determinants of unemployment flows labour market institutions and macroeconomic policies pdf

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Determinants of unemployment flows Labour market institutions and macroeconomic

policies

Ekkehard Ernst

INTERNATIONAL LABOUR ORGANIZATION

INTERNATIONAL INSTITUTE FOR LABOUR STUDIES

Copyright © International Labour Organization (International Institute for Labour Studies) 2011. Short excerpts from this publication may be reproduced without authorization, on condition that the source is indicated. For rights of reproduction or translation, application should be made to the Editor, International Institute for Labour Studies, P.O. Box 6, CH-1211 Geneva 22 (Switzerland). ISBN Print: 978-92-9014-978-1 Web/pdf: 978-92-9014-979-8 First published 2011 The responsibility for opinions expressed in this paper rests solely with its author, and its publication does not constitute an endorsement by the International Institute for Labour Studies of the opinions expressed. Requests for this publication should be sent to: IILS Publications, International Institute for Labour Studies, P.O. Box 6, CH-1211 Geneva 22 (Switzerland).

Determinants of unemployment flowsLabour market institutions and macroeconomic policies

Ekkehard Ernst∗

ILOInternational Institute for Labour Studies

Geneva, CH

[email protected]

Summer 2011

Abstract

The paper makes use of newly developped information on unemployment dynamics. On the basisof a matching model of the labour market the paper analyses the economic, institutional and policy de-terminants of unemployment in- and outflows. Standard determinants are found to be significant withthe expected sign. In particular adverse productivity shocks, higher user costs of capital, stronger realwage growth, heavier tax burden, larger unionization rate and more stringent employment protection leg-islation can be shown to depress unemployment dynamics. Moreover, the impact of the degree of wagebargaining centralization confirms the original Calmfors-Driffill insight also in the flow context. Thepaper also identifies the impact of policy interventions through fiscal and labour market spending us-ing a macroeconomic, simultaneous equation set-up. The paper assess the relative contribution of thesepolicies in stimulating unemployment outflows and analyzes the effectiveness of different policy instru-ments at different time horizons, stressing the importance of passive labour market measures to stimulateunemployment outflows and to limit unemployment inflows.

Keywords: Unemployment in- and outflows, economic, institutional and policy determinants oflabour market flows, fiscal and labour market policies

JEL-Codes: J64, J23

∗The author acknowledges helpful discussions with Matthieu Charpe, Uma Rani and Raymond Torres. The views expressed inthis paper are those of the author and cannot be imputed to the International Institute for Labour Studies or the International LabourOrganization.

1

2 DETERMINANTS OF UNEMPLOYMENT FLOWS.

Contents1 Introduction 4

2 What drives unemployment dynamics? 52.1 The base model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Macro-economic interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Solving for the steady state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Data, methodology and hypotheses 83.1 Data: Unemployment flows and labour market institutions . . . . . . . . . . . . . . . . 83.2 Methdology and hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2.1 Empirical methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2.2 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Determinants of unemployment flows 104.1 Labour market dynamics: Economic and institutional determinants . . . . . . . . . . . . 104.2 Government spending and labour market programmes . . . . . . . . . . . . . . . . . . . 144.3 Short vs. long run effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5 Conclusion 19

DISCUSSION PAPER SERIES NO. 209 3

Preface

The role of labour market policies in limiting employment losses resulting from the global crisis of 2008has received considerable policy attention. As the labour market situation worsened, countries implementedunconventional measures to support job seekers and limit job losses such as extending unemployment bene-fits and expanding work sharing arrangements. Some of the instruments used had been discarded by earlierstudies as ineffective or even counter-productive. This paper questions some of the earlier evidence pre-sented and argues that in situations of substantial macroeconomic stress, unconventional interventions inthe labour market may have significant favourable impacts on employment.

The paper makes use of a new database, looking at flow transitions in and out of employment, rather thanemployment and unemployment "stocks". In addition, macroeconomic interaction effects are taken into ac-count by estimating a small macroeconomic model with a labour market. This new empirical methodologyis based on theoretical considerations developed by recent Nobel prize winners Diamond, Mortensen andPissarides and allows for a more detailed account of transmission mechanisms of different policy options.Most importantly, it will allow for a better understanding of the total – macro- and microeconomic – effectsof labour market policies through various incentive and aggregate demand channels.

The analysis presented in this paper was undertaken as part of the World of Work Report 2010 and sup-ported this report’s assessment of fiscal and labour market policy effectiveness. The paper has successfullypaved the way for further research on the interactions between labour markets and macroeconomic policies.

Raymond Torres

Director

International Institute for Labour Studies


1 Introduction

The empirical analysis of the labour market has long concentrated on understanding the determinants ofemployment and unemployment levels, both in static and dynamic terms. With the onset and wide diffusionof labour market matching theories along the Diamond-Mortensen-Pissarides framework, however, the the-oretical questions have shifted towards understanding the flows rather than the stocks on the labour market.These theoretical advancements are only gradually matched by corresponding empirical studies, mainlyrelated to the lack of relevant data. While information on job vacancies has increasingly become available,other information related to gross worker flows or unemployment in- and outflows need to be constructedfrom scratch. In this respect, recent advancements by various researchers make the direct empirical analysisof the flow dynamics available for a wider community. The analysis of gross job and worker flows has beenpushed forward by various authors, including Davis et al. (1998) and Davis et al. (2008), often, however,on the basis of individual country analysis. More recently, the construction of unemployment flows - inand out of the pool of unemployed - allows cross-country comparisons of labour market dynamics (see, forinstance, Elsby et al. (2008), Shimer (2007) and Petrongolo and Pissarides (2008)).

In this paper, we want to exploit these newly available information on unemployment dynamics, tryingto understand the economic policy and institutional determinants. Specifically, we want to consider whethertypical determinants of unemployment stocks - such as those identified by Baccaro and Rei (2005), Bakeret al. (2005), Bassanini and Duval (2006), Bassanini et al. (2010), Nickell et al. (2005) and Stockhammerand Klär (2008) - carry over to an analysis of unemployment flows. In particular, we are interested whetherstandard theories of labour market rigidities, such as wage bargaining systems and employment protection,can explain in an economically sensible way the information related to unemployment in- and outflows.

In addition, and motivated by recent policy interventions in relation with the global financial crisis,we aim at a more detailed understanding of the impact of fiscal and labour market policy interventionson the different margins of unemployment dynamics. To properly take the dynamics of the labour mar-ket adjustment into account, we have developed a new methodology to account for the macroeconomiceffects of fiscal and labour market policies when analysing programme effectiveness. In particular, weaccount for economy-wide pecuniary externalities from these spending programmes through influences oflong-term interest rates. This is in contrast to most microeconomic studies that analyse policy effective-ness ceteris paribus, i.e. assuming that aggregated demand is not being affected. This is often justified asmany programmes are run on a relatively limited scale, with a clearly identified scope on a local or sectorallevel. Often, these programmes are introduced at an experimental level and do not develop a large, fiscallyrelevant impacts. However, at the current juncture, with a large scale increase in spending on general gov-ernment and specific labour market programmes, these will also have a macroeconomic effect, which needsto be taken into account when assessing the implications of these programmes for labour market dynamics.Moreover, by considering the programme impact on different flow margins rather than on an aggregatejob/worker reallocation measure, our analysis of unemployment flows allows to assess to what extent dif-ferent programmes will be useful in stimulating unemployment outflows, as compared to programmes thatlimit unemployment inflows. This will help in better designing programmes that better correspond to par-ticular cyclical characteristics of an economy.

The main results of this paper can be summarised as follows:

• Standard economic determinants help explain a substantial part of the (within-country) variation ofunemployment in- and outflows. Across specifications and depending on the particular specificationused up to 40% of the sample variation are explained by such variables.

• Both demand (investment, disposable income, output gap) and supply (real wages, user cost of capital,productivity, terms of trade) factors are relevant to explain the data on the basis of a standard searchand matching labour market model.

• Labour market institutions are related to unemployment flows with the expected sign: Employmentprotection legislation lowers outflows but does not lead to a significant decline in inflows. Union-ization rates on the other hand do not seem to affect outflows but increase the rate of unemployment


inflows. Finally, the relationship between wage bargaining centralization and outflows is U-shaped(as in the original Calmfors-Driffill hypothesis, see Calmfors and Driffill (1988)) whereas inflows are(only insignificantly) negatively related to this indicator (in both the linear and non-linear specifica-tion).

• Fiscal and labour market policies have significant effects on both unemployment in- and outflows buteffects wear off over the long-run. Typically, the effects are stronger on stimulating unemploymentoutflows than on preventing unemployment inflows. Unemployment benefits and hiring incentivesseem to have particularly strong effects both in the long and the short run whereas spending ontraining programmes and public employment services seem to have (small) positive effects only inthe long run.

The paper is organised as follows: In the following section, a simple model of labour market flows is intro-duced on the basis of a standard search and matching framework, extended by a more general description ofshocks and taking into account capital accumulation at the firm level. In section 3, the data and methodologyemployed in this paper are introduced and discussed. Section 4 presents the main results and examines therelative contribution of individual factors to the overall variation in our sample. A final section concludes.

2 What drives unemployment dynamics?

This section presents the empirical model underlying our analysis of unemployment dynamics. The sectionstarts by discussing the determinants of unemployment in- and outflows as they arise from current theo-retical search and matching models, including indications to possible institutional variables that influencethe dynamics. In a second part, macro-economic feedback loops of fiscal and labour market policies areanalysed. Finally, this section finishes by deriving the steady state solution of the dynamic empirical model.

2.1 The base model

In order to study the dynamics of unemployment flows, we first need to link our observables - unem-ployment in- and outflows - to typical determinants identified in the labour market search and matchingliterature, the current industry standard for labour market analysis. In the analysis below, we concentrateon labour market flows related to flows of unemployed in (INt ) and out of (OUTt ) the unemployment pool.These flows can be linked to (absolute) changes in the number of unemployed as follows:

4Ut =4Lt −4ETt = INt −OUTt (1)

i.e. the level of unemployment increases with the increase in the labour force, LFt , and decreases with therise in (total) employment. ETt . Alternatively, unemployment increases when inflows into the unemploy-ment, INt pool exceed outflows, OUTt .

In order to be operational for our purposes, this flow equation needs to be further refined, taking intoaccount the job creation and destruction process that affects the total amount of jobs available:

4ETt = JobCreationt − JobDestructiont (2)

i.e. changes in the employment level result from the difference between created vs. destroyed jobs. Fol-lowing standard labour market matching models (see Pissarides, 2000, for a classical reference in this area,and Carlsson et al., 2006), the extensive margin of labour demand1 is determined by a mix of the followingfactors:

JobCreationt = α1 +β11ETt−1 +β12wt +β13ADt +β14Ut +β15Vt +β16rt−1 (3)

where ETt−1 : past employment, wt : real wages, ADt : aggregate demand, Ut : unemployment, Vt : unfilledvacancies and rt−1 : (lagged) real long-term interest rates.

1In this set-up, we abstract from changes in working hours.


Similarly, job destruction will be affected by the rate of technological progress, the real interest rate(through the discounted future benefits of an ongoing relationship), import competition, wages and aggre-gate demand:

JobDestructiont = α2 +β21T FPt +β22rt +β23εt +β24IMPt +β25wt +β26ADt (4)

where T FPt : an indicator for total factor productivity, rt : the real (long-term) interest rate, εt : the realeffective exchange rate, IMPt : a measure of either import penetration (i.e. imports relative to GDP) or realimport growth.

Finally, unemployment dynamics are also affected by changes in the labour force. We reflect standardtheories about the determinants of labour supply by considering the following equation for changes in laboursupply (see, for instance, Burniaux et al., 2003):

4Lt = α3 +β314Lt−1 +β324ut−1 +β33Taxt (5)

where β32 represents the discouraged worker effect which depresses labour force growth (with an expectednegative sign).

Wages in equations (3) and (4) are endogenous to job creation and destruction and, therefore, need tobe instrumented. This can be done using the following wage-equation, which is based on a wage-curveapproach (à la Blanchflower and Oswald, 1994):

wt = α4 +β41Kt +β42CBt +β434ut−1 +β44Taxt (6)

where Kt : the capital stock, CBt : an indicator for the degree of centralization in collective bargaining system(either the degree of coordination or the union density),4ut−1 : lagged changes in the unemployment rate,Taxt : a measure for the tax burden (either average or marginal tax rates).2

The six equations (1)-(6) form the basis of our labour market flow model. Due to the lack of interna-tionally comparable data on job creation and destruction rates, however, the model needs to be rewritten tomatch it with our data base. This can be done by bringing in accordance job creation rates and unemploy-ment outflows on the one hand and job destruction and unemployment inflows on the other. This requiresthat the determinants of labour supply as specified in equation (5) are plugged into the appropriate unem-ployment flow equation. Indeed, unemployment inflows and outflows do not match exactly job destructionand job creation. Some unemployment inflow happens from inactivity (see chart) when the economy re-covers while some people who are loosing their job might drop out immediately to inactivity if they donot qualify for any benefits. Similarly, job creation can happen out of inactivity (for instance through self-employment), while some people might “flow out of” unemployment at the end of their benefit period andinto inactivity. As a consequence, using unemployment flows instead of job creation and destruction ratesmight overestimate employment dynamics due to the failure to take out flows back and forth from and toinactivity. It might also overestimate the variation of employment growth when the inactivity rate fluctuateswith the business cycle (as is suggested by the discouraged worker effect).

In our specification, we consider that the discouraged worker effect will create additional unemploy-ment outflows. On the other hand, increasing supply in the available workforce will show up as additionalunemployment inflows. Tax-related changes in labour supply are considered to affect both unemploymentinflows and outflows. Besides these adjustments to our specification, we consider both unemployment in-flows and outflows to follow dynamic adjustment processes, instead of estimating them in levels. This way,we cope with systematic under- or overestimation of flows over the cycle that are due to these linkagesbetween unemployment and inactivity. In addition, by considering contemporaneous interactions betweenthe two flow directions, we also take care of the possibility that we are overestimating the impact of unem-ployment flows on employment variation: Higher contemporaneous inflows will also increase outflows aspart of it goes into inactivity. Similarly, higher outflows might partly imply an increase in inactivity thatwill show up in increased inflow rates. We will therefore estimate the following two equations related to

2In a further extension we might consider estimating a separate wage inflation Phillips curve.


unemployment dynamics:

OUTt = αOUT + β̃11OUTt−1 + β̃12INt + β̃13XJobCreationt + β̃144ut−1 + β̃15Taxt−1 (7a)

INt = αIN + β̃21INt−1 + β̃22OUTt + β̃23XJobDestructiont + β̃244Lt−1 + β̃25Taxt−1 (7b)

where XJobCreationt and XJobDestruction

t correspond to the different explanatory variables in equations (3)and (4) respectively. This will form the base model for the following extensions of our labour flow model.

2.2 Macro-economic interactions

In addition to the variables considered in equations (3), (4) and (6), policy makers also have instruments attheir disposal to influence directly the rate at which jobs are being created or destroyed. On the one hand,they can generically affect aggregate demand, thereby slowing down job destruction and speeding up jobcreation. This can be done either through purchases of goods and services (non-wage government spending)or by expanding the public work force (wage government spending).

On the other hand, they can more finely try to influence labour market dynamics through active andpassive labour market measures. For instance, policy makers can set up hiring incentives for firms, re-duce labour costs via wage subsidies or strengthen the matching process through training expenditures andspending on public employment services that help job seekers in finding adequate job vacancies.

In either case, however, macro-economic feedback loops are likely to influence the impact of thesepolicies. In the following, we want to consider two such loops: First, we take the feedback from automaticstabilization into account, whereby both general government spending and labour market policies react tothe state of the labour market. In particular, we expect higher unemployment outflows to be correlated withless spending and higher unemployment inflows with more spending. Second, higher public spending willact on the long interest rates to the extent that it increases government net lending. This may happen asgovernment lending will crowd out private borrowers but also because of higher risk premia on sovereigndebt that will carry over to private investors as well. In either case, higher long-term interest rates will havean adverse effect on unemployment dynamics by both lowering unemployment outflows and increasingunemployment inflows. Taking such feedback mechanisms into account when assessing the effectivenessof government interventions on the labour market is essential for countries to properly assess the long-runimplications of their spending programs.

Taking such feedback mechanisms into account when estimating the effect of policy interventions re-quires setting up a simultaneous equation model such as the following:

OUTt = αOUT + β̃11OUTt−1 + β̃12INt + β̃13XJobCreationt + β̃144ut−1 + β̃15POLt (8a)

INt = αIN + β̃21INt−1 + β̃22OUTt + β̃23XJobDestructiont + β̃244Lt−1 + β̃25POLt (8b)

POLt = αPOL + β̃31OUTt + β̃32INt (8c)

RIRLt = αR + β̃41NLGt + β̃42LLGDPt (8d)

The system is based upon the basic in- and outflow equations (7a) and (7b) but contains additionalpolicy variables, POLt , that refer to (fiscally relevant) policy interventions (including taxes, as indicatedabove). All spending items are measured in terms of spending on particular programmes with respectto GDP, so as to properly account for the budgetary burden that is implied by different fiscal and labourmarket policy options. As discussed before, the policy equation is based on feedback effects resulting fromautomatic stabilization; no other influences are considered here. Regarding the long-term feedback effects,an aggregate supply curve has been added to the model by means of a forth equation that takes the effect ofgovernment spending dynamics on (long-term) real interest rates, RIRLt , into account. In this equation, theinterest rate is supposed to be influenced by government net lending, NLGt , - itself a function of governmentspending - and the availability of savings as measured by the amount of liquid liabilities in the economy,LLGDPt .


2.3 Solving for the steady state

The above system of equations given by (8a)-(8d) can be solved for a steady state solution, effectively givingthe long-run impact of policies on unemployment out- and inflows in contrast to the estimated coefficientsβ̃15 and β̃25 the only indicate the effect on impact:

OUTt = 1(1−β̃11)·(1−β̃21)−β̃12·β̃22

·[αOUT

(1−β̃21

)+αIN ·β̃12+β̃13

(1−β̃21

)·XJobCreation

t +β̃12·β̃23·XJobDestructiont +(

β̃15·(

1−β̃21

)+β̃12·β̃25

)·POLt+

(β̃14·

(1−β̃21

)+β̃12·β̃24

)4ut−1

](9a)

INt = 1(1−β̃11)·(1−β̃21)−β̃12·β̃22

·[αIN

(1−β̃11

)+αOUT ·β̃22+β̃13·β̃22·XJobCreation

t +β̃23

(1−β̃23

)XJobDestruction

t +

=(

β̃25·(

1−β̃11

)+β̃15·β̃22

)·POLt+

(β̃11·

(1−β̃24

)+β̃14·β̃22

)4ut−1

](9b)

In the following, we will compare the effects of institutional and policy variables both on impact (i.e. att +1) and in steady state. As can be seen from the formular, signs for different policy variables may differbetween short-run and steady state effects, depending on the size of (some) of the estimated parameters.We will see in the next section that this is indeed the case for some of the policy variables that we consider.

3 Data, methodology and hypotheses

3.1 Data: Unemployment flows and labour market institutions

The paper brings together three new (or updated) databases: Unemployment in- and outflows, generalmacroeconomic data and institutional indicators covering labour market regulation and policies. The re-sulting database covers 15 OECD countries over most of the period between 1970 and 2005 on an annualbasis (time coverage changes depending on the particular specification used).

Unemployment flows come from Elsby et al. (2008). The data is constructed on the basis of OECDinformation regarding unemployment stocks and unemployment duration at different duration lengths. Incontrast to similar information provided by Shimer (2007) or Petrongolo and Pissarides (2008), the databy Elsby et al. (2008) allow for a systematic cross-country analysis and a larger variety of determinants ofunemployment flows to be included.

Information on labour market policies has been taken from the OECD Labour Market Spending database.The data has been complemented with information taken from the OECD Economic Outlook database andthe OECD Main Economic Indicators. In particular, data regarding vacancy rates, total employment andlabour force developments, capital stock estimates and interest rates are taken from there. In addition, an in-dicator of real share price increases has been developped on the basis of OECD information, using the GDPdeflator to deflate nominal share prices. Finally, information regarding unionization rates, the degree ofwage bargaining coordination and employment protection legislation is taken from the OECD EmploymentOutlook.

3.2 Methdology and hypotheses

3.2.1 Empirical methodology

To exploit the cross-country nature of our data, we apply standard panel data techniques. The theoreticalequations developped in section 2 are both formulated in level terms. It, therefore, appears straightforwardto use simple OLS to test the different determinants of unemployment flows. Three issues arise, however,in this respect:


• Country-specific information is not always available for all variables over the entire time period.Moreover, especially the institutional variables, such as employment protection legislation, sufferfrom very limited time-variability within country panels, making them almost undistinguishable fromcountry fixed effects.

• Both unemployment in- and outflows are highly persistent within countries, introducing problems ofautocorrelation.

• Some of the right-hand side variables are endogenous to the dependent variable (in- and outflows).

In principle, these three problems could be addressed using the Arellano-Bond system GMM estimator.However, in the context of this paper, the number of available observations per country is relatively high(in comparison to the number of panels), which typically leads to a rejection of the overidentification tests.Given that - a part from auto-correlation - other forms of clustering of the error terms (see Nichols andSchaffer (2007) for an overview) are unlikely to arise in this sample, different versions of auto-correlationcorrected panel estimators were used instead. In addition, in order to take into account endogeneity ofleft-hand-side variabels, a panel-specific version of a 3SLS estimator has been used.

3.2.2 Hypotheses

The labour market literature has identified an abundance of economic and institutional determinants oflabour market stocks and flows. In this paper, we want to restrict ourselves to these factors compatible withthe search and matching labour flow model developed in the previous section. On the basis of this modelwe expect the following:

1. Wages should slow down outflow, i.e. β̃12 < 0. Wages, in turn, depend positively on installed cap-ital, wage bargaining power and taxes, and negatively on the unemployment gap (or changes in theunemployment rate), i.e. β41,β42,β 44 > 0; β43 < 0.

2. Mean reversal of unemployment flows implies β̃11 < 0 while the matching process is expected toyield β̃14 > 0.

3. Aggregate demand should strengthen outflows, i.e. β̃13 > 0.

4. Institutional variables:

(a) Employment protection should be negatively related to outflows, either directly (when firms hireless as they anticipate higher barriers to firing) or indirectly (when EPL increases bargainingpower).

(b) A higher degree of unionization is linked to more bargaining power and hence higher wages.This should, ceteris paribus, lead to higher unemployment inflows and lower outflows.

(c) The degree of wage bargaining centralization, CBt , is not monotonically linked to unemploy-ment out- and inflows. At lower levels an increase is likely to strengthen wage growth, whereasat higher levels, an increase might lower the wage-interest rate ratio.

5. Fiscal and labour market policies:

(a) General government spending should strengthen outflows and limit inflows, i.e. β̃15 > 0 , β̃25 <0. Spending on public employment can be expected to have a stronger effect on limiting inflowsthan on stimulating outflows given the stability of even slight decline in public employmentacross the sample.


(b) Labour market policies are expected to increase unemployment outflows, i.e. β̃15 > 0. Un-employment inflows may increase - at least in the short-run - for some of the programmesdue to compositional changes: Public employment services (PES) and training programmes(TRN), for instance, require registration of participants, some of whom come from inactivity,i.e. β̃

PES, T RN25 > 0. In the long-run, however, all labour market policies are expected to reduce

unemployment inflows, i.e.(

β̃25 ·(

1− β̃11

)+ β̃15 · β̃22

)< 0 (see (9b)).

4 Determinants of unemployment flows

On the basis of this empirical strategy, the determinants of unemployment in- and outflows are estimated.We first proceed at identifying the relevant contributing factors in single equation models, estimating (7a)and (7b) separately and assess the relative contribution of the different factors to the overall variation ofunemployment flows in our sample (see next section). On the basis of these estimates, we then introduce themacro-economic set-up, (8a)-(8d), to evaluate the importance of different fiscal and labour market policiesin explaining the unemployment dynamics. We conclude by differentiating between short- and long-termeffects of the different policies.

4.1 Labour market dynamics: Economic and institutional determinants

The following four tables summarise the evidence on the economic and (labour market) institutional deter-minants of unemployment in- and outflows.

Table 1: Economic determinants of unemployment inflowsDependent variable: Unemployment inflows

(1) (2) (3) (4)

Lagged inflows 0.98*** 0.98*** 0.98*** 0.98***(9.2e-3) (9.0e-3) (7.7e-3) (7.2e-3)

Output gap -1.7e-4*** -1.6e-4*** -1.3e-4*** -1.1e-4***(4.1e-5) (4.0e-5) (3.9e-5) (3.5e-5)

TFP growth 2.0e-3** 1.9e-3** 1.8e-3** 1.8e-3**(8.6e-4) (8.5e-4) (8.7e-4) (8.1e-4)

Labour force growth 1.8e-2** 2.0e-2*** 1.4e-2** 1.3e-2*(7.2e-3) (7.2e-3) (7.1e-3) (6.6e-3)

Changes in terms of trade -2.8e-3** -3.0e-3** -5.5e-3***(1.4e-3) (1.3e-3) (1.4e-3)

Real interest rate 3.9e-5** 3.4e-5**(1.7e-5) (1.5e-5)

Changes in import -1.4e-2***penetration (4.4e-3)

Observations 309 309 301 301Number of countries 12 12 12 12R-squared 98.9% 98.9% 99.1% 99.2%Correction for auto-correlation Panel-specific Panel-specific Panel-specific Panel-specific


Table 2: Economic determinants of unemployment outflowsDependent variable: Unemployment outflows

(1) (2) (3) (4)

Lagged outflows 0.96*** 0.98*** 0.97*** 0.94***(2.5e-2) (2.4e-2) (2.4e-2) (2.5e-2)

Lagged employment -4.5e-2* -0.155 -0.26** -0.0534(2.5e-2) (9.8e-2) (1.1e-1) (3.7e-2)

Output gap 2.8e-3*** 3.7e-3*** 3.2e-3*** 3.0e-3(1.1e-3) (1.2e-3) (1.1e-3) (1.9e-3)

User cost of capital -1.5e-3** -1.2e-3** -1.5e-3*** -1.9e-3**(6.0e-4) (5.2e-4) (5.1e-4) (8.0e-4)

Real wage growth -2.7e-2*** -2.7e-2*** -2.7e-2*** -2.4e-2***(6.6e-3) (6.6e-3) (6.4e-3) (7.8e-3)

Real share price increase 2.1e-2*** 1.9e-2** 2.2e-2**(8.0e-3) (7.8e-3) (1.0e-2)

Capital stock growth 0.29** 0.332(1.4e-1) (2.2e-1)

Real disposable income growth 0.32**(1.4e-1)

Observations 272 262 262 194Number of countries 11 11 11 8R-squared 95.8% 95.3% 95.3% 96.1%Correction for auto-correlation Panel-specific Panel-specific Panel-specific Panel-specific

Regarding the institutional determinants of unemployment inflows, most indicators are not significantlyrelated to inflow dynamics, even when applying vector decomposition techniques, as described in section 3,to account for slow-moving policy indicators. The only significant effect in our sample relates to changes inunionization rates that are positively related to unemployment inflows, an indication for potentially higherjob destruction rates when the degree of unionization increases. Most interesting is the absence of any effectof the strictness of employment protection legislation on inflow dynamics, quite in contrast to theoreticalpriors. Further tests will be necessary to determine whether this result is robust to changes in the estimationmethodology and the sample size. Fnally, the degree of wage bargaining centralization does not seem to berelated in any way possible with unemployment inflow dynamics.


Table 3: Institutional determinants of unemployment inflowsDependent variable: Unemployment inflows(1) (2) (3) (4)

Lagged inflows 0.97*** 0.98*** 0.97*** 0.98***(1.2e-2) (7.5e-3) (1.0e-2) (1.1e-2)

Output gap -1.1e-4** -1.2e-4*** -1.4e-4*** -1.4e-4***(4.6e-5) (3.6e-5) (4.3e-5) (4.3e-5)

TFP growth 1.8e-3* 1.7e-3** 1.8e-3** 1.8e-3*(1.0e-3) (8.1e-4) (9.2e-4) (9.2e-4)

Labour force growth 8.4e-3 1.5e-2** 1.8e-2** 1.8e-2**(7.4e-3) (6.7e-3) (8.2e-3) (8.2e-3)

Changes in terms of trade -1.86e-3 -3.4e-3*** -2.7e-3** -2.7e-3**(1.8e-3) (1.3e-3) (1.4e-3) (1.4e-3)

Real interest rate 3.5e-5* 3.5e-5** 3.4e-5* 3.4e-5*(2.1e-5) (1.5e-5) (1.9e-5) (1.9e-5)

Employment protection -5.0e-05(7.8e-5)

Changes in unionization 2.2e-4***(7.1e-5)

Degree of wage bargaining -9.4e-05 3.4e-06centralization (8.7e-5) (6.1e-4)Centralization (squared) -2.4e-05

(1.5e-4)

Observations 248 294 268 268Number of countries 12 12 12 12R-squared 99.1% 99% 99.1% 99.1%Correction for auto-correlation Panel-specific Panel-specific Panel-specific Panel-specific

In the case of unemployment outflows, main institutional determinants related to the strictness of em-ployment protection legislation (EPL) and the degree of wage bargaining centralization. As expected, EPLis negatively related to outflows. Together with the absence of any significant impact of EPL on unemploy-ment inflows, the net effect seem to be largely negative for employment dynamics.

In contrast, variations in unionization rates do not seem to be significantly related to outflow dynamics.Finally, centralization indicators show non-linear effects, in line with the original Calmfors-Driffill hy-pothesis: Both high and low degrees of wage bargaining centralization are compatible with strong outflowdynamics whereas countries with intermediate levels of centralization experience lower levels of unemploy-ment outflow dynamics.


Table 4: Institutional determinants of unemployment outflowsDependent variable: Unemployment outflows(1) (2) (3) (4)

Lagged outflows 0.95*** 0.97*** 0.95*** 0.94***(2.8e-2) (2.4e-2) (2.6e-2) (2.7e-2)

Lagged employment -9.2e-2 -2.3e-1** -0.02 -3.5e-2(8.6e-2) (1.1e-1) (2.3e-2) (2.2e-2)

Output gap 5.2e-4 2.9e-3*** 3.1e-3*** 3.1e-3***(7.3e-4) (1.1e-3) (1.1e-3) (1.0e-3)

User cost of capital -5.5e-4 -1.5e-3*** -2.6e-3*** -2.3e-3***(4.0e-4) (4.8e-4) (6.5e-4) (6.5e-4)

Real wage growth -2.1e-2*** -2.8e-2*** -3.6e-2*** -3.4e-2***(4.7e-3) (6.5e-3) (7.7e-3) (7.5e-3)

Real share price increase 1.1e-2** 1.8e-2** 2.2e-2*** 2.2e-2***(5.6e-3) (7.6e-3) (8.5e-3) (8.4e-3)

Capital stock growth 0.34*** 0.28** -6.3e-2(1.1e-1) (1.4e-1) (1.4e-1)

Employment protection -4.6e-3***(1.5e-3)

Changes in unionization -1.9e-3(1.3e-3)

Degree of wage bargaining -6.3e-3*** -4.2e-2***centralization (2.0e-3) (1.3e-2)Centralization (squared) 8.9e-3***

(3.1e-3)

Observations 223 258 233 233Number of countries 11 11 11 11R-squared 95% 95.2% 95.6% 95.6%Correction for auto-correlation Panel-specific Panel-specific Panel-specific Panel-specific

On the basis of our preferred specification in table 1 and 2 (specification 4), the following two chartsshow the relative importance of different economic determinants in explaining the overall variance of in-and outflows. As can be seen from the charts, short-term demand factors play an important role in explainingunemployment inflows: Real short-term interest rates and the output gap together explain more than 40% ofthe sample variation. These factors also seem to be important in the case of unemployment outflows, albeitsomewhat less as the output gap and the growth in real disposable income explain slightly more than 30%.In contrast, unemployment outflows are more driven by structural factors and long-term macroeconomicconditions such as the user cost of capital or the growth in gross fixed capital formation. In line withconjectures by Phelps (1998) and others, a firm’s financial conditions as reflected by the change in realshare prices has a non-negligible effect on unemployment outflows (more than 10%), but does not seem tocontribute in a statistically significant way to unemployment inflows.


Figure 1: Economic determinants of unemployment inflows

−22.9

−20.1

−9.0

10.9

14.0

23.0

−50

−25

025

50C

ontr

ibut

ions

to u

nem

ploy

men

t inf

low

s (in

%)

Gro

wth

of

real

impo

rts

Out

put g

ap

Cha

nges

inT

erm

s of

Tra

de

Labo

ur fo

rce

grow

th

Gro

wth

of T

otal

Fac

tor

Pro

duct

ivity

Rea

l sho

rt−

term

inte

rest

rat

e

Tot

al

Figure 2: Economic determinanst of unemployment outflows

−23.1

−19.0

−3.6

10.9

11.8

14.1

17.4

−40

−20

020

4060

Con

trib

utio

ns to

une

mpl

oym

ent o

utflo

ws

(in %

)

Use

r co

stof

cap

ital

Rea

l wag

egr

owth

Em

ploy

men

t rat

e(la

gged

)

Cap

ital s

tock

grow

th

Gro

wth

of r

eal

shar

e pr

ices

Out

put g

ap

Rea

l dis

posa

ble

inco

me

grow

th

Tot

al

4.2 Government spending and labour market programmes3

The previous results allowed to identify a series of economic and institutional factors that influence unem-ployment dynamics. From a policy perspective, however, these factors are often only indirectly linked tospecific government intervention (with the exception of employment protection legislation). In the follow-ing, therefore, we want to turn to more direct interventions on labour markets, either through generic publicspending or through more targeted labour market measures. As argued above, this requires to take the

3This sub-section follows the exposition in International Institute for Labour Studies, 2010, ch. 3.


state of the macroeconomic environment into account when estimating the determinants of unemploymentdynamics.

The results of these estimations come at a timely moment. Indeed, looking at the state of their labourmarket, many (advanced) countries are currently wondering which policies to implement? And whether ageneric approach does exist or whether it is necessary to identify concrete areas of policy intervention toreduce unemployment? At the current juncture of severely adverse macroeconomic conditions, the existingevidence on labour market programme effectiveness is only of limited help in selecting different policyoptions. Under more tranquil circumstances, some consensus has emerged in the past regarding the impor-tance of certain policies such as job search assistance and training programmes for stimulating employmentgrowth and bringing unemployed workers back to jobs, even though there is almost no cost-benefit evidenceof these programmes available.4 In contrast, no in-depth study exists as to the effectiveness of these labourmarket policies under macroeconomic and financial sector crisis conditions. These conditions need to betaken into account, however, if countries want to select the right mix of policies at the current juncture aspolicy multipliers vary widely depending on the general macroeconomic environment. In the following anovel approach is being presented that is meant to overcome – at least partially – this missing link betweenlabour market policies and the aggregate state of the economy and employment. In particular, on the basisof a new database on unemployment dynamics, the macro- and microeconomic implications of fiscal andlabour market policies are analysed. In particular, the analysis presented below includes bi-directional ef-fects between unemployment dynamics and fiscal variables to account for potential adverse effects from thecosts of labour market policies at the macroeconomic level. This allows to take the fiscal implications oflabour market policies explicitly into account and provides a more accurate picture of policy effectivenessunder the current circumstances.

Distinguishing in more detail between different generic fiscal and specific labour market policies helpsin the assessment of appropriate policy options that are necessary to the increasingly diverse challenges thatcountries face on their labour markets as a result of the crisis. In particular, it will allow assessing the timingwhen policies need to switch from income support policies to those that facilitate long-term adjustmentprocesses on the labour market. In this regard, total government consumption (excluding interest payments)is split into wage and non-wage government spending, the former being principally related to spending onpublic employment whereas the latter relates to policies directly relevant to support consumption in theprivate sector. Within this category also fall various labour market programmes, which have been furtherdetailed in the analysis. A first distinction of these labour market programmes has been made between activeand passive measures. The active measures have further been differentiated into direct job creation, hiringincentives, training programmes and spending on public employment services. The passive measures, onthe other hand, regroup all those pertaining to income maintenance, at least temporarily.

On the basis of this analysis, general government spending seems to have a strong impact on unemploy-ment dynamics, increasing outflows and lowering inflows in an economically and statistically significantwage (see table 5, equation 1). The impact does not seem to be particularly affected by feedback effectsresulting from higher real long-term interest rates (note that government net lending, NLGt , takes positivevalues for surpluses). Looking at the components, public employment seems (weakly) related to lowerunemployment inflows but not significantly to unemployment outflows, as expected by our hypothesis insection 3.2. In contrast, government non-wage consumption is significantly linked to both unemploymentin- and outflows. As indicated above, the lack of public employment to influence job creation may have todo with the particular sample (period) used here during which public employment remained either stable ordeclined (sometimes substantially as in the case of Sweden) in comparison to private sector employment.

The analysis also allows to give a more detailed picture of various labour market programmes, includingboth passive and active measures. Moreover, the particular macroeconomic focus and the detailed analysisof competing labour market programmes provide a more detailed understanding of the different policytrade-offs that countries are currently facing. In particular, direct job creation outside the public sectorseems to come with a high amount of deadweight costs as it only seem to have a statistically significanteffect on unemployment inflows but not on unemployment outflows. In other words, the programmes oftenseem to benefit those already in a job or who would have been hired even in the absence of such policies

4See Card et al. (2010) for a recent meta-analysis of existing studies in this area.


(see table 5, equation 4). Note also that unemployment outflows do not seem to have a significant effecton spending for direct job creation, an indication that these programmes are set up largely unrelated to thecycle. In contrast to these prorgammes, hiring subsidies seem to have the expected effect on outflows butdo not influence unemployment inflows (the estimated effect is statistically not significantly different fromzero, see equation 5), an outcome to be expected in light of the particular set-up of these incentive schemes(that only kick in when a firm is planning to open a new vacancy).

Expenditures on training programmes and public employment services have the expected (positive)effects on unemployment outflows, confirming existing evidence in the literature. The estimated effectsin table 5, equation 7, do not take into account the particular design of PES or training programs in thecountries of this sample. Some countries may actually experience much better effects of these policies onunemployment dynamics by combining them with appropriately designed unemployment benefits. Never-theless, it should be noted that these programs come with a strong increase in unemployment inflows aswell: This seems to be an indication that measured unemployment rates depend significantly on programdesign in as much as that the participation in certain programs requires official inscription in the unemploy-ment register.5 As such, these programs are not only an effective way of bringing unemployed workers backto employment they also seem to constitute a useful instrument to activate those that currently have verylimited ties with the labour market or have dropped out of the labour force altogether. Moreover, as we willsee in the following section, the steady state effects of these programs have a negative effect on inflows asmuch as other labour market policies, an indication as to their long-run effectiveness. Finally, unemploy-ment benefit schemes come with highly significant effects on both unemployment in- and outflows in theexpected direction (see equation 8).

4.3 Short vs. long run effects

Statistical significance and quantitative importance of different policy instruments are not necessarily re-lated in the estimates presented above. In addition, as mentioned earlier, the short-run - “on-impact” -estimates are different from the steady state effects that arise when the dynamics of the model set up byequations (8a)-(8d) are allowed to play out fully. In the following, we, therefore, compare the contributionsof the eight different policy instruments to the dynamics of unemployment flows (see figure 3, p. 19).

The contribution rates are calculated as the share of the panel variance of unemployment in- and outflowsexplained by the variance of each individual policy instrument weighted by its regression coefficient (usingthe estimates in equations 1-8 of table 5), i.e.

Contribution Rate = β̃Policy · Var (Policy)

Var (Unemployment f low)

where Policy stands for one of the eight policy instruments, Unemployment f low for unemployment in- oroutflow, β̃ Policy for the estimated policy regression coefficient and Var for the panel-wide variance of thevariabel under consideration.

As the following figure demonstrates, contribution rates are typically very high regarding unemploy-ment outflows and much lower regarding unemployment inflows. More importantly, short-run effects areoften much larger than steady state effects. Some noticeable exceptions to this are the effects of policy in-terventions on unemployment inflows. In particular, spending on public employment services and trainingprogrammes have positive effects on inflows in the short-run but negative effects in steady state. This relateswell with the hypothesis presented in section 3.2 regarding possible short-run programme design-related is-sues that push up measured unemployment rates. Finally, note the positive contribution of unemploymentbenefits on unemployment inflows that is even more important in the long- than in the short-run, a possibleindication for the importance of aggregate demand stabilization that these programmes allow and that helpmaintain people in employment.

5As discussed above (see section 3), the rise in unemployment inflows following an increase in expenditures on public employmentservices and training can partly be considered a statistical artefact: These measures target particularly inactive people to return themto the labour market, causing measured unemployment rates to increase while inactivity rates decline.


T abl

e5:

Polic

yco

ntri

butio

nsto

unem

ploy

men

tdyn

amic

s(1

)(2

)(3

)(4

)G

over

nmen

tcon

sum

ptio

nG

over

nmen

twag

eco

nsum

ptio

nG

over

nmen

tnon

-wag

eco

nsum

ptio

nD

irec

tjob

crea

tion

INt

OU

T tG

ovC

ons t

RIR

L tIN

tO

UT t

Gov

WC

ons t

RIR

L tIN

tO

UT t

Gov

NW

Con

s tR

IRL t

INt

OU

T tJo

b tR

IRL t

INt−

10.

961*

**0.

012*

**0.

879*

**-0

.007

**0.

934*

**0.

019*

**0.

949*

**0.

001*

**(0

.076

)(0

.004

)(0

.090

)(0

.003

)(0

.090

)(0

.003

)(0

.076

)(0

.000

)

ET t−

14.

685*

**4.

022*

**4.

73**

*2.

625*

**(0

.766

)(0

.855

)(0

.920

)(0

.788

)

∆P

OP

T t−

16.

632*

**7.

35**

*3.

473

9.66

0***

(2.2

42)

(2.6

39)

(2.3

59)

(2.6

73)

∆T

FP t

0.09

40.

182*

0.21

5**

0.25

0***

(0.0

86)

(0.0

99)

(0.0

85)

(0.0

97)

RIR

L t−

10.

019*

**1.

719*

**0.

760*

0.01

8***

(0.0

05)

(0.5

20)

(0.4

11)

(0.0

05)

TIN

Dt−

11.

872*

*1.

864*

0.33

00.

654

(0.8

84)

(1.0

83)

(0.7

52)

(0.9

92)

OU

T t-0

.378

***

-0.5

47**

*-0

.518

***

-0.2

39**

*(0

.061

)(0

.075

)(0

.074

)(0

.055

)

Gov

Con

s t−

1-3

.205

**4.

711*

**(1

.303

)(1

.125

)

EP

L t−

1-0

.009

-0.0

47-0

.039

-0.0

90**

-0.1

43**

-0.1

46**

*-0

.032

-0.0

23(0

.036

)(0

.032

)(0

.046

)(0

.035

)(0

.047

)(0

.040

)(0

.035

)(0

.036

)

OU

T t−

10.

675*

**-0

.014

***

0.55

6***

-0.0

10**

*0.

504*

**-0

.005

**0.

605*

**-0

.000

(0.0

50)

(0.0

03)

(0.0

46)

(0.0

03)

(0.0

55)

(0.0

02)

(0.0

52)

(0.0

00)

ET t

4.70

7***

3.89

2***

5.36

***

3.68

9***

(0.6

72)

(0.6

93)

(0.7

61)

(0.9

67)

UC

Ct

0.00

7-0

.005

-0.0

060.

007

(0.0

05)

(0.0

05)

(0.0

05)

(0.0

05)

WI t

-0.0

53**

*-0

.068

***

-0.0

52**

-0.0

86**

*(0

.018

)(0

.018

)(0

.017

)(0

.023

)

I NV t

3.93

4***

4.33

6***

3.63

7***

4.59

4***

(0.6

63)

(0.7

68)

(0.7

95)

(0.8

56)

∆Y

DR

Ht

0.03

70.

255

-0.0

410.

363

(0.3

91)

(0.4

65)

(0.4

20)

(0.4

89)

INt

-0.0

70-0

.066

-0.1

48-0

.038

(0.0

87)

(0.0

82)

(0.0

91)

(0.0

96)

NLG

t−1

-0.2

46**

*-0

.207

***

-0.2

25**

*-0

.166

***

(0.0

51)

(0.0

34)

(0.0

31)

(0.0

53)

LLG

DP t−

1-6

.340

***

-4.2

48**

*-2

.695

***

-6.1

09**

*(1

.253

)(0

.843

)(0

.718

)(1

.350

)

Gov

WC

ons t−

1-3

.717

*1.

938

(2.2

33)

(1.7

23)

Gov

NW

Con

s t−

1-5

.36*

*4.

448*

*(2

.702

)(2

.206

)

Job t−

1-6

2.27

6***

9.27

3(1

3.59

0)(1

4.47

2)

HIR

t−1

TR

Nt−

1

PE

S t−

1

UB t−

1

Obs

erv a

tions

150

150

150

150

130

130

130

130

130

130

130

130

140

140

140

140

R-s

quar

ed0.

972

0.98

10.

929

0.47

40.

966

0.98

70.

950

0.52

80.

971

0.98

10.

822

0.51

60.

969

0.98

60.

622

0.48

4N

ote:

All

equa

tion

syst

ems

are

estim

ated

usin

g3S

LS.

All

regr

essi

ons

cont

ain

coun

try

fixed

effe

cts.

Stan

dard

erro

rsin

pare

nthe

ses:

***

p<0.

01,*

*p<

0.05

,*p<

0.1.

Var

iabl

eco

des:

INt:

unem

ploy

men

tinfl

ows,

OU

T t:

unem

ploy

men

tout

flow

s,E

T t:e

mpl

oym

ent-

to-

popu

latio

nra

tio,∆

PO

PT t

:gr

owth

inw

orki

ng-a

gepo

pula

tion,

∆T

FP t

:gr

owth

into

talf

acto

rpr

oduc

tivity

,∆Y

DR

Ht:

grow

thin

real

disp

osab

leho

useh

old

inco

me,

RIR

L t:

real

long

-ter

min

tere

stra

te,U

CC

t:us

erco

stof

capi

tal(

i.e.

real

long

-ter

min

tere

stra

tem

inus

capi

tals

crap

ping

rate

),W

I t:w

age-

inte

rest

rate

ratio

,IN

V t:a

nnua

lcha

nge

ingr

oss

fixed

capi

talf

orm

atio

n,LL

GD

P t:s

hare

ofliq

uid

liabi

litie

sto

GD

P,T

IND

t:sh

are

ofin

dire

ctta

xes

toG

DP,

Gov

Con

s t:s

hare

ofgo

vern

men

tcon

sum

ptio

nto

GD

P,G

ovW

Con

s t:s

hare

ofgo

vern

men

twag

eco

nsum

ptio

nto

GD

P(i

.e.

publ

icem

ploy

men

t),G

ovN

WC

ons

: t:sh

are

ofgo

vern

men

tnon

-wag

eco

nsum

ptio

nto

GD

P,N

LGt:

gove

rnm

entn

etle

ndin

g(p

ositi

veva

lues

indi

cate

surp

luse

s),H

IRt:

shar

eof

hiri

ngsu

bsid

ies

toG

DP,

JOB t

:G

PDsh

are

ofsp

endi

ngon

dire

ctjo

bcr

eatio

n,T

RN

t:sh

are

oftr

aini

ngex

pend

iture

sto

GD

P,P

ES t

:GD

Psh

are

ofsp

endi

ngon

publ

icem

ploy

men

tser

vice

s,U

B t:s

hare

ofun

empl

oym

entb

enefi

tsto

GD

P,E

PL t

:str

ictn

ess

ofem

ploy

men

tpro

tect

ion

legi

slat

ion.


Table5:Policy

contributionsto

unemploym

entdynamics

(cont’d)(5)

(6)(7)

(8)H

iringincentives

Trainingexpenditures

Publicem

ploymentservices

Unem

ploymentbenefits

INt

OU

Tt

HIR

tR

IRL

tIN

tO

UT

tT

RN

tR

IRL

tIN

tO

UT

tP

ES

tR

IRL

tIN

tO

UT

tU

Bt

RIR

Lt

I Nt−

10.895***

0.001***0.894***

0.0000.972***

-0.0000.949***

0.005***(0.073)

(0.000)(0.073)

(0.000)(0.075)

(0.000)(0.076)

(0.001)

ET

t−1

2.785***3.689***

4.717***0.807

(0.749)(0.860)

(0.986)(0.781)

∆P

OP

Tt−

19.180***

11.317***7.097**

4.388**(2.373)

(2.936)(3.156)

(2.199)

∆T

FP

t0.283***

0.278***0.352***

0.120(0.085)

(0.101)(0.108)

(0.079)

RI R

Lt−

10.016***

0.016***0.034***

0.006(0.004)

(0.005)(0.006)

(0.005)

TIN

Dt−

1-1.211

-0.0222.963**

0.340(0.891)

(1.204)(1.433)

(0.839)

OU

Tt

-0.241***-0.291***

-0.236***-0.293***

(0.052)(0.057)

(0.058)(0.065)

GovC

onst−1

EP

Lt−

10.004

0.0060.006

-0.016-0.090

-0.0570.016

-0.017(0.034)

(0.037)(0.037)

(0.034)(0.070)

(0.060)(0.035)

(0.035)

OU

Tt−

10.590***

-0.0000.581***

-0.001***0.578***

-0.0000.789***

-0.006***(0.051)

(0.000)(0.050)

(0.000)(0.054)

(0.000)(0.057)

(0.001)

ET

t3.566***

4.254***4.559***

2.760***(0.940)

(0.898)(1.083)

(0.946)

UC

Ct

0.0000.006

0.0060.031***

(0.005)(0.005)

(0.006)(0.005)

WIt

-0.068***-0.070***

-0.077***-0.070***

(0.021)(0.021)

(0.025)(0.018)

I NV

t4.173***

5.307***5.718***

3.797***(0.773)

(0.762)(0.913)

(0.694)

∆YD

RH

t0.656

0.5120.247

0.116(0.460)

(0.444)(0.512)

(0.396)

I Nt

-0.1460.005

0.067-0.101

(0.101)(0.088)

(0.085)(0.098)

NLG

t−1

-0.159***-0.148***

-0.095*-0.168***

(0.053)(0.053)

(0.051)(0.051)

LLGD

Pt−

1-6.508***

-5.541***-7.362***

-4.809***(1.325)

(1.319)(1.197)

(1.228)

GovW

Const−

1

Go vN

WC

onst−1

J obt−

1

HIR

t−1

-9.27171.203***

(15.468)(18.955)

TR

Nt−

127.543*

26.080*(15.282)

(13.730)

PE

St−

170.153*

50.128(41.456)

(40.310)

UB

t−1

-19.648***8.734*

(4.498)(4.477)

Observ ations

140140

140140

140140

140140

128128

128128

140140

140140

R-squared

0.9720.985

0.8630.484

0.9720.986

0.8550.481

0.9720.986

0.8150.545

0.9710.976

0.6850.478

Note:

Allequation

systems

areestim

atedusing

3SLS.A

llregressionscontain

countryfixed

effects.Standard

errorsin

parentheses:***

p<0.01,**p<0.05,*

p<0.1.V

ariablecodes:

INt :

unemploym

entinflows,O

UT

t :unem

ploymentoutflow

s,ET

t :employm

ent-to-population

ratio,∆

PO

PT

t :grow

thin

working-age

population,∆

TF

Pt :

growth

intotalfactor

productivity,∆Y

DR

Ht :

growth

inrealdisposable

householdincom

e,RIR

Lt :

reallong-terminterestrate,U

CC

t :user

costofcapital(i.e.

reallong-terminterestrate

minus

capitalscrappingrate),W

It :wage-interestrate

ratio,INV

t :annualchangein

grossfixed

capitalformation,LLG

DP

t :shareofliquid

liabilitiesto

GD

P,TIN

Dt :share

ofindirecttaxesto

GD

P,GovC

onst :shareofgovernm

entconsumption

toG

DP,G

ovWC

onst :shareof

governmentw

ageconsum

ptionto

GD

P(i.e.

publicem

ployment),G

ovNW

Cons

:t :share

ofgovernmentnon-w

ageconsum

ptionto

GD

P,NLG

t :governm

entnetlending(positive

valuesindicate

surpluses),HIR

t :share

ofhiringsubsidies

toG

DP,JO

Bt :

GPD

shareof

spendingon

directjobcreation,T

RN

t :shareoftraining

expendituresto

GD

P,PE

St :G

DP

shareofspending

onpublic

employm

entservices,UB

t :shareofunem

ploymentbenefits

toG

DP,E

PL

t :strictnessofem

ploymentprotection

legislation.


Figure 3: Short-term vs. long-term effects of policies

−20

020

40C

ontr

ibut

ions

(in

%)

Governmentspending

Government non−wage spending

Government wagespending

Policy contributions to outflows (short− vs. long−term)

−20

020

40C

ontr

ibut

ions

(in

%)

Governmentspending

Government wagespending

Government non−wage spending

Policy contributions to inflows (short− vs. long−term)−

200

2040

Con

trib

utio

ns (

in %

)

Unemploymentbenefits

Hiringincentives


Public employmentservices

Direct jobcreation

Short−term effecton outflows

Long−term effecton outflows

−20

020

40C

ontr

ibut

ions

(in

%)


Public employmentservices

Hiringincentives

Unemploymentbenefits

Direct jobcreation

Short−term effecton inflows

Long−term effecton inflows

Note: The chart presents the contributions (in %) to unemployment in- and outflows of different fiscal and labour marketpolicies in a panel of 14 OECD countries. Contributions are calculated with respect to the average spending shock acrossthe country sample for each individual policy. Short-term effects are based on contribution rates at impact, long-term effectsare steady state contributions of the corresponding policy measures. Both calculations take the impact of an increase ingovernment debt on real long-term interest rates into account.

5 Conclusion

The paper presents evidence regarding the economic, institutional and policy determinants of unemploy-ment in- and outflows. On the basis of considerations related to labour market search- and matching models,the paper identifies a number of factors that influence the flows in and out of the unemployment pool, includ-ing investment dynamics, productivity growth and interest rates but also employment protection legislationand the degree of wage bargaining centralization. The paper also argues that given the macroeconomic di-mension of unemployment dynamics, parts of the analysis related to the impact of fiscal and labour marketpolicies need to be estimated in a systemic manner, using simultaneous equation methods.

The macroeconomic estimates confirm the importance of passive income support measures both in theshort- and the long-run to lower unemployment . Other measures, such as training measures and public em-ployment services seem to develop lasting effects only in the long-run whereas in the short-run, their effectsare either negligible or induce a further increase in measured unemployment rates due to the some designfeatures of these programmes. Moreover, some active labour market policies seem to come with importantdeadweight costs, such as direct job which mainly seem to prevent further inflows into unemployment ratherthan a substantial increase in unemployment outflows.


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