Posted: October 27th, 2022

Critical article review

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Chapter 5: Education and Empowerment.

Chapter 5 addresses various aspects of health and how inequality plays out. In chapter five, the focus is on Education and Empowerment across multiple subjects. The author seeks to deconstruct and demystify education’s perception regarding its roles, gender perspectives, and relation to health and well-being. The chapter is structured on how education creates empowerment through the concept of gender, empowerment, health, and addressing the several forms of inequality. The author understands the structural myths that have overtaken gender-based education inequalities and how misconceptions override education regarding better pay and wealth accumulation. The chapter is structured in terms of health, gender, education, fertility reduction, especially in developing countries, and child survival (Marmot, 2019). Education and empowerment are closely interrelated. According to the author, an educated child enhances its survival, improved health and awareness, and self-protection initiatives. Marmot also proposes the measures of addressing structural inequality, with the Finland model being proposed.

The author has a clear outline, structure, and model of addressing education-based misconceptions attached to gender and its roles regarding the chapter’s strengths. Marmot’s pertinent question is on the importance of education to parents, children, and society concerning their health. Thus, the article uses typical case studies in Finland to benchmark the understanding of inequality methods and strategies. The author also creates an objective approach to how education is related to health, gender, and inequality across different aspects of society. Marmot’s chapter on education and empowerment has an insightful, simple, and detailed assessment of various factors associated with education and health. Education is viewed as a tool that is more than just improved pay. The author appreciates its role concerning awareness of risk factors, gender biases, and inequalities.

However, the chapter has shortcomings regarding the complexity of the correlation between education and health in terms of gender. There is no clear clarity on which models the author uses in comparing Finland’s approach to addressing inequality to any other specific country. Thus, the vagueness creates confusion in connecting the variables. The weakness is also in the more extended similarities of the issues discussed through a language that is not smoothly comprehensive in most aspects (Marmot, 2019). These components create a lack of clarity, and the readers might lose track of what is expected of them by the author (Wiggins, 2012)There is a thin line between the chapter objectives and the prolonged narratives in the book’s chapter. These negative attributes constitute weaknesses, characterized by vague reference to claims and concepts that the author seeks to pass across. Two questions that I believe should be considered for further studies are 1. How is education creating health-related empowerment in developing countries during global pandemics such as coronavirus? 2.How make differences in educational curriculum and models impact health inequalities among developed countries?

A reflective conclusion is that it forms the benchmark on the argument between education’s roles about empowering people about their health status and well-being. The author has the masteries of various case studies on the correlation between these concepts, which underline their relevance in the modern health setup. The gap in understanding education and its implications on health and empowerment is addressed throughout the chapter. Therefore, a recommendation on the role of education on cultural empowerment should be undertaken to achieve the desired outcomes.

References

Marmot Michael. (2019). The Health Gap: The Challenge of an Unequal World. Bloomsbury Publishing.

Wiggins, N. (2012). Popular education for health promotion and community empowerment: A review of the literature. Health Promotion International, 27(3), 356–371. https://doi.org/10.1093/heapro/dar046

Critical Review Grading Rubric

KINE 3353: Health and the Human Condition in the Global Community

Fall 2020

Criteria

Ratings

Overview of the chapter

2 pts

Excellent

Reflects a full understanding of all key concepts and discusses main arguments. Takes about 1/3 of the page. Ends with a strong thesis that acknowledges both strengths and limitations.

1 pts

Satisfactory

Reflects a moderate understanding of all key concepts and arguments. Takes about 1/3 of the page. Thesis does not acknowledge both strengths and limitations.

0 pts

Needs Improvement

Overview is either too long or too short and does not accurately summarize the text. Does not end with a thesis statement or does not acknowledge either strengths or weaknesses.

Evaluation of Strengths & Weaknesses

2 pts

Excellent

Critically evaluates the text’s arguments and assumptions, discussing its strengths and weaknesses using appropriate evidence and examples. Synthesizes information and does not merely list. About 1/3rd of a page.

1 pts

Satisfactory

Adequately evaluates the arguments made. Discusses strengths and weaknesses but does not synthesize the information (instead listing) and provides adequate examples. About 1/3rd of a page.

0 pts

Needs Improvement

Does not evaluate either the strengths or weaknesses of the text. Too long or too short.

Conclusion: General Impressions or Recommendations

2 pts

Excellent

Discusses the text’s contribution to public health and any recommendations for improvement. Proficiently supports recommendations made and closing arguments.

1 pts

Satisfactory

Concludes with only either public health contributions or recommendations. Adequately supports recommendations for improvement and closing arguments.

0 pts

Needs Improvement

Does not discuss the text’s contribution to public health nor makes any recommendations for improvement.

Two Key Questions

2 pts

Excellent

At least two thoughtful questions are posed which provoke further thought AND at least one relevant external source is cited.

1 pts

Satisfactory

Only one thoughtful question is posed which provokes further thought, no external sources are cited, or one external source is cited with no questions posed.

0 pts

Needs Improvement

No questions are posed nor are any external sources cited.

Writing, Grammar & APA Format

2 pts

Excellent

Written in APA style using Zotero, in 11-point Times New Roman font, one-inch margins, double-spaced, and paginated with no errors. No spelling or grammar mistakes. 1 page or more.

1 pts

Satisfactory

Written in APA style using Zotero, in 11-point Times New Roman font, one-inch margins, double-spaced, and paginated with a few errors. A few spelling or grammar mistakes. 1 page or more.

0 pts

Needs Improvement

Not written in APA style, in 11-point Times New Roman font, one-inch margins, double-spaced, and paginated or with many errors. Does not use Zotero. Many spelling or grammar mistakes. Less than 1 page. An assignment with 30% or more in Unicheck will automatically receive a grade of zero.

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The Economic Lives of the Poor

Abhijit V. Banerjee and Esther Duflo

October 2006

Abstract

This paper uses survey data from 13 countries to
document the economic lives of the poor (those living
on less than $2 dollar per day per capita at purchasing
power parity ) or the extremely poor (those living on
less than $1 dollar per day). We describe their
patterns of consumption and income generation as
well as their access to markets and publicly provided
infrastructure. The paper concludes with a discussion
of some apparent anomalous choices. JEL:
O10,O15,016. Keyword: Poverty, Development,
Consumption Choices

Abhijit V. Banerjee and Esther Duflo are both Professors of Economics and Directors of the
Abdul Latif Jameel Poverty Action Lab, Massachusetts Institute of Technology, Cambridge,
Massachusetts.

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In what turned out to be a rhetorical master-move, the 1990 World Development Report
from the World Bank defined the “extremely poor” people of the world as those who are
currently living on no more than $1 per day per person, measured at the 1985 purchasing
power parity (PPP) exchange rate.1 Even though there have always been poverty lines─ indeed
one dollar per day was chosen in part because of its proximity to the poverty lines used by
many poor countries2─this particular one has come to dominate the conversations about
poverty in a particularly stark way.

But how actually does one live on less than one dollar per day? This essay is about the
economic lives of the extremely poor: the choices they face, the constraints they grapple with,
and the challenges they meet.

The available evidence on the economic lives of the extremely poor is incomplete in
many important ways. However, a number of recent data sets and a body of new research have
added a lot to what we know about their lives, and taken together there is probably enough to
start building an image of the way they live their lives.

Our discussion of the economic lives of the extremely poor builds on household
surveys conducted in 13 countries listed in Table 1: Cote d’Ivoire, Guatemala, India, Indonesia,
Mexico, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, South Africa, Tanzania, and
Timor Leste. We mainly use the Living Standard Measurement Surveys (LSMS) conducted by
the World Bank and the “Family Life Surveys” conducted by the Rand Corporation, all of
which are publicly available. In addition, we also use two surveys that we conducted in India
with our collaborators. The first was carried out in 2002 and 2003 in 100 hamlets of Udaipur
District, Rajasthan (Banerjee, Deaton, Duflo, 2004). Udaipur is one of the poorer districts of India,
with a large tribal population and an unusually high level of female illiteracy (at the time of the
1991 census, only 5 percent of women were literate in rural Udaipur). The second (Banerjee,
Duflo, Glennerster, 2006) surveyed 2,000 households in “slums” (or informal neighborhoods) of
Hyderabad, the capital of the state of Andhra Pradesh and one of the boom-towns of post-
liberalization India. We chose these countries and surveys because they provide detailed
information on extremely poor households around the world, from Asia to Africa to Latin
America, including information on what they consume, where they work, and how they save
and borrow. To flesh out our main themes further, we also draw freely on the existing research
literature.

From each of these surveys we identified the extremely poor as those living in
households where the consumption per capita is less than $1.08 per person per day, as well as
the merely “poor” defined as those who live under $2.16 a day using the PPP in year 1993 as
benchmark.3 The use of consumption, rather than income, is motivated by the better quality of
the consumption data in these surveys (Deaton, 2004). Table 1 provides some background
information on these surveys. It lists the countries, and the source of the survey data. It also
lists the sample sizes: the numbers and the proportions of the extremely poor and the poor in

1 In 1993, the poverty line was updated to $1.08 per person per day at the 1993 PPP exchange rate (this is the line
we use in this paper).
2 For example, the “All India Rural” poverty line used by the Indian Planning Commission was Rs 328 per person
per month, or $32 in PPP dollars in 1999/2000.
3 In keeping with convention, we call these the $1 and $2 dollar poverty lines, respectively.

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each survey. The fraction of individuals living under $1 dollar per day in the survey vary from
2 percent in Panama to 47 percent in Udaipur, and the fraction living under $2 per day varies
from 6 percent in Panama to 86 percent in Udaipur. All the numbers discussed in this paper are
available in the appendix.

There are many important issues with our identification of the poor. First, purchasing
power parity exchange rates, which are essential to compute a “uniform” poverty line, have
been criticized as inadequate, infrequently updated, and inapplicable to the consumption of the
extremely poor (Deaton, 2004, 2006). Prices are typically higher in urban than in rural areas,
and even in rural areas, the poor may pay different prices than everyone else. Also, reporting
periods vary significantly from survey to survey and this, it has been shown, systematically
affects what people report.

These problems may affect us less because we are not focused on counting the exact
number of poor. In describing what their lives look like, misclassifying a number of
households should not change anything very important about the averages we observe in the
data, unless the number affected are very large, and those artificially moved into or out of
poverty are very different than the other poor. It turns out that most of our conclusions do not
change if we look at the poor rather than the extremely poor, which is of course reassuring.
Nevertheless one cannot obviously entirely rule out the possibility that our results may have
been very different had we used a different poverty line.

We will also assume that the people we are describing as the poor are the long-term

poor in the sense that their permanent income is actually close to their observed consumption.
If, instead, they were just transiting through poverty, some of the behaviors that we will
observe (such as lack of savings) would be less puzzling, and others (like the lack of assets)
would be much more so. We feel that this is a reasonable assumption in most of the countries,
since the fraction of the population below $2.16 a day is actually sizeable (40 percent of the
population or more in the median country and more than 70 percent in quite a few) and it is
unlikely that there are quite so many transients.4 However, for this reason, the poor in Panama
(where only 6 percent of the population is poor) or South Africa (where 19 percent are) may
not be easily compared to the poor in some of the other countries, where they are much more
numerous.

The Living Arrangements of the Poor

The typical extremely poor family tends to be rather large, at least by the standards of
today’s rich countries. The number of family members varies between about 6 and about 12,
with a median value (across the different countries) of between 7 and 8, compared to 2.5 in the
2000 U.S. census, for example.

Unfortunately not all surveys report fertility rates, which would have been the ideal

way to check how much of these high numbers comes from the fact that here are a lot of
children. The data does however give us some broad measures of the age structure in these

4 The extremely poor are less numerous, but as we observed above, our conclusions are largely independent of the
poor/extremely poor distinction,

4

families (the number of those below 13, between 13 and 18, above 51, etc.). The number of
adults (i.e. those over 18) ranges from about 2.5 to about 5, with a median of about 3, which
suggests a family structure where it is common for adults to live with people they are not
conjugally related to (parents, siblings, uncles, cousins, etc.). This is a common finding in the
literature on developing countries, and has often been related to the fact that when every penny
counts, it helps to spread the fixed costs of living (like housing) over a larger number of
people. Consistent with this view, we do see a fall in family size when we go from the
extremely poor to the entire group below $2 a day, of the order of one half of one person or
more, though at least some part of this comes from the fact that the extremely poor families
have more children living with them.

The other fact is that there are a large number of children in these families. This does

not necessarily imply high levels of fertility, as families often have multiple adult women.
When we look at the number of children (ages 0 to 18) per woman in the child-bearing age
(ages 21-50) we get numbers between 2 and 4 in both the rural and the urban sample, though
the urban ratios tend to be slightly lower. These would not be especially high if they were
actually fertility rates, but they are not─for example, a 51 year-old could have a child who is
now 36, but we only include those who are below 18. A more useful exercise with this data is
to compare the number of young people (those below 18) in these families with the number of
older people (those above 51). The ratio varies between 3 and 9 in the rural sample with a
median of 6, and between 2 and 11 in the urban sample, with a median once again of around 6.
The corresponding number in the U.S. is around 1. This is a very young population.

One reason the population is young is that there are a lot of younger people, which

reflects high fertility if not now, at least in the recent past. A second reason is that there are
actually very few older people. The ratio of the number older people (over age 51) to the
number of people of “prime-age” (21-50) tends to be between 0.2 and 0.3 in both the rural and
the urban sample. The corresponding number in the U.S., for example, is approximately 0.6.
The difference probably owes a lot to the higher mortality rates among those who are older and
poor in poor countries, though it is possible that older people are underrepresented in our
sample because they tend to be richer. However in the latter case, we might have expected to
find more of the older people among the poor (as compared to the extremely poor), whereas in
the data there is no clear pattern.

How the Poor Spend Their Money

A common image of the extremely poor is that they do not get to make many real
choices. Indeed there must be some people who work as hard as they can ─ which may not be
particularly hard, because they are underfed and weak ─ and earn barely enough to cover their
basic needs which they always try to fulfill in the least expensive way. Historically, poverty
lines in many countries were originally set to capture exactly this definition of poverty─at the
budget needed to buy a certain amount of calories, plus some other indispensable purchases
(such as housing). A “poor” person was by definition someone without enough to eat.

Food and Other Consumption Purchases

5

Yet the average person living at under$1 per day does not seem to put every available
penny into buying more calories. Among our 13 countries, food typically represents from 56
to 78 percent among rural households, and 56 to 74 percent in urban areas. For the rural poor in
Mexico, slightly less than half the budget (49.6 percent) allocated to purchase food.5

Of course they could be spending the rest of the money on other commodities which
they also greatly need. Yet among the non-food items that the poor spend significant amounts
of money on, alcohol and tobacco show up prominently. The extremely poor in rural areas
spent 4.1 percent of their budget on tobacco and alcohol in Papua New Guinea, 5.0 percent in
Udaipur, India; 6.0 percent in Indonesia and 8.1 percent in Mexico; though in Guatemala,
Nicaragua, and Peru, no more than 1 percent of the budget gets spent on these goods (possibly
because they prefer other intoxicants).

Perhaps more surprisingly, it is apparent that spending on festivals is an important part
of the budget for many extremely poor households. In Udaipur, over the course of the previous
year, more than 99 percent of the extremely poor households spent money on a wedding, a
funeral, or a religious festival. The median household spent 10 percent of its annual budget on
festivals. In South Africa, 90 percent of the households living under $1 per day spent money on
festivals. In Pakistan, Indonesia, and Cote d’Ivoire, more than 50 percent did likewise. Only in
some Latin American countries in our sample─ Panama, Guatemala, Nicaragua─ are festivals
not a notable part of the yearly expenditure for a significant fraction of the households.
However in the LSMS surveys, unlike the Udaipur survey, people are not asked to account
separately for the food that they bought because there was a festival. It is therefore probably no
accident that the Udaipur number is the highest across the surveys, and there is reason to
suspect that LSMS numbers would have been higher had the data been directly collected in
those surveys.

On the other hand, the under $1 per day households spend very little on the forms of
entertainment that are common in rich countries, such as movies, theater, or video shows. In all
13 of the countries in our sample, in the month preceding the survey the average extremely
poor household spent less than 1 percent on any of these forms of entertainment. The
comparable number for the United States is 5 percent. We can only speculate about the roots of
this difference: Has the importance given to festivals and other indigenous forms of

5 The fact that the share spent on food, which is often seen as a physiological necessity, varies so much across
countries is itself interesting. One possibility is that this represents the fact that the poor have more choice in some
countries than in others, because consumption goods are relatively are cheaper relative to food in some countries.
For example, India, a large economy with a long history of being relatively closed, has evolved a large menu of
low-cost and lower-quality consumer goods that are produced almost exclusively for the domestic market,
examples include tooth-paste, cigarettes, and clothing. Other countries must buy these goods at higher prices on
the global market. If the manufactured consumer goods that the average person buys in India tend to be
inexpensive relative to their traded counterparts, the ratio between the consumption exchange rate at purchasing
power parity and the official exchange rate ought to be relatively low in India. More generally: the lower this
ratio, the lower the share of the consumption that should be made up of food. In our data, it turns out that the
correlation between the ratio of the purchasing power parity exchange rate for consumption to the official
exchange rate in 1993 and the share of expenditure spent on food is 0.33 among these 12 countries, although this
sample is of course too small to support definite conclusion.

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entertainment crowded out movie-going, or it is lack of access to movie theatres and such, that
gives festivals the place that they occupy in their lives.

The propensity to own a radio or a television, a widespread form of entertainment for
American households varies considerably across countries. For example, among rural
households living under $1 per day, ownership of a radio is 11 percent in the Udaipur survey,
almost 60 percent in Nicaragua and Guatemala, and above 70 percent in South Africa and Peru.
Similarly, no one owns a television in Udaipur, but in Guatemala nearly a quarter of
households do, and in Nicaragua, the percentage is closer to a half.

These two phenomena appear to be related. In Udaipur, where the share spent on

festivals is the highest, radio and television ownership is very low. In Pakistan, the fraction
spent on festivals is 3.3 percent and only 30 percent have a radio. By contrast, in Nicaragua
where 57 percent of the rural poor households, respectively, have a radio and 21 percent and 19
percent own a television, very few households report spending anything on festivals.6 One
wrinkle on this explanation is that the urban poor who are much more likely to own a television
than the rural poor (60 versus 33 percent in Indonesia, 61 versus 10 percent in Peru, 38 versus
17 percent in South Africa), do not spend less on festivals than their rural counterparts. While
this observation is based on only a few data points, it hints at the possibility of an unmet
demand for entertainment among the rural poor─they would also like to buy a television, but
perhaps the television signal does not reach their neighborhoods.

In either case, it is hard to escape the conclusion that the poor do see themselves as

having a significant amount of choice, and choose not to exercise it in the direction of spending
more on food─the typical poor household in Udaipur could spend up to 30 percent more on
food than it actually does, just based on what it spends on alcohol, tobacco, and festivals.
Indeed in most of the surveys the share spent on food is about the same for the poor and the
extremely poor, suggesting that the extremely poor do not feel an extra compulsion to purchase
more calories.

This conclusion echoes an old finding in the literature on nutrition: Even the extremely

poor do not seem to be as hungry for additional calories as one might expect. Deaton and
Subramanian (1996), using 1983 data from the Indian state of Maharashtra, found that even for
the poorest, a 1 percent increase on overall expenditure translates into about a two-thirds of a
percent increase in the total food expenditure of a poor family. Remarkably, the elasticity is not
very different for the poorest individuals in the sample and the richest (although nobody is
particularly rich in this sample). The Deaton and Subramanian estimate is one of the higher
estimates. Strauss and Thomas (1997) found an elasticity of demand for food with respect to
expenditure per capita of about a quarter for the poorest Brazilians.

6 The ultimate source of variation here might be the relative prices of radios and televisions. There is a strong
correlation between the ratio of the purchasing power exchange rate for consumption and the official exchange
rate and the probability that a household owns a radio (the correlation is 0.36). The logic is probably quite similar
to the argument presented earlier in the context of food consumption (viz footnote ? footnote 11?11). Radios are
tradable (they are all made in China). Since among manufactures, non-tradable goods are much less costly in
some countries than others, while traded goods tend to be more similarly priced, people at the same expenditure
levels at purchasing power parity can have widely differing levels of purchasing power in terms of traded goods.

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Another way to make the same point is to look at what edibles the extremely poor are
buying. Deaton and Subramanian (1996) note that among grains, in terms of calories per rupee,
the millets (jowar and bajra) are clearly the best buy. Yet in their data, only about two-thirds of
the total spending on grains is on these grains, while another 20 percent is on rice, which costs
more than twice as much per calorie, and a further 10 percent or so is spent on wheat, which is
a 70 percent more expensive way to get calories. In addition, the poor spend almost 7 percent
of their total budget on sugar, which is both more expensive than grains as a source of calories
and bereft of any other nutritional value. The same affinity for sugar also shows up in our
Udaipur data: The poor spend almost 10 percent of their food budget on the category “sugar,
salt and other processed foods” (this does not include cooking oil, which makes up another 6
percent of the expenditures on food). Even for the extremely poor, for every 1 percent increase
in the food expenditure, about half goes into purchasing more calories, and half goes into
purchasing more expensive (and presumably better tasting) calories.

Finally, to the extent that we can tell, the trend seems to be to spend even less money

on food. In India, for example it went from 70 percent in 1983 to 62 percent in 1999-2000, and
the share of millet in the food budget dropped to virtually zero (Deaton, 2005). Not
surprisingly, the poor are also consuming fewer calories over time (Meenakshi and
Vishwanathan, 2003), though it is possible that this change reflects the fact their work involves
less physical effort (Jha, 2004).

The Qwnership of Assets

While all the surveys have some information about assets, the list of assets varies. To
obtain a relatively coherent list across countries, we focus on radios, televisions, and bicycles.
The share of people who own these particular assets varies significantly across countries.

As we already discussed, ownership of radio and television varies a lot from country to

country, but is low in several of those countries. One reason may be the lack of signal. The
other may be that it is not easy to buy a television if you are extremely poor: It is an expensive
and lumpy transaction that one has to save up for if one is born poor. We do see a fairly steep
income gradient in the ownership of radio and television: In all countries, the share of rural
households owning a television is substantially larger for those who live on less than $2 a day
than those living on less than $1 a day. For example, the share owning a television increases
from 14 percent for those living on $1 a day to 45 percent for those living on less than $2 a
dollar a day in Cote d’Ivoire; from 7 to 17 percent in South Africa; and from 10 to 21 percent
in Peru. This pattern has been observed in other contexts (Filmer and Pritchett, 2001), and has
been the basis for using the lack of durable goods as a marker for poverty. Our data suggests
that this proxy can be appropriate within a country, but it could easily be misleading to use this
measure in a cross-country comparison.

Among productive assets, land is the one that many people in the rural surveys seem to
own though there are enormous country-to-country variation. Only 4 percent of those living
under $1 a day own land in Mexico, 1.4 percent in South Africa; 30 percent in Pakistan, 37
percent in Guatemala, 50 percent in Nicaragua and Indonesia, 63 percent in Cote d’Ivoire; 65
percent in Peru; and 85 percent in Panama. In the Udaipur sample, 99 percent of the
households below $1 a day own some land in addition to the land on which their house is built,
although much of it is dry scrubland that cannot be cultivated for most of the year. However,

8

when the extremely poor do own land, the plots tend to be quite small. The median landholding
among the poor who own land is one hectare or less in Udaipur, Indonesia, Guatemala and
Timor, between 1 and 2 hectares in Peru, Tanzania, Pakistan, and between 2 and 3 hectares in
Nicaragua, Cote d’Ivoire, and Panama.

Apart from land, extremely poor households in rural areas tend to own very little by
way of durable goods, including productive assets: 34 percent own a bicycle in Cote d’Ivoire,
but less than 14 percent in Udaipur, Nicaragua, Panama, Papua New Guinea, Peru, and East
Timor. In Udaipur, where we have detailed asset data, we find that most extremely poor
households have a bed or a cot but only about 10 percent have a chair or a stool and 5 percent
have a table. About half have a clock or a watch. Less than 1 percent has an electric fan, a
sewing machine, a bullock cart, a motorized cycle of any kind, or a tractor. No one has a
phone.

As we will see below, this is not because most of these households are employees and

therefore have no use for such assets: On the contrary, many of the extremely poor households
operate their own businesses, but do so with almost no productive assets.

The Pursuit of Health and Well-being

Should we worry about the fact that the poor are buying less food than they could?
According to Deaton and Subramanian (1996), the poorest people—the ones in the bottom
decile in terms of per capita expenditure—consume on average slightly less than 1400 calories
a day. This is about half of what the Indian government recommends for a man with moderate
activity, or a woman with heavy physical activity.7 The shortfall seems enormous, though one
could question whether the initial recommendation was appropriate. However, the Udaipur
data, which has other health indicators, suggests that health is definitely reason for concern.

Among the extremely poor in Udaipur, only 57 percent report that the members of their
household had enough to eat throughout the year. Among the poor adults in Udaipur, the
average “body mass index” (that is, weight in kilograms divided by the square of the height in
meters) is 17.8. Sixty-five percent of adult men and 40 percent of adult women have a body
mass index below 18.5, the standard cutoff for being underweight (WHO expert consultation,
2004). Moreover, 55 percent of the poor adults in Udaipur are anemic, which means they have
an insufficient number of red blood cells. The poor are frequently sick or weak. In Udaipur, 72
percent report at least one symptom of disease and 46 percent report an illness which has left
them bedridden or necessitated a visit to the doctor over the last month. Forty-three percent of
the adults and 34 percent of the adults aged under 50 report having difficulty with carrying out
at least one of their “activities of daily living”, such as working in the field, walking, or
drawing water from a well. Diarrhea is extremely frequent among children. About one-seventh
of the poor have vision problems, which may also be due to nutritional deficits (caused by
either poor nutrition, or the diseases that afflict them, or a combination of the two).

Detailed information on health is not available in all the surveys we have, but most
report health episodes that left a household member bedridden for a day or more, or required
them to see a doctor. While this data is less than perfect, given that the poor may be less prone

7 See http://www.fao.org/documents/show_cdr.asp?url_file=/DOCREP/x0172e/x0172e02.htm.

9

to recall and report such sicknesses than the rich, the general pattern is of a remarkably high
level of morbidity. Among the rural poor living under $1 a day in Peru, South Africa, East
Timor, Panama, and Tanzania, between 11 and 15 percent of households report having a
member either being bedridden for at least a day or requiring a doctor. The number is between
21 and 28 percent in Pakistan, Indonesia, and Cote d’Ivoire, and between 35 and 46 percent in
Nicaragua, Udaipur, and Mexico.

The poor generally do not complain about their health―but then they also do not complain
about life in general either. While the poor certainly feel poor, their levels of self-reported
happiness or self-reported health levels are not particularly low (Banerjee, Duflo, and Deaton,
2004). On the other hand, the poor do report that they are under a great deal of stress, both
financial and psychological. In Udaipur, about 12 percent say that there has been a period of
one month or more in the last year in which they were so “worried, tense, or anxious” that it
interfered with normal activities like sleeping, working, and eating. Case and Deaton (2005)
compare data from South Africa to the data from Udaipur and data from the United States.
They find that the answers of poor South Africans and poor Indians about stress look very
similar, while reported levels of stress are very much lower in the United States. The most
frequently cited reason for such tensions is health problems (cited by 29 percent of
respondents), with lack of food and death coming next (13 percent each). Over the last year, in
45 percent of the extremely poor households in Udiapur (and 35 percent of those living under
$2 a day) adults had to cut the size of their meal at some point during the year and in 12
percent of them, children had to cut the size of their meals. In the extremely poor households
under $1 per day, 37 percent report that, at some point in the past year, the adults in the
household went without a meal for an entire day. Cutting meals is also strongly correlated with
unhappiness.

Carrying enough savings to make sure that they never have to cut meals, should not be

too hard for these households since, as noted above, they have substantial slack in their budgets
and cutting meals is not that common. It would also make it easier for them to deal with
healthcare emergencies. As such, saving a bit more would seem like a relatively inexpensive
way to reduce stress.

Investment in Education

The extremely poor spend very little on education. The expenditure on education
generally hovers around 2 percent of household budgets: higher in Pakistan (3 percent),
Indonesia (6 percent) and Cote d’Ivoire (6 percent), but much lower in Guatemala (0.1
percent), and South Africa (0.8 percent). The fraction does not really change very much when
we go from the poor to the extremely poor, or from rural areas to urban areas, though in a few
countries like Pakistan, urban families spend substantially more than rural families. This low
level of expenditure on education is not because the children are out of school. In 12 of the 13
countries in our sample, with the exception of Cote d’Ivoire, at least 50 percent of both boys
and girls aged 7 to 12 in extremely poor households are in school. In about half the countries,
the proportion enrolled is greater than 75 percent among girls, and more than 80 percent among
boys.

The reason spending is low is that children in poor households typically attend public

schools or other schools that do not charge a fee. In countries where poor households spend

10

more on education, it is typically because government schools have fees (as in Indonesia and
Cote d’Ivoire). What they are doing might therefore be perfectly sensible, given that this is the
reason why public education exists. The one concern comes from the mounting evidence,
reported below, that public schools are often dysfunctional: This could be the reason why even
very poor parents in Pakistan are pulling their children out of public schools and spending
money to send them to private schools.

How the Poor Earn Their Money

Walking down the main street of the biggest slum in the medium sized Southern Indian
city of Guntur at nine in the morning, the first thing one notices are the eateries: In front of
every sixth house that directly faced the road, by our count, there was a woman sitting behind a
little kerosene stove with a round cast-iron griddle roasting on it. Every few minutes someone
would walk up to her and order a dosa, the rice and beans pancakes that almost everyone eats
for breakfast in South India. She would throw a cupful of the batter on the griddle, swirl it
around to cover almost the entire surface and drizzle some oil around the edges. A minute or
two later, she would slide an off-white pock-marked pancake off the griddle, douse it in some
sauce, fold it in a newspaper or a banana leaf and hand it to her client, in return for a rupee
(roughly 15 cents, at PPP).

When we walked back down that same street an hour later, the women were gone. We
found one inside her house, filling her daughter’s plate with lunch that she had cooked while
making the dosas. She told us that later that day, she was going out to vend her saris, the long
piece of decorative cloth that Indian women drape around themselves. She gets plain nylon
saris from the shop and stitches beads and small shiny pieces on them, and once a week, she
takes them from house to house, hoping that women would buy them to wear on special
occasions. And they do buy them, she said confidently. All the other dosa women we met that
day had a similar story: once they are done frying dosas, they do something else. Some collect
trash; others make pickles to sell; others work as laborers.

Entrepreneurship and Multiple Occupations Among the Poor

All over the world, a substantial fraction of the poor act as entrepreneurs in the sense of

raising the capital, carrying out the investment, and being the full residual claimants for the
earnings. In Peru, 69 percent of the households who live under $2 a day in urban areas operate
a non-agricultural business. In Indonesia, Pakistan, and Nicaragua, the numbers are between 47
and 52 percent. A large fraction of the rural poor operate a farm (except in Mexico and South
Africa, between 25 percent and 98 percent of the households who earn less than a dollar a day
report being self employed in agriculture).8 Moreover, many of the rural poor (from 7 percent
in Udaipur up to 36 percent in Panama) also operate a nonagricultural business.

8 The low level of agriculture among the extremely poor in South Africa is easily explained. The black population,
which contains almost all of the extremely poor people, were historically under the apartheid regime not allowed
to own land outside the “homelands,” and most of the land in the homelands was not worth cultivating.

11

Many poor households have multiple occupations. Like the dosa women of Guntur, 21

percent of the households living under $2 a day in Hyderabad who have a business actually
have more than one, while another 13 percent have both a business and a laborer’s job. This
multiplicity of occupations in urban areas is found in many other countries as well, though not
everywhere. Among those earning less than $2 a day, 47 percent of the urban households in
Cote d’Ivoire and Indonesia get their income from more than one source; 36 percent in
Pakistan; 20.5 percent in Peru; and 24 percent in Mexico. However, in urban South Africa and
Panama, almost no one has more than one occupation and only 9 percent do so in Nicaragua
and Timor Leste.9

This pattern of multiple occupations is stronger in rural areas. In Udaipur district, as we
discussed earlier, almost everybody owns some land and almost everybody does at least some
agriculture. Yet only 19 percent of the households describe self-employment in agriculture as
the main source of their income. Working on someone else’s land is even rarer, with only 1
percent reporting this as their main source of income. In other words, the poor cultivate the
land they own, no less and usually, no more. Yet, agriculture is not the mainstay of most of
these households. The most common occupation for the poor in Udaipur is working as a daily
laborer: 98 percent of households living under $1 per day in rural areas report doing this and 74
percent claim it is their main source of earnings.

This pattern is confirmed by data from a smaller survey of 27 villages randomly
sampled from eight districts in West Bengal (Banerjee, 2006). In this survey, even households
that claim to be the operators for a plot of land, spend only 40 percent of their time in
agricultural activities on their own land. The fraction is not very different for men and
women―women do less direct agricultural work but more animal rearing, along with growing
fruits and vegetables. Their other activities include teaching, sewing and embroidery, unpaid
household work, and gathering fuel. Strikingly, almost 10 percent of the time of the average
household is spent on gathering fuel, either for use at home or for sale. The median family in
this survey has three working members and seven occupations.

In most of the Living Standard Measurement Surveys, households are not asked their
main source of earnings, but the pattern of diversification among rural households is apparent
nevertheless. In Guatemala, 65 percent of the rural extremely poor say they get some income
from self-employment in agriculture, 86 percent work as laborers outside agriculture, and 24
percent are self-employed outside agriculture. In Indonesia, 34 percent of the rural extremely
poor households work as laborers outside of agriculture, and 37 percent earn income from self
employment outside of agriculture. In Pakistan, 51 percent of the rural extremely poor earn
income from labor outside of agriculture, and 35 percent from a business outside of agriculture.
Overall, the fraction of the rural extremely poor households who report that they conduct more
than one type of activity to earn a living is 50 percent in Indonesia, 72 percent in Cote d’Ivoire,
84 percent in Guatemala, and 94 percent in Udaipur. It is smaller, but not negligible ─between
10 and 20 percent ─ in Nicaragua, Panama, Timor Leste, and Mexico. Once again, an
exception to this general pattern is South Africa, where less than 1 percent of the rural poor or
extremely poor report multiple occupations.

9 This may however be a data problem: Anthropologists do claim that they observe multiple occupations in South
African households (Francie Lund, verbal communication to Angus Deaton).

12

Temporary Migration to Work

Where do rural households, which are often a walk of a half-hour or more from the
nearest road, find all this non-agricultural work? The answer turns out to be the obvious one:
they migrate.

Temporary migration is rarely documented in surveys, but in the Udaipur survey, which

had questions about this activity, 60 percent of the poorest households report that someone
from their family had lived outside for a part of the year in order to obtain work. For 58 percent
of the families, the head of the household had migrated. The migrants typically complete
multiple trips in a year. However, people do not leave for very long: The median length of a
completed migration is one month, and only 10 percent of migration episodes exceed three
months. Nor do most of the migrants travel very far: 28 percent stay in the district of Udaipur,
and only 42 percent leave the state of Rajasthan.

In contrast, permanent migration for work reasons is rare, although many women move
when they get married. Even if we look at households currently living in urban areas, where
the inflow of immigrants is presumably higher than in rural areas, the share of extremely poor
households who had one member that was born elsewhere and had migrated for work reasons
was just 4 percent in Pakistan, 6 percent in Cote d’Ivoire, 6 percent in Nicaragua, and almost
10 percent in Peru. The 1991 Census of India reports that only 14.7 percent of the male
population lives somewhere other than where they were born. Indonesia is the only country in
our data where the proportion is higher: 41 percent of the urban households came from
elsewhere. Indonesia is also the only country in this sample where migration was explicitly
subsidized.

Lack of Specialization

A pattern seems to emerge. Poor families do seek out economic opportunities, but they
tend not to get too specialized. They do some agriculture, but not to the point where it would
afford them a full living (for example by buying/renting/sharecropping more land). They also
work outside, but only in short bursts─they do not move permanently to their place of
occupation.

This lack of specialization has its costs. Many of these poor households receive most of
their earnings from these outside jobs, despite only being away for 18 weeks of the year on
average (in the case of Udaipur). As short-term migrants, they have little chance of learning
their jobs better or ending up in a job that suits their specific talents or being promoted.

Even the non-agricultural businesses that the poor operate typically require relatively

little specific skills. For example, the businesses in Hyerabad include 11 percent tailors, 8
percent fruit and vegetable sellers, 17 percent small general stores, 6.6 percent telephone
booths, 4.3 percent auto owners, and 6.3 percent milk sellers. Except for tailoring, none of
these jobs require the high levels of specialized competence that take a long time to acquire,
and therefore are associated with higher earnings. In several ways, the poor are trading off
opportunities to have higher incomes.

13

The Problem of Small Scale

The businesses of the poor typically operate at a remarkably small scale. As we saw,
the average landholding for those who own land is usually quite tiny, and renting land is
infrequent. Furthermore, most of this land is not irrigated and cannot be used all year round.

The scale of non-agricultural businesses run by the poor also tends to be pretty small. In
the 13 countries in our sample, the median business operated by people living under $2 dollars
a day either in a rural or an urban location has no paid staff, and the average number of paid
employees range between 0.14 in rural Nicaragua to 0.53 in urban Panama. Businesses are
operated on average by 1.38 (in Peru) to 2.59 (in Cote d’Ivoire) people ─ most of them being
family members. Most of these businesses have very few assets as well. In Hyderabad, only 20
percent of the businesses operate out of a separate room. In Pakistan, about 40 percent of the
businesses of those living under $1 or $2 dollar a day have a vehicle, but only 4 percent have a
motorized vehicle and none have any machinery. In other countries, even non-motorized
vehicles are rare. In Hyderabad, where we have an exhaustive list of business assets, the most
common assets are tables, scales, and pushcarts.

Many of these businesses are probably operating at too small a scale for efficiency. The
women making dosas spend a lot of time waiting: having fewer dosa-makers who do less
waiting would be more efficient. In fact, it might make sense in efficiency terms for the dosa-
makers to work in pairs: One to make the dosas and one to wrap them and make change.

Markets and the Economic Environment of the Poor

The economic choices of the poor are constrained by their market environment. The
amount they save, for example, should vary with their access to a safe place to put their
savings. Other constraints result from a lack of shared infrastructure. When the government
builds a water line to your neighborhood, for example, you no longer need your own well.
This section focuses on markets. The next takes up the issue of infrastructure.

The Market for Credit and the Poor

The data from our 13 countries suggests that the fraction of rural extremely poor
households having an outstanding debt varies between countries, from 11 percent in rural East
Timor to 93 percent in Pakistan. But what is consistent across the surveys is that very few of
the poor households get loans from a formal lending source.

In the Udaipur sample, about two-thirds of the poor had a loan at the time of the
interview. Of these, 23 percent are from a relative, 18 percent from a money lender, 37 percent
from a shopkeeper, and only 6.4 percent from a formal source like a commercial bank or a
cooperative. Lest one suspect that the low share of bank credit is due to the lack of physical
access to banks, a similar pattern occurs in urban Hyderabad, where households living below
$2 a day primarily borrow from moneylenders (52 percent), friends or neighbors (24 percent),
and family members (13 percent), and only 5 percent of the loans are with commercial banks.

14

The one country where a substantial share of the loans to the poor are formal in nature is
Indonesia, where thanks to efforts by the Bank Rakyat Indonesia, one-third of the rural poor
households borrow from a bank. In all the other countries, relatives, shopkeepers, and other
villagers form, by far, the overwhelming source of borrowed funds.

Credit from informal sources tends to be expensive. In the Udaipur survey, where we
have data on interest rates not available in other surveys, those living on less than $1 a day pay
on average 3.84 percent per month for the credit they receive from informal sources. Those
who consume between $1 and $2 dollar a day per capita pay a little less: 3.13 percent per
month. This is in part because they rely less on informal sources of credit and more on the
formal sources than the extremely poor, and the formal sources are cheaper; and in part it
reflects the fact that informal interest rates are lower for those with more land―the interest rate
from informal sources drops by 0.40 percent per month for each additional hectare of land
owned. The monthly interest rate we see in the Hyderabad sample is even higher: 3.94 percent
per month for those living under $2 dollars a day. This reflects the fact that few of these urban
poor households have any land that they can use as collateral.

These high interest rates seem to occur not directly because of high rates of default, but
as a result of the high costs of contract enforcement. While delay in repayment of informal
loans is frequent, default is actually rare (Banerjee and Duflo, 2005). For example, a
“Summary Report on Informal Credit Markets in India” reports that across four case studies of
money-lenders in rural India, default explains only 23 percent of the interest rate charged
(Dasgupta, 1989). A well known study of rural money-lenders in Pakistan by Aleem (1990),
find that the median rate of default across money lenders is just 2 percent.

These low default rates are however anything but automatic: Contract enforcement in
developing countries is often difficult, and in particular, it is not easy to get courts to punish
recalcitrant borrowers. As a result, lenders often spend important resources making sure that
their loans get repaid, which is what drives up the interest rates. The fact that lending depends
so much on effective screening and monitoring also means that lending to the poor is especially
difficult: The problem, at least in part, is that the poor have very little by way of collateral to
secure the loan and therefore lenders hesitate to trust them with a lot of money. Given that the
loan amount will in any case be small, it is not always clear that the profits from the transaction
will be large enough to cover the cost of monitoring/screening. As a result, a lot of lenders are
reluctant to lend to the poor. Moreover and for the same reason, informal lenders located close
to the borrowers may be the only ones who are willing to lend to the poor—since
monitoring/screening is relatively cheap for them. The trouble is that these informal lenders
have to pay more for their deposits than the more formal institutions, since they are less
capitalized and less regulated and do not have any government guarantees. This higher cost of
deposits gets passed on to poorer borrowers. The gap can be considerable―in the study by
Aleem, the cost of capital for the money-lenders was 32.5 percent in a year when banks were
only paying 10 percent for their deposits.

The Market for Savings and the Poor

A main challenge for the poor who try to save is to find safety and a reasonable return.
Stashing cash inside your pillow or elsewhere at home is neither safe nor particularly well-
protected from inflation. In addition, recent research by Ashraf, Karlan, and Yin (2005) in the

15

Philippines and Duflo, Kremer, and Robinson in Kenya (2006) suggests that the poor, like
everyone else, have problems resisting the temptation of spending money that they have at
hand.

Few poor households have savings accounts. Except in Cote d’Ivoire, where 79 percent
of the extremely poor households under $1 a day have a savings account, the fraction is below
14 percent in the other countries in our data. In Panama and Peru, less than 1 percent of such
households have a savings account. In most countries, the share of households with a saving
account is similar in rural and urban areas, and similar for those under $2 a day and those under
$1 a day. Here India appears to be an exception, since only 6 percent of the extremely poor
households in rural Udaipur have a savings account, while 25 percent of them do in the city of
Hyderabad.

A lack of access to reliable savings accounts appears common to the poor everywhere,
as documented in Stuart Rutherford’s (2000) fascinating book, The Poor and their Money.
Rutherford describes the many strategies the poor use to deal with this problem: They form
savings “clubs,” where each person makes sure that the others do their savings. Self-help
Groups (SHGs), popular in parts of India, and present in Indonesia as well, are saving clubs
which also give loans to its members out of the accumulated savings (they are also some times
linked to bank which offer them banks). In Africa, Rotating Savings and Credit Associations
(ROSCAs) allow people to lend their savings to each other on a rotating basis. Others pay
deposit collectors to collect their deposits and put them in a bank. Yet others deposit their
savings with local money-lenders, with credit unions (which are essentially larger and much
more formally organized Self-Help Groups) or in an account at the local post office. And the
reason why many of the poor respond so well to micro-credit, is not necessarily because it
offers them credit, but because once you take a loan and buy something with it, you have a
disciplined way to save ─ namely, by paying down the loan.

However even participation in semi-formal savings institutions (such as Self-help
Groups, ROSCAs and Microfinance Institutions), is not nearly as common among the poor as
one might have expected. Even in India, despite the high visibility especially of SHGs, less
than 10 percent of the poor in our Udaipur and Hyderabad surveys are part of an SHG or a
ROSCA. The majority of the households who have any savings simply have it at the bank.

The Market for Insurance and the Poor

The poor have very little access to formal insurance. In many surveys, questions about

insurance are not even asked. In the six of the seven countries where there is data on this, less
than 6 percent of the extremely poor are covered by health insurance of any kind. The
exception is Mexico where about half of them have coverage. The numbers are not much
higher in urban areas. Life insurance is a bit more common in India (and is, essentially, a form
of savings). Four percent of the extremely poor in Udaipur and 10 percent in Hyderabad have
life insurance.10

10 Surprisingly, weather insurance is also essentially absent everywhere the world over (Morduch, 2005),
although it would seem straightforward to provide insurance against observed weather patterns.

16

In principle, informal insurance can also be had through social networks. For example,
Udry (1990) shows that poor villagers in Nigeria live a life that is shaped by a dense network
of loan exchange: Over the course of one year, 75 percent of the households had made loans,
65 percent had borrowed money, and 50 percent had been both borrowers and lenders. Almost
all of these loans took place between neighbors and relatives. Both the repayment schedule and
the amount repaid were affected by both the lender’s and the borrower’s current economic
conditions, underlining the role of these informal loans in providing insurance. Rosenzweig
and Munshi (2005), argue that the same process happens in India through the jati or sub-caste
networks.

Yet these informal networks have only a limited ability to protect the households
against risk. The consumption of poor households is strongly affected by variations in their
incomes, as has been shown by Deaton (1997) in Cote d’Ivoire, Rosenzweig and Munshi
(2005) in India, Fafchamps and Lund (2003) in the Philippines, and Townsend (1995) in
Thailand. Poor households also bear most health-care risks (both expenditures and foregone
earnings) directly. For example, Gertler and Gruber (2002) find that in Indonesia a decline in
the health index of the head of the household is associated with a decline in non-medical
expenditures. In Udaipur, large expenditures on health ($70 and higher, at PPP) are covered by
borrowing or dissaving. Only 2 percent of these expenses were paid for by someone else, and
none came from the self-help groups. Twenty-four percent of the households in Hyderabad had
to borrow to pay for health expenses in the last year.

When the poor come under economic stress, their form of “insurance” is often eating
less or taking their children out of school. For example, Jacoby and Skoufias (1997) find that
poor children leave school in bad years. Rose (1999) finds that the gap in mortality of girls
relative to boys is much larger in drought years (but only for the landless households, who are
not able to sell land or borrow to weather the crisis). They also are less likely to get medical
treatment for themselves or their children: In the Udaipur sample, those who were sick in the
last months and did not seek treatment (more than half) cite lack of money more often than any
other reason (34 percent of the times). The lack of insurance also leads the poor to under-invest
in risky, but profitable, technologies, such as new seeds (Morduch, 1991).

The weaknesses of informal insurance should not really be a surprise. Ultimately,
informal insurance relies on the willingness of the fortunate to take care of those less favored,
which limits the amount of insurance provided. Moreover, informal social networks are often
not well-diversified. They tend to spread risk over households who live nearby and have
similar incomes and occupations, as Gubert and Fafchamps (2006) show for the Philippines.

Unfortunately governments in these countries are not very effective at providing
insurance either. For example, in most countries, the government is supposed to provide free
health care to the poor. Yet, health care is rarely free. Government health-care providers often
illegally charge for their own services and for medicines in reality. Also, as we will see, the
quality of care in the public system is so low that the poor often end up visiting private
providers.

A number of governments provide a form of income insurance through safety-net “food
for work” programs. Under these programs, everyone is entitled to a certain number of days of

17

government employment usually involving physical labor at a pre-announced (relatively low)
wage. In Udaipur, where the years leading up to the survey had been particularly arid, 76
percent of the poor had at least one of the household members work on a public employment
program of this kind. However, such schemes often offer only a limited number of jobs which
might end up being doled out in a way that discriminates against the poor.

The Market for Land and the Poor

The land market is an issue for the poor because, for historical reasons, land is the one
asset they tend to own. The one obvious problem with owning land is that land records in
developing countries are often incomplete and many people do not have titles to their land.
This, as many, including most famously, Hernando De Soto (2003), have emphasized, means
that it is harder to sell the land or mortgage it.

From the point of view of the poor, this is especially troubling, because they tend to
own a lot of the land that was either recently cleared or recently encroached upon, which is
typically the land where tilling is incomplete. Field (2006) suggests that, in Peru, the poor, as a
result, spend a lot of time protecting their claims to the land (since they have no title, they have
no legal recourse).

The poor also suffer because where titles are missing or imperfectly enforced, political

influence matters. In parts of Ghana, land belongs either to lineages or to the village, and
cultivators have only rights of use. In this context, Goldstein and Udry (2005) show that the
people who lack the political clout to protect them from having their land taken away from
them by the village or their lineage (which typically includes the poor), do not leave their land
fallow for long enough. Leaving land to fallow increases its productivity, but increases the risk
that someone may seize it.

Finally there is a long tradition of research in agricultural economics that argues that

the poor lack the incentives to make the best use of the land they are cultivating because they
are agents rather than owners (Shaban, 1987). Banerjee, Gertler, and Ghatak (2002) found that
a reform of tenancy that forced landlords to raise the share of output going to the sharecroppers
(improving sharecroppers’ incentives) and also gave them a secure right to the land, raised
productivity by about 50 percent.

Infrastructure and the Economic Environment of the Poor

What we call infrastructure includes the entire spectrum from roads and power
connections to schools, health facilities, and public health infrastructure (mostly water and
sanitation). While there are different models of how such infrastructure gets supplied, with
markets and the government playing differing roles, they are all usefully thought of as part of
the environment in which people live, with some characteristics of a local public good, rather
than something that can be purchased piecemeal by individuals.

18

The availability of physical infrastructure to the poor like electricity, tap water, and
even basic sanitation (like access to a latrine) varies enormously across countries. In our
sample of 13 countries, the number of rural poor households with access to tap water varies
from none in Udaipur, to 36 percent in Guatemala. The availability of electricity varies from
1.3 percent in Tanzania to 99 percent in Mexico. The availability of a latrine varies from none
in Udaipur, to 100 percent in Nicaragua. Different kinds of infrastructure do not always appear
together. For example, in Indonesia, 97 percent of rural extremely poor households have
electricity but only 6 percent have tap water. Some governments provide reasonable access to
both electricity and tap water to the extremely poor: In Guatemala, 38 percent of the extremely
poor rural households have tap water and 30 percent have electricity. Other governments do
very little: In Udaipur, Papua New Guinea, East Timor, and South Africa, the share of the rural
extremely poor with tap water or electricity is below 5 percent.

Generally, access to electricity and tap water is greater for the urban poor than the rural
poor (which is probably fortunate since lack of sanitation in very dense surroundings can be
disastrous for the disease environment). The only exception to this pattern in our 13 countries
is Cote d’Ivoire, where rural households seem to have better access. Moreover, access to both
tap water and electricity is typically higher for those under $2 a day than those under $1 a day.

In most low-income countries, there has been some attempt to make sure that poor
households have access to primary schools and to basic health centers. For example, most
Indian villages now have a school within a kilometer, and there is a health sub-center for every
10,000 people. However, the quality of the facilities that serve the poor tends to be low, even
when they are available, and it is not clear how much they actually deliver. Chaudhury et al.
(2005) report results on surveys they conducted to measure the absence of teachers and health
workers in Bangladesh, Ecuador, India, Indonesia, Peru, and Uganda. They found that the
average absence rate among teachers is 19 percent and the average absence rate among health
workers is 35 percent. Moreover, because not all teachers and health workers are actually
working when at their post, even this picture may be too favorable. Moreover, absence rates
are generally higher in poor regions than in richer areas.

In an innovative study on health care quality, Das and Hammer (2004) collected data on
the competence of doctors in Delhi, India, based on the kinds of questions they ask and the
action they say they would take faced with a hypothetical patient, suffering from conditions
they are likely to encounter in their practice. Every Delhi neighborhood, poor or rich, lives
within 15 minutes of at least 70 health providers. However, the gap in competence of the
average health practitioner between the poorest and richest neighborhoods is almost as large as
the gap between the competence of a health provider with an MBBS degree (the equivalent of
an MD in the United States) and a provider without such a qualification. In fact, an expert
panel found that the treatments suggested by the average provider in their sample are slightly
more likely to do harm rather than good, due to a combination of misdiagnosis and over-
medication.

These differences in health care and basic sanitation infrastructure can directly affect
mortality. Several surveys ask women about their pregnancies and the outcomes, including
whether the child is still alive. We compute an infant mortality measure as the number of
children who died before the age of 1 divided by the number of live births. The numbers are
startling, especially when one takes into account the fact that they are likely to be

19

underestimates (because not all children are remembered, especially if they died very early).
Among the rural extremely poor, the lowest infant mortality that we observe is 3.4 percent in
Indonesia. At the high end, infant mortality among the extremely poor is 8.7 percent in South
Africa and Tanzania, 10 percent in Udaipur, and 16.7 percent in Pakistan. The rates are lower,
but not much lower, in urban areas. The rates also remain high if the definition of poverty is
expanded to include those who live under $2 a day. That child mortality is likely related at
least in part to health infrastructure is suggested by Wagstaff (2003) who uses data from the
demographic and health surveys to estimate prevalence of malnutrition and child mortality
among those living under $1 a day in a number of countries. He finds very large difference
between survival chances of poor children in different countries, and shows that they are
correlated with health spending per capita in these countries.

The low quality of teaching in public schools has clear effect on learning levels as well.
In India, despite the fact that 93.4 percent of children ages 6-14 are enrolled in schools (75
percent of them in government school), a recent nationwide survey found that 34.9 percent of
the children age 7 to 14 cannot read a simple paragraph at second-grade level (Pratham, 2005).
Moreover, 41.1 percent cannot do subtraction, and 65.5 percent cannot do division. Even
among children in grades 6 to 8 in government schools, 22 percent cannot read a second-grade
text.

In countries where the public provision of education and in health services is
particularly low, private providers have stepped in. In the parts of India where public school
absenteeism is the highest, the fraction of rural children attending private schools is also the
highest (Chaudhury et al., 2005). However, these private schools are less than ideal: They do
have lower teacher absenteeism than the public schools in the same village, but their teachers
are significantly less qualified in the sense of having a formal teaching degree.

One sees a similar pattern in healthcare but in a more extreme version. Once again
private providers who serve the poor are less likely to be absent and more likely to examine the
patient with some care than their public counterparts, but they tend to be less well qualified
(see for example, Das and Hammer (2004)). However unlike in education where most poor
children are still in the public system even in countries and regions where public education is
of extremely poor quality, where the public healthcare system has high levels of absence most
people actually go to private providers. For example, in India, where absence of health care
providers is 40 percent, 58 percent of the extremely poor households have visited a private
health care provider in the last month. By contrast in Peru, where the absentee rate for health
care providers is fairly low (25 percent) according to Chaudhury et al. (2005), only 9 percent
of the rural extremely poor households have been to a private health provider in the last month.
Within the Udaipur district, Banerjee, Deaton, and Duflo (2005) also found that the rate of
usage of the public health facility is strongly correlated with the absence rate at the public
health facilities in the areas.

Understanding the Economic Lives of the Poor

Many things about the lives of the poor start to make much more sense once we
recognize that they have very limited access to efficient markets and quality infrastructure. The

20

fact that they usually cultivate the land they own, no more and no less, for example, probably
owes a lot to the agency problems associated with renting out land. In part, it must also reflect
the fact that the poor, who typically own too little land relative to the amount of family labor
and therefore are the obvious people to buy more land, suffer from lack of access to credit.
This gets reinforced by the difficulties, discussed above, that the poor face in getting any kind
of insurance against the many risks that a farmer needs to deal with: A second job outside
agriculture secures you against much of that risk.

Why so Little Specialization?

More generally, risk spreading is clearly one reason why the poor, who might find risk
especially hard to bear, tend not to be too specialized in any one occupation. They work part-
time outside agriculture to reduce their exposure to farming risk, and keep a foot in agriculture
to avoid being too dependent on their non-agricultural jobs.

Another reason for a second job is to occupy what would otherwise be wasted time.
The reason people almost always give for why they diversify is because they have time on their
hands. When we asked the dosa-sellers of Guntur why they did so many other things as well,
they all said: “[We] can sell dosas in the morning. What do we do for the rest of the day?”
Similarly, farmers who do not have irrigated land can only work the land when the land is not
too dry, which depends on rainfall and/or irrigation. Finding some work outside agriculture is a
way for them to make productive use of their time when the land is unusable. However this
argument is incomplete. We also need to explain what made the women opt to sell dosas: After
all they could have skipped the dosas and specialized in whatever they were doing for the rest
of the day. Risk spreading remains a possible answer, but many of them seem to be in
relatively safe occupations. And given the fact that almost everyone owns the cooking
implement that one needs to make a dosa and entry is free, it does not seem that dosa-making is
an extraordinarily profitable activity.

A final, more compelling reason for doing multiple jobs is that they poor cannot raise
the capital they would need to run a business that would fully occupy them. As we saw, most
businesses operate with very little assets and little working capital. Likewise, some poor
farmers might be able to irrigate their lands and make them useable for a larger part of the year,
but they do not have the necessary access to funds. Of course, in agriculture, some down time
will always remain, justifying a degree of multiple occupations. But this would be much more
limited.

Why so Many Entrepreneurs?

Once we make this link between the tendency of the poor to be in multiple occupations
and their access to financial markets, it is easy to see why so many of them are entrepreneurs.
If you have few skills and little capital, and especially if you are a woman, being an
entrepreneur is often easier than finding a job: You buy some fruits and vegetables (or some
plastic toys) at the wholesalers and start selling them on the street; you make some extra dosa
mix and sell the dosas in front of your house; you collect cow dung and dry it to sell it as a
fuel; you attend to one cow and collect the milk. As we saw in Hyderabad, these are exactly the
types of activity the poor are involved in.

21

It is important, however, not to romanticize the idea of these penniless entrepreneurs.
Given that they have no money, borrowing is risky, and in any case no one wants to lend to
them, the businesses they run are inevitably extremely small, to the point where there are
clearly unrealized economies of scale. Moreover, given that so many of these firms have more
family labor available to them than they can use, it is no surprise that they do very little to
create jobs for others. This of course makes it harder for anyone to find a job and hence
reinforces the proliferation of petty entrepreneurs.

Why the Poor Don’t Eat More?

Another big puzzle is why the poor do not spend more on food both on average and
especially out of the marginal dollar. Eating more and eating better (more grains and iron-rich
foods, less sugar) would help them build up their BMI (which we noted tends to be very low).

One possibility is that eating more would not help them that much, or not for long,
because they would become weak again at the first attack of disease, which will invariably
occur. For example, Deaton, Cutler, and Lleras-Muney (2006) argue that nutrition is at best a
very small part of what explains the tremendous gains in health around the world in the past
few decades. However, some improvements in nutrition (reduction of anemia in particular)
have been credibly linked to increased productivity (Thomas et al, 2004). Moreover, as we
saw, not having enough to eat does, at a minimum, make the poor extremely unhappy.

Provided that eating more would increase their productivity, it is unlikely that the low
levels of good consumption can explained by a simple lack of self control (the poor cannot
simply resist temptations to spend on other things, and don’t have enough left to eat): As we
noted above, they also spend surprisingly large amounts on entertainment―be it televisions,
weddings, or festivals. All of these involve spending a large amount at one time, which implies
some saving unless they happen to be especially credit-worthy. In other words, many poor
people save money that they could have eaten today in order to spend more on entertainment in
the future, which does not immediately fit the idea of their lacking self-control.

The need to spend more on entertainment rather than on food appears to be a strongly
felt need, not something that would go away if the poor could plan better. One reason this
might be the case is that the poor want to keep up with their neighbors. Fafchamps and Shilpi
(2005) offer evidence from Nepal in which people were asked to assess whether their level of
income as well as their levels of consumption of housing, food, clothing, health-care, and
schooling were adequate. The answers to these questions were strongly negatively related to
the average consumption of the other people living in the same village.

Why Don’t the Poor Invest More in Education?

The children of the poor are, by and large, going to primary school. What parents are
not doing is reacting to the low quality of these schools, either by sending their children to
better and more expensive schools or by putting pressure on the government to do something
about quality in government schools. Why not?

22

One reason is that poor parents, who may often be illiterate themselves, may have a
hard time recognizing that their children are not learning much. One survey shows that poor
parents in Eastern Uttar Pradesh in India have limited success in predicting whether their
school-age children can read (Banerjee et al., 2005). Moreover, how can parents be confident
that a private school would offer a better education, given that the teacher there is usually less
qualified than the public school teachers? After all, researchers have only discovered this
pattern in the last few years. As for putting pressure on the government, it is not clear that the
average villager would know how to organize and do so.

Why Don’t the Poor Save More?

The arguments based on lack of access to credit and insurance or labor market
rigidities, by themselves, do not help very much in understanding why the poor are not more
interested in accumulating wealth. As we saw above, they could easily save more without
getting less nutrition, by spending less on alcohol, tobacco, festivals, and food items such as
sugar, spice, and tea.

It is true that they typically have no bank accounts or other rewarding financial assets to
put savings into, but many of them have their own businesses, and, as we argue above, these
tend to be chronically under-funded: Why not save up to buy a new machine, or increase the
stock in the shop? Moreover, as we saw above, a very substantial fraction of them have debt,
and the interest rate on the debt often well exceeds 3 percent per month. Paying down debt is
therefore a very attractive way to save. And even if you have no business to grow, and have no
debt to repay, just holding some extra stocks for the proverbial rainy day (or more commonly
“the drought”) can save both worry and the misery of watching your children starve. In other
words, precautionary motives for saving should be especially strong for the poor.

A part of the answer is probably that saving at home is hard: The money may be stolen
(especially if you live in a house that cannot be locked) or simply grabbed by your spouse or
your son. Perhaps equally importantly, if you have money at hand, you are constantly resisting
temptation to spend:. to buy something you want, to help someone who you find difficult to
say no to, to let your child have the sweet he wants so badly. This is probably especially true of
the poor, because many of the temptations you are being asked to resist are things that
everyone else might take for granted.

The poor seem quite aware of their vulnerability to temptation. In the Hyderabad
survey, the respondents were asked to name whether there are particular expenses that they
would like to cut. 28 percent of the poor named at least one item they would like to cut. The
top item that households would like to cut is alcohol and tobacco (mentioned by 44 percent of
the households that want to cut on items). Then comes sugar, tea, and snacks (9 percent),
festivals (7 percent), and entertainment (7 percent).

This is one place where self-knowledge does not help. Knowing that you face self-
control problems makes you even less likely to try to save: You know that it would probably
just end up feeding some future indefensible craving, and the machine that you so want to save
for will never actually be acquired. Being naïve might actually help―you might just be lucky
and save enough to buy the machine before the temptation gets to you.

23

Beyond Market Failures and Self-control Problems

An interesting example that spans many of the arguments we have used above is a
study by Duflo, Kremer, and Robinson (2005) on investment in fertilizer in Kenya. According
to surveys conducted over several years, just 40.3 percent of farmers had ever used fertilizer,
and just 25 percent used fertilizer in any given year. Conservative estimates suggest that the
average return to using fertilizer exceed 100 percent, and the median return is above 75
percent. Duflo, Kremer, and Robinson conducted field trials of fertilizer on the farms of actual
randomly selected farmers, which were meant to teach the farmers how to use fertilizer and the
rewards of doing so. They found that the farmers who participated in the study are 10 percent
more likely on average to use fertilizer in the very next season after the study, but only 10
percent more likely – and the effects fade after the first season.

When farmers were asked why they did not use fertilizer, most farmers replied that they
did not have enough money. However, fertilizer can be purchased (and used) in small
quantities, so this is another investment opportunity which seems easily accessible to farmers
with even a small level of saving. This suggests that the issue, once again, is that farmers find it
difficult to put away even small sums of money. The program in Kenya offered to sell farmers
a voucher right after the harvest, which is when farmers have money in hand, which would
entitle them to buy fertilizer later.

This program had a large effect: 39 percent of the farmers offered the voucher bought

the fertilizer, and the effects are as large as a 50 percent subsidy on the cost of fertilizer. The
voucher seemed to work as a commitment device to encourage saving. But what remains
puzzling is that the farmers could have bought the fertilizer in advance on their own. Indeed, a
huge majority of the farmers who bought the vouchers for future delivery of fertilizer requested
immediate delivery, and then stored the fertilizer for later use. Moreover almost all of them
used the fertilizer they bought. They apparently had no self-control problems in keeping the
fertilizer, even though they could easily exchange the fertilizer for something more
immediately consumable. Indeed, even if there were some transaction costs in selling, they
would have to be very large indeed, given that these are people appear to be are willing to give
up a 100 percent return in a three to five months in order to consume now.

Why Don’t the Poor Migrate for Longer?

A final puzzle is why the poor do not migrate for longer periods, given that they could
easily earn much more by doing so. Munshi and Rosenzweig (2004) argue that the lack of
long-term migration reflects the value of remaining close to one’s social network, in a setting
where the social network might be the only source of (informal) insurance available to people.
However, those who migrate for short periods of up to a few months leave their entire family,
who presumably can maintain their social links, behind. The ultimate reason seems to be that
making more money is not a huge priority, or at least not a large enough priority to experience
several months of living alone and often sleeping on the ground somewhere in or around the
work premises.

In some ways this puzzle resembles the question of why the Kenyan farmers do not buy
fertilizer right after the harvest even though they are happy to buy (and use it) if someone made

24

the (small) effort to bring it to their farm. In both cases one senses a reluctance of poor people
to commit themselves psychologically to a project of making more money. Perhaps at some
level this avoidance is emotionally wise: Thinking about the economic problems of life must
make it harder to avoid confronting the sheer inadequacy of the standard of living faced by the
extremely poor.

Acknowledgements

We thank Andrei Shleifer for motivating us to undertake this exercise. We thank him and the
editors of this journal for detailed suggestions on the previous draft of this paper. We thank
Danielle Li, Marc Shotland, and Stefanie Stancheva for spectacular assistance in assembling
the data, and Kudzai Takavarasha for carefully editing a previous draft. Special thanks to
Angus Deaton for extremely useful advice and guidance, and extensive comments on the
previous draft and to Gary Becker for helpful comments.

25

REFERENCES:

Aleem, Irfan (1990), “Imperfect Information, Screening and the Costs of Informal Lending: A
Study of a Rural Credit Market in Pakistan,” World Bank Economic Review, 3: 329–349.

Ashraf, Nava, Dean Karlan, and Wesley Yin (2006). “Tying Odysseus to the Mast: Evidence
from a Commitment Savings Product in the Philippines,” Quarterly Journal of
Economics, forthcoming.

Audretsh, David The Entrepreneurial Society, Oxford: Oxford University Press, Inc., 2005.

Banerjee, Abhijit, Rukmini Barnerji, Esther Duflo, Rachel Glennerster, Stuti Khemani, Sendhil
Mullainathan, and Marc Shotland (2005), “The Impact of Information, Awareness and
Participation on Learning Outcomes” MIMEO, MIT.

Banerjee, Abhijit, Angus Deaton, and Esther Duflo (2004), “Wealth, Health and Health
Services in Rural Rajasthan,” American Economic Review 94 (2): 326-330.

Banerjee, Abhijit and Esther Duflo (2004), “Growth Theory through the Lens of Development

Economics,” Handbook of Economic Growth, Volume 1A, Steve Durlauf and Philippe
Aghion, editors, Elsevier Science Ltd.-North Holland: December 2005.

Banerjee, Abhijit, Esther Duflo, and Rachel Glennerster (2006), “A Snapshot of Micro
enterprises in Hyderabad”, MIMEO, MIT.

Banerjee, Abhijit, Paul Gertler, and Maitreesh Ghatak. “Empowerment and Efficiency:
Tenancy Reform in West Bengal.” Journal of Political Economy 110, no. 2 (2002): 239-
280.

Banerjee, Abhijit, and Rohini Somanathan (2005), “The political economy of public goods:
Some evidence from India,” Mimeo, MIT.

Banerjee, Nirmala (2006), “A survey of occupations and livelihoods of households in West
Bengal”, mimeo Sachetana, Kolkata.

Case, Anne and Deaton, Angus (2005), “Health and Wealth among the Poor: India and South
Africa Compared,” American Economic Review Papers and Proceedings, 95(2): 229-233.

Chaudhury, Nazmul, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan, and F. Halsey
Rogers (2005). “Teacher Absence in India: A Snapshot,” Journal of the European
Economic Association 3(2-3), April-May.

Das, Jishnu and Jeffrey Hammer (2004), “Strained Mercy: The Quality of Medical Care in
Delhi.” Economic and Political Weekly, 39 (9): 951-965.

Dasgupta, A. (1989). Reports on credit markets in India: Summary. Technical report, New
Delhi: National Institute of Public Finance and Policy.

26

Dasgupta, P. and Debraj Ray (1986), “Inequality as a Determinant of Malnutrition and
Unemployment: Policy,” Economic Journal, 96:1011-1034.

Deaton, Angus (1997) The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy, Baltimore, Johns Hopkins University Press for the World Bank

Deaton, Angus (2004), “Measuring poverty,” in Abhijit Banerjee, Roland Benabou, and Dilip
Mookherjee, eds., Understanding Poverty, Oxford University Press.

Deaton, Angus (2006) “Purchasing Power Parity Exchange Rates for the poor: Using
Household Surveys to Construct PPPs” – MIMEO, Princeton August 2006.

Deaton, Angus, and Shankar Subramanian (1996), “The Demand for Food and Calories,”

Journal of Political Economy, 104(1): 133-162.

Deaton, Angus, David Cutler and Adriana Lleras-Muney (2006) “The Determinants of
Mortality” Journal of Economic Perspectives, Fall 2006.

Duflo, Esther, Michael Kremer, and Jonathan Robinson (2006). “Why don’t farmer use
Fertilizer: Evidence from Field Experiments,” Mimeo, MIT.

Fafchamps, Marcel and Susan Lund (2003), “Risk-sharing networks in rural Philippines,”
Journal of Development Economics, 71(2): 261-287.

Field, Erica (2003), “Entitled to Work: Urban Property Rights and Labor Supply in Peru,”
Mimeo, Harvard, July.

Filmer, Deon and Lant Pritchett (2001), “Estimating Wealth Effects without Expenditure
Data—or Tears: An Application to Educational Enrollments in States of India,”
Demography, 38(1): 115-32,

Gertler, Paul and Jonathan Gruber (2002), “Insuring Consumption Against Illness,” American
Economic Review, 92 (1): 50-70,

Fafchamps, Marcel and Flore Gubert (2005), “The Formation of Risk Sharing Networks,”
Working Papers DT/2005/13, DIAL (Développement, Institutions & Analyses de Long
terme).

Field, Erica (2006) “Entitled to Work: Urban Property Rights and the Labor Supply in Peru”,
MIMEO, Harvard.

Goldstein, Markus and Christopher Udry (2005), “The Profits of Power: Land Rights and
Agricultural Investment in Ghana,” Working Papers 929, Economic Growth Center, Yale
University.

Jacoby, Hanan G. and Emmanuel Skoufias (1997), “Risk, Financial Markets, and Human
Capital in a Developing Country,” Review of Economic Studies, 64 (3): 311-335.

27

Jha, Raghavendra (2004), “Calories deficiency in Rural India in the Last three Quinquennial
rounds of the NSS” MIMEO, Australian National University.

Meenakshi, J.V., and Brinda Vishwanathan (2003), “Calorie Deprivation in Rural India, 1983-

1999/2000”, Economic and Political Weekly, January 25, pp 369-375.

Morduch, Jonathan (1995) “Income Smoothing and Consumption Smoothing, Journal of

Economic Perspectives, 9(3):103-114.

Morduch, Jonathan (2006), “Micro-Insurance: The Next Revolution?,” in What Have We
Learned About Poverty?, edited by Abhijit Banerjee, Roland Benabou, and Dilip Mookherjee.
Oxford University Press, forthcoming.

Munshi, K. and M. Rosenzweig (2005) “Why is social mobility in India so low? Social
Insurance, Inequality, and Growth”, BREAD working paper no. 097.

Prathman. Annual Status of Education Report, 2006. Mumbai: PRATHAM, 2006.

Ravallion, Martin (2004), “Pessimistic on Poverty?” The Economist, April 7, 2004.

Rose, Elaina (1999), “Consumption Smoothing and Excess Female Mortality in Rural
India,” Review of Economics and Statistics, 81(1): 41-49.

Rutherford, Stuart. The Poor and Their Money. New Dehli: Oxford University. Press, 2000.

Sala-i-Martin, Xavier (2004), “More or Less Equal,” The Economist, March 11, 2004.

Shaban, Radwan (1987), “Testing between Competing Models of Sharecropping,” Journal of
Political Economy, 95 (5): 893-920.

De Soto, Hernando (2003) The Mystery of Capital: Why Capitalism Triumphs in the West and
Fails Everywhere Else Basic Books.

Thomas, D., and J. Strauss (1997), “Health and wages: Evidence on men and women in urban
Brazil,” Journal of Econometrics, 77:159-85.

Thomas, Duncan, et al. (2004) `Causal Effect of Health on Labor Market Outcomes: Evidence
from a Random Assignment Iron Supplementtion Intervention,’ Mimeo, UCLA

Townsend, Robert (1995), “Financial Systems in Northern Thai Villages,” Quarterly Journal of
Economics 110 (4): 1011-1046.

Udry, Christopher (1990), “Credit Markets in Northern Nigeria: Credit as Insurance in a Rural
Economy,” World Bank Economic Review, 4(3): 251-69.

Wagstaff, Adam (2003) “Child Health on one dollar a day” Social science & medicine
2003, 57(9):1529-1538

28

World Health Organization Expert Consultation (2004), “Appropriate Body-Mass Index for
Asian Populations and Its Implications for Policy and Intervention Strategies,” Lancet,
363(9403): 157–63.

Table 1: Data sets description
Avg Monthly
Consumption

per capita Number Percent of Total Number Percent of Total
Country Source Year (In PPP$) Surveyed Surveyed HHs Surveyed Surveyed HHs
Cote d’Ivoire LSMS 1988 664.13 375 14% 1,411 49%
Guatemala GFHS 1995 301.92 469 18% 910 34%
India – Hyderabad Banerjee-Duflo-Glennerster 2005 71.61 106 7% 1,030 56%
India – Udaipur Banerjee-Deaton-Duflo 2004 43.12 482 47% 883 86%
Indonesia IFLS 2000 142.84 320 4% 2,106 26%
Mexico MxFLS 2002 167.97 959 15% 2,698 39%
Nicaragua LSMS 2001 117.34 333 6% 1,322 28%
Pakistan LSMS 1991 48.01 1,573 40% 3,632 83%
Panama LSMS 1997 359.73 123 2% 439 6%
Papua New Guinea LSMS 1996 133.38 185 15% 485 38%
Peru LSMS 1994 151.88 297 7% 821 20%
South Africa LSMS 1993 291.33 413 5% 1,641 19%
Tanzania LSMS 1993 50.85 1,184 35% 2,941 73%
Timor Leste LSMS 2001 64.42 662 15% 2,426 51%

Notes 1) To compute the $1.08 and $2.16 poverty line for the countries in our sample, we use the 1993 consumption exchange rate provided by the World Bank
(available at ) multiplied by the ratio of the country’s Consumer Price Index
to the U.S. Consumer Price Index between 1993 and the year the survey was carried out.
2) To compute average consumption per capita and the proportion of households in poverty, observations are weighted using survey weight*household size
3) The Mexican Family Life Survey is documented in Rubalcava and Teruel (2004) and available at http://www.radix.uia.mx/ennvih/
4) The LSMS are available from the World Bank LSMS project page.
5) The IFLS and GFLS are available from the RAND FLS page (http://www.rand.org/labor/FLS/)
6) The Udaipur data is available from www.povertyactionlab.org/data. The Hyderabad data is forthcoming on the same page

Households (HHs) Living On Less Than
$1.08 per person per day $2.16 per person per day

Appendix- Tables

The economic lives of the poor

Esther Duflo and Abhijit Banerjee
MIT

girls 0-12 girls 13-18 women 21-50 women 51-older boys 0-12 boys 13-18 men 21-50 men 51-older children per woman

Rural
Cote d’Ivoire 2.149157 0.7818763 1.979564 0.721778 2.666122 0.914795 1.175167 0.5598547 2.432494
Guatemala 1.796232 0.544987 1.311481 0.2802738 1.729336 0.4418486 1.156533 0.352742 3.498438
IndiaUdaipur 1.325726 0.3672199 0.966805 0.2697096 1.358921 0.3443983 0.9896265 0.2385892 2.789124
Indonesia 0.9174603 0.523725 1.304952 0.437163 0.8705046 0.4374299 1.371218 0.4232031 2.032071
Mexico 0.9 0.5070693 1.035307 0.4321416 0.853714 0.5452123 1.145439 0.331505 2.211015
Nicaragua 1.597005 0.5678074 1.07484 0.2315043 1.628777 0.76195 0.9744686 0.3372386 3.078375
Pakistan 2.016568 0.585511 1.271537 0.4109347 2.088473 0.6692291 1.367866 0.43903 3.228408
Peru 1.562229 0.421786 1.017115 0.3058037 1.442208 0.3533415 0.9837216 0.3200212 2.961281
SouthAfrica 1.473795 0.6973492 1.429532 0.6129878 1.66929 0.583054 1.074615 0.4891815 2.329741
Tanzania 1.447079 0.4819979 1.069643 0.3744389 1.526861 0.4848362 0.8318779 0.38776 2.722047
TimorLeste 1.376577 0.386832 0.9851342 0.1902925 1.310178 0.3651149 0.8517124 0.247835 2.774989
Urban
Cote d’Ivoire 2.297182 0.778813 2.337201 0.6046988 3.003163 0.8925693 1.317061 0.635378 2.267817
India – Hyderabad 1.121212 0.4742424 1.263636 0.2166667 1.039394 0.5636364 1.293939 0.2757576 2.227559
Indonesia 0.9582009 0.4653502 1.564172 0.7008179 1.018669 0.4165608 1.663275 0.8335569 1.861013
Mexico 1.301199 0.594942 1.141251 0.3630897 1.172067 0.5453798 1.061756 0.3236976 2.942235
Nicaragua 1.66856 0.5267447 1.195798 0.4157869 1.646113 0.7636554 1.013791 0.2835233 2.869345
Pakistan 1.976316 0.6990231 1.327084 0.365237 1.989362 0.7446227 1.421973 0.4749397 2.988264
Peru 1.494865 0.6035883 1.225346 0.1070044 1.47728 0.5117929 1.026184 0.2389448 2.813905
SouthAfrica 1.550764 0.4555098 1.575343 0.7533257 1.845001 0.5225847 1.05761 0.2573899 2.377336
Tanzania 1.21222 0.5618973 1.017122 0.6991292 1.42613 0.4225321 0.7071783 0.6967021 2.495158
TimorLeste 1.179673 0.4258459 1.050646 0.238627 1.697905 0.5778195 1.005795 0.2158147 2.876323

Rural
Cote d’Ivoire 1.983717 0.9397745 1.811298 0.4581137 2.335259 0.9145386 1.295418 0.4183922 2.384464
Guatemala 1.792295 0.4989813 1.222347 0.2587095 1.682279 0.4352029 1.047535 0.2842566 3.601835
India – UP/Bihar 0.6699485 0.4066677 1.358147 0.3778105 1.511564 0.49102 1.402095 0.419623 1.44092
IndiaUdaipur 1.075878 0.3522084 0.9433749 0.2887882 1.137033 0.3420159 0.9784824 0.2627407 2.350831
Indonesia 0.8259456 0.413202 1.252339 0.4174558 0.8941389 0.4064281 1.256622 0.397603 1.990475
Mexico 1.016112 0.3829419 1.253461 0.3620298 0.9411678 0.4715392 1.136004 0.3489086 2.249281
Nicaragua 1.331818 0.4888128 0.9862231 0.2519777 1.347131 0.6082729 0.9912466 0.3150775 2.719685
Pakistan 1.853165 0.5910142 1.257741 0.4318625 1.918093 0.6186932 1.38542 0.4669165 2.998437
Peru 1.32672 0.4108896 0.9707615 0.2971886 1.229715 0.3936079 0.9365241 0.3189041 2.63802
SouthAfrica 1.379225 0.6423018 1.368768 0.5451272 1.393872 0.5882369 1.064175 0.4357492 2.12633
Tanzania 1.289787 0.4567574 1.02991 0.3058478 1.392748 0.4550529 0.8360943 0.3384566 2.53434
TimorLeste 1.139498 0.3522435 0.9438348 0.1808534 1.220317 0.3294099 0.855065 0.2327247 2.48946
Urban
Cote d’Ivoire 2.108609 0.7220769 1.848683 0.5703453 2.45891 0.8077151 1.11699 0.5436647 2.470688
India – Hyderabad 0.8324541 0.4296889 1.219211 0.2366745 0.7727851 0.4447881 1.243405 0.2481353 1.779609
Indonesia 0.8485122 0.4439105 1.525618 0.5089126 0.8760152 0.4067585 1.606952 0.4441536 1.706557
Mexico 1.128694 0.5234351 1.179056 0.362634 1.017721 0.4652054 1.066386 0.3814492 2.518073
Nicaragua 1.420392 0.485482 1.140665 0.3437782 1.329313 0.6281453 0.9929218 0.2941304 2.484601
Pakistan 1.754959 0.6288284 1.300856 0.3632465 1.758454 0.7107547 1.46305 0.4470149 2.700846
Peru 1.330315 0.5576077 1.256044 0.2606437 1.559565 0.5512478 0.962274 0.3051204 2.664829
SouthAfrica 1.136331 0.5750275 1.485255 0.5224957 1.233012 0.5849377 1.184646 0.3265471 1.722113
Tanzania 1.162486 0.4838437 1.049367 0.414594 1.190848 0.4942869 0.7823718 0.4480715 2.262439
TimorLeste 1.146996 0.5370345 1.030758 0.207165 1.268939 0.4137023 1.102973 0.2388155 2.428151

Table 2 : Demographic table for the poor and the extremely poor households

Living on less than $1 a day

Living on less than $2 a day

Average number per household
Female, Age: Male, Age

% HHs with
Alcohol/ any Festival

Food Tobacco Education Health Entertainment Festivals Expenditure
Living on less than $1 a day

Rural
Cote d’Ivoire 64.4% 2.7% 5.8% 2.2% 0.0% 1.3% 59.9%
Guatemala 65.9% 0.4% 0.1% 0.3% 0.1% 7.7%
India – Udaipur 56.0% 5.0% 1.6% 5.1% 0.0% 14.1% 99.4%
India – UP/Bihar 80.1% 3.1% 0.3% 5.2% 0.1% 2.2%
Indonesia 66.1% 6.0% 6.3% 1.3% 0.0% 2.2% 80.3%
Mexico 49.6% 8.1% 6.9% 0.0% 0.7% 0.0% 2.7%
Nicaragua 57.3% 0.1% 2.3% 4.1% 0.0% 0.0% 1.8%
Pakistan 67.3% 3.1% 3.4% 3.4% 0.3% 2.4% 64.8%
Panama 67.8% 2.5% 4.0% 0.6% 0.0% 0.0%
Papua New Guinea 78.2% 4.1% 1.8% 0.3% 0.2% 1.5% 21.7%
Peru 71.8% 1.0% 1.9% 0.4% 0.0%
South Africa 71.5% 2.5% 0.8% 0.0% 0.1% 3.2% 90.3%
Timor Leste 76.5% 0.0% 0.8% 0.9% 0.0% 0.0% 49.0%

Urban
Cote d’Ivoire 65.0% 3.5% 5.1% 1.6% 0.0% 2.5% 73.9%
India – Hyderabad 59.8% 2.5% 4.7% 4.6% 1.2% 4.3%
Indonesia 58.5% 5.5% 8.8% 0.9% 0.0% 2.0% 87.2%
Mexico 59.6% 3.6% 6.3% 0.0% 0.1% 0.1% 1.6%
Nicaragua 56.3% 1.0% 3.6% 6.6% 0.0% 0.0% 2.5%
Pakistan 63.4% 3.0% 6.1% 3.8% 0.3% 2.2% 60.4%
Papua New Guinea 81.7% 0.6% 0.4% 0.0% 1.9% 0.0% 0.0%
Peru 58.5% 0.2% 2.9% 0.4% 0.0%
South Africa 57.9% 5.0% 1.2% 0.0% 0.1% 4.2% 92.2%
Timor Leste 74.1% 0.0% 0.7% 1.3% 0.0% 0.0% 51.9%

Living on less than $2 a day

Rural

Cote d’Ivoire 62.5% 2.2% 7.2% 2.3% 0.1% 1.9% 67.8%
Guatemala 53.6% 0.5% 0.1% 0.5% 0.2% 14.6%
India – Hyderabad
India – UP/Bihar 76.8% 3.0% 0.2% 6.1% 0.1% 3.0%
Indonesia 65.0% 6.8% 5.4% 1.4% 0.2% 2.5% 89.0%
Mexico 50.1% 6.5% 6.8% 0.1% 0.4% 0.4% 6.6%
Nicaragua 60.6% 0.6% 2.9% 4.2% 0.1% 0.0% 4.2%
Pakistan 66.0% 2.9% 3.2% 3.6% 0.3% 3.3% 67.8%
Panama 66.2% 3.4% 4.3% 1.2% 0.0% 1.1%
Papua New Guinea 68.5% 5.1% 2.5% 0.4% 0.2% 2.5% 36.7%
Peru 70.8% 1.3% 2.2% 0.5% 0.1%
South Africa 67.4% 3.4% 1.0% 0.1% 0.3% 3.1% 91.0%
Timor Leste 75.1% 0.0% 0.9% 0.8% 0.0% 0.0% 59.3%

Urban
Cote d’Ivoire 66.1% 3.3% 4.9% 1.8% 0.1% 2.5% 77.0%
Guatemala
India – Hyderabad 53.9% 2.7% 7.3% 5.8% 1.7% 5.4%
Indonesia 60.1% 6.3% 7.6% 1.5% 0.2% 2.1% 90.9%
Mexico 56.7% 4.2% 5.6% 0.0% 0.2% 0.3% 3.7%
Nicaragua 59.9% 0.7% 5.7% 4.6% 0.3% 0.0% 4.9%
Pakistan 60.2% 2.9% 6.3% 4.2% 0.4% 2.9% 66.7%
Panama 50.7% 6.5% 13.1% 1.9% 0.2% 9.6%
Papua New Guinea 61.6% 4.4% 0.8% 0.8% 3.4% 2.3% 30.4%
Peru 56.4% 0.8% 3.6% 0.4% 0.1%
South Africa 56.9% 5.1% 0.9% 0.2% 0.3% 2.9% 89.4%
Timor Leste 65.3% 0.0% 1.6% 0.7% 0.0% 0.0% 51.8%

As a Share of Total Consumption
Table 3: How the poor spend their money

Radio Television Bicycle Land
Living on less than $1 a day

Rural
Cote d’Ivoire 43.3% 14.3% 34.4% 62.7%
Guatemala 58.5% 20.3% 23.1% 36.7%
India – Hyderabad
India – Udaipur 11.4% 0.0% 13.5% 98.9%
India – UP/Bihar 28.3% 7.3% 65.8%
Indonesia 26.5% 49.6%
Mexico 41.3% 4.0%
Nicaragua 59.3% 8.3% 11.1% 50.4%
Pakistan 23.1% 27.0% 30.4%
Panama 43.6% 3.3% 0.0% 85.1%
Papua New Guinea 18.0% 0.0% 5.3%
Peru 73.3% 9.8% 9.8% 65.5%
South Africa 72.2% 7.2% 20.0% 1.4%
Tanzania 0.0% 92.3%
Timor Leste 14.3% 0.6% 0.9% 95.2%

Urban
Cote d’Ivoire 44.1% 5.2% 58.5% 57.3%
Guatemala
India – Hyderabad 16.2% 57.0% 39.4% 17.6%
India – Udaipur
India – UP/Bihar
Indonesia 51.7% 10.7%
Mexico 39.0% 37.1%
Nicaragua 69.3% 21.1% 14.4% 15.0%
Pakistan 36.1% 40.4% 1.5%
Panama
Papua New Guinea 0.0% 0.0% 0.0%
Peru 78.8% 46.6% 9.8% 8.6%
South Africa 71.4% 26.7% 1.3% 0.0%
Tanzania 0.3% 71.9%
Timor Leste 10.9% 0.6% 0.8% 89.8%

Living on less than $2 a day
Rural

Cote d’Ivoire 70.4% 44.9% 23.0% 58.7%
Guatemala 59.7% 18.6% 25.4% 38.8%
India – Hyderabad
India – Udaipur 16.1% 1.6% 16.1% 98.9%
India – UP/Bihar 34.2% 9.1% 68.2%
Indonesia 33.2% 50.9%
Mexico 52.2% 2.3%
Nicaragua 57.2% 19.2% 19.4% 47.9%
Pakistan 30.6% 30.1% 35.1%
Panama 55.7% 10.6% 4.2% 70.5%
Papua New Guinea 27.4% 1.4% 6.9%
Peru 76.7% 20.9% 10.2% 66.8%
South Africa 79.0% 16.5% 19.5% 5.5%
Tanzania 0.1% 91.7%
Timor Leste 13.3% 0.4% 1.2% 92.6%

Urban
Cote d’Ivoire 49.6% 11.9% 46.6% 68.4%
Guatemala
India – Hyderabad 15.7% 73.6% 42.1% 20.2%
India – Udaipur
India – UP/Bihar
Indonesia 59.9% 13.9%
Mexico 43.0% 35.2%
Nicaragua 38.1% 54.5% 33.0% 11.5%
Pakistan 42.4% 38.4% 1.6%
Panama 49.2% 70.0% 34.7% 0.0%
Papua New Guinea 46.1% 0.0% 9.6%
Peru 82.1% 62.4% 15.1% 8.8%
South Africa 78.3% 38.3% 12.8% 2.5%
Tanzania 1.0% 61.2%
Timor Leste 14.3% 3.2% 1.8% 60.3%

Percent of Households with:
Table 4: What do the poor own

In-House Toilet/
Tap Water Latrine Electricity

Rural
Cote d’Ivoire 11.8% 27.1% 45.1%
Guatemala 37.7% 50.5% 29.9%
India – Udaipur 0.0% 0.0% 8.3%
India – UP/Bihar 1.9% 3.4% 8.7%
Indonesia 5.6% 30.5% 96.9%
Mexico 99.0%
Nicaragua 12.3% 59.0% 16.4%
Pakistan 9.9% 28.5% 55.5%
Panama 37.7% 0.0%
Papua New Guinea 1.7% 95.2% 2.0%
Peru 29.7% 12.2%
South Africa 4.4% 58.9% 5.6%
Tanzania 0.7% 91.6% 1.1%
Timor Leste 2.3% 31.3% 8.8%

Urban
Cote d’Ivoire 1.6% 11.3% 9.1%
Indonesia 15.7% 34.7% 100.0%
Mexico 95.5%
Nicaragua 29.3% 67.5% 30.2%
Pakistan 50.4% 82.7% 95.2%
Panama
Papua New Guinea 28.7% 53.6% 28.7%
Peru 73.8% 59.5%
South Africa 44.2% 60.5% 15.1%
Tanzania 12.1% 96.7% 14.2%
Timor Leste 9.1% 42.8% 46.9%

Living on less than $2 a day
Rural

Cote d’Ivoire 15.7% 31.6% 68.1%
Guatemala 36.3% 51.1% 29.2%
India – Udaipur 0.0% 0.5% 15.2%
India – UP/Bihar 2.0% 5.7% 10.7%
Indonesia 8.5% 40.1% 89.0%
Mexico 99.0%
Nicaragua 17.3% 63.9% 27.3%
Pakistan 12.6% 33.1% 61.1%
Panama 54.2% 10.1%
Papua New Guinea 1.0% 92.8% 1.8%
Peru 26.1% 16.3%
South Africa 7.0% 65.9% 10.5%
Tanzania 1.5% 92.8% 1.3%
Timor Leste 5.4% 29.3% 11.0%

Urban
Cote d’Ivoire 4.6% 14.6% 18.6%
Indonesia 20.5% 57.9% 99.1%
Mexico 96.6%
Nicaragua 66.2% 88.4% 70.6%
Pakistan 55.4% 86.2% 95.2%
Panama 89.1% 81.1%
Papua New Guinea 16.0% 70.4% 16.0%
Peru 67.5% 72.4%
South Africa 59.1% 69.8% 34.2%
Tanzania 21.2% 97.3% 23.2%
Timor Leste 29.5% 34.6% 69.1%

Living on less than $1 a day

Table 5: Economics environment of the poor: Basic infrastructure
Percent of Households with:

Percent of Median Ares Percent of Households in which At Least One Member: Percent of HHs
Households Of Land Is Self Employed In Works for a Wage or Salary in That Receive Income
that own land Owned Agriculture Other Agriculture Other From Multiple Sectors

Living on less than $1 a day
Rural

Cote d’Ivoire 62.7% 300 37.2% 25.9% 52.4% 78.3% 72.1%
Guatemala 36.7% 29 64.4% 22.6% 31.4% 86.4% 83.8%
India – Udaipur 98.9% 60 98.4% 5.9% 8.5% 90.7% 94.0%
India – UP/Bihar 40 72.1% 40.2% 2.0% 18.9% 41.8%
Indonesia 49.6% 60 49.8% 36.6% 31.1% 34.3% 50.4%
Mexico 4.0% 4.9% 20.4% 2.8% 72.6% 13.2%
Nicaragua 50.4% 280 54.7% 11.6% 0.3% 42.8% 18.4%
Pakistan 30.4% 162 72.1% 35.5% 32.6% 50.8% 66.8%
Panama 85.1% 300 69.1% 17.7% 0.0% 0.0% 19.2%
Peru 65.5% 150 71.7% 25.2% 34.8%
South Africa 1.4% 0.0% 9.1% 27.9% 26.6% 0.4%
Tanzania 92.3% 182
Timor Leste 95.2% 100 78.5% 12.0% 10.4%

Urban
Cote d’Ivoire 57.3% 300 35.0% 4.8% 92.3% 26.3% 47.4%
Guatemala
India – Hyderabad 17.6% 20 0.0% 18.0% 0.8% 89.8% 11.5%
Indonesia 10.7% 5 9.6% 50.8% 35.6% 77.0% 56.9%
Mexico 37.1% 27.3% 20.7% 24.3% 36.3% 24.2%
Nicaragua 15.0% 350 24.9% 37.7% 0.0% 31.6% 8.3%
Pakistan 1.5% 121 17.6% 51.2% 4.2% 67.2% 38.3%
Peru 8.6% 100 6.2% 57.6% 21.9%
South Africa 0.0% 0.0% 6.8% 9.0% 46.4% 0.0%
Tanzania 71.9% 162
Timor Leste 89.8% 100 80.6% 7.6% 2.1%

Rural
Cote d’Ivoire 58.7% 300 25.3% 18.0% 39.1% 83.5% 46.6%
Guatemala 38.8% 31 61.9% 18.5% 30.4% 84.0% 81.2%
India – Udaipur 98.9% 63 98.1% 6.7% 7.0% 86.9% 93.2%
India – UP/Bihar 51 74.5% 41.6% 1.6% 20.6% 44.8%
Indonesia 50.9% 50 55.4% 33.4% 32.4% 34.7% 48.9%
Mexico 2.3% 7.6% 27.2% 1.1% 67.4% 18.2%
Nicaragua 47.9% 420 47.3% 23.2% 0.2% 34.8% 20.7%
Pakistan 35.1% 162 75.3% 32.1% 26.4% 53.2% 64.4%
Panama 70.5% 300 55.5% 36.1% 0.0% 0.0% 24.8%
Peru 66.8% 150 68.6% 27.0% 40.4%
South Africa 5.5% 0.7% 13.6% 18.4% 33.4% 0.7%
Tanzania 91.7% 182
Timor Leste 92.6% 100 70.7% 11.9% 12.3%

Urban
Cote d’Ivoire 68.4% 400 35.4% 5.6% 83.6% 32.0% 45.7%
Guatemala
India – Hyderabad 20.2% 40 0.1% 20.3% 1.3% 88.7% 12.2%
Indonesia 13.9% 20 13.2% 49.5% 18.6% 71.5% 46.8%
Mexico 35.2% 26.8% 21.9% 19.7% 41.4% 23.6%
Nicaragua 11.5% 630 12.1% 45.6% 0.0% 20.0% 8.7%
Pakistan 1.6% 162 17.5% 48.4% 3.0% 68.5% 35.5%
Panama 0.0% 0.0% 51.1% 0.0% 0.0% 0.0%
Peru 8.8% 150 11.4% 61.9% 18.8%
South Africa 2.5% 0.0% 12.5% 6.7% 42.2% 0.5%
Tanzania 61.2% 121
Timor Leste 60.3% 100 52.2% 18.8% 9.2%

Table 6: How the poor earn their money: Occupation

Living on less than $2 a day

In Last Month
A Household’s Percent of Households that met

Percent of HH Average # of At Least Once with a Consultant Infant
Members Sick Consultations Public Private Mortality

Rural
Cote d’Ivoire 21.4% 1.28 49.7% 3.2% 6.2%
Guatemala 6.2%
India – Hyderabad
India – Udaipur 46.1% 0.11 20.1% 58.1% 10.0%
India – UP/Bihar 12.5% 0.81 13.9% 47.3% 7.7%
Indonesia 24.2% 0.77 20.7% 27.3% 3.4%
Mexico 46.3% 1.11 47.7% 0.0% 6.9%
Nicaragua 34.9% 0.15 46.0% 5.0%
Pakistan 28.0% 0.45 24.0% 48.8% 16.7%
Panama 15.2% 0.10 23.8% 0.0%
Papua New Guinea
Peru 11.1% 0.10 20.9% 8.5%
South Africa 12.5% 0.12 16.4% 6.9% 8.6%
Tanzania 13.2% 0.07 23.2% 14.0% 8.7%
Timor Leste 11.7% 0.21 30.2% 0.5%

Urban
Cote d’Ivoire 27.8% 0.68 32.6% 2.6% 9.5%
Guatemala
India – Hyderabad
India – Udaipur
India – UP/Bihar
Indonesia 27.6% 0.88 23.5% 34.4% 2.8%
Mexico 50.1% 0.95 46.1% 0.0% 2.5%
Nicaragua 31.7% 0.14 50.7% 4.8%
Pakistan 24.4% 0.37 21.3% 43.3% 11.8%
Panama
Papua New Guinea
Peru 13.5% 0.19 34.8% 16.5%
South Africa 11.3% 0.11 17.7% 2.2% 9.2%
Tanzania 14.4% 0.10 26.5% 15.2% 7.3%
Timor Leste 12.4% 0.26 38.1% 4.5%

Rural
Cote d’Ivoire 21.9% 1.33 55.1% 11.8% 7.2%
Guatemala 5.4%
India – Hyderabad
India – Udaipur 46.4% 0.15 20.3% 60.8% 10.5%
India – UP/Bihar 13.3% 0.84 15.9% 48.8% 7.8%
Indonesia 22.8% 0.77 19.6% 24.7% 4.1%
Mexico 47.8% 1.29 55.1% 0.0% 3.2%
Nicaragua 34.2% 0.16 46.6% 6.6%
Pakistan 28.5% 0.42 24.5% 45.4% 16.7%
Panama 13.8% 0.23 42.4% 4.4%
Papua New Guinea
Peru 12.4% 0.12 25.0% 11.3%
South Africa 13.9% 0.14 17.6% 11.9% 8.3%
Tanzania 14.6% 0.09 25.1% 16.1% 9.2%
Timor Leste 11.3% 0.27 24.6% 2.8%

Urban
Cote d’Ivoire 27.3% 0.79 34.1% 2.1% 11.8%
Guatemala
India – Hyderabad
India – UP/Bihar
India – Udaipur
Indonesia 29.3% 1.06 28.3% 28.9% 2.6%
Mexico 47.7% 1.06 51.2% 0.0% 2.8%
Nicaragua 29.9% 0.15 48.4% 8.0%
Pakistan 26.6% 0.32 21.4% 39.2% 13.1%
Panama 16.5% 0.27 61.3% 14.5%
Papua New Guinea
Peru 10.3% 0.13 33.2% 14.8%
South Africa 13.5% 0.14 19.3% 12.0% 6.9%
Tanzania 15.6% 0.11 28.9% 21.7% 7.7%
Timor Leste 12.4% 0.28 32.6% 10.4%

Living on less than $1 a day
Living on less than $2 a day

Table 7: Health in the Household

Percent of Proportion of Total Laons from: % HH with a
HH with at least Microcredit Savings Savings
one Loan Bank Moneylender Instiution Credit Union Group Shopkeeper Villager Relative Friend Other Account

Rural
Cote d’Ivoire 30.5% 5.7% 0.0% 0.0% 94.3% 0.0% 0.0% 79.5%
India – Udaipur 66.3% 6.0% 15.9% 0.0% 6.0% 2.6% 36.4% 4.0% 21.6% 2.1% 2.8% 6.4%
India – UP/Bihar 6.0% 2.2% 19.2% 0.0% 1.5% 60.9% 0.0% 1.3%
Indonesia 11.6% 25.3% 2.7% 2.9% 0.0% 17.8% 0.0% 0.0% 0.0% 51.3% 6.6%
Mexico 18.5% 17.4% 2.5% 0.0% 53.5% 18.3% 8.3% 6.2%
Nicaragua
Pakistan 93.2% 1.5% 0.8% 0.0% 0.0% 15.8% 11.2% 38.1% 29.0% 3.7% 11.7%
Panama 2.8% 0.5%
Papua New Guinea
Peru 12.3% 0.0% 9.2% 23.9% 0.5%
South Africa 39.6% 1.0% 0.0% 71.3% 26.1% 16.7%
Timor Leste 10.9% 0.0% 0.0% 0.0% 13.4%

Urban
Cote d’Ivoire 40.4% 3.9% 0.0% 2.1% 92.6% 0.0% 1.5% 93.4%
India – Hyderabad 70.5% 6.9% 61.6% 1.0% 0.0% 0.0% 0.0% 10.5% 6.2% 11.1% 2.7% 24.7%
Indonesia 11.6% 32.3% 22.6% 2.4% 0.0% 3.8% 0.0% 0.0% 0.0% 38.8% 3.3%
Mexico 19.1% 1.2% 12.7% 0.0% 31.4% 40.6% 14.1% 3.0%
Nicaragua
Pakistan 95.1% 6.2% 0.4% 0.0% 0.0% 8.9% 4.7% 43.5% 23.2% 13.1% 26.2%
Papua New Guinea
Peru 8.6% 0.0% 0.0% 79.1% 0.0%
South Africa 26.0% 0.0% 0.0% 49.3% 25.5% 22.3%
Tanzania 17.7%
Timor Leste 10.3% 0.0% 0.0% 0.0% 8.5%

Living on less than $2 a day
Rural

Cote d’Ivoire 37.3% 6.8% 0.0% 1.1% 86.9% 0.0% 5.2% 86.4%
India – Udaipur 68.0% 6.4% 17.9% 0.0% 6.4% 2.6% 37.4% 4.3% 23.0% 2.2% 3.2% 12.1%
India – UP/Bihar 6.4% 3.0% 19.8% 0.0% 1.9% 58.7% 0.0% 1.2%
Indonesia 11.9% 33.3% 4.6% 6.1% 0.0% 23.0% 2.7% 1.5% 0.0% 28.8% 9.6%
Mexico 17.9% 17.1% 3.6% 0.0% 33.2% 20.1% 25.9% 10.1%
Nicaragua
Pakistan 95.4% 3.6% 0.7% 0.0% 0.1% 16.8% 9.3% 38.1% 25.9% 5.5% 16.0%
Panama 2.1% 2.5%
Peru 14.7% 0.0% 6.0% 30.6% 1.0%
South Africa 41.7% 0.9% 0.0% 61.3% 21.2% 27.4%
Timor Leste 13.2% 2.5% 0.0% 0.0% 14.5%

Urban
Cote d’Ivoire 40.0% 4.4% 0.0% 2.1% 90.0% 0.0% 3.4% 90.6%
India – Hyderabad 69.9% 5.1% 52.1% 1.3% 0.0% 0.7% 1.2% 11.5% 12.9% 12.9% 2.2% 23.2%
Indonesia 19.4% 39.6% 9.0% 7.8% 0.0% 13.7% 0.0% 0.0% 0.0% 29.9% 14.9%
Mexico 21.3% 5.3% 10.6% 0.0% 33.8% 34.2% 16.0% 5.1%
Nicaragua
Pakistan 94.0% 5.8% 0.5% 0.0% 0.0% 11.6% 3.7% 42.9% 25.5% 9.9% 31.2%
Panama 0.0% 0.0%
Papua New Guinea
Peru 14.7% 3.0% 7.8% 43.3% 0.1%
South Africa 43.1% 1.1% 0.0% 39.8% 23.0% 46.6%
Timor Leste 11.5% 0.0% 0.0% 0.0% 8.6%

Living on less than $1 a day

Table 8: Market for Credit and Savings and the poor

Percent of Children in School
Female, Age: Male, Age:
7-12 13-18 7-12 13-18

Rural
Cote d’Ivoire 32.3% 22.8% 45.5% 21.1%
India – Udaipur 60.7% 13.0% 82.6% 24.7%
India – UP/Bihar 51.4% 20.2% 72.1% 51.2%
Indonesia 93.4% 45.9% 82.4% 39.3%
Mexico 94.5% 56.5% 93.5% 38.6%
Nicaragua 67.5% 38.0% 65.4% 27.5%
Pakistan 30.7% 9.2% 64.1% 41.3%
Panama 79.0% 14.6% 85.1% 27.0%
Papua New Guinea 53.0% 33.5% 71.4% 70.9%
Peru 94.2% 64.7% 93.3% 73.7%
South Africa 83.6% 87.5% 80.5% 76.9%
Tanzania 51.2% 53.3% 47.2% 61.4%
Timor Leste 76.6% 89.7% 80.0% 86.8%

Urban
Cote d’Ivoire 20.5% 10.7% 39.8% 27.7%
India – Hyderabad 88.7% 42.6% 88.1% 47.3%
Indonesia 85.3% 39.1% 100.0% 36.5%
Mexico 97.1% 47.7% 95.9% 55.8%
Nicaragua 80.0% 52.0% 60.8% 32.2%
Pakistan 65.8% 29.2% 75.7% 40.7%
Papua New Guinea 60.8% 56.7% 62.2% 60.2%
Peru 93.0% 73.0% 95.1% 97.3%
South Africa 91.2% 87.0% 89.1% 96.2%
Tanzania 66.4% 51.8% 54.4% 65.3%
Timor Leste 84.9% 90.2% 91.4% 97.1%

Rural
Cote d’Ivoire 50.1% 34.4% 60.5% 41.4%
India – Udaipur 62.9% 16.1% 85.9% 30.1%
India – UP/Bihar 54.3% 23.1% 73.5% 54.7%
Indonesia 92.7% 45.9% 91.1% 47.4%
Mexico 95.1% 54.5% 97.0% 51.0%
Nicaragua 78.3% 48.3% 74.3% 37.4%
Pakistan 37.5% 16.5% 69.7% 46.8%
Panama 90.4% 30.9% 90.7% 34.0%
Papua New Guinea 60.7% 42.8% 64.8% 62.0%
Peru 95.5% 62.6% 94.1% 74.8%
South Africa 87.8% 85.4% 82.5% 81.9%
Tanzania 53.0% 57.2% 50.1% 63.1%
Timor Leste 79.6% 94.4% 83.5% 92.4%

Urban
Cote d’Ivoire 40.0% 19.0% 54.2% 37.0%
Guatemala
India – Hyderabad 88.6% 48.6% 89.9% 57.6%
Indonesia 97.2% 54.6% 95.7% 57.6%
Mexico 97.5% 54.5% 97.1% 61.1%
Nicaragua 87.7% 72.3% 87.4% 57.8%
Pakistan 69.8% 35.8% 77.4% 49.3%
Panama 55.2% 20.9% 44.9% 75.8%
Papua New Guinea 66.6% 37.2% 67.4% 42.6%
Peru 95.9% 72.6% 98.7% 81.1%
South Africa 91.4% 91.9% 87.2% 89.7%
Tanzania 64.8% 59.6% 55.2% 67.4%
Timor Leste 89.2% 92.8% 90.3% 95.3%

Table 9: Education

Living on less than $1 a day
Living on less than $2 a day

Table 10: Non-Agriculture Enterprises Owned by Household
Percent of HHs
with at least One In Each Business:
Non-Agricultural Average Number of Employees Percent of Businesses that Own:
Business Paid Workers Paid + Unpaid Vehicles Machines

Rural
Cote d’Ivoire 66.4% 0.14 2.48 2.6% 66.5%
India – UP/Bihar 35.0%
Indonesia 29.4% 0.11 1.55 0.0%
Mexico 7.8% 0.59 2.16 0.0%
Nicaragua 14.0% 0.08 1.39 7.5% 0.0%
Pakistan 34.3% 0.13 1.16 36.7% 0.0%
Panama 15.2% 0.00 1.58 0.0%
Papua New Guinea
Peru 34.5% 1.50

Urban
Cote d’Ivoire 19.8% 0.03 2.43 0.7% 73.0%
Guatemala
India – Hyderabad 14.8% 1.85 0.0%
Indonesia 44.4% 0.15 1.49 0.0%
Mexico 7.9% 0.75 2.30 0.0%
Nicaragua 40.6% 0.03 1.18 0.0% 0.0%
Pakistan 51.7% 0.18 1.23 40.3% 0.0%
Peru 63.2% 1.39

Rural
Cote d’Ivoire 60.6% 0.26 2.59 2.5% 64.5%
Guatemala
Indonesia 33.8% 0.17 1.61 0.0%
Mexico 16.1% 0.55 2.00 0.0%
Nicaragua 19.3% 0.14 1.39 7.6% 0.0%
Pakistan 31.1% 0.25 1.30 37.6% 0.0%
Panama 18.3% 0.39 1.80 0.0%
Peru 37.8% 1.42

Urban
Cote d’Ivoire 24.5% 0.11 2.52 2.8% 59.6%
Guatemala
India – Hyderabad 18.0% 1.72 0.0%
Indonesia 47.2% 0.23 1.70 0.0%
Mexico 11.9% 0.57 2.21 0.0%
Nicaragua 51.5% 0.06 1.40 3.2% 0.0%
Pakistan 49.3% 0.36 1.41 40.9% 0.0%
Panama 27.4% 0.53 1.53 0.0%
Peru 70.3% 1.40

Living on less than $1 a day
Living on less than $2 a day

Percent of Total Households with Insurance:
Any Type Health Life

Rural
Cote d’Ivoire
Guatemala
India – Hyderabad
India – Udaipur 3.8%
India – UP/Bihar 9.2% 4.7% 3.8%
Indonesia 6.0% 3.9% 0.0%
Mexico 50.7%
Nicaragua 0.0% 5.5%
Pakistan
Panama 0.0% 0.0%
Papua New Guinea
Peru 5.6% 0.0%
South Africa 5.4%
Tanzania
Timor Leste

Urban
Cote d’Ivoire
Guatemala
India – Hyderabad 11.2% 0.0% 10.0%
India – Udaipur
India – UP/Bihar
Indonesia 25.4% 23.1% 0.0%
Mexico 24.9%
Nicaragua 0.0% 7.6%
Pakistan
Panama
Papua New Guinea
Peru 13.3% 0.0%
South Africa 5.7%
Tanzania
Timor Leste

Rural
Cote d’Ivoire
Guatemala
India – Hyderabad
India – Udaipur 6.9%
India – UP/Bihar 10.1% 5.1% 3.7%
Indonesia 7.7% 5.5% 0.0%
Mexico 61.6%
Nicaragua 0.0% 7.2%
Pakistan
Panama 0.0% 0.0%
Papua New Guinea
Peru 9.8% 0.0%
South Africa 11.6%
Tanzania
Timor Leste

Urban
Cote d’Ivoire
Guatemala
India – Hyderabad 18.0% 0.1% 17.3%
India – Udaipur
India – UP/Bihar
Indonesia 24.1% 16.5% 0.3%
Mexico 33.1%
Nicaragua 0.0% 19.4%
Pakistan
Panama 0.0% 0.0%
Papua New Guinea
Peru 21.6% 0.0%
South Africa 15.1%
Tanzania
Timor Leste

Table 11: Market for Insurance and the poor

Living on less than $1 a day
Living on less than $2 a day

Percent of Adults who Have Migrated
Since Birth For Work

Rural
Cote d’Ivoire 26.9% 11.1%
Guatemala
Indonesia 34.3% 30.8%
Mexico 48.7% 45.6%
Nicaragua 22.4% 5.6%
Pakistan 16.7% 3.7%
Panama 34.8% 0.6%
Papua New Guinea 4.8%
Peru 15.3% 6.7%
Timor Leste 61.6%

Urban
Cote d’Ivoire 14.1% 6.0%
Indonesia 43.0% 41.3%
Mexico 37.4% 32.3%
Nicaragua 16.2% 5.9%
Pakistan 23.1% 3.6%
Papua New Guinea 18.9%
Peru 16.5% 9.6%
Timor Leste 62.6%

Rural
Cote d’Ivoire 31.9% 16.1%
Indonesia 34.9% 29.3%
Mexico 54.3% 51.9%
Nicaragua 21.6% 5.2%
Pakistan 18.4% 4.9%
Panama 34.3% 0.6%
Papua New Guinea 5.3%
Peru 16.9% 7.3%
Timor Leste 48.7%

Urban
Cote d’Ivoire 19.0% 7.8%
Guatemala
India – Hyderabad
India – Udaipur
India – UP/Bihar
Indonesia 41.5% 30.4%
Mexico 39.5% 40.3%
Nicaragua 30.9% 7.5%
Pakistan 25.9% 4.8%
Panama 57.1% 0.0%
Papua New Guinea 37.3%
Peru 27.2% 12.0%
Timor Leste 66.9%

Table 12: Migration

Living on less than $1 a day
Living on less than $2 a day

(1) (2) (3) (4)

Any Education -0.064 -0.046 Any Medical Fa -0.289 -0.037
(.076) (.039) (.14) (.059)

primary school -0.077 -0.051 health center -0.018 0.007
(.08) (.04) (.009) (.008)

middle school -0.153 -0.074 dispensary -0.158 -0.055
(.105) (.035) (.071) (.017)

high school -0.230 -0.062 hospital -0.067 -0.032
(.102) (.025) (.031) (.015)

college -0.013 -0.006 maternal and ch -0.033 0.023
(.007) (.003) (.025) (.02)

State Fixed effe No Yes State Fixed effe No Yes

any water facili -0.003 -0.005 Electricity -0.350 -0.260
(.002) (.002) (.153) (.058)

well 0.407 0.395 post office -0.200 -0.042
(.154) (.072) (.121) (.05)

tap water -0.465 -0.114 phone -0.256 -0.101
(.16) (.048) (.101) (.033)

tank 0.130 0.066 Tarmac roards -0.469 -0.306
(.1) (.046) (.197) (.047)

canal -0.101 -0.059 banks -0.073 -0.057
(.044) (.026) (.018) (.006)

State Fixed effe No Yes State Fixed effe No Yes

NOTE: The dependent variable is the fraction of villages in the districts that have a given facility (in 81 and
91). The control variables include density of population, caste fractionalization in 1931, proportion of
schedule caste and proportion of scheduled caste and a year dummy. The independent variable is the

headcount ratio using the official Indian poverty line in 1973 (for the 1981) data and 1987. 1973 data is at the
NSS region level standard errors in the odd columns are clustered at the State level. State dummies are in

even columns.

Table 13: Do Poorer Indian districts have fewer public goods?
Proportion below the Poverty line

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