This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

1. Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets.
2. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modeling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (Ficedula hypoleuca) breeding across Britain.
3. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34–64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structures to changes in short- and long-term population growth rates using transient life table response experiments (LTREs).
4. Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or non-breeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period.
5. We show that both short- and longer-term population changes of British-breeding pied flycatchers are likely linked to factors acting during migration and in non-breeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them. Methods Data collection protocols are described in the paper, and further references provided therein. Raw data were harmonised and converted to a standard format by SPI-Birds (https://spibirds.org/) and then collated into the input data provided here using code deposited on https://github.com/SPI-Birds/SPI-IPM. Details on this step of data processing will be added to https://spi-birds.github.io/SPI-IPM/. The MCMC sample data files are the outputs of the integrated population models fitted in the study. Please refer to the published article and material deposited on the associated GitHub repository for more details.

The data belong to a paper that empirically examines the correlation between population growth and real interest rates. Although this correlation is well founded in macroeconomic theory, the corresponding empirical results have been rather tenuous. Demographic interest rate theories are typically based on long-term relationships across generations. Accordingly, key population trends appear often only across decades, if not centuries, worth of data. To capture these trends, a distinction is made between population growth resulting from a birth surplus and net migration. Within a panel covering 12 countries and the years since 1820, the paper find robust evidence that the birth surplus is significantly correlated with the real interest rate.

Abstract The future population trajectory, as well as climate change, are aspects that generate uncertainties as to their probable effects on the economy, especially on agricultural production and food industry. This paper simulates the effects of population scenarios and one of climate change using the GTAP computable general equilibrium model. A version of GTAP 10 was created to identify Agriculture, Forestry and Food Industry activities, and eight regions, called Agricultural Economic Blocks, using multivariate analysis techniques. The dynamic simulations of the accumulated deviation between thebaseline and the policy scenarios up to 2050 in isolation indicated widespread negative effects of climate change on the GDP and economic activities of the blocs. The results of the population scenarios indicated that the blocks made up of richer countries and with more diversified economies would tend to win at the expense of the others in terms of GDP. On the other hand, they would generally encourage the blocks’ Agriculture, Forestry and Food Industry productions. Taken together, the negative effects of climate change would tend to outweigh the positive effects of population scenarios and more intensively on those which project less population growth.

In 2023, the annual population growth in Morocco was 1.02 percent. Between 1961 and 2023, the figure dropped by 1.58 percentage points, though the decline followed an uneven course rather than a steady trajectory.

Demographic compensation – the opposing responses of vital rates along environmental gradients – potentially delays anticipated species’ range contraction under climate change, but no consensus exists on its actual contribution. We calculated population growth rate (λ) and demographic compensation across the distributional ranges of 81 North American tree species, and examined their responses to simulated warming and tree competition. We found that 43% of species showed stable population size at both northern and southern edges. Demographic compensation was detected in 25 species, yet fifteen of them still showed a potential retraction from southern edges, indicating that compensation alone cannot maintain range stability. Simulated climatic warming caused larger decreases in λ for most species, and weakened the effectiveness of demographic compensation in stabilizing ranges. These findings suggest that climate stress may surpass the limited capacity of demographic compensation and pose a threat to the viability of North American tree populations.

Historical chart and dataset showing Japan population growth rate by year from 1961 to 2023.

In 2023, the annual population growth in the Philippines was 0.81 percent. Between 1961 and 2023, the figure dropped by 2.37 percentage points, though the decline followed an uneven course rather than a steady trajectory.

This submission contains data and R-code that enable to reproduce the data manipulations and analyses in the paper “Why are population growth rate estimates of past and present hunter-gatherers so different?” by Miikka Tallavaara and Erlend Kirkeng Jørgensen (Philosophical transactions of the Royal Society B). Please, cite the paper and this Zenodo repository if you use the files included in this Zenodo record in your work.

The submission includes a html-file titled “Why are population growth rate estimates of past and present hunter-gatherers so different? - Data analyses” (TJ2020.html) that contains R-code and instructions and comments for running the code (open this file in your browser). In addition, the submission includes Rdata-file (dataTJ2020.Rdata) containing all the data that are not created within the code and pure R-code (TJ2020.R).

This data package includes the underlying data to replicate the charts and calculations presented in As US population growth slows, we need to reset expectations for economic data, PIIE Policy Brief 25-5.

If you use the data, please cite as:

Kolko, Jed. 2025. As US population growth slows, we need to reset expectations for economic data. PIIE Policy Brief 25-5. Washington: Peterson Institute for International Economics.

An important challenge in ecology is to understand variation in species’ maximum intrinsic rate of population increase, ????, not least because ???? underpins our understanding of the limits of fishing, recovery potential, and ultimately extinction risk. Across many vertebrate species, terrestrial and aquatic, body mass and environmental temperature are important correlates of ????. In sharks and rays, specifically, ???? is known be lower in larger species, but also in deep-sea ones. We use an information-theoretic approach that accounts for phylogenetic relatedness to evaluate the relative importance of body mass, temperature and depth on ????. We show that both temperature and depth have separate effects on shark and ray ???? estimates, such that species living in deeper waters have lower ????. Furthermore, temperature also correlates with changes in the mass scaling coefficient, suggesting that as body size increases, decreases in ???? are much steeper for species in warmer waters. These findings suggest that there are (as-yet understood) depth-related processes that limit the maximum rate at which populations can grow in deep sea sharks and rays. While the deep ocean is associated with colder temperatures, other factors that are independent of temperature, such as food availability and physiological constraints, may influence the low ???? observed in deep sea sharks and rays. Our study lays the foundation for predicting the intrinsic limit of fishing, recovery potential, and extinction risk species based on easily accessible environmental information such as temperature and depth, particularly for data-poor species. This repository contains the data and a minimum working example of the model-fitting process used for the article "Body mass, temperature, and depth shape productivity in sharks and rays", which is currently in press at Ecology and Evolution.

We investigated population dynamics in chorus frogs (Pseudacris maculata) relative to extrinsic (air temperatures and snowpack) and intrinsic (density dependence) characteristics at 2 sites in Colorado, USA. We used capture--mark-recapture (cmr) data (i.e., 1 or 0, provided here) and a Bayesian model framework to assess our a priori hypotheses about interactions among covariates and chorus frog survival and population growth rates. Files include: Cameron_Lily_cmr_NOV2020.csv, Cameron_Matthews_cmr_NOV2020.csv, and Cameron_covariates_NOV2020.csv. Data associated with paper by Kissel et al. 2021.

Data contains 2022 world population data publised by the UN DESA for six most populous countries of the world. File also contains the analysis of decomposition of demographic indicators on population growth.

Abstract Challenges in the field of demographic projections include, among others, the volatility of the migration component - critical for the projection of small areas; the compatibility between projections of small and large areas; and the measurement and inclusion of uncertainty in future scenarios of population growth. This article presents a new probabilistic method to conduct interregional population forecasting dealing with these three challenges. The proposed method has the following advantages: 1) it only requires information about the last place of residence and the population distributions of the last two Censuses; 2) it generates confidence intervals for the projected populations; 3) it makes the role of migration flows in the growth dynamics explicit and; 4) it facilitates the elaboration of counterfactual scenarios and sensitivity analysis using matrices of interregional population growth and distribution. We describe the patterns and trends in migration flows in the state of São Paulo applying spatial visualization tools and identifying areas in which migration is responsible for considerable shares of the demographic dynamics. About 95% of the 572 municipal projected populations of São Paulo had good precision and were within expected confidence intervals. We used data from the 1980, 1991 and 2000 Brazilian Censuses.

About this dataset

Context

The dataset tabulates the Dallas population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Dallas across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Dallas was 1.3 million, a 0.42% increase year-by-year from 2022. Previously, in 2022, Dallas population was 1.3 million, an increase of 0.59% compared to a population of 1.29 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Dallas increased by 113,194. In this period, the peak population was 1.34 million in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Dallas is shown in this column.
• Year on Year Change: This column displays the change in Dallas population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dallas Population by Year. You can refer the same here

Invasive species offer ecologists the opportunity to study the factors governing species distributions and population growth. The Eurasian Collared-Dove (Streptopelia decaocto) serves as a model organism for invasive spread because of the wealth of abundance records and the recent development of the invasion. We tested whether a set of environmental variables were related to the carrying capacities and growth rates of individual populations by modeling the growth trajectories of individual populations of the Collared-Dove using Breeding Bird Survey (BBS) and Christmas Bird Count (CBC) data. Depending on the fit of our growth models, carrying capacity and growth rate parameters were extracted and modeled using historical, geographical, land cover and climatic predictors. Model averaging and individual variable importance weights were used to assess the strength of these predictors. The specific variables with the greatest support in our models differed between data sets, which may be the result of temporal and spatial differences between the BBS and CBC. However, our results indicate that both carrying capacity and population growth rates are related to developed land cover and temperature, while growth rates may also be influenced by dispersal patterns along the invasion front. Model averaged multivariate models explained 35–48% and 41–46% of the variation in carrying capacities and population growth rates, respectively. Our results suggest that widespread species invasions can be evaluated within a predictable population ecology framework. Land cover and climate both have important effects on population growth rates and carrying capacities of Collared-Dove populations. Efforts to model aspects of population growth of this invasive species were more successful than attempts to model static abundance patterns, pointing to a potentially fruitful avenue for the development of improved invasive distribution models.

Total current population for China in 2025 is 1,424,381,924, a 0.06% decline from 2024.

<ul style='margin-top:20px;'>

<li>Total population for China in 2024 was <strong>1,425,178,782</strong>, a <strong>1.03% increase</strong> from 2023.</li>
<li>Total population for China in 2023 was <strong>1,410,710,000</strong>, a <strong>0.1% decline</strong> from 2022.</li>
<li>Total population for China in 2022 was <strong>1,412,175,000</strong>, a <strong>0.01% decline</strong> from 2021.</li>
</ul>Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

National and subnational mid-year population estimates for England and Wales by administrative area, age and sex (including components of population change, median age and population density).

About this dataset

Context

The dataset tabulates the Palm Beach County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Palm Beach County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Palm Beach County was 1.53 million, a 0.92% increase year-by-year from 2022. Previously, in 2022, Palm Beach County population was 1.52 million, an increase of 1.09% compared to a population of 1.5 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Palm Beach County increased by 398,586. In this period, the peak population was 1.53 million in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Palm Beach County is shown in this column.
• Year on Year Change: This column displays the change in Palm Beach County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Palm Beach County Population by Year. You can refer the same here