Cross‐Country Differences in Unemployment: Fiscal Policy, Unions, and Household Preferences in General Equilibrium

• DOI: http://doi.org/10.1111/sjoe.12302
• Date: 01 July 2019
• Author: Brecht Boone,Freddy Heylen
• Published date: 01 July 2019

Scand. J. of Economics 121(3), 1270–1302, 2019

DOI: 10.1111/sjoe.12302

Cross-Country Differences in

Unemployment: Fiscal Policy, Unions, and

Household Preferences in General

Equilibrium*

Brecht Boone

Ghent University,B-9000 Ghent, Belgium

brecht.boone@ugent.be

Freddy Heylen

Ghent University,B-9000 Ghent, Belgium

freddy.heylen@ugent.be

Abstract

We develop a five-period overlapping generations model with individuals who differ by ability

and with an imperfect labour market (union wage setting) for individuals of lower ability. The

model explains human capital formation, hours worked,and unemployment within one coherent

framework. Its predictions match the differences in the unemployment rate across 12 OECD

countries remarkably well. A Shapley decomposition of these differences reveals an almost

equal role for fiscal policy variables and union preferences. As to fiscal policy, differences in

unemployment benefits play a much more important role than tax differences. Differences in

households’ taste for leisure are unimportant.

Keywords: Overlapping generations; Shapley decomposition; skill-type heterogeneity; union

preferences; wage setting

JEL classification:E24; E62; J51; J64

I. Introduction

Labour market performance differs widely across OECD countries. For

about a decade, many researchers have built gradually richer general

equilibrium models to account for these differences. Initial contributions by

Prescott (2004), Rogerson (2007), Dhont and Heylen (2008), and Ohanian

et al. (2008) tried to explain differences in aggregate per capita hours

worked. Later work introduced a life-cycle dimension in labour supply and

*We thank DirkVan de gaer, Tim Buyse, Glenn Rayp, Fr ´ed´eric Docquier, Hans Fehr, St ´ephane

Vigeant, Rigas Oikonomu, and an anonymous referee for valuable suggestions and comments.

B. Boone acknowledgesfinancial suppor t from the Research Foundation– Flanders (FWO). Any

remaining errors are ours.

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The editors of The Scandinavian Journal of Economics 2018.

B. Boone and F. Heylen 1271

employment in order to explain also the huge cross-country differences in

employment among people older than 50 (Rogerson and Wallenius, 2009;

Erosa et al., 2012; Alonso-Ortiz, 2014). Another advantage of introducing a

life-cycle dimension is that it became possible to model the time allocation

of young people between labour and education, and to explain human

capital formation as an endogenous variable (Ludwig et al., 2012; Heylen

and Van de Kerckhove, 2013; Wallenius, 2013).

Despite the enormous progress that has been made in this literature,

one clear weakness has not been dealt with. A striking observation in all

the aforementioned models is their assumption of a perfectly competitive

labour market. The models cannot explain equilibrium unemployment,

let alone the huge and persistent differences in unemployment between,

for example, high- and lower-educated individuals. Yet, as demonstrated

in Figure 1 for 12 OECD countries in the period 2001–2007, cross-

country differences in aggregate employment are strongly related to

differences in unemployment, in particular unemployment among lower-

educated individuals.1In Figure 1(a), we observe the highest aggregate

employment rates in countries such as Denmark, Norway, and Sweden,

which are relatively successful at avoiding unemployment among lower-

educated individuals. By contrast, countries that fail to fight unemployment

among the lower educated, such as Belgium and Germany, also show

relatively bad aggregate employment performance. The other panels in

Figure 1 reveal a number of interesting other regularities, which will guide

us later in this paper. Figure 1(b) establishes the fact that almost all cross-

country variation in the gap between the unemployment rates of lower-

and high-educated individuals is due to variation in the unemployment

rate among the lower educated. Correlation in this panel is almost 0.95.

Countries vary much less when it comes to the labour market situation

of the high educated. (Correlation between the unemployment rate among

individuals with a tertiary degree and the unemployment gap between

the lower and high educated is only 0.14.) Figure 1(c) shows a strong

inverse relationship between the unemployment gap and the employment

gap between lower- and high-educated individuals. Finally, Figure 1(d)

reveals that the aggregate employment rate is strongly related to this

employment gap. We conclude that if it is the objective of countries

to raise aggregate employment, an important challenge will be to fight

unemployment among lower-educated individuals. The existing (dynamic)

general equilibrium models for labour market analysis in the tradition of

1We focus on 2001–2007 as this was the last period of relative stability in the labour market

before the financial crisis and the euro crisis. To study equilibrium unemployment, it is clearly

more appropriate to use data for a relatively stable period.

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The editors of The Scandinavian Journal of Economics 2018.

1272 Cross-country differences in unemployment

(a) (b)

(c) (d)

Fig. 1. Employment and unemployment in OECD countries, 2001–2007

Notes:We compute the (un)employment rate among lower-educatedindividuals as the average of the (un)employment

rates among individuals with less than upper secondary education and among individuals with upper secondary

education, but no tertiary degree. The (un)employment rate among individualswith higher education relates to those

with a tertiary degree. Unless defined differently,all repor ted employmentand unemployment rates concern the age

group 25–64. The employmentrate indicates the fraction of individuals who have a job.

Data sources:Eurostat ( LFS series: lfsa ergaed, lfsa urgaed) and OECD Labour ForceStatistics (Total Employment).

Prescott (2004) and Rogerson (2007) have no clear answer to deal with this

challenge.

Next to excluding a potential role for labour market imperfections, the

above-mentioned general equilibrium literature also leaves little room for

differences in individual preferences across countries to show up. Blanchard

(2004) and Alesina et al. (2005) have argued that a key factor behind the

lower employment in many European countries compared to the United

States is a higher taste for leisure. Yet, the general equilibrium literature

generally imposes the same preferences upon individuals.

Our contribution in this paper is to extend the dynamic general

equilibrium literature studying employment with a labour market

imperfection, and to use our extended model to quantitatively explore which

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The editors of The Scandinavian Journal of Economics 2018.