III Labor Markets in Transition: An Econometric Analysis

Philippe Egoume Bossogo; Jerald A Schiff; Miho Ihara; Tetsuya Konuki; Kornelia Krajnyak

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Author: Mr. Philippe Egoume Bossogo, Mr. Jerald A Schiff, Ms. Miho Ihara, Mr. Tetsuya Konuki, and Ms. Kornelia Krajnyak

Language:: English

Keywords:: OP; economy; labor market performance; high-unemployment outlier; terms of trade; minimum wage; employment gain; Labor market institutions; Labor market policy; Unemployment; Labor markets

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Abstract

After a review of the main stylized facts, this section undertakes a systematic quantitative analysis of transition countries’ labor market experience to help draw policy conclusions.

A Brief Literature Review

Theoretical analyses of unemployment in transition initially predicted a hump-shaped path for unemployment during the transition process. Initial job destruction in the public sector would gradually be overtaken by private sector job creation. However, in the interim, unemployment would result either because of the costs of matching workers from the declining (public) sector with private sector jobs with different skill requirements, or because the private sector initially expands output by increasing labor productivity rather than via hiring (Chadha, Coricelli, and Krajnyák, 1993). In this context, the pace and breadth of structural reforms in general and labor policies specifically play a key role in determining unemployment.

Aghion and Blanchard (1994) use this framework to describe an optimal speed of transition. Too rapid transition (restructuring) would be detrimental to employment. It would create an initial overly large increase in unemployment that would generate a heavy tax burden on firms to support the social safety net and thereby limit private sector job creation. In addition, too fast transition would be resisted by workers in the state sector, who would have to be bought off with generous unemployment benefits. If, however, transition proceeds too slowly, private sector job creation is insufficient as well, and the benefits of transition are not fully realized. More recently, this framework has been criticized for failing to fully capture the reality of labor market dynamics in transition countries. For example, Boeri (2001) argues that such models consider only labor demand and ignore developments in labor supply, notably the sharp decline in participation rates, which drive a wedge between the recovery in employment and the decline in unemployment.

A large empirical literature for industrial countries finds that rigid labor market institutions are detrimental to employment. A number of papers argue that lower structural unemployment in the United States and United Kingdom compared with continental Europe is due largely to more flexible labor market policies and institutions in these two countries (Blanchard and Wolfers, 2000; Nickell, 1998; IMF, 2003). Other papers argue that, while stringent institutions do not necessarily lead to higher unemployment, they may lead to other problems such as low job turnover. Comparing Portugal and the United States—two countries at the extremes of the spectrum of employment protection, but with similar unemployment rates—Blanchard and Portugal (2004) show that in Portugal flows into unemployment are three times lower than in the United States, but unemployment duration is three times higher.

But this view that rigid institutions are to blame for relatively poor employment performance in Europe is not unanimous. For example, Blanchard (2004) presents evidence that the increase in unemployment in continental Europe has not coincided with an increasing rigidity of institutions. Baker and others (2002) argue that the econometric results of a number of leading contributions purporting to show a positive relationship between rigidity of institutions and unemployment do not pass close scrutiny.

A Simple Analytical Framework

A simple neoclassical analysis shows that, for some period of time, a decline in employment is consistent with a rebound in output. Figure 3.1 depicts in panel (2) the starting point in transition economies (E_t) and the profit-maximizing equilibrium E^*, where marginal prices equalize and the labor market clears with wage w^* in panel (3).⁷ Upon the elimination of central planning, firms sought to move to E^*, eliminating excess labor and capital, while improving technology. After the initial severe shock to output and labor (shift of the isoquant downward), transition economies would have moved gradually closer to market equilibrium. During the move to the equilibrium E^*, output increases and employment declines. At the same time, technological and structural progress would gradually shift the isoquant out. This process would eventually reverse the initial drop in labor as gains from increased productivity run their course.

This suggests a C-shaped relationship between output and employment during transition. Countries can be expected to go through three stages during the transition: (1) an initial period of declining output and falling employment; (2) a period of rising productivity and growth, but falling employment; and (3) the “normal” period of rising output and employment. Only in this third stage, when the recovery of output is already well under way, does employment pick up. The length of the “anomalous” second phase can be considered a measure of success for transition, which would depend on how fast the economy implements efficiency-increasing structural reforms and absorbs technological changes. Labor market institutions and policies and macroeconomic shocks may also affect the speed of the process.

A growth accounting exercise for a number of CEE countries shows that labor’s contribution to growth remained negative even while these economies were rebounding. Figure 3.2 illustrates that the contribution of labor to growth turns positive only two to five years after the uptick in output following the shock of transition, well after capital and total factor productivity gains begin to be felt.

There is, in addition, some evidence of the predicted C-shaped dynamics of employment and output in transition countries (Figure 3.3). Coincident employment and output gains are a fairly recent phenomenon, although more successful reformers—in particular Hungary, Poland, and the Czech Republic—were able to increase employment earlier as well. However, in each of these successful cases, employment gains subsequently leveled off or reversed. This may reflect in part that, even in these countries, second rounds of restructuring or cyclical developments have led to employment losses, a phenomenon that may well be repeated in other less advanced transition economies.

Econometric Analysis: Determinants of Unemployment in Transition

The relationship, described above, between employment (e) and economic growth (q) can be estimated as can be estimated as:

\begin{array}{l} e_{i t} = c_{i} + β {\dot{q}}_{i t} + ϵ_{i t} \\ or \\ e_{i t} = c_{i} + \underset{τ}{Σ} α_{τ} T_{τ}^{'} {\dot{q}}_{i t} + ϵ_{i t,} \end{array}

where α_τ is a vector of slopes associated with each transition phase and T_τ is a vector of transition phases. The transition phases need to be defined such that they are not endogenous to growth or employment developments.

This simple model, however, needs to be expanded to account for the impact of labor market institutions and macroeconomic shocks. The consensus empirical specification has sought to explain unemployment with labor market institutions, controlling for macroeconomic determinants of unemployment, with a view to testing directly whether institutions have an impact on unemployment. However, the impact of some institutions or policies on unemployment may be either reduced or magnified by other institutions. This suggests that the simple specification should be augmented by interactions between certain institutions (see IMF, 2003). In addition, rigid institutions could prevent the economy from taking advantage of positive macroeconomic shocks to reduce unemployment. For example, Blanchard and Wolfers, (2000) point to this by showing that the institution-corrected unemployment rate trends upward in EU–15 countries. They suggest that this hypothesis can be tested by including the interactions of macroeconomic and institutional variables as explanatory variables. Finally, the transition process may also influence the effects of labor market policies and institutions. Indeed, institutions themselves were in transition: for example, high firing costs on the books would not have had a strong impact on unemployment if they were not binding due to lack of enforcement mechanisms. Therefore, the reduced-form model to estimate employment growth should ideally be:

e_{i t} = c_{i} + \underset{τ}{Σ} α_{τ} T_{τ} {\dot{q}}_{i t} + \underset{j + k + μ}{Σ} δ_{j + k + μ} Γ_{j + k + μ} + ξ_{i t},

where j is the number of macroeconomic control variables included, k is the number of labor market policies and institutions, μ is the number of interacted variables in the equation, and ξ_it is assumed to be a normally distributed, independent and identically distributed (i.i.d.) residual.

Data Issues and Econometric Results

Data were compiled from various national and international sources. Despite best efforts to ensure consistency across countries and over time in definitions of variables, it is likely that some consistency problems remain. Thus, results should be viewed with more than the usual caution.

Data limitations imply that the actual specifications employed here fall well short of the ideal. The shortness of the period observed (1993–2002) and the small number of countries (11) present limitations. The lack of a full set of observations for the years 1989–92 is a particular gap, because this covers the early years of transition in several countries. Analogous studies undertaken for OECD countries have typically utilized about 40 years of data and a larger set of variables, making the results significantly more robust. Owing to these data limitations, the paper focuses on a subset of potentially relevant labor market institutions and policies: the tax rate on labor (the tax wedge), minimum wage (as a share of per capita GDP), and there placement rate of unemployment benefits (the statutory replacement ratio). Other policy variables for which data were available—duration of unemployment benefits, early retirement age, and family benefits—were consistently nonsignificant and were subsequently dropped. The lack of significance may well reflect the lack of intracountry variation. Potentially relevant but unobserved variables include the role of unions, the extent of centralization of the wage bar-gaining system, the cost of active labor market policies, and limitations on flexible work contracts.⁸ Tominimize endogeneity problems, the transition phases were derived on the basis of the average value of EBRD indices of transition. These indices are based on institutional indicators and are not directly related to output or employment. (See Appendix I for a description of data sources and availability.)

Panel estimations were carried out, correcting for bias arising from the presence of the lagged dependent variable on the right-hand side. Because the lagged dependent variable is a significant regressor and the period of observation is short, the Arellano-Bond (A-B) estimator, which corrects for the attendant bias, was used. We regressed the employment rate on macroeconomic controls, labor market institutions, labor market institutions interacted with changes in the terms of trade, and transition stages interacted with output:

\begin{array}{l} e_{i t} & = & c_{i} + {λe}_{i, t - 1} + \underset{j}{Σ} φ_{j} X_{j, i, t} + \underset{k}{Σ} θ_{k} Y_{k, i, t} \\ + \underset{j}{Σ} \underset{k}{Σ} γ_{j, k} X_{j, i, t} Y_{k, i, t} + Σ_{τ}^{3} α_{τ} {\dot{q}}_{i, t} E B R D_{τ} + ξ_{i, t}, \end{array}

where e_it is the employment rate at time t in country i; c_i is a country-specific effect; X_{j, i, t} is a vector of macroeconomic variables other than output growth; Y_{k, i, t} is a vector of labor market institutions; q_it is output growth; EBRD_τ represents the transition stages; and ξ_{i, t} is an i.i.d. residual. Identical equations were estimated for unemployment rates, which abstracts from potential responses of participation.

The results suggest that employment and unemployment rates are persistent.⁹ Lagged unemployment has a large positive effect on unemployment (Table 3.1)—an increase of 1 percentage point in the lagged unemployment rate raises unemployment by almost 0.6 percentage point. Results for employment are similar, with a 1 percentage point rise in lagged employement generating a rise in employment of about 0.6 percentage point. As a robustness test, the model was estimated sequentially by dropping, in turn, each one of the countries from the sample. The results did not change markedly.

Table 3.1.

Panel Estimation (Arellano-Bond): Employment and Unemployment¹

Note: Robust z-statistics are shown in brackets;*significant at 10 percent; **significant at 5 percent; ***significant at 1 percent; TOT-terms of trade.

^¹The Arellano-Bond estimator corrects for the lagged dependent variable bias that occurs when the period of observation is short (usually up to 10 years) by using first differences as instruments.

Table 3.1.

Panel Estimation (Arellano-Bond): Employment and Unemployment¹

		1	2	3	4	5	6	7	8	9	10	11
		None	Bulgaria	Croatia	Czech Rep.	Estonia	Hungary	Latvia	Lithuania	Polanh	Slovak Rep.	Slovenia
		Employment
Lagged employment rate		0.615	0.708	0.625	0.617	0.596	0.574	0.66	0.617	0.559	0.595	0.618
		[8.68]***	[18.12]***	[9.41]***	[8.54]***	[9.58]***	[7.65]***	[9.01]***	[9.48]***	[6.78]***	[7.72]***	[8.61]***
Transition phase 1*lagged		–0.144	–0.254	–0.063	–0.145	–0.17	–0.138	–0.098	–0.213	–0.129	–0.015	–0.145
	change in real GDP	[0.81]	[1.45]	[0.26]	[0.83]	[0.88]	[0.61]	[0.53]	[1.16]	[0.67]	[0.12]	[0.77]
Transition phase 2*lagged		0.195	0.329	0.013	0.193	0.222	0.178	0.146	0.148	0.179	0.133	0.195
	change in real GDP	[1.79]*	[3.41]***	[1.74]*	[1.77]*	[2.05]**	[1.51]	[1.52]	[1.02]	[1.57]	[1.44]	[1.73]*
Transition phase 3*lagged		0.31	0.322	0.299	0.315	0.381	0.304	0.304	0.32	0.201	0.273	0.326
	change in real GDP	[3.55]***	[3.84]***	[3.58]***	[3.27]***	[4.01]***	[3.38]***	[2.99]***	[5.37]***	[2.58]***	[3.15]***	[3.72]***
Lagged change in TOT		1.065	1.108	0.917	1.109	0.958	1.158	0.921	0.873	0.957	1.143	1.077
		[6.04]***	[5.37]***	[5.01]***	[6.29]***	[5.58]***	[5.85]***	[5.29]***	[3.10]***	[4.82]***	[6.76]***	[6.00]***
Cumulative foreign direct investment		–0.01	0.035	–0.001	–0.022	–0.035	–0.01	–0.035	0.013	–0.042	–0.001	–0.007
(in percent of GDP)		[0.20]	[1.03]	[0.02]	[0.35]	[0.66]	[0.19]	[0.67]	[0.31]	[0.78]	[0.03]	[0.13]
Tax wedge		–0.104	–0.154	–0.102	–0.102	–0.112	–0.084	–0.058	–0.119	–0.124	–0.048	–0.101
(total taxes on labor)		[1.44]	[2.02]**	[1.31]	[1.44]	[1.59]	[0.87]	[0.81]	[1.50]	[1.77]*	[0.83]	[1.38]
Replacement ratio		–0.005	–0.005	0.005	–0.008	–0.003	–0.007	–0.008	–0.003	–0.001	–0.009	–0.005
		[0.68]	[0.25]	[0.32]	[1.08]	[0.40]	[0.81]	[1.09]	[0.27]	[0.10]	[1.18]	[0.72]
Minimum-to-average wage		0.063	0.073	0.084	0.063	0.053	0.063	0.019	0.105	0.054	0.067	0.061
		[1.55]	[1.95]*	[1.37]	[1.51]	[1.25]	[1.44]	[0.80]	[2.38]**	[1.22]	[1.74]*	[1.43]
TOT*tax wedge		–0.014	–0.014	–0.014	–0.014	–0.013	–0.015	–0.014	–0.007	–0.013	–0.014	–0.014
		[7.01]***	[8.25]***	[8.33]***	[7.97]***	[7.73]***	[5.36]***	[7.16]***	[2.89]***	[7.28]***	[8.38]***	[7.60]***
TOT*replacement ratio		–0.006	–0.006	–0.004	–0.006	–0.005	–0.006	–0.005	–0.004	–0.005	–0.006	–0.006
		[4.78]***	[3.64]***	[1.58]	[5.70]***	[4.77]***	[4.47]***	[4.25]***	[2.29]**	[4.67]***	[4.92]***	[4.80]***
TOT*minimum-to-average wage		0.001	0.001	0.003	0.001	0.002	0	0.004	–0.004	0.001	0.001	0.001
TOT*minimum-to-average wage		[0.38]	[0.38]	[1.01]	[0.40]	[0.60]	[0.14]	[2.56]**	[0.98]	[0.35]	[0.47]	[0.41]
Constant		–0.08	–0.147	–0.06	–0.062	–0.012	–0.127	–0.053	–0.221	0.13	–0.09	–0.102
		[0.47]	[0.99]	[0.38]	[0.33]	[0.08]	[0.63]	[0.30]	[1.69]*	[0.77]	[0.44]	[0.52]
Observations		87	79	79	79	79	79	80	79	79	79	79
Number of country identification		11	10	10	10	10	10	10	10	10	10	10
		Unemployment
Lagged unemployment rate		0.553	0.605	0.558	0.558	0.534	0.508	0.648	0.532	0.483	0.52	0.556
		[5.25]***	[5.91]***	[5.03]***	[5.23]***	[5.24]***	[4.99]***	[8.34]***	[4.02]***	[4.63]***	[4.54]***	[5.02]***
Transition phase 1*lagged		0.228	0.378	0.327	0.228	0.257	0.211	0.14	0.344	0.215	0.081	0.229
	change in real GDP	[1.16]	[1.92]*	[1.43]	[1.18]	[1.23]	[0.90]	[0.72]	[1.78]*	[1.00]	[0.59]	[1.11]
Transition phase 2*lagged		–0.179	–0.348	–0.17	–0.176	–0.013	–0.16	–0.14	–0.091	–0.166	–0.11	–0.179
	change in real GDP	[1.64]	[3.59]***	[1.48]	[1.62]	[1.89]*	[1.38]	[1.40]	[0.75]	[1.45]	[1.24]	[1.56]
Transition phase 3*lagged		–0.284	–0.294	–0.29	–0.287	–0.366	–0.28	–0.286	–0.265	–0.161	–0.244	–0.302
	change in real GDP	[2.89]***	[2.84]***	[2.90]***	[2.63]***	[3.56]***	[2.83]***	[2.92]***	[3.47]***	[2.16]**	[2.43]**	[3.02]***
Lagged change in TOT		–0.838	–0.752	–0.917	–0.881	–0.726	–0.919	–0.923	–0.399	–0.723	–0.91	–0.844
		[4.99]***	[3.22]***	[4.65]***	[5.26]***	[5.40]***	[6.04]***	[5.36]***	[3.31]***	[5.74]***	[5.25]***	[5.06]***
Cumulative foreign direct investment		0.037	–0.006	0.024	0.052	0.065	0.035	0.04	0.02	0.071	0.031	0.037
(in percent of GDP)		[0.92]	[0.18]	[0.58]	[1.02]	[1.68]*	[0.78]	[0.78]	[0.56]	[1.87]*	[0.72]	[0.75]
Tax wedge		0.107	0.177	0.139	0.105	0.117	0.086	0.055	0.128	0.136	0.049	0.104
(total taxes on labor)		[1.40]	[2.23]**	[1.54]	[1.39]	[1.56]	[0.90]	[0.77]	[1.47]	[1.76]*	[0.79]	[1.35]
Replacement ratio		0.012	0.05	0.017	0.015	0.01	0.015	0.01	0.014	0.007	0.015	0.012
		[1.79]*	[2.08]**	[1.64]	[2.39]**	[1.40]	[1.83]*	[1.35]	[2.42]**	[1.33]	[2.28]**	[1.81]*
Minimum-to-average wage		–0.044	–0.045	–0.037	–0.044	–0.034	–0.047	–0.02	–0.088	–0.036	–0.05	0..042
		[1.51]	[1.83]*	[1.14]	[1.46]	[1.14]	[1.39]	[0.84]	[2.70]***	[1.18]	[1.88]*	[1.33]
TOT*tax wedge		0.011	0.012	0.01	0.012	0.01	0.012	0.014	0.003	0.01	0.012	0.011
		[3.63]***	[3.74]***	[3.37]***	[3.80]***	[3.64]***	[3.70]***	[7.16]***	[1.10]	[3.99]***	[3.92]***	[3.70]***
TOT*replacement ratio		0.006	0.005	0.007	0.007	0.006	0.006	0.006	0.005	0.005	0.007	0.006
		[5.51]***	[2.98]***	[3.75]***	[6.14]***	[5.27]***	[4.57]***	[4.36]***	[4.74]***	[5.32]***	[5.43]***	[5.45]***
TOT*minimum-to-average wage		–0.002	–0.004	–0.001	–0.003	–0.003	–0.002	–0.004	0.001	0..003	–0.003	–0.002
TOT*minimum-to-average wage		[1.32]	[1.98]**	[0.24]	[1.31]	[1.76]*	[0.83]	[2.54]**	[0.31]	[1.58]	[1.49]	[1.32]
Constant		–0.018	0.075	0.045	–0.044	–0.092	0.042	0.024	0.115	–0.25	–0.032	–0.003
		[0.10]	[0.40]	[0.24]	[0.24]	[0.60]	[0.20]	[0.13]	[0.72]	[1.59]	[0.15]	[0.01]
Observations		88	80	80	80	80	80	80	80	80	80	80
Number of country identification		11	10	10	10	10	10	10	10	10	10	10

Note: Robust z-statistics are shown in brackets;*significant at 10 percent; **significant at 5 percent; ***significant at 1 percent; TOT-terms of trade.

^¹The Arellano-Bond estimator corrects for the lagged dependent variable bias that occurs when the period of observation is short (usually up to 10 years) by using first differences as instruments.

Table 3.1.

Panel Estimation (Arellano-Bond): Employment and Unemployment¹

		1	2	3	4	5	6	7	8	9	10	11
		None	Bulgaria	Croatia	Czech Rep.	Estonia	Hungary	Latvia	Lithuania	Polanh	Slovak Rep.	Slovenia
		Employment
Lagged employment rate		0.615	0.708	0.625	0.617	0.596	0.574	0.66	0.617	0.559	0.595	0.618
		[8.68]***	[18.12]***	[9.41]***	[8.54]***	[9.58]***	[7.65]***	[9.01]***	[9.48]***	[6.78]***	[7.72]***	[8.61]***
Transition phase 1*lagged		–0.144	–0.254	–0.063	–0.145	–0.17	–0.138	–0.098	–0.213	–0.129	–0.015	–0.145
	change in real GDP	[0.81]	[1.45]	[0.26]	[0.83]	[0.88]	[0.61]	[0.53]	[1.16]	[0.67]	[0.12]	[0.77]
Transition phase 2*lagged		0.195	0.329	0.013	0.193	0.222	0.178	0.146	0.148	0.179	0.133	0.195
	change in real GDP	[1.79]*	[3.41]***	[1.74]*	[1.77]*	[2.05]**	[1.51]	[1.52]	[1.02]	[1.57]	[1.44]	[1.73]*
Transition phase 3*lagged		0.31	0.322	0.299	0.315	0.381	0.304	0.304	0.32	0.201	0.273	0.326
	change in real GDP	[3.55]***	[3.84]***	[3.58]***	[3.27]***	[4.01]***	[3.38]***	[2.99]***	[5.37]***	[2.58]***	[3.15]***	[3.72]***
Lagged change in TOT		1.065	1.108	0.917	1.109	0.958	1.158	0.921	0.873	0.957	1.143	1.077
		[6.04]***	[5.37]***	[5.01]***	[6.29]***	[5.58]***	[5.85]***	[5.29]***	[3.10]***	[4.82]***	[6.76]***	[6.00]***
Cumulative foreign direct investment		–0.01	0.035	–0.001	–0.022	–0.035	–0.01	–0.035	0.013	–0.042	–0.001	–0.007
(in percent of GDP)		[0.20]	[1.03]	[0.02]	[0.35]	[0.66]	[0.19]	[0.67]	[0.31]	[0.78]	[0.03]	[0.13]
Tax wedge		–0.104	–0.154	–0.102	–0.102	–0.112	–0.084	–0.058	–0.119	–0.124	–0.048	–0.101
(total taxes on labor)		[1.44]	[2.02]**	[1.31]	[1.44]	[1.59]	[0.87]	[0.81]	[1.50]	[1.77]*	[0.83]	[1.38]
Replacement ratio		–0.005	–0.005	0.005	–0.008	–0.003	–0.007	–0.008	–0.003	–0.001	–0.009	–0.005
		[0.68]	[0.25]	[0.32]	[1.08]	[0.40]	[0.81]	[1.09]	[0.27]	[0.10]	[1.18]	[0.72]
Minimum-to-average wage		0.063	0.073	0.084	0.063	0.053	0.063	0.019	0.105	0.054	0.067	0.061
		[1.55]	[1.95]*	[1.37]	[1.51]	[1.25]	[1.44]	[0.80]	[2.38]**	[1.22]	[1.74]*	[1.43]
TOT*tax wedge		–0.014	–0.014	–0.014	–0.014	–0.013	–0.015	–0.014	–0.007	–0.013	–0.014	–0.014
		[7.01]***	[8.25]***	[8.33]***	[7.97]***	[7.73]***	[5.36]***	[7.16]***	[2.89]***	[7.28]***	[8.38]***	[7.60]***
TOT*replacement ratio		–0.006	–0.006	–0.004	–0.006	–0.005	–0.006	–0.005	–0.004	–0.005	–0.006	–0.006
		[4.78]***	[3.64]***	[1.58]	[5.70]***	[4.77]***	[4.47]***	[4.25]***	[2.29]**	[4.67]***	[4.92]***	[4.80]***
TOT*minimum-to-average wage		0.001	0.001	0.003	0.001	0.002	0	0.004	–0.004	0.001	0.001	0.001
TOT*minimum-to-average wage		[0.38]	[0.38]	[1.01]	[0.40]	[0.60]	[0.14]	[2.56]**	[0.98]	[0.35]	[0.47]	[0.41]
Constant		–0.08	–0.147	–0.06	–0.062	–0.012	–0.127	–0.053	–0.221	0.13	–0.09	–0.102
		[0.47]	[0.99]	[0.38]	[0.33]	[0.08]	[0.63]	[0.30]	[1.69]*	[0.77]	[0.44]	[0.52]
Observations		87	79	79	79	79	79	80	79	79	79	79
Number of country identification		11	10	10	10	10	10	10	10	10	10	10
		Unemployment
Lagged unemployment rate		0.553	0.605	0.558	0.558	0.534	0.508	0.648	0.532	0.483	0.52	0.556
		[5.25]***	[5.91]***	[5.03]***	[5.23]***	[5.24]***	[4.99]***	[8.34]***	[4.02]***	[4.63]***	[4.54]***	[5.02]***
Transition phase 1*lagged		0.228	0.378	0.327	0.228	0.257	0.211	0.14	0.344	0.215	0.081	0.229
	change in real GDP	[1.16]	[1.92]*	[1.43]	[1.18]	[1.23]	[0.90]	[0.72]	[1.78]*	[1.00]	[0.59]	[1.11]
Transition phase 2*lagged		–0.179	–0.348	–0.17	–0.176	–0.013	–0.16	–0.14	–0.091	–0.166	–0.11	–0.179
	change in real GDP	[1.64]	[3.59]***	[1.48]	[1.62]	[1.89]*	[1.38]	[1.40]	[0.75]	[1.45]	[1.24]	[1.56]
Transition phase 3*lagged		–0.284	–0.294	–0.29	–0.287	–0.366	–0.28	–0.286	–0.265	–0.161	–0.244	–0.302
	change in real GDP	[2.89]***	[2.84]***	[2.90]***	[2.63]***	[3.56]***	[2.83]***	[2.92]***	[3.47]***	[2.16]**	[2.43]**	[3.02]***
Lagged change in TOT		–0.838	–0.752	–0.917	–0.881	–0.726	–0.919	–0.923	–0.399	–0.723	–0.91	–0.844
		[4.99]***	[3.22]***	[4.65]***	[5.26]***	[5.40]***	[6.04]***	[5.36]***	[3.31]***	[5.74]***	[5.25]***	[5.06]***
Cumulative foreign direct investment		0.037	–0.006	0.024	0.052	0.065	0.035	0.04	0.02	0.071	0.031	0.037
(in percent of GDP)		[0.92]	[0.18]	[0.58]	[1.02]	[1.68]*	[0.78]	[0.78]	[0.56]	[1.87]*	[0.72]	[0.75]
Tax wedge		0.107	0.177	0.139	0.105	0.117	0.086	0.055	0.128	0.136	0.049	0.104
(total taxes on labor)		[1.40]	[2.23]**	[1.54]	[1.39]	[1.56]	[0.90]	[0.77]	[1.47]	[1.76]*	[0.79]	[1.35]
Replacement ratio		0.012	0.05	0.017	0.015	0.01	0.015	0.01	0.014	0.007	0.015	0.012
		[1.79]*	[2.08]**	[1.64]	[2.39]**	[1.40]	[1.83]*	[1.35]	[2.42]**	[1.33]	[2.28]**	[1.81]*
Minimum-to-average wage		–0.044	–0.045	–0.037	–0.044	–0.034	–0.047	–0.02	–0.088	–0.036	–0.05	0..042
		[1.51]	[1.83]*	[1.14]	[1.46]	[1.14]	[1.39]	[0.84]	[2.70]***	[1.18]	[1.88]*	[1.33]
TOT*tax wedge		0.011	0.012	0.01	0.012	0.01	0.012	0.014	0.003	0.01	0.012	0.011
		[3.63]***	[3.74]***	[3.37]***	[3.80]***	[3.64]***	[3.70]***	[7.16]***	[1.10]	[3.99]***	[3.92]***	[3.70]***
TOT*replacement ratio		0.006	0.005	0.007	0.007	0.006	0.006	0.006	0.005	0.005	0.007	0.006
		[5.51]***	[2.98]***	[3.75]***	[6.14]***	[5.27]***	[4.57]***	[4.36]***	[4.74]***	[5.32]***	[5.43]***	[5.45]***
TOT*minimum-to-average wage		–0.002	–0.004	–0.001	–0.003	–0.003	–0.002	–0.004	0.001	0..003	–0.003	–0.002
TOT*minimum-to-average wage		[1.32]	[1.98]**	[0.24]	[1.31]	[1.76]*	[0.83]	[2.54]**	[0.31]	[1.58]	[1.49]	[1.32]
Constant		–0.018	0.075	0.045	–0.044	–0.092	0.042	0.024	0.115	–0.25	–0.032	–0.003
		[0.10]	[0.40]	[0.24]	[0.24]	[0.60]	[0.20]	[0.13]	[0.72]	[1.59]	[0.15]	[0.01]
Observations		88	80	80	80	80	80	80	80	80	80	80
Number of country identification		11	10	10	10	10	10	10	10	10	10	10

Note: Robust z-statistics are shown in brackets;*significant at 10 percent; **significant at 5 percent; ***significant at 1 percent; TOT-terms of trade.

^¹The Arellano-Bond estimator corrects for the lagged dependent variable bias that occurs when the period of observation is short (usually up to 10 years) by using first differences as instruments.

The impact of growth on labor market performance depends, as expected, on a country’s stage of transition. The impact of growth on employment in early transition is not significant and point estimates are negative. But, as predicted, as the transition advances employment growth becomes more tightly linked to output growth.¹⁰ For example, for a country in the most advanced transition stage, a rise in growth by 1 percentage point increases the employment rate by about 0.3 percentage point. This highlights the potential gains to countries completing the transition process quickly. Results for unemployment are similar: growth does not reduce unemployment in the initial phase of transition, but does so increasingly in later stages.

Labor market policies and institutions, while not a dominant influence on employment and unemployment, do appear to play a role in allowing economies to benefit from a positive terms of trade shock. The key labor policy variables are not significant on their own, except for the replacement ratio, which has the expected positive sign in the unemployment equation. However, when we interact the terms of trade—a proxy for an exogenous shock—and labor market policies, a significant impact is observed.¹¹ A higher labor tax wedge and higher replacement ratio reduce gains from positive shocks to the terms of trade, with the coefficients in both the employment and unemployment equations significant. The impact of institutions is quantitatively significant. For example, while an improvement in terms of trade of 1 percentage point would have a direct positive impact on employment of 1.07 percentage points, three-quarters of this gain is lost when the minimum wage ratio, replacement ratio, and tax wedge are at their mean levels, and a correspondingly higher fraction of the gains is lost when these variables are less favorable (Table 3.2).¹²

Table 3.2.

Impact of Terms of Trade (TOT) Shocks on the Employment Rate in the Presence of Labor Market Institutions (LMI)

Source: Author’s calculations.

^¹Abstracting from the effect of this (interacted) variable because its coefficient is not significantly different from zero.

Table 3.2.

Impact of Terms of Trade (TOT) Shocks on the Employment Rate in the Presence of Labor Market Institutions (LMI)

		Mean Value of Variable	Coefficient of Variable	Effect of Variable	Combined Effect of TOT and Individual Interaction	Combined Effect of TOT and All Interactions	“Cost” of LMI	“Cost” of LMI in Percent of TOT Effect
When the labor market variables are at their mean						0.197	0.868	81.5
	percentage change in TOT	0.734	1.065	1.065
	TOT*tax wedge	40.7	–0.014	–0.570	0.495
	TOT*replacement ratio¹	18.6	0.001	0.019	1.084
	TOT*minimum wage ratio	49.6	–0.006	–0.297	0.768
Assuming 10 percent increase to average of each labor market variable						0.111	0.954	89.6
	TOT*tax wedge	44.8		–0.627	0.438
	TOT*replacement ratio ¹	20.5		0.020	1.085
	TOT*minimum wage ratio	54.5		–0.327	0.738

Source: Author’s calculations.

^¹Abstracting from the effect of this (interacted) variable because its coefficient is not significantly different from zero.

Table 3.2.

Impact of Terms of Trade (TOT) Shocks on the Employment Rate in the Presence of Labor Market Institutions (LMI)

		Mean Value of Variable	Coefficient of Variable	Effect of Variable	Combined Effect of TOT and Individual Interaction	Combined Effect of TOT and All Interactions	“Cost” of LMI	“Cost” of LMI in Percent of TOT Effect
When the labor market variables are at their mean						0.197	0.868	81.5
	percentage change in TOT	0.734	1.065	1.065
	TOT*tax wedge	40.7	–0.014	–0.570	0.495
	TOT*replacement ratio¹	18.6	0.001	0.019	1.084
	TOT*minimum wage ratio	49.6	–0.006	–0.297	0.768
Assuming 10 percent increase to average of each labor market variable						0.111	0.954	89.6
	TOT*tax wedge	44.8		–0.627	0.438
	TOT*replacement ratio ¹	20.5		0.020	1.085
	TOT*minimum wage ratio	54.5		–0.327	0.738

Source: Author’s calculations.

^¹Abstracting from the effect of this (interacted) variable because its coefficient is not significantly different from zero.

These results are broadly similar to those from studies of industrial countries. As in this paper, IMF (2003) finds that unemployment is persistent, with a similar coefficient. However, while that paper also concludes that a positive terms of trade shock would reduce unemployment, the estimated effect is much smaller than in this paper. As for interacting macroeconomic shocks with labor market institutions, Blanchard and Wolfers, (2000) find that labor market institutions or policies can have a significant mitigating effect on the impact of positive macroeconomic shocks on unemployment. The replacement ratio tends to increase unemployment despite positive macroeconomic shocks, as does the tax wedge. The literature, in general, does not focus on estimating determinants of the employment rate, or on the impact of the transition on employment and unemployment.

In contrast to the EU–15 countries, there is no evidence that residual unemployment rates are trending upward over time. Unlike in Western Europe—where residual unemployment appears to trend upward, perhaps reflecting relatively rigid labor market policies and institutions (IMF, 2003)—both the employment and unemployment residuals appear stationary in the transition economies (Figure 3.4). As noted above-compared with the EU–15 countries, labor market policies and institutions are reasonably flexible and perhaps also moving more toward market orientation over time (Riboud, Sánchez-Páramo, and Silva, Jáuregui, 2002).

Country Case Studies: What Works Best?

The analysis to date provides some general guidance for policymakers in transition countries looking to improve labor market performance over the medium term. In particular, results suggest that completing transition as quickly as possible allows employment to begin rising more rapidly, and that avoiding high minimum wages and taxes on labor can allow labor performance to benefit from positive economic shocks.

In the absence of a far richer data base, more detailed policy recommendations would require a closer qualitative analysis of the experience of transition economies. Here we seek to take a detailed look at two transition countries that have had relatively strong labor market performances—Hungary and the Czech Republic—and several with weaker outcomes—Bulgaria, Croatia, and the Slovak Republic—with an eye toward explaining these differences. We look, then, at selected policies of these countries in some detail to see whether light can be shed on their disparate labor market performances.

Structural Reforms for Employment Generation

The three underperforming countries can be characterized as relatively slow reformers. This can be seen, for example, in various transition indicators and the private sector’s share of value added in total GDP and its employment share in total employment. All of these slow reformers have suffered from a stubbornly high unemployment rate. In addition, the share of long-term unemployment in total unemployment has been significantly higher (hovering around 60 percent in recent years) than in the rapid reformers (where it is around 45 percent).

Poor performers have no systematic disadvantage as regards wage levels or the process of wage determination. Although labor unions take a part in the collective bargaining in these countries, and the wage setting system is fairly centralized, there is no evidence that they are outliers in this regard. Moreover, there is not consistent evidence that real wage dynamics are out of line with productivity growth in Bulgaria and Slovak Republic. Average wages in Croatia are higher than most CEE counties and this does not appear to be justified by proportionately higher value added per employees (Figure 3.5). However, unit labor cost dynamics indicate that Croatia has more recently contained the labor cost increases compared with other CEE countries, partly reflecting its efforts to restrain public sector wages.¹³

Transfers and benefits also do not appear to play a major role in explaining labor market performance overall, but they can be important in particular cases. Unemployment insurance payments are generally low and do not seem to discourage job search. However, social assistance in some high-unemployment countries may blunt job search incentives, especially for workers with large families. In the Slovak Republic, income assistance to reach a “minimum subsistence income” for all households other than single individuals exceeds the net average wage for families with two or more children.

Strict employment protection legislation may also be a factor. Croatia scores poorly in this regard, with an EPL that is high among CEE countries. Individual dismissals are costly because of a long advance notice period and high severance pay, while collective dismissals are even more difficult. However, the EPL in the Slovak Republic is significantly lower (indicating labor market flexibility) and is not seen as a major contributor to unemployment.

One potentially important explanation for differences in labor market performance lies in the varying abilities of the nascent private sectors to create jobs. It is widely recognized that new small and medium-sized enterprises or foreign-owned firms, which entered after the collapse of planned economy regime, have been the engine of job creation in the CEE countries (see, for example, Jurajda and Terrell, 2002; and Rutkowski, 2003). But in high-unemployment countries, foreign direct investment inflows appear weaker, and small and medium-sized enterprises are less dynamic. In Bulgaria and Croatia, small and medium-sized enterprises account for only 41 percent and 46 percent of employment, respectively, in contrast to leading reformers where the share of employment in small and medium-sized enterprises is well over 50 percent.

Legal barriers to firm entry and job creation do not appear to be the main issue here, but implementation of laws and regulations may be. According to the “Doing business indicators, “compiled by the World Bank on the basis of relevant laws and regulations, institutional barriers to business entry or impediments in business operations in the three high-unemployment countries do not compare poorly with barriers in other transition countries. However, there is some evidence that implementation of these regulations may be a problem. For instance, the time required for firm entry is high in the Slovak Republic, while time required to enforce a contract is high in the Slovak Republic and Bulgaria (Table 3.3).

Table 3.3.

Selected Business Environment Indicators

Sources: World Bank (2004); and World Economic Forum (2004).

^¹On a scale of 1 to 100.

^²Higher indicators imply less flexibility.

^³Hours of work-leaves-and minimum wage.

^⁴Ranking among 80 countries.

Table 3.3.

Selected Business Environment Indicators

			Slow Reformers			Fast Reformers
			Bulgaria	Croatia	Slovak Rep.	Czech Rep.	Hungary
World Bank (2004), Doing Business in 2004
	Starting a business
		Number of procedures	10	13	10	10	5
		Time (days) to start a business	30	50	98	88	65
		Monetary cost (percent of income per capita)	8.3	18.2	10.2	11.7	64.3
		Minimum capital (percent of income per capita)	134.4	50.7	111.8	110.0	220.3
	Enforcing a contract
		Number of procedures	26	20	26	16	17
		Time (days) to enforce a contract	410	330	420	270	365
		Monetary cost (percent of income per capita)	6.4	6.6	13.3	18.5	5.4
		Procedural complexity index¹	69	50	40	65	57
	Rigidities in employment regulations²
		Flexibility of hiring	43	76	34	17	46
		Flexibility of employment³	90	89	89	63	92
		Flexibility of firing	26	31	60	27	23
		Overall flexibility in employment regulations	53	65	61	36	54
	World Economic Forum (2004), The Global Competitiveness Report, 2002–2003
	Quality of the national business environment ranking⁴		63	54	40	29	34

Sources: World Bank (2004); and World Economic Forum (2004).

^¹On a scale of 1 to 100.

^²Higher indicators imply less flexibility.

^³Hours of work-leaves-and minimum wage.

^⁴Ranking among 80 countries.

Table 3.3.

Selected Business Environment Indicators

			Slow Reformers			Fast Reformers
			Bulgaria	Croatia	Slovak Rep.	Czech Rep.	Hungary
World Bank (2004), Doing Business in 2004
	Starting a business
		Number of procedures	10	13	10	10	5
		Time (days) to start a business	30	50	98	88	65
		Monetary cost (percent of income per capita)	8.3	18.2	10.2	11.7	64.3
		Minimum capital (percent of income per capita)	134.4	50.7	111.8	110.0	220.3
	Enforcing a contract
		Number of procedures	26	20	26	16	17
		Time (days) to enforce a contract	410	330	420	270	365
		Monetary cost (percent of income per capita)	6.4	6.6	13.3	18.5	5.4
		Procedural complexity index¹	69	50	40	65	57
	Rigidities in employment regulations²
		Flexibility of hiring	43	76	34	17	46
		Flexibility of employment³	90	89	89	63	92
		Flexibility of firing	26	31	60	27	23
		Overall flexibility in employment regulations	53	65	61	36	54
	World Economic Forum (2004), The Global Competitiveness Report, 2002–2003
	Quality of the national business environment ranking⁴		63	54	40	29	34

Sources: World Bank (2004); and World Economic Forum (2004).

^¹On a scale of 1 to 100.

^²Higher indicators imply less flexibility.

^³Hours of work-leaves-and minimum wage.

^⁴Ranking among 80 countries.

Business surveys also help shed light on stagnant private sector job creation in the high-unemployment outliers. According to the World Economic Forum’s Quality of the National Business Environment, which ranks countries on the basis of survey scores on various factors affecting the business environment, Bulgaria, Croatia, and the Slovak Republic rank significantly behind Hungary and the Czech Republic (see Table 3.3). Croatia’s lowest ratings are for cooperation in labor-employer relations, while the Slovak Republic fails in transport infrastructure quality, and Bulgaria scores poorly in administrative burdens for startups. Corruption and inefficient bureaucracy are also among the top three problems both in Croatia and the Slovak Republic. Thus, weak law enforcement owing to corruption and red tape, combined with strict employment protection (Croatia) or low quality of infrastructure (Slovak Republic), could have played an important role in explaining the slow pace of job creation by private sector new businesses in the slow reformers.

In recent years, the poor performers started addressing these issues, which could help turn around their unfavorable labor market performance. At the beginning of 2004, Croatia amended the labor law to substantially relax employment protection (see Appendix II), while the Slovak Republic reformed the social assistance system to encourage efforts to seek work (Appendix II). These measures could enhance employment growth in these countries over the medium term. Furthermore, all three underperforming countries recently began focusing more on active labor market policies (ALMPs). Although impact studies of ALMPs in Hungary seem to indicate little positive effect (Appendix II), empirical studies in OECD countries suggest that there is a correspondence between training and employment and labor force participation both at the aggregate and individual levels (OECD, 2003a and 2004a). Such studies also provide evidence that crowding-out effects, in which individuals who receive training might partially displace those who do not, are not large within each specific labor market group. These results could indicate that properly designed ALMPs can improve the labor market position of specific targeted groups in CEE countries who are hit particularly hard by the transition shock, and can enhance economy-wide employment.

Limiting the Size of the Informal Economy

Less flexible labor market policies may also influence formal sector employment by providing incentives for firms to operate in the gray economy. A large shadow economy could have a number of unwanted economic implications. First, it leads to lower tax compliance, which will hinder fiscal consolidation. Second, an expansion of the shadow economy could lower the productivity of the entire economy.¹⁴ Firms in the unofficial sector tend to be small because they try to stay away from the authorities’ attention. Their small scale limits their ability to make the most of new technology and business practices, which can drag down the overall productivity of the economy.

A number of studies find that the gray economy is substantially larger in transition countries than in industrial countries. Schneider (2002a) finds that in CEE countries the shadow economy accounted for between 18 and 36 percent of GDP and that this share has increased over time. Among these countries, Bulgaria and Croatia were the highest, above 30 percent, and the Czech Republic and Slovak Republic were the lowest, at roughly 18 percent.¹⁵

Stringent employment protection leads to increased labor costs in the official economy and provides an incentive for firms to operate in the informal sector. Since labor costs can be shifted largely onto employees, it could also provide workers with an incentive to work in the shadow economy. Figure 3.6 plots the size of the shadow economy in percent of GDP and the EPL in 21 industrial countries and 7 CEE countries.¹⁶ It suggests that the stricter the employment protection is, the bigger the shadow economy tends to be.

Other factors also affect the size of the shadow economy. Almost all studies point out that the tax and social security burden is a key factor.¹⁷ Business regulations also affect the size of the shadow economy. Finally, it is widely recognized that the higher are the quality of infrastructure and the effectiveness of public services, the greater are the incentives to work in the official sector (see Johnson-Kaufmann, and Zoido-Lobaton, 1998).

A simple econometric analysis suggests that employment protection may help explain the size of the shadow economy. Table 3.4 reports the results of ordinary least squares (OLS) regression of the estimated size of the shadow economy on the log of one year lagged per capita GDP, the EPL index, the tax wedge on labor income, and the business regulation index.¹⁸ This result should be treated with caution due to the small sample size and inherent uncertainties regarding estimates of the size of the shadow economy. However, it is of interest that the EPL coefficient is positive and highly significant, suggesting that less employment protection is correlated with a smaller shadow economy. Neither the tax wedge on labor income nor the business regulation index is significant.

Table 3.4.

OLS Estimation on the Impact of EPL on Shadow Economy

(Dependent variable: shadow economy as a percent of GDP)

***Significant at 1 percent level.

Table 3.4.

OLS Estimation on the Impact of EPL on Shadow Economy

(Dependent variable: shadow economy as a percent of GDP)

Regressor	Coefficient	Standard Error	T-Ratio	[Prob]
Constant	7.994	14.750	0.542	[0.593]
Log GDP per capita, one-year lagged	–1.260	1.150	–1.096	[0.284]
EPL***	4.529	1.442	3.140	[0.005]
Tax wedge on labor income	0.106	0.099	1.074	[0.294]
Business regulations	0.962	1.344	0.716	[0.481]
R-bar-squared =0.356
Number of observations =28

***Significant at 1 percent level.

Table 3.4.

OLS Estimation on the Impact of EPL on Shadow Economy

(Dependent variable: shadow economy as a percent of GDP)

Regressor	Coefficient	Standard Error	T-Ratio	[Prob]
Constant	7.994	14.750	0.542	[0.593]
Log GDP per capita, one-year lagged	–1.260	1.150	–1.096	[0.284]
EPL***	4.529	1.442	3.140	[0.005]
Tax wedge on labor income	0.106	0.099	1.074	[0.294]
Business regulations	0.962	1.344	0.716	[0.481]
R-bar-squared =0.356
Number of observations =28

***Significant at 1 percent level.

The above results suggest that CEE countries could enhance employment in the official sector and expand the tax base by relaxing employment protection. Because EPLs in all CEE countries are significantly higher than those in very flexible labor markets such as the United States and the United Kingdom, there is ample room for these countries to make their market more flexible over the medium term. Such reforms could even improve the productivity of the entire economy as mentioned above, as well as enhance tax compliance.

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Abstract

A Brief Literature Review

A Simple Analytical Framework

Econometric Analysis: Determinants of Unemployment in Transition

Data Issues and Econometric Results

Country Case Studies: What Works Best?

Structural Reforms for Employment Generation

Limiting the Size of the Informal Economy

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