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Fertility rate accounting for survival until childbearing age

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Fertility Rate

About this data

Fertility rate accounting for survival until childbearing age
The number of children who live long enough to reproduce, per woman. This number is dependent on the survival of daughters to childbearing age (between 15 and 49 years old).
Source
Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025) – processed by Our World in Data
Last updated
October 22, 2025
Next expected update
October 2026
Date range
1751–2023
Unit
children per women

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Sources and processing

This data is based on the following sources

Malani and Jacob – A New Measure of Surviving Children that Sheds Light on Long-term Trends in Fertility

The world has experienced a dramatic decline in total fertility rate (TFR) since the Industrial Revolution. Yet the consequences of this decline flow not merely from a reduction in births, but from a reduction in the number of surviving children. Authors propose a new measure of the number of surviving children per female, which authors call the effective fertility rate (EFR). EFR can be approximated as the product of TFR and the probability of survival. Moreover, TFR changes can be decomposed into changes that preserve EFR and those that change EFR. Authors specialized EFR to measure the number of daughters that survive to reproduce (reproductive EFR) and the number children that survive to become workers (labor EFR).

Authors use three data sets to shed light on EFR over time across locations. First, authors use data from 165 countries between 1950-2019 to show that one-third of the global decline in TFR during this period did not change labor EFR, suggesting that a substantial portion of fertility decline merely compensated for higher survival rates. Focusing on the change in labor EFR, at least 40% of variation cannot be explained by economic factors such as income, prices, education levels, structural transformation, an urbanization, leaving room for explanations like cultural change. Second, using historical demographic data on European countries since 1750, authors find that there was dramatic fluctuation in labor EFR in Europe around each of the World Wars, a phenomenon that is distinct from the demographic transition. However, prior to that fluctuation, EFRs were remarkably constant, even as European countries were undergoing demographic transitions. Indeed, even when EFRs fell below 2 after 1975, we find that EFRs remained stable rather than continuing to decline. Third, data from the US since 1800 reveal that, despite great differences in mortality rates, Black and White populations have remarkably similar numbers of surviving children over time.

Retrieved from
https://www.nber.org/papers/w33175
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Malani, A., & Jacob, A. (2024). A New Measure of Surviving Children that Sheds Light on Long-term Trends in Fertility. https://doi.org/10.3386/w33175

The world has experienced a dramatic decline in total fertility rate (TFR) since the Industrial Revolution. Yet the consequences of this decline flow not merely from a reduction in births, but from a reduction in the number of surviving children. Authors propose a new measure of the number of surviving children per female, which authors call the effective fertility rate (EFR). EFR can be approximated as the product of TFR and the probability of survival. Moreover, TFR changes can be decomposed into changes that preserve EFR and those that change EFR. Authors specialized EFR to measure the number of daughters that survive to reproduce (reproductive EFR) and the number children that survive to become workers (labor EFR).

Authors use three data sets to shed light on EFR over time across locations. First, authors use data from 165 countries between 1950-2019 to show that one-third of the global decline in TFR during this period did not change labor EFR, suggesting that a substantial portion of fertility decline merely compensated for higher survival rates. Focusing on the change in labor EFR, at least 40% of variation cannot be explained by economic factors such as income, prices, education levels, structural transformation, an urbanization, leaving room for explanations like cultural change. Second, using historical demographic data on European countries since 1750, authors find that there was dramatic fluctuation in labor EFR in Europe around each of the World Wars, a phenomenon that is distinct from the demographic transition. However, prior to that fluctuation, EFRs were remarkably constant, even as European countries were undergoing demographic transitions. Indeed, even when EFRs fell below 2 after 1975, we find that EFRs remained stable rather than continuing to decline. Third, data from the US since 1800 reveal that, despite great differences in mortality rates, Black and White populations have remarkably similar numbers of surviving children over time.

Retrieved from
https://www.nber.org/papers/w33175
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Malani, A., & Jacob, A. (2024). A New Measure of Surviving Children that Sheds Light on Long-term Trends in Fertility. https://doi.org/10.3386/w33175

United Nations – World Population Prospects

World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to their FAQ. You can also explore data sources for each country or visit their main page for more details.

Retrieved on
July 11, 2024
Retrieved from
https://population.un.org/wpp/downloads/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.

World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to their FAQ. You can also explore data sources for each country or visit their main page for more details.

Retrieved on
July 11, 2024
Retrieved from
https://population.un.org/wpp/downloads/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.

Human Mortality Database

The Human Mortality Database (HMD) is a research resource that provides detailed mortality and population data for national populations with high-quality vital statistics. It includes original calculations of death rates and life tables, as well as the underlying data — such as birth counts, death counts, and census-based population estimates — used to produce these metrics.

Its scope is limited to countries with virtually complete death registration and census coverage, mostly wealthy and industrialized nations. The database’s core mission is to document the historical rise in human longevity and support research into its causes and implications. HMD follows a rigorous, uniform methodology focused on transparency, reproducibility, and comparability, while acknowledging limitations such as age misreporting and data coverage issues.

Each country’s dataset is curated and quality-checked by dedicated researchers, ensuring reliability for demographic and public health analysis.

Retrieved on
October 22, 2025
Retrieved from
https://www.mortality.org/Data/ZippedDataFiles
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
See also the methods protocol:
Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). Available online (needs log in to mortality.org).

The Human Mortality Database (HMD) is a research resource that provides detailed mortality and population data for national populations with high-quality vital statistics. It includes original calculations of death rates and life tables, as well as the underlying data — such as birth counts, death counts, and census-based population estimates — used to produce these metrics.

Its scope is limited to countries with virtually complete death registration and census coverage, mostly wealthy and industrialized nations. The database’s core mission is to document the historical rise in human longevity and support research into its causes and implications. HMD follows a rigorous, uniform methodology focused on transparency, reproducibility, and comparability, while acknowledging limitations such as age misreporting and data coverage issues.

Each country’s dataset is curated and quality-checked by dedicated researchers, ensuring reliability for demographic and public health analysis.

Retrieved on
October 22, 2025
Retrieved from
https://www.mortality.org/Data/ZippedDataFiles
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
See also the methods protocol:
Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). Available online (needs log in to mortality.org).

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All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

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Notes on our processing step for this indicator

For a given cohort year, we estimate the cumulative survival probability for a person to reach each age from 0 to 49. For example, the probability of a person born in 2000 reaching age 15, 16, 17, and so on up to 49. We have used HMD data for years before 1950, and UN's for years after 1950 (including).

We then estimate the Effective Fertility Rate (EFR) for each age group by multiplying the Total Fertility Rate (TFR) by the cumulative survival probability. The EFR for a given age gives us an approximation of the average number of children from a woman that will live long enough to reach that age.

For years before 1950, we have used HMD data, which does not provide TFR values. Instead, we have used an approximation of the TFR based on births and female population (in reproductive ages), as suggested by Jacob and Malani (2024).

The Reproductive Effective Fertility rate (EFR) is the average of the EFR over all reproductive ages (15-49).

Note that the Reproductive Effective Fertility rate (EFR) is an approximation of the number of daughters, so it uses the total fertility rate of female children, or equivalently, the TFR weighted by the sex ratio at birth.

So we have that: EFR_repr = (TFR * mean(EFR)) / (1 + SRB), where SRB is the male-to-female ratio and the mean is taken over all reproductive ages (15-49).

This indicator is scaled by the sex ratio to allow easy comparability with the Total Fertility Rate (TFR) and the Labor Effective Fertility rate (EFR_labor).

Read more details in the author's paper: https://www.nber.org/papers/w33175

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Citations

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To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Fertility rate accounting for survival until childbearing age”, part of the following publication: Saloni Dattani, Lucas Rodés-Guirao, and Max Roser (2025) - “Fertility Rate”. Data adapted from Malani and Jacob, United Nations, Human Mortality Database. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025) – processed by Our World in Data

Full citation

Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025) – processed by Our World in Data. “Fertility rate accounting for survival until childbearing age” [dataset]. Malani and Jacob, “A New Measure of Surviving Children that Sheds Light on Long-term Trends in Fertility”; United Nations, “World Population Prospects”; Human Mortality Database, “Human Mortality Database” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.html (archived on March 4, 2026).

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https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv?v=1&csvType=full&useColumnShortNames=false
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https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.metadata.json?v=1&csvType=full&useColumnShortNames=false

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Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear