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

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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!