5. Conclusion / Reference / Appendix

5. Conclusion

Discussion 

In the case of childbirth, it typically takes about a year from pregnancy to delivery. Therefore, it would be more appropriate to assume that policy effects also require a year to manifest. For this reason, previous studies have applied lagged variables of one year to policy variables (주석 - Hong, 2016). However, the policy variable used in this study was implemented starting in 2024, making it impossible to apply lagged variables. Consequently, we were compelled to use data from the same year. As a result, it is possible that the policy effects have not yet been fully captured in our findings. This represents the most significant limitation of our study and underscores the need for future research. To address this issue, we plan to track the long-term effects of the policy as well as its heterogeneous impacts over the coming years.

 

(주석 - Hong et al. examined the association between one- to twenty-month lagged grant variables and the crude birth rate. The correlation increased as longer lag periods were applied, peaked at twelve months, and then gradually diminished. This suggests that potential parents responded most significantly to the grant provided one year prior to childbirth—or, more plausibly, around the time the decision to conceive was made.)

 

출산의 경우 임신에서부터 출산까지 1년여의 기간이 소요되므로 정책효과 또한 1년의 기간이 걸린다고 가정하여야 타당할 것이다. 따라서 선행연구들은 정책변수에 1년 전의 후행변수(lagged variables)를 적용하였다.(주석 - Hong, 2016) 그러나 본 연구에서 사용한 정책변수는 2024년부터 시행되어, 후행변수를 적용할 수가 없어 부득이하게 동일 연도의 데이터를 사용하였다. 따라서 본 연구 결과에는 아직 정책 효과가 충분히 반영되지 않았을 가능성이 있다. 이는 본 연구의 가장 큰 한계점이자 향후 연구의 필요성을 말한다. 우리는 이 문제를 극복하기 위해 향후 정책 효과의 장기적인 영향력과 매해 heterogeenous effect를 추적해갈 예정이다. 

Implication 정책제언

In this paper, we investigate the impact of Incheon's babybirth incentive policy on birth rates and number of birth. We exploited temporal and spatial variation in outcome with a set of various control variables to identify causal effects. The results of our SDID analysis indicate that implementation of "100-Million+ I-Dream" program increased total fertility rate by 5% and number of bitrhs by 8% in 2024, respectively.  

As the low birth rate problem has intensified, Korea’s childbirth incentives have rapidly expanded in both scope and scale. Although prior studies have found some positive effects of childbirth grants on fertility rates, these effects have been minimal. Moreover, despite the continued expansion of such incentive programs, the birth rate has continued to decline. If this trend persists, it is likely to accelerate the decline in the total population and working-age population, leading to various social and economic challenges. In this context, the present study offers valuable policy insights for the development of more effective pro-natal policies.

 

In the long term, effective measures to address low birth rates should focus on encouraging childbearing intentions among young people who are delaying or forgoing marriage and childbirth. While multiple factors contribute to this delay, financial burdens are likely a primary cause. The high costs incurred throughout the child-rearing process pose significant barriers to family formation. These challenges cannot be addressed through one-time or limited cash payments. Therefore, it is essential to explore policies that substantially reduce the long-term cost of raising children. This study highlights and evaluates the impact of Incheon’s "100 Million+ I-Dream" policy. The findings may serve as a meaningful guide for developing more effective fertility policies in the future.

 

저출산 문제가 심회됨에 따라, 한국의 출산장려금은 범위와 규모가 빠르게 확대되었다. 선행연구에서 출산장려금이 출산율에 미치는 긍정적 효과를 다수 발견하였지만 그 효과는 매우 미미하였고, 출산장려금제도의 확산에도 불구하고 출산율은 계속하여 감소하고 있는 추세이다. 이러한 추세가 지속될 경우 총인구 규모 및 생산연령인구 감소의 가속화로 다양한 사회·경제적 문제를 불러온다. 이러한 상황에서, 본 연구는 출산 장려 정책의 수립에 귀중한 정책제언을 시사한다. 

 

장기적으로 효과적인 저출산 대책은 결혼과 자녀 계획을 미루거나 포기하는 청년들의 출산 의향을 고취시키는 방향으로 나아가야 할 것이다. 청년들이 출산을 미루는 데에는 복합적인 요인이 있지만 비용적 측면이 주된 원인일 것이다. 양육 전 과정에서 발생하는 고비용은 가족 형성을 어렵게 만드는 요인이 된다. 이는 일시적이고 제한적인 현금 지급으로 해결될 수 없다. 장기적으로 양육 비용의 부담을 획기적으로 감소시키는 정책을 모색할 필요가 있다. 따라서 본 연구에서는 인천시의 '100 Million+ I-Dream' 정책에 주목하고 그 효과를 검증하였다. 이는 향후 효과적인 출산정책을 마련하는 데에 이정표가 될 수 있을 것이다.  

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Appendix

A. Estimation Algorithms for SDID

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B. Placebo Variance Estimation

Arkhangelsky et al. (2021) propose three variance estimation methods to construct asymptotically valid confidence intervals based on their theoretical framework. However, both the bootstrap and jackknife approaches are tailored for settings with a large number of treated units and may yield unreliable results when the number of treated units is small—as in our case, where N_tr = 1.

Therefore, we adopt the third method—placebo variance estimator. This method evaluates the variability of the synthetic control estimator by systematically replacing the treated unit with control units and assessing the distribution of placebo effects. Specifically, Algorithm 2 implements this approach by generating placebo predictions using only unexposed units to estimate the variance V^tau, which is then used to construct confidence intervals as specified in Equation (5).

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