Advances in Applied Sociology
2012. Vol.2, No.4, 280-291
Published Online December 2012 in SciRes (
Copyright © 2012 SciRes.
Determinants of Saving among Low-Income Individuals in Rural
Uganda: Evidence from Assets Africa
Gina A. N. Chowa1*, Rainier D. Masa1, David Ansong2
1School of Social Work, University of North Carolina, Chapel Hill, USA
2Brown School of Social Work, Washington University, St. Louis, USA
Email: *,
Received September 8th, 2012; revised October 10th, 2012; accepted October 22nd, 2012
Although research has shown that poor people in sub-Saharan Africa (SSA), including those living in ru-
ral areas save, little is known about the factors that influence saving and asset accumulation among this
population. Using three theoretical perspectives on saving and asset accumulation, this study examines the
broader determinants of saving and asset accumulation among low-income individuals in rural Uganda.
Compared with the individual-oriented and sociological perspectives, institutional theory explains a large
part of the variance in saving outcome among rural, low-income households. Wealth, proximity to finan-
cial institutions, financial education, and financial incentives are positively associated with higher saving
performance. Findings suggest that poor people can and do save, particularly when institutional barriers to
saving are removed. Institutional structures, which encourage low-income individuals to save, may con-
tribute to a poverty reduction policy that shifts from just income supplementation to a more inclusive
wealth promotion policy that assists people in creating their own pathways out of poverty.
Keywords: Saving; Sub-Saharan Africa; Rural Uganda; Theory; Asset Building; Institutional Theory
Numerous reasons, including low and irregular income and
lack of access to financial services, have been posited to con-
tribute to sub-Saharan Africa’s (SSA) low formal savings rate1.
Access to financial services, including deposit or savings ac-
counts, remains a privilege for most of the population (Consul-
tative Group to Assist the Poor, 2010). In rural Uganda, where
86% of the country’s 31 million people live, only 10% of the
population has access to basic financial services (Chemonics
International, 2007). Inadequate physical infrastructure, oner-
ous documentation requirements and high account fees have
been found to be associated with lower levels of formal finan-
cial service use in developing regions (Beck, Demirguc-Kunt,
& Peria, 2008). In spite of the barriers, empirical research has
shown that poor people in SSA, including those living in rural
areas, save (Chowa, Masa, & Sherraden, 2012; Collins, Mur-
doch, Rutherford, & Ruthven, 2009; Dupas & Robinson, 2009).
However, limited empirical research has been conducted to
understand the factors that influence saving and asset accumu-
lation among rural individuals and households in SSA.
Savings and assets are of interest to policymakers and schol-
ars across disciplines because of their importance to individuals,
households, and the economy. For individuals and households,
economic security throughout the life course is inherently
linked not only to income but also to asset ownership. Savings
and assets are important because, unlike income, they are what
individuals and families accumulate and hold over time. Assets,
such as savings, also generate returns that generally increase
lifetime consumption and improve a family’s well-being over
several generations. Savings and assets provide a cushion to fall
back on during hard times and emergencies. Asset-poor fami-
lies in SSA become more vulnerable to unexpected economic
events or natural disasters and as a result, to their long-term
adverse consequences (Hoddinott, 2006). For the economy at
large, savings represents an important source for the financing
of investment in developing countries. The savings rate of a
country has been found to be strongly correlated with invest-
ment and growth rates (Attanasio & Banks, 2001).
Although two decades of experimentation and research on
saving and asset building suggest that linking low-income
families with saving and asset-building strategies has the poten-
tial to positively influence family well-being outcomes, includ-
ing economic, educational, and health-related results, (Chowa,
Ansong, & Masa, 2010; Chowa, Masa, & Sherraden, 2012;
Erulkar & Chong, 2005; Lerman & McKernan, 2010; Schreiner
& Sherraden, 2007; Ssewamala & Ismayilova, 2009; Williams
Shanks, Kim, Loke, & Destin, 2010), most of the studies on
determinants of household saving have focused on middle- to
upper-income families. Few empirical studies have investigated
savings’ determinants among low-income individuals and fami-
lies, and those that have have primarily been completed within
the United States (Curley, Ssewamala, & Sherraden, 2009; Han
& Sherraden, 2009). Building on existing theories and empiri-
cal evidence, this study contributes to knowledge in two ways.
First, the study explores factors that affect saving among par-
ticipants of an asset-building program in rural areas of Uganda.
As mentioned earlier, little is known about the factors affecting
saving and asset accumulation in rural, low-income populations
in developing economies, particularly in SSA. Data used in this
study came from an asset-building intervention targeted to
low-income individuals in rural communities in a developing
1In SSA, there are 163 bank accounts (compared with 635 bank accounts in
other developing regions) and 28 bank loans (compared with 245 bank
loans in other developing regions) per 1000 adults (Consultative Group to
Assist the Poor, 2010).
*corresponding author
country. Observed savings determinants from a quasi-experi-
mental setting that intentionally provides an opportunity to save
or build assets may be quite different than those observed from
national data sets. Second, the study examines whether diverse
theories of saving, which have been primarily developed in
more industrialized societies, can explain savings behavior
among low-income households in developing countries. This
study adds to a growing body of research (Curley, Ssewamala,
& Sherraden, 2009; Han & Sherraden, 2009) that examines
competing theories of saving in a single model with a compara-
tive approach. To the authors’ knowledge, this is the first study
to concurrently examine diverse theories of saving using quasi-
experimental household-level data from rural, low-income
populations in SSA.
Theories of Savings and Evidence from SSA and
Other Developing Economies
Examining and explaining determinants of saving and asset
building have garnered attention from scholars across numerous
disciplines. Economic theories put primary emphasis on income
and age as predictors of saving and asset accumulation (Modi-
gliani & Ando, 1957). Behavioral economists and economic
psychologists have recognized the role of self-control, motives,
and other personality characteristics on saving (Katona, 1975;
Thaler & Shefrin, 1981; Wӓrneryd, 1999). Sociologists have
been interested in how class and social stratification influence
saving and asset accumulation (D’Souza, 1981; Sorensen,
2000). Social workers have examined the effects of institutional
factors such as access, incentives, expectation, and facilitation
in promoting saving (Beverly & Sherraden, 1999; Sherraden,
1991; Sherraden, Schreiner, & Beverly, 2003). Similar to Han
and Sherraden (2009), this section classifies existing theories
into three perspectives: 1) an individual-oriented perspective; 2)
a social stratification perspective; and 3) an institutional per-
Individual-Oriented Perspectives
The individual perspective in this study includes neoclassical
economics, economic psychology, and behavioral economics.
Neoclassical economic theory assumes that individuals are
rational beings who respond in predictable ways to changes in
incentives; and assumes that individuals have perfect knowl-
edge and access to perfect markets. Two prominent neoclassical
economic theories include: 1) the life cycle hypothesis (LCH;
Modigliani & Ando, 1957); and 2) the permanent income hy-
pothesis (PIH; Friedman, 1957). Both theories assume that
individuals and households are concerned about long-term
consumption opportunities and therefore, explain saving and
consumption in terms of expected future income. The LCH
posits that savings will be used to smooth consumption when
income varies by age. A main idea of the LCH is that working
people are savers, whereas children and retired people are not.
Thus, differences in consumption and saving among house-
holds are attributed to age differences (Modigliani & Ando,
1957). While people are working, they use their income to pro-
vide for the household consumption, while at the same time
they are saving to provide for their retirement. On the other
hand, the PIH suggests that savings decisions are based on in-
come being perceived as either permanent or temporary.
Households mainly spend the permanent income, while the
temporary or transitory income is channeled into savings; and
households are freely able to save and borrow to smooth their
consumption (Friedman, 1957). Both economic theories view
savings primarily as a function of income.
Due to the limited availability of relevant data on low-in-
come households in developing economies, literature cited in
this study also includes data on middle- to upper-income
households. Evidence from SSA and other developing countries,
albeit mostly from middle- to upper-income households, sug-
gests that income positively influences saving and in ways con-
sistent with standard economic theories (Schmidt-Hebbel,
Webb, & Corsetti, 2002). In Kenya, household income was
found to be a statistically significant predictor of savings
among rural farmers, entrepreneurs, and teachers (Kibet, Mutai,
Ouma, Ouma, & Owuor, 2009). In Uganda, higher permanent
and transitory incomes significantly increased the level of net
deposits among households that reported owning bank deposit
accounts (Kiiza & Pederson, 2001). Income was also a signifi-
cant predictor of improved savings in India (Agrawal, Sahoo, &
Dash, 2007; Athukorala & Sen, 2004), Morocco (Abdelkhalek,
Arestoff, de Freitas, & Mage, 2009), Pakistan (ur Rehman,
Bashir, & Faridi, 2011), and the Philippines (Bersales & Mapa,
2006). These findings suggest that households save a larger
share of their income when that income is higher.
Aside from income, age, including the age of the head of
household and other household members, is an important pre-
dictor of saving, according to neoclassical economic theories.
Economic researchers commonly use dependency ratio, or
those under age 15 and over 65 as a share of the total household
composition, as an explanatory demographic variable. In the
LCH, households with more children at home may save less
until the children leave home, which would raise the per capita
income of the household. Thus, higher dependency ratio nega-
tively affects savings rates. Evidence from SSA and other de-
veloping countries shows contradictory results. An increase in
dependency ratio decreased saving, while a decrease in de-
pendency ratio increased saving among households in Kenya
(Kibet et al., 2009), Indonesia (Johansson, 1998), India (Ang,
2009), China (Ang, 2009), Morocco (Abdelkhalek et al., 2009),
and Pakistan (ur Rehman et al., 2011). This result suggests that
a reduction in the number of dependents relative to a working
age population has eased household budget constraints, thus
increasing savings, which is consistent with the LCH. However,
other researchers have found no significant relationship be-
tween savings rates and dependency ratio in developing coun-
tries (Cornia & Jerger, 1982; Deaton, 1992; Schmidt-Hebbel et
al., 2002). These nonsignificant findings are similar to Mason’s
(1987, 1988) studies, which challenged findings that depend-
ency ratio has a strong negative effect on saving. Mason quali-
fies the negative effect of dependency ratio on savings by in-
troducing the age factor. He demonstrates that, in some cases,
the effects of the dependency ratio depend on the age of the
dependents. In the Philippines, for instance, researchers found
that the percentage of young dependents had a negative effect
on savings, whereas, the percentage of the elderly had a sig-
nificant positive effect on savings (Bersales & Mapa, 2006).
Although evidence suggests that neoclassical economic theo-
ries can predict savings behavior of households in developing
countries, some researchers have argued that the application of
LCH and PIH to explain savings behaviors of low-income
households in developing countries can be problematic
(Rosenzweig, 2001). For instance, PIH’s assumption that
Copyright © 2012 SciRes. 281
households are freely able to save and borrow to smooth their
consumptions may not be true in developing countries where
low-income households have very limited access to a well-
developed insurance and credit market (Rosenzweig, 2001). In
addition, distinction between permanent and temporary income
may not be evident in many parts of the developing countries
where household income is minimal and irregular. Savings in
low-income settings for long-term purposes, such as retirement,
may not be substantial given that many households struggle to
meet subsistence consumption level, especially in times of
emergencies and other income shocks. Furthermore, the LCH
may not effectively predict long-term savings in low-income
settings in developing countries because many households,
particularly rural households, include more than two adults, and
adults of different generations. Since the life-cycle of the
household is not the same as the life-cycle of the individual, it
is not clear whose age matters for savings decisions (Deaton &
Paxson, 2000; Rosenzweig, 2001). In SSA, for instance, evi-
dence suggests that households change size and composition in
response to income fluctuations (Fafchamps, Udry, & Czukas,
Unlike neoclassical economic theories, the other two indi-
vidual perspectives on savings in this study—economic psy-
chology and behavioral economics—do not assume that people
are rational and all-knowing. These two perspectives assume
that personality characteristics and attitudinal variables affect
saving and asset accumulation. The inclusion of psychological
factors on savings research has been the subject of investiga-
tions by early economic thinkers such as Jevons (1965), Mar-
shall (1961), and Fisher (1977). Although they recognized that
savings depend on economic factors, particularly income and
its size and frequency, they also believe that there are various
psychological characteristics that influence the temptation to
spend and forego saving. Although fewer psychologists have
investigated psychological determinants of saving behavior
than economists, there are some established psychological
models on savings behaviors, including those by Katona (1975),
Ölander and Seipel (1970), and Lindqvist (1981). For instance,
Katona’s theory of saving (1975) is partly determined by in-
come and partly by some independent intervening factors. Two
important factors are the ability to save (mostly objective data)
and willingness to save (a variety of psychological variables).
Ability to save refers to those who can save, whereas willing-
ness to save is related to the degree of optimism or pessimism
of economic conditions (Katona, 1975). Thus, ability to save
does not guarantee savings because savings also depend on an
individual’s willingness to save. Due to constraints on the
availability of relevant data, there is no evidence thus far on the
effects of psychological factors on the saving behavior of
low-income households in developing countries. Although
some evidence from industrialized countries shows that psy-
chological predictors tend to have low explanatory power
(Furnham, 1985; Lindqvist, 1981; Lunt & Livingstone, 1991),
there is also some evidence suggesting that personality charac-
teristics, including optimism/pessimism about economic condi-
tion ( Lunt & Livingstone, 1991), perceived locus of control
(Lunt & Livingstone, 1991; Perry & Morris, 2005), perceived
ability to save (Sherraden & McBride, 2010), and future orien-
tation (Webley & Nyhus, 2006) are associated with saving be-
Behavioral economics integrates insights from psychology
and economics. Behavioral economics qualifies some of the
unrealistic assumptions of standard economic models of human
behavior, such as unbounded rationality, unbounded willpower,
and unbounded “selfishness” (Shefrin & Thaler, 1988; Thaler,
1994). According to this perspective, common human charac-
teristics such as self-control and ability to delay gratification,
mental accounting, use of rules-of-thumb, default options, and
hyperbolic discounting shape financial behaviors and economic
decisions (Ainsle, 1975; Angeletos, Laibson, Repetto, To-
bacman, & Weinberg, 2001; Laibson, 1997; Mullainathan &
Thaler, 2000; Shefrin & Thaler, 1988; Thaler, 1981). These
characteristics can lead households to behave in ways that are
inconsistent with their own priorities. However, little is known
about the explanatory powers of these factors on savings be-
havior of low-income households in developing countries. On
the other hand, studies from industrialized countries have
shown that self-control (Ameriks, Caplin, Leahy, & Tyler, 2004;
Moffitt et al., 2010; Romal & Kaplan, 1995), and use of default
options (Madrian & Shea, 2001; Thaler & Benartzi, 2004)
shape saving behaviors of middle- and upper-income individu-
als and households.
Sociological Perspective
Social stratification theory refers essentially to a distribution
of power in society. The divisions in society, based on eco-
nomically conditioned power, are called classes, which refer to
any group of people that is found in the same economic situa-
tion (D’Souza, 1981; Weber, 1967). Class and social stratifica-
tion have strengths in explaining the factors affecting savings
behavior among low-income households because class relates
to the possession (or lack) of resources (economic or otherwise)
necessary for individuals and households to save and build up
their assets. Individuals and families in lower economic classes
have limited access to information, resources, and services that
can help them save and accumulate assets over time. When
low-income families have assets, they are less likely to have
access to additional resources that they can use to generate
positive returns on the assets they already own. The economis-
tic approach to class and social stratification suggests that a
major explanation of class inequalities rests in the nature of
access to, and take up of, material resources, as well as the
institutions that govern such access (Crompton, 2008). In addi-
tion to control and possession of economic resources, class and
social stratification are powerful determinants of outcomes that
can further shape saving and asset accumulation patterns. De-
mand from social network members, particularly family mem-
bers, can make it difficult to save and accumulate assets (Stack,
1974). Evidence suggests that poverty in extended families can
impede saving and asset accumulation (Caskey, 1997; Chiteji &
Hamilton, 2005; Heflin & Patillo, 2002). Some scholars have
attributed asset poverty to other factors beyond the control of an
individual, such as cultural origins (Al-Awad & Elhiraika,
2003), gender biased-cultural norms (Chowa, 2008), and finan-
cial socialization in families, schools, or communities (Chiteji
& Hamilton, 2005; Chiteji & Stafford, 1999; Cohen, 1994), and
race (Oliver & Shapiro, 1995; Shapiro, 2004).
Evidence from SSA and other developing countries suggests
that class-related factors can explain savings behaviors of
low-income households. Education has been found to be a sig-
nificant predictor of savings in Kenya (Kibet et al., 2009), and
the Philippines (Bersales & Mapa, 2006) but not in India
(Agrawal et al., 2007). Higher education level translates to
Copyright © 2012 SciRes.
higher savings level. Occupation, which can be predicted by a
person’s level of education, was also found to be a significant
predictor of savings rates in rural Kenya (Kibet et al., 2009).
With regard to access to other resources that can increase in-
come and generate positive returns on existing assets, evidence
from SSA is mixed. In Uganda, increases in the availability of
credit resulted in higher savings levels (Kiiza & Pederson,
2001). Households with access to credit consistently hold
higher net balances of savings than household without access to
credit. However, in rural Kenya, improved access to credit re-
sulted in savings reduction (Kibet et al., 2009). The authors
cited that this contradictory finding could be due to observed
savings being used for consumption purposes and rarely con-
verted to income-generating assets.
Furthermore, evidence also suggests that class-related factors,
such as education, not only affect savings rate but also owner-
ship of a formal savings account. In Uganda, the education
level of the head of household was found to be a statistically
significant predictor on whether a household will acquire a
formal savings account (Kiiza & Pederson, 2001). Owing to the
sufficiently low-income of many poor households in develop-
ing countries, they tend to use informal savings mechanisms,
which are less secure and safe than formal savings accounts
(Collins et al., 2009). For example, in Pakistan, increases in
income led to a higher rate of participation in both formal and
informal savings sectors. However, at higher levels of income,
formal institutions (such as banks) become more widely used
than informal institutions (e.g. bisi, an informal savings com-
mittee similar to a rotating-savings-and-credit-association)
(Carpenter & Jensen, 2002). Further, the same study found that
bank use was strongly influenced by literacy and numeracy,
suggesting that access to and use of formal savings institutions
could face severe constraints in countries with low educational
attainment and literacy rates (Carpenter & Jensen, 2002).
Institutional Perspective
Institutional theory posits that individuals and households are
faced with institutional-level factors that make it impossible or
difficult to save. The main hypothesis of institutional theory
assumes that low-income individuals and families are unable to
save and accumulate assets primarily because they do not have
the same institutional opportunities that higher-income indi-
viduals and households receive (Beverly & Sherraden, 1999;
Sherraden, 1991). Otherwise, given access to the same institu-
tional support for saving and asset building that their wealthier
counterparts use, low-income families can be in a position to
save and accumulate assets. Institutions in the institutional the-
ory refer to “purposefully-created policies, programs, products,
and services that shape opportunities, constraints, and conse-
quences” (Beverly et al., 2008: p. 10). Institutional theory hy-
pothesizes that institutions affect worldviews, which in turn,
affect financial behaviors and decisions (Beverly & Sherraden,
1999). Seven institutional-level dimensions have been hypothe-
sized to influence saving and asset accumulation. These dimen-
sions are access, information, incentives, facilitation, expecta-
tions, restrictions, and security (Beverly & Sherraden, 1999;
Beverly et al., 2008; Sherraden & Barr, 2005; Sherraden et al.,
2003; Sherraden, Williams Shanks, McBride, & Ssewamala,
2004). This study focuses on the institutional factors (access,
information, incentives and expectations) that have variations in
the asset-building program used in the study.
Evidence from SSA and other developing countries suggests
that institutional factors are associated with saving and asset
building. In Uganda, proximity of the financial institution to the
household is associated with the probability of whether or not a
household will open a formal saving account, as well as the
level of net deposits among households owning a bank account
(Kiiza & Pederson, 2001). In the same study, urban households
were more likely to open a deposit account than their rural
counterparts. Higher transaction costs (due to reduced accessi-
bility) were also found to have significant negative effects on
the level of savings deposits among Ugandan (Kiiza & Peder-
son, 2001) and rural Kenyan households (Dupas & Robinson,
2009). Dupas and Robinson (2009) found that subsidizing the
opening fees for a savings account on behalf of a random sam-
ple of small business owners in rural Kenya increased the sav-
ings of women, many of them market vendors who opened the
account compared to women who were not offered the account.
Also in Kenya, Kibet and colleagues (2009) found that higher
transport costs to saving institutions had a negative impact on
the saving habits of teachers in rural areas. These results sug-
gest that poor people can be better off if it is much cheaper to
start a bank account. In these circumstances, accumulation of
savings and other assets is not solely based on individual char-
acteristics and choices; some people have greater access than
others, and this disparity in access is evident in many parts of
the developing world.
Aside from access, information, particularly general infor-
mation about financial institutions and their products and ser-
vices, was found to be associated with owning a bank account
among households in SSA. In Uganda, researchers found that
the likelihood of owning a savings account increases by
roughly 33 times when a household becomes well informed
about a particular bank and its services (Kiiza & Pederson,
2001). In the Philippines, restrictions (i.e. prohibitions or rules
that restrict access to or use of savings or assets) attached to a
commitment savings product helped low-income women save
(Ashraf, Karlan, & Yin, 2006). However, the impact of other
institutional-level factors on saving behaviors among low-in-
come households in developing countries is not yet known due
to lack of relevant data. This study aims to provide evidence on
the impact of two other institutional-level factors: incentives
and expectations on savings2.
Data and Sample
This paper studies participants of AssetsAfrica program in
rural Uganda. AssetsAfrica is a demonstration and research
2Evidence from the United States and other industrialized countries sug-
gests that other institutional constructs are associated with financial behav-
iors and decisions. Information (or financial education), for instance, has
een found to increase participation and contribution in savings plans
(Bernheim & Garrett, 2003; Clancy, Grinstein-Weiss, & Schreiner, 2001;
yce, 2005). Incentives, particularly in the form of matching contributions,
have been found to increase savings (Duflo, Gale, Liebman, Orszag, & Saez
2006; Han & Sherraden, 2009) and participation in savings plan (Duflo, et
al., 2006; Nyce, 2005). Research has also found that facilitation (i.e. assis-
tance with participation and savings), whether in the form of precommit-
ment constraints (Thaler & Benartzi, 2004), automatic enrollment (Madrian
& Shea, 2001), or direct deposit (Han & Sherraden, 2009; Sherraden &
McBride, 2010) shapes saving and asset accumulation. Evidence also sug-
gests that expectations (i.e. implicit or explicit suggestions about desired
saving; Han & Sherraden, 2009; Schreiner & Sherraden, 2007) and restric-
tions (Sherraden & McBride, 2010) help individual and families increase
their savings.
Copyright © 2012 SciRes. 283
initiative designed to test asset-building innovations in Africa.
Implemented in Masindi, Uganda between 2004 and 2008, the
Ugandan pilot project used a quasi-experimental design, com-
paring across treatment and comparison villages. This design
was chosen in part because of high risk of contamination across
treatment and control subjects if randomly assigned in the same
village. The AssetsAfrica study sample consisted of 400 indi-
viduals assigned to the intervention group (n = 200) or the
comparison group (n =200). Individuals in the intervention
group were selected by village committees, (i.e., economically
needy individuals in the community were chosen) and offered
the opportunity to participate in the project. The intervention
implemented in this project was a structured asset-building
program offered to half of the study sample for a 3-year period.
The intervention comprised a comprehensive program that
included matched funds for participants’ savings, financial
education, and training on how to manage the asset they
planned to acquire with their savings. Treatment group partici-
pants opened savings accounts in a commercial bank. Due to
the absence of banks in the six intervention-site villages and the
distance to the financial institution in the business district of
Masindi, Stanbic bank (an international company that operates
in 16 African countries) established a mobile bank that visited
the villages every week to collect savings. Participants who
wanted to complete their own banking transactions could either
wait for the weekly mobile bank visit or travel to the bank in
the Masindi business district. Deposits had to remain in the
accounts for a minimum of 6 months before participants were
eligible to receive matched funds. Restrictions were made for
lump sum deposits to encourage more regular savings over the
participation period. Participants who successfully reached their
goals had their savings matched at a ratio of 1:1 after which
each participant purchased their desired asset. To encourage
sustainability and viability of the assets, only purchases of pro-
ductive assets (i.e., those that would generate income) were
eligible for matching funds.
Data were collected by 12 locally trained interviewers who
conducted face-to-face surveys. Questionnaires were adminis-
tered twice (baseline and follow-up) over a 13-month interval.
Because this study focuses on examining the extent to which
different theories of saving explain saving in an asset-building
program for low-income households, only participants in the
treatment group were used for analysis (n = 200). Missing data
on one or more explanatory variables reduced the sample to 127.
Given the reduction of sample size, we analyzed the extent to
which respondents (n = 127) and nonrespondents (n = 73) dif-
fered. Bivariate analyses showed no significant differences (p
> .05) between the two groups in all of the demographic and
explanatory variables.
Data Analysis
Through the use of hierarchical multiple regressions (HMR),
we assessed the degree to which savings among AssetsAfrica
participants could be explained by demographic variables and
three theoretical perspectives. HMR allows researchers to de-
cide which order to use for a list of explanatory variables by
putting the predictors or groups of predictors into blocks of
variables. Unlike stepwise regression, the groups of predictors
are based on theoretical grounds. Consistent with prior studies
(Curley et al., 2009; Han & Sherraden, 2009), we entered the
individual-oriented predictors first, followed by sociological
predictors, and then the institutional-level predictors.
Outcome Variab le
The outcome variable in this study was similar to previous
evaluations of asset-building programs: the average quarterly
net saving (AQNS). We used AQNS to measure saving per-
formance at the end of the program. AQNS is defined as net
savings per quarter and is calculated by dividing net savings by
the number of participation quarters (Schreiner et al., 2001). In
this study, the number of participation quarters was 12.
Explanatory Variabl e s
In this study, we used key constructs of the saving theories
discussed in the earlier sections. Aside from the explanatory
variables, we also included three demographic variables: gender,
marital status, and gender of youngest child. Gender is a di-
chotomous variable, having 1 for female and 0 for male. Mari-
tal status is also a dichotomous variable, having 1 for married
and 0 for not married. Gender of youngest child is a dichoto-
mous variable, having 1 for male and 0 for female.
Using HMR, four models were estimated in the study. The
first model we ran was based on the individual-oriented per-
spectives. Controlling for demographic variables, we estimated
the impact of explanatory variables based on economic and
psychological theories of saving. Age and wealth were used as
measures based on neoclassical economic theories. Wealth was
used as a substitute for income. Measuring income was a chal-
lenge in this study as most respondents were seasonal income
earners who only earned income part of the year. As a result,
respondents could not accurately recall how much income they
earned in a year. Therefore, wealth was used as a substitute as it
was easy to measure and could be verified on the spot. Wealth
was measured using an index, which included the total value in
USD of financial and productive assets reported by participants
at baseline survey. Optimism/pessimism about future economic
expectation, perceived locus of control, attitude toward saving,
and self-control were used to examine how psychological fac-
tors and common human characteristics were related to saving.
Future economic expectation was measured using a 3-item,
7-point Likert scale ranging from 1 (extremely worse) to 7 (ex-
tremely better). Respondents were asked how well they ex-
pected their life and food supply to be next year, in addition to
how well they expected to be living next year. Perceived locus
of control was also measured using a 3-item, 7-point Likert
scale ranging from 1 (strongly disagree) to 7 (strongly agree).
Respondents were asked if they felt pretty sure that their lives
would work out the way they wanted them to; if they usually
performed tasks the way they expected to; and if they always
finished doing something once they started it. Attitude toward
saving was a dummy variable, with respondents choosing from
1 (agree) that saving takes too long or 0 (do not agree).
Self-control was measured using a 1-item, 7-point likert scale
ranging from 1 (never) to 7 (always). Respondents were asked
how often they were hesitant to spend money that they had
The second model based on the sociological perspective
takes into account class and social stratification variables in
addition to the control variables we added in the first model.
We included four different predictors to measure the relation-
Copyright © 2012 SciRes.
ships between savings and class and social stratification. First,
social support was measured using a 3-item, 7-point Likert
scale ranging from 0 (no help at all) to 7 (a lot of help). Re-
spondents were asked how much help they get to meet the
needs of the household from family and friends, from organiza-
tions, and from the community. Second, economic strain was
measured using a 1-item, 7-point Likert scale ranging from 1
(extremely hard) to 7 (extremely easy). Respondents were asked
how hard or easy it was to meet the needs of their household.
Third, education was a dummy variable, with respondents
choosing from 1 (secondary education or higher) or 0 (primary
education or lower). The dummy education variable was cre-
ated based on the frequency distribution of the original variable.
And finally, type of employment was a dummy variable, with
respondents choosing from 1 (formal employment) and 0 (oth-
The third model adds the three institutional theory constructs
(access, information, and expectation) to assess the relationship
between saving outcomes and institutional factors, after all the
predictors in the previous models are taken into account. First,
expectation was measured by the total amount of money re-
spondents said they would like to save. Second, incentive was
measured by the total amount of matching funds that each par-
ticipant received at the end of the program. Third, information
was measured by the total number of hours that participants
spent in financial education. And fourth, access was divided
into two sub-domains: ease of visiting financial institutions and
ease of depositing money. Both explanatory variables were
dummy variables. We created two groups for each access indi-
cator: 1 for (easy ratings) and 0 for (hard ratings).
In the fourth model, we included incentive as an institutional
theory construct to assess the relationship between saving out-
comes and financial incentives after all the predictors in the
first three models were considered. Incentives were separated
from the rest of institutional theory constructs to assess how
much of the outcomes are explained by financial incentives
when incentives are a key feature of the intervention. The deci-
sion to separate incentives was also made because incentives
might inflate the AQNS when included and might therefore
provide a different picture of the effects on savings. Separating
them provided a clearer picture for participants about the actual
money being saved without regard to incentives.
In HMR, a full model with all theoretical perspectives was
compared to models without each block of perspective. Partial
F-tests were conducted to determine to what extent each theo-
retical perspective explained saving outcomes in an asset-
building program. Results of the diagnostic tests and residual
analyses showed that the outcome variable and three predictors
were highly skewed and not normally distributed. To reduce the
influence of extreme observations on regression coefficient
estimates, we used two types of data transformation. We trans-
formed AQNS and wealth using inverse hyperbolic sine trans-
formation to handle observed zero values (Burbidge, Magee, &
Robb, 1988). We transformed information (hours of financial
education attended) and expectation (desired total amount of
money in savings) variables using logarithmic transformation.
Further, because the financial incentive variable violated the
assumption of constant variance, we used weighted least
squares regression in the final model to correct for the bias
caused by heteroscedastic data (Kutner, Nachtsheim, & Neter,
2004). For each multi-item predictor, a composite score was
calculated by taking the average score of all items.
Descriptive Results
A total of 127 individuals were included in the sample of this
study. Table 1 presents descriptive statistics for the variables in
the analysis. The median amount of savings per quarter among
AssetsAfrica participants was US $60. The median for savings
per quarter are reported because the variables for savings ex-
pectations, wealth, number of financial education hours at-
tended, and amount of matching funds received are skewed.
More females (59.84%) than males (40.16%) are represented in
the sample. The sample had more married individuals (79.53%)
than unmarried individuals (20.47%). Participants’ youngest
children included mostly girls (55.91%) compared to boys
(44.09%). Study participants were, on average, was about 35
years old, while their median wealth was valued at US $372.
Regarding self-control, 18.90% of participants reported that
they were rarely to never hesitant to spend money that they had
saved, whereas 30.71% and 50.4% of participants reported that
they were sometimes and more than often hesitant to spend
their savings. Sixty-four percent of participants agreed that
saving takes too long. On average, participants expected that
their economic conditions in the following year would be better
than the current year. Further, participants, on average, tended
to agree that they had control over their lives.
On average, AssetsAfrica participants received some help
from family, friends, organizations, and the community. Re-
garding the ability to meet the needs of their household, 45.7%
of the participants reported having some degree of difficulty
meeting the needs of the household; 28.3% reported being in
the middle, and 26% reported having an easier time meeting
their households’ needs (25.98%). Fifty-four percent of the
participants had at least a secondary education or higher,
whereas 45.67% had a primary education or lower. A lower
percentage (20.47%) of participants was formally employed.
Regarding institutional features, the median amount of
money participants said they wanted to save was US $300. The
median number of financial education hours that AssetsAfrica
participants attended was 5. A higher percentage (63.78%) of
participants reported having a hard time visiting a financial
institution. On the other hand, a lower percentage (33.86%) of
participants reported having a hard time making deposits. The
median matching funds that participants received was US $290.
Bivariate tests were conducted to examine the association
between AQNS and each explanatory variable. Table 1 shows
results of bivariate analysis. Four of five institutional-level
predictors were statistically significant (p < .10). However, this
analysis only serves an exploratory purpose because it did not
control for other explanatory variables.
Hierarchical Mul tip l e Regression Anal ysis
Table 2 presents results of the HMR analysis. Results indi-
cate that a significant amount of variance in savings perform-
ance in AssetsAfrica is explained by model 4 or the final model
in which all explanatory variables are considered. All R2 values
were statistically significant (p < .05). Two R2 increments were
also statistically significant (p < .05), (i.e. when institutional
theory variables are added in the models). The R2 in the final
model indicates that 61% of the variance in an individual’s
saving performance can be accounted for by the three theoreti-
cal perspectives on saving and demographic characteristics. The
Copyright © 2012 SciRes. 285
Table 1.
Descriptive statistics and bivariate analysis results.
Variables % or Ma (SD)AQNS
Outcome variable
Average quarterly net saving (AQNS) in 2008 US$ 60.00 -
Individual-oriented perspective
Age 34.91 (10.40).007
Wealth 372.00 .448***
Expectation of future economic condition 5.30 (0.75).254
Perceived locus of control 5.35 (1.08).041
Self-control 4.61 (1.61).216
Rarely to never hesitant to spend savings (%) 18.90
Sometimes hesitant to spend savings (%) 30.70
Often to always hesitant to spend savings (%) 50.40
Attitude toward saving (saving takes too long) (%) 63.78
Sociological perspective
Social support 3.79 (1.51).073
Economic strain 3.62 (1.28).009
Education .084
Primary or lower (%) 45.67
Secondary or higher (%) 54.33
Type of employment .121
Formal employment (%) 20.47
Others (%) 79.53
Institutional perspective
Savings expectation (in USD) 300.00 .158^
Ease of visiting bank .594^
Easy to visit bank (%) 36.22
Hard to visit bank (%) 63.78
Ease of making deposits .088
Easy to make deposits (%) 66.14
Hard to make deposits (%) 33.86
Hours of financial education attended 5.00 .508*
Financial incentive (match funds received, in
USD) 290.00 .004***
Gender .270
Female 59.84
Male 40.16
Marital status .074
Married 79.53
Not married 20.47
Gender of youngest child .344
Male 44.09
Female 55.91
Number of subjects 127
aEach entry is percentage of AssetsAfrica participants in the categorical predictor
variable or the mean of the continuous variable. Standard deviations are presented
in parentheses. Median is presented for AQNS, wealth, expectations, financial
education hours, and financial incentives. ^p < .10, *p < .05, **p < .01, ***p
< .001.
Table 2.
Hierarchical multiple regression analysis on average quarterly net sav-
variables Model 1Model 2 Model 3 Model 4
Age .022 .021 .010 .006
Future economic
Expectation .364 .476^ .325 .287
Perceived locus
of control .124 .075 .095 .063
Wealth .443*** .470*** .483*** .284***
Self-control .042 .048 .032 .011
Attitude toward
saving (saving takes
too long = 1)
.028 .048 .324 .151
Social support .095 .004 .042
Economic strain .049 .005 .059
Education (secondary
education or higher =
.358 .553 .217
Employment status
(formal = 1) .272 .095 .070
Expectation .144 .013
Ease of depositing
(easy =1) .360 .179
Ease of visiting
bank (easy = 1) .598^ .467^
Number of hours of
financial education
.506* .381*
Financial incentives
(match funds) .003***
Gender (female = 1) .205 .268 .096 .228
Marital status
(married = 1) .142 .189 .213 .113
Gender of youngest
child (male = 1) .218 .225 .323 .339
F value 3.10** 2.22* 2.40** 9.34***
R2 .1926 .2031 .2722 .6089
Incremental R2 .0105 .0691* .3367***
^p < .10, *p < .05, **p < .01, ***p < .001, two-tailed test.
greatest increase in variance occurred in the final model, which
include a financial incentive variable. R2 increased from .2722
in Model 3 to .6089 in Model 4, or a statistically significant
increase of .34.
Results of each regression model are presented next. When
individual-oriented perspective and demographic characteristics
are held constant, a test of model fit suggests that there is a
regression relation (p < .01) and at least one β is not equal to
zero. Among the individual-level predictors, wealth is the only
Copyright © 2012 SciRes.
explanatory variable that is statistically significant (p < .001).
For instance, when other variables are held constant, and when
wealth increases by 10%, AQNS is expected to increase by
about 4%. Consistent with previous research, other individ-
ual-oriented variables in the study are not statistically signifi-
cant. Model 1 explains 19% of the variance in AQNS. Results
of model 2 also showed that the model fits with the data and
that there is a regression relation in the population (p < .05).
Wealth remains statistically significant (p < .001). When other
variables are held constant, and when wealth increases by 10%,
AQNS is expected to increase by about 5%. In addition, future
economic expectation showed a statistical trend (p < .10). In the
study sample, when other variables are held constant, a one-unit
increase in future economic expectation, (i.e., when individuals
expect next year to be better than the current year), AQNS is
expected to increase by 61%. This finding contradicts what
economic psychologists have posited, which is that individuals
are likely to save more when they expect their economic future
to be worse. However, this finding is consistent with neoclassi-
cal theory, which posits that when people have more disposable
income, they tend to save more. How this translates to expecta-
tions of more disposable income rather than actual disposable
income requires more research. Further, results of model 2
showed no statistically significant sociological perspective
predictors. This finding does not support results of previous
studies in developing countries that have shown some socio-
logical-oriented variables such as education and employment to
be statistically significant predictors of savings. Model 2 ex-
plains 20% of the variance in AQNS. However, the incremental
R2 between model 2 and model 1 is not statistically significant,
suggesting that explanatory variables related to class and social
stratification may not be important to saving performance
among low-income rural Ugandans who participated in Assets-
Consistent with the first two models, an F test model fit
showed that model 3 fit the data, and that there is also a regres-
sion relation in the population (p < .01). Model 3 explains 27%
of the variance in the outcome variable. The incremental R2
between models 3 and 2 is statistically significant (p < .05);
suggesting the importance of information, access, and expecta-
tion to the saving performance of low-income rural individuals
who participated in AssetsAfrica. Wealth remains statistically
significant (p < .001). When other variables are held constant,
and when wealth increases by 10%, AQNS is expected to in-
crease by 5%. Financial education and ease of visiting a bank
(proximity) are also statistically significant. Holding other
variables constant, for any 10% increase in the number of fi-
nancial education hours attended, AQNS is expected to increase
by about 5%. Further, holding other variables constant, the
expected increase in AQNS from those individuals who found
it hard to visit a bank to those who found it easy to visit a bank
is about 82%. This finding showed a statistical trend (p < .10),
the two other measures of institutional theory—expectation and
ease of depositing—were not statistically significant.
Results of the final model showed that the model fit with the
data. Overall, all three theoretical perspectives and demo-
graphic variables explain 61% of the variance in AQNS. The
models also showed that there is a significant regression rela-
tion in the population (p < .001). As stated earlier, the greatest
variance occurred in the final model, resulting in a statistically
significant incremental R2 of .34 between model 3 and 4 (p
< .001). This finding suggests the importance of financial in-
centive, which in the current study is provided through match-
ing funds in the saving performance of low-income individuals
in rural Uganda. In the final model, four explanatory variables
were statistically significant. When other variables are held
constant, and when wealth increases by 10%, AQNS is ex-
pected to increase by about 3% (p < .001). This result is 1% to
2% lower than the estimated values from the three earlier mod-
els. Similarly, when the number of financial education hours
attended increases by 10%, AQNS is expected to increase by
about 4%, holding other variables constant (p < .05). Further,
the expected increase in AQNS from those individuals who
found it hard to visit a bank to those who found it easy to visit a
bank is about 60%, holding other variables constant (p < .10).
Finally, for a one dollar increase in matching funds received,
AQNS is expected to increase by about .3% (p < .001).
Conventional wisdom dictates that saving is more difficult
for low-income individuals and households than their wealthier
counterparts. However, this study adds evidence that rural,
low-income individuals in SSA can and do save, especially
when given the opportunities. Further, results of this study pro-
vide initial evidence about the factors affecting saving among
low-income participants of an asset-building program in rural
areas of Uganda. All three theoretical perspectives, albeit in
varying degrees, explain variation in saving performance
among rural, low-income households. The final model, which
includes all three theoretical frameworks and demographics,
accounted for the highest variance in AQNS. The statistically
significant incremental R2 in models 3 and 4 suggests that in-
stitutional theory of saving is important in predicting savings
performance in an asset-building intervention for low-income,
rural households. Further, the substantial increase in variance
that occurred in the final model, which included amount of
matching funds, suggests that financial incentives are important
in encouraging low-income individuals to save.
The strong explanatory power of institutional theory in pre-
dicting saving outcomes among participants of AssetsAfrica is
not surprising. Institutional theory of saving has been devel-
oped with the objective of increasing our knowledge of how
individuals, especially the poor, can save (Sherraden, 1991).
Although institutional theory emerged primarily from asset-
building research in the United States, results of the current
study suggest that institutional theory can also explain substan-
tially the factors affecting saving performance among rural
low-income individuals in SSA. In other words, consistent with
results from studies conducted in the United States, institutional
factors, such as information, access, and incentives, influence
saving behaviors of rural, low-income individuals in Uganda.
Thus, saving outcomes among the poor are not solely attributed
to individual characteristics and choices. In AssetsAfrica, all
participants were required to take financial education, primarily
because the intervention was a package that included access,
incentives, and information through financial education. A
Chemonics International report (2007) cited limited financial
information and lack of financial education as the two most
common barriers to increasing savings rates in rural Uganda. In
this study, results suggest that financial education (one method
of providing financial information) is positively associated with
higher savings. In other words, offering financial education to
low-income individuals in developing countries can lead to
Copyright © 2012 SciRes. 287
positive outcomes, as research on financial behaviors among
low-income individuals in the United States have shown (Hirad
& Zorn, 2002; Lusardi, 2002). Further, the current study sug-
gests that access (or proximity to financial institutions) is also
influential in encouraging low-income individuals to save. Dis-
tance remains a major barrier to formal financial services and
other markets in rural areas in SSA. In rural Uganda, only 10%
of the population has access to basic financial services
(Chemonics International, 2007). Although 80% of rural
Ugandans were active savers, only 13% of those active savers
had a savings account in a formal financial institution (Che-
monics International, 2007). Even though financial institutions
may exist, transaction costs in reaching them can make them
unavailable. These transaction costs typically refer to time,
effort, and money spent to reach a bank. This finding supports
previous findings about the positive association between prox-
imity to financial institutions and higher savings in SSA (Kibet
et al., 2009; Kiiza & Pederson, 2001). However, unlike prior
studies, the current study provides initial evidence that prox-
imity to financial institutions also influences positive saving
behaviors of low-income individuals in rural SSA. Further, the
positive association between proximity and saving suggests that
there can be benefits to both financial providers and users when
financial services are not only expanded but are easy to access,
particularly in rural areas. Finally, financial incentives in the
form of matching funds increased AQNS, albeit not as much as
expected, among AssetAfrica participants. Schreiner and Sher-
raden (2007) argued that matches in asset-building programs
may encourage low-income people to save for at least three
reasons: 1) matches increase the reward to saving and may help
compensate for the sacrifice required to defer consumption; 2)
matches may motivate people by translating a given level of
saving into a stock of wealth that is large enough to use for a
major asset; and 3) matches may be the program feature that
catches the participant’s attention and motivates him or her to
participate in the first place. The use of matching funds in As-
setsAfrica is perhaps the first of its kind in rural SSA. The posi-
tive relationship between the matching funds and saving out-
come indicates that low-income individuals will respond posi-
tively when given the opportunity to save and save more. This
finding is identical to other studies in the United States that
investigated the role of matching funds on the savings per-
formance of low-income individuals (Duflo et al., 2006)The
benefits of matching funds to AssetsAfrica participants go be-
yond encouraging them to save; individuals are rewarded for
saving more. Higher savings mean that low-income individuals
and households are better prepared to weather income shocks
and other emergencies. Savings are particularly important to
families living in developing economies, like Uganda, because
formal social safety nets that families in more developed
economies can rely on to buffer against emergencies are not
widely available.
Although saving performance in AssetsAfrica is influenced
primarily by institutional factors, wealth is positively associated
with savings performance. In all four models, wealth is a statis-
tically significant predictor, which suggests that wealth has a
strong power in explaining saving among low-income indi-
viduals in the AssetsAfrica program. The current study supports
the findings of previous studies, albeit mostly on higher income
individuals, in developing economies. A majority of studies on
saving behaviors in developing economies have included only
income in their analyses for various reasons. However, income
does not necessarily provide a reliable measure of well-being
because many people in developing countries, especially in
rural areas, engage in informal labor markets where incomes
are often highly variable and can be seasonal. Wealth provides
a better and more stable picture of long-term living standards
than an income snapshot because wealth or assets are what
individuals and households accumulate over time and what last
longer. In the final model, other than wealth, no other individ-
ual-oriented variables were found to be a statistically significant
predictor of saving. These findings are consistent with results of
prior studies that found no or weak statistical relationship be-
tween individual-oriented (economic and psychological) factors
and savings outcome in both developed and developing econo-
mies. Further, when sociological variables were added, results
showed a statistically nonsignificant incremental R2. This find-
ing indicates that the four sociological constructs (education,
employment, level of social support, and degree of economic
strain) in the study have a weak association in explaining sav-
ing among rural, low-income individuals in AssetsAfrica. In
other words, results suggest that class and social stratification
are less important than wealth and institutional factors to an
individual’s saving performance. These findings contradict
what other researchers have found. These divergent findings
may be attributed to the following: 1) observed savings deter-
minants from a quasi-experimental setting that intentionally
provides an opportunity to save might be quite different than
those observed from national, aggregated data sets; 2) the sam-
ple in the current study was primarily rural, low-income indi-
viduals, whereas the samples in prior studies included a more
representative sample of the general population; and 3) meas-
urement-related limitations might have confounded the find-
ings. For instance, economic strain is measured by only one
Limitations and Conclusion
There are several limitations to this study. First, participants
in AssetsAfrica are not a representative sample of the overall
low-income population in Uganda and SSA. Selection bias,
particularly self-selection, limits the generalizability of the
results. Second, this study examines saving performance only
in the account provided by the program. Thus, we cannot say
whether this is new savings or whether they have saved in other
ways that were not reported in the study. Third, although we
used relevant measures of each theory, this study cannot con-
firm the validity of each theory especially when tested in a
different geographic and socioeconomic population. Results
may be an artifact of weak measures of key constructs of the
saving theories. Future studies should employ, among other
things, a wider range of key measures and more reliable scales
to measure important constructs. Fourth, the study’s model
specifications may have omitted important predictors. Omission
of important predictors may lead to different results. Similarly,
the functional form between the outcome and predictors might
not have been specified correctly. For most of the variables, a
linear relationship with the saving outcome was assumed. Fu-
ture studies should also test for interaction effects among con-
structs of interest. For instance, it could be that expectations of
total savings amount are not predictive in themselves, but the
interaction with financial incentives or access might have an
effect. Fifth, the study has a small sample size relative to the
number of explanatory variables included in the analyses. Thus,
Copyright © 2012 SciRes.
the study findings may have been biased because of the rela-
tively small sample size.
Despite the limitations, some of the findings are statistically
and substantively significant. Results provide initial evidence
on the factors that affect saving by rural, low-income individu-
als. Controlling for other theoretical perspectives, the institu-
tional theory of saving has independent and significant ex-
planatory power in saving outcomes. Unlike the institutional
theory of saving, which emerged from studies of saving behav-
iors of low-income individuals and households, individual-
oriented and sociological perspectives were not developed to
explain saving by low-income populations. Findings also sug-
gest that institutional structures of saving matter to low-income
individuals in rural Uganda. Given access to the same institu-
tional support for saving and asset building that their wealthier
counterparts use, low-income individuals can be in a position to
save and accumulate assets. Unlike the other theoretical per-
spectives that may have limited practical implications, institu-
tional theory of saving can directly inform future programs and
policies designed to promote financial inclusion and help
low-income individuals and families save and build assets.
Support for AssetsAfrica comes from the Ford Foundation.
The authors thank Grace Kemirembe for her valuable assistance
in AssetsAfrica, and Susan White for her careful review and
insightful comments on the manuscript.
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