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”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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…
Outline
 The Italian social protection system
 Data collection:
 The RMI “experiment”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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
Outline
 The Italian social protection system
 Data collection:
 The RMI “experiment”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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
Outline
 The Italian social protection system
 Data collection:
 The RMI “experiment”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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 versus RUI
RUI
RMI

Period: 1999-2003

Period: 2004-2005

More generous
(in 2003, single = 279 €)

Less generous (especially because
time limited)

More developed activation
programs

Threshold: ISEE < 5.000 €

Poor 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 “support”:
•
•
•
people who
cannot work
single = 300 €
max duration 6
months (only 1
renewal)
RUI “insertion”:
•
•
•
•
people in socioeconomic distress
difficulties in finding a job
single = 350 €
max duration 6 months
(renewal always allowed)
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
2.06 (1.19)
1
7
1.68 (1.00)
1
5
43.93 (12.91)
-
-
48.05 (9.86)
26
64
1.56 (0.50)
-
-
1.29 (0.46)
1
2
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
-
-
-
Household dimension
Age (head of household)
Woman head of household
Education (head of household)
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 betterdesigned 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
1
Results:
0.9
RMI
RUI
% beneficiari ancora in assistenza
0.8
0.7
0.6
0.5
0.4
 The two
survival
functions do not
significantly
differ
(confidence
intervals
overlap)
Moreover, we
are not
controlling for
“behavioural
effects”…
0.3
0.2
0.1
0
1
1
2
3
4
5
6
7
mesi di permanenza nel programma
8
9
10
11
12
Outline
 The Italian social protection system
 Data collection:
 The RMI “experiment”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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 statistics
RMI 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
Claimants’ age
Female claimants
media
dev.std.
min
max
37.99
(10.51)
17
71
0.34
(0.47)
-
-
6.74
-
-
-
56.61
-
-
-
15.51
-
-
-
11.56
-
-
-
9.58
-
-
-
7.15
-
-
-
36.15
-
-
-
46.33
-
-
-
9.66
-
-
-
0.43
-
-
-
3.14
(0.83)
0.08
3.75
37.71
(9.97)
0.99
45
500.40
(255.65)
4.08
1824.66
Household composition
Couple
Couple with children
One parent with children
Single
Other
Education
None
Primary school
Lower secondary school
Upper secondary school
University
Years in assistance
Months in assistance
Subsidy (2003)
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
nessuna inv alidità
 The % of households
who gave up applying is
increasing over time:
4% in 2000
6% in 2001
10% in 2002
0.030
2000
inv alidità
0.010
nessuna inv alidità
 Mostly households with
disabled persons
0.033
2001
inv alidità
 features like selfemployment or owning a
house are not correlated
with increasing renounce
rate
0.024
nessuna inv alidità
0.045
2002
inv alidità
0.050
0
.01
.02
.03
.04
% di benef iciari che rinuncia o non rinnov a
.05
 There is evidence
that stricter controls
reduce welfare abuse
Outline
 The Italian social protection system
 Data collection:
 The RMI “experiment”
1. Rovigo
2. Foggia
 The Friuli Venezia-Giulia project on
guaranteed minimum income
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?
.00001 .00002 .00003 .00004 .00005
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
0
2. Income
underreporting
0
20000
40000
x
administrative data
60000
survey
80000
Evidence of fiscal evasion?
.00006
Administrative data vs survey (only welfare recipients)
 Here, we only consider
households who receive
transfers or social services
.00002
.00004
The distribution from
survey data has a peak in
the interval 10.000 –
20.000 €, while
administrative data peak at
lower values
0
 Thresholds to enter
social assistance programs
are usually in the interval
5.000 – 15.000 €
0
20000
40000
x
administrative data
60000
survey
80000
 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!