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Reddito minimo di inserimento: an analysis of local experiences

Reddito minimo di inserimento: an analysis of local experiences. Paola Monti - Fondazione RDB (joint with M. Pellizzari and T. Boeri) Moncalieri, 8 November 2007 . Outline. The Italian social protection system Data collection: The RMI “experiment” Rovigo Foggia

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Reddito minimo di inserimento: an analysis of local experiences

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

  3. The Italian social protection system • Segmented: - only limited categories are protected- mainly targeted on pensioners and scarce resources- poor targeting properties [Toso, 2000] • 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. Outline • The Italian social protection system • Data collection: • The RMI “experiment” • Rovigo • Foggia • The Friuli Venezia-Giulia project on guaranteed minimum income

  5. Data collections • Our research unit carried out data collections on: • the RMI “experiment” (Rovigo and Foggia) • 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. Outline • The Italian social protection system • Data collection: • The RMI “experiment” • Rovigo • Foggia • The Friuli Venezia-Giulia project on guaranteed minimum income

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

  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 RMI versus RUI • 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)

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

  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 20006% in 200110% 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. Outline • The Italian social protection system • Data collection: • The RMI “experiment” • Rovigo • Foggia • The Friuli Venezia-Giulia project on guaranteed minimum income

  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: • An ad hoc survey on FVG households (October 2006-March 2007) • 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: • Households that fill in ISEE declarations are poorer • 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!

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