. They include the unique bilateral trade arrangement with the former
Soviet Union that climaxed in 1983 and ended in 1991 as well as the trends in thedevelopment of hours worked and productivity. In Section III, some alternative
productivity decomposition methods are described, analytically compared and6evaluated. In Section IV, the empirical analysis of the micro-level dynamics ofproductivity growth in 15 manufacturing industries is performed. In this section
alternative decomposition methods are compared empirically. Concluding
remarks are made in Section V.
II. Finnish manufacturing industries in transition
II.1

The collapse of the trade to the Soviet Union in the 1980s

The Finnish Great depression is thoroughly described in several studies including,for example, Honkapohja et al. (1996), Kiander et al. (1998) Honkapohja andKoskela (1999), Honkapohja et al. (2009), and Gorodnichenko et al. (2009).
These studies typically share a couple of emphasis; one is the exceptional severityof the recession and another is the failure in the policy, including the deregulationof financial markets in the 1980s as well as monetary and fiscal policy. Also badluck is recognized as one ingredient referring to the collapse of export to theSoviet Union. Yet, few studies acknowledge this as a main triggering factor, thepaper by Gorodnichenko et al. (2009) being perhaps the main exception (see also
Tarkka, 1994).
This paper takes a notice to two important points pertaining to the trade
between Finland and the Soviet Union. The first is the timing of the collapse andthe second its potential implications for the micro-level dynamics, both interesting
aspects from the point of view of this study. A series of collapses in the trade to
the Soviet Union started after 1983, i.e. several years before the great depressionof the early 1990s (see Graph 1). The decline in exports to the Soviet Union
between years 1983 and 1989 can be largely attributed to a decrease in the world
crude oil prices for two reasons. First, Finland had a bilateral clearing
arrangement with the Soviet Union unique among developed market economies.

7

According to the system, trade should be balanced annually and only relatively
small temporary imbalances were allowed. This feature of the system can also
been seen in Graph 1 where imports from the Soviet Union per total value of
production in the manufacturing industries is represented by a dashed line.
Second, mineral fuels accounted for above 80 percent of the imports from theSoviet Union in the early 1980s. The sharp decline in the world crude oil prices
was found difficult to be compensated by an increase of other forms of imports,
which led to a need to cut down exporting (see Sutela, 1991). The collapse of the
trade after 1989, in turn, can be attributed to the political changes that took placein the Soviet Union in the late 1980s (the so-called Perestroika) which finally led
to an end of the bilateral trade system and a further drop in the trade (see e.g.
Gorodnichenko et al., 2009). From the point of view of this study it is worth
noting that the reasons for the collapses of the trade with the Soviet Union
between years 1983 and 1991 stem from the outside of the Finnish manufacturingindustries, first it was a world oil prices and later it was the political changes in
the Soviet Union.
< GRAPH 1 ABOUT HERE>
The second important aspect of the trade with the Soviet Union has to dowith implications for micro-level structures. For one thing, opportunities to export
to the Soviet Union concerned only a limited number of the firms (and in practiceplants), and for another exporting was very profitable to these firms. Sutela (1991)
reports that 600-700 firms exported to the Soviet Union in the seventies. By 1989
the number of these firms had increased to 1688 but exporting to the Soviet Union
was still highly concentrated. A total of 116 exporters accounted for 90 percent of
all export. As for profitability, Kajaste (1992) estimates that the prices of exports
to the Soviet Union were at least 9.5 percent higher than those for exports to8western markets. Gorodnichenko et al. (2009) estimates that this markup was aslarge as 36 percent.
So, exporting to the Soviet Union was highly profitable to the firms
involved but on the other hand this opportunity concerned only a limited numberof firms (and plants). All in all, the bilateral trade with the Soviet Union meant asignificant subvention system to a part of the incumbent firms. Consequently, thedownfall of the system since 1983 can be expected to have led a profound changes
in the structures not only between different industries, as emphasized in
Gorodnichenko et al. (2009), but also between firms (and plants) within
industries. The fact, reported by Kajaste (1992), that a large proportion of exports
was highly specialized to the Soviet markets and was difficult to convert towestern markets just strengthens the point. It seems that new technology and newplants were needed to replace the capacity build for serving the Soviet markets.
The years from 1983 to 1991 represents a period of a moderate increase in export
to other countries (mainly to western markets) which seems more like acontinuation of the trend started in the 70s rather than a reflection of a major
relocation of exports. All in all, it seems that Finland can be described as a semitransition economy during the years from 1983 to 1993 (see Tarkka, 1994).
II.2

Other developments

A trend of declining labor input in the Finnish manufacturing industries started at
the beginning of the 1980s and took a steeper slope during the yearly 1990s.
Between the years 1983 and 1990 the number of hours worked had decreased by
12.8 percent and between the years 1983 and 1993 by 34.3 percent. The years
from 1993 to 2001 represent a period of uninterrupted growth in terms of labor
input. The number of hours worked increased by 20.4 percent (see Graph A2.1).

9<GRAPH 2 ABOUT HERE>

Besides labor input growth, it is an interest of this paper to have a look at
the development of the other source of economic growth, i.e. labor productivity
growth. Graph 2 presents two measures of productivity. The first is the
productivity level of the Finnish manufacturing sector relative to that of theUnited States. The relative labor productivity level in 1987 is obtained from thedatabase of Groningen University (the Groningen Growth and Development
Centre, ICOP Industrial Database, 1987 Benchmark). Other years are extrapolated
by using productivity time-series obtained from various sources (see further
details in the note of the graph). The graph shows a great leap if in Finnish
manufacturing productivity between the years 1983 and 1993. The second
productivity measure shown in Graph 2 is the annual labor productivity growth(scale on the right). Both the observed and the smoothed series are shown. 3 It canbe seen that an important part of the catch-up is due to the acceleration of
productivity growth in Finland after 1983. Before we make use of plant-level data
to examine in depth the micro-level dynamics of productivity growth in the
Finnish manufacturing industries in Section IV, some methods useful in such ananalysis are first examined in Section III.
III. Decomposition methods
III.1 Method 1, static approach
The industry productivity index Φ can be defined as follows:

3 The growth rates are smoothed by using the Hodrick-Prescott filter with a parameter of 6.25, as

recommended by Ravn and Uhlig (2002)

10

Φ1 = ∑ si1ϕi1

(1)

i∈Ω1

where si1 and ϕi1 are the share of firm i in an industry in period 1 and its
productivity index defined as;

si1 =

Li1

∑

Li1
i∈Ω

ϕi1 =

Yi1
Li1

(2)

1

(3)

where Li1 and Yi1 denote labor input and output, respectively. 4

Inserting (2) and (3) into equation (1) gives the form

Yi1 ∑ i∈Ω1 Y=i∈Ω1 ∑ i∈Ω Li1 Li1∑ i∈Ω L

Φ1 = ∑

Li1

1

(4)

1

which is the standard aggregate (or industry) labor productivity measure that isbased on the sum-aggregate values of output and input and that can be obtained
from industry-level data such as the EU-KLEMS database. From the standpoint of
performing micro-level decompositions of productivity it is useful to note that thestandard aggregate labor productivity level is a labor input weighted arithmetic
average of the firm productivity levels (see e.g. Van Biesebroeck, 2003).
Alternatively, firm productivity can be measured in terms of the log-unitsas4 Later we discuss the use of multi-factor productivity index that include more than one input.

11

Y
Li1

ϕi1 = ln i1(5)

In this case we obtain a measure of industry productivity that is alsomeasured on the log-scaleΦ1 = ∑ si∈Ω ,1ϕi1 ∑
i∈Ω1

i∈Ω1

∑

Li1
Li1
i∈Ω

ϕi1

(6)

1

It should be noted that exp ( Φ1 ) is a weighted geometric average offirms’ productivity indices defined in (3).
The firms in period 1 can be classified into two groups: “stayers”, whichappeared also in the previous period 0, and “entrants”, which did not exist inperiod 0. The former group is denoted by Ω S and the latter by Ω N . The industry
productivity index can then be expressed as
Φ1 = ∑ si1ϕi1 + ∑ s j1ϕ j1
i∈Ω S

(7)

j∈Ω N

where ∑ si1 + ∑ s j1 = 1 by definition.
i∈Ω S

j∈Ω N

Aggregate industry productivity index can be written as

Φ1 = (1 − S1entrant ) Φ1stayer + S1entrant Φ1entrant= Φ1stayer + S1entrant ( Φ1entrant − Φ1stayer )(8)where S1entrant = ∑ j∈Ω s j1 = 1 − ∑ i∈Ω si1 is the employment share of the entrantsNS(see Maliranta, 1997b; Vainiomäki, 1999; Diewert & Fox, 2009).

12

The second component in the second row of (8) indicates the contributionof the new firms to the current industry productivity level, i.e. how much lower orhigher industry productivity level would be without the entrants. The component
is positive when the aggregate productivity (i.e. the weighted average
productivity) of the new firms ( Φ1entrant ) is higher than that of the stayer firms( Φ1stayer ). The magnitude of the effect is dependent on the share of the new firms
in period 1 ( S1entrant ) and the log-difference in the productivity level between thestayers and the entrants.
The industry productivity index can be decomposed into two components
by using the static Olley-Pakes decomposition (Olley & Pakes, 1996):
Φ1 = ϕ1 + ∑ i∈Ω ( si1 − s1 ) (ϕi1 − ϕ1 )1

= ϕ1 + cov ( si1 , ϕi1 )(9)Obviously a similar decomposition can be made separately for the stayers
Φ1stayer = ϕ1stayer + cov1stayer ( s, ϕ )(10)

Φ1entrant = ϕ1entrant + cov1entrant ( s, ϕ )(11)and for the entrants

By inserting (10) and (11) into (8) we obtain (see Melitz & Polanec, 2009)Φ1 = ϕ1stayer + cov1stayer ( s, ϕ ) + S1entrant ( Φ1entrant − Φ1stayer )(12)

and further
Φ1 = ϕ1stayer + cov1stayer ( s, ϕ ) +S1entrant (ϕ1entrant − ϕ1stayer ) + S1entrant ( cov1entrant ( s, ϕ ) − cov1stayer ( s, ϕ ) )

(13)

13

The industry productivity level consists of three (equation (12)) or four
components (equation (13)). In the latter both the stayers and the entrants have
two sub-components; the average and covariance component. The covariancecomponent of the stayers is the difference between the weighted (i.e. aggregate
productivity level) and the unweighted average of the stayers (see (10)). It is
positive when there is a positive relationship between the productivity level andthe employment share among the stayers. Accordingly, entrants can contribute to
the industry productivity level through two channels; through the averagecomponent and the covariance component. The former is positive when theunweighted average productivity of the new firms is higher than that of thestayers. The covariance (or allocation) component of the entrants is positive when
the covariance between the size and the productivity level is larger among theentrants than among the stayers (see (13)).
The productivity index is useful in the comparisons of productivity, i.e.
measuring productivity growth or relative productivity levels. Equations (12) and(13) can be used for analysing the micro-level sources of the productivity gaps
between different countries or between different regions of the same country, forexample. However, more common is to compare productivity levels between two
different points of time, which leads to a dynamic approach of analyzing
productivity and its micro-level sources.
One attractive feature in the static Olley-Pakes decomposition is that thecovariance (or allocation) component can be estimated by using ordinary leastsquare (OLS) method. Consequently, it is possible to estimate the standard errorof the covariance component and thus evaluate its accuracy. To my best
knowledge this possibility has not utilized in the literature so far.

14

Let us assume that the log-productivity of firm i, that is ϕi , includes a
deterministic component α U and a stochastic component ε i as follows;

ϕi = α U + ε i , i = 1,..., n; ε i ∼ N ( 0, σ 2 )(14)

As well-known, in this case the unweighted OLS estimate of thedeterministic component isαˆ U =∑ ϕ =ϕ
i

i

n

(15)

where n is the number of firms.
Next, let us consider a transformed model
Li ϕi = Li α W + Li ε i , i = 1,..., n; ε i ∼ N ( 0, σ 2 Li )(16)

It is straightforward to show that the OLS estimate of the deterministic
component isLLαˆ W = ∑ i i ϕi = Φ(17)Inserting (15) and (17) into (9) givescov ( si1 , ϕi1 ) = αˆ W − αˆ U = λˆ(18)After having applied a seemingly unrelated estimation to the models (14)and (16), the coefficient λ̂ and its standard error can be computed by a linear
combination post-estimation. Besides providing a gauge of the accuracy for thecovariance component, the approach also allows controlling for other potentiallyconfounding factors of productivity in a straightforward manner. Because the15

covariance component is computed by the intercepts, a suitable parametrization
for the coefficients of the control variables is needed, for example by means of theparameter constraints. The aim is that the intercept indicates the productivity levelat the sample average.
III.2 Dynamic approach

Next we are interested in the change of productivity between two periods, 0 and 1.
The industry productivity level in period 1 is defined as in (5) above. The industry
productivity level in period 0 is defined analogously except now the firms areclassified into the following two groups; the stayer firms and the exiting firms.
The former group consists of the firms that appear both in period 0 and period 1
and the latter group of those that do not appear in period 1.
Now the industry productivity level in period 0 can be written as follows
Φ 0 = ∑ si 0ϕi 0 + ∑ sk 0ϕk 0
i∈Ω S

(19)

k∈Ω X

where ∑ si 0 + ∑ sk 0 = 1 .
i∈Ω S

k∈Ω X

The industry productivity level can be derived into a form that is
analogous to that of (8);
Φ 0 = Φ 0stayer + S0exit ( Φ 0exit − Φ 0stayer )(20)

The next task is to decompose the industry productivity change ΔΦ1 :ΔΦ1 = Φ1 − Φ 0= Φ1stayer − Φ 0stayer + S1entrant ( Φ1entrant − Φ1stayer ) + S0exit ( Φ 0stayer − Φ 0exit )

(21)

16
III.2.1 Method 2, Melitz-Polanec decomposition (dynamic Olley-Pakes)

One way to proceed from expression (21) is the one proposed by Melitz andPolanec (2009). Since productivity is measured in the log-units the components
directly gives the measures for growth rates.
By noting that
Φ1stayer = ϕ1stayer + cov1stayer ( s, ϕ ) andΦ 0stayer = ϕ0stayer + cov0stayer ( s, ϕ )the formula (21) can be derived into the following form:⇔ ΔΦ1 = Δϕ1stayer + Δ cov1stayer + S1entrant ( Φ1entrant − Φ1stayer ) − S0exit ( Φ 0exit − Φ 0stayer ) (22)The first component is the firm component, which is the change of theunweighted productivity level of the stayer firms. The second component is thechange in the covariance among the stayers in period 0 and 1. The third
component of (22) , the entry component, consists of two sub-components;
S1entrant ( Φ1entrant − Φ1stayer )= S1entrant (ϕ1entrant − ϕ1stayer ) + S1entrant ( cov1entrant ( s, ϕ ) − cov1stayer ( s, ϕ ) )(23)

The first sub-component in (23) is the difference between unweighted
average productivity level of the new and stayer firms multiplied by theemployment share of the entrants. The second sub-component is the difference
between the covariance term among the new firms and stayer firms multiplied,again, by the employment share of the entrants.
Analogously, the fourth component of (22), the exit component, takes thefollowing form;

17

S0exit ( Φ 0exit − Φ 0stayer )= S0exit (ϕ0exit − ϕ0stayer ) + S0exit ( cov 0exit ( s, ϕ ) − cov 0exit ( s, ϕ ) )(24)

III.2.2 Method 3

An alternative way is to proceed from (21) by making use of a Bennett (1920)
type of decomposition for the stayer firms:⇔ ΔΦ1 = ∑ i∈Ω si stayer ⋅ Δϕi + ∑ i∈Ω ϕi ⋅ Δsistayer +S

S

S1entrant ( Φ1entrant − Φ1stayer ) + S0exit ( Φ 0stayer − Φ exit
0 )

(25)

where si stayer = 0.5 ( sistayer+ sistayer) is the average share, Δϕi = ϕi1 − ϕi0 is the0
1
productivity change rate, ϕi = 0.5 (ϕi 0 + ϕi1 ) is the average productivity level, andΔsistayer = sistayer− sistayer
is the share change of firm i . Note that all shares refer to the10
stayers only so that ∑ i∈Ω si stayers = 1 .
S

The first term on the right-hand side of (25) is the firm component. It is theweighted average of the productivity change of the stayer firms, i.e. among thefirms for which the change concept is relevant. Hence, the firm component gauges
the rate at which productivity of an average input increases over time when it
stays in the same production unit. Each firm is weighted by the average input
share (average over period 0 and period 1), which eliminates the bias in themeasure that would appear if the firms were weighted by using only the input (oroutput) share in period 0 (or period 1) (see e.g. Maliranta, 1997a, page 19; Foster
et al., 2001, page 317).
The second component is the between component, which measures thecontribution of resource reallocation between the staying firms to industry
productivity growth. Equation (25) is basically similar to the methods used by

18

Vainiomäki (1999), Maliranta and Ilmakunnas (2005), and Kyyrä and Maliranta(2008) but applied in different contexts. It is identical to that of Griliches andRegev (1995) when applied to a balanced panel (i.e. to the staying firms only).
The formula (25) has at least three important properties. First, the firmcomponent is intuitive and easy to interpret. It eliminates such labor productivity
effects that emerge when an input unit moves to another firm which may havemore capital per labor input or capital embodies better technology. Consequently
the firm component provides a kind of proxy for disembodied technologicalchange. While each firm is weighted by the input share this method, unlike thedynamic Olley-Pakes method (Method 2), Method 3 takes into account the factthat the productivity growth of large firms is more important for industry
productivity growth than that of smaller firms. Second, the method is symmetric,
which means that if the roles of periods 0 and 1 are reversed then all components
have opposite values (see Diewert & Fox, 2009). For example, a popular methodby Foster, Haltiwanger and Krizan (2001) lacks this desirable property. For an
illustrative demonstration of this, see Maliranta (2003, pages 97-8). Last but notleast, Diewert (2005) has shown that this decomposition has a strong axiomatic

justification.
The third component of formula (25) is the entry component. An importantfeature of it is that the productivity level of the new firms is compared to that of
the staying firms in the same period 1. For example, in the Foster-HaltiwangerKrizan method the productivity level of the new firms are compared to that of thestaying firms in the past. As demonstrated by Maliranta (2003) this leads to an
exaggereation of the contribution of the new firms to industry productivity growth
especially when the time-window is long (e.g. 5 years) or when the generaltechnical progress is rapid (e.g. in the manufacture of telecommunication

19

equipment in recent years) (see also Melitz & Polanec, 2009). Contrary to theother methods, the interpretation of the entry component of formula (25) isstraightforward since it provides an intuitive counterfactual. It indicates how
much higher (or lower) industry productivity growth would have been if none of
the new firms had made an entry between two periods. Of course, now it isassumed that new firms do not have an indirect productivity effect on other firms(see Aghion et al., 2009).
The fourth component, the exit component, is analogous to that of theentry component, which is one of the merits of the method. The productivity levelof the exiting firms is compared to that the staying firms in the same period 0. The
exit component shows how much higher or lower industry productivity growth
between two periods would have been if none of the firms had made an exit andwere able to improve their productivity as much as the staying firms (which ismeasured by the firm component).
The method given in (25) can be interpreted in alternative ways. The sumof the entry and exit components (i.e. net entry) is the difference in aggregate
productivity change among all firms (unbalanced panel) and among the staying
firms only (balanced panel) (Maliranta, 1997b). Alternatively, the difference
between aggregate productivity and the firm productivity change (i.e. the firmcomponent) indicates how well (or badly) the so-called representative firm model
describes the evolution of productivity. The gap consists of three distinct microstructural factors; entries, exits and the reallocation of resources between staying
firms.
For sake comparing Method 2 and Method 3 it is useful to note the link
between equations (22) and (25). The difference between these two methods is20that productivity growth among the staying firms is decomposed differently
whereas the entry and exit components are the same;

∑

i∈Ω S

si stayer ⋅ Δϕi + ∑ i∈Ω ϕi ⋅ Δsistayer = Δϕ1stayer + Δ cov1stayer

(26)

S

If the size and the productivity growth rate of the firms are perfectly
uncorrelated then we have ∑ i∈Ω si stayer ⋅ Δϕi = Δϕ1stayer . In this case the between
S

component of expression (25) indicates that the covariance between share andproductivity level has increased among the staying firms between periods 0 and 1.
III.2.3 Method 4

A problem with the Method 2 (equation (22)) and Method 3 (equation (25)) is thatthe aggregate productivity growth rate does not correspond to the standard
industry productivity measure, and not necessarily even approximately, i.e.

ln

Φ1 Φ1
≈/
Φ0 Φ0

(27)

This may be a bit bothersome when one wishes to interpret the micro-levelmechanisms behind the official industry productivity series, for example. To
tackle this problem, Maliranta (2003) proposes a use of the following industry
productivity growth as the starting point of decomposing micro-level sources of
productivity growth;Φ1 − Φ 0Φ

(28)

which is a close approximation of the standard measure of industry productivity
change, i.e. ln ( Φ1 Φ 0 ) ≅Φ1 − Φ 0(see Davis & Haltiwanger, 1999).
Φ21

This measure can be decomposed as follows
Δϕi
Δϕi ⎛ ϕi ⎞ϕiΔΦ1stayer= ∑ i∈Ω si stayer+ ∑ i∈Ω si stayer⎜ − 1⎟ + ∑ i∈ΩS ⋅ ΔsiSSΦϕi
ϕi ⎝ Φ ⎠ΦΦ entrant − Φ1stayer )entrant ( 1

+ S1

Φ

Φ 0stayer − Φ 0exit )exit (

+ S0

(29)

Φ

The first and the third components on the right-hand side of equation (29)
are the firm and between components, respectively. They are approximately thesame as those of Method 3, i.e.

∑
∑

i∈Ω S

ϕi
i∈Ω S

Φ

Δϕi

≈ ∑ i∈Ω si stayer ⋅ Δϕi(30)⋅ Δsistayer ≈ ∑ i∈Ω ϕi ⋅ Δsistayer(31)

si stayer

ϕi

S

S

The basic idea of the entry and exit components of equation (29) is thesame as those of (22) and (25). Therefore one would not expect major differences
between these three methods for the entry and exit components. Consequently,
Method 4 shares the same desirable properties with Method 3 discussed above.
The second component of equation, which can be called the crosscomponent, constitutes a departure from Method 3 since it does not exist there. As
the other components should be mutually reasonably similar in Method 3 andMethod 4, the cross-component of Method 4 should be reasonably close to the“aggregation bias” indicated in (27). The cross-component may also have an
economic interpretation. It has a resemblance with the firm component but

ϕincludes an additional factor ⎛⎜ i − 1⎟⎞ that is negative for those firms that have⎝ Φ ⎠had a lower than average productivity level in the period. The cross-component of
Method 4 indicates the combined effect of a high productivity level and a high22

productivity growth rate on standard aggregate productivity growth. When two
firms are of the same size and have the same productivity growth rate, then the
one having a higher productivity level has a greater contribution to the standard
industry productivity growth. On the other hand, one might expect that firms
having low productivity levels have high productivity growth rates due to thecatching-up potential, which may yield negative values for the cross-componentof Method 4.
III.3 Contribution to the components by firm groups

Each component is the sum of the contributions of the firms involved.
Accordingly, with a decomposition method one can gauge how much a certain
group of firms contribute to industry productivity, and more interestingly, throughwhich micro-level channel.
The splitting of the components by the firm group can be made with thefollowing extension of equation (29);
J
⎡
Δϕi ⎤
ΔΦ1
= ∑ ⎢ ∑ i∈Ω si stayer
⎥+
S ,g
ϕi ⎦
Φ
g ⎣
J

⎡

∑ ⎢∑
g

⎣

J

⎡

∑ ⎢∑
g

⎣

J

⎡

∑ ⎢⎢ Sg⎣i∈Ω S , gi∈Ω S , gentrant
1, g

si stayerΔϕi ⎛ ϕi⎞⎤⎜ Φ − 1⎟ ⎥ +ϕi ⎝⎠⎦⎛ ϕi ⎞
stayer ⎤⎜ − 1⎟ ⋅ Δsi
⎥+
⎝Φ ⎠
⎦

(Φ

entrant
1, g−Φ

stayer1

Φ⎡⎤Φ 0stayer − Φ exit
0, g )
exit (⎢⎥
S∑g ⎢ 0, gΦ⎥⎦⎣, g = 1, ..., J) ⎤⎥ +

(32)

⎥⎦

J

where Ω S , g denotes the group of the staying firms that belong to group g ,S1,entrant
is the input share of the new firms belonging to group g in period 1, S0,exitgg

23

is the input share of the exiting firms belonging to the group g in period 0,
Φ1,entrant
is the aggregate productivity of the new firms in period 1 belonging to theggroup g and Φ exit
0, g is the aggregate productivity of the exiting firms belonging to
the group g in period 0.

Each of the five components of Method 4 is split into J parts by the firmtype (g = 1,…,J). Note that the between component in the fourth row of (32) is
modified by ‘rescaling’ the relative productivity level that is done by replacing theterm (ϕi Φ ) by (ϕi Φ − 1) . The added minus 1 term is redundant for thecomputation of the between component over all staying firms sinceJ∑ ⎡⎢⎣∑
gi∈Ω S , gΔsistayer ⎤⎥ = 0 by definition. However, the term is useful for interpreting⎦the contribution of each firm and computing the contribution of a group of thefirms to the total component. A firm that has a productivity level lower (higher)
than that of the industry positively contribute to the between component if it has
decreased (increased) its input share. A group of the new firms positively
contributes to the entry component when their aggregate (or weighted average)
productivity level exceeds that of the industry in period 1. Finally, a group of
exiting firms positively contributes to the exit component when their aggregate
productivity level is lower than the industry productivity level in period 0.
For interpreting how a certain firm group has contributed to industry
productivity growth through distinct micro-level mechanisms it is useful tocompare the relative productivity contribution to its input share. In cases of thefirm, between and cross-components, the relevant baseline is the average input
share (averaged over periods 0 and 1) among the stayer firms, i.e.

24

S

stayerg∑= J

i∈Ω S , g∑∑g

si stayeri∈Ω S , gsi

= ∑ i∈Ω si stayer

(33)

S ,g

stayer

As for the entry component a relevant comparison point is the input shareof the firm group in period 1 among all firms;
S1, g = ∑ i∈Ω si1

(34)

g

Analogously, a relevant comparison point for the interpretation of the exit
component is the input share of the group in period 0 among all firms;
S0, g = ∑ i∈Ω si 0

(35)

g

III.4 Decomposition of multi-factor productivity (MFP)

So far we have only considered a one-input case in the productivity measurement.
All the analysis above can be generalized for a more comprehensive measure of
performance that takes into account the efficiency in the use of several types of
input. All that is needed is to replace input L by some appropriate input index X.
In this paper we use a very simple and common alternative that is

X = Lα K (1−α )(36)

In the growth accounting literature it has been standard to estimateparameter α by the labor compensation (wages plus supplements) to value added
ratio computed as an average of the initial and end period, i.e. the average labor
income share;⎛ wt −1 ⋅ Lt −1 wt ⋅ Lt ⎞+⎟VALt ⎠⎝ VALt −1

α = 0.5 ⎜

(37)

25

where w is the unit price of labor, L is labor input and VAL is the nominal value
added. 5
Now si1 and ϕi1 denote input share and multi-factor productivity,
respectively;

si1 =

X i1

∑

X i1
i∈Ω

ϕi1 =

Yi1
X i1

(38)

1

(39)

The relative productivity levels between different firms or productivity
growth rates are independent of the units in which L or K are measured as long as

α is constant over all firms (and years) that are compared. The point can be easily
demonstrated more formally as follows;

RMFPij =Yi
Yj( L ( aK ) ) = Y ( L ( K ) ) a
1−α i

αi

(

i

i

L j j ( aK j )
α

1−α j

1−α i

αi

)

i

Yj

(

i

i

Lj j ( K j )
α

1−α j

)

α j −α i(40)where RMFPij is the MFP level of firm i relative to that of firm j, and a is
the unit in which capital is measured in the data. We note that RMFPij isindependent of the value of a when α i = α j . Yet, some of studies use varying
coefficients including (see e.g. Disney et al., 2003). Since the results for MFP arearbitrary when α vary between firms it is not surprising that labor productivity
and MFP yield different results in such studies.

5 Van Biesebroeck (2008) has empirically compared various alternative techniques of measuring

productivity. He finds that different methods yield surprisingly similar results.

26

There are a number of different theoretical and empirical approaches todetermineα(see e.g. Diewert & Nakamura, 2007). The approachnotwithstanding, it seems natural to expect that α varies between different
industries or different phases of the economic development, so it isrecommendable to perform decompositions separately for the different time
periods and industries (see e.g. Böckerman & Maliranta, 2007).
One should note, however, that generally

∑

(

i∈Ω1

X i1 = ∑ i∈Ω Lαi1 K i(11−α ) = X 1
1≠ ∑ i∈Ω Lit1)(α∑ i∈Ω Kit
1

)

1−α

(41)

The inequality in the second row implies that in the multi-input caseindustry productivity growth obtained by the decomposition method is not equal
to that obtained by using a sum-aggregate of output and input. So, in a sense
Method 4 is not, using a concept of the index theory, ‘consistent in aggregation’(see e.g. Theil, 1967; Vartia, 1976). It should also be noted that the use of output
share instead of input shares in the decomposition method (e.g. Foster et al., 2001)
does not solve this problem for it is still the case that the industry productivity
change computed with a decomposition method cannot be derived from theindustry level variables of output and inputs.
IV. Empirical analysis
IV.1 Data sources

The data source of the baseline analysis of this study is longitudinal data on plants
in Finnish manufacturing (LDPM data). It is constructed from the annual
manufacturing surveys especially for research purposes. Nowadays these data27

cover years 1974-2007. 6 The data have at least three important advantages for ananalysis of micro-level sources productivity growth. First, data include detailed
information on labor input (hours worked), capital input (capital stock estimatedby perpetual inventory method) and output (value added and gross output).
Second, data content and coverage is comparable over a long period of time,
which is crucial for analyzing changes in trends. To improve the comparability ofdata over time further, only those plants are included in the sample that have atleast five persons and are owned by a firm that has at least 20 persons. Third, thecoverage is good. Despite removing some plants for sake of unreliable
information and fine-tuning the comparability over time the sample accounts forabout three quarters the total number of hours worked in the manufacturing sector,
as estimated on the basis of numbers obtained from the National Accounts. Moreinformation on data and computation protocols followed in the analysis is given in
Appendix 1. Maliranta (2003) provides a detailed description of the LDPM data
set and its main properties.
As for a robustness check three other alternative data sources are used inthis study. First of these is the Business Register data on plants (BR data) that
include principally all manufacturing plants (also the smallest ones) in Finland.
The disadvantages of this data set are a shorter time-span (years 1990-2007), lackof a measure for hours worked and value added. The second alternative data
source is the Structural Business Statistics data (SBS data) on firms that coveryears 1994-2007. In principal it covers all firms. It also includes both value added

6 Year 1974 is not included in the productivity analysis of this paper because of a break in theindustry-specific price index series between the years 1974 and 1975.

28

and gross output information but not hours worked. 7 As a firm-level data set it
provides an opportunity to complementary analyses to those conducted with the
LDPM data source. The third alternative data source is a commercial database on
firms (Asiakastieto Inc.) that is developed further in the ETLA for research
purposes. This data set is substantially smaller than the SBS data set both in termsof coverage and time-span (2001-2007). The quality of the Asiakastieto data islikely to correspond to the quality of a great majority of the samples used in theeconomic research. Hence a robustness check with these data gives an indicationof the reliability and comparability of the results made with less perfect data. The
checks made with alternative data sources are useful not only for evaluating theuncertainty or comparability of the results between different countries. They also
should help to identify suitable indicators of micro-level dynamics that are not
only relevant but also reliable enough for conclusions.
Value added and gross output in year t is converted in year t-1 prices byusing the implicit corresponding industry-level price indexes computed from the
National Accounts.
IV.2 Baseline results

The baseline results of the paper are based on Method 4 and LDPM data. Later
some comparisons with other methods are presented. All computations are made7 The number of persons is the average during the accounting period. Hence, a person who has

been employed in the firm for six months corresponds to half an employee. On the other hand,
part-time employees are not converted to full-time equivalents. The number of persons
engaged include workers, salaried employees and entrepreneurs. It also includes, for example,
employees on sick leave or maternity leave or laid-off for a fixed period. For more detailed
information

on

the

Structural

http://tilastokeskus.fi/til/tetipa/kas_en.html.

Business

Statistics

see

29

at the industry level (15 industries) and separately for each pair of the successive
years. The industry-level labor productivity results are aggregated to the level ofthe total manufacturing using input shares of the industries (the average of theinitial and end year). The industry MFP results are aggregated by using nominal
value added shares. 8 In this way comparisons between “oranges” and “apples” areavoided (see e.g. Sørensen, 2001) and the role of industry-level restructuring iseliminated. More technical details on these computations are given in Appendix 1.
Graph 3 displays the main general patterns in the development of
productivity at the different levels of aggregation in the Finnish manufacturingsector by means of a productivity index. 9 The index shows the cumulative effectof the plant component 10 (solid line), the aggregate component of the industrylevel computations 11 (dashed grey line) and the aggregate component of the total
manufacturing computations (dashed black line). 12 The gap between the plantlevel and industry-level productivity index indicates the cumulative effect of the

8 The use of labor hours in aggregation of the industry MFP results yielded quite similar results(not reported here).

9The

productivity

index

is

INDt = INDt −1 × (1 + 0.5 × at ) × (1 − 0.5 × at )−1

measuredasfollows:

where at is the component of the annual

growth rate of aggregate or firm component in t obtained by Method 4. The starting point is set
as IND1975 = 100.
10 Note that the observation unit of LDPM data is the plant not the firm.

11 The industry-level components are aggregated to the level of the total manufacturing by using

industries’ input shares as weights.

12 These components have been computed at the total manufacturing level.

30

micro-level restructuring and the cross-component within industries. 13 The gap
between the industry-level and the total manufacturing-level series shows thecontribution of restructuring (and the cross-component) between industries. Threeinteresting findings emerge from the graph. First, the cumulative effect of therestructuring over the period from 1975 to 2007 is considerable; according tothese estimates the level of the labor productivity would have been 41% (=(550327)/550) lower without any productivity-enhancing restructuring during theperiod from 1975 to 2007. Second, the main part of productivity-enhancing
restructuring has taken place within industries. Third, the gap between the plant
productivity and the industry productivity development started to widen in theearly 1980s.
<GRAPH 3 ABOUT HERE>

Graphs 4 and 5 showing the annual productivity growth components have
two purposes; first, changes in development are more discernible and, second, theseparate contributions of the different sub-components can be distinguished.
Graph 4 shows the components of industry labor productivity growth and Graph 5
those of MFP.
A number of findings can be made from these two graphs. First, aggregate
labor productivity growth computed from the LDPM sample is reasonably similar
to that shown in Graph 2 that is based on the National Accounts. Though, itshould be noted that some differences would not be a surprise. The effect ofindustry restructuring is eliminated in Graph 4 and Graph 5 but not in Graph 2.

13 To be more precise, here the gap is the joint effect of the structural components (the between,

entry and exit components) and the cross-component.

31

Moreover, labor productivity growth series of the National Accounts involvesome imputations whereas in this analysis the main primary data is utilized
without any further adjustments or corrections.
<GRAPH 4 ABOUT HERE><GRAPH 5 ABOUT HERE>
Second, the plant component is the most important component. Typicallyit constitutes 50-80 percent of industry labor and MFP productivity growth. On
the other hand, it should be noted that this is a tremendous departure from the 100
percent that is assumed in the representative plant/firm model having still
important role in the economic growth literature, for example.
Third, the micro-level restructuring has a significant contribution toindustry labor productivity growth and is even more important to MFP growth.
What is more important, the restructuring component has an important role inexplaining the acceleration of labor and MFP growth after 1983. In other words,
the micro-level restructuring appears to explain the increase in labor productivity
growth shown in Graph 2. It seems to have driven the catching-up of theinternational labor productivity frontier, which is here approximated by the
productivity level of the US manufacturing sector.
Fourth, some “chilling” in productivity-enhancing restructuring can befound in the mid-1990s which is about the same time when Finland almost caught
up with the United States in labor productivity level. On the other hand, theproductivity-enhancing restructuring has been a more important contributor ofindustry productivity growth in the post-transition period 1994-2007 than in the
pre-transition period 1976-1983.

32

Fifth, as for labor productivity growth, both the exit and the between
components stand out as important factors. Furthermore, these two components
share similar short-run variations and trends. They started to increase after year
1983 and peaked around year 1993. The entry component is generally negative forlabor productivity growth but positive for MFP growth. The latter finding should
be interpreted somewhat cautiously since the capital measure might underrate therelative capital intensity (capital input per labor input) of the new and young
establishments. For example, Maliranta (1997c) provides evidence that youngplants have more rented capital per labor input than older plants, which seems to
explain at least a part of the positive entry component of MFP growth. On theother hand, the entry component of MFP also has a positive slope after the late1970s giving further support to the view that there was a significant change in the
micro-level dynamics of productivity growth during the transition period 19831993.
Sixth, the cross-component, showed in Panel A in Graph 4 and 5, is foundto play a non-negligible role in explaining variation of industry productivity
growth. It has marked cyclical variation that seems to be reasonably similar forlabor and MFP growth. The cross-component of MFP was clearly negative inyears from 1992 to 1996. The cross-component of labor productivity growth isgenerally positive but had clearly negative values in 1995 and 1996.
Table 1 reports the average annual industry productivity growth and its
micro-level components by the three periods at the level of industry (15industries) and the total manufacturing for labor productivity. Table 2 presents thecorresponding numbers for industry MFP growth and its components. The resultsof the total manufacturing level replicate the aforementioned general trends. The
average annual restructuring component of labor productivity growth was 0.3, 1.3

33

and 0.8 percentage points in the periods 1976-1983, 1984-1993 and 1994-2007,
respectively. The corresponding numbers for MFP growth were larger being 1.1,
1.7 and 1.9 percentage points.
<TABLE 1 ABOUT HERE><TABLE 2 ABOUT HERE>
The industry-level results indicate that similar trends can be found inseveral industries. On the other hand, variation between industries is considerable.
An increase in the restructuring component of labor (and MFP) productivity
between the pre-transition and the transition periods can be found in all industries
apart from one out of 15 industries. The greatest increases in the restructuringcomponent of labor productivity growth during the transition period took place in
Textile (NACE 17-19), Chemicals (NACE 24), Transport equipment (NACE 3435), and in Other (NACE 36-37), the increase being no less than 1.5 percentage
points per year in each industry. According to the MFP measure the greatest
increases in the productivity-enhancing restructuring occurred in Chemicals(NACE 24), Electrical equipment (NACE 30-31) and Other (NACE 36-37). The
results for both labor productivity and MFP give indication that all aspects of“creative destruction”, i.e. the entry, exit and between components became more
intensive during the transition period 1983-1993 in most of the industries.
To better capture the trends in restructuring by industry, the annual
restructuring components of labor and MFP productivity growth are smoothed by
Hodrick-Prescott filter ( λ = 6.25 ). Graphical illustrations of the results byindustry give further support to the view that creative destruction intensified after
year 1983 in many industries and in some industries more than in others (seeAppendix 2, Graph A2.2 and Graph A2.3). The analysis provides some indication

34

of substantial creative destruction in the mid-70s but it should be noted that theboth the left and right tails of the smoothed series should always be treated withcaution. The manufacture of communication equipment is an industry thatdeserves a special attention. 14 Productivity-enhancing restructuring peaked in the
mid-90s and was clearly stronger than in other industries in those days. Thatmeans that creative destruction was in operation in the industry before the Nokiadriven ICT expansion began in Finland.
IV.3 Dynamics after entry

The entry component of Method 4 (and Method 3) gauges an immediate effect of
the entrants to the industry productivity level. Indirect effects aside, the entry
component tells how much higher or lower industry productivity growth would
have been if no entries had been taken place in the period. In what follows welook at the longer-term labor productivity dynamics of the new plants by a use of
Method 4 with an extension of a break-down by the plant cohorts (see equation(32)). The results are reported in Table 3. To show how it is convenient to read thetable we first focus on the plants that contributed to the entry component in the 5year window 1985-1990, which is the cohort 1986-90. 15

14 Communication equipment industry witnessed a huge drop in the between and entry componentof labor productivity after 2005. The decline in the smoothed series is mainly due to very
negative numbers in year 2007. A closer inspection of plant-level data revealed that thesenegative numbers can be attributed to a few observations in that industry in that year.

15 As earlier, all computations are made separately for each of 15 industries. The results areaggregated to the level of the total manufacturing sector by a use of the share of hours worked(the average of the initial and end year, to be more precise)35

Contrary to the later cohorts, the cohort 1986-90 had a (slightly) positive
entry effect (0.1 percentage points) on industry labor productivity growth. These
plants accounted for 11.2% of the total number of hours worked in 1990. A
proportion of these plants were disappeared by 1995 (i.e. during the next 5-year
window). These plants accounted for 3.2% of the total number of hours worked in
1990. The average productivity level of these plants was (slightly) higher thanthose who stayed until 1995 and hence they negatively (-0.1 percentage points)
contributed to industry productivity growth in the period 1990-95.
Those plants who did not make an exit before 1995 became the stayers of
the period 1990-95. As Table 3 shows, these plants quite strongly increased theirlabor input share among all stayers from 7.2% to 8.9%. They contributed to thebetween component by 0.6 percentage points, which is disproportionally large
effect. This is not, however, the end of the selection and restructuring mechanisms
concerning the cohort 1986-1990. Some of these plants made an exit between
years 1995 and 2000. As Table 3 shows, these plants contributed to the exit
component of the period 1995-2000, i.e. a decade later, by 0.2 percentage points.
Those who stayed increased their share from 9.1 to 10.0 percentage points andpositively contributed to the between component by 1.0 percentage points. The
numbers of Table 3 for the period 2000-2005 indicate that this was the peak of thelife-cycle of the cohort 1986-90 (their labor input share started to decline) and theend of the productivity-enhancing selection (in fact, the exit component became
negative) and restructuring process among the stayers (the between component
turned to zero).
Table 3 also shows how the cohort 1986-1990 has contributed to industry
productivity growth through the plant component (i.e. having a high productivity
growth rate). The labor input weighted average productivity growth rate of the36stayers was 26.8% (not shown in the table), 16 which contributed, as can be readfrom Table 3, to the plant component by 2.2 percentage points in the period 19901995. In the next period, i.e. the period 1995-2000, the average productivity
growth rate was accelerated to 30.1% (not shown in the table) and its contribution
increased to 2.9 percentage points. All in all, the results suggest that the cohort
1986-90 not only had a relatively high productivity level but also a highproductivity growth rate. As Table 3 indicates, the cohort still had an important
contribution to industry productivity growth in the period 2000-2005 through theplant component.
A high productivity level and a high productivity growth rate is acombination that is particularly advantageous to industry productivity. This seems
to be reflected in a relatively large cross-component contribution of the cohort
1986-1990 in the period 1990-1995 and 1995-2000. 17More generally, as Table 3 indicates, a proportion of the new plants
immediately makes an exit. If these plants have a lower-than-average productivity
level (as usually is the case) they positively contribute to industry productivity
growth through the exit component in the next period. Table 3 shows that the exit
component has had a positive contribution and it was the largest in the period
16 This is obtained by dividing the cohort’s contribution by its average labor input share, i.e.

26.8%=2.2%/(0.5x(7.2%+8.9%))

17 When two plants are of the same size (in terms of labor input) and have the same productivity

growth rate then the one who has a higher productivity level contribute more to the standard
aggregate industry productivity rate. The difference can be read from the plants’ contribution
through the cross-component.

37

1995-2000. By looking at the contribution of the different cohorts it can beconclude that the oldest cohorts account for the main part of the exit component.
However, in the early 1980s and since the mid-1990s, also the newest cohort has
had a significant and disproportionally large contribution to the exit component.
So, a more detailed examination of the entry and exit components with a breakdown by cohorts gives a clear indication of a “revolving door phenomenon” or akind of experimentation that has been prevalent especially since 1990s.
So, those of the new plants that do not make immediately an exit become
stayers. Table 3 points out the general tendency that younger staying (i.e.
surviving) plant cohorts increase their labor input share at the cost of older
cohorts. Both younger and older cohorts positively contribute to the between
component, the former by creating high productivity jobs and the latter bydestroying low productivity jobs. To sum up, Method 4 seems be a useful tool forinvestigating micro-level dynamics of productivity growth involving incessant
experimentation, selection and restructuring.
IV.4 Analysis with alternative decomposition methods
IV.4.1 Method 1, static Olley-Pakes decomposition method

The static Olley-Pakes decomposition for the labor productivity has been
computed by a use of the regression approach represented in Section III.1. Two
regression models explaining the log labor productivity of a plant is estimated 18 ;j =15

ϕi = α U + ∑ β Uj ⋅ D j + ε i ,j =1

18 I thank Pekka Ilmakunnas for useful suggestions concerning these estimations.

(42)

38
j =15

Li ϕi = Li α + ∑ β Wj ⋅ Li D j + Li ε i ,
W

(43)

j =1

Model (42) is unweighted (i.e. untransformed) and Model (43) is labor
input weighted (i.e. transformed) regression. The both models include an intercept
term and dummies for 15 industries.
In order to estimate these models and to have a suitable interpretation for
the intercepts, the following constraints to industry coefficients have been
imposed for the both regression models
j =15∑ share β = 0, X = {U ,W }
j =1

j

X
j

(44)

where share j is the labor input share of the industry j in the total plantj =15

panel ( ∑ share j = 1 ).
j =1

A seemingly unrelated estimation is applied to these models. 19 As shownin equation (18), the covariance component is obtained as a difference of theintercept of Model (43) and Model (42). This is done by a linear combination
estimator, which also provides a robust standard error for the covariancecomponent of the static Olley-Pakes decomposition. This procedure has been
performed separately for each year from 1975 to 2007 (but using the same
industry weight constraints).
The collected results are shown in Graph 6. It represents the covariancecomponent gauging the contribution of allocation within industries and its 95%
19 This is performed by suest postestimation command of Stata Statistical Software package(Release 10).

39

confidence interval. The graph provides evidence on a substantial increase in therole of micro-level allocation as a determinant of the industry productivity level
during the 1980s. The latter part of the 1990s witnessed a further increase in its
effect. However, a plateau is discerned since the late 1990s which gives indication
on a chilling in the micro-level dynamics of productivity growth. All in all, theanalysis with the static Olley-Pakes method gives a picture on the development of
micro-level dynamics that is broadly similar to that obtained with Method 4
above.
It should be noted that since the industry coefficient constraints are held
constant over all years the effect of the changes in the industry structures are
eliminated in the graph. As a robustness check the analysis has also been
performed by using year-specific (i.e. current) industry coefficient constraints,
which yields more or less similar picture. 20<GRAPH 6 ABOUT HERE>

IV.4.2 Method 2, dynamic Olley-Pakes (Melitz-Polanec)

Decomposition analysis with Method 2 is conducted in a manner analogous to thatmade with Method 4 above. Computations are performed separately for each pairsof the consecutive years and for each of 15 manufacturing years. The industrylevel results are aggregated to the level of the total manufacturing by using theindustry labor input shares. The main results are presented in Graph 7. Panel A
shows aggregate productivity growth and how it is split between the unweighted

20

The Olley-Pakes decomposition analysis has been performed as a traditional accounting
exercise by year and industry. As expected, these unreported computations give a similar
picture on the evolution of the allocation component in the Finnish manufacturing industries.

40

plant component and the restructuring component, which includes the covariancechange, entry and exit component.
The unweighted plant component dominates but occasionally the
restructuring component has both absolutely and relatively a quite large
contribution, for instance, in years 1984, 1992 and 2003. When compared to therestructuring component of Method 4 showed in Graph 4, the restructuringcomponent of Method 2 exhibits less stable patterns over time. Panel B of Graph
7 indicates that the variability of the restructuring component can largely be
attributed to the covariance change component. The other sub-components of therestructuring component, that is say the entry and exit components, clearly havesmoother patterns. In fact, they are quite similar to those of Method 4, as
expected.
As shown in equations (22) and (24), Method 2 allows spliting the entry
and exit components further into two parts; unweighted average and covariancechange effect. The results for labor productivity are shown in Table 4.a (the entry)
and Table 4.b (the exit). Generally the both sub-components of the entry effectnegatively contribute to industry productivity growth (the unweighted average
effect in the period 2002-20007 is an exception but it is mostly due to one single
industry that is the manufacture of communication equipment). So, on average,
new plants have a productivity level lower to that of the stayers. Moreover, theresults imply that covariance between the productivity level and the size is smaller
among the new plants than among the stayers. Broadly speaking, the latter
observation seems to be in line with the earlier conjecture concerning
productivity-enhancing restructuring by plant cohorts made with Method 4. It wasthen found that generally it is the youngest cohorts where the restructuring is themost effective in relative terms.

41

The break-down of the exit component by the dynamic O-P method shownin Table 4.b reveals a deeper anatomy of the increasing exit component since the1970s. The main driver of this trend is a sustained increase in the covariancechange effect of the exits. During the pre-transition period (i.e. 1978-1983) it was
negative in most industries but become gradually positive. This finding suggeststhat a change has taken place especially among the larger plants: their propensity
to exit has become increasingly sensitive to their productivity level. As aconsequence, when large and low productivity plants make an exit the relationshipbetween the productivity level and the size (i.e. covariance component) among theremaining plants becomes stronger. The unweighted average effect of the exits
has been positive all the time. A considerable increase can be found between the
pre-transition and transition periods (i.e. from the period 1978-83 to the period
1984-1989) but this component has been relatively stable since the early 1990s.
<GRAPH 7 ABOUT HERE>< TABLE 4.a-b ABOUT HERE>< TABLE 4.b ABOUT HERE>
IV.4.3 Method 3

As argued in Section III.2.3 the plant, between, entry and exit components of
Method 3 can be expected to be close to those of Method 4. An empiricalcomparison of the plant and the restructuring components between the methods
validates the proposition (see Appendix 2, Graph A2.4).
Another remark concerning Method 3 is that industry productivity growthrate obtained from it may not even approximately correspond to the standard
industry productivity growth rates computed from aggregate industry data. An

42empirical investigation with labor productivity and MFP reveals that the absolutevalue of the “log-bias” often is non-negligible (as for results concerning MFP, see
Appendix 2, Graph A2.5). Furthermore, since Method 4 is expected to provide aclose approximation to the standard industry productivity growth rate, this implies
that the cross-component of Method 4 should be close to the “log-bias” in Method
3. Indeed, empirical results give support to this (as for results concerning MFP,
see Appendix 2, Graph A2.5). 21 The bias (and the cross-component) has been
substantial and have had some striking time patterns. The early 1990s witnessed
clearly negative and at the turn of the 2000s positive values of the crosscomponent. The absolute values of these numbers are large enough to be essential
in the analysis of cycles and trends in industry productivity growth, for example.
IV.5 Robustness analysis

A number of further robustness checks have been made for two main purposes: 1)
to examine the robustness of the empirical findings of this paper made with
Method 4, and 2) to compare the robustness (and thus usefulness) of thealternative methods to Method 4. Here we focus on the between component oflabor productivity growth of Method 4 which was found to be a solid gauge of
productivity-enhancing restructuring.
IV.5.1 Robustness of Method 4

As documented, for instance, by Ali-Yrkkö et al. (2000), the manufacture ofcommunication equipment industry (i.e. NACE 32-33) with Nokia-company andits hundreds of the subcontractors has had an exceptionally decisive role to play in

21 The corresponding picture on labor productivity growth is not reported but is broadly similaralthough trends of the cross-component (and log-bias) are not as pronounced as for MFP
growth shown in Appendix 2.

43

the development of the Finnish economy especially since the latter part of the
1990s. Hence, it is of a great interest to know how it is reflected in the micro-level
dynamics of productivity growth in the Finnish manufacturing. This is examinedby looking at how the restructuring component of productivity growth changes
when the manufacture of communication equipment industry is excluded from theanalysis. The results indicates a minor role for the “Nokia-industry” in explaining
the acceleration of the between component in the latter part of the 1980s while ithas had a significant contribution in the period from 1994 to 2001 (Appendix
A2.6).
In addition, a number of other robustness checks have been made with
Method 4. The between component of labor productivity growth is robust to using
gross output measure instead of value added (see top-left panel in Graph 8). Firmlevel data (SBS) give results partly different from those obtained with the plantdata (LDPM). However, it should be noted that, contrary to the use of plant data,
the decomposition made with the firm data does not capture the productivityenhancing restructuring between plants within multi-plant firms which in an
important part of the micro-level renewal (see e.g. Disney et al., 2003; Kyyrä &Maliranta, 2008). Yet, both the plant and firm data give the same basic message;
on the basis of the between component, productivity-enhancing restructuring has
been an important source of productivity growth during the post-transition period.
The main discrepancies in the results pertain to years 2000 and 2001 which seemto be exceptional anyway (see top-right panel in Graph 8). The alternative plantlevel data source (i.e. Business Register) gives some further support to main
conclusions (see bottom-left panel in Graph 8). Finally, the results obtained withalternative firm-data (Asiakastieto-data) are consistent with those obtained with
the SBS data (see bottom-right panel in Graph 8).

44<GRAPH 8 ABOUT HERE>
Further robustness checks of Method 4 with LDPM data include a use ofan alternative threshold for the inclusion to the sample (plants employing at least
20 persons), an alternative measure of labor input (the number of persons) and alonger time-window (5-year windows). The results of these experiments are notreported here but they in no way challenge the basic results.
IV.5.2 Robustness of the alternative methods

An important property of a good method is that the results are not easily driven bysome peculiarities of data. For sake of comparing the methods in this respect, ahost of sensitivity checks has been performed with the alternative methods aswell. While Method 3 was found to be quite similar to Method 4, except for the
cross-component, this exploration focuses on the static as well as dynamic OlleyPakes decomposition method (i.e. Method 1 and Method 2).
In the following, the main findings of these experiments are briefly
described. An analysis of the static O-P method with LDPM plant-data indicatedthat neither the level nor the time pattern of the covariance component is not very
sensitive to the inclusion or exclusion of small (5-19 persons) or very small (1-4persons) plants. Neither are the results very sensitive to measuring the labor input
with the number of persons instead of hours worked.
However, the level of the covariance component is very sensitive to themeasure of productivity. The covariance-component of the MFP is roughly 10%points lower than that of labor productivity (not reported here). Moreover, thecovariance component turns out to be very sensitive to the use of plant or firm as aunit of observation. With a use of SBS firm data over the period from 1995 to
2007, the average cross component is 34.6 percentage points when all45

manufacturing firms employing at least one person are included (see Table 5).
However, the covariance component drops steeply with an increase in the size
threshold. In a sample covering all manufacturing firms employing at least 5
persons, the covariance component is 25.2 percentage points that is more than 10
percentage points lower than with no threshold. The difference is considerable
given that the firms employing less than 5 persons account for only 4.0 percent of
the employment (but 61.6 percent of the number of firms) in our data andconsequently have a minor contribution to industry employment and productivity
in manufacturing. This may not be a problem only from the point of view ofvalidity but of reliability as well. One could suspect that the quality of data ispoorer for the smaller than larger firms. On the other hand, the standard error of
the covariance component exhibits just a moderate decline with the increase in thethreshold (see Table 5).
<TABLE 5 ABOUT HERE>
Graph 9 presents the comparisons of the results with the dynamic O-P
method (i.e. Method 2) analogous to those given in Graph 8 for Method 4. We
again see that the covariance change effect, the novelty of Method 2, has annual
variation which is far greater than that of the between component of Method 4
shown earlier. Further, as with the static O-P method, it is found that the use ofplant or firm as the unit makes a difference (see top-right panel in Graph 9). The
covariance change component has particularly strong annual variation when dataalso include very small plants as it is the case with the Business Register data (seebottom-left panel). Finally, due to the great variability in the covariance change
component it does not seem to be able to yield a reliable picture on thedevelopment of micro-dynamics with short firm-level panel data (see e.g. bottomright panel). On the other hand, with longer panels and a use of wider time-

46

windows (e.g. 5-year windows) it seems possible to capture important changes in
the micro-level dynamics. For instance, the covariance change component
computed with LDPM plant-data and using 5-year windows clearly indicate animportant change in the mid-80s as well as in the mid-90s that which is in keepingwith the picture that has obtained in this study (not reported here).
<GRAPH 9 ABOUT HERE>

V. Conclusions

This paper has sought suitable methods for analyzing the micro-level dynamics
underlying the great leap in productivity of the Finnish manufacturing industries
before and after the great depression of the early 1990s.
A useful method (called Method 4 in this study) with several theoreticaland empirical advantages is proposed. It gives an unbiased view on the role ofentries and exits as well as the reallocation between staying firms in industry
productivity growth. The firm (or plant) component indicates the rate at which
productivity of an average input increases when it stays at the same production
unit. The restructuring component consists of the entry, exit and between
components. The fifth component of the method is called the cross-component. It
essentially captures the difference between the standard measure of the industry
productivity growth, based on the aggregates of output and inputs, and themeasure of industry productivity growth computed by aggregating logproductivity numbers of the firms, as typically done in the micro-levelproductivity decompositions.
By a use of plant level data covering a long period of time, a preferred
method is shown to be useful tool in detecting longer run effects of the entries and47

the underlying micro-level mechanisms. As usual, the initial entry effect on
industry labor productivity is found to be negative. However, in the next period
the newly born plants have a disproportionally large contribution to the positive
exit component. The remaining new plants become stayers and contribute toindustry productivity growth through the plant component, between component
and the cross-component. Splitting these components further by plant cohorts
reveals that young plants have a disproportionally large contribution to thebetween component, indicating that the entry is only a part of broader and longer
restructuring process that is time-consuming and gradual, and to the plantcomponent, indicating that younger plants are able to achieve greater-thanaverage productivity growth due to learning-by-doing, for instance.
The preferred method is analytically and empirically compared with someinteresting alternatives. The first of these is the popular static productivity
decomposition proposed by Olley and Pakes (1996). For computing it, a novel
regression approach is represented which provides the standard error estimate of
the covariance (allocation) component as well as an opportunity to control otherfactors of productivity in a flexible manner.
The method, however, ignores the entries and exits and, for that matter, it
is not fully compatible with the view, emphasized by the modern economic
growth literature, that incessant restructuring is an essential element of growth.
This shortcoming is corrected in the second method of comparison, which is theso-called dynamic Olley-Pakes decomposition (Method 2 in this paper) recently
proposed by Melitz and Polanec (2009). The method is shown to have some
important analytical and empirical similarities to the preferred method. Though,the preferred method appears to give a somewhat more robust picture on the
productivity-enhancing restructuring. This is found with ample empirical

48

robustness checks made with alternative data sets, measures of input or output,
units of observations (firm or plant), size thresholds and time-windows. Theseexperiments are also helpful in search of suitable methods for international
comparisons with micro-data that are potentially plagued by data differences due
to varying statistical, administrative or legal systems.
However, the dynamic Olley-Pakes method provides an interesting angle
to the role of entries and exits in the micro-level dynamics of productivity growth.
Empirical results of this paper show that emergence of new plants incessantly
lowers the relationship between the productivity level and the size of the plants
and thus decreases the covariance (i.e. allocation) component of the productivity
level in industries. At the same time, however, the relationship becomes stronger
among the staying plants (due to reallocation of resources between low and highproductivity plants). Furthermore, the exit of plants appears to have a positiveeffect on the covariance component of the industry productivity level. So, as it
was with the preferred method, empirical findings of the dynamic Olley-Pakes
method suggest that the entrants contribute negatively and the staying plants andthe exiting plants positively to the reallocation component of industry productivity
growth.
As for the empirical contribution of this study, with suitable methods andwith exceptionally rich and long panel data on plants a number of important
aspects in the micro-level dynamics of rapid industry productivity growth inFinnish manufacturing industries are unveiled. Three periods can be distinguishedin the development; 1) the pre-transition period (from 1976 to 1983), 2) thetransition period (from 1984 to 1993) and 3) the post-transition period (after1994). The average annual restructuring component of labor productivity growthin these three periods was 0.3, 1.3 and 0.8 percentage points, respectively. The

49

corresponding numbers for MFP growth were larger being 1.1, 1.7 and 1.9
percentage points. Both the productivity-enhancing restructuring between thestayers (i.e. the between component) and the exit component positively
contributed to industry labor productivity growth. The results with a MFP
measure suggest that the entrants have a positive impact on industry productivity
growth. However, the results should be treated with cautious due to the problems
in measuring capital input accurately especially for the new plants.
Evidence suggests that the collapse of the trade to the former Soviet Union
after 1983 triggered “creative destruction” in the Finnish manufacturing industries
that paved the way for a climb to the international productivity frontier by themid-90s. On the other hand, the results are at least broadly consistent with theview emphasized in the modern economic growth literature that micro-level
restructuring is more important for such economies and industries that are on theinternational technology frontier than those far away from it.

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Appendix 1.

Data

Longitudinal data on plants in Finnish manufacturing (LDPM) is constructed from annual
manufacturing surveys especially for research purposes. Nowadays the data cover years 1975-2007.
In principle, the plant is defined as a local kind-of-activity unit. In other words, In principle,
the plant is defined as a local kind-of-activity unit. In other words, it is a specific physical location,
which is specialised in the production of a certain type of product. A single local unit may thus
consist of several plants that have activities in different industries. In some special cases a plant isdelineated so that it includes parts that are geographically detached. However, it is required that theunits are located within the same municipality. This solution seems well justified, especially when
the geographically separated units are closely attached to each other operationally. Besides, thisway of grouping plants may help the firm to provide more accurate information on its activities
within a certain specific industry. The plant codes mostly stay the same throughout the life of aplant. Three criteria are taken into account when considering a change of a code: industry, address
and ownership. In principle, at least two of these conditions need to be met before a new code isgiven. A brand new plant code is given in such cases where there has been a thorough-going change
in the way the production is performed. This is the case, for example, when a substantial proportionof tangible assets is replaced. This treatment of plants’ deaths and births accords roughly with the
one needed when using this data source to analyse the life cycles of plants from the standpoint oftechnology adaptations.
Up to the year 1994 the main criterion was that the plant employed at least five persons. Since
1995 it includes basically all plants owned by firms that employ no less than 20 persons. Therefore,
since 1995 the data also include the very small plants of multi-unit firms, but, on the other hand, theplants of small firms are left outside. This break in the series needs to be taken into accountespecially when analysing entries and exits.
To ensure the comparability of the results over time plants employing less than five persons
and plants owned by a firm employing less than 20 persons are dropped from the sample.

53

As always with these kinds of data, the LDPM data include outliers that might be influential
in an economic analysis. A transparent procedure is needed to clean the data. An approach similar
to that of Mairesse and Kremp (1993) is adopted. Those observations are deemed as outliers whose
log of labor productivity differs by more than 4.4 standard deviations from the input-weighted
industry average (15 industries) in that year. The decompositions are performed separately for eachpairs of consecutive years. If a plant is classified as an outlier in either an initial (i.e. 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). Thanks to this procedure no artificial entries or exits are created by removing outliers.
Sometimes a single plant might have an impact on one of the micro-level components that is simply
unbelievable. A more detailed inspection of these cases reveals that the changes in value added orlabor input are sometimes erroneous beyond reasonable doubt. Since, on certain occasions, theseerrors are quite influential in decomposition calculations, further cleaning in needed to obtainreliable results. For this reason, the decompositions are made in two rounds. If the absolute value ofthe between component of Method 2 is greater than 2 percentage points, the plant is classified as an
outlier in the first round. A similar procedure is performed for entries and exits, too. These areconservative criteria since they lead to exclusions of a couple of plants per year on average.
Table A1.1. Descriptive statistics on LDPM sample
Year
Industry
15a6
17t9
20
21
22
24
25
26
27
28
29
30a1
32a3
34a5
36a7
Manufacturing

Observations
1980 1985

1989

1995

2000

2005

Hours worked, millions
1980 1985 1989 1995
84.1
85.7
53.9
72.3
54.5
31.0
23.2
31.2
30.9
36.8
93.8
37.6
25.4
58.6
27.6

77.5
54.4
47.8
65.0
52.4
31.2
21.0
31.1
27.6
35.3
86.7
30.5
30.6
44.0
24.3

54.5
21.3
32.7
52.9
33.8
27.4
19.0
16.1
23.6
24.8
69.0
25.9
40.5
30.9
14.6

2000

2005

50.4
16.7
34.9
56.4
34.5
27.4
24.6
20.7
24.8
37.7
80.7
24.8
58.4
28.7
16.0

42.5
13.1
32.0
41.6
32.6
23.5
21.4
20.8
24.4
44.2
79.7
21.4
68.6
24.7
13.8

696
812
458
160
300
132
141
203
78
366
559
136
90
202
291

697
743
474
181
421
163
145
301
82
376
590
141
120
212
285

677
640
430
179
444
168
178
313
70
409
610
177
127
207
279

542
381
350
165
400
172
178
302
76
393
565
166
152
152
231

418
201
290
170
324
165
179
214
80
425
500
142
180
133
176

358 87.8
171 111.1
298 74.6
132 72.2
339 37.4
154 26.1
180 25.8
233 22.8
80 32.5
573 41.8
553 108.0
153 36.8
201 21.4
144 60.8
184 32.8

4624

4931

4908

4225

3597

3753 791.8 746.6 659.2 487.1 536.8 504.2

54

Appendix 2.

Robustness checks and additional
analysis

0250

millions hours
500

750

1000

GRAPH A2.1. Development of hours worked in the manufacturing sector

1975

1980 1983

1990 1993

Source: National Accounts, Etla's database, own calculations

Note: For details, see text.

2000

2007

55

Graph A2.2. Restructuring components of labor productivity growth by industries, Method 4(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.

%

-2

0

2

4

-2

0

2

4

-2

0

2

4

(15-6) Food

1975 1983

2007 1975 1983

1993

2007

(36-7) Other

-2

0

2

4

(34-5) Transport equip.

1993

1975 1983

1993

2007 1975 1983

1993

Between

2007

Entry

Exit

-10

-5

%

0

5

(32-3) Communic. equip.

1975

1983
BetweenNotes: For details, see text.

1993
Entry

2007
Exit

56

Graph A2.3. Restructuring components of MFP growth by industries, Method 4(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.

-1 0 1 2 3%-1 0 1 2 3-1 0 1 2 3(15-6) Food

1975 1983

2007 1975 1983

1993

2007

(36-7) Other-1 0 1 2 3(34-5) Transport equip.

1993

1975 1983

1993

2007 1975 1983

1993

Between

2007

Entry

Exit

0

2

%

4

6

(32-3) Communic. equip.

1975

1983
BetweenNotes: for details, see text.

1993
Entry

2007
Exit

57

-5

0

%-points

510

GRAPH A2.4. Comparisons of Method 3 and Method 4, labor productivity growth

1975

1980 1983

1990 1993

2000

Plant, Method 3

Plant, Method 4

Restructuring, Method 3

Restructuring, Method 4Note: For details, see text.

2007

58

-5

-4

-3

-2

%-points
-1 0
1

2

3

4

5

GRAPH A2.5. Comparison of log-bias in Method 3 with the cross-component of Method 4, MFP
growth19751980 1983

1990 1993

Cross-component of Method 4Note: For details, see text.

2000
Log-bias in Method 3

2007

59

-1

-.5

%-points
0
.5

1

1.5

GRAPH A2.6. The annual between component of labor productivity growth in all industries andexcluding the manufacture of communication equipment

1975

1980

1985
All industries

Note: For details, see text.

1990

1995

2000

Exc. NACE 32-3

2005

0

10

20

%

30

40

50

GRAPH 1. Phases in the trade of the manufacturing sector

1960

1970

1975

19801983

19901993

2000

Export to USSR/Russia per value of total output
Import from USSR/Russia per value of total output
Export to other markets per value of total output
Source: National Accounts, Etla's database, own calculations

Note: For details, see text.

2007

0

4

%

8

12

Relative labor productivity, USA=100
60
80
100
120

GRAPH 2. Development of labor productivity in the manufacturing sector

1975

1980 1983

1990 1993

2000

2007

Relative labor productivity, USA=100
Prod. growth, observed (right scale)
Prod. growth, filtered, HP-6.25 (right scale)
Source: Groningen University, KLEMS, National Accounts, Etla's database, BLS, own calculations

Note: The relative labor productivity level (that is 74.3%) for year 1987 is obtained from ICOP
Industrial Database (1987 Benchmark), Groningen Growth and Development Centre. The
other years until 2005 are extrapolated by using the productivity series of the EU-KLEMSdatabase and years 2006-2007 by using National Accounts (for Finland) and the Bureau ofLabor Statistics (for USA). For further details, see text.

100

Productivity index, log-scale
200
300 400 500 600

GRAPH 3. Labor productivity development at the different levels of the aggregation, Method 4

1975

1980

1985

1990

1995

in total manufacturing
in industries
in plants

Note: For details, see text.

2000

2005

GRAPH 4. Micro-level sources of labor productivity growth in Finnish manufacturing industries,
Method 4-50%-points
5

10

15

Panel A

1975

1980 1983
Aggregate1990 1993
Plants (weighted)

2000
Cross

2007
Restructuring-2-1%-points
01

2

Panel B

1975

1980 1983
RESTRUCTURING

1990 1993

2000
Between

2007
Entry

Exit

Note: Panel A presents the annual aggregate labor productivity growth and its three main
components; the plant component, cross-component and restructuring component. Panel B
presents the annual restructuring component and its three sub-components; the between
component, entry component and exit component.

GRAPH 5. Micro-level sources of MFP growth in Finnish manufacturing industries, Method 4

-10

-5

%-points
0
5

10

15

Panel A

1975

1980 1983
Aggregate1990 1993
Plants (weighted)

2000
Cross

2007
Restructuring-10%-points
1
2

3

4

Panel B

1975

1980 1983
RESTRUCTURING

1990 1993

2000
Between

2007
Entry

Exit

Panel A presents the annual aggregate MFP growth and its three main components; the plantcomponent, cross-component and restructuring component. Panel B presents the annual
restructuring component and its three sub-components; the between component, entry
component and exit component.

-50%-points
5
10

15

20

GRAPH 6. Static allocation (i.e. covariance) component of the Olley-Pakes decomposition and its
95% confidence interval

1975

1980

1985
upper/lower

1990

1995

2000

20052007

covariance component

Note: The covariance component and its robust standard error are computed by a use of a regression
approach. For further details, see text.

GRAPH 7. Dynamic Olley-Pakes (Melitz-Polanec) decomposition, Method 2-50%-points
5

10

15

Panel A

1975

1980 1983

1990 1993

Aggregate

Plants (unweighted)

2000

2007
Restructuring-20%-points2
4

6

8

Panel B

1975

1980 1983
RESTRUCTURING
Exit

Note: see text.

1990 1993

2000

Covariance change

2007
Entry

GRAPH 8. Comparisons of the between component of Method 4
B. Comparison between plant and firm data

-1

-.5

0

%-points
01%-points
.5
1

2

1.5

A. Comparison between value added and gross output measure

1975 1980 1985 1990 1995 2000 2005
Value added

1975 1980 1985 1990 1995 2000 2005

Gross output

LDPM data

SBS data

D. Comparison between firm data sets

-.5

-.5

0

%-points
0.5%-points
.5
1

1.5

1

C. Comparison of plant data sets

1975 1980 1985 1990 1995 2000 2005
LDPM data

Note: see text.

BR data1975 1980 1985 1990 1995 2000 2005
SBS data

Asiakastieto data

GRAPH 9. Robustness of the covariance change component of the dynamic Olley-Pakes method(Method 2)
B. Comparison between plant and firm data

-10

-2

0

%-points-50%-points2
4

5

6

A. Comparison between value added and gross output measure

1975 1980 1985 1990 1995 2000 2005
Value added

1975 1980 1985 1990 1995 2000 2005

Gross output

LDPM data

SBS data

D. Comparison between firm data sets

-5

-10

0

%-points-50%-points
5
10

5

15

C. Comparison of plant data sets

1975 1980 1985 1990 1995 2000 2005
LDPM data

Note: see text.

BR data1975 1980 1985 1990 1995 2000 2005
SBS data

Asiakastieto data

TABLE 1. Micro-level sources of industry labor productivity growth, the annual average, Method 4
INDUSTRY
Agg.
Plant
Cross Restr. Betw.
Entry
Exit
Pre-transition period: 1976–1983(15-6) Food
2.2
2.4
-0.3
0.2
0.1
-0.2
0.3
(17-9) Textile
2.6
2.3
0.0
0.4
0.0
-0.4
0.7
(20) Wood
4.6
5.4
-1.4
0.6
0.8
-0.4
0.2
(21) Paper
7.0
5.7
0.6
0.8
0.8
-0.1
0.1
(22) Printing
2.7
1.5
0.6
0.7
0.8
-0.2
0.1
(24) Chemicals
3.0
3.6
-0.9
0.3
0.7
0.0
-0.4
(25) Plastic & rubber
5.3
4.7
-0.2
0.9
0.6
-0.2
0.5
(26) Minerals
4.3
3.2
-0.2
1.2
0.6
0.2
0.4
(27) Metals
9.8
7.4
1.8
0.6
0.3
0.0
0.3
(28) Metal products
5.5
5.0
0.1
0.4
0.4
-0.5
0.4
(29) Machinery
3.5
3.3
0.6
-0.4
-0.5
-0.2
0.3
(30-1) Electr. equipm.
0.6
0.7
0.2
-0.3
-0.7
0.4
0.0
(32-3) Communic. equip.
6.7
2.5
3.0
1.2
0.6
0.3
0.3
(34-5) Transport equip.
2.2
2.3
0.2
-0.4
-0.3
-0.2
0.2
(36-7) Other
3.0
2.8
-0.1
0.2
0.3
-0.6
0.5
MANUFACTURING
3.9
3.4
0.2
0.3
0.2
-0.2
0.3
Transition period: 1984–1993(15-6) Food
5.9
4.9
0.6
0.4
-0.1
-0.1
0.6
(17-9) Textile
5.3
2.8
0.6
1.9
0.4
0.0
1.5
(20) Wood
6.7
4.5
1.0
1.2
0.4
-0.1
0.9
(21) Paper
6.9
5.2
0.1
1.7
1.7
-0.2
0.2
(22) Printing
2.5
2.2
-1.2
1.4
0.5
-0.1
1.0
(24) Chemicals
5.0
3.6
-0.5
1.8
1.0
0.5
0.2
(25) Plastic & rubber
8.3
6.9
-0.1
1.5
0.7
0.5
0.2
(26) Minerals
4.1
2.6
0.6
0.9
0.6
-0.2
0.5
(27) Metals
7.2
6.3
-0.6
1.5
1.4
-0.4
0.5
(28) Metal products
6.3
3.9
0.8
1.6
0.7
-0.3
1.2
(29) Machinery
5.1
3.3
0.8
1.0
1.0
-0.2
0.2
(30-1) Electr. equipm.
13.5
11.8
0.8
0.9
0.7
-0.6
0.7
(32-3) Communic. equip.
11.7
9.0
1.1
1.6
1.5
-0.5
0.5
(34-5) Transport equip.
4.4
2.5
0.7
1.2
0.5
0.2
0.5
(36-7) Other
4.1
1.3
0.6
2.3
0.8
0.2
1.3
MANUFACTURING
6.0
4.3
0.4
1.3
0.7
-0.1
0.7
Post-transition period: 1994–2007(15-6) Food
5.1
4.1
0.3
0.6
0.2
-0.5
0.9
(17-9) Textile
3.1
1.1
0.4
1.5
0.7
-0.7
1.6
(20) Wood
2.7
1.7
0.6
0.5
0.2
-0.5
0.9
(21) Paper
4.2
3.2
0.4
0.6
0.4
-0.3
0.5
(22) Printing
2.0
1.2
0.1
0.6
0.4
-0.6
0.9
(24) Chemicals
3.3
2.0
1.1
0.2
0.1
0.2
-0.1
(25) Plastic & rubber
0.7
0.0
-0.2
0.9
0.6
-0.4
0.6
(26) Minerals
2.6
1.9
0.1
0.5
0.2
-0.4
0.8
(27) Metals
4.3
3.1
1.0
0.1
-0.1
-0.3
0.5
(28) Metal products
0.7
0.2
0.1
0.4
0.2
-0.6
0.7
(29) Machinery
4.5
3.6
0.2
0.7
0.5
-0.3
0.5
(30-1) Electr. equipm.
8.5
7.6
0.2
0.6
0.3
-0.5
0.8
(32-3) Communic. equip.
16.7
11.0
3.7
2.0
2.0
-1.5
1.5
(34-5) Transport equip.
1.2
0.0
1.0
0.2
0.2
-0.4
0.4
(36-7) Other
2.8
1.1
0.0
1.7
0.6
-0.8
1.9
MANUFACTURING
4.9
3.4
0.7
0.8
0.5
-0.5
0.8

Note: See text.

TABLE 2. Micro-level sources of industry MFP growth, the annual average, Method 4
INDUSTRY
Pre-transition period: 1976-1983(15-6) Food
(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.
(32-3) Communic. equip.
(34-5) Transport equip.
(36-7) Other
MANUFACTURING
Transition period: 1984–1993(15-6) Food
(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.
(32-3) Communic. equip.
(34-5) Transport equip.
(36-7) Other
MANUFACTURING
Post-transition period: 1994–2007(15-6) Food
(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.
(32-3) Communic. equip.
(34-5) Transport equip.
(36-7) Other
MANUFACTURING

Note: See text.

Agg.

Plant

Cross

Restr.

Betw.

Entry

Exit

-1.9
0.5
3.7
4.1
0.9
0.8
1.4
1.7
9.0
4.2
1.7
-0.9
3.5
1.3
0.7
1.5

-1.6
-0.8
4.4
3.1
-1.9
-0.4
1.3
1.0
7.6
2.6
0.7
-1.6
-2.0
0.5
0.3
0.5

-1.0
-0.1
-1.8
-0.7
1.5
-0.1
-0.7
-0.9
1.1
-0.5
0.0
-0.1
3.5
0.4
-0.3
-0.1

0.7
1.4
1.1
1.7
1.3
1.4
0.8
1.6
0.4
2.1
0.9
0.9
2.0
0.4
0.7
1.1

0.6
0.7
1.0
1.2
0.8
1.3
0.6
1.1
-0.1
0.8
0.2
0.3
0.9
0.0
0.6
0.6

0.4
0.3
0.2
0.5
0.2
0.6
0.1
0.2
0.1
1.4
0.4
1.0
0.9
0.4
0.0
0.4

-0.3
0.4
-0.1
-0.1
0.3
-0.6
0.1
0.2
0.4
-0.1
0.3
-0.4
0.2
0.0
0.1
0.0

3.6
4.1
4.9
3.7
0.8
2.5
6.2
1.3
4.8
4.6
3.4
12.3
7.3
2.9
1.4
3.7

3.2
2.1
3.0
3.5
-0.9
1.6
4.8
-0.8
4.8
2.0
1.3
8.2
3.4
1.4
-1.5
2.2

-0.5
0.2
0.6
-0.8
-0.7
-2.0
-0.2
0.1
-1.2
0.2
0.5
0.2
1.6
0.3
0.5
-0.2

0.9
1.7
1.3
1.0
2.3
3.0
1.7
2.0
1.2
2.3
1.6
3.9
2.3
1.2
2.4
1.7

0.5
0.6
0.7
0.7
1.4
2.1
0.8
1.4
0.8
1.2
1.2
2.1
2.0
0.5
0.9
1.0

0.1
0.5
0.3
0.1
0.0
0.7
0.8
0.3
0.3
0.9
0.7
1.1
0.0
0.5
0.7
0.4

0.3
0.6
0.4
0.2
0.9
0.2
0.1
0.3
0.1
0.2
-0.3
0.7
0.3
0.2
0.7
0.3

4.3
2.5
2.4
3.8
2.1
1.8
0.1
2.4
3.3
0.7
5.1
8.1
13.5
1.1
1.7
4.6

2.7
0.6
1.0
3.9
0.4
1.1
-1.4
1.6
0.7
-1.0
3.1
7.0
7.3
-0.4
-0.6
2.5

0.1
0.1
0.1
-0.5
-0.5
0.1
-0.4
-0.6
1.5
-0.6
-0.5
-1.4
1.7
1.0
-0.5
0.1

1.5
1.7
1.3
0.4
2.1
0.6
1.9
1.5
1.0
2.3
2.5
2.5
4.5
0.5
2.8
1.9

0.5
1.0
0.6
0.2
1.1
0.8
1.0
0.8
0.2
0.9
1.3
1.4
3.2
0.3
1.4
1.1

0.6
0.4
0.6
0.2
0.5
0.4
1.0
0.5
1.0
1.5
1.4
1.3
0.8
0.1
0.1
0.7

0.4
0.3
0.1
0.0
0.5
-0.6
-0.1
0.3
-0.3
-0.1
-0.2
-0.2
0.5
0.1
1.3
0.1

TABLE 3. Decomposing the contributions of plant cohorts to industry labor productivity growth, Method 4
Period

TOTAL
ENTRY
EXIT
Cohort -1975
1976-80
1981-85
1986-90
1991-95
1996-00
BETWEEN
Cohort -1975
1976-80
1981-85
1986-90
1991-95
1996-00
PLANT
Cohort -1975
1976-80
1981-85
1986-90
1991-95
1996-00
CROSS
Cohort -1975
1976-80
1981-85
1986-90
1991-95
1996-00

Notes: See text.

1980-1985
Compnent
24.5
0.9
1.1
0.7
0.4

0.5
0.6
-0.1

18.9
17.1
1.8

3.1
2.9
0.2

1985-1990

Share of hours
1980

1985
8.0

9.9
7.6
2.3

100.0

100.0

90.1

88.9

9.9

11.1

100.0

100.0

90.1

88.9

9.9

11.1

100.0

100.0

90.1

88.9

9.9

11.1

Compnent
26.2
0.1
1.6
1.4
0.2
0.0

2.0
1.5
0.4
0.1

21.6
18.0
2.6
1.0

1.0
1.1
0.1
-0.2

1990-1995

Share of hours
1985

1990
11.2

15.3
10.8
2.6
1.8

100.0

100.0

84.3

82.5

10.7

11.5

5.0

6.0

100.0

100.0

84.3

82.5

10.7

11.5

5.0

6.0

100.0

100.0

84.3

82.5

10.7

11.5

5.0

6.0

Compnent
20.5
-0.9
1.5
1.4
0.1
0.1
-0.1

2.1
0.9
0.4
0.1
0.6

17.6
11.9
2.1
1.5
2.2

0.2
-0.5
-0.1
0.1
0.7

1995-2000

Share of hours
1990

1995
11.8

21.3
13.0
2.8
2.3
3.2

100.0

100.0

77.0

74.1

10.1

10.8

5.7

6.2

7.2

8.9

100.0

100.0

77.0

74.1

10.1

10.8

5.7

6.2

7.2

8.9

100.0

100.0

77.0

74.1

10.1

10.8

5.7

6.2

7.2

8.9

Compnent
26.0
-1.7
1.9
0.9
0.2
0.2
0.2
0.3
2.9
1.5
0.5
0.1
1.0
0.0
20.1
13.4
1.7
0.9
2.9
1.2
2.9
0.8
-0.1
0.1
2.0
0.1

2000-2005

Share of
hours
1995

11.7
5.3
1.2
0.9
1.6
2.7
100.0
66.6
10.3
6.0
9.1
8.1
100.0
66.6
10.3
6.0
9.1
8.1
100.0
66.6
10.3
6.0
9.1
8.1

Compnent
2000
16.9
-1.4
13.5
0.6
0.7
0.2
0.1
-0.3
-0.6
0.4
0.4
100.0
-0.3
64.5
0.2
10.6
0.0
6.1
0.0
10.0
0.6
8.8
-0.1
16.5
100.0
8.0
64.5
2.1
10.6
1.3
6.1
3.2
10.0
0.9
8.8
1.0
0.9
100.0
1.3
64.5
-0.1
10.6
-0.1
6.1
0.1
10.0
-0.4
8.8
0.0

Share of hours
2000

2001
10.8

15.1
3.9
1.1
0.8
2.7
3.6
3.0
100.0

100.0

59.7

58.4

10.5

10.8

6.1

6.4

10.4

9.9

7.8

8.5

5.6

6.1

100.0

100.0

59.7

58.4

10.5

10.8

6.1

6.4

10.4

9.9

7.8

8.5

5.6

6.1

100.0

100.0

59.7

58.4

10.5

10.8

6.1

6.4

10.4

9.9

7.8

8.5

5.6

6.1

TABLE 4.a Decomposing entry component of labor productivity growth by dynamic O-P (Method2), selected 5-year windows
Unweighted average effectCovariance effect
Period1978-83 1984-89 1993-98 1997-02 2002-07 1978-83 1984-89 1993-98 1997-02 2002-07
Industry(15-6) Food
-1.9
-0.8
-1.1
-2.7
-1.3
0.3
0.3
-0.5
0.0
0.2
(17-9) Textile
-1.0
-1.2
-0.2
-1.2
-0.1
0.4
1.1
-1.9
-1.3
-0.7
(20) Wood
-0.8
1.0
-2.3
-1.7
-2.6
1.0
-1.0
0.0
0.9
-0.4
(21) Paper
0.2
0.7
-1.4
0.5
-0.3
-0.4
1.4
0.1
-1.8
-0.1
(22) Printing
0.4
-0.1
-0.7
-1.8
-1.2
0.1
0.2
-2.3
-2.3
-0.5
(24) Chemicals
0.5
1.4
-0.7
-1.1
-0.2
0.8
-0.1
-3.6
2.5
-2.1
(25) Plastic & rubber
6.2
0.7
-0.5
1.0
0.0
-9.4
-2.2
-0.2
-1.6
0.1
(26) Minerals
0.3
0.4
-1.6
-2.5
0.2
-1.3
-0.9
0.6
-0.9
-0.5
(27) Metals
-3.8
-1.1
-1.8
-0.9
0.7
2.7
-0.9
-1.6
-1.0
-1.9
(28) Metal products
-1.9
0.6
-1.7
-3.5
-1.7
0.3
-2.4
-0.5
-1.9
0.8
(29) Machinery
-0.8
1.7
0.7
-1.0
0.3
-3.7
-1.4
-1.0
0.9
-1.1
(30-1) Electr. equipm.
0.0
-1.7
1.7
0.4
0.5
-3.5
1.0
1.0
-0.4
-1.2
(32-3) Comm. equip.
-2.4
-0.7
-3.1
1.7
8.1
-1.0
-3.3
-2.6
-8.5
-25.7
(34-5) Transp. equip.
-0.3
0.6
0.2
-1.3
-0.5
-0.5
0.4
0.7
-0.5
1.5
(36-7) Other
-1.0
2.0
-0.3
0.4
1.7
-2.0
-1.9
-3.0
0.4
-5.8
Weighted average
Unweighted average

-0.6
-0.4

0.2
0.2

-0.8
-0.9

-0.9
-0.9

0.7
0.2

-0.8
-1.1

-0.3
-0.6

-0.9
-1.0

-1.3
-1.0

-3.9
-2.5

Notes: see text.
TABLE 4.b. Decomposing exit component of labor productivity growth by dynamic O-P (Method2), selected 5-year windows
PeriodIndustry(15-6) Food
(17-9) Textile(20) Wood(21) Paper(22) Printing(24) Chemicals(25) Plastic & rubber(26) Minerals(27) Metals(28) Metal products(29) Machinery(30-1) Electr. equipm.
(32-3) Comm. equip.
(34-5) Transp. equip.
(36-7) Other
Weighted average
Unweighted average

Notes: see text.

Unweighted average effectCovariance effect
1978-83 1984-89 1993-98 1997-02 2002-07 1978-83 1984-89 1993-98 1997-02 2002-07
2.3
2.4
1.4
-0.2
0.7
0.4
1.5
0.3
0.7
0.2
1.1
1.5
1.4
-0.2
3.1

1.3
5.3
3.8
7.0
4.7
3.8
-1.0
1.0
3.0
6.4
3.4
2.8
0.5
3.9
3.5

4.7
3.5
4.3
3.9
2.0
-0.9
0.4
5.5
1.5
0.6
2.2
-1.4
3.2
-0.1
8.9

2.9
5.0
1.4
2.6
1.1
1.8
1.5
-0.2
-1.5
1.4
1.3
1.7
4.1
1.0
2.7

4.1
5.2
1.7
3.8
4.9
3.1
1.9
2.1
0.1
2.8
0.1
0.4
2.3
0.5
7.2

-1.3
-0.6
-1.4
-1.8
0.4
-1.2
-0.3
1.9
0.3
0.6
-2.1
-2.0
-0.3
0.6
0.7

-1.1
0.9
2.8
-1.1
3.9
-0.4
-1.0
0.6
-0.2
-0.5
-0.7
-10.0
-0.8
4.1
1.1

-2.9
-0.7
1.8
4.0
-0.3
0.4
-0.3
1.6
0.3
1.5
1.7
4.2
0.3
1.0
-2.1

0.1
1.8
0.4
1.1
0.4
-2.5
-0.3
0.5
-1.3
0.2
0.4
2.0
10.4
0.4
-0.1

-1.9
2.5
-1.8
3.8
0.5
-1.8
0.5
1.4
-0.5
0.7
-3.2
-4.8
12.0
0.4
5.2

1.2
1.1

3.6
3.3

2.6
2.6

1.9
1.8

2.3
2.7

-0.7
-0.4

0.0
-0.2

0.8
0.7

1.4
0.9

1.2
0.9

73

TABLE 5. Covariance component of the static O-P method and the size threshold for the inclusion
to the sample, the SBS firm-panel, average over the period from 1995 to 2007
Size threshold,none 1
2
3
4
5
10
15
20
number of persons
Covariance component 36.2 34.6 30.1 27.7 26.2 25.2 22.4
21.4
20.7
Standard error
3.50 3.49 3.47 3.45 3.44 3.43 3.40
3.38
3.36
Notes: see text.