C:\Users\DAVE\Dropbox\Distribution Measures\+Paper Mr.24\FINAL DRAFT JUNE 25\FULL PAPER.docx

Measuring Income Inequality with Consumer Expenditure Data.

David Bunting

Department of Economics

Eastern Washington University

Cheney WA 99004

dbunting@ewu.edu

June 27, 2020

Abstract

Census Bureau CPS tables are commonly used to document income inequality. An 

unrecognized but nearly equivalent source of inequality data is the Consumer Expenditure (CE) 

survey which has published tables of quintile income and quintile income shares since 1984.

While CPS and CE money income shares are nearly equal, CE data is a source of quintile

demographic characteristics such as earners and income determinants such as taxes and income 

sources unavailable elsewhere. These finer, more precise measures of income differences suggest 

gross money income shares overstate quintile differences and trends in income inequality.

Key Words: income equality; distribution; income shares; Gini coefficients; income surveys

JEL: D310, D630, I320
1

Measuring Income Inequality with Consumer Expenditure Data.

INTRODUCTION

Income inequality as reflected by growth and changes in relative shares of personal 

income is a perennial topic of academic research and political debates [1]. Commonly the 

debates devolve into essentially statistical disputes dependent on national representative surveys 

found in Current Population Surveys (CPS) conducted by the Bureau of Census [2]. While 

inequality measures involving quintile income shares and Gini coefficients reported in these 

surveys indicate growing differences between the lowest and highest quintiles and slowly 

increasing coefficients, the CPS data is severally circumscribed because it is restricted to 

reporting household money income measures and does not consider disposable income, number 

of earners or income sources.

The Consumer Expenditure (CE) Survey, annually conducted by another Federal agency, 

the Bureau of Labor Statistics [3] is an unrecognized statistical source that can be used to 

investigate some of the income inequality measures ignored by the CPS. Closely aligned with the 

CPS, the CE is the only nationwide representative source for quintile statistics on tax payments, 

earners, and income sources, information which could provide important insights into the nature 

and causes of inequality. For example, it was not considered in a recent Center on Budget and 

Policy Priorities “Guide to Statistics on Historical Trends in Income Inequality [4].”

The basic reason for this omission seems to be that CE data is only available in separate 

annual web based tables or as microdata requiring considerable manipulation to become useful. 

However, beyond reasons involving convenience of CPS data, CE quintile shares might be 

ignored because they were alleged to represent “transitory” behavior by Friedman [5] in his 

consumption function studies of the 1950s. Curiously, CPS shares reflecting the same behavior 

as the CE data, have been excluded from this allegation. Nevertheless, CE shares, published 

since 1989, can be found in “Aggregate Expenditure Shares Tables. Quintiles of income before 

taxes: Tables 2018 to 1989” [6]. These shares are simple averages of annual quintile incomes 

found in “Quintiles of Income before Taxes,” tables for 2018 to 2012 [7], for 2011 to 1984 [8] or 

available in BLS Bulletins for 1980-81 [9] and 1982-83 [10].

Although CE income shares can add insight into the nature and causes of income 

inequality, the comparability and consistency of these shares with CPS figures, the presumptive 

official estimates, should be established. After showing that estimates between the two surveys 
2

of money income shares, Gini coefficients and Q5/Q1 quintile income dispersion ratios are not 

significantly different, CE shares are adjusted for tax payments thereby reducing inequality 

measures by about 8 percent. Behavioral units for CPS income shares are households and 

consumer units for CE shares. While distinctions between these entities is slight [Cf., Appendix 

1], both reflect the incomes of multiple earners. Annual CE earner surveys show that income 

shares are proportional to the number of earners in the reporting unit. When income shares and 

related dispersion statistics are recalculated on an earner basis, measures of inequality 

significantly decline. Finally, discussions of inequality largely ignore the income sources 

defining each income share. To a large extent, share differences are explained by the sources of 

income received with unearned sources dominating lower quintile incomes and earned incomes 

dominating higher quintile sources.

MONEY INCOME DISTRIBUTIONS

Current Population Surveys (CPS) and Consumer Expenditure Surveys (CE) are the only 

sources with enough representative national coverage and detail to provide the basic data 

necessary to examine historical inequality trends and changes in the size and share of personal 

income [11]. Derived from March Supplements and Annual Social and Economic Supplements, 

CPS Income Inequality Data Tables date from 1967 for households. Consumer Expenditure 

Survey estimates are derived from two separate surveys, an Interview Survey designed to collect 

data on large and recurring expenditures and a Diary Survey designed to collect data on small, 

frequently purchased items. CE surveys are conducted by the Census Bureau. Income 

definitions, coverage and limitations essentially replicate those of the CPS [Cf., Appendix 1]. 

Periodic reviews indicate CE aggregate income has increased from around 80 percent to around 

90 percent of Census money income from 1990 to 2017 [12].

Annual CE quintile data since 1980 are collected into a dataset CE Data1980-2018 with

CPS quintile shares and income added [13]; all monetary variables are deflated by a Personal 

Consumption Expenditure deflator based on 2012 prices. Although the current version of the 

Consumer Expenditure survey started in 1980, only results since 1984 are available 

electronically. The survey is not without its limitations, suggesting that “caution should be used 

in comparing data from current surveys with those gathered . . . during the first few years, due to 

changes in concepts and definitions; published data covered only the urban portion of the 

population. From 1984 onward ... integrated data from the Diary and Interview Surveys as well 
3

as for the total population, urban and rural, have been published [14].” Also , as will be seen, 

income for the lowest quintiles in 1984, 1985 and 1986 are suspiciously much larger or smaller 

than those in preceding or succeeding years. Real average quintile one (Q1) income fell from 

$8525 in 1983 to $6313 in 1984 and then recovered from $7375 in 1986 to $8240 in 1987. 

During these years Q2 income was never less than $20,000 while Q3 income was never greater 

than $16,000. From 1986 to 1987 Q3 income increased by $13,000, from $14,857 to $27,332, an 

implausible amount. Since these inconsistencies have no external explanation, they indicate 

something is amiss and that statistics covering 1980 through 1986 should be viewed with 

caution.

Appendix Table A contains CE and CPS annual quintile shares of money income from 

1980 through 2018 while Appendix Table B contains average real incomes, and comparative 

statistics to evaluate the similarity of the two distributional estimates. Tables of averages for 

selected years are included in the text for discussion purposes. Table 1 contains CE and CPS 

shares of quintile money income. Chi-square values found in Table 2 of annual quintile 

differences between the two sets of shares are significantly smaller than the one percent test 

statistic of 13.28 with four degrees of freedom, indicating the shares are not statistically 

different. 

Table 1

Table 2 also contains Gini coefficients and Q5/Q1 ratios which are direct measures of 

inequality—the larger the ratio, the greater the distance between the incomes of the lowest 

quintile and those of the highest one. Since 1991-94 both measures for either data sources have 

differed by a percentage point or less, but over 25 years have increased by about four percent 

points, indicating increasing inequality. The correlation of annual CE and CPS Gini coefficients 

[Cf. Appendix 1 for derivation] is .85 while the correlation of their Q5/Q1 ratios is .66 for the 

entire time period or .90 for years from 1987 to 2018. Overall, with some reservations for earlier 

years, the CE and CPS estimates of quintile distributions of money income between 1980 and 

2018 can be considered essentially identical.

Table 2

TAX ADJUSTMENTS

Because of nonresponse to survey tax questions [15], tax estimation procedures in 2013 

were changed from survey responses to estimates produced by Taxsim [16], a tax estimating 
4

model developed by the National Bureau of Economic Research that incorporated CE income

estimates and demographic characteristics [17]. The effect of the revision was dramatic. Average 

federal, state, local and other taxes for the highest income quintile, Q5, increased from $8,985 in 

2012 to $28,294 in 2013, Q4 average taxes increased from $1,982 to $7,107 and average 

household taxes rose from $11,114 to $36,695. Especially noteworthy because of the variety of 

state taxing laws and reporting procedures was the increase in Q5 state and local taxes from 

$1,826 to $5,485 and from $526 to $1,762 for Q4.

While subsequent estimates showed similar increases, tax estimates for prior years were 

not revised. Revising these estimates for years from 1980 to 2012 can involve attempting to 

replicate CE processing procedures of individual respondents found in annual microdata files, a 

huge undertaking, or assuming representative behavior and using quintile averages of household 

income and demographic characteristics. The latter method was selected for reasons of 

expediency. To construct these estimates, Taxsim was used to estimate federal taxes as FEDSIM 

for every quintile from 1980 to 2018. (State, local and other tax estimates require respondent 

data.). As compared with CE estimates of federal taxes (FEDTX) and state, local and other taxes 

(SLO) averaged over 2013-18 shown in Table 3, the FEDSIM estimate approximated the Q5 CE 

FEDTX but deteriorated for the lower quintiles, underestimating total CE federal taxes by about 

20 percent. Further CE state, local and other taxes, about 25 percent of CE federal taxes, were 

not estimated. 

Table 3

The “Adj. Factor” found in Table 3 is the ratio of total CE taxes divided by FEDSIM that 

indicates the extent FEDSIM underestimated quintile taxes as reported in CE surveys. This ratio 

times the appropriate FEDSIM estimate for each year and quintile is used to estimate quintile 

taxes; eg., for 1980, Q1 tax = 1.776 x FEDSIM, and so on for all quintiles. Annual average 

disposable income, income shares, Gini coefficients and Q5/Q1 ratios are found in Appendix 

Table C. Previously indicated data problems in years before 1987 are apparent in that the Q3 

income share ranges from 6.1 to 9.0 percent while the range of the Q2 share ranges from 11.7 to 

14.1 percent; however, thereafter the distributions reverse as the Q3 share averages 14.2 percent 

and the Q2 share is 10.5 percent. 

Ignoring pre 1987 figures, the estimates, while crude, seem reasonable. Tax payments 

caused higher incomes to fall, thereby reducing inequality as measured by Gini coefficients and 
5

Q5/Q1 ratios. Averages of CE changes in disposable income shares divided by money income 

shares from 1987 to 2018 found in Table 4 indicate the quintile effects of taxes on inequality. 

Tax adjustments reduced average income by 13.5 percent, increased Q1 income share by about 

22 percent, the share of Q2 by 17 percent, Q3 and Q4 shares 7 and 2 percent, respectively while 

reducing the Q5 share by 7 percent and the Gini coefficient by 9 percent. While changes in lower 

quintile shares are significant, overall effects seem modest as the Q5 income share fell from 49.4 

to 46.2 percent and the Gini coefficient declined from 42.8 to 39.5, changes indicating that tax 

adjustments have limited effects on inequality.

Table 4

EARNER ADJUSTMENTS

Inequality is usually measured in terms of household income shares with the implicit 

assumption that each share represents a single income earning entity. However, household 

income can reflect the incomes of multiple earners [18]. Thus, the same 2018 income quintile

could contain a household of four sisters each averaging $50,000 annually and another reflecting 

the top coded income of some Silicon Valley zillionaire [19]. 

As shown in CE earner surveys, household income is influenced by the number of 

household earners. Table 5 reports survey results for selected years of 1990, 2000, 2010 and 

2018 [20]. Real average income of households in 1990 with no earners was $22,200 while the 

average income of those with three or more earners was $82,100. By 2018 income of the lowest 

earner group was $25,600 while that of the highest group was $126,500. Income shares varied 

little over time, averaging about 8.3 percent for households with no earners and 30.1 percent for 

single earners, 46.5 percent for two earners, and 15.2 percent for three plus earners households. 

The percent of households represented by each type of earner also has been relatively constant, 

21.2, 37.5, 32.3 and 9.2 percent, respectively. In terms of average quintile income, the four 

earner groups in 2018 would fall in the second, third, fifth and fifth quintiles. Clearly household 

income is influenced by the number of household earners.

Table 5

Appendix D contains annual earner adjusted average income, quintile shares, Gini 

coefficients and Q5/Q1 ratios. Income shares were recalculated on an earner basis if earners were 

greater than one. Since the number of earners rise with income, the adjustments cause shares of 

the higher quintiles to decline as shares of the lowest ones rise, thereby reducing income 
6

inequality, Gini coefficients and Q5/Q1 ratios, declines consistent with a Gini decline from 43 to 

34 found in an “assortative matching” study.by Greenwood, et, al, [21]. 

Again, ignoring pre 1987 figures, percent changes in earner income share divided by 

money income share for selected years shown in Table 6 indicate the effects of calculating 

income shares based on earners rather than by economic unit. The rebasing reduces average 

income by 40 percent, increases both Q1 and Q2 income shares by about 66 percent because 

earners were less than one, increases the Q3 share by 23 percent, reduces Q4 and Q5 shares by 6

and 20 percent, respectively, and the Gini coefficient by 29 percent. These changes clearly 

indicate the sensitively of income inequality measures to income concepts and definitions of 

income recipients.

Table 6

Table 7 compares average Gini coefficients and Q5/Q1 ratios found for the three CE 

income measures: CE money income (YY), disposable income (YD) and earner income (YE). 

Recalculating income as disposable income reduces Gini coefficients by about 8 percent and 

dispersion ratios by about 25 percent while recalculating income on an earner basis reduces the 

coefficients by about 30 percent and the ratios more than 50 percent. These recalculations clearly 

show that finer income definitions reduce rather than increase measures of income inequality.

Table 7

INCOME SOURCES

The implicit assumption that quintiles differ only by their income size also carries over to 

income sources. Quintiles with a large fraction of total income are supposed to reflect the 

earnings of highly paid respondents while those with smaller shares reflect the earnings of lesser 

paid respondents. However, as CE surveys reveal, income sources vary significantly by quintile. 

Following CE definitions of sources, money income can be divided into market income (Mkt) 

consisting of wage and salary earnings, self-employment income and interest, dividends, rental 

and other property income and non-market income (NMkt) consisting of social security, private 

and government retirement income, public assistance, supplement security income, food stamps, 

unemployment and workers compensation, veterans benefits, regular contributions for support 

and other income.

Table 8 shows for selected years of 1990, 2010 and 2018 that market income represents 

about 86 percent of total real money income and non-market income represents 14 percent. 
7

However, the distribution of income from these different sources varies widely across the 

quintiles. In 1990, 30 percent of Q1 income was from market sources and 70 percent from nonmarket sources while 96 percent of Q5 income was market originated and 4 percent non-market. 

As the table indicates, these divisions are consistent over time suggesting that different income 

sources could explain income share differences.

Table 8

Changes in income sources offers potential for explaining changes in income inequality. 

The income of any quintile is equal to its market income plus its non-market income (Mkti + 

NMkti = Yi) or in terms of the relative share of total income, the relative income share of any 

quintile is equal to its relative share of market income plus its relative share of non-market 

income (%Mkti/ΣY + %NMkti/ΣY = %Yi/ΣY). Table 9 shows quintile shares of market and 

non-market shares. For example, in 1990 Q1 income was 3.5 percent of total 1990 income as 

represented by 1.0 percent of market income and 2.5 percent of non-market income. On the other

hand, Q5 income was 48.1 percent share of total income as represented by 46.0 percent of 

market income and 2.1 percent of non-market income. 

Table 9.

Deterioration in the income sources of lower quintile income shares over time is 

apparent. The Q1 non-market share fell from 2.5 percent in in 1990 to 2.2 percent in 2010 to 1.9 

percent in 2018 and the Q3 market share declined from 12.2 to 11.3 to 11.1 percent. However, 

these changes are offset by the Q5 market income share which increased from 46.0 to 47.7 to 

50.4 percent. Overall, measured inequality as represented by quintile income shares changed as 

the Q1 share fell from 3.5 to 2.9 percent between 1990 to 2018, the Q3 share declined from 15.4 

percent to 14 percent, Q2 and Q4 shares declined, and the Q5 share increased from 48.1 to 52.2 

percent—changes in total that caused the Gini coefficient to rise from 41.8 to 43.7 to 45.5.

Table 10 shows changes in percentage point shares from 1990 to 2010 and from 2010 to 

2018. In the first period the income share of Q1 fell 0.4 percentage points of which 80 percent 

resulted from a decline in non-market income. Q2 suffered the same disproportionate decline in 

non-market income. However, Q3 and Q4 suffered significant declines in market rather than 

non-market income. For example, the market income of Q3 fell 0.9 percentage points while its 

non-market share increased 0.1 points. On the other hand, Q5 enjoyed increases in both its 

income shares. The pattern continues from 2010 to 2018. Q1 continues to loss non-market share, 
8

Q2 suffers market income loss and Q3, Q4 and Q5s lose non-market shares. However, the Q5 

loss is offset by a significant gain in market income share. 

Table 10.

The division of income into different sources clearly shows that changes in income 

inequality can not be attributed to single causes. As shown the lower four quintiles lost income 

shares primarily because of declines in non-market incomes as represented by a wide variety of 

social payments and support systems. Meanwhile the income share of the highest quintile 

increased as a result of market based activities involving relative increases in wages and salaries, 

self-employed income and investment earnings. The effects of these different income sources 

raise questions as to the relevancy of universal measures of income in equality.

CONCLUSIONS

Statistics derived from Consumer Expenditure surveys offer new sights into the 

determinates of income inequality. These statistics, whose significance has been largely 

unrecognized because of cumbersome availability or elaborate data accessing, provide details 

from nationally representative surveys on a variety of respondent characteristics not found 

elsewhere.

Since 1987 quintile money income shares from Consumer Expenditure (CE) surveys and 

Current Population (CPS) surveys have been nearly identical. However, unlike CPS measures 

which are limited to money income, CE shares can be adjusted for tax payments, thereby 

reducing Gini coefficients and Q5/Q1 income dispersion ratios by about 8 percent. CE shares 

also can be calculated in terms of income earners rather than economic units, thereby reducing 

inequality by about 30 percent. These adjustments indicate that income inequality is very 

sensitive its measurement, especially to respondent definitions.

Consumer Expenditure data on sources of income disclose that low income quintiles have 

different income sources than high income ones suggesting that gross measures are unreliable 

indicators of inequality. Most likely comparing households with one part-time earner against 

those with three or more full time earners or comparing households whose income is largely 

composed of non-market supplements against those whose is income is largely market based are 

at best problematic, even potentially biased, comparisons. 
9

While income inequality is a controversial topic that touches many different interests and 

agendas, Consumer Expenditure data provides a heretofore underutilized means to systematically 

examine both its historical dimensions and its empirical basis.
10

Footnotes.

BLS = U.S. Bureau of Labor Statistics

CB = U.S. Census Bureau

(month/year last accessed)

[1] Compare Ryscavage, P. (1999). Income Inequality in America: An Analysis of Trends. M.E. 

Sharpe: Armonk, NY with Piketty, T., Saez, E, and Zucman, G. (2018). Distributional 

National Accounts: Methods and Estimates for the United States, Quarterly Journal of 

Economics, 133(2): 553–609, https://doi.org/10.1093/qje/qjx043, Gramm, P. and Solon, 

M. (2014). How to Distort Income Inequality. Wall Street Journal, Nov. 11, 2014 and 

Early, J. F. (2018). Reassessing the Facts about Inequality, Poverty, and Redistribution. 

Cato Institute. https://www.cato.org/sites/cato.org/files/pubs/pdf/pa-839-updated-2.pdf

[2] CB. Historical Income Tables: Income Inequality. https://www.census.gov/data/tables/timeseries/demo/income-poverty/historical-income-inequality.html

[3] BLS. CE Tables. https://www.bls.gov/cex/tables.htm .

[4] Stone, C, Trisi, D., Sherman, S., and Beltran, J. (2020). Guide to Statistics on Historical 

Trends in Income Inequality. Center on Budget and Policy Priorities. 

https://www.cbpp.org/research/poverty-and-inequality/a-guide-to-statistics-on-historicaltrends-in-income-inequality ).

[5] Friedman, M. (1957). Theory of the Consumption Function. Princeton, Princeton U.P.

[6] BLS. Aggregate Expenditure Shares Tables. Quintiles of income before taxes: Tables 2018 to 

1989. https://www.bls.gov/cex/csxashar.htm. (06/2020)

[7] BLS. Combined Expenditure, Share, and Standard Error Tables. Quintiles of income before 

taxes: Tables 2018 to 2012. https://www.bls.gov/cex/csxcombined.htm (06/2020)

[8] BLS. Expenditure Tables. Quintiles of income before taxes. Tables 2011 to 1984, 1972-72, 

1960-61. https://www.bls.gov/cex/csxstnd.htm (06/2020)

[9] BLS Bulletin 2225 (1985). Consumer Expenditure Survey: Interview Survey 1980-81.

[10] BLS Bulletin 2246 (1986). Consumer Expenditure Survey: Interview Survey 1982-83.
11

[11] Both CPS and CE sources are used by the Congressional Budget Office for its distributional 

estimates; see Congressional Budget Office. (2014). The Distribution of Household 

Income and Federal Taxes, 2011. Appendix: Data and Methods. 

https://www.cbo.gov/publication/49440 06/2020).

[12]. See generally BLS. (2020). Data Quality in the Consumer Expenditure Surveys: CE income 

to the Current Population Survey (CPS). 

https://www.bls.gov/cex/cecomparison.htm#CPS. For a recent review see Hubener, E. 

(2019). Table 1A. Aggregate income and ratios for Current Population Survey (CPS) and 

three alternative measures of CE income, 2002–2017.

https://www.bls.gov/cex/cecpstotalincome2002-17.xlsx . (06/2020

[13] CB. Table H-2. Share of Aggregate Income Received . . .; Table H-3. Mean Household 

Income Received by Each Fifth and Top 5 Percent, All Races: 1967 to 2018. 

https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-incomehouseholds.html (06/2020).

[14] BLS. Are historical data from the Consumer Expenditure Surveys available?

https://www.bls.gov/cex/csxfaqs.htm (06/2020).

[15] BLS. Why are 2013 income taxes so different than earlier years?

https://www.bls.gov/cex/csxfaqs.htm (06/2020).

[16] TAXSIM (nd). Internet TAXSIM Version 9. National Bureau of Economic Research. 

http://users.nber.org/~taxsim/taxsim9/ .

[17] Paulin, G.D. and Hawk, W. (Mar 2015). Improving data quality in Consumer Expenditure 

Survey with TAXSIM. Monthly Labor Review, pp 1-13.

https://doi.org/10.21916/mlr.2015.5. (06/2020)

[18] Literature reviewed in Donovan, S. A., Labonte, M. and Dalaker, J. (2016). The U.S. 

Income Distribution: Trends and Issues. Congressional Research Service. 

https://fas.org/sgp/crs/misc/R44705.pdf (06/2020)

[19] Crain, V. and Wilson, T. J. (May 2017). Use with caution: interpreting Consumer 

Expenditure income group data. Beyond the Numbers: Prices & Spending, vol 6 (5), pp 
12

1-7. https://www.bls.gov/opub/btn/volume-6/use-with-caution-interpreting-consumerexpenditure-income-group-data.htm. (06/2020)

[20] CE Table. Number of earners in consumer unit, 1990, 2000, 2010, 2018.

https://www.bls.gov/cex/csxcombined.htm, https://www.bls.gov/cex/csxstnd.htm

[21] Greenwood, J., Guner, N., Kocharkov, G., and Santos, C. (2014). Marry Your Like: 

Assortative Mating and Income Inequality. American Economic Review, vol. 104, pp. 

348-353.
13

Appendix 1. Definitions, Data Organization, Gini Coefficient 

CENSUS BUREAU DEFINITIONS (edited, from Census Glossary, 

https://www.census.gov/glossary/

Household: Includes related family members and all the unrelated people, if any, such as lodgers, 

foster children, wards, or employees who share the housing unit; a person living alone; a group 

of unrelated people sharing a housing unit.

Household Income: The income of all people 15 years and older living in the household. 

Income. "Money income" is the income received on a regular basis (exclusive of certain money 

receipts such as capital gains and lump-sum payments) before payments for personal income 

taxes, social security, union dues, medicare deductions; includes income received from wages, 

salary, commissions, bonuses, and tips; self-employment income; interest, dividends, net rental 

income, royalty income; Social Security or Railroad Retirement income; Supplemental Security 

Income (SSI); any cash public assistance or welfare payments; retirement, survivor, or disability 

benefits; other income received regularly such as Veterans' (VA) payments, unemployment 

and/or worker’s compensation, child support, and alimony.

CONSUMER EXPENDITURE SURVEY DEFINITIONS (edited, from Consumer Expenditure 

Definitions. https://www.bls.gov/cex/csxgloss.htm), 

Consumer unit: Comprises all members of a household related by blood, marriage, adoption, or 

legal arrangements; a person living alone who is financially independent; or two or more persons 

living together using their income to make joint expenditure decisions. Financial independence is 

determined by housing, food, and other living expenses.

Earner: A consumer unit member, 14 years of age or older, having worked at least 1 week during 

the 12 months prior to the interview date.

Income: The combined income of all consumer unit members (14 years of age or over); money 

income before taxes includes: wages and salaries before deductions for taxes, pensions, union 

dues; self-employment income; Social Security, private and government retirement income; 

interest, dividends, rental income, other property income; unemployment and workers' 

compensation; veterans' benefits; public assistance, supplemental security income, food stamps; 

supplemental security income; regular contributions for support; other income; cash 

scholarships, fellowships, or stipends. 
14

DATA ORGANIZATION

Complied Consumer Expenditure data for 1980 to 2018 referenced as “CE Data1980-2018” was 

compiled into a single spreadsheet as a Pivot Table with records identified by year and quintile. 

The dataset contains annual quintile figures on a variety of consumer unit survey items involving 

demographic characteristics, income types and sources, expenditures and financial situation. 

Data was taken from Quintiles of income before taxes tables found as Combined Expenditure, 

Share, and Standard Error Tables. Tables 2018 to 2012 [7]; Expenditure Tables. Tables 2011 to 

1984 [8]; Consumer Expenditure Survey: Interview Survey 1980-81, 1982-83 9 [9, 10]. Also 

included in the table are CPS 1980- 2018 quintile shares and a BEA PCE price deflator base 

2012. Data was not edited

GINI COEFFICIENT

The Gini coefficient is calculated as G = (

2

N

) ∑risi − (n + 1)/n where ri represents integer 

ranking, the same as quintile order, 1,2, ... n, and si represents relative share. To illustrate using 

2018 CE money income as an example,

rank (ri) Income share(si) risi

1 11,285 .0287 .0287

2 31,237 .0795 .1590

3 54,900 .1397 .4192

4 90,478 .2303 .9212

5 204,975 .5217 2.6087

Total 392,875 1.0000 4.1368

With n =5, G = 0.4547. Rstated in EXCEL the function becomes G = 0.4*SUMPRODUCT(r1:rn, 

s1:sn)-1.2. It suffers small sample bias. With n of 5, the range of Gini coefficient is 0 to .80, with 

n of 10, the range is 0 to .90; with n of 100 the range is 0 to .99, the expected range of inequality 

measures, from perfect equality to perfect inequality. Small sample adjustments simply increase 

coefficients to unfamiliar sizes, not comparable with other published quintile coefficients such as 

those from the Census Bureau. Consequently, the function as shown above was used.
15

Tables

Table 1. Household Money Income: CE and CPS Quintile Shares.

Consumer Expenditure Survey Current Population Survey

GRP1 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5

1980-86 3.25 9.17 16.02 24.92 46.64 3.94 9.92 16.45 24.59 45.10

1987-90 3.51 8.88 15.45 24.25 47.91 3.81 9.60 15.97 24.13 46.49

1991-94 3.51 8.69 15.08 23.97 48.76 3.67 9.21 15.45 23.82 47.85

1995-98 3.47 8.64 15.08 24.27 48.54 3.62 8.98 15.06 23.27 49.08

1999-02 3.37 8.47 14.80 23.91 49.45 3.53 8.83 14.78 23.10 49.77

2003-06 3.29 8.70 14.84 23.61 49.56 3.39 8.66 14.64 23.11 50.20

2007-10 3.22 8.66 14.69 23.25 50.19 3.37 8.60 14.65 23.32 50.06

2011-14 3.06 8.31 14.34 23.20 51.08 3.15 8.28 14.31 23.05 51.20

2015-18 3.04 7.99 14.00 23.07 51.90 3.09 8.23 14.17 22.82 51.69

Ave. 3.30 8.66 15.01 23.91 49.13 3.54 9.00 15.16 23.55 48.74

Source: CE Data 1980-2018.

Table 2. CE, CPS Comparative Statistics.

Real Average Gini Q5/Q1

Money Income Coefficients Dispersion

GRP1 CE CPS Chi-Sq CE CPS CE CPS

1980-86 46,551 52,796 .321 41.0 38.8 14.6 11.5

1987-90 49,913 58,907 .147 41.7 40.0 13.7 12.2

1991-94 51,000 59,291 .120 42.3 41.2 13.9 13.1

1995-98 52,974 65,590 .076 42.3 42.1 14.0 13.6

1999-02 58,812 72,335 .072 43.0 42.7 14.7 14.1

2003-06 65,491 72,743 .046 43.0 43.2 15.1 14.8

2007-10 67,116 72,313 .014 43.4 43.2 15.6 14.9

2011-14 64,611 72,532 .013 44.4 44.3 16.7 16.2

2015-18 70,357 80,693 .059 45.1 44.7 17.1 16.7

Ave. 57,614 66,338 .114 42.8 42.0 15.0 13.9

Source: CE Data 1980-2018.
16

Table 3. Average Quintile Real Taxes ($), 2013 -2018

Tax Calculation Q1 Q2 Q3 Q4 Q5 Total

FEDSIM -249 -4,183 197 4,989 27,915 28,669

CE:FEDTX -409 -568 1,859 6,295 28,518 35,694

CE:SLO -34 190 862 2,074 6,662 9,754

CE:Total -443 -378 2,721 8,369 35,180 45,448

Adj. Factor 1.776 0.090 13.812 1.677 1.260 1.585

Source: CE Data1980-2018.

Table 4. Percent Change Income, Shares and, Inequality Statistics

with Disposable Income as Divisor Rather than Money Income

%chg %chg %chg %chg %chg %chg %chg %chg

GRP1 Y Q1 Q2 Q3 Q4 Q5 Gini Q5/Q1

1987-90 -17.29 25.38 21.66 -15.31 5.08 -3.53 -5.62 -22.94

1991-94 -13.75 23.65 17.12 6.49 1.02 -7.17 -9.36 -24.92

1995-98 -13.35 27.64 16.78 9.88 0.04 -8.03 -10.55 -27.67

1999-02 -13.75 21.14 17.28 9.61 1.92 -8.19 -9.78 -24.21

2003-06 -12.25 19.55 15.35 9.76 2.17 -7.93 -9.29 -22.97

2007-10 -12.03 18.64 15.26 11.64 2.31 -8.29 -9.50 -22.68

2011-14 -10.69 16.34 13.56 20.50 1.01 -9.38 -10.35 -22.11

2015-18 -14.90 22.59 19.12 0.36 4.55 -6.37 -7.50 -23.61

Ave -13.50 21.87 17.01 6.62 2.26 -7.36 -8.99 -23.89

Source: CE Data1980-2018
17

Table 5. Average Real CE Money Income, Income

Share and Consumer Units by Earners

Number Earners

Year 0 1 2 3

Average Income

1990 22,214 40,160 68,516 82,099

2000 22,288 47,063 80,459 88,102

2010 25,553 53,350 96,564 110,878

2018 25,568 56,105 110,483 126,519

Income Share

1990 8.69 29.45 44.80 17.09

2000 7.84 30.60 46.78 14.89

2010 8.56 31.94 46.10 13.40

2018 7.97 28.32 48.16 15.55

Percent Consumer Units

1990 19.69 36.92 32.92 10.48

2000 20.08 37.10 33.18 9.64

2010 21.86 39.08 31.17 7.89

2018 22.66 36.71 31.70 8.94

Source: CE Table Number of earners in consumer 

unit, 1990, 2000, 2010, 2018.

Table 6. Percent Change Income, Shares and, Inequality Statistics

with Earner Income as Divisor Rather than Money Income

%chg %chg %chg %chg %chg %chg %chg %chg

GRP1 Y Q1 Q2 Q3 Q4 Q5 Gini Q5/Q1

1987-90 -40.09 66.91 66.91 19.22 -5.92 -20.52 -30.47 -52.38

1991-94 -38.91 63.73 63.73 23.65 -6.38 -20.11 -29.46 -51.19

1995-98 -39.92 66.45 66.45 23.43 -6.19 -20.74 -30.24 -52.38

1999-02 -40.06 66.87 66.87 21.44 -5.94 -19.56 -28.75 -51.79

2003-06 -40.01 66.71 66.71 19.08 -4.69 -19.64 -28.64 -51.79

2007-10 -38.65 63.00 63.00 20.87 -5.47 -18.50 -27.05 -50.00

2011-14 -39.02 64.00 64.00 26.16 -3.53 -19.97 -27.46 -51.19

2015-18 -40.23 67.34 67.34 26.41 -7.04 -18.34 -26.71 -51.19

Ave -39.61 65.63 65.63 22.53 -5.64 -19.67 -28.60 -51.49

Ave. Earners 0.58 0.93 1.36 1.75 2.08 -- --

Source: CE Data1980-2018
18

Table 7. Inequality Statistics, CE Money Income (YY), 

CE Disposable Income (YD, CE Earner Income (YE)

Gini Coefficients Q5/Q1

GRP1 YY YD YE YY YD YE

1980-86 41.0 40.3 27.8 14.6 10.4 6.7

1987-90 41.7 39.3 29.0 13.7 10.5 6.5

1991-94 42.3 38.3 29.8 13.9 10.5 6.8

1995-98 42.3 37.9 29.5 14.0 10.1 6.7

1999-02 43.0 38.8 30.7 14.7 11.1 7.1

2003-06 43.0 39.0 30.7 15.1 11.6 7.3

2007-10 43.4 39.3 31.7 15.6 12.1 7.8

2011-14 44.4 39.8 32.2 16.7 13.0 8.1

2015-18 45.1 41.7 33.1 17.1 13.1 8.3

Ave 42.8 39.5 30.3 15.0 11.3 7.2

%Ave./YY 100.0 92.2 70.8 100.0 75.4 48.1

Source: CE Data1980-2018.

Table 8. Quintile Shares Market, NonMarket Money Income (%).

1990 2010 2018

Quin Mkt NMkt Mkt NMkt Mkt NMkt

Q1 29.5 70.5 30.6 69.4 33.2 66.8

Q2 58.9 41.1 61.5 38.5 58.0 42.0

Q3 79.6 20.4 77.7 22.3 79.3 20.7

Q4 90.9 9.1 87.7 12.3 89.8 10.2

Q5 95.5 4.5 94.7 5.3 96.0 4.0

Total 86.4 13.6 85.7 14.3 87.4 12.6

Ave. YY 43,432 6,853 55,973 9,311 63,497 9,161

Source: CE Data1980-2018.
19

Table 9. Shares Market (MktT) and NonMarket (NMkt) Money Income (%).

1990 2010 2018

Quin Mkt NMkt Total Mkt NMkt Total Mkt NMkt Total

Q1 1.0 2.5 3.5 1.0 2.2 3.2 1.0 1.9 2.9

Q2 5.2 3.6 8.9 5.3 3.3 8.6 4.6 3.3 8.0

Q3 12.2 3.1 15.4 11.3 3.3 14.6 11.1 2.9 14.0

Q4 21.9 2.2 24.1 20.4 2.9 23.3 20.7 2.3 23.0

Q5 46.0 2.1 48.1 47.7 2.6 50.4 50.1 2.1 52.2

Total 86.4 13.6 100.0 85.7 14.3 100.0 87.4 12.6 100.0

Ave. YY 43,432 6,853 50,285 55,973 9,311 65,283 63,497 9,161 72,658

Source: CE Data1980-2018.

Table 10. Changes Market, NonMarket Income, 2010-1990 and 2018 -2010.

Change 2010 - 1990 Change 2018 - 2010

Quin Mkt NMkt Total %Mkt %NMkt Mkt NMkt Total %Mkt %NMkt

Q1 -0.1 -0.3 -0.4 -20.5 -79.5 0.0 -0.3 -0.3 -5.8 -94.2

Q2 0.1 -0.3 -0.3 18.4 -118.4 -0.7 0.0 -0.6 -106.5 6.5

Q3 -0.9 0.1 -0.8 -114.5 14.5 -0.2 -0.4 -0.6 -40.5 -59.5

Q4 -1.4 0.7 -0.8 -182.5 82.5 0.2 -0.5 -0.3 89.8 -189.8

Q5 1.7 0.5 2.2 77.7 22.3 2.3 -0.5 1.8 129.7 -29.7

Total -0.6 0.6 0.0 -- -- 1.7 -1.7 0.0 -- --

Source: CE Data1980-2018.
20

Appendix 2 Annual Tables.

Appendix A. CE, CPS Quintile Money Income Shares

Consumer Expenditure Survey Current Population Survey

YR Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5

1980 3.38 9.74 16.88 25.21 44.79 4.09 10.19 16.81 24.76 44.15

1981 3.56 9.88 16.80 25.14 44.62 4.04 10.06 16.67 24.85 44.38

1982 3.74 9.54 16.10 24.94 45.68 3.94 9.97 16.51 24.55 45.03

1983 3.50 9.20 15.90 24.88 46.52 3.95 9.91 16.39 24.59 45.16

1984 2.70 8.75 15.65 24.75 48.15 3.96 9.86 16.34 24.60 45.24

1985 2.86 8.61 15.45 24.67 48.40 3.86 9.79 16.25 24.44 45.66

1986 3.00 8.46 15.36 24.86 48.33 3.75 9.66 16.16 24.35 46.08

1987 3.38 8.76 15.34 24.37 48.16 3.78 9.62 16.08 24.30 46.22

1988 3.47 9.03 15.83 24.53 47.14 3.80 9.59 16.05 24.25 46.31

1989 3.66 8.88 15.25 23.99 48.21 3.83 9.53 15.84 23.96 46.84

1990 3.54 8.86 15.38 24.09 48.13 3.83 9.64 15.92 24.01 46.59

1991 3.53 8.75 15.40 24.13 48.18 3.81 9.57 15.90 24.24 46.48

1992 3.54 8.64 14.85 23.82 49.16 3.74 9.36 15.77 24.21 46.92

1993 3.67 8.65 14.95 24.07 48.66 3.55 9.01 15.10 23.46 48.88

1994 3.29 8.74 15.11 23.84 49.03 3.58 8.91 15.02 23.37 49.13

1995 3.42 8.74 15.31 24.27 48.26 3.71 9.08 15.18 23.33 48.69

1996 3.45 8.53 14.91 24.38 48.73 3.65 8.95 15.06 23.31 49.03

1997 3.55 8.64 15.18 24.30 48.32 3.56 8.89 14.96 23.18 49.41

1998 3.45 8.64 14.90 24.15 48.87 3.56 8.98 15.03 23.24 49.19

1999 3.31 8.21 14.52 23.83 50.14 3.62 8.90 14.89 23.17 49.42

2000 3.44 8.55 14.75 23.89 49.36 3.56 8.88 14.78 22.98 49.80

2001 3.35 8.56 14.97 23.97 49.15 3.48 8.75 14.65 22.97 50.15

2002 3.37 8.57 14.97 23.96 49.13 3.45 8.78 14.80 23.28 49.69

2003 3.21 8.41 14.69 23.93 49.76 3.38 8.69 14.76 23.36 49.80

2004 3.37 8.86 15.29 23.92 48.56 3.39 8.67 14.69 23.16 50.09

2005 3.30 8.71 14.53 23.11 50.35 3.36 8.64 14.62 22.99 50.39

2006 3.30 8.81 14.85 23.46 49.57 3.41 8.65 14.49 22.93 50.52

2007 3.34 8.78 14.66 22.98 50.24 3.42 8.71 14.78 23.40 49.69

2008 3.23 8.64 14.86 23.32 49.95 3.41 8.63 14.65 23.31 50.00

2009 3.13 8.67 14.65 23.37 50.18 3.40 8.61 14.57 23.15 50.27

2010 3.17 8.57 14.58 23.30 50.37 3.26 8.47 14.59 23.41 50.27

2011 3.08 8.52 14.51 23.25 50.65 3.23 8.38 14.31 22.99 51.10

2012 3.05 8.42 14.42 23.17 50.95 3.22 8.33 14.36 23.04 51.04

2013 3.03 8.24 14.36 23.37 51.01 3.08 8.20 14.29 23.00 51.43

2014 3.08 8.08 14.07 23.03 51.73 3.08 8.21 14.27 23.19 51.24

2015 3.14 8.16 14.27 23.25 51.18 3.14 8.23 14.34 23.22 51.06

2016 3.05 7.75 13.53 22.52 53.15 3.11 8.30 14.23 22.90 51.46

2017 3.10 8.10 14.24 23.46 51.10 3.04 8.10 14.00 22.60 52.26

2018 2.87 7.95 13.97 23.03 52.17 3.06 8.29 14.12 22.57 51.96

Ave. 3.28 8.62 14.92 23.82 49.36 3.50 8.92 15.05 23.47 49.05

Source: CE Data1980-2018.
21

Appendix B. CE, CPS Inequality Statistics.

Money Income Gini Q5/Q1

YR CE CPS Chi-Sq CE CPS CE CPS

1980 46,288 51,050 .161 39.3 37.9 13.2 10.8

1981 46,309 50,684 .066 39.0 38.2 12.5 11.0

1982 46,833 51,224 .054 39.7 38.7 12.2 11.4

1983 46,661 51,758 .161 40.7 38.8 13.3 11.4

1984 45,653 53,493 .741 42.8 38.9 17.8 11.4

1985 47,244 54,705 .605 42.8 39.3 16.9 11.8

1986 46,866 56,659 .461 42.8 39.7 16.1 12.3

1987 48,796 57,915 .236 42.1 39.8 14.3 12.2

1988 49,032 58,501 .084 41.1 39.9 13.6 12.2

1989 51,540 60,174 .114 41.7 40.2 13.2 12.2

1990 50,285 59,038 .155 41.8 40.0 13.6 12.2

1991 51,727 57,920 .170 41.9 40.0 13.6 12.2

1992 50,317 57,782 .235 42.6 40.5 13.9 12.6

1993 50,569 60,134 .036 42.2 42.0 13.3 13.8

1994 51,388 61,328 .038 42.6 42.2 14.9 13.7

1995 51,364 62,578 .078 42.1 41.7 14.1 13.1

1996 51,777 64,246 .083 42.6 42.0 14.1 13.4

1997 53,468 66,592 .089 42.1 42.4 13.6 13.9

1998 55,288 68,943 .055 42.5 42.2 14.2 13.8

1999 57,536 71,704 .118 43.7 42.3 15.2 13.6

2000 57,027 73,029 .056 42.9 42.6 14.3 14.0

2001 59,534 72,999 .080 42.8 43.0 14.7 14.4

2002 61,150 71,610 .036 42.8 42.8 14.6 14.4

2003 62,047 71,721 .033 43.5 43.0 15.5 14.7

2004 64,480 71,633 .100 42.2 43.2 14.4 14.8

2005 67,593 72,967 .003 43.4 43.4 15.3 15.0

2006 67,844 74,650 .046 42.9 43.4 15.0 14.8

2007 68,958 73,940 .017 43.2 42.9 15.0 14.5

2008 67,455 72,653 .012 43.2 43.1 15.5 14.7

2009 66,770 72,242 .024 43.5 43.3 16.0 14.8

2010 65,283 70,416 .005 43.7 43.6 15.9 15.4

2011 64,898 71,005 .018 44.0 44.1 16.4 15.8

2012 65,560 71,274 .012 44.2 44.1 16.7 15.8

2013 62,957 74,196 .011 44.4 44.6 16.8 16.7

2014 65,027 73,655 .010 44.9 44.5 16.8 16.6

2015 67,451 76,920 .001 44.5 44.3 16.3 16.2

2016 71,817 79,875 .134 46.0 44.5 17.4 16.5

2017 69,501 82,736 .064 44.5 45.2 16.5 17.2

2018 72,658 83,243 .037 45.5 44.8 18.2 17.0

Ave. 57,614 66,338 .033 42.9 42.3 15.0 14.0

Source: CE Data1980-2018.
22

Appendix C. CE Disposable Income (YD), Inequality Statistics.

YR YD Q1 Q2 Q3 Q4 Q5 Gini Q5/Q1

1980 33,562 4.88 13.49 6.17 28.32 47.15 39.7 9.7

1981 32,503 5.32 14.12 5.18 28.87 46.50 38.8 8.7

1982 34,153 5.37 13.13 7.42 28.10 45.98 38.5 8.6

1983 34,894 4.89 12.35 8.24 27.72 46.80 39.7 9.6

1984 34,262 3.68 11.71 9.01 27.75 47.84 41.7 13.0

1985 34,960 4.01 11.69 8.18 27.83 48.29 41.9 12.1

1986 35,040 4.21 11.37 8.48 27.84 48.10 41.7 11.4

1987 40,444 4.07 10.63 13.52 25.77 46.01 39.6 11.3

1988 40,863 4.16 10.90 13.66 25.65 45.63 39.1 11.0

1989 42,142 4.82 10.93 11.99 25.35 46.91 39.4 9.7

1990 41,658 4.57 10.76 13.19 25.15 46.33 39.2 10.1

1991 42,037 4.35 10.85 11.96 25.64 47.21 40.2 10.9

1992 44,712 4.31 9.80 18.04 23.44 44.42 37.5 10.3

1993 44,681 4.55 9.88 17.63 23.72 44.22 37.3 9.7

1994 44,453 4.13 10.22 16.45 24.06 45.14 38.3 10.9

1995 44,711 4.54 10.16 16.71 24.26 44.34 37.5 9.8

1996 45,087 4.11 9.92 17.16 24.25 44.56 38.1 10.8

1997 45,418 4.94 10.30 14.94 24.58 45.25 38.0 9.2

1998 48,386 4.11 9.98 17.44 24.04 44.43 37.9 10.8

1999 49,817 3.98 9.59 16.83 23.98 45.62 39.1 11.5

2000 48,841 4.20 10.09 15.84 24.30 45.57 38.8 10.9

2001 51,505 4.05 10.01 16.20 24.60 45.14 38.7 11.2

2002 52,752 4.09 10.05 16.01 24.61 45.25 38.8 11.1

2003 56,073 3.71 9.41 18.05 23.92 44.91 38.8 12.1

2004 56,466 4.03 10.23 16.26 24.48 45.00 38.5 11.2

2005 58,537 3.98 10.17 15.44 23.86 46.55 39.5 11.7

2006 58,655 4.03 10.31 15.39 24.20 46.06 39.2 11.4

2007 59,245 4.08 10.34 14.89 23.86 46.83 39.6 11.5

2008 58,069 3.92 10.17 15.15 24.16 46.61 39.7 11.9

2009 59,715 3.64 9.83 17.38 23.69 45.46 39.0 12.5

2010 59,041 3.65 9.62 18.13 23.42 45.18 38.8 12.4

2011 57,485 3.60 9.75 16.76 23.64 46.25 39.7 12.8

2012 57,833 3.58 9.67 16.47 23.65 46.63 40.0 13.0

2013 57,084 3.47 9.22 18.23 23.32 45.76 39.5 13.2

2014 58,385 3.59 9.13 17.64 23.13 46.52 39.9 13.0

2015 58,565 3.77 9.52 15.27 23.99 47.45 40.7 12.6

2016 61,177 3.73 9.22 14.14 23.55 49.36 42.2 13.2

2017 58,975 3.81 9.67 13.80 24.55 48.17 41.4 12.7

2018 60,687 3.59 9.64 13.01 24.38 49.39 42.5 13.8

Ave. 48,792 4.14 10.46 14.26 24.91 46.23 39.5 11.3

Source: CE Data1980-2018.
23

Appendix D. CE Earner Income (YE), Inequality Statistics.

YR YE Q1 Q2 Q3 Q4 Q5 GINI Q5/Q1

1980 26,778 5.84 16.83 19.45 24.21 33.66 25.2 5.8

1981 27,641 5.96 16.55 20.11 23.40 33.98 25.2 5.7

1982 28,615 6.12 15.62 20.27 24.01 33.98 25.6 5.6

1983 28,798 5.67 14.90 19.82 23.72 35.89 27.7 6.3

1984 26,969 4.58 14.81 18.92 24.65 37.05 29.9 8.1

1985 27,887 4.85 14.59 18.70 24.59 37.27 29.9 7.7

1986 28,149 4.99 14.09 18.26 24.35 38.31 30.8 7.7

1987 29,452 5.60 14.51 18.15 23.75 37.99 29.6 6.8

1988 29,361 5.79 15.08 18.89 22.76 37.49 28.4 6.5

1989 30,782 6.12 14.87 18.24 22.32 38.44 28.8 6.3

1990 30,014 5.93 14.85 18.41 22.42 38.40 29.0 6.5

1991 30,848 5.92 14.68 18.44 22.48 38.48 29.2 6.5

1992 30,701 5.80 14.16 18.72 22.97 38.36 29.6 6.6

1993 31,112 5.97 14.06 18.69 21.74 39.54 29.9 6.6

1994 31,954 5.28 14.05 18.69 22.55 39.42 30.7 7.5

1995 30,592 5.74 14.67 18.37 22.64 38.59 29.5 6.7

1996 31,169 5.72 14.18 19.06 22.50 38.54 29.6 6.7

1997 31,841 5.96 14.52 18.21 22.67 38.64 29.4 6.5

1998 33,738 5.65 14.16 18.78 23.28 38.13 29.6 6.7

1999 35,092 5.42 13.46 18.31 21.70 41.10 31.8 7.6

2000 34,268 5.73 14.23 17.54 23.39 39.12 30.4 6.8

2001 35,318 5.64 14.43 18.03 22.45 39.45 30.3 7.0

2002 36,285 5.67 14.44 18.03 22.43 39.43 30.2 6.9

2003 37,891 5.26 13.77 17.19 23.05 40.74 32.1 7.8

2004 38,910 5.58 14.68 18.10 23.32 38.32 29.6 6.9

2005 40,015 5.57 14.71 17.53 21.69 40.50 30.7 7.3

2006 40,272 5.55 14.85 17.87 21.96 39.77 30.2 7.2

2007 41,704 5.52 14.51 17.31 21.11 41.54 31.4 7.5

2008 41,267 5.28 14.12 17.35 22.43 40.82 31.8 7.7

2009 41,335 5.06 14.00 18.20 22.21 40.53 31.7 8.0

2010 40,380 5.13 13.86 18.13 22.16 40.72 31.8 7.9

2011 40,077 4.99 13.79 18.07 22.14 41.01 32.2 8.2

2012 40,423 4.94 13.65 17.99 22.11 41.32 32.5 8.4

2013 37,993 5.02 13.65 18.31 22.78 40.25 31.8 8.0

2014 39,129 5.12 13.43 17.99 22.51 40.94 32.3 8.0

2015 40,999 5.17 13.42 18.06 21.25 42.10 32.7 8.1

2016 42,767 5.12 13.02 16.23 21.01 44.63 34.8 8.7

2017 41,366 5.20 13.61 18.41 21.90 40.88 31.9 7.9

2018 43,022 4.85 13.43 18.15 21.61 41.96 33.0 8.6

Ave. 34,741 5.46 14.32 18.25 22.61 39.36 30.4 7.2

Source: CE Data1980-2018.