WORKING
P A P E R
Targeting Cash Transfer
Programs for an Older
Population
EMMA AGUILA, ARIE KAPTEYN AND CAROLINE
CAROLINE TASSOT
WR-950
June 2012
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Targeting Cash Transfer Programs for an Older Population
Emma Aguila, Arie Kapteyn, Caroline Tassot •
RAND Γ
June 2012
Abstract
Targeting based on individuals or households needs instead of applying universal
programs helps distribute scarce resources to those who need it most, avoiding “leakage” of the
poverty budget to non-poor households. In this paper, we explore the use of different household
and individual characteristics for targeting needy households. We estimate a Heckman selection
model to explain household income and use the estimated model to simulate the effects of
different means testing schemes. Our analysis focuses on the evaluation of cash transfer
programs for older populations. Using data from the Survey of Household Socioeconomic
Characteristics in the State of Yucatan, Mexico, we evaluate the feasibility and target efficiency
of different possible means-tested programs. Our analysis shows that a means-tested “flat rate”
option would be more effective in alleviating poverty than the less costly option of a sliding
scale. The tradeoff between raising welfare for the poor, thereby prioritizing low under coverage
rates; and allocating limited resources, thus prioritizing low overall costs of a program, remains
however a political challenge for all administrations designing poverty alleviation programs.
JEL classification: I32, I38, J14
Keywords: Targeting, Cash Transfer Programs, Elderly
•
We acknowledge the support from the State of Yucatan, Mexico; the RAND Corporation; the National
Institute on Aging (NIA) under grants R01AG035008, P01AG022481, and R21AG033312 and the
RAND Center for the Study of Aging (with grant P30AG012815 from NIA). We would like to thank
Norely Martinez for her excellent research assistance and Joanna Carroll, Sandy Chien, Uriel Ezequiel
Palma, and José Eduardo del Torno for helping process and clean the data. We would like to thank
Alejandra Michaels Obregon and Claudia Diaz for their advice.
Γ
RAND Corporation, 1776 Main Street, Santa Monica, California, 90407, US. Tel: +310 393 04 11.
1
1. Introduction
Poverty alleviation programs generally face the challenge of targeting the population that
is in most economic need. Raising the incomes of this segment of the population can have a
positive effect on the overall welfare of society (Coady et al., 2004). However, there are tradeoffs in assisting the poor, since other segments of society must pay for this assistance as well as
for program administration costs through taxes. Targeting groups based on their level of need
instead of applying universal programs serves to distribute scarce resources to those who need it
most, thereby avoiding “leakage” of the poverty budget to non-poor households. Targeting could
help allocate resources better thereby increasing the level of benefits transferred by a program or
lowering expenses for governments (Coady et al., 2004, Skoufias and Coady, 2007).
In this paper we analyze different mechanisms for targeting a cash transfer program to an
older population. Specifically, we explore alternatives of targeting mechanisms for the noncontributory social security program of the government of the State of Yucatan, Mexico. The
non-contributory social security program is a monthly cash transfer for persons 70 years old or
older of 550 pesos or US$78 in PPP.
In many countries, particularly high-income ones, reported income is often used to
identify those qualifying as poor; individuals or households under a certain income threshold are
given forms of state-sponsored assistance to raise their consumption level and utility. This
targeting method has a number of potential problems, including potentially high administrative
costs, error in identifying beneficiaries, incentive effects, and fairness issues (Besley and Kanbur,
1990). A particularly challenging aspect of targeting is the potential for error in identifying
beneficiaries. Material wealth such as income, savings, and assets is considered the first best
option for targeting but the difficulty in observing and correctly measuring these indicators in
developing countries can diminish their efficacy in targeting the poor (De Wachter and Galiani,
2006, Ravallion and Chao, 1989). Income is difficult to assess and track in countries that do not
have well-developed tax registries, that have large informal labor markets, or where the
definition of a household is an issue (Besley and Kanbur, 1990, De Wachter and Galiani, 2006).
Moreover, income can be considered an imperfect indicator of welfare in developing countries,
as it is unlikely to reflect the imputed value of self-produced goods and transfers as well as selfoccupied housing. Finally, volatility due to seasonality (particularly for agricultural workers) and
informal work can highly distort measurements of income (Narayan, 2005).
A second-best option in identifying the poor is to use more easily observable
characteristics of a household. Akerlof (1978) was one of the first to introduce this idea of
targeting using observable characteristics, referred to in his terminology as “tagging”.
Observable characteristics, or proxies, are used as instruments correlated with welfare levels to
predict household income and determine individual or household eligibility. The underlying
principle behind this methodology is that household income might be easier to predict based on
2
household characteristics rather than through self-reports. Typical proxies include household
demographics, housing conditions (e.g. material used to construct the roof or floor), and
ownership of durable assets. Information about these proxies can be gathered by program staff
during household surveys, thereby avoiding issues with misreporting income when determining
eligibility (Narayan, 2005).
There is a growing amount of evidence on the evaluation of targeting mechanisms
throughout the world. Overall, the success of proxy means testing depends on the ability to pick
observable variables that are highly correlated with household income and to reliably gather the
necessary information (Coady et al., 2004). Studies focusing on proxy means testing in Cote
d’Ivoire (Glewwe, 1989) and Sri Lanka (Narayan, 2005) show that regression predictions based
on different combinations of observable variables can significantly improve targeting. Evidence
presented by Grosh and Baker (1995) regarding Jamaica, Bolivia and Peru shows that household
characteristics are reasonable predictors of income for assessing eligibility for social programs.
Though the models evaluated suffered from significant errors of under-coverage, they reduced
leakages by a magnitude large enough to yield on net a better impact on poverty with imperfect
targeting than with none. A study led by Coady, Grosh and Hoddinott (2004) reviewed 122
antipoverty programs in 48 countries. Their analysis of efficiency in targeting the poor shows
that wealthier countries, where governments are likely to be held accountable and have higher
implementation capabilities, provide a better environment for targeting programs. Furthermore,
the authors concluded that targeting works better in countries where inequality is more
pronounced, that is, where different standards of living are presumably easier to distinguish.
Another option to categorize the poor is geographic targeting. Low-income
neighborhoods can be identified and used as an eligibility criterion for their population to
universally benefit from a program in their area (Besley and Kanbur, 1990). In countries with
substantial disparities in the living conditions between geographical areas, low-income areas are
easier to identify than individual incomes. Furthermore, administrative costs may be lower,
making geographic targeting a less costly option. If geographic divisions are sufficiently
correlated with income, geographic targeting can be effective, particularly when applied in small
areas, for example across neighborhoods (Bigman and Srinivasan, 2002).
Moreover, in order to design an effective targeting mechanism, one must consider the
feasibility of the different methods and the costs involved in applying them. The literature
identifies three types of costs as possible barriers to a successful targeting procedure. These are
monitoring costs, participation costs for potential beneficiaries, and political costs. First,
administrative costs may be high, as frequent monitoring of households and administrative
training of auditors are required to ensure a successful means testing process (Besley and
Kanbur, 1990). Grosh and Baker (1995) show that collecting information during household visits
can be expensive, although this provides the advantage of verifying individuals’ reports. It may
be more efficient to allow social workers to determine eligibility and enroll individuals in a
program during an interview at a central office. The required information for means testing often
3
involves frequent updating. If criteria for the means test are not updated frequently, ideally, on an
annual basis, targeting is likely to fail to take into account households who recently slid into or
came out of poverty (Glewwe, 1992). Consequently, the costs of collecting information should
be weighed against the likelihood of misreporting when deciding what data collection method to
use or how to combine data from both office and household interviews.
Second, due to imperfect information, certain individuals might refuse to participate in
extensive interviews and detailed assessment of their assets because of the cost involved in
obtaining the benefit. These individual costs can be non-monetary, such as the time spent filing
the necessary documents or obtaining required certifications (such as identity cards or proof of
residency). Moreover, these individual costs could include social stigma attached to participating
in a poverty alleviation program (Besley and Kanbur, 1990, Coady et al., 2004). Social stigma
tied to participation in programs specifically aimed at the poor can lead to a low take-up, thus
negating the purpose of the program (Besley and Kanbur, 1990). Finally, the political cost of
targeting poverty alleviation programs implies that excluding the middle and higher income
brackets from transfer programs may undermine broad-based support and make targeting
programs vulnerable if voter support determines budget allocation. However, if a targeting
program proves to be efficient and thus minimizes tax outlays, political support might increase,
as even non-beneficiaries may support it based on their indirect benefit from reduced poverty,
including a feeling of social justice and increased political stability (Coady et al., 2004). In this
study, we exploit a comprehensive survey called ENCAHEY (Encuesta de Características
Socioeconómicas del Hogar en el Estado de Yucatán- Survey of Household Socioeconomic
Characteristics in the State of Yucatan) broadly similar to the US Health and Retirement Study
(HRS) and the Mexican Health and Aging Study (MHAS). This survey includes detailed
information on income, wealth, pensions, assets, and individual and household characteristics.
We use different demographic, socioeconomic, and household characteristics to predict income,
and thereafter compare the predicted with the self-reported income. We simulate different means
testing mechanisms, their impact on poverty alleviation, and costs. We describe the sample
design in detail in section three.
The paper is organized as follows. Section two describes a simple targeting model.
Section three describes the sample we used in the analysis. Section four presents the empirical
model to predict income as well as a comparison of predicted and self-reported income
distributions. Section five presents simulations of different means testing schemes and discusses
their implications for poverty alleviation and costs. Finally, Section six presents brief final
remarks.
2. A Simple Targeting Model
In 2005, 58 percent of the labor force in Mexico was in the informal sector (Perry, 2007).
Moreover, informal mechanisms such as family transfers or work at advanced ages provide most
4
of the income of older people in Mexico (Aguila et al., 2011). Therefore, reported income may
not the most reliable source of information for targeting. Tagging and geographic targeting could
be suitable in this case. In this study we explore the tagging approach to predict income (Akerlof,
1978). 1 Consider the following simple model:
𝑦 = 𝑥′𝛽 + 𝜖
(1)
where y is some measure of economic well-being (which we will take to be household income)
and x is a vector of observables. Let there be some cut-off point y0 such that a household is only
eligible if y ≤ y0 . As noted earlier, it may be easier to observe the vector x than income y . So we
would not observe y , but only some estimate x ' β (where x is observed in our data and β is
estimated). In addition to the fact that x is easier to observe than income, we may also choose x
such that it is less amenable to incentive effects. 2 This would mitigate the labor supply
disincentives that tend to be present in targeted poverty alleviation programs. 3 The estimation of
a model like (1) also yields an estimate of the variance of ε . We can then calculate the
probability that y ≤ y0 for each household for which we observe x .
Under the assumption that y is not observable for all members of the population, or at
least too costly to make it an eligibility criterion for the administrators of the program, the rule
y ≤ y0 cannot be applied. An obvious alternative is a rule that defines a household as eligible
when x ' β ≤ z0 . We can then calculate Pr[ y ≤ y0 | x ' β ≤ z0 ] . This then also gives us the
probability that a potentially eligible household does not get the pension and vice versa. In other
words, we can control the size of type I and type II errors. Clearly, a more sophisticated scheme
uses a sliding scale where the level of the pension is a function of x ' β and z0 . An example of
such a scheme would be the following. Let’s normalize x ' β so that a higher value means more
resources.
In
that
case
one
could
determine
a
pension P as
follows:
z − x'β
P = max{min{550,550 0
}, 0} . Recall that the flat rate current pension benefit is $550. If
z0
we take z0 = 550 then the formula for the pension simplifies
to P max{min{z0 , z0 − x ' β }, 0} .
=
The approach suggested in this study is similar to Glewwe’s solution of the targeting
problem (1992); a similar approach was taken by De Wachter and Galiani (2006). Glewwe
(1992) considers an explicit welfare maximization (poverty minimization) problem based on a
1
We also tested geographic targeting and the results of that analysis will be presented in another research paper.
Observability of x mitigates but will probably not eliminate misreporting issues. Using data from Oportunidades,
Martinelli and Parker (2009) find that underreporting of goods and desirable home characteristics is widespread,
while overreporting is common for goods linked to social status. Validation and verification should therefore be part
of the design of a means testing program.
3
One may ask how important labor supply effects are in this age group and even argue that a major purpose of the
non-contributory pension is to reduce the need to keep working in old age.
2
5
parameterization of the poverty index. In estimating the parameter vector β , he chooses the value
of β that minimizes the poverty index. Rather than following that approach we will consider
somewhat simpler approaches as sketched above and investigate sensitivities to functional
specification and estimates of type I and type II errors. 4
3. Data from an Experimental Non-contributory Pension Program
The social security program being implemented in the State of Yucatan is called
Reconocer Urbano, a non-contributory, universal pension program for individuals 70 and older.
Reconocer Urbano is being implemented in 11 municipalities with more than 20,000 inhabitants.
The roll-out of the pension program is done in stages. The first phase began in 2008 in the
eastern region of the state, where the cities of Valladolid (a town with 45,868 inhabitants
according to the 2005 Census) and Motul (population 21,508 according to the 2005 Census)
were selected after identifying eligible cities with similar characteristics. Valladolid was
randomly chosen as treatment and Motul as control group. To be able to evaluate the effect of the
introduction of a non-contributory pension program, baseline data were collected in Valladolid
between August and September 2008. Moreover, the same data were also collected (between
October and November 2008) among individuals 70 and over in the town of Motul, which serves
as a control town. First follow up interviews in both treatment and control towns were conducted
approximately six months after the treatment group received the intervention (the first pension
payment).
Although the current pension is universal (a fixed amount of $550 pesos per month for
every eligible individual, about US$78 in PPP), the long-term viability of the pension program
may require a concentration of the benefits on the most needy households. In the empirical work
below we use the data from the baseline surveys in Valladolid and Motul to investigate the
feasibility of a targeting scheme. The non-contributory pension program was announced a month
after the baseline survey was collected to avoid announcement effects of the program in the data.
Both baseline and follow up surveys collect self-reported data on health, depression,
chronic conditions, activities of daily living (ADLs), instrumental activities of daily living
(IADLs), physical functioning, anthropometric measurements, a number of biomarkers,
individual and household characteristics, as well as income and wealth measures. The survey
instrument largely overlaps with the Mexican Health and Aging Study (MHAS) and the US
Health and Retirement Study (HRS). Where appropriate, respondents are asked for continuous
4
A Type I error refers to a “false negative”, or error of exclusion, occurring when a person whose “true” welfare
level is below the threshold but whose predicted welfare is above the threshold will be incorrectly categorized as
ineligible for a poverty alleviation program. It thus represents “under-coverage” of the program. A Type II error
refers to a “false positive”, or error of inclusion, occurring when an individual’s “true” welfare level is above the
threshold but whose predicted welfare is below this threshold will be incorrectly identified as eligible for enrollment,
thus representing a “leakage” of the program (Grosh and Baker, 1995).
6
answers (e.g., when asked for monetary quantities). If the respondent is unable to answer,
unfolding brackets are used to reduce the number of missing responses. This mimics the current
practice in the HRS.
To build the sampling frame for the study, a complete listing of all households in the two
towns was done to identify households with at least one age-eligible individual. Next, all
households with an age-eligible household member were recruited into the sample. The baseline
survey in Valladolid comprised 1,403 interviews representing an 88.9 percent response rate and a
refusal rate of 1.9 percent. The remainder consisted of deceased or non-contact. In Motul the
baseline survey comprised 1,204 interviews representing a 86.6 percent response rate and a 3.5
percent refusal rate. The remainder consisted of deceased or non-contacts.
Table 1 shows descriptive statistics of the sample. The first column describes the
characteristics of the total sample and the second and third columns show frequencies for
households that report income and those that do not report any income. The fourth column shows
the difference between households that report income and those that do not report income in
order to understand the differences between these samples. Income is defined as the sum of all
wages, income from businesses or farms, rental income, capital assets income, as well as all
transfers -monetary and in-kind -, from relatives and friends, and from governmental institutions.
In our analysis, we model income exclusive of government monetary transfers. Transfers
resulting from the program Oportunidades, Programa de Atención a los Adultos Mayores,
Programa 70 y más, and Procampo are not considered when predicting income.
7
Table 1: Descriptive Characteristics of the Sample
Total Sample
(a)
Households reporting positive income
(b)
Households reporting no income
(c)
Difference
(c)-(b)
Demographics
Male (%)
54.6
59.1
44.4
-14.7 ***
Married (%)
39.9
44.1
30.5
-13.6 ***
No schooling (%)
31.9
30.4
35.4
5.0 **
Incomplete primary (%)
55.4
55.4
55.3
-0.1
Primary or more (%)
12.7
14.2
9.3
-4.9 ***
Age 70-79 (%)
63.3
66.5
56.3
-10.2 ***
Age 80-89 (%)
30.1
28.2
34.5
6.4 ***
Age 90-99 (%)
6.2
4.9
9.2
4.2 ***
Age 100-110 (%)
0.3
0.5
0.0
-0.5
2nd respondent 70-79 (%)
21.4
23.7
16.2
-7.5 ***
2nd respondent is 80 or more (%)
19.1
17.4
23.0
5.5 ***
Can read and write Spanish (%)
64.6
65.9
61.5
-4.4 *
Number members in the household
2.4
2.3
2.6
0.2 **
Household with 1 member (%)
59.5
58.9
60.8
1.9
Household with 2 members (%)
37.2
38.0
35.6
-2.4
Household with 3 members (%)
3.2
3.1
3.5
0.4
Household with 4 members (%)
0.1
0.1
0.0
-0.1
Household with 5 members (%)
0.1
0.0
0.2
0.2
Receives pension (%)
23.6
31.6
5.7
-25.9 ***
No health insurance (%)
29.0
24.2
39.7
15.5 ***
Works for pay (%)
19.7
27.1
3.1
-24.0 ***
Assets (%)
Owns home
83.4
83.1
83.9
0.8
Own other real estate than home
2.5
3.3
0.7
0.8
Owns business or farm
3.1
3.3
2.8
-0.5
Owns animals
32.6
34.6
28.2
-6.5 ***
Owns vehicle
29.3
35.2
15.9
-19.3 ***
Receive money transfers
25.2
35.5
2.1
-33.4 ***
Receive in-kind transfers
13.6
18.0
3.8
-14.2 ***
Give money transfers
2.7
3.4
1.2
-2.1 ***
Give in-kind transfers
1.6
2.1
0.4
-1.7 ***
Amount money received ($)
180.1
260.1
0.0
-260.1 ***
In-kind value received ($)
45.7
66.0
0.0
-66.0 ***
Amount money given ($)
50.4
69.2
8.0
-61.1
In-kind value given ($)
3.8
5.4
0.2
-5.2
Receive food from government
3.0
2.8
3.5
0.6
Receive food from private org.
1.5
1.9
0.5
-1.4 **
Receive food from neighbor
22.5
22.4
22.8
0.4
Receive food for free
12.2
12.7
10.9
-1.9
Receive food transfers
32.9
33.2
32.5
-0.7
Monthly Income in pesos
Including government transfers
1221.4
1764.1
0.0
-1764.1 ***
Without government transfers
1105.1
1596.2
0.0
-1596.2 ***
Dwelling characteristics
No toilet (%)
23.5
25.1
19.9
-5.2 **
No sewage (%)
20.8
22.1
18.0
-4.1 **
No water in dwelling (%)
47.3
48.3
45.1
-3.2
Dirt floor (%)
5.1
5.4
4.5
-0.9
Sleep in kitchen (%)
15.4
15.3
15.5
0.2
Number of rooms
263.8
261.3
269.3
7.9
Number of observations
1,882
1,303
579
Notes: The classification of households with positive income and zero income is based on total income including government transfers. *** p<0.01, ** p<0.05, * p<0.1
Source: ENCAHEY Baseline Valladolid and Motul, 2008.
8
In our sample, which includes individuals 70 years old or older, 30 percent of the older
persons report zero total income. MHAS, which surveys persons 50 years old and older at the
national level, found that in 2003, 13.8 percent of the respondents reported no individual income
(Wong and Espinosa, 2004). There are two potential reasons for individuals not to report income
in MHAS. First, a respondent may live with other family members and does not have any
income. Second, a respondent may refuse or does not know their income. In table 1, we observe
that women, less-educated and non-married individuals are disproportionately represented in the
group of households reporting no income. The very old, for instance between ages 80 to 99, are
also overrepresented in this group. Moreover, a smaller proportion of persons with zero income
report having access to health care insurance or owning a business or a farm compared with
those individuals that report a positive income. Finally, we also observe that households not
reporting any income tend to be larger, with on average 2.6 household members, which could
indicate potential misreporting or confusion for respondents when reporting income. It is worth
mentioning that a higher proportion of persons with income report owning animals or a vehicle
than those with zero income. Furthermore, a lower proportion of persons with no income report
no toilet or sewage. The latter may indicate that some elderly in Mexico move to live with their
relatives due to a lack of other sources of income. A small proportion, 5.7 percent of households
with no income report receiving pensions in comparison to 31.6 percent of households reporting
some income. Similarly, about 3 percent of individuals not reporting any income declare to be
currently active in the labor force and earning a pay, compared to about a third of those
individuals who do report an income. This might indicate the presence of measurement error in
income variables due to individuals’ misreporting, refusal or lack of knowledge. We have not yet
analyzed the unfolding bracket income responses of the survey, which represent 3.5 percent of
the sample of respondents. We will incorporate unfolding bracket income responses in
subsequent versions of the paper.
4. Predicted Income as an Indicator for Targeting
In practice, targeting in the context of poverty alleviation is based on predictions of the
household’s living standard obtained from ordinary least squares regressions on the observed
characteristics (Skoufias and Coady, 2007). The observable characteristics need to be chosen
carefully, since they should correlate highly with income so that the targeted program will reach
the intended group of beneficiaries. While earlier studies limited the choice of variables to two or
three (Ravallion and Chao, 1989), a large number of household characteristics have since been
used in poverty alleviation programs such as Ficha CAS in Chile. While Ficha CAS started with
initially 14 indicator variables related to housing characteristics and location, education and labor
activity in 1980, it evolved to a more complex algorithm including questions on income, wealth,
and health in 1987 (Grosh, 1995). In general, the variables should be few enough to allow the
9
application of the proxy means test to a large population, while they should be relatively difficult
for the household to manipulate for example, through hiding of appliances during household
visits (Coady et al., 2004). Once the variables have been chosen, weights associated with each
variable in the regression analysis may take into account possible misreporting (Martinelli and
Parker, 2009).
In this study, we consider a simple model in which we regress the log of household
income excluding government transfers on a set of background variables such as education level,
marital status, age, and characteristics of the dwelling. Household income encompasses various
components, such as wages, business or farm income, asset income, rental income, pensions, as
well as transfers of money and in-kind from children, relatives, neighbors and friends. As noted,
in the definition of income we do not include monetary transfers from programs such as
Oportunidades, the “70 and more” (70 y mas) program or Procampo, or institutional in-kind
transfers. Institutions providing in-kind transfers include charitable or social welfare groups
supplying food or clothing for example.
Though our sample comprises 1,882 respondents, only 1,303 observations (69 percent)
have non-zero or non-missing values for household income. Assuming that all households must
have positive consumption, one possible interpretation is that these households receive transfers
in kind. We can consider the zero-value data on income as de facto missing values (of in-kind
income) - one third of the sample’s income values are therefore missing values. The predicted
income resulting from a simple regression on a number of observable covariates would be useful
if the missing income data for one third of the sample were missing at random. We can however
reasonably question this assumption, implying that there is self-selection into the reporting of
zero income, thus violating the assumption missing information is random.
In order to deal with the missing information, we use a Heckman selection model
(Heckman, 1979). We assume that income is a function of a set of respondent and housing
characteristics, whereas the likelihood of not having zero or missing values, i.e. the selection into
the observed observations, is generated by a second equation.
Formally,
I∗ = X′ β + ϵ
Pr[I = 1|X] = Pr[𝐼 ∗ > 0|𝑋]
(2)
(3)
Where ε is a standard normally distributed error term and I is a dummy, indicating positive
income ( I = 1 ) or zero or missing income ( I = 0 ). The latent income process is
𝑦 ∗ = 𝑍𝛾 + 𝑢
10
(4)
For the errors we assume
1
𝜖
0
� � ~𝑁 �� � , �
𝑢
𝜎𝜖𝑢
0
𝜎𝜖𝑢
��
𝜎𝑢2
(5)
The conditional expectation of income, given that it is observed is:
𝐸(𝑦|𝑋, 𝐼 = 1) = 𝑍 ′ 𝛾 + 𝐸(𝑢|𝑋, 𝐼 = 1) = 𝑍 ′ 𝛾 + 𝜌𝜎𝑢 𝜆(𝑋 ′ 𝛽)
(6)
Here ρ is the correlation between the error terms u and ε and λ is the Mills ratio. ρσ u is the
coefficient of the Mills ratio.
Once the model has been estimated, it can be used to simulate income distributions. An
unconditional prediction of income is:
𝐸(𝑦|𝑋, 𝑍) = 𝑍 ′ 𝛾
(7)
The simplest procedure to simulate incomes is to draw from the joint distribution of the
error terms. If I=0 this generates a zero income. If I=1 we calculate y* which is then the
simulated observed income. We simulate five values per observation in order to obtain a stable
distribution of predictions, using the following definitions of the errors terms:
𝜖 = 𝑒1
𝜎𝜖𝑢
𝜎𝑢
𝑢 = 𝑒1 𝜎𝑢
+ 𝑒2 �1 −
2
𝜎𝜖𝑢
𝜎𝑢2
(8)
(9)
Where 𝑒1 and 𝑒2 are two random variables drawn independently from standard normal
distributions.
We are aiming at a parsimonious model. If the analyses of the data might suggest that
targeting is both feasible and worthwhile from a viewpoint of target efficiency, we will need to
keep the number of variables used to predict income and therefore also eligibility, to a minimum.
These restrictions should allow us to limit complexity of an income maintenance scheme while
maintaining reasonable measurements of income levels.
We initially tested the model by including numerous explanatory variables, and thereafter
excluding variables based on their contribution to the explanatory power of the model. We
further reviewed the literature on existing algorithms for proxy targeting to decide on the final
set of explanatory variables.
Based on this pretesting, we include the following variables as predictors of household
income: marital status, levels of education (the maximum value for each household), age
indicators, a literacy indicator, household size, ownership of real estate (respondent’s home,
business or farm), ownership of a vehicle (whether bike or car), household characteristics (no
11
toilet, no sewage, no running water, dirt floor, number of rooms, breed animals, respondent
sleeping in the kitchen), work status, and finally an indicator of the household’s geographic
location, i.e. a dummy which is equal to 1 for the treatment town and 0 for control town. Since
income typically has a skewed distribution, the equation for income takes the log of income as a
dependent variable.
The likelihood of not reporting any income may be related to the informal work status of
the respondents. Respondents who do not have health insurance (typically the self-employed or
in general, informal workers) would be more likely to have missing values for income. The
selection equation thus differs from the income equation by adding an indicator showing whether
the respondent has health insurance.
12
Table 2: Income and Selection Equation Results
Demographics
Marital status (Dummy married=1)
Incomplete primary education
Complete primary education or more
Respondent aged 80-89
Respondent aged 90-99
Respondent aged 100-109
Second adult aged 80+
Can read and write message in Spanish
Number of members in household
Respondent receives pension
Respondent works for pay
Respondent without health insurance
Asset ownership
Home ownership
Ownership of real estate other than home
Ownership of business/farm
Ownership of at least one vehicle
Household has some breed animals
Dwelling characteristics
No toilet in dwelling
No available sewage in dwelling
No running water inside dwelling
Dwelling with dirt floor
Log number of rooms
Respondent sleep in kitchen
Dummy (treatment=1)
Income
Selection
0.177**
(0.0819)
0.236**
(0.103)
0.648***
(0.137)
-0.0627
(0.105)
0.165
(0.187)
-1.340***
(0.510)
-0.0660
(0.127)
-0.00311
(0.101)
-0.0223
(0.0181)
1.215***
(0.165)
0.218
(0.167)
0.103
(0.0798)
-0.0116
(0.0954)
0.159
(0.141)
0.0206
(0.0946)
-0.115
(0.149)
6.839
(0)
-0.121
(0.112)
0.0578
(0.0942)
-0.0390**
(0.0166)
1.413***
(0.102)
1.611***
(0.120)
-0.228***
(0.0750)
0.0599
(0.0941)
0.0328
(0.215)
0.437**
(0.201)
0.218***
(0.0837)
-0.0250
(0.0757)
-0.121
(0.0915)
0.587**
(0.261)
0.212
(0.203)
0.188**
(0.0881)
0.138*
(0.0742)
-0.0650
(0.133)
-0.356***
(0.134)
0.127
(0.0803)
0.0119
(0.172)
0.221***
(0.0853)
-0.133
(0.0997)
0.0586
(0.0765)
0.0848
(0.119)
-0.00865
(0.119)
-0.0867
(0.0763)
-0.140
(0.162)
0.115
(0.0824)
0.0901
(0.0936)
0.130*
(0.0724)
lambda
Mills ratio
-0.511**
(0.249)
Constant
5.852***
(0.279)
-0.264*
(0.150)
Observations
1,879
1,879
1,879
Notes: The dependent variable in the income equation is the log of household income without government transfers; the dependent variable for the
selection equation is an income dummy variable indicating whether the respondent reported any income excluding government transfers. Reference
category for education is no schooling, for age is age category 70 to 79 years old. Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: ENCAHEY Baseline Valladolid and Motul, 2008.
13
Our results, reported in Table 2, indicate a number of expected patterns in the income
equation. Being married is related to higher income as are a higher education level and pension
receipt. Home ownership or ownership of other real estate or a business does not appear
significantly related to the level of income reported. Working for pay is also insignificant, which
for this age group may be plausible. The nature of work is often very irregular and leads to little
formal income. The dwelling characteristics are mostly not significant either, with the exception
of the number of rooms and the presence of sewage.
The likelihood of reporting any income is negatively related to the lack of health
insurance, as expected. Working for pay, owning real estate other than home, receiving a
pension, having breed animals, as well as owning a vehicle increase the likelihood of reporting
income. The coefficient of the hazard rate λ suggests indeed that incomes are not missing at
random.
Figure 1, shows the actual income distribution at the household level, both including and
excluding government transfers. As mentioned above, a large fraction of the sample reported no
income. These are shown as zeroes in the income distribution.
Figure 2 shows the predicted (simulated) distribution. Reported income including
government transfers shows a lower proportion of the sample reporting zero or low income than
reported income excluding government transfers. However, the distribution of reported income
including government transfers is similar to reported income without government transfers for
households with an adjusted income above 1,000 pesos. Comparing Figures 1 and 2, one sees
that predicted income follows a similar but smoother distribution than reported income.
Figure 1: Reported Income Distribution with and without Government Transfers
14
>2000
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
50
45
40
35
30
25
20
15
10
5
0
0-125
Percentage of sample
Figure 2: Actual and Predicted Income Distribution
Income Categories
Reported income excluding gov. transfers
Predicted income excluding gov. transfers
Notes: Predicted income excludes monetary transfers from government programs and is equivalized by
household size using OECD scales. The OECD equivalence scale assigns a value of 1 to the first household
member, of 0.7 to each additional adult and of 0.5 to each child. Incomes higher than 2,500 pesos were
grouped in the bar on the right.
5. Simulation of Means Testing Mechanisms
In this paper, we compare five policy options incorporating varying degrees of targeting.
We use OECD equivalence scales throughout our analysis, thereby assigning a value of 1 to the
first household member and of 0.7 for each additional adult member of the household (OECD). 5
The first policy option pays out a flat rate that would provide m0 , corresponding to a noncontributory pension of 550 pesos, if the predicted income (see equation (7)) — adjusted for
household size — falls below an eligibility cutoff or poverty line, z0 , equal to 550 pesos. The
second option represents a sliding scale, whereby the benefit makes up for the shortfall of the
predicted income compared to the cut-off point or poverty line, adjusting for household size,
with the same eligibility criteria as the first option.
We refer to the third option as a demogrant approach or universal coverage, whereby no
targeting scheme is applied and all individuals in the sample would receive a cash transfer of 550
5
For more information about the OECD equivalence scale or Oxford scale see
http://www.oecd.org/dataoecd/61/52/35411111.pdf
15
pesos. Next, we analyze the option adjusting the demogrant policy option with equivalence
scales to account for household size (equivalized demogrant). Finally, we also consider the “best
case” scenario in which income is observed, the fifth option is therefore perfect targeting, where
transfers are made to provide a pension equal to the shortfall of predicted income – excluding
government transfers - compared to the poverty line. The poverty line is set at 550 pesos.
In all simulations we use predicted income for targeting and determining eligibility but
we show the results of the various post transfer programs using reported income. For observed
income we set all missing incomes to zero before adding the income transfer and include
government monetary transfers.
All targeted programs may have unintended incentive effects on labor supply and on
reporting of income or other targeted characteristics because of imperfect information (Besley
and Kanbur, 1990). Individuals can have a behavioral response to receiving benefits. For
example, people may modify their standards of living in order to become or remain eligible for
enrollment in a social program. A common criticism of threshold programs is therefore that they
inadvertently create a poverty trap; individuals cannot increase their income or possessions
without losing eligibility, which incentivizes them to remain in poverty in order to receive the
benefit. Thus, a beneficiary may choose not to work, or may lower the number of hours worked,
in order to qualify for a program, thereby creating a labor market distortion (Besley and Kanbur,
1990, Kanbur et al., 1994).
There is also evidence that poverty programs can discourage savings, particularly in
countries that include savings in their assets test for benefits, such as the United States (Powers,
1998). These distortions can reduce the increase in income following an individual’s enrollment
in a poverty alleviation program compared to the case where behavior would not have been
affected by eligibility status. However, Glewwe (1992) argues that this phenomenon does not
necessarily decrease welfare when welfare is measured in terms of utility rather than income
levels.
A method that may help to avoid the poverty trap and may seem fairer is that of a sliding
scale of eligibility; rather than a strict cutoff threshold, benefits decrease as poverty decreases.
The incentive to stay in poverty to receive benefits is lessened and the problem of behavioral
response to misaligned incentives for individuals who are close to the cutoff threshold can be
mitigated. Though the issue of labor disincentive effects are a problem for developed countries,
they might be less important in developing countries, where transfers are low, leading
beneficiaries to have more incentives to choose additional earnings over additional leisure when
possible. Coady et al. (2004) notice that sliding scale transfers are rare in developing countries.
This study analyzes a non-contributory pension program for individuals 70 and older, so that
labor supply effects are probably less of a concern and one might even argue that a benefit that
allows people to reduce work effort at that age is actually beneficial. Nevertheless, we simulate
the effect of both sliding scales and strict cut-off points.
16
Figure 3 shows the simulated income distributions after receiving the non-contributory
pension in the five types of targeting policy options. Those income distributions are calculated
by adding the transfer under each policy option to the total reported income, including
government transfers. To appreciate the effect of the various policies, these distributions should
be compared to Figure 1 and Figure 2. Panel (a) of Figure 3 shows the income distribution with
the flat rate, sliding scale and perfect targeting programs. The eligibility criterion for the flat rate
and the sliding scale is the same, as the simulated incomes are used to determine whether a
household falls below the poverty threshold of 550 pesos. The amount however varies gradually
for the sliding scale, depending on the poverty gap identified through the simulated income,
whereas it is fixed at 550 pesos for the flat rate. The perfect targeting alternative is here shown to
offer the counterfactual of eligibility based on respondents’ reports of income, with transfers
equal to the poverty gap if the households were below the 550 pesos threshold. We observe that
with the flat rate and sliding scale options, about 10 and 24 percent of the households still fell
below the poverty line. This is due to errors in our predicted income simulations.
Figure 3: Income Distributions after Receiving the Non-contributory Pension
35
25
20
15
10
5
Income Categories
Flat Rate
Sliding Scale
(a)
17
Perfect Targeting
>2500
2375-2499
2250-2374
2125-2249
2000-2125
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
0
0-125
Percentage of sample
30
35
Percentage of sample
30
25
20
15
10
5
Demogrant
>2500
2375-2499
2250-2374
2125-2249
2000-2125
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
0-125
0
Income Categories
Equivalized Demogrant
Perfect Targeting
(b)
Notes: Post-transfer incomes represent the sum of reported income including government transfers and the transfer
for each scheme. They are equivalized by household size using OECD scales. Incomes higher than 2,500 pesos are
grouped in the right hand bar.
Type I (under coverage) and Type II (leakage) errors are calculated by looking at the
difference between eligibility based on predicted income and eligibility based on reported
income. The undercoverage rate is calculated by dividing the number of Type I errors (i.e.,
households classified as living above the poverty line but whose true income, excluding
government transfers, falls below the poverty line) by the total number of “true” poor household.
This measure thus represents the percentage of those whom the program is meant to cover but
are not covered using the predicted income measure (Grosh, 1995). For the flat rate and sliding
scale schemes, 11.1 percent of households have been incorrectly classified as non-poor based on
our model, and thus are not covered by those transfers. About 16.2 percent of transfers are
received by households who should not be eligible based on their “true” level of income in the
flat rate and sliding scale options, thus representing Type II errors. This rate is calculated by
dividing the number of households considered being Type II errors of inclusions divided by the
total number of households receiving transfers.
18
Finally, we also observe a spike of about 16 percent of the sample at the cut-off point of
550 pesos with the flat rate and the sliding scale transfers, which corresponds to households
having zero income in Figure 2, and correctly predicted to receive a cash transfer.
Panel (b) in Figure 3 shows the income distribution following the demogrant, equivalized
demogrant, and the perfect targeting policy options. The demogrant option implies universal
coverage, therefore no household falls below the cut-off point. Since the equivalized demogrant
pays less if there is more than one eligible adult in a household, the post cash transfer income
distribution is located somewhat more to the left than with the pure demogrant or universal
coverage. For the demogrant and equivalized demogrant approaches, given the universal aspect
of those transfers, there is no Type I error, but 39.2 percent of households under the demogrant,
and 37.3 percent of households under the equivalized demogrant, are receiving transfers without
being poor.
5.1 Costs of Targeting and Poverty Rates
In the following section, we analyze the impact of the five targeting mechanisms on
population poverty rates and costs of each policy option. Table 3 shows the relative costs and
savings obtained through the targeting programs in comparison with the demogrant approach, the
most costly policy options. The total cost of perfect targeting, i.e. the poverty gap according to
reported income, would be 3,656,168 pesos 6. This is 3,796,332 pesos less than the cost of the
demogrant. The least expensive policy option when basing eligibility on predicted income is the
sliding scale, but this comes at the cost of leaving many households below the cut-off point as
illustrated by Figure 3, due to errors both when determining eligibility and when estimating the
gap to the poverty line for each household. The sliding scale is therefore more sensitive to the
predicted income measure.
Table 3: Costs of Targeting Mechanisms Compared to Demogrant or Universal Coverage
of the Cash Transfer
Savings compared to Demogrant
Costs relative to Demogrant (%)
Without transfers
Flat rate
Sliding Scale
Equivalized demogrant
Perfect targeting
0
62.4
51.1
90.8
49.1
7,452,500
2,806,705
3,644,157
683,100
3,796,332
6
The cost of each program can be derived from table 3 by subtracting the savings of each program from the savings
without transfers
19
5.2 Impact of Targeting on Poverty
The poverty gap is the mean, over all households, of the distance (or the gap) between
households’ income and the poverty line. In this case, the poverty line is 550 pesos for one
individual, and is further increased for each additional individual in the household. It is in other
words equal to the total amount of money that would be needed to raise the income of all poor
households exactly to the poverty line, divided by the total number of households in society. In
our analysis of the impact of each poverty alleviation program, we will focus on the poverty gap
index – expressing the poverty gap as a percentage of the poverty line – as well as the squared
poverty gap index.
Consider the Foster-Greer-Thorbecke poverty measure:
𝑞
1
𝑦0 − 𝑦𝑖 𝛼
𝑃𝑜𝑣𝑒𝑟𝑡𝑦𝛼 = � �
�
𝑁
𝑦𝑜
𝑖=1
where N represents the total number of households, yo is the poverty line, and q is the number of
poor households. The parameter α is a measure of the sensitivity of the index to poverty, so that
α = 0 calculates the poverty headcount index (the number of poor as a proportion of total
population size), α = 1 measures the poverty gap index, and α = 2 measures the squared poverty
gap index (Foster et al., 1984). The latter, although more difficult to interpret, places the highest
weight on the largest poverty gap, thereby allowing comparisons of inequalities in the poverty
distribution – for instance showing a difference depending on whether a person close to the
poverty line receives a transfer or if the recipient is located far from the line.
In Table 4, we show the three poverty measures for each of the five policy options as well
as when maintaining the status quo. In the latter case - not implementing any transfers - the
poverty headcount is about 60 percent of the sample. The sliding scale is the second worst option
in terms of poverty measurements, as about one fourth of the sample would still be living below
the poverty threshold, while the poverty gap index would be reduced by about three quarters
compared to the status quo. The flat rate scheme allows for a reduction of the poverty headcount
to about 11 percent, while also significantly lowering the poverty gap index. As explained
earlier, with an unlimited budget, no household is left below the poverty line with universal
transfers – i.e. the demogrant approaches – or with the perfectly targeted transfers.
20
Table 4: Impact on Poverty with Selected Targeting Options, unlimited budget
Without transfers
Flat rate
Sliding scale
Demogrant
Equivalized demogrant
Perfect targeting
Poverty headcount
Poverty gap index
59.5
10.6
24.1
0
0
0
47.0
8.0
12.7
0
0
0
Squared Poverty Gap
index
42.0
7.0
9.5
0
0
0
In Table 5, the average income levels for the sample and for the remaining poor
households after the transfers, as well as the average poverty gap are reported. We observe here
that while the average poverty gap is lowest with the sliding scale, and the mean post transfer
income among the poor remains virtually identical for the status quo and the flat rate policy
options, one needs to keep in mind the rate of remaining households living in poverty.
Table 5: Average Income Before and After Targeting, unlimited budget
Mean
Mean post-transfer
Mean
income
income among poor
poverty gap
Without transfers
Flat rate
Sliding scale
Demogrant
Equivalized demogrant
Perfect targeting
1221.4
1715.1
1626.1
2013.4
1940.8
1609.9
151.3
166.3
344.6
NA
NA
NA
21
575.2
521.7
366.5
NA
NA
NA
5.3 Budget constraints
In order to further analyze the relation between poverty reduction and budgetary costs,
we specify different budget levels and determine the effect of these budgets on the various
poverty measures. We will consider fixed budgets between 1,000,000 and 10,000,000 pesos in
increments of 1,000,000 pesos. When dealing with fixed budgets, we set the pension level for the
flat-rate scheme at the level of the total budget divided by the total number of eligible
households, adjusting for household size, thereby allowing a pension of 118.4, 591.9, and
1,183.8 pesos for a one person household in 1,000,000, 5,000,000, and 10,000,000 pesos budgets
respectively. The sliding scale and perfect targeting schemes are simulated by multiplying the
necessary pension that would fill the poverty gap by the fraction of the total poverty gap allowed
by the budget. Finally, the demogrant schemes use a similar approach to the flat rate, with the
total budget being divided by the total number of households for the equivalized demogrant, and
the total number of respondents for the demogrant scheme. The results for the different indexes
are presented in Figure 4, Figure 5, and Figure 6.
Figure 4: Poverty Indicator- Headcounts
70
50
40
30
20
10
Budget categories
Without transfers
Demogrant
Flat Rate
Equivalized Demogrant
22
Sliding scale
Perfect targeting
10,000,000
9,000,000
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
0
1,000,000
Percentage of sample
60
First consider the poverty head count in Figure 4 and the poverty gap index in Figure 5.
Given that the total poverty gap, i.e. the amount needed to allow perfect targeting, amounts to
3,656,168 pesos, it is not surprising that a successful poverty alleviation policy can be found only
at the 4,000,000 pesos budget with perfect targeting. Changes in the budget have modest effects
on the results produced by the demogrant approaches until the budget is so high (at 8,000,000)
that everyone receives a benefit equal to the poverty line, given the high “leakage” rates of about
40 percent. Although the poverty headcounts under flat rate and sliding scale policies decrease
more sharply with increasing budgets, they also suffer from a 16.2 percent leakage rate, and
perhaps more importantly, about 11 percent under-coverage of the poor. We note therefore that
even with unlimited budget, poverty rates do not fall to zero for the sliding scale and flat rate
schemes (see Tables 4 and 5).
Figure 5: Poverty Gap Index
70
50
40
30
20
10
Budget categories
Without transfers
Demogrant
Flat Rate
Equivalized Demogrant
23
Sliding scale
Perfect targeting
10,000,000
9,000,000
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
0
1,000,000
Percentage of sample
60
The flat rate scheme requires a maximum budget of 3,645,795 pesos to cover the
households predicted as poor with a transfer of 550 pesos per person, equivalized by the
household size. At that level the poverty headcount stagnates at 10 percent, corresponding to the
percentage of household incorrectly classified as non-poor, at budgets of 5,000,000 pesos or
higher. Turning to the demogrant approaches, both require more than eight million pesos in order
to be successful, and will only gradually decrease the number of poor households as the budget
increases. Finally, using reported income as the basis of eligibility, perfect targeting would
require a budget of 4,000,000 pesos to succeed, but would not decrease poverty headcounts until
reaching this budget, though decreasing the poverty gaps.
The patterns in Figure 6 are qualitatively similar to Figure 5, but show a steeper slope at
lower budget levels, reflecting its higher sensitivity of this index to inequality among the poor.
Figure 6: Poverty Gap Index Squared
40
35
30
25
20
15
10
5
Budget categories
Without transfers
Demogrant
Flat Rate
Equivalized Demogrant
5.3 Sensitivity of Outcomes to the Poverty Line
24
Sliding scale
Perfect targeting
10,000,000
9,000,000
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
0
1,000,000
Percentage of Poverty Line Squared
45
In order to evaluate the sensitivity of our results to the choice of the poverty line, we
conduct the initial policy simulations under an “unlimited” budget with the official, national
poverty line. This poverty line is calculated by CONEVAL (Consejo Nacional de Evaluación de
la Política de Desarrollo Social) based on a basic food basket. In 2008, this basic food basket in
rural areas had an estimated value of 706.69 pesos. We however maintain the level of transfers to
a maximum of 550 pesos, corresponding to the current experimental non-contributory pension
program. Given that more households will be categorized as eligible under this setting, the costs
of each program are higher compared to the previous lower poverty line of 550 pesos, as shown
in Table 6.
Table 6: Costs of Targeting Options
Costs relative to demogrant (%)
Saving compared to Demogrant
Without transfers
Flat rate
Sliding Scale
Equivalized demogrant
0
66.0
54.1
90.8
7,452,500
2,536,600
3,423,377
683,100
Perfect targeting
51.2
3,634,415
Because the non-contributory pension does not match the level of the CONEVAL
poverty line, the poverty headcounts, even under demogrant policy options, remain high as
shown in Table 7. The reductions in the poverty gap index and squared poverty gap index show
however that the severity of poverty is substantially alleviated. The poverty headcount under
perfect targeting remains identical to the poverty headcount without any type of transfer due to
the fact all households will be identified as poor at the 706.69 pesos line, but will only receive a
pension equivalent to max{min �550, 550 ∗
pesos.
706.69−𝑥 ′ 𝛽
706.69
25
� , 0} , which is never more than $550
Table 7: Impact on Poverty with Selected Targeting Options, unlimited budget,
CONEVAL poverty line
Poverty headcount
index
64.3
Poverty gap
index
50.2
Squared Poverty Gap
index
44.6
Flat rate
45.5
13.9
7.6
Sliding scale
49.6
18.5
10.9
Demogrant
37.2
5.7
1.1
Equivalized Demogrant
41.3
7.9
1.7
Perfect Targeting
55.5
10.0
2.0
Without transfers
In Table 8, we observe that both the rate of undercoverage and leakage are lower when
using the CONEVAL poverty line than when using the previous poverty line set at 550 pesos.
This result is intuitive as the households with an adjusted predicted income between 550 and
706.69 pesos will now qualify as eligible to receive transfers, while previously they were not.
Table 8: Targeting Errors and Average Income after Targeting, unlimited budget,
CONEVAL poverty line
Undercoverage Leakage Mean post transfer
rate
rate
income across poor
Without transfers
NA
NA
10.2
15.4
Demogrant
0
Equivalized demogrant
Perfect targeting
Flat rate
Sliding scale
Mean poverty gap
198.4
729.9
656.3
277.7
593.1
340.0
36.1
780.6
121.5
0
33.5
763.1
176.9
0
0
761.5
167.4
Note: NA is not applicable.
The simulations of outcomes with a different poverty line allow us to estimate the
sensitivity of our results to the eligibility threshold when keeping maximum transfer amounts
constant. The undercoverage and leakage rates are not very sensitive to the poverty line chosen,
but as may be expected, the poverty headcounts are extremely sensitive to the threshold. For
instance, even the universal approaches show high rates of remaining poverty after implementing
the transfers. This is primarily due to the high proportion of the sample with zero income,
26
thereby remaining below the poverty line even after receiving the “full” transfers, as those are
not covering an amount equal to at least the poverty line.
5.4 Assessing Targeting Performance: Net Cost
Even with perfect targeting, it is not a priori clear that this is superior to an equivalized
demogrant, despite the prevalence of type II errors, i.e. households with incomes above the
poverty line receiving benefits, in the latter policy option. One has to take into account the
administrative cost of targeting. By comparing the budgetary cost of a demogrant or universal
coverage and the budgetary cost of perfect targeting, one can easily calculate what level of
administrative costs would be allowable to make targeting superior to a demogrant, if the sole
purpose is to make sure that no households fall below the poverty line. We have seen in Table 3
that the total cost of perfect targeting would be equal to 3,656,168 pesos, while the equivalized
demogrant would cost 6,769,400 pesos. Thus total administrative costs equal to 3,113,232 pesos
per year, or 259,436 pesos per month would make perfect targeting equally expensive as an
equivalized demogrant. In other words only when administrative costs of targeting are roughly
equal to the costs of the benefits, will an equivalized demogrant be more efficient than perfect
targeting.
We can apply the same logic to transfers based on predicted income. We show in Table 3
that a sliding scale would be the least costly option in terms of transfers, but would also be the
least effective policy in terms of poverty alleviation. Figures 4 and 5 show that the Poverty Gap
Index and Poverty Gap Index Squared for the sliding scale transfers would be higher at any
budget than with the flat rate transfers. Both types of transfer schemes show targeting errors in
their coverage, with an undercoverage rate of 11.1 percent and a leakage rate at about 16.2
percent. Comparing the sliding scale to the equivalized demogrant approach, we conclude that
administrative costs of targeting of up to 246,754.8 7 pesos per month would make the sliding
scale more cost-effective than the universal equivalized transfers.
6. Panel data analysis
Finally, in this section, we analyze the income distributions following the intervention
described above, namely, the distribution of a universal pension of $550 pesos per month for
every individual aged 70 and older in the city of Valladolid. Figure 7 shows the distribution of
household income including government transfers in Valladolid (a) and Motul (b) as reported in
the baseline and follow-up surveys. The follow-up survey was conducted six months after the
implementation of the non-contributory pension program in Valladolid.
7
Corresponding to the difference between the cost of the equivalized demogrant (6,769,400) and the cost of the
sliding scale (3,808,343), divided by 12.
27
>2000
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
50
45
40
35
30
25
20
15
10
5
0
0-125
Percentage of sample
Figure 7: Reported household income, equivalized by household size in baseline and followup
Income categories
Baseline
Follow-up
(a) Valladolid
Percentage of sample
35
30
25
20
15
10
5
2000-2125
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
0-125
0
Income categories
Baseline
Follow-up
(b) Motul
We observe a major shift in the distribution of income in Valladolid: while about 43 percent
of household reported zero income in the baseline survey, only 5 percent did so in the follow-up.
The distribution has shifted instead to the poverty threshold at 550 pesos. This indicates high
28
quality of the data, as they reflect the transfers implemented during the intervention in the
treatment group in Valladolid. We also observe a decrease on the order of 10 percentage points
in Motul for the percentage of the sample reporting zero income, while the rest of the distribution
remains quite stable.
We confirm this analysis by looking at the distribution of income excluding government
transfers in both cities as reported in the baseline and follow-up surveys. Given the nature of the
intervention - the pension being a government program - the newly distributed pensions are not
reflected in the distribution of income excluding government transfers in Valladolid. Figure 8
shows in fact an increase in the proportion of the sample reporting zero income in Valladolid in
the follow-up survey. One possible explanation for this increase would be a crowding out of
sources of income other than government transfers, such as family transfers or earnings,
following the distribution of the universal pensions in Valladolid.
Figure 8: Reported household income excluding government transfers, equivalized by
household size in baseline and follow-up
70
50
40
30
20
10
(a) Valladolid
29
>2000
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
Income categories
Baseline
Follow-up
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
0
0-125
Percentage of sample
60
45
Percentage of sample
40
35
30
25
20
15
10
5
>2000
1875-1999
1750-1874
1625-1749
1500-1624
1375-1499
Income categories
Baseline
Follow-up
1250-1374
1125-1249
1000-1125
875-999
750-874
625-749
500-624
375-499
250-374
125-249
0-125
0
(b) Motul
Table 9 provides an overview of the changes of all poverty indicators used in previous parts
of our analysis. The drop in poverty headcounts in Valladolid can be seen in the first column,
with headcounts dropping from 66.6 percent of the population in Valladolid to 13.6 percent in
the follow-up. As expected, the poverty gap index and poverty gap index squared also drop
significantly in Valladolid. The poverty indicators should be zero in the follow-up survey with a
100 percent take-up rate of the program given that it was available for all individuals age 70 or
older in Valladolid. However, not all the persons age 70 or older requested the cash transfer. The
positive poverty rates estimates in the follow-up could be due to measurement error or a take-up
rate below 100 percent. These estimates may indicate that some low-income individuals did not
claim the non-contributory pension in Valladolid.
Table 9: Poverty indicators in baseline and follow-up by city
Poverty headcount Poverty Gap Index Poverty Gap Index Squared
Reported
income
Reported
income
excluding
government
transfers
Valladolid Motul Valladolid Motul Valladolid
Baseline
66.6
50.3
52.9
39.3
47.6
Follow-up
13.6
41.7
8.2
29.2
6.4
Motul
34.9
25.9
Baseline
69.1
54.5
58.3
47.4
53.9
44.1
Follow-up
70.2
52.8
61.7
45.4
58.2
42.6
30
In Motul, we also observe a decline in the poverty indicators but much less than in
Valladolid. The decline in Motul could be explained by the introduction of a cash transfer
program from the federal government around the time of the follow-up survey. Thirty percent of
individuals age 70 or older in Motul report in the follow-up survey receiving the federal
government cash transfer program. In subsequent versions of the paper we will obtain the
administrative records of the non-contributory pension program in Valladolid and the cash
transfer program of the federal government in Motul to analyze the changes in poverty rates in
Motul and the characteristics of the individuals that did not claim the non-contributory pension in
Valladolid.
7. Conclusions and Policy Recommendations
In this paper we present an analysis of the cost and impact on poverty alleviation of
various cash-transfer policies. We estimate a Heckman selection model to predict income, using
demographic characteristics, asset ownership and dwelling characteristics as explanatory
variables. Comparing the household eligibility for these programs based on observed versus
predicted income enables us to evaluate the targeting accuracy of our model.
The use of observable characteristics in targeting is not without its own set of challenges.
One issue tied to observable characteristics is the likelihood of false reporting when eligibility is
determined through an individual’s report of possessions; depending on the item in question and
the individual, there may be incentives to underreport or over-report. On the one hand,
underreporting causes those who would be above the eligibility threshold to wrongly qualify for
benefits; usually referred to as a Type II error. This phenomenon can occur if underreporting is
not made costly by program requirements and if individuals can increase their probability of
obtaining benefits by understating their material possessions and quality of housing or
misreporting their location of residence. This type of behavior could be somewhat reduced by
keeping information regarding eligibility criteria secret (Glewwe, 1992).
On the other hand, over-reporting may cause the truly poor to be classified as above the
threshold, a Type I error. This can be the consequence of individuals’ responses to stigma,
leading to a bias towards over-reporting due to embarrassment or fear of being considered poor
by neighbors and family members. Under- and over-reporting behaviors have been found to be
strongly linked to the type of material good. Goods with “status value” that cause embarrassment
or social stigma for the individuals who do not own them are likely to be over-reported by
individuals. For most assets however, underreporting is more likely. The eligibility index for
Oportunidades, for instance, placed different weights on typically underreported goods (such as
washing machines or cars) and on over-reported goods (e.g. toilets or running water) to attempt
31
correcting for the bias from under- and over-reporting (Martinelli and Parker, 2009). 8 In this case
as mentioned previously the data was collected one month before the non-contributory pension
program was announced. We believe therefore that anticipatory effects of the program were
negligible, but the issue of misreporting should be considered when the government would
implement a means testing policy based on tagging, requiring regular updates of the information
and list of beneficiaries.
We find an undercoverage rate of about 11.1 percent, and leakage rates varying between
16 percent for the flat rate and sliding scale, and about 40 percent for the demogrant approaches,
our model is more accurate than other simulations in the literature in determining household
eligibility to receive transfers. See for instance Grosh and Baker (1995) for an analysis of proxymeans testing program simulations in Jamaica, Peru and Bolivia, as well as Narayan and Yoshida
(2005) for an analysis in Sri Lanka.
We have also evaluated the sensitivity of our model, using a different threshold –the
CONEVAL poverty line - and using fixed budgets. Our analysis shows that at a cost similar to
that of perfect targeting, the flat rate option would be more effective in alleviating poverty than
the sliding scale. The sliding scale however remains the least costly policy option.
Overall, comparing leakage and undercoverage of each policy option remains a difficult
task. The tradeoff between raising welfare for the poor, thereby prioritizing low undercoverage
rates; and saving limited resources, thus prioritizing low overall costs of a program, remains a
political challenge for all administrations designing poverty alleviation programs.
Finally we analyzed the actual distribution of income following the distribution of a
universal pension to all individuals aged 70 and older in the city of Valladolid. We found that the
poverty headcount rate was 66.6 percent according to data from the baseline survey before the
implementation of non-contributory pension program, while the poverty rate declined to 13.6
percent according to data from the follow-up survey, conducted 6 months after the
implementation of the program. We still observe positive poverty rates after the universal
introduction of the program in Valladolid, which could be due to measurement error or a take-up
rate below a 100 percent.
This paper contributes to the existing literature by providing an analysis of the impact and
costs of various means-tested and universal cash transfers on poverty alleviation of the elderly
population. The well-being of increasingly aging populations represents an important challenge
for policy-makers, in particular in developing countries. Future research is needed to determine
8
Oportunidades is a conditional cash transfer program launched in Mexico in 2002. It is the expansion of the
PROGRESA program, a social safety-net program focusing on rural areas since 1997. Oportunidades uses a
combination of geographic, self-selection and proxy-means testing to identify poor households (Coady and Parker,
2005)
32
the factors contributing to misreports of income in households, possible improvements of models
to predict income, as well as possible behavioral responses to the implementation of meanstested poverty alleviation programs.
33
References
AGUILA, E., DIAZ, C., FU, M. M., KAPTEYN, A. & PIERSON, A. 2011. Living Longer in Mexico:
Income Security and Health. RAND Corporation.
AKERLOF, G. A. 1978. The Economics of "Tagging" as Applied to the Optimal Income Tax, Welfare
Programs, and Manpower Planning. The American Economic Review, 68, 8-19.
BESLEY, T. & KANBUR, R. 1990. The principles of targeting. The World Bank.
BIGMAN, D. & SRINIVASAN, P. V. 2002. Geographical targeting of poverty alleviation programs:
methodology and applications in rural India. Journal of Policy Modeling, 24, 237-255.
COADY, D. P., GROSH, M. & HODDINOTT, J. 2004. Targeting of transfers in developing countries:
review of lessons and experience, World Bank.
COADY, D. P. & PARKER, S. W. 2005. Program participation under means-testing and self-selection
targeting methods. International Food Policy Research Institute (IFPRI).
DE WACHTER, S. & GALIANI, S. 2006. Optimal income support targeting. International Tax and
Public Finance, 13, 661-684.
FOSTER, J., GREER, J. & THORBECKE, E. 1984. A Class of Decomposable Poverty Measures.
Econometrica, 52, 761-766.
GLEWWE, P. 1992. Targeting assistance to the poor : Efficient allocation of transfers when household
income is not observed. Journal of Development Economics, 38, 297-321.
GLEWWE, P., KANAAN, O. 1989. Targeting Assistance to the Poor Using Household Survey Data. The
World Bank, PPR Working Paper Series.
GROSH, M., BAKER, J. 1995. Proxy Means Test for Targeting Social Programs, Simulations and
Speculations. 118.
HECKMAN, J. J. 1979. Sample Selection Bias as a Specification Error. Econometrica, 47, 153-161.
KANBUR, R., KEEN, M. & TUOMALA, M. 1994. Labor Supply and Targeting in Poverty Alleviation
Programs. The World Bank Economic Review, 8, 191-211.
MARTINELLI, C. & PARKER, S. W. 2009. Deception and Misreporting in a Social Program. Journal of
the European Economic Association, 7, 886-908.
MARTINELLI, C., PARKER, S. 2009. Deception and Misreporting in a Social Program. Journal of the
European Economic Association, 7, 886-908.
NARAYAN, A. & YOSHIDA, N. 2005. Proxy Means Tests for Targeting Welfare Benefits in Sri Lanka.
Report No. SASPR–7, Washington, DC: World Bank, http://siteresources. worldbank.
org/EXTSAREGTOPPOVRED/Resources/49344 0-1102216396155/5728611102221461685/Proxy+ Means+ Test+ for+ Targeting+ Welfare+ Benefits. pdf, accessed
February, 5, 2009.
NARAYAN, A., YOSHIDA, Y. 2005. Proxy Means Test for Targeting Welfare Benefits in Sri Lanka.
The World Bank
OECD What are equivalence scales? OECD Project on Income Distribution and Poverty. Paris: OECD.
PERRY, G. 2007. Informality: Exit and exclusion, World Bank Publications.
POWERS, E. T. 1998. Does means-testing welfare discourage saving? evidence from a change in AFDC
policy in the United States. Journal of Public Economics, 68, 33-53.
RAVALLION, M. & CHAO, K. 1989. Targeted policies for poverty alleviation under imperfect
information: algorithms and applications. Journal of Policy Modeling, 11, 213-224.
SKOUFIAS, E. & COADY, D. P. 2007. Are the Welfare Losses from Imperfect Targeting Important?
Economica, 74, 756-776.
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