区间 1：19962002 (1)

区间 2：20042010 (2)

区间 3：20122018 (3)

增长：1 至 3（4）


Panel A. Variance, in levels 面板 A. 方差，水平 

Total variance 总方差  0.794  0.862  0.915  0.121  
Withinfirm 企业内部  0.512  0.532  0.531  0.018  
Betweenfirm, withinindustry 企业间，行业内 
0.112  0.127  0.140  0.028  
Betweenindustry 行业间  0.170  0.203  0.245  0.075  
Panel B. Variance, as percent of total 面板 B。方差，占总数的百分比 

Withinfirm 企业内部  64.6  61.7  58.0  14.9  
Betweenfirm, withinindustry 企业间，行业内 
14.0  14.7  15.3  23.1  
Betweenindustry 行业间  21.4  23.6  26.8  61.9  
Panel C. Other measures 面板 C. 其他措施 

Sample size (millions) 样本大小（百万）  239.4  249.2  269.7  
Number of firms (thousands) 企业数量（千家） 
470  460  466  
Number of NAICS industries 北美行业分类系统（NAICS）行业数量 
301  301  301 
行业间方差增长的行业份额

Number of industries 行业数量  总就业份额 (%)

行业间方差增长的总贡献

行业间方差增长的总份额 (%)


5 industries 五个行业  8.8  0.8  
25 industries 25 个行业  30.5  0.031  40.7  
71 industries 71 个行业  21.8  0.017  57.4  
145 industries 145 个行业  19.3  0.000  22.3  
55 industries 55 个行业  19.7  0.015  0.1  
Overall 总体  301 industries 301 个行业  100.0  0.075  20.3 
\hline & & Average
(1) & Change
(2) & Average
(3) & \begin{tabular}{l}
Change \\
(4)
\end{tabular} & \\
\hline 2111 & Oil and Gas Extraction & 0.3 & \(0.0\) & 1.012 & 0.247 & 1.8 \\
\hline 2131 & Support Activities for Mining & 0.5 & 0.3 & 0.374 & 0.191 & 1.4 \\
\hline 3254 & Pharmaceutical Manufacturing & 0.5 & \(0.1\) & 0.799 & 0.203 & 1.6 \\
\hline 3344 & Semiconductor Manufacturing & 0.8 & \(0.5\) & 0.556 & 0.299 & 1.4 \\
\hline 4234 & Professional Equipment Wholesaler & 0.7 & \(0.0\) & 0.557 & 0.190 & 1.9 \\
\hline 4441 & Building Material and Supplies & 0.9 & 0.1 & \(0.293\) & \(0.180\) & 1.5 \\
\hline 4451 & Grocery Stores & 2.4 & 0.0 & \(0.378\) & \(0.194\) & 4.7 \\
\hline 4481 & Clothing Stores & 0.7 & \(0.0\) & \(0.607\) & \(0.244\) & 2.6 \\
\hline 4529 & Other General Merchndse. Stores & 1.4 & 1.5 & \(0.539\) & \(0.051\) & 6.8 \\
\hline 5112 & Software Publishers & 0.5 & 0.2 & 1.009 & 0.186 & 5.6 \\
\hline 5182 & Data Processing Services & 0.3 & \(0.0\) & 0.545 & 0.301 & 1.3 \\
\hline 5191 & Other Information Services & 0.2 & 0.3 & 0.798 & 0.699 & 5.8 \\
\hline 5221 & Depository Credit Intermediate. & 2.1 & 0.0 & 0.189 & 0.234 & 2.5 \\
\hline 5231 & Securities Brokerage & 0.5 & \(0.1\) & 0.866 & 0.204 & 1.1 \\
\hline 5239 & Other Financial Investment Activity & 0.3 & 0.1 & 0.834 & 0.388 & 3.3 \\
\hline 5241 & Insurance Carriers & 1.6 & \(0.4\) & 0.488 & 0.167 & 2.3 \\
\hline 5413 & Archt. and Engineering Services & 1.2 & 0.1 & 0.469 & 0.161 & 2.6 \\
\hline 5415 & Computer Systems Design & 1.7 & 0.9 & 0.663 & 0.012 & 5.6 \\
\hline 5416 & Mgmt. and Scientific Services & 0.9 & 0.6 & 0.381 & 0.069 & 1.8 \\
\hline 5417 & Scientific Research Services & 0.8 & \(0.1\) & 0.741 & 0.244 & 3.3 \\
\hline 5511 & Management of Companies & 2.0 & \(0.1\) & 0.471 & 0.201 & 5.0 \\
\hline 5613 & Employment Services & 3.9 & 0.6 & \(0.685\) & 0.017 & 2.5 \\
\hline 5617 & Services to Buildings and Dwell & 1.1 & 0.3 & \(0.493\) & \(0.002\) & 1.1 \\
\hline 6211 & Offices of Physicians & 1.7 & 0.5 & 0.254 & 0.099 & 1.6 \\
\hline 6216 & Home Health Care Services & 0.8 & 0.4 & \(0.525\) & \(0.016\) & 1.7 \\
\hline 6221 & General Medical and Hospitals & 4.5 & 0.5 & 0.205 & 0.162 & 4.2 \\
\hline 6233 & Continuing Care Retirement & 0.6 & 0.4 & \(0.493\) & \(0.001\) & 1.2 \\
\hline 6241 & Individual and Family Services & 0.8 & 0.6 & \(0.490\) & \(0.155\) & 3.5 \\
\hline 7139 & Othr. Amusement and Recreation & 0.6 & 0.1 & \(0.594\) & \(0.106\) & 1.7 \\
\hline 7225 & Restaurants and Othr. Eat Places & 4.9 & 2.0 & \(0.739\) & \(0.027\) & 16.9 \\
\hline
\end{tabular}
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. Average log earnings for industry \(k\) are relative to the economy average. The 19962002 and 20122018 intervals are averaged. Changes are the growth (or decline) from 19962002 to 20122018. See equation (2) for definitions. Top 11 lowpaying industries in bold.
transportation and material moving, building and grounds cleaning and maintenance, and food preparation and serving are increasingly employed in industries that provide services to other firms. Important industries in the top 11 lowpaying industries that fit this description include Employment Services (5613) and Services to Buildings and Dwellings (5617). As we will see below using the OEWS, we find patterns consistent with the shift of such occupations away from the top 19 highpaying industries.
What about the other 271 fourdigit NAICS industries? Figure 1 highlights they make relatively little contribution. Using Table 2, there are 145 industries that each
Table 4Industry Contributions to BetweenIndustry Variance Growth, by Average Earnings
\begin{tabular}{ccccccc}
\hline \multirow[b]{2}{*}{Industry relative earnings} & \multirow[b]{2}{*}{Number of Industries} & \multirow[t]{2}{*}{Total employment share (1)} & \multirow[t]{2}{*}{Total contribution to betweenindustry growth
(2)} & \multirow[t]{2}{*}{Total share of betweenindustry growth} & \multicolumn{2}{l}{Shiftshare} \\
\hline & & & & & \begin{tabular}{l}
Employment \\
(4)
\end{tabular} & Earnings
(5) \\
\hline Overall & 301 & 100.0\% & 0.075 & 100.0\% & \(14.0 \%\) & 86.0\% \\
\hline \multicolumn{7}{l}{Panel A. 30 industries with variance contribution \(>1 \%\)} \\
\hline Highpaying & 19 & \(21.1 \%\) & 0.041 & \(54.1 \%\) & \(16.1 \%\) & 83.9\% \\
\hline Lowpaying & 11 & \(18.1 \%\) & 0.033 & \(44.1 \%\) & \(68.3 \%\) & \(31.7 \%\) \\
\hline \multicolumn{7}{l}{Panel B. 271 industries with variance contribution \(\leq 1 \%\)} \\
\hline Highpaying & 146 & \(34.9 \%\) & 0.001 & \(1.3 \%\) & & \\
\hline Lowpaying & 125 & \(25.9 \%\) & 0.000 & 0.6\% & & \\
\hline
\end{tabular}
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. Employment shares are calculated as the average of 19962002 and 20122018 employment shares. Industry \(k\) 's contribution to betweenindustry variance growth is specified in equation (2). The shiftshare calculations for changing employment and earnings follow equation (3). Shiftshare results are summed across industries and normalized by the total contribution so that the two components sum to 100 percent. The two rows for the 271 industries with variance contribution \(\leq 1\) percent have missing cells because the denominator for the shiftshare decomposition is close to zero.
contribute approximately 0.0 percent (to be precise, greater than 0.05 percent and less than 0.05 percent) to betweenindustry variance growth. This says that almost onehalf of all fourdigit NAICS industries contribute essentially nothing to inequality growth. There are 71 industries that contribute between 0.05 percent and 1.0 percent, accounting for 22.3 percent of betweenindustry variance growth. These industries are basically offset by another 55 industries that have a negative contribution ( \(<0.05\) percent), accounting for 20.3 percent of betweenindustry variance growth.
As seen in Table 4, the top 30 industries include 19 highpaying industries that account for 54.1 percent of betweenindustry variance growth, and 11 lowpaying industries that account for 44.1 percent of betweenindustry variance growth. The other 271 industries that have small contributing and offsetting contributions to increasing inequality do not occur systematically among highpaying versus lowpaying industries. 146 highpaying industries account for 1.3 percent of betweenindustry variance growth, and 125 lowpaying industries account for only 0.6 percent of betweenindustry variance growth.
Changes in earnings and employment share determine an industry's contribution to growth in inequality. This is seen in the expression defining industry \(k\) 's contribution to betweenindustry variance growth: \(\Delta\left(\frac{N^{k, p}}{N^{p}}\right)\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}\). If an industry with relatively high earnings exhibits an earnings increase, then, ceteris paribus, inequality will increase. Analogously, inequality will increase if an industry with relatively low earnings exhibits an earnings decrease. In contrast, when average earnings in an industry moves closer to the overall average, inequality decreases.
Employment shares also determine industrylevel contributions to inequality. An industry's earnings changes will have larger effects on inequality when its employment share is larger. Changes in an industry's employment share will have smaller effects on inequality when that industry's pay is more similar to the overall average.
Employment gains among very high and very lowpaying industries tend to increase inequality.
In Table 4, we report the relative importance of earnings changes versus employment changes using a shift share decomposition. Industry \(k\) 's contribution to betweenindustry variance growth is \(\Delta\left(\frac{N^{k, p}}{N^{p}}\right)\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}\). We can use a standard shiftshare decomposition to express this change in terms of the components attributable to changes in employment versus earnings:
\[
\underbrace{\Delta\left(\frac{N^{k, p}}{N^{p}}\right)\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}}_{\begin{array}{c}
\text { industry } k^{\prime} \text { s contribution to } \\
\text { betweenindustry variance growth }
\end{array}}=\underbrace{\overline{\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}} \Delta\left(\frac{N^{k, p}}{N^{p}}\right)}_{\text {shiftshare: employment }}+\underbrace{\frac{\overline{N^{k, p}}}{N^{p}} \Delta\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}}_{\text {shiftshare: earnings }}
\]
where \(\overline{\left(\bar{y}^{k, p}\bar{y}^{p}\right)^{2}}\) and \(\overline{\frac{N^{k, p}}{N^{p}}}\) are averages of intervals 1 and 3 . We do this for our top 30 industries, distinguished by highpaying and lowpaying industries (we do not present the shift share estimates for the other 271 industries since the denominator of the shift share is very close to zero). Among the 19 highpaying industries, 83.9 percent of betweenindustry variance growth is accounted for by changing relative earnings, and the remaining 16.1 percent is accounted for by changing employment shares. Among the 11 lowpaying industries, the relative importance of earnings versus employment is reversed: 68.3 percent of betweenindustry variance growth is accounted for by changing employment shares, and the remaining 31.7 percent is accounted for by changing relative earnings. These results highlight different explanations for why betweenindustry variance growth is increasing at the opposite tails of the earnings distribution. Inequality growth at the top of the earnings distribution is a story of increasing earnings, whereas inequality growth at the bottom of the earnings distribution is a story of increasing employment.
These two different explanations for increasing inequality among low versus highpaying industries is evident in the earnings and employment changes of the thirty industries listed in Table 3. All of the 19 highpaying industries exhibit earnings increases during our time period. The most rapid growth is found in Other Information Services (5191), which had a 69.9 log point (101.2 percent) increase in relative earnings. \({ }^{17}\) Of the remaining highpaying industries, nine had earnings increases in excess of \(20 \log\) points ( 22.1 percent), six had increases between 10 (10.5 percent) and \(20 \log\) points, and three had increases less than \(10 \log\) points.
Most of the 11 lowpaying industries exhibit earnings decreases, yet they are smaller in absolute value than the earnings increases among the highpaying industries. The only lowpaying industry with a decline greater in magnitude than 20 \(\log\) points ( 22.1 percent) is Clothing Stores (4481), which had a 24.4 log point ( 27.6 percent) decrease in relative earnings. Of the remaining lowpaying industries, four had earnings declines between 10 ( 10.5 percent) and 20 log points, and five had
\footnotetext{
\({ }^{17}\) We convert \(\log\) differentials to proportionate changes using the expression \(e^{x}1\). For small differences, \(\log\) points are approximately equal to the percentage change.
}
earnings declines between 0 and 10 log points. One industry, Employment Services (5613), exhibited a relatively small increase in earnings.
On the other hand, changes in employment are more important for the 11 lowpaying industries than for the 19 highpaying industries. Two lowpaying industries in Table 3 stand out: Restaurants and Other Eating Places (7225) had a 2.0 percentage point increase in employment share, and Other General Merchandise Stores (4529) had a 1.5 percentage point increase in employment share. Eight of the other lowpaying industries have smaller employment share increases (less than one percentage point), and one industry (Clothing Stores, 4481) had a declining employment share. Among the 19 highpaying industries, none had employment share increases exceeding 1 percentage point, ten had small employment share increases (less than 1 percentage point), and about onehalf (9) of the highpaying industries had declining employment shares.
In the analysis that follows, we also use the CPSLEHD integrated data, the OEWS and LBD data to provide further insights into the role of rising betweenindustry dispersion. It is worth highlighting that all of these alternative sources provide a similar quantification of the contribution of the top 30 industries listed above to rising betweenindustry dispersion. While more detail is provided below, the share of the betweenindustry increase in dispersion from 19962002 to 20122018 accounted for by the top 30 industries is 98.1 percent in the LEHD data, 105.5 percent in the CPSLEHD data, 96.2 percent in the OEWS data, and 94 percent in the LBD data.
\section*{IV. Firm and Worker Composition in the Top 30 Industries}
\section*{A. Mega Firms}
Changes in the employment shares and sizeearnings premia for mega (10,000+) firms play a critical role in accounting for rising betweenindustry earnings inequality. Table 5 shows descriptive statistics of employment and earnings in mega firms and nonmega firms in our four industry groups. One immediate result in Table 5 is that employment has shifted over time to the top 30 industries. The employment share of the top 30 industries increased by 8.2 percentage points, with most of this increase ( 6.0 percentage points) among the 11 lowpaying industries. The employment share of the other 271 industries analogously declined by 8.2 percentage points, with most of this decline ( 6.8 percentage points) among the 146 highpaying industries.
The substantial increase in the employment share of the top 30 industries is driven by mega firms. This is evident in both Table 5 and Figure 2. Figure 2 shows the change in employment share by detailed size class for each of our four industry groups. \({ }^{18}\) The employment share of the 11 lowpaying industries increased in every size class, with mega firms exhibiting the largest increase ( 2.5 percentage points). The 19 highpaying industries had a smaller increase in employment, but most of this increase ( 1.4 of a total of 2.2 percentage points) is accounted for by mega firms. Given the high average relative pay of mega firms in the highpaying
\footnotetext{
\({ }^{18}\) The corresponding employment share levels in the first interval (19962002) and in the third interval (20122018) are given in online Appendix Figure F1.
}
Table 5Changes in Employment and Earnings, by Industry Earnings, Mega Firms versus Others
\begin{tabular}{ccccccc}
\hline \multirow[t]{2}{*}{Industry relative earnings} & \multirow[t]{2}{*}{Number of industries} & \multirow[t]{2}{*}{Firm employment} & \multicolumn{2}{l}{Employment share} & \multicolumn{2}{l}{Relative earnings} \\
\hline & & & Average
(1) & Change
(2) & \begin{tabular}{l}
Average \\
(3)
\end{tabular} & Change
(4) \\
\hline \multicolumn{7}{l}{Panel A. 30 industries with variance contribution \(>1 \%\)} \\
\hline \multirow[t]{3}{*}{Highpaying} & 19 industries & Any & \(21.1 \%\) & \(2.2 \%\) & 0.440 & 0.177 \\
\hline & & \(10,000+\) & \(3.8 \%\) & 1.4\% & 0.579 & 0.145 \\
\hline & & <10,000 & 17.3\% & 0.8\% & 0.410 & 0.174 \\
\hline \multirow[t]{3}{*}{Lowpaying} & 11 industries & Any & 18.1\% & 6.0\% & 0.586 & 0.069 \\
\hline & & 10,000+ & 4.3\% & 2.5\% & 0.492 & \(0.125\) \\
\hline & & \(<10,000\) & 13.8\% & \(3.5 \%\) & \(0.613\) & \(0.061\) \\
\hline \multicolumn{7}{l}{Panel B. 271 industries with variance contribution \(\leq 1 \%\)} \\
\hline \multirow[t]{3}{*}{Highpaying} & 146 industries & Any & \(34.9 \%\) & \(6.8 \%\) & 0.281 & 0.046 \\
\hline & & \(10,000+\) & \(3.9 \%\) & \(1.2 \%\) & 0.646 & 0.042 \\
\hline & & \(<10,000\) & \(31.0 \%\) & \(5.7 \%\) & 0.236 & 0.052 \\
\hline \multirow[t]{3}{*}{Lowpaying} & 125 industries & Any & 25.9\% & \(1.3 \%\) & 0.325 & 0.002 \\
\hline & & 10,000+ & 3.3\% & 0.5\% & 0.404 & \(0.061\) \\
\hline & & <10,000 & 22.6\% & 0.9\% & \(0.314\) & 0.006 \\
\hline
\end{tabular}
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. Averages and changes use the employment shares and earnings from the 19962002 and 20122018 intervals. Average log earnings are relative to the economy average.
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g15.jpg?height=714&width=1073&top_left_y=1186&top_left_x=325)
Figure 2. Change in Employment Share by Size Class, by Industry Group
Notes: Authors' tabulations of linked LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. Changes in the employment shares are expressed in terms of percentage points. The denominator is total employment across all size classes and industry groups.
industries ( \(57.9 \log\) points, or 77.9 percent) and the low pay of mega firms ( 49.2 log points, or 63.6 percent) in the lowpaying industries, these shifts
in employment to mega firms contributed to rising betweenindustry earnings inequality. \({ }^{19}\)
Mega firms also play a key role in the changing earnings of the top 30 industries. For the 11 lowpaying industries, the relative pay of mega firms decreased by \(12.5 \log\) points ( 13.3 percent) compared to a decline of \(6.1 \log\) points ( 6.3 percent) for the nonmega firms. Both mega firms and nonmega firms in the 19 highpaying industries exhibit large earnings increases: \(14.5 \log\) points ( 15.6 percent) for mega firms and 17.4 log points ( 19.0 percent) for nonmega firms. Earnings at mega firms increased relative to the smallest firms in the toppaying industries but not by as much as the increase in relative earnings at large but not mega firms. \({ }^{20}\) In contrast, relative earnings increases at mega firms in the 146 remaining highpaying industries are modest ( 4.2 log points, or 4.3 percent) compared to \(14.5 \log\) points in the top 19 highpaying industries. Similarly, relative earnings declines at the mega firms in the remaining 125 lowpaying industries are modest ( 6.1 log points, or 6.3 percent) compared to the \(12.5 \log\) points in the top 11 lowpaying industries.
\section*{B. Education and Occupation}
To shed light on the changing education composition of the top and bottom industries, we turn to the CPSLEHD integrated data. Figure 3 shows the change in employment in the top 30 industries from 19962002 to 20122018. Both low and highpaying industries had increases in the educational attainment of the workers that they employ. These changes were much more dramatic in the top 19 highpaying industries. The share of workers with bachelor's degrees at these highpaying industries increased by 7.0 percent, workers with advanced degree increased by 8.4 percent, and workers with a high school diploma declined by 8.7 percent. 21
For occupation, we turn to the OEWS published data. \({ }^{22}\) In Figure 4, we consider employment changes across all 22 occupation groups in the top 30 industries. The occupation groups are ranked from left to right by the changes in employment shares in the top 30 industries. There are substantial differences in the changing mix of occupations across the top 19 and bottom 11. The top 19 industries have large increases in Business and Financial Operations (13) and Computer and Mathematical Science (15) with accompanying large declines in Office and
\footnotetext{
\({ }^{19}\) Online Appendix Table H2 shows that the patterns of changes in employment shares by mega firms is robust to using the national and enterprise concepts available in the Business Dynamic Statistics (BDS). The BDS is derived from the LBD.
\({ }^{20}\) Online Appendix Figure F3 shows the crosssectional sizeearnings premia for the 19962002 and the 20122018 intervals. Among the top 19 highpaying industries, the sizeearnings profile shifts upward, with increases in all size classes. Online Appendix F also provides more details of the changing patterns by firm size.
\({ }^{21}\) Online Appendix Figure D1 shows the distribution of employment by education group in the top 19 highpaying and 11 lowpaying industries for the 19962002 and 20122018 periods. In the initial period, the top 19 highpaying industries had significantly higher shares of workers with a bachelor's or advanced degree while the top 11 lowpaying industries had higher shares of workers with only a high school diploma or less. These differences became much more pronounced by 20122018 with sharp increases in the share of workers with bachelor's and advanced degrees in the top 19 highpaying industries and accompanying declines in the share of workers with a high school diploma or less.
\({ }^{22}\) Online Appendix Figure G1 shows that the CPSLEHD yields similar patterns on changes in occupations by industry.
}
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g17.jpg?height=726&width=1063&top_left_y=218&top_left_x=343)
Figure 3. Change in Employment Share by Educational Attainment, by Industry Group
Notes: Authors' tabulations of linked CPSLEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\). Changes in the employment shares are expressed in terms of percentage points. The denominator is total employment in the respective industry group.
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g17.jpg?height=721&width=1088&top_left_y=1212&top_left_x=330)
Figure 4. Change in Employment by Occupation and Industry Group
Note: Authors' calculations of published OEWS aggregates.
Administrative Support (43), a low wage occupation group. These patterns are consistent with the dramatic innovations in information and communication technology largely developed by toppaying industries. Related, much of the role of Office and Administrative Support (43) tasks in these highpaying industries are
Table 6Changes in Employment and EarningS, by Industry EarningS, by Occupation Groups
\begin{tabular}{cccccc}
\hline \multirow[b]{2}{*}{Industry group earnings} & \multirow[b]{2}{*}{Occupation group earnings} & \multicolumn{2}{l}{Employment share(\%)} & \multicolumn{2}{l}{Relative earnings} \\
\hline & & Average
(1) & Change
(2) & \begin{tabular}{l}
Average \\
(3)
\end{tabular} & Change
(4) \\
\hline \multicolumn{6}{l}{Panel A. 30 industries with variance contribution \(>1 \%\)} \\
\hline \multirow[t]{3}{*}{19 highpaying} & All & 16.3 & 1.3 & 0.405 & 0.107 \\
\hline & 12 highpaying & 9.7 & 2.0 & 0.707 & 0.067 \\
\hline & 10 lowpaying & 6.6 & \(0.7\) & \(0.039\) & 0.029 \\
\hline \multirow[t]{3}{*}{11 lowpaying} & All & 19.8 & 3.1 & \(0.437\) & \(0.010\) \\
\hline & 12 highpaying & 2.1 & 0.3 & 0.269 & 0.031 \\
\hline & 10 lowpaying & 17.7 & 2.8 & \(0.521\) & \(0.013\) \\
\hline \multicolumn{6}{l}{Panel B. 251 industries with variance contribution \(\leq 1 \%\)} \\
\hline \multirow[t]{3}{*}{141 highpaying} & All & 30.0 & \(2.8\) & 0.213 & 0.001 \\
\hline & 12 highpaying & 13.0 & \(0.1\) & 0.496 & 0.005 \\
\hline & 10 lowpaying & 17.1 & \(2.7\) & \(0.002\) & \(0.036\) \\
\hline \multirow[t]{3}{*}{110 lowpaying} & All & 33.9 & \(1.6\) & \(0.130\) & \(0.011\) \\
\hline & 12 highpaying & 12.8 & \(0.2\) & 0.238 & \(0.017\) \\
\hline & 10 lowpaying & 21.1 & \(1.5\) & \(0.353\) & \(0.020\) \\
\hline
\end{tabular}
Notes: Authors' calculations of OEWS published aggregates. Changes in employment and earnings compare year intervals 20022003 with 20152016. Industry pay designations and contributions to variance growth follow the LEHD administrative records data.
now increasingly accomplished with adoption of information and communication technologies. Some of the reduction in Office and Administrative Support (43) may also reflect outsourcing of these tasks to service firms. Consistent with the latter, there have also been decreases in production and building and ground maintenance and cleaning. The employment share of the bottom 11 industries increases, with particularly strong growth in Food Preparation and Serving Related (35) and Personal Care and Service (39). These lowpaying industries also exhibit a nontrivial decline in Management (11) occupations.
To get a sense of the contribution of occupations to industrylevel wage inequality, we divide occupations into 2 broad categories, 12 that are highpaying relative to the overall average, and ten that are analogously lowpaying (see Table 6). Occupational shifts appear to occur, at least broadly, at the industry level. Neanly all of the growth in the employment share of highpaying occupations occurs in the 19 highpaying industries, and all of the growth in lowpaying occupations occurs in our 11 lowpaying industries. Our 19 highpaying industries increase their employment share by 1.3 percent in the OEWS data. This reflects strong growth among our 12 highpaying occupations, whose share of employment grows by 2.0 percent. The employment share of our 10 lowpaying occupations in these 19 highpaying industries contracts by 0.7 percent. The 11 lowpaying industries increase their employment share by 3.1 percent. This mostly reflects an increase among our 10 lowpaying occupations, whose share of overall employment increases by 2.8 percentage points.
Outside the thirty industries that contribute to inequality, lowpaying occupations have a declining employment share. The employment share of our other 141 highpaying industries declines by 2.8 percentage points, and nearly all of this (2.7 percentage points) occurs among lowpaying occupations. The employment
share of the 110 lowpaying industries declines by 1.6 percentage points, which almost matches ( 1.5 percentage points) its decline in the share of lowpaying occupations.
Table 6 also illustrates the role of industryoccupation pay differentials in rising inequality. Our 19 highpaying industries have a strong ( 40.5 log point) earnings differential. This reflects an even larger ( 70.7 log point) differential among our 12 highpaying occupations in these industries. These industries also had the highest earnings growth, both overall ( 10.7 log points), and especially among highpaying occupations ( 6.7 log points). Workers in our 10 lowpaying occupations in these 19 highpaying industries had earnings gain as well ( 2.9 log points).
Earnings changed by relatively less among our top 11 lowpaying industries. The overall change in earnings was a decline of only \(1.0 \log\) points. This reflects a gain in earnings of 3.1 log points among the relatively rate highpaying occupations. Lowpaying occupations in these industries had an earnings decline of 1.3 log points. There were smaller changes among the other 251 industries (an increase of 0.1 log points among highpaying industries, and a decline of 1.1 log points among lowpaying industries), with consistent declines in the earnings of lowpaying occupations (of 3.6 log points, and 2.0 log points, respectively). \({ }^{23}\)
\section*{V. Inequality in Terms of Sorting and Pay Premia}
In this section, we present decompositions of earnings in the LEHD data using the AKM approach, in the CPSLEHD data using a human capital approach, and in the OEWS data using a related but distinct approach given the limitation of only having occupation by industry data.
\section*{A. Sorting and Industry Premia Using AKM}
To understand the role of workers and firms in the generation of earnings inequality, we begin by using the linear model of AKM. We estimate our model separately for each of three sevenyear intervals: 19962002, 20042010, and 20122018. Following Song et al. (2019), we assume that earnings \(y_{t}^{i, j, k, p}\) are the sum of the effect \(\theta^{i, p}\) of worker \(i\) in interval \(p\), a firm effect \(\psi^{j, k, p}\) when employed by employer \(j\) in industry \(k\) during interval \(p\), and a vector of timevarying observable characteristics \(X_{t}^{i, p}\) for worker \(i\) at time \(t\), which have distinct marginal effects \(\beta^{p}\) by interval \(p\). We express this as
\[
y_{t}^{i, j, k, p}=X_{t}^{i, p} \beta^{p}+\theta^{i, p}+\psi^{j, k, p}+\varepsilon_{t}^{i, j, k, p} .
\]
Our observable characteristics include a set of year dummies that capture calendar year effects on earnings. Following Card, Cardoso, and Kline (2016), we center age around 40 , include a quadratic and cubic transformation of worker age, and omit the
\footnotetext{
\({ }^{23}\) One potential issue is whether greater occupational detail may change the inferences in this section. However, there is a high degree of concentration of occupations across detailed industries. Online Appendix Figure G3 shows that for the twodigit occupations we use in the current analysis, occupations are highly concentrated in detailed industries. Online Appendix Figure G4 shows that using sixdigit occupations, the median occupation has a top 20 industry concentration ratio of 100 percent.
}
linear term. To solve this model, we implement the iterative method proposed by Guimarães and Portugal (2010).
The AKM approach to decomposing earnings has received substantial scrutiny in terms of the interpretation of the estimated person and firm effects. Recent research has highlighted the potential for limited mobility bias arising from the small number of transitions per firm. Bonhomme et al. (2022) find that limited mobility bias yields an upward bias in the variance of firm effects and a downward bias in the covariance between firm and worker effects. However, they find little bias on the contribution of the change in the role of premia and sorting for the change in inequality (and it is the latter that is the focus of our work). \({ }^{24}\) Our use of AKM makes our results directly comparable to Song et al. (2019) which enables us to highlight that the betweenfirm effects they identified are mostly occurring at the industry level. However, we also include (see Section VB) alternative decompositions based on the CPSLEHD and OEWS data.
We use the AKM approach to decompose the betweenfirm components into those that occur within and betweenindustries. \({ }^{25}\) To explore crossindustry differences, we calculate industrylevel averages. For a given interval \(p\) (and hereafter omitting this superscript), we define the average worker effect in industry \(k\) as \(\bar{\theta}^{k}\), the average effect of observable characteristics as \(\bar{X}^{k} \beta\), and the average firm effect as \(\bar{\psi}^{k}\). Given this notation, it is possible to distinguish between how firmlevel pay premia relate to within versus betweenindustry earnings dispersion. This is given by
(5) \(\operatorname{var}\left[y_{t}^{i, j, k}\right]\)
\[
\begin{aligned}
& =\underbrace{\operatorname{var}\left[\theta^{i}\bar{\theta}^{j, k}\right]+\operatorname{var}\left[X_{t}^{i} \beta\bar{X}^{j, k} \beta\right]+2 \operatorname{cov}\left[\theta^{i}\bar{\theta}^{j, k}, X_{t}^{i} \beta\bar{X}^{j, k} \beta\right]}_{\text {withinfirm person effect and observables }} \\
& +\underbrace{\operatorname{var}\left[\bar{\psi}^{k}\right]}_{\begin{array}{c}
\text { betweenindustry } \\
\text { pay premia }
\end{array}}+\underbrace{\operatorname{var}\left[\psi^{j, k}\bar{\psi}^{k}\right]}_{\begin{array}{c}
\text { withinindustry, } \\
\text { betweenfirm } \\
\text { pay premia }
\end{array}}+\underbrace{2 \operatorname{cov}\left[\bar{\theta}^{k}, \bar{\psi}^{k}\right]+2 \operatorname{cov}\left[\bar{\psi}^{k}, \bar{X}^{k} \beta\right]}_{\text {betweenindustry covariance }} \\
& +\underbrace{2 \operatorname{cov}\left[\left(\bar{\theta}^{j, k}\bar{\theta}^{k}\right),\left(\psi^{j, k}\bar{\psi}^{k}\right)\right]+2 \operatorname{cov}\left[\left(\psi^{j, k}\bar{\psi}^{k}\right),\left(\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right)\right]}_{\text {withinindustry, betweenfirm covariance }} \\
& +\underbrace{\operatorname{var}\left[\bar{\theta}^{j, k}\bar{\theta}^{k}\right]+\operatorname{var}\left[\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right]+2 \operatorname{cov}\left[\left(\bar{\theta}^{j, k}\bar{\theta}^{k}\right),\left(\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right)\right.}_{\text {withinindustry, betweenfirm segregation }}] \\
& +\underbrace{\operatorname{var}\left[\bar{\theta}^{k}\right]+\operatorname{var}\left[\bar{X}^{k} \beta\right]+2 \operatorname{cov}\left[\bar{\theta}^{k}, \bar{X}^{k} \beta\right]}_{\text {betweenindustry segregation }}+\underbrace{\operatorname{var}\left[\varepsilon_{t}^{i, j, k}\right]}_{\text {residual (withinfirm) }} .
\end{aligned}
\]
\footnotetext{
\({ }^{24}\) There has also been concern raised about exogenous mobility. Bonhomme et al. (2022) have highlighted that this is less of an issue than limited mobility bias. The reason is that what is required is that mobility is unrelated to the residual from the AKM model after controlling for person and firm effects.
\({ }^{25}\) Our betweenindustry decomposition builds on Song et al. (2019). In online Appendix E, we show the original Song et al. (2019) decomposition and compare our results to theirs.
}
Withinfirm dispersion is given by the collection of terms in the first line of equation (5). Workerlevel effects are given by
\[
\operatorname{var}\left[\theta^{i}\bar{\theta}^{j, k}\right]+\operatorname{var}\left[X_{t}^{i} \beta\bar{X}^{j, k} \beta\right]+2 \operatorname{cov}\left[\theta^{i}\bar{\theta}^{j, k}, X_{t}^{i} \beta\bar{X}^{j, k} \beta\right]
\]
Residual dispersion \(\operatorname{var}\left[\varepsilon_{t}^{i, j, k}\right]\) occurs within firms given the inclusion of firm effects.
The firmlevel premia contributions can be decomposed into withinand betweenindustry components. Specifically, \(\quad \operatorname{var}\left[\psi^{j, k}\right]=\operatorname{var}\left[\bar{\psi}^{k}\right]+\) \(\operatorname{var}\left[\psi^{j, k}\bar{\psi}^{k}\right]\), where \(\operatorname{var}\left[\bar{\psi}^{k}\right]\) reflects the betweenindustry dispersion in average firm effects, that is, industrylevel pay premia. The remaining term \(\operatorname{var}\left[\psi^{j, k}\bar{\psi}^{k}\right]\) captures the withinindustry dispersion of firmlevel pay premia.
In addition to pay premia, we can distinguish the within versus betweenindustry components of a covariance contribution and segregation. The betweenindustry covariance contribution is defined as \(2 \operatorname{cov}\left[\bar{\theta}^{k}, \bar{\psi}^{k}\right]+2 \operatorname{cov}\left[\bar{\psi}^{k}, \bar{X}^{k} \beta\right]\), which reflects the extent to which highly paid workers are employed in industries with a high pay premium (and viceversa). This is distinct from the withinindustry covariance contribution \(2 \operatorname{cov}\left[\left(\bar{\theta}^{j, k}\bar{\theta}^{k}\right),\left(\theta^{j, k}\bar{\theta}^{k}\right)\right]+2 \operatorname{cov}\left[\left(\theta^{j, k}\bar{\theta}^{k}\right),\left(\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right)\right]\). This is the component of the covariance contribution where relatively highly paid workers tend to work at highpaying firms, apart from industrylevel differences. For example, workers and firms in the Restaurants and Other Eating Places (7225) industry may tend to have low worker effects, while those among Software Publishers (5112) may have high effects. The betweenindustry component reflects these industry level differences. The withinindustry component reflects the extent to which relatively low versus highpaid workers work for relatively low versus highpaying firms in those industries.
Segregation also can be decomposed into its within versus betweenindustry components. Betweenindustry segregation is given by industrylevel average worker effects. Formally, this is expressed as \(\operatorname{var}\left[\bar{\theta}^{k}\right]+\operatorname{var}\left[\bar{X}^{k} \beta\right]+2 \operatorname{cov}\left[\bar{\theta}^{k}, \bar{X}^{k} \beta\right]\). This is the extent to which low versus highly paid workers tend to work with each other. To continue with the previous example, Restaurants and Other Eating Places (7225) may employ workers with a low person effect, on average, while employers among Software Publishers (5112) may employ workers with a high average person effect. The extent to which this is related to the firmlevel pay differences reflects the covariance component. The extent to which it reflects similar workers grouped together is segregation. Segregation that occurs within industries is expressed as \(\operatorname{var}\left[\bar{\theta}^{j, k}\bar{\theta}^{k}\right]+\operatorname{var}\left[\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right]+2 \operatorname{cov}\left[\left(\bar{\theta}^{j, k}\bar{\theta}^{k}\right),\left(\bar{X}^{j, k} \beta\bar{X}^{k} \beta\right)\right]\).
While the covariance and segregation components are distinct in the above decomposition and potentially conceptually, in practice we find that the covariance and segregation components of the between industry increase in earnings inequality are closely related. For the complete set of fourdigit NAICS industries, the correlation of the changes in the two components is 0.93 and for the top 30 industries it is 0.90 . This tight relationship reflects our finding that highwage workers work increasingly in high wage industries and work together while lowwage workers work increasingly in low wage industries and work together. Given this tight relationship, we combine these covariance and segregation components into a combined sorting contribution and in turn for expositional convenience primarily focus our
Table 7IndustryEnhanced Variance Decomposition
\begin{tabular}{lcccc}
\hline \hline & Interval 1: & Interval 2: & Interval 3: & Growth: \\
& \(19962002\) & \(20042010\) & \(20122018\) & 1 to 3 \\
& \((1)\) & \((2)\) & \((3)\) & \((4)\) \\
\hline Panel A. Variance, in levels & & & & \\
Total variance & 0.794 & 0.862 & 0.915 & 0.121 \\
Withinfirm & 0.512 & 0.532 & 0.531 & 0.018 \\
Person effect and observables & 0.382 & 0.401 & 0.405 & 0.023 \\
\(\quad\) Residual & 0.130 & 0.131 & 0.125 & 0.005 \\
Betweenfirm, withinindustry & 0.112 & 0.127 & 0.140 & 0.028 \\
\(\quad\) Firm sorting & 0.087 & 0.098 & 0.111 & 0.025 \\
\(\quad\) Firm pay premium & 0.025 & 0.029 & 0.028 & 0.004 \\
Betweenindustry & 0.170 & 0.203 & 0.245 & 0.075 \\
\(\quad\) Industry sorting & 0.137 & 0.162 & 0.201 & 0.065 \\
\(\quad\) Industry pay premium & 0.033 & 0.042 & 0.044 & 0.011 \\
Panel B. Variance, as percent of total & & & & \\
Withinfirm & 64.6 & 61.7 & 58.0 & 14.9 \\
\(\quad\) Person effect and observables & 48.2 & 46.5 & 44.3 & 18.8 \\
\(\quad\) Residual & 16.4 & 15.2 & 13.7 & 3.9 \\
Betweenfirm, withinindustry & 14.0 & 14.7 & 15.3 & 23.1 \\
\(\quad\) Firm sorting & 10.9 & 11.3 & 12.2 & 20.3 \\
\(\quad\) Firm pay premium & 3.1 & 3.4 & 3.1 & 2.9 \\
Betweenindustry & 21.4 & 23.6 & 26.8 & 61.9 \\
Industry sorting & 17.2 & 18.8 & 22.0 & 53.2 \\
Industry pay premium & 4.2 & 4.8 & 4.8 & 8.7 \\
\hline
\end{tabular}
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. See equation (5) and discussion in text for definitions.
discussion on this combined contribution in the remainder of the paper. \({ }^{26}\) In addition, while often expositionally convenient to combine these terms, it is useful to emphasize the especially large contribution of the covariance component of what we denote as sorting to rising inequality. This covariance contribution highlights that rising dispersion in between industry premia contributes directly and indirectly via this rising covariance. We draw this point out in various places in the main text.
Table 7 exploits our industryenhanced AKM decomposition to understand rising earnings inequality. \({ }^{27}\) The first three columns of Table 7 show results for our three intervals while the last column computes the terms underlying the change in inequality from our first to last intervals (19962002 to 20122018). 28
Betweenindustry dispersion accounts for 61.9 percent of the growth in inequality over the time period covered by our dataset. 53.2 percent of the total rise in
\footnotetext{
\({ }^{26}\) In the online Appendix (Appendix Table A4), we provide the separate contribution of these components. In using sorting for the combined contribution of the covariance and segregation components, we deviate from the labels used by Song et al. (2019) who denoted the covariance component sorting. We have a further discussion of the alternative decompositions and terminology in the literature in the online Appendix.
\({ }^{27}\) Online Appendix Table A1 presents estimates that use the Song et. al (2019) approach without reference to industry, and online Appendix Table A2 aggregates these estimates into firmlevel segregation, pay premium, and covariance contributions. Online Appendix Table A3 presents estimates of equation (5) before aggregating them as done in Table 7 .
\({ }^{28}\) While their focus is on inequality in the cross section rather than its change over time, Card, Rothstein, and Yi (2024) demonstrate that there is substantial variation in industry premia not explained by worker sorting, which is consistent with our findings in Table 7.
}
inequality can be attributed to rising betweenindustry sorting. Rising dispersion in industrylevel pay premia accounts directly for a smaller but still substantial 8.7 percent of the total rise in inequality in addition to its indirect contribution via sorting.
What firmlevel inequality is left after we account for industrylevel differences? Table 7 shows that, in the crosssection, less than onesixth ( 14.0 percent to 15.3 percent) of earnings dispersion occurs between firms in the same industry. Looking at growth, we find that 23.1 percent of variance growth is between firms, within industries. Of this, sorting accounts for 20.2 percent. Rising withinindustry, betweenfirm pay premia play a smaller role in rising inequality accounting for 2.9 percent of the increase in inequality. \({ }^{29}\)
Table 7 also describes withinfirm inequality. In the cross section, most of the variation in earnings is withinfirm rather than betweenfirmbut notably the share is declining from 64.6 percent in the first interval to 58.0 percent in the last. Although its share of overall earnings dispersion falls over time, rising withinfirm inequality accounts for a modest amount ( 14.9 percent) of the growth in inequality. This mostly reflects an increase in the dispersion of worker effects (18.8 percent), as the residual has a relatively small role offsetting inequality growth ( 3.9 percent). \({ }^{30}\)
We next turn to two alternative methods of analyzing the critical role of industry in rising inequality in recent decades. These methods allow us to assess the robustness of our AKM approach and also provide detail on the mechanisms by which worker characteristics may lead to earnings dispersion.
\section*{B. Sorting and Industry Premia: Alternative Approaches}
We use the CPSLEHD and OEWS to decompose rising inequality into related but distinct sorting and industry premia contributions. Details are in the online Appendix but we summarize the results here. \({ }^{31}\) Starting with the CPSLEHD data, we estimate the contributions of age by education, occupation, and industry using the specification from Hoffman, Lee, and Lemieux (2020). This allows us to decompose the variance of earnings into the contribution of withinindustry dispersion arising from observable (age by education and occupation) differences in earnings for workers in the same industry, betweenindustry pay premia, and betweenindustry sorting. The latter reflects the contribution of the covariance between average industry worker observable effects and industry premia along with the segregation of observable worker effects between industries.
Using the OEWS, we do not have personlevel information but we can estimate a related decomposition using the industry by occupation by time interval data. First, we estimate a model in which each occupation has an additive effect on earnings, as does each industry. We then decompose the variance of earnings into withinindustry occupation effects (and a withinindustry residual), betweenindustry premia, and
\footnotetext{
\({ }^{29}\) Details about industry contributions to the betweenfirm, withinindustry contributions are provided in online Appendix C.
\({ }^{30}\) These estimates are quite close to analogous results reported by Song et al. (2019) for a similar time period. We elaborate on this in online Appendix E.
\({ }^{31}\) The CPSLEHD decomposition is described in online Appendix D and that of the OEWS is in online Appendix G.
}
Table 8Variance Decomposition: AKM versus Human Capital versus Occupation
\begin{tabular}{lcccc}
\hline \hline Data source: \\
Specification: & \begin{tabular}{c}
LEHD \\
AKM \\
\((1)\)
\end{tabular} & \begin{tabular}{c}
CPSLEHD \\
AKM \\
\((2)\)
\end{tabular} & \begin{tabular}{c}
CPSLEHD \\
Human capital \\
\((3)\)
\end{tabular} & \begin{tabular}{c}
OEWS \\
Occupation \\
\((4)\)
\end{tabular} \\
\hline Variance, as percent of total \((\%)\) & & & & \\
Betweenindustry growth & 61.9 & 66.2 & 66.2 & 87.8 \\
\(\quad\) Sorting & 53.2 & 56.5 & 44.8 & 85.0 \\
\(\quad\) Pay premia & 8.7 & 9.6 & 21.4 & 2.8 \\
\hline
\end{tabular}
Notes: The first column is taken from Table 7. The second and third columns are from the linked CPSLEHD dataset, including workers with annual real earnings \(>\$ 3,770\), see discussion in text and online Appendix equation (D2) for definitions. The fourth column is from the OEWS dataset, see discussion in text and equation (G2) for definitions. In the first 3 columns total variance growth is measured at the person level. In the OEWS, total variance growth is measured at the industryoccupation level.
betweenindustry sorting (reflecting the covariance between industry premia and average industry occupation effects and the segregation of occupation effects across industries). \({ }^{32}\)
Table 8 compares the decomposition of the contribution of industry using the full LEHD with AKM as in Table 7 with the linked CPSLEHD analysis using the human capital equation approach. In taking advantage of the data infrastructure from both approaches, we can also integrate the AKM estimates from the full LEHD analysis into the linked CPSLEHD. Also, included in the table is the decomposition using the OEWS data.
In comparing the columns 1 and 2 in Table 8 we find that the overall contribution of industry is very similar in the full LEHD and the linked CPSLEHD. The same is true of the respective contributions of industry premia and sorting using AKM estimates. This finding is reassuring as the CPSLEHD linked data replicates the core patterns from the LEHD. It also highlights that the firm size restriction, imposed in column 1 but not in column 2, has a limited impact.
In comparing columns 2 and 3 in Table 8, we find that, whether using AKM or human capital equation based estimates, the contributions of industry premia and sorting are broadly similar. By construction, the overall contribution of industry is the same in columns 2 and 3 . Industry premia appear to be especially important when we use the human capital approach. In contrast, when we use AKM, betweenindustry sorting becomes more important. The AKM approach attributes more of interindustry earnings differentials to timeinvariant worker characteristics, some of which are not captured by our human capital framework. \({ }^{33}\)
The last column in Table 8 presents the results using the OEWS. The overall magnitudes of the change in inequality are not comparable to those from the first three columns given that this reflects the changing betweenindustry variance from industry by occupation data rather than from personlevel data. However, it is still striking that the overwhelming fraction (87.8 percent) of the increase in dispersion
\footnotetext{
\({ }^{32}\) See online Appendix G for details and formulas.
\({ }^{33}\) The covariance components from AKM versus the human capital approach are almost identical. It is the segregation component that is smaller with the human capital approach reflecting the smaller role of observable human capital variables compared to worker effects in AKM.
}
Table 9—Sources of BetweenIndustry Variance Growth: Top 30 Industries
\begin{tabular}{cccc}
\hline \multirow[b]{3}{*}{Industry group} & \multirow[b]{3}{*}{Total contribution to betweenindustry variance growth} & \multicolumn{2}{l}{Share of contribution explained by betweenindustry (\%)} \\
\hline & & Sorting & Premium \\
\hline & & (2) & (3) \\
\hline \multicolumn{4}{l}{Panel A. LEHD AKM decomposition} \\
\hline Top 19 highpaying & 0.041 & 86.2 & 13.8 \\
\hline Top 11 lowpaying & 0.033 & 84.2 & 15.8 \\
\hline \multicolumn{4}{l}{Panel B. CPSLEHD human capital decomposition} \\
\hline Top 19 highpaying & 0.039 & 63.5 & 36.5 \\
\hline Top 11 lowpaying & 0.030 & 66.1 & 33.9 \\
\hline \multicolumn{4}{l}{Panel C. OEWS occupationindustry decomposition} \\
\hline Top 19 highpaying & 0.016 & 77.3 & 22.7 \\
\hline Top 11 lowpaying & 0.008 & 81.8 & 18.2 \\
\hline
\end{tabular}
Notes: "LEHD AKM decomposition:" Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. See discussion in text and equation (5) for definitions. "CPSLEHD human capital decomposition:" Authors' tabulations of linked CPSLEHD dataset including workers with annual real earnings \(>\$ 3,770\), see discussion in text and equation (D2) for definitions. "OEWS occupationindustry decomposition:" Authors' tabulation of OEWS published aggregates, 281 fourdigit industries and 22 occupations. See discussion in text and equation (G2) for definitions.
in earnings in the published OEWS data derives from increasing betweenindustry dispersion. Furthermore, the betweenindustry contribution is dominated by sorting (high wage workers based on occupation increasingly sorting into high wage industries and working together and low wage workers increasingly sorting into low wage industries and working together).
\section*{C. Sorting and Industry Premia in the Top 30 Industries}
We now focus our attention on the top 30 industries that contribute to rising betweenindustry inequality taking advantage of the decompositions from the LEHD, CPSLEHD, and OEWS data. Table 9 presents the betweenindustry sorting and firm pay premia contributions from these sources for the top 30 industries. Both of these components contribute substantially to rising betweenindustry dispersion. However, it is apparent that sorting dominates. However, industry premia contribute directly and also via the covariance component of sorting. \({ }^{34}\) In interpreting Table 9, it is useful to recall the dominance of the top 30 industries in accounting for rising between industry dispersion from the alternative data sources and decompositions. The share of the betweenindustry increase in dispersion from 19962002 to 20122018 accounted for by the top 30 industries is 98.1 percent in the LEHD data, 105 percent in the CPSLEHD data, and 96.2 percent in the OEWS data.
\footnotetext{
\({ }^{34}\) The covariance component is large in all approaches and datasets as shown in the online Appendix, e.g., online Appendix Tables A4, D1, and G3.
}
\section*{VI. Industry Premia and Worker Effects in the Top 30 Industries}
In Table 3, we show that over our sample period all of the top 19 highpaying industries exhibit earnings per worker increases relative to the mean and ten of the top 11 lowpaying industries exhibit earnings per worker decreases relative to the mean. \({ }^{35}\) In this section, we show that this translates into rising industry premia and worker effects for the top 19 highpaying industries and falling industry premia and worker effects for the top 11 lowpaying industries.
At the industrylevel, the average change in earnings per worker is equal to the sum of the change in the average worker effect plus the sum of the change in the industry premia. For the AKM decomposition, we have
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g26.jpg?height=159&width=922&top_left_y=758&top_left_x=411)
We use analogous expressions for the CPSLEHD human capital decomposition and OEWS decomposition. \({ }^{36}\) We start with the LEHD AKM based decomposition of the change in average industry earnings, shown in Figure 5. For each of the 30 industries, we report the change in average worker effects and industry premia, where industries are ranked by the changes in the industry's average pay.
In 23 of the top 30 industries, the change in the average worker and industry premia have the same sign. The correlation in the top 30 industries between the changing industry premia and changing average worker effects is 0.53 . Notable contributors to this correlation are highpaying industries like Other Information Services with a very large increase its average worker effect of \(42.3 \log\) points ( 52.7 percent) and its industry premia of \(27.8 \log\) points ( 32.1 percent) and lowpaying industries like Grocery Stores with a large decline in its average worker effect of 11.5 log points ( 12.2 percent) and its industry premia of \(7.9 \log\) points ( 8.2 percent).
In 8 of our 19 highpaying industries (Figure 5, panel B), the average worker effect contributes more than \(10.0 \log\) points ( 10.5 percent) to that industry's change in average earnings, while the average firm effect contributes less than 5.0 \(\log\) points ( 5.1 percent). These include the two manufacturing industries, as well as Professional and Commercial Equipment and Supplies Merchant Wholesalers (4234), and Management of Companies and Enterprises (5511).
We show that the same patterns hold on average for the top 19 highpaying industries and top 11 lowpaying industries using the CPSLEHD human capital and OEWS occupation decompositions in Figure 6. First, panel A of the figure shows the average changes from the AKM based decomposition consistent with Figure 6. Panel B shows the results using the CPSLEHD data with the earnings decomposition
\footnotetext{
\({ }^{35}\) The one exception in the top 11 industries is the very lowpaying Employment Services (5613) industry that on average has earnings that are more than 50 percent lower than average and has a modest increase of about 2 percent.
\({ }^{36}\) The exact formulas for these CPSLEHD and OEWS decompositions are online Appendices D and G, respectively. The CPSLEHD decomposition is given as online Appendix equation (D3) and that of the OEWS decomposition is given as online Appendix equation (G3).
}
Panel A. Top 11 lowpaying industries
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g27.jpg?height=687&width=1073&top_left_y=267&top_left_x=367)
Panel B. Top 19 highpaying industries
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g27.jpg?height=679&width=1121&top_left_y=1013&top_left_x=321)
Figure 5. Change in IndustryLevel Average Worker and Firm Effects
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. See equation (6) for definitions. Average earnings are normalized by the overall average in each time interval. "Worker" combines the contribution of the worker effect with that of observable characteristics.
based upon observable human capital variables. The top 19 highpaying industries have positive changes on average in industry premia and human capital premia. The top 11 lowpaying industries have negative changes on average in each of these components. Panel C depicts the patterns for the OEWS decomposition. Recall the magnitudes are not directly comparable to the LEHD AKM or CPSLEHD human capital decompositions since the starting point is industry by occupation data. However, for the top 19 highpaying industries we find that on average increases
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g28.jpg?height=1034&width=1298&top_left_y=220&top_left_x=230)
Figure 6. Worker and Employer Components of the Change in Industry Average Earnings
Notes: Panel A: Authors' tabulations of LEHD microdata, including workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. "Worker" combines the contribution of the worker effect with that of observable characteristics. See equation (6) for definitions. Panel B: Authors' tabulations of linked CPSLEHD microdata, including workers with annual real LEHD earnings \(>\$ 3,770\) who match to the CPS. See online Appendix equation (D3) for definitions. Panel C: Authors' tabulations of published OEWS aggregates. See online Appendix equation (G3) for definitions.
in industry premia and occupation effects. For the top 11 lowpaying industries, we find on average decreases in occupation effects with modest offsetting industry premia effects. Notably the modest positive industry premia change effects on average in the top 11 lowpaying industries are much smaller than the positive industry premia changes on average in the top 19 highpaying industries.
\section*{VII. Taking Stock}
Our findings provide a distinct perspective on the role of polarization in increasing inequality (e.g., Autor, Katz, and Kearney 2006, 2008; Goos and Manning 2007; and Acemoglu and Autor 2011). This literature highlights the increasing bifurcation of the occupational skill and pay structure. Our findings show that this manifests itself via low and highpaying industries becoming more sorted on worker skill, becoming more skillsegregated across industries, and becoming relatively higher and relatively lower paid due to rising industry premia in highpaying sectors and falling industry premia in lowpaying sectors.
Panel A. Employment change by initial premium
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g29.jpg?height=477&width=660&top_left_y=264&top_left_x=224)
Panel C. Employment change by premium change
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g29.jpg?height=480&width=648&top_left_y=817&top_left_x=228)
Panel B. Premium change by initial premium
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g29.jpg?height=475&width=631&top_left_y=268&top_left_x=888)
Panel D. Worker effect change by initial premium
![](https://cdn.mathpix.com/cropped/2024_10_28_afef6c19081ade40df36g29.jpg?height=477&width=628&top_left_y=816&top_left_x=892)
Figure 7. Changes in Employment and Earnings (Top 30 Industries)
Notes: Authors' tabulations of LEHD microdata. Tabulations include workers with annual real earnings \(>\$ 3,770\) in EINs with 20 or more employees. Graphs plot local polynomial estimates of the variable on the vertical axis on the horizontal axis. Shaded regions indicate 95 percent confidence intervals. Changes compare 20122018 with 19962002.
This industry polarization is concentrated in relatively small set of industries in the tails of the industry earnings distribution. Figure 7 provides a visual summary of pattern of polarization in the top 30 industries that dominate rising earnings inequality. Both the low industry premia industries and high industry premia industries in the top 30 exhibit (upperleft panel) increases in employment shares with especially large employment share increases in the low premia industries. High initial premia industries exhibit increases in industry premia while the low premia industries exhibit declines in industry premia (upperright panel)—with high premia industries having especially large increases. Industries with declines in the industry premia (the low premia industries) and industries with increases in the industry premia (the high premia industries) have had increases in employment shares (lowerleft panel). Increases in worker effects are large in the high initial premia industries and declines in worker effects are present in the low premia industries.
In short, the uppertail component is due to rising industry premia without much change in employment; the lower tail is driven by falling industry premia and rising
employment. These findings suggest that different forces are at play. At the top, the patterns are consistent with a positive demand shift, yielding higher wages and slightly higher quantities. At the bottom, the patterns are consistent with a positive supply shift, yielding lower wages and higher quantities.
The results are thus related but distinct from the existing polarization literature, which argues that the growth of employment in lowwage sectors is in part driven by the decline of middleskill bluecollar production and whitecollar officeadministrative occupations. Relatedly, Acemoglu and Restrepo (2022) argue that skill groups that have been displaced by automation of routine tasks have seen falling earnings as these groups are reallocated to activities where they have weaker comparative advantage. These findings and perspective have a natural mapping to our findings on the growth of lowpaid service industries, which have substantially expanded even as their alreadylow pay premia have fallen further.
In emphasizing the role of industry, it is important to emphasize the prominent role for industry premia in our findings. Conceptually, industry premia (and more generally firm premia) are consistent with the presence of frictions or market imperfections (e.g., monopsony power) in labor markets. Industry and firm premia may also arise from other factors than market imperfections such as environments where the marginal product of labor depends on the unique product attributes of the firm or industry, see Tervio (2008). While we acknowledge there are issues for mapping such mechanisms to the estimated firm and industry effects, the robustness of our findings across different source data and methodologies suggests that it is important to understand the sources of the direct and indirect effects of the industry premia in accounting for rising earnings inequality. That is, whether we use AKM to control for worker effects, or a human capital approach to control for age, education, and occupation effects, or rich occupation data to control for occupation effects, we find a role for rising betweenindustry premia and especially a role for a rising covariance between those premia and alternative estimates of worker premia (AKM worker effects, education and occupation premia).
Viewed from this perspective, our findings rule out explanations of rising inequality that don't have a prominent role for industry premia through both direct and sorting effects. While we have noted our results are complementary to Acemoglu and Restrepo (2022), the structural model in their paper has competitive labor markets without frictions. Hence, their model implies that the prominent role of industry they find is associated with rising segregation of workers between industries without any role for industry premia and sorting associated with such premia. In other words, their model focuses on the changing composition of workers across industries without any interaction of that changing composition with industry premia. Our empirical results suggest that enhancing the structural model to permit such interactions is critical. One way to do this would be to consider the role of frictions and imperfect competition in labor markets for capturing this important aspect of variation in the data. This argument is not restricted to this specific mechanism (which is a form of skill biased technical change). Our results imply that any mechanism (whether driven by frictions, distortions, or some other mechanism) must account for the combined contribution of increased betweenindustry sorting and rising betweenindustry premia.
\section*{VIII. Conclusion}
Rising earnings inequality is dominated by rising betweenindustry inequality. High earnings workers are more likely to work in high earnings industries and with each other, and low earnings workers are more likely to work with in low earnings industries and with each other. This polarization of the industry earnings and employment structure is concentrated in a relatively small number of industries. About 10 percent of the 301 detailed fourdigit NAICS industries account for almost all of rising betweenindustry dispersion, while accounting for less than 40 percent of employment. These thirty industries are drawn from the top and bottom of the earnings distribution in terms of industrylevel averages. The top 19 highpaying industries exhibit increases in industry pay induced by both increases in industry premia and average worker effects. The top 11 lowpaying industries exhibit decreases in industry pay induced by both decreases in industry premia and average worker effects. These inferences are robust to identifying industry premia and worker effects using the AKM approach, a standard human capital approach using worker observables including age, education, and occupation, and using high quality industry by occupation data.
The top 10 percent of industries that account for virtually all of rising betweenindustry inequality are not randomly spread across the distribution of industries but concentrated in specific industry clusters in the tails of the earnings distribution. At the high end, dominant industries are drawn from hightech and STEM intensive industries, finance, mining, and selected industries in health. At the low end, dominant industries are drawn from selected industries in retail and health. The top 30 industries are in industry clusters that have exhibited structural transformations that have been the subject of independent study. Notably absent are the vast majority of industries in manufacturing. Part of the polarization story is the decline in production workers in manufacturing and the rise of employment and decline in earnings in key low pay service sectors.
The dominance of industry effects is closely linked to the rising importance of mega \((10,000+\) ) firms in the US economy. The increasing share of employment accounted for by mega firms is concentrated in the thirty fourdigit industries that account for virtually all of rising betweenindustry dispersion. This rising share of employment at mega firms is accompanied by a declining sizeearnings premium in the 11 lowpaying industries. For mega firms in the 19 highpaying industries in the top 30, earnings premia rise sharply relative to other industries (albeit not as rapidly as other large but not mega firms in these industries).
We find there is a close connection between changes in the occupation distribution and our top 30 industries. Most of the increase in employment in toppaying occupations is accounted for by our 19 toppaying industries, while all of the increase in employment in lowpaying occupations is in our 11 lowpaying industries. For the toppaying industries, there is a substantial increase in earnings differentials for the toppaying occupations. Likewise there is a substantial decline in earnings differentials for the lowpaying occupations in our lowpaying industries.
In the top 19 highpaying industries there is a sharp increase in the share of workers with a bachelor's or advanced degree with accompanying declines in workers without such degrees. In the top 11 lowpaying industries there is no such shift in
the distribution. On average, this distribution is dominated by workers with a high school diploma or less.
Our findings imply that understanding rising earnings inequality during the last several decades requires understanding the evolution of how firms organize themselves in a relatively small set of industries. Moreover, since it is the betweenindustry contribution that dominates, the common effects of reorganization across firms in the same industry matter. Many mechanisms such as changing technology, market structure, and globalization likely underlie rising earnings inequality. The focus of future research on the impact of such changes on rising earnings inequality should be on the uneven and concentrated impact of such mechanisms across industries. In addition, since betweenindustry sorting and industry premia play important roles, the mechanisms must interact with the factors that yield such effects including possibly frictions and imperfect competition in labor markets.
\section*{REFERENCES}
Abowd, John, Francis Kramarz, and David Margolis. 1999. "High Wage Workers and High Wage Firms." Econometrica 67 (2): 251333.
Abowd, John M., Kevin L. McKinney, and Nellie L. Zhao. 2018. "Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked EmployerEmployee Data." Journal of Labor Economics 36 (S1): S183300.
Abowd, John, Bryce Stephens, Lars Vilhuber, Fredrik Andersson, Kevin McKinney, Marc Roemer, and Simon Woodcock. 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators." In Producer Dynamics: New Evidence from Micro Data, edited by Timothy Dunne, J. Bradford Jensen, and Mark J. Roberts, 149230. Chicago: University of Chicago Press.
Acemoglu, Daron, and David H. Autor. 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings." In Handbook of Labor Economics, Vol. 4, edited by Orley Ashenfelter and David Card, 10431171. Amsterdam: ElsevierNorth Holland.
Acemoglu, Daron, and Pascual Restrepo. 2022. "Tasks, Automation, and the Rise in US Wage Inequality." Econometrica \(90(5): 19732016\).
Autor David, David Dorn, Lawrence F. Katz, Christina C. Patterson, and John Van Reenen. 2020. "The Fall of the Labor Share and the Rise of Superstar Firms." Quarterly Journal of Economics \(135(2): 645709\).
Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2006. "The Polarization of the U.S. Labor Market." American Economic Review 96 (2): 18994.
Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. "Trends in U.S. Wage Inequality: Revising the Revisionists." Review of Economics and Statistics 90 (2): 30023.
 Barth, Erling, Alex Bryson, James C. Davis, and Richard Freeman. 2016. "It's Where You Work: Increases in the Dispersion of Earnings across Establishments and Individuals in the United States." Journal of Labor Economics 34 (S2): S6797.
Bloom, Nicholas, Fatih Guvenen, Benjamin S. Smith, Jae Song, and Till von Wachter. 2018. "The Disappearing LargeFirm Premium." AEA Papers and Proceedings 108: 31722.
Bureau of Economic Analysis. 2021. "Personal Consumption Expenditures Price Index." Bureau of Economic Analysis. https://www.bea.gov/data/personalconsumptionexpenditurespriceindex (accessed November 22, 2021).
Bureau of Labor Statistics. 2022. "Occupation Employment and Wage Survey Data." Bureau of Labor Statistics. https://www.bls.gov/oes/tables.htm (accessed July 26, 2022).
Bureau of Labor Statistics. 2022. "Quarterly Census of Employment and Wages." https://https://www. bls.gov/cew/ (accessed July 26, 2022).
Bonhomme, Stéphane, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad and Bradley Setzler. 2022. "How Much Should We Trust Estimates of Firm Effects and Worker Sorting?" Journal of Labor Economics 41 (2): 291322.
Briskar, Juraj, José Rodríguez Mora, Edoardo di Porto, and Cristina Tealdi. 2022. "Decomposition of Italian Inequality." Unpublished.
 Card, David, Ana Cardoso, and Patrick Kline. 2016. "Bargaining, Sorting, and the Gender Wage Gap: Quantifying the Effect of Firms on the Relative Pay of Women." Quarterly Journal of Economics 131 (2): 63386.
Card, David, Jorg Heining, and Patrick Kline. 2013. "Workplace Heterogeneity and the Rise of West German Wage Inequality." Quarterly Journal of Economics 128 (3): 9671015.
Card, David, Jesse Rothstein, and Moises Yi. 2024. "Industry Wage Differentials: A Firm Based Approach." Journal of Labor Economics 42 (S1): S11S59.
Davis, Steve J., and John Haltiwanger. 1991. "Wage Dispersion Between and Within U.S. Manufacturing Plants, 19631986." Brookings Papers: Microeconomics 21: 11520.
Dey, Matthew, Susan Houseman, and Anne Polivka. 2010. "What Do We Know About Contracting Out in the United States? Evidence from Household and Establishment Surveys." In Labor in the New Economy, edited by Katharine Abraham, James Spletzer, and Michael Harper, 267304. Chicago: University of Chicago Press.
Dorn, David, Johannes Schmeider, James Spletzer, and Lee Tucker. 2023. "Domestic Outsourcing in the United States." Unpublished.
Dunne, Timothy, Lucia Foster, John Haltiwanger, and Ken Troske. 2004. "Wage and Productivity Dispersion in United States Manufacturing: The Role of Computer Investment." Journal of Labor Economics 22 (2): 397429.
Fernald, John. 2014. "Productivity and Potential Output before, during, and after the Great Recession." NBER Macroeconomics Annual 29 (1): 151.
Foster, Lucia, John Haltiwanger, and C. J. Krizan. 2006. "Market Selection, Reallocation, and Restructuring in the U.S. Retail Trade Sector in the 1990s." Review of Economics and Statistics 88 (4): 74858.
Goos, Maarten, and Alan Manning. 2007. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain." Review of Economics and Statistics 89 (1): 11833.
—Guimarães, Paulo, and Pedro Portugal. 2010. "A Simple Feasible Procedure to Fit Models with HighDimensional Fixed Effects." Stata Journal 10 (4): 62849.
Haltiwanger, John, Henry Hyatt, and James Spletzer. 2023. "Increasing Earnings Inequality: Reconciling Evidence from Survey and Administrative Data." Journal of Labor Economics 41 (S1): S6193.
Haltiwanger, John, Henry Hyatt, and James Spletzer. 2024. Data and Code for: "Rising Top, Falling Bottom: Industries and Rising Wage Inequality." Nashville, TN: American Economic Association; distributed by Interuniversity Consortium for Political and Social Research, Ann Arbor, MI. https://doi.org/10.3886/E198751V1.
Haltiwanger, John, and James Spletzer. 2020. "Betweenfirm changes in earnings inequality: the dominant role of industry effects." NBER Working Paper 26786.
Haltiwanger, John, and James Spletzer. 2022. "Rising BetweenFirm Inequality and Declining Labor Market Fluidity: Evidence of a Changing Job Ladder." In Measuring Distribution and Mobility of Income and Wealth, edited by Raj Chetty, John Friedman, Janet Gornick, Barry Johnson, and Arthur Kennickell, 4567. Chicago: University of Chicago Press.
Hecker, Daniel. 2005. "HighTechnology Employment: A NAICSBased Update." Monthly Labor Review 128 (7): 5772.
Hoffman, Florian, David S. Lee, and Thomas Lemieux. 2020. "Growing Income Inequality in the United States and Other Advanced Economies." Journal of Economic Perspectives 34 (4): 5278.
Hoffman, Florian, David S. Lee, and Thomas Lemieux. 2020. Replication data and code for: "Growing Income Inequality in the United States and Other Advanced Economies" Nashville, TN: American Economic Association; distributed by Interuniversity Consortium for Political and Social Research, Ann Arbor, MI. https://doi.org/10.3886/E122201V1.
Krueger, Alan, and Lawrence Summers. 1988. "Efficiency Wages and the InterIndustry Wage Structure." Econometrica 56 (2): 25993.
Oliner, Stephen, Daniel Sichel and Kevin Stiroh. 2007. "Explaining a Productive Decade." Brookings Papers on Economic Activity 37 (2).
Ruggles, Steven, Sarah Flood, Matthew Sobek, Daniel Backman, Annie Chen, Grace Cooper, Stephanie Richards, Renae Rodgers, and Megan Schouweiler. "IPUMS USA: Version 15.0 [dataset]." IPUMS. Minneapolis, MN: IPUMS, 2024. https://doi.org/10.18128/D010.V15.0.
Song, Jae, David J. Price, Fatih Guvenen, Nicholas Bloom, and Till von Wachter. 2019. "Firming Up Inequality." Quarterly Journal of Economics 134 (1): 150.
 Tervio, Marko. 2008. "The Difference That CEOs Make: An Assignment Model Approach." American Economic Review 98 (3): 64268.
US Census Bureau. 2021. "Current Population Survey (CPS)  Annual Social and Economic Supplements (ASEC) microdata." US Census Bureau. https://www.census.gov/data/datasets/timeseries/ demo/cps/cpsasec.html (accessed November 15, 2021).
US Census Bureau. 2023. "Longitudinal Business Database (LBD) microdata." US Census Bureau. https://www.census.gov/programssurveys/ces/data/restrictedusedata/longitudinalbusinessdatabase.html.
US Census Bureau. 2023. "LongitudinalEmployer Household Dynamics (LEHD) micro data." US Census Bureau. https://lehd.ces.census.gov/data/lehdsnapshotdoc/latest/.
Walker, Ed. 1997. "The Census Bureau's Business Register: Basic Features and Quality Issues." Paper presented at the Joint Statistical Meetings of the American Statistical Association, Anaheim, CA.