Table 2. Annuwl decline in TFR: Percectiles across countries obtained from three data set

|Percentile|A|B|C|Mean|
|---|---|---|---|---|
|10|-0.160|-0.224|-0.176|-0.187|
|25|-0.116|-0.176|-0.163|-0.151|
|_50_|-0.084|-0.150|-0.136|-0.123|
|75|_-0.055_|-0.104|-0.111|-0.090|
|90|-0.022|-0.085|-0.089|-0.066|
|(Number of<br>countries)|(49)|(32)|(41)||

Note: Data set A uses 1980-85 and 1985-90 Bank estimates. Data set B uses survey, census, or registration data from the date of transition. Data set C uses Bank estimates adjusted to start at the date of transition.

# Botswana, and .29 points a year in 1981/82-86 in Zimbabwe.

One question this analys.; raises is whether TFR decline should be represented as linear. We examined the shape of the TFR curve over the entire transition period, regressing TFR on number of years since the start of the transition for a selection of countries with frequent survey-based data. A linear formulation fit quite well, with R<sup>2</sup> ranging from .86 to .96. Little improvement was obtained from modeling the decline with a quadratic or cubic term. Figure 4 shows six representative countries with linear and quadratic curves fitted to fertility rates. For the Republic of Korea, Mauritius, Thailand, Taiwan, and Trinidad and Tobago, a stiaight line fits the data well. Only for Sri Lanka does a quadratic formulation give a slightly better fit, because of a stall in fertility decline. Stalls present an obstacle to modeling fertility decline for two reasons. First, their occurrence is hard to predict, partly because they occur for a variety of different reasons (Gendel! 1984; Bongaarts 1986). Second, they are of variable duration: Korea's stall in fertility decline was a brief interlude before rapid fertility decline resumed, whereas Costa Rica's and Fiji's have lasted much longer. In general, many of the countries that have experienced stalls in fertility have resumed fertility decline after a variable number of years. For thes reasons, modeling fertility decline as a simple linear process that does not attempt to repre.sent stalls (but takes them into account in determining the general pace of decline) appears justified. However, during the late stages of the tmnsition, when TFR has dropped below 3, fertility decline slows down and becomes nonlinear. The level at which this happens depends on how far fertility will ultimately fall, which varies from country to country. This segment of the transition might be treated separately if a linear formulation is applied.

We attempted to predict the rate of decline in the three quinquennia following the start of the fertility transition with regression analysis, using as explanatory variables the following socioeconomic indicators from World Bank data: (a) year of start of the transition; (b) infant mortality rate (IMR) at the start of the tasition; (c) the rate of change in the IMR in the preceding quinquennium; (e) secondary school enrollment of women at the start of the transition; (f) TFR at the start of the transition; (g) region (Asia, Latin America, and other regions); and (h) percent of population living in urban areas. (lif expectancy was initially included but was dropped because of a strong correlation with the IMR.) Table 3 shows the characteristics of the sample countries and the correlations between rate of change in each quinquennium since the start of the transition and the predictor variables. Table 4 presents the results of multiple regression analyses. Models are presented with the mte of change in TFX for each of the quinquennia following the start of the fertility transition as the dependent varia',le. Two models are given 10