ITEM-NON-RESPONSE AND IMPUTATION OF LABOR INCOME IN PANEL SURVEYS: A CROSS-NATIONAL COMPARISON
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ITEM-NON-RESPONSE AND IMPUTATION OF LABOR INCOME IN PANEL SURVEYS: A CROSS-NATIONAL COMPARISON Joachim R. Frickand Markus M. Grabka DIW Berlin and IZA Bonn DIW Berlin Presentation at the IARIW 29th General Conference, Joensuu, Finland, 22 August 2006 Presented by:

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ITEM-NON-RESPONSE AND IMPUTATION OF LABOR INCOME IN PANEL SURVEYS: A CROSS-NATIONAL COMPARISON

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Item non response and imputation of labor income in panel surveys a cross national comparison

ITEM-NON-RESPONSE AND IMPUTATION OF LABOR INCOME IN PANEL SURVEYS: A CROSS-NATIONAL COMPARISON

Joachim R. FrickandMarkus M. Grabka

DIW Berlin and IZA Bonn DIW Berlin

Presentation at the IARIW 29th General Conference,

Joensuu, Finland, 22 August 2006

Presented by:

Professor Ian Plewis

Centre for Longitudinal Studies

Bedford Group for Lifecourse and Statistical Studies

Institute of Education, University of London


Item non response and imputation of labor income in panel surveys a cross national comparison

Main features of the paper:

1.Item non-response for income (as for Hawkes and Plewis)

2.Panel data

3.Cross-national comparisons (SOEP, Germany; HILDA, Australia; BHPS, GB)

4.Imputation as used in the three studies.


Item non response and imputation of labor income in panel surveys a cross national comparison

  • Prevalence of income non-response:

  • HILDA<10%

  • 2. SOEP14%

  • 3. BHPS15%

  • These appear to be average prevalences – do they

  • change with the age of the panel?

  • Are there separate figures for ‘don’t know’ and

  • ‘refusals’?


Item non response and imputation of labor income in panel surveys a cross national comparison

Income non-response at time t predicts income non-

response at time t+1 (supported by Hawkes and

Plewis).

Income non-response at time t predicts attrition at

time t+1 (also supported by Hawkes and Plewis).

More generally, the literature suggests that the more

item non-response there is at time t in any

longitudinal study, the more likely is attrition at time

t+1.

This suggests that it might be worth directing more

resources at these ‘frail’ respondents.


Item non response and imputation of labor income in panel surveys a cross national comparison

Predictors of income non-response (combining waves

using (?) probits or logits):

A very strong effect of being self-employed: the self-

employed are very much less likely to report their

income (supported by Hawkes and Plewis), although

less so in Germany than in GB and Australia.

Is change in employment status associated with

change in response behaviour?


Item non response and imputation of labor income in panel surveys a cross national comparison

Two kinds of imputation methods are used:

1.Predictive mean matching from a regression model in BHPS.

2.‘Row and column’ imputation as set out by Little and Su (1989), in HILDA and SOEP.

The authors argue, on the basis of previous research, that the second method is the better of the two.


Item non response and imputation of labor income in panel surveys a cross national comparison

Both are single imputation methods, presumably

devised to fill in holes in public release datasets.

However, most of the statistical literature now

favours multiple imputation in order properly to

represent the sampling variability induced by

imputation.


Item non response and imputation of labor income in panel surveys a cross national comparison

The authors consider the effects of the imputation methods used for three issues:

1. Cross-sectional measures of inequality.

2. Longitudinal measures of income mobility.

3.Fixed effects wage regressions – are the fixed effects individuals or sweeps?


Item non response and imputation of labor income in panel surveys a cross national comparison

We collect panel data to measure and model change

and so we should perhaps focus on the effects of

imputation on change and on dynamic models.


Item non response and imputation of labor income in panel surveys a cross national comparison

The authors show that income mobility across

quintiles is considerably higher when imputed cases

are combined with observed or complete cases than

it is when using only the observed cases.

However, this difference emerges because there is

considerable mobility for the imputed cases and

some of this must be due to measurement error

generated by the imputations.


Item non response and imputation of labor income in panel surveys a cross national comparison

A difficulty here is that the authors use cross-

sectional imputation i.e. imputing an income value

for each sweep whereas the real interest is in

imputing mobility or change across sweeps.


Item non response and imputation of labor income in panel surveys a cross national comparison

Suppose we have a panel study with just two sweeps

with income measured in quintiles at each sweep

and with item non-response at each sweep.

We have three sets of information:

1.Cases with measured income at each sweep, located in the internal 25 cells of a five by five contingency table.

2.The marginal distribution for cases measured at

sweep one but not at sweep two.


Item non response and imputation of labor income in panel surveys a cross national comparison

3.The marginal distribution for cases measured at

sweep two but not at sweep one.


Item non response and imputation of labor income in panel surveys a cross national comparison

Little and Rubin (2002, Ch. 13) show how to use the

EM algorithm to estimate the contingency table for

all cases, both fully and partially classified, and this

approach (or a variant of it that accounts for the

ordering of the quintiles) might be more appropriate

for this particular question.


Item non response and imputation of labor income in panel surveys a cross national comparison

One of the interesting findings from the estimated

wage equations is that the effect of being self-

employed on wages is, for all three studies, more

positive once the imputed cases are introduced into

the analysis.


Item non response and imputation of labor income in panel surveys a cross national comparison

Concluding remarks

1.This is a very interesting and thought-provoking

paper.

2.It shows that imputation for missing income responses can alter substantive conclusions about, for example, income mobility.

3.BUT the single imputation methods currently used by these panel studies are not those most favoured in the statistical literature.


Item non response and imputation of labor income in panel surveys a cross national comparison

Concluding remarks

4.AND imputing levels and taking differences might not be the best way of imputing for change.

5.ALSO income non-response is just one facet of missing data and ideally needs to be considered along with unit non-response at the outset of the panel and attrition as the panel ages.

6.AS ALWAYS, SENSITIVITY ANALYSES ARE CRUCIAL.


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