# S6. Relation between top earner isolation and wage inequality measures

How do top earner isolation measures relate with classical inequality measures? To understand further this relationship, we use both simulations and econometric exercises.

## Simulation

To explore the relation between inequality and exposure measures, we simulate changes in the between, within and total variance of the French BTS 2016 log-wage distribution. For this purpose, we modify the $\beta$ parameters in the following formula:

$$ lwage_{ij}^* = E_{ij}(lwage_{ij}) + \sqrt{\beta_{tot}} \left[ \sqrt{\beta_{wi}} [lwage_{ij} - E_i(lwage_{ij})] + \sqrt{\beta_{be}} [E_i(lwage_{ij}) - E_{ij}(lwage_{ij})] \right] $$

where $E_k$ stands for the arithmetic mean along the $k$ dimension.

For instance, in line 1, we set $\beta_{tot}$ parameter to 1.1 and other $\beta$ to 1.

<table><caption>**Table S6.1.** Variation in wage variance and impact on top 10% isolation measures</caption><thead><tr><th></th><th>Total variance</th><th>Within variance</th><th>Between variance</th><th>Between var. share</th><th>Top 10% isolation</th></tr></thead><tbody><tr><td><strong>0. Baseline</strong></td><td><strong>0.272</strong></td><td><strong>0.132</strong></td><td><strong>0.140</strong></td><td><strong>0.514</strong></td><td><strong>36.2%</strong></td></tr><tr><td>1. +10% in total variance</td><td>0.299</td><td>0.145</td><td>0.154</td><td>0.514</td><td>36.2%</td></tr><tr><td>2. -10% in total variance</td><td>0.244</td><td>0.119</td><td>0.126</td><td>0.514</td><td>36.2%</td></tr><tr><td>3. +10% in within variance</td><td>0.285</td><td>0.145</td><td>0.140</td><td>0.490</td><td>34.6%</td></tr><tr><td>4. -10% in within variance</td><td>0.258</td><td>0.119</td><td>0.140</td><td>0.540</td><td>38.1%</td></tr><tr><td>5. +10% in between variance</td><td>0.286</td><td>0.132</td><td>0.154</td><td>0.538</td><td>37.9%</td></tr><tr><td>6. -10% in between variance</td><td>0.258</td><td>0.132</td><td>0.126</td><td>0.488</td><td>34.5%</td></tr><tr><td>7. -10% in within variance and -5% in between variance</td><td>0.251</td><td>0.119</td><td>0.133</td><td>0.528</td><td>37.2%</td></tr><tr><td>8. -1% for all wages in establishments with no top earners</td><td>0.291</td><td>0.131</td><td>0.159</td><td>0.548</td><td>36.2%</td></tr><tr><td>9. +1% for wages both in bottom 50% and top 10% workplaces</td><td>0.264</td><td>0.133</td><td>0.131</td><td>0.495</td><td>40.0%</td></tr><tr><td>10. -1% for wages both in bottom 50% and top 10% workplaces</td><td>0.283</td><td>0.130</td><td>0.153</td><td>0.541</td><td>32.7%</td></tr></tbody></table>

Note: This simulation is based on the French BTS 2016. n=14,202,911

This exercise shows that a simple change in total variance does not affect top earner isolation (Table S6.1, rows 1 and 2). For a change in wage variance to do so, we need a decoupling in the evolution of the between and within components. *Ceteris paribus*, an increase in within workplace wage variance decreases top earner isolation (rows 3 and 4) and an increase in between workplace wage variance increases it (rows 5 and 6). However, our segregation
measure can also increase in a context of declining wage inequality, for example, if the decline in the within component is more pronounced than that in the between component (row 7). Thus, our exercise shows that our measure varies globally with the between variance share (column 4).

We can create even more complex cases where between variance share and top earner isolation evolve in opposite direction (rows 8 to 10). The reason for this discrepancy is that the between variance share is a measure based on the whole distribution, while the top earner isolation index only focuses on the top. The advantage of our exposure measures is precisely that they enable us to estimate an accordion phenomenon, with some extreme bellows spreading while others are regrouping.

## Econometric approach

In Table S6.2, we use our country-level estimates of top earner segregation and various classical measures of inequality to measure the correlations between the two sets of variables. To do this, we use a classical two-way fixed effects regression with country and year fixed effects. This exercise shows that the top 10% income share is not correlated with top 10% isolation. We find a correlation of +0.1 between top earner isolation and log earnings variance. This is largely driven by the +0.25 correlation between our segregation index and the between-workplace log variance. Conversely, there is no correlation with the within-workplace log variance. As one would expect, the share of between workplace variance is the inequality variable most related to top earner isolation: one within-country standard deviation of the former is associated with 0.56 variation of the latter.
<table><caption>**Table S6.2.** Impact of various inequality measure on top 10% isolation at work</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Top 10% earnings share</td><td>0.034<br>(0.058)</td><td></td><td></td><td></td><td></td></tr><tr><td>Total log earnings variance</td><td></td><td>0.097<sup>**</sup><br>(0.042)</td><td></td><td></td><td></td></tr><tr><td>Within-workplace log earnings variance</td><td></td><td></td><td>-0.028<br>(0.025)</td><td></td><td></td></tr><tr><td>Between-workplace log earnings variance</td><td></td><td></td><td></td><td>0.250<sup>***</sup><br>(0.064)</td><td></td></tr><tr><td>Between-workplace variance share</td><td></td><td></td><td></td><td></td><td>0.557<sup>***</sup><br>(0.050)</td></tr><tr><td>Number of observations</td><td>251</td><td>251</td><td>251</td><td>251</td><td>251</td></tr><tr><td>R<sup>2</sup></td><td>0.674</td><td>0.681</td><td>0.674</td><td>0.718</td><td>0.808</td></tr><tr><td>Number of country fixed effects</td><td>12</td><td>12</td><td>12</td><td>12</td><td>12</td></tr><tr><td>Number of year fixed effects</td><td>30</td><td>30</td><td>30</td><td>30</td><td>30</td></tr></tbody></table>

Note: Robust standard errors clustered by years in parentheses. Dependent and independent variables are country-demeaned and standardized. Hence, one within-country standard deviation of top 10% earnings share increase by 0.03 within-country top 10% isolation standard deviation.

***p<0.01, **p<0.5, *p<0.1
S7. The role of earnings increase and employee mobility in top earner segregation evolution

Evolution of top earner isolation combines change in workplace composition and between workplace heterogeneity in wage increase. Following Manduca (2019), which measures the respective role of sorting and wage evolution in the increase of US spatial earning segregation, we try to decompose for one country, France, the role of mobility and wage increase on our segregation measure.

For this purpose, we take advantage of our panelization of the BTS year-files (cf. Appendix A1) in order to estimate the counterfactual top earner isolation measure if no wage change had happened. We proceed as follows.

1) We use $t-3$ wage ranks for wage-earners who are both present in year $t$ and $t-3$.

2) We use $t$ wage ranks for wage-earners who are only present in year $t$.

3) We append these two rank distributions and recompute a new one in order for the new rank distribution to be fully consistent.

This enables us in table S7.1 to compare the real isolation measures with the counterfactual one, had wages not changed from year $t-3$ to year $t$.
<table><caption>**Table S7.1.** Comparison of true and counterfactual top earner isolation measures</caption><thead><tr><th>Year</th><th>Isolation</th><th>Counterfactual isolation</th><th>3-year evolution</th><th>3-year counterfactual evolution</th><th>Share of evolution</th></tr></thead><tbody><tr><td colspan="6" style="text-align: center;">Panel A. Top 10%</td></tr><tr><td>2002</td><td>30.77%</td><td></td><td></td><td></td><td></td></tr><tr><td>2005</td><td>31.76%</td><td>31.14%</td><td>0.99%</td><td>0.37%</td><td>37%</td></tr><tr><td>2008</td><td>33.32%</td><td>32.10%</td><td>1.56%</td><td>0.34%</td><td>22%</td></tr><tr><td>2011</td><td>35.11%</td><td>34.05%</td><td>1.79%</td><td>0.73%</td><td>41%</td></tr><tr><td colspan="6" style="text-align: center;">Panel B. Top 1%</td></tr><tr><td>2002</td><td>12.94%</td><td></td><td></td><td></td><td></td></tr><tr><td>2005</td><td>13.74%</td><td>13.01%</td><td>0.81%</td><td>0.07%</td><td>9%</td></tr><tr><td>2008</td><td>15.51%</td><td>13.90%</td><td>1.76%</td><td>0.16%</td><td>9%</td></tr><tr><td>2011</td><td>16.63%</td><td>15.67%</td><td>1.12%</td><td>0.16%</td><td>15%</td></tr></tbody></table>

Note: This simulation is based on the French BTS 2002, 2005, 2008, 2011, years for which we can rely on the panelization of the full BTS and during which the increase in top earner isolation is more pronounced (after 2012, top earner isolation stabilizes after 2012).

This exercise shows that change in coworkers' wages accounts for two thirds and between workplace sorting of coworkers for one third of French top 10% isolation evolution between 2002 and 2011. When we shift to the top 1% (Panel B), the role of wage change is even stronger (89%) and that of sorting lower (11%) (average of the last column).

This exercise gives a first decomposition of sorting over heterogeneity in wage increase. However, we must keep in mind that its rests on the fact that we conventionally use *t* wage rank as the counterfactual wage of wage-earners who were not present in year *t*-3. Other working hypotheses could be made yielding a different decomposition of sorting over wage increase.
S8. Supplementary tables

<table><caption>**Table S8.1.** Top 10% Isolation trend controlling for geography, industry, and workplaces</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Canada × year</td><td>0.137<sup>***</sup><br>(0.013)</td><td>0.099<sup>***</sup><br>(0.009)</td><td></td><td>0.151<sup>***</sup><br>(0.005)</td><td>0.112<sup>***</sup><br>(0.001)</td><td>0.122<sup>***</sup><br>(0.001)</td></tr><tr><td>Denmark × year</td><td>0.143<sup>***</sup><br>(0.008)</td><td>0.173<sup>***</sup><br>(0.015)</td><td>0.141<sup>***</sup><br>(0.025)</td><td>0.066<sup>***</sup><br>(0.012)</td><td>-0.036<br>(0.027)</td><td>0.007<br>(0.018)</td></tr><tr><td>Norway × year</td><td>0.010<br>(0.017)</td><td>-0.007<br>(0.023)</td><td>-0.0467<br>(0.046)</td><td>0.015<br>(0.017)</td><td>-0.165<sup>***</sup><br>(0.038)</td><td>-0.313<sup>***</sup><br>(0.033)</td></tr><tr><td>Sweden × year</td><td>0.198<sup>***</sup><br>(0.017)</td><td>0.132<sup>***</sup><br>(0.014)</td><td>0.129<sup>***</sup><br>(0.023)</td><td>0.152<sup>***</sup><br>(0.015)</td><td>0.142<sup>***</sup><br>(0.017)</td><td>0.020<br>(0.017)</td></tr><tr><td>France × year</td><td>0.431<sup>***</sup><br>(0.014)</td><td>0.412<sup>***</sup><br>(0.013)</td><td>0.389<sup>***</sup><br>(0.018)</td><td>0.374<sup>***</sup><br>(0.015)</td><td>0.271<sup>***</sup><br>(0.018)</td><td>0.175<sup>***</sup><br>(0.017)</td></tr><tr><td>Netherlands × year</td><td>0.272<sup>***</sup><br>(0.019)</td><td>0.261<sup>***</sup><br>(0.015)</td><td>0.234<sup>***</sup><br>(0.064)</td><td>0.265<sup>***</sup><br>(0.015)</td><td>0.222<sup>***</sup><br>(0.047)</td><td>0.264<sup>***</sup><br>(0.033)</td></tr><tr><td>Germany × year</td><td>0.278<sup>***</sup><br>(0.023)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spain × year</td><td>0.327<sup>***</sup><br>(0.029)</td><td>0.286<sup>***</sup><br>(0.025)</td><td>0.287<sup>***</sup><br>(0.058)</td><td>0.285<sup>***</sup><br>(0.022)</td><td>0.247<sup>***</sup><br>(0.053)</td><td>0.433<sup>***</sup><br>(0.046)</td></tr><tr><td>Czechia × year</td><td>0.140<sup>***</sup><br>(0.028)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hungary × year</td><td>0.209<sup>***</sup><br>(0.027)</td><td>0.220<sup>***</sup><br>(0.023)</td><td>0.201<sup>***</sup><br>(0.053)</td><td>-0.194<sup>***</sup><br>(0.016)</td><td>-0.110<sup>*</sup><br>(0.058)</td><td>-0.084<sup>*</sup><br>(0.046)</td></tr><tr><td>Korea × year</td><td>0.446<sup>***</sup><br>(0.019)</td><td></td><td></td><td>0.451<sup>***</sup><br>(0.017)</td><td>0.432<sup>***</sup><br>(0.117)</td><td>0.459<sup>***</sup><br>(0.037)</td></tr><tr><td>Japan × year</td><td>0.335<sup>***</sup><br>(0.017)</td><td>0.342<sup>***</sup><br>(0.013)</td><td>0.353<sup>***</sup><br>(0.019)</td><td>0.313<sup>***</sup><br>(0.017)</td><td>0.271<sup>***</sup><br>(0.063)</td><td>0.261<sup>***</sup><br>(0.050)</td></tr><tr><td>Fixed effects</td><td></td><td>Country × Aggregate region</td><td>Detailed region</td><td>Country × Aggregate industry</td><td>Detailed industry</td><td>Workplace</td></tr></tbody></table>

Note: In models 1, 2, and 4, we opted for pooled regressions. In models 3, 5, and 6, each line is a separate regression with robust standard errors clustered in parentheses at the workplace level (except for Canada where clustering was not available). Robust standard errors clustered at the year level in model 1, at the country and year levels in models 2, and 4.

<footnote>***p<0.01, **p<0.5, *p<0.1.</footnote>
<table><caption>**Table S8.2.** Top 1% workplace isolation trends controlling for aggregate sector</caption><thead><tr><th></th><th>Workpl.<br>Isolation<br>Trend</th><th>Average<br>categorical<br>isolation</th><th>Category<br>isolation<br>Trend</th><th>Within cat.<br>Isolation<br>Trend</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A. Category: 2 regions (11 countries)</strong></td></tr><tr><td>Global financial center × year</td><td>0.219<sup>***</sup><br>(0.042)</td><td>2.6%</td><td>0.008<br>(0.005)</td><td>0.210<sup>***</sup><br>(0.038)</td></tr><tr><td>Rest of the country × year</td><td>0.064<br>(0.037)</td><td>0.8%</td><td>-0.002<br>(0.001)</td><td>0.066<sup>*</sup><br>(0.036)</td></tr><tr><td colspan="5"><strong>Panel B. Category: Industry (10 countries)</strong></td></tr><tr><td>Manufacturing × year</td><td>0.125<br>(0.071)</td><td>1.3%</td><td>0.013<sup>**</sup><br>(0.006)</td><td>0.112<br>(0.067)</td></tr><tr><td>Wholesale and retail trade × year</td><td>0.080<br>(0.049)</td><td>1.0%</td><td>-0.009<sup>**</sup><br>(0.003)</td><td>0.089<sup>*</sup><br>(0.047)</td></tr><tr><td>Transportation and storage × year</td><td>-0.069<br>(0.149)</td><td>1.0%</td><td>-0.016<br>(0.013)</td><td>-0.054<br>(0.140)</td></tr><tr><td>Finance × year</td><td>0.273<sup>***</sup><br>(0.074)</td><td>4.7%</td><td>0.053<sup>**</sup><br>(0.018)</td><td>0.220<sup>**</sup><br>(0.074)</td></tr><tr><td>Support service activities × year</td><td>0.039<br>(0.076)</td><td>1.0%</td><td>-0.039<sup>***</sup><br>(0.010)</td><td>0.078<br>(0.069)</td></tr><tr><td>Other activities × year</td><td>0.033<br>(0.081)</td><td>1.6%</td><td>-0.014<br>(0.012)</td><td>0.047<br>(0.070)</td></tr><tr><td>FE (country × Aggregate sector)</td><td>Yes</td><td></td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: In the first column, the dependent variable is top earner workplace isolation, in the third top earner category isolation and in the last top earner within category workplace isolation, calculated as the difference of the two previous. In the second column, average categorical isolation is calculated in 2011, the last year when all country are present.

***p<0.01, **p<0.5, *p<0.1. Robust standard errors clustered both at the country and year in parentheses.
# S9. Supplementary material on the impact of workplace restructuring on top earner isolation in France

## Complementary information on estimation

In the case of outsourcing events, we use the first workplace outsourcing event as the treatment, we drop workplaces' years when a second treatment has occurred (very rare), and we use “never treated” and “not yet treated” as counterfactual groups. In the case of layoffs, we use a multi-event framework (Sandler and Sandler 2014).

Some heterogeneity in the treatment effect for different levels of treatment intensity can also lead to biases in the estimation of the average treatment effect (de Chaisemartin and D'Haultfoeuille 2023). We did not correct for this problem. However, we think this issue remains minor. It only affects estimations where we weight events by their magnitude and estimations with or without multiplying by treatment intensity give the same qualitative results (cf. Figures 6 and S9.1). As the events we study are rare, treatment effects are mostly driven by comparison between treated and non-treated (as shown by the similarity of result of stacked and non-stacked regressions).

## *Measuring the impact of an event on segregation both in departure and arrival workplaces*

Generally, we can measure the impact of restructuring events on segregation only in workplaces where they occur. Hence, we don't have a full picture of the distributional impact of these reorganizations. We overlook the fact these events also impact other workplaces, via workers' moves.

The database on outsourcing and subsidiarization enable us to address this issue. We can combine departure and arrival events and measure their joint effect. We do such exercise on outsourcing and subsidiarization events (Table S9.1 models 5, 6, 10 and 11).

## *Estimates*
<table><caption>**Table S9.1.** Impact of outsourcing and subsidiarizing events (2001-2017) on top 10% isolation in France</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th><th>Model 5</th><th>Model 6</th><th>Model 7</th><th>Model 8</th><th>Model 9</th><th>Model 10</th><th>Model 11</th></tr></thead><tbody><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> - 4)]</td><td>-0.39<br>(1.07)</td><td>-1.56<br>(1.88)</td><td>-0.42<br>(1.10)</td><td>-0.35<br>(1.03)</td><td>-0.85<br>(1.01)</td><td>-2.25<sup>**</sup><br>(1.15)</td><td>-0.48<br>(3.11)</td><td>-0.85<br>(3.32)</td><td>0.41<br>(3.11)</td><td>-3.03<br>(2.71)</td><td>-1.76<sup>**</sup><br>(0.78)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -3)]</td><td>-0.82<br>(0.73)</td><td>-1.48<br>(1.69)</td><td>-0.76<br>(0.75)</td><td>-0.63<br>(0.66)</td><td>-1.46<sup>**</sup><br>(0.72)</td><td>0.09<br>(0.53)</td><td>-2.12<br>(1.92)</td><td>-1.97<br>(2.02)</td><td>-1.55<br>(1.75)</td><td>-2.29<br>(1.88)</td><td>1.86<br>(1.21)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -2)]</td><td>0.07<br>(0.95)</td><td>-1.54<br>(1.76)</td><td>0.03<br>(0.96)</td><td>0.11<br>(0.92)</td><td>-0.83<br>(0.86)</td><td>-0.41<br>(0.27)</td><td>-0.22<br>(2.56)</td><td>-0.15<br>(2.67)</td><td>-0.22<br>(2.57)</td><td>-2.12<br>(1.85)</td><td>-0.83<br>(0.52)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -1)]</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>S<sub>event</sub> × [year = year<sub>event</sub>]</td><td>2.39<sup>***</sup><br>(0.81)</td><td>0.11<br>(0.39)</td><td>2.37<sup>***</sup><br>(0.82)</td><td>1.79<sup>**</sup><br>(0.84)</td><td>2.00<sup>***</sup><br>(0.66)</td><td>0.56<sup>**</sup><br>(0.26)</td><td>10.96<sup>*</sup><br>(5.92)</td><td>10.73<sup>*</sup><br>(6.00)</td><td>7.5<br>(5.47)</td><td>8.31<sup>*</sup><br>(4.85)</td><td>2.31<sup>*</sup><br>(1.21)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 1)]</td><td>2.92<sup>***</sup><br>(0.89)</td><td>1.4<br>(0.96)</td><td>3.00<sup>***</sup><br>(0.90)</td><td>2.21<sup>**</sup><br>(0.90)</td><td>2.00<sup>**</sup><br>(1.00)</td><td>0.54<sup>*</sup><br>(0.29)</td><td>36.82<sup>***</sup><br>(10.32)</td><td>37.77<sup>***</sup><br>(10.72)</td><td>18.29<br>(11.52)</td><td>13.69<sup>***</sup><br>(4.04)</td><td>7.91<sup>***</sup><br>(1.35)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 2)]</td><td>2.88<sup>***</sup><br>(0.72)</td><td>3.03<sup>***</sup><br>(0.95)</td><td>2.90<sup>***</sup><br>(0.73)</td><td>2.44<sup>***</sup><br>(0.81)</td><td>1.49<sup>**</sup><br>(0.76)</td><td>0.82<sup>**</sup><br>(0.33)</td><td>30.06<sup>***</sup><br>(8.03)</td><td>31.49<sup>***</sup><br>(8.67)</td><td>16.23<sup>*</sup><br>(8.81)</td><td>13.10<sup>***</sup><br>(4.06)</td><td>8.81<sup>***</sup><br>(1.83)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 3)]</td><td>3.37<sup>***</sup><br>(1.22)</td><td>3.11<sup>***</sup><br>(0.78)</td><td>3.51<sup>***</sup><br>(1.23)</td><td>2.66<sup>***</sup><br>(0.98)</td><td>1.42<br>(1.27)</td><td>1.05<sup>***</sup><br>(0.39)</td><td>36.07<sup>***</sup><br>(9.76)</td><td>40.91<sup>***</sup><br>(10.55)</td><td>23.75<sup>**</sup><br>(10.30)</td><td>11.34<sup>**</sup><br>(4.90)</td><td>9.31<sup>***</sup><br>(2.15)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 4)]</td><td>3.11<sup>***</sup><br>(1.07)</td><td>4.76<sup>***</sup><br>(1.27)</td><td>3.25<sup>***</sup><br>(1.10)</td><td>1.92<sup>**</sup><br>(0.90)</td><td>1.98<sup>**</sup><br>(0.80)</td><td>1.30<sup>***</sup><br>(0.42)</td><td>32.73<sup>***</sup><br>(10.63)</td><td>37.24<sup>***</sup><br>(11.37)</td><td>17.23<br>(11.30)</td><td>11.48<sup>**</sup><br>(5.43)</td><td>9.00<sup>***</sup><br>(1.60)</td></tr><tr><td>Size control</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Establishment fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>4,516,193</td><td>4,513,329</td><td>3,228,358</td><td>3,228,358</td><td>3,476,877</td><td>3,480,314</td><td>4,516,193</td><td>3,228,358</td><td>3,228,358</td><td>3,476,877</td><td>3,480,314</td></tr><tr><td>R<sup>2</sup></td><td>0.92</td><td>0.92</td><td>0.92</td><td>0.92</td><td>0.94</td><td>0.94</td><td>0.92</td><td>0.92</td><td>0.92</td><td>0.94</td><td>0.94</td></tr><tr><td>Establishment (fixed effects)</td><td>828,501</td><td>828,490</td><td>808,923</td><td>808,923</td><td>884,927</td><td>891,511</td><td>828,501</td><td>808,923</td><td>808,923</td><td>884,927</td><td>891,511</td></tr><tr><td>Event: Departure (D) or Depature and arrival (D&A)</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D&A</td><td>D&A</td><td>D</td><td>D</td><td>D</td><td>D&A</td><td>D&A</td></tr><tr><td>Destination of the flow</td><td>Low-skill outsourced sector</td><td>Low & high skill outsourced sector</td><td>Low-skill outsourced sector</td><td>Low-skill outsourced sector</td><td>Low-skill outsourced sector</td><td>New subsidy</td><td>Low-skill outsourced sector</td><td>Low-skill outsourced sector</td><td>Low-skill outsourced sector</td><td>Low-skill outsourced sector</td><td>New subsidy</td></tr><tr><td>S<sub>event</sub> =</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>Workforce share in flow</td><td>Workforce share in flow</td><td>Workforce share in flow</td><td>Workforce share in flow</td><td>Workforce share in flow</td></tr><tr><td>Stacked regression</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size. ***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.
<table><caption>**Table S9.2.** Impact of layoffs on top 10% isolation in France</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th><th>Model 5</th><th>Model 6</th></tr></thead><tbody><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> - 4)]</td><td>0.23<br>(0.24)</td><td>0.164<br>(0.23)</td><td>0.321<br>(0.40)</td><td>0.248<br>(0.39)</td><td>6.31<br>(4.44)</td><td>6.554<br>(4.30)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -3)]</td><td>0.273<br>(0.28)</td><td>0.398<br>(0.27)</td><td>-0.114<br>(0.71)</td><td>-0.015<br>(0.63)</td><td>3.378<br>(2.66)</td><td>4.211<br>(2.60)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -2)]</td><td>0.351<sup>*</sup><br>(0.20)</td><td>0.392<sup>**</sup><br>(0.18)</td><td>0.172<br>(0.19)</td><td>0.195<br>(0.18)</td><td>3.194<br>(1.95)</td><td>4.162<sup>**</sup><br>(1.94)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> -1)]</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>S<sub>event</sub> × [year = year<sub>event</sub>]</td><td>0.251<br>(0.15)</td><td>-0.107<br>(0.14)</td><td>0.127<br>(0.14)</td><td>-0.142<br>(0.14)</td><td>15.156<sup>***</sup><br>(2.61)</td><td>5.129<sup>**</sup><br>(2.45)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 1)]</td><td>0.224<br>(0.22)</td><td>-0.08<br>(0.21)</td><td>0.266<br>(0.17)</td><td>-0.009<br>(0.16)</td><td>13.713<sup>***</sup><br>(2.88)</td><td>3.298<br>(2.72)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 2)]</td><td>0.162<br>(0.19)</td><td>-0.115<br>(0.20)</td><td>0.26<br>(0.22)</td><td>-0.052<br>(0.19)</td><td>13.326<sup>***</sup><br>(2.81)</td><td>3.081<br>(2.68)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 3)]</td><td>0.524<sup>***</sup><br>(0.17)</td><td>0.226<br>(0.16)</td><td>0.738<sup>**</sup><br>(0.33)</td><td>0.442<br>(0.29)</td><td>13.640<sup>***</sup><br>(3.32)</td><td>3.479<br>(3.05)</td></tr><tr><td>S<sub>event</sub> × [year = (year<sub>event</sub> + 4)]</td><td>0.885<sup>***</sup><br>(0.20)</td><td>0.525<sup>***</sup><br>(0.19)</td><td>0.547<sup>*</sup><br>(0.31)</td><td>0.324<br>(0.30)</td><td>11.539<sup>***</sup><br>(3.31)</td><td>1.24<br>(3.24)</td></tr><tr><td>Size control</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Establishment fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>309,177</td><td>309,177</td><td>1,693,291</td><td>1,693,291</td><td>1,693,291</td><td>1,693,291</td></tr><tr><td>R<sup>2</sup></td><td>0.95</td><td>0.95</td><td>0.96</td><td>0.96</td><td>0.96</td><td>0.96</td></tr><tr><td>Establishment (fixed effects)</td><td>67,855</td><td>67,855</td><td>307,637</td><td>307,637</td><td>307,637</td><td>307,637</td></tr><tr><td>S<sub>event</sub> =</td><td>1</td><td>1</td><td>1</td><td>1</td><td>Share of workforce laid off</td><td>Share of workforce laid off</td></tr><tr><td>Stacked regression</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size.

***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.
<table><caption>**Table S9.3.** Impact of offshoring between 2009 and 2011 on top 10% isolation in France</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th></tr></thead><tbody><tr><td>S<sub>event</sub> × [year = 2005]</td><td>-1.62<br>(1.23)</td><td>-1.57*<br>(0.87)</td><td>-10.25<br>(7.73)</td><td>-4.77<br>(5.42)</td></tr><tr><td>S<sub>event</sub> × [year = 2006]</td><td>-0.33<br>(1.17)</td><td>-1.40<br>(1.21)</td><td>-0.59<br>(6.31)</td><td>2.34<br>(6.24)</td></tr><tr><td>S<sub>event</sub> × [year = 2007]</td><td>0.67<br>(1.20)</td><td>-2.16**<br>(0.93)</td><td>-1.41<br>(5.09)</td><td>-0.98<br>(4.53)</td></tr><tr><td>S<sub>event</sub> × [year = 2008]</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>S<sub>event</sub> × [year = 2009]</td><td>1.44<br>(1.31)</td><td>-0.41<br>(0.69)</td><td>16.29**<br>(7.15)</td><td>12.57*<br>(7.24)</td></tr><tr><td>S<sub>event</sub> × [year = 2010]</td><td>3.41*<br>(1.80)</td><td>0.99<br>(1.14)</td><td>27.43***<br>(8.76)</td><td>14.26<br>(11.13)</td></tr><tr><td>S<sub>event</sub> × [year = 2011]</td><td>6.34**<br>(3.23)</td><td>-1.98<br>(1.62)</td><td>28.15**<br>(11.67)</td><td>22.46*<br>(12.56)</td></tr><tr><td>S<sub>event</sub> × [year = 2012]</td><td>2.99*<br>(1.53)</td><td>0.50<br>(1.57)</td><td>22.23***<br>(8.23)</td><td>12.40<br>(9.87)</td></tr><tr><td>S<sub>event</sub> × [year = 2013]</td><td>4.82**<br>(2.17)</td><td>1.47<br>(2.02)</td><td>25.64***<br>(8.16)</td><td>11.40*<br>(6.12)</td></tr><tr><td>S<sub>event</sub> × [year = 2014]</td><td>5.74***<br>(2.17)</td><td>1.35<br>(1.90)</td><td>37.96***<br>(11.35)</td><td>25.79**<br>(11.73)</td></tr><tr><td>S<sub>event</sub> × [year = 2015]</td><td>5.76***<br>(2.20)</td><td>1.34<br>(2.10)</td><td>37.97***<br>(12.39)</td><td>22.47**<br>(9.95)</td></tr><tr><td>Size control</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Establishment fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>290,192</td><td>290,192</td><td>290,192</td><td>290,192</td></tr><tr><td>R<sup>2</sup></td><td>0.89</td><td>0.89</td><td>0.89</td><td>0.90</td></tr><tr><td>Establishment (fixed effects)</td><td>55,410</td><td>55,410</td><td>55,410</td><td>55,410</td></tr><tr><td>S<sub>event</sub> =</td><td>1</td><td>1</td><td>Share of the workforce offshored</td><td>Share of the workforce offshored</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size.

***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.
<table><caption>**Table S9.4.** Impact of subcontracting between 2005 and 2011 on top 10% isolation in France</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th></tr></thead><tbody><tr><td>[ Year = 2001 ]</td><td>-0.34<br>(2.65)</td><td></td><td></td><td></td></tr><tr><td>[ Year = 2002 ]</td><td>0.28<br>(2.25)</td><td></td><td></td><td></td></tr><tr><td>[ Year = 2003 ]</td><td>-13.48<br>(9.65)</td><td></td><td></td><td></td></tr><tr><td>[ Year = 2004 ]</td><td>-1.46<br>(1.62)</td><td>-1.07<br>(2.21)</td><td></td><td>-1.00<br>(2.24)</td></tr><tr><td>[ Year = 2005 ]</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>[ Year = 2006 ]</td><td>-0.39<br>(2.07)</td><td>-0.60<br>(2.00)</td><td></td><td>-0.43<br>(2.06)</td></tr><tr><td>[ Year = 2007 ]</td><td>1.77<br>(2.00)</td><td>3.05<br>(2.21)</td><td></td><td>3.12<br>(2.28)</td></tr><tr><td>[ Year = 2008 ]</td><td>-1.92<br>(2.77)</td><td>0.08<br>(3.03)</td><td></td><td>0.17<br>(3.09)</td></tr><tr><td>[ Year = 2009 ]</td><td>1.67<br>(2.37)</td><td>2.98<br>(2.77)</td><td></td><td>3.18<br>(2.82)</td></tr><tr><td>[ Year = 2010 ]</td><td>2.53<br>(1.64)</td><td>4.15*<br>(2.20)</td><td></td><td>4.32*<br>(2.25)</td></tr><tr><td>[ Year = 2011 ]</td><td>1.69<br>(1.64)</td><td>3.45<br>(2.21)</td><td>3.25*<br>(1.87)</td><td>3.59<br>(2.25)</td></tr><tr><td>[ Year = 2012 ]</td><td>0.65<br>(3.18)</td><td>2.48<br>(3.43)</td><td></td><td>2.67<br>(3.46)</td></tr><tr><td>[ Year = 2013 ]</td><td>0.77<br>(3.49)</td><td></td><td></td><td></td></tr><tr><td>[ Year = 2014 ]</td><td>-1.65<br>(4.26)</td><td></td><td></td><td></td></tr><tr><td>[ Year = 2015 ]</td><td>-33.81<br>(20.91)</td><td></td><td></td><td></td></tr><tr><td>Size control</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Establishment fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>2751</td><td>1732</td><td>396</td><td>1732</td></tr><tr><td>R<sup>2</sup></td><td>0.89</td><td>0.96</td><td>0.98</td><td>0.96</td></tr><tr><td>Establishment (fixed effects)</td><td>198</td><td>198</td><td>198</td><td>198</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size.

***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.
<table><caption>**Table S9.5 Panel B Impact of subcontracting intensity between 2011 and 2019 on top 10% isolation in France**</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th></tr></thead><tbody><tr><td>S<sub>event</sub> × [year = 2008]</td><td>1.65<br>(8.95)</td><td></td><td></td><td></td></tr><tr><td>S<sub>event</sub> × [year = 2009]</td><td>9.76<br>(6.96)</td><td></td><td></td><td></td></tr><tr><td>S<sub>event</sub> × [year = 2010]</td><td>-6.37<br>(4.95)</td><td>-6.46<br>(4.89)</td><td></td><td>-6.96<br>(5.11)</td></tr><tr><td>S<sub>event</sub> × [year = 2011]</td><td>0</td><td>0</td><td></td><td>0</td></tr><tr><td>S<sub>event</sub> × [year = 2012]</td><td>0.17<br>(6.33)</td><td>0.78<br>(6.43)</td><td></td><td>0.22<br>(6.11)</td></tr><tr><td>S<sub>event</sub> × [year = 2013]</td><td>17.56<br>(11.22)</td><td>17.10<sup>*</sup><br>(10.01)</td><td></td><td>14.43<sup>**</sup><br>(6.91)</td></tr><tr><td>S<sub>event</sub> × [year = 2014]</td><td>14.64<sup>**</sup><br>(7.30)</td><td>15.71<sup>**</sup><br>(7.11)</td><td></td><td>16.29<sup>*</sup><br>(8.66)</td></tr><tr><td>S<sub>event</sub> × [year = 2015]</td><td>19.28<sup>*</sup><br>(10.63)</td><td>20.22<sup>*</sup><br>(10.45)</td><td></td><td>20.48<sup>*</sup><br>(11.86)</td></tr><tr><td>S<sub>event</sub> × [year = 2016]</td><td>32.78<sup>*</sup><br>(18.13)</td><td>32.07<sup>*</sup><br>(17.28)</td><td></td><td>31.66<sup>*</sup><br>(17.83)</td></tr><tr><td>S<sub>event</sub> × [year = 2017]</td><td>43.14<sup>**</sup><br>(18.72)</td><td>43.48<sup>**</sup><br>(18.32)</td><td>44.68<sup>**</sup><br>(19.01)</td><td>43.30<sup>**</sup><br>(19.21)</td></tr><tr><td>S<sub>event</sub> × [year = 2018]</td><td>43.21<sup>*</sup><br>(22.25)</td><td>45.14<sup>**</sup><br>(22.01)</td><td></td><td>46.27<sup>**</sup><br>(23.78)</td></tr><tr><td>S<sub>event</sub> × [year = 2019]</td><td>-55.79<br>(55.38)</td><td></td><td></td><td></td></tr><tr><td>Size control</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Establishment fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Number of observations</td><td>4,282</td><td>3,235</td><td>726</td><td>3,235</td></tr><tr><td>R<sup>2</sup></td><td>0.90</td><td>0.92</td><td>0.95</td><td>0.92</td></tr><tr><td>Establishment (fixed effects)</td><td>363</td><td>363</td><td>363</td><td>363</td></tr><tr><td>S<sub>event</sub> =</td><td colspan="4">Share of value added Share of value added Share of value added Share of value subcontracted</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size.

***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.
<table><caption>**Table S9.6.** Contribution of organizational events to the within workplace top 10 percent isolation trend</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Contribution to the trend</th><th>Field</th></tr></thead><tbody><tr><td colspan="5"><em>Panel A</em></td></tr><tr><td>Year linear trend</td><td>0.191<sup>***</sup></td><td>0.189<sup>**</sup></td><td rowspan="3">1%</td><td rowspan="3">All 2001-2017 BTS workplaces with n&gt;1</td></tr><tr><td>Cumulative outsourcing events</td><td></td><td>3.484<sup>***</sup></td></tr><tr><td>Number of observations</td><td>4,081,192</td><td>4,081,192</td></tr><tr><td colspan="5"><em>Panel B</em></td></tr><tr><td>Year linear trend</td><td>0.418<sup>***</sup></td><td>0.347<sup>***</sup></td><td rowspan="3">17%</td><td rowspan="3">2002-2014 BTS workplaces matched in MMO</td></tr><tr><td>Cumulative layoff events</td><td></td><td>0.396<sup>**</sup></td></tr><tr><td>Number of observations</td><td>309,177</td><td>309,177</td></tr><tr><td colspan="5"><em>Panel C</em></td></tr><tr><td>Year linear trend</td><td>0.384<sup>***</sup></td><td>0.340<sup>***</sup></td><td rowspan="3">11%</td><td rowspan="3">2008 and 2012 BTS workplaces matched with CAM 2012 and present in 2008</td></tr><tr><td>2009-2011 offshoring event</td><td></td><td>2.20<sup>*</sup></td></tr><tr><td>Number of observations</td><td>53,752</td><td>53,752</td></tr><tr><td colspan="5"><em>Panel D</em></td></tr><tr><td>Year linear trend</td><td>0.373<sup>**</sup></td><td>-0.09</td><td rowspan="3">123%</td><td rowspan="3">2005 and 2011 BTS workplaces present both in REPONSE 2005 and 2011, which had not subcontracted their activity in 2005</td></tr><tr><td>2006-20011 subcontracting event</td><td></td><td>3.254<sup>*</sup></td></tr><tr><td>Number of observations</td><td>396</td><td>396</td></tr></tbody></table>

Note: All regressions are OLS regressions, with workplace fixed effects and robust standard errors clustered at the firm level. ***p < 0.01; **p < 0.05; *p < 0.1. Given the small number of workplaces involved in panel D (n=198), the apparent strong contribution of subcontracting to the segregation trend should be considered carefully.
<figure><img src="image_18.png" /><figcaption>**Figure S9.1.** Impact of workplace discrete restructuring events on top 10% isolation in France</figcaption></figure>

Note: We measure the impact in France of four types of restructuring events (outsourcing, layoffs, offshoring, subcontracting) on top 10% isolation. Hence, an outsourcing event increases top earner isolation on year *t* by 0.24 percentage point more than in workplace which do not outsource. As workplace size decrease could be the mediator, we also estimate in separate models (triangle points) the impact of those events controlling for size. All models are OLS with workplace and year fixed effects, clustered at the firm level. We plot the 95% confidence interval. To avoid the issue of time-heterogeneity of treatment for the staggered outsourcing and layoff models, we use a stacked version of two-way fixed effects models, where database are constructed separately for each year of events and further aggregated.

Source: Figure 5 and Appendix A2.
# S10. Digitalization and top earner isolation in France

To test the relationship between digitalization and top earner isolation at the micro level, we use the French COI (Changement organisationnel et innovation) 2006 survey on organizational change and innovation, which collects information on 14,508 firms in the private sector. The survey includes questions on the use of various forms of IT (intranet, extranet, website, databases, ERP software, etc.) both in 2006 and retrospectively for 2003. We consider a digitalization event to have occurred if the number of digitalized items (26 items in questions B11, B12, B14, B15 and C17) increased by at least one between 2003 and 2006, or if the firm undertook a major reorganization project involving IT (question G32). In Table S10.1, we use an event-study framework similar to the one developed for other workplace restructuring events.

<table><caption>**Table S10.1.** Impact of digitalization between 2003 and 2006 on top 10% isolation in France</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th></tr></thead><tbody><tr><td>[ Year = 2000 ]</td><td>0.67<br>(1.39)</td><td>0.83<br>(1.24)</td></tr><tr><td>[ Year = 2001 ]</td><td>-0.28<br>(1.33)</td><td>-0.25<br>(1.17)</td></tr><tr><td>[ Year = 2002 ]</td><td>-0.51<br>(1.32)</td><td>-0.70<br>(1.15)</td></tr><tr><td>[ Year = 2003 ]</td><td>0</td><td>0</td></tr><tr><td>[ Year = 2004 ]</td><td>0.31<br>(0.77)</td><td>0.51<br>(0.71)</td></tr><tr><td>[ Year = 2005 ]</td><td>0.54<br>(0.94)</td><td>0.76<br>(0.85)</td></tr><tr><td>[ Year = 2006 ]</td><td>0.23<br>(1.08)</td><td>0.55<br>(1.04)</td></tr><tr><td>[ Year = 2007 ]</td><td>3.09<sup>**</sup><br>(1.35)</td><td>3.39<sup>**</sup><br>(1.37)</td></tr><tr><td>[ Year = 2008 ]</td><td>1.66<br>(1.28)</td><td>1.95<br>(1.23)</td></tr><tr><td>[ Year = 2009 ]</td><td>1.63<br>(1.19)</td><td>2.09<sup>*</sup><br>(1.18)</td></tr><tr><td>Number of observations</td><td>265,791</td><td>265,791</td></tr><tr><td>R<sup>2</sup></td><td>0.90</td><td>0.92</td></tr><tr><td>Year fixed effects</td><td>10</td><td>10</td></tr><tr><td>Establishment fixed effects</td><td>39,469</td><td>39,469</td></tr><tr><td>Size control</td><td>No</td><td>Yes</td></tr></tbody></table>

Note: Size control includes log size and cumulative decrease in log size.

***p < 0.01; **p < 0.05; *p < 0.1, robust standard errors clustered at the firm level in parentheses.

It shows that in 2007, just after the end of the 2003-2006 period during which digitalization is measured, top 10% isolation increased by 3 percentage
points relative to 2003. The absence of a significant divergence between
treated and counterfactual firms before 2003 supports a causal interpretation
of the relationship between digitalization and workplace segregation.
# S11. Replication Package

A replication package can be found at the following address:

http://olivier.godechot.free.fr/hopfichiers/Godechot_et_al_Great Separation_Replication.zip
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