ARE IMMIGRANTS COMPETING WITH NATIVES IN THE ITALIAN LABOUR MARKET? THE EMPLOYMENT EFFECT. Alessandra Venturini *, University of Padua, and IZA, Bonn Claudia Villosio, R&P Ricerche e Progetti, Torino Preliminary version, not to be quoted Abstract In a previous paper the authors (Gavosto, Venturini, Villosio, Labour, 1999) analyse the impact of foreign workers on the wage of natives in Italy. The effect was found to be positive for unskilled labour, for small firms and for the North where immigrants are more concentrated. This result was partly interpreted as complementarity between the two production factors and partly was expected given the rigidity of the Italian labour market where the adjustment takes place more on the quantity side than on the price one. To clear up all doubts about the role played by immigrants in Italian labour market, we inquired into their effect on the native employment. We focused on two aspects of the unemployment experience: i) the displacement risk, measured by the probability to move from employment to unemployment; ii) the job-search effectiveness measured by the probability to move from unemployment to employment within one year. In the empirical model the transition probabilities depend on two sets of independent variables at time t: the first related to individual characteristics and the second to external condition of the market. We have performed two different estimates: a probit model and a linear probability model for repeatedcross-sections. The quarterly Labour Force Survey (ISTAT) from 1993 to 1997 was used. The results show that after controlling for individual characteristics and the external condition of the market where the individuals make their choices the share of immigrants has no effect on the native transition from employment to unemployment. As well no effect or at least a complementary effect is detected on the probability of transition from unemployment to employment for workers looking for a new job while for people looking for a first job (the young) the negative effect is limited in amount, restricted to the first year and to the South, while the effect is positive in the most recent periods and in the North. * Correspondence: Università di Padova, Dipartimento di Economia, Via del Santo 33, 35123 Padova, e-mail: venturini@decon.unipd.it, tel.+39 049-8274242 Correspondence: R&P Ricerche e Progetti Via Bava 6, 10124 Torino, Italy e-mail: rep@repnet.it, tel. +39 011 888100 This research has received a financial support from the "Commissione per le Politiche di Integrazione degli Immigrati - Presidenza del Consiglio dei Ministri" and from the University of Padua. We would like to thank Giorgio Brunello, Stefano Fachin, Chiara Monfardini, Enrico Rettore and the participants at the XV AIEL Conference for useful suggestions and comments to a previous version of this paper. Usual disclaimers applies. 1
1. Introduction During the 80s, the Southern European countries, including Italy, were no longer exporters of labour but became importers. During this period, the stock of foreign residents in Italy increased from 300,000 in 1980 to one million in 1996 and reached 2% of the population. This increase was almost exclusively made up of immigrants from non-european Union countries, such as from Morocco, Tunisia, the Philippines and more recently from the former Yugoslavia and the Albania. The inflows of foreigners in Italy has followed the subsequent legalisation laws implemented in the period by the Governments. The novelty of the immigration phenomenon forced the Government to pass a first legislation in 1987 which was designed to legalise the presence of an unexpected and feared large number of immigrants. The difficulties of handling this new phenomenon in a satisfactory manner forced the Italian Government, in 1990, to replace the previous law with new legislation, which was extended until 1991. The number of illegal immigrants who took advantage of these two laws to regularise their position was lower than expected, amounting to about 120,000 under the first law and 200,000 under the second 1. The pressure of public opinion brought the rightwing Government in 1996 to implement a third legalisation 2 and the left-wing Government in 1998 to pass a new law which granted limited political rights to legal resident foreigners while tightened controls and introduced immediate expulsion for immigrants who have been involved in criminal activities or have entered Italy illegally. The debate about the effect immigrants have on the labour market has been heated, on the one hand natives fared the competition of immigrants in the labour markets and on the other hand there was an excess demand for labour not matched by natives. The issue of the competition rose recently in the Italian debate not only because the novelty of the phenomenon has focused the attention to the access to the country, to the illegal presence of foreigners and to the laws revisions but also because no dataset was available to study this issue. Only recently the availability of data from the social security archive made possible the analysis on the effect of immigration on the Italian labour market. Gavosto, Venturini, Villosio (1999) tested the effect on the native wages of the share of foreign work. Their results show that the inflow of immigrants raises the wages of native manual workers (i.e. it has a complementary effect), and this effect is larger in small firms and in the North part of the country. No analysis has been made on the effect of immigration on the Italian unemployment. Object of this paper is to investigate if there is competition between natives and immigrants on the occupational side. The paper is organised as follow: section 2 briefly describes the data we have used, in section 3 the relationship between the presence of foreign and unemployment rates in the different regions of Italy is presented; section 4 and 5 try to find out if immigrants displace natives by answering to the following questions: have the recent migrations affected the probability of the Italian workers to find a job? Has the 1 On the competition between illegal immigrant and legal native employment see Venturini 1999. 2 During the thrid legalization 230.000 foreigners got the residency permits 2
foreign employment increased the risk for natives to be displaced? Section 6 concludes. 2. Foreign employment The information on foreign workers that we are using in this paper have been derived from the Social Security (INPS) Archives on private employment. This archive represents about 70% of the relevant total employment for foreigners because family workers and the employees in the agricultural sector are registered in other two archives. For long time the information on foreign employment from the administrative INPS dataset were not exploited, mainly because the number of foreign workers registered by INPS underestimated the total legal foreign employees, according to the Italian Statistical Office (Istat). The underreporting was caused by the use of nationality as the selection criterion: in fact the field nationality is often left blank or incomplete. To avoid the underreporting we selected foreign workers in a different way. We used the place of birth as the selection criterion, and defined foreign workers those workers born abroad. The shortcoming of this procedure is that, as we are not able to control for real foreigners, we may count Italians born abroad as immigrants; thus we end up with an over representation of those groups where descendent of Italians emigrated abroad still hold Italian passports. If they still hold the Italian passport, in fact, they are considered Italian nationals upon arrival and they are not requested to have a residence permit. In order to avoid counting Italians born abroad as immigrants, only workers born outside the European Union and the main industrialised countries have been chosen 3. In this way we exclude the countries where there has been a lot of Italian emigration as well as countries that contribute very little to the stock of immigrants to Italy and even less to the recent inflows. Moreover, for the same reason we have excluded workers coming from Argentina, Brazil and Venezuela. Those are the three countries with the highest return migration form Latin America to Italy, as described in Natale, Casacchia, Strozza (1999). The dataset built in this way provides a much higher total number of immigrants than the previous statistics computed from the INPS data using the nationality as the selection criterion and, what is more important, the total foreign employment with which we end up is much more coherent with the ISTAT revised estimates of foreign employment. Table 2.1 4 shows that our total foreign employment represents on average 70% of the ISTAT revised estimates of foreign employees which cover also domestic help and agriculture workers, who are not included in our dataset. 3 We have excluded from our definition of foreign workers, those born in European countries and in Iceland, Switzerland, Canada, Greenland, United States, Australia, New Zealand 4 Data of the Ministry of the Interior are an overestimation of foreign employment because they include also some expired permits. 3
Tab. 2.1 Foreign workers in Italy. Comparison of different sources 1987 1989 1991 1993 1995 1996 Ministry of the Interior Total Work permits 160.550 153.473 455.963 559.294 556.826 628.694 a. employees 149.004 126.602 285.229 336.382 347.068 448.561 ISTAT Total work permits 285.318 399.940 433.833 656.585 b. employees 255.233 275.774 301.798 479.391 INPS c. foreign workers 58.913 69.887 168.346 186.365 209.849 282.758 %INPS/M.I. (100*c/a) 59 55.4 60.5 63.0 %INPS/ISTAT (100*c/b) 66 67.6 69.5 59.0 In Fig.2.1 the evolution of the foreign workers from 1986 to 1996 is shown by country of origin as reported in our elaboration of the INPS data-set. The relevance of the amnesty of 1990-91, when illegal immigrants were granted working permits and resident status, is highlighted by the data. Immigrants from all areas took advantage of the amnesty but those from North Africa displayed the largest increase, followed by the Non-Mediterranean Africans and by the Asians while foreign employment from East Europe is increasing only in the last period. In clear evidence in the graph is also the effect on foreign employment of the legalisation law of 1996. Fig. 2.1 Immigrant workers by area of origin - Absolute values 1986-1996 300000 250000 200000 EUROPA EST 150000 ASIA AME LATINA 100000 AFRICA MEDIT. 50000 AFRICA NON MEDIT. 0 86 87 88 89 90 91 92 93 94 95 96 4
3. Immigration and native unemployment A simple correlation between unemployment rates and foreign workers employed in each region, shows a clear distinction between northern and central regions and southern regions. Those regions with an higher foreign employment are also those with the lower unemployment rates. Moreover, from the comparison of two different years, it is possible to notice an increase in the polarization between the two area of the country. This is the results of two different effects: on one hand the unemployment which remained unchanged in the period in the northern and central regions but has substantially increased in the south. On the other hand the increase in foreign employment has particularly invested the northern regions. Fig. 3.1 Unemployment rates and share of foreign workers by region - 1992 7.0 6.0 quota di occupati stranieri 5.0 4.0 Trentino-A.A Emilia-Romagna 3.0 Veneto Friuli VG Lombardia Lazio 2.0 Marche Piemonte Toscana Abruzzo Liguria Umbria 1.0 Basilicata Sicilia Puglia Sardegna Calabria Molise Campania 0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Tasso di disoccupazione 5
Fig. 3.2 Unemployment rates and share of foreign workers by region - 1996 7.0 quota di occupati stranieri 6.0 Trentino-A.A 5.0 Emilia-Romagna Veneto Friuli VG 4.0 Lombardia Marche Toscana Lazio 3.0 Abruzzo Piemonte Umbria Liguria 2.0 1.0 Puglia Sicilia Sardegna Calabria Campania Basilicata Molise 0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Tasso di disoccupazione In order to point out if the employment prospects of natives worsened due to the presence of immigrants, we have performed an analysis of the probability of transition from employment to unemployment, and viceversa for the period 1993-1997, after controlling for the different degree of concentration of foreign workers in the different areas of the country. In particular we focused on two aspects of the unemployment experience: the displacement risk and the job-search effectiveness. Both of them can be affected by the presence of immigrants in the labour market. The displacement risk occurs to employed workers who can be displaced by foreign workers. It is presumed that this competition could be stronger for specific categories of workers: youth, workers with low education. Also the job search can be affected by the presence of immigrants who may decrease the probability for natives to find a job. The effect of immigrants on the job search for natives can be different for first job seekers or for workers looking for a new job. The analysis is developed as follow. 1. Job-search effectiveness: analysis of the transition from unemployment to employment to point out if immigrants have decreased the probabilities for natives to find a job. 2. Displacement risk: analysis of the transition from employment to unemployment to point out if foreign workers have increased the probabilities for natives to loose their job. 6
Our analysis is carried on in different period of time to point out if subsequent immigration flows have modified, in time, the employment opportunities for natives. 4. The model 4.1 Data used The empirical analysis is carried out on microdata from the Italian Labour Force Survey from 1993 to 1997. This survey collects, quarterly, information on the main characteristics of the labour supply on a sample of about 76.000 household. The second quarter of the survey contains a question on the professional situation of individuals in the previous year. The answer to this question makes it possible to qualify movements from unemployment to employment and viceversa. Data on individuals are merged to some aggregate data drawn from different statistical sources in order to control for the "environment" in which individuals make their choices. Due to matter of availability of data we have analysed the transition of the following years: 1. Transition from employment to unemployment: 1992-93, 1993-94, 1994-95, 1995-96. 2. Transition from unemployment to employment: 1992-93, 1993-94, 1994-95, 1995-96, 1996-97. 4.2 Specification of the transition probabilities The analysis is carried, for the transition from employment to unemployment only on those workers employed as dependent in the manufacturing, commerce, and transport sectors. The reason for this selection is that only for this group of workers foreigners can have a competition effect. In fact immigrants cannot compete in the public sector, in self-employment and in the financial services. Our data, unfortunately, do not allow us to analyse the role played by immigrants in agriculture and in the sector of family help where is strong the presence of foreign workers 5. In the transition from unemployment to employment, for the same reasons, we include in the analysis only unemployed who search employment as dependent workers 6. 5 Infact data on foreign workers in agriculture are available only at the aggregate level, for the family workers, in the Labour force survey there are not enough data to replicate the analysis for this sector. 6 Data do not allow us to use more detailed information (for instance, sector of activity or occupation) about the job searched 7
As an indicator for displacement risk we use the probability to move from employment to unemployment and as an indicator for the job-search effectiveness we use the probability to move from unemployment to employment within one year. Thus, we have estimated the empirical model separately on those who are employed as dependent workers on the probability to loose a job 7 and on those who are unemployed on the probability to find a job. In this last case we have been able to consider individuals who are looking for the first job separately from those who are looking for a new job. For individual i we have built a dichotomous variable Y defined in this way: 1) Transition from employment to unemployment Yi = 1 if x i,t-1 = "employed" and x i,t = "unemployed" 0 if x i,t-1 = "employed" and x i,t = "employed" 1) transition from unemployment to employment Yi = 1 if x i,t-1 = "unemployed" and x i,t = "employed" 0 if x i,t-1 = "unemployed" and x i,t = "unemployed" where x i represent the condition reported by the individual on the survey. We have that: Pr(Y i =1) = f( z i t, w t ) That is to say the transition probabilities depends on two sets of independent variables: z i,t related to individual characteristics at time t and w t related to external condition of the market at time t. We have performed two types of estimation: a) probit model b) linear probability model In models where aggregate information are considered jointly with characteristics of individuals, the disturbance terms might be correlated within the aggregation groups. Moulton (1990) shows that in such a case the estimated coefficients are consistent but not efficient, in particular the estimated standard errors are biased downwards. To solve the problem of the aggregation bias we have used, in the probit estimation, the White heteroschedasticity consistent estimator; and in the linear probability model a two-stage procedure, as suggested by Moulton (1990). 7 As we are not able to discriminate between quit and layoffs, in the definition of "loosing a job" we includes also all the voluntary movements. 8
4.3 Description of the variables used Variables z refers to individual characteristics: age (linear and squared term), gender, marital status number of component in the household separately for individuals married and not 8, education. Tab. 4.1 Individual variables used (z) DFEM Dummy=1 if gender=female ETA Age of the individual ETA2 Age squared SINGLE Dummy=1 if never married CONIUG Dummy=1 if married (Benchmark ) SEPAR Dummy=1 if separated, divorced or widowed FAM_SIN Number of household component when not married FAM_CON Number of household component when married FAM_SEP Number of household component when separated, divorced or widowed DEDU_UNI Dummy=1 if education = university degree or higher DEDU_SUP Dummy=1 if education = secondary level education DEDU_MED Dummy=1 if education = first level education DEDU_EL Dummy=1 if education = less than first stage of education or nothing (Benchmark ) Variables w are aimed at representing the external condition of the market where individuals make their choices. Among these variables we have introduced the foreign share. Particular attention is given to the definition and the level of aggregation of the variables w. Effects from the demand side are captured by the growth in the added value between time t-1 and t, and the unemployment rate. Labour demand in the dependent employment is measured by net firms' creation rate at time t-1. Immigration is measured by foreign employment share (foreign employment divided by native employment) 9. The level of aggregation of those variables (with the exception of the unemployment rate) is, for the employment-unemployment transition, branch (5) by region (20); for the unemployment-employment transition, region (20). The idea is to control for the external condition of the market where the individuals make their choices 8 This variable (number of component interacted with the marital status) is used as a proxy of the number of children, information which is not present in the survey. 9 Analogous study for other countries use as measure for immigration or the foreign share (Winter-Ebmer and Zweimuller 1999) or the changes in the share of foreign employment in a giver region or industry (Card 1990; DeNew e Zimmermann 1994). 9
Tab. 4.2 Variables related to macroeconomic conditions (w) Employment-unemployment transition Unemployment-employment transition Variable Period Level of aggregation Period Level of aggregation DVA Change in the added value Between Region by branch Between Region t-1 and t t-1 and t DISOC Unemployment rate t-1 Region t-1 Region IMP_NE Net firms' creation rate t-1 Region by branch t-1 Region PERC Foreign employment share t Region by branch t-1 Region Moreover we have included industry and area dummies to control for area or industry fixed effects. At first the immigrant share was considered as an exogenous variable, however it is reasonable to assume that the supply of foreign labour is itself driven by labour market conditions like the unemployment rate. To test for the endogeneity of the immigrant share, following Blundell and Smith (1986) we have used a two stage procedure. At the first stage we have estimated, by a linear equation, the immigrant share as a function of appropriate instruments, at the second we have estimated the probit equation with, among the independent variables, the error term estimated at the first stage. If the error term comes out to be significantly different from zero, then the exogeneity of the variable is rejected and it instrumentation is necessary. The variables chosen to instrument the foreign share are the lagged foreign share, the share of women and blue collars in a certain region or industry (as measures of the structure of employment) and the average wage among immigrants as a measure of the attractiveness for a foreign worker to enter in that sector of the market. The test for endogeneity rejects the exogeneity of the foreign share only in few cases 10. For this reason we have run our probit estimation first without instrument and then, as a further control, with the foreign share by region or sector instrumented. We have followed a two-stage procedure similar to Nelson and Olsen (1976) and followed also by Winter-Ebmer et al. (1999) in which the endogenous variable is substituted with its predicted values in the probit estimation. The obtained coefficients are correct but not efficient. We have chosen to properly estimate the correct standard errors using bootstrap 11. The use of instrumental variables did not substantially modified initial results. 10 Over the 14 different regression run, only in two cases the exogeneity is rejected at a confidency level of 1% and in other two at a confidency level of 5%. 11 In principle the variance-covariance matrix can be corrected following Maddala (1983). In practice, due to the difficulties in computing the correction term very often standard errors are not corrected, but in this case statistical inference could be misleading 10
5. Results 5.1 Transition from unemployment to employment A) Search of the first job In this section we concentrate our analysis on those who are looking for their first job. In order to focus only on young unemployed, and not to consider also those who have had some previous experiences not recorded as is the case if they have had some job in the black economy, we have restricted our sample to individuals aged less than 30 12. The full set of results is reported in the appendix. Before discussing the effect of immigration on native employment, let's first have a look at the results for the other variables of the specification. The probability to find a job is lower for women, is decreasing with age up to a certain level and then is increasing (the linear coefficient is negative and its square is positive). And finally the likelihood to find a job is positively correlated with education. The marital status and the number of household components do not show any significant effect. Among the variables of the macro economic condition, we find that the unemployment rate is always significant with the (expected) negative sign: when unemployment decreases, the probability to find a job increases. Variation in the added values re never significant, while the net firms' creation rate, when significant, has the expected positive sign. Last, in Tab. 5.1 we can find the effect of immigration in the different estimations run. Tab. 5.1 Effect of foreign workers on the probability to find a job for the unemployed searching for the first job. 1993 1994 1995 1996 1997 Probit -0.28 (-3.8) -0.17 (-2.6) -0.12 (-1.4) -0.08 (-1.2) -0.05 (-0.7) Probit IV -0.49 (-3.8) -0.18 (-2.6) -0.15 (-2.0) -0.18 (-2.2) -0.002 (-0.2) LPM IV (a) -0.04 (-1.4) -0.01 (-0.3) 0.03 (1.1) 0.01 (0.5) 0.02 (1.1) Only North and Centre Probit -0.35 (-2.5) -0.03 (-0.3) 0.01 (0.1) 0.20 (1.3) 0.27 (1.9) Probit IV -0.72 (-2.6) -0.06 (-0.5) -0.09 (-0.6) 0.29 (1.2) 0.24 (1.3) (a) Linear two-stage regression with instrumented foreign share. Dependent variable at second stage: coefficients of the regional dummies /std. errors. Controls includes time and geographical area dummies. t-statistics in parenthesis Results from the probit equation seem to suggest a negative effect of the presence of immigrants on the probability to find the first job, for the first years, while the effect is not significant in 1997. It is necessary to be very careful in discussing these results; it is in fact possible that the territorial effects are not properly controlled. In fact if we look at the results of the linear probability model, which, due to the two 12 Anyway they are the majority of those searching the first job. 11
stage procedure, should better control for the regional effects, we see that the role of immigrants is not significant In order to better control for the regional dimension we have replicated the analysis only for individuals in the north and centre of Italy (the areas more industrialised and where we expect the stronger effect of immigration). Results are reported in the last part of Tab. 5.1. For this sub-area, the presence of immigrants reduces the probability to find a job only in 1993, soon after the procedures of legalisation, while after that a complementary effect seem to prevail. B) Search of a new job Similarly we have replicated the probit analysis for those searching for a new job. Results are in line with the above findings: probability to find a new job is lower for women, decreases with age, is not affected by marital status and by the number of cohabitant, and is higher for those with higher education. Among the macroeconomic variables, only the net firm's creation rate has a positive effect, unemployment rate negatively affect the probability to find a new job only in some years, while no effect is shown by the value added. What is more important for our analysis is the effect of immigrants (Tab. 5.2). In this case, differently from what obtained before, the impact of foreign workers is not significant up to 1995, while the effect is significant and positive for the period 1996-97. Thus, a complementary effect seems to exist between foreign workers and natives with previous work experience. Also in this case we must proceed very carefully. First of all, when we use instrumental variables, the statistical significance of the coefficients is reduced, probably suggesting a problem of endogeneity of our variable. Secondly, if we restrict our analysis to the Centre-North of Italy the complementary effect that comes out for the 1997 is no confirmed by the analysis with instrumental variables. However, from the estimation with the linear probability model a stronger effect of complementarity comes out, expecially in recent years. Tab. 5.2 Effect of foreign workers on the probability to find a job for the unemployed searching for a new job. 1993 1994 1995 1996 1997 Probit 0.07 (0.9) 0.03 (0.6) 0.04 (0.6) 0.05 (0.7) 0.11 (2.0) Probit IV 0.01 (0.1) 0.05 (0.8) -0.01 (-0.1) -0.01 (-0.1) 0.01 (0.1) LPM IV (a) 0.06 (2.4) 0.06 (2.7) 0.084 (4.4) 0.06 (3.4) 0.06 (4.1) Solo Nord e Centro Probit 0.25 (2.1) 0.07 (0.9) -0.07 (-0.7) -0.04 (-0.3) 0.31 (3.0) Probit IV 0.35 (1.4) 0.06 (0.7) -0.09 (-0.9) 0.03 (0.2) 0.12 (1.0) (a) Linear two-stage regression with instrumented foreign share. Dependent variable at second stage: coefficients of the regional dummies /std. errors. Controls includes time and geographical area dummies. t-statistics in parenthesis Summarising the analysis of the relationship between probability to find a job and the presence of immigrants, different effects are detected for people looking for the 12
first job and people looking for a new job. In the first case, for individuals without any job experience the negative effect is limited in amount and restricted to the first year and to the South, while the effect is positive in the most recent periods and in the North. For more older and experienced workers, who are looking for a new job, the presence of immigrants has no effect or at least a complementary effect expecially in the northern and central part of the country. 5.2 Transition form employment to unemployment In the estimation of the transition from employment to unemployment we have included the share of foreign workers at time t (see Tab. 4.2). Due to unavailability of data after 1996 we are able to analyse transition only up to 1995-96. However, the great number of observation related to employed workers allow us to replicate the analysis for different sub-groups that may react differently to the presence of immigrants. Again, most of the findings for this transition are in line with the previous: the probability to loose a job is higher for women, decrease with age, is higher for unmarried or divorced, and decreases when education increases. The likelihood to become unemployed is higher in those regions where unemployment is higher, is reduced when the net number of new firms increases and, limited to 1996, when value added increases. The share of immigrants employed seems to be complementary with the domestic workers employed in the same region and sector of activities in 1994 and competing in 1996 (Tab. 5.1). The use of instrumental variables, however, reduces the complementary effect detected in 1994. In the other years no significant effect emerges for the immigrants share. Unfortunately, as we do not have data after 1996, we are not able to see if the competition effect that comes out for 1996, is confirmed also for the subsequent years and therefore conclude that it can be a consequence of the legalisation of the 1996-97. 13
Tab. 5.1 Effect of the foreign employment on the probability to loose a job for domestic workers 1993 1994 1995 1996 Probit -0.02 (-1.6) -0.05 (-4.1) -0.02 (-1.7) 0.03 (3.1) Probit IV -0.01 (-1.0) -0.04 (-2.1) -0.01 (-0.3) 0.04 (2.7) LPM IV (a) 0.004 (2.1) -0.003 (-1.5) -0.007 (-3.3) -0.001 (-0.4) Sub-groups (b) Centre & North -0.02 (-1.4) -0.03 (-2.2) 0.03 (2.2) 0.04 (2.3) South 0.01 (0.1) 0.09 (1.4) -0.15 (-2.6) 0.00 (0.0) Youth (<41) -0.02 (-1.3) -0.04 (-2.4) 0.02 (1.4) 0.04 (2.2) Old (>40) -0.01 (-0.6) -0.07 (-2.9) -0.07 (-2.8) 0.04 (1.7) Low education -0.02 (-1.2) -0.04 (-2.2) 0.01 (0.3) 0.05 (2.8) High education -0.01 (-0.6) -0.02 (-1.0) 0.00 (0.2) 0.02 (1.0) (a) Linear two-stage regression with instrumented foreign share. Dependent variable at second stage: coefficients of the regional dummies /std. errors. Controls includes time, region and sector dummies. t-statistics in parenthesis (b) Instrumental probit regression t-statistics in parenthesis The analysis has been replicated also on specific groups of workers. Discriminating by geographical areas we can notice that the competition effect detected for the 1996 is limited to the northern workers, for whom it emerges also in 1995, while for southern workers no significant effect comes out. The same happen for the complementary effect that the aggregate analysis indicated for 1994: this result is confirmed only for the north of Italy, while a complementary effect for southern workers emerges in 1995 Interesting is the separate analysis for young and old workers: the complementary effect seems stronger for the older rather then the youth, while the competition effect is restricted to young workers. Finally discriminating between educational levels, we find that the share of foreign workers do not affect the probability to loose the job for those with higher educational degrees (university or high school degrees). Turning to the estimation with the linear probability model, we observe that the competition effect that the probit analysis indicates for some groups of workers, is not confirmed by the linear model. This results, may indicate that our model is not able to fully catch all the effects that contribute to explain the negative behaviour of employment in 1996. A possible explanation deals with the impossibility to distinguish between quits and layoffs and to the fact that we have no information about the nature of the contract of the job held in 1996. This could be particularly important in 1996: in that year we had a strong increase in the number of nonstandard contracts and temporary contracts. It may be possible that the effect we attribute to the presence of immigrants could be reduces if we where able to include in our analysis the type of contract and separately replicate the estimation on the workers with temporary contracts and with open-end contracts. 14
6. Conclusion Immigration in Italy is no longer a recent phenomenon, however the debate about the effect of immigration on the domestic labour market, due to the unavailability of appropriate data, is often dominate by aprioristic belief and little empirical evidence is available for the different hypothesis proposed. In this paper, we have use data form the Italian Labour Force Survey to which we have merged information about foreign employment Our analysis is conducted on the 1993-1997 period. In this period the first important procedure of legalisation of the 1991 ended, and the new of 1996, started. We restrict our analysis to foreign workers legally employed in the private sector as dependent workers in some selected sectors of activities (manufacturing, construction, commerce and transports). First significant differences emerge in the reaction to the presence of foreign workers between unemployed looking for their first job and unemployed looking for a new job. In the first case of young unemployed with no job experiences, the presence of foreign workers could have had a negative effect, limited in amount and restricted to the first year and to the South, while the effect is positive in the most recent periods and in the North. For unemployed looking for a new job no effect or at least a complementary effect, between immigrants and native can be detected expecially in the centre-north of Italy. Among the employed, at a first analysis the legalisation procedure of 1996 seems to determine a competition between foreign and domestic workers limited to the young, with low education and located in the centre-north of Italy. This results however is no longer assessed by subsequent analysis. This makes us concludes that probably our model is not able to fully control for some other factors, like the increase in the temporary jobs (for which we do not have information in our dataset) that may have influenced the probability of loosing a job for natives. 15
7. References Amemiya, T. (1979), The estimation of a simultaneous equation Tobit model, International Economic Review vol. 20 pp. 169-181 Blundell R.W., Smith R.J. (1986), An exogeneity test for a simultaneous equation Tobit model with an application to labour supply, Econometrica, 54, 679-685. Card D., (1990) The Impact of the Mariel Boatlift on the Miami Labor Market, Industrial and Labor Relations Review 43, n.2: 245-257. Commissione per le politiche di integrazione degli immigrati (2000) "Primo rapporto sull'integrazione degli immigrati in Italia", Il Mulino, Bologna DeNew J.P., Zimmermann K.F., (1994) Native Wage Impacts of Foreign Labor: A Random Effects Panel Analysis, Journal of Population Economics, 7: 177-192. Gavosto, A. Venturini, A., Villosio, C. Do immigrants compete with natives? Labour no. 3:13, 1999 ISTAT (1999) Forze di lavoro: media 1988, Annuario, n. 4. ISTAT, (2000) La presenza straniera in Italia caratteristiche demografiche, Collana Informazione, n.7. Maddala G.S (1983), "Limited dependent and qualitative variables in econometrics", Cambridge University Press Moulton B.R., (1990) An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units, The Review of Economics and Statistics, 32: 334-338. Natale, Casacchia, Strozza (1999) "Migrazioni interne, migrazioni internazionali: il nuovo ruolo del Mezzogiorno" in Bonifazi C. (eds.) "Mezzogiorno e migrazioni interne", IRP, monografie IO/1999. Nelson F., Olsen L. (1978), Specification and estimation of a simultaneous-equation model with limited dependent variables International Economic Review vol. 19 pp. 695-709 Pischke, J. S., Velling, J. 1997, Employment effects of immigration to Germany: an analysis based on local labor markets. Review of Economics and Statistics, Vol. 79, No. 4, pp. 594-604 SOPEMI (1997) Trends in International Migrations, OCDE, Paris. Venturini A., (1999) Do Immigrants working illegally reduce the natives legal employment? Evidence from Italy, Journal of Population Economics 12, n.1: 135-154 Venturini, A. e Villosio C. (1998) Foreign workers in Italy: are they assimilating to natives? Are they competing against natives? An analysis by the SSA dataset Quaderni del dipartimento di Scienze Economiche, Università di Bergamo no. 3 Winter-Ebmer R., Zimmermann K, 1999, East-West Trade and Migration: The Austro- German case in Faini R., De Melo J., Zimmermann K., 1999, Migration. The controversies and the evidence, Cambridge University Press, 296-327. Winter-Ebner R., Zweimuller J., (1999), Do immigrants displace young native workers: the Austrian Experience, Journal Of Population Economics, vol. 12 N.2 pp. 327-340 16
8. Appendix 8.1 Transitions matrices The LFS survey second quarter contains a question on the professional condition of individuals in the previous year. This question allows the construction of transition matrices at one year interval. Tab. 8.1 Transitions between t and t-1 for years 1992-1995 a. Unemployed looking for a first job (aged less than 31) Status in year t Unemployed looking for the first job in t-1 Unemployed Employed % employed on unemployed in t-1 1992 4789 3849 940 19.6 1993 4912 4055 857 17.4 1994 5268 4264 1004 19.1 1995 5478 4422 1056 19.3 1996 5615 4570 1045 18.6 b. Unemployed looking for a new job Status in year t Unemployed looking for a new job in year t-1 Unemployed Employed % employed on unemployed in t-1 1992 3630 2506 1124 31.0 1993 4581 3221 1360 29.7 1994 5268 3649 1619 30.7 1995 5452 3646 1806 33.1 1996 5478 3673 1805 32.9 c. Employed Status in year t Employed at year t-1 Employed Unemployed % unemployed on employed in t-1 1992 26776 25690 1086 4.1 1993 25692 24600 1092 4.3 1994 25078 24178 900 3.6 1995 24843 23994 849 3.4 17
Tab. 8.2 Results from the probit regression on the probability of the transition from unemployment to employment for those looking for the first job (t-statistics in parenthesis) variable 1993 1994 1995 1996 1997 intercep 4.34 (5.2) 5.43 (6.6) 4.05 (4.3) 6.07 (7.0) 4.56 (5.1) dfem -0.32 (-6.4) -0.21 (-4.2) -0.29 (-5.9) -0.32 (-6.5) -0.35 (-7.1) eta -0.30 (-4.4) -0.40 (-5.7) -0.21 (-2.9) -0.42 (-6.1) -0.25 (-3.5) eta2 0.01 (4.0) 0.01 (5.2) 0.00 (2.7) 0.01 (5.9) 0.01 (3.3) single -0.16 (-0.6) 0.07 (0.2) -0.49 (-1.8) -0.41 (-1.6) -0.64 (-2.5) separ 10.39 (0.0) 0.50 (0.6) -25.14 (-0.1) -1.24 (-1.1) -0.24 (-0.2) fam_sin -0.07 (-2.4) -0.05 (-1.6) 0.01 (0.2) -0.04 (-1.6) -0.04 (-1.6) fam_con -0.07 (-0.9) 0.06 (0.8) -0.08 (-1.1) -0.11 (-1.4) -0.17 (-2.3) fam_sep -3.66 (-0.0) 0.03 (0.2) 5.21 (0.1) 0.22 (0.8) -0.12 (-0.4) dedu_uni 0.56 (3.9) 0.32 (2.1) 0.18 (1.3) 0.47 (3.2) 0.28 (2.0) dedu_sup 0.25 (2.4) 0.30 (2.6) 0.13 (1.1) 0.25 (2.2) 0.21 (1.9) dedu_med 0.02 (0.2) 0.01 (0.1) 0.00 (-0.0) 0.13 (1.2) -0.02 (-0.2) dva 0.04 (1.2) 0.03 (1.1) -0.03 (-1.1) -0.02 (-0.6) -0.06 (-1.5) disoc -0.07 (-5.5) -0.08 (-4.7) -0.11 (-7.5) -0.08 (-10.4) -0.08 (-8.4) imp_ne -0.06 (-1.3) 0.07 (1.8) 0.06 (1.5) 0.13 (3.2) 0.11 (2.2) Perc -0.29 (-3.8) -0.17 (-2.6) -0.12 (-1.4) -0.08 (-1.1) -0.05 (-0.7) d_ne 0.36 (2.4) 0.27 (2.1) 0.32 (2.5) 0.36 (2.5) 0.54 (3.9) d_ce 0.09 (0.7) -0.06 (-0.7) -0.15 (-1.6) -0.01 (-0.1) 0.11 (1.1) d_su -0.14 (-0.9) -0.19 (-1.2) 0.07 (0.5) -0.05 (-0.3) 0.21 (1.5) d_is 0.03 (0.2) -0.03 (-0.2) 0.40 (2.4) 0.13 (0.7) 0.27 (1.7) N. obs. 4789 4921 5268 5478 5615-2 Log L 3600.9 3434.9 3770.3 3826.3 3848.1 18
Tab. 8.3 Results from the probit regression on the probability of the transition from unemployment to employment for those looking for a new job (t-statistics in parenthesis) variable 1993 1994 1995 1996 1997 intercep 0.79 (1.9) 0.83 (2.5) 1.13 (2.6) 1.15 (3.0) 0.25 (0.6) dfem -0.44 (-8.0) -0.27 (-5.6) -0.40 (-8.8) -0.50 (-11.1) -0.42 (-9.4) eta -0.04 (-2.2) -0.04 (-2.6) -0.06 (-4.2) -0.04 (-2.8) -0.03 (-2.4) eta2 0.00 (1.1) 0.00 (2.0) 0.00 (2.8) 0.00 (1.7) 0.00 (1.2) single -0.19 (-1.2) 0.05 (0.4) 0.06 (0.5) 0.01 (0.1) -0.09 (-0.7) separ -0.22 (-0.6) -0.22 (-0.7) -0.13 (-0.5) 0.07 (0.3) 0.24 (0.9) fam_sin 0.01 (0.2) -0.04 (-1.5) -0.04 (-1.4) -0.01 (-0.4) -0.03 (-1.1) fam_con 0.01 (0.3) 0.03 (1.2) 0.04 (1.4) 0.06 (2.2) 0.02 (0.9) fam_sep -0.01 (-0.1) 0.05 (0.5) 0.08 (0.9) -0.05 (-0.6) -0.20 (-1.9) dedu_uni 0.63 (4.1) 0.61 (4.6) 0.61 (4.9) 0.58 (5.5) 0.47 (4.2) dedu_sup 0.21 (2.6) 0.27 (3.9) 0.26 (3.9) 0.25 (3.9) 0.28 (4.3) dedu_med 0.05 (0.7) 0.09 (1.5) 0.13 (2.2) -0.01 (-0.2) 0.13 (2.2) dva -0.02 (-0.7) 0.02 (0.9) 0.01 (0.4) -0.04 (-1.9) 0.02 (0.6) disoc -0.02 (-1.2) -0.05 (-3.2) -0.02 (-1.7) -0.03 (-4.2) -0.02 (-1.8) imp_ne -0.01 (-0.1) 0.07 (2.2) 0.06 (1.7) 0.14 (4.2) 0.05 (1.1) perc_rs 0.07 (0.9) 0.03 (0.6) 0.04 (0.6) 0.05 (0.7) 0.11 (2.0) d_ne 0.07 (0.5) -0.05 (-0.5) 0.10 (0.9) 0.07 (0.6) 0.07 (0.7) d_ce -0.04 (-0.3) -0.05 (-0.6) -0.12 (-1.5) -0.16 (-2.2) -0.04 (-0.5) d_su -0.06 (-0.3) 0.16 (1.0) -0.20 (-1.4) 0.07 (0.5) 0.04 (0.3) d_is 0.18 (0.8) 0.44 (2.5) -0.01 (-0.1) -0.01 (-0.1) 0.23 (1.6) N. Obs 3630 4581 5268 5452 5478-2 Log L 3579.9 4471.5 5073.7 5370.0 5446.9 Tab. 8.4 Results from the probit regression on the probability of the transition from employment to unemployment (t-statistics in parenthesis) variabile 1993 1994 1995 1996 intercep -1.50 (-9.0) -1.23 (-8.0) -1.85 (-10.8) -1.59 (-8.9) dfem 0.19 (9.4) 0.21 (9.6) 0.24 (10.8) 0.24 (11.2) eta -0.04 (-6.7) -0.04 (-6.3) -0.03 (-5.2) -0.05 (-7.7) eta2 0.00 (4.3) 0.00 (4.2) 0.00 (3.3) 0.00 (5.0) single 0.18 (2.9) 0.21 (3.5) 0.32 (5.3) 0.13 (2.1) separ 0.45 (3.8) 0.19 (1.5) 0.40 (2.9) 0.27 (2.0) fam_sin 0.00 (0.3) -0.01 (-0.8) -0.04 (-2.8) 0.00 (0.3) fam_con -0.02 (-1.3) 0.00 (0.3) 0.02 (1.4) 0.02 (1.4) fam_sep -0.10 (-2.2) 0.01 (0.1) -0.13 (-2.3) -0.06 (-1.2) dedu_uni -0.28 (-4.2) -0.84 (-8.5) -0.19 (-3.1) -0.55 (-6.9) dedu_sup -0.34 (-11.7) -0.38 (-13.2) -0.38 (-12.6) -0.40 (-13.2) dedu_med -0.19 (-7.5) -0.27 (-10.9) -0.22 (-8.2) -0.24 (-9.1) dva 0.00 (-0.2) 0.00 (-0.5) 0.00 (0.2) -0.01 (-2.5) disoc 0.02 (4.0) 0.04 (6.9) 0.05 (10.2) 0.02 (4.4) imp_ne -0.02 (-2.3) -0.03 (-3.7) -0.02 (-4.2) -0.06 (-8.3) perc_t -0.02 (-1.6) -0.05 (-4.1) -0.02 (-1.7) 0.03 (3.1) N. Obs 26766 25692 25078 24843-2 Log L Regression includes also 4 sectorial dummies and 19 regional dummies 19