Reddito minimo di inserimento:
an analysis of local experiences
Paola Monti - Fondazione RDB(joint with M. Pellizzari and T. Boeri)
Moncalieri, 8 November 2007
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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…
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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
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
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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)
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
Summary statistics RMI RUI
Programs entry and exit dates
min max min max
Entry date 13-gen-99 10-gen-03 14-lug-04 28-nov-05
Exit date 01-apr-99 02-apr-04 31-ago-04 31-dic-05
Household features
mean dev.std. min max mean dev.std. min max
Household dimension 2.06 (1.19) 1 7 1.68 (1.00) 1 5
Age (head of household) 43.93 (12.91) - - 48.05 (9.86) 26 64
Woman head of household 1.56 (0.50) - - 1.29 (0.46) 1 2
Education (head of household)
None 3.42 - - - 0 - - -
Primary school 34.25 - - - 33.96 - - -
Lower secondary school 49.09 - - - 54.72 - - -
Upper secondary school 10.27 - - - 9.43 - - -
University 2.97 - - - 1.89 - - -
Months in assistance 22.81 (15.26) 0.8 57.5 3.98 (3.06) 0.26 12.14
Subsidy (2004 prices) 359.12 (195.66) 0.00 1139.05 331.89 (111.04) 74.38 654.00
Number of observations
Households 313 - - - 63 - - -
Individuals 649 - - - 105 - - -
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
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
Survival probability
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 1 2 3 4 5 6 7 8 9 10 11 12
mesi di permanenza nel programma
% b
enef
icia
ri a
ncor
a in
ass
iste
nza
RMI RUI
Results:
The two survival functions do not significantly differ (confidence intervals overlap)
Moreover, we are not controlling for “behavioural effects”…
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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
Summary statisticsRMI in Foggia (1999-2003)
Program entry and exit dates
min max
Entry date 1-May-99 1-Jan-03
Exit date 1-Oct-99 1-Jun-03
Household features
media dev.std. min max
Claimants’ age 37.99 (10.51) 17 71
Female claimants 0.34 (0.47) - -
Household composition
Couple 6.74 - - -
Couple with children 56.61 - - -
One parent with children 15.51 - - -
Single 11.56 - - -
Other 9.58 - - -
Education
None 7.15 - - -
Primary school 36.15 - - -
Lower secondary school 46.33 - - -
Upper secondary school 9.66 - - -
University 0.43 - - -
Years in assistance 3.14 (0.83) 0.08 3.75
Months in assistance 37.71 (9.97) 0.99 45
Subsidy (2003) 500.40 (255.65) 4.08 1824.66
Number of observations
Households 2.655 - - -
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
Decreasing renewal rates
0.050
0.045
0.024
0.033
0.010
0.030
0 .01 .02 .03 .04 .05% di benef iciari che rinuncia o non rinnov a
2002
2001
2000
inv alidità
nessuna inv alidità
inv alidità
nessuna inv alidità
inv alidità
nessuna inv alidità
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
The Italian social protection system Data collection:
The RMI “experiment”
1. Rovigo
2. Foggia
The Friuli Venezia-Giulia project on guaranteed minimum income
Outline
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
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
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.
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)
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
Evidence of fiscal evasion?
ISEE values distribution: administrative vs survey data
0.0
0001
.000
02.0
0003
.000
04.0
0005
kden
sity
ISE
E
0 20000 40000 60000 80000x
administrative data survey
Average ISEE value is higher (+20%) from survey data
Two possible explanations:
1. Households that fill in ISEE declarations are poorer
2. Income underreporting
Evidence of fiscal evasion?0
.000
02.0
0004
.000
06kd
ensi
ty IS
EE
0 20000 40000 60000 80000x
administrative data survey
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
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 …
Thanks for your attention!