Sibling Rivalry Evidence from Ghana

Sibling Rivalry: Evidence from Ghana

As in many other low-income economies, parents in Ghana often invest less in the human capital of their daughters than their sons. Primary school enrollments are fairly even, but by secondary school only 28% of females between age 16 and 23 attend school, whereas 42% of boys are enrolled.

A study by Garg and Morduch [1997] explored how economic constraints exacerbate gender differences in Ghana. The starting point for this study is that even if parents desire to invest a given amount in their children’s human capital, they may lack the personal resources to do so, and even if expected returns are high, parents may find it difficult to borrow for such long-term investments. Children must then compete with their siblings for the resources currently available to parents. Boys have an advantage in this competition if parents perceive higher returns to this investment. If the total number of their siblings is held constant, children with fewer brothers also may get more resources than they would otherwise.

The Garg–Morduch study supports this hypothesis in the case of Ghana. For instance, the study shows that children aged 12–23 with three siblings are over 50% more likely to attend middle or secondary school when all three of their siblings are sisters than when the three are brothers. The effects are similar for boys and girls and for other sibling groups. Similar results hold for health outcomes as well. The study is consistent with the idea of “sibling rivalry” caused by parents’ difficulty in borrowing to make human capital investments in their children. The study illustrates the importance of considering issues of gender within the context of markets and institutions available to households. The results suggest that improving financial systems can have important indirect benefits for the health and education of children in Ghana.

What we have learned so far is that there are dimensions along which females are discriminated against, but the obvious indicator of discrimination—nutrition—does not hold up well unless we have a precise notion of requirements. There is the additional problem that direct intrahousehold data are hard to obtain. Where they do exist —as in the Ghana study described in the box—and where data are collected on outcomes other than nutrition, such as medical care and education, there is clear evidence of discrimination against girls (see also Subramanian [1994]).

We must, therefore, seek to supplement this sort of research with indicators of differential educational attainment, direct anthropometric indicators of differential nourishment, or indicators of differential mortality and morbidity. These indicators are not without problems either,35 but they serve as another route to understanding the relationship between poverty and intrahousehold allocation.

Consider educational attainment. The World Development Report (World Bank [1996]) noted that for low- income countries as a whole, there were almost twice as many female illiterates as there were males in 1995 (the illiteracy rates were 45% for females and 24% for males). This disparity is echoed by enrollment figures: in low- income countries taken together, male enrollment in primary schools exceeded female enrollment by over 12%, and the difference exceeded 30% for secondary schools.36 Note well that these are averages for the countries as a whole. To the extent that the relatively rich in these countries are free of the resource constraints that lead to discrimination, the corresponding figures for the poor in these countries must be more dramatic still.37

Consider sex ratios: estimates of female-to-male population in the developing world. In North America and Europe, the life expectancy of women is somewhat longer than for men. The roots of this difference are unclear: they may be biological, but there are also possible social and occupational factors at work. The average ratio of female-to-male population in these countries is around 1.05; that is, there are approximately 105 females for every 100 males. Figure 8.4 displays the corresponding sex ratios for many developing countries. The first panel shows the African data, the second shows the data for Asia, and the last panel shows the data for Latin America. It is evident that the problem of low female-to-male ratios is predominantly an Asian problem. The figure for Asia is peppered with data points in the range of the mid-90s, and there are several instances that are lower still.

These differences imply enormous absolute discrepancies. If the ratio of females to males is 93 (for every 100 males) in India, and India has approximately 440 million males (United Nations [1993]), then about 30 million women are unaccounted for in India alone.38 Thus sex ratios around 95 or so represent prima facie evidence of substantial discrimination, which might include neglect in infancy or childhood (leading to death) or practices such as sex-selective abortion.

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Figure 8.4. Sex ratios (females per 100 males) in the developing world. Source: United Nations Secretariat [1996].

The relative absence of skewed sex ratios in Africa makes an interesting point. As we’ve noted before, poverty alone cannot be responsible for the gender biases that we do see in Asia, although poverty serves to reinforce these biases. The overall social context of discrimination also plays a role. Take, for instance, the institution of dowry. Families might react to dowry by resorting to sex-selective abortion, female infanticide, or discriminatory neglect during the infancy of a girl (which amounts to infanticide). Boys are preferred because they are a source of income and support; girls are not because they impose costs. Nevertheless, once a girl survives there may be less evidence of discrimination in matters of nutrition and medical care. After all, the costs, say, in terms of the potential for marriage, are only enhanced in the absence of this care. Testing for gender discrimination is therefore a complicated issue, and it may be unevenly manifested through various potential channels. There is no reason to expect that all ways and forms of discrimination will be equally in evidence.

8.5. Summary Poverty, just like inequality, has intrinsic as well as functional aspects. We are interested in poverty in its own right, as an outcome that needs to be removed through policy, but poverty also affects other forms of economic and social functioning. It creates inefficiencies of various kinds and can exacerbate existing forms of discrimination, such as those against women.

We first studied issues of poverty measurement. The measurement of poverty is based on the notion of a poverty line, which is constructed from monetary estimates of minimum needs. We noted several problems with the concept even at this fundamental level: should income or item-by-item expenditure be used to identify the poor, are notions of the poverty line “absolute” or “relative,” is poverty temporary or chronic, should we study households or

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individuals as the basic unit, and so on. We then turned to well-known poverty measures: among these are the head-count ratio, which simply measures

the fraction of the population below the poverty line. The head-count ratio is a popular measure, but it fails to adequately account for the intensity of poverty. In particular, a planner who uses the head-count ratio as a political yardstick for poverty reduction will be tempted to target the segment among the poor who are very close to the poverty line (and who are arguably not in the greatest need of help). To remedy this shortcoming, we can use measures such as the poverty gap ratio or the income gap ratio, which look at the total shortfall of poor incomes from the poverty line and express this shortfall as a fraction of national income (as in the poverty gap) or as a fraction of the total income required to bring all the poor to the poverty line (as in the income gap ratio). These measures add to the information contained in the head count, but have their own drawbacks: in particular, they are indifferent to the relative deprivation of the poor (see the Appendix to this chapter for more).

We then described some of the characteristics of the poor. Even going by conservative estimates, such as India’s poverty line applied to the world as a whole, we see that in 1990, over 600 million people were poor. Poor households tend to be large (though there are some qualifications attached to this statement) and they are overrepresented by female heads of households. Rural areas tend to display more poverty. Poverty is highly correlated with the absence of productive asset holdings, such as holdings of land. Poverty is correlated with lack of education, and there is an intimate connection between nutrition and poverty, although nutrition levels do not seem to rise as quickly with household income as we might suppose a priori.

The fundamental implication of poverty is that the poor lack access to markets, most notably the markets for credit, insurance, land, and labor. We discussed how the absence of collateral restricts access to credit markets and how problems of moral hazard and incomplete information restrict access to insurance. We then began a study of imperfect access to the labor market (the threads of this story will be taken up again in Chapter 13). The basic idea is that poverty and undernutrition affect work capacity. The relationship between nutrition and work capacity can be expressed through the use of a capacity curve. The capacity curve creates the possibility of a low-income undernutrition trap. Just as low incomes are responsible for low levels of nutrition, low levels of nutrition work through the capacity curve to diminish earnings. We argued that the existence of such a trap is far more likely in countries that have low per capita incomes overall (because of labor supply effects), that it is difficult to borrow one’s way out of an undernutrition trap (lack of access to credit is again relevant here, though not necessary), and that long-run contracts may not spontaneously come into play to overcome the undernutrition trap, although examples of long-run relationships that have an effect on the trap do exist.

Finally, we turned to the relationship between poverty and resource allocation within the household. We argued that extreme poverty promotes unequal treatment within the household, because of a “lifeboat problem”: certain minima are needed for people to lead a productive life, and equal treatment may simultaneously deny everyone those minima. We showed how inflexible minima are unnecessary to derive this result by using the capacity curve to analyze an intrahousehold allocation problem. We then asked the question, Which subgroups are on the receiving end of such unequal treatment (when it occurs)? The elderly (notably widows) are among such groups. Females are generally on the receiving end as well, although this phenomenon requires more careful exploration. In particular, observations of actual nutritional treatment of surviving females do not reveal the same sorts of disparities as those implicit in skewed sex ratios, suggesting that much of the discrimination occurs through active neglect leading to death in infancy or perhaps practices such as sex-selective abortion. However, some other indicators of unequal treatment, such as access to education, certainly reveal more pronounced evidence of gender bias even among surviving children.

Appendix: More on poverty measures Poverty lines suggest that there is some sort of magic threshold to poverty: people below the line are poor, whereas people above the line are not. Quite apart from the serious conceptual difficulties associated with this, there are operational problems as well. Policy makers who have an incentive to reduce poverty as measured by the head count may not cater to the poorest, but rather only to those who are easily nudged above the line. The poverty gap measure gets around this to some extent, but problems remain. Consider a natural application of the Pigou–Dalton transfer principle to the measurement of poverty:39

Weak Transfers Principle. A transfer of income from any person below the poverty line to anyone less poor, while keeping the set of poor unchanged, must raise poverty.40

This sounds innocuous, but as we have seen in the text, both the head-count ratio and the poverty gap (or the income gap) fail to satisfy this criterion. Is this just nitpicking or are there real-world phenomena that correspond to

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these conceptual problems? The World Development Report (World Bank [1990, Box 2.2]) discussed the effect of an increase in rice prices on poverty in Java, Indonesia, in 1981. Many poor households are farmers: they are net producers of rice, so the price hike presumably helped them, and indeed, the head-count index of poverty fell. However, this masks another phenomenon: many of the poorest people are not rice producers but landless laborers or farmers with other sources of income. They are net consumers of rice and they are adversely hit. Measures of poverty that are “transfer sensitive” could pick up this change, whereas traditional measures register a decline in poverty.

The best known measures that address the distributional underpinnings of poverty is the class proposed by Foster, Greer, and Thorbecke [1984]. The idea is very simple. Look at a variant of the poverty gap ratio in equation (8.2), given by

which is just the sum of all individual poverty gaps, expressed as a fraction of the poverty line, and then divided by the total number of people in the society. Distributional sensitivity is achieved by raising the poverty gaps to a power, much as we did in our discussion of the coefficient of variation as a measure of inequality. For any power α, define a class of poverty measures, called the Foster–Greer–Thorbecke (FGT) class, by

As we vary α over different values, we obtain interesting implications. First note that for α = 0, the measure P0 is just the head-count ratio. For α = 1, the measure P1 is the poverty gap ratio in (8.4). As α rises beyond 1, larger poverty gaps begin to acquire greater weight and the measure becomes increasingly sensitive to these gaps and, therefore, to questions of distribution, such as those raised by the Java price hike.

The case α = 2 is of separate interest. With some manipulation, we can show that

where HCR is the head-count ratio, IGR is the income gap ratio, and Cp is just the coefficient of variation among the set of poor people (see Chapter 6 for a definition). This is a very useful way to see the FGT index for α = 2. It tells us that when there is no inequality among the poor, poverty can be captured by some simple function of the head-count ratio and the income gap ratio alone, but the presence of inequality raises poverty. To see this, imagine that the Lorenz curve of incomes among the poor worsens, while both the head-count ratio and the income gap ratio are kept unchanged. Then, because the coefficient of variation is Lorenz-consistent, Cp will rise and the FGT index will rise as well.

There is another reason why the case α = 2 is of interest. It marks the boundary between poverty measures that not only satisfy the transfer principle, but satisfy what one might call transfer sensitivity:

Principle of Transfer Sensitivity. A given regressive transfer between two poor people must matter more if both (starting) incomes of the persons involved are reduced equally.

It can be checked that the transfer-sensitivity principle is satisfied if and only if α > 2. At α = 2 the FGT index is just about insensitive to the principle.

The FGT family of poverty measures also satisfies a convenient decomposability property. Suppose we are interested in how much overall poverty in a country is contributed by various subgroups: for instance, we may be interested in looking at poverty across women and men or across various ethnic groups.41 It would be useful if these “subgroup poverty measures,” appropriately weighted by the numerical strengths of the groups, summed to the total poverty as measured by the same index. The FGT indices have this property (see Foster, Greer and Thorbecke [1984] for a more extended discussion).

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(1) Is poverty an absolute concept or a relative concept? There are clearly some components (such as access to food, clothing, and shelter) that we would consider necessary in any society, but there are other components that are clearly society–specific.

(a) Identify some components of “minimum needs” that you feel are specific to one society but not another.

(b) Do you think these relative components are purely social (or cultural) or are they apt to change with the per capita income of a country?

(c) Because poverty has these relative components, consider the following poverty measure: anybody who has less than half (or some predetermined fraction) of the per capita income of a society is poor. Why is this a bad approach to poverty measurement?

(d) Try and identify some basic “capabilities” that you might want any human being to have: for example, every person should be capable of obtaining adequate nutrition, every person should be capable of obtaining “adequate” housing, means of transportation, and so on. Treat the right to such capabilities as absolute. Now can you reconcile the relative and absolute notions of poverty using these absolute capabilities as a starting point? On these and related matters, read Sen [1985].

(2) Read the 1990 World Bank Development Report to see how international poverty calculations are carried out. In the light of question (1), how would you evaluate such calculations? Study the Report for a clear account of the characteristics of the poor and for additional material on poverty not contained in this text.

(3) Evaluate the following statements by providing a brief explanation or analysis.

(a) The income gap ratio and the head count, as measures of poverty, may lead to very different uses of antipoverty resources by policy makers.

(b) World poverty shows a steadily diminishing trend all through the 1970s and 1980s.

(c) The poverty gap ratio and the income gap ratio focus attention on different aspects of the poverty problem.

(d) Both the poverty gap ratio and income gap ratio are insensitive to the inequality among the poor.

(e) The FGT indices (see Appendix) are increasingly sensitive to the income distribution among the poor, the greater is the value of α.

(4) Suppose that you are comparing two economies, A and B. The FGT indices (with α = 2) for the two countries are the same. However, the head-count and the income gap ratio are both higher for economy A than for B. What can you say about the coefficient of variation of income distribution among the poor in these two economies? What about the inequality of the entire income distributions in these two economies?

(5) Explain why a moneylender who relies on future credit cutoffs to enforce loan repayment today will be less willing to advance loans to a poor individual for projects that guarantee future income security. Discuss the role of collateral in obtaining such loans.

(6) Discuss the capacity curve and explain why the curve has an initial segment in which work capacity exhibits increasing returns with respect to nutritional input. In Chapter 13, we will discuss the implications of this in more detail, but the following exercise will provide you with some advance intuition.

Suppose that you need 8,000 units of work (in capacity units) to be performed, and you can hire all the laborers that you want. Assume that all income earned by the laborers is paid to them by you, and that all income is spent on nutrition. The capacity curve for each laborer is described as follows: for all payments up to $100, capacity is zero and then begins to rise by 2 units for every additional dollar paid. This happens until an income of $500 is paid out. Thereafter, an additional dollar paid out increases capacity by only 1.1 units, until total income paid is $1,000. At this point additional payments have no effect on work capacity.

(a) Assume that you would like to get your work done at minimum cost. Describe how many laborers you would hire to get your work done and how much you would pay each of them.

(b) Redo the exercise assuming that capacity is zero for all payments up to $275, then follows exactly the same rules for additional dollars paid as in the original problem. Interpret your answer.

(7) Consider the same capacity curve as in problem (6a). Suppose that a family of five members each have this 193

 

 

capacity curve. Assume that this family has access to a source of nonlabor income, valued at $400. Assume furthermore that each unit of capacity can fetch an income of 50 cents, and that all income is spent on nutrition. We are going to examine the division of income among the family members.

(a) Show that if all nonlabor income is divided equally among the family members, then no one will be able to sustain any work capacity so that labor income will be zero.

(b) Find allocations of the nonlabor income that give rise to positive wage income. Compare and contrast these allocations with the equal division allocation, using various criteria (including Pareto-optimality).

1 See the World Development Report (World Bank [1990, Table 3.1]). The figures pertain to consumption in 1985 PPP prices. 2 On this, see, for example, Behrman and Deolalikar [1987] and the box on nutrition and income in South India later in this chapter. 3 For a detailed discussion of these matters, see Sen [1983]. 4 There are conceptual questions regarding the construction of such scales, although the existing practice of using per capita expenditure (or

income) for a household can certainly be improved. For further discussion, see Deaton [1997, Section 4.3]. 5 This is taken to be denominated in the same units of currency as income or expenditure. Thus, for instance, if the poverty line is calorie-

based, p represents the amount of money that is required to attain the acceptable calorie threshold. 6 Of course, this measure has the opposite problem: by ignoring the overall wealth of the society, it tells us little about how easily the

problem can be tackled, at least domestically. 7 For a more detailed treatment of this issue, see Sen [1976]. 8 These are Bangladesh, Egypt, India, Indonesia, Kenya, Morocco, and Tanzania. The lower limit, $275, coincides with a poverty line used

for India. 9 Anand and Morduch [1996] used the Bangladesh Household Expenditure Survey, 1988–89, to do this. Let x be aggregate household

expenditure and m be household size. Then x/m is per capita household expenditure. Now introduce a scaling factor α between 0 and 1, and think of mα as effective household size. Because 0 < α < 1, mα rises slower than α, and this is a way of capturing returns to scale. The smaller the value of α, the higher the returns to scale. This procedure captures some of the adult equivalence issues as well, because it implies that the larger the household, the greater the proportion of children, and so effective household size (in adult equivalents) rises more slowly Values of α around 0.8 or lower are sufficient to overturn the observed positive correlation between household size and poverty in the Bangladesh data. Whether this value of α represents “high” or “moderate” returns to scale requires more careful investigation, however.

10 As Meesook [1975] and Fields [1980] observed, Thailand appears to be an exception to this rule. Social custom there provides greater assistance to women who are without a principal male earner in the household.

11 This is not to say that poverty should be identified with undernutrition. For one thing, persons counted below the poverty line in any one year might be “temporarily poor” (recall our previous discussion). For another, nutritional requirements vary from person to person, whereas the food adequacy standard used to measure poverty is an overall average.

12 See, for example, the exercise conducted by Glewwe and van der Gaag [1990] for Côte d’Ivoire, using data from the 1985 Côte d’Ivoire Living Standards Survey. Côte d’Ivoire, however, did not visibly suffer from an overall inadequacy of food supply in 1985. Children were relatively well nourished even among the poor. This is not true of countries where the overall nutritional base is much lower.

13 A classic application of linear programming is to the so-called diet problem: find the lowest-cost bundle of foods that will give you at least so many calories, so much protein, certain minima of various vitamins, and so on. The typical solutions to the diet problem offer a very low cost of attaining the required minima, but the foods will not look very appetizing.

14 Pure wastage of food also may be an indicator of social standing. It is unfortunate that very often the deliberate wastage of a scarce resource is a powerful way of signaling one’s social rank. Viewed from this angle, the wastage of food is no more horrific than the excessive consumption of power, wood, paper, geographical space, and many other resources in developed countries.

15 Other nutrients are of importance as well: see the case study box on nutrition and income in South India. 16 The survey by Behrman [1993] discusses some of these estimates. 17 For an introduction to the ICRISAT villages, see Chapter 10. 18 Of course, the nutrient intakes are themselves calculated with respect to a basket of food items and are therefore logically subject to the

same problem. However, direct observations on 120 foods were made, and this is a very rich sample indeed, so it reduces, to a large extent, the compositional errors that we have discussed.

19 The figures that we report are estimates that control for village and household fixed effects by the use of differencing. The overall results are similar without these controls, although the elasticity estimates for particular items, noticeably milk, do change pretty significantly. For details, see Behrman and Deolalikar [1987].

20 Thus if the local pawnbroker accepts your greatgrandmother’s watch, left to you as a family heirloom, as collateral for a loan, it is not so much that the watch will fetch a good price if you default on the loan. The point is that the watch is valuable to you, so that if you are contemplating a default in a situation where you can afford to pay, you will think twice about it.

21 For a more complete discussion of this issue, see Banerjee and Newman [1994] 194

 

 

22 A classic example of moral hazard comes from health insurance. The United States is a leading instance of the problem. High levels of insurance create an overuse of the medical system, because patients run to their doctors on the slightest provocation and receive treatments on a scale unparalleled elsewhere in the world. Is this all for free? Of course not. Over time, insurance premiums climb to staggering heights, which creates a situation that is very costly both at the personal and the social level.

23 The material in this subsection draws on Dasgupta and Ray [1986, 1987, 1990], Ray and Streufert [1993], and Ray [1993]. 24 Take the case of the rural labor market in India, in which the majority of India’s labor force participates. There seems to be little doubt

that such markets are characterized by large and persistent levels of unemployment, at least for significant fractions of the year. The evidence comes from a number of sources. For example, Krishnamurty [1988] observed from National Sample Survey data that rural unemployment rates were high and increasing in the 1970s, although there was significant interstate variation. Visaria [1981] and Sundaram and Tendulkar [1988] observed, moreover, that for agricultural households that were primarily engaged in the rural labor market, these rates were very high indeed. Mukherjee’s thesis [1991] contains a careful review of the relevant literature and, in addition, carries out a detailed study of Palanpur village, which reinforces the foregoing findings. High unemployment is such an accepted feature for researchers studying the Indian case that theoretical analysis of labor markets is often driven by the objective of explaining and understanding this one crucial feature. The excellent survey by Drèze and Mukherjee [1991] of theories of rural labor markets illustrates this point well.

25 This is the argument made in Dasgupta and Ray [1986]. 26 The cautious reader will notice that the argument is a bit slippery here. There may be effects on relative prices that do change

consumption allocations for the nonparticipants, but in the simple one-commodity model that we consider in Chapter 13, these claims are true. 27 Fogel and Engerman [1974, p. 111] pointed out that among the “plantation products that slaves consumed were beef, mutton, chickens,

milk, turnips, peas, squashes, sweet potatoes, apples, plums, oranges, pumpkins and peaches,” in addition to corn and pork. 28 In this context, see also Rodgers’ [1975] study of some Bihar villages, though in this study the reasons for on-the-job feeding are

considerably more ambiguous. 29 Middle- and upper-class Indian households display an extremely high degree of paternalistic concern regarding the nutrition and medical

care available to their servants. Such concern seems particularly out of line with the monetary wages paid to servants. Even though this paternalistic care has been molded by social custom to appear as genuine caring, there is little doubt regarding the fundamental motives behind such behavior.

30 Of course these two extreme options represent an exaggeration. Other intermediate divisions are obviously possible, but we neglect them for simplicity.

31 For more detailed analysis these lines, see Mirrless [1976] and Stiglitz [1976]. 32 To be sure, the fear of being without a son for support may in turn impact on fertility decisions earlier on, and this may account in part

for high fertility among groups known to discriminate against widows (see Chapter 9 for more on this). 33 The problem is exacerbated in the presence of women and children, who accentuate the trend in allocation away from the elderly. The

analysis is complicated, however, by the puzzling observation that savings are more likely to be run down for the treatment of the elderly than for young males, after controlling for the severity of the illness. Kochar’s paper contains an insightful discussion of the possible causes of this seeming anomaly.

34 This discussion is drawn from Sen [1984, Chap. 15]. 35 Kumar [1991], in his insightful study of Kerala, noted that the incidence of illness in that state of India far exceeds the national average.

This is especially true of diseases such as tuberculosis. Does this prove that Kerala is the sickest state in India? It does not. Data on morbidity, or the incidence of illness, combine two features: the actual incidence of illness, which is not observed by the researcher, and its perception (which includes reporting the illness). Kerala, with its higher rates of education and literacy, may do very well on the latter, thus raising observed morbidity. The same ideas can be applied to the use of morbidity as a test for discrimination between boys and girls. If girls fall ill more often but the illness goes unreported, morbidity rates might look much lower for girls.

36 These figures are for 1993. 37 For middle-income countries these discrepancies begin to fade, at least in the aggregate terms of measurement used by the World Bank.

Nevertheless, male rates of illiteracy are consistently lower than their female counterparts. 38 This is the case if we adopt the counterfactual scenario that there “should be” 440 million males as well. There are two reasons why the

number 30 million is probably an underestimate. First, there are also males who died in infancy or childhood because of high rates of child mortality (of course, the additional female count so implied would not all be attributable to discrimination). Second, the counterfact assumes a 1:1 parity: if the European or North American figures are taken as a benchmark, then the number of missing females would be higher still. On these and related matters, see, for example, Coale [1991], Coale and Banister [1994], Klasen [1994], and Sen [1992].

39 This is the approach to poverty pioneered by Sen [1976]. A discussion of the poverty index developed by him can be found in Foster [1984].

40 This is called the weak transfers principle because it restricts consideration of transfers to those occurring between poor people. For more discussion on this matter, consult Foster [1984].

41 See, for example, Anand [1977].

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Chapter 9 Population Growth and Economic Development

9.1. Introduction The world is populated today as it has never been before. Although rates of population growth have fallen and will continue to fall, we currently add about a million people every four days to the world population, net of deaths. According to projections carried out by the United Nations, annual additions to the population are likely to remain close to the ninety million mark until the year 2015.

It took 123 years for world population to increase from one billion (1804) to two billion (1927). The next billion took 33 years. The following two billions took 14 years and 13 years, respectively. The next billion is expected to take only 11 years and will be achieved by 1998, at which time we will arrive at the staggering figure of six billion. Such is the power of exponential growth.

However, more than just exponential growth is hidden in this story. Population growth through the millennia has not proceeded at an even exponential pace. The growth rate of population has itself increased, and the trend has reversed only in the last few years. Part of our purpose in this chapter is to tell this complex and interesting story.

Yet a description of trends is not our only purpose, because this is a book about economics, not demographic statistics. We are interested primarily in how the process of development has spurred (or retarded) population growth and, more important, we want to know how population growth in turn affects economic development. As with the evolution of now-familiar variables such as per capita income and economic inequality, population and development are intertwined, and we seek to understand both strands of the relationship.

The question of how population growth affects development runs into an immediate difficulty. How do we value the lives of the people yet unborn? Is a small population living in luxury better off than a large population living under moderate circumstances? How do we compare the fact that a larger number of people are around to enjoy the “moderate circumstances” with the alternative in which luxuries are available to a smaller number, simply because the births of the rest were somehow prevented?

This is a difficult question and we do not pretend to provide an easy answer. Indeed, we simply sidestep this issue by using per capita welfare (and its distribution) as our yardstick. The implicit ethical judgment, then, is that we are “neutral” toward population: once someone is born, we include that someone as worthy of all the rights and privileges of existing humanity. At the same time, our focus on per capita welfare means that we are indifferent to the unborn and are even biased toward keeping population growth down if it affects per capita welfare adversely.

This ethical judgment is implicit in the dire warnings that we see all around us, especially in developed countries where population growth in the “Third World” appears most frightening. Population growth cannot be good. It eats into resources and into production. There is less per head to go around.

That is fair enough. We adopt the per capita perspective as well. However, this does not imply that we need be averse to population growth from a functional viewpoint. The existence of a population of nontrivial size may have been essential to many important advances to the world. It is unclear how much Robinson Crusoe would have accomplished on his own, even with the help of his man Friday. For one thing, there are limits to what one or two brains can think up. For another, necessity is the mother of invention, and without the pressure of population on resources, there may be no necessity and consequently no invention. Just how large population needs to be for the full realization of these salubrious effects is open to debate, but the point remains that the total quantity of available resources may itself be positively affected through population growth.

The doomsday predictions associated with population growth also have a particular slant to them. On the heels of the (perhaps defensible) feeling that population growth is unambiguously bad for humanity, there is also the observation, sometimes made with a great deal of sophistication, that unless we do something about population growth in developing countries, the world will somehow be unbalanced in favor of the peoples of these countries. That would be “unfair.”

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Both of these misconceptions are, to some extent, unfounded. Moreover, taken to extremes, they can be dangerous. However, clearing up misconceptions is not our main goal. These statements are corollaries of more serious questions regarding the interaction of population growth and economic development that we shall address in this chapter.

(1) What are the observed patterns of population growth across different countries and how do these patterns correlate with other features of development in these countries? Specifically, is there a close relationship between what the now-developed countries have demographically experienced in the past and what is currently being experienced by developing countries? This will take us into a discussion of the demographic transition, a phenomenon you were introduced to briefly in Chapter 3.

(2) What connects these societywide patterns in population growth to the decisions made by individual households regarding fertility? What features of the social and economic environment affect these household-level decisions? In particular, how does economic development affect fertility choices?

(3) Can observed household decisions regarding the number of children be “rationalized” by the environment in which they find themselves? Alternatively, do households have more children than is good for them? This is a difficult question that we must address at two levels. The first level is what might be called the “internal level”: given some economically rational level of fertility at the level of the couple, do couples systematically depart from this level, either because of miscalculation or because of the absence of effective contraception? The second level is “external” and comes from pondering the meaning of the italicized phrase in the previous sentence. Are there reasons to believe that a couple’s decisions regarding family size have a social impact that is not fully internalized by them?

(4) Finally, reversing the causality from economics to demography, is it unambiguously true that population growth is harmful to the economic development of a country? What explains the interesting dichotomy between the belief that world population growth is “bad” and the belief, so widespread in developed countries, that population growth will make “them” powerful at “our” expense?

We do not pretend to have comprehensive answers to all these questions, but you will certainly find some of the issues that we discuss very provocative and worthy of further study. However, before we begin a serious discussion, it will be useful to review some basic concepts and terminology that are used by demographers. This is the task of the next section.

9.2. Population: Some basic concepts

9.2.1. Birth and death rates To conduct a useful analysis of population and its interaction with economic development, it is necessary to

understand a few basic concepts and terms. Most of what we study in this section are just definitions, and with a little patience, they are very easy to understand. These definitions set down the language in which we discuss demographic issues.

Fundamental to the study of population is the notion of birth rates and death rates. These are normally expressed as numbers per thousand of the population. Thus, if we say that the birth rate of Sri Lanka is 20 per 1,000, this means that in each year, Sri Lanka adds 20 newborn babies for every thousand members of the population. Likewise, a death rate of 14 per 1,000 means that in each year, an average of 14 people die for every 1,000 members of the population.

The population growth rate is the birth rate minus the death rate. Even though this works out as a number per 1,000 (6 in our example above), it is customary to express population growth rates in percentages. Thus, the population growth rate is 0.6% per annum in our example.

Table 9.1 provides us with data on birth rates, death rates, and population growth rates for selected low-income, middle-income, and high-income countries. There is a cross-sectional pattern here that we will take up in more detail when we study the demographic transition, but certain features come to mind.

First, very poor countries such as Malawi and Guinea-Bissau appear to have both high birth rates and high death rates, ranging around 50 per 1,000 for births and 20 per 1,000 for deaths. This is Group I in the table. Countries in Group II are not as poor: their death rates are much lower relative to the Group I countries, but their birth rates are still high. This isn’t uniformly true of all poor countries though: some, such as India and Bangladesh (Group III), seem to have begun a fall in birth rates that is gathering momentum. Other relatively poor countries, such as China and Sri Lanka (Group IV) have already taken significant strides in this direction: both birth and death rates are low

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and getting lower. Group V lists some Latin American countries, where the experience is mixed: countries such as Guatemala and Nicaragua have (like the Group II countries) benefited from the drop in death rates, but the accompanying fall in birth rates has not yet occurred. Countries such as Brazil and Colombia are well into the process, as are East and much of Southeast Asia (Group VI): countries such as Korea and Thailand have very low birth and death rates (others, such as Malaysia, have not completed this process).

Table 9.1 is constructed very roughly in ascending order of per capita income. The following broad trend appears: at very low levels of per capita income, both birth and death rates are high. Indeed, this is probably an understatement: age-specific death rates are probably higher still (see following text). Then death rates fall. This is finally followed by a fall in the birth rates. We will see this much more clearly when we track a single country over its history.

Now for a different concept. It is worth understanding that aggregative figures such as birth rates and death rates, and especially population growth rates, hide significant information about the underlying “demographic structure” of the country.

Table 9.1. Birth and death rates (1992) and population growth rates for selected countries.

Source: World Development Report (World Bank [1995]) and Human Development Report (United Nations Development Programme [1995]).

For instance, two countries with the same population growth rates may have dramatically different age structures. This is because one of the two countries (call it A) may have a significantly higher birth rate and a significantly higher death rate than the other country (B) (so that the two cancel out in the comparison of net population growth rates). At the same time, it is true that country A is adding more young people to its population than country B. Unless the higher death rates in country A are entirely concentrated among the young, which is

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unlikely, there will be more young people in A than in B. We might then say that country A has a “younger age distribution” than country B. As we will soon see, age distribution plays an important role in determining overall birth and death rates.

9.2.2. Age distributions The age distribution of a population is given by a list of proportions of that population in different age groups.

Table 9.2 gives us the age distribution of populations in different parts of the world, as of 1995. It is apparent from the table that the age distribution of developing countries is significantly younger than in their developed counterparts. I have never met a person who failed to be amazed by these figures when seeing them for the first time, and you will be too. The developing world is very young.

Just as birth rates and death rates affect age distributions, these rates are in turn affected by the age distribution prevailing at any particular moment in time. An aggregate birth rate is the outcome of the age distribution in a country, the age-specific fertility rates of women in that country, and the fraction of the population in different age groups. Similarly, the aggregate death rate is a composite that comes from age-specific death rates in a particular country, as well as the overall age distribution in that country.

These observations have important implications, as we will see. At the moment, let’s pursue the more disaggregated view a bit further. An age-specific fertility rate is the average number of children per year born to women in a particular age group. The total fertility rate is found by adding up all the age-specific fertility rates over different age groups: it is the total number of children a woman is expected to have over her lifetime. In developing countries, this number can be as high as 7 or 8, and often higher. In the typical developed country, this number is 2, perhaps lower.

Of course, high total fertility rates contribute to a high birth rate, but from our discussion, it should be clear that the total fertility rate is not the only factor that determines the overall birth rate. In a country with a young age distribution, the birth rate can be significantly high, even if the total fertility rate is not. This is simply because the younger country has a larger percentage of the population in their reproductive years.

A parallel observation holds for death rates. Young populations are biased toward low death rates, and this is true even if age-specific death rates are high. It is worth noticing that even though most developing countries have higher death rates in each age group relative to their developed counterparts, these differences are not adequately reflected in the overall death rates, which lie far closer together. Indeed, it is perfectly possible for country A to have higher age-specific death rates at every age group than country B, and yet have a lower death rate overall. This is the effect of a young age distribution at work.


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