1

Earnings Volatility in the U.S.: Short-Term and Long-Term 

Trends 

Beethika Khan and David Beede1

Version: January 7, 2010 

Prepared for the 2009 Federal Committee on Statistical Methodology 

(FCSM) Research Conference (November 2-4, 2009) 

Abstract: This paper uses data from the U.S. Census Bureau’s Survey of Income and 

Program Participation (SIPP) and Current Population Survey (CPS) to analyze the yearover-year earnings fluctuations experienced by the American work-force. Using the 2004 

Panel of the SIPP, we find that a significant share of the U.S. work force experiences 

year-over-year economic fluctuations, although people with more stable labor force 

attachment experiences less volatility. Earnings volatility is widespread across 

demographic groups as well as industry and occupation groups. The earnings of selfemployed workers, however, are much more volatile than their wage and salaried 

counterparts. The CPS results spanning the last three decades suggest that earnings 

volatility changes seasonally and over business cycles, and has risen over time. It is, 

however, not clear how much of the upward trend in measured earnings volatility is due 

to changes over time in the design of the CPS itself. 

Disclaimer: This paper represents the work and judgments solely of the authors; it is 

not a document that reflects the formal policies or stances of the U.S. Department of 

Commerce. 

 

1

 Beethika Khan and David Beede are both economists at the Economics and Statistics Administration 

(ESA) of the U.S. Department of Commerce. Please send all correspondence to Beethika Khan: 

bkhan@doc.gov and David Beede: dbeede@doc.gov
2

1. Introduction

People’s earnings in general rise over their lifetime, primarily because of human 

capital investment (educational attainment, work experience, job tenure), but in the short 

run earnings can fluctuate for a number of reasons. Some fluctuations are the direct 

results of changes in individual-specific attributes, like changes in employment 

characteristics like occupation and industry and movements in and out of labor force. 

Some earnings fluctuations are due to aggregate shocks, like macroeconomic, 

technological or geographic factors that affect the overall economic and employment 

conditions including wage and price levels, and bonus and commission. Other earnings 

fluctuations could occur because of random shocks at the individual level, such as illness, 

crime, climate, lotteries and other idiosyncratic events. These categories are not always 

independent from one another and a number of these factors could be at work 

simultaneously. In addition, while some of these factors are voluntary, others are entirely 

exogenous or involuntary at the individual level. 

This paper attempts to measure the extent of earnings fluctuations experienced by 

the American work-force. The extent of ups and downs in people’s earnings is an 

important indicator of wellbeing. Yet, such aspects are not captured in static or aggregate 

statistics. Static measures like the unemployment rate, the poverty rate, per capita GDP or 

median household income provide an overall snapshot of the population. While they are 

essential for understanding aggregate economic trends, they do not provide much 

information about the extent of volatility experienced at the individual level from one 

year to another. This is because the static measures are not based on true panels but on 

randomly selected samples of population which are different every year. In order to 

measure economic volatility, it is necessary to follow the economic outcomes of the same 

group of people over time. 

The findings of this paper are presented in two main sections. The first main 

section (Section 3) uses data from the Census Bureau’s longitudinal household survey, 

the Survey of Income and Program Participation (SIPP), to shed light on the following 

questions: how much year over year economic fluctuations do Americans experience and 

how do demographic characteristics, poverty status and employment characteristics 
3

correlate with the extent of earnings volatility. Hence, the first section analyzes how 

earnings volatility during a short span of time (two years) is affected by individualspecific characteristics. We find that a significant share of the U.S. work force 

experiences year-over-year economic fluctuations, although people with more stable 

labor force attachment experiences less volatility. One in every five year-round employed 

workers saw their earnings change by at least 25% between 2006 and 2007 whereas close 

to one in ten saw changes of at least half. Earnings volatility is widespread across 

demographic groups --- while there is some variation in volatility across demographic 

groups, no specific age, race or gender group seems to be driving the overall trend. The 

earnings of self-employed workers (who constitute a small fraction of the overall work 

force), however, are much more volatile than their wage and salaried counterparts 

Earnings volatility is also particularly high among the work force in poverty. Finally, 

earnings volatility varies quite a bit across industry and occupation sectors. 

The second main section (Section 4) of this paper uses Current Population Survey 

data to put the results of the first half of the paper in the context of longer-term trends and 

business cycle effects. The CPS results spanning the last three decades suggest that 

earnings volatility changes seasonally and over business cycles, and has risen over time. 

It is, however, not clear how much of the upward trend in measured earnings volatility is 

due to the evolution of the CPS itself. The results exhibit similar relationships between 

earnings volatility and age, gender, marital status, race, ethnicity, education, and industry 

as in the results derived from the SIPP. In addition, changes in demographic variables 

over time are associated with sharp increases in earnings volatility. 

2. Literature Review

 A large volume of studies have focused on short-term absolute changes or 

fluctuations in earnings at the individual level. The existing studies have primarily looked 

at long term trends, i.e., whether earnings volatility has risen or declined over long 

periods of time. 

 Much of the theoretical framework for studying earnings fluctuations originate 
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from Gottschalk and Moffitt (1994). They decompose the variance in annual earnings2

into a permanent and a transitory component. The transitory variance reflects transitory 

shifts in earnings and can be seen as “earnings instability” or short-term earnings 

volatility because these reflect short-term changes that gradually diminish over time, i.e., 

are mean-reverting. Permanent shifts, on the other hand, reflect a permanent change in a 

person’s earnings trajectory. Using data from the Panel Study of Income Dynamics 

(PSID), Gottschalk and Moffitt (1994) define a person’s permanent earning by averaging 

the person’s annual earnings over a 9-year period. Transitory shifts are defined as 

deviations in annual earnings from the 9-year average. Their study looks at white, male, 

non-student heads of households, aged between 20 and 59, for two periods, 1970-1978 

and 1979-1987, and finds that the transitory variance of male earnings rose between the 

1970s and1980s. They also find that the rise in short-term earnings volatility or transitory 

variance accounts for about half the rise in earnings inequality among white men during 

this period. In order to understand how labor market characteristics affect the rise in 

volatility, they look at industry of employment, union status and job turnover. Industries 

vary across a number of dimensions, including but not limited to, the required level of 

worker skill, the seasonal or cyclical pattern of demand, the extent of unionization, the 

extent of temporary workers, exposure to trade and globalization, firm size and 

innovation. All of these are likely to affect the overall variability of earnings across 

industries. These are important areas to study not only for understanding earnings 

volatility but also the overall earnings distribution because of the potential impact of 

short-term volatility on earnings inequality.3

 Gottschalk and Moffitt (1994) find that the 

extent of volatility at a given (9 year) period varies across industry sectors, however, all 

sectors saw a rise in volatility during the 1980s relative to the 1970s. Similarly, they find 

that while unionized workers experienced significantly lower levels of variability at a 

given period than their non-unionized counterparts, both groups saw a rise in earnings 

volatility during the 1980s. As a result, the rise in the overall transitory variance in 

earnings in their sample is not explained by a particular sector. Excluding part-time and 

 

2

 They decompose earnings residual which is the residual portion of earnings after controlling for a 

person’s age and education. 

3

 As opposed to explanations like skill-biased technological change which reflect a change in price of skill 

and therefore permanent shifts in earnings. 
5

self-employed individuals from their sample lowers both the level and rise in volatility 

implying that these groups experience more earnings fluctuations than their counterparts. 

However, the pattern of widespread rise across sectors still holds. Finally, Gottschalk and 

Moffitt (1994) compare the transitory variance for workers who stayed in the same job 

vs. who switched jobs. They again find that earnings variability rose for both movers and 

non-movers. 

 Gottschalk and Moffit (1995) update their 1994 study by using a formal model of 

earnings dynamics for the decomposition exercise. Using data from the PSID for white, 

male heads of household, they find that the transitory variance of earnings have risen 

during the 1969-1991 periods. Gottschalk and Moffitt (2008) use both a formal model as 

well as an extension of the method used in their 1994 paper to estimate transitory 

variance. They find that the transitory variance in earnings rose during the 1980s and 

began to level off during the 1990s, although the variance stayed at the high level reached 

in the 1980s through 2004. They also find that the transitory variance has a cyclical 

component, so earnings volatility varies with the business cycle. In addition, they find 

that the rise in volatility accounts for about a third of the rise in earnings inequality 

between the 1990s and mid-2000s. The authors did not provide an update of their results 

by industry, union status and job movers in the later versions of their 1994 paper. 

 Cameron and Tracy (1998) use a similar model of earnings dynamics and 

decomposition method to study the long-term trends of permanent and transitory variance 

in earnings residual using data from the Current Population Survey (CPS). They look at 

men, between 18 and 63 years of age during the 1967-1996 period. They exclude selfemployment earnings from their earnings measure. They find that earnings volatility vary 

with the business cycle; apart from the cyclicality, however, volatility rose during the 

1970s, reached a peak during early 1980s and then slowly and gradually fell until the mid 

1990s, although the levels in the 1990s were still higher than that during the beginning of 

their sample period in the late 1960s. In addition, they find that the trends in volatility 

vary across industries --- while some industries (like manufacturing and government) saw 

only mild rises in volatility, others (like trade and construction) experienced larger 

increases during their sample period. 

 Other studies have also looked at long-term trends of earnings volatility but none 
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to my knowledge has looked at volatility across detailed industry and occupation sectors. 

For example, Haider (2001) used data from the PSID on white, male heads of 

households, performed a similar model-based decomposition as Gottschalk and Moffit 

and found that earnings volatility rose between the early 1970s and late 1980s. The 

Congressional Budget Office (2008) used administrative data from the Social Security 

Administration’s Continuous Work History Sample (CWS) to look at earnings volatility 

for male and female workers between 25 and 55 years of age. They define earnings 

volatility by the fraction of workers experiencing at least a 25% and a 50% change4 in 

annual earnings (their measure of earnings does not include self-employment earnings). 

CBO finds that earnings volatility has a cyclical component; however, apart from the 

cyclicality, overall volatility during the 1984-2003 period has not changed much. 

 

3. Short-Term Context

As mentioned above, this paper has two main sections. The first measures how 

much year over year earnings fluctuations Americans experience and how demographic 

and employment characteristics correlate with the extent of the earnings volatility. For 

this purpose, we use data from the Census Bureau’s Survey of Income and Program 

participation (SIPP) which contains detailed demographic and economic data at the 

household level. 

3.1 Data and Methods

The SIPP is a longitudinal survey of a representative sample of the U.S. noninstitutional civilian population, and provides detailed monthly data on labor force 

participation, income, assets, welfare program participation, and demographic 

characteristics of households. The SIPP collects monthly data on individuals of 

participating households for 3 or 4 years.5

 

Because the SIPP provides detailed demographic and labor force data for every 

member, age 15 and over, of participating households, we do not have to limit ourselves 

 

4

 Earnings volatility is defined as the deviation in earnings in a given year from the 2 year average (the 

denominator). 

5

 The 2004 panel is the most recently completed panel of the SIPP. It started in February 2004 with about 

46,500 households. The 2001 panel interviewed households from February 2001 through January 2004. 
7

to looking at only heads of households. The studies based on the PSID data suffer from 

this limitation. In addition, for working individuals the SIPP reports earnings, industry 

and occupation data for up to two jobs and businesses. This enables us to study in detail 

the self-employed population. Because of their highly variable earnings (Gottschalk and 

Moffit; 1994), they are an important group for getting a comprehensive understanding of 

overall earnings fluctuations. The CBO (2008) study excludes this group because of lack 

of data. In addition, because the SIPP data are monthly, we can more precisely calculate 

job tenure, identify individuals and businesses that go through transitions, and correlate 

earnings fluctuations with job transitions. The identification of such transitions are not 

completely precise in annual data. For example, if a person moves from one industry to 

another in a particular year, it is not clear how much of her annual earnings from that year 

come from which industry. 

The SIPP data, however, does not enable us to study long-term trends in earnings 

because it follows the same households for a maximum of 3-4 years. As a result, while 

we can study earnings volatility across detailed demographic and labor force categories at 

a given point in time, we are unable to say much about trends over long periods of time, 

which is why the next section of this paper uses data from the CPS to look at longer term 

trends. 

In this section, we measure earnings volatility between 2006 and 2007, the two 

most recent years covered by the 2004 panel of SIPP (which is the most recently 

completed panel of the SIPP). In order to make sure the trends seen during 2006-2007 

are not unique, we also look at 2004-2005 and these results are presented in the 

Appendix. The volatility measures are not based on model-based parameters, so they are 

much easier to understand. Earnings volatility is defined by the fraction of the 25-65 year 

old work force whose year-over-year change in real earnings exceeds a minimum 

threshold amount. The thresholds used in this report are 25% and 50%, so earnings 

volatility is measured by the share of the 25-65 year old work force experiencing a 25% 

or larger change in real earnings, and a 50% or larger change in real earnings between 

two consecutive years. Annual earnings are adjusted for inflation using the Consumer 

price Index Research Series (CPI-U-RS). 
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In order to minimize the impacts of schooling and retirement on earnings 

volatility, we restrict our sample to the 25-65 year old age group. While schooling and 

retirement decisions may still impact economic volatility between the ages of 25-65, they 

are likely to be less prevalent for this age group than their younger and older 

counterparts. We also exclude those with zero earnings in either year in order to limit the 

impact of very large fluctuations on overall volatility. 

We look at two samples. The first sample includes all 25 to 65 year old people 

who are in the labor force (either working, on layoff, or looking for a job) for at least one 

month during the two year sample period. So people who move in and out of the labor 

force during the sample period are included in this sample as long as they have positive 

earnings in both years. The second sample includes those who report working (i.e. no 

unpaid absence from job) every month during the two year period. The 2006-2007 

sample size for the first group is 11,971 and for the year-round worker sample is 8,539 

observations.6

 Since the first sample includes people who move in and out of the labor 

force whereas the second sample strictly focuses on year-round employed people, 

comparing the two samples enables us to see how earnings volatility varies with labor 

force attachment. 

3.2 Results

Table 1 presents data on real average annual earnings for 2006 and 2007 as well 

as on earnings volatility between 2006 and 2007 by demographic characteristics for the 

first sample. The last two columns show that a sizeable share of the population 

experiences earnings volatility: 17% or almost 1 out of every 5 people saw their real 

earnings change by at least half between 2006 and 2007, while 31% or almost 1 out of 3 

saw their earnings change by 25% or more.

 It is useful to simultaneously look at mean earnings for two reasons. First, it gives 

an idea about the magnitude of a 25% or 50% change in earnings on average. With 

average earnings at $44,600 in 2006, a 25% and 50% change from average earnings is 

equivalent to roughly $11,100 and $22,300 respectively. These are not small changes 

 

6

 The 2004-2005 sample sizes are 28,904 and 18,892 respectively. The number of households in the 2004 

panel of SIPP declined significantly after Wave 8, which is why the 2006-2007 samples are smaller than 

the 2004-2005 samples. 
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year over year. Second, the change in average earnings between 2006 and 2007 masks the 

extent of fluctuations seen at the individual level. For example, comparing the two 

columns with average annual earnings for 2006 and 2007 shows that real average 

earnings remained unchanged (the drop is not statistically significant) between 2006 and 

2007 --- while this tells us that workers on average earned the same in 2007 as they did in 

2006, it does not give any information about the extent of earnings fluctuations 

experienced at the individual level during this period. 

 In general, women are slightly more likely than men to experience earnings 

volatility.7

 The differences across the race categories are not statistically significant.8 In 

addition, people in the middle of the age distribution are less likely to experience 

earnings volatility than their younger and older counterparts. For example, 15% of the 

25-34 year old and 35-44 year old groups saw their earnings change by half or more 

compared to 19% and 21% of the youngest and oldest group. People in the youngest 

group are much more likely than their middle-aged counterparts to be completing school 

and going through other transitions like part-time to full-time employment, switching 

jobs to find a better match and so on. The 55-65 year old group is closer to retirement 

than the two younger groups. All of these would translate into significant earnings 

volatility. 

 Those with less education are more likely than their more educated counterparts 

to experience earnings volatility. For example, 21% of those with less than a high school 

degree saw large earnings volatility compared to 16% of college graduates and 13% of 

advanced degree holders. The more educated workers are likely to hold jobs with more 

job security and stable earnings paths. 

 Table 2 presents the same results for the year-round employed sample. Excluding 

people who were either not in the labor force or on layoff or looking for a job part of the 

year significantly reduces the extent of earnings volatility. For example, 9.1% of yearround workers between 25-65 years of age experienced large earnings fluctuations, 

compared to almost double or 17.2% in the first sample. This pattern holds across the 

 

7

 All comparisons made in this paper are significant at least at the 10% level. 

8

 During 2004-05, however, Whites were slightly less likely than Blacks and Hispanics to experience 

volatility of 25% or more. For large volatility of 50% or more, there was no difference. 
10

demographic groups. This implies that the extent of earnings volatility is strongly related 

to a person’s labor force attachment. 

 Contrary to the first sample, women in the year-round worker sample are slightly 

less likely than men to experience earnings volatility (50% or larger). This means that 

large volatility among women who are either not in the labor force or on layoff or job 

searching for part of the year are much higher than their male counterparts, which is why 

the overall result by gender reverses in the first sample. 

 Some of the differences by age, particularly between the youngest and two 

middle-aged groups, diminish while looking at year-round workers. The oldest group, 

those between 55 and 65, however, still experiences more volatility than the younger 

groups. 

 The correlation between education and volatility is also weaker in the year-round 

employed sample.9

 This means that although people with less education earn less on 

average than their more educated counterparts, once we condition on year-round work 

status, the less educated don’t always see more volatility than their more educated 

counterparts. This again reiterates that labor force attachment is a strong indicator of the 

extent of economic fluctuations experienced by people, perhaps a stronger indicator than 

other characteristics like the level of education and age. 

 Table 3 presents mean earnings and volatility results by poverty status. The top 

half of Table 3 shows that, in the first sample, 45% or almost half the people in poverty in 

2006 saw their earnings change by at least half, compared to only 13% of their wealthier 

counterparts. The data on mean earnings show that those in poverty in 2006 experienced 

almost a 36% rise in average earnings by 2007, compared to a 4% drop for higher income 

individuals. This implies that the people in poverty experience much larger fluctuations 

in earnings than their wealthier counterparts. 

 The bottom half of Table 3 presents the results for year-round workers. The 

poverty rate is lower, average earnings higher, and the fluctuations lower among this 

group than the first sample. This makes sense since we are conditioning on year-round 

 

9

 For example, in the 2006-2007sample, the differences between people with less than a college degree and 

those with a college degree or more disappear once we condition on year round work status. In the 2004-

2005 sample, the difference remains only between high school dropouts and those with college degree or 

more for 25% or more volatility only. 
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employment status in the second group. Almost a third of the people in poverty, 29.7%, 

see their earnings change by half or more by the next year, compared to 8% of their 

wealthier counterparts. Real average earnings rose by 24% for the people in poverty in 

2006, compared to a 4% drop for higher income individuals. 

 An interesting question (and a short digression from earnings volatility) that arises 

from this analysis is: how persistent is poverty? What fraction of the population in 

poverty move out of poverty within a year? In the first sample (top half of Table 3), 3.9% 

of our 25-65 year old sample is poor in 2006 and 2.4% remain poor in 2007. This implies 

that 38% (1.5/3.9)of the poor in 2006 are no longer poor in 2007. In the second sample, 

33% of the poor in 2006 move out of poverty by 2007.10 

Since the likelihood of experiencing earnings volatility is strongly related to labor 

force attachment, the rest of this section will focus on employment characteristics of 

people. Does a certain segment of the labor force, either an industry or occupation sector 

or business owners vs. salaried employees, have significantly more stable earnings? We 

will focus on the year-round employed sample for the rest of the analysis. The SIPP asks 

respondents about whether they worked for employers or owned their business (i.e. selfemployed). As a result, it is possible to identify on a monthly basis whether a person held 

a job for an employer or was self-employed or both. Table 4 reports mean earnings and 

volatility for wage and salary earners vs. self-employed people. The vast majority of the 

sample worked for an employer year-round. A much smaller, 11%, were self-employed 

only and did not hold any job for an employer. There is a big difference in the extent of 

earnings volatility experienced by the two groups. Almost 40% of the self-employed 

group or 2 out of every 5 saw their earnings change by half or more, compared to only 

4%-5% of wage and salary earners and 9% of the overall sample. This means that 

excluding the small share of the self-employed people reduced overall large volatility by 

half. 

 Another interesting question is: do workers in certain industries and occupations 

experience more earnings fluctuations than their counterparts in other sectors? Also how 

do variations across industry and occupation sectors compare for wage and salaried 

workers vs. self-employed workers? The last four tables focus on shedding light on these 

 

10 In the 2004-05 sample, about 45% of the people in poverty in 2004 moved out of poverty by 2005. 
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issues. Since the group consisting of wage and salaried workers are much larger 

compared to the self-employed, Tables 5 and 6 focus on the former sample to take a 

detailed look at volatility across industries and occupations. Tables 7 and 8 compare 

volatility across aggregate industries and occupations for workers vs. business owners. 

The SIPP collects information for up to two jobs and/or businesses for each 

employed individual. The analysis in this paper focuses only on the main industry and 

occupation reported by the respondent. The SIPP reports 4-digit industry codes (based on 

NAICS) on a monthly basis. In order to look at year-over-year earnings fluctuations 

conditional on a person’s industry, it is necessary to identify the pertinent industry for the 

entire sample period. People who move from one industry to another during the sample 

period are assigned the industry where they worked the longest, i.e. spent the most 

number of months during the 2006-2007 period. The 4-digit industries are then 

aggregated into broader 2-digit categories. The same steps were implemented to identify 

a respondent’s occupation. 

 Tables 5 and 6 provide data on real average earnings and earnings volatility by 

major industry and occupation sectors for year-round wage and salaried workers. Because 

of the relatively small sample size of some of the sectors, the tables focus on aggregated 

industry and occupation sectors. Both mean levels of earnings as well as earnings 

volatility vary quite a bit across sectors. For example, as shown in Table 5, mean earnings 

in 2007 ranged from $34,000 (arts, entertainment, recreation, accommodation and food 

services) to $63,000 (professional, scientific, management, administration, support, 

waste). Between a tenth (public administration) and almost a fifth (18% in arts, 

entertainment, recreation, accommodation and food services) of the workers saw their 

earnings change by 25% or more; between 3% (manufacturing) and 6% (arts, 

entertainment, recreation, accommodation and food services) saw changes of half or 

more. 

Higher average earnings do not necessarily indicate more stability. For example, 

trade and the combined professional, scientific, management sectors have similar 

volatility but the latter pays significantly more than the former. There are also examples 

of higher paying sectors that have more stable earnings. For example, public 

administration has lower volatility and higher earnings than the combined arts, 
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entertainment, recreation, accommodation, and food sectors. The cyclicality of demand 

and required levels of skill and education as well as the average length of tenure, the 

share of part-time and full-time workers, rate of unionization, sensitivity to trade and 

globalization, and sensitivity to business cycles vary across industry sectors which all in 

turn are likely to impact the volatility of earnings across sectors. 

Table 6 provides data on earnings and volatility by major occupation sectors. Just 

like industry, both mean levels of earnings as well as earnings volatility vary by 

occupation sectors. For example, mean earnings in 2007 ranged from $33,000 in services 

to $77,000 in management, business and financial occupations. Between 8% (installation, 

maintenance, repair) and 18% (service) of the workers saw their earnings change by 25% 

or more; between 3% (production; installation, maintenance, repair) and 6% (service; 

sales and related) saw changes of half or more. Just like industries, higher earnings did 

not always indicate more stability. For example, the management, business and financial 

sector had the highest levels of earnings on average but higher volatility than some lower 

paying occupations. 

So far, the analysis by industry and occupation has focused on year-round wage 

and salaried employees only. Tables 7 and 8 compare the industry and occupation 

specific volatility for wage and salaried employees and self-employed workers. The high 

volatility among self-employed workers is widespread. In other words, self-employed 

workers have much more volatile earnings than their wage and salaried counterparts in all 

industry and occupation sectors. The distribution of the two groups of workers also varies 

significantly. For example, education, health, social services and manufacturing have the 

two largest concentrations of workers whereas professional, scientific, management, 

administration and construction contain the largest shares of business owners. 

4. Business Cycle and Longer-Term Contexts

The results discussed above focus on a relatively narrow period of time – the mid2000s – which was a period of economic growth and expanding employment. In this 

section we attempt to place these results within a broader timeframe to examine business 

cycle and longer-term trends in year-to-year income volatility. To do this, we use 

Current Population Survey (CPS) data, which contain many more observations than the 
14

SIPP data and are available over a much longer period. Preliminary graphical analysis of 

CPS data suggests procyclical, long-term rising weekly earnings volatility. 

4.1 Data and Methods

The data used in this section are drawn from the CPS Merged Outgoing Rotation 

Group (MORG) data published on the National Bureau of Economic Research website 

(Feenberg and Roth, 2007). In the CPS, sampled housing units are surveyed for four 

months, at the end of which much additional economic data are collected; then the 

housing units are out of sample for 8 months, and then surveyed again for four months, at 

the end of which the additional economic data are again collected and the housing unit 

drops out of the sample. Thus, the more extensive economic data are collected once for 

each housing unit and then again one year later, providing the opportunity to examine 

changes in economic and other characteristics of persons over a one-year period. 

The CPS, however, is not expressly designed to perform such longitudinal studies. 

This means that certain adjustments and safety measures have to be added into the 

analysis to ensure the accuracy of the results. In order to study the change in economic 

data between two consecutive years for a person in the sample, the housing unit data must 

be matched to the corresponding data for the following year, and then checked for 

consistency by age, gender, and race and other characteristics in an attempt to ensure that 

the same persons are being matched over the two years and to avoid duplications. (We 

adapted programs originally developed by Madrian and Lefgren (2000) to match the 

March CPS data to match the NBER MORG data.) Data couldn’t be matched between 

parts of 1984-86 and 1993-95 because of sample redesigns, so these periods are not 

included in our analysis. We exclude self-employed persons from our sample since some 

researchers have suggested that earnings data for self-employed people in the CPS 

MORG data are of suspect quality (Feenburg and Roth). In addition, we exclude persons 

with allocated earnings and/or hours in either of years t or t+1. (For purposes of 

reporting monthly or annual statistics, CPS uses other person-specific data to allocate 

hours and earnings to the records of persons who do not respond to questions on those 

topics. The estimation methodology for filling in such data gaps is not necessarily 

designed for estimating year-to-year changes in hours and earnings in a longitudinal 
15

sense.) Data are dropped for t=1988-1994 because only about 25 percent of allocation 

flags were available in the data for those years (consequently, estimated earnings and 

hours volatility for those years -- not reported here -- were much higher than in the years 

when complete allocation flags were available, even after dropping those observations 

with allocation flags). 

Table 10a in the Appendix shows sample means for a wide range of economic and 

demographic variables for persons that have allocated earnings in year t or t-1 (or both) 

versus persons with allocated earnings in neither year t nor year t-1. Roughly one-third 

of the persons in the sample have allocated earnings in year t or t-1, and they have much 

higher earnings volatility than persons without allocated earnings, which presumably 

reflects the noisiness of the allocation process, which is heightened by looking at 

differences in earnings over time. Persons with allocated earnings tend to be more 

concentrated in later periods, consistent with Hirsch and Schumacher (2004), who found 

that allocation rates have increased since the 1980s. Allocated earnings are more likely 

to be associated with changes in pay status (to or from hourly pay) and industry; 

otherwise the two groups are fairly similar. 

4.2 Results

Table 9 provides a kind of “bridge” between the SIPP-based results reported 

earlier and (roughly) corresponding CPS-based results. The table reports percentages of 

workers with earnings rising or falling by more than 25 and 50 percent between 2006 and 

2007 (all earnings are adjusted for inflation using the CPI-U). The SIPP results based on 

data for persons in the labor force in both 2006 and 2007 (column (a)) are roughly similar 

to the CPS results for persons employed in both 2006 and 2007 (column (c)); and the 

SIPP results based on persons employed in all months in 2006 and 2007 (column (b)) are 

also roughly similar to the CPS results for persons working 35 or more hours per week 

(column (d)), which provides some basis for using the CPS data to provide longer-term 

context for the SIPP findings. (Hereafter the focus is on full-time workers in both t and 

t+1.) 

Unfortunately, the combination of the allocation flag problem and sample 

redesign leaves rather wide holes in the CPS data in the mid-1980s and between the late 
16

1980s and the mid-1990s. This gap makes it a bit difficult to interpret what happened 

before and after that period: do changes in the variables under examination reflect 

changes in the economy over that period or do they reflect changes in CPS methodology? 

Moreover, the weekly earnings data in the CPS are top-coded and infrequently adjusted 

for inflation ($999 in 1979-1988; $1,923 in 1989-97; and $2,884 in 1998-2008). 

However, it appears that top-coding may not have a huge impact on our findings. The 

left panel of Figure 1 shows for each quarter the percent of persons with 25% and 50% 

changes in weekly earnings (raw percentages in red; smoothed percentages in green). It 

appears that weekly earnings mobility increased in the 1980s and plateaued at a higher 

level in the 1995-2007 data. The right panel of Figure 1, shows the same chart but 

imposing the minimum (in 2000 dollars) top-coding for the entire 1979-2008 period 

($1,454 in 1988). Even this rather drastic solution to the top-coding problem only mutes 

but does not eliminate the long-term trend shown in the left panel of figure 1. 

Figure 2 also shows that earnings volatility has a distinctly seasonal dimension. 

Simple smoothing of the data reveals no clear cyclicality in earnings volatility: volatility 

peaked after the 1980-82 recessions, but it peaked before the 2001 recession (the data 

limitations noted above imply that no analysis of earnings volatility can be made around 

the 1990-91 recession). Since our focus is on persons employed full-time, both the 

seasonal and business cycle movements in earnings mobility are likely to be related to 

movements in overtime hours. 

We estimate ordinary least squares regressions of various measures of earnings 

volatility on demographic and economic variables in levels and changes in an attempt to 

explore the characteristics of high- versus low-volatility workers and the circumstances 

under which they may experience higher or lower earnings volatility. The results are 

summarized in Tables 10-12 (full results including standard errors are shown in Table 

10b in the Appendix). For each table, the dependent variable in the first column is the 

absolute value of the change in log weekly earnings; in the second third columns the 

dependent variables are indicator variables taking the value of one if a person 

experienced an absolute change in earnings greater than 25 percent and 50 percent, 

respectively (and zero otherwise). 
17

Table 10 focuses on time and age independent variables. There appears to be a 

slight downward trend in earnings volatility, though dummies for the late 1980s and the 

mid-1990s through 2007 (controlling for changes in CPS sample design and other 

changes) are positive. Together these findings suggest that much of the increase in 

earnings volatility shown in Figure 1 may be more due to changes in the way CPS has 

been sampled, worded, and conducted than structural changes in the economy, but more 

research would be needed before any definitive conclusions could be reached (Hirsch and 

Schumacher note, however, that allocation rates jumped after the 1994 changes in the 

CPS). Coefficients on second, third, and fourth calendar quarters suggest that earnings 

volatility typically peaks in the fourth quarter (October–December) perhaps because of 

holiday seasonal effects on overtime hours. The coefficient on an indicator variable for 

recession is very small and not statistically significant, which is consistent with Figure 1. 

Table 10 shows coefficients for a quadratic in age (as is typically done in wage 

regressions). Since these coefficients are hard to interpret, Figure 2 shows the marginal 

effect of age on earning volatility (more details are shown in Table 10d in the Appendix). 

As workers age from their twenties to their thirties earnings volatility decreases (perhaps 

because their wage/experience profile flattens), while it increases starting in their late 

fifties (perhaps because they begin to work more hours). 

Table 11 shows coefficients for demographic right-hand side variables. 

Interestingly (but consistent with the SIPP results reported in Table 9), women full-time 

workers have less earnings volatility than men (perhaps because working mothers are 

more constrained from working over time than working fathers), while black, Hispanic, 

and other race workers have more earnings volatility than non-Hispanic whites. Married 

workers experience more earnings volatility than unmarried workers (as was the case 

with the SIPP results reported in Table 9), although the effect is small; change in marital 

status has a much greater (positive) impact on earnings volatility (it seems reasonable to 

expect that changes in any status are likely to increase earnings volatility). Workers with 

higher educational attainment have less earnings volatility, but increasing educational 

attainment from one year to the next appears to sharply increase earnings volatility. 

Table 12 focuses on hourly pay status and industry. As expected, workers in 

public administration have relatively low earnings volatility; workers in non-
18

manufacturing, private-sector industries generally have higher earnings volatility than 

manufacturing workers (the omitted industry category); exceptions are professional 

services and utilities. Hourly workers have less earnings volatility than salaried workers 

which is somewhat surprising (and which may be reversed if the sample were broadened 

to include part-time workers, which we will look at in future research), but changing pay 

status from hourly to salaried or vice versa results in sharply higher earnings volatility.11

In conclusion, the CPS results spanning three decades suggest an upward trend in 

measured earnings volatility, although it is not clear how much of this trend is due to the 

evolution of the CPS itself. The results exhibit similar relationships between earnings 

volatility and age, gender, marital status, race, ethnicity, education, and industry as in the 

results for this decade reported in Table 9. In addition, changes in demographic variables 

are associated with sharp increases in earnings volatility. 

5. Future Work

 We plan to expand this paper in a number of ways. First, we would like to 

analyze how much of the year-over-year earnings volatility seen on the SIPP results from 

earnings rises vs. declines. Second, we would like to perform a more in-depth analysis on 

job transitions using the SIPP. The monthly nature of the SIPP data makes it possible to 

identify transitions and may enable us to answer questions such as: 

 Do workers who transition from job to job experience more volatility than those 

who do not? 

 Are job transitions more prevalent in certain sectors? 

 For self-employed people, is there a correlation between the size and age of the 

business with volatility? 

In addition, we will examine subgroup decomposable mobility measures (such as those 

Fields and Ok, 1999). 

 

11 We also include the full regression results when observations with allocated earnings are included (see 

Appendix Table 10c) to show the impact on our results of omitting such observations. 
19

Table 1: Earnings Volatility, 25-65 year old in Labor Force one or more months, 2006-07 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. 

Bureau of the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and 

subtracted from the estimate provides the 90-percent confidence interval. 

Sample includes all 25-65 year old people who are in the labor force (working, on layoff or looking for a 

job) for at least one month during the 2006-2007 period. Those with zero earnings in either year are 

excluded. The data are weighted by longitudinal panel weights. Sample includes 11,971 person records.

Sample 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2006 2007 >=25% change >=50% change 

All 100 44,612 44,106 31.4 17.2

 (813) (734) (0.6) (0.4)

Gender 

Men 53 54,270 53,301 29.9 15.9

 (1197) (1086) (0.8) (0.6)

Women 47 33,814 33,827 33.1 18.7

 (666) (597) (0.8) (0.6)

Race 

White, not Hispanic 72 47,705 47,115 30.6 17.0

 (878) (764) (0.6) (0.4)

Black 10 34,392 34,061 33.0 17.8

 (1521) (1620) (1.9) (1.4)

Hispanic (of any origin) 12 30,446 30,364 32.6 17.8

 (1307) (1258) (1.9) (1.3)

Age 

25-34 25 37,535 38,186 34.7 19.1

 (1200) (1138) (1.1) (1.0)

35-44 30 46,372 46,546 29.2 15.2

 (1025) (996) (1.0) (0.7)

45-54 29 48,088 47,348 28.4 15.4

 (1198) (1127) (0.9) (0.7)

55-65 17 46,078 42,983 35.6 21.2

 1911 1438 (1.3) (1.0)

Marital Status 

Married 63 48,541 48,109 30.6 16.6

 (976) (926) (0.6) (0.5)

Not Married 32 37,824 37,003 32.1 17.8

 (940) (851) (1.0) (0.7)

Education 

Less than HS Degree 5 21,259 20,418 36.5 20.5

 (686) (671) (2.6) (2.0)

HS Degree/Some College 63 35,139 35,113 32.5 18.1

 (551) (554) (0.8) (0.6)

College Degree 21 58,175 56,537 30.0 15.8

 (1738) (1357) (1.1) (0.8)

Advanced Degree 12 80,989 80,059 26.2 13.4

 (2572) (2480) (1.4) (1.1)
20

Table 2: Earnings Volatility, 25-65 Year Old Year-Round Employed, 2006-2007 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. 

Bureau of the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and 

subtracted from the estimate provides the 90-percent confidence interval. 

Sample includes all 25-65 year old people who report having paid employment (i.e. no unpaid absence) 

during all months of 2006 and 2007. Those with zero earnings in either year are excluded. The data are 

weighted by longitudinal panel weights. Sample includes 8,539 person records. 

Sample 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2006 2007 >=25% change >=50% change 

All 100 50,608 50,020 20.2 9.1

 (953) (870) (0.6) (0.4)

Gender 

Men 53 59,771 58,782 21.0 9.7

 (1390) (1272) (0.8) (0.5)

Women 47 38,950 38,873 19.6 8.4

 (778) (696) (0.9) (0.6)

Race 

White, not Hispanic 72 53,880 53,198 20.2 9.5

 (1040) (911) (0.7) (0.5)

Black 10 38,352 37,402 18.8 7.0

 (1890) (1939) (1.8) (1.2)

Hispanic (of any origin) 12 34,899 35,338 19.6 8.4

 (1632) (1616) (1.9) (1.2)

Age 

25-34 25 43,375 43,821 21.2 8.5

 (1662) (1580) (1.2) (0.8)

35-44 30 51,976 51,803 18.8 7.5

 (1231) (1162) (1.0) (0.6)

45-54 29 53,250 52,783 18.8 9.7

 (1372) (1339) (0.9) (0.7)

55-65 17 53,317 50,284 23.7 12.2

 2537 1792 (1.3) (0.9)

Marital Status 

Married 63 54,684 54,049 20.4 9.7

 (1171) (1101) (0.7) (0.5)

Not Married 32 43,018 42,217 19.0 8.0

 (1157) (1027) (1.0) (0.6)

Education 

Less than HS Degree 5 24,831 24,035 23.3 11.6

 (820) (819) (2.9) (2.3)

HS Degree/Some College 63 39,707 39,816 20.3 9.2

 (671) (688) (0.8) (0.5)

College Degree 21 64,502 62,923 19.8 8.9

 (2169) (1727) (1.2) (0.8)

Advanced Degree 12 89,007 86,809 18.7 8.2

 (2936) (2795) (1.3) (1.1)
21

Table 3: Earnings Volatility by Poverty Status, 25-65 Year Old, 2006-2007 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. 

Bureau of the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and 

subtracted from the estimate provides the 90-percent confidence interval. 

Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel 

weights. The labor force sample includes 11,971 person records and the year round employed sample 

includes 8,539 person records. 

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2006 2007 >=25% change >=50% change 

Sample: Those in labor force at least one month during 2006-2007 

All 100 44,612 44,106 31.4 17.2

 (813) (734) (0.6) (0.4)

Poverty Status 

Poor in 2006 3.9 8,888 12,065 58.8 45.2

 (364) (481) (2.7) (2.7)

Remain poor in 2007 2.4 9,220 9,177 48.1 32.0

 (465) (425) (3.5) (3.1)

Not poor in 2007 1.5 8,356 16,684 76.0 66.1

 (535) (1085) (4.0) (5.1)

Income-to-poverty 

ratio>=4 in 2006 48.3 65,231 62,548 25.8 13.0

 (1165) (1049) (0.7) (0.5)

 

Sample: Those with paid employment during all months in 2006-2007

All 100 50,608 50,020 20.2 9.1

 (953) (870) (0.6) (0.4)

Poverty Status 

Poor in 2006 2.4 11,120 13,770 42.5 29.7

 (581) (884) (3.8) (3.8)

Remain poor in 2007 1.6 11,705 11,538 32.3 18.7

 (662) (671) (4.7) (3.9)

Not poor in 2007 0.8 9,964 18,186 62.6 51.6

 (1012) (2283) (6.4) (8.4)

Income-to-poverty 

ratio>=4 in 2006 52.6 70,423 67,933 17.6 7.6

 (1388) (1249) (0.7) (0.5)
22

Table 4: Earnings Volatility, Wage and Salary Earners vs. Self-Employed (Business 

Owners), 25-65 Year Old, 2006-2007 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. 

Bureau of the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and 

subtracted from the estimate provides the 90-percent confidence interval. 

Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel 

weights. Total sample size is 8,539 observations.

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2006 2007 >=25% change >=50% change 

Sample: Those with paid employment during all months in 2006-2007

All 100 50,608 50,020 20.2 9.1

 (953) (870) (0.6) (0.4)

 

Year round (YR) Wage 

and Salary Earners only 83 48,849 49,236 13.1 4.3

 (761) (811) (0.5) (0.3)

YR Wage and Salary 

Earners (plus selfemployed YR or part-year) 86 49,728 49,992 14.1 4.8

 (824) (865) (0.5) (0.3)

YR Self-employed only 11 56,716 50,531 61.6 39.8

 (3941) (2446) (1.9) (2.0)

YR Self-employed (plus 

work for employer YR or 

part-year) 14 59,067 53,023 58.2 36.2

 (3821) (2568) (1.8) (1.8)

YR Self-employed plus 

YR wage and salary earner 2 77,196 71,982 37.0 15.4

 (10,735) (10,378) (4.3) (3.2)
23

Table 5: Earnings Volatility by Industry, Wage and Salary Earners, 2006-2007 

Major Industry Categories 

Population 

Share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

(Based on NAICS) % 2006 2007 >=25% change >=50% change 

 

All YR Wage and Salaried workers 100 49,728 

(824) 

49,992 

(866) 

14.1 

(0.5) 

4.8 

(0.3) 

 

Construction & Natural Resource (Agriculture, 

Mining, Forestry, Fisheries) 6.3 

49,336 

(2160) 

48,805 

(2073) 

15.3 

(1.9) 

5.0 

(1.1) 

Manufacturing 

 14.4 

54,233 

(1913) 

54,110 

(1812) 

10.9 

(1.0) 

2.6 

(0.5) 

Wholesale & Retail Trade 

 13.8 

43,120 

(1418) 

42,868 

(1302) 

14.4 

(1.4) 

5.4 

(0.8) 

Transportation, Warehousing, Utilities 

 5.3 

52,599 

(2470) 

52,801 

(2834) 

12.6 

(2.1) 

3.7 

(1.0) 

Information, Finance, Insurance, Real Estate, 

Rental, Leasing 9.3 

59,165 

(2657) 

58,072 

(2702) 

15.3 

(1.5) 

3.9 

(0.8) 

Professional, Scientific, Technical, Management, 

Administration, Support, Waste Management, 

Remediation 9.7 

62,112 

(2687) 

63,397 

(2786) 

16.8 

(1.6) 

4.9 

(0.9) 

Education, Health Care and Social Assistance 23.2 

47,076 

(1724) 

47,115 

(1615) 

13.3 

(1.0) 

5.0 

(0.6) 

Arts, Entertainment, Recreation, 

Accommodation and Food Services, Other 

services 9.4 

33,783 

(1190) 

33,988 

(1262) 

17.5 

(1.7) 

6.2 

(1.1) 

Public Administration 

8.3 

55,152 

(2026) 

56,840 

(2280) 

10.0 

(1.3) 

3.8 

(1.0) 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from 

the estimate provides the 90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the 

Census. 

Sample includes all 25-65 year old people who report working for an employer during all months of 2006 and 2007. 

Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel weights. Total sample 

size is 7,396 observations.
24

Table 6: Earnings Volatility by Occupation, Wage and Salary Earners, 2006-

2007 

 Major Occupation Categories Population 

Share 

Mean Annual Earnings 

 ($2007) 

Earnings Volatility 

(%) 

 Based on 2000 SOC % 2006 2007 >=25% change >=50% change 

Management, Business, Financial 

15.3 77,210 

2441 

77,077 

2480 

14.3 

(1.2) 

4.8 

(0.7) 

Professional and Related 

24.2 60,234 

1242 

60,558 

1244 

12.5 

(0.9) 

4.2 

(0.5) 

Service 12.7 33,494 

2648 

33,145 

2294 

18.1 

(1.4) 

6.1 

(0.9) 

Sales and related 8.9 47,751 

1882 

47,788 

1904 

16.3 

(1.6) 

6.1 

(1.0) 

Office and Administrative Support 15.3 35,466 

784 

36,649 

921 

12.6 

(1.1) 

3.9 

(0.7) 

Construction, Extraction & 

Farming, Fishing, Forestry 

4.6 42,109 

1665 

40,404 

1353 

14.6 

(2.3) 

3.7 

(1.1) 

Installation, Maintenance, Repair 

3.9 47,200 

2283 

47,425 

2291 

8.2 

(1.9) 

2.9 

(1.0) 

Production 8.0 37,213 

995 

37,711 

1032 

11.5 

(1.5) 

2.9 

(0.8) 

Transportation and Material Moving 6.3 39,311 

1675 

38,526 

1882 

14.8 

(1.8) 

5.0 

(1.1) 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted 

from the estimate provides the 90-percent confidence interval. Source: Author’s calculations from the 2004 Panel of 

Survey of Income and Program Participation, U.S. Bureau of the Census. Sample includes all 25-65 year old who 

report working for an employer during all months of 2006-2007. Those with zero earnings in either year are excluded. 

The data are weighted by longitudinal panel weights. Total sample size is 7,396. 
25

Table 7: Earnings Volatility by Industry, Wage and Salary Earners vs. 

Self-Employed (Business Owners), 2006-2007

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from 

the estimate provides the 90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the 

Census. 

Sample includes all 25-65 year old people who have paid employment (either for an employer or owning business) during 

all months of 2006 and 2007. Sample excludes those with zero earnings in either year. The data are weighted by 

longitudinal panel weights.

Workers (7396 obs) Business owners (1204 obs) 

Major Industry Categories Population 

Share 

Earnings Volatility 

(%) 

Pop 

Share 

Earnings Volatility 

(%) 

(Based on NAICS) 

 

>=25% 

change 

>=50% 

change 

 >=25% 

change 

>=50% 

change 

 

Construction & Natural Resource 

(Agriculture, Mining, Forestry, 

Fisheries) 

6.3 15.3 

(1.9) 

5.0 

(1.1) 

22.6 61.5 

(4.0) 

38.0 

(3.8) 

Manufacturing 14.4 10.9 

(1.0) 

2.6 

(0.5) 

4.3 64.1 

(7.3) 

34.2 

(8.3) 

Wholesale & Retail Trade 13.8 14.4 

(1.4) 

5.4 

(0.8) 

13.1 61.7 

(4.1) 

39.9 

(4.3) 

Transportation, Warehousing, 

Utilities 

5.3 12.6 

(2.1) 

3.7 

(1.0) 

3.3 66.1 

(8.1) 

34.9 

(8.9) 

Information, Finance, Insurance, 

Real Estate, Rental, Leasing 

9.3 15.3 

(1.5) 

3.9 

(0.8) 

9.5 57.3 

(5.1) 

41.8 

(5.3) 

Professional, Scientific, Technical, 

Management, Administration, 

Support, Waste Management, 

Remediation 

9.7 16.8 

(1.6) 

4.9 

(0.9) 

22.0 57.8 

(3.5) 

33.1 

(3.6) 

Education, Health Care and Social 

Assistance 

23.2 13.3 

(1.0) 

5.0 

(0.6) 

9.1 63.3 

(5.0) 

37.3 

(5.3) 

Arts, Entertainment, Recreation, 

Accommodation and Food Services, 

Other services 

9.4 17.5 

(1.7) 

6.2 

(1.1) 

16.2 56.9 

(4.0) 

37.1 

(3.7) 

Public Administration 8.3 10.0 

(1.3) 

3.8 

(1.0) 

NA NA NA
26

Table 8: Earnings Volatility by Occupation, Wage and Salary Earners 

vs. Self-Employed (Business Owners), 2006-2007

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from 

the estimate provides the 90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the 

Census. 

Sample includes all 25-65 year old people who have paid employment (either for an employer or owning business) during 

all months of 2006 and 2007. Sample excludes those with zero earnings in either year. The data are weighted by 

longitudinal panel weights.

Wage & Salaried workers (7396 obs) Self-employed (1204 obs) 

Major Industry Categories Population 

Share 

Earnings Volatility 

(%) 

Pop 

Share 

Earnings Volatility 

(%) 

(Based on SOC) 

 

>=25% 

change 

>=50% 

change 

 >=25% 

change 

>=50% 

change 

 

Management, Business, Financial 

15.3 14.3 

(1.2) 

4.8 

(0.7) 

25.5 56.7 

(3.5) 

34.5 

(3.4) 

Professional and Related 

24.2 12.5 

(0.9) 

4.2 

(0.5) 

15.6 53.4 

(4.0) 

32.7 

(3.9) 

Service 12.7 18.1 

(1.4) 

6.1 

(0.9) 

14.1 64.3 

(4.1) 

34.2 

(4.1) 

Sales and related 8.9 16.3 

(1.6) 

6.1 

(1.0) 

17.8 59.4 

(3.7) 

40.6 

(3.9) 

Office and Administrative Support 15.3 12.6 

(1.1) 

3.9 

(0.7) 

3.9 52.7 

(8.2) 

26.0 

(6.7) 

Construction, Extraction & 

Farming, Fishing, Forestry 

4.6 14.6 

(2.3) 

3.7 

(1.1) 

12.6 66.9 

(4.3) 

42.6 

(5.2) 

Installation, Maintenance, Repair 

3.9 8.2 

(1.9) 

2.9 

(1.0) 

3.2 61.5 

(8.4) 

42.9 

(8.8) 

Production 8.0 11.5 

(1.5) 

2.9 

(0.8) 

3.6 

67.8 

(8.1) 

48.4 

(8.4) 

Transportation and Material Moving 6.3 14.8 

(1.8) 

5.0 

(1.1) 

3.4 

70.3 

(7.9) 

43.9 

(8.8) 
27

Table 9: Comparison of Earnings Volatility in SIPP and CPS Data, 2006-07 

 (percents) 

Sources: Columns (a) and (b): See notes to Tables 1 and 2. Columns (c) and (d): Author’s calculations from 

NBER’s 2006 and 2007 Current Population Survey Merged Outgoing Rotation Group data (Feenberg and Roth, 

2007). 

Note: CPS sample does not include self-employed persons or persons with hourly wages averaging less than $1. 

Earnings are adjusted for inflation using CPI-U. The sample for column (c) includes all persons who were 

employed the week prior to their interview (N=22,974); for column (d), the sample includes all persons who 

worked 35 hours or more in the week prior to their interview (N=19,182). 

 SIPP CPS 

(a) 

Table 1 (in labor 

force one or more 

months) 

(b) 

Table 2 (worked all 

months) 

(c) 

Employed both years 

(d) 

Employed 35+ 

hours both years 

 >=25% >=50% >=25% >=50% >=25% >=50% >=25% >=50% 

All 31.4 17.2 20.2 9.1 28.2 11.4 24.3 8.6

Gender 

Men 29.9 15.9 21.0 9.7 28.0 10.7 26.3 9.4

Women 33.1 18.7 19.6 8.4 28.3 12.1 21.9 7.6

Race 

White, not Hispanic 30.6 17.0 20.2 9.5 26.5 11.0 22.5 8.2

Black 33.0 17.8 18.8 7.0 35.1 13.8 32.1 11.3

Hispanic (any origin) 32.6 17.8 19.6 8.4 33.7 12.8 30.1 9.8

Age 

25-34 34.7 19.1 21.2 8.5 32.4 12.7 28.1 9.7

35-44 29.2 15.2 18.8 7.5 28.6 12.3 25.2 9.8

45-54 28.4 15.4 18.8 9.7 25.6 10.8 21.9 7.1

55-65 35.6 21.2 23.7 12.2 25.3 10.0 21.6 7.6

Marital Status 

Married 30.6 16.6 20.4 9.7 27.8 11.1 23.9 8.5

Not Married 32.1 17.8 19.0 8.0 29.0 12.1 24.7 8.9

Education 

Less than HS Degree 36.5 20.5 23.3 11.6 32.6 12.8 28.7 9.6

HS Degree 30.0 11.0 25.7 8.3

HS Degree/Some 

College 32.5 18.1 20.3 9.2

Some College 28.2 12.1 24.3 9.2

College Degree 30.0 15.8 19.8 8.9

College/Adv. Degree 25.7 10.8 22.4 8.3

Advanced Degree 26.2 13.4 18.7 8.2
Table 10

Regression Analysis of Weekly Earnings Volatility: 

Time and Age Variables 

 |∆(log earnings)| |∆(earnings)| > 25% |∆(earnings)| > 5Time trend -0.000112*** -0.000110** -2.612nd Quarter -0.00144 -0.0021 -0.03rd Quarter 0.00044 0.00286 -0.004th Quarter 0.00427*** 0.00662*** 0.002In 1985_1987 0.00450*** 0.0119*** 0.0090In 1995_2007 0.0222*** 0.0356*** 0.017Recession 0.000136 0.000826 0.0Age 0.0309*** 0.0516*** 0.030Age2 0.00105*** 0.00167*** 0.001Age3 -1.60e-05*** -2.44e-05** -1.58e-Age4 9.02e-08*** 1.34e-07** 8.86e-R-squared 0.033 0.027 0

*** p<0.01, ** p<0.05, * p<0.1. Standard errors available on request. 
Table 11 

Regression Analysis of Weekly Earnings Volatility Using 

Demographic Variables 

 |∆(log earnings)| |∆(earnings)| > 

25% 

|∆(e

Female -0.0185*** -0.0324*** Married 0.00267*** 0.00444*** Married status change 0.0279*** 0.0421*** Black 0.0315*** 0.0566*** Hispanic 0.0183*** 0.0364*** Other race 0.0150*** 0.0203*** High school only -0.00338*** -0.00468** Some college -0.00277** -0.00571** College degree or more -0.00639*** -0.0120*** Increase education attainment 0.0289*** 0.0409*** 

*** p<0.01, ** p<0.05, * p<0.1 
Table 12 

Regression Analysis of Weekly Earnings Volatility Using 

Industry and Pay Status Variables 

 |∆(log earnings)| |∆(earnings)| > 25% |∆(ea

Paid hourly -0.0279*** -0.0453*** Paid hourly status change 0.0710*** 0.123*** Accommodation and Food & Drink 0.0409*** 0.0648*** Administration and Waste Management 0.0118*** 0.0208*** Agriculture, Forestry and Fishing 0.00163 0.000563 Arts & Recreation 0.0129*** 0.0246*** Construction 0.0133*** 0.0247*** Education 0.0251*** 0.0483*** FIRE & Leasing 0.0165*** 0.0215*** Health & Social Services 0.00405*** 0.00563** Information -0.0024 -0.00792* Mining 0.0296*** 0.0444*** Organizations & Households 0.00942*** 0.0120** Personal & Services 0.0205*** 0.0353*** Professional Services -0.00844*** -0.0170*** Public Administration -0.0125*** -0.0241*** Retail 0.0141*** 0.0230*** Transportation & Warehousing 0.0208*** 0.0320*** Utilities -0.00667*** -0.00942** Wholesale 0.000668 0.0037 Industry change 0.0477*** 0.0784*** 
31

Reference

Cameron, Stephen and Joseph Tracy (1998). “Earnings Variability in the United States: An 

Examination Using Matched- CPS Data,” Mimeo, October 1998; 

www.newyorkfed.org/research/economists/.../earnings_variability.pdf

Congressional Budget Office (2008). “Recent Trends in the Variability of Individual Earnings 

and Household Income,” June 2008; 

http://cbo.gov/ftpdocs/95xx/doc9507/Frontmatter.1.2.shtml

Dynan, Karen, Douglas Elmendorf and Daniel Sichel (2007). “The Evolution of Household 

Income Volatility,” Finance and Economics Discussion Series, no 2007-61, Federal 

Reserve Board. 

Feenberg, Daniel and Jean Roth. “CPS Labor Extracts.” January 2007. 

(http://www.nber.org/data/morg.html) 

Fields, Gary S. and Efe A. Ok. ”Measuring Movements of Income.” Economica. 66 (1999) 455-

471. 

Gottschalk, Peter and Robert Moffitt (1994). “The Growth of Earnings Instability in the U.S. 

Labor Market,” Brookings Paper on Economic Activity, 1994, Issue 1, pages 217-272. 

Haider, Steven (2001). “Earnings Instability and Earnings Inequality of Males in the United 

States: 1967-1991,” Journal of Labor Economics, October 2001, pp 799-836; 

www.msu.edu/~haider/Research/2001-jole.pdf

Hacker, Jacob and Elizabeth Jacobs (2008). “The Rising Instability of American family Incomes, 

1969-2004: Evidence from the Panel Study of Income Dynamics.” Economic Policy 

Institute Briefing paper no. 213, May 2008; www.epi.org/content.cfm/bp213

Hirsch, Barry T. and Edward J. Schumacher. "Match Bias in Wage Gap Estimates Due to 

Earnings Impuation." Journal of Labor Economics. 22 (2004), pp. 689-722. 

Madrian, Brigitte C. and Lars John Lefgren. “An Approach to Longitudinally Matching Current 

Population Survey (CPS) Respondents.” Journal of Economic and Social Measurement:

26 (2000), 31-62. 

Moffitt, Robert and Peter Gottschalk (1995). “Trends in the Covariance Structure of Earnings in 

the United States.” Institute for Research on Poverty Discussion Paper, no 1001-93; 

http://www.irp.wisc.edu/publications/dps/pdfs/dp100193.pdf

 

Moffitt, Robert and Peter Gottschalk (2008). “Trends in the Transitory Variance of Male 

Earnings in the U.S., 1970-2004,” Mimeo, December 2008; 

http://fmwww.bc.edu/ec/research.php
32

Appendix
33

Table 1b: Earnings Volatility, 25-65 year old in Labor Force one or more months, 2004-05 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from the 

estimate provides the 90-percent confidence interval. 

Sample includes all 25-65 year old who are in the labor force (working, on layoff or looking for a job) for at least one month 

during the 2004-2005 period. Those with zero earnings in either year are excluded. The data are weighted by longitudinal panel 

weights. Sample includes 28,904 person records.

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2004 2005 >=25% change >=50% change 

All 100 43,841 44,247 36.4 20.3

 (561) (604) (0.3) (0.3)

Gender 

Men 53 53,034 53,674 35.3 19.5

 (805) (876) (0.5) (0.4)

Women 47 33,534 33,678 37.5 21.3

 (388) (444) (0.5) (0.4)

Race 

White, not Hispanic 72 47,259 47,545 35.5 19.9

 (620) (666) (0.4) (0.3)

Black 11 33,969 34,913 38.3 20.9

 (849) (1159) (1.2) (0.9)

Hispanic (of any origin) 12 30,405 30,476 39.6 21.6

 (829) (788) (1.3) (1.0)

Age 

25-34 27 36,189 37,954 38.2 21.0

 (616) (692) (0.7) (0.6)

35-44 30 45,981 46,386 34.7 19.2

 (730) (827) (0.6) (0.5)

45-54 28 48,511 48,462 33.6 18.7

 (740) (837) (0.6) (0.5)

55-65 15 44,562 43,344 41.8 24.7

 (1106) (1187) (0.8) (0.8)

Marital Status 

Married 62 47,815 48,124 34.9 19.7

 (650) (696) (0.4) (0.4)

Not Married 32 36,609 37,265 38.9 21.5

 (589) (647) (0.6) (0.5)

Education 

Less than HS Degree 9 23,677 23,410 42.3 24.3

 (556) (495) (1.1) (1.0)

HS Degree/Some College 60 35,367 35,118 37.2 20.7

 (332) (339) (0.4) (0.4)

College Degree 20 57,429 57,951 34.0 19.1

 (921) (957) (0.7) (0.6)

Advanced Degree 11 80,930 85,167 31.3 17.2

 (1885) (2218) (1.0) (0.8)
34

Table 2b: Earnings Volatility, 25-65 Year Old Year-Round Employed, 2004-2005 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of 

the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted 

from the estimate provides the 90-percent confidence interval. 

Sample includes all 25-65 year old who report having paid employment (i.e. no unpaid absence) during all months 

of 2004 and 2005. Those with zero earnings in either year are excluded. The data are weighted by longitudinal panel 

weights. Sample includes 18,892 person records. 

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2004 2005 >=25% change >=50% change 

All 100 51,714 52,449 23.6 11.1

 (667) (752) (0.4) (0.3)

Gender 

Men 56 60,433 61,571 24.7 12.3

 (930) (1055) (0.5) (0.4)

Women 44 40,500 40,717 22.1 9.7

 (488) (570) (0.5) (0.4)

Race 

White, not Hispanic 73 55,128 55,852 23.6 11.5

 (730) (824) (0.4) (0.3)

Black 10 40,130 41,745 22.5 9.1

 (1145) (1713) (1.2) (0.8)

Hispanic (of any origin) 11 37,087 36,919 25.0 10.7

 (1100) (1113) (1.5) (1.0)

Age 

25-34 24 43,721 45,767 21.2 9.1

 (750) (891) (1.2) (0.5)

35-44 31 53,842 53,964 18.8 10.5

 (925) (1046) (1.0) (0.5)

45-54 30 55,470 55,705 18.8 11.1

 (871) (1025) (0.9) (0.5)

55-65 14 52,648 53,556 23.7 16.1

 (1547) (1663) (1.3) (0.8)

Marital Status 

Married 65 55,373 56,064 23.3 11.3

 (776) (886) (0.5) (0.4)

Not Married 30 44,162 44,901 24.1 11.1

 (765) (842) (0.7) (0.5)

Education 

Less than HS Degree 7 28,952 28,393 26.0 11.6

 (888) (784) (1.5) (0.9)

HS Degree/Some College 59 41,395 41,414 23.6 11.0

 (411) (438) (0.5) (0.4)

College Degree 22 65,267 65,986 23.0 11.1

 (1089) (1222) (0.7) (0.5)

Advanced Degree 12 90,958 95,918 22.9 11.6

 (2123) (2629) (1.0) (0.8)
35

Table 3b: Earnings Volatility by Poverty Status, 25-65 Year Old, 2004-2005 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of 

the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted 

from the estimate provides the 90-percent confidence interval. 

Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel weights. 

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2004 2005 >=25% change >=50% change 

Sample: Those in labor force at least one month during 2004-2005 

All 100 43,841 44,247 36.4 20.3

 (561) (604) (0.3) (0.3)

Poverty Status 

Poor in 2004 4.5 8,364 14,243 72.3 53.6

 (175) (527) (1.4) (1.6)

Remain poor in 2005 2.5 7,998 8,442 62.9 42.5

 (233) (253) (1.9) (1.9)

Not poor in 2005 2.1 8,802 21,176 83.5 66.8

 (272) (1043) (1.8) (2.5)

Income-to-poverty 

ratio>=4 in 2004 48.1 64,319 62,602 29.5 15.1

 (680) (789) (0.4) (0.3)

 

Sample: Those with paid employment during all months in 2004-2005

All 100 51,714 52,449 23.6 11.1

 (667) (752) (0.4) (0.3)

Poverty Status 

Poor in 2004 2.2 10,050 16,830 60.4 41.5

 (379) (1133) (2.7) (2.9)

Remain poor in 2005 1.2 10,094 10,494 44.0 28.0

 (533) (485) (3.7) (3.8)

Not poor in 2005 1.0 9,997 24,515 80.3 58.0

 (515) (2202) (3.5) (4.6)

Income-to-poverty 

ratio>=4 in 2004 54.7 69,946 69,255 20.8 9.4

 (807) (963) (0.4) (0.3)
36

Table 4b: Earnings Volatility, Wage and Salary Earners vs. Self-Employed (Business 

Owners), 25-65 Year Old, 2004-2005 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of 

the Census. 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted 

from the estimate provides the 90-percent confidence interval. 

Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel weights. Total 

sample size is 18,892.

Population 

share 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

 % 2006 2007 >=25% change >=50% change 

Sample: Those with paid employment during all months in 2004-2005

All 100 51,714 52,449 23.6 11.1

 (667) (752) (0.4) (0.3)

 

Year round (YR) Wage 

and Salary Earners only 81 50,599 50,865 14.9 5.0

 (667) (687) (0.4) (0.2)

YR Wage and Salary 

Earners (plus selfemployed YR or part-year) 84 51,154 51,451 15.4 5.3

 (685) (711) (0.4) (0.2)

YR Self-employed only 11 55,809 58,323 69.6 45.2

 (1950) (2665) (1.2) (1.3)

YR Self-employed (plus 

work for employer YR or 

part-year) 13 56,185 58,903 68.3 44.0

 (1815) (2469) (1.1) (1.2)

YR Self-employed plus 

YR wage and salary earner 0.5 77,657 74,355 34.1 14.5

 (8188) (7184) (5.1) (3.8)
37

Table 5b: Earnings Volatility by Industry, Wage and Salary Earners, 2004-2005 

Major Industry Categories 

Sample Share 

(15,949 obs) 

Mean Annual Earnings 

($2007) 

Earnings Volatility 

(%) 

(Based on NAICS) % 2004 2005 >=25% change >=50% change 

 

All YR Wage and Salaried workers 100 51,154 

(685) 

51,451 

(711) 

15.4 

(0.4) 

5.3 

(0.2) 

 

Natural Resource – Agriculture, Mining, 

Forestry, Fisheries 1.0 

48,860 

(4074) 

48,194 

(3646) 

12.0 

(2.7) 

3.3 

(1.5) 

Construction 

4.7 

48532 

(1298) 

48,394 

(1346) 

15.7 

(1.5) 

5.1 

(0.9) 

Manufacturing 

 15.4 

56181 

(1408) 

56,502 

(1,472) 

13.4 

(0.8) 

4.3 

(0.5) 

Wholesale Trade 

 4.2 

51,937 

(1660) 

53,753 

(1931) 

16.0 

(1.6) 

5.4 

(1.1) 

Retail Trade 

 9.1 

40,968 

(1365) 

41,381 

(1421) 

16.3 

(1.4) 

6.2 

(0.9) 

Transportation, Warehousing, Utilities 

 5.8 

55,451 

(1448) 

55,486 

(1497) 

15.5 

(1.4) 

3.8 

(0.7) 

Information 

 2.7 

63,135 

(2594) 

64,661 

(2,976) 

14.7 

(2.1) 

5.0 

(1.3) 

Finance and Insurance 

 6.1 

62,426 

(2538) 

63,809 

(2516) 

17.9 

(1.4) 

6.7 

(1.0) 

Real Estate, Rental, Leasing 

1.5 

43453 

(2267) 

43,087 

(1838) 

16.3 

(2.9) 

10.0 

(2.8) 

Professional, Scientific, Technical PLUS 

Management of Companies and Enterprises 6.0 

75863 

(2372) 

75,882 

(2251) 

14.6 

(1.4) 

4.7 

(0.8) 

Administration, Support, Waste Management, 

Remediation 2.8 

41569 

(2169) 

41,023 

(2170) 

18.1 

(2.4) 

6.3 

(1.4) 

Educational Services 

 9.8 

48267 

(1088) 

48,571 

(1118) 

12.3 

(0.9) 

4.5 

(0.6) 

Health Care and Social Assistance 

 12.9 

45,787 

(1255) 

45,540 

(1241) 

17.0 

(0.9) 

5.2 

(0.5) 

Arts, Entertainment, Recreation 

1.1 

40903 

(3266) 

41,389 

(3090) 

20.7 

(4.3) 

8.2 

(2.4) 

Accommodation and Food Services 

 3.6 

29011 

(1143) 

28,620 

(1075) 

22.4 

(2.1) 

7.3 

(1.2) 

Other Services (except Public Administration) 

 3.7 

36882 

(1378) 

36,193 

(1405) 

20.4 

(1.9) 

6.8 

(1.2) 

Public Administration 

9.3 

54937 

(1076) 

55,846 

(1242) 

10.8 

(0.9) 

3.8 

(0.6) 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from the estimate provides the 

90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the Census. 

Sample includes all 25-65 year old people who report working for an employer during all months of 2004 and 2005. Sample excludes those 

with zero earnings in either year. The data are weighted by longitudinal panel weights. Total sample size is 15,949.
38

Table 6b: Earnings Volatility by Occupation, Wage and Salary Earners, 2004-2005 

 Major Occupation Categories Sample Share 

(15,949 obs) 

Mean Annual Earnings 

 ($2007) 

Earnings Volatility 

(%) 

 Based on 2000 SOC % 2004 2005 >=25% change >=50% change 

Management 

 11.6 

82,439 

1957 

84,430 

2048 

15.2 

1.0 

5.7 

0.7 

Business and Financial Operations 

 4.8 

61,308 

1701 

63,416 

1955 

12.7 

1.4 

4.8 

0.9 

Computer and Mathematical 

3.8 

73,218 

1591 

73,801 

1606 

11.1 

1.6 

3.1 

0.9 

Architectural and Engineering 

 2.8 

76,140 

2508 

75,080 

2381 

8.5 

1.4 

2.5 

0.7 

Life, Physical and Social Science 

1.3 

67,239 

2157 

67,315 

2239 

7.1 

1.8 

2.0 

1.0 

Community and Social Service 

 1.9 

43,600 

1827 

43,175 

2008 

16.3 

2.3 

4.1 

1.2 

Legal 

 1.2 

100,229 

9258 

98,972 

8698 

16.0 

3.0 

5.8 

2.0 

Education, Training, Library 

6.0 

49,256 

1163 

49,383 

1157 

12.9 

1.2 

4.5 

0.8 

Arts, Design, Entertainment, Sports, Media 

 1.3 

53,895 

2886 

54,473 

3156 

16.0 

2.9 

4.4 

1.6 

Health Care Practitioner and Technical 

 5.5 

64,507 

2210 

63,286 

2012 

18.3 

1.4 

6.3 

0.9 

Healthcare support 

1.9 

30,040 

2898 

30,963 

3566 

18.1 

2.3 

7.6 

1.6 

Protective Service 

 2.9 

50,412 

1488 

51,107 

1694 

14.1 

1.9 

3.5 

0.9 

Food Preparation and Serving-Related 

 2.8 

22,923 

848 

23,196 

789 

24.9 

2.5 

8.6 

1.4 

Building and Grounds Cleaning and 

Maintenance 3.0 

26,536 

969 

25,812 

774 

15.5 

1.8 

7.7 

1.4 

Personal care and Service 

 1.5 

27,041 

1511 

26,488 

1495 

21.7 

3.2 

10.7 

2.5 

Sales and Related 

8.4 

49,354 

1331 

49,878 

1350 

19.1 

1.4 

7.8 

0.9 

Office and Administrative Support 

 16.0 

36,674 

506 

36,857 

525 

14.7 

0.9 

4.6 

0.5 

Farming, Fishing, Forestry 

 0.4 

29,014 

1696 

30,317 

2115 

8.4 

3.5 

7.2 

0.7 

Construction and Extraction 

3.8 

43,166 

1107 

43,203 

1024 

13.9 

1.7 

4.5 

0.9 

Installation, Maintenance, Repair 

 4.1 

46,617 

912 

47,192 

1196 

13.3 

1.5 

3.6 

0.7 

Production 

 8.6 

38,102 

693 

37,709 

688 

15.0 

1.3 

3.9 

0.6 

Transportation and Material Moving 

5.4 

40,502 

1077 

40,149 

1036 

18.0 

1.5 

5.7 

1.1 

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from the 

estimate provides the 90-percent confidence interval. Source: Author’s calculations from the 2004 Panel of Survey of Income and

Program Participation, U.S. Bureau of the Census. Sample includes all 25-65 year old people who work for an employer during 

all months of 2004 and 2005. Sample excludes those with zero earnings in either year. The data are weighted by longitudinal 

panel weights. Total sample size is 15,949. 
39

Table 7b: Earnings Volatility by Industry, Wage and Salary Earners vs. SelfEmployed (Business Owners), 2004-2005

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from the 

estimate provides the 90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the Census. 

Sample includes all 25-65 year old people who have paid employment (either for an employer or owning business) during all 

months of 2004 and 2005. Sample excludes those with zero earnings in either year. The data are weighted by longitudinal panel 

weights.

Workers (15,949 obs) Business owners (2,459 obs) 

Major Industry Categories Population 

Share 

Earnings Volatility 

(%) 

Pop 

Share 

Earnings Volatility 

(%) 

(Based on NAICS) 

 

>=25% 

change 

>=50% 

change 

 >=25% 

change 

>=50% 

change 

 

Natural Resource – Agriculture, 

Mining, Forestry, Fisheries 

10.0 12.0 

(2.7) 

3.3 

(1.5) 

5.1 66.7 

(4.0) 

43.4 

(4.5) 

Construction 4.7 15.7 

(1.5) 

5.1 

(0.9) 

17.4 66.6 

(2.7) 

42.3 

(2.6) 

Manufacturing 15.4 13.4 

(0.8) 

4.3 

(0.5) 

4.8 72.1 

(4.6) 

35.5 

(4.7) 

Wholesale Trade 4.2 16.0 

(1.6) 

5.4 

(1.1) 

4.0 69.7 

(5.8) 

52.0 

(7.0) 

Retail Trade 9.1 16.3 

(1.4) 

6.2 

(0.9) 

9.9 70.4 

(3.4) 

47.2 

(3.9) 

Utilities, Transportation and 

Warehousing 

5.8 15.5 

(1.4) 

3.8 

(0.7) 

3.0 77.2 

(5.4) 

43.7 

(7.0) 

Information 2.7 14.7 

(2.1) 

5.0 

(1.3) 

1.2 40.5 

(11.7) 

32.3 

(10.6) 

Finance, Insurance, Real Estate, 

Rental, Leasing 

7.6 17.5 

(1.3) 

7.4 

(1.0) 

8.5 70.2 

(4.1) 

45.5 

(4.2) 

Professional, Scientific, Technical, 

Management of Companies and 

Enterprises, Administration, 

Support, Waste Management, 

Remediation 

8.8 15.7 

(1.3) 

5.2 

(0.7) 

20.5 67.0 

(2.5) 

44.1 

(2.5) 

Education, Health, Social Services 22.7 15.0 

(0.7) 

4.9 

(0.4) 

9.6 67.4 

(3.5) 

40.2 

(4.0) 

Arts, Entertainment, Recreation, 

Accommodation and Food Services 4.6 22.0 

(1.8) 

7.5 

(1.1) 

6.2 75.7 

(3.5) 

53.4 

(4.2) 

Other Services (except Public 

Administration) 

3.7 20.4 

(1.9) 

6.8 

(1.2) 

9.5 65.9 

(3.4) 

42.8 

(3.5) 

Public Administration 9.3 10.8 

(0.9) 

3.8 

(0.6) 

0.2 68.8 

(25.1) 

49.8 

(25.9) 
40

Table 8b: Earnings Volatility by Occupation, Wage and Salary Earners vs. 

Self-Employed (Business Owners), 2004-2005

Note: Standard errors in parentheses. The margin of error, calculated by 1.6*standard error, added to and subtracted from the 

estimate provides the 90-percent confidence interval. 

Source: Author’s calculations from the 2004 Panel of Survey of Income and Program Participation, U.S. Bureau of the 

Census. 

Sample includes all 25-65 year old people who have paid employment (either for an employer or owning business) during all 

months of 2004 and 2005. Sample excludes those with zero earnings in either year. The data are weighted by longitudinal 

panel weights. 

Workers (15,949 obs) Business owners (2,459 obs) 

Major Industry Categories Population 

Share 

Earnings Volatility 

(%) 

Pop 

Share 

Earnings Volatility 

(%) 

(Based on SOC) 

 

>=25% 

change 

>=50% 

change 

 >=25% 

change 

>=50% 

change 

 

Management, Business, Financial 

16.5 14.5 

(0.8) 

5.6 

(0.6) 

26.1 64.1 

(2.4) 

39.7 

(2.3) 

Professional and Related 

23.8 13.7 

(0.6) 

4.3 

(0.4) 

16.4 67.1 

(2.4) 

45.2 

(2.7) 

Service 12.1 18.8 

(1.0) 

7.5 

(0.7) 

14.1 75.6 

(2.6) 

48.6 

(3.2) 

Sales and related 8.4 19.1 

(1.4) 

7.8 

(0.9) 

17.0 68.2 

(2.4) 

46.4 

(2.8) 

Office and Administrative Support 16.0 14.7 

(0.9) 

4.6 

(0.5) 

3.7 65.3 

(5.2) 

31.7 

(5.1) 

Farming, Fishing, Forestry 

0.4 8.4 

(3.5) 

0.7 

(0.7) 

1.6 62.0 

(7.7) 

37.7 

(7.8) 

Construction and Extraction 3.8 13.9 

(1.7) 

4.5 

(0.9) 

11.0 70.6 

(3.1) 

42.9 

(3.4) 

Installation, Maintenance, Repair 4.1 13.3 

(1.5) 

3.6 

(0.7) 

3.6 69.0 

(5.3) 

53.5 

(5.7) 

Production 

8.6 15.0 

(1.3) 

3.9 

(0.6) 

3.4 67.3 

(5.8) 

47.2 

(6.0) 

Transportation and Material Moving 5.4 18.0 

(1.5) 

5.7 

(1.1) 

2.7 78.6 

(5.7) 

49.3 

(7.8) 
41

Table 10a: Variable Means by Earnings Allocation Status 

 Allocated Unallocated 

Number of Observations 243,191 430,278

|∆(log earnings)| 0.441 0.172

|∆(earnings)| > 25% 0.609 0.220

|∆(earnings)| > 50% 0.328 0.075

Time trend 74.964 58.116

12t Quarter 0.207 0.232

2nd Quarter 0.273 0.267

3rd Quarter 0.275 0.259

4th Quarter 0.245 0.241

1979-1984 0.178 0.319

1985-1987 0.063 0.137

1995-2007 0.759 0.544

Recession 0.156 0.186

Age 43.347 41.425

Female 0.419 0.427

Married 0.702 0.732

Married status change 0.022 0.022

White, non-Hispanic 0.755 0.806

Black 0.116 0.076

Hispanic 0.079 0.077

Other race 0.049 0.041

Didn't complete High School 0.095 0.113

High school only 0.348 0.334

Some college 0.263 0.249

College degree or more 0.294 0.305

Increase education attainment 0.061 0.045

Paid hourly 0.530 0.502

Paid hourly status change 0.268 0.130

Manufacturing 0.196 0.223

Accommodation and Food & Drink 0.031 0.028

Administration and Waste Management 0.024 0.019
42

Table 10a: Variable Means by Earnings Allocation Status 

 Allocated Unallocated 

Agriculture, Forestry and Fishing 0.008 0.012

Arts & Recreation 0.011 0.010

Construction 0.057 0.054

Education 0.095 0.116

FIRE & Leasing 0.076 0.063

Health & Social Services 0.109 0.101

Information 0.026 0.025

Mining 0.009 0.011

Organizations & Households 0.016 0.016

Personal & Services 0.018 0.015

Professional Services 0.047 0.042

Public Administration 0.076 0.074

Retail 0.087 0.083

Transportation & Warehousing 0.056 0.048

Utilities 0.019 0.019

Wholesale 0.040 0.041

Industry change 0.217 0.168
43

Table 10b: Complete Regression Results for Tables 10-12 

(Omitting Observations with Allocated Earnings) 

 Dependent Variables 

Explanatory Variables |∆(log earnings)| |∆(earnings)| 

> 25% 

|∆(earnings)| > 

50% 

-0.000112*** -0.000110** -2.61E-05 Time trend

2.94E-05 5.56E-05 3.54E-05 

-0.00144 -0.0021 -0.00119 2nd Quarter 0.00093 0.00177 0.00113 

0.00044 0.00286 -0.00068 3rd Quarter 0.00095 0.00179 0.00114 

0.00427*** 0.00662*** 0.00263** 4th Quarter 0.00096 0.00182 0.00116 

0.00450*** 0.0119*** 0.00908*** In 1985_1987 0.00129 0.00244 0.00156 

0.0222*** 0.0356*** 0.0178*** In 1995_2007 0.00248 0.00469 0.00299 

0.000136 0.000826 0.00062 Recession 

0.00094 0.00178 0.00113 

-0.0309*** -0.0516*** -0.0308*** Age 0.00939 0.0178 0.0113 

0.00105*** 0.00167*** 0.00105** Age2

0.00034 0.00064 0.00041 

-1.60e-05*** -2.44e-05** -1.58e-05** Age3

5.27E-06 9.99E-06 6.37E-06 

9.02e-08*** 1.34e-07** 8.86e-08** Age4

3.01E-08 5.70E-08 3.64E-08 

-0.0185*** -0.0324*** -0.0130*** Female 

0.00074 0.00139 0.00089 

0.00267*** 0.00444*** 0.000938 Married 

0.00077 0.00146 0.00093 

0.0279*** 0.0421*** 0.0255*** Married status change 0.00224 0.00423 0.0027 

0.0315*** 0.0566*** 0.0247*** Black 

0.00127 0.0024 0.00153 

0.0183*** 0.0364*** 0.0120*** Hispanic 0.00129 0.00245 0.00156 

0.0150*** 0.0203*** 0.0107*** Other race 

0.00168 0.00317 0.00202 

-0.00338*** -0.00468** -0.00065 High school only 0.00118 0.00224 0.00143 

-0.00277** -0.00571** 0.00126 Some college 0.00128 0.00242 0.00154 

-0.00639*** -0.0120*** -0.00151 College degree or more 0.00137 0.00259 0.00165 

0.0289*** 0.0409*** 0.0330*** Increase education attainment 

0.0021 0.00397 0.00253 

-0.0279*** -0.0453*** -0.0248*** Paid hourly 0.00076 0.00145 0.00092 
44

Table 10b: Complete Regression Results for Tables 10-12 

(Omitting Observations with Allocated Earnings) 

 Dependent Variables 

Explanatory Variables |∆(log earnings)| |∆(earnings)| 

> 25% 

|∆(earnings)| > 

50% 

0.0710*** 0.123*** 0.0569*** Paid hourly status change 0.00099 0.00187 0.00119 

Accommodation and Food & 0.0409*** 0.0648*** 0.0394*** 

Drink 0.00209 0.00396 0.00252 

Administration and Waste 0.0118*** 0.0208*** 0.0130*** 

Management 0.00251 0.00476 0.00303 

0.00163 0.000563 0.00593 Agriculture, Forestry and Fishing 0.0031 0.00588 0.00375 

0.0129*** 0.0246*** 0.0168*** Arts & Recreation 

0.00343 0.0065 0.00414 

0.0133*** 0.0247*** 0.00788*** Construction 

0.00159 0.00301 0.00192 

0.0251*** 0.0483*** 0.0199*** Education 

0.00131 0.00249 0.00159 

0.0165*** 0.0215*** 0.0181*** FIRE & Leasing 0.00154 0.00292 0.00186 

0.00405*** 0.00563** 0.00805*** Health & Social Services 

0.00132 0.00249 0.00159 

-0.0024 -0.00792* 0.00154 Information 

0.00222 0.00421 0.00268 

0.0296*** 0.0444*** 0.0217*** Mining 0.0032 0.00606 0.00386 

0.00942*** 0.0120** 0.0132*** Organizations & Households 0.0027 0.00512 0.00326 

0.0205*** 0.0353*** 0.0165*** Personal & Services 

0.00279 0.00528 0.00337 

-0.00844*** -0.0170*** -0.00425* Professional Services 

0.00181 0.00343 0.00219 

-0.0125*** -0.0241*** -0.00495*** Public Administration 

0.00143 0.00271 0.00173 

0.0141*** 0.0230*** 0.0166*** Retail 

0.00135 0.00256 0.00163 

0.0208*** 0.0320*** 0.0179*** Transportation & Warehousing 0.00166 0.00315 0.00201 

-0.00667*** -0.00942** -0.00158 Utilities 

0.00248 0.00469 0.00299 

0.000668 0.0037 0.000505 Wholesale 

0.00179 0.0034 0.00217 

0.0477*** 0.0784*** 0.0426*** Industry change 0.00091 0.00173 0.0011 

0.499*** 0.797*** 0.395*** Constant 

0.0951 0.18 0.115 

Observations 430,278 430,278 430,278 

R-squared 0.033 0.027 0.017 

Standard errors in italics under coefficient estimates. *** p<0.01, ** p<0.05, * p<0.1 
45

Table 10c: Regression Analysis of Earnings Volatility Using CPS 

(Including Observations with Allocated Earnings) 

Dependent Variables 

|∆(log 

earnings)| 

|∆(earnings)| > 

25% 

|∆(earnings)| > 

50% 

0.000473*** 0.000568*** 0.000462*** 

Time trend (3.24e-05) (4.95e-05) (3.85e-05) 

0.00630*** 0.00977*** 0.00713*** 

2nd Quarter (0.00108) (0.00164) (0.00128) 

0.00697*** 0.0116*** 0.00629*** 

3rd Quarter (0.00108) (0.00166) (0.00129) 

0.0107*** 0.0155*** 0.00877*** 

4th Quarter (0.00111) (0.00169) (0.00132) 

-0.0144*** -0.0167*** -0.00777*** 

In 1985_1987 (0.00158) (0.00241) (0.00187) 

0.0275*** 0.0421*** 0.0276*** 

In 1995_2007 (0.00278) (0.00424) (0.00331) 

0.00348*** 0.00629*** 0.00417*** 

Recession (0.00108) (0.00166) (0.00129) 

-0.0822*** -0.0876*** -0.0624*** 

Age (0.0108) (0.0164) (0.0128) 

0.296*** 0.302*** 0.223*** 

Age^2/100 (0.0386) (0.0589) (0.0459) 

-0.0456*** -0.0447*** -0.0340*** 

Age^3/1000 (0.00599) (0.00916) (0.00713) 

0.00258*** 0.00244*** 0.00190*** 

Age^4/10000 (0.000341) (0.000521) (0.000406) 

-0.0314*** -0.0419*** -0.0275*** 

Female (0.000841) (0.00128) (0.00100) 

-0.00756*** -0.00830*** -0.00801*** 

Married (0.000871) (0.00133) (0.00104) 

0.0212*** 0.0284*** 0.0209*** 

Married status change (0.00256) (0.00392) (0.00305) 

0.0527*** 0.0797*** 0.0469*** 

Black (0.00134) (0.00205) (0.00160) 

0.0160*** 0.0305*** 0.0109*** 

Hispanic (0.00147) (0.00225) (0.00175) 

0.0236*** 0.0317*** 0.0203*** 

Other race (0.00185) (0.00283) (0.00220) 

0.00545*** 0.00930*** 0.00704*** 

High school only (0.00138) (0.00210) (0.00164) 

0.00285* 0.00313 0.00603*** 

Some college (0.00148) (0.00226) (0.00176) 

0.0103*** 0.00397* 0.0153*** 

College degree or more (0.00157) (0.00240) (0.00187) 
46

Table 10c: Regression Analysis of Earnings Volatility Using CPS 

(Including Observations with Allocated Earnings) 

Dependent Variables 

|∆(log 

earnings)| 

|∆(earnings)| > 

25% 

|∆(earnings)| > 

50% 

0.0318*** 0.0399*** 0.0408*** Increase education 

attainment (0.00228) (0.00349) (0.00272) 

Accommodation and Food 0.0484*** 0.0598*** 0.0485*** 

&Drink (0.00237) (0.00362) (0.00282) 

Administration and Waste 0.0135*** 0.0177*** 0.0129*** 

Management (0.00276) (0.00422) (0.00329) 

Agriculture, Forestry and -0.000989 -0.0157*** 0.00492 

Fishing (0.00374) (0.00571) (0.00445) 

0.0244*** 0.0310*** 0.0277*** 

Arts & Recreation (0.00383) (0.00585) (0.00456) 

0.0142*** 0.0227*** 0.0119*** 

Construction (0.00181) (0.00277) (0.00215) 

0.0106*** 0.0260*** 0.00940*** 

Education (0.00153) (0.00234) (0.00182) 

0.0313*** 0.0359*** 0.0320*** 

FIRE & Leasing (0.00172) (0.00262) (0.00204) 

0.0102*** 0.0118*** 0.0143*** 

Health & Social Services (0.00150) (0.00230) (0.00179) 

0.00412 -0.000244 0.00723** 

Information (0.00252) (0.00386) (0.00300) 

0.0212*** 0.0301*** 0.0141*** 

Mining (0.00379) (0.00580) (0.00451) 

Organizations & 0.0297*** 0.0221*** 0.0312*** 

Households (0.00310) (0.00474) (0.00369) 

0.0298*** 0.0434*** 0.0302*** 

Personal & Services (0.00309) (0.00473) (0.00368) 

-0.00404** -0.00932*** 0.00158 

Professional Services (0.00204) (0.00312) (0.00243) 

-0.0150*** -0.0244*** -0.00753*** 

Public Administration (0.00163) (0.00249) (0.00194) 

0.0261*** 0.0307*** 0.0293*** 

Retail (0.00154) (0.00236) (0.00184) 

Transportation & 0.0238*** 0.0301*** 0.0219*** 

Warehousing (0.00186) (0.00285) (0.00222) 

-0.00508* -0.00541 -0.00197 

Utilities (0.00285) (0.00435) (0.00339) 

0.00815*** 0.0106*** 0.00847*** 

Wholesale (0.00206) (0.00315) (0.00245) 

0.0485*** 0.0727*** 0.0438*** 

Industry change (0.00100) (0.00153) (0.00119) 

-0.0344*** -0.0416*** -0.0256*** 

Paid hourly (0.000860) (0.00131) (0.00102) 
47

Table 10c: Regression Analysis of Earnings Volatility Using CPS 

(Including Observations with Allocated Earnings) 

Dependent Variables 

|∆(log 

earnings)| 

|∆(earnings)| > 

25% 

|∆(earnings)| > 

50% 

0.0992*** 0.142*** 0.0903*** 

Paid hourly status change (0.000993) (0.00152) (0.00118) 

1.012*** 1.172*** 0.707*** Constant (0.109) (0.167) (0.130) 

Observations 673469 673469 673469 

R-squared 0.046 0.037 0.028 

Standard errors in parentheses under coefficient estimates. *** p<0.01, ** p<0.05, * p<0.1 
48

Table 10d: Marginal Age Effects 

 |∆log(earnings)| |∆(earnings)| > 25% |∆(earnings)| > 50% 

Age dy/dx s.e. p-value dy/dx s.e. p-value dy/dx s.e. p-value 

25 -0.0026 0.0005 0.0000 -0.0054 0.0010 0.0000 -0.0025 0.0006 0.0000

30 -0.0011 0.0001 0.0000 -0.0028 0.0003 0.0000 -0.0011 0.0002 0.0000

35 -0.0005 0.0001 0.0000 -0.0014 0.0002 0.0000 -0.0004 0.0001 0.0030

40 -0.0003 0.0001 0.0010 -0.0008 0.0002 0.0000 -0.0002 0.0001 0.1100

45 -0.0004 0.0001 0.0000 -0.0008 0.0001 0.0000 -0.0002 0.0001 0.0130

50 -0.0004 0.0001 0.0000 -0.0007 0.0002 0.0000 -0.0003 0.0001 0.0480

55 -0.0001 0.0001 0.2600 -0.0004 0.0002 0.0780 0.0000 0.0001 0.9130

60 0.0007 0.0003 0.0210 0.0007 0.0006 0.2210 0.0008 0.0004 0.0350

65 0.0024 0.0009 0.0050 0.0030 0.0016 0.0680 0.0024 0.0010 0.0200

dy/dx = effect on dependent variable of increasing age by one year. 

s.e. = standard error 
Work in Progress – Not for Distribution 

Figure 1 

Impact of Top-Coding Inflation Adjustments on Estimated Long-Term Trends in 

No Top-Coding Adjustment 

18 20 22 24 26

1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1

ma: x(t)= weekgt25pct: weights(1 2 <2> 2 1) Recession

allfulltimers weekgt25pct

Weekly Earnings Top-Coded at 

18 20 22 24 26

1980q1 1985q1 1990q1 1995q1 2

ma: x(t)= weekgt25pct: weights(1 2 <

allfulltimers weekgt2

56789 10

1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1

ma: x(t)= weekgt50pct: weights(1 2 <2> 2 1) Recession

allfulltimers weekgt50pct

56789 10

1980q1 1985q1 1990q1 1995q1 2

ma: x(t)= weekgt50pct: weights(1 2 <

allfulltimers weekgt5
Work in Progress – Not for Distribution 

Figure 2