_______________________________________________________
POVERTY and ECONOMIC POLICY (PEP) Research Network
Call for Research Proposals
_______________________________________________________
Poverty and Employability Effects
of Workfare Programs in Argentina
Sandra Fachelli
Lucas Ronconi
Juan Sanguinetti
First Draft: March 2004
_______________________________________________________
Sandra Fachelli works at the Ministerio de Economia, Argentina. Juan Sanguinetti at
the Centro de Estudios para el Desarrollo Institucional (CEDI), Argentina; and Lucas
Ronconi at Universidad de San Andres and CEDI, Argentina. This work was carried
out with the aid of a grant from Poverty and Economic Policy (PEP) Research
Network, financed by the International Development Research Centre (IRC).
* We appreciate the valuable comments of Dorothee Boccanfuso, Evan Due, JeanYves Duclos and seminar participants at the Hanoi PEP meeting. Ignacio
Franceschelli and Virginia Casazza provided excellent research assistance.
Introduction
Argentina suffered a deep economic, social and political crisis during the last
few years. The economy shrink by about 11% in 2002, and due to the
currency’s depreciation, GDP per capita drop off to US$ 2,850 (down from
US$ 8,210 at its peak in 1998).
The crisis sharply aggravated the already difficult social situation. During
2002 poverty and unemployment were at their maximum historical level:
55% of argentine households were below the poverty line, and almost 20%
of the labor force was unemployed. Unemployment is particularly severe
among the least-skilled workers, being higher than 30% (INDEC, October
2002). This extremely negative context also had an impact on the education
and health sectors where there is a growing evidence of deterioration in
service delivery. The combined effect of all these factors has resulted in an
increasingly conflictive social situation with high levels of violence and
protests (see Fiszbein 2002).
One of the main policies implemented by the government to deal with the
crisis was to significantly increase the budget allocated to active labor
demand policies. The number of beneficiaries of workfare programs
increased from 90,000 at December 2001, to 1,200,000 at October 2002,
and to 2,000,000 in 2003 (see table 1).
The recent decline in unemployment (form 21.5% in May 2002 to 15.6% in
May 2003) and in poverty (from 54.3% to 47.8%) has been presented by
the government as evidence of the positive income and employability
effects of workfare programs (Ministerio de Trabajo, 2003).
Table 1: Long term trends in Poverty, Unemployment, Economic Growth and Workfare
programs (1980-2002)
Year
1980
1985
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003*
Poverty Rate
Unemployment
(Buenos Aires) (% of labor force)
8,0
16,0
41,2
26,4
18,7
16,9
17,0
22,6
25,5
25,2
24,9
26,7
28,9
38,3
54,3
47.8
2,0
6,1
7,5
6,5
7,0
9,6
11,5
17,5
17,2
14,9
12,8
14,3
15,1
18,3
17,8
15,6
Workfare programs
Growth of
real GDP
(%)
Nº Participants
(monthly average)
Expenditure
(millions of $)
4,5
-2,0
-1,8
10,5
9,9
5,7
5,9
-2,7
5,5
8,1
4,0
-3,4
-0,5
-4,4
-10,9e
5,4e
26.236
33.365
48.909
62.083
126.264
112.076
105.895
85.665
91.806
1.282.000e
1.992.000e
94
118
125
134
299
259
241
162
160
2.312e
3.586e
Source: Own elaboration based on Fachelli (2002), World Bank (2000) and INDEC. Notes:
(*) May 2003. e estimated
Allocating more funds to social sectors, and particularly to labor programs,
seems to be an adequate policy considering the current difficult situation.
However, several questions have been raised in Argentina regarding the
fairness and effectiveness of workfare programs. The program has been
pointed out as a source of political clientelism and corruption1, and many
analysts argued that their employment effects are questionable. In spite of
the importance of the topic, most of the arguments are based on anecdotal
evidence. There are very few empirical evaluations of the program. Our
research objective is to contribute to the debate, providing an econometric
evaluation of the poverty and employability effects of workfare programs in
Argentina, using the most reliable argentine database: The “Encuesta
Permanente de Hogares” (Permanent Household Survey, hereafter EPH).
While our focus is on the argentine case, we consider that is relevant to
other countries, particularly in Latin America, where active labor policies
have been advocated as a way to soften the shocks generated by marketoriented reforms (Heckman et al., 1998; Goldbert L. and C. Giacometti,
1998; Marquez, 1999).
The paper is organized in five sections. The second section briefly describes
the characteristics of workfare programs in Argentina. The third section
presents our research objectives, a review of the empirical evidence and the
knowledge gaps. The forth sections presents the methodology and the
results of our research (at this stage is only a draft). The fifth section
concludes.
Brief background of workfare programs in Argentina
The “Jefes de Hogar” workfare program was implemented few weeks after
president Duhalde took office in February 2002. However, workfare
programs in Argentina have been implemented since 1993, and while their
names have changed2, they have all the same basic characteristics and
objectives3:
•
The program is targeted at the least-skilled unemployed workers,
preferably if they are head of household. People who receive
1
Ronconi (2001) surveys the main argentine newspapers, and finds that most of the press
reports related to workfare programs mention the existence of political clientelism and
corruption in the funds allocation process.
2
In 1993 it was called “Programa Intensivo de Trabajo”, from 1995 to 2001 “Programa
Trabajar”, and since 2002 “Programa Jefes de Hogar”. Provincial governments also
implemented their own workfare program with similar characteristics to the federal ones. In
terms of magnitude the most important program was “Barrios Bonaerenses” implemented by
the province of Buenos Aires.
3
See the Argentine executive’s decree number 327/1998 for the “Programa Trabajar”, and
the executive’s decrees number 165/2002 and 565/2002 for the “Programa Jefes de Hogar”.
A detailed description of workfare programs in Argentina is provided in Fachelli (2002) and
Ronconi (2001).
•
•
•
unemployment insurance benefits, a pension or hold an informal job are
not allowed to participate4.
Participants receive a monthly benefit below the minimum wage5, during
a certain period (between three and six months) paid by the
government6.
During that period participants receive training and have to work
(between twenty and forty hours per week7) on communitarian projects
at public or non-profit organizations8.
The objectives of the program are: To act as a short-term safety net,
and to increase employability among the least-skilled unemployed
workers.
Research objectives and knowledge gaps
Our research objective has three components:
1.- How well targeted is the program?
A review of workfare programs in OECD and some developing countries,
found that public-service jobs are well targeted at low-income unemployed
workers when the wage rates have been set very low (See Dar and
Tzannatos, 1999).
In Argentina, the benefit is below the minimum wage so we might expect
self-targeting. However, several concerns have been raised. Over 50% of
total employment is informal and the government has no capacity to detect
if the candidate holds an informal job. Hence, it could be the case that some
benefits are assigned to people who also hold a job. But a second and
potentially more important concern is that, due to lack of sound institutions,
benefits are allocated in a political-clientelar basis, and not necessarily to
the most needed.
Kremenchutzky (1997) and Ministerio de Trabajo (1999) have surveyed a
small number of workfare program participants (60 and 159 respectively),
and they find few cases (less than 10%) where participants do not meet the
requirements to receive the benefit (for example, she/he is well-educated,
or already has an informal job). However, the sampling techniques used in
both studies are questionable9. Fachelli, Ronconi and Sanguinetti (2002)
present ad-hoc evidence showing several cases where the jobs are assigned
4
“Programa Jefes de Hogar” includes the following additional restriction: Candidates, in
order to be eligible, have to show proof that their children are attending school and receiving
appropriate medical treatment (such as vaccines).
5
The monthly benefit in the “Programa Trabajar” was $200 per month, while the monthly
benefit in the “Programa Jefes de Hogar” is $150 per month. The minimum wage in
Argentina is between $300 and $350 per month depending on the industry.
6
Both the “Programa Trabajar” and the “Programa Jefes de Hogar” were partially financed
by through a World Bank loan.
7
In the “Jefes de Hogar” the work requirement is 20 hours per week, while in the Trabajar
was between 30 and 40 hours per week.
8
“Programa Jefes de Hogar” allows a participant to work in private companies provided that
the employer pays the payroll tax and some complementary benefit.
9
See Ronconi (2001)
through a political-clientelar basis, or different forms of corruption in the
allocation process10.
Our first objective is to describe the socio-economic characteristics of
participants and non participants, using the Permanent Household Survey,
in order to verify if the participants are in fact those who need the program
most. We answer several questions, such as: Do participants have any
other source of income? How many of them are heads of poor households?
Do participants have a low education level? Is there any unemployed and
poorly educated worker who does not receive the program11?
2.- Poverty effects
A second concern is related to the poverty effect of workfare programs.
Even in the case where the program is well targeted, is necessarily to
measure the income gain conditional on income in the absence of the
program to assess the impact of the program. As mentioned by Subbarao et
al. (1997), common practice has been to estimate the gains by the gross
wages paid, assuming that the labor supply to the program came only from
the unemployed and from people who where out of the labor force. But,
even if a participating worker was unemployed at the time she joined the
program does not mean that she would have remained unemployed had the
program not existed.
Ministerio de Trabajo (2003) argues that the “Programa Jefes de Hogar”
helped 29.3% of households that were below the indigence line to move out
of indigence, and 6.5% of households that were below poverty to become
non-poor. Their “estimation” is done assuming that benefits are targeted
towards the poorest, and that the income gain of participating in the
program is equal to the benefit. For obvious reasons this analysis does not
provide much value added.
Jalan and Ravallion (1999) estimated the net income gains of workfare
programs in Argentina during 1997 constructing the counterfactual from a
group of non-participants. They have exploited the cross section
characteristic of the Encuesta de Desarrollo Social (EDS), and find average
gains of approximately $100 per participant per month (i.e. 50% of the
benefit). While this is the first serious attempt to measure the effects of the
program, the results may suffer a bias as suggested in Ronconi (2001)12.
10
Just to mention a few examples: In the suburban area of Buenos Aires, participants
received 2/3 of the benefit, while the remaining third was hold by a political leader. In La
Matanza, funds were distributed by local leaders instead of been assigned directly from the
government to the participant as the legal procedure stipulates. Some participants were
forced to participate in political manifestations in order to receive the benefits.
11
Regrettably, the EPH does not include any political variable. Hence we are not able to check
if in fact corruption and political clientelism characterize funds allocation. However, we
consider that providing a reliable estimation of the percentage of participants who do not
meet the requirements constitutes an improvement given the poor quality of the existing
empirical evidence, and also an input for further studies.
12
Jalan and Ravallion’s (1999) results are based on a sample of 3,500 participants. However,
the random sample was composed of approximately 6,000 participants. But they dropped
2,500 observations because the address of the participants could not be found, or because
the participant did not want to respond. We consider that the dropped observations have a
high probability of including participants who do not meet the program requirements.
In this paper we compute the net income gain of the program13, using a
different database (the Permanent Household Survey) and a matching pairs
approach (see the methodological section). The data and our empirical
approach allow us to estimate the short and medium run poverty effect of
the program14,15.
3.- Employability effects
Finally, we assess the employability effects of the program. According to
Bartik (2001), public service programs significantly increased the long-run
earnings of participants in the US, since they provide some work experience
and the needed soft skills. Do we observe this effect in the argentine case?
How did participants performed in the labor market after the program? Are
participants more or less likely to be employed than individuals in the
control group after program completion? Does participation affects the odds
of getting a formal job16? Do workers receive higher wages due to
participation? Or is the workfare program a disguised income transfer?
Furthermore, did the program have any negative impact, such as
“signaling” or stigma effects on the participants? Or did the program
generated dependency among participants?
None of these questions have been appropriately answered in Argentina. As
far as we are aware, there are no statistically reliable evaluations of the
employment effects of workfare programs. Our objective is filling this gap,
exploiting the panel characteristic of the Permanent Household Survey and
implementing a matching pairs approach to construct the control group.
Briefly, our research objective is to analyze how well the program is
targeted, and its effectiveness in reducing poverty and increasing
employability.
13
Since we do not follow a general equilibrium approach, we ignore indirect effects (such as
an increase in income due the increase in aggregate demand generated by the program).
These indirect effects are probably small before December 2001 because the number of
participants was 1% of the labor force. However, they might have become important after
the government increased the number of participants up to 15% of the labor force.
14
We estimate if the direct income gains generated by the program, helped the participants
to move out of poverty and indigence. We use the official poverty and indigence lines. The
poverty line is calculated based on the 1986/87 income and expenditure survey, and updated
using prices indices for its food and non foods components. For 1998, the poverty line is
$160 per male adult, per month. To calculate poverty, household composition is converted
into male adult equivalents using standard conversion factors. The indigence line, is based on
the food consumption portion of the poverty line, and is equal to $69 per male adult, per
month. See more details in Annex 2.
15
The long-run poverty effect of the program is much harder to assess. It would be
necessarily to measure how the program affects several outcomes. For example,
Franceschelli and Ronconi (2002) argue that there exists a causal relation between the
introduction of workfare programs and the emergence of the “Piquetero” movement in
Argentina.
16
We define formal jobs as those which include unemployment insurance and pension’s
benefits.
Methodology and data sources
The empirical strategy adopted in this study is the result of the research
objectives advanced in the previous discussion and the characteristics of the
available data.
The analysis is based on the Permanent Household Survey (EPH). The data
is collected and processed by the Argentine National Statistical and Census
Institute (Instituto Nacional de Estadísticas y Censos, INDEC). The survey
has been conducted bi-annually in May and October since 1974, and covers
28 urban centers which represent 70% of total population of the country
and 98% of the population living in centers with more than 100,000
inhabitants. The sampling and data collection techniques used by the INDEC
ensures the validity and reliability of the information (See Annex 1 for more
details)17.
The EPH contains information related to occupational, educational and
income characteristics, both at the individual and household level. Since
October 2000, it includes a specific question that allows determining if the
individual participates in a workfare program. Therefore, we are able to
verify, at six different points in time, if the participants were in fact those
who needed the program most. (October 2000, May 2001, October 2001,
May 2002, October 2002 and May 2003).
To assess how well targeted the program is, we analyze several variables,
mainly years of schooling and household income per capita. We follow
INDEC’s definition of poverty and indigence to measure the proportion of
beneficiaries below poverty, and the proportion of poor people not receiving
the benefit.
In order to compute the short run poverty effects of the program, we need
to measure the income gain conditional on income in the absence of the
program (Heckman, Lalonde, Smith, 1998). The “with” data is provided by
the EPH (i.e. we observe the income of participants); but the “without” data
(i.e. what would have been the income of participants in the absence of the
program) is fundamentally unobserved, since an individual cannot be both a
participant and a non-participant at the same time. Following the
conventional evaluation literature, we assume there exists a group of
individuals comparable to participants except for not having received
benefits. We use propensity score matching methods (see Heckman,
Ichimura, Todd, 1998) to draw a comparison group to workfare participants
form the large number of non-participants available in the EPH18. More
specifically:
Let Di=1 if individual i participates in the program, and Di=0 if does not
participates. Let Xi be a vector of variables that helps predict participation in
17
The EPH can be download form INDEC’s web page www.indec.gov.ar
We plan to estimate income gains for 2000 or 2001, since at that time the number of nonparticipants was sufficiently large to ensure that participants are matched with nonparticipants over a common region of matching variables. During 2002, almost every
unemployed worker received a benefit, so it might not be possible to obtain a reliable
matching pair.
18
the program; and P(X) = Prob(D=1/X) is the probability of participating
conditional on X, the “propensity score”.
We calculate the propensity score for each individual in the participant and
the comparison-group samples using standard logit model. Then we
compute the odds ratio pi = Pi / (1-Pi), where Pi is the estimated propensity
score for individual i. Finally, we minimize [(p(Xi) – p(Xj)] 2 over all j, in
order to obtain the “nearest neighbor” to the ith participant.
Then, the mean income gain of the workfare program is given by the firstdifference:
GFD = ∑i=1,..P [(Y1i - ∑j=1,..NP (wjiY0ji)] / P
where Y1i is the household income of participant i, Y0ji is the household
income of the jth non-participant matched to the ith participant, P is total
number of participants, NP total number of non participants and wij are the
weights applied in calculating the average income of the matched nonparticipants19. Hence, we compute the mean income gain of the program as
the difference between the average income of participants and the weighted
average income of non-participants.
Finally, we compute the percentage of participant households who moved
out of poverty due to the income gain G. (See Annex 2 for details regarding
how the poverty line is calculated in Argentina). While this strategy does not
allow us to claim any long term effects over poverty reduction, at least will
assess the importance of workfare programs as short run safety nets.
Our third objective is to estimate employability effects. We want to evaluate
if participants are more or less likely to find a job, and earn a higher wage
after program completion. As in the previous case, the fundamentally
unobserved data is the employment performance and salaries of
participants “without” treatment. Again, our empirical strategy is to use
propensity score matching methods to draw a comparison group to
workfare participants form the large number of non-participants available in
the EPH20.
But unlike the previous case, we need data “before” and “after” treatment.
The Permanent Household Survey has a rolling panel structure: once a
household is chosen it remains in the sample for four waves (two years).
Each period, 25% of the families are replaced. Therefore, we follow
participants and the comparison group through time.
19
In this first version of the paper we only compute the nearest neighbor. Hence, the
number of non-participants included in the control group (NP) is equal to the number of
participants (N) and the weights are all equal to 1.
20
We plan to follow the matched pair technique since the procedure is less arbitrary and
program impact measures are easier to interpret (See Dar and Tzannatos, 1999). We
consider matching estimates to be reliable since the same questionnaire was administered to
both groups, and the EPH contains a wide range of socio-economic characteristics for each
individual, allowing to control for a wide range of observable factors.
The difference in difference estimate of the mean income gain of the
program before and after treatment is21:
GDD = [∑i=1,..P (Y1it – Y1is) - ∑j=1,..P (Y0jt – Y0jt)] / P
Y1it is the income of participant i at time t and Y1is the income of participant i
at time s. Where t refers to a time period after treatment and s refers to a
period before treatment. Hence, GDD measures the difference between the
average income of participants after and before treatment relative to the
average income of non-participants during the same time period. The
sample formula applies to other outcomes such as the rate of
unemployment or the labor force participation rate.
At this stage an important point should be emphasized: During the period
under consideration (from 2000 to 2002), the overall state of the argentine
economy suffered major changes. GDP per capita decreased 25%, the
unemployment rate went up 3 percentage points, and the share of informal
employment increased from 37% to 50%. Under such a crisis, it would be
incorrect to attach all the negative changes in participants’ outcomes
(probability of being employed and wages) to the workfare program. In
other words, the “before and after” estimator should be discarded, or at
least taken with extreme caution. However, since we also work with a
comparison group of non-participants, and under the reasonable
assumption that the crisis had a similar effect over the outcomes of
participants and non-participants, we can isolate the workfare programs
effects from the economic crisis by computing a difference-in-difference
estimator.
Briefly, we follow the conventional evaluation literature. The value added of
our paper is that we use these standard methods to explore a database and
answer several questions that have not been analyzed yet.
Preliminary Results
1. - Targeting
As mentioned above, since October 2000 the EPH includes a specific
question that allows determining if the individual participates in a workfare
program. Therefore, we are able to verify, at six different points in time, if
the participants were in fact those who needed the program the most.
(October 2000, May 2001, October 2001, May 2002, October 2002 and May
2003)22.
NOTE: In this first draft of the paper we only analyze how well target to the
poor the program was in October 2000 and October 2002. In the next
version of the paper we will include the remaining periods.
Targeting in October 2000
21
This is the formula when only the nearest neighbor to each participant is selected.
The October 2003 survey has not been released yet, but it might be in the next months so
we could also include it in the analysis.
22
The EPH includes 561 observations where the person declares that she/he is
participating in a public workfare program. These observations represent
approximately 0.1 million people. It is important to mention that during
October 2000 the number of benefits was a very small share of those who
needed support. The number of unemployed people was 1.4 million, and the
number of people living in households below the poverty line was 6.9
million. Therefore, we expect to find that a large share of poor and
unemployed people did not receive the benefit. But were the scarce benefits
allocated properly?
Table 2, 3 and 4 present some basic socio-economic characteristics of
beneficiaries and their households, and for the rest of the surveyed
individuals.
Table 2
Characteristics
Age
Gender (% of female)
Head of Household
Work experience (in months)
Number of members in the household
Residence located in a shantytown
Lack of access to water, electricity
and bathroom
Residence ownership (yes=1)
# observations
Participants of
workfare program
34.6 years
58.1%
38%
31.4 months
4.9
2.5%
5.7%
Non-participants
57%
561
62%
45,647
37.1 years
52%
39%
53.3 months
4.4
1.4%
6.8%
Source: Own elaboration based on EPH (INDEC)
Almost 60% of program participants are female, less than 40% are head of
household, and 2.5% of the participants live in a shantytown. The average
participant is 2.5 years younger, has less work experience, and a slightly
higher probability of living in a shantytown than the average nonparticipant. The differences are not large.
Regarding years of schooling we observe that participants are on average
less educated than non-participants. But again, the difference is not very
large. While 44% of the beneficiaries have 7 or less years of schooling and
18.3% have some college education (i.e. more than 12 years of schooling),
the average figures for the group of non-participants is 34% and 25%
respectively.
Table 3
Maximum education attained
Incomplete primary school
Completed primary school
Incomplete high school
Completed high school
Incomplete college
Completed college
Participants
12.5%
31.5%
21.2%
16.5%
13.0%
5.3%
Non-participants
8.9%
25.1%
21.9%
19.1%
14.6%
10.3%
Source: Own elaboration based on EPH (INDEC). Note: “Completed primary school”
means 7 years of schooling. “Completed high school” means 12 years of schooling.
This is preliminary evidence that a significant share of the benefits have not
been assigned to the poorest and least skilled workers as established in the
normative. The inadequate allocation of benefits becomes more evident
when we analyze household income per capita: Only 19.9% (95,000 out of
481,000) of participants are below the indigence line. 40.1% are below the
poverty line but above the indigence line, and the remaining 40% are above
the poverty line23. On the other hand, only 4.8% of the indigent households
have at least one member who participates in the program.
Table 4
Beneficiaries
Without Benefit
Total
Above
Poverty
line
192
13,843
14,035
Below
Poverty
line
288
6,577
6,865
Below Poverty
line but above
Indigence line
193
4,668
4,861
Below
Indigence
line
95
1,908
2,004
Total
481
20,420
20,901
Source: Own elaboration based on EPH (INDEC)
We also observe that, while most of participants are members of
households located in the poorest quintiles, 24.6% of the beneficiaries are
members of a household that ranks in the top 50% of the income per capita
distribution.
Table 5 Distribution of beneficiaries according to income per capita of the household
1st decile (Poorest 10%)
2nd
3rd
4th
5th
6th
7th
8th
9th
10th (Richest 10%)
Beneficiaries
30.6 %
13.8 %
11.1 %
11.8 %
8.2 %
6.7 %
5.8 %
6.0 %
3.6 %
2.5 %
Source: Own elaboration based on EPH (INDEC)
Finally, we found that 25.1% of the beneficiaries declare income higher than
$200 per month, which was the workfare program benefit in October 2000.
This might be evidence that, contrary to what was established in the
normative, these participants are holding a job in addition to the workfare
benefit.
Summing up, we found evidence that in October 2000 the limited number of
benefits were not appropriately distributed. On the one hand, while the
average participant was less-skilled and poorer than the average non23
NOTE: In order to properly analyze how well target was the workfare program according to
income is necessary to compute the income of participants in the absence of the program.
This analysis would be included in the next version of the paper. At this stage we have
analyzed the income of participants including the benefit. The other extreme assumption is
to analyze the income of participants with out including the benefit. Under this alternative
assumption the program appears to be better targeted, but there are still a significant
number of participants who are above the poverty line.
participant, the difference was small. On the other hand, many high-skilled
workers and members of non-poor households received the benefit, while a
large number of low-skilled, unemployed and poor workers did not receive
the benefit24.
Targeting in October 2002
After the December 2001 political and economic crisis, the new government
significantly expanded the number of funds allocated to workfare program
(which became to be called “Jefes de Hogar”). The number of benefits
increased from 90,000 in December 2001 to 1,200,000 in October 2002.
While the increase in the number of benefits was significant, the crisis was
so severe that the number of people living in households with income below
the poverty line went up from 6.9 million people in October 2000 to 12.3
million people in October 2002. This section analyzes how well targeted
were the funds allocated in October 2002.
The EPH for October 2002 contains 2,329 observations were the person
declares that she/he is participating in a public workfare program. These
observations represent almost 1 million people. Tables 6 and 7 present
some basic socio-economic characteristics of beneficiaries.
Table 6
Characteristics
Age
Gender (% of female)
Head of Household
Years of Schooling
Incomplete primary school
Completed primary school
Incomplete high school
Completed high school
Incomplete college
Completed college
Beneficiaries of workfare programs
34.4 years
71.6%
39.3%
15.3%
37.4%
26.1%
13.6%
5.7%
1.9%
Source: Own elaboration based on EPH (INDEC)
Table 6 shows that more than 70% of the benefits were allocated to
women. Regarding education, we observe that 52.7% of beneficiaries had 7
or less years of schooling. Compared to the figures for October 2000, we
find that the program was better target towards the least skilled in October
2002. However, we also observe that in October 2002 there was still a
significant share of beneficiaries (7.6%) who had some college education.
The EPH indicates that 61.6% of members of households receiving a benefit
are below the indigence line. 33.2% are below the poverty line but above
the indigence line and 5.2% are above the poverty line. On the other hand,
we find that 41% of people living in a household below the indigence line
are receiving a benefit.
24
As a caveat it should be mentioned that, while the INDEC assures the confidentiality of the
collected information, it could be the case that some of those individuals who are
participating in the program and do not meet the eligibility criteria might not provide
accurate information.
Table 7
Beneficiaries
Without Benefit
Total
Above
Poverty
line
203
8,813
9,015
Below
Poverty
line
3,731
8,591
12,322
Below Poverty
line but above
Indigence line
1,307
5,102
6,409
Below
Indigence
line
2,423
3,490
5,913
Total
3,934
17,404
21,337
Source: Own elaboration based on EPH (INDEC)
Finally, we observe that 10.4% of the beneficiaries declare an income
higher than $150 per month, which was the workfare program benefit in
October 2002. As mentioned before, this might be evidence that, contrary
to what was established in the normative, these beneficiaries are holding a
job in addition to the workfare benefit.
Therefore, the evidence shows that the program was better targeted
towards the least skilled and poor workers in October 2002 relative to
October 2000. However, even during October 2002, the targeting was far
from perfect, since many poor workers did not receive a benefit, while
approximately 10% of the benefits were assigned to highly-skilled and nonpoor workers.
2.- Poverty and employability effects
The first and critical step in our estimation is to find a comparison group of
non-participants who has sufficiently similar characteristics to the
participants except for not participating in the program.
Following common practice in the evaluation literature we use propensity
score matching methods to draw a comparison group to workfare
participants form the large number of non-participants available in the EPH.
We run standard logit models (one for each province) to estimate the
propensity score by regressing D (an indicator of participation in the
program) on a vector X of individual and household characteristics such as:
age, gender, work experience, years of schooling, if the person is head of
household, number of members in the household, quality of the residence
(if the residence has access to water, electricity and sanitary installations),
location of the residence, ownership of residence.
Then, we compute the odds ratio pi = Pi / (1-Pi), where Pi is the estimated
propensity score for individual i obtained in the logit regressions. Finally, we
minimize [(p(Xi) – p(Xj)] 2 over all j, in order to obtain the “nearest
neighbor” to the ith participant.
Note: in this first version of the paper we only included one “nearest
neighbor” in the comparison group for each program participant. While this
is a reasonable strategy when a large number of observations are available,
it seems to be less appropriate in our case (As explained below, the results
for the difference-in-difference estimator for the period October 2000- May
2002 are based on 82 observations, which is too low). Regrettably, we
become aware of this problem too late. Therefore, in this first draft we
present the results based on the selection of only one “nearest neighbor”.
But for the PEP-June-Conference we will include estimates based on the “3
or 5 nearest neighbors” to each participant.
We take October 2000 as the base state. The reason we discarded using
May 2002, October 2002 or May 2003 as base state was because they are
quite recent and do not allow to analyze the performance of ex-participants
several months after treatment. On the other hand, since the workfare
program become almost universal by October 2002, it might not be possible
to find a reliable matching pair to each participant from the group of nonparticipants, since most of the poor and unemployed people was in the
program.
Note: The reason we choose October 2000 instead of May 2001 or October
2001 as the base state was arbitrary. However, we realize that it was a bad
decision. The October 2000 survey was the first to include the specific
questions that allows determining if the person is a workfare program
participant. Hence, when we intend to analyze the “before” performance of
participants, we are not able to know if those who were participants during
October 2000, were participating or not during May 2000. We return to this
issue below. For the PEP-June-Conference the paper would include the
estimates obtained from taking May 2001 or October 2001 as the base
state.
In October 2000 the EPH reports 561 individuals who were participating in
the six month workfare program. The group of non-participants includes
45,647 observations. From this large group we extracted the 561 nearest
neighbor implementing the methodology described above. The following
table presents some basic socio-economic characteristics of participants and
their respective nearest neighbors.
Table 8
Characteristics
Age
Gender (% of females)
Head of Household
Work experience (in months)
Number of members in the household
Residence located in a shantytown
Lack of access to water, electricity and
bathroom
Residence ownership (yes=1)
Schooling
Incomplete primary school
Completed primary school
Incomplete high school
Completed high school
Incomplete college
Completed college
# of observations
Source: Own elaboration based on EPH (INDEC)
Beneficiaries of
workfare programs
34.6 years
58.1%
38.0%
31.4 months
4.9
2.5%
5.7%
Control Group
57%
60%
12.5%
31.5%
21.2%
16.5%
13.0%
5.3%
561
12.5%
33.9%
23.7%
12.3%
11.4%
6.2%
561
34.7 years
58.0%
39.0%
32.2 months
4.8
2.5%
5.8%
As we observe, both groups have very similar observable characteristics.
The first question we consider is by how much did the income of
participants change during the participation in the program due to the
program. In other words, which would have been the income of participants
during October 2000, if they were not beneficiaries?
We find that, on average, participants had an income $40.48 per month
higher than the comparison group25. Considering that in October 2000 the
workfare program benefit was $200 per month, the net income gain of
participating in the program was 20% of the benefit.
Our estimated effect is quite smaller than the one computed by Jalan &
Ravallion (1999). They estimated a net income gain of $100 per month or
50% of the benefit. Their estimation is for the year 1997 and they used a
different database. These two factors might explain the discrepancy in the
results. But we suspect that the difference could also be due to a potential
bias on the sample used in Jalan & Ravallion.
The positive effect on income could be explained by the fact that many
participants would have remained unemployed or inactive, and hence
without income, in the absence of the program. Specifically, the labor force
participation rate was 60.7% and the unemployment rate was 17.5%
among the control group. While the participation and employment rate of
participants is by definition 100%. However, the effect on income was
smaller that the benefit because many participants would have got a job
and worked more hours in the absence of the program. The number of
hours worked by an average participant was 27 per week, while the average
employed nearest neighbor worked 40.5 hours per week.
The described first-difference estimator has two major shortcomings. On the
one hand, it does not allow measuring “before and after” effects of the
program. On the other hand, while the first-difference estimator controls for
observed heterogeneity between participants and non-participants, one
cannot eliminate latent heterogeneity that could bias the impact estimates
of the program using the nearest neighbor as a counterfactual. For
example, it could be argued that participants have higher social capital. And
it is this higher level of social capital what explains both the higher
probability of participating in the program and also the higher level of
earnings. Since we do not observe social capital we can control for it.
Therefore, the first-difference estimator would inappropriately consider the
effect of social capital as part of the program effect.
However, if the source of heterogeneity is time-invariant we can eliminate it
by computing difference-in-difference estimators. For example, the level of
social capital might be quite constant through short periods of time. Hence,
we can get rid of the problem by analyzing the performance of participants
25
We also estimated the income gain excluding all those “suspicious” participants and their
respective nearest neighbor. The result was basically the same: an income gain of $40.81
per month. (By suspicious we mean those participants who reported income above $300 per
month).
and the control group at different points in time. This estimator also allows
measuring “before and after” effects.
“Before treatment” May 2000
In the EPH for May 2000 we find that 328 people were surveyed, out of the
561 who were workfare program participants in October 2000. However,
when we only retain those observations for which the nearest neighbor was
also surveyed, we end up with 251 pairs.
The average income of the 251 individuals who were participants in October
2000, was $9.3 higher in October 2000 than in May 2000. Considering that
during that period Argentina was suffering a recession, the results appears
to be quite remarkable. Indeed, the average income of the 251 nearest
neighbors went down between May 2000 and October 2000 by $100.4.
Therefore, the difference-in-difference estimate for the program is a
positive effect of $109.7 per month.
Table 9 “Before treatment”
# hours worked per week
Unemployment rate
Labor force participation rate
Works in the public sector
# observations
Participants
May
October
2000
2000
29.4
26.2
11.7%
0%
84.5%
100%
78%
84%
251
251
Control Group
May
October
2000
2000
37.2
40
18.9%
21.4%
59%
61.1%
20%
20%
251
251
Source: Own elaboration based on EPH (INDEC)
The difference-in-difference for the labor force participation rate is equal to
13.4 percentage points, and for the unemployment rate -14.2 percentage
points. As shown in Table x, while the rate of unemployment increased by
2.5 percentage points for the control group between May 2000 and October
2000, it went down by 11.7 percentage points for those who were
participants during October 2000.
However, there may be a severe problem with these estimates. They are
based on the assumption that people who were participating in the sixmonth workfare program during October 2000, were not participating
during May 2000. Since, there is a five months gap between May and
October, it could be argued that approximately 1/6 of those receiving the
benefit in October were also receiving the benefit in May. Regrettably, we
cannot control for that problem. The October 2000 survey was the first to
include the question that allows us to determine if the person is
participating in a workfare program. Moreover, as we argue in the next
paragraphs, there is evidence to suspect that while the program’s length is
only 6 months, participants continued receiving benefits for a longer period.
Therefore, it could be the case that a substantial number of those who
received the benefit during October 2000 were also receiving the benefit
during May 200026.
26
Another piece of evidence that supports this possibility is the fact that 78% of the October
2000 participants, were working in the public sector during May 2000. And most of workfare
projects are implemented by the public sector.
Note: As mentioned before, we became aware of this problem too late. For
the PEP-June Conference we would present the results using May of October
2001 as the base state. Therefore, we would be able to determine the
participants who did not received the benefits during October 2000, and
hence properly measure the “before treatment” situation.
“After treatment” May 2001
During May 2001, i.e. 7 months after October 2000, the EPH surveyed 327
out of the 561 individuals who were program participants during October
2000. The average income for this group was $51.8 higher in May 2001
than in October 2000. We find that their labor participation rate went down
and unemployment up. This result was expected since the transition from
program participation to obtaining a job is obviously not frictionless.
However, we are surprised by the fact that only 10% of those who were
program participants went out of the labor force and the unemployment
rate among ex-participants was only 9.2%. These results are quite
surprising considering that the labor force participation rate among the
control group was 63.4% and the unemployment rate 15.2%.
One of the explanations for the previous result is the fact that 47.1% of
those who were participants during October 2000 appear to still be
participants during May 2001. This result was unexpected since the
normative established that the length of the program is six months. While
renewal of benefits was not explicitly prohibited, there was an implicit
solidarity objective in the program. The idea was to distribute the scare
benefits among as many poor people as possible. Hence, those candidates
who did not participate before had preference over those who did
participate.
In order to compute the before and after estimate we drop all those
observations where the individual declares that she/he participated in the
workfare program during both October 2000 and May 2001. The number of
pairs becomes 129. We find that the average income of ex-participants was
$32 higher during May 2001 relative to October 2000, while the average
income of their respective nearest neighbor was $59 higher during May
2001 relative to October 2000.
Table 10 also shows that the average number of hours per week worked by
ex-participants during May 2001 was very similar to the number of hours
worked by the control group (37.2 and 37.8 respectively). However, during
treatment participants were only working 26.9 hours per week. Hence,
income per hour was quite high during treatment. This fact may explain
why many participants had an incentive to continue participating in the
program. What remains to be explained is why de government continued
allocating the scare funds to those that already received the benefits
instead of assigning the funds to those needy candidates who never got it.
Table 10 “After treatment”
# hours worked per week
Unemployment rate
Labor force participation rate
Employment rate
# observations
Participants
May
October
2001
2000
37.2
26.9
17.5%
0%
79.8%
100%
65.9%
100%
129
129
Control Group
May
October
2001
2000
37.8
40.2
15%
22.1%
63.1%
60.6%
54.3%
47.2%
129
129
Source: Own elaboration based on EPH (INDEC)
The difference-in-difference estimate between ex-participants and the
control group between May 2000 and May 2001 is $82.727. The average
income of those who received treatment was $41.3 higher after treatment –
May 2001- than before treatment –May 2000-, while the average income of
the control group was $41.4 lower during May 2001 relative to May 2000.
The labor force participation rate, the unemployment rate and the rate of
employment were higher for the group of ex-participants relative to the
control group during May 2001. While 79.8% of ex-participants remained in
the labor force during May 2001, the participation rate for the control group
was 63.1%. On the other hand, 17.5% of ex-participants were actively
looking for a job but could not find it, while the unemployment rate for the
control group was 15%. Finally, 65.9% of ex-participants were working
while the employment rate for the control group was 54.3%.
This analysis provides estimates of how participants performed seven
months after completion of the workfare program. Our next step is to
analyze the performance of ex-participants during May 2002, a year and a
half after program completion.
“After treatment” May 2002
The EPH for May 2002 surveyed 119 individuals out of the 561 who received
treatment during October 2000. However, not all these individuals are exbeneficiaries as expected: 32.4% of those who were participating in the six
month workfare program during October 2000 declare to continue
participating one-year-and-a-half later. We drop those observations in order
to compare the performance of ex-beneficiaries to their respective nearest
neighbor and we end up with 41 pairs of observations.
The average income of ex-participants was $7 higher during May 2002
relative to October 2000. While the average income of the control group
was $9 higher during May 2002 relative to October 2000.
The labor force participation rate was still higher among ex-beneficiaries
than their respective nearest neighbors, but the difference was much
smaller than the one found for May 2001. During May 2002, 68.3% of exparticipants were actively looking for a job while only 63.4% of the control
group. The rate of employment was also higher for the group of ex27
This estimator is reliable under the assumption that those individuals who received
treatment during October 2000, were not receiving treatment during May 2000.
beneficiaries (56.1% relative to 48.3%) meaning that participation in the
program had a positive effect on the chances of getting a job.
Preliminary Comments
Besides all the caveats and the methodological aspects that need to be
improved, we consider that two preliminary results are worth mentioning:
On the one hand, while the average participant was less skilled and poorer
that the average non-participant, targeting was far from adequate. On the
other hand, we found that many participants received treatment for a
longer period contrary to what was established in the normative. We found
that one third of those who received treatment during October 2000 were
still receiving treatment a year and a half later.
Conclusion
To be written
References
Bartik T. (2001), Jobs for the Poor. Can Labor Demand Policies Help? Russell Sage
Foundation, New York.
Dar A. and Z. Tzannatos (1999), “Active Labor Market Programs: A Review of the Evidence
from Evaluations”, Working Paper, World Bank.
Elías V. and F. Ruiz Nuñez (2000), “An Evaluation of the Impact of the Young Training
Program in Argentina”, working paper, Universidad Nacional de Tucumán.
Fachelli S., Ronconi L. and Sanguinetti J. (2002), “Política Laboral Activa en Argentina”,
mimeo, Centro de Estudios para el Desarrollo Institucional.
Fachelli (2002), “Programas de Empleo de Ejecución Provincial”. Dirección de Gastos Sociales
Consolidados. Secretaría de Política Económica. Ministerio de Economía. 2002. En imprenta.
Franceschelli I. and L. Ronconi (2003), ““Comportamiento político de nuevos grupos: El
Movimiento Piquetero y la política pública asistencial”. Segundo Premio de Concurso.
Sociedad Argentina de Analisis Politico. Revista SAAP forthcoming.
Fiszbein, Giovagnoli and Aduriz (2002), “Argentina’s crisis and its impact on household
welfare” Working Paper No, World Bank Office for Argentina, Chile, Paraguay and Uruguay.
Goldbert L. and C. Giacometti (1998), Programas de Empleo e Ingresos en América Latina y
el Caribe, Banco InterAmericano de Desarrollo y Oficina Internacional del Trabajo, Atenea
Impresores-Editores, Perú.
Heckman J. and J. Hotz (1989), “Choosing among alternative Nonemperimental methods for
estimating the impact of social programs”, Journal of the American Statistical Association,
Vol.84, No. 408.
Heckman J., H. Ichimura and P. Todd (1998), “Matching as an Econometric Evaluation
Estimator”, Review of Economic Studies, 65, 261-294.
Heckman J., Lalonde R. and J. Smith (1998), “The Economics and Econometrics of Active
Labor Market Programs”, in Ashenfelter and Card (eds) Handbook of Labor Economics,
Volume 3A. Amsterdam: North Holland.
Jalan J. and Ravallion M. (1999), “Income gains to the Poor from Workfare: Estimates for
Argentina’s Trabajar Program”; Working Paper, World Bank.
Kremenchutzky (1997), “Evaluación Diagnóstica del Programa Trabajar I”, Crisol Consultores
Económicos de Empresas Industriales; SIEMPRO.
Marquez (1999), “Unemployment Insurance and Emergency Employment Programs in Latin
America and the Caribbean: an Overview” Conference on Social Protection and Poverty,
Inter- American Development Bank.
Ministerio de Trabajo (1999), “Evaluación del Programa Trabajar III”, Noviembre 1999,
Argentina.
Ministerio de Trabajo (2003), “Impacto del Plan Jefes y Jefas de Hogar en la Pobreza”,
Direccion General de Estudios y Formulacion de Politicas de Empleo, Argentina.
Ronconi L. (2001), “Determinates Político-Institucionales de los Programas de Empleo en
Argentina”, Working Paper #63, CEDI.
World Bank (2000a), “Poor People in a Rich Country: A Poverty Report for Argentina” vol I y
II, World Bank.
World Bank (200b), “Gestion del riesgo social en Argentina”, World Bank Office for Argentina,
Chile, Paraguay and Uruguay.
Annex 1. Encuesta Permanente Hogares – Permanent Household Survey
The EPH is a sampling survey, developed 28 in urban agglomerates. The rigorous
application of statistical methods ensures the validity and reliability of collected
information (selection of sample members is conducted using random selection
techniques, data collection methods are uniform, etc). A detailed description of
sampling and data collection techniques is available at www.indec.gov.ar. The
sample has a wide representation as the following table shows:
Provinces
ratio (%)
Total
population of province
to total
Census 91
(A)
population
EPH Agglomerates
ration (%) of
Total
% EPH
urban
population of sampling
population
EPH
over
to total
agglomerate agglomera
population
Census 91 (B)
te
(B/A)
population
2965403
100,0
10,4
7948443
99,7
27,9
Ciudad de Bs As
Buenos Aires
2965403
7969324
9,1
24,4
Ciudad de Bs As
Partidos del Conurbano
Catamarca
Cordoba
264234
2766683
0,8
8,5
Corrientes
Chaco
Chubut
795594
839677
375189
2,4
2,6
1,1
Entre Ríos
1020257
3,1
Formosa
Jujuy
La Pampa
La Rioja
Mendoza
Misiones
Neuquén
Resto de Bs As
398413
512329
259996
220729
1412481
788915
388833
4625650
1,2
1,6
0,8
0,7
4,3
2,4
1,2
14,2
Gran Catamarca
Rio Cuarto(*)
Gran Córdoba
Corrientes
Gran Resistencia
C. Rivadavia-Rada Tilly
Rawson-Trelew (***)
Concordia(*)
Gran Paraná
Formosa
Jujuy-Palpala
Santa Rosa-Toay
La Rioja
Gran mendoza
Posadas
Neuquén-Plottier
Bahia Blanca-Cerri
Mar del Plata-Batán(*)
Gran La Plata
San Nicolás de los Arroyos
(***)
Carmen de Patagones (***)
121815
138853
1175400
258103
292287
127038
97355
116485
207041
147636
219924
80592
103727
773113
210755
183579
265885
519065
642979
119302
46,1
5,0
42,5
32,4
34,8
33,9
25,9
11,4
20,3
37,1
42,9
31,0
47,0
54,7
26,7
47,2
5,7
11,2
13,9
2,6
0,4
0,5
4,1
0,9
1,0
0,4
0,3
0,4
0,7
0,5
0,8
0,3
0,4
2,7
0,7
0,6
0,9
1,8
2,3
0,4
17075
0,4
0,1
Viedma (***)
Salta
Gran San Juan
San Luis-El Chorrillo
Rio Gallegos
Gran Santa Fé
Gran Rosario
Villa Constitución (***)
Sgo del Estero-La Banda
40398
368659
352691
113074
64640
396991
1117322
41161
261824
8,0
42,6
66,7
39,5
40,4
14,2
39,9
1,5
39,0
0,1
1,3
1,2
0,4
0,2
1,4
3,9
0,1
0,9
Ushuaia-Río Grande
Gran Tucumán-Tafí Viejo
67303
652882
20208800
97,0
57,2
61,9
0,2
2,3
71,1
Río Negro (**)
Salta
San Juan
San Luis
Santa Cruz
Santa Fe
Santiago del
Estero
Tierra del Fuego
Tucumán
Total País
Total Urbano
506772
866153
528715
286458
159839
2798422
1,6
2,7
1,6
0,9
0,5
8,6
671988
2,1
69369
1142105
32633528
28439499
(*)Aglomerates included in October 1995
Source: EPH-INDEC
0,2
3,5
100,0
87,1
Annex 2.- Indigence and Poverty line methodology
The method currently in use by the INDEC to measure poverty and indigence is
presented below.
Indigence
The concept of «indigence level» (or indigence line), IL, aims to assess whether the
households earns enough income to purchase a food basket that will satisfy a
minimum threshold of energetic and protein needs. Thus, the household that does
not meet that threshold or line is considered indigent. The procedure is based on
the use of a “Canasta básica de alimentos” -basic food basket- (CBA) of minimum
cost, determined as a function of the consumption patterns of a reference
population defined according to the results of the 1985-86 Household Expenditure
and Income Survey. The procedure also takes into account the prescribed
kilocalories and protein requirements for that population (as specified in the «Basic
Food Basket for the Equivalent Adult», included below). Once the CBA components
have been established, their prices are assigned according to the Consumer Price
Index (IPC) for each measurement period.
Since human nutritional requirements vary according to age, sex and person’s
activity, INDEC adjust for each person’s characteristics, taking as reference the
requirements of a male adult aged between 30 and 59 and exerting moderate
activity. This reference unit is called the «equivalent adult» and is assigned the
value 1. The table of equivalences of energetic requirements for each consumer
unit in terms of equivalent adult is the following:
Table of equivalences
Energetic needs and consumer units by age and sex
Greater Buenos Aires
Energetic
Consumer unit /
Sex and age
needs (Kcal) Equivalent adult
Boys and girls
Under 1 year old
880
0.33
1 year old
1170
0.43
2 yrs. Old
1360
50
3 yrs. Old
1500
0.56
4 to 6 yrs. Old
1710
0.63
7 to 9 yrs. Old
1950
0.72
Men
10 to 12 yrs. Old
2230
0.83
13 to 15 yrs. Old
2580
0.96
16 to 17 yrs. Old
2840
1.05
Women
10 to 12 yrs. Old
1980
0.73
13 to 15 yrs. Old
2140
0.79
16 to 17 yrs. Old
2140
0.79
Men
18 to 29 yrs. Old
2860
1.06
30 to 59 yrs. Old
2700
1
60 yrs. old and over
2840
1.05
Women
18 to 29 yrs. Old
2000
0.74
30 to 59 yrs. Old
2000
0.74
60 yrs. old and over
1730
0.64
Note: Extracted from the table by MORALES, Elena, Canasta básica
de alimentos, Gran Buenos Aires, Documento de trabajo
n°3,INDEC/IPA, 1988.
Each household’s composition in equivalent adults determines a specific CBA value
for that household. In September, 2000, the CBA value for an equivalent adult was
62,44 pesos. As a final step, the specific value of each household’s CBA is
compared to the household’s total income. If the total income is less than the
household’s CBA, the household and its members are considered to be under the
indigence level.
Poverty
The measurement of poverty by the poverty level or «poverty line» (PL) method is
based on determining, from the household income reported, whether the household
in question is able to satisfy --through the purchase of goods and services-- a set of
nutritional and non-nutritional needs considered essential. In order to calculate the
poverty level the INDEC determines the CBA value and compound it with the
inclusion of non-nutritional goods and services (clothing, transportation, education,
health care, etc.) so as to obtain the value of the Total Basic Basket (CBT).
For the purpose of compounding the CBA value, the so-called Engel coefficient (EC)
is used. The EC is defined as the ratio of food expenditures to total expenditure
observed in the reference population in the base year (1985-86). Thus:
Engel coefficient = Food expenditures / Total expenditure.
In each period, both the numerator and the denominator of the Engel coefficient
are updated with the price variations obtained from the CPI. According to the
relative price variation, the EC is determined each month for the purpose of
measuring poverty. In order to compound the CBA value, in practice its value is
multiplied by the reciprocal of the Engel coefficient:
CBT = CBA x 1/Engel coefficient.
In September, 2000, the reciprocal of the Engel coefficient was 2.42 and the CBA
was 62.44 pesos. Thus we have $ 62.44 (CBA) x 2.42 (reciprocal of EC) = $ 151.10
(CBT) for an equivalent adult). As a last step, each household’s CBT value is
compared to the household’s total income. If the household’s income is less than
the CBT value, the household and its members are considered under the poverty
line; otherwise, they will be considered as non-poor.
ANNEX N°2
Basic food basket for the equivalent adult (monthly)
Component
Grams
Specifications
Bread
6060
Salt crackers
420
Sweet biscuits
720
Rice
630
Wheat flour
1020
Other flours (corn)
210
Noodles
1290
Potatoes
7050
Sweet potatoes
690
Sugar
1440
Sweets and jams
240 made with milk
made with sweet potatoes
marmalades
Dry legumes
240 Lentils
Beans
Green peas
Vegetables
3930 Chard
Onions
Lettuce
Tomatoes
Carrots
Pumpkins
Canned tomatoes
Fruits
4020 Bananas
Tangerines
Apples
Oranges
Meats
6270 Short ribs
Chuck
Minced meat
Rump beef
Navel and foreshank
Bottom round beef
Shoulder clod
Chicken
Eggs
630
Milk
7950
Cheese
270 fresh
spread
Quartirolo
grated
Cooking oil
1200 blended
Sweet/sweetened beverages
4050 Concentrated (fruit) juices
drinks
Unsweetened carbonated beverages
Table salt
Kitchen salt
Vinegar
Coffee
Tea
Maté
3450 Soda water
150
90
90
60
60
600
Sources: Documento de trabajo n° 3, Idem n° 8, INDEC / IPA