Reddito minimo di inserimento: an analysis of local experiences

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Reddito minimo di inserimento an analysis of
local experiences

• Paola Monti - Fondazione RDB
• (joint with M. Pellizzari and T. Boeri)
• Moncalieri, 8 November 2007

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Outline

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

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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

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Outline

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

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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

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Outline

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

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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

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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

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Outline

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

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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)

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RMI versus RUI

• RMI
• Period 1999-2003
• More generous (in 2003, single 279 )
• More developed activation programs
• Unlimited duration
• Threshold equivalent income lt 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 lt 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)

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Summary statistics
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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

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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

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Survival probability

• Results
• ? The two survival functions do not significantly
  differ (confidence intervals overlap)
• Moreover, we are not controlling for behavioural
  effects

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Outline

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

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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

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Summary statistics
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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

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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

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Outline

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

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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

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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

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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 ISEElt5.000 (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.

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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)

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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

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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

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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

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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

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Thanks for your attention!