In 2021, the population of the Sacramento-Roseville Folsom metropolitan are was about 2.41 million people. This was a slight increase from the previous year, when the population was about 2.4 million people.

Chart and table of population level and growth rate for the Sacramento metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

About this dataset

Context

The dataset tabulates the data for the Sacramento, CA population pyramid, which represents the Sacramento population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Sacramento, CA, is 26.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Sacramento, CA, is 20.2.
• Total dependency ratio for Sacramento, CA is 47.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Sacramento, CA is 4.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Sacramento population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sacramento for the selected age group is shown in the following column.
• Population (Female): The female population in the Sacramento for the selected age group is shown in the following column.
• Total Population: The total population of the Sacramento 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 Sacramento Population by Age. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Sacramento County, CA (B03002010E006067) from 2009 to 2023 about Sacramento County, CA; Sacramento; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in El Dorado County, CA (B03002021E006017) from 2009 to 2023 about El Dorado County, CA; Sacramento; latino; hispanic; CA; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Yolo County, CA (B03002019E006113) from 2009 to 2023 about Yolo County, CA; Sacramento; latino; hispanic; CA; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Placer County, CA (B03002019E006061) from 2009 to 2023 about Placer County, CA; Sacramento; latino; hispanic; CA; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset presents the median household income across different racial categories in Sacramento County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Sacramento County population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 47.16% of the total residents in Sacramento County. Notably, the median household income for White households is $93,257. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $97,534. This reveals that, while Whites may be the most numerous in Sacramento County, Asian households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Sacramento County.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Sacramento County median household income by race. You can refer the same here

We use Monte Carlo methods which draw parameters from Bayesian posterior distributions to generate a distribution of population size estimates and trajectories, thus giving managers a fuller accounting of the uncertainty in the population status. We then propagate this population estimate and its associated uncertainty into a model using Monte Carlo methods to assess the impact of fishing bycatch on the species. We show that the population is below the recovery goal of 3,000 adults. The current total population estimate (including juveniles) is approximately 10,000 fish. Our model finds that fishing bycatch pressure reduces an otherwise assumed stable population by a median value of 0.4% per year, which could impede the recovery of the species. Fisheries bycatch is only one of many threats this population faces, and future work is needed to assess how other threats, such as spawning habitat alteration through dams and water diversions, may affect this population’s trajectory. The framework presented here is suitable for further data integration or modular expansion to incorporate the cumulative effects of challenges facing green sturgeon recovery. Methods We assessed the number of spawning green sturgeon adults in annual surveys of the Sacramento River, CA, USA from the Irvine Finch Boat Ramp (river kilometer 320, just west of Chico) up to Redding (river kilometer 480) (Fig. 1). Acoustic tag data and egg mat studies have confirmed that this is the extent of the spawning grounds (Poytress et al. 2013; Thomas et al. 2014). We surveyed any site along that section of the river with depths greater than 5 m (Erickson et al. 2002). Generally, we see green sturgeon in approximately 40 sites. This section of the river provides the vast majority (effectively all) of the southern DPS green sturgeon spawning locations. 2.3 Spawner survey A detailed description of the methods is published by Mora et al. (2015) and is only briefly described here. The survey has taken place continuously since 2010. There are three phases of the survey conducted over three weeks in mid-June. In phase one, a survey crew drifts downstream over the deepest parts of the channel with a depth sounder. The crew contour maps areas of the river with depths greater than 5 m using a sonar system. The survey generally finds approximately 70 areas with a depth over 5 m within the study area (Fig. 1). The crew marks locations with spawner observations in the last 5 years for an automatic revisit in phase three. In phase two, the crew uses a Dual frequency IDentification SONar (DIDSON; Sound Metrics, Bellevue, Washington) video camera to scan for green sturgeon during 3 passes at sites without spawners in the previous 5 years. In phase three, the crew visits all sites where sturgeon have been seen in the past 5 years as well as any new ones added during phase two. At each site, the crew makes 7 passes recording DIDSON footage. The DIDSON footage from phase three is reviewed in random order three times and counts are combined into an estimate of the number of sturgeon at each site location. The sum of counts from all sites is the total number of spawners observed. 2.4 Life table, IPM, and sensitivity analysis 2.4.1 Literature parameters Both the IPM and life table models need some parameters describing demographics, behavior, and physiology. We took a subset of these parameters directly from the literature (Appendix S1). All these parameters are for the northern DPS green sturgeon as similar data is unavailable for the southern population. 3.4.2 Length vs. age relationship We used the age vs length data for southern DPS green sturgeon on the Sacramento River from supplement 1 of Ulaski and Quist (2021). We fit these data with Bayesian regression in R using JAGS (packages used rjags, purr, ggplot, dplyr, patchwork, viridis, minpack.lm, ggextra, mcmcplots, and furrr) (Plummer 2003; R Core Team 2015; RStudio Team 2015; Wickham 2016b; Elzhov et al. 2016; Wickham 2016a; Curtis 2018; Wickham et al. 2018; Garnier 2018; Attali & Baker 2019; Pedersen 2019; Vaughan & Dancho 2021). The model and priors are as follows:

L ~ normal(Μ_L, Τ) Μ_L = L_∞ (1-e^((-k(A-t_0)))) L_(∞ ) ~ normal(μ = 190 cm, τ = 0.05 (1/cm)) k ~ gamma(ϕ = 1, θ = 0.2 (1/yrs)) t_0 ~ normal(μ = -3 yrs, τ = 0.001 (1/yrs)) Τ ~ gamma(ϕ = 0.001, θ = 0.001 (1/cm))

Eq. 1

where L is the length, ML is the mean of the length distribution, T is the precision of the length distribution, L∞ is the asymptotic length, k is the growth coefficient, A is age in years, and t0 is agee at zero length. Priors are loosely informed by data from northern green sturgeon (Adair et al. 1983; Farr et al. 2002). We ran three MCMC chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples per chain at 90% thinning. Each chain had random starting values based on draws from the prior distributions. All chains converged based on visual inspection or running means. We compared these results to fits for the northern DPS of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.3 Annual survival Appendix S1 presents an estimate of mortality based on a catch curve analysis from a fishery in the Columbia Estuary. We used the telemetry data from the sturgeon on our system to calculate annual mortality. We then used these two values to bookend the estimate of annual mortality in the life table model and IPM. We used data from the BARD for all green sturgeon tags from 2007-2018. We only used data where length was labeled as either total length or fork length. We converted all lengths to fork lengths and all lengths reported in this manuscript are fork lengths. We used the mean parameters from Eq. 1 to convert the lengths to ages. We grouped the data in 5-year bins to reduce noise. Instantaneous mortality is equal to the slope of the change in counts with age after natural-log transformation. We calculated the slope of the descending arm and converted it from instantaneous mortality to annual survival using (annual survival) = exp(-instantaneous mortality) (Ricker 1975). Subsequent calculations involving survival drew mortality from a uniform distribution over the range between the Columbia River and this estimate. 3.4.4 Probability of being an adult Rather than using published ages of maturity or the maturity curve implied by the Beamesderfert al. (2007) cohort model, we based the timing of maturity on data specific to the southern population. We calculated the probability that fish of a certain length are adults (i.e. potential spawners) using the same raw data set from the BARD. We flagged fish as adults if they were marked as “mature”, “adult,” or “eggs” (meaning they were caught with eggs) or if detections were above river kilometer 320 (the bottom of the spawning ground) (note the BARD uses a different river kilometer 0, thus river kilometer 320 equates to 410 in the BARD). Only tagging-year records were used so that length and maturity data were contemporaneous. We grouped individuals by sex into females and others (males and unknown). We obtained two separate estimates of the probability of maturation with length using Bayesian logistic regression. The first estimate was a sex-specific hierarchical model fit divided between females and others in which the sexes shared a common slope but had separate intercepts. We used the female parameters from this model in the sensitivity analysis calculation because that analysis needed fecundity. The second estimate was a fit with all the data for use in the population estimate, which needed the total number of spawners. The models are as follows:

P ~ Bernoulli(Μ_A) Μ_A = (1+e^(-(b_0+b_1 L)))^(-1) b_(1 ) ~ normal(μ = 0 (1/cm), τ = 10^(-12) cm) b_0 ~ normal(μ = 0, τ = 10^(-12))

Eq. 2

where P is the probability of being an adult, MA is the mean of the probability distribution, L is the length, b0 is the intercept and b1 is the slope. Thus, in the hierarchical model, females and others share the b1 term but have separate b0 terms. We used broad normal priors (Eq. 2). We ran three chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples at 90% thinning per chain. Each chain had random starting values based on draws from the prior distributions. All chains converged. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.5 Spawning interval distribution The population estimate portion of the IPM needs the distribution of spawning intervals for all adults and the sensitivity analysis requires the average spawning interval of females. To calculate these data, we took all detections from the BARD above river kilometer 320 for months during the spawning season (March - September). We removed any detections from the same year the fish was tagged as well as any detections for fish without a detected outmigration between upstream records. For each fish, we then found both the interval between tagging and the first return and, if available, the interval between the first and second return. We divided these into two groups (all fish and females). We then constructed a distribution showing the fraction of fish that have a return interval greater than each interval and calculated the average return interval. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.6 Calculate the fraction of the population that is adults and spawners We then sampled from the estimated distributions of survival, maturation, and spawning interval described above to make 10,000 life tables (the average parameter values converged at approximately 8,000 samples) from which we calculated the fraction of the population that is