1. Reddito minimo di inserimento: an analysis of local experiences Paola Monti - Fondazione RDB (joint with M. Pellizzari and T. Boeri) Moncalieri, 8 November 2007

2.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

3. The Italian social protection system 1.Segmented: - only limited categories are protected - mainly targeted on pensioners and scarce resources - poor targeting properties [Toso, 2000] 2.Fragmented: many local administrations have created independent programs, but low coverage and irregular geographic distribution  territorial inequality  A more general approach is needed in order to introduce a guaranteed minimum income (GMI)  However, before extending a measure like a GMI at national level one may want to know its properties and predict its costs…

4.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

5. Data collections  Our research unit carried out data collections on: 1.the RMI “experiment” (Rovigo and Foggia) 2.the FVG project for the introduction of a guaranteed minimum income  Partly funded by the PRIN, partly by the fRDB  For the RMI, we look for detailed information on recipients  For the FVG project, we collect information on potential beneficiaries using both survey and administrative data

6.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

7. The RMI “experiment”  Introduced in 1998 as a pilot scheme in 39 municipalities (Law 237/98, Prodi Government)  Extended to 267 in 2001  Features: Unit of entitlement: the household Cash transfer + activation programs Benefits = difference between a predefined threshold and the household “equivalent income” Eligibility conditional to participation in activation programs (employment programs, training, care services, etc.) 90% centrally funded

8. An experiment?  Emphasis on its “experimental” nature, but in reality nothing to do with scientific experiments  Municipalities/recipients not randomly chosen (actual criteria far from being random…)  No detailed data collection on recipients  Evaluation commissioned to independent research institutes (IRS), but they could only work on very aggregated data and the final report was not made public by the new government

9.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

10. 1) Rovigo  RMI starts in 1999 (39 municipalities)  Local services already provided economic assistance to the poor  Network of local actors collaborating with public services  RMI continued until 2003  In 2004 a new program was introduced: RUI (Reddito di Ultima Istanza – Last Resort Income)  We collect detailed information on recipients from both programs (RMI and RUI)

11. RMI  Period: 1999-2003  More generous (in 2003, single = 279 €)  More developed activation programs  Unlimited duration  Threshold: equivalent income < 3.500 €  When computing the household “equivalent income”, a coefficient is applied, based on household dimension and features RUI  Period: 2004-2005  Less generous (especially because time limited)  Threshold: ISEE < 5.000 €  Poor activation programs RUI “support”: people who cannot work single = 300 € max duration 6 months (only 1 renewal) RUI “insertion”: people in socio- economic distress difficulties in finding a job single = 350 € max duration 6 months (renewal always allowed) RMI versus RUI

12. Summary statistics

13. A possible application: survival functions  Assistance programs typically create disincentives to labour force participation. We look at the role of activation programs in reducing disincentive effects.  We use RUI recipients as a control group for RMI recipients in order to test whether better- designed activation programs may compensate disincentive effects related to a more generous subsidy  We compare the survival functions of the two programs

14. Comparable groups? In order to use RUI beneficiaries as a control group for RMI beneficiaries we need to be sure that the two groups are comparable (the only difference must be in the “treatment”): Focus on last years of RMI program (2001-2003) We look at individuals during their first 12 months into the program We exclude beneficiaries of both programs We check for variations in main labour market indicators during the observed period

15. Survival probability Results:  The two survival functions do not significantly differ (confidence intervals overlap)  Moreover, we are not controlling for “behavioural effects”…

16.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

17. 2) Foggia  RMI starts in 1999  Starting from 2000, special efforts to implement stricter controls in order to check claimants’ requisites : coordination of different local authorities (INPS, catasto, etc.) controlled households discretionally chosen by the local administration for being “suspect” (no random controls) There was a concrete probability of being checked

18. Summary statistics

19. A possible application: do controls reduce cheating? In order to check for possible effects of improved controls…  We looked at households who gave up applying for the subsidy without any observable change in their economic situation  We excluded households who left the program because their economic situation improved

20. Decreasing renewal rates  The % of households who gave up applying is increasing over time: 4% in 2000 6% in 2001 10% in 2002  Mostly households with disabled persons  features like self- employment or owning a house are not correlated with increasing renounce rate  There is evidence that stricter controls reduce welfare abuse

21.  The Italian social protection system  Data collection: The RMI “experiment” 1.Rovigo 2.Foggia The Friuli Venezia-Giulia project on guaranteed minimum income Outline

22. Friuli Venezia-Giulia  The FVG has planned to introduce a GMI  Research group to evaluate sustainability of the measure and to decide eligibility criteria and target  Subsidy = cash transfer equal to the difference between a pre-defined ISEE threshold and the household ISEE indicator  What is the ISEE indicator? Homogeneous criteria to evaluate households economic situation Info on income, assets, household composition and features (children, disabled person, working parents) Based on self-certification

23. Two data sources  We collected data from: 1.An ad hoc survey on FVG households (October 2006-March 2007) 2.Administrative data on “ISEE declarations” from the INPS archive

24. 1. The survey  Two samples: Random sample of FVG households (1.376 households) Random sample from households that filled in an “ISEE declaration” between July 2005 and June 2006 and have ISEE<5.000 € (474 households)  Two questionnaires: Family-based: quality of the place where the family lives (rented flat? home owners?), savings, social services or transfers they can benefit from, disabled people Individual-based: age, education, sex, health status, labour market status, occupation, income, etc.

25. 2. ISEE administrative data  Data on “ISEE declarations” from INPS archives 43.000 declarations ISEE values for all households that filled in an ISEE declaration between July 2005 and June 2006 Data not available (privacy issues)

26. A possible application: looking for evidence of fiscal evasion  How extensive is cheating when households apply for a subsidy?  We compare survey and administrative data in order to check for income underreporting phenomena of welfare claimants  Method: For each household from the survey (random sample of FVG households) we construct a household-specific ISEE indicator We compare the distribution of ISEE values from our survey data (estimated ISEE values) with ISEE administrative data

27. Evidence of fiscal evasion? ISEE values distribution: administrative vs survey data  Average ISEE value is higher (+20%) from survey data  Two possible explanations: 1.Households that fill in ISEE declarations are poorer 2.Income underreporting

28. Evidence of fiscal evasion? Administrative data vs survey (only welfare recipients)  Here, we only consider households who receive transfers or social services  The distribution from survey data has a peak in the interval 10.000 – 20.000 €, while administrative data peak at lower values  Thresholds to enter social assistance programs are usually in the interval 5.000 – 15.000 €  Households underreport their income in order to enter assistance programs

29. Conclusions  All data we collected are available for the other PRIN units, and  they will become available for researchers in the future  More analysis Take-up rates Implications of definition of beneficiaries on costs Labour supply effects …

30. Thanks for your attention!