Tackling unemployment in recessions: The effects of short-time work policy

Transcription

1 Tackling unemployment in recessions: The effects of short-time work policy Britta Gehrke 1,2 Brigitte Hochmuth 1 1 Friedrich-Alexander University Erlangen-Nuremberg (FAU) 2 Institute for Employment Research (IAB) Young Economists Conference 2016, Vienna October 5th, 2016

2 STW over the business cycle in Germany Upper panel: Share of short-time workers of employment. West Germany until Lower panel: GDP growth (qoq). Shaded areas show OECD recession periods. 1 / 19

3 STW Take Up Not only a story about Germany: In 2009, 25 out of 33 OECD countries used short-time work (STW) as a fiscal stabilizer. More than 2% of the workforce was affected in Germany, Italy, and Japan. Fiscal expenditures of more than 5 billion Euros in each of these countries. In Austria, the STW take up rate was 1.2% compared to 4.2% in Germany. Germany has used STW since the 1970s. Also outside of recessions. 2 / 19

4 Background: Short-time work Firms apply for STW subsidies at the Federal Employment Agency. The agency will approve if the firm convincingly argues that its expected demand is (temporarily) lower than its production potential and, as a result, it has to lower labor input. If approved, firms reduce hours worked of their employees (and wage payments). The agency covers 60% of the workers net wage loss. Maximum duration of STW is set by law (6 months, but frequently prolonged up to 24 months). 3 / 19

5 Ongoing Debate on the Effectiveness of STW Several cross-country studies find positive employment effects. (Cahuc and Carcillo, 2011; Hijzen and Martin, 2013) Others are sceptical (Burda and Hunt, 2011; Boysen-Hogrefe and Groll, 2010) Balleer et al. (2016): Rule-based vs. discretionary component STW is a strong and cost effective automatic stabilizer for unemployment. Effectiveness of discretionary part less clear. What is discretionary STW policy? Legal changes: extended eligibility period and loose criteria, additional coverage of social security contributions. Others:Increased advertising, less stringent interpretation of existing rules. We pay special attention to potential interactions with the business cycle. 4 / 19

6 Why should STW interact with the business cycle? Wage subsidies support liquidity/credit constraint firms in recessions. Subsidies resolve downward nominal wage rigidities that may cause sharp increases in real labor cost in recessions if inflation declines (Abbritti and Fahr, 2013). Rationing unemployment (Michaillat, 2012). Unemployment in recessions is not necessarily search (or frictional) unemployment. 5 / 19

7 Our contribution We analyse time-varying effects of labor market policy in a nonlinear VAR. 1 We show that the discretionary component can be effective. BUT: Timing is crucial! High effectiveness in deep recessions. The deeper the recession, the more jobs are saved. In normal times and expansions, effects are less pronounced or may even be negative. 2 We compute historical STW multipliers Discretionary STW policy may save up to 0.7 jobs per short-time worker 3 To do: Explain this finding in a theoretical labor market model 6 / 19

8 Our Paper We estimate a smooth transition VAR (STVAR) on German time series data. As in Balleer et al. (2016), we use microeconomic firm level data to pin down the elasticity of STW to output. This elasticity serves as a short-run restriction to identify our STVAR (following Blanchard and Perotti (2002)). 7 / 19

9 The empirical model 8 / 19

10 STVAR setup, estimation and interpretation more Quarterly data 1973q1 to 2014q4 for log GDP, log STW and log employment. Data Sources Baseline STVAR with 2 lags, regime specific intercept and reunification dummy. ML estimation using MCMC methods (Chernozhukov and Hong, 2003, JoE). Interpretation: GIRFs show the model responses while the regimes switch endogenously conditional on an initial regime in the impact period (Koop et al., 1996). 9 / 19

11 A smooth transition VAR Auerbach and Gorodnichenko (2012) X t = ( 1 F (z t 1 ) ) Π E (L)X t 1 + F (z t 1 )Π R (L)X t 1 + u t (1) u t N(0, Ω t ) (2) ( Ω t = Ω E 1 F (zt 1 ) ) + Ω R F (z t 1 ) (3) F (z t ) = exp( γ(z t c) ) 1 + exp ( ), γ > 0 (4) γ(z t c) var(z t ) = 1 (5) E(z t ) = 0 (6) with vector of endogenous variables X t, business cycle indicator z t, and γ governing the speed of switching. 10 / 19

12 Weight on recession regime F (z) Notes: The business cycle indicator z t is set equal to a four quarter backward moving average of the output growth rate and normalized, γ = 2.3. The economy spends about 38 percent of the time in recession. 11 / 19

13 Identification of STW policy As in Blanchard and Perotti (2002), we can disentangle the response of policy to output shocks and the response of output to policy using a short-run restriction. Balleer et al. (2016) propose to use microeconomic firm level data to estimate the rule-based STW response of firms to output changes. We adapt this setting and add a distinction of the effects in expansion and recession. The derived elasticity is 4.8 in expansions and 3.4 in recessions. more Shocks 12 / 19

14 Results 13 / 19

15 Responses to STW policy shock Figure: Generalized Impulse Responses Notes: Median Employment Responses to a STW policy shock normalized to one. Shaded areas denote 95 percent confidence intervals. 14 / 19

16 STW effects in extreme events (a) Extreme Events (b) Great Recession vs. normal expansion Figure: Generalized Impulse Responses in Extreme Events Notes: Median Employment Responses to a STW policy shock normalized to one. Shaded areas denote 95 percent confidence intervals. Linear Model Unemployment Specification Sign Size 15 / 19

17 Historical multipliers Notes: Shaded Regions denote OECD recession periods. The solid black line is the mean maximum multiplier in terms of employment effects in response to a one percent STW shock (max h= EMPL h \ max h= STW h ). Dashed lines denote 95 percent confidence intervals. Individuals 16 / 19

18 Conclusions This paper analyzes the effects of discretionary STW policy over the busines cycle using regime switching SVARs. We find strong evidence that the effects differ depending on whether the economy is in a recession or a boom. In a recession, expansionary STW policy stabilizes employment. It should, however, not be used to avoid job losses due to structural change in expansions. STW turns out to be an effective policy in terms of automatic stabilization and discretion, if implemented in recessions. 17 / 19

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20 Appendix

21 Structural discretionary STW shocks Notes: The dashed line denote the raw shock series, the solid line represents a five quarter moving average of structural policy shocks. Gray shaded areas denote recession periods according to the OECD definition. Back.

22 The mechanism in expansions Positive shocks Only firms with very low productivity use STW in expansions. This ties resources (workers and capital) to these firms and prevents structural adjustments and growth. Negative long-term effects. We know that firms use the intensive margin of STW by a lot more in expansions than in recession (hours reductions by up to 100%). Effects may be very different compared to an hours reduction of approximately one third in expansions. STW usage after reunification probably had detrimental effects for the labor market as structural adjustments were hindered (see also Snower and Merkl, 2006).

23 Analyzing the mechanism Notes: The figure shows the responses to STW policy shocks of labor market flow rates (each estimated from a separate VAR with GDP, STW, the respective variable and employment).

24 Analyzing the mechanism Notes: The figure shows the response to STW policy shocks of unit labor costs (estimated from a separate VAR with GDP, STW, unit labor costs and employment).

25 Analyzing the mechanism STW develops through job separation. In recessions, unit labor costs are stable, whereas in an expansion they rise due to STW policy. This reduces labor demand in expansions.

26 Robustness Our results are robust to STW policy anticipation controlling for Hartz reforms different switching parameters γ, A larger γ, i.e., faster switching, reduces the scale of the effects, but not the sign or the significance. different identifying elasticities, different VAR specifications.

27 Robustness

28 Related literature Theory Normative aspects of STW (Burdett and Wright, 1989, Braun et al., 2012) Positive analysis in DSGE framework (Faia et al., 2010, Krause and Uhlig, 2011) Empirical evidence Cross-country studies of the Great Recession (e.g. Cahuc and Carcillo, 2011) Microeconometric firm-level studies (e.g. Bellman et al. for Germany) Dynamic labor demand estimation (Burda and Hunt, 2011, Boysen-Hogrefe and Groll, 2010)

29 Notes: Median Responses to an output policy shock normalized to one. Shaded areas denote 95 percent confidence intervals. GIRFs of output shock Figure: Generalized Impulse Responses for a 1 s.d output shock

30 GIRFs of linear SVAR for output shock

31 GIRFs of linear SVAR for policy shock Figure: Generalized Impulse Responses including linear model responses Notes: Median Responses to an output and STW policy shock normalized to one. Shaded areas denote 95 percent confidence intervals. Back.

32 Specification with Unemployment (a) Responses to Output Shock (b) Responses to Policy Shock Figure: Generalized Impulse Responses for Unemployment Specification Notes: Median Responses to an output and STW policy shock normalized to one. Shaded areas denote 95 percent confidence intervals.

33 The unemployment response to STW: extreme events (a) Responses to a policy shock in extreme events (b) Responses to a policy shock in the Great Recession vs. normal expansion Back.

34 Different sign of STW policy (c) Responses in Expansions Figure: Different Shock Signs (d) Responses in Recessions Back.

35 Different size of STW policy (a) Responses in Expansions Figure: Different Shock Size (b) Responses in Recessions Back.

36 Historical multipliers in Individuals Notes: Shaded Regions denote OECD recession periods. The solid black line is the mean maximum multiplier in terms of employment effects in response to a one percent STW shock (max h= EMPL h \ max h= STW h ). Dashed lines denote 95 percent confidence intervals. Back.

37 Elasticity estimation: linear What is the automatic response of STW with respect to changes in output? y }{{} it = x it β }{{} 1 +α i + γ t + z it β 2 + u it STW EMP exp. revenue Micro data: IAB establishment panel for Germany 16,000 firms in 2003, 2006, 2009 and 2010 Panel estimation with firm and time fixed effects Additional control: firm size in t 1 Account for nonlinearities at zero with pseudo-panel tobit and Heckman selection model Elasticity ranges between (baseline) and

38 Results of linear elasticity estimation Linear fixed effects log exp. derived year fixed employees observations revenue elasticity effects in firm (1) , 545 [0.306] (2) yes 39, 545 [0.299] (3) yes yes 31, 824 [0.342] Fixed effects tobit (5) yes 31, 824 [0.286] (6) yes yes 31, 824 [0.311] Fixed effects heckman (7) yes 31, 824 [2.57] (8) yes yes 35, 264 [2.75] Notes: denotes 1% significance, denotes 5% significance, denotes 10% significance.

39 Elasticity estimation: Non-linear y }{{} it = x it (β }{{} 1 + Dt rec β1 rec ) + α i + γ t + z it β 2 + u it STW exp. revenue EMP 2003 and 2009 recession log exp. exp.rev. D rec elasticity observations revenue expansion recession (1) , 824 [0.342] [0.087] 2009 recession (2) , 824 [0.340] [0.099] Notes:Results from microeconomic elasticity estimation on IAB establishment panel. We control for the number of employees, the change of employment, and year fixed effects in the estimation. denotes 1% significance, denotes 5% significance, denotes 10% significance. Back.

40 Data sources Real GDP growth: National accounts (cleared for reunification break) Number of STWorkers: Federal Employment Agency (West Germany until 1992) Employment: National Accounts (w/o self-employed) Unemployment rate: Federal Employment Agency (cleared for reunification break and trends) Wages: National Accounts (Total compensation) Flows: IAB data Back.

41 Estimation I ML estimation (as in Auerbach and Gorodnichenko, 2012) log L = const 1 2 T log Ω t 1 2 t=1 T t=1 u tω 1 t u t where u t = X t (1 F (z t 1 ))Π E (L)X t 1 F (z t 1 )Π R (L)X t 1 Model Parameters: ψ = {γ, Ω R, Ω E, Π E (L), Π R (L)} Given a guess for {γ, Ω R, Ω E }, {Π R, Π E } can be estimated using WLS with weights Ω 1 t The objective function is: where 1 2 T t=1 (X t ΠW t ) Ω 1 t (X t ΠW t ) W t = [(1 F (z t 1))X t 1 F (z t 1)X t 1... (1 F (z t 1))X t p F (z t 1)X t p]

42 Estimation II Rewriting and taking the FOC w.r.t Π gives T vec Π = ( [Ω 1 t W t W t ) 1 vec( t=1 T t=1 W t X t Ω 1 t ) To ensure a global optimum, the MCMC method by Chernozhukov and Hong (2003, JoE) is used (Metropolis-Hastings). 1 Draw candidate vector Θ (n) = Ψ (n) + ϕ (n) for the n+1st chain value, where Ψ (n) = current state and ϕ (n) = i.i.d shocks taken from N(0, Ω Ψ ). 2 accept candidate vector with probability min {1, exp[log L(Θ n ) log L(Ψ n )]} Back.

43 Generalized Impulse Responses I 1 Randomly draw a history λ i Λ R corresponding to a recessionary period. 2 For a given impulse response horizon h, randomly sample h + 1 values of residuals. 3 Compute the inverse of the A0-Matrix at corresponding time t, A0 1 t : A0 1 t = F (z)a0 1 R + (1 F (z))a0 E 1. 4 Transform the randomly drawn vector of residuals into structural shocks using Σ e = A t Σ u A t. 5 Add the one standard deviation shock at h = 1 and transform the structural shocks back into residuals using Σ u = A 1 t Σ e A 1 t.

44 Generalized Impulse Responses II 6 Simulate a time path of Y t0 using the history for the vector of original residuals and another path Y t1 using for the vector of residuals containing the one standard deviation shock. Take the difference between the paths: GIRF i = Y t1 Y t0. 7 Repeat the previous steps 2-5 B = 500 times and calculate the median GIRF conditional on the specific history draw. GIRF i = median(girf i b=1:b ). 8 Repeat steps 1-7 R = 500 times and compute the median GIRF, which corresponds to the average GIRF under recessions, GIRF R = median(girfr=1:r i ). In addition, compute the 95% confidence bands by picking the 97.5th and 2.5th percentile. 9 Repeat steps 1-8 for R = 500 expansionary periods and compute the average GIRF under expansions,girf E and the 95 % confidence bands.