@incollection{KWW2007,
    TITLE = {Methods for Census 2000 and Statistical Adjustments},
   AUTHOR = {David A. Freedman and Kenneth W. Wachter},
BOOKTITLE = {The SAGE Handbook of Social Science Methodology},
   EDITOR = {William Outhwaite and Stephen P. Turner},
PUBLISHER = {Sage Publications},
  ADDRESS = {London},
  CHAPTER = {13},
    PAGES = {232--245},
     YEAR = {2007},
     ISBN = {1412901197,9781412901192},
   }

        

@incollection{FREEDMAN:2003:OTLOIT:MR1993596,
   AUTHOR = {Freedman, David A. and Wachter, Kenneth W.},
    TITLE = {On the likelihood of improving the accuracy of the census through
             statistical adjustment},
   EDITOR = {Goldstein, Darlene R.},
BOOKTITLE = {Science and Statistics: A {F}estschrift for {T}erry {S}peed},
     ISBN = {0940600560, 9780940600560},
   SERIES = {IMS Lecture Notes---Monograph Series},
   VOLUME = {40},
     YEAR = {2003},
    PAGES = {197--230},
PUBLISHER = {Institute of Mathematical Statistics},
  ADDRESS = {Beachwood, OH},
  MRCLASS = {62-06 (00B30)},
       ID = {info:mr/MR1993596 (2004d:62008)},
 ABSTRACT = {In this article, we sketch procedures for taking the census, making
             adjustments, and evaluating the results. Despite what you read in
             the newspapers, the census is remarkably accurate. Statistical
             adjustment is unlikely to improve on the census, because adjustment
             can easily put in more error than it takes out. Indeed, error rates
             in the adjustment turn out to be comparable to errors in the
             census. The data suggest a strong geographical pattern to such
             errors, even after controlling for demography---which contradicts a
             basic premise of adjustment. The complex demographic controls built
             into the adjustment mechanism turn out to be counter-productive.
             Proponents of adjustment have cited \u201closs function analysis
             \u201d to compare the accuracy of the census and adjustment,
             generally to the advantage of the latter. However, the chosen
             analyses make assumptions that are highly stylized, and quite
             favorable to adjustment. With more realistic assumptions, loss
             function analysis is neutral, or favors the census. At the heart of
             the adjustment mechanism, there is a large sample survey---the post
             enumeration survey. The size of the survey cannot be justified. The
             adjustment process now consumes too large a share of the Census
             Bureau's scarce resources, which should be reallocated to other
             Bureau programs.},
   }

        

@article{FREEDMAN:2001:CASPOI,
   AUTHOR = {Freedman, David and Wachter, Kenneth W.},
    TITLE = {Census adjustment: Statistical promise or illusion?},
  JOURNAL = {Society},
   VOLUME = {39},
     YEAR = {2001},
    PAGES = {26--33},
       ID = {info:doi/10.1007/BF02712617},
     NOTE = {without abstract},
   }

        

@article{FREEDMAN:2001:APMFCA:0990.91050,
   AUTHOR = {Freedman, David A. and Stark, Philip and Wachter, Kenneth W.},
    TITLE = {A probability model for census adjustment},
  JOURNAL = {Mathematical Population Studies},
     ISSN = {0889-8480},
   VOLUME = {9},
     YEAR = {2001},
    PAGES = {165--180},
ZBLABSTRACT = {The census can be adjusted using capture-recapture techniques:
             capture in the census, recapture in a special Post Enumeration
             Survey (PES) done after the census. The population is estimated
             using the Dual System Estimator (DSE). Estimates are made
             separately for demographic groups called post strata; adjustment
             factors are then applied to these demographic groups within small
             geographic areas. We offer a probability model for this process, in
             which several sources of error can be distinguished. In this model,
             correlation bias arises from behavioral differences between persons
             counted in the census and persons missed by the census. The first
             group may on the whole be more likely to respond to the PES: if so,
             the DSE will be systematically too low, and that is an example of
             correlation bias. Correlation bias is distinguished from
             heterogeneity, which occurs if the census has a higher capture rate
             in some geographic areas than others. Finally, ratio estimator bias
             and variance are considered. The objective is to clarify the
             probabilistic foundations of the DSE, and the definitions of
             certain terms widely used in discussing that estimator.},
 ZBLCLASS = {91D20},
       ID = {info:zbl/0990.91050},
   }

        

@article{WACHTER:2000:MLHWCD,
   AUTHOR = {Wachter, Kenneth W. and Freedman, David A.},
    TITLE = {Measuring local heterogeneity with 1990 census data},
  JOURNAL = {Demographic Research},
   VOLUME = {3},
   NUMBER = {10},
     YEAR = {2000},
      URL = {http://www.demographic-research.org/volumes/vol3/10/},
 ABSTRACT = {A sample covering 204,394 blocks from the 1990 U.S. Census permits
             measurement of residual heterogeneity from local area to local area
             after controlling by stratification for demographic characteristics
             such as race, ethnicity, age, sex as well as geographic
             characteristics such as region and place-type. The local areas have
             populations on the order of 10,000 people. The variables studied
             are four indices of enumeration difficulty. The results show that
             variance due to heterogeneity from area to area is comparable to
             (if not larger than) variance from stratum to stratum and can be
             expected to dominate sampling variance---especially with samples as
             large as the ones used in the U.S. Census Bureau's Post-Enumeration
             Surveys. These findings constrain the viable estimation strategies
             that could be employed for local tallies in the U.S. 2000 Census.},
   }

        

@article{WACHTER:2000:TFCCBI,
   AUTHOR = {Wachter, Kenneth W. and Freedman, David A.},
    TITLE = {The fifth cell: Correlation bias in U.S. census adjustment},
  JOURNAL = {Evaluation Review},
   VOLUME = {24},
     YEAR = {2000},
    PAGES = {191--212},
      URL = {http://erx.sagepub.com/cgi/content/abstract/24/2/191},
 ABSTRACT = {One form of error that can affect census adjustments is correlation
             bias, reflecting people who are doubly missing--from the census and
             from the adjusted counts as well. This article presents a method
             for estimating the total national number of doubly-missing people
             and their distribution by race and sex. Application to the 1990
             U.S. census adjustment leads to an estimate of 3 million doubly-
             missing people. Correlation bias is likely to be a serious problem
             for census adjustment in 2000. The methods of this article are well
             suited for measuring its magnitude.},
   }

        

@article{BROWN:1999:SCIC,
   AUTHOR = {Brown, Lawrence D. and Eaton, Morris L. and Freedman, David A. and
             Klein, Stephen P. and Olshen, Richard A. and Wachter, Kenneth W.
             and Wells, Martin T. and Ylvisaker, Donald},
    TITLE = {Statistical controversies in census 2000},
  JOURNAL = {Jurimetrics Journal},
   VOLUME = {39},
     YEAR = {1999},
    PAGES = {347--375},
      URL = {http://www.stat.berkeley.edu/~census/537.pdf},
 ABSTRACT = {This paper discusses Census 2000, focusing on sampling techniques
             for adjustment. Experience with similar adjustment methods in 1980
             and 1990 suggests that the design for Census 2000 is quite risky.
             The use of sampling to obtain population counts for apportionment
             was rejected by the Supreme Court as violating the Census Act.
             Statistical adjustments for the purpose of redistricting may or may
             not be constitutional.},
   }

        

@article{FREEDMAN:1996:PFTCIT,
   AUTHOR = {Freedman, David A. and Wachter, Kenneth W.},
    TITLE = {Planning for the census in the year 2000},
  JOURNAL = {Evaluation Review},
   VOLUME = {20},
     YEAR = {1996},
    PAGES = {355--377},
       ID = {info:doi/10.1177/0193841X9602000401},
 ABSTRACT = {Considering the difficulties, the Census Bureau does a remarkably
             good job at counting people. There are two current techniques for
             evaluating or adjusting the census: (a) demographic analysis uses
             administrative records to make independent population estimates,
             which can be compared to census counts; (b) datafrom an independent
             sample survey can be used to estimate population coverage. If there
             is a large undercount, these techniques may be accurate enough for
             adjustment. With a small undercount, it is unlikely that current
             adjustment methodologies can improve on the census; instead,
             adjustment could easily degrade the accuracy of the data. This
             article reviews plans for Census 2000, including proposals for
             adjustment, in the light of past experience.},
   }

        

@article{FREEDMAN:1994:HACAFT,
   AUTHOR = {Freedman, David A. and Wachter, K.W.},
    TITLE = {Heterogeneity and census adjustment for the intercensal base},
  JOURNAL = {Statistical Science},
   VOLUME = {9},
     YEAR = {1994},
    PAGES = {476--485},
       ID = {info:doi/10.1214/ss/1177010260},
 ABSTRACT = {Current techniques for census adjustment involve the ``synthetic
             assumption'' that undercount rates are constant within ``post-
             strata'' across geographical areas. A poststratum is a subgroup of
             people with given demographic characteristics; poststrata are
             chosen to minimize heterogeneity in undercount rates. This paper
             will use 1990 census data to assess the synthetic assumption. We
             find that heterogeneity within poststrata is quite large, with a
             corresponding impact on local undercount rates estimated by the
             synthetic method. Thus, any comparison of error rates between the
             census and adjusted counts should take heterogeneity into account.},
   }

        

@article{FREEDMAN:1994:ATCOLF,
   AUTHOR = {Freedman, David A. and Wachter, K.W. and Cutler, R. and Klein, S.},
    TITLE = {Adjusting the census of 1990: Loss Functions},
  JOURNAL = {Evaluation Review},
   VOLUME = {18},
     YEAR = {1994},
    PAGES = {243--280},
       ID = {info:doi/10.1177/0193841X9401800301},
 ABSTRACT = {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.},
  EDNOTES = {<contact>http://erx.sagepub.com/cgi/content/abstract/18/3/243 gives
             subtitle as one of the authors},
   }

        

@article{FREEDMAN:1993:ATCOTS,
   AUTHOR = {Freedman, David A. and Wachter, Kenneth W. and Coster, Daniel C.
             and Cutler, Richard and Klein, Stephen P.},
    TITLE = {Adjusting the census of 1990: The smoothing model},
  JOURNAL = {Evaluation Review},
   VOLUME = {17},
     YEAR = {1993},
    PAGES = {371--443},
       ID = {info:doi/10.1177/0193841X9301700401},
 ABSTRACT = {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.},
   }