Skip to main content
SpringerLink
Log in

Do marriage markets respond to a natural disaster? The impact of flooding of the Kosi river in India

  • Original Paper
  • Published:
Journal of Population Economics Aims and scope Submit manuscript

Abstract

This paper studies the impact of the flooding of the Kosi river on the timing of marriage in the Indian state of Bihar, one of the world’s poorest regions. Using a difference-in-differences design, we show that the Kosi floods reduced men’s age at marriage by almost a year and women’s age at marriage by over 4 months. The Kosi floods also decreased the secondary school completion rates of married men and women and married women’s command over economic resources. We interpret these results within a framework of marriage markets, where in the absence of complete credit markets, marriage market payments (dowry) help smooth consumption in response to adverse income shocks. In support of this framework, we find that the impact of the Kosi floods is more pronounced among Hindus (for whom dowry is the traditional marriage payment norm) and among the landless (who are more credit-constrained).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data and materials

All data used in this paper is obtained from public sources. Description of data used and links to these public sources are in Appendix A of Online Appendix.

Code availability

Code will be made available upon request.

Notes

  1. Many of these papers use the timing of the onset of menstruation as an instrument for the timing of marriage, which may not be an excluded instrument for recent cohorts as it is likely to affect the education decision as well (Khanna 2020).

  2. Corno and Voena (2016) show that bride price smooths consumption when credit markets are incomplete.

  3. The framework used in this paper is a simplistic representation of marriage markets. While many parameters are normalized for simplicity, male wages in adulthood play a critical role in parents’ decision to marry their sons. Refer to the paper by Corno et al. (2020) for technical details on other parametric assumptions.

  4. The total mortality associated with the 2001 Gujarat earthquake (studied by Das and Dasgupta (2019)) was over 15,000 and with the 1970 Ancash earthquake (studied by Caruso and Miller (2015)) was over 65,000. The Great Chinese Famine (studied by Douglas et al. (2007) and Brandt et al. (2016)) was one of the worst famines in history, with between 16.5 and 30 million deaths.

  5. Natural disasters have been known to have demographic effects beyond the immediate mortality effects. For instance, Nandi et al. (2018) show that the 2001 Gujarat earthquake increased fertility and reduced birth spacing among some social groups. The desire to have more family members after a demographic shock could spur early marriages, and therefore, it is difficult to isolate the impact of the aggregate economic shock due to a natural disaster if it also entailed demographic changes.

  6. Globally, flooding events are responsible for massive damages to property, crops, livestock, and human lives, with the average annual loss estimated to be USD 7.4 billion (UNISDR 2015).

  7. Sankritization is the process wherein castes places lower in the social hierarchy emulate the traditions of upper castes (such as dowry) to seek upward mobility.

  8. At its peak, the intensity of water force went up to 166,000 cubic feet per second compared with the regular 25,744 cubic feet per second, running straight down south through a new course 15–20 kms wide and 150 kms long (Government of India 2010).

  9. Marrying in childhood is equivalent to marrying under age eighteen.

  10. Marriages are almost universal in India (see Fig. C1 of Online Appendix). Therefore, we assume that every individual in the cohort gets married either in childhood or adulthood.

  11. We believe this framework closely mimics the reality in the Indian context where income is highly variable (Dercon 2002).

  12. The linear demand and supply curves can be derived under the assumption of logarithmic utility. Corno et al. (2020) assume that households have a constant relative risk aversion utility function, where the main results of the model rely on households being at least moderately risk averse.

  13. When adult wages are large enough, the supply for child grooms will be more price responsive than demand for any concave utility function that exhibits prudence or decreasing absolute risk aversion. See Corno et al. (2020) for detailed discussion and proof.

  14. As shown in Fig. 4, the average wages for men are low in many other states such as Chattisgarh, Madhya Pradesh, Odisha, West Bengal and Uttar Pradesh. In these contexts, the results of this paper are likely to apply.

  15. Indian Demographic and Health Surveys are also known as National Family Health Surveys.

  16. A woman enters the survey at the time of her marriage and exits the survey at age 49 or the year of the survey, whichever is earlier.

  17. A potential objection to using these data comes from the measurement error in retrospective data on marriage age, which could introduce a greater imprecision in the estimates of the impact of the Kosi floods. Pullum (2006) shows that discrepancies in age and date are not a serious concern even in the worst data quality setting.

  18. By doing this, we exclude only nineteen women from our sample.

  19. Although the river flows through Katihar, it was not as severely affected and was not categorized as a flood-affected district by the government since the floods affected the regions in the river’s basin.

  20. Married after August 2008.

  21. We include a categorical variable for caste (scheduled caste (SC/ST), other backward caste (OBC), or forward caste (FC)), and an indicator variable for religion (Hindu or others). Assets include indicators for ownership of agricultural land, house, access to protected water source, dwelling type, access to toilet, refrigerator, bicycle, electricity, car/scooter, cooking fuel, livestock, radio, television, sewing machine, pressure cooker, electric fan and mattress, and the number of rooms in the household. Table D2 of Online Appendix presents averages for all controls across Kosi and non-Kosi districts before the floods.

  22. Since the majority of women(men) in Bihar got married by 30(35), we restrict the sample to those who got married by this age (Fig. C1 of Online Appendix).

  23. These results depict long-term changes in migration patterns because the survey data was collected in 2015, which is seven years after the Kosi floods.

  24. An individual is defined as having married as a child if they married before the age of eighteen.

  25. In the restricted sample of 3033 households, where a married couple was interviewed, the incidence of female child marriage increases by 7.1 percentage points (p-value: 0.13), which is similar in magnitude to the increase in the incidence of male child marriage (Table D5 of Online Appendix).

  26. For the country as a whole in 2015, men were about 4.07 years older than their wives. In Bihar, men were about 4.4 years older than their wives in 2015.

  27. We use individual and household level characteristics that we used as controls in our main specification to estimate these propensity scores with a logit specification. In addition to controlling for these characteristics, we include district and year of marriage fixed effects while implementing the difference-in-differences specification.

  28. Since we have a much smaller sample for men’s outcomes, we restrict the analysis to women’s age at marriage in this section. Small sample size for men’s outcomes means that some control districts in some pre-treatment years have no observations. Therefore, we cannot use outcome averages for those districts in those districts when re-weighting.

  29. Since the majority of women(men) in Bihar got married by 30(35), we restrict the sample to those who got married by this age.

  30. This specification is not estimated at each age separately but on the whole sample.

  31. We repeat this exercise 2,000 times. Young (2018) shows that typically 2000 replications suffice to recover the non-parametric distribution of estimated effect size under placebo treatment. By doing this, we eliminate any systematic relationship between being a resident of a Kosi district and age at marriage.

  32. Thinking about inference is especially relevant in this paper because each district arguably represents a marriage market, and error terms are likely correlated for observations within a district. We have addressed this concern by clustering the standard errors at the district level. However, the validity of inference may still be a concern due to the small number of districts in this sample (thirty-seven).

  33. In the worst affected district (Madhepura), the Kosi floods decreases monthly consumption expenditure by about 18%.

  34. Using Rural Economic and Demographic Survey (2006), we confirm that dowries are prevalent among both communities in Bihar, and the average amount of dowry payment among Hindus is almost 2.5 times the dowry payments among Muslims. The average amount of dowry reported for Hindus is INR 23,820, while that for Muslims is INR 9,718.

  35. We also replicate this analysis in a pooled specification, and the results are available upon request. We focus on this specification to explore if there is a sudden drop in the quality of schooling infrastructure of school enrollment after the Kosi floods.

  36. The difference-in-differences estimate with the incidence of consanguineous marriage as the outcome is \(-\)0.6% against the average of 4.3% for non-Kosi districts after 2008.

  37. Secondary education is typically completed around age sixteen, and we define it as having completed ten years of formal education.

References

  • Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. J Am Stat Assoc 105(490):495–510

    Article  Google Scholar 

  • Anderson S (2003) Why dowry payments declined with modernization in Europe but are rising in India. J Polit Econ 111(2):269–310

    Article  Google Scholar 

  • Anderson S (2007) Why the marriage squeeze cannot cause dowry inflation. J Econ Theory 137:140–152

    Article  Google Scholar 

  • Anukriti S, Kwon S, Prakash N (2022) Saving for dowry: Evidence from rural India. J Dev Econ 154:102750. https://doi.org/10.1016/j.jdeveco.2021.102750

    Article  Google Scholar 

  • Athey S, Imbens GW (2017) The state of applied econometrics: Causality and policy evaluation. J Econ Perspect 31(2):3–32

    Article  Google Scholar 

  • Banerjee A, Duflo E, Ghatak M et al (2013) Marry for what? Caste and mate selection in modern India. Am Econ J Microeconomics 5(2):33–72

    Article  Google Scholar 

  • Becker GS (1991) A treatise on the family: enlarged edition. Harvard University Press

  • Bhat PM, Halli SS (1999) Demography of brideprice and dowry: Causes and consequences of the Indian marriage squeeze. Popul Stud 53(2):129–148

    Article  Google Scholar 

  • Biswas N (2017) Policy or perception? Women’s political liberalization and sex-selection in India. JMP

  • Bloch F, Rao V (2002) Terror as a bargaining instrument: A case study of dowry violence in rural India. Am Econ Rev 92(4):1029–1043

    Article  Google Scholar 

  • Bloch F, Rao V, Desai S (2004) Wedding celebrations as conspicuous consumption signaling social status in rural India. J Hum Resour 39(3):675–695

    Article  Google Scholar 

  • Blundell R, Dias MC (2009) Alternative approaches to evaluation in empirical microeconomics. J Hum Resour 44(3):565–640

    Google Scholar 

  • Borooah V, Iyer S (2004) Religion and fertility in India: The role of son preference and daughter aversion. Cambridge Working Papers in Economics (0436)

  • Boserup E (1970) Woman’s role in economic development. Allen and Unwin, London

    Google Scholar 

  • Botticini M, Siow A (2003) Why dowries? Am Econ Rev 93(4):1385–1398

    Article  Google Scholar 

  • Brandt L, Siow A, Vogel C (2016) Large demographic shocks and small changes in the marriage market. J Eur Econ Assoc 16(4):1437–1468

    Article  Google Scholar 

  • Caldwell JC, Reddy PH, Caldwell P (1983) The causes of marriage change in South India. Popul Stud 37(3):343–361

    Article  Google Scholar 

  • Caruso G, Miller S (2015) Long run effects and intergenerational transmission of natural disasters: A case study on the 1970 ancash earthquake. J Dev Econ 117:134–150

    Article  Google Scholar 

  • Chari A, Heath R, Maertens A et al (2017) The causal effect of maternal age at marriage on child wellbeing: evidence from India. J Dev Econ 127:42–55. https://doi.org/10.1016/j.jdeveco.2017.02.002. www.sciencedirect.com/science/article/pii/S0304387817300123

  • Chiplunkar G, Weaver J (2021) Marriage markets and the rise of dowry in India. Available at SSRN 3590730

  • Collver A (1963) The family cycle in India and the United States. Am Sociol Rev 28(1):86–96

    Article  Google Scholar 

  • Corno L, Voena A (2016) Selling daughters: Age of marriage, income shocks and the bride price tradition. Institute of Fiscal Studies Working Paper W16/08

  • Corno L, Hildebrandt N, Voena A (2020) Age of marriage, weather shocks, and the direction of marriage payments. Econometrica 88(3):879–915

    Article  Google Scholar 

  • Das S, Dasgupta S (2019) Marriage market responses in the wake of a natural disaster in India. unpublished

  • Dercon S (2002) Income risk, coping strategies, and safety nets. World Bank Res Obs 17(2):141–166

    Article  Google Scholar 

  • Disaster management department (2009) Bihar Floods- As on 26th May 2009. Disaster Management Department. http://disastermgmt.bih.nic.in/Downloads/PHASE-III-Report-26.05.09.pdf

  • Dixit A (2009) Kosi embankment breach in Nepal: Need for a paradigm shift in responding to floods. Econ Polit Wkly 44(6):70–78

    Google Scholar 

  • Douglas A, Edlund L, Li H, et al (2007) Long-term effects of the 1959-1961 China famine: Mainland China and Hong Kong. NBER Working paper 13384

  • Emmanuel Hospital Association ND (2008) Bihar Flood 2008, Situation Report- 1

  • Field E, Ambrus A (2008) Early marriage, age of menarche, and female schooling attainment in Bangladesh. J Polit Econ 115(5)

  • Garcia-Hombrados J (2022) Child marriage and infant mortality: Causal evidence from Ethiopia. J Popul Econ 35:1163–1223. https://doi.org/10.1007/s00148-021-00873-y

    Article  Google Scholar 

  • Government of India (2010) Bihar Kosi Flood (2008) Needs assessment report. Government of India, World Bank and Global Facility for Disaster Reduction and Recovery

  • Hotte R, Lambert S (2020) Marriage payments and wife’s welfare: All you need is love. https://doi.org/halshs-02611667

  • ICRW (2016) Child Marriage in Southern Asia: Policy Options for Action. AFPPD, Australian Aid, International Center for Research on Women and UNFPA

  • IHDS-II (2012) Sonalde Desai and Reeve Vanneman, India Human Development Survey-II (IHDS-II), 2011-12. Inter-university consortium for political and social research [distributor], https://doi.org/10.3886/ICPSR36151.v6

  • Jensen R, Thornton R (2003) Early female marriage in the developing world. Gend Dev 11(2):9–19. http://www.jstor.org/stable/4030636

  • Kahn-Lang A, Lang K (2020) The promise and pitfalls of differences-in-differences: Reflections on 16 and pregnant and other applications. J Bus Econ Stat 38(3):613–620. https://doi.org/10.1080/07350015.2018.1546591

    Article  Google Scholar 

  • Khanna M (2020) The precocious period: the impact of early menarche on schooling in India. https://madhulika-khanna.github.io/WebsiteStuff/Khanna_JMP.pdf

  • Kirbas A, Gulerman HC, Daglar K (2016) Pregnanyc in adolescence: Is it an obstetrical risk? J Pediatr Adolesc Gynecol 29:367–371

    Article  Google Scholar 

  • Kone ZL, Liu MY, Mattoo A et al (2018) Internal borders and migration in India. J Econ Geogr 18(4):729–759

    Google Scholar 

  • Mirza MMQ (2011) Climate change, flooding in South Asia and implications. Reg Environ Chang 11(1):S95–S107

    Article  Google Scholar 

  • Mobarak AM, Kuhn R, Peters C (2013) Consanguinity and other marriage market effects of a wealth shock in Bangladesh. Demography 50(5):1845–1871. http://www.jstor.org/stable/42919954

  • Munshi K, Rosenzweig M (2016) Networks and misallocation: insurance, migration, and the rural-urban wage gap. Am Econ Rev 106(1):46–98. http://www.jstor.org/stable/43821397

  • Nandi A, Mazumdar S, Behrman JR (2018) The effect of natural disaster on fertility, birth spacing, and child sex ratio: evidence from a major earthquake in India. J Popul Econ 31(1):267–293

    Article  Google Scholar 

  • Pullum TW (2006) An assessment of age and date reporting in the DHS surveys. DHS Methodological Reports No 5

  • Rao V (1993) The rising price of husbands: A hedonic analysis of dowry increases in rural India. J Polit Econ 101(4):666–677

    Article  Google Scholar 

  • Relief Web (2008) India declares flood a ‘national calamity’. https://reliefweb.int/report/india/india-declares-flood-national-calamity

  • Rural economic and demographic survey (2006) Rural economic and demographic survey. National Council of Applied Economic Research

  • Scott S, Nguyen PH, Neupane S et al (2021) Early marriage and early childbearing in south Asia: Trends, inequalities, and drivers from 2005 to 2018. Ann N Y Acad Sci 1491(1):60

    Article  Google Scholar 

  • Sekhri S, Debnath S (2014) Intergenerational consequences of early age marriages of girls: Effect on children’s human capital. J Dev Stud 50(12):1670–1686

    Article  Google Scholar 

  • Shreshtha RK, Ahlers R, Bakker M, et al (2010) Institutional dysfunction and challenges in flood control: A case study of the Kosi flood 2008. Econ Polit Wkly XLV(2):45–53

  • Srinivas MN (1956) A note on sanskritization and westernization. J Asian Stud 15(4):481–496

    Google Scholar 

  • Sunder N (2019) Marriage age, social status, and intergenerational effects in Uganda. Demography 56(6):2123–2146

    Article  Google Scholar 

  • UNDP (2009) Kosi lloods 2008: How we coped! What we need? Perception survey on inpact and recovery strategies. United Nations Development Programme. https://www.undp.org/india/publications/kosi-floods-2008-how-we-coped-what-we-need-perception-survey-impact-andrecovery-strategies

  • UNISDR (2015) Making Development sustainable: The future of disaster risk management. Global assessment report on disaster risk reduction. United Nations Office for Disaster Risk Reduction (UNISDR), Geneva

    Google Scholar 

  • United Nations (2018) Economic losses, poverty and disasters 1998-2017. Centre for Research on Epidemiolody of Disasters

  • Vogl TS (2013) Marriage institutions and sibling competition: Evidence from South Asia. Q J Econ 128(3):1017–1072

    Article  Google Scholar 

  • Waheed A (2009) Dowry among Indian Muslims: Ideals and practices. Indian J Gend Stud 16(1):47–75

    Article  Google Scholar 

  • Young A (2018) Channeling fisher: Randomization tests and the statistical insignificance of seemingly significant experimental results. Q J Econ 134(2):557–598

    Article  Google Scholar 

Download references

Acknowledgements

We are indebted to Erich Battistin, Garance Genicot, and Martin Ravallion for constant advice and encouragement. We are grateful to Ashish Agarwal, Shreyasee Das, Shatanjaya Dasgupta, Francisco Garrido, Shareen Joshi, Umberto Muratori, JJ Nadeo, Deniz Sanin, Yangfan Sun, Milan Thomas, and Andrew Zeitlin for helpful discussions on this project. This paper also benefited from comments during presentations at Economics Graduate Student Organization (Georgetown University), Georgetown Center for Economic Research, Sustainability and Development Conference (Michigan University), Midwest International Economic Development Conference (Purdue University), North East Universities Development Consortium (Dartmouth College), and Southern Economic Association. We also benefited from discussions with several seminar speakers and co-workers at Georgetown University. We appreciate the editor Terra McKinnish and two anonymous referees for their insightful and constructive feedback during the review process. All errors remain our own.

Author information

Authors and Affiliations

  1. Department of Economics, Georgetown University, 3700 Ost NW, Washington, DC, 20057, USA

    Madhulika Khanna & Nishtha Kochhar

Authors
  1. Madhulika Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nishtha Kochhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Authors’ have contributed equally to this work.

Corresponding authors

Correspondence to Madhulika Khanna or Nishtha Kochhar.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Consent for publication

Authors’ grant their consent for publication.

Additional information

Responsible editor: Terra McKinnish.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Madhulika Khanna and Nishtha Kochhar contributed equally to this work

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 2136 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khanna, M., Kochhar, N. Do marriage markets respond to a natural disaster? The impact of flooding of the Kosi river in India. J Popul Econ 36, 2241–2276 (2023). https://doi.org/10.1007/s00148-023-00955-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-023-00955-z

Keywords

JEL Classification

Search

Navigation