Reconciling Trends in Volatility: Evidence from the

SIPP Survey and Administrative Data

Michael D. Carr∗ Robert A. Moffitt† Emily E. Wiemers‡

FCSM Research & Policy Conference

October 26, 2020

∗Department of Economics, University of Massachusetts Boston. Email:

michael.carr@umb.edu

†Department of Economics, Johns Hopkins University. Email: moffitt@jhu.edu

‡Department of Public Administration and International Affairs, Syracuse University. Email:

eewiemer@syr.edu

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 1 / 20
Research Questions

What are the trends over time in earnings volatility in the SIPP survey and

SIPP-matched administrative earnings data?

How do choices about the treatment of low earnings and imputation effect the

trends?

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 2 / 20
Background

Recent work on men’s earnings volatility has come to different conclusions

1 Declines in aggregate volatility at the macro level have been accompanied by

increasingly stable earnings at the micro level

2 As earnings inequality has grown, so has volatility for workers

But studies use different sources of data and do not use comparable samples

and methods

This study is part of a group of studies using similar samples and methods to

compare trends in earnings volatility in six datasets: CPS (survey and admin

earnings), SIPP (survey and admin earnings), LEHD, and PSID.

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 3 / 20
Methods

Volatility

Var 

Yit − Yi,t−τ

Yit+Yi,t−τ

2

!

(1)

where Yit is annual earnings, and τ = 1.

We estimate Equation 1 using residuals from a regression of Equation 1 on a

quadratic in age separately by year.

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 4 / 20
Data

Survey of Income and Program Participation (SIPP)

Nationally representative sample of 14,000 to 52,000 households

16 SIPP panels from 1984

Data from 1985-2012

Retrospective questionnaire covering previous four months

Panels vary in length from 2 to 4 years

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 5 / 20
Data

Baseline Measure of Annual Earnings

Monthly Earnings

I Two measures of employment status: worked during wave, weeks worked

during month

I Five components of monthly earnings: 2 jobs, 2 self-emp, and casual

I Sum all non-imputed components of earnings

I Set earnings to zero if not working during the month, and earnings are not

imputed

Annual earnings are the sum of monthly earnings, including months of zero

earnings

Volatility requires:

I 24 months of non-imputed monthly earnings

I Two consecutive years of non-zero earnings

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 6 / 20
Data

Sample Size and Imputations

Average 4200 observations per year

Imputations are non-trivial (Czaka and Denmead, 2008)

I 49.5% of those with wage and salary income in the SIPP had imputed values

I 29% of total wage and salary income imputed and 39.5% of self-employment

income

Imputation tends to bias estimates of volatility in household income upwards

in 1996-2004 panels

Imputation rate for earnings and self-employment income increased from 35%

to 54% between 1993 and 1996 panels (Dahl, DeLeire, and Schwabish, 2008)

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 7 / 20
Data

SIPP Gold Standard File (GSF)

All SIPP participants in 1984 and 1990–2008 panels

Linked to DER maintained by SSA/IRS

Non-topcoded earnings from 1978 - 2014

Basic demographic/education data from SIPP survey

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 8 / 20
Data

Sample Size and Matching

Linked prospectively and retrospectively

Data on 700,000+ individuals

Average 103,106 observations per year

Use only individuals matched to DER

Annual match rates between 74% and 83 %

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Data

Baseline Sample

Men age 25 to 59 with positive annual earnings in two consecutive years.

Additional SIPP Sample Restrictions

Baseline: only non-imputed earnings components for 24 months

Imputed: use SIPP-imputed earnings for 24 months (including unit

non-response and item non-response)

Also use SIPP-imputed earnings on baseline sample (include item

non-response only)

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Data

How Samples Differ

1 GSF pools across panels in year t, SIPP does not

2 Datasets have different earnings measures with different reporting issues

3 GSF sample includes only individuals matched to DER

4 Matching samples across datasets is a challenge

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Data

Table 1: Demographic Characteristics in the SIPP GSF and Survey Samples

Volatility Samples

All Matched SIPP GSF SIPP

<High School 0.185 0.165 0.141 0.101

High School 0.305 0.302 0.305 0.310

Some College 0.264 0.273 0.283 0.283

College 0.155 0.164 0.172 0.185

College+ 0.090 0.096 0.100 0.121

White 0.725 0.750 0.771 0.834

Black 0.116 0.108 0.099 0.064

Other 0.053 0.050 0.046 0.044

Hispanic 0.107 0.092 0.083 0.058

Age 40.380 40.720 40.150 39.404

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Main Results

Figure 1: Trends in Male Earnings Volatility

0

.05

.1

.15

.2

.25

.3

.35

.4

Variance

198019821984198619881990199219941996199820002002200420062008201020122014

Year

SIPP GSF−Arc SIPP−Arc Notes: Authors’ calculations based on SIPP GSF and SIPP. Sample includes men age 25 to 59 with positive annual earnings in two consecutive years.

Earnings are trimmed at the bottom and top 1% of the full distribution of earnings. Earnings changes are adjusted using a quadratic in age, separately by

year. Earnings volatility is measured in arc percent change.

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 13 / 20
Imputations in the SIPP

Figure 2: Trends in Male Earnings Volatility, With and Without Imputations

0

.05

.1

.15

.2

.25

.3

.35

.4

Variance

198019821984198619881990199219941996199820002002200420062008201020122014

Year

SIPP SIPP−Imputed Earnings SIPP−All Imputes SIPP GSF Notes: Authors’ calculations based on SIPP. Sample includes men age 25 to 59 with positive annual earnings in two consecutive years. SIPP-Arc and

SIPP-GSF Arc are the baseline series, SIPP-Arc Imputed Earnings includes imputed earnings components, SIPP-Arc All Imputes includes unit nonresponse

and imputed earnings components..

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Adjusting for Differences in the Distribution of Earnings

Table 2: Selected Earnings Percentile Points: SIPP GSF and SIPP Survey Data

Year P1 P5 P10 P25 P50 P75 P90 P95 P99 N

SIPP

1984 1,398 8,379 14,909 26,307 39,411 55,455 76,738 94,568 155,647 6,131

1990 944 7,492 13,444 23,669 37,827 55,669 77,797 98,054 149,043 8,424

1996 1,461 8,268 14,906 24,750 39,483 58,912 86,289 110,306 236,896 5,298

2001 1,608 9,696 16,503 27,005 42,538 64,624 95,877 123,628 303,540 7,346

2004 1,888 8,820 15,535 27,088 43,764 67,592 100,150 129,402 309,243 12,522

2009 1,373 6,448 12,540 24,410 42,076 67,951 103,258 132,221 368,604 10,418

GSF

1980 951 5,693 12,020 25,770 42,550 60,070 80,420 100,700 165,900 82,000

1990 705 4,523 9,618 22,020 38,510 58,480 79,170 100,900 202,900 109,000

1996 606 4,332 9,364 21,690 38,260 60,130 88,610 117,900 265,600 119,000

2001 605 4,780 10,450 24,740 42,940 67,550 103,200 141,900 318,100 124,000

2004 516 4,142 9,503 23,940 42,770 68,610 105,300 143,900 328,000 122,000

2009 387 3,325 7,749 21,340 41,190 68,650 108,600 148,600 337,500 118,000

Notes: Authors calculations on SIPP survey and SIPP GSF. Sample is men age 25 to 59 with positive earnings in year t. SIPP is limited to men with

non-imputed earnings. Constant 2010 dollars deflated using the PCE.

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Adjusting for Differences in the Distribution of Earnings

Figure 3: Trends in Male Earnings Volatility, Weighted to PSID

0

.05

.1

.15

.2

.25

.3

.35

.4

Variance

198019821984198619881990199219941996199820002002200420062008201020122014

year

SIPP GSF SIPP SIPP GSF−Weighted SIPP−Weighted Notes: Authors’ calculations based on SIPP. SIPP earnings are trimmed at the PSID top and botttom 1%, and at the min/max of two-year average

earnings, separately by year. SIPP trends are weighted using inverse share of individuals in each PSID ventile and are weighted using annual weights and

not age-adjusted. Earnings volatility is measured in arc percent change.

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Role of Low Earnings in Estimates of Volatility

Figure 4: Share Trimmed by Threshold

0

.01

.02

.03

.04

.05

.06

.07

.08

Share

198019821984198619881990199219941996199820002002200420062008201020122014

Year

P1 P5 SSA Threshold $3770 (2011$) Min Wage Recession

Notes: Author’s calculations using SIPP-DER for 1979 to 2014.

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Role of Low Earnings in Estimates of Volatility

Figure 5: Volatility by Trim

0

.05

.1

.15

.2

.25

.3

Variance

198019821984198619881990199219941996199820002002200420062008201020122014

Year

Untrimmed P1 P5 SSA Threshold $3770 (2011$) Min Wage Recession

Notes: Author’s calculations using SIPP-DER for 1979 to 2014.

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Conclusions

1 Trends in SIPP survey and SIPP GSF are similar

2 Volatility increases modestly in the SIPP GSF and declines modestly in the

SIPP survey data between mid-1980s and early 2010s

3 Levels in the SIPP GSF are higher than in the SIPP survey

4 Differences in the underlying cross-sectional earnings distribution explain

difference in level of volatility and minor differences in trends

5 Imputations pose a problem in consistent series of volatility in SIPP survey

data

Carr, Moffitt, and Wiemers Reconciling Trends in Volatility 19 / 20
Czaka, John L. and Gabrielle Denmead. 2008. “Income Data for Policy Analysis:

A Comparative Assessment of Eight Surveys.” Final Report to the U.S.

Department of Health and Human Services, Mathematica Policy Research.

Dahl, Molly, Thomas DeLeire, and Jonathan A Schwabish. 2008. “Trends in

Earnings Variability over the Past 20 Years.” Tech. rep., Congression Budget

Office.

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