본문 바로가기
KERD
논문KCI 등재

Do Attending Universities in the Capital Region Delay the Age of Marriage?

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류경제학

초록

This study analyzes whether students from non-capital regions experience a delay in the age of first marriage when they attend universities in the capital region. Using propensity score matching and survival analysis, we investigate this phenomenon. To ensure the homogeneity between the treatment group and control group, we consider individuals who attended universities in the capital region as the entire sample. Among them, the treatment group consists of individuals who relocated to the capital region for university education, while the control group consists of individuals who did not relocate to the capital region for education. Kaplan-Meier survival analysis reveals that individuals who relocated to the capital region for university education experience a later age of first marriage. Cox proportional hazards model analysis also indicates that the experience of relocating to the capital region is significant across all models and lowers the marriage rate by approximately 70-75% in each interval. This suggests that individuals from non-capital region high schools who enter the capital region for university education face challenges that affect their marriage preparations negatively. These challenges include the time required for adaptation, disadvantages in terms of local information, and potentially higher living costs such as rent. Therefore, it can be inferred that the difficulties encountered during university years have a negative impact on marriage preparations.

Abstract

This study analyzes whether students from non-capital regions experience a delay in the age of first marriage when they attend universities in the capital region. Using propensity score matching and survival analysis, we investigate this phenomenon. To ensure the homogeneity between the treatment group and control group, we consider individuals who attended universities in the capital region as the entire sample. Among them, the treatment group consists of individuals who relocated to the capital region for university education, while the control group consists of individuals who did not relocate to the capital region for education. Kaplan-Meier survival analysis reveals that individuals who relocated to the capital region for university education experience a later age of first marriage. Cox proportional hazards model analysis also indicates that the experience of relocating to the capital region is significant across all models and lowers the marriage rate by approximately 70-75% in each interval. This suggests that individuals from non-capital region high schools who enter the capital region for university education face challenges that affect their marriage preparations negatively. These challenges include the time required for adaptation, disadvantages in terms of local information, and potentially higher living costs such as rent. Therefore, it can be inferred that the difficulties encountered during university years have a negative impact on marriage preparations.

참고문헌 (44)

  1. Kim, S. J., "Why is the age of marriage delayed?", Labor Economics Journal, 38(4), pp.57~81 (2015)
  2. Kim, G., "The relationship between housing costs and marriageability: Focusing on the deposit system", Master, pp.2014
  3. Lee, C. H., "Analysis and future prospects of factors affecting changes in the number of births", Korean Economic Analysis, 25(1), pp.37~83 (2019)
  4. Lee, C. H., "Decomposition of the factors affecting the total fertility rate in Korea : The effects of marriage and fertility rates of married women", Korean Journal of Population Studies, 35(3), pp.117~144 (2012)
  5. Lee, C. H., "Have Korea's pro-natal policies failed? Decomposition of factors affecting the fertility rate from 2000 to 2016", Economic Studies, 166(3), pp.1~38 (2018)
  6. Seo, M. S., "A study on the relationship between housing price changes and women's fertility rate", Journal of Women's Economics, 10(1), pp.63~79 (2013)
  7. Jeong, C. M., "Socio-economic variables influencing the age of first marriage", National Planning Journal, 43(4), pp.105~118 (2008)
  8. Choi, S. E., "Factors determining the age of first marriage for men and women in their 30s", Journal of the Korean Association for Family Relations, 8(2), pp.53~73 (2003)
  9. Angrist, J. D., "Mostly harmless econometrics: An empiricist's companion" (2009), Princeton University Press
  10. Austin, P. C., "Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples", Statistics in Medicine, 28(25), pp.3083~3107 (2009)
  11. Becker, Sascha O., "Estimation of average treatment effects based on propensity scores", The Stata Journal, 2(4), pp.358~377 (2002)
  12. Bloom, D. E., "Age patterns of women at marriage, cohabitation, and first birth in India", Demography, 23(4), pp.509~523 (1986)[DOI]
  13. Branisa, B., "Gender Inequality in Social Institutions and Gendered Development Outcomes", World Development, 45, pp.252~268 (2013)
  14. Caliendo, M., "Some Practical Guidance for the Implementation of Propensity Score Matching", The Institute for the Study of Labor, 1588, pp.1~29 (2005)
  15. Cummins, N., "Marital fertility and wealth during the fertility transition : rural France, 1750-1850", The Economic History Review, 66(2), pp.449~476 (2013)
  16. Casual Effects in Non-Experimentra Studies: Re-Evaluating the Evaluation of Training Programs, "Dehejia, H.", Harvard University, ~ (1997)
  17. Elser, D. M., "Comparison of "subjective" and "objective" measures of severity of urinary incontinence in women. Program for Women Research Group", Neurourol and Urodynamics, 14(4), pp.311~316 (1995)
  18. Propensity Score Matching and Policy Impact Analysis: A Demonstration in EViews, "Essama-Nssah, B.", World Bank Policy Research, ~, pp.World Bank Policy Research Working Paper 3877 (2006)
  19. Foreman-Peck, J., "The Western European Marriage Pattern and Economic Development", Explorations in Economic History, 48(2), pp.292~309 (2011)
  20. Propensity Score Matching: An Application using the ABS Business Characteristics Survey, "Franklin, S.", Australian Bureau of Statistics, ~ (2012)
  21. Garrido, M. M., "Methods for constructing and assessing propensity scores", Health services research, 49(5), pp.1701~1720 (2014)
  22. Heckman, J. J., "Sample Selection Bias as a Specification Error", Econometrica, 47(1), pp.153~162 (1979)
  23. Heckman, J. J., "Matching as an Econometric Evaluation Estimator", The Review of Economic Studies, 65(2), pp.261~294 (1998)
  24. Heckman, J. J., "Assessing the Case for Social Experiments", Journal of Economic Perspectives, 9(2), pp.85~110 (1995)
  25. Heckman, J. J., "Matching as an Econometric Evaluation Estimator : Evidence from Evaluating a job Training Programme", The Review of Economic Studies, 64(4), pp.605~654 (1997)
  26. Kamal, S. M. M., "Socio-economic determinants of age at first marriage of the ethnic tribal women in Bangladesh", Asian Population Studies, 7(1), pp.69~84 (2011)[DOI]
  27. Keats, A., "Women’s schooling, fertility, and child health outcomes : Evidence from Uganda’s free primary education program", Journal of Development Economics, 135, pp.142~159 (2018)
  28. The Impact of Schooling on the Timing of Marriage and Fertility : Evidence from a Change in Compulsory Schooling Law, "Kirdar, M. G.", 1~30, pp.MPRA Paper Vol.13410 (2009)
  29. Maitra, P., "Effect of socioeconomic characteristics on age at marriage and total fertility in Nepal", Journal of Health, Population and Nutrition, 22(1), pp.84~96 (2004)
  30. Singh, M., "Patterns in age at first marriage and its determinants in India: A historical perspective of last 30 years (1992–2021)", SSM - Population Health, 22, pp.101363~ (2023)[DOI]
  31. Mitchell, R. E., "Changes in fertility rates and family size in response to changes in age at marriage, the trend away from arranged marriages, and increasing urbanization", Population Studies, 25(3), pp.481~489 (1971)[DOI]
  32. Moffit, R. A., "Introduction to the Symposium on the Econometrics of Matching", The Review of Economics and Statistics, 86(1), pp.1~3 (2004)
  33. Morgan, S. P., "Is Low Fertility a Twenty-First-Century Demographic Crisis?", Demography, 40(4), pp.589~603 (2003)
  34. The Causal Effects of Education on Age at Marriage and Marital Fertility, "Cummins, N.", LSE, ~, pp.Economic History Working Papers No: 341 (2022)
  35. Rosenbaum, A. P., "The central role of the propensity score in observational studies for causal effects", Biometrika, 70(1), pp.41~55 (1983)
  36. Rosenbaum, P., "The Central Role of the Propensity Score in Observational Studies for Causal Effects", Biometrika, 70(1), pp.41~55 (1983)
  37. Rosenbaum, P. R., "The Central Role of the Propensity Score in Observational Studies for Causal Effects", Biometrika, 70(1), pp.41~55 (1983)
  38. Rossi, H. P., "Evaluation : a systematic approach" (2004), Sage Publications, Inc
  39. Rubin, D. B., "Using Propensity Scores to Help Design Observational Studies : Application to the Tobacco Litigation", Health Services and Outcomes Research Methodology, 2, pp.169~188 (2001)
  40. Seguino, S., "Gender inequality and economic growth : a cross-country analysis", World Development, 28(7), pp.1211~1230 (2000)
  41. Song, S., "The impact of the sent-down movement on Chinese women’s age at first marriage", Demographic Research, 34(1), pp.797~826 (2016)
  42. Maubrigades, S., "CONNECTIONS BETWEEN WOMEN`S AGE AT MARRIAGE AND SOCIAL AND ECONOMIC DEVELOPMENT, Programa de Historia Económica y Social, Unidad Multidisciplinaria", Facultad de Ciencias Sociales, pp.2015
  43. Smith, J. A., "Does Matching Overcome LaLonde's Critique of Nonexperimental Estimators?", Journal of Econometrics, 125(1-2), pp.305~353 (2005)
  44. Stuart, A. E., "Using propensity scores in difference-in-differences models to estimate the effects of a policy change", Health Services and Outcomes Research Methodology, 14(4), pp.166~182 (2014)

관련 논문AI추천

이 간행물의 다른 논문 (5건)