In this paper we study the wage curve with both cross-sectional and pooled Finnish data. Hourly wage is used as a dependent variable throughout the paper as well as a fairly detailed definition of the local labour market. Results indicate that there exists a relationship similar to downward sloping wage curve in the Finnish data. Results are sensitive to the inclusion of regional fixed effects.

2. THE WAGE CURVE: FINNISH EVIDENCE
TUOMAS PEKKARINEN*
ABSTRACT
In this paper we study the wage curve with both cross-sectional and pooled
Finnish data. Hourly wage is used as a dependent variable throughout the
paper as well as a fairly detailed definition of the local labour market.
Results indicate that there exists a relationship similar to downward sloping
wage curve in the Finnish data. Results are sensitive to the inclusion of
regional fixed effects.
Keywords: Wage curve, local unemployment.
JEL Classification: J31, J64.
*
I would like to thank Erkki Koskela for helpful comments as well as the
Confederation of Finnish Industry and Employees (Teollisuus ja työantajat), Juhana
Vartiainen and Aila Mustonen at the Labour Institute for Economic Research,
Pekka Myrskylä at the Statistics Finland and Ilkka Nio at the Ministry of Labour for
providing the data. The usual disacclaimer applies.
LABOUR INSTITUTE FOR ECONOMIC RESEARCH
DISCUSSION PAPERS 144
HELSINKI 1998

3. ISBN 952–5071–20–0
ISSN 1236–7184

4. 1
The countries studied by Blanchflower and Oswald are United States, Great
Britain, Canada, South-Korea, Austria, Italy, Netherlands, Switzerland, Norway,
Ireland, Australia and Germany. The largest single regression uses data of over 1,7
million individuals.
3
1. INTRODUCTION
The wage curve is an empirical observation originally reported by
Blanchflower and Oswald (1990, 1994, 1995), that describes a relationship
between a worker’s pay and the unemployment rate in the local labour
market. The causality is thought to run from the unemployment rate to the
wage rate. The wage curve is estimated using microeconomic data and a
standard microeconometric wage function with the local unemployment rate
as an additional independent variable. Original studies by Blanchflower and
Oswald use repeated cross-sectional data from 12 different countries,
containing information on approximately 3,5 million individuals.1
The results achieved by Blanchflower and Oswald indicate that there exists
a negative relationship between local unemployment and wages. A worker
who is employed in a high unemployment region is expected to be paid a
lower wage than a correspondent worker in a region with low
unemployment. According to Blanchflower and Oswald (1994) the wage
curve relation is well-approximated by a simple log-linear function of the
following form:
ln w = -0.1 ln Ur + other variables
In other words the coefficient of the logarithm of the local unemployment
rate Ur takes approximately the value of -0.1 in an equation where the

5. 2
As an interpretation of the wage curve results Blanchflower and Oswald (1994)
present three different theoretical models that give an outcome which is similar to
downward sloping wage curve. All the models presented by Blanchflower and
Oswald are based on the assumption of imperfect competition in the labour market
4
dependent variable is the logarithm of the individual’s wage and other
variables are a typical set of measured individual characteristics, such as
gender, age and education. This relationship can be drawn as a negatively
sloped, convex curve in a wage-unemployment -space and, following the
example of Blanchflower and Oswald (1990), it has been called the wage
curve in the literature.
The finding of a negative relationship between local unemployment and the
worker’s wage contradicts traditional thinking on the wage-unemployment
relationship, which is based on the theoretical model of Harris and Todaro
(1970). The model predicts that wages and unemployment are positively
correlated across regions due to the compensating differentilas2
.
Furthermore Blanchflower and Oswald argue that this negative relationship
is an international phenomenon. The estimated coefficient of the local
unemployment rate takes approximately same values regardless of the
country or period which the data are from. After the original contributions of
Blanchflower and Oswald (1990, 1994,) a number of recent studies have
documented the existence of the wage curve in micro data sets from
several countries (Groot et al. 1992; Wagner 1994; Bratsberg and Turunen
1996; Hoddinot 1996). The wage curve seems to be a general result which
holds regardless of the institutional structure of the labour markets under
study. This has led authors like Blanchflower and Oswald (1994, 1) and
Card (1995, 798) to claim it “an empirical law of economics”.
In this paper we set out examine whether there exists a similar kind of
relationship in Finnish data. There is only one earlier example of Finnish
wage curve estimations in the literature. The Finnish data used here allow
us to distinguish between workers annual and hourly wage. This distinction
is important since for example commentators like Card (1995) have pointed
out that the original results of the studies by Blanchflower and Oswald

6. 5
(1994) may be biased due to the authors’ failure to control for the changes
in the number of working hours by using simply annual or monthly wages as
dependent variables. This possible source of bias can be avoided with the
use of hourly wages. The data also make it possible to define the worker’s
local labour market as a regional county (seutukunta) of which there are 88
in Finland. This may be considered as a fairly detailed definition of the local
labour market and it should allow for a efficient control of region-specific
fixed effects.
First the earlier results relevant to this paper are studied shortly. These
include the small body of Scandinavian results as well as the only earlier
Finnish study by Parjanne (1997). The data used here are then presented in
the third section. The fourth section presents the wage curve results. In the
concluding section the interpretation of the results is discussed.

7. 3
See for example Pekkarinen, Pohjola and Rowthorn (1992).
6
2. SCANDINAVIAN WAGE CURVE RESULTS
There exists only one earlier wage curve study conducted on Finnish data.
Parjanne (1997) studies the flexibility of the hourly wages with four different
cross-sectional data sets. The data are from the annual Labour Market
Surveys conducted by Statistics Finland. Both regional and industry-specific
unemployment rates are used as independent variables. Parjanne divides
Finland into 13 regional labour markets. The results from the cross-
sectional regressions obtained by Parjanne (1997) support the hypothesis
that there exists a relationship similar to downward sloping wage curve in
the Finnish data. Estimated coefficients of the regional as well as the
industry-specific unemployment rates are negative, significant and
relatively close to the standard result of Blanchflower and Oswald (-0.1).
The study does not include results from a pooled data.
Small body of Scandinavian results also exist. As Finnish labour markets
are often grouped together with other Scandinavian countries and Austria
as representing a typical corporatist labour market structure, it is useful to
review other Scandinavian results before examining the results obtained
here.3
Original contributions by Blanchflower and Oswald (1994) contain
results estimated on Norwegian data that support the wage curve
hypothesis. The results from pooled data, covering the period of 1989–91,
are sensitive to the inclusion of regional fixed effects in the regression: the
coefficient of the local unemployment rate remains negative but does not
reach significance. However according to Blanchflower and Oswald (1994,
335) only a part of the regional dummies used to control for the fixed
effects “are significantly different”. When the regression is repeated using
only these significant regional dummies the coefficient of the regional

8. 4
See for example Blomskog (1993).
7
unemployment rate is again significant and negative: -0.08. Blanchflower
and Oswald do not have their own results from the Swedish data to present.
Instead they refer to the work done in Sweden. According to results of for
example Holmlund and Skedinger (1990) there are some grounds to
conclude that the Swedish data supports the wage curve hypothesis.
Contradicting results have however also appeared.4
Blanchflower and
Oswald nevertheless conclude that there is evidence on the existence of
the wage curve in Scandinavian data, even though these countries with
their centralised bargaining systems and nationwide wage parities are not
necessarily “likely to exhibit signs of a regional wage curve” (Blanchflower
and Oswald 1994, 355).

9. 8
3. FINNISH WAGE CURVE
The results presented by Blanchflower and Oswald give relatively strong
reasons to consider the wage curve as a “stylised fact” of the labour
markets. Scandinavian results however play only a small role in their work
and they are based on a small sample sizes. Therefore new Scandinavian
results are interesting from the point of view of the generality of the wage
curve. In the following we present a set of results from estimations where
the motivation has been to replicate the original studies of Blanchflower and
Oswald using Finnish data.
3.1. Data
The data used here are a sample from manufacturing workers’ wage
statistics by the Confederation of Finnish Industry and Employers –
Teollisuus ja työnantajat (TT) – from the period of 1992–94. Wage statistics
contains data on all the manual workers who are above 15 years old and
are employed in a firm affiliated with TT and have worked during the
calendar year.
The sample examined here has been used in a number of earlier Finnish
studies on the Finnish wage structure, wage drift, and the wage
discrimination. It has been restricted to include only the five most important
industries in Finland: metal, paper, timber, textile and clothing industries.
According to Vartiainen (1994) the sample is supposed to represent the
structure of the manufacturing sector’s labour force in each year of cross-
section as well as its dynamics. The sample has been picked by choosing
the year 1990 as a base year and ordering the 1990 wage statistics by
firms. Every fourteenth worker is then picked for the sample. A longitudinal
data base has been constructed from 1990 onwards and backwards all the

10. 9
way to the year 1980. The period 1992–94 was chosen here because the
data on individuals’ county of residence are available only from 1992
onwards. The sample sizes for the years 1992, 1993, and 1994 are 7
399, 6 807, and 7 140 respectively. Furthermore a balanced panel was
constructed for the pooled analysis: 5 460 workers were present in the each
year of the panel, which therefore contains 16 380 observations.
Data do not contain a very detailed information on the individual
characteristics of the worker. Only age and gender can be identified.
However the data on the working hours and the different compensation
forms are very detailed. Working hours are divided into normal working
hours, piecework hours, added pay hours, extra time hours, and working
hours on Sunday. Worker’s earnings form each of these hours are also
separated. Data also include “the wage group” of the individual. The wage
group is determined in the wage negotiations and it serves as a proxy of the
job’s requirement level.
3.2. Variables of the wage equation
In their original studies Blanchflower and Oswald (1994) use monthly or
annual wages as dependent variables. This method may be a source of
bias in the wage curve result since the annual earnings are a product of
annual working hours and hourly wages. It is to be expected that the annual
hours may react to changes in the local unemployment rate. This has led
commentators like Card (1995) to express their concern that Blanchflower
and Oswald have not distinguished between an “hours curve” and the wage
curve. According to the results of Card (1995) and Bratsberg and Turunen
(1996), the coefficient of the local unemployment rate is closer to zero in an
equation where the dependent variable is an hourly wage than when the
annul earnings are used as a dependent variable. We chose to use hourly
wages as a dependent variable. First annual earnings from normal working
hours, piecework hours and added pay hours were calculated. Hourly wage
was given by dividing this sum by the number of hours worked. Since

11. 10
Blanchflower and Oswald end up preferring the log-linear equation form,
the dependent variable was included as a logarithm.
Naturally the most interesting independent variable in the equation is the
logarithm of the regional unemployment rate. The original data included the
worker’s county of residence. Using a county (kunta) as a definition of the
local labour market isn’t however reasonable, since there are over 300
counties in Finland. Here we chose to use the regional counties
(seutukunta) of which there are a total of 88 in Finland. Regional county is
usually seen as a typical go-to-work area in Finland. The sample contained
observations from 75 regional counties. Unemployment rates of the
regional counties were provided by the Statistics Finland. The chosen
period was rather exceptional in the Finnish unemployment history, since
the Finnish unemployment experienced it’s most dramatic rise in the whole
20th
century during this period: the sample average reached a peak of 23 %
in 1993.
As already mentioned above, the data did not include a rich set of variables
describing individual’s characteristics. Dummy variables controlling for the
individual’s gender and age were included in the equation. Individual’s
wage group was included to control for the job’s requirement level. A set of
variables controlling the way in which the individual has been compensated
for the hours worked was also used. These variables included the
proportion of the piece rate hours worked by the individual to the total
number of hours worked and dummy variables controlling for work on extra
time, Sundays or in shifts. Although the earnings from these hours were not
included in the dependent variable, we considered that these variables
control for the possibility that the individuals who work on extra time or on
Sundays tend to earn larger wages also from normal working hours.
Different working schedules – there are 10 of them in the official contracts
– were controlled for as for the industry in which the individual works. In
some of the pooled regressions a full set of regional county -dummies was
used to control for the region specific fixed effects. Naturally in the cross-
sectional regressions this wasn’t possible. However the data included a
variable that splits Finnish counties in two according to their price level.

12. 5
To save space, only the results relevant to the wage curve debate are presented
here. The complete regression results are available on request. For the comparison
with the results of Blanchflower and Oswald (1994) the regressions that use annual
wages as a dependent variable were also conducted. To save space the results are
not included in the current version of the text. They are however generally in line
with the results from hourly wage equations and are also available on request.
6
Blanchflower and Oswald (1994) present three different tehoretical models that
generate a downward sloping wage curve-type realtionship between wages and
regional unemployment. The relationship is explicitly defined to exist between
wages and unemployment, not between hours of work and unemployment.
11
This variable could be used as a rough approximation of the regional
characteristics in the cross-sectional regressions. In the pooled regression
the yearly dummies were included to control for the changes in the price
level.
3.3. On the results
We present here results obtained from the estimation of the wage equation
of the type described above.5
The estimation procedures are ordinary least
squares and regional fixed effects model. There are results from the cross-
sections 1992, 1993 and 1994 as well as from the pooled sample of 1992–
1994. As the hourly wage clearly is the “theoretically” correct dependent
variable, the following results are mainly from equations that use hourly
wages.6
We shall first focus on the results from the cross-sections. Table 1 presents
the estimated coefficients of the regional unemployment rates from the
cross-section equations that use hourly wage as a dependent variable.

13. 12
Table 1. Wage curve regressions with cross-sectional data – log hourly
wage
Year 1992 1993 1994
log Ur
(t-statistic.)
-0.08
(8.69)
-0.04
(4.09)
-0.04
(4.16)
R2
0.5574 0.5545 0.5903
N 7 399 6 807 7 140
Log Ur is the estimated coefficient of the regional unemployment rate in the wage
equation. The regressions include following control variables: constant, age,
gender, proportion of the hours worked on piece rates, controls for work on
Sunday, extra time and in shifts, controls for the work schedule, industry, wage
group and the price level of the region. All the regressions use hetereoscedasticity-
consistent covariance matrix.
As can be seen from the table 1, the estimated coefficients of the regional
unemployment rate are negative and significant throughout the cross-
sections. Especially the coefficient of the year 1992, -0.08, is particularly
close to the stylised fact of Blanchflower and Oswald (1994): -0.1. There
are signs of a relationship similar to downward sloping wage curve in these
results.
The results presented above give evidence that there might exist a
relationship similar to the downward sloping wage curve. However most of
the results presented by Blanchflower and Oswald (1994) are from the
pooled data. The pooled data also make it possible to control for the region-
specific fixed effects. For this we constructed a balanced panel data set
from the sample, covering the whole sample period 1992–94. This could be
done since the data give information on whether the individual is an entrant
or a leaver in the sample. 5 460 workers were present throughout the whole
sample, the number of observations was thus 16 380. In six observations
the individual had not worked normal hours at all so his hourly wage could
not be calculated. This makes the number of observations that could be
used for the estimation 16 374. Table 2 presents the results from a

14. 13
regression that uses the logarithm of the hourly wage as a dependent variable.
Table 2. Wage curve regressions with the pooled data – log hourly wage
Year (1)
1992–1994
(2)
1992–1994
(3)
1992–1994
log Ur
(t-statistic.)
-0.06
(10.13)
-0.02
(0.8)
-0.09
(7.203)
Regional dummies None All (75) 64
R2
0.5603 0.6235 0.6232
N 16 374 16 374 16 374
Log Ur is the estimated coefficient of the regional unemployment rate in the wage
equation. The regressions include following control variables: constant, age,
gender, proportion of the hours worked on piece rates, controls for work on
Sunday, extra time and in shifts, controls for the work schedule, industry, wage
group and region-specific fixed effects where indicated. All the regressions use
hetereoscedasticity-consistent covariance matrix.
When the region-specific fixed effects are not controlled for, the results
give strong evidence on the downward sloping wage curve. The estimated
coefficient of the regional unemployment rate in the first column is clearly
negative and significant and relatively close to the standard of Blanchflower
and Oswald (1995). The inclusion of a full set of regional dummies to
control for the region-specific fixed effects drives the coefficient to
insignificance, although it remains negative (second column). However only
64 of the regional dummies were found to be statistically significant. When
the estimation was repeated with only 64 regional dummies, the estimated
coefficient of the regional unemployment rate was once again negative and
significantly different from zero. The result in the third column, -0.09, is also
very near the result of Blanchflower and Oswald (1994).

15. 14
3.4. Interpretation of the results
The results presented above can be considered to be in accordance with
the typical wage curve results as defined in Blanchflower and Oswald
(1994). The estimated coefficients of the regional unemployment rate are
all negative. With the pooled data the estimates tend to be driven to
insignificance by the inclusion of a full set of regional dummies to control
for the region-specific fixed effects. However when only initially significant
dummies are used the estimate turns significant again and its value, -0.09,
is surprisingly close to the typical results of Blanchflower and Oswald
(1994). The results from cross-sectional data also support the existence of
a relationship similar to the downward sloping wage curve.
Certain reservations are in place however. Firstly, it is not at all clear that
the exclusion of initially insignificantly different regional dummies is wholly
justified. The role that regional fixed effects play in these results is an
unsolved question. Secondly, the data on the individual characteristics of
the workers are poor and it might be reasonable to control for the
unobserved individual effects. The balanced panel constructed for this
study makes it possible. Thirdly, it should be remembered that the period
studied here is an exceptional one in the Finnish unemployment history.
The results reported above may therefore fail to give a right account of how
the wages react to changes in regional unemployment under normal
conditions.
The result in the third column of the table 2 – with the pooled data and 94
regional dummies and hourly wage as a dependent variable – may be
considered a most representative one. Figure 1 then gives an idea of the
shape of the Finnish wage curve.

16. 15
Figure 1. Finnish wage curve 1992–94: ln(w) = 8.3146 S 0.09 ln Ur
In figure 1 x-axis reports the regional county unemployment rates, which
varied approximately between 12 and 30 percent in the sample, and y-axis
the calculated hourly wages. The picture depicts the relationship of wages
and unemployment for only those unemployment rates that are actually
observed in the sample. If the x-axis had started from full employment the
wage curve would have turned sharply steeper below the 10 percent
unemployment rate. Figure 1 is thus the flat part of the wage curve.
Some commentators, for example Paldam (1990), have suspected that the
wage curve is being forced onto data by the chosen functional form. To
correct for this, Blanchflower and Oswald (1994) estimate an unrestricted
specification of the wage curve. Here the method is to use the usual control
variables but to replace the regional unemployment rate with a series of
dummy variables denoting intervals of unemployment rate. To get a fuller
picture of the shape Finnish wage curve we ran a similar regression with

17. 16
the data in hand. The unemployment intervals were constructed so that
each cell contains approximately 5 % of the observations, so there are
nineteen dummy variables for unemployment intervals in the regression the
lowest interval 12.8 %–14.8 % being the omitted interval. Table 3 reports
the results from this regression. The dependent variable is the logarithm of
the hourly wage.
Table 3. Wage curve regression with pooled data – unrestricted functional
form, log hourly wage
Regional unemployment Coefficient (t-statistic)
-14.8S16.6 -0.0529 (9.27)
16.7S16.9 -0.0159(2.7)
17S17.8 -0.0085 (1.67)
17.9S18.4 -0.0505 (7.65)
18.5S19 -0.018 (3.14)
19.1S19.5 -0.0444 (8.45)
19.6S19.9 -0.02 (3.73)
20S20.5 -0.0236 (4.29)
20.6S21.4 -0.0336 (6.42)
21.5S21.7 -0.0202 (3.82)
21.8S21.9 -0.0740 (12.21)
22S22.6 0.0067 (1.11)
22.7S23.1 -0.0644 (10.71)
23.2S23.6 -0.0376 (7.34)
23.7S24.2 -0.036 (6.24)
24.3S24.7 -0.042 (6.63)
24.8S25 -0.0377 (4.94)
25.1S27 -0.0556 (10.27)
27.1+ -0.073 (10.83)
The control variables are otherwise the same as in the regression of the first
column of table 2.

18. 7
The estimated coefficient of the interval 22–22.6, which was positive but not
highly significant, was left out of the picture. It would have been a clear out-lier: the
anti-log of it’s estimated coefficient is approximately 1.007.
17
If the results were to resemble the typical wage curve-results, we would
obtain more negative estimates for the coefficients of the larger
unemployment rates. This is not clear from looking at the table 3. As can be
seen, the dummy variable for the interval 22–22.6 obtains a positive but not
highly significant coefficient. To make the interpretation of these results
easier, we plotted the anti-logs of the estimates of the coefficients togethter
with the mid-points of the unemployment rate intervals.7
Figure 2. Non parametric wage curve

19. 18
There are some signs of downward sloping wage curve in figure 2,
especially if one takes into account that the estimated coefficient of the
interval 14.8–16.6 is clearly an outlier. Although the figure does not give
such a clear support to the downward sloping wage curve as do for
example the ones presented by Blanchfower and Oswald (1994, figures
4.7–4.19) with US data, there are still no grounds to conclude that the
results from the unrestricted estimation would somehow contradict the
results presented above.

20. 19
4. CONCLUSIONS
It may be concluded that these results indicate that Finnish data do not
present an exception to the argument of Blanchflower and Oswald (1994),
according to which there exists a downward sloping curve, linking the
regional unemployment rate and wages, that seems to be of approximately
same shape regardless of the country or period under study. Region-
specific fixed effects are a problem that makes the interpretation of these
results complicated, but it seems that this can be dealt with the same way
as Blanchflower and Oswald (1994) have done in their estimations with the
Norwegian data. When only significantly different regional dummies are
used the estimated coefficient of the regional unemployment rate is
negative, significant, and very close to the typical results obtained by
Blanchflower and Oswald (1994): -0.09.

21. 20
REFERENCES
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Press, Cambridge, Massachusetts.
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Bratsberg, B. & J. Turunen (1996), Wage curve evidence from panel
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Card, D. (1995), The wage curve: A review, Journal of Economic Literature,
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