Impact of Birth Rate Incentives

Timeframe Jan - Mar 2025
GitHub View GitHub
tools Python cvxpy scikit-learn
00. Overview

Causal Inference Birth Rate.

South Korea 2001: Pronatalist cash transfer policy
Russia 2006: "Maternal capital" program
Japan 1994: Angel Plan


Actual Birth Rate
Policy Introduction
Counterfactual Projection

Policy Impact Summary

Select a country and adjust parameters to see the estimated policy impact.

01. Overview

Introduction.

Birth rates in developed countries are plummeting, with most nations now falling below the population replacement rate (source). This unprecedented demographic shift threatens to reshape economies, as shrinking workforces struggle to support aging populations. Without a major economic transformation, the next generations may face declining living standards and a labor shortage too severe to sustain long-term growth.

Discussion

My friend and I had a lengthy discussion where we explored potential solutions, particularly the role of artificial intelligence in mitigating workforce shortages. We shared the assumption that reversing the declining birth rate trend is extremely difficult, if not impossible.

Some key points from our discussion:

- Once a country reaches a certain level of development, having children may become a lower priority.
- Japan serves as a strong case study since it resists immigration and faces a rapidly aging population.
- Even in countries that welcome immigration, if global birth rate declines persist, there may eventually be too few people to fill workforce gaps—even through immigration.
- The trend appears to be universal, cutting across cultures and persisting despite government incentives.
The Central Question

Given that the articles argue governmental aid and incentives have had limited success, I wanted to investigate from a causal inference perspective:

Are these incentives actually making a difference?
02. Methodology

Methodology.

Methods Used:
- Difference-in-Differences (DiD)
- Synthetic Control Method
- Doubly Robust Estimation
- Causal Graph Visualizations (DAGs)
03. Sources

Sources.