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An Impact Study of the Heifer Project: Effects of Dairy Cow and Meat Goat Donation on
Household Nutrition in Rural Rwanda
Rosemary Rawlins, Master’s Candidate University of San Francisco
April 2012
Abstract: Animal-source foods provide protein, calories, and an array of micronutrients that are often limited or unavailable in rural peasant diets. Using household survey data, I evaluate the impact of two Heifer International animal donation programs in Rwanda to determine their impact on animal-source food consumption. I find a highly significant, positive correlation between dairy cow donation and household dairy consumption. I also find a significant and positive correlation between meat goat donation and household meat consumption.
Special thanks to Bruce Wydick, Chris Barrett, Sarah Pedersen, Svetlana Pimkina, Tim Ong, Rienzzie
Kern, Charles Kayumba, and all of the staff at Heifer Rwanda.
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1. Introduction
The World Health Organization estimates that worldwide more than 800-million
people are chronically undernourished. One-third of the world’s children are affected by
delayed growth and development due to malnutrition, in addition to the 40,000 children who
die each day from starvation and disease (Behrman et al., 2003). A lack of balanced diets,
particularly the absence of animal-source foods, causes 500,000 children to become blind
every year and 14 million to develop eye damage due to vitamin-A deficiency (World Health
Organization, 2010).
Nutrition is particularly important for maternal and child survival, as it is a key
determinant in reducing morbidity and mortality. During pregnancy and in the first two
years of life nutritional losses take their greatest toll, causing damage that is largely
irreversible. Undernourished children are likely to start school later and complete fewer
years of schooling, they demonstrate a reduced capacity to learn, and this has long-term
impacts on earnings. The United States Agency for International Development
acknowledges that improving nutrition has been a struggle during the past decades, that both
developing and developed countries have had difficulty achieving more balanced diets and
optimal caloric intake (USAID, 2009). In many developing countries, decades of aid have
had essentially no impact on reducing poverty and food insecurity (Sabates-Wheeler and
Devereux, 2010).
There is a rising demand in the developed world for programs that allow first world
consumers to directly aid people in the developing world. To meet this demand, there has
been an increase in child sponsorship programs and person-to-person microfinance
institutions such as Kiva; fair-trade coffee, chocolate, and bananas are available in grocery
stores across the United States; and in-kind transfer programs such as Heifer International
have seen greater and greater popularity. If the developed world is really serious about
aiding developing countries, then it has a responsibility to evaluate the impact of these
programs to ensure that well-intentioned efforts are in fact benefitting the recipients.
This paper explores the current approaches to increasing dietary diversity and the
consumption of animal-source foods, and presents evidence that Heifer International’s
animal-donation program is improving household dietary diversity in Rwanda by increasing
the consumption of animal-source foods.
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2. Literature Review
2.1 The Importance of Animal-Source Foods
Malnutrition results from a combination of an inadequate intake of calories,
micronutrients, and high-quality protein (Sabates-Wheeler and Devereux, 2010). Research is
increasingly showing that animal-source foods provide complete protein, energy, and an
array of micronutrients that are often limited or unavailable in the rural peasant diet
(Scrimshaw, 1994). In addition to providing a good source of protein and calories, meat and
milk products are an important source of calcium, vitamin B-12, zinc, and vitamin A.
Inadequate intake of these nutrients is associated with anemia, poor growth, rickets,
impaired cognitive performance, blindness, and neuromuscular deficits. Even relatively
small amounts of animal-source foods can substantially increase nutrient adequacy (Murphy
and Allen, 2003). While there are other methods of obtaining these micronutrients, it is
difficult for a child to even approach meaningful levels of nutrients such as calcium and
vitamin B-12 on a plant-based diet (Murphy, Beaton, and Calloway, 1992). Low incomes, in
tandem with limited access to animals, often means that poor households consume very few
animal-source products and are forced to rely on less expensive substitutes (Neumann et al.,
2002).
Generally, there are four strategies used to improve dietary diversity and animal-
source food consumption: nutrition education, cash transfers, food aid, and in-kind transfers
such as animal donation.
2.2 Nutrition Education
Nutrition education is a cost-effective and significant means of changing eating
behaviors. In a study of adolescent eating behaviors by Mariane Fahlman, children who
received nutrition education were significantly more likely to eat fruits and vegetables and
less likely to eat junk food than the control group (Fahlman, 2008). In a study focused on
low-income women and children, researchers found that the treatment group benefitted
from lessons on what makes a balanced diet, but that families needed to be convinced that
healthy food could be affordable and that children would eat it (Hampson, 2009). There is
generally robust evidence that nutrition education is a key piece of the puzzle, but if a diverse
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diet is too expensive or not available, then households are unable to get the nutrients they
need. The next three approaches focus on the means to supply food, but may also
incorporate an education component.
2.3 Conditional Cash Transfer Programs
Conditional cash transfer (CCT) programs are popular among policy makers because
they are relatively inexpensive (generally governments expend less than 1 percent of their
gross domestic product), and because they seem to have a positive impact on nutrition,
education, and poverty. The impact of CCT programs on household food consumption has
been studied in several Latin American, Asian, and African countries. CCT programs have
positive effects on short-term poverty alleviation, total household expenditure, and
household food expenditure (Adato and Bassett, 2009). They are also found to have a
positive effect on the quality of the household diet, and increase spending on animal-source
foods. According to an analysis of 20 CCT programs around the world by Adato and
Bassett, most programs are associated with lower reported rates of hunger, and an increase
in average daily meals consumed (Adato and Bassett, 2009).
Positive reports of the impact of CCT programs can be found around the world. In
Malawi, participants consumed nearly twice as many food groups as the control group, and
were more likely to eat high-quality foods (Miller and Reichert, 2008). In Nicaragua,
recipients consumed more food items than non-recipient households and consumed foods
with higher nutritional quality (Maluccio and Flores, 2005). In Colombia, beneficiary
households consumed a greater share of protein than control households (Attanasio and
Mesnard, 2006).
However, there are limitations to CCTs. In Brazil, the Bolsa Familia program has
seemingly failed to significantly impact health and nutrition (Soares et al., 2010). Soares et al.
attribute this failure mainly to constraints on the supply of food and the quality of household
diets. In Ethiopia, cash transfer programs were associated with increased food-price
inflation (Sabates-Wheeler and Devereux, 2010). By nature, cash transfers are vulnerable to
price inflation of the commodities that they are intended to purchase. Where supplies are
constrained, such as in Ethiopia, injecting cash transfers can even exacerbate inflation. This
risk was highlighted when world food prices surged in 2007 and 2008, reducing the
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purchasing power of low-income households and those who depended on cash-based
transfers (Sabates-Wheeler and Devereux, 2010).
2.4 Food Aid
While cash transfers are generally cost efficient and flexible, food aid has been
criticized as expensive to store, ship, and distribute. Food basket programs have in some
cases damaged local production and trade, and are viewed as paternalistic. However, food
transfers are typically controlled by women and benefit children directly, while cash transfers
are more likely to be controlled by men and may be used for expenses other than food and
necessities. In Mexico, one study used a randomized control trial to examine the effect of
the Programa de Apoyo Alimentario (Leroy et al., 2007). The study looked at the effect of a
program cash transfer versus a food basket among the rural poor. According to Leroy et al.,
the food basket led to a significantly greater impact on energy and nutrient consumption
than the cash transfer. Additionally, they found a positive effect on fruit and vegetable
consumption, but also a significant increase in excess (i.e., unnecessary) calorie consumption.
They concluded that the food basket program had net positive impacts, but that the
composition of the basket needed to be revisited.
2.5 In-Kind Transfers—Animal Donation
The impact of animal donation on household nutrition has not been extensively
studied, but thanks in part to programs like Heifer International, it has lately received more
notice. Organizations like Heifer donate a variety of large and small animals to poor
households around the world with the goal of increasing their income and nutritional status.
Lacking any previous studies on animal donation specifically, we are forced to infer their
impact from the effect of livestock ownership on household nutrition and animal-source
food consumption. Studies in Africa have indicated a positive relationship between child
nutrition and dairy cow ownership. These findings include better nutritional status,
increased child height, and increased height for age (Leegwater, 1991, and Vella, 1995).
Nicholson et al., found that in Kenya, if all households owned between one and two cows,
the percentage of children with moderate or severe stunting would be reduced from 54
percent to 32 percent for households with no current access to milk (Nicholson et al., 2004).
Studies in China, Jamaica, Mexico, Nicaragua, and Brazil have found that children who
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consume cow’s milk or other dairy products have a lower incidence of stunting (Neumann,
Harris, and Rogers, 2002).
Basic economic theory dictates that if purchasing livestock were the most efficient
choice for a household, then given a cash transfer, the household would make the rational
choice. However, de Janvry and Sadoulet point out that under incomplete or absent
markets, behaviors arise that are apparently contrary to economic logic, but that make sense
given the prevailing market constraints (de Janvry and Sadoulet, 2006). For Heifer
International’s targeted households, there intuitively may be many constraints that would
prevent their procurement of animal-source foods when given a cash transfer or presented
with nutrition education. Cultural norms and traditions, as well as lack of education, may
decrease the preference for animal products. There may be missing markets both for the
end food products and for the animals themselves, making it difficult to obtain livestock
even with the proper counseling. Finally, lack of experience caring for animals, as well as an
aversion to the risk of the animal getting sick or dying, could prevent the purchase of
livestock or other small animals.
While certainly there are advantages and disadvantages to each approach, animal
donation could, in theory, provide households with a direct, sustainable source of high-
quality protein, energy, and micronutrients. This approach may be the best of the four as it
could potentially combine nutrition education with a steady source of meat, eggs, or dairy.
3. Empirical Hypothesis
3.1 Heifer International in Rwanda and Description of the Sample
My data consist of 406 household surveys collected during the summer of 2011 in
Rwanda. Rwanda is a small country in East Africa. It is approximately the size of the state
of Maryland, and is home to 10.7 million people. Since coming to power in 2003, President
Paul Kagame has instituted many far-reaching reforms including improving access to
schools, mandating that all houses have metal roofs, and banning the use of plastic bags in
the country. Rwanda is sometimes called “Africa’s Singapore” and is widely regarded as
being one of the most efficient, well-organized countries in Africa.
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Our research team conducted 224 surveys in the north of Rwanda in Heifer
International’s dairy cow region, Ruli. In this region, Heifer donates dairy cows to coffee
farmers belonging to two different coffee cooperatives. The goal of the coffee farmer
program is to increase dairy consumption and production in the region, but it is also
designed to increase coffee output via access to fertilizer. In order to qualify to receive a
dairy cow, applicant households must meet certain requirements. The most stringent
requirements are that first, households have at least one hectare of land, and second, they
must be able to construct a shed with a cement floor and metal roof to house the cow (once
selected, Heifer loans beneficiaries the funds to purchase building materials). Beyond the
land and shed requirements, Heifer International was unable to provide us with specific
selection criteria. However, in discussion with Heifer staff, we were able to ascertain that
beneficiaries are selected based on need as determined by a team of assessors. During the
selection process, Heifer staff visit applicant households and review various household
characteristics such as income, number of children, widow(er) status, etc. Upon selection,
Heifer gives each beneficiary household an imported, pregnant cow. The imported cows
produce much more milk than the local cow breeds, able to produce three to six liters (.8 to
1.6 gallons) of milk per day after they give birth. Heifer beneficiaries are required to donate
the first female offspring of their cow to the “Pass on the Gift” program, which Heifer
International also coordinates. If beneficiaries are lucky enough to get a male (bull)
offspring, they may sell it for a considerable profit (approximately $250-$1,000). Heifer
International provides ongoing insemination assistance to beneficiaries, so that their donated
cows can continue to produce milk and offspring.
In addition to the dairy cow program, I also analyze 182 surveys from households in
the east in Heifer’s meat goat region, Kirehe. In Kirehe, the meat goat program is designed
specifically as a poverty-reduction program and thus the participants are much poorer than
in the north. Kirehe is near the Tanzanian border and is home to a large population of
refugees that returned to Rwanda after the 1994 genocide. The area is much poorer overall,
with limited access to markets and less developed infrastructure. Researchers noted that
Heifer’s meat goat program is not as well organized or operated as the dairy cow program.
While there is still officially a requirement that beneficiaries build sheds to house the donated
goats, the research team noted many households where goats were tied to trees, or housed
on concrete slabs with no roof. Upon selection, each beneficiary household is given two
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female goats. A few beneficiaries in each area are also given a male goat to breed with the
area’s female goats. Heifer expects households to breed the goats for sale and/or
consumption.
In both regions, the sample is divided into three treatment groups. The first group
consists of households that received an animal (dairy cow or meat goat, depending on the
region) one year ago; we call this group “beneficiaries.” The second group consists of
households that applied to Heifer to receive an animal and were approved to receive one in
the near future (the next month or so); we call this group “potentials.” The last group
consists of households that applied to receive a Heifer animal in the current cycle, but were
excluded by Heifer during the screening process; we call this group “nevers.” For the
purpose of this paper, the group “qualified” includes both beneficiaries and potentials—that
is, all households in the sample that were qualified to receive animals from Heifer
International either the year before data collection or at the time of data collection.
Heifer staff provided lists of beneficiaries, potentials, and nevers in each region and
we attempted to survey every listed household, as time permitted. We made sure that each
list included any households that may have left the program, voluntarily or otherwise, to
ensure that the sample was not biased upwards by leaving out beneficiaries that did not fulfill
Heifer program obligations. If household members were not easily contacted, we made
multiple attempts to reach them, including phoning friends and neighbors, and visiting
houses multiple times to establish contact. In the event that the animal recipient or applicant
was not present when we visited a household, we interviewed either the spouse or the eldest
member of the household present.
Dietary information is very hard to gather using surveys. If given a lengthy reporting
period, respondents often forget what they ate or report what they think they eat on average
during a normal week or month. To avoid these issues, I gave respondents defined
categories of food and asked them how many times they ate foods in each category in the
last two days. The survey questions both prompted the respondents to remember
everything they ate, and limited the recall period for more accurate results. The survey
questions were modified from a 2006 Demographic and Health Survey in Uganda. To
measure the monthly consumption of dairy per person in the household, we asked
respondents how much milk or meat their household produces each month, how much they
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sell, and how much they consume. The amount of total household consumption was then
divided by the number of members in the respondent’s household to arrive at the amount of
monthly per-person consumption. If respondents did not know how much meat or milk
was produced or consumed in a month, we recorded the information per week or per day
and converted the amount into a monthly figure upon entering the data. The enumerators
were trained to make sure while surveying that the combined amount of meat or milk
consumed and sold was equal to the amount produced by the household.
3.2 Hypothesis
Economic theory suggests that receiving an in-kind transfer may increase household
consumption of the transferred good. In-kind transfers may also increase consumption of
other goods through an income effect if the transferred good is easily sold. The
fundamental question of this paper is: what is the impact of receiving a dairy cow or meat
goat on household nutrition as expressed by dietary diversity? In order to quantify this
effect, I use six measures of dietary diversity:
" number of food groups consumed in the last two days (individual),
" number of non-dairy food groups consumed in the last two days (individual),
" number of times dairy was consumed in the last two days (individual),
" number of times meat was consumed in the last two days (individual),
" monthly consumption of dairy (per-person in household), and
" monthly consumption of meat (per-person in household).
My hypothesis is that the strongest effects will be seen in dairy cow donation
increasing household dairy consumption. I also expect dairy cow donation to have positive,
although perhaps mild, effects on the amount of dairy consumed at the individual level. If
the sale of milk is large enough to create an income effect, we might also expect to see a
modest increase in the number of food groups consumed overall by individuals. Similarly, I
expect that receiving a meat goat will increase individual and household meat consumption,
and perhaps have a small positive effect on the other measures.
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3.3 Functional Form
For each dependent variable, the functional form is stated below:
(1) Y = !0 + !1(Qualified) + !2(Cowben) + !3(Goatben) + !4(North) + !5(Controls) + ei
In the equation above, “Qualified” is a dummy variable indicating that the household
either received an animal last year or will receive one soon (i.e., is either a beneficiary or
potential household). Thus, the estimated !2 and !3 coefficients measure the marginal effect
of receiving a cow or goat given that a household was qualified by Heifer International to
receive an animal. By using the Qualified variable, I am able to control for any unobservable
characteristic that caused the “Never” households to be rejected for animal donation in the
current round. The dummy variable “North” ensures that the groups in the cow and goat
regions are only compared to others within their region (i.e., cow beneficiaries, cow
potentials, and cow nevers are compared to each other, but not to households in the goat
region). This control is important as the regions vary greatly in terms of weather,
infrastructure, and incomes. Controls used in the models include household head
characteristics, household size, land size, and asset ownership. I also include variables for
non-Heifer animal ownership to be sure that the effect captured by my analyses is from
receiving the Heifer animal specifically, and not picking up a general effect of animal
ownership.
3.4 Specification
To test my first two dependent variables, total number of food groups and number
of non-dairy food groups consumed by the respondent in the last two days, I use Ordinary
Least Squares regressions with robust standard errors. This approach is appropriate for this
dependent variable because the data are normally distributed, but show signs of
heteroskedasticity. For both dependent variables I run three separate regressions, each
adding more controls than the last. The first is a simple model with no controls; the second
includes controls for household characteristics, and the third includes controls for non-
Heifer livestock ownership.
To test the effect of animal donation on dairy and meat consumption by the
respondent in the last two days, I use Poisson regressions. These dependent variables are
described by count data between zero and six, with a large number of observations censored
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at zero. A Poisson regression is the best approach for these variables because the model is
designed specifically for rare events where most observations have a value of zero. As stated
in the introduction, animal-source food consumption (meat and milk) are often limited in
the peasant diet and certainly qualify as “rare events” in this case. As with the OLS
regressions, I estimate three separate Poisson models for dairy and for meat consumption.
To test household-level dairy and meat consumption, I use robust Tobit regressions.
The Tobit model is appropriate for my household dairy and meat consumption variables
because the data are count data censored at zero (but have ranges too large for a Poisson
regression). The Tobit model allows us to estimate the effect of the independent variables
on the zero and positive values of the dependent variable separately, but reports a coefficient
that is conveniently interpreted as a marginal effect. As with the OLS and Poisson
regressions, I estimate three Tobit models for household dairy and meat consumption.
As a check on the Tobit results, I also run Propensity Score Matching (PSM) models
to estimate average treatment effects on the treated. PSM provides an estimate of the effect
of treatment on an outcome variable that is largely free of bias when selection intro
treatment is based on observable characteristics. The model estimates propensity scores for
each household, which is an estimate of their likelihood to be treated, based on control
variables such as household size, education, and wealth. The model then matches treated
households (beneficiaries) with un-treated households (potentials) based on their propensity
scores to essentially create “pre-” and “post-treatment” groups. The mean difference
between matched treated and untreated households is then computed to arrive at the average
treatment effect on the treated. For my sample size, PSM should only be run on a select few
control variables. In keeping with the literature on matching models, I selected my controls
by finding the top four or five variables that were most correlated with both treatment and
the outcome variable. After running my PSM models, I ran balancing tests to ensure that
there was no statistical difference between the means of matched pairs.
PSM is not robust against bias arising from unobserved variables that affect both
assignment to treatment and the measured outcome. Because of this, I test my matching
results using the Rosenbaum bounds approach. Rosenbaum bounds measure how
confounded treatment selection would need to be under the worst-case scenario in order for
causal effects from a matching analysis to lose their significance (See DiPrete and Gangl,
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2004). In other words, Rosenbaum bounds measure how strongly a variable that is
correlated with treatment, and perfectly correlated with the outcome variable, would need to
affect selection into treatment in order to invalidate the results. The Rosenbaum bounds
output gives a value for !, which is a threshold level of the effect of an unobserved covariate
on the odds ratio of treatment at which we must question a significant result. For example, a
! of 1.5 means that if an unobserved variable caused the odds ratio of treatment assignment
to differ between treatment and control groups by 1.5, we would need to question our
conclusion of a significant effect.
4. Results
Overall, I find that being a Heifer recipient has no significant effect on the number
of non-dairy food groups consumed by respondents in the last two days. I do, however,
find significant positive impacts of dairy cow donation on household and individual dairy
consumption, and positive impacts of meat goat donation on household meat consumption.
4.1 Respondent Food Groups Consumption
As seen in Table 3, there are many factors associated with the number of total food
groups eaten in the last two days by survey respondents. The coefficient on being a cow
beneficiary is positive and significant at the 1 percent level, indicating that receiving a Heifer
cow is highly correlated with an average increase of 1.2 food groups consumed in the last
two days. This result is intuitive as I expected the increased household income from milk
sales to increase the number of foods that beneficiary households purchase and consume.
Thus, the correlation between being a cow beneficiary and eating a more diverse diet is
consistent with my hypothesis.
However, this effect appears to be driven by the respondent’s consumption of
dairy, as we can see from Table 4. When dairy products are removed from food groups
consumed, there is no significant effect of being a cow beneficiary or goat beneficiary on the
number of food groups consumed. This implies that either there is no income effect of
receiving a Heifer animal on the number of non-dairy food groups consumed, or the effect
is too subtle to be picked up by our limited sample size. The variable “North” is highly
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significant in both regressions which is unsurprising as, noted earlier, Ruli is a much
wealthier region overall than Kirehe.
Our suspicion above is confirmed by Table 5—we see that cow beneficiaries
consume dairy an average of one more time in the last two days compared to the control
groups. While this may seem like a small effect, it is quite significant considering that the
entire sample consumed dairy 1.4 times in the last two days, on average. Table 6 shows that
while wealth indicators such as the number of machetes owned by the household, number of
cell phones owned, and the amount of land owned, are highly correlated with respondent
meat consumption, we see no effect of being a goat beneficiary on meat consumption in the
last two days by respondents. This may be because there is no effect, or because meat is
consumed too infrequently to be picked up by a dietary diversity measurement based on the
last two days.
4.2 Household Dairy Consumption
Table 7 shows the correlations between the independent variables and the amount of
household monthly per-person dairy consumption in liters. The regression results show a
positive correlation between being a cow beneficiary and household dairy consumption. I
find that receiving a Heifer cow one year ago is correlated with a 9.15-liter (2.4-gallon)
increase in monthly per-person dairy consumption today compared to the control groups.
This represents a nearly three-fold increase over the mean value of per-person household
dairy consumption of 3.6 liters per month. This result is significant at the 1 percent level.
The marginal effect seems a little high and may indicate measurement error on the part of
respondents (although, as seen in section 4.4, the PSM matching technique estimates a very
similar marginal effect). It may also be worth noting that “consumption” in the economic
sense is not the same as consumption in the nutritional sense. It’s possible that some of the
dairy that households “consume” is given to neighbors or guests. Additionally, Rwandan
households mainly consume a yogurt-like substance made from fermenting milk. This
process reduces the volume of dairy so that what started as a liter of milk does not end up
being a full liter of yogurt. Regardless, the result is highly significant, indicating a strong
effect even if we are unsure of the exact magnitude of the marginal effect. Not surprisingly,
having a non-Heifer cow is also highly correlated with dairy consumption, highlighting the
importance of controlling for non-Heifer animal ownership.
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4.3 Household Meat Consumption
Table 8 shows the correlations between the independent variables and monthly per-
person meat consumption in kilograms. The results show that being a Heifer meat goat
recipient one year ago is correlated with an increase in monthly per-person meat
consumption of .2 kilograms, or about half a pound. This constitutes a nearly 100 percent
increase above the sample mean of .27 kilograms of meat per-person per month. This result
is significant at the 10 percent level. The lower significance may be due to the subtlety of the
effect, or may be because Rwandans eat meat so infrequently. Many respondents reported
eating meat only a few times per year. As before, households in the North consumed more
meat regardless of beneficiary status. Also of note, owning pigs or sheep was significantly
correlated with meat consumption.
4.4 Propensity Score Matching Results
As seen in Table 9, the results for PSM for dairy consumption are very similar to the
results found using the Tobit regression. I find that receiving a dairy cow is correlated with
an average treatment effect on the treated of 9.32 more liters of milk consumed per-person
per month. The result is significant at the 1 percent level. This is very close to the 9.15 liters
estimate obtained in section 4.2. Because the propensity score estimate is higher than the
Tobit estimation, it implies that the Tobit result may be slightly biased downwards. Still, the
result remains very similar and continues to be highly significant.
Table 10 shows the results of the balance test for the PSM model for household
dairy consumption. The balance test shows that the differences in means of the matched
pairs for the control variables are not statistically significantly different from zero for any of
the controls, or for the model overall. Since all of the observations are on support, and
because the sample is balanced, we can be confident that the PSM was created to the best of
our ability.
The results for the Rosenbaum Bounds test on the dairy consumption PSM results
may be found in Table 11. I find that significance at the 1 percent level is invalidated at a !
of 4.5; the 5 percent level is invalidated at a ! of 5.5. This means that if there is an
unobservable characteristic that causes the odds ratio of self-selection to be 5.5 times higher
for those who were eventually treated, and the characteristic is perfectly correlated with dairy
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consumption, my results would no longer be valid at the 5 percent level. While there is no
hard and fast rule on what ! threshold is “good,” it seems unlikely that self-selection into
dairy cow treatment would be so biased as to invalidate this result.
Table 12 shows the results for the PSM for goat donation on meat consumption.
The estimated average treatment effect on the treated is .17, indicating that being a goat
recipient is correlated with increased per-person monthly meat consumption of .17
kilograms (.37 pounds). The result is significant at the 5 percent level. This is very close to
the effect of .20 kilograms found above using the Tobit regressions. Unlike the dairy
consumption results above, the PSM results for meat consumption seem to indicate that our
Tobit estimation may have been slightly biased upwards. Table 13 shows the results of the
balance test for this estimation. As with dairy consumption above, we see that the difference
between the means of the matched samples is not statistically different from zero for either
the controls or the model overall.
The results for the Rosenbaum Bounds test on the meat consumption PSM results
may be found in Table 14. I find that the critical level of ! at which we would have to
question our conclusion of a significant positive effect at the 5 percent level is between 1.2
and 1.3.! ! This means that households characterized by an unobserved covariate that is
perfectly correlated with meat consumption and correlated with selection into treatment
would need to have odds ratio of treatment only 1.3 times higher than other households. A
! of 1.3 indicates that the results are fairly sensitive to endogeneity. As indicated previously,
meat consumption is so rare that the effects of meat goat donation are likely very subtle and
hard to measure. It is therefore unsurprising that we find a smaller ! for meat consumption
than for dairy.
5. Limitations
My results look encouraging for the ability of animal-donation programs to improve
household animal-source food consumption. However, there is reason to believe that some
of my independent variables may be endogenous. Since the purpose of animal donation is
to relieve poverty, I expect that the outcomes I’m looking at—dietary diversity, milk and
meat consumption—are themselves correlated with whether or not a household gets a cow.
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For example, it is very possible (and likely) that income affects both milk consumption and
whether or not someone received a cow. Through similar channels, I expect that dietary
diversity is negatively correlated with being chosen to receive a cow (because if income is
already high enough to have a diverse diet, it may disqualify the applicant).
We do our best to control for this in our study by selecting our three treatment
groups: beneficiaries, potential beneficiaries, and “nevers” who asked for an animal but are
not going to receive one. This strategy keeps preferences consistent across households (all
of them asked for an animal), and controls for endogeneity by looking at the differences
between groups rather than comparing them to the population as a whole. Thus, the only
endogeneity that would affect my results would be variables that were correlated with
becoming a beneficiary in the first round of donations rather than in the second round. For
example, there may be omitted variable bias from factors such as motivation or political
connectedness that may have been unobservable to us, but are correlated with receiving an
animal in the first round. If this is the case, we may be over-estimating the effects of animal
donation. Rosenbaum Bounds tests this by assuming the worst-case scenario: that political
connectedness, for example, is perfectly correlated with the outcome and correlated with
self-selection into treatment in the first round. On the other hand, the group that received
animals last year may have been the poorest of the three groups one year ago, and the animal
donation may have enabled them to “catch up.” If this is the case, then we may be under-
estimating the effect of animal donation.
Encouragingly, I find no correlation between being a potential beneficiary or
“qualified” and the dietary diversity measures. This indicates that households that qualify to
receive an animal do not systematically consume significantly more dairy or meat as a group
than the “nevers” group. I also find no relationship between cow donation and meat
consumption, or between goat donation and dairy consumption. If more motivated
households received animals in the first round (rather than the neediest), then we might
expect to see a correlation between cow donation and meat consumption or goat donation
and dairy consumption because “motivated” households might be expected to consume
more animal-source foods regardless of animal donation.
Finally, because we only surveyed households in treated areas, we are unable to
measure any spillover effects that may have occurred. It’s possible that the first round of
16!
dairy cow donation increased dairy access for untreated friends, family, or neighbors of
beneficiaries. It is also worth noting that Rwanda is not a “typical” African country in terms
of efficiency and attitudes about work and bureaucratic rules. Thus, we must be careful to
qualify our results as applying to Rwanda only. We cannot necessarily expect that the
program would have the same effects elsewhere. Ideally, a follow-up study would use
instrumental variables, or even better, a randomized control trial in several countries to
address the issues identified above.
6. Summary and Conclusions
The results of my research are very encouraging for organizations looking to
improve household nutrition in developing countries. Using Tobit regressions, I find that
receiving a dairy cow is correlated with an increase in monthly per-person dairy consumption
of 9.15 liters. This relationship is highly significant. Further, I find that receiving a meat
goat is correlated with an increase in monthly per-person meat consumption of .20
kilograms. Using PSM as a check on the Tobit results, I find very similar marginal effects.
Additionally, I find that the Rosenbaum bounds indicate the dairy consumption results are
fairly insensitive to endogeneity, while the meat consumption analysis is quite vulnerable if
there is an unobservable characteristic determining treatment and outcome. Current
research shows that there is room for improvement in the fight against malnutrition. A
follow-up study such as a randomized control trial would enrich our understanding of how
animal donation can contribute to improving dietary diversity in the developing world.
17!
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Appendix A: Dietary Diversity Survey
In the last 7 days, did you (adult taking survey) eat: (Answer: Yes, No, or Don’t Know) A) STAPLE FOODS 1) Starchy fruits such as cooking banana-matoke? 2) Cassava, yams, sweet potatoes, Irish potatoes or other roots and tubers? 3) Rice, posho, porridge, bread, chapatti, pasta/macaroni, pizza, or other foods made
from maize, millet, sorghum or other grains? B) PROTEIN 4) Beans, peas, cow peas, nuts, seeds ,oil seeds, soya beans or other legumes or seeds 5) Meat (beef, pork, goat, lamb, chicken, duck) or other meat? 6) Eggs (Chicken eggs, duck eggs etc)? 7) Fresh fish, dry fish or shell fish? C) VEGETABLES AND FRUITS 8) Dark green leafy vegetables like dodo, nakati spinach, amaranths, bugga, sungsa,
jjobyo, Marakwang? 9) Orange-colored vegetables such as pumpkins, carrots, orange fleshed sweet
potatoes? 10) Any bio-fortified food (Orange-fleshed sweet potatoes)? 11) Orange colored fruits like ripe mangoes, pawpaw? 12) Other fruits or vegetables (passion fruit, jack fruit, pineapples, oranges etc)? D) OTHER FOODS 13) Any milk? 14) Cheese, yogurt, or other milk products? 15) Cooking oil, margarine, butter or other oils/fats? 16) Any sugary foods such as chocolates, sweets, candies pastries, cakes or biscuits?
20!
Appendix B: Description of Variables
Table 1: Description of Dependent Variables
Variable name Description Mean Standard Min Max Deviation
Groups Number of food groups consumed by the beneficiary in the last two days (out of 16)
6.63 2.74 1 16
Grpnd Number of non-dairy food groups consumed by the beneficiary in the last two days (out of 14)
6.11 2.36 1 14
Ind. Dairy Number of times milk or yogurt was consumed in the last two days by the respondent
1.40 2.18 0 12
Ind. Meat Number of times meat was consumed in the last two days by the respondent
.39 .82 0 4
PPMeat Kilograms of meat consumed by the household per person per month .27 .48 0 3
PPDairy Liters of dairy (milk and yogurt) consumed by the household per person per month
3.60 7.02 0 45
Table 2: Description of Independent Variables
Variable name Description Mean Standard Min Max Deviation
North Dummy variable for location; =1 if household is in the dairy cow zone (Ruli), =0 if the household is in the meat goat zone (Kirehe)
.55
.50
0
1
Qual Dummy indicating that the household either received an animal or will receive one soon (beneficiary or potential)
.80
.40
0
1
Cowben Dummy indicating that the household received a cow one year ago
.21
.40
0
1
Goatben Dummy indicating that the household received a goat one year ago
.20
.40
0
1
HHSize Number of persons in household
5.53 2.00 1 11
Age Age of household head (respondent) 45.88 13.57 16 86
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LLandsize Amount of land owned by household (logged, in hectares)
.70 .43 0 2.30
Phones Number of cell phones owned by household
.84 1.0 0 5
Education Respondent’s level of education (in years)
4.14 3.55 0 14
Labor2 Amount spent on hired labor (thousands of Francs/month)
4,501.65 12,950.28 0 120,000
Machetes Number of machetes owned by the
household 1.85 .85 0 5
NHCow
Number of non-Heifer cows a household currently has (includes Heifer offspring)
.38 .77 0 7
NHGoat
Number of non-Heifer goats a household currently has (includes Heifer offspring)
1.26 1.56 0 10
Beef Number of beef cattle owned by the household
.09 .38 0 4
Bull Number of bulls owned by the household
.11 .34 0 2
Pigs
Number of pigs owned by the household
.28 .83 0 9
Sheep Number of sheep owned by the household
.26 .83 0 8
Chickens Number of chickens owned by the household
.87 1.89 0 13
Rabbits Number of rabbits owned by the household
.51 1.39 0 8
22!
Appendix C: Regression Tables
Table 3: Food Groups Dependent Variable: Food Groups Consumed by the Respondent in the Last Two Days (Max 16)
--OLS Regression with Robust Standard Errors---
(1) (2) (3) North 2.12*** 1.29*** 1.11*** (0.34) (0.40) (0.41) Qual -0.16 -0.26 -0.24 (0.37) (0.33) (0.35) Cowben 1.58*** 1.22*** 1.19*** (0.39) (0.37) (0.36) Goatben 0.32 0.24 0.36 (0.36) (0.32) (0.34) HHSize -0.22*** -0.24*** (0.06) (0.07) Age -0.02** -0.02** (0.01) (0.01) LLandsize 1.16*** 0.94*** (0.34) (0.35) Phones 0.61*** 0.66*** (0.13) (0.14) Education -0.05 -0.05 (0.04) (0.04) Labor2 0.04*** 0.04*** (0.01) (0.01) Machetes 0.60*** 0.64*** (0.17) (0.16) NHCow 0.23 (0.17) NHGoat -0.07 (0.07) Beef 0.61* (0.35) Bull -0.60 (0.39) Pigs -0.1 (0.12) Sheep 0.13 (0.13) Chickens 0.01 (0.01) Rabbits 0.21* (0.11) Constant 5.19*** 5.59*** 5.74*** (0.33) (0.68) (0.68) Observations 369 369 369 R-squared 0.251 0.399 0.425
* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses
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Table 4: Non-Dairy Food Groups Dependent Variable: Non-Dairy Food Groups Consumed by the Respondent in the Last Two Days (Max 14)
--OLS Regression with Robust Standard Errors---
(1) (2) (3) North 1.99*** 1.25*** 1.16*** (0.31) (0.36) (0.37) Qual -0.047 -0.15 -0.19 (0.33) (0.29) (0.31) Cowben 0.68** 0.38 0.42 (0.34) (0.31) (0.32) Goatben 0.30 0.22 0.31 (0.33) (0.31) (0.32) HHSize -0.22*** -0.23*** (0.059) (0.059) Age -0.02* -0.02* (0.00) (0.00) LLandsize 1.03*** 0.86*** (0.29) (0.31) Phones 0.44*** 0.49*** (0.12) (0.13) Education -0.03 -0.03 (0.03) (0.03) Labor2 0.03*** 0.03*** (0.00) (0.00) Machetes 0.60*** 0.63*** (0.15) (0.14) NHCow 0.11 (0.14) NHGoat -0.04 (0.07) Beef 0.30 (0.30) Bull -0.62* (0.37) Pigs -0.10 (0.10) Sheep 0.07 (0.12) Chickens 0.01 (0.01) Rabbits 0.17* (0.09) Constant 4.81*** 5.15*** 5.25*** (0.28) (0.60) (0.60) Observations 369 369 369 R-squared 0.21 0.36 0.38
* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses
24!
Table 5: Respondent Dairy Consumption Dependent Variable: Dairy Consumed by the Respondent in the Last Two Days (Number of Meals)
--Poisson Regression---
(1) (2) (3) North 0.40 0.41 0.33 (0.30) (0.31) (0.30) Qual -0.32 -0.29 -0.18 (0.32) (0.32) (0.31) Cowben 1.38*** 1.29*** 1.17*** (0.21) (0.21) (0.21) Goatben 0.12 0.17 0.25 (0.36) (0.36) (0.35) HHSize -0.02 -0.02 (0.03) (0.04) Age -0.02*** -0.02** (0.01) (0.01) LLandsize 0.29 0.07 (0.18) (0.21) Phones 0.21*** 0.28*** (0.06) (0.07) Education -0.05** -0.06** (0.02) (0.02) Labor2 0.01* 0.01* (0.00) (0.00) Machetes 0.02 -0.01 (0.09) (0.09) NHCow 0.19** (0.07) NHGoat -0.08* (0.05) Beef 0.23** (0.10) Bull 0.06 (0.17) Pigs -0.06 (0.08) Sheep 0.11* (0.06) Chickens -0.01* (0.01) Rabbits 0.10** (0.04) Constant -0.17 0.34 0.40 (0.25) (0.43) (0.46) Observations 369 369 369
* significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors in parentheses
25!
Table 6: Respondent Meat Consumption Dependent Variable: Meat Consumed by the Respondent in the Last Two Days (Number of Meals)
--Poisson Regression---
(1) (2) (3) North 0.74** 0.52 0.49 (0.36) (0.37) (0.38) Qual -0.18 -0.24 -0.23 (0.32) (0.30) (0.32) Cowben -0.11 -0.30 -0.29 (0.26) (0.25) (0.27) Goatben 0.01 0.06 0.04 (0.46) (0.45) (0.47) HHSize -0.27*** -0.27*** (0.06) (0.06) Age -0.01 -0.01* (0.01) (0.01) LLandsize 0.54** 0.53* (0.23) (0.28) Phones 0.20* 0.23** (0.12) (0.12) Education -0.09*** -0.10*** (0.03) (0.03) Labor2 0.00 0.00 (0.01) (0.01) Machetes 0.46*** 0.44*** (0.11) (0.12) NHCow 0.12 (0.08) NHGoat -0.02 (0.09) Beef -0.08 (0.28) Bull -0.17 (0.27) Pigs -0.01 (0.07) Sheep 0.02 (0.15) Chickens -0.07 (0.07) Rabbits 0.03 (0.05) Constant -1.23*** -0.21 -0.03 (0.34) (0.61) (0.64) Observations 369 369 369
* significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors in parentheses
26!
Table 7: Household Dairy Consumption Dependent Variable: Monthly Dairy Consumption Per-Person (Liters)
--Tobit Regression with Robust Standard Errors---
(1) (2) (3) North 2.45*** 2.35** 1.09 (0.93) (1.14) (1.05) Qual 0.21 -0.04 0.96 (0.93) (0.89) (0.83) Cowben 11.45*** 10.65*** 9.15*** (1.50) (1.47) (1.34) Goatben 0.26 -0.04 -0.35 (1.05) (0.98) (0.91) HHSize -0.95*** -0.98*** (0.24) (0.21) Age -0.05 -0.05 (0.03) (0.03) LLandsize 2.53* 1.59 (1.40) (1.31) Phones 1.61*** 1.37*** (0.50) (0.47) Education -0.07 0.04 (0.13) (0.11) Labor2 0.04 0.02 (0.03) (0.03) Machetes 0.30 -0.14 (0.61) (0.57) NHCow 3.38*** (0.59) NHGoat -0.36 (0.24) Beef -0.13 (0.61) Bull 2.37 (1.76) Pigs -0.21 (0.49) Sheep 0.25 (0.53) Chickens -0.11** (0.05) Rabbits 0.16 (0.50) Constant -1.84** 2.79 3.65* (0.89) (2.03) (1.91) Observations
322
322
322
* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses
27!
Table 8: Household Meat Consumption Dependent Variable: Monthly Meat Consumption Per-Person (Kilograms)
--Tobit Regression with Robust Standard Errors---
(1) (2) (3) North 0.51*** 0.35*** 0.30** (0.12) (0.13) (0.13) Qual -0.05 -0.09 -0.06 (0.12) (0.11) (0.10) Cowben -0.08 -0.10 -0.23 (0.12) (0.13) (0.15) Goatben 0.25** 0.24** 0.20* (0.12) (0.12) (0.11) HHSize -0.09*** -0.10*** (0.02) (0.02) Age -0.01* -0.01* (0.00) (0.00) LLandsize 0.28** 0.30** (0.14) (0.13) Phones 0.15*** 0.13** (0.05) (0.05) Education -0.01 -0.00 (0.01) (0.01) Labor2 0.00 0.00 (0.00) (0.00) Machetes 0.22*** 0.20*** (0.06) (0.06) NHCow 0.02 (0.08) NHGoat -0.01 (0.02) Beef -0.03 (0.07) Bull 0.16 (0.16) Pigs 0.07* (0.04) Sheep 0.16*** (0.06) Chickens 0.01 (0.02) Rabbits -0.02 (0.04) Constant -0.05 0.20 0.25 (0.11) (0.21) (0.21) Observations 284 284 284
* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses
28!
Table 9: Household Dairy Consumption Dependent Variable: Monthly Dairy Consumption Per-Person (Liters)
--Propensity Score Matching: Nearest Neighbor, with Replacement---
Table 10: Household Dairy Consumption Dependent Variable: Monthly Dairy Consumption Per-Person (Liters)
--Propensity Score Matching: Balance Test---
Mean % Reduction t-test Variable Sample Treated Control %bias |bias| t p>|t|
Phones Unmatched 1.14 0.78 39.60 2.23 0.03 Matched 1.14 0.97 18.10 54.40 1.11 0.27 Machetes Unmatched 2.41 2.25 21.60 1.23 0.22 Matched 2.41 2.36 5.80 73.20 0.35 0.73 Rabbits Unmatched 1.07 0.71 20.30 1.15 0.25 Matched 1.07 1.42 -20.10 1.20 -1.03 0.31 NHCow Unmatched 0.78 0.42 38.00 2.15 0.03 Matched 0.78 0.76 2.90 92.50 0.16 0.87 Llandsize Unmatched 0.87 0.79 21.40 1.22 0.22 Matched 0.87 0.90 -7.90 63.00 -0.49 0.62 Sample Pseudo R2 LR chi2 p>chi2
Unmatched 0.057 10.4 0.065 Matched 0.013 2.71 0.745
Variable
Sample Treated Controls Difference Standard Error T-Statistic
Unmatched 12.51 2.40 10.11 1.45 6.98 ATT 12.51 3.18 9.32 1.56 5.98 ATU 2.40 9.42 7.02 . .
Dairy Per- Person
ATE . . 8.30
29!
Table 11: Household Dairy Consumption Dependent Variable: Monthly Dairy Consumption Per-Person (Liters)
--Rosenbaum Bounds: Gamma (!) = Log-odds of differential assignment due to unobserved factors---
Gamma sig+ sig- t-hat+ t-hat- CI+ CI- 1 1.40E-10 1.40E-10 8.375 8.375 6 10.8929
1.5 4.40E-07 8.90E-16 6.62321 10.2576 4.25 13.2714 2 0.000025 0 5.43333 11.6429 3.09524 14.9586
2.5 0.000284 0 4.5 12.8571 2.38095 16.5833 3 0.001433 0 3.78571 13.8095 1.64286 17.9167
3.5 0.004556 0 3.28571 14.5 0.928571 18.75 4 0.01082 0 2.9631 15.3214 0.442857 20
4.5 0.021144 0 2.5506 16 0.071429 20.7143 5 0.036029 0 2.35714 16.6429 -0.285714 21.6667
5.5 0.055549 0 2.07143 17 -0.5 22.5 6 0.079437 0 1.829 17.5714 -0.928572 23.25
6.5 0.107194 0 1.58239 18 -1.23214 23.9286
30!
Table 12: Household Meat Consumption Dependent Variable: Monthly Meat Consumption Per-Person (Kilograms)
--Propensity Score Matching: Nearest Neighbor, with Replacement---
Variable
Sample Treated Controls Difference Standard Error T-Statistic
Unmatched 0.31 0.14 0.16 0.09 1.88 ATT 0.31 0.13 0.17 0.09 2.04 ATU 0.14 0.30 0.15 . .
Meat Per-Person
ATE . . 0.17
Table 13: Household Meat Consumption Dependent Variable: Monthly Meat Consumption Per-Person (Kilograms)
--Propensity Score Matching: Balance Test--- Mean % Reduction t-test Variable Sample Treated Control %bias |bias| t p>|t|
Hhsize Unmatched 4.62 4.90 -16.60 -0.87 0.39 Matched 4.62 4.54 4.30 73.80 0.26 0.79 Phones Unmatched 0.46 0.48 -2.70 -0.14 0.89 Matched 0.46 0.40 9.00 -239.00 0.69 0.49 Machetes Unmatched 1.25 1.35 -15.60 -0.82 0.41 Matched 1.25 1.26 -1.90 88.00 -0.13 0.90 Llandsize Unmatched 0.49 0.43 19.00 0.97 0.33 Matched 0.49 0.46 7.20 61.80 0.47 0.64 Sample Pseudo R2 LR chi2 p>chi2
Unmatched 0.018 2.70 0.609 Matched 0.005 1.02 0.906
31!
Table 14: Household Meat Consumption Dependent Variable: Monthly Meat Consumption Per-Person (Kilograms)
--Rosenbaum Bounds: Gamma (!) = Log-odds of differential assignment due to unobserved factors---
Gamma sig+ sig- t-hat+ t-hat- CI+ CI-
1 9.11E-03 9.11E-03 0.075 0.075 0.006917 0.182143 1.1 2.27E-02 3.21E-03 0.046667 0.096666 -0.00000033 0.2 1.2 0.046699 0.001092 0.03254 0.107639 -0.000278 0.213889 1.3 0.083413 0.000362 0.024271 0.125 -0.008333 0.225 1.4 0.133265 0.000117 0.016666 0.127778 -0.016111 0.25 1.5 0.194947 0.000038 0.013889 0.144444 -0.017857 0.275
Appendix D: Household Survey
CONSENT TO BE A RESEARCH SUBJECT
Purpose and Background
Ms. Rosemary Rawlins, Ms. Svetlana Pimkina, and Mr. Timothy Ong, are doing a study on the impacts of giving livestock to rural households on health and nutrition, child labor, education, and gender inequalities. Millions of individuals around the world are living far below the poverty line, resulting in hunger, low education levels, and gender inequalities. It is the mission of Heifer International to help end world hunger and poverty, while at the same time caring for the Earth. Prior Heifer International evaluation found that participants have healthier home environments. This is a result of improved nutrition due increasing food sources, a greater probability of self-employment, better lighting, and an increased sense of hope. The goal of their research is to see if they get similar results as previous evaluations. In particular, they want to see the effects of Heifer International on nutrition, child labor, and gender equality. They hope that participating in the program will have a positive effect on all three facets because it would mean Heifer International is achieving its mission to help end world hunger and poverty. I am being asked to participate because I am over 16 years of age and am qualified to receive an animal donation from Heifer International.
Procedures
If I agree to be a participant in this study, the following will happen: I will participate in an interview with a research assistant, during which I will be asked about my personal and family’s age, gender, race, job history, nutrition, and animal ownership.
Risks and/or Discomforts
1. It is possible that due to the length and subject matter of the interview and process of collecting a child’s anthropometric data I may become bored or uncomfortable, but I am free to decline to answer any questions I do not wish to answer or to stop participation at any time.
2. Participation in research may mean a loss of confidentiality. Study records will be kept as confidential as is possible. No individual identities will be used in any reports or publications resulting from the study. Study information will be coded and kept in locked files at all times. Only study personnel will have access to the files.
3. Because the time required for my participation may be up to 2 hours, I may become tired or bored.
Benefits
There will be no direct benefit to me from participating in this study. The anticipated benefit is a better understanding of the effects of animal donation by Heifer International and recommendations for its improvement, which, if implemented, will benefit the program recipients in the future.
Costs/Financial Considerations
There will be no direct financial costs to me as a result of taking part in this study.
Payment/Reimbursement
I will not be reimbursed for participation in this study.
Questions
I have talked to Prof. Bruce Wydick or his research assistants about this study and have had my questions answered. If I have further questions about the study, I may call him at (415) 422-2765. If I have any questions or comments about participation in this study, I should first talk with the researchers. If for some reason I do not wish to do this, I may contact the IRBPHS, which is concerned with protection of volunteers in research projects. I may reach the IRBPHS office by calling (415) 422-6091 and leaving a voicemail message, by e-mailing [email protected], or by writing to the IRBPHS, Department of Psychology, University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117-1080.
Consent
I have been given a copy of the "Research Subject's Bill of Rights" and I have been given a copy of this consent form to keep. PARTICIPATION IN RESEARCH IS VOLUNTARY. I am free to decline to be in this study, or to withdraw from it at any point. My decision as to whether or not to participate in this study will have no influence on my present or future status as a donation recipient candidate. My signature below indicates that I agree to participate in this study.
Subject's Signature Date of Signature
Signature of Person Obtaining Consent Date of Signature
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