Estimating the Missing People in the Uk 1991 Population Census

© 2015 H.M. Wasiul Islam. All rights reserved.

No part of this book may be reproduced, stored in a retrieval system, or transmitted by any means without the written permission of the author.

Published by AuthorHouse 12/03/2015

ISBN: 978-1-5049-9420-0 (sc)

ISBN: 978-1-5049-9423-1 (e)

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Contents

Acknowledgements

Chapter 1.   Introduction

Chapter 2.   Census Evaluation: Review of the Literature

2.1 Introduction

2.2 Demographic Analysis

2.2.1 Estimation of subgroups

2.2.2 Estimation of Components

2.2.3 Merits and Demerits

2.3 Post Enumeration Survey (PES)

2.3.1 Dual-System Estimate (DSE)

2.3.3 Alternative Estimation

2.3.4 PES in the U.K.

2.3.5 PES in the U.S.

2.3.6 PES in Bangladesh

2.3.7 Triple system

2.4 Administrative Record Match (ARM)

2.5 Reverse Record Check (RRC)

2.6 Multiplicity

2.7 CensusPlus

2.8 SuperCensus

2.9 Conclusions

Chapter 3.   1991 Census and Census Validation Survey (U.K.)

3.1 Census

3.2 Census Validation Survey (CVS)

3.3 Sample Design and Methodology of (CVS)

3.3.1 Sample Design:

3.3.2 Stratification

3.3.3 Selecting the Sample to Check Enumeration Error:

3.3.4 Handling unresolved addresses

3.4 CVS Results and Discussion

3.5 Conclusions

Chapter 4.   Comparison Among Different Dual System Estimates

4.1 Introduction

4.1.1 Greenfield Method

4.1.2 El-Sayed Nour Method

4.2 National Estimate of Net Undercount/ Overcount:

4.3 Data for the Dual System Method

4.4 Procedure of the C-D Technique

4.4.1 Estimate by Race

4.4.2 Estimate by Sex

4.5 Procedure of the Greenfield Method

4.6 Procedure of El-Sayed Nour Technique

4.7 Results and Discussion

4.8 Conclusion

Chapter 5.   Triple System Estimation Using Log-linear Models

5.1 Introduction

5.2 Data for Triple System Method:

5.3 Log-Linear Models

5.4 Triple-System Estimation

5.5 Models for Varying Catchability

5.5.1 Independence Model

5.5.2 No Second Order Interaction Model

5.5.3 Quasi-symmetry Model

5.5.4 Partial Quasi-symmetry Model

5.6 Result

5.7 Conclusions

Chapter 6.   Regression Models to Estimate the Local Population

6.1 Introduction

6.2 Regression Models

6.2.1 Assumptions of the Models

6.3 Estimating the Undercount Rate

6.4 Procedure for fitting the Regression Models

6.5 Selecting Explanatory Variables

6.6 Estimating the Regression Equation

6.7 Assessment

6.8 Results

6.9 Conclusion

6.10 Discussion

Chapter 7.   Dealing With Missing Data

7.1 Introduction

7.1.1 Types of Nonobservation

7.1.2 Effect of Nonobservation

7.1.3 Compensation Procedure for Missing Data:

7.1.4 Imputation:

7.1.5 Multiple Imputation:

7.1.6 Proposed Method of Imputation

7.1.7 Introduction

7.1.8 Missing units from the six CVS samples

7.1.9 Missing units from the dual system estimation

7.2 Conclusion

Chapter 8.   Assessing Error

Introduction

8.2 Background

8.3 Empirical DSE

8.4 Total Error and Partitioning the Total Error

8.5 Sampling Error:

8.5.1 Sources of Error

8.5.2 Definition

8.5.3 Example

8.5.4 Jackknife Method in the Case of a Stratified Sample

8.5.5 Random Groups Method

8.6 Model Error

8.6.1 Sources of Error

8.6.2 Definition

8.6.3 Measurement

8.6.4 Example

8.7 Measurement Error

8.7.1 Estimate of (ŇP – x+1)

8.7.2 Estimate of (ŇCE - x1+)

8.7.3 Estimate of (ŇCP - x11)

8.8 Conclusion

Chapter 9.   Recommendations

9.1 Cost-benefit Analysis

9.2 Design Issues

9.3 Response and Coverage Issue

9.4 Sampling and Statistical Estimation

9.5 Creating Administrative Record Lists

9.6 Small Area Estimates of Population

9.7 Hard-to-Enumerate Population

9.8 Conclusion:

Appendix 1

Appendix 1.1

Appendix 1.2

Appendix 1.3

Appendix 2

Appendix 2.1

Appendix 3

Appendix 4

Bibliography

In memory of my parents: Badrul Islam Nurnunnabi

And

Hasina Banu

Acknowledgements

I express my sincere gratitude to my M. Phil supervisor Mr. C. A. O’Muircheartaigh, Senior Lecturer, Department of Statistics, London School of Economics and Political Science (LSE), for his keen interest, co-operation, guidance over the whole duration of this study.

I also acknowledge with gratitude the support I received from Professor Ian Diamond, Chairman, Social Statistics Department, Southampton University. Thanks also go to Dr. M. Knott, Chairman, Statistics Department, LSE, for his valuable advice and to Dr. Irini Moustaki, Lecturer, Department of Statistics, LSE, for her help and support right from the beginning as a friend.

Regarding publishing the original M. Phil thesis dissertation into this book form, I have received valuable help, encouragement and practical support from Dr. Arbab Akande, a Fellow of the Chartered Institute of Profesional and Development, London; Mr Md. Soukat Ali, writer and author; Mr. Md. Shahadat Ali, Rtd. Civil Servant of Gants Hill, Ilford, London, without which this book would may not have been possible.

Finally, this book simply would never have been completed without help from home. My wife Mehbuba Shirin and son Sakkhar Islam not only provided encouragement and support in completing this book but also share all the difficulties we faced in life.

Chapter 1

Introduction

In recent years, interest in improvements in the taking of censuses of population and housing have been realized in many countries. This is due, among other things, to their growing use in formulating policies and programmes, in the dispensation of Government funds, and in planning. In 1961, England and Wales first marked the formal statistical checks on the completeness and quality of census enumeration. Some limited demographic checks, using birth and death Registration records for instance, took place immediately after the 1951 census; but these were not as extensive, nor as pre-planned, as those devised for subsequent censuses (OPCS, 1983). The best results of the 1991 Census Validation Survey suggested that the census missed about 288,000 people or 0.5 per cent of the population present in private households in England and Wales on census night. Among these people 162,000 were residents, 98,000 were visitors, while the residential status of the remaining 28,000 was not known. The CVS estimates (best) of overall undercoverage of household spaces was estimated as 198,000 — a little under one percent of the total while the estimated overall undercoverage of households was 125,000 — a little over 0.5 percent of the total. However, there was no statistically significant difference between the net underenumeration of different areas — of household spaces, households, and resident households (Heady et al, 1994).

The degree of under-enumeration from the 1981 PES (Post Enumeration Survey) is of the same magnitude as the extent of under-enumeration established in the coverage checks of the 1991 CVS. From the 1981 PES it was observed that there was a big difference between London — especially Inner London — and the rest of the country. The same result held in 1991. Under enumeration of persons in the Inner London area is estimated to be about 2.5 per cent compared with 0.3 per cent outside London – the level in Outer London is about 1.0 per cent. Some 2.8 per cent of households were missed in Inner London compared with about 0.4 per cent elsewhere. From the U.S. census reports it is also known that i) men tend to be missed at a higher rate than women and ii) blacks tend to be missed at a higher rate than non-blacks. If the rate of net undercount were nearly constant across races, sexes, age-groups and regions, few people would be concerned. Some national and Local Government agencies rely on census figures to determine how to allocate funds and resources to various Government programs. If large demographic groups are differentially undercounted in the census, such funds and resources, which are allocated to administrative units partly or wholly on the basis of their estimated sizes, are inequitably distributed. A group with a large relative undercount receives somewhat less of these resources per capita. Moreover, all inhabitants of an area with a high proportion of members of such a group probably suffer as well.

In this situation it is very important to investigate the ability of different methods to identify the extent of undercoverage. It is needless to mention that the ability of different methods to produce accurate estimates essentially depends on accurate and reliable data. If the quality of the data collected is not good, even the best method of estimating the undercoverage will not be able to give good estimates.

The purpose of this book is to compare different methodologies for estimating census coverage and to investigate how well they work and to provide a methodology for distributing estimated missing people throughout the country. This dissertation also gives an imputation technique and develops a methodology to estimate the total error of any census and/or survey.

An Outline of the book

The book is divided into nine chapters. Chapter 2 contains a review of the different methods of estimating the undercoverage around the world with their merits and demerits. We mainly focus on one of the post enumeration survey estimates known as dual system estimate (DSE). We describe the method in detail and also the main methodological problems of the method. We also discuss some alternative estimates of the DSE as well as the triple system method of estimation.

In Chapter 3 we describe the 1991 U.K. Census process and methodology and sampling design of the Census Validation Survey (CVS). Some of the CVS findings were also compared with demographic estimates and we discuss the main reasons for the discrepancies between the two estimates. We also discuss the ways of handling the unresolved cases of the census by the CVS interviewers.

In Chapter 4 we present an example with the help of hypothetical data of dual-system models to estimate the missing people as well as the undercount of the census. We present three alternative estimation procedures of the people missed by both the census enumerators and the CVS interviewers. The three estimation procedures were chosen because we believe these three estimates will produce a range of missing people under most of the data collection situations.

In Chapter 5 we give details of the log-linear model as an extension of the dual system method to estimate the undercoverage by using information from three different sources one of which may be administrative lists. We discuss four alternative log-linear models and use hypothetical data to estimate the population total.

In Chapter 6 we present a regression model to estimate the local authority population total. We discuss in detail why and how we estimate the dependent variable for the regression model for 403 local authorities. For evaluation purposes we use another independent estimate known as the `Goldstandard’ estimate.

In Chapter 7 we briefly describe several types of missing unit, the effect of Missing unit, different methods of imputation and our proposed methods of imputation.

In Chapter 8 we deal with the total error model. We divide the total error into three components and describe each of these components with the complete procedure of measuring the error from each of these three components.

In Chapter 9 we present recommendations which we believe are necessary for the improvement of the future U.K. census.

Chapter 2

Census Evaluation: Review of the Literature

2.1 Introduction

Pre-modern censuses or counts of population were carried out with some specific purpose - such as taxation or military conscription - in mind. The modern censuses are much more than headcounts: it also implies the collection and publication of a great variety of information on the characteristics or `attributes’ of the individual, such as age, sex, marital status, birthplace, economic activity, etc., and the composition of the households among which the population is distributed (Dewdney, 1983). In 1967 the United Nations defined the census as the total process of collection, compiling, evaluating, analysing and publishing demographic, economic and social data pertaining, at a specified time, to all persons in a country or in a well-defined part of a country.

According to Yaukey (1985), a modern census has four key elements: (1) It should be universal, that is everyone in the census area should be enumerated. (2) It should be simultaneous, that is, everyone should be counted at the same time to minimize the underenumeration or overenumeration. (3) It should be periodic, that is, everyone should be counted at regular intervals in order to permit measurement of changes in the population. (4) Finally a census should be individual, that is, the enumeration of each should include different descriptive variables about that person (age, sex, race, etc.) so that individual-level variables can be cross-classified.

Every census in every country faces a challenge to meet the above criteria. The two strongest criticisms of the present census are first that unit costs have increased significantly and second the problem of differential undercount by sex, race and region. The volume of gross error also contributes to the growing momentum and advocacy for fundamental change in the census operations.

An important indicator of census data quality is to measure the gross census error. Such measures consider not only omissions (that produce undercounts) but also double or multiple enumerations (that produce overcounts). There are several ways to measure these errors but demographic and survey estimates are the main and more popular. In the following we will describe some of the methods with their advantages and disadvantages.

2.2 Demographic Analysis

The general methods of making demographic estimates of the population for valuation purposes are based on the estimates of the components of population change, which can occur only in two ways–through reproductive change (also known as natural increase) and through migration. If the number of births (B) during a given period exceeds the number of deaths (D), the reproductive change is positive, and the population increases in size. On the other hand if the number of deaths exceeds the number of births, reproductive change is negative and the population declines. A similar situation holds for migration. If the number of immigrants (I) exceeds the number of emigrants (E), the population increases; if the reverse migration situation exists the population becomes smaller.

In countries in which vital registration system and migration data are relatively complete or accurately measured, the population estimates Pt for census evaluation based on demographic analysis are derived by the basic demographic accounting equation.

Pt = B - D + I – E                                          (2.1)

Current population totals can also be estimated by using census counts from a previous year in conjunction with vital registration and migration data. This was the practice in the U.K. 1991 demographic estimates, in which 1981 census counts were used as baseline estimates Pb (adjusted for coverage error), with recorded births and recorded deaths data used to estimate the natural growth of the population in the last ten years and immigration and emigration data used for estimating the net effect of migration at national level.

Pt = Pb + B - D + I – E                                          (2.2)

Population estimates from the above equation are then compared with the corresponding census counts to yield a measure of net census coverage.

Coverage error = Demographic estimate – Census count                             (2.3)

In the U.S. the estimated total population Pt based on demographic analysis in 1990 involves first developing estimates for the population in various categories, such as age-sex-race groups. The particular method used to estimate the population total for the various demographic subgroups depends primarily on the nature and availability of the required demographic data. Different demographic techniques were used for the population under age 55, 55-64, and 65 and over; the total population is the sum of these subgroups. In the following we will discuss some of them in brief.

2.2.1 Estimation of subgroups

Age under 55

Estimates of the population under age 55 in 1990 are based directly on the estimates of the components of population change for both sexes and each race category. Births for 1935 to 1990 corrected for underregistration are carried forward to later census dates with statistics and estimates for deaths, immigration, and emigration. The population estimates P1 are derived by the basic demographic accounting relationship:

P1 = B - D + I – E                                          (2.4)

Age 55-64

For the population age 55-64 different analytic techniques are used to develop the demographic estimates in 1990 for this age group as there were no national data on registered births and underregistration factors for this group (i.e. birth from 1925 to 1935). Estimates for births to the white population for 1925- 1935 developed by Whelpton (1950) are carried forward to 1940 with lifetable survival rates and to 1990 with components of change to estimate the population age 55-64. Coale and Rives (1973) developed revised population estimates for the black population which were carried forward to 1990 with components of change. Estimates for the other races of the population aged 55-64 were derived from assumptions about the consistency of age patterns of coverage in earlier censuses and the use of expected sex ratios. The equation used to estimate the population of the age-group 55-64 is,

P2 = T - D + I – E                                          (2.5)

where T is the estimate in a previous time period (1925-1935 births for White, 1960 population for Blacks, 1990 population for other Races) and D, I, and E are same as before.

Age 65 and over

In 1990 in the U.S.A. to estimate the population age 65 and over (P3), administrative data on Medicare enrollments were used for both sexes