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Social Welfare Programs, Stigma, and Trust: Experimental Evidence for Six Latin American Cities
Alberto Chong and Vanessa Rios1
Abstract
We argue that social welfare programs are linked with the destruction of social capital, as measured by interpersonal trust in laboratory games. To test for this, we employ experimental data for representative samples of individuals in six Latin American capital cities (Bogota, Buenos Aires, Caracas, Lima, Montevideo, and San Jose). In fact, we find that participation in welfare programs damages trust, a result that is robust to the inclusion of individual risk measures and a broad array of controls. Whereas we argue that endogeneity is not a critical issue, we also control for it using an instrumental variables approach. Our findings also support the notion that low take up rates may be due to stigma linked to trust and social capital, rather than to transaction costs.
JEL Classification Code: D01, O12, O10Key Words: Experiments, Surveys, Social Programs, Social Capital, Trust, Stigma, Latin AmericaWord Count: 10,452
1 Chong, Georgia State University, Rios: University of Wisconsin, Madison. We are grateful to Juan Camilo Cardenas, Hugo Nopo, Suzanne Duryea, Jorge Guillen. for comments and suggestions. An earlier draft circulated under the title “Do Welfare Programs Damage Interpersonal Trust? Experimental Evidence from Representative Samples for Four Latin American Cities”. All remaining errors are our own.
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1. Introduction
Social welfare programs, in particular, those designed and implemented by governments
to help the neediest cope with long term vulnerabilities play a critical role as a policy instrument
in developed and developing countries alike. In fact, the proliferation of these long-term need-
based programs that include nutritional assistance, health assistance, educational support, and
even outright cash transfers have been actively pursued under the premise that they can help
provide a smooth transition towards reaching long-term improved welfare to the neediest. In fact,
it may be difficult to argue against these strategies, in particular, in the case of Latin America
where social welfare programs have become rather ubiquitous, but appear to have delivered on
the promise of improving the welfare of the neediest (Fiszbein and Schady, 2009). In fact, the
abundance of welfare programs in the developing world has spurred a coterie of academic papers
that seek to evaluate their impact and effectiveness, which in turn have generated ever improving
waves of such welfare programs aimed at better targeting the most disadvantaged.
Interestingly, whereas social welfare programs have become widely accepted as a tool to
effectively reach and positively impact the neediest, there is an on going debate on whether or
not they may also have undesirable effects on the society. In particular, several sociologists and
psychologists have argued that stigma in social programs may be linked with disapproval or
discontent with oneself and the society-at-large, as the very same characteristics that make them
eligible to such programs may also distinguish them, which may have negative connotations in
the society. After all, stigma is defined as a sign of disgrace or discredit, which sets a person
apart from others as a marker of adverse experiences reflected in a deep sense of shame, and thus
secrecy and withdrawal. Individuals who try to pursue a secrecy strategy and withdraw have a
more insular support network (Byrne, 2000). Furthermore, family and friends may also endure
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stigma by association, the so-called “courtesy stigma”. In fact, negative cultural sanction and
myths may combine to ensure scapegoating in the wider community, which translates in an
increase in social distance of those stigmatized by social programs with the rest of the
community (Goffman, 1963; Byrne, 2000).
In the context above, this paper provides empirical evidence on the possible link between
participation in social welfare programs and social capital, as measured by one of its key
components, interpersonal trust. Unlike typical studies on the latter, we do not employ
perception data, but offer representative new data at the city level from experimental games
collected from the following six Latin American capital cities, Bogota, Colombia; Buenos Aires,
Argentina; Caracas, Venezuela; Lima, Peru; Montevideo, Uruguay, and San Jose, Costa Rica. In
particular, we apply a standard, well-known, trust games with a “tried and true” protocol and
methodology that have been widely applied in the literature as a proxy for interpersonal trust
(Burks et al., 2003; Berg et al., 2005; Levitt and List, 2007). In addition, we also collected
representative information at the city level on the type and extent of social welfare program
participation by individuals, along with their corresponding basic socio-economic information.
We ask individuals about participation on mean-tested programs in education, health, nutrition,
and child care specific programs.
It is important to mention that since our focus is on welfare programs that may elicit
stigma, for instance, the use of distinguishable food stamps in stores or similar public locations,
we exclude income maintenance programs where in-cash or in-kind transfers are typically done
either using electronic tools, or related indistinguishable means, which tends to common in Latin
America. This is the case of program delivery of individual unemployment savings plans,
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severance pay, and conditional cash transfers, which are common in many countries in the
region2.
We find that being in a social welfare program is negatively linked to trusting behavior
and thus, to social capital formation. Furthermore we also test whether this association is causal
by applying a two-stage instrumental variables approach to provide additional evidence that the
uncovered link appears to go causally, from social welfare program participation to a reduction
of interpersonal trust, and thus to social capital destruction. Overall, we believe that our findings
make a strong case on the link between welfare programs and interpersonal trust as we employ
representative samples at the city level, and to the extent that we are not only making emphasis
on methodological and experimental protocols, but also on sampling design and
representativeness, we increase the external validity of our results3.
The paper is organized as follows. The next section provides a brief review of the
literature. Section 3 describes the data collection process, including sampling experimental
design. Section 4 describes the empirical methodology applied. Section 5 provides empirical
evidence on the possible link between participation in social programs and interpersonal trust,
including an identification strategy that employs an instrumental variables approach. Finally,
section 6 summarizes and concludes.
2. Brief Review of the Literature
In spite of the fact that the issue of possible negative externalities related to social welfare
programs due to stigma and discrimination can be very relevant for policy design, it has been
largely overlooked by economists and policymakers alike. However, there is relevant related 2 For instance, in the case of the Dominican Republic funds for most social program transfers are delivered electronically to individual card holders who can check balances and make transactions anonymously or with little interaction with other members of the society (Busso and Galiani, 2014).3 Our experimental set up focuses on interpersonal trust but it does not allow us to study whether being in a welfare program is associated with trustworthiness, an issue that escapes our research scope, which we refer to in our conclusions.
4
research in other fields, including social psychology, sociology, and the medical sciences, where
researchers have studied the issue of stigma and its impact in terms of social interactions (e.g.,
Crocker, Major, and Steele, 1998; Watson and Corrigan, 2003). Furthermore, some researchers
have distinguished between public stigma, defined as ways in which the general public reacts to
stigma attached to a group, and self-stigma, defined as the reactions that individuals turn against
themselves because they are members of a stigmatized group (Augoustinos and Ahrens, 1994;
Judd and Park, 1993; and Krueger, 1996). Researchers have also identified several behaviors
associated with public stigma, all leading to discrimination, prejudice, and loss of trust and social
capital. According to this literature, stigmatized individuals are considered responsible for their
handicaps, as these are seen as arising from problems created by themselves. In this context, help
is withheld and avoidance follows as people choose not to interact with a stigmatized person that
is believed to be responsible for her lot in life (Watson and Corrigan, 2003). Furthermore, these
individuals are viewed as incompetent and thus requiring authority figures to care for them. This
also brings prejudice as well as discrimination (Hilton and von Hippel, 1996; Devine, 1995;
Crocker, Major and Steele, 1998). Thus, isolation and difficulties in social interactions follow
leading to a loss in trust and social capital (Stuber, et al., 2000; Watson and Corrigan, 2003).
Individuals from stigmatized groups tend to be aware of the beliefs about their groups so much,
that a sizeable fraction of them interiorize the stigma and apply it against themselves. As a result
of this sort of self-stigmatization, they end up suffering diminished self-esteem and self efficacy,
and isolation (Bowden, Schoenfield, and Adams, 1980; Kahn, Obstfeld, and Heiman, 1979). As
in the case of public stigma, people perceiving or feeling stigmatization are less likely to interact
with other individuals thus reinforcing the negative feelings of the stigmatized, to the point that
sentiments of marginalization, hatred, resentment, and lack of trust become predominant.
5
Paradoxically, entitlement programs that are designed to help excluded groups may end up
further marginalizing them as the social construct or social capital of the society becomes
structurally damaged (Gronfein and Owens, 2000).
According to this literature, the existence of networks of civil engagement and basic
norms of reciprocity are the most important prerequisites to accumulate social capital within a
society (Kumlin and Rothstein, 2005). Personal welfare state experiences tend to be generalized
into collective judgments of what population is experiencing as a global, thus influencing
political orientation and attitude formation and also affecting trust in politics, institutions, and
even trust among peers (Kumlim, 2002; Kumlin and Rothstein, 2005; Rothstein, 2000). The link
between personal experiences and trust is crucial as it determines the attitude for a voluntary
civil engagement and reciprocity, thus affecting the accumulation of social capital. In this sense,
when there is a general perception of trust and fairness in political and legal institutions, such pro
social behavior that enhances social capital is more likely to develop; while, a contrary context,
might influence citizens’ views on the reliability of other people as well, reducing interpersonal
trust to the detriment of the production of social capital. In other words, the formulation and
implementation of welfare programs implies a great scope for bureaucratic discretion and gives
rise to suspicions of arbitrary and discriminatory treatment in comparison with universal welfare
programs (Kumlin and Rothstein, 2005). These programs may also incite resentment on the
portion of the population who is not qualified for the benefits; being more exposed to political
resistance when non qualifiers are numerous and politically effective (Rothstein, 1998). On the
contrary, universal programs do promote equal treatment and principles of fairness, generating
experiences that build support for the welfare state and the political system. In fact, Kumlin and
Rothstein (2005) provide empirical evidence to sustain that personal experience that employ
6
means-tested welfare state tools seem to reduce interpersonal trust while experiences with
universal institutions tend to build it. In particular, they empirically test whether specific design
matters for the production of social capital in the case of Sweden. Their argument is that welfare
state institutions are not necessarily unfavorable to the production of social capital, being the
specific design of the welfare state policies a key issue on such production of social capital.
Thus, they find that contacts with universal welfare state institutions tend to increase social trust,
whereas experiences with means-tested welfare programs undermine it, suggesting that by
designing welfare-state institutions, governments can invest in social capital.
2. Data and Experimental Design4
As mentioned above, one of the advantages in our study is that we employ objective
measures of interpersonal trust based on experimental games. By using these data, instead of
subjective data, we avoid potential biases widely discussed in the literature (e.g., Bertrand and
Mullainathan, 2000)5. Our full sample consists of more than 3,000 individuals (half of them
assume the role of Player 1 and the other half the role of Player 2) and include samples collected
in Bogota, Buenos Aires, Caracas, Lima, Montevideo and San Jose. Not only are these data the
most comprehensive experimental data in Latin America, but they are also particularly unique
since the samples are specifically collected to be representative at the city level as the goal of the
sampling procedure was to obtain empirical distributions of individuals within combinations of
4 This section draws heavily from Candelo, et al. (2009) and Cardenas et al., (2012) as these papers are also part of a broader research initiative on trust and pro-social behaviors funded by the Research Department of the the Inter-American Development Bank, a multilateral institution based in Washington, DC. The paper by Candelo, et al., (2009) gives details related of the methodological approach of the overall field work and experimental protocols employed in both Cardenas, et al (2012) and this research. The research questions pursued in Cardenas (2012) and this piece are distinct and independent from each other. In particular, in the former paper the authors create a pro-social index using survey data and compare it with pro-social experimental data to show that both sources of information are complementary.5 Furthermore, our experimental approach allows us to measure interpersonal trust in an indirect way, which may help reduce reverse causality issues. Endogeneity issues are addressed in the next section.
7
characteristics resembling those of the populations in the cities (Candelo, et al., 2009). In
particular, we employed stratified random samples where strata were chosen on the basis of
education, average family income of the districts, gender, age, and the territorial units that make
up each city under study –in either quartiles or quintiles, depending on data availability.6
In order to guarantee homogeneity in the application of experimental protocols, the field
teams participated in a training workshop at the launching of this project7. This workshop
provided a uniform approach to implementation and related fieldwork details such as sampling
procedures, writing style, protocol, timing of actions (i.e., invitations, pre-survey, experiments,
post-surveys), elements to be included in experimental sessions and the construction of
questionnaires8. The individuals participating in the experiments were recruited randomly from
neighborhoods and invited a few days prior to the application of the experimental session in
order to gather information regarding their socio-economic background and social welfare
participation characteristics and to receive information about the expected gains from
participating in the experiments, which included a show-up fee and potential monetary gains
from participating in the games. The day before the experimental sessions, the participants were
reminded of it with a phone call or visit, and transportation was agreed or discussed. Information
on the socio-economic composition of the groups in each particular session was delivered as the
sessions progressed. The participants met in one room where they were able to see each other,
but they were not allowed to communicate during the session. During the recruitment process we
avoided having two people who knew each other within one session. As the sessions progressed,
participants received information about their peers, depending of the particular activity. Social
6 The age groups were: (i) 17-27; (ii) 28-38; (iii) 39-59 and (iv) 60-72.7 The launching workshop for this project was conducted in Bogota, Colombia during the fourth quarter of 2007.8 All experiments and related questionnaires were conducted in Spanish. A translation of the questionnaire and protocols is available from the authors upon request.
8
heterogeneity on individuals’ decisions in each particular session was made as salient and clear
as possible using the information collected on the socio-economic composition of the groups.
In order to carry out the field work we conducted a series of approximately 25
experimental sessions per city. The sessions were arranged so that at least three sessions per city
included only individuals from high-income strata and at least three other sessions included only
individuals from low-income strata; the rest combined individuals from all strata. Around 30
individuals were invited for each session, under the assumption that approximately one third
would not show up, thus allowing each experimental session to go forward with roughly 20 to 25
participants that lasted between two and three hours (Candelo, et al., 2009). Each experimental
session followed the exact same protocol, with the exact same sequence of activities, as a team of
researchers with experience in survey and field methods was selected to undertake the sample
design and conduct the experiments and surveys in each city. After participants completed the
surveys, which were designed to collect information on whether individuals participated in
education, health nutrition, or child care welfare programs, the payoffs from the experiments
were computed and the participants received their payments.
The sessions consisted of several activities each, all played in the same order. For this
paper we focus on the results of the first activity, a straightforward Trust Game, which was
applied using the strategy method (Berg, et al, 2003; Carpenter, et al., 2005). As it is well
known, session participants in this game are randomly assigned in pairs: half assume the role of
player 1 and the other half, that of player 2. The assignment of pairs was randomly made. Both
groups are simultaneously located in different rooms, and identities of the pairs are never
revealed, although each player receives information on key demographic characteristics of their
pairs: sex, age, schooling level and socio-economic stratum. Other socio-demographic variables,
9
such as if the person was or not in a welfare program, were not included as having done so may
have affected expectations about what the experiment would be, and also may have affected both
the behavior of the players, which could have compromised our findings. In this game, both
players receive an equal endowment, and player 1 is then asked to decide how much of this
endowment he or she wants to send to player 2, knowing that player 2 will then receive three
times that amount on top of the initial endowment everyone initially receives. In another room,
player 2 is asked to decide the amount to be returned to player 1 for each possible offer from
player 1, from a discrete set of fractions of amounts sent (0%, 25%, 50%, 75% and 100%).
Immediately before making their decisions, the individuals are also asked to predict the decisions
to be made by the other player. That is, the amount expected by player 2 from player 1, and
player 1’s expected returned amount from player 2. After both players make their decisions the
matching of their choices is made. This procedure allows us to separate the effects of
expectations from those of social background. Replications of this game around the world have
shown that people on average send half of the initial endowment to player 2, and that the returns
from player 2 to player 1 generate a net positive return for player 1 of about ten to twenty percent
of what was originally sent (Carpenter et al, 2005; Ashraf, Camerer and Loewenstein, 2005).
Provided that individuals’ attitudes towards risk may be a crucial determinant of a
player’s offering in this game, in this paper we also use information from a related experimental
activity based on the simple risk games first applied by Binswanger (1980) and later on by Barr
(2003). In this activity (which was the third activity of the experimental sessions) each player
makes individual decisions over three games that measure individual attitudes over risk,
ambiguity, and losses. In this paper we only use information that measures attitudes towards
10
risk9. Such measure comes from a game were participants are given a set of outcomes for six
50/50 lotteries that go from a sure low payoff to an all-or-nothing higher expected payoff. The
lotteries in between gradually increase both in expected value and in the spread between the low
and high payoff. Based on the outcomes of this game, we classify participants into three groups:
low risk averse, medium risk averse and high risk-averse. We use this resulting variable as a
control in our empirical specifications.10 For the purpose of this study, the sample only includes
those players who made a initial offer, which consists of 1,465 observations.
3. Econometric Approach
Following the standard interpretation of the trust game, we use the offer of the first player
to the second player, the measure of interpersonal trust, as our dependent variable. As mentioned
above, after both players receive an equal endowment player 1 is then asked to decide how much
of this endowment he or she wants to send to player 2. This amount is considered a good
measure of interpersonal trust, as player 1 knows that player 2 will receive three times the
amount given by him or her on top of the initial endowment everyone initially receives, and then
player 2 will return an amount to player 1 from a discrete set of fractions of amounts sent. In
addition, we include a vector of measures of welfare participation as our key variables of interest
along with basic controls, as described below. In particular, our reduced form follows the
specification:
(1)
where VolOfferi represents the voluntary offer of the particular individual i to another
individual in the trust game. We use the percentage of the initial endowment that player 1
9 Details on this specific game are provided in Candelo, et al. (2009) and Cardenas, et al., (2012). The other two experimental games applied were a Voluntary Contributions Game and a Risk Pooling Game. (Cardenas, et al., 2012).10 The exclusion of these variables among our controls delivers qualitatively similar results.
11
offered to player 2. Additionally, Xi is a vector of individual characteristics that includes age,
schooling, gender, and socio-economic level. Vector Zi captures information obtained from the
experimental sessions, in two dimensions: attitudes towards risk and (pre-game) expectations
about the behavior of the matched players. The vector M i contains socio-economic information
about the matched player; in particular, we control for gender and for differences between the
matched players in terms of schooling, age, and socio-economic level.
Our variable of interest, Wi , a measure of individuals’ participation in social welfare
programs, is defined in three alternative ways: (i) a simple dummy variable that captures whether
the individual is participant in a social welfare program; (ii) a variable defined as the percentage
of social programs of which the participants are beneficiaries, out of the list of social programs
that were listed in our survey; and (iii) an index that ranges from 0 to 4 depending on the groups
of social programs for which the individuals benefit, as the list of social programs is divided in
four general groups, education, health, nutrition, and child care For example, if the participant is
beneficiary of social programs related to only health and education, then his or her index will be
211. The exact definitions of these and all the other variables used in the paper are presented in
Table 112. Finally, i is a residual term.
Table 2 presents summary statistics, and Table 3 shows correlations between variables
used in the analysis. Related to the basic characteristics of the sample we find that the average
age of game and survey participants is 39, and that there is a reasonable gender balance as 54
percent of participants are women. Also, in our sample, 52 percent of the participants reside in a
low socio-economic level neighborhood and 30 percent in a medium-level neighborhood.
11 We also introduce this variable in percentages. Results do not change.12 Appendix 1 shows coverage of social welfare programs by city.
12
Finally, the average number of years of education reached by participants is almost 11 years13.
As mentioned above, Table 1 provides detailed definitions of variables.
4. Findings
In this paper we focus on the outcomes of the trust game and their link to participation in
social programs. In particular, Table 4 shows ordinary least squares results obtained when the
dependent variable is the voluntary offer of the first player in the trust game. All our regressions
include session dummies and include robust standard errors that are computed clustered at the
session level.14 We find that participation in social programs is negatively linked to the voluntary
amount offered by player 1, this variable interpreted as a measure of trust from the individuals to
their corresponding pairs. This result is statistically significant at conventional levels regardless
of the proxy employed to capture social welfare participation. Thus, our finding is consistent
with the idea that social welfare beneficiaries are associated with lower levels of interpersonal
trust, possibly due to stigma or discrimination, which is consistent with the literature of
disciplines such as sociology, social psychology and the medical sciences. This result, however
does not fully hold when studying each city separately. Whereas, overall our results are strongly
statistically significant, in some cities the sign is consistent but the significance is limited.15 On
the other hand, not all of our controls are statistically significant. We find that if the participant is
female and the second player is male, the participant will make a lower offer–compared to the
case where the participant is a male and the other player is female. In line with previous results
by Rabin (1993), the expectation of generosity of the matched individual (e.g., the second player)
appears to matter as well.
13 Further description of the sample can be found in Cárdenas et al. (2009).14 Results do not change when using ordered probit regressions instead. 15 Since our focus is on Latin America as a whole we do not present our city-level results. However they are available upon request.
13
It may be argued that endogeneity and in particular, reverse causality may be an issue
when testing the link between participation in social welfare programs as an explanatory variable
for interpersonal trust. In fact, as it stands it may be difficult to claim that participation in social
welfare programs causes a reduction in interpersonal trust and social capital. At the extreme, one
may argue that the opposite causal link is as likely to occur, that is, that more interpersonal trust
may drive higher participation in social programs either because interpersonal trust may also be
positively correlated to government and political trust or because more trusting individuals may
tend to have better social networks. While theoretically possible, as reverse causality is always
very difficult to be ruled out in empirical work, there are several reasons to be skeptical about
such issues between participation in social welfare programs and interpersonal trust. In fact, it is
unclear whether there is a link between interpersonal trust and political trust as several political
scientists have shown (Seligman, 1997; Benson and Rochon, 2004). In fact, some studies have
demonstrated a negative link between these two variables. For instance, Benson and Rochon
(2004) use World Value Surveys data to show that interpersonal trust is an important factor in
motivating political protest participation and raising the intensity of protest, thus negating a
positive link between interpersonal trust and political trust. These researchers further suggest that
high levels of trust make individuals likely to anticipate low expected costs of political trust and
participation.
Similarly, in the case of interpersonal trust linked to better social networks, it is difficult
to envision how improved social networks may increase participation in social welfare programs
at least in the context of most Latin American countries, for at least three reasons. First, it seems
unlikely that eligible individuals may not want to participate in a program that provides
substantial benefits at very-low-to-zero costs, at least under the basic assumption of rational
14
behavior. Thus, while strictly speaking it cannot be ruled out that an eligible, poor, needy
individual will not participate in, say, a government-sponsored social nutrition program, one
would have to argue that this may occur because of mistrust to such programs, tradition, personal
beliefs, and the like. Again, given that the cost of participating in such programs in both
monetary and non-monetary terms is essentially zero it does not seem likely that potential
beneficiaries would not participate in such programs, at least under basic standard neoclassical
considerations and if so, it would be reasonable to believe that such individuals choosing not to
participate would not be economically or statistically significant. Second, it is unclear why
better social networks may be able to provide potential beneficiaries with better transmission of
information of social welfare programs in the context of government oversaturation of such
social programs as, in fact, most Latin American countries own national networks (newspapers,
radio, and television), control affiliated media, and have dedicated Ministries that constantly
market the benefits of social welfare programs (Djankov, et al, 2003). Third, there is a very long
historical tradition of assistentialism in Latin American countries that date back to 1940s by
which populist dictators would provide a host of social programs and related favors to the lowest
income segments of the population in an exchange for implicit political support. The most
notorious historical example in Latin America is the case of Peron in Argentina, which has not
been, by any means unique in the region16. It is unlikely that there would be aversion by potential
beneficiaries to participating in welfare social programs sponsored by Latin American
governments and thus as, indeed, this has been historically expected. Thus, there is little reason
to see why social networks would needed influential in welfare program participation, further
undermining the idea of reverse causality between social welfare program participation and
16 In fact, Venezuela’s Chavism or Neo-Chavism is the most recent example of this long tradition (Hsieh, et al, 2011).
15
interpersonal trust, as stated in this paper. Finally, and in addition, the objective dependent
variable employed in this research seeks to reflect interpersonal trust in an “indirect” manner,
which we believe further helps minimize possible reverse causality issues.
Nevertheless, it may be argued that there may be some other channels that may drive
reverse causality from interpersonal trust to social welfare program participation to the ones
described above. Furthermore, it may be argued that endogeneity due to the presence of omitted
variables may still be an issue in our empirical results. Thus, in order to potentially correct for
this issue we proceed to employ an instrumental variables approach where the instrument chosen
is a categorical variable based on the ratio of children over income earners in the household. In
fact, chances are that households with higher household ratio of children over income earners
will have a higher probability of participating in social programs as the correlation coefficients
between participation in a social program and the ratio of children over earners using any our
three measures of participation is 0.478, 0.271, and 0.459 (See Table 3). On the other hand, there
appears to be no direct reason to expect that households with more children than income earners
will show different patterns of interpersonal trust as a result of a direct causal relationship
between them, since it is a measure of the households’ need.17
In fact, our main results hold when using the instrumental variables approach, as shown
in Table 5. The estimations presented are 2SLS, where the instrument included is the predicted
probability of the regressions of social programs participation on our proposed instrument and all
the control variables included in Table 4 estimations. For our first participation measure (i.e.
receives any social program), as it is a binary variable, this initial stage is a probit regression. For
17 The simple correlation between our instrument and the percentage offered to player 2 is -0.105. Also, we apply corresponding under-identification tests, namely, that the excluded instruments are relevant or correlated with the endogenous control. The null hypothesis that the equation is under-identified is rejected.
16
the other participation measures, given that they are left-censored, we run tobit regressions.
These initial stages of all instrumental variables regressions are in Appendix 2 and Appendix 3.18
We find that our three measures of participation in welfare social programs continue to yield
coefficients that are negative and statistically significant at conventional levels. The instrumented
estimators for the role of social programs on trust show higher magnitudes than the ordinary least
squares estimators, as expected. With respect to the other control variables, we find that some
gender combinations, the return offer expected from the second player, and medium risk
aversion are all still statistical significant. Initial probit and tobit estimations include session
dummies and city dummies.
In Tables 6 we go a step further and explore the linkages between social program
participation and interpersonal trust for three different social welfare domains separately,
education, health, and nutrition programs.19 In our ordinary least squares results we find an
analogous result of negative linkages between interpersonal trust and social welfare program
participation for every category, which is statistically significant at conventional levels.20
As before, we repeat the exercise of correcting for possible endogeneity with the use of
an instrumental variables approach. The results are shown in Table 7. In this case we confirm our
findings with respect to education, social health and nutritional programs. As expected, when
18 Additionally, it may be possible to argue that both trust and welfare participation may be linked to a third unobserved control, for instance, households’ shocks or life hardships (job loss, death of a bread winner, natural disasters, etc.). The occurrence of such negative shocks may be linked to a simultaneous change in interpersonal trust and individuals’ need of welfare, causing a spurious correlation among the two variables of interest. The design of the experiments and the data captured with the accompanying surveys do not allows us to explore such possibilities and thus leave these issues beyond the scope of this paper.
19 We are unable to consider the category child care programs because of lack of variation. In fact, less than two percent of our sample indicates being enrolled in such type of program.20 For the sake of completeness, in Appendix 4 we run our reduced form (1) but employ the offer of the second player. Unsurprisingly, we find that in this case, social participation in welfare programs has no bearing on reciprocity although the percentage expected to be received from matched player does matter.
17
using an instrumental variables approach, we find evidence of substantially higher coefficients in
our variables of interest, which suggest causal linkages between participation in specific social
welfare programs and interpersonal trust. Based on these instrumental variables findings, it may
be claimed that the participation of welfare programs appears to impact interpersonal trust
negatively, and thus contribute to damage social capital.
5. Summary and Conclusions
In this paper we offer experimental data on trust, which for the first time is studied in the
context of social welfare program participation. We apply a trust game to representative samples
of six Latin American cities with the aim of testing the extent to which the reception of social
programs is linked to a decrease in interpersonal trust. From the analysis of these experimental
data for representative samples of individuals we conclude that participation in social welfare
programs are linked with the destruction of social capital, as measured by interpersonal trust.
According to related literature, this may occur because of stigma or discrimination which
contributes to the increase of social distance between recipients and non-recipients. Furthermore,
our findings touch on the debate on whether low take up rates are due to transaction costs or
stigma and provide supporting evidence that they are due to the latter as stigma has been linked
with trust and social capital, but not with transaction costs. The results are robust to both the
inclusion of individual risk measures and to changes in specification when pooling our sample
and when testing most cities individually.
While, to the best of our knowledge, this is the first paper that addresses the issue of
welfare participation and social capital, the policy implications of this research may be even
more extensive. On the one hand, promoting higher take up rates would not necessarily be sound
policy as the trade off of damaging social capital would have to be taken into account. In the
18
context of Latin American countries, where social capital is not particularly high, there is a real
risk that social programs may be depleting such capital. On the other hand, under the assumption
that our dependent variable better reflects stigma rather than administrative costs, one shop stops
or centralized administrative offices for the delivery of social services would not be advisable.
It is important to highlight, however, that this paper studies only one aspect of trust,
interpersonal trust among agents. It is unclear whether trustworthiness issues may be affected,
measured by trust in the recipient of the trust game, which, at least in theory appear to be as
relevant as trust in the sender of the game. We expect that our future research will help address
these issues.
19
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22
Table 1 Variables Definitions
Variable DefinitionIndividual’s socio-demographic characteristicsAge (years) Age of the participant. Education (years) Number of years of education of the participant.Socio-economic level Categorical variable that indicates the socio-economic level of the participant:
low, medium or high. Each category was converted to a dummy variable. In all regressions, the first category (low socio-economic level) was the omitted dummy.
Matched players’ characteristics (related to individuals)Matched players that are woman
Dummy variable that takes the value of 1 when the matched player with the participant is a female and 0 otherwise.
Matched players that are men
Interaction variable that results from multiplying the participant’s gender by the matched player’s gender. It takes a value of 1, when both players are women; and 0, otherwise.
Average age difference between matched players
Difference in age of the matched player with the participant.
Average schooling difference between matched players
Difference in number of years of education of the matched player with the participant.
Socio-economic level compared to the matched player
Categorical variable that ranges between -2 to 2 as a result of subtracting the matched player’s socio-economic level from the participant’s one. Each category was converted into a dummy. In all regressions, the omitted dummy was the category “0”, when there was no difference between the matched players.
Experiment variablesInitial offer by Player 1 Percentage of money offered by player 1 to player 2 in the Trust Game. From
the amount received by player 1, he/she had five options: to give 0%, 25%, 50%, 75%, or 100% of his/her money to player 2.
Return offer by Player 2 The fraction of money that player 2 decided to send at the time of her/his decision. The numerator is the monetary amount sent at her/his move. The denominator is the initial endowment plus three times the amount sent by player 1.
Risk aversion Categorical variable that indicates the risk aversion level of the participant: low, medium or high. Each category was converted to a dummy variable. In all regressions, the first category (low risk aversion) was the omitted dummy.
Percentage expected to be returned by matched player (For player 1 in Trust Games regressions)
This variable can take values from 0% to 100%. It reflects player’s 1 expectation about the percentage to be returned by player 2, considering the different set of options he has (that set of options depends on the percentage of money gave by player 1).
Participation in social programsReceives any social program
Dummy variable that takes the value of one when the participant indicates that he/she or a member of his/her household is a beneficiary of a social program. The list of social programs is divided in four groups: education (E), health (H), nutrition (N), and child care (C). The list of specific programs included in each group varies depending on the city (programs in Buenos Aires and Caracas were not identified separately): Bogota: E: “Familias en Acción”, Labor training –SENA, “Jóvenes en
Acción”, “Hogares Infantiles” (ICBF or DABS), “Jardines Comunitarios”, and “Centro de Educación para Adultos”. H: “Régimen Subsidiado en Salud”, and “Jornada Nacional de Vacunación”. N: “Comedores comunitarios”, “Comedores Escolares” (ICBF), “Hogar de Bienestar” (FAMI), “Desayunos Infantiles” (ICBF), “Adulto Mayor” (PPSAM), and
23
Variable Definition“Red de Seguridad Alimentaria” (RESA). C: “Hogares Comunitarios”, “Club Juvenil o prejuvenil ICBF”
Lima: E: School uniform and shoes, Textbooks and school supplies, Labor training, and PRONOEI. H: “Seguro Integral de Salud”, “Campaña Nacional de Vacunación”, “Campaña de Planificación Familiar”, and “Control de Tuberculosis”. N: “Desayuno escolar”, “Vaso de leche”, “Comedor Popular”, “Canasta familiar” (PANFAR), “Alimento por trabajo”, “Comedor parroquial”, direct food aid, “Papilla u otro alimento para menores”. C: “Wawa Wasi”, and “Cuna”.
Montevideo: E: Textbooks and school supplies, School uniform and other clothing, Full time public school, “Asignaciones familiares”, and “Beca lineal”. H: “Programa de Educación Sexual y Planificación Familiar”, “Vacunación contra la gripe”, “Apoyo económico para control de embarazos”, and “Apoyo económico para control de niños”. N: “Comedores y merenderos”, “Reparto de canasta alimenticia”, “Reparto de leche en polvo”. C: “Verano solidario”, and CAIF.
San Jose: E: “Bono escolar”, Scholarships, “Transporte escolar”, and labor training. H: “Régimen no contributivo”, and “Vacunación gratuita”. N: “Comedor escolar”, “Comedor universitario”, “Comedor comunitario”, “CEN CINAI hogar comunitario”, “Leche”, “Paquetes de Alimentos”. C: “Guardería”.
Percentage of programs received
Percentage of social programs that the first players’ (in the trust games) households receive (out of all). If there are 10 social programs listed for that city, and the participant’s household received 2 of them, the variable value for this observation will be 20. It could take values from 0 to 100.
Reception of social programs (index)
Index that takes values from 0 to 4 depending on the groups of social programs that the first players’ (in the trust games) households receive. For example, if the participant is beneficiary of social programs related to health and education, then his index will be 2.
Reception of social program related to education, health, nutrition or child care
Dummy variable that takes the value of one when the participant indicate that his/her household is beneficiary of a social program related to education, health, nutrition or child care in his city; and zero, otherwise.
InstrumentsRatio of children over income earners in the household
Ordinal variable indicating the ratio of children over income earners in the household: Zero, 0 to 0.5, 0.5 to 1, 1 to 2, and More than 2. In all regressions, the category equal to zero was omitted.
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Table 2Summary Statistics
Variable Mean Std. Dev.
Min Max
Household's participation in social programs Receives any social program 0.281 0 1Percentage of programs received by the players' household (%) 3.199 6.805 0 61Reception of social programs (index) 0.445 0.806 0 3Receives a social program related to education 0.157 0 1Receives a social program related to health 0.174 0 1Receives a social program related to nutrition 0.088 0 1Receives a social program related to child care 0.031 0 1Individuals' socio-demographic characteristics Age 39.172 15.176 17 80Years of education 10.942 3.591 0 22SE level
Low 0.52 0 1Medium 0.30 0 1High 0.18 0 1
CityBogotá 0.29 0 1Buenos Aires 0.38 0 1Caracas 0.06 0 1Lima 0.22 0 1Montevideo 0.04 0 1San José 0.01 0 1
Ratio of children over income earners in the household (Instrument)Zero 0.380 to 0.5 0.16 0 10.5 to 1 0.25 0 11 to 2 0.14 0 1More than 2 0.06 0 1
Differences between participants and matched playersGender difference
Both are women 0.31 0 1Both are men 0.24 0 1Participant is a woman and her match is a man 0.24 0 1Participant is a man and her match is a woman 0.22 0 1
Age difference 3.25 20.507 -54 53Schooling difference -1.12 4.351 -15 14SE level difference
Participant is at a lower SE level than his/her match 0.27 0 1Both are at the same SE level 0.55 0 1Participant is at a higher SE level than his/her match 0.19 0 1
Experimental variablesFirst player's initial offer 42.24 29.078 0 100Percentage expected to be returned by matched player 36.16 23.103 0 100Risk aversion
Low 0.17 0 1Medium 0.34 0 1High 0.50 0 1
Note: Summary statistics are based on weighted data using sample of main empirical specifications presented in Table 4 (1,465 observations). Standard deviations are only presented for continuous variables. Variables are defined in Table 1.
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Table 3 Correlations matrix
First player's initial offer (Trust
measure)
Receives any
social program
Percentage of
programs received by the
players' household
Reception of social programs (index)
Receives a social program related to education
Receives any
social program related
to health
Receives a social program related
to nutrition
Receives a social program related to child
care
Age Years of
education
SE level
Percent. expected
to be returned
by matched player
Risk aversi
on
Differences between participants and matched players
Ratio of children
over income
earners in the
household
Age diff.
Schooling diff.
SE level diff.
First player's initial offer
1.000 -0.236 -0.165 -0.247 -0.355 -0.164 -0.118 -0.307
Age 0.035 -0.130 -0.062 -0.126 -0.119 -0.078 -0.089 -0.119 1.000Years of education 0.138 -0.306 -0.196 -0.298 -0.286 -0.213 -0.312 -0.235 -0.266 1.000SE level 0.127 -0.314 -0.259 -0.343 -0.238 -0.331 -0.459 -0.172 -0.192 0.636 1.000Percentage expected to be returned by matched player
0.260 0.077 0.076 0.068 -0.006 0.104 0.159 -0.183 0.084 -0.047 0.058 1.000
Risk aversion -0.033 0.078 0.038 0.065 0.048 -0.025 0.100 0.274 -0.030 0.066 -0.030 -0.020 1.000Gender differenceBoth are women -0.009 0.157 0.102 0.154 0.199 0.071 0.169 0.120 -0.033 -0.003 -0.055 -0.067 0.116 -0.159 0.117 -0.008 0.218Both are men 0.039 -0.201 -0.186 -0.196 -0.122 -0.201 -0.232 -0.162 0.014 -0.010 0.037 0.070 -0.174 0.124 -0.078 0.028 -0.136Participant is a woman and her match is a man
-0.076 0.018 0.001 -0.013 -0.179 0.062 -0.011 0.114 0.133 -0.014 0.004 0.018 0.047 0.170 -0.120 -0.033 -0.010
Participant is a man and her match is a woman
0.050 -0.008 0.023 0.017 0.048 0.044 0.013 -0.143 -0.120 0.028 0.021 -0.015 0.001 -0.127 0.062 0.015 -0.115
Age difference 0.053 -0.133 -0.064 -0.126 -0.157 -0.041 -0.114 -0.131 0.746 -0.212 -0.231 0.056 -0.018 1.000Schooling difference 0.020 0.025 -0.001 0.018 0.060 0.018 -0.042 -0.008 -0.244 0.623 0.333 -0.088 0.016 -0.332 1.000SE level difference 0.046 -0.023 -0.057 -0.035 0.046 -0.100 -0.137 0.098 -0.198 0.288 0.609 -0.029 -0.013 -0.214 0.510 1.000Ratio of children over income earners in the household (Instrument)
-0.105 0.478 0.271 0.459 0.518 0.298 0.360 0.416 -0.181 -0.192 -0.159 0.082 0.032 -0.146 -0.037 -0.027 1.000
Note: The matrix includes the Pearson, polychoric (tetrachoric), and polyserial (biserial) correlation coefficients, which were calculated according to their appropriateness for different continuous and categorical variables combinations.
27
Table 4Welfare Social Programs and Interpersonal Trust
Dependent variable: First player's initial offer(1) (2) (3)
Participation in social programs Receives any social program=1 -9.5251***
(2.5423)Percentage of programs received -0.7173***
(0.1989)Reception of social programs (index1) -7.0492***
(1.4775)
Medium Risk Aversion 4.9799 4.5632 4.6809(3.6050) (3.6304) (3.5817)
High Risk Aversion -1.5221 -1.5188 -1.4968(3.2023) (3.2198) (3.2009)
Constant 36.5769*** 35.9658*** 38.1116***(9.6649) (9.5134) (9.4256)
Observations 1,465 1,465 1,465Individuals' socio-demographic characteristics Yes Yes YesMatched Players’ Characteristics Yes Yes YesCity Fixed Effects Yes Yes YesClusters 149 149 149Adjusted R-squared 0.2361 0.2417 0.2476* Significant at ten percent; ** significant at five percent; *** significant at one percent. Also controlled for matched players characteristics (gender, age, schooling, socio-economic status, and expectations of money return), individuals’ socio-demographic characteristics (age, education, and socio-economic status).
28
Table 5Welfare Social Programs and Interpersonal Trust, Instrumental Variables
Dependent variable: First player's initial offer(1) (2) (3)
Participation in social programs Receives any social program=1 -15.7470*
(8.0837)Percentage of programs received -1.3456**
(0.5369)Reception of social programs (index) -9.5327**
(3.7503)Medium Risk Aversion 6.6696* 4.2082 4.5795
(3.4517) (3.4667) (3.3795)High Risk Aversion -1.3176 -1.2348 -1.3749
(3.1416) (3.0657) (3.0227)Observations 1,067 1,465 1,465Individuals’ socio-demographic characteristics Yes Yes YesMatched Players’ Characteristics Yes Yes YesCity Fixed Effects Yes Yes YesClusters 105 149 149Under identification test (Kleibergen-Paap LM statistic) 19.4670*** 23.1321*** 27.3927***Robust standard errors in parentheses, clustered at the session level. All regressions are run using 2SLS and include dummies per session. In (1), we calculated the predicted probabilities of a probit regression of the endogenous variable on the instruments and all control variables included in the main specifications. These predicted probabilities were used as instruments in the 2SLS regressions. A similar procedure was followed for (2) and (3), but using the predicted probabilities of tobit models. * Significant at ten percent; ** significant at five percent; *** significant at one percent.
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Table 6Type of Social Programs and Interpersonal Trust
Dependent variable: First player’s initial offer(1) (2) (3)
Participation in social programs Receives an education social program=1 -14.7525***
(3.1133)Receives a health social program=1 -10.6239***
(3.6944)Receives a nutrition social program=1 -6.5318
(4.0547)Medium Risk Aversion 5.1287 4.4100 4.8693
(3.6183) (3.5590) (3.6368)High Risk Aversion -1.4460 -2.1294 -1.6170
(3.2565) (3.2308) (3.2604)Constant 37.1910*** 35.4520*** 33.1797***
(9.6157) (9.4782) (9.6842)Observations 1,465 1,465 1,465Clusters 149 149 149Adjusted R-squared 0.2476 0.2355 0.2241* Significant at ten percent; ** significant at five percent; *** significant at one percent. Also controlled for matched players characteristics (gender, age, schooling, socio-economic status, and expectations of money return), individuals’ socio-demographic characteristics (age, education, and socio-economic status).
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Table 7 Type of Social Programs and Interpersonal Trust, Instrumental Variables
Dependent variable: First player’s initial offer(1) (2) (3)
Participation in social programs Receives an education social program=1 -16.9924**
(7.1625)Receives a health social program=1 -33.1684**
(14.5450)Receives a nutrition social program=1 -47.8183**
(22.4485)Medium Risk Aversion 8.2312** 2.0575 2.5738
(3.7810) (4.2274) (6.8829)High Risk Aversion 0.5570 -4.5467 0.3807
(3.5504) (3.8114) (6.5555)Observations 889 823 450Individuals’ socio-demographic characteristics Yes Yes YesMatched Players’ Characteristics Yes Yes YesCity Fixed Effects Yes Yes YesClusters 86 79 43Underidentification test (Kleibergen-Paap LM statistic) 27.4146*** 10.7141*** 6.4851***Robust standard errors in parentheses, clustered at the session level. Robust standard errors in parentheses, clustered at the session level. All regressions are run using 2SLS and include dummies per session. We calculated the predicted probabilities of a probit regression of the endogenous variable on the instruments and all control variables included in the main specifications. These predicted probabilities were used as instruments in the 2SLS regressions. * Significant at ten percent; ** significant at five percent; *** significant at one percent.
31
Appendix 1Individuals that Participate in Social Programs, By City
(percentage of all the first players)
Bogotá Buenos Aires Caracas Lima Montevideo San JoséAny social program 43.9% 5.5% 10.5% 53.9% 18.6% 17.4%Related to education 34.1% 0.7% 9.7% 19.3% 15.4% 9.6%Related to health 22.3% 0.3% 4.1% 46.9% 5.1% 10.6%Related to nutrition 8.3% 0.0% 3.8% 27.5% 3.6% 1.6%Related to child care 4.3% 4.5% 0.0% 0.3% 2.4% 0.4%
Note: Calculations are based on weighted data using sample of main empirical specifications presented in Table 4 (1,465 observations). 1/ Data is for 2006, excepting Uruguay, for which only 2005 was available. It includes expenditure in education, health and housing. Source: CEPAL (Web).
32
Appendix 2Instrumental Variables Initial Stage, Table 5
(1) (2) (3)Receives any
social programPercentage of
programs receivedReception of social programs (index)
Ratio of children over income earners in the household (Instrument, base=zero)0 to 0.5 0.5014** 6.9121*** 0.7927***
(0.2268) (2.2435) (0.2812)0.5 to 1 0.9807*** 11.9581*** 1.4720***
(0.2151) (2.2742) (0.2704)1 to 2 1.2478*** 13.1298*** 1.6473***
(0.2645) (2.4333) (0.2854)More than 2 1.3300*** 13.0662*** 1.7422***
(0.2905) (2.6198) (0.3025)Individuals' socio-demographic characteristicsAge (years) 0.0008 -0.0097 -0.0004
(0.0071) (0.0751) (0.0087)Years of education -0.0478 -0.2784 -0.0497
(0.0408) (0.3394) (0.0382)SES (base=Low)
Middle -0.4614** -5.7201** -0.6812**(0.2330) (2.3020) (0.2656)
High -1.3411*** -13.8560*** -1.8013*** (0.4089) (4.3737) (0.5391)Matched player' charcteristics (related to individuals)Gender (base= Participant is a man and the matched is a woman)
Both players are women=1 0.2334 2.3477 0.2232(0.1544) (1.7564) (0.1841)
Both players are men=1 -0.0869 -2.0977 -0.1619(0.2194) (2.2023) (0.2708)
Participant is a woman and the matched is a man=1
0.1050 1.1629 0.0433(0.1889) (2.0374) (0.2203)
Age difference -0.0061 -0.0245 -0.0044(0.0049) (0.0537) (0.0057)
Schooling difference 0.0177 0.0841 0.0174(0.0224) (0.2029) (0.0248)
SES difference (base=Both are equal)Participant is at a lower SES -0.1279 -1.1534 -0.2284
(0.2164) (2.0417) (0.2538)Participant is at a higher SES 0.4578* 4.3783 0.5111
(0.2690) (2.6728) (0.3226)Experimental variablesPercentage expected to be returned by matched player
0.0019 0.0055 0.0001(0.0028) (0.0262) (0.0030)
Risk aversion levels (base=Low)Medium -0.0043 -1.3994 -0.1187
(0.2369) (2.3488) (0.2957)High 0.0440 0.4124 0.0045
(0.2152) (2.2076) (0.2709)Observations 1,067 1,466 1,466Clusters 105 149 149Robust standard errors in parentheses, clustered at the session level. For (1), we used a probit model; and for (2) and (3), tobit models (left-censored, lower limit equals zero). The predicted probabilities from these regressions were used as instruments in the 2SLS regressions presented in Table 5. * Significant at ten percent; ** significant at five percent; *** significant at one percent.
33
Appendix 3Instrumental Variables Initial Stage, Table 7
(1) (2) (3)Receives an education
social programReceives a health
social programReceives a nutrition
social programRatio of children over income earners in the household (Instrument, base=zero)
0 to 0.5 1.5018*** 0.5504** -0.2324(0.3521) (0.2580) (0.4227)
0.5 to 1 2.1667*** 0.7842*** 0.4151(0.3147) (0.2506) (0.4342)
1 to 2 2.3311*** 0.7866*** 0.5464(0.3148) (0.2554) (0.4434)
More than 2 2.5989*** 0.3815 0.8798* (0.3295) (0.3489) (0.4640)Individuals' socio-demographic characteristicsAge (years) 0.0065 -0.0058 0.0003
(0.0082) (0.0104) (0.0126)Years of education -0.0841** 0.0161 -0.0007
(0.0426) (0.0431) (0.0404)SES (base=Low)
Middle -0.4357 -0.6877*** -0.6612*(0.2655) (0.2450) (0.3430)
High -1.8359*** -1.2584*** -0.5596 (0.4883) (0.4884) (0.6353)Matched player' characteristics (related to individuals)Gender (base= Participant is a man and the matched is a woman)
Both players are women=1 0.0448 0.1077 0.4618(0.2268) (0.1733) (0.3076)
Both players are men=1 -0.0795 -0.2097 -0.2772(0.2882) (0.2484) (0.4138)
Participant is a woman and the matched is a man=1 -0.5809** 0.2159 0.2103
(0.2459) (0.2197) (0.2953)Age difference -0.0064 0.0057 -0.0073
(0.0052) (0.0058) (0.0075)Schooling difference 0.0143 0.0069 -0.0176
(0.0314) (0.0255) (0.0344)SES difference (base=Both are equal)
Participant is at a lower SES -0.1904 -0.2189 -0.2057(0.2953) (0.2675) (0.3523)
Participant is at a higher SES 0.4659 0.1774 -0.1707 (0.2995) (0.2786) (0.3968)Experimental variablesPercentage expected to be returned by matched player
-0.0039 -0.0006 0.0014(0.0046) (0.0032) (0.0048)
Risk aversion levels (base=Low)Medium 0.0747 -0.3686 -0.1921
(0.3282) (0.2647) (0.3445)High 0.0220 -0.2153 0.2449
(0.3018) (0.2477) (0.3734)Observations 889 823 450Clusters 86 79 43Robust standard errors in parentheses, clustered at the session level. For all regressions, we used probit models. The predicted probabilities from these regressions were used as instruments in the 2SLS regressions presented in Table 7. * Significant at ten percent; ** significant at five percent; *** significant at one percent.
34
Appendix 4The Case for Reciprocity
Dependent variable: Second player's return offer(1) (2) (3)
Participation in social programs Receives any social program=1 -0.7558
(2.2290)Percentage of programs received 0.0092
(0.1494)Reception of social programs (index) -0.0981
(1.2087)Individuals' socio-demographic characteristics Age (years) 0.0977 0.0983 0.0982
(0.0868) (0.0869) (0.0870)Years of education -0.1323 -0.1166 -0.1233
(0.4010) (0.3981) (0.3953)SES (base=Low)
Middle -2.4342 -2.3741 -2.3951(2.4056) (2.4109) (2.4072)
High -4.0897 -3.9374 -3.9735(3.7729) (3.7145) (3.7375)
Matched player' characteristics (related to individuals) Gender (base= Participant is a man and the matched is a woman)
Both players are women=1 -4.2464** -4.2710** -4.2646**(2.1089) (2.1165) (2.1138)
Both players are men=1 -4.0156* -4.0432* -4.0464*(2.2026) (2.2206) (2.2113)
Participant is a woman and the matched is a man=1
-4.2895** -4.3104** -4.3107**(1.9920) (1.9986) (1.9972)
Age difference -0.0021 -0.0006 -0.0011(0.0617) (0.0619) (0.0618)
Schooling difference -0.1712 -0.1686 -0.1673(0.3028) (0.3000) (0.3001)
SES difference (base=Both are equal)Participant is at a lower SES -2.6715 -2.6530 -2.6526
(3.0256) (3.0188) (3.0242)Participant is at a higher SES 5.3047** 5.2898** 5.2866**
(2.2507) (2.2340) (2.2343)Experimental variables Percentage expected to be returned by matched player
0.2122*** 0.2131*** 0.2129***(0.0325) (0.0328) (0.0326)
Constant 19.1998*** 18.7073*** 18.8701***(6.3130) (6.2625) (6.2205)
Observations 1,522 1,522 1,522Clusters 149 149 149Adjusted R-squared 0.2806 0.2804 0.2804Robust standard errors in parentheses, clustered at the session level. All regressions are run using OLS and include dummies per session. * Significant at ten percent; ** significant at five percent; *** significant at one percent.
35
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