A mature science has reached agreement on definitions of key concepts, measurement strategies, and appropriate analysis procedures. Fertility research is clearly institutionalized in each of these domains. Fertility's success as a scientific area of study rests on fortuitous features of the phenomenon itself, the broader interest in fertility (for administrative and other reasons) that have encouraged data collections and standardization of measurement strategies, and an immense amount of research attention on a decadal time scale.
Fertility shares with the study of mortality fortuitous features of the study phenomenon. Births and deaths, the core events in demography, focus on observable events that are relatively easily measured, naturally quantifiable, highly structured, and can be easily incorporated into accounting frameworks or represented by descriptive demographic models (Morgan and Lynch 2001). In any science, conceptual clarity and intersubjective agreement across observers are essential for good measurement. Births are biologically based and are thus fixed in a universally accepted truth. Another important characteristic of births is that they are categorical by nature and thus inherently quantifiable, making measurement reliability attainable. The actual occurrence of a birth is universally recognized, although the actual meaning and consequences of a birth may be socially constructed. Therefore, valid cross-national and cross-temporal measurement of fertility is feasible. This is not to say that fertility measurement is easy or error free. But the inherent features of fertility provide a leverage for good measurement that is not found for many other concepts (Morgan and Lynch 2001).
The interest in fertility data for administrative purposes aids fertility measurement, increases data availability, and improves data quality. The importance of fertility data for administrative purposes has led to wide-scale collection. The usefulness of comparable fertility data across administrative units encourages the codification of definitions and standardization of measurement procedures.
While births are the event to be measured, the concept of an event/exposure rate is fundamental to all demographic measurement. The additional key concept needed for rate calculation is the population at risk or person-years of exposure. The essential measurement task is to estimate the risk of a specific event (e.g., a birth, a first birth, a nonmarital birth). The accepted strategy utilizes a ratio of a count of events (births to a specified group) to an estimate of the person-years exposed to the risk of an event in a given time period (Preston, Heuveline, and Guillot 2001). In the year 2000, for instance, a woman who survives the full year contributes a full year at risk of a birth and thus adds one to the denominator of a year 2000 birth rate. There is a range of strategies for estimating years at risk (Preston, Heuveline, and Guillot 2001).
Once rates have been estimated, how does one conceptualize social change? In general, birth rates can be examined by alternative dimensions of time: period or cohort. Period fertility measures look at fertility cross-sectionally, or births/exposure occurring at one period in time (usually across a set of age categories). Cohort analysis, con versely, follows a group longitudinally or over a women's reproductive history, again across age. Data for calculating period measures are more widely available, they have useful and interpretable meanings and, consequently, they are more frequently used (Newell 1988).
The two most commonly used period measures of fertility are age-specific fertility rates (ASFR) and the total fertility rate (TFR). When calculating age-specific fertility rates, the numerator is restricted to births occurring to women of a specified age interval, and the denominator is restricted to the number of person-years lived by women in the age interval (see Preston, Heuveline, and Guillot 2001). The teenage (age 15 to 19) birth rate is an age-specific birth rate, as is the rate for women aged 35 to 39.
The total fertility rate (TFR) is the most frequently used indicator of period fertility; it is the simple sum of the ASFRs across the childbearing years. Thus, the TFR is an age-standardized, single-value, summary measure of fertility. The TFR has a powerful yet easily understood interpretation. Specifically, the TFR is the number of children a woman would bear if she experienced, at each age, the current period age-specific fertility rates (and she survived to the end of her reproductive cycle). In the absence of mortality, a TFR of 2.0 would equal replacement level fertility. This means that the women are having enough births to replace themselves and their male partner. Other measures estimate replacement-level fertility in the presence of mortality (see Preston et al. 2001). Table 8.1 presents estimates of the highest and lowest TFRs for countries in 2002.
The TFR can be calculated from cohort data, that is, age-specific rates for an actual cohort. This measure (sometimes called children ever born) can be interpreted as the mean number of children produced by a birth cohort.
Data for fertility rate estimation come from several sources. Vital registration systems, if birth certificates are filed for all births, can provide an accurate count of births. One can then use various demographic procedures to estimate the denominator of desired rates, usually from census data projected forward or backward to correspond to the year in question. For instance, since birth certificates usually include the age of the mother, one can get a count of births to 20-year-old women. The census estimate of the midyear, 20-year-old, female population provides a commonly used estimate of years at risk, which is the denominator of the rate.
Frequently censuses contain data that can be used to measure fertility. Many censuses include the number of children ever born (a cohort measure of fertility). Also, since censuses generally include a household roster, one can count a woman's number of surviving children in the household. Strategies exist for estimating fertility from this count of own children. Specifically, one makes a set of adjustments to the count
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