Great Expectations: Responses to Current and Future Transfers for Low-Income Individuals
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Great Expectations: Responses to Current and Future
Transfers for Low-Income Individuals
Preliminary draft: Do not cite or distribute without the authorisation of the authors*
Achyuta Adhvaryu, Jean-François Gauthier,
Pamela Jakiela, and Dean Karlan†
January 30, 2023
Abstract
We study how the expectation of future transfers affects current expenditures, investment,
saving, work hours, health and well-being. We conducted a randomized controlled trial among
low-income individuals in Uganda with four arms: 1) a lump-sum cash transfer of $135; 2) the
same $135 transfer accompanied by a light-touch financial planning exercise; 3) an ”expecta-
tions” treatment in which individuals receive the $135 lump-sum transfer with at 12 month delay
and were informed upfront of the future transfer; and 4) control. Contemporaneous transfers
and the promise of future transfers both increased work hours, consumption, and health in the
short and medium run (11 months and shortly before individuals with delayed-treatment receive
their transfer, respectively). Immediate transfers also increase entrepreneurship and investment
in own businesses. We propose a life-cycle model to explain these movements. Traditionally,
individuals are treated as wage earners in these models which predicts a reduction in work hours
following contemporaneous or the promise of future transfers, unless frictions are imposed.
We show that treating individuals as self-employed who can increase their marginal product of
labor, and thus their self-employment earnings, by investing in their health and their business
can explain the rising work hours and the movements in the other variables. Our findings
suggest that the workhorse life-cycle model should be amended when modeling the behavior
of individuals in similar low-income settings, fundamentally changing key implications of
the model with respect to policy. Rich outcome data coupled with the trial’s novel design
allows us to reject many other standard variations of the life-cycle model. Our results also
highlights an internal validity peril of promising future treatments to control group participants
*Thanks to participants in U of Michigan’s H2D2 and Labor Lunch seminars, participants at CIRANO’s Applied
Micro Day in particular Sonia Laszlo and Fabian Lange.
†Adhvaryu: University of Michigan, NBER, BREAD, J-PAL, Good Business Lab, William Davidson Institute
(adhvaryu@umich.edu); Gauthier: HEC Montreal (jean-francois.gauthier@hec.ca); Jakiela: Williams College, BREAD,
CGD, IPA, IZA, J-PAL (pamela.jakiela@williams.edu); Karlan: Northwestern University, IPA, J-PAL, NBER (kar-
lan@northwestern.edu).
1in experimental research.
Keywords: expectations, cash transfers, life-cycle model, consumption, labor supply, saving,
borrowing, Uganda
JEL: D15, J22, O12
21 Introduction
In the field of development economics, the evaluation of the impact of relaxing specific con-
straints is a common method for testing theories. However, for certain questions, the promise of
future transfers can provide more precise answers to specific theoretical inquiries. Cash transfers,
for example, are often used to examine the tradeoffs between labor and leisure and income effects.
Simple life-cycle models such as the ones presented by Hall, 1978, and Deaton, 1991, imply
that cash transfers will weakly lead to reduced work hours. However, evidence from developing
countries has not supported this prediction (Banerjee et al., 2017; Haushofer and Shapiro, 2016).
A potential explanation for this discrepancy is that cash transfers are complex and can be spent in
various ways, potentially leading to positive effects on labor productivity (Banerjee et al., 2020).
Hence, while cash transfers may lower ones labor supply curve, it may also raise labor demand, and
depending on the relative magnitude of the shifts, result in increased work hours.
In this study, we investigate the impact of the anticipation of a large lump-sum transfer from a
trusted source among low-income individuals in Uganda, by promising it one year in the future. No
additional planning exercises, training, or accompanying information were provided. This allows
us to see if increased work hours can also stem from the expectation of future expected income
shocks, and at the same time, inform us about constraints faced by individuals in our population.
Our findings indicate that the mere promise of a future transfer leads to an increase in consumption,
personal business profits, and investments in health, sustained by an increase in work hours rather
than borrowings. These effects consistently move in the same direction, although they tend to
be of smaller magnitude, compared to those observed in the case of an immediate cash transfer,
suggesting that access to borrowing may not be stringent here. Through this examination, we are
able to gain several valuable theoretical insights and derive accompanying policy implications.
Indeed, cash transfer policies are integral components of the social safety net in most countries
(World Bank, 2018). In recent years, and particularly during economic crises, the number, scope, and
generosity of such programs have greatly increased. For example, the combined safety net programs
reached up to 310 million caseloads in the aftermath of the Great Recession in the United States,
and over 1.3 billion people worldwide were beneficiaries of cash transfers during the Covid-19
pandemic (Gentilini, 2022; Gentilini et al., 2022; Moffitt, 2013; Moreira and Hick, 2021). The rapid
expansion of such programs – as well as the fraught political climate surrounding them – generates
considerable policy uncertainty that may directly impact potential beneficiaries (Altig et al., 2020;
Baker et al., 2016). It also raises the important question of how expectations about future transfers
affect the current economic behaviors and outcomes of the target population. Individuals who expect
to be beneficiaries may consume, save, invest, and spend their time differently from those who do
3not have such an expectation. This fact, in turn, may fundamentally change the social return of a
given transfer program, generating additional gains or losses from the policymaker’s perspective
depending on the behavior induced by these expectations (Ashenfelter, 1978; Ashenfelter and Card,
1985; Deshpande and Dizon-Ross, 2022). Much has been written about the short- and long-term
impacts of cash transfers on low-income households, yet little is known about how the expectation
of future transfers changes current economic behaviors and outcomes. Documenting responses to
changes in expectations regarding future income is thus of first order academic as well as of policy
interest.
We implemented a cash transfer RCT in rural Uganda to gain insight into this question. We
studied a population of individuals with HIV who were enrolled in antiretroviral therapy (ART) at
The AIDS Support Organization (TASO), one of the leading AIDS support organization in Africa.
Transfers were delivered through the organization’s network. To ensure representativeness, we
selected participants from TASO clinics, community drug distribution points (also run by TASO),
and through home visits. We recruited one participant per household when multiple members were
TASO patients.
The sample was mostly comprised of low-income individuals who are generally smallholder
farmers or working in their own micro-enterprises. We randomized individuals into one of four
experimental study arms. In the grant treatment group (T1), we provided unconditional lump-sum
cash grants of 350,000 Ugandan shillings or UGX (equivalent to PPP US $336, about two months
household income).1 In the planning+grant treatment arm (T2), we provided individuals with the
same cash transfer conditional on completing a very light touch counseling session that was meant
to help the grant recipients plan better regarding how they could use the cash transfers.2 In the
expectation treatment arm (T3), we set up the expectation of receipt of the same cash transfer, but
in one year’s time. We had no further interaction with this group until the year had elapsed at which
point participants were provided the transfer that was promised previously. In the control arm,
no intervention was provided. Comparing outcomes of individuals in the control group with the
outcomes of individuals randomized into the expectation treatment arm allows us to estimate the
causal impact of future transfer expectations. Comparing the outcomes of the two treatment arms in
which individuals received cash transfers immediately let us benchmark how rapidly measures of
well-being would have improved if individuals received cash transfers immediately as opposed to
1This is in the same order of magnitude as other studies of unconditional cash transfers in sub-Saharan Africa
(Haushofer and Shapiro (2016) $870 in PPP, Egger et al.(forthcoming) $1900 in PPP) and in Uganda (Blattman et al.
(2016) $460 in PPP, and Blattman et al. (2014, 2020) $761 in PPP).
2 Participants learned about interest rates, saving and investment opportunities, as well as temptations to share the
transfer. The sessions allowed the participants to see what they could, not what they should, do with the transfers. It
was up to them to decide how they wanted to use the transfer.
4with a one-year delay. Finally, comparing T1 to T2 allows us to measure the effects of the planning
sessions.
To evaluate the impacts of the expectation of future transfers on current behavior, as well as the
short-term impacts of receiving cash transfers in the same population, we conducted three surveys:
at baseline; one month post-transfer; 12 months post-transfer (and just before the expectations arm
received their transfer). The surveys covered a wide variety of demographics, consumption, work
hours, health, and entrepreneurship information. Surveys were done through Innovations for Poverty
Action (IPA), an independent survey organization. Importantly, individuals in the third treatment
arm that were promised a future transfer, did not receive the cash until after the endline survey was
completed. Hence, all effects measured for this group are not due to the receipt of transfers, but
rather solely due to the expectation of a future transfer.
We find large effects of the expectation of future transfers both one month after (short run) and
12 months after (medium run) treatment assignation on a host of important outcomes related to
consumption, work hours, and health, to enumerate some of the major outcomes measured here.
These effects are substantial in their own right and they are also quite large relative to the effects of
receiving cash immediately. We observe a 40% average increase in consumption expenditure, a 74%
increase in business expenditure, a 21% increase in work hours, and a 52% increase in own-business
profits for the participants that received an early transfer, relative to the control group, after one
month. For participants that expect a future transfer, there is little change in business expenditure or
in consumption at this point, other than food expenditure, but work hours and own-business profits
increase by 12% and 26%, respectively. After one year, impacts on savings and consumption-related
outcomes for the expectation group are about 30 to 50% the size of the impacts of receiving the
cash right away, and work hours increase shows persistence. Participants that received an early
transfer save 60% more after one month and 80% more after one year than the control group. By
endline, their consumption expenditure and work hours are 32% and 12% higher than that of the
control group, respectively. At this point, participants expecting a future transfer increased their
consumption expenditure and work hours by 18%. Results suggest an increase in net savings for
this group both after one month and more so after one year.
To better interpret the patterns and the relative size of adjustment between groups, we build
on the workhorse life-cycle model. That is, we assume that consumption and leisure are normal
goods and that there are no financial frictions (motivated by the fact that participants in our context
have access to different sources of savings and borrowing and that we observe an increase in net
savings in all treatment groups). The basic model assumes that labor productivity is constant and
does not factor in the labor earning of individuals. This leads to the prediction that consumption
5increases are accompanied by a reduction in work hours following contemporaneous or the promise
of future transfers unless frictions are imposed. The added consumption is then fueled by the
transfer for an immediate grant or fueled by dissaving for an expected future transfer. As mentioned
above, we find that individuals in all treatment groups see an increase in work hours and we find no
evidence of dissaving in any groups, violating key predictions of the basic model. We first consider
simple extensions of the model by allowing the health stock of individuals to be a component of
their utility or by allowing health to endogenously affect survival into future periods, but these
extensions do not predict increases in work hours on their own. That is not to say that health may
be an important channel here as we further explore below. Before doing so, we consider a class
of constrained models that can deliver increases in work hours in response to a transfer in similar
contexts. In particular, Banerjee et al. (2015) proposes a lumpy durable consumption model where
individuals face a borrowing constraint. In response to a transfer, they borrow as much as possible,
use the transfer in its entirety and work more to purchase a lumpy and expensive durable. In the
data, however, we see no change in the purchase of more expensive durable goods and if anything,
borrowing falls which allows us to rule out such constrained models.
The key challenge with unconstrained models with leisure as a normal good is that an increase
in wealth leads to a decrease in labor supply due to the income effect it generates. Hence, we let
labor income depend endogenously on labor productivity which can increase following a transfer or
an expected future transfer, shifting labor demand upward. Allowing labor productivity to affect
labor income makes sense if wages can be renegotiated based on productivity, if individuals are
paid as a function of their output, and if they are self-employed, for example. We believe that this
assumption represents our sample well. Indeed, more than 80% of participants have some form
of self-employment and most of the self-employed grow crops and raise animals for profit, which
depends on the output that can be generated in a set amount of time. Hence, the productivity of each
hour put in these businesses is important. We let labor income depend on production, which can
grow in work hours and in the productivity of these hours. In particular, we let the product of labor
depend in an increasing and concave way on an asset that we broadly think of as the individual’s
health stock, although other assets could improve productivity as mentioned below. Investing in
one’s health stock raises the marginal product of labor, the labor income (ceteris paribus), and can
lead to increased work hours following an immediate or an expected future transfer. Consistent with
this, we observe an increase in profits and work hours in the participants’ household businesses for
all treatment groups. At the same time, we find that participants in all treatment arms improve their
food security and they eat more of the food groups that are particularly important to build up energy
reserves in general, and especially for people with HIV, without substituting away from other food
groups (World Bank, 2007). Recent work has provided further evidence of the intrinsic link between
nutrition and productivity, particularly in physical work, leading us to believe that participants in all
6treated groups invest in their health and become more productive (Adhvaryu et al., 2022; Aragon
et al., 2017; Graff Zivin and Neidell, 2012). Participants in groups receiving an immediate transfer
also invest more in own-business inputs, which would also make their own labor more productive
for a broad array of production functions.
We then explore an extension of our preferred model in which transfers and expected future
transfers also have a positive psychological productivity effect on recipients as in Banerjee et al.,
2020. In our preferred framework, the desire to smooth consumption across periods drives the
adjustment in outcomes. We cannot rule out that, for example, being more hopeful about the future
contributes to the decision to re-optimize. However, we also do not have conclusive evidence for
this effect empirically given the mental health variables measured in the trial.
Our paper contributes to our understanding of how people smooth consumption, work hours,
income, and other outcomes in low-income contexts. Many have pointed out that the ability
to smooth and the manner in which smoothing is done can be deeply influenced by saving and
borrowing constraints that tend to bind more strongly for poor individuals (e.g. Fink et al., 2020;
Jayachandran, 2006). Our research highlights that both contemporaneous and the expectation of
income shocks can also be productive as they can lead to improved health and increased business
investments. At the same time, we observe increased work hours and improvement in health that
is intrinsically linked to nutrition here, suggesting the presence of a physiological productivity
channel highlighted in early poverty-trap models as discussed by Banerjee et al., 2020. We are
able to quantify such effects through novel variation in expected income with some participants
receiving an immediate cash grant and others being promised the same transfer in a year’s time.
Comparing the outcomes between these two groups and to a pure control group that receives no
grant or promises allows us to test many predictions of life-cycle models and distinguish which
models fit in the context at hand.
We also contribute to the rich literature on cash transfers. See for example Haushofer and
Shapiro, 2016, Haushofer et al., 2020, and Egger et al.(forthcoming) for recent examples. See
also Baird et al., 2013, Fiszbein and Schady, 2009, and Hanlon et al., 2012 for reviews. We
add to the literature by studying both immediate cash transfers and the promise of future cash
transfers, with the latter having received less attention. We find that both cash transfers and
the expectation of them lead to adjustments in many outcomes. As other studies in low-income
settings have found, we observe that immediate cash transfers do not discourage work and provide
novel evidence that this also holds true for the promise of future transfers (see Banerjee et al.,
2020, 2017; Egger et al.(forthcoming); Gerard et al.(forthcoming) for example). Bianchi and
Bobba, 2013, study the incidence of contemporaneous and future cash transfers on entrepreneurship
7decisions among recipient households of the PROGRESA program in Mexico, making them one
of the few who paid attention to the expectation of future cash transfers. Households receive
a cash transfer of various amounts based on the demographics of their children, and depending
on these demographics, the participants may be entitled to future cash transfers (also of various
amounts). Focusing on individuals working for wages or unemployed at baseline, the researchers
find that the expectation of future transfers has a strong incidence on the probability of becoming
entrepreneurs while immediate transfers have little effect, suggesting that credit constraints are
more important than liquidity constraints in their context. Importantly, they look at individuals
who receive a contemporaneous and expect future transfers, while we compare individuals who
receive a contemporaneous transfer to individuals who expect a future transfer, but do not receive a
contemporaneous one. Moreover, recipients of the Mexican program are financially constrained
with only 1.2% PROGRESA communities having formal credit institutions, while virtually all of our
participants have savings and borrowings and over 70% of them have formal savings or borrowings
(Gertler et al., 2012). We find that immediate transfers raise investment in own businesses and in
the number of income-generating activities, but expectation of future transfers have little effect on
these variables. However, there’s very little impact on the probability of becoming an entrepreneur
as 84% of our participants have at least one business at baseline.
We also provide a methodological contribution to the evaluation RCTs and policy evaluations.
Many studies on the effect of policies, including cash transfer policies, use delayed-treatment
groups as controls (e.g., Blattman et al., 2016; Miguel and Kremer, 2004). We observe that both
immediate and the promise of future cash transfers have the power of improving the well-being
of precarious populations through higher consumption, higher labor income, and better health.
Methodologically, however, the results and the theory both provide important warnings when
using delayed-treatment groups as controls for internal validity. A wide array of studies have have
shown that individuals often adjust their behavior prior to participating in various programs (see
Ashenfelter, 1978, Ashenfelter and Card, 1985, and Deshpande and Dizon-Ross, 2022, for a more
recent example). Similarly, we find that anticipatory effects can be important in the course of
treatment. Since participants expecting a transfer also respond to the intervention, using them as
control would severely bias our estimates with regards to the impact of contemporaneous cash
transfers in the present context. Researchers need to be cognizant of expectation impacts when
designing evaluations of social-net policies. The PROGRESA program, for example, is often
evaluated by comparing early treatment groups to delayed-treatment groups. In this case, however,
delayed-treatment groups were not aware that they would receive cash transfers up to two months
prior. This has been shown to limit anticipatory effects in this group (Gertler et al., 2012).
We also provide an important methodological contribution to experimental designs. Many
8studies employ a randomized roll-out design in which everyone in a study receives treatment but
with the timing randomized over long enough time periods to then measure impact in the interim
(e.g., Blattman et al., 2016; Miguel and Kremer, 2004). While this can be done without announcing
to the later recipients, often they are told.3 This may create bias, however, if individuals shift
behavior (accelerate or postpone investments, as studied here) because of the expectation of the
later treatment. Indeed, A wide array of studies have have shown that individuals often adjust their
behavior prior to participating in various programs (see Ashenfelter, 1978, Ashenfelter and Card,
1985, and Deshpande and Dizon-Ross, 2022, for a more recent example). Similarly, we find that
anticipatory effects can be important in the course of treatment. Since participants expecting a
transfer also respond to the intervention, using them as control would severely bias our estimates
with regards to the impact of contemporaneous cash transfers in the present context. Researchers
need to be cognizant of expectation impacts when designing evaluations of social-net policies.
2 Context and Data
We partner with The AIDS Support Organization (TASO). This NGO headquartered in Uganda
is a clinical delivery and support entity providing care to over 100 000 Ugandans suffering from
HIV/AIDS and their families. TASO is nationally representative with 54 public health facilities and
11 regional centers across the country (Bakanda et al., 2011a,b; Chu et al., 2013; Mills et al., 2011).
Patients receive Antiretroviral Therapy pills (ART pills) as well as monthly counseling sessions to
help them and their families to cope with the illness.4 We recruited over 2000 participants from
18-60 years old enrolled in two of TASO’s clinics located in the rural districts of Masindi in the
West and of Soroti in the East.5 The Masindi center provides care to over 3800 patients from
that district and the surrounding districts of Buliisa, Hoima, Nakasongola, and Kibale. The Soroti
center is larger with over 5900 patients from the Soroti, Kumi, Katakwi, Amuria and Kaberamaido
districts. We recruited one person per household in the case where more than one member was
a TASO patient and participants were recruited from the clinics themselves, the community drug
distribution point (also run by TASO) and through home visits to ensure representativeness. The
recruitment took place between October 2013 and May 2014. Participants were informed that some
3The PROGRESA program, for example, is often evaluated by comparing early treatment groups to delayed-
treatment groups. In this case, however, delayed-treatment groups were not aware that they would receive cash transfers
up to two months prior. This has been shown to limit anticipatory effects in this group (Gertler et al., 2012).
4Antiretroviral Therapy had a large impact on the life expectancy of people with HIV. Although, participants have
HIV, only 7% of them have AIDS. Given that they are around 40 years old on average, and given their immunity profile,
males can expect to live for another 20 years while females can expect to do so for another 30 years on average (Johnson
et al., 2013).
5This age group ranges from the legal age of maturity to the retirement age.
9of them would receive a cash transfer and completed the baseline survey. This extensive survey and
following ones allowed us to measure a wide array of demographics and probed the participants on
their businesses and income-generating activities, time use, household and business expenditures,
dwelling quality and asset ownership, as well as saving and borrowing histories.
Participants were randomly assigned to a control group or one of three treatment arms in a
stratified based on which TASO center they are affiliated with (Masindi or Soroti), on whether they
are female or not, and on their age.6 The four arms are as follows:
Transfer (T1)- Participants in this group are told that they have been selected to receive the
cash grant to improve their overall welfare at their next monthly counseling session, but received no
guidance on how the money could or should be spent.
Transfer with Planning Exercise (T2)- Participants are told the same and also receive the transfer
at approximately the same date as T1. However, within a month of the group assignation and
the reception of the transfer, they attend two financial information sessions held one week apart.
These sessions were not designed to indicate to the participants how they should spend the transfer,
but provided information on how they could spend it as well as the temptations and pressure that
they may face. In the first information sessions, the following topics were discussed: how to set
expectations, possible uses of the grant, the temptations that the participants may face and pressures
to share grant money. Then, they were asked to formulate a spending plan and strategies were
discussed on how to carry out the spending. This first session was held at the community drug
center close to where they live. The second information session was held at the TASO center
after which they received the transfer. There, participants received information on opportunities
for investing in current or new income-generating activities, savings, loans, and potential ways to
address emergencies. Then, they were asked to review and revise their original spending plan if
they wished to.
Expectation of Future Transfer (T3)- Participants are told that although they will not receive the
grant within the next month like the other groups, they will receive it in approximately one year;
at the end of the study. This group received their grant shortly after the endline survey following
the same procedure as participants in T1. They additionally had the option to attend the financial
information sessions offered to participants in T2.
Control- Participants were informed that they would not receive the grant.
6 For this exercise, age was split into 3 intervals, namely, 18-35, 36-50, and 51-65 years old.
10All grantees received the same amount of 350 000 Ugandan shillings (UGX) which is approxi-
mately 425 USD in purchasing power parity of 2022. This amount is not trivial as it corresponds to
two months of the average household income. Approximately one month after participants in T1
and T2 receive the transfer, all participants are asked to complete the midline survey and the endline
survey around 11 months later. During the entire study period, all participants continue to receive
their usual monthly counseling sessions.
2.1 Balance and Key Variables
In Tables 1 and 2, we present summary statistics and show that the sample is well balanced both
in terms of demographics and with respect to the key dependent variables used in analysis.
Approximately 540 participants were assigned to each arm. There is very little attrition in any of
the follow-up surveys. Attrition ranges from 2.4-3.2% for the one-month follow-up survey and from
2.05-4.06% for the endline survey.7 Attrition is lower at endline for the expectation group which is
likely due to the fact that they expected to receive their transfer after that survey. Unfortunately,
0.9-1.1% of the original participants died during the course of the study which is not counted as
part of attrition.
In terms of demographics, 69% of participants are women.8 Approximately half of them are
married. 7-9% of participants are in a polygynous union, and the majority are either Catholic
(36-39%) or Protestant (44-47%). The average participant is approximately 41 years old and with
roughly 5 years of education, both of which are close to the national average.
Most participants declared having worked for pay (28-32%) or self-employment (42-45%) with
a small fraction doing both (7-9%) during the last 7 days. Overall, 93% of people in the sample is
working in some capacity. The vast majority of participants are in a household owning at least one
business (80-84%) and practically all of them have savings and/or borrowings. The vast majority of
households have formal savings and/or borrowing through banks or village saving/borrowing groups.
These facts are important as they indicate that households are not completely credit constrained
here.
7Attritionis not compounding here as participants who missed the midline survey could complete the endline survey.
8 InAfrica, women are 2.3 times more likely to contract HIV from men, than men from women from sexual relations,
and as much as 60% of people infected by the virus are women (Magadi, 2011).
11Table 1: Balance Table: Demographics
C ONTROL G RANT P LANNING + E XPECTATIONS
G RANT
(T1) (T2) (T3)
Arm size 548 536 544 542
Attrition midline 3.13% 2.46% 3.16% 2.43%
Attrition endline 4.60% 3.59% 4.09% 2.05%*
Deaths 0.91% 1.31% 1.10% 1.11%
Female 68.98% 69.22% 69.12% 69.19%
Married 51.09% 52.80% 50.18% 52.21%
Polygynous 8.76% 8.96% 8.09% 7.75%
Catholic 39.23% 37.13% 36.76% 35.98%
Protestant 43.98% 44.96% 46.69% 46.31%
Working last week 28.95% 28.07% 29.71% 31.73%
Self empl. last week 42.29% 45.42% 43.11% 42.50%
Both 8.19% 9.55% 7.38% 8.85%
Own business 83.76% 80.60% 79.78%* 82.66%
Saving and/or borrowing 97.14% 95.71% 96.31% 95.96%
Formal savings and/or bor. 72.00% 74.27% 71.65% 74.04%
Age 41.13 41.05 40.96 41.20
(8.66) (8.78) (8.74) (8.33)
Years educ 5.15 4.84 5.00 5.01
(3.99) (3.82) (3.75) (3.58)
Note: ∗ indicates that the proportion is different from that of the control group at the 10% level. We present the proportion of various demographics
and the average and standard deviation for age and years of education across the four arms: Control, Transfer (T1), Planning and transfer (T2), and
Expectation (T3).
Next, we turn our attention to the key variables used in the analysis. As a first way of addressing
multiple hypotheses tests, we construct aggregate measures and indexes. We further detail how the
12variables are constructed in Appendix B. In particular, we look at work hours (in the week prior
to the survey) which is the sum of hours working for wages and self-employment hours. We also
look at total household spending and total spending in the household’s businesses in the 3-week
period prior to the survey, as well as the total amount saved and owed by the participants and their
household up to the survey date. To account for outliers and misreporting mistakes, we winsorize
the top and bottom 1% of values for these continuous variables.
We find that participants spend on average 20 hours per week working, with 2/3 of these hours
spent working for their own businesses. The average household expenditure is between 182-194
thousand UGX and they spend on average 108-122 thousand UGX on their businesses in the 3 weeks
prior to the baseline survey. Among the household expenditures we measure, we find that households
dedicate 12% of spending on food, whether as direct purchases or as spending for growing their
own food. Another 30% goes towards nondurable household items such as soap, cleaning products,
toilet paper, etc. Rent, durable purchases, repairs to the dwelling and durable goods, and gifts or
donations each account for 16% of household expenditures. Among business expenditures, 14-17%
is spent on each of the following categories: rent, machine inputs, and employee wages. 50% is
split equally between non-machine inputs and goods purchased for reselling purposes. 10% is spent
on transport and 5% on repairs. Households have 110-124 thousand UGX in savings and owe a
little over twice that amount on average (238-282 thousand UGX). 48% of the amount saved is done
informally and 37% is done through village savings accounts. The remainder is saved in banks or
cooperative savings institutions. On average, 24% of the borrowing comes from family members,
16% from village savings groups, 37% comes from schools as an advance on school fees.
We also create indexes to capture the number of Income-Generating Activities (IGA) which
depends on the number of businesses owned by the household, whether crops are grown and animals
raised for profit. We create an index capturing the number of durable assets owned ranging from
dwelling material and vehicles to phones and radios. Finally, we create a measure of food security
which broadly captures the ease with which meals can be procured and an index of dietary diversity
based on the variety of foods consumed. These indexes are normalized to be mean 0 and standard
deviation 1 in the control group at baseline. At baseline, participants in T2 and T3 have a slightly
lower score than the control group on IGA and food diversity respectively and the expectation group
has a slightly larger score on the durable assets index than participants in T2. Overall, the sample
is well balanced, but we include baseline outcomes as regressors in our regressions to account for
these small differences.
Looking at the components of these indexes, we find that 57% of the households had no food to
eat for at least a full day over the four weeks prior to the baseline survey with 32% of households
13not having food for 3 or more days. 50% of households had members going to bed hungry for
at least a night and 10% had members who didn’t eat for at least a full day and a full night over
that period. Typically, households eat grains, roots, nuts, and vegetables 4-5 days a week. They
eat fruits, fat and oils, and sugars 3-4 days a week. But they eat meat and dairy products only 1-2
times a week. Together with the fat and oils food group, meat and dairy constitute key food groups
for people with HIV/AIDS as they allow them to build up energy reserves necessary to perform
daily activities (World Bank, 2007). Consequently, if cash transfers lead participants to eat more of
these food groups, then they could see a rise in their productivity. 74% of households own their
dwellings and 90% have a protected water source, but over 60% have no flooring in their dwellings
and don’t have solid cement walls, with 90% of households living without electricity. 75% and 67%
of households own at least one cellphone and one radio, respectively, with 8% owning one or more
televisions. 57% own at least a bicycle, 10% own one or more motorcycle, and only 1% own cars.
14Table 2: Balance Table: Outcome Variables
C ONTROL G RANT P LANNING + E XPECTATIONS
G RANT
(T1) (T2) (T3)
Work hours 20 20 20 19
(26) (25) (25) (24)
Household 182 194 185 182
Expenditure (248) (244) (247) (229)
Business 112 102 108 122
Expenditure (294) (260) (284) (308)
Total saved 121 110 116 124
(277) (248) (248) (269)
Total owed 238 282 279 271
(466) (598) (624) (573)
IGA 0.000 -0.075 -0.115* -0.079
(1.000) (1.025) (1.012) (1.019)
Durable assets 0.000 -0.007 -0.081 0.034†
(1.000) (0.993) (0.946) (1.000)
Food security 0.000 -0.127** -0.049 -0.035
(0.979) (1.068) (0.997) (1.032)
Dietary 0.000 -0.014 -0.080 -0.078
diversity (0.979) (0.996) (1.019) (1.128)
Note: ∗ and ∗∗ indicates that the mean is different from that of control group at the 10% and 5% level, respectively. † indicates that the mean is
different from the mean in T2 at the 10% level. We present the mean and standard deviation for the variables used in the main analysis across the four
arms: Control, Transfer (T1), Planning and transfer (T2), and Expectation (T3).
2.2 Sample
Our study is internally valid as there is not contamination across arms and all individuals in
the treatment groups have been treated according to the original design. While participants in this
sample are HIV-positive, there is a large overlap with Uganda’s general population. Drawing from
15Uganda’s reports on their National Household Survey (UNHS) for the 2012/2013 wave, we compare
our sample to the aggregate population (UBOS, 2013). Figures 1, 2, and 3 compare, respectively,
the age and education attainment of the respondents, and household monthly earnings to that of the
population. Figure 1 shows that our sample represents well the 25-39 and 40-59 age groups, but
perhaps not surprisingly, less so the younger population that may not yet be sexually active and the
older population given that we recruited people of working age (18-60 years old).
Figure 1: Age Distribution
Note: We plot the proportion of different age groups in our sample against the proportion of the same age groups in the Ugandan population from the
Ugandan National Household Survey wave of 2012/2013.
In terms of education attainment, our sample is very similar to the population both for males
and females as can be seen from panel (a) and (b) of Figure 2. In these two panels, we plot the
proportion of individuals without formal education, with some or completed primary schooling,
with some or completed secondary education, and with some or completed tertiary education.
16Figure 2: Education Attainment
(a) Females (b) Males
Note: We plot the distribution of educational attainment of our sample against the distribution in the Ugandan population from the Ugandan National
Household Survey wave of 2012/2013. We plot the distribution for females in panel (a) and for males in panel (b).
In our survey, we asked about the income of the respondents in the last 7 days while the UNHS
asks about the average household monthly income. To establish a rough comparison, we rely on the
fact that the average Ugandan household has 5 members, and the dependency ratio is 120 suggesting
that 2.27 of the household members are in the working-age population on average.9 We multiply
our estimate of the monthly earnings of the respondents by the number of working-age adults and
obtain an estimate of household monthly earnings.
As we can see from the figure, our sample is more representative of low earners. Part of this
can be explained by the fact that we build our estimates only from the earnings of HIV-positive
individuals and affected people very often see a decrease in productivity (Azomahou et al., 2016;
World Bank, 2007).10 Hence, our estimates are likely to be a lower bound of household income.
Despite this, we see that all earning groups are represented in our sample.
9The dependency ratio indicates that for every 100 working age adult, 120 individuals are below 15 or above 65. We
obtain the average number of working age adult from this proportion: (1- fraction of working age adults)/(fraction of
working age adults)=1.2 . Hence, 45.45% of the household members on average are of working age.
10 Moreover, individuals may not work with the same intensity every week.
17Figure 3: Distribution of Monthly Earnings
Note: We plot the distribution of estimated individual monthly earning in our sample against the distribution of individual monthly earnings in the
Ugandan population from the Ugandan National Household Survey wave of 2012/2013.
3 Empirical Strategy and Results
To measure the effect of the intervention on the treated participants, we adopt the following
ANCOVA specification:
yi,t = β0 + β1 T1,i + β2 T2,i + β3 T3,i + γyi,t=0 + δstrata + εi,t , (1)
where yi,t is the dependent variable for participant i at time t ∈ 1 month, 12 months. T1,i , T2,i , and
T3,i are indicator variables equal to one if the participant is in the unstructured transfer group,
the planning with transfer group, and the expectation group, respectively. yi,t=0 is the dependent
variable at baseline.11 Finally, δstrata are strata fixed effects. In our main results, we report Eicker-
Huber-White standard errors, and apply the Benjamini-Hochberg (BH) correction to the p-values to
further account for multiple hypotheses tests.
11Wealso asked questions relating to mental health, but only at endline. Hence, we perform a post estimation for
these variables and we do not control for baseline values.
183.1 Results After One Month
We present results after one month of the intervention in Table 3. At this point, participants
in the groups with early transfers received them a month prior and participants in the expectation
group expect to receive theirs 11 months ahead. We first present the coefficients of interest (β1 − β3 )
and present the p-value of t-tests associated with the null hypothesis for equality of coefficient tests
between the treatment groups.12
We start our presentation of the results by focusing on the groups that received the contempora-
neous transfers (T1 and T2). The participants in these groups adjust similarly to the transfer with
some important nuances that we will point out along the way. First, both groups see a 0.3 standard
deviation (SD) increase in food security, which captures the ease of access to food, and in dietary
diversity. These results indicate that compared to the control group, the contemporaneous transfer
groups have a better access to and eat more diverse food due to the transfer. Next, we find that
they work more (for wage or on self-employed work) by 2.4-4.7 hours every week, with the group
that received financial planning (T2) seeing the largest increase. Compared to the control group,
this represents a 14-27% increase in work hours. As we can see, even a large unconditional cash
transfer does not discourage work. This lack of discouragement is consistent with many experiments
conducted in developing countries (see Banerjee et al. (2017)). As we show in further details below,
treated participants eat more of the foods important for productivity, especially for people with
HIV/AIDS (World Bank, 2007). We believe that this increase plays a key role in explaining the
increase in work hours of these participants as we explore further in the model section below.
The two contemporaneous-transfer groups also spend more on household and business expendi-
tures as a result of the transfer. Participants in T1 spend 45% (97,000 UGX) more on household
expenditures and 76% (176,000 UGX) more on business expenditures than the control group in
the three weeks prior to the survey. Participants in the financial-planning group (T2) also see large
increases in expenditures, but they are slightly more conservative in their spending (33% or 70,000
UGX for household expenditures and 71% or 163,000 UGX). Both groups also see an increase in
business profits of 46% for T1 (28,000 UGX) and of 58% for T2 (35,500 UGX). They additionally
have 58% and 64% more in total household savings than the control group following treatment
(68,000 UGX and 80,500 UGX respectively). While not significant, the point estimates on the
household debt suggest a decrease of 11% and 15% in the total amount owed by participants in T1
and T2, respectively. Finally, we observe that the two groups experience a 0.2 SD increase in their
sources of income as measured by the income-generating activity index. Overall, participants in T1
12A small p-value indicates that we can reject that the coefficients are equal between treatment groups.
19and T2 eat more and better, they consume more, work more, invest in their businesses, make more
profit from these businesses, and save more.
The financial planning sessions received by participants in T2 seem to lead them to be more
prudent in that they work more, spend less, earn more profit, and save more than participants in
T1. This is perhaps because they planned for more long-term projects and/or because the sessions
helped them identify better investment opportunities. However, little difference remains between
these two groups after a year as we show below.
When adding up changes in household and business expenditure, we find that participants in T1
spend 270,000 UGX, which represents 77% of the transfer size. Participants that received financial
planning sessions spend 233,000 UGX or 67% of the transfer. As a result, we expect that additional
work hours, business profits, and savings will be used to smooth consumption in future periods,
which consistent with our findings after one year of treatment.
Next, we focus on the expectation group that has yet to receive the transfer. The most noticeable
result is that this group also sees an adjustment in many outcomes, typically around 30-50% the
size of the adjustments made by participants in T1 and T2. In particular, food security and dietary
diversity increase by 0.1 SD. Similar to participants in T1, people in the expectation group work 2
additional hours per week (11% increase) and their business profit increases by 16,000 UGX (26%).
Also similar to the other treatment groups, there is no evidence of dissaving for the expectation
group. While savings don’t increase, the point estimate on the total amount owed is negative and
close to being significant.13 We see little change in household expenditures other than for food.
Given the results above, this additional consumption must be largely driven by a rise in work hours
and in additional business profits. The expectation group differs from the other treatment groups in
that they do not invest in their businesses or increase their income-generating activities.
13The p-value is 0.09 which is close to the BH p-value cutoff of 0.078 for this coefficient.
20Table 3: Effects After One Month
F OOD D IETARY W ORK T OTAL B USINESS B USINESS T OTAL T OTAL IGA
S ECURITY D IVERSITYH OURS E XPEND . E XPEND . P ROFITS S AVED OWED I NDEX
Grant (T1) 0.28*** 0.27*** 2.38** 96.97*** 173.14*** 28.09*** 67.73*** -24.29 0.17***
(0.04) (0.05) (0.99) (14.75) (32.02) (8.94) (12.71) (22.15) (0.04)
Planning (T2) 0.28*** 0.30*** 4.68*** 70.28*** 162.67*** 35.42*** 80.57*** -32.65 0.19***
(0.04) (0.05) (1.09) (13.48) (29.88) (8.79) (12.55) (21.02) (0.04)
Expectations (T3) 0.09* 0.12** 1.96* 13.35 -12.92 15.88* 2.67 -30.80 0.04
(0.04) (0.05) (0.96) (13.19) (27.10) (7.93) (12.23) (18.17) (0.04)
Pr(T1 = T2) 0.874 0.514 0.039 0.078 0.767 0.464 0.330 0.692 0.563
Pr(T3 = T1) < 0.001 0.001 0.672 < 0.001 < 0.001 0.190 < 0.001 0.720 < 0.001
Pr(T3 = T2) < 0.001 < 0.001 0.013 < 0.001 < 0.001 0.033 < 0.001 0.912 < 0.001
R-squared 0.38 0.24 0.47 0.33 0.44 0.25 0.57 0.60 0.31
Control Mean 0.18 0.17 17.11 211.86 226.97 61.26 125.97 212.52 -0.00
Control S.D. 0.93 0.93 21.62 264.12 605.28 133.02 317.36 467.89 0.78
Observations 2101 2101 2101 2101 2101 2101 2101 2101 2101
Note: ∗∗∗ , ∗∗ , and ∗ indicate significance at the 1%, 5%, and 10% level, respectively, after Benjamini-Hochberg (BH) corrections of the p-values to
account for multiple hypotheses tests. We report regression coefficients and the associated Eicker-Huber-White standard errors in parentheses. We
regress the various outcomes of interests on dichotomous variables for whether participants are in the first treatment arm (T1), second treatment arm
(T2), or last treatment arm (T3), leaving the control group as the excluded group. Hence, the coefficients can be interpreted as unit change with
respect to that latter group. We also include strata fixed effects and control for the outcomes at baseline in all regressions. Food security, dietary
diversity, and the Income-Generating Activity (IGA) indexes are measured in standard deviations from the control mean at baseline. The latter
variable captures the number of sources of income for the household participants belong to. Work hours are the hours worked for wages and in the
household’s business(es) per week. Total expenditure or household expenditure, business expenditure, business profits, total saved, and total owed are
all measured in thousands of UGX. Below the main estimates, we present the standard p-values (not adjusted for BH corrections) of t-tests of equality
between the coefficients of different treated groups. A small p-value indicates that we can reject that the coefficients are equal between treatment
groups. In the table notes, we report the R2 , the control mean and its standard deviation, and the number of observations.
3.2 Results After One Year
All participants were surveyed one year after the treatment assignation, just before the expecta-
tion group received their grant. The changes observed after one month, and detailed above, carry
for the most part 11 months later. The groups that received the transfer at the beginning (T1 and
T2) still have more secure food access (0.1SD) and eat more diversely (0.3SD) than the control
group. Participants in T1 and T2 work 1.4-2.4 hours more, spend 37% (49,000 UGX) and 27%
(35,500 UGX) more on household goods and services than the control group, respectively. The
two contemporaneous transfer treatment groups also have about 80% (76,000 UGX) more in total
savings than the control group at this point, and they maintained the additional income-generating
21activities. As we saw above, the total spending in these groups was close to the value of the transfer
after one month. Hence, the results of Table 3 indicate that increases in work hours and savings
help smooth consumption across future periods. We also measure aspects of mental health and
durable asset ownership in this endline survey. We find that participants who received the grant see
a 0.13-0.18SD increase in mental health and a 0.04-0.08SD in durable asset ownership. While it
looked like participants in T2 who received financial information were more frugal than participants
in T1 initially, we detect little differences between these groups at endline. The only statistical
difference is with regards to durable asset ownership suggesting that participants in T2 buy slightly
fewer durables as a result of the transfer than participants in T1.
By endline, participants in the expectation group who still have yet to receive the grant adjust
in a more similar way compared to participants in T1 and T2 than they did 11 months prior. They
see a similar increase in food security, dietary diversity, work hours, total household expenditure,
and durable asset consumption. Once again, there is no evidence of dissaving for this group. As
opposed to the other treatment groups however, the expectation group still sees little increase in its
sources of profits or in mental health.
22Table 4: Effects After One Year
F OOD D IETARY M ENTAL W ORK T OTAL D URABLE T OTAL T OTAL IGA
S ECURITY D IVERSITYH EALTH H OURS E XPEND . A SSETS S AVED OWED I NDEX
Grant (T1) 0.10* 0.25*** 0.13** 1.43 48.37*** 0.08*** 75.78*** 27.57 0.13***
(0.05) (0.06) (0.06) (1.27) (11.35) (0.02) (16.02) (28.92) (0.04)
Planning (T2) 0.12** 0.29*** 0.18*** 2.37* 35.53*** 0.04** 77.48*** 10.93 0.14***
(0.05) (0.06) (0.06) (1.28) (10.67) (0.02) (15.10) (26.79) (0.04)
Expectations (T3) 0.14*** 0.17** 0.09 2.74** 22.80** 0.04** 24.07 -19.54 0.03
(0.05) (0.06) (0.06) (1.28) (10.16) (0.02) (13.86) (24.61) (0.04)
Pr(T1 = T2) 0.681 0.499 0.423 0.466 0.277 0.038 0.925 0.563 0.903
Pr(T3 = T1) 0.326 0.158 0.420 0.311 0.024 0.075 0.003 0.076 0.010
Pr(T3 = T2) 0.558 0.033 0.120 0.778 0.233 0.803 0.001 0.212 0.006
R-squared 0.13 0.15 0.05 0.29 0.24 0.51 0.30 0.44 0.21
Control Mean 0.33 -0.18 0.00 15.58 130.18 0.00 96.33 246.62 0.00
Control S.D. 0.84 0.99 1.00 24.04 184.45 0.41 231.17 509.29 0.74
Observations 2069 2069 2069 2069 2069 2069 2069 2069 2069
Note: ∗∗∗ , ∗∗ , and ∗ indicate significance at the 1%, 5%, and 10% level, respectively, after Benjamini-Hochberg (BH) corrections of the p-values to
account for multiple hypotheses tests. We report regression coefficients and the associated Eicker-Huber-White standard errors in parentheses. We
regress the various outcomes of interests on dichotomous variables for whether participants are in the first treatment arm (T1), second treatment arm
(T2), or last treatment arm (T3), leaving the control group as the excluded group. Hence, the coefficients can be interpreted as unit change with
respect to that latter group. We also include strata fixed effects and control for the outcomes at baseline in all regressions except for the mental
health regression since the questions related to that variable were only asked at endline. Food security, dietary diversity, mental health, durable
assets, and the Income-Generating Activity (IGA) indexes are measured in standard deviations from the control mean at baseline. The latter variable
captures the number of sources of income for the household participants belong to. Work hours are the hours worked for wages and in the household’s
business(es) per week. Total expenditure or household expenditure, business expenditure, total saved, and total owed are all measured in thousands of
UGX. Below the main estimates, we present the standard p-values (not adjusted for BH corrections) of t-tests of equality between the coefficients of
different treated groups. A small p-value indicates that we can reject that the coefficients are equal between treatment groups. In the table notes, we
report the R2 , the control mean and its standard deviation, and the number of observations.
3.3 Discussion
3.4 Contemporaneous and Expected Transfers Matter
Figures 4 and 5 plot the effect sizes for the treatment groups relative to the control groups
expressed in standard deviations, relative to the control group at the time of the survey. The effects
measured after one month and one year clearly reveal that contemporaneous unconditional transfers,
but also the promise of future transfers can have strong effects on many outcomes. For example,
we find that all treatment groups see significant changes in food security and dietary diversity,
23work hours, household and durable expenditure, by endline with most changes between 0.1SD and
0.3SD. These large effects indicate that contemporaneous and the promise of future transfers can be
powerful tools in improving the lives of poorer individuals, at least in the short and medium run.
This intuition can be harnessed when designing transfer programs by policy-makers who may not
always have the financial cash flow to treat all the target population at once.
Figure 4: Effect Sizes in Standard Deviations After One Month
Note: ∗∗∗ , ∗∗ , and ∗ indicate significance at the 1%, 5%, and 10% level, respectively, after Benjamini-Hochberg (BH) corrections of the p-values to
account for multiple hypotheses tests. We plot the effect sizes measured in standard deviations from the control mean at the time of the survey for the
main outcomes. We plot the effect size for each treatment groups separately.
24Figure 5: Effect Sizes in Standard Deviations After Twelve Months
Note: ∗∗∗ , ∗∗ , and ∗ indicate significance at the 1%, 5%, and 10% level, respectively, after Benjamini-Hochberg (BH) corrections of the p-values to
account for multiple hypotheses tests. We plot the effect sizes measured in standard deviations from the control mean at the time of the survey for the
main outcomes. We plot the effect size for each treatment groups separately.
One thing to notice, however, is that the expectation group remains ”behind” the immediate
transfer group on many outcomes. This suggests that, although expectations of future transfers
can help improve the well-being of individuals, it remains better to receive an early transfer from
the perspective of the recipients. Our preferred model presented in the next section, predicts that
individuals who are certain of receiving a future transfer, should adjust very similarly to those
receiving a transfer immediately. The fact that we see large adjustments in the expectation group
indicates that large part of the participants in this group believed in our promise. It is entirely
possible that some didn’t believe our promise or had some doubts as to weather they would actually
receive the transfer. The model predicts no adjustment for the former and a smaller adjustment for
the latter compared to those who believe that a transfer will come with probability one. Hence,
when looking at the average response in the expectation group, it is not surprising to see smaller
adjustments in magnitude than in the groups that received an early transfer.
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