The adjustment of birth rates
Macro- and micro-inertia The preceding story of the demographic transition relies on an enormously important feature—the well-documented failure of the birth rate to instantly chase the death rate downward. Recall from the previous section that the main impetus to the rise in population growth rates comes from the fact that death rates decline rapidly, while at the same time, birth rates hold firm. There are several reasons for this.
It is certainly true that over the past centuries, the factors that precipitated the fall in death rates were also linked with an increase in the carrying capacity of the earth. The leading example of this was a rise in agricultural productivity. This is one explanation for why birth rates did not fall (in those times). Unfortunately, this argument cannot be applied today. Many of the factors bringing down death rates in developing countries today are sanitation and health related: they do not go toward increasing carrying capacity.
We study in this section the various factors that keep the birth rate high. At the outset, it will be useful to distinguish between two forms of inertia in the birth rates: one at the level of the overall population (macro-inertia) and one at the level of the family (micro-inertia).
As discussed already, the distribution of the population by age plays an important role. The fact that both birth 203
and death rates are initially high in a poor country makes the net population growth rate low, just as in rich countries, but there is a second implication that is quite different: the populations of the former type of countries will be very young, on average. This feature tends to keep overall birth rates high even if fertility rates are reduced at different age groups. The sheer inertia of the age distribution guarantees that young people of reproductive age continue to enter the population. One might think of this as macro-inertia—inertia at the aggregate level.
Macro-inertia is not the only form of inertia keeping birth rates high. There is also what we might call micro- inertia—inertia at the household level, perhaps in conjunction with the operation of societal norms regarding children and other socioeconomic factors. This will be our focus of attention for the rest of this section.
Fertility choice and missing markets The angle that we explore in this section is that offspring are generally a substitute for various missing institutions and markets, notably the institution of social security in old age. This absence often compels a couple to make fertility choices based on the recognition that some of their children will die. These potential deaths must be compensated for by a larger number of births.
Of course, children bring enjoyment to their parents, as they undoubtedly do in all societies, but this is not the only reason why they are produced. On top of this “consumption-good” aspect of children is their role as an “investment good”; that is, as a source of support to the family in old age, and more generally as a form of insurance. If it were possible to obtain insurance or old-age security from a more efficient source, these effects would go away. As we have seen already and will see on several occasions again, the fact that there is a missing market somewhere spills over into other seemingly disparate aspects of economic life.
To begin, then, let us get a sense of what markets are missing in this context. If you live and work in a developed country, you pay a good fraction of your earned income into a government fund that often goes under the name of a social security fund. When you retire, this fund pays you a retirement pension. It is necessary to contribute to this fund to receive benefits from it, although in many countries the pension is progressive (larger contributors do not get back all their payments). A second source of old-age funds is an employer-subsidized retirement plan (where both you and your employer makes contributions). Finally, you can save for your own retirement, not necessarily under the umbrella of any retirement plan.
Next, there are various forms of insurance that are available to you, both in your working life and in your old age. Perhaps the most important of these is medical care, but there are also other forms of insurance. Life insurance is among the most important of these. If you die, your spouse receives a payout from the insurance company that helps to support him or her. There is also insurance that you can buy to protect you from sudden loss of employment, or from disability, or from natural disasters, or from theft. This is not the case that in developing countries: these markets are completely missing. By and large, these institutions are only available to people who work in the formal sector. In the informal sector, where employment is largely casual and wages are abysmally low, there is little or no incentive to set up a retirement scheme between employer and employee, and even if the law says that this should happen, it is impossible to implement. Likewise, appropriate contributions to a government-run social security system are difficult, if not impossible, to assess. Large sections of the population live in rural areas or work in informal urban areas. For the same reasons of limited information, it is very difficult for an insurance company to assess the validity of claims, such as a crop failure or a sudden drop in the income of a streetside hawker. Agriculture is particularly hampered by the fact that income shocks may be highly correlated across policy holders, which necessitates large payouts for insurance companies. Of course, these correlations can be avoided by companies that operate at a national level, but such companies may lack the local expertise to collect adequate information. Therefore, insurance markets in the agricultural and urban formal sectors are often missing.
What about life insurance or personal savings? Both these avenues are somewhat more viable. With reasonable banking systems, individuals can save for their own retirement. It may be impossible to verify a crop failure, but it is certainly easier to verify death. Thus these routes to old-age support are often available.
At the same time, people often do not avail themselves of these routes. The reason why this is so has to do with low incomes. Consumption needs today are often so pressing that there is little left over to save. People therefore often hold on to assets that they might have inherited, such as land or jewelry, and sell these assets only under conditions of extreme duress. These assets form their security in old age.
Note that the more difficult it is to sell an asset for current consumption, the easier it is to save using that asset. You might respond that if it is difficult to sell that asset now, why should it be easier to sell when funds are truly needed? The answer has to do with the nature of the difficulty. Society sprouts norms around the sale of assets such as land and jewelry. It is all right to sell them under severe duress, but the sale of these assets in “normal times”
might be frowned upon or regarded as a signal that the family is completely indigent. Thus the difficulty of marketing these assets is created by the emergence of social norms that protect savings in some form.
In this context, take a fresh look at children. Children are assets par excellence. They do not need to be bought, although there are costs to child rearing (see subsequent text) and they embody income-earning possibilities, both now and in the future. Because slavery is banned (and socially unacceptable anyway), it is generally not possible (though unfortunately, not impossible) to market them for cash. At the same time, when children grow up they can convert their labor power into income, both for themselves and their parents. Little wonder, then, that individuals who lack insurance and old-age security, choose to invest in the future in the form of children. This is the background against which we investigate theories of fertility choice.
Mortality and fertility Consider the probability that a child will grow up to look after its parents. This probability is given by several factors. The child may die young; infancy is the biggest hurdle. As we know from Chapter 2, infant mortality may be close to 150 or 200 per 1,000 in several developing countries, which translates into a 15% probability of death by the end of the first year of existence. After this barrier, there are still the diseases of childhood, which are still a significant killer in many developing countries up to the age of five or so.
Third, there is the possibility that the child may not be an adequate income earner. The poorer the economy, the greater this fear.
Fourth, a child may not look after its parents in their old age. This is an interesting social factor that may cut in either direction. In societies where the norm of looking after one’s parents has practically vanished or is relatively nonexistent to start with, the mental calculations that we are going to talk about may have no relevance at all for fertility decisions. For instance, economic historians such as Williamson  have argued that fertility reductions in nineteenth century United Kingdom can be explained by the increasing emigration rates of adult children. If emigrees send limited remittances, this reduces the present value of children (as investment goods) considerably.
At the same time, in societies where it is accepted practice to care for one’s parents, the limited possibility that some child might not do so may have the opposite effect on fertility: instead of lowering it, it may increase it as parents attempt to compensate for this contingency.
Finally, there is the possibility that the parents themselves might not anticipate being around in their old age. This is certainly a possibility in very high-mortality societies, but in general it is of second-order importance. At the stage in their lives when individuals are making their fertility decisions, they have already lived through the bulk of the (non-old-age) high-mortality phase.
Summarize the overall probability of having a given child grow up to look after you by p. This includes, then, infant and child mortality, the eventuality that the child survives but is not an adequate income earner, and the possibility that the child earns adequate income but nevertheless does not look after you. What value might p take? It is hard to tell without detailed data on each of these possibilities, but child mortality by itself might be responsible for raising p to well above 1/5. With the other factors accounted for, p may well be higher than 1/3, and the possibility that parents regard p as a one-half (or close to it) is certainly not unreasonable.
Now contrast this with the probability—call it q—that a couple finds acceptable as a threshold probability of receiving support from at least one child. This is a matter of attitudes toward risk and varies greatly from couple to couple. Try introspection: what probability would you find acceptable to be without any form of old-age support? If you could honestly tolerate a probability that is significantly greater than 1/10, you are an unusual person. We may therefore think of q as having values above 9/10—perhaps even as high as 95/100—certainly greater than p. The rest is a matter of simple arithmetic: how many children do you need to have—each child looking after you with probability p—so that the overall probability of having at least one child look after you is at least q?
This is easy to calculate (or it should be!). Suppose you have n children. Then the probability that none of them will loo a ter you is (1 − p)n. Consequently, your rule would be to choose n—the number of your offspring—just large enough so that
Let us check this out with some numbers. Say that p = 1/2 and q = 9/10. Then it is easy enough to see, using (9.1), that n must be at least 4! If you are more risk-averse than that, so that your acceptable q is 95/100, then you will need five children, and that, too, brings you barely to your acceptable threshold, as you can check by direct calculation.
Gender bias In this context, gender bias can be immensely costly. Suppose that for some reason, a couple wishes to receive support from a son. Households will then see n as their desired number of male offspring. Quite simply and devastatingly, it doubles the expected number of children that the household will have.
For instance, if p = 1/2, if q = 9/10, and if the couple desire support from a male child in their offspring, then that couple will have, on average, eight children, all for the sake of ensuring just one son!
In many societies, the provision of old-age support is thought to be exclusively the task of male offspring. Although support (especially in nonmonetary form) from female children is just as valuable, there may be a stigma associated with receiving support from daughters as opposed to sons. This bias is, of course, a source of discrimination in favor of male children.
To be sure, this argument does not explain the rationale behind such a bias, and there may be many reasons. For instance, Cain’s [1981, 1983] study of Bangladesh illustrates the importance of sons as support for widows: the ability of widows to hold on to land depends on whether they have able-bodied sons. This will be especially true in situations where property rights are either not well-defined or difficult to enforce by the law.
Information, income, and fertility Let us summarize the discussion so far. Individuals choose the number of their offspring with the intention of receiving support in their old age. This support may not be forthcoming from a child for several reasons: (1) the child may die, as an infant or later in life, (2) the child may not earn enough income to support the parents, and (3) the child may break parental ties and deliberately not support its parents, even though it has the economic capability to do so. The probability of these uncertain events taken together we denoted by p.
The uncertainty described in the preceding paragraph has to be compared with the tolerance threshold of the parents, which is the minimum probability that they need old-age support, and this threshold changes with the degree of risk aversion of the parents. The degree of risk aversion, in turn, depends in part on the economic security of the parents. A higher level of security generally implies a lower degree of aversion to risk.
These factors help us to uncover, to some extent, the reasons behind a sticky fertility rate in the face of rapidly falling death rates. The first element is information. How is the social phenomenon of a falling death rate translated to the level of individual decision making? We have already commented on the rapidity of the decline in death rates in developing countries. For twenty-one developing countries during the period from just before World War II until 1950, the death rate dropped on average by seven per thousand population every ten years (Coale and Hoover [1958, p. 14]). For a historical demographer and indeed for any social scientist, this is a remarkable change indeed and is unparalleled in history for its rapidity. As Coale and Hoover [1958, p. 14] observed, “this rate of improvement surpasses anything from the records of areas inhabited by northern and western Europeans.” Yet it would be wondrous indeed if these changes made the newspapers at the time! The fact of the matter is that individuals must often go by their own experience, by which I mean their vision of the experiences of their parents and the siblings and friends of their parents. It is the preceding generation that provides the only direct experience that is relevant in this context.
Thus the fall of death rates may not instantly translate into a revised estimate of mortality (see box, Three Generations).
The village of Rampur in India was surveyed by Lewis  and then resurveyed by Das Gupta . The story of Umed Singh comes from them. Umed Singh’s father was Siri Chand, who was born around 1900. Epidemics of plague and cholera decimated his family including his father and mother. Siri Chand was brought up by his uncle. As a farmer, he faced the kind of uncertainty that is difficult for us to even contemplate: consecutive crop failures, famine, the occasional bumper crop, the loss of six out of nine live births: two girls and one boy survived to adulthood. The life of the boy, Umed Singh (who was born around 1935), stands in sharp contrast to that of his father.
Umed Singh completed secondary school and became a policeman. He earned a regular salary and also received income from his land (left to him by his father). However, the uncertainties of his father’s life never ceased to haunt him. He was the sole surviving son in a family that had given birth to nine children. With no objective reasoning to back his insecurity, Umed Singh worried and then worried some more. His first two children were girls. Because he wanted a boy, he insisted on having more children. He had three more children,
and two of them were boys, but he continued to worry that his children would die, and this fear did not leave him until his third son was born. All his children survived.
As Umed Singh relived the anxieties of his father, people around him were already changing. His wife, when interviewed, felt that they should have stopped having children much earlier. So did Umed Singh’s cousins and his colleagues in the police force.
Das Gupta ends the story thus: “The second generation of people who lead a secure, ordered life do not experience the anxieties left over from past insecurities. Umed Singh’s oldest daughter has completed a course in teacher training and will be married shortly. She says she has no intention of childbearing in the way her mother had; three children were the maximum she would have. She is a relaxed, confident woman, who is inclined to be a little amused by her father’s anxieties on behalf of his family.”
Although falling death rates are central to the fertility decline, there are other factors in the construction of p that have little to do with the fall of death rates. These are the previously mentioned items (2) and (3), which may well go the other way even as death rates fall. These depend on the economic conditions of the region. The poorer the region, the greater the anticipated probability that a single child will not earn enough in adulthood to support parents; hence, the greater the incentive to have more children to compensate for this possibility. Likewise, falling death rates cannot in any way affect the social possibilities of fulfilling parental obligations. These are independent phenomena that continue to leave their mark even as death rates fall, and they might keep birth rates high.
Finally, there are the additional complications introduced by gender bias. Again, there is no guarantee that a fall in the death rates will have any impact on the degree of bias. In making this statement, we actually distinguish between two types of bias. One is what might be called observable bias; that is, measurable indicators of differential treatment of boys and girls. With development, such bias indeed lessens as resource constraints loosen. A second sort of bias has to do with the intrinsic valuation of women in society and it feeds into the perception of women as sources of old-age support. This bias actually increases with economic progress, at least to a certain extent. One important reason for the potential increase is that economic progress is associated with a decline in the importance of agriculture. To the extent that women play an important role in agriculture, they may now be perceived as relatively less capable of providing old-age support on their own. We have already seen that such biases, apart from their obvious intrinsic shamefulness, can brutally affect fertility decisions.
Hoarding versus targeting Our discussion so far contains an implicit assumption: that parents must make their fertility decisions about later children without being able to use information about the fate of their earlier children. Is this reasonable? Again, the answer depends on just which components of p are dominant in parental psychology. For instance, if an individual worries that the child may not earn enough in adulthood to support his aging parent, this is not an outcome that lends itself to a wait-and-see strategy. By that time, it will not be possible to have a new child! If the source of the uncertainty resides in such features, all the tickets will have to be bought in advance, as it were. We may refer to this phenomenon as one of hoarding: children have to be stockpiled in advance, before we know which (if any) among them will provide the requisite support.
Contrast this with a situation where infant mortality (death before the age of one) is the dominant form of uncertainty. In such a situation a wait- and-see strategy acquires greater feasibility. A couple can have a child and condition its next fertility decision on the survival of this child. The desired number of children can be attained sequentially; this strategy is called targeting. Obviously targeting generally is associated with lower fertility rates, because the total number of children do not have to be created “in advance.”
A change in the demographic regime from hoarding to targeting can lead to a drastic lowering of the fertility rate. Again, the rate at which this switch of regime occurs depends critically on the kinds of uncertainties that the couple is most concerned about. It is true, however, that a fall in the death rate can only assist in bringing about this change of regime.
The costs of children So far we have neglected the costs of child rearing. These costs take two forms. First, there are what might be called the direct costs of children: they need to be fed, clothed, kept in good health, looked after, and schooled. Second, there are the indirect or opportunity costs of children that are measured by the amount of income foregone in the process of bringing up the child. Time spent at home with the child is time not spent earning income, so the opportunity cost of children is roughly proportional to the going wage rate multiplied by the number of hours spent in parenting.
In societies where this opportunity cost is low, fertility rates tend to be high. Gender bias plays a role in this as well. In many societies (including many developed countries), it is presumed that women must allocate the bulk of their time to the upbringing of children. In such societies wages for women’s work are low as well. This brings down the opportunity cost of having children and keeps birth rates high.
Similarly, if there are high rates of unemployment, the opportunity costs of children comes down. Again, this can push fertility upward.
This cost–benefit approach to fertility choice is natural to economists. Becker  introduced this approach to other social scientists. Often, the methodology is not very useful: simply stating that parents have children up to the point where marginal benefit equals marginal cost may be an impressive piece of jargon, but does not convey much information. To make the cost–benefit approach useful, we must either discuss benefits, or costs, or both in a way that is relevant to the situation at hand. This is what we have done so far with the notion of benefits. Instead of stating that parents derive “utility” out of children, we describe it specifically as old-age support, and this description allows us to draw the specific conclusions that we have arrived at so far. So it is with costs. We need to understand how different kinds of costs have different sorts of demographic implications. In the discussion that follows, we illustrate this point by considering a specific case: the effect of income improvements on fertility.
Figure 9.1 considers the preferences of a couple over the number of children it wishes to have and “other goods,” denominated in terms of money. Children are on the horizontal axis; other goods are on the vertical axis. In what follows, we do not pay much attention to the exact form of preferences, which are represented by indifference curves in Figure 9.1. For instance, these preferences may be a reduced form of the desire for old-age support or may simply arise from the intrinsic pleasure of having children. Our focus is on the costs.
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