14 intensity had greater demand for jobs related to green technologies. The interaction term between firm-level energy intensity and country-level import dependence in the second row also yields a positive coefficient, significant at the 5 percent level. This shows that more energy-intensive businesses, which were more vulnerable to rising energy costs, increased their posting of green technology-related roles after the energy shock relative to their less energy-intensive peers.

||Baseline||Robustness Che|cks||
|---|---|---|---|---|---|
||Dep. var. IHS(# of<br>green tech. postings)<br>(1)|Drop fossil<br>fuel frms<br>(2)|Control for<br>fuel subsidies<br>(3)|Control for<br>industrial policy<br>(4)|Dep. var. **1**(Any<br>green tech. posting)<br>(5)|
|Post-February 2022||||||
|_×_Country exposure|0.153***<br>(0.049)|0.153***<br>(0.050)|0.189***<br>(0.045)|0.105***<br>(0.019)|0.086***<br>(0.027)|
|Post-February 2022||||||
|_×_Country exposure<br>_×_Firm exposure|0.034**|0.038**|0.044**|0.038***|0.024**|
||(0.015)|(0.015)|(0.015)|(0.010)|(0.009)|
|Year-month FE|Yes|Yes|Yes|Yes|Yes|
|Establishment FE<br>Post-Feb ’22|Yes|Yes|Yes|Yes|Yes|
|_×_Firm Exposure|Yes|Yes|Yes|Yes|Yes|
|Observations|1,982,647|1,948,455|1,954,918|1,796,280|1,982,647|
|Adjusted R-squared|0.608|0.608|0.609|0.613|0.444|

Note: Regressions of the number of green technology-related job postings by an establishment each month on indicators for a month after February 2022 interacted with measures of the establishment’s ex-ante exposure to the energy price shock. Standard errors clustered at the country level in parentheses. Sample is firm establishments (defined by a firm-country-region triplet) with monthly data spanning from Q2 2021 to Q4 2022 (with singleton observations automatically dropped). Dependent variable in Columns (1) to (4) is inverse hyperbolic sine of total number of green technology-related job postings by establishment in each month. Country exposure measure is the fraction of total energy use from imported natural gas in 2019. Firm exposure is energy intensity, calculated as fuel and SHEC consumption of the firm divided by its sales (both in 2020). Only firms with information in CDP included in the regressions. Coefficients on Post-February 2022 × Country exposure × Firm exposure multiplied by 10,000. Robustness check in Column (2) excludes establishments of fossil fuel firms, defined those with NAICS codes categorized as upstream or midstream oil and gas or upstream coal industries in Greenspon and Raimi (2024); Column (3) includes a control for fossil fuel subsidies (% of GDP) from the OECD Inventory of Support Measures for Fossil Fuels; Column (4) includes a control for green industrial policies from the Global Trade Alert database; and Column (5) repeats the baseline analysis but with a dependent variable equal to one if an establishment had any green technology-related job postings that month. Levels of significance denoted by (*) _<_ 0.10, (**) _<_ 0.05, and (***) _<_ 0.01.

# Table 1: Baseline results and robustness

We show these results are robust to a range of alternative specifications in Columns 2 through 5 of Table 1.<sup>17</sup> In Column (2) we exclude 2,948 establishments of fossil fuel producing-firms,

# which may have been positively impacted by the energy shock, and find virtually unchanged

17In addition, placebo test results provided in Supplementary Appendix Table A4 show that establishments more exposed to the 2022 energy price shock did not differentially increase job openings unrelated to green technologies after February 2022.