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INEQUALITY AND POVERTY DURING THE RECESSION IN ITALY

di Massimo Baldini ed Emanuele Ciani

1. Introduction

The aim of this paper is to discuss some quantitative simulations of the changes in inequality and poverty levels in Italy during the recent recession and to study the role of public subsidies in integrating the incomes of those affected by a reduction of employment.

Focusing on the years 2007-2010, for a given demographic structure of the population we ask what has been the impact on inequality and poverty of both the reduction in employment and the increase in the number of employ-ees in Cassa Integrazione guadagni, a job sharing scheme that is part of the backbone of the Italian employment protection. We do not, therefore, mean to simulate the total impact of the economic crisis on the distribution of in-come and on poverty. To this aim, we would need data on the distribution of income changes for those who have not lost their job. Indeed, it is reasonable to claim that the recession has had a profound effect on the economic struc-ture of the country, with distributional consequences across individuals, areas and sectors that are only partially reflected by the employment statistics. Fur-thermore, in the absence of sufficient data on the distribution of the reduc-tion in capital incomes we cannot simulate the performances of the housing and capital markets. The same problem applies to cash benefits or losses from self-employment, except for the case where people have closed their activity. Finally, it is not possible to separate the effect of the crisis from other phe-nomena that have continued to take place in the Italian labour market.

POLITICA ECONOMICA / a. XXVII, n. 3, dicembre 2011

This is an update to 2010 data of the policy report appeared in Commissione di indagine sull’esclusione sociale (2010, pp. 81-95) and of Baldini and Ciani (2010). A previous version cir-culated as a working paper (Baldini and Ciani, 2011). We thank participants to the ESPANET conferences in Valencia (8/9/11) and Milan (29/9/11) for useful comments. We are also thankful to two anonymous referees, who provided helpful suggestions on different aspects of the simula-tion. Emanuele Ciani received funding from the Economic and Social Research Council, which is gratefully acknowledged.

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Nevertheless, if we want to use «real» data, we should wait for 2010 or 2011 households’ survey, which are not yet available. As a consequence, it seems useful to show some simulations based on the information currently available 1. The results presented in this ex-ante analysis would remain of some interest also when new data will become available, for two reasons. First of all, changes in poverty and inequality in «real» data are subject to a wide variety of influences, while the simulation allows one to consider one change at a time, in this case the variation in employment rates. Secondly, we also simulated the changes in the income distribution that would have occurred if the unemployment benefits had been made more universal, intro-ducing a general transfer for all the individuals losing their job, as suggested by Boeri and Garibaldi (2008) and Berton et al. (2009) 2.

The present paper updates to 2010 data a previous simulation that we carried out for the annual report of the Italian Commission on Social Ex-clusion (Commissione di indagine sull’esclusione sociale, 2010; Baldini and Ciani, 2010). Almost contemporarily to the development of that report, Add-abbo et al. (2010b) had worked on imputing the transition from employment to unemployment from the Spanish and Italian Labour Force Surveys to the two countries’ SILC surveys 3. Their approach is different in two respects. First, they calibrated the simulation to the proportion of transitions between different employment states (employed, unemployed, CIG, inactive) that oc-curred in the Labour Force Surveys between 2008 and 2009 (Addabbo et al., 2010b, p. 24). Differently, we calibrate the simulation to the reduction in the employment rate for different socio-economic groups between 2007 and 2010. Secondly, our simulation of the Cassa Integrazione is based on the ag-gregate data for 2010, while, again, they focused on the transitions observed in the 2008-2009 Labour Force Surveys.

In the meantime, other papers have studied a similar subject for Italy. Addabbo and Maccagnan (2011), expanding on their previous work for Italy, consider another approach, based on reweighting the survey sample weights in order to calibrate the data to the unemployment rates estimated in the 2009 Labour Force Survey (Addabbo and Maccagnan, 2011, p. 14). Another related paper is D’Amuri (2011), which is focused on the Italian labour mar-ket and on the changes in earnings inequality. He finds that the recession has increased the probability of unemployment mainly for workers on fixed-term contracts, with a consequent rise in earnings inequality. Our results are con-sistent with these findings, but we also study the implications on the more

1 See also Boeri (2010) and Misiani (2010).2 We thank an anonymous referee for suggesting this improvement.3 The authors carried out a similar work for Italy in Addabbo et al. (2010a).

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general changes in inequality and poverty among households’ incomes. Fi-gari et al. (2011), using the Euromod microsimulation model, study the role of social protection systems of various countries in contrasting the negative distributional effects of the downturn. Differently from them, we consider only a limited set of policy instruments, but we simulate in greater detail the reduction in employment probabilities and we also provide an exhaustive reconstruction of the recipients of the Cassa Integrazione. The most recent work is Brandolini et al. (2011): this paper simulates the distributive effects of the recession on the LFS data, where detailed information on job transi-tions are available, imputing to each individual in the LFS labour incomes and pensions from the Bank of Italy Survey on households income and wealth. Unemployment and CIG benefits are simulated using entitlement rules. They find that average equivalent disposable income across individu-als fell by 1.5% and that the Gini index rose by 1.4 percentage points. The poverty headcount index rose in particular for households with two or more children and fell for those whose head is aged more than 64 years.

Sections 2 and 3 are devoted to description of the data used and of the steps followed in the simulations. Section 4 presents the results for the changes occurred during the crisis, while section 5 discusses the alternative scenario with reformed unemployment benefits. Section 6 concludes.

2. The data and the simulation of the changes in employment rates

The simulation is based on three datasets. The first two are the samples obtained by pooling all quarterly samples of the Labour Force Survey for 2007 and 2010, respectively. Each single survey is a stratified sample from the population of all the components of Italian households, so that every member of the selected households is interviewed 4. We used these datasets only to estimate the average employment rate along the four quarters, distin-guishing different demographic groups.

The changes are simulated in the 2008 EU-SILC survey for Italy (IT-SILC), because it contains detailed information on earnings and other sources of income, both at the individual and at the household level. The reason why we used the 2008 survey is that, by design, all the information on income in IT-SILC refers to the year previous to the interview, that is 2007. Therefore we set the latter as the first year in the simulation. Table 1

4 See pp. 1-2 in the «Methodological note» for the 2010 survey, available at http://www.istat.it/it/archivio/27135 (last access: 27/10/2011).

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reports the number of observations and a few descriptive statistics on the three samples.

The definition of employment in the two different surveys can be made coherent, as both the LFS and IT-SILC include the same set of questions. Basically, the employed are those who have worked for at least one hour in the week previous to the interview, or who had a job from which they were absent. However, in IT-SILC the professional condition is reported at the moment of the interview (2008), while income data are relative to the previous year (2007). Unfortunately, the job characteristics in 2007 are sur-veyed with a detail that is not enough to simulate the Cassa integrazione 5. As a consequence, we decided to use the 2008 status in employment, defining it in the same way as in the LFS, but we tried to avoid two extreme cases: in-dividuals who declared to be employed in 2008, but without having received any labour income during 2007, and respondents who reported to be not employed in 2008, but who had worked during all 12 months in 2007 6. In the first of these two cases, we re-classified the individuals as not employed, including them in the category of student, unemployed or other non-profes-sional condition according to the prevalent status during 2007. In the second case, we re-classified the respondents as employed and we used the informa-

5 For the previous year, we only know whether the individual was or not employed during each month, and whether he was an employee or self-employed. Therefore we miss information on key characteristics, such as the sector of activity and the position in the employment.

6 For the self-employed with zero earnings, we checked whether they have worked for at least one month during 2007.

TAB. 1. Descriptive statistics on the LFS and SILC samples

Labour force surveys SILC

2007 2010 Raw data After the employment correction

After the post-

stratification

After the simulation

Employment rate [15-64] (%) 58.7 56.9 60.3 59.9 58.7 56.0% aged 15-64 66.1 65.9 64.5 64.5 66.1 66.1% male 48.6 48.6 48.6 48.6 48.6 48.6% employees on total employed 15-64 76.0 76.5 78.0 78.5 78.5 78.6% fixed term on total employees 15-64 15.6 14.8 17.4 16.7 16.9 16.8

Observations 677,746 662,986 52,433 52,433 52,433 5,243,300

Source: authors’ computations on LFS and IT-SILC data; all estimates use the sample weights.

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tion on their activity in 2007 7. For all other cases, we assumed that nothing changed between 2007 and 2008.

In the simulation we focused on the population aged between 15 and 64 years. The reduction in the employment rate observed in the LFS was repli-cated for 24 categories (see Table 1), defined according to the following de-mographic variables:

– Gender.– Age (less than 40, 40 or more).– Education level (up to lower secondary, upper secondary, degree).– Area of residence (North-Centre, South).Note that we avoided using characteristics related to the type of job or

the sector of employment. Otherwise, we should have taken into account the occurrence of transitions across different sector, on which we do not have enough information at the individual level. The fact that we focus on aggre-gate changes in the employment rate in each group implies that we neglect the job creation and job destruction that generally occurs in each single class. The main drawback is that our simulation does not take into account distributional effects due to the different characteristics of job separations and new hires.

Given that the distribution of individuals across classes and the rate of em-ployment in each of them was different between the IT-SILC sample and the 2007 LFS, we post-stratified the SILC weights in order to make them similar 8. The distribution of employment and demographic classes in the SILC weighted sample is now approximately the same as in the 2007 LFS weighted sample (see Tables A.1 and A.2 in Appendix 1). The indexes of inequality and poverty

7 These data are collected in section 7 of the individual questionnaire only for persons currently not employed. They refer to the most recent job. We do not have information on whether, during 2007, these individuals were employed in the public or private sector. There-fore, we estimated a probit model for the dummy «public sector». We then imputed it for those individuals to whom we changed status from not employed to employed, using Monte Carlo techniques, which simply means that we predicted the probability of each category and we decided which to assign drawing a number from an uniform distribution. Full results are available on request.

8 We used Nick Winter’s survwgt poststratify command for Stata (http://ideas.repec.org/c/boc/bocode/s427503.html). We focused on 50 classes: 48 generated after splitting the classes in table A.1 in employed and not employed individuals, plus 4 classes collecting respectively all individuals aged less than 15, divided by sex, and all aged 65 or more, again distinguishing males and females. We included the last four in order to use the new weights in the distribu-tional analysis. The command survwgt poststratify adjusts the sample weights in each class so that the total sum of the new weights in each class of the SILC sample corresponds to the total sum of weights in the 2007 LFS sample (divided by four because we pooled the four quarterly surveys, where the weights in each of them sum up to the total Italian population).

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in the «status quo» scenario are nevertheless not sensibly different from the ones obtained using the original SILC weights (results available on request).

The Labour Force Surveys were used to estimate the change in the em-ployment rate for each group between 2007 and 2010. The difference was then applied to each corresponding group in the IT-SILC survey. For exam-ple, if for the group composed of young workers living in the South and with low education we observed a 4% fall in the employment rate, we applied the same percentage change within the IT-SILC survey.

Within each of the 20 groups where the change in employment rate was negative, we randomly selected some employed persons in the private sector and re-classified them as not-employed. For these classes, we distinguished the decline in the proportion of fixed term employees and in the proportion of other employed individuals, including the self-employed, as observed in the LFS samples. In those cases where the reduction is the sum of a fall in the former and an increase in the latter, we imputed the total fall to the fixed term employees, and vice versa. The reason is that, given that our imputation works on aggregates and not on individual transitions, we cannot work on transitions between fixed term and permanent contracts 9. For the fixed term category, the individuals losing their job were chosen among all employees with this kind of contracts, including those in the public sector. For the rest of the employed, we randomly selected among all individuals working in the private sector, either employees with a permanent contract or self-employed. Apart from the aggregate statistics in Table 1, we do not report the propor-tion of fixed term employees in each class, as Tables A.1 and A.2 will be-come hardly legible. Full tables are nevertheless available on request.

Those individuals whose employment status was changed to not-em-ployed lost their recorded earnings, replaced by the unemployment ben-efit 10. If the workers did not meet all the requirements for receiving the full

9 This is a concern for six classes. However, in four of them there is only a slight increase in fixed-term contracts, smaller than 0.25%, and a decrease in other employed. In one class (Up to lower secondary education; female; old; South) there is a little decrease in fixed term contract (0.33%) and a small increase in other employed (0.17%). The only class for which the com-pensation plays a more important role is «Degree; female; young; North-Centre», where there is a decrease of 2.55% in fixed term contracts and an increase in other employed by 1.80%. One criticism might be that, as we work on aggregate variations, we should have focused on the changes in fixed term and non-fixed term employment in each single class. However, this would have required to post-stratify the SILC weights in order to account also for this distinction. We preferred to calibrate only on the aggregate employment rate in each class, and on the classes distribution, in order to avoid an excessive transformation of the original SILC weights.

10 To have a clear understanding of rules of unemployment benefits and the Cassa Inte-grazione we relied on Berton et al. (2009). To update their tables to the most recent legisla-

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amount, we applied the reduced rate (in Italian, «requisiti ridotti», reduced requirements) 11. Further, we assumed that the length of the unemployment condition is 12 months. This assumption, clearly strong, is due to the dif-ficulty of estimating transitions from non-employment to employment dur-ing the crisis, and of distinguishing between discouraged workers and unem-ployed. It is also in any case unclear how to simulate spells of unemployment shorter than one year, because we do not have panel data that allow us to follow individuals thorough every month of 2010 12.

In the four groups for which the employment rate during the period increased, we simulated a corresponding increase in the proportion of em-ployed in IT-SILC, by randomly selecting a suitable number of respondents from the pool of those who were not employed (excluding disabled per-sons, pensioners and soldiers). We assigned them the annual earnings of an individual of the corresponding group randomly selected among those who were already employed. Although we could have used the prediction from a regression, it should be noted that through the distinction in demographic classes we are implicitly accounting for sex, geographic area, degree and whether the individual is aged less than 40 13.

Lastly, we accounted for the variability generated by the random selection by repeating the simulation 100 times. All the following results are therefore estimated as the averages across all the replications.

tion we relied on the INPS website. See the following url and the related links (last access: 07/10/11) http://www.inps.it/portale/default.aspx?sID=%3b0%3b5673%3b&lastMenu=5673&iMenu=1&p4=2&bi=22&link=.

11 We do not know all the details necessary to distinguish neatly between the two cases. In practice, we assigned the reduced unemployment benefit to those unemployed with no more than two years of paid contribution. Those who had not paid any contribution in the past are left without any subsidy. For people with short term contracts, we took into account the characteristics of the particular benefits that apply to the various contract types.

12 In the LFS dataset unemployed individuals are nevertheless asked to report the length of the current spell of unemployment. Pooling all quarterly samples, the average duration in all quarterly samples was 19.5 months (median 10) in 2007 and 18.7 months in 2010 (median 10). See also the recent report Employment in Europe 2009 (European Commission, 2009), where the average length of unemployment is estimated also using the longitudinal component of EU-SILC. The values reported for Italy (ibidem, Tab. 15, p. 91) are not inconsistent with our assumption.

13 The estimate clearly suffers from selection bias, as the earning distribution of new hires (or new self-employed) is likely to differ from the one for those currently employed. In order to produce the statistics in Table 1, we also assigned the new employed to the position in employment and the type of contract of earnings donor. In any case, it should be taken into account that these individuals account only for 5.41% of those whose employment status is changed in the simulation.

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Table A.2 in Appendix 1 shows the employment rates for the 24 groups of workers, in 2007 and in 2010 for the labour force survey, while in 2007 and after the simulated change in the IT-SILC dataset. The four groups for which the employment rate increased are all composed by mature work-ers, some of them probably being obliged to keep working after the recent changes in the pension system rules. Even a rise in the employment rate for a group could hide a negative impact of the crisis, if the employment rate would have increased by a greater extent without it. In particular, in the years before the crisis the female employment rate has significantly increased, from 37.3% in 1998 to 46.3% in 2006 (source: EUROSTAT) 14. On the other hand, from 2006 to 2010 it remained fairly stable.

As reported in Table 1, the post-simulation total employment rate is equal to 56.0%. Although the simulated change in the employment rate in each group is approximately the same as observed in the two LFSs, it should be added that it is lower than the one estimated in the 2010 LFS (56.9%), re-sulting in a larger drop. The reason is that the weights are still calibrated to the 2007 LFS sample, in order to keep constant the demographic structure, at least as represented by the 24 demographic classes. If we had changed them to reproduce the distribution of the groups in the 2010 LFS survey, we would have obtained the same aggregate employment rate 15.

Table 2 presents the main characteristics of those individuals to which the simulation changed the economic status from employed to not-employed. It turns out, for example, that 4% of workers living in the North of the coun-try lost their job after the recession, while the drop was significantly larger in the Southern regions. In general, the downturn affected more the work-ers with low educational levels, particularly younger individuals aged 40 or less. Lastly, it is important to note that the group of workers born outside the EU countries, accounting for 5% of the total population, is over-repre-sented among those who lost their job, with a probability of becoming not employed higher than that for Italian workers.

3. The simulation of the wage supplement fund (Cassa integrazione guadagni)

The Italian government decided to face the recession by relying upon the existing social protection schemes, increasing their total expenditure without

14 The table consulted for these data is «Employment rate by gender», [tsiem010], avail-able on the EUROSTAT website (last access: 30/05/2010). In 2004 the series has a break.

15 A simple Excel table showing this result is available on request. The final employment rate in each single class is not exactly equal to LFS 2010 because of the number of observations.

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embarking in systemic reforms. As it is well known, the current Italian so-cial insurance benefits form a system which is complex and unfair, and leaves many unemployed without any support. This is true in particular for tempo-rary «atypical» workers with fragmented careers 16. Before the crisis, only a minority of the unemployed received a benefit.

Another scheme that has recently seen a rapid increase is the Cassa Inte-grazione Guadagni (CIG), a wages guarantee fund that protects workers’ in-comes and jobs in case of a temporary crisis of the firm where they are em-ployed. It is administered by the Social Insurance Institute (INPS), and pro-vides up to 80% of the salary for a short period (usually less than one year, but longer periods are allowed). Traditionally reserved to big industrial firms, this scheme has been extended during the recession to sectors so far excluded (small firms) and to wider time intervals. The CIG preserves the place of work and maintains a link between the worker and the firm, but slows the inevitable process of reallocation of workers due to changing market conditions.

16 See Anastasia et al. (2009).

TAB. 2. Shares of employed individuals that after the simulation lose their job by demographic characteristics, and their group composition

Geographic area Age class

Shareof individuals

(%)

Composition(%)

Shareof individuals

(%)

Composition(%)

North West 3.8 23.6 ≤ 30 8.3 33.7North East 3.8 17.3 31-40 7.8 51.4Centre 3.6 15.2 41-50 1.5 9.0South 7.6 30.0 51-64 1.4 5.8Islands 7.3 13.9Total 4.8 100.0 Total 4.8 100

Education Citizenship

Shareof individuals

(%)

Composition(%)

Shareof individuals

(%)

Composition(%)

Up to lower secondary 7.2 58.4 UE 5.1 2.5Upper secondary 3.7 34.9 Italian 4.7 90.2Degree 2.0 6.8 Other countries 6.2 7.3Total 4.8 100 Total 4.8 100

Source: authors’ computations on IT-SILC data; population 15-64. All estimates use the post-stratified sample weights (see Section 2), and are the average across 100 replications.

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In order to simulate its distributional effects, we need data on the number of hours of CIG conceded during 2010 17. Again ideally, we would need to know the distribution among workers of the reduction in hours worked. Unfortunately, we only know the frequency distribution of the total number of hours authorised by the Social Insurance Institute (INPS), by sec-tor of activity and geographic area, as provided by the Statistical Observatory on CIG available at the INPS website (www.inps.it). We also know, from the INPS Annual Report 2010, the share of total authorised hours that have been actually used during 2010, without any disaggregation by area or sector. In 2010 a total of 1.204 billion hours have been authorised, with an utilisa-tion rate of 49.1%, corresponding to 591 million (INPS, 2011).

Usually each worker benefits from CIG for a limited number of hours: according to the INPS Report, during 2010 the total number of workers interested by CIG has been 1.56 million, corresponding to an average of two months in CIG for each of them 18. Since we do not have informa-tion on the actual distribution of CIG among workers, it seems reasona-ble to simulate the receipt of this scheme by 1.56 million workers, corre-sponding to some 7% of total employment. Given the distribution of total CIG hours between sectors (industry, construction, artisans, trade), areas (North, Centre, South) and condition in employment (blue and white col-lars), we constructed 24 groups and computed the number of CIG ben-eficiaries in each of them. Using their distribution across the groups, we randomly selected a corresponding number of beneficiaries in each group, in such a way that the proportion of the employed individuals covered by this scheme is equal to the total number of beneficiaries reported by INPS (2011) divided by the total number of individuals in employment accord-ing to official statistics (as reported in Banca d’Italia, 2011). As before, we repeated the simulation 100 times, and we report the average of the results across all replications.

To each selected worker, we assigned 771 hours of authorised CIG, of which on average 49% are actually used, i.e. about 378 hours, corresponding to two months of full-time work. We excluded from the sample of poten-tial CIG users the self-employed and the public employees. We also took ac-count of those workers that in the original IT-SILC sample declare to be us-ing the CIG scheme. All individuals who are extracted as CIG beneficiaries received a transfer equal to 80% of their monthly wage, for two months 19.

17 We consider jointly all forms of CIG (ordinary, extraordinary, «in deroga»).18 Two months of working hours correspond to 49% of the ratio between the total number

of authorised CIG hours and the number of workers involved in the scheme during 2010. 19 We also imposed the cap prescribed by CIG rules. Moreover, certain type of fixed

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They lost twice the ratio between their total yearly earnings and the number of monthly payments received in the year 20. The average amount of CIG re-ceived is 1,928 euro.

From Table 3, one third of authorised hours went to industrial manual workers resident in the North. The next more significant groups belong to the manufacturing sector as well: manual workers in the South (11.8%) and

term contracts are excluded. See INPS website for more details: http://www.inps.it/portale/default.aspx?itemdir=5902 (last access: 30/10/11).

20 In this way we can allow for the fact that in the two months under CIG the workers lose not only 20% of their wage, but also the additional payments due to extra work hours and fringe benefits.

TAB. 3. Distribution of workers interested by the CIG scheme

INPS data(year 2010)

IT-SILC data afterCIG simulation

number % %

Blue collars Manufacturing North 564,490 36.2 36.7Centre 112,330 7.2 7.3South and Islands 183,108 11.8 11.9

Artisans North 134,951 8.7 8.7Centre 36,291 2.3 2.3South and Islands 5,296 0.3 0.3

Construction North 50,064 3.2 3.2Centre 17,516 1.1 1.1South and Islands 31,342 2.0 2.0

Trade North 36,458 2.3 2.3Centre 11,383 0.7 0.7South and Islands 16,712 1.1 1.0

White collars Manufacturing North 181,743 11.7 11.8Centre 39,432 2.5 2.5South and Islands 33,403 2.1 2.1

Artisans North 16,395 1.1 1.0Centre 3,083 0.2 0.2South and Islands 996 0.1 0.0

ConstructionNorth 2,757 0.2 0.1Centre 949 0.1 0.0South and Islands 1,475 0.1 0.0

Trade North 46,812 3.0 3.0Centre 11,515 0.7 0.7South and Islands 20,489 1.3 1.3

Total 1,558,991 100.0 100.00

Note: the number of workers in each group has been reconstructed on the basis of the data provided by the observatory on the CIG available at the INPS website. We have assumed that workers benefiting from CIG are distributed among the groups proportionally to the number of authorised hours. Popula-tion 15-64. All estimates in the last column use the post-stratified sample weights (see Section 2) and are the average across 100 replications.

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in the Centre (7.3%), together with industrial white collars in the North (11.8%). As in the analysis of the previous paragraph, in Table 4 we show some characteristics of the persons selected as CIG beneficiaries by our simulation. Compared with the fall in employment levels, the CIG is much more concentrated in the North than in other areas. The age of beneficiar-ies is higher than in the case of the simulation of the probability of job loss. Finally, since many immigrants are often employed as manual workers in the factories of the North, it is not surprising that the diffusion of the CIG among them is greater than for Italian workers.

4. The distributive impact of the recession

In this section we use the dataset built according to the criteria described above to simulate the change in the distribution of household incomes that took place during the crisis. We also provide some quantitative results on how this reduction has been tempered by increased expenditure on unem-ployment benefits and on the wage supplement fund.

We consider in particular three scenarios:A) The first case corresponds to the distribution of income before the

crisis.

TAB. 4. Share of employed workers to which the simulation has imputed the CIG and their group composi-tion

Geographic area Age class

Shareof individuals

(%)

Composition(%)

Shareof individuals

(%)

Composition(%)

North West 8.4 37.1 ≤ 30 7.7 21.1North East 9.0 29.3 31-40 8.0 35.8Centre 4.8 14.8 41-50 6.4 28.8South 5.3 14.4 51-65 4.7 14.3Islands 3.4 4.4Total 6.8 100.0 Total 6.8 100.0

Education Citizenship

Shareof individuals

(%)

Composition(%)

Shareof individuals

(%)

Composition(%)

Up to lower secondary 9.1 51.1 UE 8.0 2.7Upper secondary 6.4 43.0 Italian 6.6 89.2Degree 2.5 5.9 Other countries 10.0 8.1Total 6.8 100.0 Total 6.8 100.0

Source: authors’ computations on IT-SILC data; population 15-64. All estimates use the post-strati-fied sample weights (see Section 2) and are the average across 100 replications.

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B) The second scenario considers the impact of the changes in the employ-ment rates and of the increase in the unemployment benefits and in the CIG.

C) Finally, scenario C corresponds to the income distribution that would have been produced by the recession without changes in social benefits.

The analysis is performed on the individuals of the IT-SILC sample. To each person we assigned the total disposable income of his/her household, corrected using the modified OECD equivalence scale. Disposable income includes earnings, pensions, welfare benefits, social assistance and capital in-come including imputed rents 21.

The impact of the crisis and of social benefits is measured by observing the changes in the Gini and poverty headcount indexes, the latter computed by defining two poverty lines: the first one set at 60% of median equivalent income across persons, the second one set at 40% of the median, in order to select the most serious cases.

After the recession and the reduction in earnings, the whole income distri-bution should present, ceteris paribus, a fall in median and average family val-ues 22. If the poverty line is computed ex novo, in a purely relative approach to the study of poverty, it should be lower than the poverty line of the pre-crisis distribution. The use of a variable poverty line would therefore narrow the in-crease in poverty, because a person could now be defined as non-poor even if his/her income is lower that the previous line. To take this into account, we

21 We use the IT-SILC variable «fytot_imp», which is defined as total disposable income including imputed rents.

22 As discussed in the previous paragraphs, for a few groups employment has increased from 2007 to 2010, and with it also household incomes. For workers benefiting from the wage supplement fund, however, the change in earnings is always negative.

TAB. 5. Average equivalent income by quintiles of equivalent income, before and after the crisis – all indivi-duals of the sample

A) Beforethe crisis

B) After the crisis with

social benefits

C) After the crisis without social benefits

% change in income:

from A to B

% change in income:

from A to C

% increase in income from

benefits

Share of income loss regained through the benefits (%)

1 9,036 8,796 8,672 –2.65 –4.02 1.43 342 15,000 14,687 14,512 –2.09 –3.26 1.21 363 19,720 19,337 19,124 –1.94 –3.02 1.12 364 25,362 24,909 24,662 –1.79 –2.76 1.00 355 40,622 40,046 39,838 –1.42 –1.93 0.52 27Total 21,946 21,554 21,360 –1.79 –2.67 0.91 33

Source: authors’ computations on IT-SILC data- All estimates use the post-stratified sample weights (see Section 2), and are the averages across 100 replications. The quintiles are defined on the equivalent income before the crisis.

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TAB. 6. Average equivalent income by quintiles, before and after the crisis – individuals living in households with head aged less than 65 years

A) Beforethe crisis


social benefits

C) After thecrisis withoutsocial benefits

% change in income:

from A to B

% change in income:

from A to C

% increase in income from

benefits

Share of income loss regained through the

benefits

1 8,889 8,588 8,436 –3.39 –5.10 1.80 342 15,004 14,583 14,352 –2.81 –4.34 1.61 353 19,752 19,255 18,980 –2.51 –3.91 1.45 364 25,402 24,855 24,559 –2.15 –3.32 1.21 355 40,305 39,629 39,390 –1.68 –2.27 0.61 26Total 22,144 21,653 21,415 –2.22 –3.29 1.11 33

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see Section 2), and are the averages across 100 replications. The quintiles are defined on the equivalent income before the crisis, including those with head aged more than 65.

show poverty indexes computed both with a variable poverty line and keeping the poverty line fixed at the pre-crisis level (scenario A). Table 5 shows, for quintiles of the pre-crisis income distribution, the average equivalent income before the crisis (A), after the crisis without changes in social benefits (C), and after the crisis considering also the impact of social benefits (B) 23. Case C is the counterfactual, i.e. the hypothetical situation without changes in benefits. The right section of the table contains the percentage changes in average in-come by quintiles, the percentage change in post-crisis incomes due to social benefits and a measure of how much of the income loss caused by the cri-sis has been recovered thanks to the social benefits. The recession would have caused disposable incomes, ceteris paribus, to fall on average by 2.7%. Social benefits have filled about one third of this loss, reducing the fall in average in-come to 1.8% 24. The impact of the crisis has been harder for the lower quin-tiles, in percentage terms. Social benefits have had a greater impact on lower incomes. If we restrict the analysis only to the households that have seen a fall in income (representing 12% of all households, i.e. about 3 millions), on av-erage the equivalent incomes of the persons living in these households have fallen, before changes in benefits, by 23%, and by 16% after the increase in benefits (see later section 5, tab. 11).

23 Since total pretax income is changing, also marginal tax rates and allowances will change. We do not take account of these changes, which would have a stabilizing impact on incomes. Therefore, in this sense our simulations are an upper bound result, which could be also interpreted as an immediate result of the recession, since many changes in taxes and other benefits are likely to happen several months after the change in the employment condition.

24 Brandolini et al. (2011) find that disposable income fell on average, after the change in benefits, by 1.5%.

311

Since the losses are particularly high for households where labour in-comes are present, Table 6 repeats the content of the previous one, but only for the subsample of persons who live in households where the head is aged less than 65 years 25. In this case the recession would produce a greater re-duction of average income (3.3%), softened by social transfers in particular for the lowest quintiles.

Tables 7 and 8 provide an overall view of the impact of the crisis, in terms of changes in the inequality and poverty indexes (both with variable and fixed line) 26. Without benefit increases, the Gini index would rise by

25 The household head is defined as the person with the highest individual income in the family.

26 With respect to the results in Brandolini et al. (2011), we find a slightly greater per-centage increase in both the Gini index (2.4% against 1.4%) and in the poverty rate with fixed line. The Gini index in IT-SILC is quite low when compared with the Bank of Italy Sur-

TAB. 7. Inequality and poverty before and after the crisis – all individuals of the sample

Gini Poverty diffusion with line at 60% of the median

Poverty headcount ratio with line at 40% of the median (%)

Variableline

Line fixedat scenario A

Variableline

Line fixed at scenario A

A) Before the crisis 0.2878 16.90 16.90 5.64 5.64B) After the crisis and the increase in

unemployment benefits and CIG 0.2949 17.69 18.46 6.54 6.82C) After the crisis without changes in

social benefits 0.2996 18.15 19.33 7.03 7.44

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see Section 2), and are the averages across 100 replications.

TAB. 8. Inequality and poverty before and after the crisis – individuals living in households with head aged less than 65 years

Gini Poverty diffusion with line at 60% of the median

Poverty headcount ratio with line at 40% of the median (%)

Variable line


Variable line


A) Before the crisis 0.2904 17.37 17.37 6.10 6.10B) After the crisis and the increase in

unemployment benefits and CIG 0.2995 18.62 19.32 7.29 7.58C) After the crisis without changes in

social benefits 0.3054 19.30 20.40 7.95 8.35

Source: authors’ computations on IT-SILC data All estimates use the post-stratified sample weights (see Section 2), and are the average across 100 replications. The poverty line is defined on the earnings distribution for all household, including those with head aged more than 65.

312

more than one percentage point, not a small variation, and poverty head-count ratio with fixed line by more than 2 points 27. The increase in poverty with the line set at 40% of the median would also be significant.

The impact of the recession seems to vary across age classes: even after the extension of social benefits, those more hardly hit are the younger, there-fore making more marked the concentration of poverty among households with children. Figure 1 contains the values of the poverty headcount ratios fixed at the pre-level crisis, by age classes.

vey on Household Income and Wealth (SHIW), which was estimated at 0.323 in 2006 (Banca d’Italia, 2008). We compared the index in IT-SILC cross-sections 2006-2007-2008 and SHIW 2006-2008. It seems that most of the difference is due to imputed rents, but even removing them from the disposable income is not enough to account for the variation. Given that we focus on changes in the indexes, these differences do not create problem for the present anal-ysis, though they might deserve further research.

27 Since we do not have detailed information on the changes in capital incomes, these distributive results depend only on changes in the labour market position of the population and in the associated social transfers. We made, however, two checks of the robustness of our results, reducing by 30% first the financial incomes (variable hy090n in SILC) of each house-hold, and then also the incomes from rental property (hy040n). In both cases, the values of the inequality and poverty indexes would be very similar to those shown in the tables.

FIG. 1. Poverty before and after the crisis (fixed line at 60% or 40%) by age classes – all the individuals of the sample. Poverty line fixed at the pre-crisis level.

Source: authors’ computations on IT-SILC data. All estimates use the sample weights and are the average across 100 replications.

0-14 15-24 25-34 35-44 45-54 55-64 65-74 75+

30%

25%

20%

15%

10%

5%

0%

A) Before the crisis (60%)

B) After the crisis with social benefits (60%)

C) After the crisis without social benefits (60%)

A) Before the crisis (40%)

B) After the crisis with social benefits (40%)

C) After the crisis without social benefits (40%)

313

Table 9 takes a deeper look into the effect of the crisis on the younger generations, focusing on individuals aged up to 35 years, distinguishing also those who live in the original family and those who have left it. This last group has been hit much more severely by the recession, with disposable in-come falling on average by 5% even after the increase in benefits. On the other hand, the disposable incomes of the youth still living with their parents have hardly changed after the recession: the original family has performed once again its traditional role of social safety net which is typical of the South-European welfare regime (Ferrera, 1996), supplementing the falling incomes of the younger generations through the sharing of parents’ incomes. The gap in incomes between those who live in the original family and those who have left it has increased, making still more difficult the transition into adulthood.

With fixed line, the crisis would produce a much stronger effect in Northern regions (Table 10), if we evaluate the impact in relative terms with

TAB. 9. Average equivalent income for young individuals, before and after the crisis – all individuals aged 0-35

Share of total population

aged 0-35 (%)

A) Before the crisis


social benefits


% change in income: from

A to B

% change in income: from

A to C

Aged 0-17 42.6 19,263 18,754 18,528 –2.6 –3.8Aged 18-35 living with parents 30.3 22,380 21,978 21,759 –1.8 –2.8Aged 18-35 living without parents 27.1 19,888 18,934 18,529 –4.8 –6.8Total aged 0-35 100.0 20,377 19,780 19,508 –2.9 –4.3

Source: authors’ computations on IT-SILC data; all estimates use the post-stratified sample weights (see Section 2). All estimates are the averages across 100 replications. The quintiles are defined on the equivalent income before the crisis, and are computed on the total population.

TAB. 10. Poverty headcount ratios before and after the crisis (line at 60%) by area of residence –all the indi-viduals of the sample

Variable line Fixed line


(%)


social benefits (%)


(%)


(%)


social benefits(%)


(%)

North 8.4 9.0 9.7 8.4 9.4 10.4Centre 10.1 10.4 10.7 10.1 10.9 11.6South 31.7 33.0 33.2 31.7 34.3 35.2Total 16.9 17.7 18.2 16.9 18.5 19.3

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see Section 2) and are the averages across 100 replications.

314

respect to the starting points. Social benefits would anyway be more effective in contrasting the rise in poverty indexes in the North, due to the greater concentration of industrial jobs, and therefore of the wage supplement fund, in that area.

5. What would have happened with universal benefits?

The Italian system of unemployment benefits is fragmented and categori-cal (Anastasia et al., 2009, Berton et al., 2009). Reflecting the dual nature of the labour market, significant benefits are available only for workers with permanent contracts, while for fixed-term contracts only a very small benefit is available after the end of the job, covering also a limited proportion of these workers. As shown in section 2, the crisis has had a much greater im-pact on the younger generations of workers, i.e. the side of the labour mar-ket where many fixed-term contracts are concentrated. The patchy nature of unemployment benefits has therefore exacerbated the effects of the reces-sions, leaving without significant help precisely those workers who have been more hardly hit by job losses.

Many proposals have been advanced in the last few years arguing for the creation of a more universal and robust safety net against unemployment risk. In this section we ask what could have been the effects of such a uni-versal system during the last recession. We consider a very stylized scheme, which resembles (with simplifications and differences) some previous pro-posals (for example, Boeri and Garibaldi, 2008, Berton et al., 2009): in our simulation, after losing the job a person is entitled a transfer of 65% of the previous wage for the first six months, and of 55% for the next 18 months. No contributory requirement is needed to access the benefit, which is also available to all types of contract. The previous cap is removed and replaced with a maximum monthly payment equal to 2,500 euro, while we also im-pose a minimum of 500 euro (see Boeri and Garibaldi, 2009). We keep un-changed the CIG scheme, for two reasons. First, Boeri and Garibaldi suggest to maintain a scheme with the same broad characteristics, albeit financed on a voluntary basis by the firms and employees. Second, it would be very dif-ficult to imagine how to replace this scheme in the simulation, since the sim-ple allocation of an unemployment benefit for two months does not seem realistic, and it seems hard to account for the different behaviour of the em-ployer whether the CIG scheme was changed.

With this reform, expenditure for new unemployment benefits (i.e., the benefits delivered to those who have been simulated as newly not employed after the crisis) would nearly double with respect to the amount simulated

315

for the pre-crisis period, rising by about four billion euro. This result is in line with what Boeri and Garibaldi have computed for their proposal 28. Above we saw that, following the crisis, the Gini index would increase from 0.288 (case A) to 0.300 (case C, i.e. after the crisis without changes in ben-efits). The increased expenditure in existing benefits would reduce the Gini to 0.295; the reform in unemployment benefits would further cut it to 0.292. For poverty rates the story is the same: more general unemployment benefits would have further contained the impact of the crisis. The headcount ratio after the increase in benefits would fall from 18.5% (current benefits) to 18.0% (reformed benefits) with the fixed 60% line, and from 6.8% (current benefits) to 6.4% (reformed benefits) with the fixed 40% line: values still higher that the pre-crisis ones, but in any case lower than those guaranteed by the current unemployment benefits.

Table 11 shows, only for the subset of households with a simulated de-crease in income following the crisis (i.e., where at least a member has be-come not employed or CIG beneficiary), the reduction in disposable equiva-lent income before and after the crisis, including the hypothesis of this new unemployment scheme. For those hit by the crisis and belonging to the poor-est 20% of the total population, average equivalent income would have fallen, without any benefit adjustment, by 42%, a very significant amount. Current benefits would reduce this fall to 30%, but with the reformed, universal un-employment benefits the income loss would be further reduced to only 20%, half the initial percentage reduction. The same is true for the other quintiles,

28 See Boeri and Garibaldi (2009). We do not simulate the total cost of a new unemploy-ment benefit, but only the cost of covering with this new scheme those who have lost their job during the crisis.

TAB. 11. Average equivalent income by quintiles of equivalent income, before and after the crisis – all indi-viduals living in households with a reduction in income

A) Beforethe crisis


social benefits


D) After the crisis with CIG

and reformed UB

% changein income:

from A to B

% changein income:

from A to C

% changein income:

from A to D

1 9,810 6,878 5,706 7,848 –30 –42 –202 15,077 12,089 10,742 12,938 –20 –29 –143 19,755 16,676 15,182 17,519 –16 –23 –114 25,429 22,009 20,379 22,869 –13 –20 –105 37,567 32,219 30,540 33,257 –14 –19 –11Total 21,954 18,410 16,928 19,314 –16 –23 –12

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see Section 2), and are the averages across 100 replications. The quintiles are defined on the equivalent income before the crisis, and are computed on the total population.

316

FIG. 2. Change in poverty headcount ratios (fixed line) with new UB.

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see section 2), and are the average across 100 replications.

0-14 15-24 25-34 35-44 45-54 55-64 65-74 75+

0%

–0,2%

–0,4%

–0,6%

–0,8%

–1,0%

–1,2%

60% line 40% line

but the effect of the reform would be stronger for the poorest ones. Such a reform, therefore, would have also a positive distributional impact.

Reforming the unemployment benefits towards universalism would mainly benefit not only the poorest quintiles, but in particular the youngest cohorts of workers and their relatives: Figure 2 shows the changes in the poverty headcount rates for individuals belonging to different age classes, after the transition from current benefits to the new general unemployment scheme (i.e., from case B to case D of the previous table): compared to the current situation, the risk of poverty would fall in particular for those aged between 25 and 44 years, i.e. the younger workers, and for their children, belonging to the first age class.

Turning to the distinction among individuals on the basis of the type of households in which they live, we can ask which groups have been more af-fected by the crisis, and which ones would benefit more from a transition to a universal system of unemployment benefits. Table 12 shows that single per-sons, in particular those younger than 50 years of age, and young couples with children aged less than 18 years suffer on average the strongest reductions in disposable incomes. The suggested reform of unemployment benefits (last col-umn) would have a very low impact on the living standards of those aged more than 50 years and their families, while would significantly increase the incomes of workers aged less than 50 years and of households with children. This ev-idence is confirmed by Table 13, which reports poverty headcount ratios by family type: the new reformed benefits would reduce the rise in the risk of poverty in particular for young adults and for households with small children.

317

TAB. 12. Average equivalent income by household type, before and after the crisis – all individuals living in households with a reduction in income

Share of total population

(%)

A) Beforethe crisis

B) Afterthe crisis

with social benefits

D) Afterthe crisis

with CIG and reformed UB

% changein income:

fromA to B

% changein income:

fromB to D

Single aged 50 + 8 29,189 22,387 22,864 –23.3 2.1Single aged less than 50 4 24,202 16,666 18,520 –31.1 11.1Couple without co-resident children, at least one part-ner 50 + 12 28,452 24v689 25,022 –13.2 1.3Couple without co-resident children, both partner less than 50 4 27,786 22,882 24,151 –17.6 5.5Couple or single with co-re-sident children, all under 18 31 19,360 15,575 16,499 –19.6 5.9Couple or single with at least one co-resident child aged 18-35 31 23,068 20,240 21,033 –12.3 3.9Other households 10 23,526 20,638 21,419 –12.3 3.8Total 100 21,954 18,410 19,314 –16.1 4.9


TAB. 13. Poverty before and after the crisis (fixed line at 60%) by household type – all the individuals of the sample (values %)

A) Beforethe crisis

B) After thecrisis with

social benefits

D) After thecrisis with CIG

and reformed UB

FromA to B

FromB to D

Single aged 50 + 15.7 15.8 15.8 0.1 0.0Single aged less than 50 16.3 19.2 18.0 2.9 –1.2Couple without co-resident chil-dren, at least one partner 50 + 9.2 9.3 9.3 0.0 0.0Couple without co-resident children, both partner aged less than 50 8.0 10.0 9.3 2.0 –0.7Couple or single with co-resi-dent children, all under 18 23.1 25.9 25.2 2.8 –0.8Couple or single with at least one co-resident child aged 18-35 16.4 17.7 17.2 1.3 –0.5Other households 12.8 13.5 13.2 0.7 –0.3Total 16.9 18.5 18.0 1.6 –0.5


Finally, Table 14 concentrates again on the younger section of the popula-tion, repeating the group classification of Table 9, this time considering only those who live in households with a fall in income due to the recession. The

318

TAB. 14. Average equivalent income for young individuals, before and after the crisis – all individuals aged 0-35 living in households with a reduction in income

A) Beforethe crisis


socialbenefits

D) After the crisis with CIG and reformed

UB

% changein income:

fromA to B

% changein income:

fromB to D

Change in headcount

rate:from

A to B(%)

Change in headcount

rate:from

B to D(%)

Aged 0-17 18,858 15,356 16,210 –18.6 5.6 2.5 –0.7Aged 18-35 living with parents 23,528 20,650 21,482 –12.2 4.0 1.3 –0.5Aged 18-35 living with-out parents 20,811 16,044 17,322 –22.9 8.0 4.0 –1.3Total aged 0-35 20,819 17,079 18,066 –18.0 5.8 2.6 –0.8

Source: authors’ computations on IT-SILC data. All estimates use the post-stratified sample weights (see section 2), and are the averages across 100 replications. The last two columns are estimated on the whole population aged 0-35, including those without a reduction in income, and refer to the 60% fixed line.

loss in income (and the corresponding increase in the risk of poverty) has been much more severe for children aged less than 18 and for young adults living on their own than for those who still live with their parents. A reform in un-employment benefits would be more effective in increasing the disposable in-comes of the first two groups, and in particular of the young adults living in new families, given the strong diffusion of fixed-term contracts among them, and the impossibility of relying on the incomes of older household members.

6. Conclusions

Like in other European countries (Jenkins et al., 2011; Ward et al., 2009), also in Italy the reduction in employment rates during the 2008-2010 crisis hit younger workers more than the rest of the population, as well as those with low education levels and with foreign citizenship. The extension of the Cassa integrazione guadagni mainly interested the Northern regions, the mid-dle-aged workers and those with Italian citizenship. Together with the reli-ance on unreformed unemployment benefits, which exclude many fixed term contracts and atypical workers, these measures have therefore reinforced the dual nature of the Italian job market and social protection system: young employees and workers of small firms on the one side, middle-aged and me-dium or big firms workers on the other. Almost no effort has been made towards a more universal system.

319

According to our simulations, the current recession, like the previous deep crisis of 1993, should have increased inequality and poverty (with the usual caveat of the ceteris paribus assumption). Keeping at 60% poverty line fixed at the pre-crisis level, the headcount ratios would have increased by about 2.4 percentage points. Inequality should have worsened too. The reac-tion of public policies, in terms of greater expenditure for traditional social benefits, has had a significant impact on the extent of poverty, in any case far from being able to bring back the indexes at their pre-crisis levels. The reform of unemployment benefits in a more universalistic sense would have benefited most the younger generations, particularly those who have already made the transition to adulthood and have children.

Appendix 1: Additional tables

TAB. A.1. Distribution of the population aged 15-64 years, for each of the 24 groups used in the simulation of the reduction in employment (values %)

Group LabourForce Surveys

SILC

2007 2010 Before the post-stratification

After the post-stratification

Up to lower secondary education; male; young; South 4.79 4.27 3.93 4.79Up to lower secondary education; male; young; North-Centre 6.40 5.83 5.59 6.40Up to lower secondary education; male; old; South 5.08 5.15 5.17 5.08Up to lower secondary education; male; old; North-Centre 8.78 8.53 8.73 8.78Up to lower secondary education; female; young; South 4.28 3.79 3.47 4.28Up to lower secondary education; female; young; North-Centre 5.03 4.69 4.30 5.03Up to lower secondary education; female; old; South 5.64 5.64 5.66 5.64Up to lower secondary education; female; old; North-Centre 9.14 8.66 9.03 9.14Upper secondary education; male; young; South 3.70 3.79 4.11 3.70Upper secondary education; male; young; North-Centre 7.22 7.12 7.49 7.22Upper secondary education; male; old; South 2.44 2.57 2.45 2.44Upper secondary education; male; old; North-Centre 6.12 6.87 6.48 6.12Upper secondary education; female; young; South 3.76 3.74 4.14 3.76Upper secondary education; female; young; North-Centre 7.23 6.96 7.33 7.23Upper secondary education; female; old; South 2.29 2.47 2.39 2.29Upper secondary education; female; old; North-Centre 6.07 6.91 6.33 6.07Degree; male; young; South 0.77 0.75 0.91 0.77Degree; male; young; North-Centre 1.88 1.91 2.06 1.88Degree; male; old; South 0.83 0.91 0.96 0.83Degree; male; old; North-Centre 1.97 2.17 2.14 1.97Degree; female; young; South 1.15 1.20 1.27 1.15Degree; female; young; North-Centre 2.69 2.79 2.88 2.69Degree; female; old; South 0.83 0.98 0.95 0.83Degree; female; old; North-Centre 1.91 2.30 2.22 1.91Total 100.0 100.0 100.0 100.0

Source: authors’ computations on IT-SILC and Labour Force Survey data; population 15-64; all esti-mates use the sample weights.

TAB. A.2. Employment rates for each of the 24 groups used in the simulation of the changes in employment rates (values %)

Group LabourForce Surveys

OriginalSILC

sample (after employment correction)

2008

Simulation

2007 2010 Change Beforethe crisis

Afteremployment

change

Change

Lower secondary; male; young; South 49.8 40.5 –9.3 56.9 49.8 40.6 –9.2Lower secondary; male; young; North-Centre 64.9 56.8 –8.1 70.9 64.9 56.8 –8.1Lower secondary; male; old; South 63.2 58.5 –4.7 65.7 63.2 58.6 –4.6Lower secondary; male; old; North-Centre 67.8 68.2 0.4 68.5 67.8 68.2 0.4Lower secondary; female; young; South 17.6 15.9 –1.7 18.7 17.6 15.9 –1.7Lower secondary; female; young; North-Centre 40.7 33.3 –7.4 49.5 40.7 33.3 –7.3Lower secondary; female; old; South 18.6 18.4 –0.2 18.7 18.6 18.4 –0.2Lower secondary; female; old; North-Centre 37.4 38.1 0.7 36.5 37.4 38.1 0.7Upper secondary; male; young; South 58.5 53.8 –4.8 53.7 58.5 53.8 –4.7Upper secondary; male; young; North-Centre 80.7 77.0 –3.8 79.0 80.7 77.0 –3.7Upper secondary; male; old; South 80.1 78.0 –2.1 81.4 80.1 78.0 –2.1Upper secondary; male; old; North-Centre 83.4 82.0 –1.4 84.0 83.4 82.0 –1.4Upper secondary; female; young; South 35.0 31.2 –3.8 30.2 35.0 31.3 –3.7Upper secondary; female; young; North-Centre 65.6 62.1 –3.5 63.6 65.6 62.1 –3.5Upper secondary; female; old; South 51.9 51.5 –0.3 52.6 51.9 51.6 –0.3Upper secondary; female; old; North-Centre 66.7 67.5 0.8 65.8 66.7 67.5 0.8Degree; male; young; South 65.2 60.5 –4.7 54.4 65.2 60.8 –4.4Degree; male; young; North-Centre 82.9 80.5 –2.4 79.3 82.9 80.6 –2.3Degree; male; old; South 89.5 88.7 –0.7 91.7 89.5 88.9 –0.6Degree; male; old; North-Centre 90.1 88.9 –1.2 88.1 90.1 89.0 –1.1Degree; female; young; South 54.2 50.7 –3.5 55.3 54.2 50.8 –3.4Degree; female; young; North-Centre 74.7 74.0 –0.8 76.4 74.7 74.0 –0.7Degree; female; old; South 79.4 76.0 –3.4 81.7 79.4 76.2 –3.2Degree; female; old; North-Centre 77.1 78.1 1.0 78.8 77.1 78.0 0.9Total 58.7 56.9 –1.8 59.9 58.7 56.0 –2.7

Source: authors’ computations on IT-SILC and Labour Force Survey data; population 15-64; all esti-mates use the sample weights; the last two columns are estimated as the average across 100 replications.

321

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Inequality and poverty during the recession in Italyby Massimo Baldini and Emanuele Ciani

Summary: This paper simulates the effects of the recent economic crisis on income in-equality and poverty in Italy. We impute changes in employment rates estimated from the La-bour force survey on the Italian SILC sample. The resulting increase in unemployment ben-efits is simulated in detail, together with the expansion of the Cassa Integrazione Guadagni, a wage supplement fund that has played a crucial role during the crisis. Our simulations suggest that the crisis has increased inequality and poverty levels, in particular for households with children and for the younger cohorts of workers. We also show that a reform of unemploy-ment benefits towards universalism would have significantly contained these effects.

Keywords: Italy, economic crisis, employment rates, unemployment benefits, inequality, poverty, microsimulation.

J.E.L. Classification: C63; I30; I38.

Address:

Massimo Baldini, Università di Modena e Reggio Emilia, Dipartimento di Economia Politica, via Berengario 51, I-41121 Modena. E-mail: [email protected].

Emanuele Ciani, University of Essex, Department of Economics, Wivenhoe Park, CO4 3SQ Colchester, UK. E-mail: [email protected].

03 baldini ciani - facoltà di economia marco biagi -...

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