Out of vVork,Out of Mind: Response Errors in ... - CiteSeerX

@ 1988 American Statistical Association

Journal of Business & Economic Statistics,

April 1988, Vol. 6, No.2

Out of vVork,Out of Mind: Response Errors in Retrospective Reports of Unemployment Nancy A. Mathiowetz National Center for Health Services Research, Rockville, MD 20857

Greg J. Duncan SurveyResearchCenter,Universityof Michigan,AnnArbor,MI 48106 This article examines

several aspects of the validity of retrospective

reports of unemployment,

obtained by a comparison of respondent reports with company records for a sample of workers from a large manufacturing firm. We find that reports of the total amount of unemployment in the calendar year prior to the interview are reasonably accurate, but reports of the timing of spells of unemployment within the year are much less accurate. Estimates of a regression model relating response error to measures of respondent characteristics, the length of the recall period, the difficulty of the reporting task, and the likely salience of the events show that the salience measures are clearly the most important and that time and demographic factors are relatively unimportant predictors of response error.

KEY WORDS: Telescoping; Memory decay; Estimation versus episodic recall.

1. INTRODUCTION

there is substantial error in reports of the timing of unemployment spells within the year. Short unemployment spells are likely to go unreported. A detailed examination of the month-to-month patterns of response error shows that the length of the recall period is, surprisingly, a relatively unimportant explanatory variable. Much more important is the salience of the month's employment events as measured by the length of the unemployment spell in which it is embedded. This article contains six remaining sections. Sections 2 and 3 describe the overall research design of the validation study and the methods used to collect and validate the unemployment reports. Section 4 shows patterns of correspondence between the annual amounts of unemployment as reported in the interview and in the company record. Section 5 describes analogous patterns of reports of spells of unemployment. Section 6 focuses on monthly response-error patterns-providing first simple description and then the specification and estimation of a response-error model that incorporates measures of time salience and task factors as independent variables. Section 7 summarizes our results.

Past studies of the reliability of survey-respondent reports of unemployment have produced a variety of unsettling results. Whether comparing amounts of unemployment reported in different surveys using different methodologies (Horvath 1982; Morgenstern and Barrett 1974) or comparing reports on overlapping periods from the same respondents in successive panel waves (Bowers and Horvath 1984; Statistics Canada 1982) all of these studies have found substantial recall error, and some have also indicated that the error increases as the recall time period is extended. But because these studies have been forced to rely on comparisons of potentially erroneous respondent reports, they have never been able to estimate precisely the extent and temporal patterns of the response errors found. The research reported in this article is based on a validation study in which reports of unemployment obtained in interviews of company employees are checked against a highly accurate company record report of actual unemployment experienced by those employees. Although limited to a sample of workers from a single large company, this method provides precise estimates of response error in the reports of unemployment and permits the estimation of a s9phisticated model of response error that incorporates as explanatory variables measures of the length of the recall period, the likely salience of the unemployment event, and the overall reporting task facing the respondent. Our specification of the model is guided by recent theoretical and empirical work on memory. We find that the extent of report error on annual amounts of unemployment is relatively small; however,

2.

RESEARCH DESIGN

The data presented in this article are part of a larger validation study designed to assess the quality of data obtained in the Panel Study of Income Dynamics (PSID) (Survey Research Center 1984). Respondents were selected from the personnel records of an established manufacturing company with several thousand employees. The hourly work force for this company is completely unionized, and all of the workers, both hourly and salaried, work full time. The company work force is considerably older (with more job tenure) than would 221

222

Journal of Business & Economic Statistics, April 1988

be true of a national sample of workers, as a result of layoffs and relatively few new hires in the two years prior to the interview. These deviations were offset by a sampling procedure that stratified the employee list by age and type of worker (hourly vs. salaried) and selected a larger proportion of younger and salaried workers. The resulting sample was evenly divided between salaried and hourly workers and had a fairly uniform age distribution. Even with this sampling plan, however, the information on unemployment reflects largely temporary layoffs of manufacturing workers. The unemployment experience of neither workers in other industrial sectors nor the very long-term unemployed is represented in this study. A substantial fraction of company employees were laid off at least once during the investigation period. Layoffs tended to be of short duration and affected many workers. There was a particularly high level of layoffs, mostly lasting two weeks or less, approximately eight to nine months prior to the June 1983 interview. Interviews were conducted by telephone using a questionnaire similar to that used in the PSID, with 78.3% of the 520 potential respondents participating. Nonresponse rates across the various sampling strata were not significantly different. A more detailed explanation of study procedures was given in Duncan and Mathiowetz (1985). 3.

METHODS

Unemployment information was collected in two ways, requiring different estimation and recall techniques on the part of the respondent. The first sequence of questions involves estimation procedures to account for weeks of work and nonwork during the two calendar years prior to July 1983. The sequence of questions requires that the respondent account for the year's 52 weeks by estimating the number of weeks lost from work because of unemployment, vacation, illness, and strikes. Our analysis focuses on the reported amounts of time the respondent was laid off from the company and ignores periods of nonwork due to health, vacation, or disciplinary reasons. The key questions were, "Did you miss any work in 1982 (1981) because you were unemployed or temporarily laid off? (If yes) How much work did you miss?" Other questions in the sequence distinguish work time lost because of illness, vacation, and strike. These questions are identical to those asked in the PSID. Responses to these questions were converted to hours using a conversion factor of eight hours per working day. A separate sequence of questions focuses on the respondent's ability to recall accurately specific unemployment episodes. Months of unemployment were reported in the interview in response to the following questions: "Were there any periods since the beginning of the year before last, January, 1981, when you were unemployed and looking for work or temporarily laid off for a week or more?" "What month(s) and year(s)

(was that/were those)?" "Any other such periods?" "Were there any periods since the beginning of the year before last, January, 1981, when you were completely out of the labor force, that is, neither unemployed nor temporarily laid off nor looking for work for a week or more?" Rather than estimate total unemployment for a given year, the task requires that the respondent retrieve specific episodes from memory, a task usually believed to be more difficult than estimation (Andersen, Kasper, and Frankel 1979; Tversky and Kahneman 1973). Detailed employee records covering the same reference period permitted precise measurement of the validity of each respondent's report of both total annual unemployment and each month's employment status. Strictly speaking, the company records could validate only reports of periods of time when an individual was not working for that company. Periods of employment with other firms were identified in the interview. Only four respondents reported employment with a firm other than the company of interest. These respondents were eliminated from the analysis, since it would be difficult to validate this secondary employment and, therefore, assess their reports of unemployment. There was no attempt to distinguish between states of "unemployment" and "out of the labor force," since no validation of the distinction was possible. Validating the first set of questions about calendar-year unemployment provides a means of assessing a respondent's ability to make accurate estimations. Validation of the second set on months in which unemployment occurred provides information on the accuracy of episodic recall. Although previous general research is inconclusive as to which procedure will yield more accurate responses, evidence from previous survey research appears to indicate that estimation procedures are more precise (Andersen et al. 1979). It is important to note at this point why the company records are regarded as infallible and all discrepancies are credited as respondent error. Several authors (e.g., Marquis 1981) have cited the potential overestimation or underestimation of error caused by the design of a validation study. Retrospective record checks, in which respondents' reports are validated, underestimate omissions, since only reported events are verified. Similarly, prospective record checks, which sample from events and then question respondents, have the potential for overestimating telescoping errors and cannot accurately measure underreporting, since the universe of events is not enumerated completely. The present design in which the employment records of all respondents are reviewed to assess unemployment spells eliminates these problems. As noted previously, those respondents who have some events outside the universe of interest, specifically employment outside the sampled company, are eliminated from the analysis. Errors in coding the employment records and matching are also not considered significant, since both tasks were completed by us.

Mathiowetz and Duncan: Response Errors in Reports of Unemployment

4.

REPORTS OF ANNUAL UNEMPLOYMENT

and considerably more of it, in relative terms, in 1981, although the average simple difference between interview and record reports of unemployment was not significantly different from 0 in either year. But, although these insignificant simple differences indicate little bias, the size and statistical significance of the average absolute differences between interview and record reports indicate that there is considerable response-error variance. The average absolute difference between interview and record reports was 52 hours in 1982 and 45 hours in 1981. Are these amounts large or small for most analytic purposes? Perhaps the single best summary measure of the relative error in the unemployment reports is the ratio of the error variance of the interview report to the true variance of the company reports. This ratio (not shown in Table 1) is .129 for 1982 and four times as large (.518) for 1981. If VI is the interview report of unemployment hours and VR is the company record report, then this ratio is var( VI - VR)/var( VR). Suppose that the actual amount of unemployment (VR) is related to some dependent variable Y according to the bivariate regression relationship

For some analytic purposes it is sufficient for unemployment to be estimated on an annual basis. Evidence from the validation study suggests that the July 1983 interview reports of annual unemployment for 1982 have some error, but that it is not unacceptably high for most purposes. The relative error in the reports of unemployment in 1981 is substantially and perhaps unacceptably higher. Table 1 provides a simple description of the pattern of response error in reporting annual unemployment. The left half of the table shows a simple cross-classification of respondents reflecting whether or not any unemployment is recorded in the company record or reported in the interview. The overwhelming majority of the 37% of respondents shown by company records to have experienced unemployment in 1982 reported unemployment in the interview. There was considerably less actual unemployment in 1981 and considerably more reporting error; nearly half of the 26% of the respondents who experienced some unemployment failed to report any for that year. There was also a small fraction of respondents each year who reported unemployment in the interview although the company record showed no evidence of unemployment. The higher 1981 rate of omissions (12% vs. 1982's 3%) is the product of two confounding factors. First, the longer recall period for a respondent's reports of 1981 activities may contribute to higher rates of omission; however, the nature of 1981 unemployment spells is different from that for 1982. In 1981,.63% of the spells were one week in duration. This compares with only 34% of the 1982 spells lasting one week. As will be discussed in later sections, shorter spells are more difficult for the respondent to recall. The confounding of these two factors makes it impossible to assess their relative importance with respect to yearly estimation. The right half of Table 1 describes the differences in the annual unemployment hours appearing in the company record and those reported in the interview. Many of the patterns noted previously are also evident in these calculations. There was some underreporting in 1982 Table 1.

Summary of Response

e;

1982 unemployment 1981 unemployment Simple change (1982-1981) Absolute change (1982-1981)

In other words, the .129 error-to-true variance ratio for the interview report of 1982 unemployment hours indicates that the coefficient on an error-free unemployment measure used as an independent variable in a bivariate regression is 12.9% larger than the coefficient that would be obtained from using the erroneous measure. Error-to-true variance ratios for reports of prioryear unemployment hours are roughly comparable to ratios for reports of annual earnings, but they are con-

Error for Reports of 1982 and 1981 Annual Unemployment

Mean hours

None Some

Some Some

Some Some

Total

Interview

Record

Simple" difference

Absoluteb difference

59 71

4 3

3 12

34 14

100 100

169 39

189 63

-11 -16

52" 45

131

126

5

151

151

0

· Meansfor simpledifferencesdo not equaldifferencesin meanscausedby itemnonresponse. H: .. = 0 (two.tailed test).

Source:

PSID Validation Study.

Hours

None None

NOTE: Number 0/ observaUons lor the table ;s 387. b H: ,. = 0 (one-tailed test). cp < .05. d P < .01.

- N(O, u~).

If reported unemployment is related to actual unemployment according to VRi = VIi + V;and cov(v;, ei) = cov( Vi>Y;) = cov( Vi, e;) = 0, then the estimate of PI obtained from using VI as a measure of VR in the regression is given by

Percent of sample in which Company record shows: Interview shows:

223

77d

224

Journal of Business & Economic Statistics, April 1988

siderably smaller than ratios for reports of annual work hours (see Duncan and Mathiowetz 1985). Calculations of the simple and absolute change in the amount of unemployment between 1981 and 1982 as shown in the interview reports and the company record are also shown in Table 1. It is often thought that estimates of change constructed from differencing two reports of levels are likely to overstate the true amount of change because of measurement error. The final row shows that this is not necessarily true. The average absolute change in the interview report of unemployment hours between the two years turns out to be exactly the same as the average absolute change in unemployment hours according to company records. 5.

UNEMPLOYMENT SPELLS

A second way of casting information about the unemployment experiences of the respondents is in terms of spells. Company records gave the precise dates on which all spells of unemployment among its workers began and ended. The information obtained in the interviews was not as precise, dating unemployment only to the month in which it occurred. Nevertheless, it was almost always possible to determine whether the respondent reported accurately each spell of unemployment that appeared in the company records. Since respondents were only asked to report months in which unemployment occurred, any spell falling completely within the months reported was considered an accurate report. For example, if the respondent reported unemployment in September, and the company record indicated that there were two one-week spells in September, each spell was counted as reported accurately. Table 2 shows the performance of respondents in reporting unemployment spells of various lengths. It is obvious that respondents have great difficulties in recalling unemployment spells, especially short ones. Twothirds (66%) of all unemployment spells that appeared in the company records were not reported in the interview. Even very long spells (more than 29 weeks) were seriously underreported; the fraction not reported was more than one-third. And only one-fourth of very short Table 2.

Length of spell in weeks

Whether Spells of Unemployment Were Reported in Interview, According to Length of Spell

Was spell reported in interview? No. of spells

Percent of spells

Percent yes

Percent no

1 2 3-4 5-12 13-20 21-28 29-t

243 117 31 14 34 23 19

51 24 6 3 7 5 4

25 34 39 43 56 51 63

75 66 61 57 44 49 37

All

481

100

34

66

Source:PSIDValidationStudy.

spells lasting one week were reported. These findings contrast sharply with the relatively greater reliability of the more aggregate measures of annual unemployment hours, discussed previously, and argue for extreme caution in analyzing spell-level information of retrospective reports of unemployment. 6.

MONTHLY PATTERNS OF RESPONSE ERRORS

The third and most informative way of analyzing the validity of retrospective interview reports of unemployment is at the monthly level. Not only is it possible to describe patterns of response errors over the 30month recall period, but information about each month can be incorporated into a model of response error as well. Throughout Sections 6.1 and 6.2, response error for unemployment status in a given month is defined as existing if either (a) the company record showed at least one week of unemployment for that particular month and the respondent reported none or (b) the company record indicated no unemployment of a week or more and the respondent reported a week or more of unemployment in that month. This procedure ignores response error in the respondent's dating within a month; more precise measurement was not possible. No distinction is made between overreports and underreports in the descriptive analysis at the monthly level, however, that distinction is incorporated implicitly into the response-error model we estimate.

6.1 Simple Relationship Between Length of Recall and Response Error Figure 1 presents the error rates in unemployment reports according to the number of months between the 45

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Figure 1. Peraent Error in Reports of Unemployment by Time Between Interview and Event: m, respondents with one or more unemployment spells; -, total sample. Source: PSID Validation Study.

Mathiowetz and Duncan: Response Errors in Reports of Unemployment

interview and the month in question for two groups of interest-all respondents and the subsample of respondents for whom company records showed at least one unemployment spell at any time in the 30-month recall period. The greater difficulty of the reporting task for those with at least one episode of unemployment within the previous 30 months is reflected in the higher error rates in the subgroup.

6.2 PastWork Several factors, including interviewer behavior, question wording, the social desirability of the response, the saliency of the event, the number of related events' to be distinguished, and the length of recall period have all been linked to a respondent's ability to report accurately (e.g., Bradburn, Sudman, and Associates 1979; Cannell, Fisher, and Bakker 1965; labine, Straf, Tanur, and Tourangeau 1984; Lansing, Ginsburg, and Braaten 1961; Tulving and Thomson 1973). Three of these factors will be examined in the present analysis-length of recall period, saliency, and the overall difficulty of the reporting task. As not~d before, the general conclusion of work on varying recall periods has been that increasing the length of recall periods results in a monotonic increase in response error. The saliency of an event has been noted by several researchers as an important factor affecting recall ability (Linton 1982; Loftus 1979). Although the definition of salience is somewhat imprecise, a salient event appears to evoke emotion at the time of occurrence, mark a transition point, have economic or social costs and benefits, or have continuing consequences after the event. Clearly unemployment could qualify as salient under each part of this definition. Furthermore, the first and third criteria suggest that longer unemployment spells are likely to be more salient than short spells. The difficulty of the reporting task facing the respondent is an additional correlate of response error to be examined in this analysis. Interference theory (Crowder 1976) suggests that the probability of reporting a particular event is inversely related to the number of related events an individual experiences. Therefore, individuals with a history of more than one unemployment spell are expected to report less accurately than other individuals about their employment status in any given month. 6.3

Model of Response Error

As noted previously, past work suggests that time, salience, and task factors may all affect response error. Demographic measures have also been shown to be predictive of such error, but there has been no clear explanation of why certain groups show higher or lower error rates. We include these measures in our model in the hope of explaining their effects in light of the more basic factors at work.

225

Our general model is: Error;t = j(Time;t, Salience;t;Task;, Demog;) + e;t where Error;t = probability that individual i gives an erroneous report of his or her unemployment status at time t Timeit = amount of time that has elapsed between t and the date of individual i's interview Salience;t

=

measures

of the psychological

im-

portance of the status at time t for individual i Task; = measures of overall reporting task facing individual i Demogi = a set of demographic measures characterizing individual i.

(1) The residual error term e;tis assumed to have zero mean and constant variance. For reasons to be explained, we cannot assume that cov(e;t, eis) = 0 for t ¥s and must, therefore, make adjustments for the effects of these nonzero error covariances in our empirical work. It is unlikely that j is both linear and additive. Past research (Sudman and Bradburn 1973) suggests that error rates are exponential functions of time. Furthermore, patterns of time "decay" may interact with the salience of the event and the reporting task. We allow for these patterns by using flexible functional forms relating time, salience, and task measures to response error, by investigating a number of possible interactions among these factors, and by estimating both linear probability and logistic forms for f. To estimate the model given in Equation (1), each monthly observation was treated as a separate case, a procedure that resulted in approximately 12,000 observations (i.e., 30 monthly observations from 387 individual respondents). Some of the explanatory variables are specific to the given month, some are formed from information on the same individual regarding adjacent months, and some (such as the demographic measures) are specific to the individual and identical for a given individual throughout all months of data. This procedure of breaking apart each respondent's 30-month reports into 30 observations causes nonindependence of eit. The situation is analogous to the problem posed when sample cases are clustered into geographic concentrations that may be more homogeneous than in the case of a completely random sample. In this instance, the 30-month clusters for each respondent are completely homogeneous for characteristics such as demographic measures that are invariant over the 30 months. Other measures included in the response-error models are specific to the month and should show much less homogeneity. Some variables, such as the summary variable measures of time between the given month and the interview will actually have a neg-

226

Journal of Business & Economic Statistics, April 1988

ative covariance within each respondent "cluster." Estimates of the sampling errors of the coefficients in our multiple regressions are obtained by forming 387 jackknife replications of the sample and then calculating the variance of the coefficients across all replicates. 6.4

Measures of Salience, Task Difficulty, and Recall Period

The specific set of salience, task, and demographic factors included in our models are spelled out here. Salience. Two measures of the saliency of the given month's employment status are included in the response-error analysis. The first is the actual amount of unemployment in the given month as revealed in the company records. A third measure of salience, the fraction of all workers in the sample laid off in a given month, was included in preliminary analyses but was not found to have statistically significant effects. We hypothesize that the response-error rates are lowest for individuals with either no unemployment or complete unemployment in a particular month, and greatest for respondents who, according to company records, were unemployed for some but not all of the month. We expect, then, a nonlinear relationship between response-error rates and the amount of actual unemployment, with error rates jumping sharply between record reports of no unemployment and reports of small amounts of unemployment, and then declining as the actual amount of unemployment in the month increases. To allow for this nonlinearity, we use a piecewise linear ("spline") function with differently sloped segments for 0-1 weeks of unemployment and 1-4 weeks of unemployment. The salience of unemployment status in a given month is also related to the timing of the spell of employment or unemployment in which that month is embedded. By "timing" we mean whether an unemployment spell begins or ends in that month or whether an individual is in the middle of an extended period of continuous employment or unemployment. The salience of long spells of unemployment or employment should produce fewer response errors for months embedded within the long spells. The beginnings or endings of unemployment spells are important events in most life-event scales and may mark important and, therefore, salient transitions. Employment status should, therefore, be most accurately reported for months embedded in long spells of unemployment or employment and for those months in which long spells begin or end than for months in which the respondents's employment status changes frequently (e.g., several short spells of unemployment within a month). The unemployment experience of the respondent was summarized from the company record information over a three-month period-the given (t), previous (t - 1), and subsequent months (t + 1). Five categories were formed: (a) no unemployment during the three months;

(b) month t begins an unemployment spell; (c) month t ends an unemployment spell; (d) respondent was unemployed during the entire three-month period; and (e) mixture of unemployment spells over the three months. The first four categories are self-explanatory; the last refers to an odd three-month unemployment history-for example, when an individual is unemployed for two weeks in each of the three months. Because this last group has no definite pattern of unemployment spells (e.g., no clear beginning or ending date), months that are embedded in such spells are expected to produce the highest levels of response error. Task. In the case of reporting unemployment, respondents who have been fully employed for the 30month recall period face the easiest reporting task. The task becomes more difficult for those with a single unemployment spell, and most difficult for those with multiple spells. As the number of months of separate unemployment spells increases, the response-error level should rise monotonically, although perhaps not linearly. To allow for a greater effect on response error for the first month of actual unemployment than for subsequent months, we use a spline function that allows for different sloped segments of 0-1 months and 1 or more months of actual unemployment. Note that this task measure is specific to the respondent but not to any given month; its value is identical for all months reported on by a given respondent. Demographic Factors. Findings from both experimental and survey studies suggest that response error increases with age, is higher for black respondents, and is lower for women and more highly educated groups (Cannell et al. 1965; Kaess and Witryol 1955). By including these factors in our response-error model, we are able to ascertain to what extent they act as proxy measures of the potentially more important salience and task factors. Time Between Interview and the Given Month. Five dummy variables, each representing a six-month segment, are included to show the effects of the elapsed time between interview and the month in question. The coefficients on these dummy variables represent the average difference in response error between the given six-month segment and the omitted category, 0-5 months. The summary variables are used to permit the greatest flexibility in the functional form of temporal effects. 6.5

Estimates of Response-Error Models

Table 3 presents estimates for two linear probability regression models of errors in unemployment reports. The first column shows the regression estimates and standard errors for a model that includes the time segments and demographic measures. The standard errors reflect the cluster effect in which the 11,460 observations are based on only 387 independent respondents.

Mathiowetz and Duncan: Response Errors in Reports of Unemployment

227

Table 3. Regression Coefficients and Standard Errors for Two Models of Reporting Error for Reports of Unemployment Full model

Model 1

Time between event and interview 0-5 months 6-11 months 12-17 months 18-23 months 24-29 months Demographic variables Age Whether black Whether female Education

Coefficients

DEFT

Coefficients

DEFT

.043d (.010) -.009 (.010) -.002 (.011) - .035d (.011)

1.33

.010 (.007) .005 (.007) .008 (.007) .007 (.006)

1.30

- .004d (.001) .022 (.025) -.018 (.026) - .011d (.003)

2.68

-.000 (.001) .008 (.010) -.014 (.017) -.001 (.002)

1.66

Amount of unemployment in current month" 0-1 segment 1 + segment Total months with unemploymentb 0-1 segment 1 + segment Timing of spellsc No unemployment Begin unemployment End unemployment Continuous unemployment Mixture of unemployment Adjusted Rb Number of observations

.039 11,460

1.41 1.49 1.33

3.02 2.78 2.22

1.26 1.66 1.14

1.82 2.62 2.07

.686d (.060) - .080d (.021)

3.46

.014 (.016) .003 (.004)

2.98

.106" (.044) .051" (.019) .003 (.079) .047" (.022) .532 11,460

2.06

3.65

5.19

1.52 3.52 1.61

NOTE: Standard errors are shown in parentheses. Amount of unemployment for the current month is based on the record report of amount of unemployment for that month. A spline function allowing for differently sloped segments for 0-1 weeks and 1-4 weeks of unemployment was used. b Total months with unemployment is based on the record report of the number of months with any unemployment during the 30. month recall period. A spline function allowing for differently sloped segments for 0-1 months and more than one month of unemployment was used. CA set of dummy variables was formed from record information about unemployment in past. current, and subsequent months. Coefficients represent deviations from an omitted category, "no unemployment in three months." dp < .01.

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