Adjusting the 1980 Census of Population and Housing

Ericksen, Eugene P.; Kadane, Joseph B.; Tukey, John W.

doi:10.1080/01621459.1989.10478857

Cited by 35 publications

(18 citation statements)

References 7 publications

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“…Though the RM approach described in this paper can be applied to any level of geography for which data are available, we limit our illustrations of interracial mobility to the national level. Unlike the case with smaller levels of geography, the use of adjusted counts at the national level is “neither difficult nor controversial” (Ericksen et al 1989: 927).…”

Section: A Reduced-form Population Accounting Modelmentioning

confidence: 99%

2. Estimating Net Interracial Mobility in the United States: A Residual Methods Approach

Perez

Hirschman

2009

Sociological Methodology

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This paper presents a residual methods approach to identifying social mobility across race/ethnic categories. In traditional demographic accounting models, population growth is limited to changes in natural increase and migration. Other sources of growth are absorbed by the model residual and can only be estimated indirectly. While these residual estimates have been used to illuminate a number of elusive demographic processes, there has been little effort to incorporate shifts in racial identification into formal accounts of population change. In light of growing evidence that a number of Americans view race/ethnic identities as a personal choice, and not a fixed characteristic, mobility across racial categories may play important roles in the growth of race/ethnic sub-populations and changes to the composition of the U.S. To examine this potential, we derive a reduced-form population balancing equation that treats fertility and international migration as given and estimates survival from period life table data. After subtracting out national increase and migration and adjusting the balance of observed growth for changes in racial measurement and census coverage, we argue that the remaining error of closure provides a reasonable estimate of net interracial mobility among the native born. Using recent Census and ACS microdata, we illustrate the impact that identity shifts may have had on the growth of race/ethnic sub-populations in the past quarter century. Findings suggest a small drift from the non-Hispanic white population into race/ethnic minority groups, though the pattern varies by age and between time periods.

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Section: A Reduced-form Population Accounting Modelmentioning

confidence: 99%

2. Estimating Net Interracial Mobility in the United States: A Residual Methods Approach

Perez

Hirschman

2009

Sociological Methodology

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“…There is simply no evidence that validates the assumptions in the smoothing model. For Ericksen and Kadane (1985), Freedman and Navidi (1986), Ericksen, Kadane, and Tukey (1989), Freedman and Navidi (1992).…”

Section: Assumptions In the Smoothing Modelmentioning

confidence: 99%

“…For a discussion of adjustment from other perspectives, see Citro and Cohen (1985), Ericksen and Kadane (1985), Ericksen, Kadane, and Tukey (1989), Ericksen et al (1991), Fay (1992), Freedman (1991), Freedman andNavidi (1986, 1992), Hengartner and Speed (forthcoming), Hogan (1991), Hogan and Wolter (1988), Mitroff, Mason, and Barabba (1983), Schenker (forthcoming), Schirm (1991), Schirm and Preston (1987), Singh (1992), Wolter ( 1986Wolter ( , 1991, Wolter and Causey ( 1991 ), Ylvisaker ( 1991 ).…”

Section: Introductionmentioning

confidence: 99%

Adjusting the Census of 1990

Freedman

1993

Eval Rev

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Considering the difficulties, the Census Bureau does a remarkably good job at counting people. This article discusses techniques for adjusting the census. If there is a large undercount, these techniques may be accurate enough for adjustment. With a small undercount, they are unlikely to improve on the census; instead, adjustment could easily degrade the accuracy of the data. The focus will be sampling error, that is, uncertainty in estimates due to the luck of the draw in choosing the sample. Sampling error is a major obstacle to adjusting the 1990 census, even at the state level. To control sampling error, the Census Bureau used a smoothing model. However, the model does not solve the problem, because its effects are strongly dependent on unverified and implausible assumptions. This story has a broader moral. Statistical models are often defended on grounds of robustness, that is, estimates do not depend strongly on assumptions.But the standard errors, which are internally generated measures of precision, may be critical. Then caution is in order. If the model is at all complicated, the standard errors may turn out to be driven by assumptions not data—the antithesis of robustness.

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“…Citro and Cohen (1985) provide a review. The merits of adjusting the 1980 census are discussed in Ericksen and Kadane (1985), Freedman and Navidi (1986), Wolter (1986), Ericksen, Kadane, and Tukey (1989), Freedman and Navidi (1992); also see Mitroff, Mason, and Barabba (1983). Ericksen, Estrada, Tukey, and Wolter (1991) make the case for adjusting the 1990 census; also see Wolter (1991).…”

Section: Introductionmentioning

confidence: 99%

Adjusting the U.S. Census of 1990

Functions¹,

Freedman

Wachter

et al. 1994

Eval Rev

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Considering the difficulties, the Census Bureau does a remarkably good job at counting people. This article discusses techniques for adjusting the census. If there is a large undercount, these techniques may be accurate enough for adjustment. With a small undercount, adjustment could easily degrade the accuracy of the data. The Bureau argued that errors in the census were more serious than errors in the proposed adjustment, using "loss function analysis" to balance the risks. This procedure turns out to depend on quite unreasonable assumptions. With other and more realistic assumptions, the balance favors the census. The story has a broader moral. Statistical models are often defended on grounds of robustness. However, internally generated measures of precision may be critical. If the model is at all complicated, these measures of precision may turn out to be driven by assumptions not data—the antithesis of robustness. AUTHORS' NOTE: Freedman andWachter testified for the Department of Justice in City of New York et al. v. Department of Commerce et al. ; Cutler and Klein were consultants. We thank

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Adjusting the 1980 Census of Population and Housing

Cited by 35 publications

References 7 publications

2. Estimating Net Interracial Mobility in the United States: A Residual Methods Approach

2. Estimating Net Interracial Mobility in the United States: A Residual Methods Approach

Adjusting the Census of 1990

Adjusting the U.S. Census of 1990

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