Adverse shocks and asymmetric adjustments

Table 2 Unit root tests of relative regional unemployment rates: P-values Region WS ADF Z Region WS ADF Z Piemonte 0.088 0.207 0.340 Lazio 0.702 0.451 0.757 Lombardia 0.208 0.887 0.599 Abruzzo 0.949 0.147 0.396 Trentino 0.489 0.668 0.385 Molise 0.257 0.224 0.299 Veneto 0.303 0.330 0.620 Campania 0.174 0.086 0.332 Friuli 0.817 0.193 0.340 Puglia 0.853 0.464 0.598 Liguria 0.203 0.380 0.153 Basilicata 0.090 0.238 0.125 Emilia 0.283 0.625 0.711 Calabria 0.049 0.156 0.143 Toscana 0.458 0.280 0.190 Sicilia 0.184 0.768 0.893 Umbria 0.434 0.351 0.184 Sardegna 0.789 0.903 0.602 Marche 0.163 0.185 0.403 Relative unemployment rates are defined as u r u , where u is the unemployment rate of region i i ITA i Ž and u is the national unemployment rate. WS is the Weighted Symmetric test see Pantula et al., ITA . Ž . 1994 ; ADF is the augmented Dickey–Fuller test Said and Dickey, 1984 ; Z is the Phillips–Perron test Ž . Phillips and Perron, 1988 . All the tests include the constant and a drift: the number of lags is selected by using the AIC criterion. of the data is that not only unemployment differentials exist, but they also diverge in a nonstationary way. In other words, there are no signs of a reversion of the observed diverging tendency towards a common equilibrium. This conclusion is formally supported by the results of tests for unit roots in regional relative unemployment rates. In no instance it is possible to reject the null hypothesis that u ru has a unit root, when u is the regional unemployment rate and u is the i ITA i ITA Ž . nationwide unemployment rate see Table 2 . Interestingly, this outcome is Ž . squarely different from the results obtained by Eichengreen 1993 , who finds instead no evidence against stationarity. This difference can be easily explained with the choice of the sample period. Eichengreen’s data stop in the mid 1980s, before the real action starts.

3. Adverse shocks and asymmetric adjustments

Widening differences in regional unemployment rates can be explained either by the fact that regional shocks are more important than common aggregate shocks or by the fact that regional employment and unemployment respond asymmetri- cally to common aggregate shocks. Clearly, these are not mutually exclusive hypotheses, but we try to assess empirically their relative importance. We start by investigating employment data using the regional accounts. Follow- Ž . Ž . ing Blanchard and Katz 1992 and Jimeno and Bentolila 1998 , we estimate the following empirical model for each region 1 Dln N s a q b Dln N q ´ 1 Ž . Ž . Ž . Ý i t i i j tyj i t i js0 Table 3 Responsiveness of regional employment to aggregated shocks a 2 Region R AR Norm b t H bs0 bs1 P-values P-values P-values P-values Piemonte 0.496 0.867 0.284 1.097 0.000 0.580 Lombardia 0.516 0.603 0.910 0.920 0.000 0.639 Trentino A. Adige 0.392 0.710 0.809 1.484 0.000 0.177 Veneto 0.380 0.058 0.121 0.876 0.000 0.563 Friuli V. Giulia 0.549 0.508 0.319 1.452 0.000 0.080 Liguria 0.171 0.403 0.035 0.683 0.023 0.275 Emilia Romagna 0.550 0.900 0.042 1.025 0.000 0.889 b Toscana 0.440 0.008 0.000 1.149 0.000 0.559 Umbria 0.475 0.118 0.988 1.319 0.000 0.234 Marche 0.464 0.606 0.248 1.426 0.000 0.153 Lazio 0.152 0.100 0.490 0.551 0.033 0.080 Abruzzo 0.339 0.560 0.708 1.024 0.001 0.930 Molise 0.065 0.137 0.512 0.901 0.175 – Campania 0.166 0.315 0.476 0.678 0.025 0.271 Puglia 0.285 0.083 0.036 1.084 0.002 0.798 Basilicata 0.045 0.817 0.299 0.773 0.262 – Calabria 0.014 0.007 0.072 0.307 0.535 – Sicilia 0.080 0.216 0.708 0.396 0.142 – Sardegna 0.280 0.188 0.375 1.085 0.003 0.799 a Ž . The table reports the results from the estimation of Eq. 1 . AR is the test for residual autocorrelation up to the second order. Norm is the normality test for residuals. b is the estimated coefficient. t is the marginal significance of the parameter. H is the test for b s1; this test is bs0 bs1 carried out only for significant estimated parameters. b Toscana is the only region for which N is significant and has been added to the regression. b i t y 1 Ž and t in this case, refer to the sum of the coefficients on N and N t in this case, is the bs0 i t i ty1 bs0 . P-value of the joint F-test . where i is for region, t is for time, N is regional employment in the private i t sector, measured by standard labor units, and N is aggregate employment in the i t private sector, region i excluded. 6 The R 2 of these regressions is an indicator of how much of the variation over time in regional employment is accounted for by Ž . variations in aggregate employment. The estimates of Eq. 1 are presented in Table 3 for the period 1965–1994. The table shows that the R 2 is much lower in the South than in the rest of the country. This result suggests that idiosyncratic shocks are relatively more important in southern regions. The presence of persis- tent and widening unemployment differentials also points to the possibility that common trends among regional unemployment rates exist. Given p 2 variables, the existence of one common trend implies that there are p y 1 cointegrating Ž . Ž . vectors. Using Johansen’s approach see Johansen, 1995 , we find see Table 4 6 We exclude region i from the aggregate in order to avoid bias in the estimates of the b ’s. Table 4 Ž . Ž . Cointegration analysis of log- regional unemployment rates P-values Geographical area p r s 0 r F1 r F 2 r F 3 r F 4 North–West 3 0.010 0.212 0.803 North–East 3 0.284 0.463 0.658 Center 5 0.125 0.242 0.360 0.579 0.968 South–West 5 0.211 0.798 0.867 0.959 0.907 South–East 3 0.145 0.247 0.689 p is the number of regions in the area; r is the cointegration rank. The P-values are from MacKinnon Ž . et al. 1996 . that in none of the geographical areas is it possible to find only one common trend. The view that there are clusters of regions where unemployment rates follow a common long run path might be too simple. At the same time, the idea that idiosyncratic shocks are important seems reasonable. Next, we describe in a parsimonious way the long run evolution of regional Ž . unemployment rates u by associating them to the movements of variables i t affecting both the supply and the demand for labor. We capture labor demand Ž . variations with the changes over time in the tax wedge t and in the real price of i t Ž . imported energy and materials PM . In the presence of real wage resistance, an t increase in the tax wedge leads to an increase of labor costs and to a reduction of labor demand. Changes in the price of raw materials also affect production costs and shift labor demand. Further, they may also affect local unemployment through changes in competitiveness, depending on local industrial structure. Labor supply Ž . variations are captured instead by government social transfers per head z . The i t Ž . bulk of these transfers consists of pension payments 79.6 of the total in 1991 , because the share of unemployment related benefits is very low by international Ž . standards see Peracchi and Rossi, 1995 . As remarked in the literature, one potential reason for the persistently high unemployment rate in the South is the presence of income transfers within southern households, from the employed and Ž the retired to the young unemployed see Attanasio and Padoa Schioppa, 1991; . Bentolila and Ichino, 1998; Faini et al., 1996 . These transfers finance wait Ž unemployment and maintain high reservation wages in the South see Brunello, . 1992; Mazzotta, 1998 . We do not have a direct measure of intra-household transfers, but we expect the size of these transfers to be influenced by government social transfers per head. 7 Social transfers affect labor supply because they have a significant role in shaping regional migration flows and individual participation decisions in the labor market. Notice that real social transfers per head could also affect regional labor demand when the production of goods and services is not 7 Notice that the relationship between social transfers per head and unemployment is not a causal relationship, since higher unemployment can induce higher transfers. symmetrically distributed across regions. In this case, higher social transfers increase household income and expenditure in all regions, but affect mainly the Ž . derived demand for labor in the regions where production is concentrated. We carry out the analysis using the standard non-stationary VAR framework, as Ž . described by Johansen 1995 . For each region i we estimate the following VAR X s m q P X q . . . qP X q ´ i s 1, . . . ,19 2 Ž . Ž . i t i i ,1 i , ty1 i , k i , tyk i , t Ž . Ž . Ž . Ž .4 where X ln u , ln t , ln z , ln PM . Given that the sample size is small i t i t i t i t t Ž . 1964–1994 , we are forced to use a very parsimonious representation. Notice, however, that cointegrating vectors are invariant to variable addition. Moreover, as Ž . shown by Abadir et al. 1999 , finite sample estimator biases in a purely nonstationary VARs are proportional to the dimension of the system and the addition of irrelevant variables has more serious consequences than in the standard Ž . stationary case. Lag length selection in Eq. 2 is accomplished for each region by initially setting k s 4 and by sequential simplification, checking at each stage the standard system diagnostics. 8 Using a 5 confidence level, we find at least one cointegrating vector for most of the regions, the only exception being Molise, a small region. We summarize our results on cointegrating vectors in Table 5. 9 Perhaps the strongest result is that real social transfers per head are positively associated to the rate of unemployment in most of the South and negatively associated in the North–Center. The positive association found in the South 10 induces us to think that, by increasing household income and the opportunities for intra-household transfers, the main effect of higher social transfers per head in these areas of the country has been both to reduce the incentive to migrate of the young unemployed and to increase their reservation wages, thus encouraging wait unemployment and queueing for public sector jobs. In the North–Center, instead, the prevailing factor behind the increase in social transfers per head has been employment substitution of the retiring old with young labor market entrants, especially in the manufactur- ing industry during the second part of the 1980s, a process described as Ayoung in, Ž . old outB by Contini and Rapiti 1994 . Compared to the South, labor force outflows due to retirement in the North–Center have been accompanied by employment inflows rather than by wait unemployment. We also speculate that real social transfers per head have affected regional labor demand in an asymmet- 8 The main diagnostics and the tests for cointegration rank in the selected models are available from the authors upon request. 9 When more than one cointegrating vector is present for a specific region, the table reports only the first vector. The other cointegrating vectors and the loading factors are available from the authors upon request. 10 Association, not causation. Table 5 Regional cointegrating vectors Region t z PM P-restr Piemonte y8.820 1.409 y0.918 0.283 Lombardia y8.400 1.498 y1.001 0.582 Trentino A. Adige y19.710 4.464 y1.523 0.366 Veneto y17.500 4.370 y0.690 0.724 Friuli V. Giulia y11.030 1.686 y1.557 0.793 Liguria 0.000 y0.370 0.000 0.601 Emilia Romagna y5.357 0.982 y1.024 0.109 Toscana y3.460 0.189 y0.323 0.236 Umbria 0.000 y0.262 0.000 0.790 Marche y2.753 0.233 y0.199 0.474 Lazio 0.449 y0.458 0.331 0.700 Abruzzo 0.000 y0.136 0.298 0.844 Campania 0.000 y0.974 0.525 0.794 Puglia 5.337 y0.924 2.387 0.533 Basilicata 0.000 y0.644 0.484 0.995 Calabria y2.487 y0.487 0.000 0.785 Sicilia y1.302 y0.687 0.485 0.696 Sardegna 0.000 y0.537 y0.414 0.376 Ž . Ž . Ž . The cointegrating vectors are reported in the form cs ln u q b ln t q b ln z q b PM. An asterisk 1 2 3 on the right of a coefficient indicates that the corresponding variable is weakly exogenous with respect to the long run parameters. P-restr is the P-value of the restrictions imposed for each region on the cointegrating vectors and the loading factors. ric way, because the induced higher consumer expenditure is not equally dis- tributed across regions. Since the production of goods and services is more concentrated in the North–Center, the positive effect on local labor demand has been stronger in these areas and weaker in the South, where the negative labor supply effects on regional unemployment have prevailed. We also find a positive association between the tax wedge and regional unemployment. This association is particularly strong in the North–Center and can be explained both by the greater importance in these areas of local bargaining and insider power, that increase real wage resistance, 11 and by the presence in the South of a larger share of the underground economy, that insulates labor costs from changes in labor taxes. Finally, there is evidence of a positive long run association between the real price of energy and imported materials and regional unemployment in the industrialized areas of the North–Center and of a negative association in the South. Since labor and energy can be either substitutes or complements in production, depending on the technology, this finding could be 11 Real wage resistance implies that increases in labor taxes are not completely absorbed by lower net pay. driven by the fact that the composition of value added differ in the two areas. While manufacturing is more important in the North–Center, both agriculture and public services are more important in the South. These differences in productive structure can influence unemployment also via the price wedge in opposite directions in the North and in the South. 12 While average tax rates have increased during the 1980s and 1990s, the real price of imported energy has significantly declined. On the other hand, real social transfers per head have increased both in the North–Center and in the South. The uncovered asymmetries in the long run association of social transfers per head, the real price of energy and the tax wedge with regional unemployment suggest that the positive association of regional unemployment differentials with higher real social transfers per head and with the lower real prices of energy has prevailed over the negative association between these differentials and the increase in the tax wedge.

4. Wages and unemployment