The welfare impact of population growth
Mohamad Adhami, Mark Bils, Charles I. Jones, and Peter J. Klenow, in this paper, challenge the standard practice of measuring economic growth solely in per capita terms. The authors argue that social welfare may also depend on the number of people, and they develop a quantitative framework to assess how much population growth contributes to overall welfare growth. The authors adopt a “total utilitarian social welfare function” defined as the sum of individual utilities of all people alive at any point in time. The key innovation of their model is to express growth in social welfare in consumption-equivalent (CE) units, allowing direct comparison of the contributions from both per capita consumption growth and population growth. Adhami et al. use annual data from the Penn World Table 10.0 for 101 countries from 1960–2019. Consumption is defined as the sum of private and government consumption.
Key findings:
- Population growth is a major contributor to welfare growth
– Across countries, average annual CE welfare growth is 6.2%, compared to 2.1% for per capita consumption growth. Population growth accounts for about two-thirds of CE welfare growth, due to the high value-of-life factor.
– For the US, CE welfare growth is 6.5% per year, with population growth contributing 66% of the total. - Country rankings change dramatically
– Countries with high population growth but modest per capita consumption growth (e.g., Mexico, South Africa, Kenya) move up sharply in welfare growth rankings.
– Countries with low or negative population growth (e.g., Germany, Japan, China) fall significantly in the rankings, despite strong per capita consumption growth. - Robustness to alternative assumptions
The results are robust to:
– Different calibrations of the value-of-life factor and utility function curvature.
– Adjustments for within-country consumption inequality and migration flows.
– Incorporating leisure, fertility, and investments in children: When accounting for parental utility from children and time use, the contribution of population growth to welfare growth is modestly diminished but remains substantial. - Long-run perspective
Over the very long run (1500–2018), CE welfare rises by a factor of 3,700, compared to a 20-fold increase in per capita consumption, illustrating the compounding power of population growth over centuries.
Conclusion
By quantifying the value of population growth in consumption-equivalent terms, the authors reveal that population growth has been a dominant driver of global welfare growth since 1960. These results are robust to a wide range of alternative assumptions and have significant implications for how economists and policymakers assess progress and design policy.
Measuring Growth when Life is Worth Living
Authors: Mohamad Adhami, Mark Bils, Charles I. Jones, Peter J. Klenow
From: Stanford University, University of Rochester
New evidence on fertility policy effectiveness
Many countries are experiencing persistent below-replacement fertility rates, threatening economic growth and pension sustainability. While anti-fertility (restrictive) policies in the past have been effective at reducing birth rates, recent pro-fertility (encouraging) policies have had limited success. In this paper, Sam Engle, Chong Pang, and Anson Zhou investigate whether fertility responds asymmetrically to anti- versus pro-fertility policies. The authors use several complementary empirical strategies to document and analyze the asymmetric effects of pro- and anti-fertility policies:
Aggregate-level analysis: They use panel regressions with country and year fixed effects to estimate how changes in national fertility policy regimes (sourced from the UN World Population Policies Database) affect the total fertility rate (TFR) across countries from 1976 to 2019. The regressions control for economic and demographic variables such as GDP per capita, urbanization, infant mortality, female labor force participation, and women’s education.
Individual-level analysis: They match country-level policy regimes to individual fertility outcomes using World Value Survey data. A cohort exposure design is employed, assigning each respondent a policy exposure index based on the policies in place during their likely childbearing years. Regression models estimate the effect of exposure to pro- or anti-fertility policies on the number of children, with controls for age, gender, and macroeconomic conditions.
Theoretical model: Finally, the authors develop a model of fertility choice with reference-dependent preferences (loss aversion) to explain the observed empirical asymmetry and simulate its implications for fertility dynamics and policy.
Findings:
- Fertility rates are much more responsive to anti-fertility policies than to pro-fertility policies. This asymmetry holds across both country-level and individual-level analyses, and is robust to various empirical specifications and controls. For example, exposure to anti-fertility policies during the childbearing window reduces the number of children by 0.63–0.88 on average, while pro-fertility policies have a much smaller effect.
- This asymmetry cannot be explained by standard economic models, which predict symmetric responses to policy changes. Instead, the authors’ model based on loss aversion highlights that households are more reluctant to reduce their living standards (by having more children) than to increase them (by having fewer children), leading to asymmetric fertility responses.
Asymmetric Fertility Elasticities
Authors: Sam Engle, Chong Pang, Anson Zhou
From: University of Exeter Business School, University of Hong Kong
