The United Nations cities data take the form of a panel data set containing population counts for individual cities over time, generally recorded at irregular intervals. To organize its records, the United Nations has maintained three
‘statistical concepts’ that serve to defi ne city boundaries. The term city proper refers to the formal administrative boundaries of a city as set out by local authorities. The urban agglomeration includes the city proper and also incorporates contiguous areas that are populated at urban levels of density. A number of countries (especially, but not exclusively, in Latin America) have adopted a more spatially elastic measure, categorizing their populations in terms of metropolitan regions that include rural-dwellers falling within the sphere of infl uence (or ‘catchment area’) of large urban places. Some countries have devised further variations on these boundary defi nitions, and the UN endeavours to fi t them within its three-category framework.
Member countries of the United Nations are asked to provide city population data for urban agglomeration boundaries; however, they may respond with data coded in terms of city proper or metropolitan region or may provide the counts without any accompanying explanation of units. Where possible, these data are adjusted to conform to the agglomeration concept – but, of course, this is not always possible. Indeed, in only a small percentage of cases – 4.5 per cent in the provisional 2006 version of the UN’s database – are all of the city’s records expressed in terms of urban agglomerations. The city proper is a far more common concept in these data, with the populations of 39.8 per cent of cities being consistently recorded in this way. For another 23 per cent of cities, no information is available on the concept by which population is reported for any of the recorded dates, while
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in the remaining 32.5 per cent of cities the population time series mixes two or more boundary concepts.
The diffi culties stemming from such mixed-unit time series are illustrated for Luanda, Angola, in Figure 1.7. The units in which this city’s population was recorded are unknown for the 1950, 1960 and 1970 entries, whereas in 1982, population counts were provided for both the city proper and the urban agglomeration concepts. The next entry in the series is again of unknown type, and it is followed by one report on the agglomeration and a fi nal record whose defi ning concept is not specifi ed. In each new revision of World Urbanization Prospects, UN researchers succeed in eliminating some of these anomalous cases. Nevertheless, far more heterogeneity remains in the city time series than is commonly realized.
Figure 1.7 City population time series for Luanda, Angola
Source: Provisional data provided by the United Nations Population Division (see note 4).
0500100015002000City Population (000s)
1950 1960 1970 1980 1990 2000
Year
Unknown City Proper Agglomeration
Luanda, Angola
Forecasting city growth
As part of the research summarized in World Urbanization Prospects, the UN Population Division prepares medium-term forecasts of both total urban and city- specifi c population growth. These forecasts are grounded in the city population
THEDEMOGRAPHY OFTHEURBAN TRANSITION 29 series that have already been discussed, with all their attendant heterogeneity. For reasons that are not yet well understood, the UN forecasts, which are essentially extrapolations of each city’s time series, have consistently projected city growth rates (and thus population sizes) that are too high. The tendency to over-project is not evident in the UN’s forecasts of total population at the national level, but it persists in the city population forecasts despite the insertion of an algorithm in the forecasting model that is designed to slow projected growth rates as city size increases (recall Figure 1.6). The Panel on Urban Population Dynamics (Montgomery et al, 2003) explains the forecasting method and provides a critical review of the issues, as does Bocquier (2005).
The pattern of forecast error for total urban populations is illustrated in Table 1.1. The entries in this table refer to population-weighted averages of country-level percentage errors. As can be seen, the mean percentage forecast errors are large for the 20-year and 10-year forecasts. The 20-year forecast for Latin America that was made in 1980 proved to be 19.8 percentage points too high when the region’s 2000 urban population was counted; the forecast for South Asia was 27.2 percentage points above the mark. Of course, as the forecast baseline moves closer to the 2000 end-line, some improvement in performance occurs, with the mean forecast error dropping to 5.4 per cent for Latin America for the decade-ahead forecast made in 1990. However, the error for South Asia remains 19.7 points too high even in the decade-ahead case. As Bocquier (2005) has noted, the tendency for over- projection that is exhibited by the UN forecasts raises doubt about the scale and pace of urban population change (if not the direction) that has been forecast for
Table 1.1 Urban population forecast errors for 2000
Mean Percentage Forecast Errors (%)
1980–2000 1990–2000 1995–2000
Region
East Asia and Pacifi c 3.9 26.7 −2.8
EAP excluding China 18.4 9.8 −0.4
Latin America and Caribbean
19.8 5.4 −0.9
Middle East and North Africa
13.3 6.8 8.5
South Asia 27.2 19.7 2.7
Sub-Saharan Africa 21.8 23.4 5.5
Level of Development
Low 23.1 18.3 3.2
Lower Middle 6.9 26.1 −1.3
Lower Middle excluding China
25.6 9.9 3.7
Upper Middle 12.8 8.9 0.8
Source: Montgomery et al (2003).
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the 21st century. Understanding the sources of forecast errors is a research priority of some urgency.
Given the uncertainties and measurement errors that plague the city population series in particular, it is clear that there are limits to how ambitious any forecast should strive to be. The UN Population Division has long couched its forecasting efforts in the most cautious of terms and has made plain its reservations about the proper scope of the effort. The United Nations (1980) warned that:
Projection of city populations is fraught with hazards. . . . There are more than 1600 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 (p45).
Even so, to an extent that probably could not have been foreseen in the early 1980s, several streams of new data – on demographic behaviour as well as on land cover, water supply and environment – have recently emerged. These new materials may well support more informed and credible city population estimates and projections than the experts of 1980 could have envisioned.