Behind South Dakota’s Population Growth
The most recent state and county estimates released by the Census Bureau conveyed a continuation of prior moderate growth and similar trends among counties. As in past years, this article will detail specifics of state and county growth. The main focus will be on natural increase, decomposing births and deaths into demographic structure effects and more behavioral elements, providing an interesting perspective on differences among counties. A brief examination of rural-urban contrasts in both demographics and in income and property ownership follows.
Population Change from 2024 to 2025
Continued growth in South Dakota state population is apparent in the 2024 to 2025 data, but interpreting the annual estimates is not as simple this year after the Census Bureau made an update to the 2023-24 estimates that was larger than usual. The primary change was the addition of about 3,000 international migrants. While a modest update to a single component of population change may seem minor, it makes a significant difference in measures of change. The update bumped the 2023-24 growth rate from the earlier estimate of 0.69% to 1.01%, much more in line with prior years with the exception of the higher growth between 2021 and 2022.
Given the updated 2023-24 estimate, the state population grew by 7,984 or 0.86 percent between July 2024 and July 2025, again similar to most prior years (Table 1). Natural change as a percentage of population was the same 0.28% as the prior two years, while growth from net migration added 0.57%. Like the previous year, there was a greater net influx of international immigrants than domestic immigrants.
This contrasts with the prior three years when domestic migration was more pronounced.
South Dakota continues to compare favorably to the nation and the region in terms of population growth (Table 2). The overall growth rate was about half again the national rate and more than double the Midwest region’s rate. It was higher than any of the neighboring states. While natural change was similar in Nebraska and North Dakota, South Dakota’s percentage growth attributed to migration was second only to Montana, which had virtually no change due to natural increase.
South Dakota’s performance relative to the nation is also reflected in its ranking among all states. The overall growth rate ranked twelfth among all states. South Dakota’s growth due to births was fourth among states, while deaths were 29th, combining for a natural increase contribution that was ninth in the nation. Migration performance was strikingly similar for both international and domestic migration: the state was sixteenth in international migration and seventeenth in domestic migration.
County summary
As usual, population change is not uniform across counties, and neither were the adjustments to the 2023-24 numbers. About two-thirds of the revised immigration increase went to Minnehaha (+1246), Brookings (+489), and Pennington (+304) counties. Other notable gains occurred in Beadle (+186), Yankton (+100), and Hamlin (+98) counties. Those same six counties led the state in immigration between 2024 and 2025. As shown in Table 3, Minnehaha had about 40% of the immigration, Brookings had close to 20%, and Beadle had close to 10%.
Minnehaha (+3275) and Lincoln (+2022) counties combined for about two-thirds of the total population increase in the 2024-25 period. Brookings (+821) and Lawrence (+750) were the only other counties with increases of over 500. The largest counties, those with populations over 10,000, accounted for almost all of the growth. One-third of South Dakota’s counties lost population, with the largest numerical decrease being -164 in Meade County. The largest percentage decrease was about 2% in Mellette County, although it should be noted that Mellette’s reported birth numbers were informally adjusted for this report to account for what appeared to be an assignment of many Todd County births to Mellette. The adjusted numbers better reflect births as reported by the state’s Department of Health. Five counties – Brookings, Campbell, Hamlin, Lincoln, and Lawrence – had growth rates between 2.21 and 2.66 percent.
Exactly half of South Dakota’s counties had negative net migration, with seven even reporting slightly negative net international migration. Hanson, Jackson, and Mellette counties had net outflows equal to about two percent of county population, while Campbell, Lawrence, and Lincoln had net inflows of more than two percent. Forty of South Dakota’s 66 counties experienced negative net domestic migration, including ten of the eighteen largest counties. Beadle had the largest net outflow, almost enough to counter the large immigration inflow in a manner similar to recent trends in states like California, New York, and Illinois. Lincoln and Lawrence counties’ large inflows amounted to more than two percent of each county’s population.
Natural Increase
Birth rates varied by a factor of four, from 0.61 to 2.46, across the state. In order to preserve scale comparability, these are indicated as percentages of population, rather than as crude birth rates, which are reported per thousand rather than per hundred. Several counties (Figure 1) had rates between 0.6 and 0.7, while only a couple of counties exceeded 2. The most notable region of low rates is in the southwest corner of the state, with Clay County also very low. Higher rates are observed in the western counties with large Native American population percentages along with a few other counties. The discussion below will dive deeper into these rates.
Death rates varied from 0.5 to 1.92, also roughly a factor of 4. Five counties had death rates between 0.5 and 0.6, while eight had rates between 1.5 and 1.92. Less regional concentration appears for deaths. Several eastern counties had low rates, as did Harding in the far northwest of the state. Higher rates are also quite scattered. These will also be discussed in more detail below.
Combining birth and death rates into natural increase (Figure 3), we find variation from -1 to +1.23. Two Native American concentrations and Hamlin County stand out with high rates of natural increase, while Fall River and Jerauld counties stand out with low rates. Interestingly, two of the Native American counties, Mellette and Todd, exhibit negative natural increase.
Considering Demographic Structure and Behavior
Insights into natural increase can be gained by decomposing births and deaths into key structural and behavioral components of change. For births, both the relative size of the cohort of child-bearing females and the fertility of that cohort are examined. For deaths, we will focus on median age and a comparison of actual deaths to those predicted by applying national death rates to the sex and age structure of each county.
Births
South Dakota counties vary significantly in their age structures. Focusing first on births, we can see significant differences in the relative sizes of the female cohorts between 15 and 44, which generate almost all births. Extreme differences in the percentage of the population in the child-bearing cohort are illustrated in the population pyramids below for Custer and Todd counties (Figure 4). The Custer County pyramid is clearly concentrated in older age groups, while the Todd County graph is close to a true ‘pyramid’ with a wider base of larger cohorts at younger ages.
NOTE: A set of 2020 pyramids for all SD counties constructed by State Demographer WeiWei Zhang is available at https://openprairie.sdstate.edu/census_data_sets-demographic/9/)
Using the 2024 Census Bureau estimate of population by age and sex and South Dakota Department of Health counts of births, the percentage of total county population which is in the 15-44 year old female group was calculated and mapped (Figure 5). Unsurprisingly, high values are found in Brookings and Clay counties, home to the state’s two largest universities. Clay County in particular stands out with close to thirty percent of its population in the child-bearing cohort. The next highest group consists of the Reservation counties of Todd, Oglala Lakota, Ziebach, and Buffalo. The remaining counties above twenty percent are Minnehaha and Lincoln, most likely due to their attractiveness to young immigrants.
Custer County has only about twelve percent of its population, less than half of the university counties and more than ten percentage points lower than Todd. Several other counties have percentages less than thirteen percent, with many more in the 13-15% range in the child-bearing cohort.
The relative size of the child-bearing cohort is combined with the fertility of the cohort to determine the number of births. As with many other measures, we have computed the number of births per one hundred females in the child-bearing cohort, rather than the more common general fertility rate measure, which is scaled per thousand. Fertility rates among South Dakota counties (Figure 6) range from an extremely low 2.76 in Clay County, not surprising given the large percentage of college students, to a very high 13.19 in Hamlin County, most likely attributable at least partly to recent high immigration. For reference, the statewide fertility rate was 6.66 in 2024, and the national rate was 5.38.
If one applies an informal computation using the thirty years of age span in the child-bearing cohort, total fertility can be estimated as 30 fertility rate 100 or 0.3 times the fertility rate. Using this transformation, a fertility rate of 6.66 translates into a total fertility rate of 2 children per woman for the state of South Dakota, just short of the 2.05 to 2.1 replacement level. The U.S. total fertility rate translates to about 1.6 children per woman, much lower than the South Dakota level that has typically led the nation in recent years.
Eleven South Dakota counties have fertility rates below 6, translating into approximate total fertility of about 1.8 children per woman. In addition to Clay and Brookings, this group includes Fall River, Jones, Lincoln, Meade, Potter, Stanley, Sully, and Union counties. In addition to Hamlin County, nine other counties have fertility rates above 10, which translates roughly to three children per woman. Contrary to common perception of high fertility driven only by the Native American population, only two of those counties, Dewey and Mellette, have majority Native American populations. Exactly half of South Dakota’s counties have fertility rates between 7 (replacement rate implied total fertility) and 10, again illustrating that the high fertility phenomenon is widespread in the state.
Combining the demographic structure cohort size and behavioral fertility measures, we see several intriguing groupings. Five Reservation (Bennett, Buffalo, Oglala Lakota, Todd, and Ziebach) counties have both very high relative cohort sizes and above-average fertility rates. This is a logical combination, since high previous fertility typically generates larger child-bearing cohorts. Several other counties have high relative cohort sizes but low fertility rates. This includes the university counties (Brookings and Clay) as well as Meade and Lincoln. Minnehaha combines a large child-bearing cohort with about average fertility (close to replacement level), suggesting sizable continued numbers of births.
The lower end of the births spectrum is occupied by a set of counties with both low relative cohort sizes and low fertility rates. Here we find Fall River, Lawrence, Potter, and Sully counties. Clay County, whose fertility rate is so low that it overwhelms the cohort size, has a slightly higher expected birth rate. It is joined by Custer, Stanley, and Jones, which aren’t low on both measures, but are low enough on one measure that the combined effect leads to an expectation of births contributing less than one percentage point of growth per year, as opposed to values above two percent for the highest group.
It should be noted that the result of combining the child-bearing cohort percentage and the fertility rate generates values only relevant for the 2024 data. The birth rates as a percentage of population discussed above will differ from the 2024 values due to the shift of years to 2024-2025 in the most recent Census numbers, including year-to-year differences in birth rates, changes to the population base which include shifts in other factors, and differences in how the SDDOH tabulates births relative to the Census Bureau tabulation.
Deaths
Death rates are obviously affected by demographic age structure. We will use median age to summarize the structure. The range of values is extremely wide across the state, varying from 23.9 in Todd County to 57.5 in Custer County. These values should, of course, correlate strongly, although not perfectly, with the female child-bearing cohort percentage. Given this association, it is not surprising that the other counties with average ages above 48 are Campbell, Fall River, Jones, Potter, and Sully (Figure 7). Likewise, the other lower-age counties are Brookings, Clay, Buffalo, Corson, Dewey, and Oglala Lakota.
The behavioral component, combined to some extent with social conditions, is captured by a relative measure of mortality. The most common measure, age-adjusted death rate, requires age- and sex-specific death rates by county, which are of questionable value with many small numbers and a time span including the Covid era. Instead, expected deaths were computed using national life tables applied to the age-sex profiles of each county. The ratio of the actual deaths to expected, the Standardized Mortality Rate (SMR), was then computed for each county. Values significantly higher than one would indicate an actual death rate much larger than the national standard. Since actual deaths can be quite volatile from year to year, the numbers should be regarded with some caution.
As with so many other measures, we find pronounced variation among counties (Figure 8). The lowest SMR of 0.37 in Jones County represents actual deaths less than 40% of what would have occurred if the county had the same death rates as the nation. Harding and Sully each had less than half the expected deaths, while Bon Homme, Brookings, Custer, Edmunds, Lincoln, and Moody counties had less than 75% of the expected deaths. Overall, slightly more than half of South Dakota counties had actual deaths below the expected number.
Unfortunately, similar extremes were seen on the high end of the distribution. Buffalo County had actual deaths more than three times the expected based on national mortality rates. Oglala Lakota and Todd had rates more than 2.5 times the expected, and Corson, Dewey, and Ziebach had rates more than double the expected. Other than Mellette County, another majority Native-American county, at 1.81, the next highest value was 1.56 in Hyde County.
Urban/Rural Location, Income, and Home Ownership
Demographic patterns are related to important urbanization and economic indicators. Many of the rural counties display distinct demographic characteristics. Most of the counties with low Standardized Mortality Rates are rural counties with White majorities. Among all of the White-majority counties, rural counties also tend to have higher fertility rates, while the lowest fertility rates are found in urban counties. Older median ages and lower child-bearing cohort percentages are also concentrated in more rural counties with White majorities.
The highest mortality counties belong to a widely acknowledged low-income group (Figure 9). All of the Native-American majority counties have median household incomes below two-thirds of the state median income, with the lowest, Buffalo County, having a median income less than half of the state median.
Among other counties the most urbanized areas of the state, those included in metropolitan statistical areas (MSAs), all have median incomes near or above the state median. By far the highest median incomes, over $100,000 in 2024, are found in Lincoln County, home to southern Sioux Falls, and Union County, part of which would be considered suburban Sioux City. Most of the other counties with at least 10,000 residents are in the top half of counties in median household income. The exceptions are Oglala Lakota, Roberts, most of which is a portion of the Sisseton Wahpeton Reservation, and Clay, whose indicators are greatly affected by the large university presence, and Butte. Less populous counties display a range of median incomes ranging from about $57,000 in Walworth County to $88,000 in Stanley County.
Property ownership levels, in particular the percentage of housing units which are owner-occupied, were derived from the full count of the 2020 Census. Property ownership percentages vary from a low of just under half in Todd County to 83 percent in Hanson County. Numerous other Native-American counties have ownership rates below sixty percent, while many other rural White-majority counties have rates near or above eighty percent. Clay and Brookings are notably low, not unexpected with the university presence, while many of the larger urban counties such as Minnehaha and Pennington have ownership percentages in the mid-60s.
The various measures discussed document a number of widely known tendencies. The decomposition is useful in understanding determinants and extending our understanding of driving forces. Hopefully, the deeper understanding will help in anticipating possible future developments and implications of policies. And one final note: if South Dakota continues the current annual increment to population, the million mark would occur in 2033.
