# RDP 2023-10: Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects 6. Adoption and Hiring

Above we have considered the role of a director's skills in adoption. But workers' skills are also likely to be important. While we do not have information about the stock of employees, we can obtain information on hiring for at least a subset of firms, using the dataset created in Bahar and Lane (2022) that covers 2012 to 2020.

First, we can consider whether firms that adopt are more likely to place job advertisements for workers with these skills. To do so we run a simple regression of the following form

$Hire a skilled worker by 2022 i =β*Adopt by 2022 i + γ ind +log( Asset s i,2022 )+ ε i$

Our outcome variable Hire a skilled worker by 2022i takes on the value 1 if the firm has put a job ad looking for GPT-skilled workers during the sample, and 0 if none of their job ads asked for these skills. We regress this on an indicator for whether or not the firm has adopted a GPT by 2022. Again we control for industry and size to capture other factors that might explain outcomes.

For the sub-sample of firms for whom we can observe job advertisements, those that have adopted a GPT are far more likely to have advertised for GPT skills at some stage, with the probability being around 16 percentage points higher than for non-adopters once we control for firm characteristics (Table 5). This suggests that firms increase their demand for these skills when trying to use these technologies.

Table 5: Relationship between Hiring Skilled Workers and Technology Adoption
Linear probability regression of ever advertised for GPT skills on ever adopted
With no controls With industry controls With industry and size controls
(0.079)
0.397***
(0.083)
0.162**
(0.070)
Number of observations 215 215 215

Notes: Excludes firms in the IT industry. Only includes firms that have job ads captured in the Bahar and Lane (2022) sample. *, **, *** indicate significance at the 10, 5 and 1 per cent level, respectively. Standard errors are shown in parentheses and are clustered at the industry level. Size control accounts for firm's largest ever (log) assets.

Sources: Authors' calculations; Bahar and Lane (2022) using Lightcast data; Morningstar; Refinitiv.

We see something similar when we look at the pattern of hiring over time. To show this we apply the same event study as outlined in Equation (2), with the outcome variable of interest being a dummy variable that takes on the value 1 if any of the job advertisements the firm posted during the year were for GPT, and 0 if none were asking for these skills.[16] Focusing on late adopters, there is a pick-up in hiring post-adoption (Figure 7). This is particularly evident for firms with Board members with GPT experience. As noted, these are also the same firms that have the most evidence of increased profitability post-adoption. While certainly not definitive, this provides some tentative evidence that hiring skilled workers is an important step in the profitable adoption of some of these GPT. Such a finding is also somewhat consistent with overseas findings that firms with more skilled workers are more likely to invest in AI/ML in the form of further hiring of skilled workers (Babina et al 2023).

More generally though, taken together these results suggest that adoption of GPT is associated with increased demand for skilled workers and therefore highlight the important role a skilled workforce plays in the effective adoption of GPT. Future work could further explore these results using more complete information on the stock of workers at these firms.[17]

## Footnotes

Those for whom we cannot see a job advertisement in the year are not considered. [16]

We explored whether firms that have placed job advertisements for GPT-skilled workers are more likely to increase profitability post-adoption. There is no evidence of a significant difference. In part this may reflect a lack of information on the stock of workers. For example, firms may already have skilled workers, and not need to hire. Further work could look to incorporate information on the stock of workers to better consider this question. [17]