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Desired Fertility and Number of Children Born Across Time and Space

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Demography

Abstract

Economists have often argued that high fertility rates are mainly driven by women’s demand for children (and not by family planning efforts) with low levels of unwanted fertility across countries (and hence with little room for family planning efforts to reduce population growth). We study the relationship between wanted fertility and number of children born in a panel of 200 country-years controlling for country fixed effects and global time trends. In general, we find a close relationship between wanted and actual fertility, with one desired child leading to one additional birth. However, our results also indicate that in the last 20 years, the level of unwanted births has stayed at 2 across African countries but has, on average, decreased from 1 to close to 0 in other develo** countries. Hence, women in African countries are less able to translate child preferences into birth outcomes than women in other develo** countries, and forces other than fertility demand have been important for previous fertility declines in many develo** countries. Family planning efforts only partially explain the observed temporal and spatial differences in achieving desired fertility levels.

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Notes

  1. The number of surveys and countries in the analysis of Pritchett (1994a) varies depending on the applied measure of wanted fertility.

  2. We exclude Botswana, Afghanistan, and Angola due to missing information on fertility. We exclude Cape Verde, Eritrea, Mauritania, and Samoa due to data restrictions. We exclude Brazil 1991 given that it covers only Northeast Brazil and is hence not comparable with the other survey years of Brazil. We exclude Nigeria 1999 because of low-quality data.

  3. Egypt and Morocco are included in the sub-Saharan African sample.

  4. The TFR is calculated as the sum of the age-specific fertility rates (ASFR) for the seven 5-year age groups 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49. The ASFR is the quotient of the number of births that occurred in the three years before the survey to women in the respective age group and the number of women-years of exposure in the respective age group. We apply the tfr2 (Schoumaker 2012) Stata command.

  5. Two types of measurement errors directly affect the calculation of fertility rates in the DHS data: the first is the possible omission of births in the birth history. For example, omissions of births are of particular relevance for children who died at very young ages. The second problem is the increasing complexity of more recent DHS questionnaires, which has led to longer interviews, resulting in a potential loss of quality of the birth histories.

  6. Because the measurement of fertility preferences is not the major concern of this article, we omit a detailed discussion of the advantages and disadvantages of existing measures. See Casterline and El-Zeini (2007) for a review of this literature.

  7. In addition to DTFR and WTFR, Pritchett (1994a) also applied the average ideal number of children (AINC), averaging women’s ideal number of children across a country. Even though the AINC is an intuitive and simple measure of fertility desire, it faces severe shortcomings (for a detailed discussion, see Bongaarts 1990).

  8. Additional shortcomings are related to child mortality and gender preferences.

  9. In particular, final WTFR = WTFR + 1 – c, where the correction factor c contains four components: a correction for not achieving the wanted family size (c1), a correction for wanting no children (c2), a correction for the time trend in fertility preferences (c3), and a correction for the duration of the reference period (c4) (Bongaarts 1990). For simplicity, the correction factor c can be approximated by WM(40–44), which is equal to the proportion of all married women aged 40 to 44 who want more children (Bongaarts 1990). We additionally calculated the WTFR based on the calculation of all adjustment factors c = c1 + c2 + c3 + c4. Results based on the more detailed adjustment factor, which do not differ significantly from the results presented here, are available from the authors upon request. To calculate WTFR and DTFR, we also use the tfr2 command (Schoumaker 2012) after the adjustments for unwanted births have been made.

  10. In a first step, for women with parity p (p = 0, 1, 2, . . . , 6+), the quotient of the proportion of women who report that they do not want another child minus the proportion of women who report that they do not want another child and also had no births during the last 30 months, divided by the proportion of women with parity p that had a birth within the last 30 months is calculated. In a second step, the age-specific proportion of unwanted births for each age group of unwanted births is calculated as the weighted sum of proportions for each parity p. In a third step, the unwanted total fertility rate is calculated by multiplying the age-specific fertility rates by the age-specific proportions of unwanted births. Because we focus on the impact of wanted fertility (wTFR) on actual fertility (TFR), in a fourth step, we use the relationship TFR = wTFR + uTFR, where uTFR refers to unwanted TFR, to calculate the wTFR = TFR – uTFR. Using wTFR instead of uTFR allows for a direct comparison with the estimation results of WTFR and DTFR.

  11. To proxy contraceptive access, Pritchett (1994a) further analyzed the impact of contraceptive use on TFR. Because of severe endogeneity problems (contraceptive use is not only a function of contraceptive access but is to the same extend influenced by WTFR, wTFR, or DTFR), we refrain from using this variable.

  12. DHS Wave 1 is from 1985 to 1990, DHS Wave 2 is from 1990 to 1992, DHS Wave 3 is from 1993 to 1998, DHS Wave 4 is from 1998 to 2005, DHS Wave 5 is from 2005 to 2009, and DHS Wave 6 is from 2009 to 2011.

  13. The estimated coefficients in Pritchett’s (1994a) table 1 are slightly different from the results in the first figure of his article—a difference that Knowles et al. (1994) noted previously—and from the results presented here (Table 2).

  14. Furthermore, according to estimation results presented by Casterline (2009) and Casterline and El-Zeini (2012, 2013), the largest drivers of changes in TFR in sub-Saharan Africa are declines in the nuptiality rate and the postponement of births (see also Moultri et al. 2012).

  15. Adding the fixed effects reported in Fig. 1 to the time dummy variable for 2009–2012.

  16. Even if a reduction of 0.4 children per woman seems low for sub-Saharan Africa (with an average fertility level at 5), according to UN population projections, a reduction of just one-half a birth per woman would already reduce the African population by 0.25 billion, down from 2 billion in 2050 (Bongaarts and Sinding 2011).

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Acknowledgments

We thank Stephan Klasen, Michael Grimm, and Lant Pritchett for valuable discussions and comments on earlier drafts of this article. We also thank John Casterline for kindly providing estimates on unwanted fertility rates based on the method of Casterline and El-Zeini (2007).

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Authors and Affiliations

  1. Center for Development and Cooperation, ETH Zurich, Clausiusstrasse 37, 8092, Zurich, Switzerland

    Isabel Günther & Kenneth Harttgen

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  1. Isabel Günther
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Correspondence to Isabel Günther.

Appendix

Appendix

Table 4 Mean TFR, DTFR, WTFR, wTFR, and FPE by country and survey year
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Table 5 Distribution of countries across DHS waves
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Table 6 Composition of family planning effort (FPE) index
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Fig. 3
figure 3

Country fixed effects of panel regression: DTFR/wTFR on TFR. The figure shows the impact of the country of residence on TFR on the y-axis (with 95 % confidence intervals) and the respective country on the x-axis for the panel regression of DTFR/wTFR on TFR (see Table 2 for details). The bars show the impact of the country of residence on TFR; the lines atop the bars show the 95 % confidence intervals. The omitted category is Bangladesh. Source: Demographic and Health Surveys. Calculations by the authors

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Fig. 4
figure 4

Impact of a 30-unit increase in FPE on TFR (dependent on DTFR/wTFR). The figure shows the impact of a 30-unit increase in FPE on TFR on the y-axis (with 95 % confidence intervals) at different levels of DTFR/wTFR on the x-axis. The levels of DTFR/wTFR refer to the quintile means of the DTFR/wTFR distribution of the sample. The bars show the impact of FPE on TFR (dependent on DTFR/wTFR). The lines atop the bars show the 95 % confidence interval. Estimations based on all countries with at least two periods and with available data on the FPE index (n = 136). Source: Demographic and Health Surveys. Calculations by the authors

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Günther, I., Harttgen, K. Desired Fertility and Number of Children Born Across Time and Space. Demography 53, 55–83 (2016). https://doi.org/10.1007/s13524-015-0451-9

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