Università degli Studi di Salerno Centro di Economia del Lavoro e di Politica Economica

Sergio Destefanis, Giovanni Pica

Università di Salerno - CELPE

The Wage Curve an Italian Perspective

Corresponding author:

destefanis@unisa.it, gpica@unisa.it

Discussion Paper 117

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Scientific Comnitee:

Adalgiso Amendola, Floro Ernesto Caroleo, Cesare Imbriani, Pasquale Persico C.E.L.P.E. Centro di Ricerca Interdipartimentale di Economia del Lavoro e di Politica Economica Università degli Studi di Salerno Via Ponte Don Melillo, 84084 Fisciano, I- Italy http://www.celpe.unisa.it E-mail: celpe@unisa.it

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Index

Abstract 5

Introduction 7

1. The Issue 7

2. The Data 10

3. The Econometric Framework 11

4. Empirical Results 13

5. Concluding Remarks 16

References 17

CELPE's Discussion Paper 19

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The Wage Curve an Italian Perspective

Sergio Destefanis, Giovanni Pica

Università di Salerno - CELPE

Abstract

In this paper we appraise the existence of a negative relationship between the wage level and the unemployment rate (the wage curve) across Italian regions, using data from the Bank of Italy’s Survey on Household Income and Wealth. The main advantage of this data-set is the availability of information on human capital characteristics of individuals (such as gender, age and education) and, more importantly, on hours worked. Our main finding is that, even though a wage curve exists in Italy, at least after the 1992-93 wage reforms, for annual and monthly wages, no such relationship exists for hourly wages. Consistently, after the reforms we find a negative elasticity of annual hours and months worked with respect to the unemployment rate.

Keywords: Wage Drift, Unemployment, Labour Market Flexibility. JEL Classification: J30, J60

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Introduction

In this paper we appraise the existence of an inverse relationship between individual wages and local unemployment across Italian regions. Such a relationship (the wage curve) has been the object of considerable interest in empirical labour economics since the seminal contributions of Blanchflower and Oswald (1990, 1994a, 1994b). By and large (see the survey in Njikamp and Poot, 2005) empirical support has been found for this relationship in many countries, Italy being somewhat an exception in this respect (Lucifora and Origo, 1999; Manacorda and Petrongolo, 2006, Ammermüller et al. 2009).

The Italian wage bargaining setup has often been blamed for this state of affairs, being deemed as unable to fully allow for local labour market conditions. Until 1993 this setup consisted of two non-coordinated bargaining levels (industry- and firm-level), on the top of automatic cost-of-living adjustments. However the 1992-93 reforms, and particularly the July 1993 Wage Agreement virtually abolished the cost-of-living allowance and in-troduced a new bargaining set-up, centred upon two specialized contractual levels. The Wage Agreement had two explicit objectives: curbing the inflationary pressure, and making wages more responsive to local conditions. The first target has been indubita-bly achieved (Fabiani et al., 1998, Destefanis et al., 2005), while its effectiveness with respect to the second has been often doubted (Casadio, 2003).

Recently Devicienti et al. (2008) have provided some evidence according to which a resurrection of the previously absent wage curve took place in Italy after the wage bar-gaining reforms using data on weekly wages. In this paper we intend to provide new evidence on this issue, using data on hourly wages. In Section 2 we provide a short survey of the literature on the Italian wage curve. Our data are described in Section 3. Unlike Devicienti et al. (2008), who rely on administrative data from social security re-cords, we use data from the Bank of Italy’s Survey on Household Income and Wealth (SHIW) from 1987 to 2004. Among the main advantages of this data-set, for the pre-sent purposes, are the inclusion of information on human capital characteristics of the individuals (such as gender, age and education), and of information about hours worked from 1987. The econometric framework is described in Section 4: we carry out a set of econometric estimates, also along the lines of Bell et al. (2002). Our main re-sults are described in Section 5. We find that a significant inverse relationship between annual wages and unemployment across Italian regions exists, at least after the 1992-93 wage reforms, but that no such relationship exists for hourly wages. Consistently, after the reforms we find a negative elasticity of annual hours and months worked with respect to the unemployment rate. Section 6 concludes.

1. The Issue

A huge empirical literature has put to test the existence of various relationships between wages and unemployment. Traditionally, empirical studies have focused on the Phillips curve, the aggregate relationship between the variation of wages and the rate of unem-ployment. Since the seminal contributions of Blanchflower and Oswald (1990, 1994a, 1994b), however, much attention has been paid to a long-run “equilibrium” relationship be-tween the level of wages and the rate of unemployment, mainly set at a fairly disaggre-gated level. This relationship is widely known as the “wage curve” (Card, 1995), and typi-cally pitches individual (or regional) wages against regional rates of unemployment.

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In this section we provide a short survey on the wage curve literature for Italy, concentrat-ing on the analysis of individual wage data.1 From Table 1 it clearly appears that most works relate to data from the 1980s onwards and find, if at all, a very weak relationship be-tween wages and regional rates of unemployment. The Italian wage bargaining setup has often been blamed for this state of affairs, being deemed as unable to fully allow for local labour market conditions.2

Until 1993, this setup consisted of two non-coordinated bargaining levels (industry- and firm-level), on the top of automatic cost-of-living adjustments. The system in place was structured as follows. Until 1968, institutional rules allowed for the existence of regional wage differentials in union contracts (gabbie salariali) and determined a substantial wage gap between Northern and Southern regions, which gradually disappeared after their de-mise. In 1994, the North-South wage differential was less than 20 percentage points with respect to its 1970 value. This did not go together with a reduction in the North-South pro-ductivity differentials, impacting upon the competitiveness of Southern firms. Simultane-ously the unemployment rate gap between North and South underwent a fivefold increase, and the propensity to emigrate from the Mezzogiorno underwent a decisive reduction. Faini (1996) then concludes that the wage convergence induced by the post-1968 bargaining setup did not bring about the desired convergence in other economic aggregates.

The 1992-93 reforms, and particularly the July 1993 Wage Agreement virtually abolished the indexation system and introduced a new bargaining setup. The nation-wide industry contracts rule now wage determination over a two-year time horizon (while dictating other normative aspects of the labour contract over four years). Wage rises implied by this con-tractual level should be consistent with a target rate of inflation annually decided by the government.3 The second contractual layer relates to plant-level bargaining, and should emphasise the nexus between wages and firm productivity. Finally, an indexation scheme of sorts still exists as a guarantee to workers if nation-wide industry contracts are not rene-gotiated within the prescribed two years. The nation-wide contractual wage rates increase by 30% of the target rate of inflation after three months of delay in renegotiation and by 50% after six months. There is some evidence that the wage reforms achieved their aim of curbing the inflationary pressure (Fabiani et al., 1998, Destefanis et al., 2005). On the other hand, their effectiveness in making wages more responsive to local conditions has been often doubted (Casadio, 2003).

Among the works carried on more recent data, Devicienti et al. (2008) stand out in provid-ing evidence according to which the Italian wage curve, very flimsy in the 1980s and early 1990s, has regained strength after the reforms, due to the greater nexus between wages and firm productivity attached to the second contractual layer. In particular, this mechanism is likely to have been stronger for top-up wage components (comprising overtime wages, collective and individual wage premia). A shortcoming of the data used by Devicienti et al. (2008), the INPS data-set, is however that it cannot allow to distinguish between effects arising from hourly wage rates and number of hours worked. In the present paper, relying on data from the Bank of Italy’s SHIW, we aim to provide evidence on this issue.

1 Lucifora e Origo (1999), Destefanis (2007) carry out estimates on regional data. Their findings do not

imply however substantially different conclusions from those expounded in the text. 2 Following Brunello et al. (2001), Manacorda and Petrongolo (2006) find evidence that the weakness of

the Italian wage curve relationship may be due to the fact that wage determination in Italy was influenced mainly by the economic conditions prevailing in the leading economic areas of the economy (the Northern regions). 3 The eventual discrepancy between actual and target rate of inflation is one of the elements taken into

account when nation-wide industry contracts are renegotiated after a two-year period.

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Authors and Datasets

Sample (structure and numerosity)

Specification Wage curve

coeff.

Bodo and Sestito (1994) INPS (archivio imprese) 1985-90 (blue-collars)

6(r), 6(t) N. obs.: 459 569

Real annual earnings ln (UR t-1)

2-stage (only sector*area in 1st stage; no controls in the 2nd stage?)

No individual (fixed-) effects No IV procedures

North: -0.042* South: -0.007*

Faini (1995) INPS (archivio imprese) 1988-92 (manufacturing)

95(r); (5(t) N. obs.: n.a.

Nominal annual earnings U t-1

2-stage Card (only T-effects in the 2nd stage)

No individual (fixed-) effects No IV procedures

large firms: 1.37 small firms: -0.35

Casavola et al. (1995) INPS (archivio imprese) 1985-93 (small firms)

95(r); 9(t) N. obs.: 42 750

Nominal annual earnings (1/Ut-1) (+ lagged dep. var.)

Mean-cell regr. (2-stage too, but as a check only)

No individual (fixed-) effects No IV procedures

North: 0.0378* South: -0.0060

Canziani (1997) SHIW 1989-93

20(r); 3(t) N. obs.: 16 963

Nominal annual earnings ln (Ut)

2-stage Card No individual (fixed-) effects

No IV procedures

-0.052*

Lucifora and Origo (1999) INPS (archivio individui) 1981-93 (all firms)

95(r); 5(t) N. obs.: 444 600

Nominal weekly earnings (also log. diff.) ln (Ut) (+ lagged dep. var.)

2-stage Card No individual (fixed-) effects

IV procedures (as a check only)

blue collars: 0.006 white collars: -0.002

North: -0.002 South: 0.012

Montuenga et al. (2003, 2006) ECHP 1994-96

20(r); 3(t) N. obs.: 3 589

Nominal hourly earnings ln (Ut)

Random effects (2003) IV (2003)

Fixed effects (2006)

RE: -0.075* IV: -0.151* FE: -0.039*

Manacorda and Petrongolo (2006) SHIW 1977-98

2(r); 15(t) N. obs.: 57 446

Real annual earnings ln (UR t)

GLS on repeated cross-sections No individual (fixed-) effects

No IV procedures

North: -0.125* South: 0.080

Ammermüller et al. (2009) SHIW 1991-04

19(r); 7(t) N. obs.: 28 000

Nominal hourly and monthly earnings (also log. diff.)

ln (Ut) (+ lagged dep. var.) 2-stage Card

No individual (fixed-) effects IV procedures (as a check only)

hourly = -0.005 monthly = -0.027

North: -0.028 South: 0.190

Devicienti et al. (2008) INPS (archivio individui) 1985-99 (full-time employees at least 3 months in contin. empl).

20(r); 15(t) N. obs.: 150 000

Nominal weekly earnings Nominal top-up components

ln (Ut) Fixed effects

No IV, but test for exogeneity

Pre-93: -0.005 Post-93: -0.029*

Pre-93 t-u’s: -0.002

Post-93 t-u’s: -0.076*

Table 1 – The Italian Wage Curve Literature

Note: N. obs. = number of observations; r = number of areas (95 = provinces; 19 or 20 = re-gions, 6 or 2 = wider areas); t = number of years. The acronym t-u’s stands for top-up compo-nents. A * indicates a 5%-significant coefficient. Where available we present disaggregated re-sults: North-South, small-large firms, blue-white collars. Small firms have less than 100 employ-ees in Faini (1995), less than 10 employees in Casavola et al. (1995) and Lucifora and Origo (1999).

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2. The Data

We use a series of repeated cross-sections drawn from the Bank of Italy Survey of Households’ Income and Wealth (SHIW) (for a description see Brandolini, 1999, and Cannari and D’Alessio, 2003) from 1977 onwards. The survey has been run continu-ously from 1977 to 1984, then in 1986, 1987 and in every other year thereafter. Our estimates refer to the period 1987-2006, the longest time span in which we can have a consistent series for wages, hours worked and unemployment. We restrict the analysis to non-agricultural private sector employees aged 18-65. This excludes from the analy-sis the sectors where wage or price determination is most heavily affected by direct public intervention.

Among the main advantages of this data-set, for the present purposes, are its inclusion of information on hours worked and on human capital characteristics of the individuals (such as gender, age and education). The SHIW also contains information on occupa-tion and sector of activity for all industries, although at some coarse degree of disag-gregation. All these features differentiate our data-set from the social security data that have been most often utilised in the Italian wage curve literature.

Of particular importance is the presence of information about hours worked. When specifying the curve, wages should be expressed in hourly terms in order to eliminate the bias generated by the negative correlation between the response in worked hours to changes in aggregate demand and the local unemployment rate. Indeed, an impor-tant reason for the inappropriateness of annual earnings is that working hours tend to are procyclical (Card, 1995). Nevertheless, most empirical estimations of the wage curve are on the basis of annual or monthly data (Nijkamp and Poot, 2005). In the pre-sent paper we demonstrate the size of this effect by using both hourly and annual wages. In fact, although Card (1995) stresses the potential relevance of this phenome-non, the relative importance of hours vs. wage adjustments has seldom been consid-ered in the literature.

The SHIW data base directly provides series for annual wages. It also provides infor-mation on the number of worked months and on weekly hours. Therefore, we can ob-tain hourly and monthly wages as follows:

monthly wages = annual wages / (working months)

hourly wages = annual wages / (weekly hours × 4 × working months)4

Indeed, we calculate real wages for annual, monthly and hourly measures, dividing them by the national index of consumer prices. As will become apparent below, it is al-so convenient to alternative measures for hourly wages.

Figure 1 plots the standard deviation of various measures of Italian wages in recent years and shows that since 1993 wage dispersion increased markedly, suggesting that the reforms might have helped making wages more responsive to local labour market conditions. This is consistent with Devicienti et al. (2008) who provide evidence of the “resurrection” of the wage curve in Italy after the 1992-93 reforms, using weekly wages from social security records. However, Figure 1 also gives the visual impression that

4 Obviously, using the figure of 4 weeks per month is an approximation. As we are going to use a log-linear

specification, it has however no impact on the estimates. Similar considerations apply to the expressions that follow.

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wage dispersion increased more for annual than for monthly and hourly wages, sug-gesting that response to the reform might have partly taken place through adjustments in hours worked. We pursue this line of inquiry below in our econometric analysis.

.3

5.4

.45

.5.5

5.6

Sta

ndard

de

via

tion

1985 1990 1995 2000 2005Year

Hourly wage

.35

.4.4

5.5

.55

.6

1985 1990 1995 2000 2005Year

Hourly wage net of overtime

.35

.4.4

5.5

.55

.6S

tanda

rd d

evia

tion

1985 1990 1995 2000 2005Year

Monthly wage

.35

.4.4

5.5

.55

.6

1985 1990 1995 2000 2005Year

Annual wage

Figure 1: Wage dispersion in Italy

3. The Econometric Framework

A baseline wage curve can be specified through the following equation:

ln(wirt) = a + b Xirt + β ln(urt) + εirt (1)

for each individual i in region r and at a given point in time, t, where w is the wage, u the unemployment rate and X a set of individual and labour characteristics (such as gender, education, experience, occupation, etc.). As both wages and unemployment

are expressed in logs, β is the elasticity of wages with respect to unemployment. Al-though Blanchflower and Oswald (1994) take (1) as their preferred specification, esti-mating this equation potentially implies various problems, as was already pointed out by Blanchflower and Oswald (1995a,b) and Card (1995). The main problems can be gathered under four headings: unobserved heterogeneity, endogeneity, dynamics, effi-ciency.

In order to deal with unobserved heterogeneity, time and region fixed effects have been included in the wage curve. The former controls for annual macro shocks common to all regions, whereas the latter controls for time-invariant regional characteristics. Con-sequently, the equation to be estimated takes the form:

ln(wirt) = a + fr + dt + Trt + b Xirt + β ln(urt) + εirt (2)

where fr is a regional fixed effect, dt a time fixed effect and Trt a region-specific time trend. In this way, any permanent component of the relationship between wages and regional unemployment is captured by region fixed effects, and the unemployment co-

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efficient β only reflects the temporary component of that relationship (see Card, 1995). Furthermore, region-specific wage pressures are likely to vary systematically both over

time and across regions. Hence, the importance to include region-specific time trends

in order to control for these factors (see on this Bell et al., 2002).5

Second, the wage curve has often been specified (and estimated) as a reduced form, positing that the rate of unemployment is exogenous. However, the latter hypothesis may be unwarranted. Besides, if the curve is to be interpreted as a structural relation, it is necessary to specify a model of labour-market equilibrium. A relationship presiding to the determination of the unemployment rate (a price equation à la Blanchard, or a la-bour demand schedule) must be included in this model, which can be written as follows (we only keep subscript r for simplicity):

wr = φ [ ƒ(ur ) | Xr ] (3)

ur = Φ ( wr, γr | Zr ) (4)

Ε ( υr ) = υυυυ* (5)

where γ is a demand shock, Z is a vector of control variables - for the price/labour de-mand curve (4), and the other variables have been defined above. The model is closed

by the “no-migration condition” according to which, in equilibrium, expected utility (υ) should be equalised across areas. Identifying structural parameters in equation (3) re-quires the assumption that regional shocks only affect the rate of unemployment

(hence, only equation (4) through γr). Alternatively, Instrumental Variable techniques can be used to instrument local unemployment. A further option is to consider a recur-sive model, in which wages depend (only) on lagged unemployment. This opens up of course the wider issue of the dynamic specification of the wage curve.

Many authors have posited that wages follow a dynamic adjustment process, and have subsequently include a lagged dependent variable in the curve, leading a model that nests the wage curve and Phillips curve specifications within the same equation:

ln(wirt) = fr + dt + Trt + b Xirt + α ln(wirt-1) + β ln(urt) + εirt (6)

An oft-debated point in the literature (se for instance Blanchard and Katz, 1997; 1999),

is whether α is equal to unity. In the latter case, the curve would take the form of a Phil-lips Curve. However, an arguably more fundamental issue is whether the time dimen-sion of the panel is high enough to allay fears on occurrence of a Hurwicz bias in the estimation of a dynamic fixed-effect models (Nickell, 1981). The extent of this bias is also related to the specification of region-specific changes such as those arising from variations in unionisation, product market competition and so on.

Finally, the rate of unemployment is usually measured at a more aggregated level than that of the other independent variables, and particularly at a more aggregated level than that of the dependent variable. This may generate a correlation across individuals belonging to the same region, giving rise to an upwards bias in the estimate of the t-statistics (see Moulton, 1986).

5 Unfortunately, the use of repeated cross-sections does not allow to control for changes in the

unmeasured characteristics of the individuals over the cycle. This may be a source of bias in the elasticity of real wages with respect to unemployment (Solon et al., 1994).

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A first way to overcome this problem is to estimate a ‘cell-mean’ wage regression (Blanchflower and Oswald, 1994 and Baltagi and Blien, 1998) where the dependent variable and all explanatory variables are defined by the region and by yearly aver-ages. Alternatively, a two-step approach, as suggested in Card (1995), can be used. This works as follows. In the first stage, equation (2) is estimated excluding ln(urt), but including unrestricted region per year dummies. Then, in the second stage the esti-mated region per year dummies are regressed on year dummies, region dummies, and the regional unemployment rate. This method uses micro-level data to estimate the co-efficients of the individual-level variables. The second step then fully accounts for the presence of correlation across individuals in the same market. Note that there may be subgroups in the second stage with no individuals represented in the survey.6

More recently, Bell et al. (2002) have refined the two-step approach. They take a sepa-rate cross-section regression for each year, pooling individuals across all regions. The same regressors as in the previous equation, as well as regional dummies, are in-cluded in these estimates. For each year t we estimate:

ln(wirt) = a0t + art + bt Xirt + νirt (7)

Then, the region-specific time effects ârt are used as composition-corrected wages in the second-stage regional panel model.7 Notice that the b coefficients are the same across regions but differ over time. This allows for more accurate composition correc-tion over a period where industry and skill effects have become increasingly dispersed. However, the b’s will reflect any correlation between the Xirt’s and the individual ef-fects.8

4. Empirical Results

Given the above discussion, we estimate both a wage and a hours curve for individual i in region r at time t using the following specification:

ln(yirt) = fr + dt + Trt + b Xirt + β ln(urt) + βpost ln(urt) × Dpost + εirt (8)

where yirt is either the (annual, monthly or hourly) wage rate or the number of (weekly or annual) hours worked or the number of months worked; urt is the regional unem-ployment rate, also interacted with Dpost, a dummy for the post-reform period; Xirt is a set of individual and market characteristics that includes gender, age dummies, educa-tion dummies, occupation dummies, sector dummies, a part-time dummy, a primary occupation dummy, city size dummies and the number of members of the household that do not perceive income. A full set of year dummies dt controls for aggregate macro shocks, whereas regional fixed effects fr control for time-invariant regional characteris-tics. Thus, any permanent component of the relationship between yirt and regional un-

6 Some authors (Blanchard and Katz, 1997, and Canziani, 1997, among others) have estimated equation

(2) excluding the unemployment variable and the time-period dummies. Then, in the second stage the estimated region dummies are regressed on year dummies and the regional unemployment rate. 7 They also consider an alternative approach that uses the panel nature of the data and estimates an

individual fixed-effect equation for each region. Given the cross-sectional nature of our data we cannot pursue this strategy. 8 It should be emphasised that Bell et al. (2002) estimate a dynamic equation in order to investigate the

process of wage adjustment. On the other hand, throughout our paper, we present only the results from static estimates, while taking into account the other three aspects of a correct specification. As will be clearer below, this is mostly due to the paucity of the panel component in our data.

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employment is controlled for and the coefficients β and βpost only reflect the temporary component of the relationship. As region-specific wage pressure is likely to vary sys-tematically both over time and across regions, we include region-specific time trends Trt (Bell et al., 2002). We cluster standard errors at the regional level to account for within-region correlation of shocks. For robustness purposes we also adjust for composition effects adopting the alternative two-stage approach of Bell et al. (2002) described above.

The upper panel of Table 2 shows results from the estimates of the wage curve for hourly, monthly and annual wages using pooled OLS and the two-stage procedure. The post-reform total wage elasticity is the sum of the coefficients attached to log(urt) and to

log(urt) × Dpost. The formal F-test that the post-reform total wage elasticity is significantly different from zero is presented at the bottom of each panel.

We find a negative and significant relationship between both annual and monthly wages (columns 3-6) and the local unemployment rate after the wage reforms (even though the estimate is only marginally significant for monthly wages when using OLS). The elasticity varies from between -0.038 and -0.060 for monthly wages to between -0.084 and -0.106 for annual wages. The former compares well with the -0.029 reported by Devicienti et al. (2008) using weekly wages. The latter is in line with the traditional -0.1 found by Blanchflower and Oswald (1994) for many countries including Italy. How-ever, we find no significant relationship between hourly wages9 and the unemployment rate (columns 1-2).

If wage reforms have made it easier for wage-setters to adapt annual and monthly wages to local labour market conditions this must be linked to some extent to arrange-ments affecting the numbers of hours. The lower panel of Table 2 explores this idea and shows results from the estimates of a hours curve for the number of weekly hours, the number of annual months and the number of annual hours. A hours curve emerges

for both annual months and annual hours, with an elasticity of about -0.04.10

These findings complement those of Devicienti et al. (2008) who find that top-up wage components are especially responsive to local conditions. Indeed these components are institutionally linked to weekly hours. This link is straightforward for overtime wages, but is also very strong for collective and individual wage premia, which are very often conditional on the achievement of sales and output targets involving variations in work-ing hours.

Finally, it may be worthwhile to compare our findings with the results obtained in some papers that rely on the SHIW: Canziani (1997), Manacorda and Petrongolo (2006), Ammermüller et al. (2009). The estimation set-ups in Canziani (1997) and Ammer-müller et al. (2009) are pretty close to each other (and to some of our estimates, too), being based on a two-step approach à la Card. Based on our evidence, we can easily rationalise why Canziani obtains a significant wage curve coefficient on annual wages and Ammermüller et al. get the opposite result for hourly wages. It is less easy to ex-plain why in the latter study no significant wage curve is found for monthly wages. Ap-parently, specification differences must be the key to this issue. The difference in re-sults may probably be driven by our inclusion of region-specific time trends among the control variables.

9 Using hourly wages net of overtime does not change the results. 10

We address the issue of the possible endogeneity of the unemployment rate conducting a C-type

endogeneity test. This entails estimating an IV model on regionally aggregated data using lagged variables as instruments (Baltagi and Blien, 1998). We never reject the null hypothesis of exogeneity of urt both for the wage and the hours specification.

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(1) (2) (3) (4) (5) (6)

Panel A. Wages

Hourly wages Monthly wages Annual wages

OLS Two-stage OLS Two-stage OLS Two-stage log(urt) 0.037 0.017 0.032 0.008 0.019 -0.017 (0.031) (0.035) (0.029) (0.033) (0.045) (0.038) log(urt) F Dpost -0.066 -0.049 -0.070 -0.068 -0.103 -0.089 (0.021)*** (0.032) (0.023)*** (0.029)** (0.025)*** (0.033)** N 34611 190 34724 190 34902 190 R2 0.38 1.00 0.49 1.00 0.46 0.99 Post-reform elasticity: Prob > F 0.35 0.35 0.16 0.07 0.03 0.00 Panel B. Hours

Weekly hours Annual months Annual hours

OLS Two-stage OLS Two-stage OLS Two-stage log(urt) -0.000 0.005 -0.010 -0.020 -0.011 -0.012 (0.015) (0.020) (0.019) (0.020) (0.024) (0.022) log(urt) F Dpost -0.006 -0.024 -0.030 -0.019 -0.034 -0.040 (0.012) (0.018) (0.008)*** (0.013) (0.015)** (0.023)* N 35390 190 35328 190 35211 190 R2 0.38 0.98 0.10 1.00 0.28 1.00 Post-reform elasticity: Prob > F 0.73 0.37 0.04 0.04 0.08 0.05

Table 2: Unemployment elasticity of wages and hours

Note: OLS and first stage regressions include regional dummies, year dummies, regional trends and city size dummies. Individual controls include gender, age dummies, education dummies, occupation dummies, sector dummies, a part-time dummy, a primary occupation dummy and number of members of the household that do not perceive income. Second stage controls: year dummies and regional trends. Robust standard errors in parentheses clustered by region in OLS regressions. * significant at 10%; ** significant at 5%; *** significant at 1%.

The two-area set-up in Manacorda and Petrongolo (2006) markedly differs from the other specifications (including our own ones). As a consequence, drawing a compari-son with their results is not straightforward. However, our 20-region (actually 19-region, as we take Piedmont and Val D'Aosta together) specification is more general and, in some sense, can be used to encompass the findings of Manacorda and Petrongolo. Accepting the punch line of that work, to the effect that cross-region wages are only related to the unemployment rate of the leading area (the Northern regions), does not affect the basic interpretation that we (and Devicienti et al., 2008) give to the evidence. The unemployment rate of the leading area may well rule the roost in determining na-tionally bargained wages, but this does not obscure the fact that, after 1992, the re-gional dispersion of components of annual take-home pay linked to working hours has been affected by the regional dispersion of unemployment rates.

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5. Concluding Remarks

In this paper we assess the existence of an inverse relationship between wages and unemployment across Italian regions. By and large (see the survey in Njikamp and Poot, 2005) empirical support has been found for this relationship, the wage curve, in many countries, Italy being somewhat an exception in this respect (Lucifora and Origo, 1999; Manacorda and Petrongolo, 2006, Ammermüller et al. 2009).

The Italian wage bargaining setup has often been blamed for this state of affairs, being deemed as unable to fully allow for local labour market conditions. Until 1992 this setup consisted of two non-coordinated bargaining levels (industry- and firm-level), on the top of automatic cost-of-living adjustments. However the 1992-93 reforms introduced a new bargaining set-up, centred upon two specialized contractual levels. We tested whether the demise of the old bargaining has favoured the existence of an inverse rela-tionship between wages and unemployment (the wage curve) across Italian regions. Unlike Devicienti et al. (2008), who rely on the administrative data from social security records, we use data from the Bank of Italy’s SHIW from 1987 to 2006. Among the main advantages of this data-set, for the present purposes, are its inclusion of informa-tion on human capital characteristics of the individuals (such as gender, age and edu-cation), and, above all, of various kinds of information about hours worked (also allow-ing us explore the possibility that our results are driven by the development of part-time contracts).

Our main findings are that a wage curve exists in Italy, at least after the 1992-93 wage reforms, for annual and monthly wages but not for hourly wages. Consistently, after 1992-93 we find a negative elasticity of annual hours and months worked with respect to the unemployment rate. Thus, it seems that the reforms has made it easier for wage-setters to adapt wages to local labour market conditions through the number of hours worked.

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