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Potential (Mis)match? Marriage Markets Amidst Sociodemographic Change in India, 2005–2050

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Abstract

We explore the impact of sociodemographic change on marriage patterns in India by examining the hypothetical consequences of applying three sets of marriage pairing propensities—contemporary patterns by age, contemporary patterns by age and education, and changing propensities that allow for greater educational homogamy and reduced educational asymmetries—to future population projections. Future population prospects for India indicate three trends that will impact marriage patterns: (1) female deficit in sex ratios at birth; (2) declining birth cohort size; (3) female educational expansion. Existing literature posits declining marriage rates for men arising from skewed sex ratios at birth (SRBs) in India’s population. In addition to skewed SRBs, India’s population will experience female educational expansion in the coming decades. Female educational expansion and its impact on marriage patterns must be jointly considered with demographic changes, given educational differences and asymmetries in union formation that exist in India, as across much of the world. We systematize contemporary pairing propensities using data from the 2005–2006 Indian National Family Health Survey and the 2004 Socio-Economic Survey and apply these and the third set of changing propensities to multistate population projections by educational attainment using an iterative longitudinal projection procedure. If today’s age patterns of marriage are viewed against age/sex population composition until 2050, men experience declining marriage prevalence. However, when education is included, women—particularly those with higher education—experience a more salient rise in nonmarriage. Significant changes in pairing patterns toward greater levels of educational homogamy and gender symmetry can counteract a marked rise in nonmarriage.

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Notes

  1. 1.

    Most studies of the impact of SRBs on nuptiality have focused on the Chinese scenario (Attané 2006; Guilmoto 2012; Jiang et al. 2007; Sharygin et al. 2013; Tucker and Van Hook 2013; Tuljapurkar et al. 1995), with few studies of the Indian scenario. Guilmoto (2012) is an exception.

  2. 2.

    Sharygin et al. (2013) considered education in their marriage projections for China.

  3. 3.

    All forward population projections and backward reconstructions by age and educational attainment used in this article are taken from the IIASA/VID Population and Human Capital data sets. The data set for the forward population projections and backward reconstruction of populations for 120 countries by age and educational attainment is available online on the IIASA website (http://www.iiasa.ac.at/web/home/research/researchPrograms/WorldPopulation/Research/ForecastsProjections/DemographyGlobalHumanCapital/EducationReconstructionProjections/education_reconstruction_and_projections.html).

  4. 4.

    There are two unique aspects of marriage in India that we do not consider in detail here: caste endogamy and an arranged marriage system. Although we focus on educational asymmetries in union formation, we recognize that caste is a significant social dimension structuring the marriage market in India. Population-level representative data on caste beyond broad categorizations are not available, nor are population projections by caste to facilitate the type of analysis that we do here. Anthropological literature has historically emphasized the importance of caste endogamy and hypergamy (Kaur and Palriwala 2014). More recently, in their urban middle-class sample from West Bengal, Banerjee et al. (2013) found a strong preference for horizontal (in-caste) rather than vertical asymmetry along caste lines, as is the case with age and education. Given in-caste matching, their theoretical model suggests that matching patterns along noncaste preferences should be very similar to those that would be observed in the absence of in-caste preferences. Within in-caste marriages, age and educational asymmetries may be assumed to move similarly, with men generally older and equally educated, if not more so, compared with their spouses. Existing work has suggested that arranged marriages remain common and that even among the most-educated women, arranged marriages are not replaced entirely by choice-based marriages but instead by a more consensual form of arranged marriage in which daughters are also involved in the matchmaking process (Banerji et al. 2013).

  5. 5.

    The data on demographic trends in this section are taken from the UN World Population Prospects 2012 database (United Nations 2013).

  6. 6.

    Global Education Trend (GET) Scenario projections from the IIASA/VID database assume that a country’s educational expansion will converge on an expansion trajectory based on a historical global trend. More details are available in the appendix.

  7. 7.

    We use Reed-Merell’s life table method of converting rates into probabilities (Keyfitz and Frauenthal 1975).

  8. 8.

    The number of individuals marrying within an age interval ( n d x ) functions like the n d x in a standard life table, where n d x = l x × n q x .

  9. 9.

    The visualization of the new matrix of the forces of attraction under this scenario in 2050 is available from the authors.

  10. 10.

    In their marriage projections for China until 2050, Sharygin et al. (2013) also anticipated that less-educated, low-status men will be most negatively impacted. The authors devoted less attention to the marriage prospects of highly educated women.

  11. 11.

    Although out attention is on education here, status is undoubtedly a more complex outcome of caste, education, and social class, among other identities.

  12. 12.

    In their study of marriage timing in India, Desai and Andrist (2010) also noted this tension. They eloquently characterized Indian parents as conflicted between “status attainment through gender performance,” referring to the ritual mobility gained through the practice of high-caste norms that stress female seclusion and modesty, and “status attainment through the performance of modernity” that valorizes female education (Desai and Andrist 2010:682).

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Acknowledgments

Most of the research presented here was carried out when all three authors were based at Center for Demographic Studies (CED) in Barcelona and supported by the WorldFam-ERC project (Grant No. ERC-2009-StG-240978) at CED. The research is also partially supported by NIH Grant R24HD041023 at the Minnesota Population Center. We greatly appreciate the support of both institutes. We thank Clara Cortina and Inaki Permanyer for valuable feedback. Tim Riffe and Hannaliis Jaadla provided especially helpful guidance on the visualizations and figures, and we are very grateful for their support. We also thank three anonymous reviewers for their helpful suggestions.

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Correspondence to Ridhi Kashyap.

Appendix

Appendix

The Harmonic Mean Marriage Function

The harmonic mean marriage function proposed by Schoen (1988) relates the number of marriages occurring to all hypothetical possible encounters of both sexes with given characteristics. The functions allows for the estimation of a composition-independent propensity to marry between males and females with given characteristics (“force of attraction”). By estimating such a magnitude of attraction, we can decompose the effect changing population composition (changes in the population at risk) versus the effect of a marriage propensity on the number of marriages and marriage rates between different groups. Schoen demonstrated that relating the number of marriages to the harmonic mean of the male and female populations at risk of marriage provides a theoretical solution to the two-sex problem—that is, how both male and female vital rates can be reconciled within population models (Schoen 1988). Schoen’s model has been criticized for not accounting for the effect of competition from age groups other than i or j (Choo and Siow 2006). Schoen, however, has shown that because the population in groups i and j are not independent from the age-sex structure of the whole population, the harmonic mean function serves well in disentangling the effects of the change in population composition versus that related to changes in propensities to marry between different groups (Schoen 1981).

Data Sources and Methodology for Estimating α ij and α ijkl

Estimating α ij and α ijkl requires data on (1) observed heterosexual marriages for a base period with information on the age (for α ij ), and age and educational level (for α ijkl ) for both male and female spouses who entered the union; and (2) the population at risk, defined as never-married males and females, in each age and educational category. Data on first unions formed between 1999–2004, with age and education characteristics of the male and female spouse who entered the union, as well as the population at risk (defined as never-married individuals), are obtained and harmonized from the latest wave of India’s National Family Health Survey (NFHS) 2005–2006 (International Institute for Population Sciences IIPS 2007) and the Indian Socio-Economic Survey (1999 and 2004) available from the IPUMS international database (Minnesota Population Center MPC 2011).

The NFHS follows the format of the Demographic and Health Surveys (DHS), which are large-scale household surveys conducted in Asia, Africa, and Latin America. The Socio-Economic Survey, run by the National Sample Survey Organization of the Government of India, is a population-representative survey that covers 0.06 % of India’s population. The women’s questionnaire of the NFHS provides data on the age at marriage of both spouses, the year of the marriage, and the educational attainment of both spouses who form a union. From these data, we select first unions that occurred in the last five years of the survey, thereby extracting all unions that occurred between 1999 and 2004. We sample marriages from this recent period to minimize effects of union dissolution and to capture data on the largest number of intact marriages. The survival of marital union is likely higher for recent marriages than those formed several years before. If we do not impose a period restriction on the marriages that we capture, we run the risk of picking up a biased sample of unions that had not dissolved and for which all required data on spousal characteristics were available. Another reason to capture recent marriages is to be able to describe contemporary marriage behavior by drawing on a broad cohort of marriages that faced similar social circumstances when forming a union. One limitation of this criterion for selecting marriages is that we cannot pick up on very rare but theoretically possible marriages happening at the very youngest and oldest ages.

These data on spousal characteristics of unions formed in the period 1999–2004 from the NFHS are tabulated by age and education, where age is categorized in five-year age groups (20–24, 25–29, until 49 years old), with the exception of the first age group that is a single-year age group (15 years) and the second comprising 16- to 19-year-olds. Educational attainment is classified into four categories: no education, primary, secondary, and university education. Because completing one’s educational career before entry into a marital union is common in India, it is a fair assumption that educational attainment is a fixed attribute that is the same at the time of survey as at the time of marital union formation. Consequently, the earliest age group for which we estimate tertiary education forces of attraction is 20–24 years. This classification differs slightly from the educational variable classification in the NFHS. The four-level classification provides the best harmonization across different sources of data—the NFHS, the Socio-Economic Survey, and the IIASA/VID population projections for 2005–2050—that we use in this article.

We define the risk population as never-married individuals across each of our age groups in each of the four educational categories. Given negligible rates of unmarried cohabitation and divorce in India and limited remarriage except in cases of widowhood, a measure of the never-married population closely approximates the population at risk of marriage. The NFHS data do not allow for an easy estimation of the population of never-married men: data collected on men are exclusively for men in unions with women. To acquire population-representative proportions of never-married individuals by age and across each of the four educational categories, we obtain data on the never-married population using the marital status variable in the Indian Socio-Economic Survey. We estimate the never-married population by calculating the mean of the never-married population of men and women in each age group by educational level between two waves of the survey, 1999 and 2004, given that we are examining data on marriages that occurred between 1999 and 2004. Because we are forced to use data on observed marriages and a population at risk from different data sources, we create consistency between the two sources by adjusting the number of observed marriages in the NFHS data to fit with the observed proportions of the never-married population in the Socio-Economic Survey.

Constructing the Harmonization Coefficient

We inflate the number of marriages observed in the NFHS by multiplying the marriages by an age and cohort adjustment coefficient to harmonize the data across the two data sources. We compute a coefficient calculated as the ratio of the total proportion of women in each age of the five-year age groups (and the first one-year age group of 15-year-olds) for each of the four educational categories in the Socio-Economic Survey (IPUMS) divided by the same proportion for the respective age and educational categories in the NFHS. Our marriages, however, are for a period extending up to five years before the survey. Thus, a woman who is 25 years old and married four years ago was 21 years old at time of marriage. Applying the coefficient for the age group 25–29 years here would wrongly apply the numbers of an older cohort to her case. As a result, we take a mean of the coefficients across each group to adjust for the fact that differences between current age and age at marriage of men and women may sort them into two different cohorts across our two data sources.

Population Projections by Age and Educational Attainment

The IIASA/VID population projection data are multistate population projections that account for differential fertility, mortality, and migration rates by educational attainment to provide estimates for future populations for 2005–2050 by four educational categories: no education or less than primary, primary-level completed, secondary, and tertiary education (KC et al. 2010). Given that the projections are available in five-year age groups, the projections move forward in five-year time steps. These data modify standard age-sex population projections of the UN WPP by adding the educational dimension and estimating populations across four educational categories using educational group specific transition parameters from the Demographic and Health Surveys (DHS). For India, the projections use data from the Indian DHS (NFHS 2005–2006), which is also the data source we use to estimate forces of attraction. In this article, we use the Global Education Trend (GET) Scenario projections from the IIASA/VID database, which assume that a country’s educational expansion will converge on an expansion trajectory based on a historical global trend. This is a midrange scenario between a worst-case scenario that assumes no change in enrollment and an optimistic fast-track scenario that assumes acceleration in global educational expansion (KC et al. 2010:407).

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Kashyap, R., Esteve, A. & García-Román, J. Potential (Mis)match? Marriage Markets Amidst Sociodemographic Change in India, 2005–2050. Demography 52, 183–208 (2015). https://doi.org/10.1007/s13524-014-0366-x

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Keywords

  • Marriage
  • Skewed sex ratios at birth
  • Educational hypergamy
  • Female educational expansion
  • India