This paper provides evidence for the size of firing costs for eight countries. In contrast to the existing literature, we use the optimality conditions obtained in a search and matching model to find a reduced form equation for firing costs. We find that our estimates are slightly larger

compared with other studies. Finally, we offer three explanations for the observed cross-country patterns

Wesselbaum, Dennis

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveHow Large are Firing Costs? ACross-Country StudyWesselbaum, DennisUniversity of Hamburg, German Physical Society, EABCN23 June 2014Online at https://mpra.ub.uni-muenchen.de/58762/MPRA Paper No. 58762, posted 25 Sep 2014 02:01 UTCHow Large are Firing Costs? A Cross-Country StudyDennis Wesselbaum∗University of Hamburg, German Physical Society, and EABCNJune 23, 2014AbstractThis paper provides evidence for the size of firing costs for eight countries. In contrast to theexisting literature, we use the optimality conditions obtained in a search and matching modelto find a reduced form equation for firing costs. We find that our estimates are slightly largercompared with other studies. Finally, we offer three explanations for the observed cross-countrypatterns.Keywords: Employment protection, Firing costs, Optimality conditions.JEL codes: C22, J41, J63.∗University of Hamburg, Chair of Computational Economics. Von Melle Park 5, 20146, Hamburg, Germany.Email: dennis.wesselbaum@wiso.uni-hamburg.de11 IntroductionThe effects of firing costs on welfare (e.g. Hopenhayn and Rogerson (1993) or Alvarez and Veracierto(1998)) and business cycle dynamics (e.g. Veracierto (2008) or Lechthaler et al. (2010)) have beenstudied in great detail. However, there is a fundamental challenge in calibrating and, therefore,applying dynamic models with firing costs: how large are they?Garibaldi and Violante (2005) show that layoff costs have two components, (i) transfers fromfirm to worker and (ii) a tax that is paid outside the firm-worker pair. While there is evidence onthe size of severance payments (i) for a number of countries, firing costs (i+ii) additionally containadministrative and procedural costs and, therefore, are hard to estimate.For severance payments, Cozzi et al. (2010) show that there is sizable variation across countries.For example, in Italy severance payments equal 20 monthly wages, while they equal 1.2 monthlywages in the United Kingdom. For firing costs, the literature uses different approaches. Abowdand Kramarz (2003) use establishment data for French firms and a statistical model to estimatehiring and firing costs. They find that firing costs are a increasing and concave function in thenumber of layoffs with a large fixed component. Further, Dolfin (2006) uses firm-level data for theUnited States from the 1982 Employment Opportunity Pilot Project. She finds that the costs offiring documentation have significant effects on turnover, vacancies, hours, and number of workers.Often, one relies on the estimates of Bentolila and Bertola (1990). Besides the fact that thepaper is 23 years old, the authors use an ad hoc equation for expected dismissal costs. They findthat the value of firing costs in the UK is 25 percent of annual wages, while it is 75 percent ofannual wages in Germany (on an annual basis).1This paper uses a different approach to find a value for firing costs in search and matchingmodels. We follow the contribution by Burnside et al. (1993): they use the model’s optimalityconditions to derive a reduced form equation describing the evolution of effort over the cycle. Todetermine the value of firing costs we use the optimality condition for job creation from a standardsearch and matching model with firing costs. At this point it should be noted that we use highlyaggregated data to obtain a value for the firing costs and, therefore, disregard the fact that firingcost vary across workers, shown by Dolado et al. (2005, 2007). Hence, we interpret the result asan economy-wide average value of firing costs.Finally, using data for eight countries, we provide a cross-country analysis of firing costs andpresent three explanations for the observed patterns.2 Reduced Form EquationWe assume that the good market is perfectly competitive, while the labor market is imperfect dueto the assumption of search and matching frictions. Trade in the labor market is uncoordinated,costly, and time-consuming. Search takes place on a discrete and closed market. Workers can be1A different approach is to make firing costs proportional to productivity. However, this is just a transformationfrom an estimate for the share of firing costs from wages to the share from productivity.2either employed or unemployed, such that there is no out of labor force option. Similarly, eachfirm has one job that is either filled, or vacant. If the job is filled, it is subject to the time-varyingprobability of being exogenously destroyed, ρt > 0. Firms create jobs at the rate M (Ut, Vt) at thenon-state-contingent cost of c > 0 units of output per vacancy, where M (·) is the homogeneous-of-degree-one-matching-function,M (Ut, Vt) = mUµt V1−µt , (1)wherem > 0 gives the match efficiency, µ > 0 is the elasticity of the matching function with respectto unemployment and Vt is the vacancy rate.The vacancy-to-unemployment ratioθt =VtUt, (2)reflects labor market tightness. Then, the vacancy filling probability is q (θt) = M (Ut, Vt) /Vt =m (Ut/Vt)µ. Combining entry and exit definitions yields the evolution of employment which statesthat employment today will be driven by the number of non-separated workers from the last periodand the non-destroyed matches from the last period. Along this line, the evolution of aggregateunemployment can be written as Ut = 1−Nt.We assume the existence of a representative firm that solves its profit maximization problemmax{Nt,Vt}Et∞t=0βtλt [Yt −WtNt − cVt − ρtNtΓt] , (3)subject to the evolution of employmentNt = (1− ρt) (Nt−1 +Mt−1) , (4)and the production functionYt = ZtNt. (5)Here, Zt is a Hicks-neutral aggregate technology shock.The second term in parenthesis gives total wage costs. The third term reflects total vacancyposting costs and firing costs Γt ≥ 0, for all separated workers.Finally, the FONCs are∂N t : τ t =YtNt−Wt + (1− ρt)Etβt+1τ t+1− ρtΓt, (6)∂V t : c = (1− ρt)q(θt)Etβt+1τ t+1, (7)where βt+1 = βλt+1λtis the stochastic discount factor and τ t is the multiplier on the evolutionof employment. Assuming a logarithmic utility function in consumption the marginal utility ofconsumption, λt, isλt =1Ct. (8)Using the FONCs gives the optimality condition for job creationcq(θt)= Et(1− ρt)βt+1Yt+1Nt+1−Wt+1 +cq(θt+1)− ρtΓt. (9)3The left-hand side of this equation gives the (time-varying) hiring costs which equal the benefitsof creating a new job. The latter depends on the marginal product of labor depleted by the wageand increased by saved hiring costs in the next period in case of non-separation.Solving this equation for Γt givesΓt = Et1ρtYt+1Nt+1−Wt+1 +cq(θt+1)−c(1− ρt)βt+1q(θt). (10)In principal, we can use this equation to back out the value of firing costs. However, often oneassumes that firing costs are proportional to the wage and we therefore assumeΓt = γtWt. (11)Then, (10) reads asγt = Et1ρtWtYt+1Nt+1−Wt+1 +cq(θt+1)−c(1− ρt)βt+1q(θt), (12)= Et1ρtWt Yt+1Nt+1−Wt+1 +cmUt+1Vt+1µ −c(1− ρt)βCtCt+1mUtVtµ. (13)3 DataIn order to compute firing costs using equation (13) we need the following time series: output,employment, unemployment, wages, vacancies, and consumption.Time series for the United States are taken from the Bureau of Labor Statistics and the Bureau ofEconomic Analysis. For the seven European countries (Austria, Czech Republic, Finland, Hungary,Poland, Portugal, and UK) time series are taken from Eurostat and the OECD.2 We use seasonallyadjusted, quarterly data from 2001:Q1 to 2012:Q2.3 Time series for output, consumption, andwages are detrended using the GDP deflator.Finally, we also use a time series for the separation rate but, due to data limitation, only forthe United States. For the remaining seven countries we take the individual separation rates fromthe estimates by Hobijn and Sahin (2009). The only exception is Austria, for which no estimate ispresented in Hobijn and Sahin (2009). We assume a value of four percent which is an intermediatevalue across all estimates.Before we present the estimation results, we need to calibrate the following parameters: thematch efficiencym, vacancy posting costs c, the discount factor β, and the elasticity of the matchingfunction µ. The discount factor is set to 0.99 as common in the literature. Match efficiency is setto a value of 0.7 which is a value usually obtained in search and matching models. We assume thatthe elasticity of the matching function is 0.4 as usual in the literature. Finally, vacancy postingcosts are taken from the steady state and are equal to 0.06.2The dataset as well as the estimation codes are available upon request.3The limiting factor here is data for vacancies.4Finally, in order to put our results into perspective, we use data on the strictness of employmentprotection legislation (overall and for temporary employment), spending and participation in activelabor market programs, and union density all obtained from the OECD database.4 ResultsFigures 1 and 2 present the estimated values of firing costs as a share of quarterly wages (withineach quarter) over the 2001:Q1 to 2012:Q2 period. For simplicity, we disregard the expectationoperator in the estimation. Further, table 1 shows the average firing cost value over the sampleperiod.Table 1: Average firing costs.Country mean(γt)USA 0.3UK 0.31Austria 0.38Czech Republic 0.78Finland 0.37Hungary 0.61Poland 0.72Portugal 0.55For the United States we obtain a value of 0.3 over the entire sample. The graph shows thatfiring costs increase at the beginning of our sample, remained almost constant at a value of roughly0.3 and in 2009 increased to a value of 0.32. Here, one might interpret this increase with thefinancial crisis triggering a large recession increasing the cost of firing a worker.The estimated series for the UK shares the initial upward trend that we observed for the UnitedStates. This trend continues until the end of 2006 when a slight downward trend until 2009 kicksin. About one year later compared the US a positive upward shift is visible. On average we find avalue of 0.31 for the UK.For Austria, the time series for firing costs shows a slight upward trend up to the end of 2007.This upward trend is followed by a significant drop of firing costs from 0.4 to 0.35. At the end ofour sample we find that firing costs tend to increase. Overall, we find an average value of 0.38 forfiring costs in Austria.In contrast to the upward trends we observed before, Finland shows a slight downward trendfrom the beginning of our sample. This downward trend becomes even steeper from the first quarterof 2007 on, decreasing from 0.42 to 0.32. This negative trend continues until the beginning of 2009,when firing costs stabilize around a value of 0.35. In summation, the mean value of firing costs forFinland is 0.37.501 02 03 04 05 06 07 08 09 10 11 120.260.280.30.320.340.360.380.40.420.44Firing CostsQuartersPercent from Wages  USAUKAustriaFinlandFigure 1: Firing costs over the cycle.Turning to the countries in Figure 2, we find that firing costs in Hungary reveal a slight down-ward trend from 0.65 to 0.57 at the end of our sample period. On average, firing costs equal 0.61.A similar pattern is obtained for the Czech republic. Starting from a level of roughly 0.8 firing costsdecrease over the entire sample to reach a value of almost 0.73 at the end of our sample. The meanvalue of firing costs for the Czech republic is equal to 0.78. Very small deviations in firing costs areobtained for Portugal. Here, firing costs show little variation and stay close to the mean value of0.55 over the entire sample. Only towards the end of the sample we observe a strong upward trend.Finally, the estimated time series for Poland shows much more variation as the other time series.We observe a sharp upward trend at the beginning of the sample period that continues until 2005.Then, for roughly three years firing costs stay put at a level of 0.77, before they decrease andstabilize around a level of about 0.7. On average, firing costs in Poland equal 0.72.5 RobustnessIn this section we want to provide a robustness check for the United States. Since we have a timeseries for the separation rate for the United States, we want to compare our firing cost time seriescomputed using a fix separation rate of 3.18 percent and the observed time series. Figure 3 presentsthe time series for the US firing costs. The figure shows that our interpretation as an upper boundseems reasonable. The two time series share the same trends over the entire time span. We observean upward trend at the beginning of the sample until the mid-2003’s. Then, from 2004 to 2007we find little variation in the value until 2008, when a upward shift becomes visible. As pointedout, the time series with the fixed separation rate is significantly above the time series with theobserved time series. Until 2009 the difference is stable at roughly six percentage points. After thelevel shift of 2009 the variable time series is above the fix time series, while the difference is much601 02 03 04 05 06 07 08 09 10 11 120.50.550.60.650.70.750.80.850.9Firing CostsQuartersPercent from Wages  PortugalCzechHungaryPolandFigure 2: Firing costs over the cycle.01 02 03 04 05 06 07 08 09 10 11 120.240.260.280.30.320.340.360.380.40.420.44Firing CostsQuartersPercent from Wages  USA − VariableUSA − FixFigure 3: Robustness check for the United States.7smaller at a value of about two percentage points.6 Explaining the ResultsAfter describing our results, we want to test and discuss several explanations for the observedpatterns. Of course, this regression suffers from the limited number of observations such that weshould be careful in interpreting the results.First, let us begin with discussing the relation between firing costs and potential explanatoryvariables. Employment protection legislation (EPL, for short). One would expect that countrieswith a c.p. stricter EPL would feature a high value of firing costs. Furthermore, spending and par-ticipation in active labor market programs (ALMP, for short) varies significantly across countries.As those ALMP programs affect the incentives and the skill level of (unemployed) workers, theremight be a relation between the cost of firing a worker and ALMP. Finally, a higher union densitymight increase the costs of firing a worker.Table 2: Firing Cost Regression. Significance levels: ∗: 10 % level, ∗∗: 5 % level, ∗∗∗: 1 % level.Mixed is cubi for EPL and union density and quadratic for ALMP.Parameter Linear Quadratic Cubic MixedConstant 0.35∗∗∗ 0.36∗∗∗ 0.36∗∗∗ 0.39∗∗∗EPL 0.06∗∗ 0.02∗∗ 0.01∗∗ 0.01∗∗ALMP -0.05 -0.03 -0.02∗ -0.05∗Union Density -0.0002 0 0 0LLN 11.36 10.98 10.93 10.9Table 2 summarizes our OLS estimation results. We show four different specifications: a linearmodel, two models with a quadratic and a cubic non-linearity and a mixed model with a cubiceffect of EPL and union density and a quadratic relation for ALMP.The resulting coefficient for EPL is positive and is significant at the five percent level. Its valuevaries between 0.06 as an upper bound and 0.01 as a lower bound. Second, the regression coefficientof firing costs on spending as percentage of GDP we find a coefficient varying between -0.05 and-0.02. The coefficient is significant at the 5 percent level, once we allow for a cubic relationshipand insignificant in the linear and quadratic scenarios. Finally, the coefficient on union density isinsignificant in all considered cases.We can draw the conclusion that in our limited sample (i) non-linear effects exist and (ii) thatEPL and active labor market programs do have a significant impact on firing costs.87 Final RemarksThis study is concerned with providing cross-country evidence for the value of firing costs in orderto calibrate state-of-the-art search and matching models with labor turnover costs. In contrastto existing studies, we use the approach pursued by Burnside et al. (1993) and use optimalityconditions to find a reduce form equation for the share of firing costs from wages.Using time series for eight different countries, we estimate the value of firing costs across oursample period from 2001 to 2012. We find that firing costs vary significantly across countries.The lowest values are obtained for the UK (31 percent of quarterly wages) and the United States(3 percent of quarterly wages). On the other side of the spectrum, Poland (0.72) and the Czechrepublic (0.78) show the largest values of firing costs. If we compare our results with the existingvalues used in the literature, we find that for most countries they are larger compared with theexisting literature. However, for the United States the value of roughly one third of quarterlywages seems to be in line with the usual values found in the literature. Further, we offer threeexplanations for the observed differences across countries. In summation, strictness of employmentprotection and active labor market programs seem to have a non-linear, significant effect on firingcosts.At the end, we would like to stress several limiting factors that should be considered in theinterpretation of our results and could be considered in future work. First, the size of searchfrictions might also vary significantly across countries which would bias our results. Second, weassumed that the values of deep parameters are identical across countries. More evidence on thoseparameters would also increase the accuracy of our results. Finally, one might think of introducinghours and time-varying hiring costs (parameter c).9References[1] Abowd, John M. and Kramarz, Franics (2003): "The costs of hiring and separations." LabourEconomics, 10: 499-530.[2] Alvarez, Fernando and Veracierto, Marcelo L. (1998): "Search, self-insurance and job-securityprovisions." Working Paper Series WP-98-2, Federal Reserve Bank of Chicago.[3] Bentolila, Samuel and Bertola, Guiseppe (1990): "Firing costs and labour demand: How badis eurosclerosis?" Review of Economic Studies, 57(3), 381-402.[4] Burnside, Craig, Eichenbaum, Martin, and Rebelo, Sergio (1993): "Labor Hoarding and theBusiness Cycle." Journal of Political Economy, 101: 245-73.[5] Cozzi, Marco, Fella, Giulio, and Violante, Gianluca (2010): "The Non-neutrality of SeverancePayments with Incomplete Markets." mimeo.[6] Dolado, Juan J., Marcel Jansen, and Juan F. Jimeno (2005): "Dual Employment ProtectionLegislation: A Framework for Analysis." IZA Discussion Paper, No. 1564.[7] Dolado, Juan J., Marcel Jansen, and Juan F. Jimeno (2007): "A Positive Analysis of TargetedEmployment Protection Legislation." IZA Discussion Paper, No. 2679.[8] Dolfin, Sarah (2006): "An examination of firms’ employment costs." Applied Economics, 38:861-878.[9] Garibaldi, Pietro, and Violante, Giovanni L. (2005): "The Employment Effects of SeverancePayments with Wage Rigidities." Economic Journal, 105(506): 799-832.[10] Hobijn, Bart and Sahin, Aysegül (2009): "Job-finding and separation rates in the OECD."Economics Letters, 104(3): 107-111.[11] Hopenhayn, Hugo A., and Rogerson, Richard (1993): "Job Turnover and Policy Evaluation:A General Equilibrium Approach." Journal of Political Economy, 101(5): 915-938.[12] Lechthaler, Wolfgang, Merkl, Christian, and Snower, Dennis J. (2010): "Monetary persistenceand the labor market: A new perspective." Journal of Economic Dynamics and Control, 34(5):968-983.[13] Veracierto, Marcelo L. (2008): "Firing Costs and Business Cycle Fluctuations." InternationalEconomic Review, 49(1): 1-39.10

How Large are Firing Costs? A Cross-Country Study

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How Large are Firing Costs? A Cross-Country Study

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