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Demography. 2009 August; 46(3): 589–603.
PMCID: PMC2831349

Drawing Statistical Inferences from Historical Census Data, 1850–1950


Virtually all quantitative microdata used by social scientists derive from samples that incorporate clustering, stratification, and weighting adjustments (Kish 1965, 1992). Such data can yield standard error estimates that differ dramatically from those derived from a simple random sample of the same size. Researchers using historical U.S. census microdata, however, usually apply methods designed for simple random samples. The resulting p values and confidence intervals could be inaccurate and could lead to erroneous research conclusions. Because U.S. census microdata samples are among the most widely used sources for social science and policy research, the need for reliable standard error estimation is critical. We evaluate the historical microdata samples of the Integrated Public Use Microdata Series (IPUMS) project from 1850 to 1950 in order to determine (1) the impact of sample design on standard error estimates, and (2) how to apply modern standard error estimation software to historical census samples. We exploit a unique new data source from the 1880 census to validate our methods for standard error estimation, and then we apply this approach to the 1850–1870 and 1900–1950 decennial censuses. We conclude that Taylor series estimation can be used effectively with the historical decennial census microdata samples and should be applied in research analyses that have the potential for substantial clustering effects.

Decennial census microdata are a key component of social science infrastructure. Census microdata are among the most frequently used data sources in the leading journals of population, economics, and sociology; indeed, during the past decade, census microdata have been used more frequently in the pages of Demography than any other data source.1 Most of these publications have used the Integrated Public Use Microdata Series (IPUMS), which makes freely available to scholars large, nationally representative samples of every surviving census from 1850 though 2000; these data are in harmonized format and are available through a user-friendly data access system with comprehensive documentation (Ruggles et al. 2004). Since 1995, more than 25,000 researchers have registered to use the IPUMS data extraction system, and they have produced 2,000 publications and working papers.

Census microdata samples are individual-level data clustered by household, often incorporate stratification, and sometimes use differential probabilities of selection (in addition to differential nonresponse), resulting in heterogeneity in sample weights. The clustering of individuals within households can significantly increase standard errors of estimates because the number of independent observations is less than the number of actual observations in each census file. Standard errors in cluster samples depend on both the size of the sampled clusters and the homogeneity of variables within clusters, measured by an intraclass correlation coefficient (Graubard and Korn 1996; Hansen, Hurwitz, and Madow 1953; Kish 1992; Korn and Graubard 1995, 1999). In the worst case, with perfect homogeneity within clusters, the standard errors for variables would be inversely proportional to the square root of the number of clusters rather than the number of people. Thus, variables such as race and poverty status, which tend to be comparatively homogeneous within households, have underestimated standard errors if clustering was ignored. Conversely, for variables that are heterogeneous within clusters, such as age and sex, clustering may have little effect on sample precision.

The loss of efficiency resulting from clustered design is partially counterbalanced by stratification (Kish 1992). The IPUMS samples for the years prior to 1960 were designed to capitalize on geographically sorted source materials, which enhance precision through implicit geographic stratification. Such procedures can lower standard errors, especially for variables that are highly correlated with geography

In this article, we examine the impact of complex sample designs on standard error estimates using IPUMS historical U.S. census microdata samples for 1850 through 1950. We compare standard errors computed using a simple random sampling assumption––the usual way of computing standard errors using the historical U.S. census data––with estimates that take the complex sample design into account. We develop and test a new variable that allows us to apply modern standard error estimation software to IPUMS census micro-data. Based on the results of this evaluation, we develop recommendations for standard error estimation when using the IPUMS samples from 1850 through 1950.2


Sophisticated methods for standard error estimation are now available in easy-to-use procedures in statistical packages. In particular, Taylor series estimation procedures are now incorporated into most statistical analysis packages (SAS 1999; Stata 2001; SPSS 2003), and these procedures yield reliable estimates (Dippo and Wolter 1984; Hammer, Shin, and Porcellini 2003; Kish and Frankel 1974; Krewski and Rao 1981; Weng, Zhang, and Cohen 1995). Researchers frequently use these products for analysis of survey data, but they are seldom applied to decennial census microdata. In part, this is because the census microdata samples do not include all the variables required to take advantage of the new software algorithms.

Implicit Geographic Stratification

The source materials for the IPUMS samples of the pre-1960 censuses are the census enumerators’ manuscripts, which had a similar format from 1850 though 1950. A sample page from the census of 1930 appears in Figure 1. Each line represents one individual, and for most census years, there are 50 lines on each page. Enumerators identified the divisions between one dwelling or household and the next by numbering each unit and identifying the head of each household in the family relationship field. The criteria used to distinguish between households were closely comparable across the entire period from 1850 to 2000 (Ruggles and Brower 2003).

Figure 1.
1930 Census Enumeration Form

The enumerators collected census information by going from house to house. Each enumerator was responsible for one enumeration district. In most census years, the average district included between 1,000 and 1,500 persons (Magnuson 1995; Magnuson and King 1995). Urban districts tended to be compact, but rural ones could be geographically extensive, especially in sparsely settled places.

The publicly available IPUMS samples created at the University of Minnesota for the censuses of 1850 through 1930 all employ the same basic design, with minor variations to accommodate differences in source materials and innovations in data-entry technology. The sampling strategy is based on the census page. We generate a random starting point for each microfilm reel between 1 and 5, and then designate every 10th page thereafter as a sample page. Thus, for example, if the starting point is 3, we designate the 3rd, 13th, and 23rd pages, continuing in that fashion until the end of the reel. On each sample page, we randomly select a set of sample points. The number of designated sample points depends on the desired sample density. For example, if the census page has 50 lines and a group of 10 pages therefore has 500 lines, we need to designate five sample points on each sample page to obtain a 1% sample. To ensure that dwellings have an equal probability of being included in the sample regardless of their size, they are entered only if a sample point falls on the line containing the first person in the dwelling. When a sample point falls on any other dwelling member, the dwelling is skipped. For example, if the sample point falls within a dwelling of five members, the dwelling will have only a one in five chance of being included in the sample; if it is included, all five members will be entered. Under this procedure, each dwelling, household, and individual in the population has exactly a 1-in-100 probability of inclusion.

We modify these procedures for persons residing in group quarters. We sample members of large units on an individual basis simply by treating each member as if he or she lived in his or her own one-person household. This procedure increases the efficiency of the sample by raising the number of independent observations while maintaining representativeness. Without individual-level sampling of residents in group quarters, the precision of estimates for this population would be very low. In all the IPUMS samples between 1850 and 1930, large units are defined as households with more than 30 members. We sample within such households simply by accepting all individuals who fall on a sample point.

In 1940 and 1950, the sampling procedures were somewhat different, but the result was very similar. In 1940, the Census Bureau introduced the practice of asking extra questions of a sample of the population. Instead of asking the supplemental questions of an entire household, as was done for the censuses of 1960 through 2000, in 1940 and 1950, enumerators asked the questions of a systematic sample of individuals. The IPUMS samples were designed to ensure that every sampled household included a person who was asked the supplemental questions. Because larger households had a higher probability of containing such a person, households were selected into the sample in inverse proportion to their size. Thus, all one-person households were included if the respondent was asked the extra questions, half the two-person households were included if either respondent had answered the questions, and so on. Data-entry staff consulted a random number table to determine if a household of a given size should be included. The effect of this procedure is very similar to taking households only if the sample point falls on the first individual in the household. For 1950, the procedure was the same as for 1940, but without the control for household size: it is a flat sample of all households containing an individual with extra questions. As a result, the 1950 sample overrepresents large households and must be weighted to compensate.3

The sample designs for these early censuses differ fundamentally from those of the censuses of 1960 through 2000 because they were drawn from microfilm images of the original census enumerator manuscripts instead of from machine-readable files. Explicit stratification was not feasible, but the organization of historical census enumeration forms incorporated implicit geographic stratification. Unlike recent mail-in U.S. censuses, the pre-1960 censuses were created through direct enumeration: an enumerator went from house to house to interview residents in person. A byproduct of this enumeration method is that the census forms are sorted according to the sequence of enumeration within each enumeration district. In practice, this means that the enumeration manuscripts are geographically organized within districts.

The systematic samples of the historical censuses capitalize on this low-level geographic sorting. By ensuring a representative geographic distribution of sampled cases, they are equivalent to extremely fine geographic stratification with proportional weighting. Since many economic and demographic characteristics are highly correlated with geographic location, this implicit stratification can yield substantially greater precision than a simple random sample of households. By capitalizing on implicit stratification of the pages, this design yields higher precision for many estimates.

Pseudo-Strata and Taylor Series Linearization

Taylor series linearization is the easiest and most widely used method for estimating variance with complex sample designs, but it is not designed for samples with implicit stratification. Because the stratification in census records is implicit, there is no geographic unit in the data that corresponds precisely to the geographic stratification embedded in the page ordering of the manuscripts. This poses a major problem for Taylor series linearization, since the method requires explicit information about strata.

To create a proxy for the implicit geographic stratification of the historical IPUMS samples, we used the Census Bureau’s page numbering scheme in combination with explicit geographic information written on the census page. The manuscripts from each census are stored on several thousand microfilm reels. Most reels contain several hundred pages. Each of these pages contains between 40 and 50 lines, with each line containing information on one person. For the samples from 1850–1930, we created pseudo-strata of approximately 10,000 lines. These pseudo-strata, on average, record information for about 2,000 households. We ensured that each stratum included cases from a single county; at each break between counties, we started a new stratum.4 For the 1940–1950 samples, we adopted a slightly different strategy, since we lack information on reel, page number, and most counties. We therefore defined strata as enumeration districts during these years. Enumeration districts consist of contiguous groups of neighborhoods or minor civil divisions. The specific boundaries of enumeration districts were defined as the area that a door-to-door enumerator could be expected to cover in a two-week period. Enumeration districts are consistently identified in these samples.5

Subsample-Replicate Approach

An alternative to Taylor series variance estimation is the subsample-replicate approach (Rust 1985; Verma 1993; Wolter 1985).6 The replicate approach divides a sample into subsamples (or replicates) that reflect the complex design of the entire sample. Each sub-sample incorporates the same stratification and clustering used to select the sample as a whole.7 Iterative computer procedures are then used to estimate standard errors. It has not been established, however, whether the subsample replicate method is reliable for samples that incorporate implicit geographic stratification. Subsample replicate estimates could be biased if the degree of geographic homogeneity varies greatly with geographic scale. For example, a typical 1-in-100 sample includes one household approximately every fifth manuscript census page; if we divide that sample into 100 subsample replicates, however, cases occur only once every 500 pages and thus have high heterogeneity. This difference in geographic scale could have significant implications for variance.


To validate both the Taylor series linearization with pseudo-strata and the subsample-replicate approach, we needed a “true” estimate of variance in the census samples. Fortunately, a new source can provide near-perfect estimates for the 1880 census. The 1880 population database provides individual-level data on the entire population of 50 million Americans, assembled with the help of 11 million hours of volunteer effort by members of the Church of Jesus Christ of Latter-Day Saints (Goeken et al. 2003). This remarkable database provides an ideal laboratory for the evaluation of sample designs. The database allows us to simulate any sample design precisely, and by repeatedly drawing samples from the full 1880 census, we can develop highly accurate variance estimates for the IPUMS sample design.8 We can then compare these estimates with results based on both the IPUMS subsample replicates and Taylor series linearization to assess the reliability of both methods.


Table 1 compares alternate methods for estimating standard errors of selected variables in the 1880 census. The first two columns are based on sample replication of the entire 1880 population. We drew 100 independent 1% samples from the complete-count database, mimicking the sample design used to create the historical samples. The standard errors derived from these replicates are unbiased estimates of the standard error that would be expected in a 1% sample.

Table 1.
Standard Error Computations Comparing Replicate Estimates From the Complete 1880 Census With Estimates Derived From Sample Data Using Alternative Methods

The last three columns present the ratio of the standard error calculated from the 1% IPUMS sample for 1880 using three methods: subsample replicate, Taylor series linearization with pseudo-strata, and simple random sample assumptions to the standard error using the full 1880 census subsample replication. We regard the full 1880 census subsample replicates as a gold standard, so the ideal ratio would be 1.0. Ratios under 1.0 represent underestimated standard errors, and ratios over 1.0 represent overestimated standard errors.

The top row of Table 1 shows that in 1880, the average age was estimated to be 24.2 years, with a full census replication standard error estimate of 0.03. The ratio of the 1% IPUMS sample replicate to the full census replication was 1.1. The ratio for the Taylor series was 1.1, and the simple random sample was 0.9. These estimates are all quite close to one another, suggesting that for this variable, the particular method of standard error estimation does not matter much. This is not surprising, since age is not highly correlated within clusters (i.e., households) or by strata (i.e., geographic strata).

The poorest performing estimates in Table 1 were for the nonwhite and nonrelative simple random sample estimates, with a ratio of 0.5. When using statistics that assume a simple random sample, which is the default in most statistical packages, the standard error estimates would be about half as large as the standard errors that take the sample design into account. This is the result of household clustering; these characteristics were both highly correlated within households.

For a few characteristics, the estimation methods overstate true standard error. This is most noticeable for the characteristic male. This variable varied greatly across geographic areas in 1880, so the implicit stratification significantly reduces standard errors. The other variance estimation methods pick this up, and the ratios reported in Table 1 are much closer to 1.

On the whole, however, the Taylor series and IPUMS 1% subsample-replicate estimates both performed well; the overall average ratios were 1.1 for the IPUMS 1% subsample-replicate estimates and 1.0 for the Taylor series estimates. By contrast, assuming a simple random sample leads to substantial underestimates of standard errors for several characteristics that are highly correlated within households.

When we turn to other historical samples from the IPUMS—1850, 1860, 1870, 1900, 1910, 1920, 1930, 1940, and 1950—we no longer have the gold standard of the full census replication because only the IPUMS sample data exist for those years. Table 2 therefore varies in structure from Table 1. The first column contains the population parameter estimate from the IPUMS sample, and the second column contains the standard error estimates based on the assumption that the data were collected as a simple random sample. The remaining two columns present the Taylor series and subsample-replicate estimates as ratios to simple random sample estimates. A high ratio indicates that the standard error estimation method yields larger standard errors than would be obtained from a simple random sample of the same size.

Table 2.
Standard Errors Assuming Simple Random Samples Compared With Taylor Series and Subsample-Replicate Estimates: Selected Characteristics

The key finding is that the Taylor series estimates using the dwelling unit/household as the clustering variable and the stratification variable we created based on sample pages (which allows us to recognize the implicit stratification in the sample) yielded results for most of the domains examined in Table 2 that are very similar to those of the replicate estimates from the IPUMS samples. On average, the Taylor series and replicated estimates led to standard errors that were 1.4 times larger than the simple random sample estimates. When the ratio of the Taylor series estimates to the simple random sample estimates was below 1, the replicate estimates ratio also tended to be below 1 (and vice versa). The two approaches to estimating the standard errors yielded very similar results. The one exception is with the estimate of urban residence, in which the replicate estimates were consistently larger than the Taylor series across census years.

Several characteristics had consistently high ratios across all census years. In addition to nonwhite and nonrelative mentioned in the discussion of 1880, we examined two variables with extremely high ratios: urban residence and farm residence. These are, in fact, household-level variables, not person-level variables, but we rectangularized the file and added the household-level characteristics to each person record to demonstrate the effects of clustering. Therefore, these two characteristics are identical for every individual in the household. With this perfect correlation, standard errors based on a simple random sample assumption are severely underestimated.


Our validation analysis using the 1880 population database shows that both the Taylor series and the subsample-replicate method compared favorably to the full census replication estimates of the standard error. We were concerned about the impact of not being able to control for the implicit stratification for the Taylor series estimates, but our pseudo-strata variable tracks the full 1880 sample replication with only minor deviations. We also had concerns that the subsample replicate estimates could be biased if the importance of geographic homogeneity varies with geographic scale. The analysis demonstrates that the IPUMS replicate estimates were not severely biased by differences in geographic scale. Based on the 1880 analysis, both types of standard error estimates that attempt to adjust for complex sample design work reasonably well to estimate standard errors. This is crucial, since we do not have the same gold standard to evaluate the other years of IPUMS data. Because the IPUMS samples for 1850 through 1950 are all drawn in a similar fashion, we believe it is reasonable to infer that methods that work in 1880 will also work for the entire period.

The subsample replicate and Taylor series produce very similar results for 1850–1870 and 1900–1950. Although we cannot compare these estimates to full census replication as we did in 1880. We did not find that the Taylor series estimates for the 1850–1950 period substantially overstated standard errors because they cannot explicitly include the effects of implicit stratification; the pseudo-strata variable we created for performing Taylor series worked to incorporate the implicit stratification in the sample design.

We have added the pseudo-strata variable (PSTRAT) to the publicly available IPUMS data files for the period 1850–1950. This will make it easy for researchers to create Taylor series standard error estimates using the major statistical packages (SPSS, SAS, and STATA). These Taylor series estimates in the statistical programs are coupled with procedures for regression, cross-tabulation, and univariate analyses. The general approach to variance estimation discussed in this article is currently being tested on U.S. census data from 1940 to the present (which have a different sample design), and also on more than 200 non-U.S. census data from 75 countries.

The results presented here demonstrate that in certain circumstances, treating historical IPUMS data as simple random samples will yield underestimates of standard errors, and this could cause researchers to draw unwarranted statistical conclusions. The results also show, however, that for characteristics that are not highly correlated within clusters (i.e., households)—such as sex, socioeconomic index, or labor force participation—a simple random sample assumption can provide a reasonable estimate of standard errors.

Many analyses of IPUMS data do not pose standard error estimation problems. Recent IPUMS-based publications have focused, for example, on elderly persons residing with their adult children (Ruggles 2007), mothers of young children (Short, Goldscheider, and Torr 2006), men aged 20–39 (Rosenfeld 2006), and married couples in which the wife is aged 18–40 (Schwartz and Mare 2005). In each of these cases, the researchers examined a population subgroup that typically appears just once per household. For example, most households contain no more than one intergenerational coresident group, one mother of small children, one young adult man, or one young married couple. In such cases, there is little or no clustering and thus little reduction in statistical power. It follows that for most analyses, assuming a simple random sample will yield acceptable estimates of standard errors. Furthermore, regression models that have control variables related to sample design and clustering are less affected by the stratification and clustering adjustments (e.g., see Davern et al. 2007; Kish and Frankel 1974).

There are some situations, however, in which the likelihood of large clustering effects is greater. Analyses of historical school attendance pose risk because if one child in a family attended school, the odds are high that all the school-age children were in school. Many schooling analyses, however, subdivide the schoolchildren by age and sex; such studies avoid clustering effects because a given household is unlikely to have multiple children of a particular age and sex. The worst clustering arises with analysis of population characteristics that almost by definition apply to entire households or families, such as poverty or urban residence. Even with these topics, however, the clustering problem evaporates if the unit of analysis does not usually occur more than once per household. Thus, for example, studies of the poverty status of families, householders, or mothers would be virtually unaffected by clustering.

In the end, researchers must evaluate their research designs and judge whether they pose a potential risk of clustering that might lead to underestimated standard errors. Where a significant risk exists, we recommend that data users make use of the new strata and cluster variables on the IPUMS Web site to produce Taylor series standard error estimates using the statistical package of their choice. This methodology can be used both for calculating percentages and means (as it was in this article) and for calculating regression models (e.g., ordinary least squares and logistic regression). These procedures will allow researchers to take advantage of implicit geographic stratification while also paying attention to the clustering of people and their characteristics within sampled households. The subsample-replicate estimates were also found to produce reasonable standard error estimates and the subsample identifiers are available in the IPUMS data files; but these estimates are harder to obtain because with this method, each analysis needs to be run 100 times, and standard errors must be calculated from the resulting sampling distribution.


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This research was supported by Grant Number R01 HD043392-03S1 from the National Institute of Child Health and Development of the National Institutes of Health.

1.In the decade from 1997 through 2006, U.S. census microdata were used in 54 Demography articles, substantially more than any other data source.

2.We reviewed 20 recent articles in Demography that used the 1850–1950 microdata for original analysis (the full set of references is in the appendix), and none of them mention having adjusted standard errors for household clustering and stratification even though this functionality is available in most modern statistical packages (e.g., SPSS, SAS, and Stata). This does not mean these articles made invalid inferences; as discussed later, for many analyses, ignoring the effects of clustering and stratification can yield conservative variance estimates.

3.There are several other differences between the 1850–1930 and the 1940–1950 designs. First, for 1940 and 1950, the basic sampling unit was the household. Before 1940, the basic sample unit was the dwelling, and because there were multiple sample points for each sampled page, occasionally more than one unit was sampled from the same enumeration page. Before 1940, group quarters units were defined as units with more than 30 members, and in 1940 and 1950 they were units with more than five persons who were unrelated to the head of the household. Moreover, in the pre-1940 period, group quarters units were sampled in clusters of five contiguous individuals; in 1940–1950, they were sampled as isolated individuals, eliminating clustering effects for residents in group quarters. See Ruggles et al. (2004) and Ruggles and Brower (2003) for a full description of differences between sample designs.

4.For this reason, some strata cover fewer than 10,000 lines. Strata that initially contained only a single sample household were allocated to other strata via a multistage procedure that takes advantage of geographic information we have about the household. Of the 2.5 million households in all of the 1850–1930 samples combined, only 4,200 were in one-household strata. If the household was in the same county as the previous or following stratum, we placed the household in that stratum. Almost 90% of these households were placed in this way. Otherwise, single-household strata were grouped with other single-household strata in the same State Economic Area (SEA); state; or, in a few cases, the entire country.

5.When enumeration districts had only one household in the sample, we created a merged stratum by combining all single-household strata in the same SEA. The 1940 and 1950 data sets together had 852,164 households. Of those, 84,487 were the sole sampled household within their stratum. All of these cases were placed in SEA-level strata with others of their kind.

6.Statisticians are currently developing model-based variance estimates (e.g., Little 2003) to improve on the design-based variance estimates we examine, but there is not a specific algorithm available in statistical packages to implement them. The model-based variance estimates are beyond our current scope of work for two reasons: (1) the standards for implementing model-based variance estimates are not set for routine statistical analysis, and (2) it is still not clear how model-based variance estimates will be used for complex sample designs like the census (Kalton 2002).

7.The IPUMS subsample standard error estimates were calculated using the subsample variable on the 1850–1950 IPUMS 1% samples, which systematically divides the IPUMS sample into 100 replicate subsamples. For the historical IPUMS samples, the values are assigned sequentially from 0 to 99 for each dwelling or group quarters member. The IPUMS replicate standard errors are constructed by calculating the mean for each subsample separately and then averaging the 100 subsample means. The standard deviation of these 100 replicates is the IPUMS subsample-replicate standard error estimate.

8.The 1880 replicate reconstruction from the full 1880 census universe was completed using the household selection rules for the IPUMS 1880 1% sample (Ruggles and Menard 1995). In order to replicate the IPUMS sample design, we used the SAS procedure PROC SURVEYSELECT to randomly select one person per microfilm page. This process was carried out for 100 replicate 1% samples, and the sampling rules determined the eligibility of the household or individual for inclusion in the final sample. The sampling rules selected households or families if the randomly selected sample line was the household or family head and selected individuals for group quarters larger than 31 persons. If the randomly selected sample line did not meet the sampling rule criteria, the sample line was disregarded. We used PROC SURVEYSELECT to select 100 1% samples, following the IPUMS sampling rules from the full 1880 microdata census file (Ruggles and Menard 1995). The full 1880 census 1% sample replication has variance estimates that are on a different scale than the replications of the 1% IPUMS file, which are based on a sample of only 0.1% of the records. As a result, the full 1880 census 1% replication results are divided by the square root of 100 (i.e., 10) because the standard errors from the full 1880 census replication are based on samples that are 100 times larger than those based on the 1% IPUMS.


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