UNEMPLOYMENT IN TRANSITION COUNTRIES (CENTRAL EUROPE): WHY SO HIGH?

1. UNEMPLOYMENT IN TRANSITION COUNTRIES (CENTRAL EUROPE): WHY SO HIGH?

2. Lecture plan • Introduction of the issue • Research approaches • Matching function approach • Theory • Simple statistics • Estimation strategy • Various empirical problems • Findings and remaining questions • Presentation will be available • Readings

3. Basic Ideas • Unemployment unknown under communism • But emerged rapidly • Is a major problem in most CE economies • Q: Is unemployment the result of • unfinished transition from plan to market => need to complete it • macro policies and external shocks => macro policies key • economic structures (mismatch) => focus on labor market institutions, labor mobility and skill formation

5. Research approach 1: Labor Market Institutions and Unemployment (WB, OECD) • Calculate measures of L mkt institutions • Unemployment Insurance (UI): net replacement rates (declining) • UI: strictness (flat or increasing) • Wage bargaining (high or increasing decentralization) • Employment protection (not strong by EU standards) • Tax wedge, employer + employee income tax (high and stable) • U not related to institutions in regressions • Except possibly for initial UI benefits and tax wedge • Conclude: U not explained by labor market institutions alone • If institutions matter, likely in combination with other factors • Heckman’s critique (simplistic indicators, small datasets, noise)

6. Research approach 2: Job Destruction, Job Creation and Unemployment • L in new sector has not replaced L lost in old sector • Q: Is labor reallocation (transition) still at work? • Looks at JC, JD and U as initial conditions and policies vary • Amadeus database => construct JC ad JD rates for 10 TEs • Macro-level regression findings: • Unemployment has a negative effect on JC in new firms • High U associated with higher UI benefits and taxes => lower JC? • Current long-term U depends on history of short term U and hence JC and JD • Firm-level regression results: • Foreign ownership has a positive effect on employment growth

7. Research approach 3: Initial Human Capital (HC) and Regional L Mkt(Stepan Jurajda) • Transition: High dispersion and lack of convergence in regional unemployment rates (URs) • focus on regional differences in HC endowments • Idea: Skill and skill-capital complementarities explain high regional dispersion in unemployment • Findings (BU, CR, HU, UKR): • Over one-half of variation in regional URs explained by concentration of HC • Regional variation in HC is wide and rising • K and skilled L move to regions with high skill concentration

8. Research approach 4: Skill Endowments in the CE Countries (Janos Köllo) • Thesis: Presence of many workers with only primary or vocational education => low employment rates • Industry and agriculture (simple tasks) declined in CEs • Growing tertiary sector demands higher skills (communication) • => Employment of low skilled workers fell dramatically • Evidence: IALS, workplace skill requirements, panel data on L and W of unskilled in occupations, firm-level skill share equations (response to tech. change) • => Policy issues related to education and training

9. UNEMPLOYMENT AND WORKER-FIRM MATCHING IN CENTRAL EUROPE Daniel Münich Jan Svejnar

10. Basic Ideas • Q: Is unemployment a result of • ongoing transition (restructuring ) • macroeconomic policies and external shocks • economic structures (mismatch) => focus on L mkt institutions (as in Western Europe), labor mobility and skill formation • Use district-level panel data on • the unemployed U, vacancies V, inflow S into unemployment, and outflow O from unemployment • in CR, HU, PO, SR, and East and West parts of Germany • Examine the three hypotheses in the context of the efficiency of matching of the U and V

11. MATCHING FUNCTION APPROACH • Matching of unemployed U and vacant jobs V with frictions (notion like production function) leads to outflow from unemployment O (flow chart) • Using flow identity (inflow=outflow; S=O)equilibrium unemployment rate u* for given inflow rate s, vacancy rate v, and matching function O=(U,V) • where

12. Structural Model • Probability of a job offer p=p(V/U); probability of a job offer to match = 1 – G(mpr) • Steady-state unemployment rate (UV curve): • Vacancy supply curve (VS): s … exogenous inflow mpr… reservation marginal product from a match z … income while unemployed γ0… worker’s costs of search v VS UV u Literature: Petrongolo B. and C. Pissarides (2001), “Looking into the Black Box: A Survey of the Matching Function,” Journal of Economic Literature 39, June: 392–431. Jackman R., C. Pissarides, and S. Savvouri (1990), “Unemployment Policies and Unemployment in the OECD,” Economic Policy, October: 449–490.Berman E. (1997), “Help Wanted, Job Needed: Estimates of a Matching Function from Employment Service Data,” Journal of Labour Economics 15(1): S251–S292.

13. Beveridge curve dynamics

14. Beveridge curve during 1970-1990

15. Beveridge curve (Czech Republic, seasonally adjusted data)

16. Beveridge curves for CE countries

17. REDUCED FORM • In equilibrium,O = Sand U = U*= const. • For given level of (exogenous) inflow S and vacancies V, equilibrium U* (not necessarily observed!) is defined implicitly by the matching function • Implying • Allows for determination of parameters not observing equilibrium

18. Conceptual framework of matching functions • O = M(U,V) • Some authors expect the matching function M to display constant returns to scale • Others have identified reasons such as externalities in the search process, heterogeneity in the unemployed and vacancies and lags between matching and hiring, why increasing returns may prevail • Increasing returns are important because they may constitute a necessary condition for multiple equilibria and provide a rationale for government intervention. • We find that increasing returns appear to be an important phenomenon • especially in the later (1997-2003) than the earlier (1993-96) period • more pronounced in some of the economies than others

19. Hypotheses about reasons for high U • H1: restructuring still at work -- inflow S (from old jobs) high => U high due to high turnover • H2: U-V matching “fine”, high U caused by low L demand (macro policies, exchange rate, shocks) => low V relative to S (irrespective of U) • H3: inefficient U-V matching (L mkt institutions or geographical or skill mismatch) => U and V both high but not in the same districts or skill groups

20. AGGREGATE TIME SERIES OF KEY VARIABLES

21. Figure 2 – Evolution of U, S, O, and V in west part of Germany (benchmark case) • West Germany an “intermediate” case in 1991-2005 • unemployment rate rising in two waves from 5% to 10% • inflow rate rising in two waves from 0.9 to 1.6 • outflow rate decreasing in two waves from 18.5 to 13.6 • Vacancy rate fluctuating (in two waves ) between 0.7 and 1.2

22. Figure 2

23. Figure 3 – Evolution of U, S, O, and V in the Czech Republic • CR is somewhat similar (intermediate) • U rate rising in two waves from 3% in early-to-mid 1990s to 10% • inflow rate has risen • outflow rate has declined from a high level • vacancy rate rose to 1.9 and then declined to 0.8-1.1

24. Figure 3

25. Czech example of seasonality in the data

26. Figure 4 – U, S, O, and V in East Germany • East Germany – one extreme case • unemployment rate rising from 11.5% to 18.6% • inflow rate rising dramatically • Outflow rate first rising and then stabilizing around 13-14% • a vacancy rate rising from 0.4% in 1991 to about 1% in the late 1990s and remaining at or below that level in the 2000s

27. Figure 4

28. Figures 5 and 6 – Evolution of U, S, O, and V in Poland and Slovakia • Poland and Slovakia – also extreme cases • unemployment rate rising quickly to the 14%-20% range • relatively steady high inflow rates • low outflow rates • vacancy rates well below 1%

29. Figure 5

30. Figure 6

31. Figure 7 – Evolution of U, S, O, and V in Hungary • Hungary is also special • lowest unemployment rate; after reaching 10-11.5% in mid 1990s, lowered to around 8-9% • inflow rate as a share of the labor force at 1.2-1.3% • outflow rate as a share of the labor force at 1.2-1.4% • kept the vacancy rate at 1.0-1.1% • Hungary’s success brought about by keeping the outflow rate relatively high and inflow rate relatively low

32. Figure 7

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34. Figures suggest • West Germany consistent with H1-3; U risen with increasing inflows S (H1), V declined while inflow risen (H2), the U and V rates are relatively high (H3) • CR starts with low U but increasingly conforms to H1 (higher U and S) and H2 (V low relative to S and U) • East Germany conforms to H1 as well as H2 • Slovakia and Poland consistent with H1 and H2 throughout the 1990s and 2000s • Because of low unemployment, Hungary does not fit clearly into any H -- has an element of all three Hs: inflow is relatively sizable (H1), the vacancy rate is low relative to inflow (H2), unemployment and vacancies are relatively high (H3)

35. Literature on matching in TEs • Grown rapidly • Produced contradictory results • Studies use different methodologies and data • Methodologically, they differ especially with respect to the • specification of the matching function and treatment of returns to scale • inclusion of other explanatory variables that might affect outflows • extent to which they use static or dynamic models • In terms of data, the studies differ in whether they • use annual, quarterly or monthly panels of district-level or more aggregate (regional) data • cover short or long time periods • None adjusts the data for the varying size of the (district or region)

36. Our approach • Unlike other studies, we use a more up-to-date empirical methodology and superior data • control for the endogeneity of explanatory variables • account for the presence of a spurious scale effect introduced by the varying size across units of observation (districts) • use long panels of comparable monthly data from all districts in the countries that we analyze

37. Empirical Specification (simple, but…!) • Cobb‑Douglas function which may be written in a deterministic form as (2) • Ui,t‑1 = number of unemployed in district i at the end of period t-1 • Vi,t‑1 = number of vacancies in district i at the end of period t-1 • Oi,t = outflow to jobs during period t • A captures the efficiency of matching.

38. Empirical Specification • Let lowercase letters stand for logarithms of variables • ai be district specific effects • εi,t be an idiosyncratic error term • Can write (2) as • (3)

39. Estimation problems (3) • OLS not appropriate if ai are correlated with u and v • Correlation likely to exist due to differences between districts (draw graph) • Specific factor is district size (spurious scale effect) • With panel data, one can use means deviation or first differencing to remove ai • But RHS u and v are predetermined through previous matching (endogenous)  inconsistent estimates  IV needed  first differencing preferred

40. First difference transformation contaminates the transformed variables only with recent error terms {εt: t = T-1, T-2} • To see this, rewrite (5) in a first difference form (6) Lagged outflows in (4) in turn given by a lagged version of (3) • … and further lags of U (or S), and V can be used as valid instruments. • District mean deviations transformation (fixed-effects): contaminates variables with all error terms.

41. Disentangle ai • From 1st differences back to levels • Problem with poor measurement of vi,t

42. Newly unemployed • Studies (e.g., Coles and Smith,1994) suggest propensity to match higher at time of entry into unemployment • Newly unemployed search through all existing vacancies • May have not experienced depreciations of skills • Remaining unemployed match only with the newly posted vacancies • To reflect this, include inflow into unemployment as an additional explanatory variable

43. Total outflow v. outflow to jobs • Data on outflow to jobs are available only for the Czech Republic, while data on total outflow are available for all the countries • We carry out the estimation for the Czech Republic using both measures and find that the estimates based on total outflow and outflow to jobs are similar • Assume the lack of data on outflow to jobs in other countries should not have a dramatic impact on our results (see also Petrongolo and Pissarides, 2000, for similar evidence from other countries)

44. Other empirical problems • Measurement error • Continuous vs. discrete process • Segmented labor market • ….

45. Data • Panel of data on 76 Czech, 38(79) Slovak, 21 Hungarian, 34 East German and 140 West German districts. The data cover January 1991- 2005 and contain monthly observations for the following variables: • Oi,t = the number of individuals flowing from unemployment in district i during period t; • Ui,t = the number of unemployed in district i the end of period t; • Si,t = the normalized number of individuals flowing into unemployment (the newly unemployed) in district i during period t;[2] • Vi,t = the number of vacancies in district i at the end of period t; • [2] Although the individuals flow into unemployment in the same calendar month, they enter on different days within the month. This means that they face different probabilities of finding vacancies during the calendar month. Assuming, that the inflow is approximately uniform over the month, we multiply the total monthly inflow by .5.

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47. Table 3

48. Conclusions • West Germany -- rising unemployment and inflow, declining vacancies and relatively efficient matching (high returns to scale) -- outcome most consistent with H1 and H2 • Czech Republic similar -- rising unemployment, inflow and outflow, and a declining vacancy rate and high returns to matching, it increasingly gives support to H1 and H2; since CR has increasingly pursued low interest rates and fiscal deficits, the support for H2 implies the presence of negative exogenous demand shocks • East Germany also in line with H1 and H2 -- relatively high unemployment and inflows, a low vacancy rate and very efficient matching (training) • Slovakia -- low returns to scale in matching, and high unemployment, rising inflow rates and a low vacancy rate; loose monetary and fiscal policies and a floating exchange rate. Its outcome is hence consistent with a combination of H1, H2 and H3 • Hungary has lowered its unemployment rate to around 8% and it has the highest estimated returns to matching. Given its low vacancy rate relative to inflows, the existing unemployment seems to be consistent with H1 and H2

49. Conclusions (2) • Overall, our findings suggest that the transition economies contain two broad groups of countries • First group = CR, Hungary and (possibly) East Germany • resembles West Germany -- efficient matching and unemployment appears to be driven by restructuring and low demand for labor • The East German case is complex -- major active labor market policies => in some sense it resembles more the second group, exemplified by Slovakia and Poland • These countries, in addition to restructuring and low demand for labor, appear to suffer from a structural mismatch (i.e., display less efficient matching)