Abstract
Across a number of countries in Asia and the Caucasus, fertility decline in recent decades has been accompanied by an unprecedented and anomalous rise in the sex ratio at birth (SRB). Although the micro-level factors—persistent son preference within a context of fertility decline and growing access to pre-natal sex determination technology—are known, their specific levels, trends and interactions in explaining macro-level SRB trajectories are hard to discern with existing data and approaches. We present an agent-based model (ABM) that examines the contribution of each of these micro-level factors to the emergence of distorted SRBs at the macro-level. Calibrating our model to the South Korean and Indian scenarios, we show that even as son preference was declining in both settings SRB distortions emerged due to the diffusion of technology along with increases in probabilities to sex-selectively abort at lower parities as norms shifted towards smaller families. In South Korea, we find that SRBs peaked at 114 at relatively low levels of son preference of ∼ 30 % wanting a son. The SRB increase was due to the joint effects of technology diffusion combined with steady increases in the readiness to abort, including small increases at parity 0, that is, before the transition to first birth. In India, our model suggests that the SRB rise was less steep than South Korea’s as the readiness to abort was not as high as in South Korea, due to higher fertility levels when SRBs rose and slower technology diffusion.
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Notes
- 1.
The United Nations (UN) Population Division publishes a comparative global SRB time series from 1950 onward and produces SRB forecasts until 2100 (United Nations 2013). SRB forecasts, unlike more recent probabilistic approaches adopted in the UN’s fertility forecasts, are deterministic. These estimates as well as other demographic studies documenting SRB trends make use of different types of data sources with varying levels of reliability: birth registration data, which are the most reliable when available; birth-history estimates from large surveys; and census data on recent births or age structure (Guilmoto 2009). Data quality problems for estimating SRB trends in Asia are detailed extensively elsewhere (Attané and Guilmoto 2007).
- 2.
Sex-selective abortion requires pre-natal sex-determination technologies as well as methods for abortion. Different technologies for pre-natal sex-determination technologies presently exist—amniocentesis, an invasive procedure that is conducted between 15 and 20 weeks of pregnancy, ultrasonography, which can determine the sex of the foetus as early as 11 weeks, and blood tests involving the analysis of the foetal DNA floating in the mother’s bloodstream, which are minimally invasive procedures and can be done at home as early as 7 weeks into the pregnancy (Bongaarts 2013).
- 3.
Differential stopping behaviour (DSB) refers to fertility behaviour in which couples continue childbearing until they reach a desired number of sons by regulating their contraceptive use and childbearing behaviour based on the sex composition of existing children. Several papers report the presence of DSB—manifested in higher levels of contraceptive use after bearing sons, higher levels of parity progression in daughter-only families, or high sex ratios at last birth—across different son-preferring populations (Amin and Mariam 1987; Arnold et al. 1998; Bongaarts 2013; Clark 2000; Retherford and Roy 2003).
- 4.
Whether her son preference gets recoded or not will depend on prevailing period- and cohort-levels of son preference for that time-step in the simulation. This takes into account the fact that a woman who might have had a son preference at one time-step and have acted on it then might not have the preference in a later period.
- 5.
The National Family Health Survey (NFHS) is the name by which the Demographic and Health Survey (DHS) is called in India.
- 6.
Since an agent’s current parity is determined at the beginning of the period t and TFR is calculated at the end of the period t, the most recently observed TFR with respect to an agent’s parity when she is at risk of childbearing in that period is TFR(t − 1).
- 7.
At parity i (t) = 1, the probability to abort will be 1∕3 ×σ = 0. 33 ×σ when TFR(t − 1) = 3, and 1∕2. 5 ×σ = 0. 4 ×σ when TFR(t − 1) = 2. 5.
- 8.
The UN World Population Prospects data only offer data from 1950 onwards. Consequently, we approximated the population structure of 1945 with the one from 1950, and for the period 1945–1950 we used the same age-specific fertility rates and death rates as for the period 1950–1955.
- 9.
Details on model calibration are available in Kashyap and:̧def:̧def Villavicencio (forthcoming).
- 10.
UN SRB and fertility trends are issued for 5-year periods (e.g. 1970–75, 1980–85) where the 5-year period is assumed to run from July to July (e.g. July 1 1970–July 1 1975) and the estimates are assumed to refer to the mid-point of the period concerned (1 January 1973). Our model runs produce yearly estimates. To enable a better comparison between the UN estimates and our simulated results, we present smoothed UN estimates, in which we linearly interpolate the 5-year values for each year. For this, as advised in UN metadata we assume the value for each of the 5-year intervals corresponds to mid-point within the interval (e.g. 1970–75 rate corresponds to mid-point 1973).
- 11.
If TFR = 2. 4, min{1, (parity ×σ∕TFR)} = min{1, (1 × 1. 7∕2. 4)} = 0. 708. If TFR = 1. 6, min{1, (parity ×σ∕TFR)} = min{1, (1 × 1. 7∕1. 6)} = min{1, 1. 0625} = 1.
- 12.
Although here we treat them as roughly approximating proportions desiring one son at different time points in the model, we acknowledge these data come from different surveys and from questions regarding sex preference that were worded very differently in each of the surveys.
- 13.
The authors of that paper report this proportion by regions for India and estimate this proportion for births during each year between 1999 and 2008. We average across all regions to approximate national-level technological diffusion. We recognize this erases the heterogeneity across different regions and in future amendments to the model we hope to incorporate issues surrounding regional heterogeneity in the model. We discuss these issues further at the end of the chapter. We should also note that we could not find similar data for South Korea so we estimated technological diffusion for South Korea by conducting sensitivity analyses for the model across a range of technology diffusion (υ and ϕ) parameters.
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Kashyap, R., Villavicencio, F. (2017). An Agent-Based Model of Sex Ratio at Birth Distortions. In: Grow, A., Van Bavel, J. (eds) Agent-Based Modelling in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-32283-4_12
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