The Effect of Parental Leave Policies on Increasing Fertility. A Systematic Review


Low fertility is set to worsen economic problems in many developed countries, and parental leave has emerged as a key pro-natal policy. However, the literature contends that evidence for the effect of parental leave on fertility is mixed. We conduct the first systematic review on this topic. By applying a rigorous search protocol, we identify and review empirical studies that quantify the impact of parental leave policies on fertility. We focus on experimental or quasi-experimental studies that can identify causal effects. We identify 11 papers published between 2009 and 2019, evaluating 23 policy changes across Europe and North America from 1977 to 2009. Results are a mixture of positive, negative, and null impacts on fertility. To explain these apparent inconsistencies, we propose a new conceptual framework which decomposes the total effect of parental leave on fertility into the “current-child”" and “future-child”" effects. We decompose these into effects on women at different birth orders, and specify types of study design to identify each effect. We classify the 23 studies in terms of the type of effect identified, revealing that all the negative or null studies identify the current-child effect, and all the positive studies identify the future-child or total effect. Since the future-child and total effects are more important for promoting aggregate fertility, our findings show that parental leave does in fact increase fertility when benefit increases are generous. Furthermore, our conceptual framework provides a new way of understanding and classifying the effects of pro-natal policies on fertility. Additionally, we propose ways to adapt the ROBINS-I tool for evaluating risk of bias in pro-natal policy studies.

Humanities & Social Sciences Communications
Francisco Rowe
Francisco Rowe
Senior Lecturer in Quantitative Human Geography

My research interests include human mobility and migration; economic geography and spatial inequality; geographic data science.