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4 Learning and Lags in Mortality Perceptions
Pages 112-137

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From page 112...
... Imagine oneself semiliterate, situated in a rural area of a developing country, and isolated to a great degree from the media and from the formal health-care system. Suppose that even in such an environment, a decline in child mortality is under way.
From page 113...
... considered child mortality perceptions in two Taiwanese townships and for Morocco they gathered data on perceptions of change. Cleland et al.
From page 114...
... In the first section I consider a model of Bayesian learning about child survival in which individual perceptions are determined by a prior distribution that summarizes initial subjective beliefs about mortality probabilities, these beliefs then being updated by reference to a sample of information on mortality experience, yielding a posterior distribution that summarizes how beliefs change in the light of experience. Bayesian learning supplies a natural benchmark for learning models, in that the approach assumes that individuals process information in an optimal fashion, acting much as statisticians do.
From page 115...
... In developing the examples below, I take ~ to be the probability of surviving to age 5, that is, as the complement of the child mortality probability.2 Known Survival Probabilities To begin, suppose that the survival probability ~ is known with certainty. Figures 4-1 and 4-2 illustrate the optimal fertility distributions for a range of values of the survival probability O
From page 116...
... 6 5.6 5.4 5.2 t .~ . ~ 4.6 4.8 4.4 4.2 4.0 LEARNING AND LAGS IN MORTALITY PERCEPTIONS \ 0.70 0.75 0.80 0.85 O.gO 0.95 Child Survival Probability FIGURE 4-1 Mean fertility by child survival probability.
From page 117...
... Uncertain Survival Probabilities Having sketched the main features of the case in which ~ is known, I now ask how fertility decisions might differ if the survival probability ~ is an unknown quantity about which individuals have more or less certain prior beliefs. Two questions must be addressed.
From page 118...
... It illustrates the evolution of perceptions for a family beginning in 1948 with a prior distribution whose mean is identical to the true survival probability (0.699) , but with considerable uncertainty about 0, as evident in the 25th to 75th percentile range that stretches from 0.57 to 0.86.
From page 119...
... One might ask whether larger social networks, which contain more annual observations on children's deaths and survival, would bring the subjective mean into line with the true survival probability. A change in the sample size N does have some effect, but the gap shown in Figure 4-3 persists even if N is doubled to 20 annual observations.
From page 120...
... In the case of updating in mortality perceptions, the main features of Figure 4-3 above would probably not change greatly if individuals simply split the difference between their prior beliefs and the latest sample evidence. However, there is considerable doubt that individuals consistently behave as if they were Bayesians.
From page 121...
... Rather, it seems that the type of non-Bayesian reasoning that individuals employ is situation-specific, depending in a complicated fashion on the nature of the problem. Where mortality perceptions are concerned, the experimental literature suggests that, on the whole, perceptions may be even more resistant to change than was illustrated in the simple Bayesian model above.
From page 122...
... This bias might cause certain unusual events, such as a case in which a single family in a rural village lost all of its children, to acquire a disproportionate influence in mortality perceptions.
From page 123...
... Taylor argues, however, that humans in fact find it more difficult to recall negative events. Following the receipt and the intense cognitive processing of negative information, a "minimization" phase evidently sets in, during which humans make concentrated efforts to explain, explain away, mute, or otherwise suppress the negative event in question.
From page 124...
... There is a link to the psychological literature on the phenomenon known as anchoring, whereby initial estimates of probabilities are resistant to revision (Tversky and Kahneman, 1974~. Order effects may be of particular interest in relation to mortality trends.
From page 125...
... Thus, a dominance of recency effects would seem to draw additional attention to low-mortality eras, and might thereby tend to accentuate the effects of mortality decline on fertility. Although the concepts of primacy and recency have to do with the time ordering of events, they might be extended to refer to social ordering or social proximity (Barney Cohen, personal communication, 1996~.
From page 126...
... If mortality decline is to be held credible, then the empirical facts suggestive of decline must be supported by a credible theory. If this is to happen, then older theories about cause and effect may have to give way or otherwise adjust, and one would expect the adherents of such theories to resist and offer counterexplanations for the observed changes (Nisbett and Ross, 1980: 171~.
From page 127...
... This literature hints that cultural differences may exist, but leaves uncertain their extent and depth. Summary If the literature described above can be taken as a guide, I would argue that on the whole, improvements in child mortality are likely to be perceived with a greater lag than suggested by optimal Bayesian calculations.
From page 128...
... Diffusion models lay emphasis on factors affecting the demand for children, such as mortality, as well as those that affect the costs of fertility regulation. Some of the arguments made above with regard to mortality perceptions might also apply to learning about the properties of modern contraceptive methods or the risks and expected returns to be derived from educating children.
From page 129...
... There was an increasing faith in the controllability of mortality, although few effective means existed for prevention and even fewer for cure. Until the early twentieth century, the rising belief in controllability did little but increase anxiety and promote a sometimes frantic search for cures; when effective medicines finally emerged, however, the net effect was to improve child survivorship.
From page 130...
... These methods might be adapted profitably for use in developing country settings in which mortality and fertility transitions are now under way. APPENDIX In the simple Bayesian fertility decision model, parents are assumed to choose the number of their births B to maximize the expected value of the utility function U(S)
From page 131...
... The above is recognizable as the probability derived from a conditional-logit choice problem. Subjective Beliefs It is analytically convenient to summarize prior beliefs regarding ~ by means of the beta distribution, whose density is f(~3 1 0C,'(~)
From page 132...
... I illustrate this last case in Figure 4A-l, for which or and 13 have been chosen so that the subjective mean of the survival probability ~ = 0.699, a value equal to Hill's initial estimated probability of child survival in postwar Ghana. Social Learning When beta-distnbuted prior beliefs about ~ are updated by reference to a sample of N external observations on child survival, this sample being generated by the binomial distnbution, the posterior distnbution for ~ is also a beta.
From page 133...
... 133 Although flexible, the beta distribution is not an ideal choice for an application to child survival in that the distribution always assigns some subjective probability to values of ~ near 1 and 0. This gives a potentially unrealistic representation of subjective beliefs, as few individuals believe either that all children will survive or that none will.
From page 134...
... LEARNING AND LAGS IN MORTALITY PERCEPTIONS max B(a - b)
From page 135...
... 135-159 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility.
From page 136...
... Sah, R 1991 The effects of child mortality changes on fertility choice and parental welfare.
From page 137...
... 1991 Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychological Bulletin 110(1)


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