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like her father) ¬ve years from now and will then live on her
husband™s land in a neighbouring village. She expects her life to be
similar to that of her mother.

The economist™s agenda
That the lives people are able to construct differ enormously across
the globe is a commonplace. In our age of travel, it is even a
common sight. That Becky and Desta face widely different futures is
also something we have come to expect, perhaps also to accept.
Nevertheless, it may not be out of turn to imagine that the girls are
intrinsically very similar. They both enjoy playing, eating, and
gossiping; they are close to their families; they turn to their mothers
when in distress; they like pretty things to wear; and they both have
the capacity to be disappointed, get annoyed, be happy.

Their parents are also alike. They are knowledgeable about the ways
of their worlds. They also care about their families, ¬nding
ingenious ways to meet the recurring problem of producing income
and allocating resources among family members “ over time and
allowing for unexpected contingencies. So, a promising route for
exploring the underlying causes behind their vastly different
conditions of life would be to begin by observing that the
opportunities and obstacles the families face are very different, that

in some sense Desta™s family are far more restricted in what they are
able to be and do than Becky™s.

Economics in great measure tries to uncover the processes that
in¬‚uence how people™s lives come to be what they are. The
discipline also tries to identify ways to in¬‚uence those very
processes so as to improve the prospects of those who are hugely
constrained in what they can be and do. The former activity involves
¬nding explanations, while the latter tries to identify policy
prescriptions. Economists also make forecasts of what the
conditions of economic life are going to be; but if the predictions are
to be taken seriously, they have to be built on an understanding of
the processes that shape people™s lives; which is why the attempt to
explain takes precedence over forecasting.

The context in which explanations are sought or in which
prescriptions are made could be a household, a village, a district, a

country, or even the whole world “ the extent to which people or
places are aggregated merely re¬‚ects the details with which we
choose to study the social world. Imagine that we wish to
understand the basis on which food is shared among household
members in a community. Household income would no doubt be
expected to play a role; but we would need to look inside
households if we are to discover whether food is allocated on the
basis of age, gender, and status. If we ¬nd that it is, we should ask
why they play a role and what policy prescriptions, if any, commend
themselves. In contrast, suppose we want to know whether the
world as a whole is wealthier today than it was 50 years ago. As the
question is about global averages, we would be justi¬ed in ironing
out differences within and among households.

Averaging is required over time as well. The purpose of the study
and the cost of collecting information in¬‚uence the choice of the
unit of time over which the averaging is done. The population
census in India, for example, is conducted every ten years. More
frequent censuses would be more costly and wouldn™t yield extra

information of any great importance. In contrast, if we are to study
changes in the volume of home sales across seasons, even annual
statistics would miss the point of the inquiry. Monthly statistics on
home sales are a favourite compromise between detail and the cost
of obtaining detail.

Modern economics, by which I mean the style of economics taught
and practised in today™s leading universities, likes to start the
enquiries from the ground up: from individuals, through the
household, village, district, state, country, to the whole world. In
various degrees, the millions of individual decisions shape the
eventualities people face; as both theory, common sense, and
evidence tell us that there are enormous numbers of consequences
of what we all do. Some of those consequences have been intended,
but many are unintended. There is, however, a feedback, in that
those consequences in turn go to shape what people subsequently
can do and choose to do. When Becky™s family drive their cars or use

electricity, or when Desta™s family create compost or burn wood for
cooking, they add to global carbon emissions. Their contributions
are no doubt negligible, but the millions of such tiny contributions
sum to a sizeable amount, having consequences that people
everywhere are likely to experience in different ways. It can be that
the feedbacks are positive, so that the whole contribution is greater
than the sum of the parts. Strikingly, unintended consequences can
include emergent features, such as market prices, at which the
demand for goods more or less equals their supply.

Earlier, I gave a description of Becky™s and Desta™s lives.
Understanding their lives involves a lot more; it requires analysis,
which usually calls for further description. To conduct an analysis,
we need ¬rst of all to identify the material prospects the girls™
households face “ now and in the future, under uncertain
contingencies. Second, we need to uncover the character of their
choices and the pathways by which the choices made by millions
of households like Becky™s and Desta™s go to produce the prospects
they all face. Third, and relatedly, we need to uncover the

pathways by which the families came to inherit their current

These amount to a tall, even forbidding, order. Moreover, there is
a thought that can haunt us: since everything probably affects
everything else, how can we ever make sense of the social world?
If we are weighed down by that worry, though, we won™t ever
make progress. Every discipline that I am familiar with draws
caricatures of the world in order to make sense of it. The modern
economist does this by building models, which are deliberately
stripped down representations of the phenomena out there. When
I say ˜stripped down™, I really mean stripped down. It isn™t
uncommon among us economists to focus on one or two causal
factors, exclude everything else, hoping that this will enable us to
understand how just those aspects of reality work and interact.
The economist John Maynard Keynes described our subject thus:
˜Economics is a science of thinking in terms of models joined to

the art of choosing models which are relevant to the contemporary

As economists deal with quanti¬able objects (calories consumed,
hours worked, tonnes of steel produced, miles of cable laid, square
kilometres of equatorial forests destroyed), the models are almost
always mathematical constructs. They can be stated in words, but
mathematics is an enormously ef¬cient way to express the structure
of a model; more interestingly, for discovering the implications of a
model. Applied mathematicians and physicists have known this for
a long time, but it was only in the second half of the 20th century
that economists brazenly adopted that research tactic; as have
related disciplines, such as ecology. The art of good modelling is to
generate a lot of understanding from focusing on a very small
number of causal factors. I say ˜art™, because there is no formula for
creating a good model. The acid test of a model is whether it
discriminates among alternative explanations of a phenomenon.
Those that survive empirical tests are accepted “ at least for a while
“ until further evidence comes along that casts doubt on them, in

which case economists go back to their drawing board to create
better (not necessarily bigger!) models. And so on.

The methodology I have sketched here, all too brie¬‚y, enables
economists to make a type of prediction that doesn™t involve
forecasting the future, but instead to make predictions of what the
data that haven™t yet been collected from the contemporary world
will reveal. This is risky business, but if a model is to illuminate, it
had better do more than just offer explanations after the events.

Until recently, economists studied economic history in much the
same way historians study social and political history. They tried to
uncover reasons why events in a particular place unfolded in the
way they did, by identifying what they believed to be the key drivers
there. The stress was on the uniqueness of the events being studied.
A classic research topic in that mould involved asking why the ¬rst
industrial revolution occurred in the 18th century and why it took

place in England. As you can see, the question was based on three
presumptions: there was a ¬rst industrial revolution; it occurred in
the 18th century; and it was based in England. All three premises
have been questioned, of course, but there was an enormous
amount of work to be done even among those who had arrived at
those premises from historical study. In the event, the literature
built round those questions is one of the great achievements of
economic history.

In recent years economists have added to that a statistical approach
to the study of the past. The new approach stays close to economic
theory, by laying emphasis on the generality of the processes that
shape events. It adopts the view that a theory should uncover those
features that are common among economic pathways in different
places, at different times. Admittedly, no two economies are the
same, but modern economists work on the commonality in the
human experience, not so much on its differences. Say, you want to
identify the contemporary features in Desta™s and Becky™s worlds
that best explain why the standard of living in the former is so much

lower than in the latter. A body of economic models tells you that
those features are represented by the variables X, Y, and Z. You look
up international statistics on X, Y, and Z from a sample of, perhaps,
149 countries. The ¬gures differ from country to country, but you
regard the variables themselves as the explanatory factors common
to all the countries in the sample. In other words, you interpret the
149 countries as parallel economies; and you treat features that are
unique to each country as idiosyncrasies of that country. Of course,
you aren™t quite at liberty to model those idiosyncrasies any way you
like. Statistical theory “ which in the present context is called
econometrics “ will set limits on the way you are able to model them.

On the basis of the data on the 149 countries in your sample, you
can now test whether you should be con¬dent that X, Y, and Z are
the factors determining the standard of living. Suppose the tests
inform you that you are entitled to be con¬dent. Then further
analysis with the data will also enable you to determine how much

of the variation in the standard of living in the sample is explained
by variations in X in the sample, by variations in Y, and by
variations in Z. Those proportions will give you a sense of the
relative importance of the factors that determine the standard of
living. Suppose 80% of the variation in the standard of living in
those 149 countries can be explained by the variation in X in the
sample; the remaining 20% by variations in Y and Z. You wouldn™t
be unjusti¬ed to conclude, tentatively, that X is the prime
explanatory variable.

There are enormous problems in applying statistics to economic
data. For example, it may be that your economic models, taken
together, suggest that there could be as many as, say, 67 factors
determining the standard of living (not just X, Y, and Z). However,
you have a sample of only 149 countries. Any statistician will now
tell you that 149 is too small a number for the task of unravelling the
role of 67 factors. And there are other problems besetting the
econometrician. But before you abandon statistics and rush back to
the narrative style of empirical discourse, ask yourself why anyone

should believe one scholar™s historical narrative over another™s. You
may even wonder whether the scholar™s literary ¬‚air may have
in¬‚uenced your appreciation of her work. Someone now reassures
you that even the author of a historical narrative has a model in
mind. He tells you that the author™s model in¬‚uenced her choice of
the evidence displayed in her work, that she chose as she did only
after having sifted through a great deal of evidence. You ask in
response how you are to judge whether her conceptual model is
better than someone else™s. Which brings us back to the problem of
testing alternative models of social phenomena. In the next chapter
we will discover that historical narratives continue to play an
important role in modern economics, but they are put to work in
conjunction with model-building and econometric tests.

There are implicit assumptions underlying econometric tests that
are hard to evaluate (how the country-speci¬c idiosyncrasies are
modelled is only one of them). So, economic statistics are often at

best translucent. It isn™t uncommon for several competing models
to co-exist, each having its own champion. Model-building, data
availability, historical narratives, and advances in econometric
techniques reinforce one other. As the economist Robert Solow
expresses it, ˜facts ask for explanations, and explanations ask for
new facts™.

In this monograph, I ¬rst want to give you a feel for the way we
economists go about uncovering the economic pathways that shape
Becky™s and Desta™s lives. I shall do that by addressing the three
sorts of questions that were identi¬ed earlier as our concern. I shall
then explain why we need economic policies and how we should go
about identifying good ones. We will certainly build models as we go
along, but I shall mostly use words to describe them. I shall also
refer to empirical ¬ndings, from anthropology, demography,
ecology, geography, political science, sociology, and of course
economics itself. But the lens through which we will study the social
world is that of economics. We will assume a point of view of the
circumstances of living that gives prominence to the allocation of

scarce resources “ among contemporaries and across the
generations. My idea is to take you on a tour to see how far we are
able to reach an understanding of the social world around us and


Chapter 1
Macroeconomic history

I said one of the things we need to do if we are to understand
Becky™s and Desta™s lives is to uncover the pathways by which their
families came to inherit their current circumstances. This is the
stuff of economic history. In studying history, we could, should we
feel bold, take the long view “ from about the time agriculture came
to be settled practice in the northern part of the Fertile Crescent
(roughly, southeast Turkey today) some 11,000 years ago “ and try
to explain why the many innovations and practices that have
cumulatively contributed to the making of Becky™s world either
didn™t reach or didn™t take hold in Desta™s part of the world.

Scholars have tried to do that. The geographer Jared Diamond, for
example, has argued that people in the supercontinent of Eurasia
have enjoyed two potent sets of advantages over people elsewhere.
First, unlike Africa and the Americas, Eurasia is oriented along
an east“west axis in the temperate zone and contains no
overpowering mountain range or desert to prevent the diffusion of
people, ideas, seeds, and animals. Second, Eurasia was blessed with
a large number of domesticatable species of animals, which made it
possible for humans there to engage in tasks they wouldn™t have
been able to undertake on their own. Economies grew and declined
in different parts of Eurasia at different times “ now India, now
China, now Persia, now Islam, now one region in Europe, then
another “ but the supercontinent™s size and orientation meant that,

during the past 11,000 years, humanity™s achievements there have
been rather like the performance of ¬nancial stocks: declines in
some regions have been matched by growth in others. By the 16th
century, the technological gap between the seafaring nations of
Western Europe and the Americas was so large that a combination
of guns, steel, and European germs enabled tiny groups of invaders
to conquer the New World. Becky™s very successful part of the world
is in effect the outgrowth of a societal transplant that took place less
than 500 years ago.

GDP as measuring rod
In order to talk of success and failure, as we are doing here, we need
a measuring rod. The one most commonly used today is gross
domestic product per person, or GDP per capita. Economists may

Macroeconomic history
have invented the concept and may have also warned against its
many limitations; but, like it or not, the term is so ingrained in
public consciousness, that if someone exclaims, ˜Economic growth!™,
we don™t need to ask, ˜Growth in what?™ “ we know they mean
growth in real GDP per capita; which is growth in GDP per capita,
corrected for in¬‚ation or de¬‚ation.

A country™s GDP is the value of all the ¬nal goods that are produced
by its residents in a given year. It is a measure of an economy™s total
output. But when a commodity is produced and sold, the price
paid for the purchase ¬nds its way into someone™s pocket. So, GDP
can be measured also by adding up everyone™s incomes “ wages,
salaries, interests, pro¬ts, and government income. GDP and
national income are therefore two sides of the same coin.

Although GDP is often said to measure wealth, it doesn™t do so.
GDP is a ¬‚ow (dollars per year, say), whereas wealth is a stock
(dollars “ period). As the concept of GDP was developed originally
for market economies, the values imputed to the goods were market
prices. But by a clever construction of notional prices (called
˜shadow prices™; Chapters 7“8), economists have adapted GDP even

for economies like Desta™s, where much economic activity is
undertaken in non-market institutions. It was by imputing values
to the produce taken from the local commons in Desta™s village that
economic statisticians concluded that one-¬fth of her household™s
income amounts to the value of goods obtained directly from the
natural resources in her locality, a ¬gure I reported when describing
Desta™s world.

Adjusting for differences in the cost of living across the world,
global income per head today is about $8,000 a year. But for most
of humanity™s past, people have been abysmally poor. The economic
statistician Angus Maddison has estimated from the very
fragmentary evidence that exists, that, at the beginning of our
Common Era (CE 0) the per capita income of the world was about
$515 a year in today™s prices. If Maddison™s estimate is even
approximately correct, it means that the average person 2,000 years
ago enjoyed not much more than a dollar a day, a ¬gure deemed by

the World Bank as the line below which a person is in extreme
poverty. Maddison has also suggested that the distribution of

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