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Cities Transformed: Demographic Change and Its Implications in the Developing World (2003)

Chapter: 4. Urban Population Dynamics: Models, Measures, and Forecasts

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Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 110
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 111
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 112
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 113
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 114
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 115
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 116
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 117
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 118
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 119
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 120
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 121
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 122
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 123
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 124
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 125
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 126
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 127
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 128
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 129
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 130
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 131
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 132
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 133
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 134
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 135
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 136
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 137
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 138
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 139
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 140
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 141
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 142
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 143
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 144
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 145
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 146
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 147
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 148
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 149
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 150
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
Page 151
Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
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Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
×
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Suggested Citation:"4. Urban Population Dynamics: Models, Measures, and Forecasts." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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4 Urban Population Dynamics: Models, Measures, and Forecasts This chapter brings analytic tools to bear on the urban levels and trends described in the previous chapter. Our treatment of the issues is narrowly demographic, particularly at the outset, focusing on what might be termed the proximate causes of urban growth. Rural-to-urban migration is one of these proximate causes; of equal importance are the rates of urban and rural natural increase and the relative sizes of the urban and rural populations. Territorial reclassification must also be considered. In placing emphasis on this small set of demographic variables, we are mindful of their uncertain causal status. Rates of migration and natural increase are at once the cause and the consequence of larger social and economic forces. Even reclassification touches on economic, fiscal, and political concerns. In the chapters to follow, the socioeconomic content of the demographic variables will be explored in depth. This chapter begins by describing the features of urban population dynamics that can be seen even with the simplest of analytic mechanisms. A model of urban and rural population growth is developed to show how an initial urban and rural population distribution, when subjected to fixed demographic rates, can produce a variety of demographic outcomes: annual increases in the total urban population, the share of those increases due to migration, rates of urban growth, levels of urbanization, and their rates of change. Using projections, we highlight several regularities that can help in understanding the empirical record. The chapter then draws upon the Demographic and Health Surveys (DHS) for evidence on the main contributors to urban population growth fertility, mortal- ity, and migration which act together to determine urban age composition. As will be seen, urban populations are much more concentrated in the productive and reproductive ages than are rural populations. Where age is concerned, ur- ban populations are configured for higher potential economic productivity. With 108

URBAN POPULATION DYNAMICS 109 respect to the reproductive ages, however, these populations are also configured in a way that enhances the potential for high fertility although later marriage and greater contraceptive use generally keep this potential from being realized and that raises the profile of reproductive health concerns and other diseases affecting young adults. To shed light on the contribution of migration, we analyze the DHS data on recent moves, linking these data to measures of city size. This analysis exposes several weaknesses in the basic infrastructure of urban population research. First, because the DHS surveys are generally restricted to women of reproductive age, they reveal little about the situations of men and other migrants. Second, the ge- ographic identifiers supplied with DHS datasets are so coarse that it is difficult to determine even the name of the city in which a survey respondent lives, unless that city happens to be the nation's capital. Third, the population of the city in ques- tion is available from United Nations sources if the city is a capital, but otherwise can be determined only for cities above 100,000 in population. Moreover, only "raw" estimates of city size, taken from the United Nations Demographic Year- books, are available for cities in the range of 100,000 to 750,000 population. The expertise of the United Nations Population Division, as expressed in its influential series World Urbanization Prospects, is focused only upon cities larger than this. In short, a rather mundane analysis task brings alarming research gaps into view. The next section turns a critical eye on the two United Nations databases used in urban population research the annual Demographic Yearbooks and the bien- nial World Urbanization Prospects. In this field, World Urbanization Prospects has assumed the role of a standard reference work; it is the authoritative, com- prehensive source of urban population estimates and projections upon which most researchers and institutions rely. Because it assembles data over time, World Ur- banization Prospects provides an especially rich set of materials on urban growth, with detailed time series for all of the world's large cities. But the occasional user of these data is apt to be misled by their attractive packaging, and may need to be reminded of the weaknesses and heterogeneities of the population series that are available to the United Nations and the difficulties it faces in adequately estimat- ing and projecting urban populations. The chapter then examines the record of urban population projections, an issue of fundamental importance that has attracted curiously little attention apart from the efforts of the United Nations and Brockerhoff (1999~. As will be seen, the his- tory of city size projections does not inspire confidence. The urban record of suc- cess is so thin that a recent authoritative assessment of national-level demographic projections (National Research Council, 2000) does not even consider the possi- bility of projecting the national populations of developing countries by conducting separate rural and urban projections. Evidently we are still some distance from being able to apply modern statistical techniques to urban population time series. Having reviewed the aggregate databases, we turn to new developments in the area of spatially disaggregated geographic information systems (GIS). As

110 CITIES TRANSFORMED a locus of several new technologies, GIS holds promise for restoring a spatial dimension to the (typically) aspatial data gathered through demographic surveys and related sources. There is at least the possibility that data collected according to GIS principles might strengthen the foundation for urban population projection. GIS technology is perhaps even more promising as a political device, that is, as a mechanism for fostering dialogue among the units of government that collect data, those that supply services, and the urban residents who wish to make use of such services. We document several of the encouraging efforts now under way. The chapter ends by emphasizing the infrastructure that will be needed to support urban population research. THE SIMPLE ANALYTICS Five demographic indicators are required to sketch the main developments in an urban transition: the absolute annual increase in the urban population, the share of that increase attributable to migration, the urban growth rate, the level of ur- banization, and the rate of urbanization. In the model presented below, all five indicators result from the repeated application of constant demographic rates to an initial population distribution. ~ Key Concepts and Notation To highlight the essentials, we abstract from the problem of reclassification and focus on a hypothetical country divided into rural and urban sectors that are fixed in geographical terms. Age dependence in fertility, mortality, and migration rates is initially ignored. The results thus obtained would generally continue to hold in an age-differentiated simulation (Rogers, 1995~. In this stylized representation, national population size in year t is denoted by Pi, the size of the urban population by Us, and that of the rural population by Rt. Given the sizes of the rural and urban populations in a base year Ro and TO, respectively the totals Us and Rt evolve in a manner determined by four demographic rates, each of which is expressed on a per annum basis: raft the rate of natural increase in the urban population, that is, the difference between urban birth and death rates Ilr the rate of- natural increase in the rural population mama the migration rate from rural to urban areas, expressed per rural resident mll,r the migration rate from urban to rural areas, expressed per urban resident iFor more detail on the equations and their derivation, see Appendix B and United Nations (1974, 1998b).

URBAN POPULATION DYNAMICS 111 As discussed in Appendix B. which presents more of the mathematical detail, from these simple ingredients the model can generate time paths for the five urban outcome measures mentioned above. To proceed, we must define some terms. The urban population increment is the net addition to the urban population from year t—1 to year t, or /\Ut = Ut—Ut-1 - We make use of an equivalent representation, /\Ut = Ut_1 (~n—marry + Rt-l mr'n' (4.1) in which the roles of the demographic rates and the population distribution are more clearly evident.2 The urban growth rate is another measure of change in the total urban population over a single year; it is expressed not in absolute terms, but as a fraction of the initial urban population: UGR' This, too, can be given a useful alternative form: UGRt = rat—m~,r + /\Ut Ut_ - Rt-1 U mr'n (4.2) The migrant share of urban growth, denoted MSt, is the proportion of net urban growth that is due to migration from rural areas. The share can be written as MSt=(l+Ut-l nn—mUrN —1 mr'~ (4.3) As is evident in equations (4.2) and (4.3), the urban growth rate and the share of growth due to migration are determined by several constants the rate of urban natural increase and rates of migration to and from urban areas as well as a time-varying factor, Ut- 1 /Rt - 1, the urban/rural population balance. Because urban population increments, growth rates, and migrant shares are closely related, they are often discussed as a group. So, too, are the following measures, which concisely summarize levels and trends. The level of urbanization is simply the urban proportion (or percentage), Aft= p, 2A counterpart expression for the rural increment would include the term for—mom. This term is generally positive. See, for instance, Oucho and Gould (1993) on net rural increase in sub-Saharan Africa.

112 CITIES TRANSFORMED and the rate of urbanization is the rate of growth in this proportion over time. It can be written as /\P7l,,t Ut—Ut- = Pll,,t- 1 It- 1 Pt—Pt- Pt—1 which is the difference between the urban and national population growth rates. Even a model as simple as this can help clarify otherwise puzzling and coun- terintuitive aspects of urban transitions. We use it to address several questions. First, if urban growth results from both migration and natural increase, which of these accounts for the greater share of growth? Should the migration share be re- garded as a constant or as a time trend? How are urban growth rates linked to the rate of urbanization? Do national population growth rates translate directly into rates of urban growth (according to Preston [1979], a 1-point decline in national growth reduces urban growth by the same amount), or can we antici- pate a systematic change in the relationship between the rates as urbanization proceeds? City Growth: Migration or Natural Increase? Much of the concern surrounding urban growth has to do with the annual additions to their populations that cities must somehow absorb, and with the contribution to growth that is made by rural-to-urban migrants. As discussed earlier, demogra- phers often find themselves emphasizing the role of urban natural increase (see United Nations, 1980; Chen, Valente, and Zlotnik, 1998), if only to counter the impression that migration must be the dominant factor. To disentangle migration from natural increase is more difficult than might be supposed; an analytic model is helpful in showing just where the problems lie. As Rogers (1982) explains, to understand whether migration or natural in- crease is the dominant source of growth, one must first decide on terms. Much depends on whether one conceives of the problem in terms of JqOWS or stocks. Flows are, by definition, short-term measures. Their empirical counterparts are found in the decompositions of intercensal urban growth that separate urban nat- ural increase on the one hand from the sum of net migration and reclassification on the other. According to such flow estimates as discussed in Chapter 3 the share of migration in urban growth is in the neighborhood of 40 percent in most developing countries. Stocks, by contrast, are cumulative measures. If the mi- grant contribution is to be assessed in terms of stocks, the estimate should take into account not only the migrants themselves, but also their descendants. To understand the cumulative contribution of migration to urban growth, one would compare the size of an urban population with what it would have been in the ab- sence of rural-to-urban migration (i.e., with mr,~' = 0) or with lower rates of

URBAN POPULATION DYNAMICS 113 migration than occurred. Of course, if migration is permitted cities will grow larger than they would otherwise, but the size of the difference is of interest.3 Because flows depend on stocks for example, the flow of rural-to-urban mi- grants is mr,ll Rt_l, with Rt_1 being the stock of rural population one cannot cleanly separate them. For analytic purposes, it is preferable to distinguish direct elects from feedback elects and to focus attention on the implications of changes in the fundamental rates. Consider, then, the consequences of a change in rail, the urban rate of natural increase. Returning to the migration share equation (4.3), we see that at time t, the direct effect of an increase in rail is to reduce the share of migration in urban growth, as would be expected. Likewise, the direct effect of an increase in the rural-to-urban migration rate, m,, is to increase the migration share, again as would be expected. In either case, the amount of change produced in the migration share depends on several factors, one of which is the urban/rural balance, U~_~/R~_~. Once rates have changed, feedback effects come into play, and these effects exert further influence on the migrant share. Higher rates of urban natural in- crease, rail, tip the population balance toward urban areas, causing U~_~/R~_~ to rise with time. The more rapid population shift toward cities diminishes the rel- ative size of the rural sector, and this in turn diminishes the relative contribution of rural migrants to city growth. The direct and feedback effects of rail work in the same direction; through both routes, a higher rate of urban natural increase reduces the migrant share of urban growth. Applying the same kind of analysis to the rural-to-urban migration rate, we find that an increase in mr,~' generates feedbacks that work against the direct ef- fect. As explained above, the direct effect is to increase the share of urban growth due to migrants; but with faster rural outmigration, the population balance begins to shift toward urban areas. Over time, this feedback acts to reduce the migrant share. The opposition of forces can be seen in Figure 4-1, which shows the share of urban growth due to migration for values of mr,~' ranging from 0.5 percent per annum to 3.0 percent.4 Higher migration rates are associated initially, and through most of the projection period, with larger migrant shares. As feedback 3 The flows-stocks perspective has often proven helpful; for instance, it was used by the National Research Council (1997) to analyze the full contribution of international migration to the population of the United States. 4 All projections begin with an urban proportion of Pu,o = 0.15. For the other parameters of the projections, we have been guided by Livi Bacci (1997) for natural increase and by the United Nations (1980) and Chen, Valente, and Zlotnik (1998) for migration. Chen, Valente, and Zlotnik (1998) present regional estimates of mr,U that range from 0.5 percent (Africa in the 1980s) to 2.65 percent (Latin America in the 1970s). Earlier, the United Nations (1980) provided estimates ranging from 0.05 percent for Nepal in the 1960s to 3.7 percent for Venezuela in the 1950s. We restrict mr,U to lie between 0.5 and 3 percent. The reverse urban-to-rural migration rate, mu, r, is set to 0.25 percent throughout.

114 o.s - s 0.8- 0.7- o s "' 0.6- cat i= CITIES TRANSFORMED Ha, ~ . 0 005 -~` m: 0.030 mr U = 0.010 -. :'` mrU= 0.015 .. `' ~ i. . ~ 0.4 - 0 10 20 30 40 50 Projection Period FIGURE 4-1 Declining migrant shares of urban growth. effects exert their influence, however, all these curves decline and show a tendency to converge. The point at which the migrant share of growth reaches one-half has been termed the cross-over point by Keyfitz and Ledent (Rogers, 1982, citing their papers). As can be seen in the curve with mr,~' = 0.03, depicted by a dashed line in Figure 4-1, very high rates of rural-to-urban migration can hasten the arrival of the cross-over point and produce lower migrant shares thereafter than would be seen in a regime of lower migration rates. The apparent paradox is an expression of a feedback effect. Urban Growth and the Rate of Urbanization The rate of urban growth obeys a similar logic. Urban natural increase, rail, has a positive direct effect on the rate of urban growth (see equation (4.2~. The direct effect of the rural-to-urban migration rate, m,, is also positive, but its strength varies with the urban/rural population balance. When man' rises, the rural sector begins to decline in relative importance, and the urban growth rate then falls. Over the long term, the urban growth rate will approach UGR = ran—m~,r + b

URBAN POPULATION DYNAMICS 0.08 - O 0.06- . _ N ~ 0.04- s a 0.02- 0.00 - 115 ~ \ \ ~;0.02 Rate of Urbanization Rates of Urban Growth By/ ~ _ ma_ _, ~ _ 0 10 20 30 Projection Period FIGURE 4-2 Rates of urban growth and urbanization. 40 50 where b is the long-term urban/rural balance, that is, the value taken by U~/R~ in the limit.5 As was shown earlier, high rates of natural increase have been a defining fea- ture of the demographic regimes of many developing countries and distinguish their urban transitions from the Western historical experience. As just noted, a higher rail produces more rapid urban growth; working indirectly through migra- tion, a higher rural Ilr also produces more rapid urban growth. Although they have powerful effects on the urban growth rate, rail and Or need not have any particular implications for the rate of urbanization. As can be seen in Appendix B. equa- tion (B.6), if roll and Ilr happen to be equal, they can be scaled up or down with no direct effect on the rate of urbanization. The projections depicted in Figure 4-2 illustrate the point. In this figure, the rates of natural increase are set to 2 percent (the upper curve) and 1 percent (the middle curve). These two curves exhibit high initial urban growth rates that are followed by growth rate declines. Meanwhile, the rate of urbanization shown with the dashed line remains wholly unaffected, adhering to the same trajectory whether the natural rates of increase are high or low. If equality in urban and rural rates of natural increase appears to be a spe- cial case, consider data from the United Nations (1980: Tables 10-12~. These 50f course, the rates must be such that the limit exists; see Appendix B.

116 CITIES TRANSFORMED data show that in the 1950s and 1960s, a number of developing countries had values of rail and fir that were roughly equal. In the 27 developing countries ex- amined by the United Nations, fertility and mortality rates were lower in urban than in rural areas, but the differences between fertility and mortality were about the same.6 When rail is approximately equal to fir, as in these cases, the rate of urbanization is all but entirely attributable to migration. If raft 76 rtr or if these rates change by different amounts, their effects will be expressed in the rate of ur- banization, producing a different path of urbanization than that which appears in Figure 4-2. For example, countries with slower rates of urban than rural natural increase (rat' < rtr) will tend to have slower rates of urbanization, other things being equal. Urban and National Population Growth As noted in Appendix B. equation (B.5), national population growth rates can be written as a weighted average of the urban and rural rates of natural increase, with the weights being the proportions of urban and rural residents in the national total. In the fixed-rate analytic model compare equations (B.2) and (B.5) in Appendix B it is clear that national and urban growth rates depend on rail and fir in much the same way, and this implies that national and urban growth will tend to be positively correlated.7 Figure 4-3 charts the relationship between the two rates of growth over the course of one projection.8 The two rates are positively associated, as expected, although they are linked in a nonlinear fashion. At the outset of the projection (see the upper right portion of the figure), both the urban and national growth rates are high; as the country urbanizes, both rates fall. With increasing urbanization, the slope of the relationship between urban and national growth rates flattens. One might well expect to see the main features of Figure 4-3 reproduced in empirical urban growth regressions. Indeed, demographers have uncovered strong regularities in the association between urban and national rates of growth. In one analysis using a sample of cities in both developing and developed countries (Pre- ston, 1979; United Nations, 1980), a regression of urban growth rates on national growth rates yielded a coefficient for national growth that was very close to unity. The relationship was revisited by Brockerhoff (1999) with a sample limited to 6The United Nations tables show that even in this era, rural rates of natural increase were often slightly higher than urban rates. It is not clear whether the generalization ran ~ rtr still stands. An inspection of recent data (United Nations, 2000: Tables 9 and 18) shows that in the 10 countries with data available for the l990s (most of these 10 being in West Asia), the urban rate of natural increase falls well short of the rural rate. However, according to Visaria (1997), rates of natural increase in India are about the same in urban and rural areas. 7Of course, if rid = rtr, the national growth rate would be invariant to the distribution of population between urban and rural areas. sin this projection, ran = 0.01, rtr = 0.02, and mr,U = 0.01. Recall that mU,r = 0.0025 in this and all other projections.

URBAN POPULATION DYNAMICS 0.06 - 0.05- 53 ° 0.04- 0.03 - 0.02 - 117 Start of Projection (proportion urban = 0.15) / - 0.0160 0.0165 0.0170 0.0175 0.0180 0.0185 Rate of National Population Growth FIGURE 4-3 Rates of urban and national population growth, given roll < for. developing countries; his regression yielded a coefficient estimate of about 0.8. Such differences in regression coefficients are to be expected when the analy- sis samples differ in the average level of urbanization.9 In this case, however, the countries in the Brockerhoff sample are less urbanized on average, leaving it doubtful that the lower coefficient estimate found by Brockerhoff is due to the changing curvature of Figure 4-3. Note that when roll < for, a higher rural-to-urban migration rate mr,ll reduces the national population growth rate because it speeds the transfer of population to the urban sector where the natural rate of growth is lower. At the same time, a higher mr,~' increases the rate of urban growth. Hence, changes in the migra- tion rate would cause urban and national population growth rates to be negatively correlated, other things being held equal. Migration and Urban Age Structure As is well known, rural-to-urban migration rates are strongly influenced by age, and this age dependence is reflected in urban and rural age distributions. As 9In reaching these conclusions, both Preston (1979) and Brockerhoff (1999) use multivariate re- gression and include covariates other than national growth rates in their specifications. The initial level of urbanization, used in both analyses, proves to have a negative, strongly significant influence on the rate of urban growth. The link to the level of urbanization is implicit in Figure 4-3.

118 CITIES TRANSFORMED will be documented shortly using DHS data, in urban populations proportion- ately more residents are found in the prime working and reproductive ages, and proportionately fewer residents are children. Such differences are attributable to lower urban fertility and the age selectivity of rural-to-urban migration. But the relationship between migration and urban age composition is more complex than it might at first appear. In supplying cities with more young adults, rural-to-urban migration also tends to increase the urban crude birth rate, and higher birth rates, in turn, partly offsets the direct effects of migration on age structure. To further clarify the role of migration, we constructed an age-differentiated demographic simulation that makes use of model schedules of fertility, mortality, marriage, and migration. The approach is inspired by Rogers (1986) and draws on demographic schedules developed by Rogers (1995), Coale and Trussell (1974), and Ewbank, de Leon, and Stoto (1983~. Consider two hypothetical populations, one rural and the other urban, between which there is no migration. Both popula- tions share the same (high) mortality rate (eO = 45) and total fertility rate (TFR = 6.0), and we assume that they have each attained stable age distributions. We then open the border between the two populations and allow for migration in both di- rections. We assume, however, that the rural-to-urban migration rates are higher. it Figure 4-4 depicts the consequences. The first wave of rural-to-urban mi- grants increases the proportion of urban residents of reproductive age (shown on the right scale of the figure). As these new urban residents marry and bear chil- dren, the urban crude birth rate increases from its initial stable population value. Meanwhile the rural crude birth rate declines from its stable value; although there is migration in both directions, the schedules we have adopted ensure a net trans- fer of young adults to urban areas. Once the urban crude birth rates have been driven higher, the share of those aged 15-49 in the urban population begins to fall as the proportion of children rises (the latter proportion is not shown). The inflation of urban crude birth rates is temporary; it subsides as the first cohorts of rural migrants work their way through the urban age distribution, and the urban and rural birth rates then approach each other. The urban crude birth rate remains higher than the rural rate, however, as a result of the continuing influence of rural-to-urban migration. All this occurs even though the rural and urban rates of fertility, marriage, and mortality are assumed to be equal at each age. The effects displayed in the figure are purely compositional. Even so, they serve as a reminder of one important role of rural-to-urban migration: it increases the share of the urban population in the reproductive ages. The analytic models we have been using are based on assumptions of fixed demographic rates whether these are aggregate rates of natural increase and mi- gration in the simpler projection model, or fixed underlying schedules in the model with age structure. If such models are to help in sorting out the empirical record, i°Initially, the urban share of the combined population is 15 percent; by the end of the projection, this share has risen to about 56 percent.

URBAN POPULATION DYNAMICS 0.047 - 0.043 - 0.039 - 0.035 \ \ Rural Crude Birth Rate Urban Crude Birth Rate Share of Urban Population Aged 15-49 \ \ . 0 20 119 - 0.53 o) l - 0.52 ~' 0 .~ - 0.51 Q a - 0.50 ° - s a) - 0.49 40 60 80 Projection Period FIGURE 4-4 Changes in rural and urban crude birth rates and age structure with migration. Results from projections using model schedules. care must be taken to separate the dynamic implications of fixed rates from the implications of changing rates. To appreciate this point, consider again the ur- ban growth rate and the migrant share of that growth. In a fixed-rate regime, we would expect to see a decline in the urban growth rate with time as rural-to-urban migration diminishes in relative importance (equation (4.2~. The migrant share of growth would also be expected to decline with time (equation (4.3~. But when the demographic rates are changing, the forces that propel the trends may need to be reinterpreted. For example, the urban rate of natural increase, rail, may fall as a result of reductions in urban fertility. If roll falls, urban growth rates will also tend to fall, but the share of urban growth due to migration will tend to rise an outcome not predicted by a fixed-rate model. On balance, then, it is sensible to view empirical trends as resulting from a combination of fixed-rate dynamics and the dynamics stemming from demo- graphic transitions. Without additional evidence, empirical time series provide ambiguous testimony as to the relative importance of these dynamic forces. The ambiguity is evident in the empirical record of urban Brazil, described in Box 4.1. Here, over the course of three decades, one can see clear evidence of decline in the growth rates of the country's six largest cities. However, that decline can be variously interpreted: as the workings of a fixed-rate model, as the result of de- clines over time in urban fertility and rates of natural increase, or as a combination of the two.

120 CITIES TRANSFORMED BOX 4.1 Declining Growth Rates Experienced by Brazil's Six Largest Cities, 1970-1996 Sao Paulo's average annual growth rate (UGR) fell from 4.36 percent in the 1970s to 1.42 percent during 1991-1996. Annual growth in Rio de Janeiro also fell, from 2.41 to 0.75 percent (Lam and Dunn, 20014. Likewise, growth slowed in the next four of Brazil's largest cities. For all these cities combined they contained nearly 40 million persons in 1996 the growth rate fell from 3.59 percent in the 1970s to to 1.29 percent by 1991-1996, leaving the UGR at slightly more than one-th~rd of its 1970s level. Reductions in fertility rates are believed to have played an important part in the UGR decline. Meanwhile, the city populations continued to grow in absolute terms even as their rates of growth waned. The annual population increments (/\U~) to these six large cities were on the order of 1 million persons in the 1970s, fell to about 600,000 persons in the 1980s, and fell further to some 500,000 persons in the l990s. Total Population (in thousands) Average Annual Growth Rate City 1970 1980 1991 1996 1970-80 1980-91 1991-96 Sao Paulo 8,137 12,589 15,445 16,582 4.36 1.86 1.42 Rio de Jar~eiro 7,082 9,014 9,815 10,192 2.41 0.77 0.75 Belo Honzonte 1,606 2,540 3,436 3,803 4.59 2.75 2.03 Porto Alegre 1,531 2,231 3,028 3,245 3.77 2.78 1.39 Recife 1,793 2,347 2,920 3,088 2.69 1.99 1.12 Salvador 1,149 1,767 2,497 2,709 4.30 3.14 1.63 TOTAL 21,298 30,488 37,140 39,619 3.59 1.79 1.29 SOURCE: Lam and Dunn (2001). FERTILITY, MORTALITY, MIGRATION, AND URBAN AGE STRUCTURE This section brings data from the DHS to bear on the demographic concepts out- lined in the previous section. We preface the discussion with a note on how mi- grant shares of growth are usually calculated from aggregate census data. This aggregate procedure, which offers estimates of a component of urban growth that cannot be reliably obtained from sample surveys of the size usually fielded, has both weaknesses and strengths. We then present estimates from the DHS for re- cent migration, examine the place of urban-origin migrants among all migrants, and conduct an analysis of migration by the size of the destination city. Finally, estimates of urban and rural fertility and mortality are presented, and urban age structures are illustrated. Migrant Shares as Calculated from Censuses Methodological problems bedevil all attempts to determine the relative contribu- tions of migration, natural increase, and reclassification to urban growth. When data are lacking on migration as such, the contribution of migrants to urban growth can be estimated only imprecisely. The usual "residual" method, which generated

URBAN POPULATION DYNAMICS 121 the findings of Chen, Valente, and Zlotnik (1998) described in the previous chap- ter, begins with a comparison of population counts in two censuses. The urban population of the second census is projected from that of the first, making an allowance for intercensal mortality. When the projected urban population is subtracted from the total counted in the second census, what remains is an es- timate of the sum of net migration and territorial reclassification. Errors in the census data and in the assumptions are also embedded in this residual. Although errors are always a concern, the residual method gives at least a rough estimate of the relative contributions of natural increase on the one hand and migration cou- pled with reclassification on the other. Migration data can be gathered by other means, such as through sample surveys, but it is difficult to imagine how survey interviewers could collect any meaningful information about reclassification from their respondents. In this limited sense, the aggregate census-based method deliv- ers information that surveys cannot. iiSuppose that the two censuses are exactly a decade apart. Let Nufa) be the number of persons of age a recorded in the first census. Let ,uu (a) be the urban mortality hazard function. If all urban residents found in the first census remain urban, a decade later one would expect to find Pu2 (a + 10) = Nu (a) exp ( - / An (Ada ) survivors in urban areas. Let Nu (a + 10) represent the number of urbanites aged a + 10 who were actually recorded in the second census. The difference Data + 10) = Nu (a + 10)—Pu2 (a + 10) is then an estimate of the sum of net migration and reclassification for this age group. Net migration is the difference between the number of rural migrants, Mr~ufa + 10), who arrived between the censuses (and survived to the date of the second census) and the number of formerly urban residents, Mu, r (a+ 10), who left. Any remainder in Du (a+ 10) is attributable to net reclassification. Summing Du (a + 10) over all a > 0 yields a total for net migration and reclassification in the population above 10 years of age at the time of the second census. There remains a need to calculate something akin to Dufa) for the population aged 10 and under, and assumptions about urban fertility must be invoked to do so. The United Nations (1980) explains the method, which relies on urban child-woman ratios. The key assumption is that the same fertility schedule applies to all women in urban areas, whether they are intercensal migrants or not. As will be seen in Chapter 6, there is some empirical justification for the assumption that, relatively soon upon arrival, rural-to-urban migrants exhibit about the same age-specific fertility rates as urban natives. But counterexamples doubtless exist, and the literature has not yet settled into consensus. Another complication is that it is rather rare to have estimates of ,uu (a) that are actually derived from urban data. Generally the urban mortality curve must be calculated from national data that include the rural population, and assumptions about relative risks are required to extract an urban estimate. As explained by Chen, Valente, and Zlotnik (1998), the United Nations assumes that the rural mortality hazard function ,ur~a) = 1.25 · ,UU(a), that is, the rural hazard rate is assumed to be 25 percent higher than the urban at each age a. Taken together with estimates of urbanization, this proportionality assumption yields estimates of the urban mortality hazard. The United Nations (1980) describes some experiments in which rural mortality was assumed to be as much as 50 percent higher than urban. These experiments revealed that the decomposition of urban growth into natural increase and net migration is robust to variations in assumptions about the relative risks of mortality. i2Scattered, order-of-magnitude estimates suggest that of the migration and reclassification total, about one-quarter is attributable to reclassification; see United Nations (1980: 25) and Visaria (1997: Table 13.5).

122 CITIES TRANSFORMED Migrants as Recorded in the Demographic and Health Surveys Another way to gauge the contribution of migrants is to examine their numbers in relation to the urban population total, rather than in relation to urban growth 33 Data from the DHS provide a measure akin to this, although it is restricted to an urban subpopulation. Using these surveys, one can examine the migration status of urban women of reproductive age. That men and other women are excluded is unfortunate, but the DHS surveys do not usually gather migration data for these groups. {4 Some DHS surveys collect month-by-month retrospective migration his- tories that cover the 5 years before the survey and occasionally go back further in time. Other surveys simply ask the woman how long she has resided in her cur- rent community and record the responses in terms of years of residence 35 In what follows, we define recent migrants to be those women who moved to their current (urban) residence in the 5 years before the DHS survey. Among urban women of reproductive age, recent migrants are a numerically important group. As Table 4-1 shows, nearly one urban woman in four in the age range 15-49 is a recent migrant36 The figure ranges from a low of 15.6 per- cent in the Latin American surveys to a high of 29.4 percent in the surveys from sub-Saharan Africa37 As would be expected given the age pattern of migration, younger women are much more likely to be recent migrants. Some 36 percent of urban women under age 25 are recent migrants, as compared with only 9-12 percent of women in their 40s. Even this lower percentage might be judged high by providers of reproductive health and related services if migrants have special i3In the analytic model developed above, this measure could be expressed as (R~_~ /U~_~ ~ mr,u. i4Census data from Mexico in 1990 show that, compared with male migrants, female migrants were more heavily concentrated in the age groups 15-19 and 20-24. Migrants were slightly more likely to be married or in a consensual union than nonmigrants (De la Paz Lopez, Izazola, and de Leon, 1993). The concentration of female migrants in the 15-24 age range is generally greater than is the case for males and is greatest for migrants moving to metropolitan areas rather than to urban areas in general (Hugo, 1993). See Recchini de Lattes and Mychaszula (1993), Findley and Diallo (1993), and Alvi and Wong (1993) for studies of the age and marital status of female migrants compared with nonmigrants. i5According to the Demographic and Health Surveys (2001), the term "community" refers to the village, town, or city of residence. i6Here and throughout the volume, we report summaries of estimates derived from analyses of individual DHS surveys. The statistical models were estimated with an allowance for unmeasured effects specific to sampling clusters, so that all discussions of statistical significance refer to robust standard errors. Sampling weights were not used at the estimation stage, but were applied to convert the estimates to representative summary values for each survey. In assembling the tables, we then averaged such survey-specific values. Survey results from countries that fielded more than one DHS survey were downweighted in proportion to the number of surveys fielded. Hence, the unit described in the tables is the country. i7Note that the "total" row of the table is dominated by estimates from sub-Saharan Africa and Latin America, where the greatest number of DHS surveys have been fielded. In no region of the developing world have all countries participated in the DHS program, and within regions some countries have fielded more surveys than others. Hence, the "total" row cannot be interpreted in terms of averages across the whole of the developing world.

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124 CITIES TRANSFORMED TABLE 4-2 Percentages of Urban Women of Reproductive Age Who Are Recent Migrants, by City Population Size and Region 31.5 21.3 21.9 17.9 27.4 29.0 26.8 23.5 16.6 DHS Surveys in Region North Africa Sub-Saharan Africa Southeast Asia South, Central, West Asia Latin America City Population Size Under 100,000 to 500,000 to 100,000 500,000 1 million 5 million 22.1 27.4 20.9 29.6 22.9 26.7 16.8 1 to Over 5 million 10.0 22.5 26.5 28.0 9.0 16.7 22.9 26.0 20.3 14.1 TOTAL 25.5 24.8 23.8 19.6 22.0 NOTE: The entries in this table are summaries of age-standardized predictions from a probit model; they represent the migrant status of women aged 25-29. See also notes to Table 4- 1. health needs or deficits in knowledge compared with urban natives. In any case, it is plain that migrants account for a substantial percentage of urban women at both ends of the reproductive age span. Table 4-2 presents data on migrant status according to the population size of the city in which the woman resides. A methodological note is in order here. Appendix C describes how the panel linked DHS survey records to United Nations data on city size. The linkage is very difficult to effect, involving approximations and a good measure of guesswork, because the DHS datasets that were available to the panel did not include adequate spatial identifiers. In concluding this chapter we will revisit this point, which has a bearing on how spatially coded urban data can be used in survey-based studies of individuals and families. Returning to the question of migration, the results for the average DHS coun- try, shown in the "total" row of Table 4-2, indicate that cities with 1 million or more residents have proportionately fewer migrants than smaller cities, although these differences are on the order of a few percentage points. In Latin America and sub-Saharan Africa, clearer evidence of a negative relationship emerges.~9 A negative relationship is also apparent in North Africa if one sets aside the smallest cities. There is little here to suggest that migrants are systematically overrepresented in the larger cities, and yet that would appear to be a common belief. i8See Table C-3 in Appendix C for a list of cities in the population size range from 1 to 5 million whose countries have fielded a DHS survey. The cities of over 5 million are, by region; North Africa, Cairo; sub-Saharan Africa, Lagos; Southeast Asia, Bangkok, Jakarta, and Manila; South, Central, and West Asia, Dhaka, Madras, Delhi, Calcutta, Mumbai, Karachi, and Istanbul; and Latin America, Rio de Janeiro, Sao Paulo, Mexico City, and Lima. i9For similar findings in Mexico using a special migration survey, see Brambila Paz (1998).

URBAN POPULATION DYNAMICS TABLE 4-3 Type of Origin Area for Recent Urban Migrants, by Region DHS Surveys in Region North Africa Sub-Saharan Africa 23 Southeast Asia 3 Na Type of Origin Area City Town Rural 40.4 33.3 31.2 South, Central, West Asia 7 34.5 38.7 Latin America 9 28.9 28.0 23.6 28.0 31.8 30.7 38.8 45.2 37.5 29.6 TOTAL 45 34.9 28.5 36.6 NOTE: Towns are defined by the DHS as urban areas with fewer than 50,000 residents; cities are all urban areas larger than this. See also notes to Table 4-1. a Number of countries with DHS survey data on migrant origin. 125 Most of the DHS surveys with data on migration also gather data on migrants' areas of origin, classified as city, town, or rural.20 Table 4-3 shows that roughly equal percentages of urban migrants come from cities and rural areas, with smaller but still substantial percentages coming from towns. As can be seen, only about one in three urban migrants is of rural origin; the table shows that, taken together, cities and towns are far more important sources of urban migrants. When city size differences are examined (see Table 4-4), a somewhat mixed picture emerges. The percentage of migrants with city origins is generally higher in the larger cities of destination, but there are exceptions and irregularities in the evidence. In Latin America, the larger the destination city, the greater is the share of city-origin mi- grants among all migrants. This pattern is also evident in North Africa, although it is less apparent in other regions. All of these findings cast doubt on the common view of migrants as predom- inantly rural folk. The DHS data also call into question the value of simplistic analytic models, such as ours, which treat the urban population as an undifferen- tiated mass. As these data show, considerable migration takes place within the urban sector, and the implications of circulation among towns and cities may be quite different from the implications of rural-to-urban migration. Urban and Rural Levels of Fertility and Mortality Unlike data on migration, which are available only for a subset of the DHS sur- veys, all of the surveys provide information on levels of fertility, infant mortality 20See Appendix C. In this context, "origin" refers to the nature of the area from which a migrant has most recently come, rather than to place of birth. Bilsborrow (1998) warns that survey respondents may describe their origin areas in terms that bias the urban percentages upward. The panel is not aware of any empirical assessments of such a bias.

126 CITIES TRANSFORMED TABLE 4-4 Type of Origin Area for Recent Urban Migrants, by Region and Population Size of Current Residence City Size DHS Surveys in Under 100,000 to 500,000 to 1 to Over Region Origin 100,000 500,000 1 million 5 million 5 million North Africa City 22.0 42.9 46.8 59.9 42.5 Town 41.6 26.3 24.5 16.2 23.9 Rural 36.4 30.8 28.7 23.9 33.6 Sub-Saharan City 28.0 36.3 40.1 30.0 35.6 Africa Town 31.1 27.5 30.1 32.7 47.2 Rural 41.0 36.2 29.8 37.2 17.2 Southeast Asia City 21.3 22.2 26.2 15.9 42.0 Town 17.9 24.4 40.4 29.6 20.4 Rural 60.7 53.4 33.4 54.5 37.6 South, Central, City 24.7 38.7 39.1 23.7 12.0 West Asia Town 30.4 28.1 25.5 30.9 57.6 Rural 44.9 33.2 35.4 45.4 30.3 Latin America City 36.6 46.5 40.3 38.2 60.7 Town 26.5 27.9 38.9 33.2 26.8 Rural 37.0 25.6 20.8 28.6 12.5 TOTAL City 28.5 38.1 39.4 35.1 42.2 Town 30.1 27.3 32.2 29.8 30.4 Rural 41.4 34.6 28.4 35.1 27.4 NOTE: See notes to Tables 4-1 and 4-3. (deaths under age 1), and child mortality (deaths under age 5~. Both fertility and mortality are examined later in this report (see Chapters 6 and 7, respectively), and the treatment of these data here is brief and introductory in nature. Table 4-5 provides estimates of total fertility rates (TFRs) for urban and rural women. This table confirms that urban and rural areas have quite different fertility rates. The widest gaps in fertility are seen in Latin America, where the difference is on the order of 2.1 children, and in sub-Saharan Africa, where urban women are estimated to have 1.4 fewer children than rural women over a reproductive life- time. The urban/rural differences are smaller in the other regions, although still appreciable. Of course, much of this is due to urban/rural differences in socioe- conomic composition, as will be seen in Chapter 6. But lower urban fertility is hardly a modern development it is a well-documented feature of the European historical record (Sharlin, 1986~. Lower fertility is, and long has been, an indica- tor of urban-specific productive and reproductive family strategies. Table 4-6 gives an overview of urban/rural differences in infant (iqO) and child (5-to) mortality levels. Here again we see sizable differences between urban and rural areas. That urban mortality falls below rural is unsurprising, perhaps, but it

URBAN POPULATION DYNAMICS TABLE 4-5 Total Fertility Rates, Rural and Urban Areas, by Region DHS Surveys Rural Urban in Region Na Fertility Fertility North Africa 3 4.82 3.59 Sub-Saharan Africa 27 6.50 5.07 Southeast Asia 3 3.37 2.81 South, Central, West Asia 10 3.93 3.29 Latin America 13 5.49 3.36 TOTAL 56 5.55 4.16 NOTE: Calculated from 90 DHS surveys, with survey-specific results downweighted for countries with multiple surveys. The fertility estimates are derived from a Poisson model with a set of age dummies. The Poisson coefficients are then converted to estimated rates, using additional correction factors supplied by the DHS for the surveys restricted to ever-married women. a Number of countries with survey information on fertility. 127 is worth remembering how recently this advantage has emerged and how tenuous it may be. Communicable diseases are a predominant cause of deaths in infancy and childhood, and if other things were equal, urban residents would be placed at greater risk by their spatial proximity and dependence on common resources, such as water. After all, it was not until the late nineteenth and early twentieth centuries in the West that urban mortality levels fell below rural levels (Preston and van de Walle, 1978; Preston and Haines, 1991~. The marked urban mortality advantage seen in Table 4-6 is thus a departure from the historical norm; it reflects advances in public health and scientific knowledge, and testifies to the ability of TABLE 4-6 Infant and Child Mortality, Rural and Urban Areas, by Region Child Mortalityb Infant Mortalitya DHS Surveys in Region Nc Rural North Africa 3 73.8 Sub-Saharan Africa 27 101.7 Southeast Asia 3 49.7 South, Central, West Asia 10 69.7 Latin America 13 63.3 Urban 45.8 81.0 30.4 54.2 46.9 Rural 88.5 153.6 60.6 84.6 80.7 115.9 Urban 50.3 122.0 36.8 62.2 57.0 3 TOTAL 56 82.8 63.7 87.8 a Table entries are means of Kaplan-Meier estimates of ~qO derived from 90 DHS sur- veys, with survey-specific estimates downweighted for countries with multiple surveys. b Kaplan-Meier estimates of 5-to, from the same set of surveys. c Number of countries with information on infant and child mortality.

128 CITIES TRANSFORMED higher-income urbanites to purchase protection against disease. In Chapter 7 we return to the issue, asking whether poor urban residents can avail themselves of any similar protections. Urban Age Structure The fact that urban fertility rates are generally lower than rural, when combined with the influence of rural-to-urban migration, yields urban age profiles with a distinctive shape. The age composition of urban populations is illustrated in Fig- ures 4-5 and 4-6 for Brazil (based on its 1996 DHS survey) and in Figures 4-7 and 4-8 for Ghana (based on its 1998-99 survey). For each country, the urban pop- ulation pyramid is shown first; below that pyramid, the figures depict the urban proportion at a given age in relation to the rural proportion at that age. Figures 4- 6 and 4-8 reveal a relative deficit of children in the urban areas of Brazil and Ghana and a relative surplus of men and women in the working and reproductive ages. As these illustrations suggest, urban populations are older than rural popu- lations on average. Table 4-7 shows that, as in Brazil and Ghana, cities gener- ally have lower percentages of children (those aged 0-14) in their populations, higher percentages of working-age and reproductive-age adults (aged 15-64), and slightly lower proportions of older adults (aged 65 and above). Table 4-8 provides a further analysis of age structure in urban areas of differ- ent population size, focusing on the proportion of the household population in the working ages. As can be seen, there is a noticeable increase with city size in the working-age proportion, with the occasional exception of the largest city size cate- gory. These differences may well stem from the lower fertility rates characteristic of larger cities. CORE ISSUES IN DEFINITION AND MEASUREMENT The preceding section presented results by city size, which required that a link- age be made from DHS survey data on individuals to aggregate data from United Nations sources on the population sizes of cities. We have already mentioned the difficulties involved in establishing such a linkage; at this point we must assess the quality of the city population data themselves. They are derived from reports made by national statistical agencies to the United Nations, and therefore reflect the criteria applied by these agencies to define urban areas and delimit the bound- aries of individual cities. The reports are usually (if not always) taken from na- tional censuses, and thus depend on the regularity with which censuses are con- ducted and the completeness of population counts. What is known of the quality of such urban and city size data? It was not until the nineteenth century that formal urban and rural classifi- cations were introduced into the compilations of European population statistics. The systematic compilation of such data is still more recent. In 1948, shortly

URBAN POPULATION DYNAMICS 100 - 95 - 90 - 85 - 80 - 75 - 70 - 65 - 60 - 55- 50- 45 - 40 - 35 - 30 - 25 - 10 - Male ~ Female 0.06 0.04 0.02 0.00 0.02 0.04 0.06 Proportion of Urban Population FIGURE 4-5 Population pyramid for urban Brazil, 1996. 1.5— 1.4— ~ 1.3 — fir ~ 1.2— :> ~ 1.1 — a .= a Q .° 1.0 — tL 0.9 0.8 - 0.7 - Male Urban/Rural | Female Urban/Rural b 1 1 1 1 1 1 1 1 1 1 1 1 1 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Age 129 FIGURE 4-6 Urban relative to rural age composition, men and women by age, Brazil, 1996.

130 CITIES TRANSFORMED 100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 1 I |l 1 Female| _:::::::::::::::::::1 Male 0.06 0.04 0.02 0.00 0.02 0.04 0.06 Proportion of Urban Population FIGURE 4-7 Population pyramid for urban Ghana, 1998-1999. 1.6 1.5— 1.4— 1.0— 0.9 - 0.e - 0.7 - 0.6 - 0.5 - / \ | ~ Male Urban/Rural | \ I · Female Urban/Rural | ~1 ~ ~~ I\ 1 1 1 1 1 1 1 1 1 1 1 1 1 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Age FIGURE 4-8 Urban relative to rural age composition, men and women by age, Ghana, 1998-1999.

URBAN POPULATION DYNAMICS TABLE 4-7 Household Age Composition, Rural and Urban Areas by Region 131 Percentages of Population DHS Surveys Aged O-14 Aged 15-64 Aged 65+ in Region Na Rural Urban Rural Urban Rural Urban North Africa 3 43.2 35.5 52.4 60.5 4.5 4.1 Sub-Saharan 27 48.3 42.8 47.5 54.8 4.3 2.4 Africa Southeast Asia 3 37.2 31.0 58.3 65.2 4.6 3.8 South, Central, 10 40.6 34.9 54.3 60.1 5.1 4.9 West Asia Latin America 12 43.4 35.9 51.8 59.6 4.8 4.5 TOTAL 55 44.9 38.8 50.5 57.7 4.5 3.5 NOTE: Calculated from the household rosters of DHS surveys, with survey-specific results downweighted for countries with multiple surveys. a Number of countries with age data in the DHS household roster. after its founding, the United Nations assembled information on the rural and ur- ban populations of 58 countries with recent censuses (United Nations, 1949~. By 1952, the United Nations Demographic Yearbook already contained rural and ur- ban population counts for 160 countries and could offer an introductory chapter on urban/rural differentials (United Nations, 1953~. These early efforts paved the way for systematic international research on ur- banization, and problems in standardizing urban definitions and measures were among the first issues to be studied (Davis, 1958, 1969; Gibbs and Davis, 1958~. From the outset, researchers at the United Nations and elsewhere wrote frankly about the deficiencies of urban data. As they discovered, the difficulties involved TABLE 4-8 Percentages of Urban Population in the Working Ages (15-64), by Region and City Population Size City Population Size DHS Surveys Under 100,000 to 500,000 to 1 to Over in Region 100,000 500,000 1 million 5 million 5 million North Africa 58.2 59.6 61.3 63.6 61.8 Sub-Saharan 52.1 55.4 56.3 60.1 55.2 Africa Southeast Asia 59.6 64.3 62.7 64.3 68.7 South, Central, 56.9 59.7 61.8 62.5 62.3 West Asia Latin America 56.1 59.4 61.0 61.9 64.7 TOTAL 54.5 58.1 59.6 61.7 63.9

132 CITIES TRANSFORMED in establishing concepts and improving measures could not be speedily overcome. Even by 1969, the United Nations (1969: 22) was forced to concede that ". . . at least a rough overview of world urbanization trends is now so much needed that tolerably usable results had to be preferred over more refined estimates obtainable only with much additional labor." This 1969 report presents estimates and pro- jections of urban population according to national urban definitions, but contrasts them with results from a proposed alternative standard whereby urban areas are defined as agglomerations with populations of 20,000 or more. Noting the many interdependencies of rural and urban areas and the ambiguous status of much ter- ritory surrounding urban areas, the report also expresses concern over simplistic urban/rural classifications. As compilations of urban data were being expanded to cover more nations and longer spans of time, methods for estimating and projecting urban popula- tions were being devised and then revised. By 1974 the United Nations had de- veloped the urban/rural growth difference (URGD) method, now usually termed "the United Nations method," which continues to feature centrally in its estimates and projections (United Nations, 1974~. The method (see Appendix D for more detailed discussion) is based on an extrapolation of differences between urban and rural growth rates, with the results expressed in terms of levels of urbanization. A method similar in spirit (if different in details) has been devised to project the pop- ulations of individual cities. In the late 1970s, the URGD method was modified so as to force a decline in projected urban/rural growth differences as the level of urbanization rises (United Nations, 1980), and the city projections were similarly revised. Over the past three decades, while making such minor adjustments to its meth- ods, the United Nations Population Division has continued to prepare estimates and projections of total urban and rural populations and of urban agglomerations, issuing reports and major updates on a biennial basis. The results are widely cited by researchers and journalists alike. At present, the United Nations provides the only comprehensive, international source of urban population data all the more reason, then, to understand the data limitations. Inconsistent Urban Definitions No definition of urban places has been universally adopted by national govern- ments, and it must be said that the prospects for consensus are dim. A great variety of national definitions is now in use. To briefly summarize current practice, in just over half of the 228 countries for which the United Nations Statistical Office com- piles data, urban definitions are based on administrative considerations equating urban areas with the capitals of provinces or with areas under the jurisdiction of certain types of local authority. In less than a quarter of countries (22 percent) are urban areas distinguished mainly on the basis of population size and density, and in these countries, the lower limit at which a settlement is considered urban varies

URBAN POPULATION DYNAMICS 133 from 200 to 50,000 inhabitants. In 39 countries, including all of the successor states of the former Soviet Union and many Eastern European countries, explicit mention is made of socioeconomic criteria, such as the proportion of the labor force employed in nonagricultural activities and the availability of urban-type fa- cilities (e.g., streets, water supply, sewerage systems, electric lighting). Some two dozen countries provide the United Nations with no explanation at all of their defining criteria. A few examples will illustrate the variety of urban definitions in use. Some countries define urban residents as those people living within well-specified ad- ministrative boundaries in municipios (as in E1 Salvador); municipality councils (as in Iraq); or places having a municipality or municipal corporation, a town committee, or a cantonment board (as in Bangladesh and Pakistan). In Angola, Argentina, and Ethiopia, urban areas are those localities with 2,000 inhabitants or more; in Benin, the threshold is set at 10,000 inhabitants. Botswana sets the threshold at 5,000 people but requires that 75 percent of economic activity be nonagricultural. In Cuba, places with as few as 2,000 inhabitants are considered urban, but even smaller places than this can qualify if they have paved streets, street lighting, piped water, sewerage, a medical center, and educational facilities (United Nations, 2001~. As Hardoy, Mitlin, and Satterthwaite (2001) note, the percentage of the world's population living in urban areas could be increased or decreased by several percentage points if China, India, or a few other large coun- tries were simply to adopt new urban definitions. It is not implausible to think that such changes might occur; as Box 4.2 shows, China made major revisions to its urban criteria in the 1980s. What, then, are the prospects for a uniform international standard in urban definitions? Some researchers have defended current practice on the grounds that "national statistical offices are in the best position to distinguish between urban and rural-type areas in their own countries" (United Nations, 1980~. But in keep- ing with its earlier scientific reviews, as late as 1980 the United Nations (1980: 5) was urging consideration of a four-fold classification distinguishing urban and rural places both inside and outside metropolitan regions to no apparent effect. As one United Nations report drily observes (United Nations, 1969: 9), "greater homogeneity of definition could be achieved in the unlikely event that forms of local government became more standardized throughout the world." If the proba- bility of such an event appeared remote in 1969, it has now reached the vanishing point as developing countries decentralize their governments and establish wholly new municipal and regional entities. The lack of an official consensus greatly constrains the efforts of the United Nations, but it need not deter all researchers. If nationally determined urban cri- teria were to be made fully and publicly available in the major urban datasets, published alongside population size and density data, researchers would be free to study the implications of applying alternative urban criteria. Many recent censuses contain detailed information on the percentages of population living in

134 CITIES TRANSFORMED BOX 4.2 Changing Urban Definitions in China China's current urban definition reflects both settlement patterns and administrative regu- lations for persons and places. China's urban concept has taken several forms over the last two decades (Zhang and Zhao, 19984. At present, four factors determine the size of the urban population and the urbanization level: the criteria for designating a settlement as urban, the physical and administrative boundaries of places so designated, the household registration (huLou) system, and the urban status of the unregistered or "floating" popula- tion. The criteria for urban designation have changed over time, reflecting the prevailing urbanization policy, economic development, and political ideologies. The criteria focus on the administrative status of a settlement, the minimum absolute size of its resident popu- lation, and its occupational structure. The 1984 revisions in urban classification reduced the requirements for minimum population size and nonagricultural workforce share (Gold- stein, 19904. These revisions increased both the number of cities and their populations. In further revisions since 1993, additional characteristics of settlements have been taken into account. The Chinese urban system now consists of two main components: cities (shi or cheng- shi) and towns (when). The urban hierarchy is divided into three levels, roughly analogous to provinces, prefectures, and counties. Provincial status has been granted to four urban re- gions: Shanghai, Beijing, Tianjin, and Wuhan. Towns fall under the authority of counties. Territorial reorganization and annexation leave the number of counties in the municipal jurisdiction of each city far from standardized. The Chinese government has long exerted influence over the growth of urban popu- lation; since 1954 it has sought to maintain control through the huLou system. This sys- tem divides the population into agricultural (nongye renkou) and nonagricultural (fed nonye renkou) categories, which among other things determine rights of access to public-sector subsidies (e.g., grain distributions). In the huLou system, there are two official indicators of urban population: the total population of cities and towns (TPCT) and their nonagri- cultural population (NPCT). TPCT counts as urban those who are living in a designated urban area under the administration of residents' committees for at least a year and absent from their former huLou registration place for over a year. NPCT, by contrast, counts as urban those living in a designated urban area who are engaged in nonagricultural work. A person's huLou status, defined administratively as "agricultural" or "nonagricultural," need not reflect his or her actual occupation. settlements in specified size ranges. Although these census data could be em- ployed to develop a uniform standard for research, they have not yet been put to this use (Satterthwaite, 1996a). A number of regional databases could also support the development of a consensus standard. For instance, the GEOPOLIS database for sub-Saharan Africa has adopted a homogeneous definition of urban areas, counting as urban only those settlements with populations of more than 10,000 (Moriconi-Ebrard, 1994~. Although such alternatives are promising, re- searchers will doubtless continue to rely on the United Nations data, with their heterogeneous urban criteria, for the forseeable future.

URBAN POPULATION DYNAMICS 135 Differences in urban definitions affect mainly the status of smaller towns and cities, those settlements that might be classified as either rural or urban. In cross-national comparisons, one can skirt much of the problem by focusing not on a country's total urban population, but on the urban population that resides in settlements above a given size. In following this line of reasoning, we are led away from the national estimates and toward the United Nations' city-level esti- mates and projections. City-Level Population Data At present, the United Nations offers two sources of data on city population size- one gathered by its Statistical Office and the other prepared by the Population Di- vision. The more extensive data, processed by the Statistical Office, are found in the annual Demographic Yearbook. Every year since 1955, this publication has recorded the population sizes of capital cities and all cities of 100,000 or more population according to the most recent official counts. The population counts are themselves taken from national or municipal censuses.2i The Demographic Yearbook presents only the most recently reported census results and estimates. For countries that do not regularly conduct censuses, do not tabulate population at the level of cities, or do not report their data to the United Nations, the fig- ures may refer to counts taken years or even decades earlier. For such coun- tries, city population data are available only at isolated or irregularly spaced points in time. The second major source of data, the biennial World Urbanization Prospects volumes (the most recent edition being United Nations, 2002a), presents pop- ulation estimates and projections at regular 5-year intervals for urban agglom- erations with populations of 750,000 and above; it includes all capital cities, irrespective of size. In preparing World Urbanization Prospects, the United Na- tions Population Division evidently draws its raw materials from the same pop- ulation counts that are published in the Demographic Yearbook, which it then extrapolates to cover years for which census counts are unavailable.22 Curve- fitting techniques akin to the URGD method are used to form city estimates and projections, involving city population growth rates (where they are available) and growth rates of the total population; further adjustments are made (it appears) on the basis of country-specific factors. Appendix D describes these procedures in more detail. Din addition to census counts, estimates of city population based on sample surveys and other sources are presented in the Demographic Yearbook. According to the United Nations (2000: 44), data drawn from sources other than a census or complete survey are potentially unreliable. 22The data files and empirical methods used by the Population Division are not publicly accessible, so we can only speculate about the details of its procedures. The panel's understanding is that the Pop- ulation Division gives considerable attention to the possibility of errors and cross-country differences in reporting.

136 CITIES TRANSFORMED It is not clear just why the estimates and projections in World Urbanization Prospects are limited to cities of 750,000 and above, with exceptions for capitals.23 Perhaps the URGD approach has proven unreliable when applied to smaller cities. The exclusion of smaller cities is unfortunate given the United Nations' projection that over the next few decades, roughly half of the urban population of developing countries will be found in cities with populations of 500,000 and below (recall Chapter 1, and see United Nations, 1998b: 274. City boundaries As noted in Chapter 1, the delineation of city boundaries affects both popula- tion counts and growth rates. Indeed, cities such as Buenos Aires, Mexico City, London, and Tokyo can correctly be said to be declining or expanding in pop- ulation, depending on how their boundaries are defined. In the United Nations publications, urban population counts are reported for several types of boundaries or city concepts: · City proper: the inhabitants residing within the formal administrative boundaries of the city. · Urban agglomeration: the population found within the contours of a con- tiguous territory inhabited at urban levels of residential density, without re- gard to administrative boundaries. · Metropolitan area: the most expansive of the measures. It includes the territory covered by the urban agglomeration, but also incorporates lower- density settlements, including areas that might otherwise be designated as rural when under the direct influence of the city through networks of trans- port and communication. That, at any rate, is the principle; in practice, metropolitan regions can be defined as large administrative entities that in- clude rural areas even if these areas have no particular city linkages. The United Nations (2000: 43-45) provides further discussion of these urban concepts. In World Urbanization Prospects, the urban agglomeration is the preferred unit for which urban estimates and projections should be prepared. Unfortunately, as 23 According to the United Nations (1998b: 34), it is the responsibility of the Population Division to "monitor the size of all of the world's cities once they reach 100,000 as recorded by a population census or other reliable observational procedure." As recently as 1985, the populations of all cities with populations of 100,000 and above were estimated, although they were not listed city by city in the annexes of the report. (The 1985 report includes a size category of under 100,000, but the estimates for this smallest size class may have been derived by subtracting the total of the other size classes from the estimated all-urban total.) The United Nations (2001) provides estimates by city size class that include all urban areas with populations under 500,000, but gives city-specific estimates only for urban agglomerations with populations of 750,000 and above.

URBAN POPULATION DYNAMICS 137 noted by the United Nations (1998b: 34-35, 37, 55-80), when countries do not report their city populations in terms of agglomerations or when their reporting criteria vary over time, the United Nations' estimates and projections cannot be interpreted in terms of urban agglomerations as such. In these cases, the World Urbanization Prospects estimates often represent the size of the city proper; oc- casionally, they represent metropolitan areas rather than urban agglomerations. Examples The figures that follow illustrate some of the difficulties of interpretation that sur- round the United Nations' city population estimates. Because the estimates that attract most attention are those published in World Urbanization Prospects, these are emphasized in the discussion. Figures 4-9 and 4-10 present the full data series available to the panel for the Egyptian cities of Cairo and Shubra-El-Khema. The line of connected points is taken from the World Urbanization Prospects dataset (United Nations, 2001~. The points marked by boxes are estimates of the population size of the city proper, as presented in various years of the Demographic Yearbook. (Egypt does not publish estimates for the agglomeration of Cairo.) Figure 4-9 thus conforms to expectations: it shows that the World Urbanization Prospects estimates, which refer to the urban agglomeration of Cairo, lie well above the various Demographic Yearbook estimates for the city proper. 9- | · World Urbanization Prospects | · City Proper; Demographic Yearbook | / A_ In o7- ~ ~ ~ . _ o cat Q 5 - / i' 3- An/ / l r 1950 1960 1970 1980 1990 2000 Year FIGURE 4-9 Cairo: United Nations population estimates.

138 CITIES TRANSFORMED 800 - _` O 600- . _ o ~ 400- iL 200 - O- · World Urbanization Prospects · City Proper; Demographic Yearbook - - ~- 1950 1960 1970 1980 1990 2000 Year FIGURE 4-10 Shubra-El-Khema: United Nations population estimates. It is then disconcerting to discover that for Shubra-El-Khema, the World Ur- banization Prospects estimates are very close to those published in the Demo- graphic Yearbook for the city proper; a larger gap would have been expected if the World Urbanization Prospects estimates faithfully represented urban agglom- erations. The United Nations (1998b: 62) provides the explanation, indicating that whereas the World Urbanization Prospects estimate for Cairo refers to the urban agglomeration, its estimate for Shubra-El-Khema refers to the city proper. Although listed in the tables as if it were a physically separate entity, Shubra-El- Khema actually lies within the greater Cairo metropolitan area, and perhaps this is why its population is reported in terms of the city proper (no rationale is stated explicitly). The second case we consider is that of Brazil. Here, according to the De- mographic Yearbook (United Nations, 1998a: 301, footnote 13), city population sizes are recorded in terms of the populations of "municipios which may contain rural areas as well as urban centreLs]." This description is ambiguous it is sug- gestive of both administrative and metropolitan area definitions. The units issue is complicated by World Urbanization Prospects (United Nations 1998b: 58), which declares that different Brazilian cities employ different reporting schemes. Sao Paulo, for instance, is said to record its population data in terms of the metropoli- tan area, whereas Brasilia is said to use the city proper concept. A comparison of the Yearbook and Prospects estimates for Sao Paulo injects further confusion.

URBAN POPULATION DYNAMICS 15 i_ o . _ ~ 10 . _ o Q o 5 o · Metropolitan Area; World Urbanization Prospects O Metropolitan Area; Demographic Yearbook o i.' O 0 00 A/ T ~ ~ ~ ~ 1950 1960 1970 1980 1990 2000 Year FIGURE 4-11 Sao Paulo: United Nations population estimates. 139 Figure 4-1 1 shows a marked difference between these estimates, with the Prospects figure exceeding that of the Yearbook by more than a million persons. It is not ob- vious how discrepancies of this size can be resolved. If both estimates refer to the population of the metropolitan area, the metropolitan measure used by Prospects must be considerably more expansive. For Brasilia (Figure 4-12), the Yearbook counts are higher than the extrapolated values of Prospects in the mid-1980s and 1990, as expected given the difference in urban concepts, but this gap unexpect- edly closes in the mid-199Os. Finally (see Figures 4-13 and 4-14), a city such as Niamey, the capital of Niger, has had too small a population to be included in the main series of estimates and projections in World Urbanization Prospects. Nevertheless, because Niamey is a capital city, its population is estimated using techniques similar to those applied to larger cities. In Kitwe, Zambia a small city in Zambia's Copper Belt, but not a national capital only the Demographic Yearbook population counts are available. Evidently, there is a great deal of heterogeneity in the nature of the United Na- tions' city population estimates. The fundamental problem is the variety of units used to report city populations to the United Nations; as noted, the city proper, the urban agglomeration, and the metropolitan area are all used, and some countries employ two of these measures. The units problem is explicitly acknowledged in the footnotes and technical appendices of the United Nations publications, but is not much appreciated, we suspect, by the casual user.

140 CITIES TRANSFORMED 2000 - City Proper; World Urbanization Prospects O Metropolitan Area; Demographic Yearbook /, O ,~ _` 1500— / / I) 0 / G o 1 000— 500 - o o 1950 1960 1970 1980 1990 2000 Year FIGURE 4-12 Brasilia: United Nations population estimates. 700 - 600 - 500- ._ City Proper; Demographic Yearbook Capitals; World Urbanization Prospects 300 - 200 - f 1950 1960 1970 1980 1990 2000 Year FIGURE 4-13 Niamey, Niger: United Nations population estimates.

URBAN POPULATION DYNAMICS 340 - 330 - _` In ~ 320- ~n o ~ 310- o 0 300- tL 280 - 141 · Urban Agglomeration; Demographic Yearbook · City Proper; Demographic Yearbook 1950 1960 1970 1980 1990 2000 Year FIGURE 4-14 Kitwe, Zambia: United Nations population estimates. Further interpretive difficulties may arise from applying the URGD method- involving simple extrapolation and projection assumptions to these heteroge- neous data series. Although the broad outlines of the URGD method are known from the United Nations publications, the details of its application to city popu- lations have not been placed in the public domain. Hence, little is known about the gains that might result from more sophisticated statistical modeling. In the panel's view, it is doubtful that more sophisticated methods could surmount the many measurement errors and other problems, stemming mainly from differing units, that plague the raw data series. Nevertheless, rigorous research on alterna- tive projection methods is in order. PROJECTING URBAN POPULATIONS A first issue with regard to urban projections is to identify the populations of inter- est: projections can be applied to national urban poulations, to the populations of individual cities, and to metropolitan subregions and even neighborhoods. Such diversity calls for the use of diverse techniques and data sources, and there are interrelationships that need to be considered. As national populations grow, es- pecially in conjunction with economic growth, the number, spatial location, and size distribution of cities can be expected to evolve. As individual cities grow, they often become more diverse internally, and their neighborhoods and subareas

142 CITIES TRANSFORMED can take divergent paths. The smaller the unit to be examined, the greater are the demands placed on data and methods to achieve any given level of forecast accu- racy. Indeed, at the submetropolitan level, it may be that the greater part of the benefit to be derived from forecasts lies in the processes that are bound up in their generation the gathering, collating, and reconciling of diverse sources of disag- gregated population and socioeconomic data. But some measure of accuracy is required to sustain the exercise and to give sensible guidance to city planners. Even for the largest units consider the total urban population of the develop- ing world demographic forecasts have been found to exhibit substantial error af- ter the fact. The United Nations' 1999 projection of the urban population for 2000 is fully 12.4 percent below the level of its 1980 projection. As Brockerhoff (1999) shows, the United Nations projections of total urban populations have tended to be overestimates. Although the United Nations has also had to revise downward its projections of national populations, these reductions have been small by com- parison with the urban reductions. For instance, the projected total populations of developing countries were reduced by just 2 percent in the 1996 projection relative to the 1980 projection (Brockerhoff, l999~. It is easy to find examples of projections at the city level that have proven to be wildly in error. For instance, United Nations projections of the population of La- gos for 2000 have fluctuated with each successive update of World Urbanization Prospects. The 1994 Revision indicated that the 2000 population of Lagos would be 13.46 million; the 1996 Revision slashed about 3 million from that total, reduc- ing the forecast to 10.47 million; and the 1999 Revision added these people back, raising the projected total to 13.43 million in 2000. After finally being granted ac- cess to more recent Nigerian data, the United Nations (2002a) again cut the 2000 population of Lagos, which it found in retrospect to have been only 8.67 million. Although Lagos can hardly be considered a fair test case most demographers would be skeptical of the accuracy of any data available for this city gyrations of this magnitude are worrisome. Error is to be expected in any forecast, no matter how sophisticated and well grounded in the data. The question is whether the level of error is tolerable given the purposes for which a forecast is required. Unfortunately, population forecasts are often understood differently by demographers and nondemographers. To de- mographers, forecasts and projections are devices for extrapolating the logical implications of current demographic forces by simple mathematical or (less of- ten) statistical means. Economic, social, and environmental considerations are not generally factored into the forecasting equations. The process of urbaniza- tion is exceedingly complex, involving feedbacks and counterpressures on many social, political, spatial, and economic fronts and these are too varied to be in- corporated in formal projection models. However, nondemographers often as- sume that population forecasts are based on professional judgments about the full range of socioeconomic and environmental influences. This interpretation may be encouraged by the presentation of high, low, and medium projection variants, a

URBAN POPULATION DYNAMICS 143 demographic practice that might suggest that medium projections represent con- sensus views. Clearly mindful of the difficulties involved, the United Nations has generally advanced only the most modest of claims for its city size projections. The United Nations (1980: 45) warns: Projection of city populations is fraught with hazards.... There are more than 1,600 cities in the data set, and it is obviously impossible to predict precisely the demographic future of most of them.... In most cases, national and local planners will have access to more detailed information about a particular place and could supply more reliable information about its prospects. With reference to Mexico City, whose population was to rise to 31 million by the turn of the century according to the 1980 projection, the United Nations (1980: 57) observes: Whether such size can actually be attained is, of course, questionable. It has been noted, for example, that population growth at Mexico City theaters to destroy tree cover that is necessary to prevent erosion and flooding. Water-supply also appears to be a potentially constraining factor in this case. Natural or social limits to growth could be en- countered well before a size of 31 million is reached, or of 26 million for Sao Paulo, and so on down the line. In the event, neither Sao Paulo nor Mexico City reached the sizes predicted for them in the 1980 United Nations projections. The 2000 Mexican census recorded a population of 18.1 million for Mexico City, and Sao Paulo's population is reck- oned at 17.9 million for the same year.24 Are these isolated cases that illustrate the inevitable errors of any projection, or are the United Nations projections assembled in a way that somehow tends to impart an upward bias to the projections for large cities? Because the United Nations does not place its city projection materials and methods fully in the public domain, we cannot say whether particular assumptions or data errors might pro- duce systematic biases. But the United Nations cannot be accused of neglecting the possibility of error. As discussed in Appendix D, the Population Division has imposed a number of restrictions in an effort to rein in its projected city growth rates. Nevertheless, the record of projections gives considerable cause for concern. Table 4-9 reports mean percentage errors (MPEs) and mean absolute percent- age errors (MAPEs) for 169 countries and territories whose boundaries have not changed substantially over the past 20 years (i.e., it excludes countries in the for- mer Soviet Union). The MPE is positive if projections are too high on average, 240f course, it is possible that the projected massive population increases spurred government action to deter growth in these cities in favor of growth in smaller cities.

144 CITIES TRANSFORMED TABLE 4-9 Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) in Urban Population Projections for the Year 2000, by Length of Forecast, Region, Level of Development, and Size of Country Category Region East Asia and Pacific (EAP) EAP excluding China Europe Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa Organisation for Economic Cooperation and Development Other High-Income Level of Development Low Lower Middle Lower Middle excluding China Upper Middle High Size of Country 0-2 million 2-10 million 10-50 million 50+ million 50+ million excluding China MPEa MAPEb 20 yearsC 10 yearsd 5 yearse 20 years 10 years 5 years 0.039 0.184 0.140 0.198 0.133 0.272 0.218 0.267 -0.028 0.098 -0.004 0.130 0.088 0.054 -0.009 0.068 0.085 0.197 0.027 0.234 0.055 0.068 -0.024 -0.018 -0.183 -0.102 -0.056 0.231 0.069 0.256 0.099 0.037 0.128 0.089 0.008 0.060 -0.027 -0.019 0.183 0.032 0.261 -0.013 World Excluding China 0.113 0.295 0.140 0.226 0.245 0.291 0.382 0.289 0.043 0.166 0.053 0.130 0.088 0.075 0.021 0.123 0.105 0.197 0.070 0.274 0.097 0.110 0.048 0.020 0.334 0.199 0.072 0.312 0.115 0.279 0.199 0.117 0.074 0.063 0.030 0.120 0.098 0.019 0.216 0.108 0.027 0.124 0.192 0.001 0.189 0.126 0.018 0.141 0.171 0.007 0.190 0.120 0.020 0.199 0.080 0.283 0.049 0.161 0.066 0.115 0.026 0.053 0.022 0.528 0.268 0.169 0.282 0.199 0.082 0.329 0.163 0.070 0.168 0.208 0.049 0.227 0.149 0.054 0.206 0.199 0.055 0.257 0.156 0.060 NOTE: Based on 169 countries and territories whose boundaries have not changed substantially over the last 20 years. Excludes former Soviet Union. All figures are weighted by population size. MPE = mean percentage error. Positive error associated with projections being too high and negative error with projections being too low. b MAPE = mean absolute percentage error. c 20-year comparison based on comparing 1980 projections for the year 2000 in United Nations (1980) with actual data in United Nations (2002a,b). 10-year comparison based on comparing 1990 projections for the year 2000 in United Nations (1991) with actual data in United Nations (2002a,b). e 5-year comparison based on comparing 1996 projections for the year 2000 in United Nations (1998b) with actual data in United Nations (2002a,b).

URBAN POPULATION DYNAMICS 145 and negative if they are too low; as such, the MPE is a measure of bias. By contrast, the MAPE is always positive and is usually taken to be a measure of . . . mpreclslon. As is evident from the positive values for MPEs in the first three columns of Table 4-9, urban projections have been too high more often than too low. This is attributable in part to the fact that fertility has declined in many places more rapidly than was expected. At the global level, the forecasts of the urban popula- tion in 2000 that were made 20 years ago were approximately 14 percent too high; forecasts made 10 years ago were approximately 17 percent too high; and fore- casts made 5 years ago were nearly perfect as a result of the fortuitous canceling out of roughly equal numbers of high and low errors. The general pattern is much as expected, in that projections typically are more accurate over shorter projection periods. The inclusion of China in the calculations makes a considerable difference to the results. Urbanization trends in China, which is home to 30 percent of the ur- ban population of Asia, have fluctuated greatly over the years. These fluctuations stem both from historical events, such as the Cultural Revolution and its aftermath, that retarded or even reversed urbanization in China, and from several revisions (since 1983) in the official criteria defining cities and towns (recall Box 4.2~. If the Chinese data are set aside, a more consistent pattern is revealed in which projec- tions over shorter periods are more accurate. Table 4-9 also shows that there has been considerable diversity in the quality of urban projections by geographic region, level of economic development, and size of country. The United Nations urban projections have been most reliable for Organisation for Economic Cooperation and Development (OECD) countries, on average, and least reliable for countries in sub-Saharan Africa and other high- income countries, many of which are quite small. United Nations projections also tend to be better for larger than for smaller countries, perhaps because larger countries tend to receive more attention. Similarly, focusing on the largest countries in each developing region and the largest city in each, Brockerhoff (1999) compares two projections for 2000 one taken from the 1980 United Nations projections (United Nations, 1980) and the other from the 1996 projections (United Nations, 1998b). He finds the 1996 pro- jections of city size to be far lower, implying that they had to be revised substan- tially downward. Among all cities of 750,000 or more residents whose popula- tions were projected in 1980, the median reduction in projected city size was 15.1 percentage points. Upward revisions in the projections were far less common than downward revisions. Such forecast errors stem from several sources. The baseline data can be ab- sent or unreliable. Censuses may well undercount urban populations: crowded cities with their mobile populations present a challenge to census takers in every part of the world. Recent censuses in Indonesia and Pakistan are believed to have seriously undercounted the populations of Jakarta and Karachi (Jones, 2002~. As

146 CITIES TRANSFORMED demographers incorporate the results of recent census rounds, they often find that their estimates of urban populations at earlier points in time need to be revised. Also, as noted above, total population growth in developing countries has been slower than projected, apparently as a result of fertility declines that were more rapid than anticipated. Many economic and social changes have taken place that simply could not have been foreseen 20 years ago. We conclude that urban and city size projections must be treated with a good deal of caution. It appears that projections at higher levels of aggregation (such as total urban populations) have been slightly more reliable. Regionally aggregated data may benefit to a certain extent from the cross-cancellation of country-level errors. Nevertheless, the urban future is highly uncertain even for some regions. The United Nations projection for Africa, for example, is that by 2025, the conti- nent will have become predominantly urban. This is a reasonable extrapolation of current trends, but of course one wonders whether the decoupling of urbanization from economic growth in Africa and the economic crises plaguing the continent's cities will again cause the level of urbanization to fall short of the prediction. STATISTICAL SYSTEMS FOR DISAGGREGATED DATA Evidently, the aggregate databases on city size and growth are in need of substan- tial repair. But the United Nations demographers cannot be charged with this task: although they have great expertise and a store of critical knowledge, they must de- pend on the figures contributed by national statistical agencies. What factors are likely to motivate these agencies to rethink and reform their procedures and give them the means to do so? One thinks first of the role of national censuses. Censuses are large and of- ten politically charged undertakings, and although they are regularly fielded in some developing countries, in others they are held irregularly, while in still oth- ers the census taking enterprise appears to have ground to a halt. In any case, not all statistical agencies will process census data into the small spatial units that are needed for accurate counts of city populations and informative assessments of socioeconomic conditions within cities. At the local level, planning is further hindered by limited information about local land markets. Most large cities lack sufficient, accurate, and current data on patterns of land conversion and infras- tructure deployment. Urban maps are not infrequently 20 to 30 years out of date, lacking any information on newly emerging periurban areas. We cannot hope to understand the motivations and constraints of national and local statistical agencies, but we can point to two developments that may be encouraging. The first is the move toward governmental decentralization that is occurring in many countries of the developing world, which places new re- sponsibilities in the hands of municipal and regional government units. In the national debates that accompany decentralization, the appropriate role for infor- mation in the processes of governance is often discussed. For instance, as noted in

URBAN POPULATION DYNAMICS 147 Chapter 2, national budget allocations to regions can be based on regional popu- lation sizes and indicators of poverty, and similar criteria can be applied to the transfers from regional to municipal levels of government. The need for informa- tion exchange and feedback among units of government is often recognized in the national debates, although what is likely to come of the insight is generally less than clear. The second development, not unrelated to the first, is the rise of civil soci- ety and the recognition that when they can be assembled, proper socioeconomic maps can be powerful political tools. A city map of differences in service delivery or health conditions can give residents a means of assessing their relative stand- ing and staking claims to resources. For example, maps of Accra and Sao Paulo showing differentials in health status, mortality rates, and environmental condi- tions among city districts produced considerable local debate and some policy change in both of these cities (Stephens, Akerman, Avle, Maia, Campanareio, Doe, and Tetteh, 1997~. Maps highlighting the city neighborhoods with far-above- average mortality rates or unusual concentrations of environmental health prob- lems both inform and help mobilize the inhabitants of these areas and the politi- cians who represent them. Assembling such maps is a daunting task, however. In most large cities, each municipal agency or department maintains its own database, often organizing the data in an idiosyncratic manner and rarely sharing them with other agencies. Computerization of data is still relatively uncommon. Many city agencies con- tinue to rely on paper files and paper maps, which may be stored in formats and at scales that all but prohibit comparison, collation, and revision (Bernhardsen, 1999~. There are almost no examples of fully integrated databases for the con- stituent parts of large metropolitan regions. At best one finds data of reasonable quality for the central areas of the city, with little comparable information for the outlying areas. Looking to the skies for help, some countries have seen remotely sensed and geocoded data as an alternative to data gathered on the ground. Here there are encouraging technical developments. Sutton (2000), for example, has made esti- mates of intraurban population density based on measures of light intensity; total city populations were estimated by measuring the areal extent of the city in the imagery. These remotely sensed data alone were found to be strongly correlated with census population counts, and the use of ancilliary socioeconomic informa- tion further strengthened the correlation. Methods such as these have good po- tential to improve estimates of the spatial extent of city populations. They can be used to inform "smart interpolation" programs that can improve on existing maps and other population data in areas where good census data are unavailable. The possibilities are attracting considerable research interest Weeks (2002) provides a guide to some of the very recent technical developments. The essential principle of GIS is that when information is systematically geocoded, it becomes possible to integrate data from highly diverse sources. Many

148 CITIES TRANSFORMED recent GIS applications draw information from maps, satellite images, videos taken from low-flying aircraft, statistical data from tables, photographs, and other sources. When such data are overlaid, they permit cities to to be described and their socioeconomic conditions monitored more quickly and accurately than was previously possible. Geocoded data can assist governance in many ways, such as planning for infrastructure and transportation, tracking crime and improving law enforcement planning, allowing comparisons of program effectiveness across jurisdictions, strengthening taxation bases and record keeping, facilitating site se- lection for services, and promoting better evacuation plans in the event of emer- gencies (O'Looney, 2000~. Under ideal circumstances, common databases can help instill habits of cooperation among the units of local, regional and even na- tional governments, or at least among their technical departments. GIS technology is still in its early stages of development in most poor coun- tries, and even where the enterprise is under way, it tends to be a single-office op- eration, usually located in a planning or engineering department. And of course, the usefulness of GIS technology is dependent on the availability of appropriate GIS-coded data. But encouraging initiatives can be seen in a number of develop- ing countries, as described below. Qatar At the forefront of geographic information technology in the developing world stands Qatar, whose GIS activities began in 1988. The country is now completely covered by a high-resolution, digital topographic database, which draws together images; estimates of land elevation; and information on streets, buildings, zoning, land use, soils, and urban utilities (Tosta, 1997~. These data are meant to be avail- able to all government agencies that need them. In one successful application, the availability of digital parcel and building records for the entire country allowed the Central Statistical Organization to conduct an extremely comprehensive housing and population census in a single day. To be sure, Qatar's situation must be very nearly unique. Factors favoring the country's advanced use of GIS technology include its small geographic size, high-level political support for GIS initiatives (including the authority to mandate and enforce uniform standards), outstanding technical leadership, and adequate funding (Tosta, 1997~. African Initiatives Many African governments record substantial amounts of data in the form of maps. The major sources of spatial data are the national mapping agencies; many municipal authorities also gather spatial data, with particular attention to cadastral records. Efforts are under way in a number of African countries to create dig- ital databases through the conversion of such maps. In Botswana, for example,

URBAN POPULATION DYNAMICS 149 the Department of Town and Regional Planning has developed a digital database to monitor land use compliance in Gaborone. Lesotho's Mapping Agency is en- gaged in a large-scale digital mapping exercise for its urban areas. In most coun- tries, however, metadata the sets of organized spatial data and information about those data (e.g., where the data are located, how and by whom they were collected and maintained, how they can be accessed, and what their major characteristics are) remain in a rudimentary form. In Lagos, GIS technology has been employed effectively to resolve conflicts over land use. The goverment owns large portions of land in certain sections of the city, and residents are supposed to pay "ground rents." But owing to the multiple claims on many parcels and a history of poor record keeping, the government has been collecting only 5 to 10 percent of the rents it is due. To improve collection rates, a land information system is being developed that will provide access to all documents for each parcel of government-owned land. The geographic bound- aries of each parcel have been derived from digital orthophotos in conjunction with various legal plot maps that have been digitized. India Although circumstances in India appear to strongly favor GIS advances, the coun- try's spatial data infrastructure remains curiously limited. The great Survey of India, which dates to the mid-eighteenth century, covered the entire country with rigorous cartographic surveys. India is also the birthplace of the IRS (Indian Re- mote Sensing) series of satellites, which provide high-resolution remote sensing data to global markets. And India is home to a remarkable software industry. Why, then, has its use of GIS technology not progressed further? Most Indian government agencies simply do not understand the value of their data for government functions or for the private sector. Much as in African coun- tries, enormous quantities of valuable material are stored in paper form and sel- dom computerized. Security concerns have led to restrictions on access to maps, as well as to aerial photographs. Despite these obstacles, a number of diverse GIS initiatives are under way in some of India's largest cities; examples are described in Box 4.3. Malaysia Since the mid-1980s, several federal and state land agencies have explored GIS technology and developed stand-alone systems with valuable information. But these systems have not been integrated across agencies. In an effort to draw the information together, the Malaysian government is developing a national land in- formation system, which will provide access to spatial data for all levels of gov- ernment, the commercial sector, the nonprofit sector, academia, and the general

150 CITIES TRANSFORMED BOX 4.3 GIS Initiatives Across India Greater Mumbai Remote sensing and GIS are much involved in land use planning, with indicators ranging from soil type to air pollution. Maps have been produced on decadal population growth, population distribution, employment, the distribution of socioeconomic facilities, agriculture and forest land uses, and traffic patterns. These maps have illuminated spatial and temporal trends in each settlement within the Mumbai Metropolitan Region. GIS technology has also been used to assess al- ternative locations for a proposed second international airport and for a solid waste disposal site, and is assisting in the preparation of a rehabilitation and resettlement program for encroachments at Bandra-Kurla Complex, site of a planned interna- tional finance and business center. The government is using GIS technology to map features related to fire hazards and risk assessment, as well as service delivery. Perhaps the most interesting development in Mumbai is that a proposed land use plan for the metropolitan region has been transferred to village maps. Citizens and other concerned groups are thereby able to understand the implications of the pro- posed plan and to file objections and suggestions. Hyderabad The Center for Resource Education undertook a project on spatial mapping of industrial estates and environmental hazardous sites in or near residential areas. The maps depict the contribution of each industry to pollution, show the likely environ- mental impact, and indicate monitoring points. Hyderabad has also been develop- ing a GIS-based integrated emergency response management system for Hyderabad City. The project incorporates maps depicting land use, road networks (including travel time estimates), and the locations and numbers of fire and police stations and water-filling points. Chennai City (Madras) A GIS database has been developed for road networks. With this database, priorities can be assigned to road improvements in the context of an integrated transportation information system. Bangalore The Bangalore Development Authority has used GIS applications for route planning and tracking of 200 (and eventually 2,000) private buses. The Global Po- sitioning System (GPS) is used to monitor the locations of the buses and to generate appropriate bills based on distance traveled. public. The private sector is being encouraged to contribute products and services (Mahid Bin Mohamed, 1998). Other Applications Elsewhere around the world, GIS technology is being applied in innovative ways to improve urban management. The Kuwaiti Ministry of Public Works has launched a large-scale computerized management system to assist in the main- tenance of roadways, bridges, sanitary and storm sewers, and street rights-of-way. The Water Authority of Jordan has employed GIS technology to restructure the water supply network in Amman. This project has involved a complete redesign

URBAN POPULATION DYNAMICS 151 of the water supply system in the city's congested, densely populated core. In Dhaka, GIS technology has been applied to the problem of drainage in Dhaka City. A digital elevation model was established for the catchment area; inunda- tion maps were then produced, and various flooding scenarios of the past were simulated. Use of this technology, found to be cost-effective, has enabled the government to develop sustainable flood alleviation schemes. The Future of GIS Although the above are all promising developments, each involves substantial costs, ranging from those of personnel and training to those of purchasing and converting maps. GIS entails much more than technology, and because it requires cooperation among units and levels of government that have little experience in this regard, it may be perceived as threatening. Effective use of the technology requires both new organizational structures and experienced staff. The few case studies available do not demonstrate that the novel techniques and ways of think- ing about information and interrelated services made possible by GIS will nec- essarily make successful transitions from the technical staffs of engineering and planning departments to the broader (and more powerful) realms of government. But clearly this is a development that bears watching, and one that may well bring new energies to bear on the collection of spatially disaggregated data. CONCLUSIONS AND RECOMMENDATIONS This chapter has addressed a wide range of issues, touching on both methods and substance. In concluding, we pass rather lightly over the empirical findings presented earlier in the chapter and emphasize implications for the infrastructure needed to support urban population research. Conclusions The analytic models examined in this chapter highlight a point that is often over- looked: urban growth rates and the migrant shares of growth will both tend to be high when a country is in its initial stages of urbanization; both will then tend to decline as the level of urbanization rises. The linkages of urban natural in- crease to the rate of urbanization can also be misperceived. If urban natural in- crease happens to equal rural natural increase, rural-to-urban migration will be the dominant factor in urbanization. This is the argument of the United Nations (19804. Although there was evidence of equal rates of natural increase in the 1950s and 1960s, it is unclear whether equality in the rates persists. If it does not, differences in the rates of natural increase will also exert an important influ- ence on the rate of urbanization. Migration has a distinctive role to play in af- fecting urban age structures together with lower urban fertility, it confers upon

152 CITIES TRANSFORMED city age distributions a distinctive shape in which greater proportions of the pop- ulation are found in the productive and reproductive ages. Hence, other things being held equal, rural-to-urban migration will tend to inflate urban fertility rates. Moving back one link in the chain, we find that rural natural increase, working through migration, exerts an influence on urban fertility. The interlinkages of ur- ban and rural populations are as clearly evident in analytic models as in empirical studies. Using data from the DHS, we have found that among urban women of repro- ductive age, nearly one woman in four is a recent migrant, having moved to her current city or town less than half a decade earlier. In studies of urban change, the term "migrant" calls up the image of a rural-to-urban migrant. The DHS data show, however, that most urban migrants come from other towns and cities; only about one migrant in three arrives directly from a rural area. It appears that the common view of migrants needs to be tempered by empirical realities. Re- searchers need to consider more carefully the implications of migration within the urban sector. There is little evidence to support another common perception- that migrants are more prevalent in the populations of large than small cities. For women, at least, the DHS data do not confirm this supposition. Turning to the aggregate data sources on urban and city populations, we un- derscored a familiar point, one that is mentioned in many scientific reviews of urbanization: countries define urban areas in a great variety of ways. This defi- nitional heterogeneity is of concern mainly with respect to small settlements, but because these are so numerous, differences in definition can have a large impact on the urban totals reported at national levels. In an ideal world, it might be thought desirable for countries to adopt a common definition, but this is unlikely to occur. How damaging is the absence of consensus? As discussed in Chapter 2, the theories that animate contemporary urban research are increasingly dismissive of simple urban/rural dichotomies, and point toward richer conceptualizations in- volving centrality, communication, and relational networks. These theoretical de- velopments would appear to be leading away from simple prescriptions and def- initions of urban areas. As discussed earlier, however, the measurement of such concepts is still in the early stages, and much remains to be learned about their value for empirical research, planning, and policy making. Furthermore, as can be seen throughout this report, simple urban and rural classifications retain con- siderable explanatory power. It is probably unwise to set such useful measures aside while better ones are being developed. In any case, because much of the international heterogeneity in definition applies to smaller settlements, analyses based on cities above a certain size (e.g., 100,000 population) can escape many of the difficulties. Unfortunately, the United Nations estimates of city size, as presented in World Urbanization Prospects, are more heterogeneous and subject to measurement error than is commonly realized. We reviewed several cases and found that only the most attentive and dogged researcher would be likely to understand the

URBAN POPULATION DYNAMICS 153 idiosyncrasies of the city population data. As discussed below, if the United Na- tions were to make its data and methods publicly available, a wider community of researchers could assist in improving measures and methods. Much the same can be said of the United Nations projections, which have often proven to be so far off the mark that consideration of alternative projection methods is now badly needed. Recommendations for Urban Research Infrastructure As countries urbanize, the proliferation of cities and increases in average city sizes heighten the need for adequate urban population data. There is, first, a need for acceptable estimates of city population size. Second, and especially for the larger cities, there is a greater need for intracity data, which are required both for un- derstanding social and economic diversity and for extending services. Spatial information is essential in both of these areas, for not even city sizes can be de- termined without good information on city boundaries. The potential for use of spatially collected data is perhaps even greater within cities. The difficulty lies in determining where among the many local, national, and international statistical systems there exists a combination of motivation and re- sources sufficient to generate such spatially disaggregated data. Of course, the major burden of responsibility must rest with the national statistical agencies themselves, but international researchers and agencies can make a contribution through focused research and coordination. The panel is hopeful that GIS and related technology advances will bring new energy and ideas to the problem, but sustained efforts and international technical assistance will clearly be required. The panel's impression is that where city and urban population data for de- veloping countries are concerned, most demographers believe the United Nations Population Division will somehow take care of things. Yet the Population Di- vision is but one small group of expert professionals with many responsibilities extending beyond the maintenance of urban databases. If the panel understands correctly, the Population Division manages to assemble its urban estimates and projections with very few resources, evidently dedicating less than the equivalent of a single full-time staffer to the task. The United Nations Statistical Office like- wise has many responsibilities. It would be unrealistic to suppose that these units are about to receive major new infusions of funds and personnel. Yet the status quo is a precarious arrangement. It places responsibility for urban databases essential to the demographic field on the shoulders of a very few individuals. If city and urban population data series are to be adequately and critically reviewed on an ongoing basis and if alternative forecasting methods are to be explored in any depth, a way must be found to bring greater resources to bear. More researchers, especially from the countries that contribute the data series, need to be involved, and more methodological perspectives taken into account. In the panel's view, on which we elaborate in this volume's concluding chapter, the

154 CITIES TRANSFORMED best way to attract more resources to the problem is to place the United Nations data and methods in the public domain, giving the Population Division the task of coordinating full-scale critical reviews. There is an urgent need for review of the empirical basis for city and urban population projections. United Nations projections of the populations of large cities have displayed a tendency toward upward bias. Total urban populations have also been projected to grow at rates that, in retrospect, were much too rapid. Although the United Nations has taken special care to restrain projections of city and urban population growth, it appears that these efforts have been insufficient. If the United Nations were to place its sources and methods fully in the public do- main, the problems that produce such projection errors might be diagnosed more effectively. Where the DHS program is concerned, the problem on which this chapter has shed light is the lack until quite recently of adequate spatial identifiers in the datasets released for public use. The problem, as we understand it, is that disclo- sure of the spatial locations of sampling clusters might compromise the privacy of respondents and threaten the exposure of confidential information. These are important concerns. As the research for this volume drew to a close, the DHS moved to address such concerns with a permissions policy that gives researchers access to GIS spatial identifiers for its recently fielded surveys. This is a welcome change in procedures; over time it will much enhance the value of DHS data for urban demography. We hope that the DHS will now do what it can to provide spatial identifiers for the surveys it fielded before 1999. There are potential ben- efits for rural analyses as well as urban. It may be that indicators of distance to nearby cities would suffice to measure concepts of rural "remoteness." In a world that is increasingly urban and in which rural areas are coming under the influ- ence of city economies and societies, it is difficult to imagine a next generation of demographic research that does not attend more closely to the implications of space.

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Cities Transformed: Demographic Change and Its Implications in the Developing World Get This Book
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Virtually all of the growth in the world’s population for the foreseeable future will take place in the cities and towns of the developing world. Over the next twenty years, most developing countries will for the first time become more urban than rural. The benefits from urbanization cannot be overlooked, but the speed and sheer scale of this transformation present many challenges. A new cast of policy makers is emerging to take up the many responsibilities of urban governance—as many national governments decentralize and devolve their functions, programs in poverty, health, education, and public services are increasingly being deposited in the hands of untested municipal and regional governments. Demographers have been surprisingly slow to devote attention to the implications of the urban transformation.

Drawing from a wide variety of data sources, many of them previously inaccessible, Cities Transformed explores the implications of various urban contexts for marriage, fertility, health, schooling, and children’s lives. It should be of interest to all involved in city-level research, policy, planning, and investment decisions.

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