The graph illustrates the number of babies born in the United States from 1995 to 2025. The x-axis represents the years, labeled from '95 to '25, while the y-axis shows the annual number of births. Over this 30-year period, birth numbers peaked at 4,316,233 in 2007 and reached a low of 3,596,017 in 2023. The data reveals relatively stable birth rates from 1995 to 2010, with slight fluctuations, followed by a gradual decline starting around 2017. The information is presented in a line graph format, effectively highlighting the long-term downward trend in U.S. birth numbers over the specified timeframe.

In 2023, around 85 percent of infants in the United States were being breastfed at discharge from the hospital, highlighting a strong trend towards early breastfeeding. This statistic shows select medical and health characteristics of mothers during pregnancy and birth in the United States in 2023.

Maternal health and birth characteristics The data reveals that 59.7 percent of delivering mothers in the U.S. were overweight or obese in 2023, a concerning statistic for maternal health. Additionally, 32.3 percent of births were via cesarean delivery, while only 1.5 percent were home births. Home birth rates vary by state, with Idaho having the highest at 4.7 percent. Despite the low overall rate of home births, some women choose this option for reasons including less medical intervention, location preference, cost, and cultural or religious factors. Declining birth rates and changing demographics The overall birth rate in the United States has been steadily declining over the past few decades. In 2022, there were 11 births per 1,000 population, down from 16.7 in 1990. This decline is influenced by various factors, including financial concerns and increased focus on careers among women. Interestingly, birth rates vary significantly across different ethnic groups, with Native Hawaiian and Pacific Islander women having the highest birth rates, while Asian and white women have the lowest.

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

In the United States, the crude birth rate in 1800 was 48.3 live births per thousand people, meaning that 4.8 percent of the population had been born in that year. Between 1815 and 1825 the crude birth rate jumped from 46.5 to 54.7 (possibly due to Florida becoming a part of the US, but this is unclear), but from this point until the Second World War the crude birth rate dropped gradually, reaching 19.2 in 1935. Through the 1940s, 50s and 60s the US experienced it's baby boom, and the birth rate reached 24.1 in 1955, before dropping again until 1980. From the 1980s until today the birth rate's decline has slowed, and is expected to reach twelve in 2020, meaning that just over 1 percent of the population will be born in 2020.

Number and percentage of live births, by month of birth, 1991 to most recent year.

This dataset includes all births for a given year and includes all items released in the public-use file. Additional information in this file includes state and county of residence (cities with a population of 100,000 or greater) and exact date of birth (which includes day of month, month, and year).

While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination, the number of births in the U.S. has steadily been decreasing since 1990, with about 3.6 million babies born in 2023. In 1990, this figure was 4.16 million. Birth and replacement rates A country’s birth rate is defined as the number of live births per 1,000 inhabitants, and it is this particularly important number that has been decreasing over the past few decades. The declining birth rate is not solely an American problem, with EU member states showing comparable rates to the U.S. Additionally, each country has what is called a “replacement rate.” The replacement rate is the rate of fertility needed to keep a population stable when compared with the death rate. In the U.S., the fertility rate needed to keep the population stable is around 2.1 children per woman, but this figure was at 1.67 in 2022. Falling birth rates Currently, there is much discussion as to what exactly is causing the birth rate to decrease in the United States. There seem to be several factors in play, including longer life expectancies, financial concerns (such as the economic crisis of 2008), and an increased focus on careers, all of which are causing people to wait longer to start a family. How international governments will handle falling populations remains to be seen, but what is clear is that the declining birth rate is a multifaceted problem without an easy solution.

About this dataset

Context

The dataset tabulates the data for the John Day, OR population pyramid, which represents the John Day population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for John Day, OR, is 27.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for John Day, OR, is 28.9.
• Total dependency ratio for John Day, OR is 56.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for John Day, OR is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the John Day population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the John Day for the selected age group is shown in the following column.
• Population (Female): The female population in the John Day for the selected age group is shown in the following column.
• Total Population: The total population of the John Day for the selected age group is shown in the following column.

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 John Day Population by Age. You can refer the same here

The average for 2022 based on 195 countries was 18.38 births per 1000 people. The highest value was in Niger: 45.03 births per 1000 people and the lowest value was in Hong Kong: 4.4 births per 1000 people. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

This data collection consists of three data files, which can be used to determine infant mortality rates. The first file provides linked records of live births and deaths of children born in the United States in 1990 (residents and nonresidents). This file is referred to as the "Numerator" file. The second file consists of live births in the United States in 1990 and is referred to as the "Denominator-Plus" file. Variables include year of birth, state and county of birth, characteristics of the infant (age, sex, race, birth weight, gestation), characteristics of the mother (origin, race, age, education, marital status, state of birth), characteristics of the father (origin, race, age, education), pregnancy items (prenatal care, live births), and medical data. Beginning in 1989, a number of items were added to the U.S. Standard Certificate of Birth. These changes and/or additions led to the redesign of the linked file record layout for this series and to other changes in the linked file. In addition, variables from the numerator file have been added to the denominator file to facilitate processing, and this file is now called the "Denominator-Plus" file. The additional variables include age at death, underlying cause of death, autopsy, and place of accident. Other new variables added are infant death identification number, exact age at death, day of birth and death, and month of birth and death. The third file, the "Unlinked" file, consists of infant death records that could not be linked to their corresponding birth records.

The US Consumer Date of Birth (DOB) file contains the month, day, and year date of birth fields for each individual in the Consumer Database.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 280 million records.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Over the past 30 years, the birth rate in the United States has been steadily declining, and in 2023, there were 10.7 births per 1,000 of the population. In 1990, this figure stood at 16.7 births per 1,000 of the population. Demographics have an impact The average birth rate in the U.S. may be falling, but when broken down along ethnic and economic lines, a different picture is painted: Native Hawaiian and other Pacific Islander women saw the highest birth rate in 2022 among all ethnicities, and Asian women and white women both saw the lowest birth rate. Additionally, the higher the family income, the lower the birth rate; families making between 15,000 and 24,999 U.S. dollars annually had the highest birth rate of any income bracket in the States. Life expectancy at birth In addition to the declining birth rate in the U.S., the total life expectancy at birth has also reached its lowest value recently. Studies have shown that the life expectancy of both men and women in the United States has been declining over the last few years. Declines in life expectancy, like declines in birth rates, may indicate that there are social and economic factors negatively influencing the overall population health and well-being of the country.

Context

Births in U.S during 1994 to 2003.

Content

The data set has the following structure:

• year - Year

• month - Month

• date_of_month - Day number of the month

• day_of_week - Day of week, where 1 is Monday and 7 is Sunday

• births - Number of births

Acknowledgements

Data set from the Centers for Disease Control and Prevention's National National Center for Health Statistics

Inspiration

Make a dictionary that shows total number of births on each day of week?

About this dataset

Context

The dataset tabulates the population of John Day by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for John Day. The dataset can be utilized to understand the population distribution of John Day by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in John Day. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for John Day.

Key observations

Largest age group (population): Male # 60-64 years (85) | Female # 10-14 years (102). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the John Day population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the John Day is shown in the following column.
• Population (Female): The female population in the John Day is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in John Day for each age group.

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 John Day Population by Gender. You can refer the same here

"This CD-ROM contains tables and other pertinent documents for Vital Statistics of the United States, 1994, Volume I, Natality. The collection provides information on live births in the United States during calendar year 1994. Data are presented in table format and include live births, birthrates, and fertility rates by several variables including geographic area; mother's age, race, education, marital status, and Hispanic origin; father's age, race, and Hispanic origin; child's race; Apgar s core; birthweight; live-birth order; parity; place of delivery and attendant; plurality; prenatal care; day of birth, and resident status. Births to nonresidents of the US are excluded from all tabulations by place of residence. Births occurring to US citizens outside of the US are not included. Geographic variables describing residence for births include state, county, city, standard metropolitan statistical area (SMSA), urban places, and the United States or all reporting areas."

Note to Users: This CD is part of a collection located in the Data Archive at the Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

About this dataset

Context

The dataset tabulates the data for the Day County, SD population pyramid, which represents the Day County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Day County, SD, is 31.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Day County, SD, is 47.0.
• Total dependency ratio for Day County, SD is 78.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Day County, SD is 2.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Day County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Day County for the selected age group is shown in the following column.
• Population (Female): The female population in the Day County for the selected age group is shown in the following column.
• Total Population: The total population of the Day County for the selected age group is shown in the following column.

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 Day County Population by Age. You can refer the same here

This data collection consists of six data files, which can be used to determine infant mortality rates in the United States in 1995. For the first time, data for Puerto Rico, the Virgin Islands, and Guam were included. Another change in 1995 is a change in format of the linked files. They are now released in two different formats, period data and birth cohort data. This collection represents the period data. Parts 1 and 2 are the Denominator files for the United States and for Puerto Rico, the Virgin Islands, and Guam, respectively. These files consist of all births in 1995. Variables in these files include year of birth, state and county of birth, characteristics of the infant (age, sex, race, birth weight, gestation), characteristics of the mother (Hispanic origin, race, age, education, marital status, state of birth), characteristics of the father (Hispanic origin, race, age, education), pregnancy items (prenatal care, live births), and medical data. A new variable in the Denominator files for 1995 is clinical estimate of gestation. Parts 3 and 4 are the Numerator files. They provide records of all infant deaths that occurred in 1995 linked to their corresponding birth certificates, whether the birth occurred in 1995 or 1994. Variables in these files include age at death, underlying cause of death, autopsy, place of accident, infant death identification number, exact age at death, day of birth and death, and month of birth and death. New variables in the linked Numerator files for 1995 include a weight and a clinical estimate of gestation. Parts 5 and 6 are the "unlinked" files. They consist of infant death records that could not be linked to their corresponding birth records. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR02285.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) and then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best-performing alternative models. Conclusions: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0–36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve. Methods Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from birth to at least 36 months of age with at least five well-baby care visits over the first year of life.
Inclusion of data for modeling: All observed weight and height values were evaluated in kilograms (kg) and centimeters (cm), respectively. Any values assessed beyond 1,125 days (roughly 36 months) and values for height and weight deemed implausible by at least two reviewers (e.g., significant losses in height, or marked outliers for weight and height) were excluded from the analysis. Additionally, weights assessed between birth and 19 days were excluded. At least five observations across the 36-month period were required: babies with fewer than five weight or height values after the previous criteria were excluded from analyses. Model: We developed our weight model using values from STORK babies and then replicated it with values from the STARR babies. Height models were evaluated in STARR babies only because STORK data on height were scant. The Michaelis-Menten equation is described as follows: v = Vmax ([S]/(Km + [S]) , where v is the rate of product formation, Vmax is the maximum rate of the system, [S] is the substrate concentration, and Km is a constant based upon the enzyme’s affinity for the particular substrate. For this study the equation became: P = a1 (Age/(b1+ Age)) + c1, where P was the predicted value of weight (kg) or height (cm), Age was the age of the infant in days, and c1 was an additional constant over the original Michaelis-Menten equation that accounted for the infant’s non-zero weight or length at birth. Each of the parameters a1, b1 and c1 was unique to each child and was calculated using the nonlinear least squares (nls) method. In our case, weight data were fitted to a model using the statistical language R, by calling the formula nls() with the following parameters: fitted_model <-nls(weights~(c1+(a1*ages)/(b1+ages)), start = list(a1 = 5, b1 = 20, c1=2.5)), where weights and ages were vectors of each subject’s weight in kg and age in days. The default Gauss-Newton algorithm was used. The optimization objective is not convex in the parameters and can suffer from local optima and boundary conditions. In such cases good starting values are essential: the starting parameter values (a1=5, b1=20, c1=2.5) were adjusted manually using the STORK dataset to minimize model failures; these tended to occur when the parameter values, particularly a1 and b1, increased without bound during the iterative steps required to optimize the model. These same parameter values were used for the larger STARR dataset. The starting height parameter values for height modeling were higher than those for weight modeling, due to the different units involved (cm vs. kg) (a1=60, b1=530, c1=50). Because this was a non-linear model, goodness of fit was assessed primarily via root mean squared error (RMSE) for both weight and height. Imputation tests: To test for the influence of specific time points on the models, we limited our analysis to STARR babies with all recommended well-baby visits (12 over three years). Each scheduled visit except day 1 occurred in a time window around the expected well-baby visit (Visit1: Day 1, Visit2: days 20–44, Visit3: 46–90, Visit4: 95–148, Visit5: 158–225, Visit6: 250–298, Visit7: 310–399, Visit8: 410–490, Visit9: 500–600, Visit10: 640–800, Visit11: 842–982, Visit12: 1024–1125). We considered two different sets: infants with all scheduled visits in the first year of life (seven total visits) and those with all scheduled visits over the full three-year timeframe (12 total visits). We fit these two sets to the model, identifying baseline RMSE. Then, every visit, and every combination of two to five visits were dropped, so that the RMSE or model failures for a combination of visits could be compared to baseline. Prediction: We sought to predict weight or height at 36 months (Y3) from growth measures assessed only up to 12 months (Y1) or to 24 months (Y1+Y2), utilizing the “last value” approach. In brief, the last observation for each child (here, growth measures at 36 months) is used to assess overall model fit, by focusing on how accurately the model can extrapolate the measure at this time point. We identified all STARR infants with at least five time points in Y1 and at least two time points in both Y2 and Y3, with the selection of these time points based on maximizing the number of later time points within the constraints of the well-baby visit schedule for Y2 and Y3. The per-subject set of time points (Y1-Y3) was fitted using the modified Michaelis-Menten equation and the mean squared error was calculated, acting as the “baseline” error. The model was then run on the subset of Y1 only and of Y1+Y2 only. To test predictive accuracy of these subsets, the RMSE was calculated using the actual weights or heights versus the predicted weights or heights of the three time series. Comparison with other models: We examined how well the modified Michaelis-Menten equation performed interpolation in STARR babies compared to ten other commonly used interpolation methods and pediatric growth models including: (1) the ‘last observation carried forward’ model; (2) the linear model; (3) the robust linear model (RLM method, base R MASS package); (4) the Laird and Ware linear model (LWMOD method); (5) the generalized additive model (GAM method); (6) locally estimated scatterplot smoothing (LOESS method, base R stats package); (7) the smooth spline model (smooth.spline method, base R stats package); (8) the multilevel spline model (Wand method); (9) the SITAR (superimposition by translation and rotation) model and (10) fast covariance estimation (FACE method). Model fit used the holdout approach: a single datapoint (other than birth weight or birth length) was randomly removed from each subject, and the RMSE of the removed datapoint was calculated as the model fitted to the remaining data. The hbgd package was used to fit all models except the ‘last observation carried forward’ model, the linear model and the SITAR model. For the ‘last observation carried forward’ model, the holdout data point was interpolated by the last observation by converting the random holdout value to NA and then using the function na.locf() from the zoo R package. For the simple linear model, the holdout-filtered data were used to determine the slope and intercept via R’s lm() function, which were then used to calculate the holdout value. For the SITAR model, each subject was fitted by calling the sitar() function with df=2 to minimize failures, and the RMSE of the random holdout point was subsequently calculated with the predict() function. For this analysis, set.seed(1234) was used to initialize the pseudorandom generator.

About this dataset

Context

The dataset tabulates the data for the Day, New York population pyramid, which represents the Day town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Day, New York, is 11.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Day, New York, is 64.3.
• Total dependency ratio for Day, New York is 75.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Day, New York is 1.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Day town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Day town for the selected age group is shown in the following column.
• Population (Female): The female population in the Day town for the selected age group is shown in the following column.
• Total Population: The total population of the Day town for the selected age group is shown in the following column.

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 Day town Population by Age. You can refer the same here

Graph and download economic data for Single-Parent Households with Children as a Percentage of Households with Children (5-year estimate) in Day County, SD (S1101SPHOUSE046037) from 2009 to 2023 about Day County, SD; single-parent; SD; households; 5-year; and USA.