determinants of unemployment flows labour market institutions and macroeconomic policies pdf
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The International Institute for Labour Studies was established in 1960 as an autonomous facility of the International Labour Organization (ILO). Its mandate is to promote policy research and public discussion on issues of concern to the ILO and its constituents — government, business and labour. The Discussion Paper Series presents the preliminary results of research undertaken by or for the IILS. The documents are issued with a view to eliciting reactions and comments before they are published in their final form.
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
ernste@ilo.org
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
4 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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
DISCUSSION PAPER SERIES NO. 209 5
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.
6 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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.
DISCUSSION PAPER SERIES NO. 209 7
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 .
8 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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:
DISCUSSION PAPER SERIES NO. 209 9
• 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.
10 DETERMINANTS OF UNEMPLOYMENT FLOWS.
(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
DISCUSSION PAPER SERIES NO. 209 11
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.
12 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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.
DISCUSSION PAPER SERIES NO. 209 13
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.
14 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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.
DISCUSSION PAPER SERIES NO. 209 15
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.
16 DETERMINANTS OF UNEMPLOYMENT FLOWS.
(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.
DISCUSSION PAPER SERIES NO. 209 17
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.
18 DETERMINANTS OF UNEMPLOYMENT FLOWS.
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.
DISCUSSION PAPER SERIES NO. 209 19
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
Trainingexpenditures
Public employmentservices
Direct jobcreation
Short−term effecton outflows
Long−term effecton outflows
−20
020
40C
ontr
ibut
ions
(in
%)
Trainingexpenditures
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.
20 DETERMINANTS OF UNEMPLOYMENT FLOWS.
ReferencesBaccaro, L.; Rei, D. 2005. Institutional determinants of unemployment in OECD countries: A time se-
ries cross-section analysis (1960-98), Discussion Paper 160 (Geneva, International Institute for LabourStudies (IILS)).
Baker, D. et al. 2005. “Labor market institutions and unemployment: A critical assessment of the cross-country evidence”, in Fighting unemployment: The limits of free market orthodoxy (ed. D.R. Howell)(Oxford, Oxford University Press).
Bassanini, A.; Duval, R. 2006. “The determinants of unemployment across OECD countries”, in OECDEconomic Studies, vol. 42, no. 1, pp. 7–86.
Bassanini, A. et al. 2010. Institutional determinants of worker flows: A cross-country/cross-industry ap-proach, Social, employment and migration paper 107 (Paris, Organization for Economic Co-operationand Development (OECD)).
Blanchflower, D.G.; Oswald, A.J. 1994. The wage curve (Cambridge, MA., MIT Press).
Burniaux, J.M.; Duval, R.; Jaumotte, F. 2003. Coping with ageing: A dynamic approach to quantify theimpact of alternative policy options on future labour supply in OECD countries, Working Paper 371(Paris, OECD).
Calmfors, L.; Driffill, J. 1988. “Bargaining Structure, Corporatism and Macroeconomic Performance”, inEconomic Policy, vol. 9, pp. 14–61.
Card, D.; Kluve, J.; Weber, A. 2010. Active labor market policy evaluations: A meta-analysis, DiscussionPaper 16173 (Cambridge, MA., National Bureau of Economic Research (NBER)).
Carlsson, M.; Eriksson, S.; Gottfries, N. 2006. Testing theories of job creation: Does supply create its owndemand?, Discussion Paper 2024 (Bonn, Institute for the Study of Labor (IZA)).
Davis, S.J.; Schuh, S.; Haltiwanger, J.C. 1998. Job creation and destruction (Cambridge, MA., MIT Press).
Davis, S.J. et al. 2008. Business volatility, job destruction and unemployment, Working Paper 14300 (Cam-bridge, MA, National Bureau of Economic Research (NBER)).
Elsby, M.; Hobijn, B.; Sahin, A. 2008. Unemployment dynamics in the OECD, Working Paper 14617(Cambridge, MA., National Bureau of Economic Research (NBER)).
International Institute for Labour Studies. 2010. World of Work Report 2010. From one crisis to the next ?(Geneva, International Labour Organization (ILO)).
Nichols, A.; Schaffer, M. 2007. “Clustered errors in STATA”, in 13th UK Stata Users Group Meeting.
Nickell, S.; Nunziata, L.; Ochel, W. 2005. “Unemployment in the OECD since the 1960s. What do weknow?”, in Economic Journal, vol. 115, no. 1, pp. 1–27.
Petrongolo, B.; Pissarides, C.A. 2008. “The ins and outs of European unemployment”, in American Eco-nomic Review, vol. 98, no. 2, pp. 256–262.
Phelps, E. 1998. Structural slumps: The modern equilibrium theory of unemployment, interest, and assets(Cambridge, MA, Harvard University Press).
Pissarides, C. 2000. Equilibrium unemployment theory (Cambridge, MA, The MIT Press).
Shimer, R. 2007. Reassessing the ins and outs of unemployment, Working Paper 13421, National Bureau ofEconomic Research (NBER), Cambridge, MA., Available at: http://www.nber.org/papers/w13421.
Stockhammer, E.; Klär, E. 2008. Capital accumulation, labour market nstitutions, and unemployment inthe medium run, Discussion Paper 834 (Berlin, German Institute for Economic Research (DIW)).
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