André Netzén Örn, Viktor Boman

Spring 2017

Bachelor, 15 ECTS

Economics

Okun’s law within the OECD

A cross-country comparison

Author: André Netzén Örn & Viktor Boman

Supervisor: Mattias Vesterberg

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Acknowledgements!

We would like to pay deep gratitude to our supervisor, Mattias Vesterberg, for his guidance

during the writing of this thesis. Also, we would like to thank Humberto Barreto and Frank

Howland for their providence of information during the research process. It has been of

immense help.

Sincerely

________________________

Viktor Boman

&

André Netzén Örn

06-09-2017

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IV

Table of content 1. Introduction ....................................................................................................................... - 1 -

1.1. The relationship of output and unemployment .......................................................... - 1 -

1.2. Development over time .............................................................................................. - 1 -

1.3. Purpose ....................................................................................................................... - 3 -

1.4. Delimitation ................................................................................................................ - 3 -

2. Theoretical framework ...................................................................................................... - 4 -

2.1. Okun’s law ................................................................................................................. - 4 -

2.2. The first difference model .......................................................................................... - 5 -

2.3. The Gap version. ........................................................................................................ - 6 -

2.4. The Dynamic model ................................................................................................... - 8 -

2.5. Summary of the models .............................................................................................. - 8 -

2.6. Leaving the US ........................................................................................................... - 9 -

2.7 Earlier studies ............................................................................................................ - 10 -

2.8. Discussion of earlier literature ................................................................................. - 12 -

3. Methodology ................................................................................................................... - 14 -

3.1. Raw data ................................................................................................................... - 14 -

3.2. Descriptive statistic .................................................................................................. - 15 -

3.3. The Least Squares Assumptions .............................................................................. - 17 -

3.4. The estimation of potential output and long-term rate of unemployment ................ - 18 -

3.5. Decomposition Procedure ........................................................................................ - 19 -

3.6. Endogeneity .............................................................................................................. - 20 -

4. Results ............................................................................................................................. - 23 -

4.1. The ordinary least squares assumptions ................................................................... - 23 -

4.2. Estimation of the Okun coefficient .......................................................................... - 23 -

4.3. Country interaction model ........................................................................................ - 24 -

4.4. Union Density model ............................................................................................... - 26 -

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4.5. The European Union model ..................................................................................... - 27 -

5. Discussion and analysis ................................................................................................... - 28 -

6.Conclusion ........................................................................................................................ - 32 -

Reference list ....................................................................................................................... - 33 -

Appendix ............................................................................................................................. - 37 -

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VII

Abstract

In the 60’s, the first article identifying the relationship between output growth and

unemployment were released, with the purpose of providing a tool for US authorities to estimate

the effect of labour policy on output. This article, presented by Arthur Okun, came to lay the

foundation for the commonly known empirical relationship, named Okun’s law.

However, since the 60’s, the world has gone through political and economic shocks, such as

the oil crisis, fall of the berlin wall, the crisis of the 90’s, the financial crisis and crisis of 2008.

These events open up the question: has the relationship changed?

This study focuses on 21 OECD countries for the time period 1991-2016, with the purpose to

identify their respective relationship between output growth and unemployment, namely their

Okun coefficient. The test that will be performed calculates the marginal effects of respective

country to observe differences. Further, this study aims to give the reader a greater

understanding of the complexity underlying the simple model Okun presented in the 60’s.

This is done by investigating whether there are any differences in the coefficient for countries

within the EU, compared to those out of the EU. To explain the complexity further we check

whether factors that affects labour market rigidity, such as union density, create differences in

the Okun coefficient. The results from the study shows that the Okun coefficient differs between

different countries. They also show that countries belonging to the European Union has a lower

Okun’s coefficient on average. Finally, the results show that countries with a union density of

over 75 % have a lower coefficient on average.

Keywords: OECD, Okun’s coefficient, Output growth, Unemployment.

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1. Introduction ___________________________________________________________________________

In this chapter, the reader will be given an understanding about the purpose and objectives

of this study. It will give a brief understanding about the relationship between unemployment

and output, that is, the Okun’s Law.

___________________________________________________________________________

1.1. The relationship of output and unemployment

In the beginning of the 21st century, many European countries started to recover from the recent

recession taking place during the year of 2000. For Sweden, the time-period between 2001 and

2004 the GDP grew by between 1.5 % to 4.3 % annually, while for their neighbour, Germany,

the same time period corresponded to a slowdown in their annual growth rate from 1.7 % in

2001 to -0.71 % in 2003. During the same time-period, Sweden and Germany both experienced

an increase in the unemployment rate. From macroeconomic theory and reasoning, increasing

unemployment should correspond to a slowdown of the GDP growth for both Germany and

Sweden, so one could ask: should not these geographically close countries experience similar

trends suggested by macroeconomic theory? That is, when slowdowns in GDP occurs, the

unemployment rate is expected to increase and vice versa.

In the 1960’s, the relationship between unemployment and output growth where documented

by the economist Arthur Okun, who suggested a negative relationship which became known as

Okun’s law. National authorities and economist need to consider the relationship between the

two variables when they try to build economic models or when they try to forecast the effect

from a fiscal policy on unemployment. Hence, the relationship between GDP growth and

unemployment has become important when analysing economic forecasts within countries or

regions. Due to the macroeconomic relevance of the relationship, Okun’s law has come to been

widely studied and over time gained empirical support.

1.2. Development over time

The majority of the data that is used when estimating the Okun’s coefficient come from the US,

and the studies has over time come to focus on the dynamics and robustness of the relationship

following recessions. Furthermore, one interesting term named jobless recoveries has come to

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been widely discussed and studied among economist. Owyang & Sekhposyan (2012) has

suggested that Okun’s law is too simple and that during times following a recession

unemployment lagged the recovery of GDP suggesting that the Okun’s original relationship

should not be considered as a law.

Nickell (1997), Siebert (1997) and Blanchard and Wolfers (1999) started to direct their attention

towards factors that characterized the labour market. Together with new data from other

countries than the United States, Guisinger et al. (2015), Palombi et al. (2015) and Sögner and

Stiassny (2002) began to map the underlying factors on country and regional level that could

help to explain the dynamics of Okun’s coefficient and the difference dynamics between

countries. From their result, they could observe that the level of union participation among

workers, employment protection indicators and the degree of unemployment benefits

contributed to the differences in the Okun coefficient between countries and states.

A common assumption within labour market theory has been that high market rigidities

corresponds to high unemployment rate and that relaxed labour market policies should lead to

lower unemployment rate (Blanchard & Wolfers, 1999; Nickell, 1997). The assumption relies

heavily on the fact that Europe, historically, has come to experience high unemployment rate

for a lengthy period, relative to its trans-Atlantic neighbour, namely the United States. Cross-

country studies made by Ball et al. (2013) and Nickell (1997) challenges if this assumption is

valid and if institutional differences in the labour market has an impact on the unemployment

rate. Moreover, the advancements made by recent research mapping the different labour market

characteristics of countries, together with their institutional differences, creates an interesting

link that could further explain the country differences of Okun’s coefficient. This sets the

purpose of our study, namely to investigate the cross-country differences of Okun’s coefficient

of 21 OECD countries and try to identify if labour union participation and employment

protection could explain these differences. Furthermore, since earlier research base their results

comparing Europe and North America, we will perform a regression to see if we can see similar

result comparing Europe against the rest of the OECD countries.

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1.3. Purpose

The purpose of this paper is to observe and test if there are any cross-country differences in the

Okun relationship between 21 OECD countries, and to test if some of the differences could be

explained by the specific country’s grade on union density or by whether it is in the Euopean

Union or not. To estimate our models, we will use the Gap-version of Okun’s Law to see how

actual output and unemployment behave around their long-term trends. This is done using a

band-pass filter called the Hodrick-Prescott (HP) – filter.

1.4. Delimitation

This thesis will be limited to the study of the Okun’s coefficient for 21 OECD countries. The

main purpose for dropping out some of the OECD countries is the lack of data in both the

unemployment rate and the GDP. For the same reason, we will also limit the study to investigate

the period ranging from 1991 to 2016. A further argument to drop out specific countries and

hence increase the length of the period investigated is the interest in capturing the recent

recessions in 1991, 2001 and 2008. These three recessions have been followed by the so called

“jobless recovery”, which is a phenomenon where the recovery in the unemployment rate lags

the recovery in the GDP (Andolfatto & MacDonald, 2004; Ball et.al., 2013; Knotek, 2007;

Koenders & Rogerson, 2005).

The study will aim to investigate whether there are cross country differences in the Okun’s

coefficient or not. The study also tries to describe the reason why these potential differences

exists. However, the study will not explain the variation in the coefficients by econometrics,

rather it will focus on mapping the cross-country differences by inspection, logic and economic

theory. It would be of interest to conduct the analysis for more countries and over a longer

period. However, due to the lack of time and resources this is not possible.

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2. Theoretical framework ___________________________________________________________________________

This theoretical chapter involves previous research on the subject. The reader gets a deeper

understanding about what the Okun’s law is and about its complexities. The chapter will

present Okun’s original work, and how the relationship has evolved since then.

___________________________________________________________________________

2.1. Okun’s law

Okun’s law is within the economic field a widely-known empirical relationship between the

unemployment and real GDP growth. As mentioned earlier, the purpose of the research done

by Okun (1962) on the relationship was to give policy makers in the United States a tool to

forecast what impact labour programmes had on output growth. In his original work, based on

data for the US between 1947-1960, Okun used two different models to estimate the

relationship between unemployment and its impact on GNP. From the estimates, he found a

negative relationship between the two variables and suggested that a 1 % increase in

unemployment would lower the output growth with approximately 3 %.

Graph 1 Visual presentation of actual and potential GNP (Okun, 1962)

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By using the data from the same time-period Knotek (2007) investigated the finding of Okun’s

work. He found that, to keep unemployment constant, a real output growth of 4 % had to be

reached. From this he concluded that zero growth of output would correspond to an increase in

the unemployment rate of 0.3 %. Hence, Knotek’s research suggest the following model:

∆𝑈𝑡 = 0.3 − 0.07∆𝑌𝑡

In the model presented, ∆𝑢 defines the change in the unemployment rate and ∆𝑦𝑡 defines the

change of output. The value of Okun’s coefficient suggests that each percentage point of real

output growth above 4 % decreases the unemployment rate with 0.07 percentage points.

Okun used two approaches when he estimated the Okun coefficient, the two models are

presented below. In the next section, we will present the two models in more detail and shortly

introduce a third model used to estimate the coefficient.

First difference version:

∆𝑈𝑡 = 𝛽0 + 𝛽1∆𝑌𝑡 + 𝜀𝑡 𝛽1 < 0

Gap version:

(𝑈𝑡 − 𝑈𝑡∗) = 𝛽2(𝑌𝑡 − 𝑌𝑡

∗) + 𝜀𝑡 𝛽 < 0

2.2. The first difference model

The first difference model also called the growth model estimates the Okun coefficient by using

a simple linear regression model where the rate of change in unemployment rate is used as the

dependent variable.

𝛥𝑈𝑡 = 𝛽0 + 𝛽1𝛥𝑌𝑡 + 𝜀𝑡

Where:

• Given zero growth of unemployment rate, 𝛽0 gives the percentage change in output

during the period

• β1 gives the percentage change in Y given a 1 % change in U.

• 𝛥𝑌𝑡 is the change in output between period t-1 and t.

• 𝛥𝑈𝑡 is the percentage change in unemployment rate between period t-1 and t.

• The error term ε is a random term that catches all other effects.

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2.3. The Gap version.

“If, in fact, aggregate demand is lower, part of potential GNP is not produced; there is

unrealized potential or a ´gap´ between actual and potential output.” (Okun, 1962)

Compared to the first difference model that relies on accessible data, Okun’s second approach

tries to estimate how much output would grow under the scenario where the economy

experiences full employment. The second approach, named the Gap-version, measured the gap

between actual and potential output to estimate the unemployment level.

According to Chamberlin (2011) potential output can be defined as “an equilibrium level of

output where the economy can grow without experiencing inflationary or deflationary

pressure”. The problem that arise with this model and which creates uncertainty is that potential

output and the level of full unemployment must be estimated and cannot be gathered from

statistical sources.

Ball et al. (2013) argues that to fully understand Okun’s reasoning of the underlying variables

in the Gap-model, the relationship can be explained through the inflation mechanism. The

natural rate of employment, is defined as the equilibrium state within the economy where

inflation does not cause unemployment and vice versa. Additionally, Ball et al. (2013) points

out that an accelerating inflation in the economy causes a shift in domestic demand that have a

negative effect on the competitiveness of the domestic goods, changing the unemployment rate

in the economy. From the changes in domestic demand, output starts to fluctuate around the

potential output level causing firms to react on these movements. Hence, firms start to layoff

or hire workers, and two underlying relationships let us understand the original gap model

estimating the Okun coefficient.

𝐸𝑡 – 𝐸𝑡∗ = 𝛼 (𝑌𝑡– 𝑌𝑡

∗) + 𝜈𝑡 , 𝛼 > 0; (1)

𝑈𝑡 – 𝑈𝑡∗ = ώ (𝐸𝑡 – 𝐸𝑡

∗) + 𝜌𝑡 , ώ < 0; (2)

Where:

• 𝑈∗ is the long term unemployment rate

• 𝑌∗ is the potential output

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• 𝐸𝑡 is the logged value of employment

• 𝑌𝑡 is the logged value of output

• 𝑈𝑡 is the unemployment rate in time t.

• The error term 𝜈𝑡 is a random term that catches all other effects.

• The error term 𝜌𝑡 is a random term that catches all other effects.

As mentioned earlier, changes in output changes employment and according to Ball et al.

(2013), given a value of 𝛼 less than 1.5, it is costly for firms to lay off workers in the short run,

so other adjustments are used for firms to handle the short-run fluctuations in the economy.

From the equations above, Okun’s coefficient, namely 𝛽2, is derived from the coefficient α

multiplied by ώ from equations (1) and (2) above:

(𝑈𝑡 − 𝑈𝑡∗) = 𝛽2(𝑌𝑡 − 𝑌𝑡

∗) + 𝜀𝑡, 𝛽 < 0

Even though Okun in his original work choose to set unemployment as the dependent variable,

economist such as Lee (2000) and Guisinger et al. (2015) choose to set output as dependent

variable when estimating Okun’s coefficient, arguing that shocks does not affect unemployment

but affects output. A deeper discussion about the potential problem with endogeneity in Okun’s

law will be discussed later in this paper. The model becomes:

𝑌𝑡 − 𝑌𝑡∗ = 𝛿(𝑈𝑡 − 𝑈𝑡

∗) + 𝜀𝑡 , 𝛽 < 0

Where:

• (𝑈𝑡 − 𝑈𝑡∗) is the gap between the actual unemployment rate and the natural

unemployment rate in time period t and captures the cyclical level of unemployment rate.

• (𝑌𝑡 − 𝑌𝑡∗) is the gap between logged actual output and the potential output in time period

t and captures the cyclical level of output.

• 𝛿 gives the percentage change of the output gap given a 1 % change in the unemployment

gap. From economic theory, 𝛿 should take a negative value.

• The error term 𝜀 is a random term that catches all other effects.

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2.4. The Dynamic model

Over time this first difference model has been extended using time lagged variables, the

developed model is called the dynamic. According to Knotek (2007), the dynamic version

corrects for omitted effects of past output on the unemployment rate, which the first difference

model fails to include. However, the drawback of the model compared to the first difference

model is that it includes a lagged coefficient and is not as easy to interpret as the first difference

model (Knotek, 2007; Stock et.al., 2010; Sögner & Stiassny, 2002). The dynamic model is

defined as follows:

𝛥𝑌𝑡 = 𝛽0 + 𝛽1𝛥 𝑈𝑡 + 𝜃1 𝛥𝑈𝑡−1 + 𝜀𝑡

Where:

• Where β0 is the constant.

• β1 gives the percentage change in Y given a 1 % change in U in time period t.

• 𝜃1 gives the percentage change in Y given a 1 % change in U in time period t-1.

• 𝛥𝑌𝑡 is the change in output between period t-1 and t.

• 𝛥𝑈𝑡 is the percentage change in unemployment rate between period t-1 and t.

• The error term ε is a random term that catches all other effects.

2.5. Summary of the models

Comparing the three different models we can see both advantages and drawbacks in the process

of estimating the Okun coefficient. The two most common models used to estimate the Okun

coefficient are the first difference and the gap model, the same two used by Arthur Okun in the

60’s. In our effort to interpret the Okun coefficient and to see potential differences between

countries we will use the gap model. This version is well examined in papers investigating

differences on national and regional level, which provides guidance in the effort to interpret our

results. Furthermore, as Ball et al. (2012) argues, if we are choosing the first difference version

we need to assume a constant long run growth rate and a constant unemployment rate. Using

the gap-version with its estimated variables of natural unemployment and potential output we

do not need to consider this assumption and could avoid a potential error in our results. Since

we estimate deviations around a trend, the means for output and unemployment rate will be

very close to zero. This means that there is no economic interpretation in the coefficient, and

we therefore chose to leave them out of the regression. Hence, we end up estimating the

following models:

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The Okun coefficient:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜀𝑖𝑡

The Okun coefficient with country specific effects:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷1𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1 + 𝜀𝑖𝑡

The Okun Coefficient with Union Density as interaction:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷2𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1+𝜀𝑖𝑡

The Okun Coefficient with EU as interaction:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷3𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1 + 𝜀𝑖𝑡

Where:

• 𝑌𝑖is logarithm of actual real GDP for country i.

• 𝑌𝑖∗is potential GDP for country i.

• (𝑌𝑖 − 𝑌𝑖∗)𝑡 is the output gap at time t.

• 𝑈𝑖 is the actual harmonized unemployment rate for country i

• 𝑈𝑖∗is the natural rate of unemployment for country i.

• (𝑈𝑖 − 𝑈𝑖∗)𝑡−1 is the unemployment rate gap at time t-1.

• 𝛿 is the Okun’s coefficient for the reference category.

• 𝜃 is the country-, union- and EU specific effects for each model.

• 𝜀𝑖𝑡 is the error term that catches all other effects.

2.6. Leaving the US

Even if a majority of the research made on the relationship between unemployment and GDP

growth is based on US data, later research estimates the relationship based on data on the OECD

and EU. Findings from the research made from the OECD countries shows evidence of different

coefficients between countries within the OECD, and that the relationship between

unemployment and output growth are different in times when the economy experiences a

recession or boom (Lee, 2000; Moosa et al, 2004; OECD,2012)

Moreover, economist such as Lee (2000) and Nickell (1997) suggest that countries with

relatively high flexibility within their labour market tends to have a higher responsiveness from

changes in unemployment on economic growth compared to countries with more strict

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regulations, since it cost firms more to lay-off workers within these countries. Hanusch (2013)

suggest that another factor explaining the phenomena of jobless recoveries is that firms entering

a recession hoard labour, and later when exiting the recession firms has an excess of workers.

To hire workers includes training them for their task and that is why firm may choose to use

this type of labour hoarding, and this could be one factor explaining why the phenomena of

jobless recoveries exists.

Blachard & Wolfers (2000), Nickell (1997), Moosa (1997) and Sögner & Stiassny (2002) are

presenting studies suggesting that factors within the labour market such as levels of union

density, minimum wages and grade of employment protection influences the level of

unemployment and the relationship of unemployment and output. Hence, there should exist

differences in the Okun coefficient between countries, something that is both interesting and

necessary to investigate.

Another thing to account for when studying the relationship is the choice of data for the output

growth. Okun did use GNP is his original paper which is GDP plus the total capital gains from

abroad, but economist that have examined the dynamics of Okun’s coefficient debate whether

output should be measured with GDP or GDI. Some economist, Meyer & Tasci (2012) and

Baker & Rosnick (2011) suggests that real GDP represent a closer indicator for the home

economy of countries and is less volatile than GNP, while other argues that GNP should be

used since it correlates closer to other business cycle indicators. Without going into depth, we

assume that GDP is an at least as good economic indicator as GNP to estimate the Okun

coefficient for the OECD countries.

2.7 Earlier studies

The robustness of Okun’s law: evidence from the OECD countries.

(Lee, 2000)

In this paper, Lee choose to set output as the dependent variable and uses post-war data from

countries within OECD to estimate the robustness of the coefficient between 1955-1996. Lee

argues that most of the coefficients are statistically valid for many of the countries within OECD

but the result differs depending on the choice of model, which is the purpose of his study. The

models he chooses to estimate the coefficient with are the gap and first difference method

arguing that a higher value of the coefficient correspond to a more rigid labour market. He

further discusses and uses three different bandpass-filters to observe the long-term

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unemployment rate that is required for estimating using the gap-method. In his findings, he

argues that some evidence of asymmetric behaviour of the coefficient exist and finds

convincing evidence of structural breaks in the 70’s.

How useful is Okun’s law?

(Knotek, 2007)

The author of this paper updates Okun’s law with quarterly data on the US stretching from the

second quarter of 1948 until the second quarter of 2007. The author further mimics the same

models that Okun used in his original research to observe potential differences compared to the

results from Okun’s original paper. He further estimates Okun’s coefficient over different time-

periods to observe potential dynamic features of Okun’s coefficient, discussing the different

coefficients the Gap and first difference model presents.

The conclusion by Knotek is that Okun’s law has not been stable over time and changes

depending on which time-period the data is gathered for both the gap and first difference

version. Furthermore, he found that the coefficient was different during times of economic

recession or expansions for the US economy and observed that periods after recession

experienced an increase in output growth while the unemployment rate did not fall. Knotek

called this phenomenon jobless growth. He argued that the first difference method of Okuns

law is too simple to capture the dynamics of the relation between output and the labour market

following by a recession.

Okun’s law: Fit at fifty?

(Ball et al., 2013)

In this paper, Ball et al. discusses Okun’s law in the United States and 20 advanced countries,

where the data begins on the year 1948 for the United States and in 1980 for the advanced

economies. They examine the data on quarterly and yearly basis with both the gap model, first

difference model and the lagged first difference model and uses the HP filter to generate the

long-term trends. They find that for the US the coefficient differs depending on which of the

three models used. They further discuss the term jobless recovery which is widely argued

among economist and explains that following a recession, employment growth is weak and

unemployment becomes higher than Okun suggest in his paper.

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In this paper, the results are estimated for 21 OECD countries and the researchers finds that

Okun’s coefficient differs across countries. Additionally, the author of the paper tries to explain

the countries with extreme values of their coefficients by looking at their respective labour

protection measured by the employment protection (EPL) - index.

Unemployment and labour market rigidities: Europe vs North America

Stephen Nickell (1997)

In this paper, the author examines institutional factors that could explain why some countries

has consistently higher unemployment than for example the US. The time-period that Nickell

choose to examine ranges between 1989-1994, he strengthens his choice by arguing that this

period followed a recession. The author examines factors such as wage flexibility, employment

protection, benefit replacement rate and trade union density to address which factors that could

potentially characterize high unemployment rate.

The result from this paper highlights the fact that countries with high unemployment rate are

characterized at some extent by high unionization density, with wages bargained collectively.

Furthermore, the length of the period that the unemployed workers can participate in generous

benefit programs, with no pressure to find work, increases the unemployment level. The author

also points out low minimum wages for young people and low educational level as factors

contributing to high unemployment rate within economies. However, in his result, Nickell

(1997) shows that some labour market institutional factors have a significant impact on the

unemployment level, while others do not and concludes that there are institutional differences

among countries within Europe.

2.8. Discussion of earlier literature

To summarize the four articles presented, the research focus mainly on the differences between

the gap and first difference model and the choice of filter when estimating potential output and

the long-term unemployment rate for the gap version. The research made in the early days was

mainly based on data from the US but over time, with new data available, the research came to

focus on the differences between countries, especially between the European economies.

Furthermore, the main conclusion about why the coefficients differ between economies has

come to focus on the impact of shocks and institutional factors within the labour market. Ball

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et al.’s and Nickell’s research highlight the fact that different labour market policies, together

with institutional differences in the labour market, partly explains the cross-country differences.

With that said, due to limited time and data, our paper will focus on mapping the difference of

Okun’s coefficient between Europe and the rest of the world within the OECD. Furthermore,

even if we should consider many different institutional factors to estimate Okun’s coefficient

for our chosen country our time is limited. The choice of the variable Union Density is based

on our limited time-frame, has been used in earlier research when estimating Okun’s coefficient

and possesses good data point for our research question.

Also, the results from earlier research of the coefficient differ depending on the time-period and

econometric method used to estimate Okun’s relationship. However, one should understand

that, based on the literature presented, the estimation of the Okun coefficient has come to take

large criticism for being too simple and for not accounting for the dynamics of changing

economies.

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3. Methodology ___________________________________________________________________________

This chapter describes and argues for the choice of method. It also explains how we handle

the problem with estimating long-term trends for unemployment and output. Further, the

parameters used in the study will be explained more in detail. Finally, the chapter also gives

a deeper understanding about potential problems in Okun’s law, such as endogeneity, and

how they have been handled.

___________________________________________________________________________

3.1. Raw data

The data is gathered from the national accounts and labour database from OECD statistical

database. They provide quarterly panel-data for cross-country comparison between our selected

economies within OECD from the first quarter of 1991 until the fourth quarter of 2016. The

advantages with using quarterly data instead of yearly is that we get a bigger dataset, and that

it is more informative. Since quarterly data better represents short term fluctuation, the cyclical

deviations from the long-term trend will be more realistic. Data on NAIRU is available from

the OECD database but the disadvantage is that the data are annually. Instead we will use OECD

harmonized unemployment rate which is gathered quarterly. Furthermore, the period 1991 to

2016 includes the fall of DDR which give us data on Germany and allows us to include three

economic crises that the economies within the OECD faced in the years 1991, 2001 and 2008.

The data for trade union density is gathered from the same OECD database as mentioned earlier.

According to OECD, the data measures the ratio of the wage- and salary earners that are trade

union members, divided by the total numbers of wage- and salary earners. The data is gathered

on annual basis during the time-period between 1991 and 2014. Furthermore, we transform this

data into a union index rating between 1-3, where 1 represents countries with a union density

up to 25 %, 2 represent countries with a union density above 25 % and up to 75 %, and 3

represent countries with over 75 %.

The data on real GDP uses 2010 as reference year, is in US dollar and is seasonally adjusted by

OECD with the X-12-ARIMA method, which smoothen out our quarterly data from its sig-saw

shape and makes it easier to analyse the real GDP growth over the given time-period. According

to Franses et al. (2005), the X-12-ARIMA procedure is one of the most popular procedures used

- 15 -

for seasonal adjustment and calendar effects. Also, Ghysels and Osborn (2001) points out that

the X-12-ARIMA is based on the procedure called X-11 which is a moving average filter and

together with the routine called regARIMA the model is fitted to the data which creates the X-

12-ARIMA procedure. The X-12-ARIMA is standardized among statistic bureaus such as US

Census Bureau and Canada Statistic for removing seasonal and calendar effects. Finally, to

extract the potential output from the data we will be using the HP-filter. Hodrick & Prescott

(1986) views the timeseries as a trend component, a cyclical component and a seasonal

component, and argues that the seasonal component must be eliminated for the HP-filter to be

consistent. Hence, the X12-ARIMA adjustment to the data is necessary.

As for unemployment, it is gathered from OECD statistical database. The OECD uses a method

for making it easier to compare the data between different countries (harmonization). This

method is according to OECD (2017) a standardized method for estimating unemployment for

international comparison. They lift the fact that the uniform application of the definition makes

the data more comparable between countries. OECD defines the unemployed as: people aged

15-64, who are without work, available for work and have taken certain steps to find work.

Where 15 is defined as the minimum age for the labour force for every country except for Spain,

UK and US, which uses 16 as minimum age. The unemployment rate is gathered between the

time-period 1990 until 2017 and is seasonally adjusted.

3.2. Descriptive statistic

Our variable (𝑈𝑖 − 𝑈𝑖∗) shows how the actual unemployment rate fluctuates around the natural

unemployment rate. Here actual unemployment corresponds to variable 𝑈𝑖 while the natural

unemployment corresponds to variable 𝑈𝑖∗. Hence our unemployment gap variable becomes

(𝑈𝑖 − 𝑈𝑖∗).

The variable for output, (𝑌𝑖 − 𝑌𝑖∗), uses real GDP which is measured in US dollars. To estimate

the variable of potential GDP for our model we use the HP-filter on the logarithmic values of

our countries real GDP. Here the logarithm of real GDP corresponds to 𝑌𝑖 while potential GDP

corresponds to 𝑌𝑖∗. Our output gap variable becomes (𝑌𝑖 − 𝑌𝑖

∗).

- 16 -

In earlier research done by Adanu (2005), the statistical tests have been done with GDP in

logarithmic form and with unemployment rate in its original form. We will use the same

method, that is, the GDP will be logged but not the unemployment rate.

Variable Description Database

Actual Real GDP

Q1 1991-Q4 2016

Real GDP measured in US dollar on quarterly

data gathered from 21 selected OECD countries.

OECD

Potential GDP

Q1 1991-Q4 2016

Estimated variable from Real GDP using its

logarithm value and HP-filter

OECD

Harmonized

unemployment rate

Q1 1991-Q4 2016

Quarterly data for the harmonized

unemployment rate from 21 selected OECD

countries.

OECD

Natural unemployment

rate

Q1 1991-Q4 2016

The natural unemployment rate is estimated

from the harmonized unemployment rate.

OECD

Trade Union density

1991-2014

Percentage of total wage- and salary earners

participating in a trade union., Rank 1=0-25%,

2=25-75%, 3=75-100%.

OECD

Table 1: descriptive statistic

- 17 -

The characteristics of the data, such as means, standard deviations etc. are summarized in the

table below.

Summary of treated data.

Variable Observations Mean Standard

Deviation

Min Max

Real GDP 2184 1680160 2860047 21607.54 1.70e+07

Unemployment

Rate

2184 .0725632 .0370324 .015 .2626667

Logarithmic

GDP

2184 13.43305 1.386837 9.980798 16.64982

GDP Gap 2184 -2.46e-12 .0156989 -.0913857 .096339

Unemployment

GAP

2184 -2.46e-12 .0067079 -.0383317 .0350896

Union Density 1764 33.78666 20.78529 7.547659 83.86255

Table 2

From table 2, we see that the unemployment rate takes a mean value of 7.25 %. We can also

see that the unemployment rate for the countries takes values between 1.5 % and 26 %, which

is not surprising considering the geographical and institutional differences between them. The

same reasoning follows considering the union density for the countries. With the lowest value

of 7.5 % and the highest of 83.9 % the density of workers in trade unions differ remarkedly and

could potentially contribute to explain the cross-country differences among the OECD

members.

3.3. The Least Squares Assumptions

For our model to be consistent and for our estimators for the OECD countries to be good, we

need to check the five OLS assumption for panel data. This is needed for our economic

interpretation, when discussing the differences between countries and to justify our findings.

• The residuals have the mean of zero

We test for normality, that is, if our sample has residuals with mean of zero. Since we have

2184 observation in our data we can rely on the Central Limit Theorem which states that when

the number of observations in the sample is large, the sampling distribution of the sample

average is approximately normally distributed (Stock & Watson, 2015). To check for this

assumption, we perform a kernel density test (see Appendix 3).

- 18 -

• (𝑿𝒊𝟏,, 𝑿𝒊𝟐, … , 𝑿𝑰𝒕´𝑻,,𝒀𝟏𝒕,𝒀𝟐𝒕, … , 𝒀𝒊𝒕), i = 1,…,n are i.i.d. across entities.

Stock and Watson (2015) argues that this assumption is fulfilled if the data are collected by

simple random sampling. Our data should be considered trustworthy and reliable since we

collect the raw data from Stat OECD database. The sampling method that is used is well

described by the OECD database, and are based on household surveys from most of the

National Statistical Institutes, which uses multi-staged stratified random sample design.

However, according to Stock and Watson (2015) we should check for serial correlation. Serial

correlation occurs when the value of 𝑋𝑖𝑡 correlates over time and is a pervasive feature of time-

series.

• Large outliers are unlikely

Observations with values that are far outside the usual range of the data can negatively influence

the OLS estimators of the coefficients in the regression (Stock and Watson, 2015). Since we

have a very large dataset, large outliers should have a relatively insignificant effect on our

estimators. Hence, there is no risk that this assumption is not fulfilled.

• No perfect multicollinearity

The problem with perfect multicollinearity arises when two or more of the regressors are perfect

linear functions of each other (Stock & Watson, 2015). According to Stock and Watson it is

impossible to compute the OLS estimator if perfect multicollinearity exists. Stata 14 has a built-

in feature that omits regressors that are perfectly correlated, hence, this problem is easily

handled in the study.

3.4. The estimation of potential output and long-term rate of unemployment

In our model, we are using the Gap-version approach of Okun’s Law. This means that the model

involves a measurement of how much the real output and unemployment rate deviates from

their respective long-term trend, that is how they deviate from the long-term unemployment

rate and the potential GPD. These values are not observable in the economy and are required to

be estimated. Adanu (2005) argues that the method of choice to estimate these variables can

have a significantly influence on the estimated coefficients. Further Adanu (2005), Baxter &

King (1999), Freeman (2001) and Dennis and Razzak (1999) highlights that when estimating

the long-term unemployment rate and the Potential GDP there are several ways to go. Among

these are the removal of deterministic trends and the use of first differencing. They also mention

- 19 -

more sophisticated methods such as band pass filtering and the use of a Hodrick Prescott filter,

with the advantage that they remove both high and low frequencies from the time series. In

addition to these methods Giorno et al. (1995) also points out the possibility to use a production

function for the estimation. Since this is a method that requires a lot of economic data, we find

it more suitable to use the HP-filter for the purpose mentioned above. Furthermore, in earlier

research (Crespo Cuaresma, 2003; Kim et.al. 2015; Lee, 2000), the HP-filter has been the

method of choice for breaking out the cyclical component when estimating Okun’s coefficient

using the gap-method.

The purpose of using the HP-filter is the fact that it decomposes the time series into a cyclical

and a growth component, that is 𝑦(𝑡) = 𝑔(𝑡) + 𝑐(𝑡), where 𝑦(𝑡) is the natural logarithm of

given series, 𝑐(𝑡) is the cyclical component and g(t) is the growth component. (Cogley &

Nason, 1995; Ravn & Uhlig, 2002). Furthermore, the method – unlike other method such as

first difference – does not suffer from a loss of data when applied. That is, for a time series y(t)

for 𝑡 = 1, … 𝑇, the procedure estimates the cyclical component, 𝑐(𝑡), for 𝑡 = 1, … 𝑇. (Baxter &

King, 1999).

Although there are many advantages with using the HP-filter to de-trend and breaking the

cyclical component out of time series, it is important to consider the fact that the method has

received criticism over the years. The most important is lifted by the Giorno et al. (1995) and

Ball et.al (2013), and is a problem with end-point bias. By using the HP-filter we fit a trendline

symmetrically through the data. This brings a problem if the starting- and the ending point of

the dataset does not reflect similar points in the cycle. Hence, there is a possibility that the trend

is moved either upwards or downwards towards the actual path of the end-points. Ball et.al

(2013) has performed a test of robustness to check whether the end-point bias has a considerable

influence on the estimation of the Okun’s coefficient or not. However, they find that influence

of end-point bias in the decomposition of the time series does not affect the estimation of the

coefficient. Hence, we do not find it necessary to address the problem with end-point bias in

this study.

3.5. Decomposition Procedure

It is important to state that Hodrick and Prescott (1986) view the time series as a cyclical

component and a trend component, as described by Cogley and Nason (1995) and Ravn and

Uhlig (2002), but also as a seasonal component. But, as discussed above in the chapter about

- 20 -

raw data, OECD has smoothened out the seasonal effects by applying a X-12-ARIMA filter to

the data. Hence, there is no necessity for us to handle this effect. According to Giorno et al.

(1995), when applying the HP-filter we fit a trend to the data points of the logarithm of the real

GDP, and by making the regression coefficients vary over time, the HP-filter takes structural

breaks into account.

The variation in the growth component, g(t), is penalized by a smoothing parameter, λ (Ravn

and Uhlig, 2002; Hodrick & Prescott, 1997; Dennis & Razzak, 1999). This parameter is

described by the Giorno et al. (1995) as a weighted factor that determine to which grade the

trendline should be smoothed. Thus, the smoothing parameter will regulate the sensitivity of

the trend to short-term fluctuations. That is, with a lower value of λ, the trend will follow the

actual output more closely, while a higher value of λ will create a trend that is less sensitive to

the short-term fluctuations, and which follows the mean growth rate of the time series more

closely. When choosing the value of λ, there are several different methods. For example, it is

possible to choose a dynamic value of λ, which adapts depending on which country is estimated

(Giorno et al., 1995). Although there are different methods, such as the one described, which

could give a more accurate estimation of the Okun’s coefficient, we will choose to go with the

approach that is described by the OECD to be the industry standard, e.g. choosing the same λ,

which is 1600, for every country. This choice of λ is also strengthened by Baxter & King

(1999), Hodrick & Prescott (1986) and Dennis & Razzak (1999), who points out that 𝜆 = 1600,

is the common choice when working with quarterly data. For deeper knowledge of the

technicalities of the HP-filter we recommend reading Theory Ahead of Business-Cycle

Measurement by Edward C. Precott.

There are also several methods for estimating the unemployment rate gap. Earlier researchers

(Cogley & Nason 1995; Lee (2000); Adanu (2005) uses the HP filter with at smoothing

parameter of 1600 on the data points to estimate the deviations from the long-term

unemployment rate. This is the method of choice that will be used in this study as well.

3.6. Endogeneity

When we are estimating economic models, we assume that the causality runs from the

dependent variable to the independent variable (Stock & Watson, 2015). One problem that

arises in Okun’s law is that there is uncertainty in whether the unemployment rate affects the

- 21 -

output or if it is the other way around. Stock and Watson (2015) describes this problem as

simultaneous causality. Further, according to Stock and Watson this problem causes biases and

inconsistency in the OLS estimator. The phenomenon is often referred to as endogeneity.

Barreto and Howland (1993) states that there is no doubt that simultaneity exists in the Okun’s

law and that unemployment and output are endogenous. This brings a potential problem to this

study. Earlier researchers, among others; Aschoff & Smith (2008), Brinks & Coppedge (2006),

Clemens et al. (2011), Cornett et al. (2007) and Green et al. (2005) suggest several methods for

handling endogeneity, the most mentioned being the use of instrumental variables and the use

of lags in the independent variable. To address this potential problem with biases in the

estimators from endogeneity we will go with the approach of lagging the unemployment gap.

By doing this we can regress a model where unemployment gap affects output gap, but where

output gap does not affect unemployment gap. That is, unemployment gap, (𝑈 − 𝑈∗), at time 𝑡

influences output gap, (𝑌 − 𝑌∗), at time 𝑡 + 1. At the same time, output gap at time 𝑡 + 1

cannot influence unemployment gap at time 𝑡. Furthermore, there is an advantage in the

economic interpretation when lagging the unemployment gap. Intuitively the effects of

unemployment on output does not happen in an instant, but rather there should exist inertia in

the process.

Barreto and Howland (1993) argues that the choice of the dependent variable when regressing

the Okun’s coefficient should be made based on the purpose of the study. The main purpose for

Okun (1962) when investigating the relationship between unemployment and output was to

give policymakers an instrument to measure the impact of labour programmes on output

growth. They did this by estimating how output affected unemployment, and then taking the

reciprocal of the coefficient to show how unemployment affected output. The problem with this

is, according to Barreto and Howland (1993), that a decreasing unemployment are caused only

partly by an increasing output. The other way around, an increasing output is caused only partly

by a decreasing unemployment. They describe this by pointing out that some of the changes

should be attributed to unobserved shocks in productivity and working hours. This means that

there exist different biases in the two estimations. They strengthen this argument by using both

methods of estimating the coefficient on the same data, and show that they differ. By estimating

using unemployment as dependent and take the reciprocal of it tends to overestimate the

coefficient compared to using output as the dependent variable. It is obvious that taking the

reciprocal of any one of the coefficients give an incorrect measure of the other coefficient. Since

- 22 -

we are interested in investigating the original Okun’s law, that is how changes in unemployment

change output, we find it more justified to use output as the dependent variable in the model.

- 23 -

4. Results ___________________________________________________________________________

In this chapter, the reader will get a brief presentation of this study’s results and empirical

findings. For an easier understanding, the presentation will be divided into different sections

for each of the regressions we make.

___________________________________________________________________________.

4.1. The ordinary least squares assumptions

We have checked for the assumptions regarding the ordinary least squares method. As argued

above, stata omits variables if they are perfectly multicollinear. We have no omitted variables

in our results, hence no multicollinearity. Since we have a very large sample, the influence of

outliers is very small, hence, the distortion from outliers is negligible. According to the Central

Limit Theorem, the large sample also makes it feasible to assume that the sampling distribution

of the sample average is approximately normally distributed (Stock & Watson, 2015). We have

also checked for heteroskedasticity and autocorrelation, the tests (see Appendix 6), shows that

there exists both autocorrelation and heteroskedasticity. According to Stock and Watson (2015),

this problem can be handled by using heteroskedasticity- and autocorrelation-consistent

standard errors, which has been done.

4.2. Estimation of the Okun coefficient

We begin with presenting the standard model of the Okun’s coefficient, followed by additional

models with different interaction terms. We estimate the following regression for the standard

model:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = −1.226(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜀𝑖𝑡

We can see that the Okun’s coefficient is estimated to -1.226. This result corresponds with what

earlier research has found. Although, one should know that since the method of choice

influences the estimated coefficients, a comparison with earlier research is hard to make.

- 24 -

4.3. Country interaction model

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷1𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1 + 𝜀𝑖𝑡

The model consists of a dependent variable, output gap, an independent variable,

unemployment gap and an interaction variable between unemployment gap and a dummy for

country, the results are presented in table 3 below. The empirical tests are conducted with

Australia as the reference country in the dummy variable, this means that the coefficients are

estimates of the difference in slope between a given country and Australia. The results show a

negative relationship between output gap and unemployment gap for all countries included in

the study. This is a result that is expected. We can also see that there are country-specific effects,

on at least a 5 % significance level, for all countries except Belgium.

Okun’s

Coefficient

Standard

Error

t-value P>t 95 % Confidence

Interval

Australia -.255∗ .131541 -1.94 0.052 -.5133368 .0025859

Country:

Belgium −.367 .2228058 -1.65 0.100 -.8038446 .0700321

Canada −1.231∗∗∗ .203051 -6.06 0.000 -1.629112 -.8327163 Germany −.980∗∗∗ .2896714 -3.38 0.001 -1.548533 -.4123997 Denmark −.862∗∗∗ .2347178 -3.67 0.000 -1.322037 -.4014398 Spain −. 582∗∗∗ .1396495 -4.17 0.000 -.8558213 -.3080958 Finland −.926∗∗∗ .2046208 -4.53 0.000 -1.327475 -.5249226 France −.592∗∗ .2409756 -2.46 0.014 -1.064334 -.119193 Great Britain −1.104∗∗∗ .2802261 -3.94 0.000 -1.653651 -.5545634 Ireland −1.391∗∗∗ .3013152 -4.62 0.000 -1.981867 -.8000647 Italy −1.069∗∗∗ .2174275 -4.92 0.000 -1.495569 -.6427867 Japan −2.062∗∗∗ .5728531 -3.60 0.000 -3.185409 -.9385956 South Korea −1.386∗∗∗ .3221012 -4.30 0.000 -2.017627 -.7542993 Luxemburg −2.211∗∗∗ .5385568 -4.11 0.000 -3.267463 -1.155165 Mexico −2.517∗∗∗ .273609 -9.20 0.000 -3.053835 -1.980701 Netherlands −.914∗∗∗ .2069308 -4.42 0.000 -1.319456 -.5078437 Norway −.947∗∗∗ .3152998 -3.00 0.003 -1.565081 -.3284295 New Zealand −.684∗∗∗ .2191367 -3.12 0.002 -1.114 -.2545138 Portugal −.859∗∗∗ .1867109 -4.60 0.000 -1.225283 -.4929763 Sweden −.937∗∗∗ .2248225 -4.17 0.000 -1.378274 -.4964874 United States −.793∗∗∗ .1679579 -4.72 0.000 -1.122569 -.4638131

Table 3: Regression of OECD countries. ***, ** and * corresponds to 1-, 5-, and 10 percent significant level.

- 25 -

To calculate the marginal effects, we take the derivative of the model with respect to

unemployment rate, and test whether 𝛿𝑖 + 𝜃𝐷1𝑖 ≠ 0. We can see from the results that 𝛿𝐷𝑖,

together with 𝛽, are significantly different from zero at a 5 % significant level for all countries

in the study. The coefficients that are estimated are mostly in line with what earlier research has

found. However, we find a couple of coefficients that have extreme values compared to earlier

research. Among those countries are for example Australia, where a 1 % change in

unemployment gap leads to a change in output gap by -0.255 %, We can see that Mexico (-

2.773) is the country where changes in the unemployment has the most powerful effect on the

output. On the other hand, Australia (-0.255) is the country with the lowest effect from

unemployment on output. These results are presented in table 4 below.

𝝏(𝒀𝒊 − 𝒀𝒊∗)

𝝏(𝑼𝒊 − 𝑼𝒊∗)

Standard

Error

t-value P>t 95 % Confidence

Interval

Country

Australia −.255∗ .131541 -1.94 0.052 -.513337 .0025859 Belgium −.622∗∗∗ .1798316 -3.46 0.001 -.974945 -.269619 Canada −1.486∗∗∗ .1546825 -9.61 0.000 -1.78963 -1.18295 Germany −1.235∗∗∗ .2580824 -4.79 0.000 -1.74196 -.729724 Denmark −1.117∗∗∗ .194395 -5.75 0.000 -1.49834 -.735891 Spain −.837∗∗∗ .0468929 -17.86 0.000 -.929295 -.745374 Finland −1.182∗∗∗ .1567376 -7.54 0.000 -1.48895 -.874201 France −.847∗∗∗ .2019065 -4.20 0.000 -1.24309 -.451186 Great

Britain

−1.359∗∗∗ .2474341 -5.49 0.000 -1.84472 -.874246

Ireland −1.646∗∗∗ .2710864 -6.07 0.000 -2.17796 -1.11472 Italy −1.325∗∗∗ .1731234 -7.65 0.000 -1.66406 -.985046 Japan −2.317∗∗∗ .5575461 -4.16 0.000 -3.41077 -1.22399 South

Korea

−1.641∗∗∗ .2940173 -5.58 0.000 -2.21793 -1.06475

Luxemburg −2.467∗∗∗ .5222455 -4.72 0.000 -3.49085 -1.44253 Mexico −2.773∗∗∗ .2399143 -11.56 0.000 -3.24313 -2.30216 Netherlands −1.169∗∗∗ .1597415 -7.32 0.000 -1.48229 -.855761 Norway −1.202∗∗∗ .2865501 -4.20 0.000 -1.76408 -.640185 New

Zeeland

−.940∗∗∗ .1752651 -5.36 0.000 -1.28334 -.595925

Portgual −1.115∗∗∗ .1325063 -8.41 0.000 -1.37436 -.854651 Sweden −1.193∗∗∗ .1823242 -6.54 0.000 -1.55031 -.835205 United

States

−1.049∗∗∗ .1044359 -10.04 0.000 -1.25337 -.84376

Table 4: Marginal effects OECD countries. ***, ** and * corresponds to 1-, 5-, and 10 percent significant level.

- 26 -

4.4. Union Density model

To investigate whether the grade of union density affects the Okun coefficient or not, we do a

regression with an interaction term between union density and the unemployment rate:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷2𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1+𝜀𝑖𝑡

The results show us that we have a significant effect from the grade of union density on the

relationship between unemployment and output. We have significance on a 5 % level for the

countries that achieved rank three on the grade of union density, both when compared to those

countries that achieved one and those that achieved two. What the results show is that countries

that have rank number three on the grade of union density have a lower Okun’s coefficient by

0.386 on average compared to those with rank two, and 0.477 compared to those with rank one

(see Appendix 5). Hence, we can see that countries grade of union density affects the Okun

coefficient, which is in line with what earlier research suggests. Countries with a high grade of

union density have a lower coefficient on average. This result differs from what earlier research

shows. They argue that a higher union density should lead to a higher Okun’s coefficient. When

we calculate the marginal effects, and check if 𝛿 + 𝜃𝐷2𝑖 ≠ 0, the results show that they differ

from zero and that they together influence the relationship.

Union

Density

𝝏(𝒀𝒊 − 𝒀𝒊∗)

𝝏(𝑼𝒊 − 𝑼𝒊∗)

Standard

Error

t-value P>t 95 % Confidence

Interval

Rank 1 −1.235∗∗∗ .0750863 -16.44 0.000 -1.38190 -1.08737 Rank 2 −1.325∗∗∗ .10412 -12.73 0.000 -1.52971 -1.12128 Rank 3 −.849∗∗∗ .0901955 -9.41 0.000 -1.02540 -.671599

Table 5: Marginal effects, union density % 0-100, rank 1=0-25%, 2=25-75, 3=75-100. ***, ** and * corresponds

to 1-, 5-, and 10 percent significant level.

- 27 -

4.5. The European Union model

We also want to check whether belonging to the European Union has any effect on the

relationship or not. This is done by regressing the model with an interaction term between if the

country is in the EU or not and Unemployment rate:

(𝑌𝑖 − 𝑌𝑖∗)𝑡 = 𝛿(𝑈𝑖 − 𝑈𝑖

∗)𝑡−1 + 𝜃𝐷3𝑖(𝑈𝑖 − 𝑈𝑖∗)𝑡−1 + 𝜀𝑖𝑡

The results from the regression shows that the countries that are a part of the European Union

have a lower Okun’s coefficient of 0.311 on average. In table 7, where we present the test for

marginal effects, we can see that 𝛿 + 𝐷3𝑖 ≠ 0, hence the interaction effect together with the

unemployment gap affects the output gap.

Okun’s

coefficent

Standard

Error

t-value P>t 95 % Confidence

Interval

Non-EU −1.458∗∗∗ .1042318 -13.98 0.000 -1.662071 -1.253263

EU . 311∗∗∗ .1167294 2.67 0.008 .0821887 .5400139 Table 5: EU member vs non-EU. ***, ** and * corresponds to 1-, 5-, and 10 percent significant level. Non-EU as

reference.

𝝏(𝒀𝒊 − 𝒀𝒊∗)

𝝏(𝑼𝒊 − 𝑼𝒊∗)

Standard

Error

t-value P>t 95 % Confidence

Interval

Non-EU −1.458∗∗∗ .1042318 -13.98 0.000 -1.662071 -1.253263

EU −1.147∗∗∗ .0525498 -21.82 0.000 -1.249619 -1.043513

Table 6: Margins-test EU vs non-EU. ***, ** and * corresponds to 1-, 5-, and 10 percent significant level.

- 28 -

5. Discussion and analysis ___________________________________________________________________________

This chapter of discussion will connect our empirical findings to the theoretical framework

and earlier research presented. We will try to give a nuanced picture of Okun’s Law and the

underlying factors that determine the coefficient.

___________________________________________________________________________

This paper studies the relationship between output growth and unemployment for the OECD

countries, except the Soviet Union and some countries that lacks the necessary data for the time-

period 1991-2016. This paper also investigates whether the Okun coefficient differs between

countries, and try to explain what the reason are for these differences. The main focus is on

whether there is an impact of different countries union density on the Okun coefficient. This is

done in an attempt to explain if these differences labour market rigidities and institutions can

help to determine the variation in the Okun coefficient between the OECD countries.

We can see that our coefficients correspond well compared to earlier research done by Lee

(2000), Sögner and Stiassny (2002), Ball et al. (2012) and Freeman (2001). Moreover, from the

regression we see that there are differences between countries within the OECD area and that

all coefficients are lower than the one suggested by Okun in his original paper. This goes in line

with the findings suggested by Freeman (2001), who argues that the coefficient should be closer

to two rather than Okun’s original estimate of three. Part of this difference compared to Okun’s

original paper could occur because we chose to use output as the dependent variable, compared

to Okun who used unemployment. As Barreto and Howland (1993) suggest, the coefficient

tends to be overestimated when using unemployment as the dependent variable.

Looking at the regression from table 4 above, we see that Japan together with Mexico takes on

the highest value for the coefficient while Australia and Spain has the lowest. Plotting the

coefficients against average unemployment, we can see that a higher coefficient corresponds to

a lower average unemployment rate and vice versa. This corresponds well with the findings of

the results from Ball et al. (2012), that the factors underlying the Okun coefficient also has an

impact on average unemployment. Observing graph 2 we can see that Spain stands out with a

high average unemployment rate and that both Japan and Mexico have had a low unemployment

rate and a high coefficient value. This relation is in line the findings of Ball et al. (2012).

- 29 -

Graph 2: Unemployment rate in % on Y-axis, Year on X-axis. Source OECD database

In their findings, Blanchard and Wolfers (2000) and Nickell (1997) try to explain the relatively

high historical unemployment rates in the European countries by linking the differences in their

institutions to their respective unemployment levels. Comparing our result with the findings of

Nickell (1997), we can see from table 5 that the union density in an interaction with the

unemployment rate does have an effect on the Okun coefficient. Looking at graph 3 we can

observe that the northern countries differ substantially in their union density compared to the

countries of Japan and Mexico. Furthermore, from the same regression measuring the impact

of the union density we can conclude that this is indeed the case, that the northern country

deviates from the rest of the OECD members. However, the result from our regression cannot

help us explain the differences between the rest of the OECD countries.

0

.05

.1.1

5.2

.25

1990q1 1995q1 2000q1 2005q1 2010q1 2015q1time

Mexico Spain

Japan

- 30 -

Graph 3: Union density% Y-axis, Okun coefficients on X-axis

The result can be linked to the findings of Guisinger et al. (2015) and Nickell (1997), however,

their findings suggest that higher unionization level potentially corresponds to a higher

unemployment level and hence a more rigid labour market. Furthermore, they state, based on

their findings, that a more rigid labour market should correspond to a higher value of the Okun

coefficient. Our regression suggest that a higher union density lowers the absolute value of the

coefficient with about 0.38 compared to the countries with lower union density. As we

mentioned before, our coefficients are similar to finings of Guisinger et al. (2015) Ball et al.

(2012), Freeman (2001) and Lee (2000), but we do not observe the same relation between the

Okun coefficient and the union density that they do.

Graph 4: OECD base line in black. unemployme nt in percent on Y-axis Source OECD database

aus

bel

candeu

dnk

esp

fin

fra

gbr

irl ita

jpn

kor

lux

mexnld

nor

nzlprt

swe

usa

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

-3,00 -2,50 -2,00 -1,50 -1,00 -0,50 0,00

Ave

Un

ion

den

sity

%

Okun coefficent

Okuns coeff and average Union density

- 31 -

As discussed above, from graph 3, we can see that the European countries experience higher

values of the union density. Also by inspection of graph 4, we can see that during the time-

period between 1991-2015, European countries has a high unemployment rate compared to the

rest of the OECD countries. From these observations, and from the findings by Guisinger et al

(2015) and Blanchard and Wolfers (2000), a higher long-term unemployment rate should have

an impact on the Okun relationship, generating a higher value of the coefficient. Also,

Blanchard and Wolfers (2000) compared the European labour market to the US labour market,

and found that higher employment protection was one of the factors that increased the duration

of unemployed workers in Europe, and hence the unemployment level. They also reasoned that

countries using a more generous unemployment insurance system decreased the opportunity

cost of being unemployed and hence lowered the incentives to search for new work, increasing

the unemployment duration. Siebert (1997) also discusses the problem with higher

unemployment duration in Europe, he lifts the fact that the European countries adjusted badly

to the intensified global competition and the labour-saving technology developed during the

90’s. However, our results suggest the opposite of what Blanchard and Wolfers and Guisinger

et al. found. The value of the Okun coefficient for the countries within the EU are lower than

for those outside the EU. This is contradictory to what is discussed above, since a more rigid

labour market should correspond with a higher Okun coefficient.

The differences in our results compared to earlier researchers could possibly be explained by

the fact that we include Japan, Mexico and South Korea, which, in our study, all show a high

value for the coefficient. Earlier researchers have mainly drawn their conclusions comparing

North America to the European countries. According to our results, Mexico has the highest

coefficient, but a small amount of the economic research includes the country due to lack of

data. In a report, the OECD (2012) finds that the Okun coefficient for Europe takes a value

between Japan and the US, this is something that presents a potential explanation for our results.

They argue that compared to Japan, the euro area has taken greater steps in their labour market

reform which results in a less rigid labour market. This is something that is also supported by

the findings of Lee (2000), where he sees pattern in Europe of more relaxed labour markets.

These findings from the OECD and Lee corresponds well with our results for Europe, Japan

and the US.

- 32 -

6.Conclusion ___________________________________________________________________________

Here we will present the findings and conclusions from this study. We will also give

recommendation for further research in this subject.

___________________________________________________________________________

In this empirical study, we have investigated the Okun coefficient of 21 countries within the

OECD. We can conclude that there are significant country-specific effects in the Okun

coefficient, that is, the coefficient differs between countries. In order to identify potential

underlying factors influencing the Okun coefficient, we did test if the specific countries union

density has any significant effect. We can see significant effects for countries that have over 75

% in union density cover. This indicates that union density is something that should be

considered when examining Okun’s law. However, since we compare many geographically and

institutionally different economies, other factors than union density needs to be considered.

Primarily other factors that affect labour rigidities should be considered. We have also tested if

being a member of the European Union influences the estimation and we find that it does.

However, due to lack of time and limited resources we cannot test for any underlying factors

that could explain this. Instead, we rely on economic reasoning and earlier studies within the

area to identify potential factors contributing to our result. It is clear that the relationship

between unemployment and output varies between countries. For further research, we suggest

that the labour market rigidities influence on the Okun coefficient should be deeply

investigated. We also recommend that further research be done on the more extreme countries,

such as Mexico, Japan and Australia, in an attempt to explain how and why they differ so much

from other countries.

- 33 -

Reference list

Adanu, K. (2005). A cross-province comparison of Okun’s coefficient for Canada. Applied

Economics, 37, 561–570.

Andolfatto, D., & MacDonald, G. (2004). Jobless recoveries. Macroeconomics, 412014.

Aschhoff, B., & Schmidt, T. (2008). Empirical Evidence on the Success of R&D Cooperation—

Happy Together? Rev Ind Organ, 33, 41–62.

Baker, D., & Rosnick, D., (2011). When Numbers Don’t Add Up: The Statistical Discrepancy

in GDP Accounts. Center for Economic and Policy Research.

Ball, L.M., Leigh, D., Loungani, P. (2013). Okun’s Law: Fit at Fifty? National Bureau of

Economic Research.

Barreto, H., & Howland, F. (1993) There Are Two Okun's Law Relationships between Output

and Unemployment. Wabash College Working Paper.

Baxter, M., & King, R.G. (1999). Measuring Business Cycles: Approximate Band-Pass Filters

for Economic Time Series. Review of Economics and Statistics, 81, 575–593.

Blanchard, O., & Wolfers, J. (2000). The Role of Shocks and Institutions in the Rise of

European Unemployment: The Aggregate Evidence. The Economic Journal, 110, 1–33.

Brinks, D., & Coppedge, M. (2006). Diffusion Is No Illusion: Neighbor Emulation in the Third

Wave of Democracy. Comparative Political Studies, 39, 463–489.

Chamberlin, G. (2011). Okun’s Law revisited. Econ Lab Market Rev, 5, 104–132.

Clemens, M.A., Radelet, S., Bhavnani, R.R., & Bazzi, S. (2012). Counting Chickens when they

Hatch: Timing and the Effects of Aid on Growth*. The Economic Journal, 122, 590–617.

- 34 -

Cogley, T., & Nason, J.M. (1995). Effects of the Hodrick-Prescott filter on trend and difference

stationary time series Implications for business cycle research. Journal of Economic Dynamics

and Control, 19, 253–278.

Cornett, M.M., Marcus, A.J., Saunders, A., & Tehranian, H. (2007). The impact of institutional

ownership on corporate operating performance. Journal of Banking & Finance, 31, 1771–1794.

Cuaresma, J.C., (2003). Okun’s Law Revisited*. Oxford bulletin of economics and statistics,

65, 439–451.

Dennis, R.J., & Razzak, W. (1996). The output gap using the Hodrick-Prescott filter with a non-

constant smoothing parameter: an application to New Zealand. Reserve Bank of New Zealand.

Franses, P. H., Fok, D., & Paap, R. (2005). Performance of seasonal adjustment procedures:

simulation and empirical results.

Freeman, D.G. (2001). Panel Tests of Okun’s Law for Ten Industrial Countries. Economic

Inquiry, 39, 511–523.

Ghysels, E., & Osborn, D.R. (2001). The econometric analysis of seasonal time series.

Cambridge: Cambridge University Press.

Giorno, C., Richardson, P., Roseveare, D., & Van den Noord, P. (1995). Estimating potential

output, output gaps and structural budget balances.

Green, R.K., Malpezzi, S., Mayo, S.K. (2005). Metropolitan-Specific Estimates of the Price

Elasticity of Supply of Housing, and Their Sources. American Economic Review, 95, 334–339.

Guisinger, A.Y., Hernandez-Murillo, R., Owyang, M., & Sinclair, T.M. (2015). A State-Level

Analysis of Okun’s Law, Federal Reserve Bank of Cleveland, 15-23.

Hanusch, M. (2012). Jobless Growth? Okun’s Law in East Asia. Social Science Research

Network, Rochester, NY.

- 35 -

Hodrick, R.J., Prescott, E.C., 1997. Postwar U.S. Business Cycles: An Empirical Investigation.

Journal of Money, Credit and Banking, 29, 1–16.

Huang, H.-C., & Yeh, C.-C., (2013). Okun’s law in panels of countries and states. Applied

Economics, 45, 191–199.

Kim, M.J., Park, S.Y., & Jei, S.Y. (2015). An empirical test for Okun’s law using a smooth

time-varying parameter approach: evidence from East Asian countries. Applied Economics

Letters, 22, 788–795.

Knotek II, E.S. (2007). How Useful is Okun’s Law? Economic Review, 92, 73–103.

Koenders, K., & Rogerson, R. (2005). Organizational dynamics over the business cycle: a view

on jobless recoveries. Review, 87.

Lee, J., (2000). The robustness of Okun’s law: Evidence from OECD countries. Journal of

Macroeconomics, 22, 331–356.

Meyer, B., & Tasci, M. (2012). An unstable Okun’s Law, not the best rule of thumb. Economic

Commentary.

Moosa, I.A. (1997). A Cross-Country Comparison of Okun’s Coefficient. Journal of

Comparative Economics, 24, 335–356.

Moosa, I.A., Silvapulle, M.J., & Silvapulle, P. (2004). Asymmetry in Okun’s law. Canadian

Journal of Economics/Revue canadienne d’économique, 37, 353–374

Nickell, S., (1997). Unemployment and Labor Market Rigidities: Europe versus North

America. The Journal of Economic Perspectives, 11, 55–74.

OECD (2012): Economic Outlook, Section 1. General Assessment of the Macroeconomic

Situation, Vol. 1, p.35

- 36 -

OECD (2017), Harmonised unemployment rate (HUR) (indicator). doi: 10.1787/52570002-en

(Accessed on 25 May 2017)

Okun, A. M. (1962) Potential GNP: its measurement and significance, in Proceedings of the

business and

economic statistics section, American Statistical Association.

Owyang, M.T., & Sekhposyan, T. (2012). Okun’s law over the business cycle: was the great

recession all that different? Federal Reserve Bank of St. Louis Review, 94, 399–418.

Palombi, S., Perman, R., & Tavéra, C. (2015). Commuting effects in Okun’s Law among British

areas: evidence from spatial panel econometrics. Papers in Regional Science.

Prachowny, M.F.J. (1993). Okun’s Law: Theoretical Foundations and Revised Estimates.

Review of Economics & Statistics, 75, 331–335.

Prescott, E.C. (1986). Theory ahead of business-cycle measurement. Carnegie-Rochester

Conference Series on Public Policy, 25, 11–44.

Ravn, M.O., & Uhlig, H. (2002). On adjusting the Hodrick-Prescott filter for the frequency of

observations. Review of Economics and Statistics, 84, 371–376.

Siebert, H., (1997). Labor Market Rigidities: At the Root of Unemployment in Europe. Journal

of Economic Perspectives, 11, 37-54.

Stock, James H. (2015). Introduction to econometrics. 3. rev. ed., Global ed. Harlow: Pearson

Education

Stock, L., & Vogler-Ludwig, K. (2010). NAIRU and Okun’s Law–The Macro-Economy in a

Nutshell? Final report.

Sögner, L., & Stiassny, A. (2002). An analysis on the structural stability of Okun’s law–a cross-

country study. Applied Economics, 34, 1775–1787.

- 37 -

Appendix Appendix 1: Hodrick-Prescott filter

Logged real GDP together with potential output

Figure 1

Figure 2

14

.314

.414

.514

.614

.7

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

France

14

.714

.814

.915

15

.1

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Germany

11

11

.512

12

.5

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Ireland

14

.35 1

4.41

4.4

5 14

.514

.55 1

4.6

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Italy

15

.15 1

5.21

5.2

5 15

.315

.35 1

5.4

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Japan

14

.214

.414

.614

.8

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Great Britain

13

13

.213

.413

.613

.8

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Australia

12

.612

.712

.812

.913

13

.1

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Belgium

13

.613

.814

14

.214

.4

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Canada

12

12

.112

.212

.312

.412

.5

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Denmark

11

.611

.812

12

.212

.4

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Finland

- 38 -

’’

Figure 3

Figure 4

13

.113

.213

.313

.413

.513

.6

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Netherlands

11

.211

.411

.611

.812

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

New Zealand

12

12

.212

.412

.612

.8

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Norway

12

.212

.312

.412

.512

.6

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Portugal

12

.412

.612

.813

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Sweden

13

13

.514

14

.5

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

South Korea

10

10

.210

.410

.610

.811

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Luxemburg

13

.814

14

.214

.414

.6

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Mexico

16

16

.216

.416

.6

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

United States

13

.613

.814

14

.214

.4

ln G

DP

1991q1 1999q3 2008q1 2016q3

time

Trend lnGDP

Spain

- 39 -

Actual unemployment with long term unemployment

Figure5

Figure 6

.04

.06

.08

.1.1

2

Un

em

plo

ym

ent

Rate

1991q1 1999q3 2008q1 2016q3

time