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on Technology and Industrial Dynamics |
| By: | Ajay K. Agrawal; John McHale; Alexander Oettl |
| Abstract: | We model a key step in the innovation process, hypothesis generation, as the making of predictions over a vast combinatorial space. Traditionally, scientists and innovators use theory or intuition to guide their search. Increasingly, however, they use artificial intelligence (AI) instead. We model innovation as resulting from sequential search over a combinatorial design space, where the prioritization of costly tests is achieved using a predictive model. We represent the ranked output of the predictive model in the form of a hazard function. We then use discrete survival analysis to obtain the main innovation outcomes of interest – the probability of innovation, expected search duration, and expected profit. We describe conditions under which shifting from the traditional method of hypothesis generation, using theory or intuition, to instead using AI that generates higher fidelity predictions, results in a higher likelihood of successful innovation, shorter search durations, and higher expected profits. We then explore the complementarity between hypothesis generation and hypothesis testing; potential gains from AI may not be realized without significant investment in testing capacity. We discuss the policy implications. |
| JEL: | O31 O33 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31558&r=tid |
| By: | Yael Hochberg; Ali Kakhbod; Peiyao Li; Kunal Sachdeva |
| Abstract: | Implementing a state-of-the-art machine learning technique for causal identification from text data (C-TEXT), we document that patents authored by female inventors are under-cited relative to those authored by males. Relative to what the same patent would be predicted to receive had the lead inventor instead been male, patents with a female lead inventor receive 10% fewer citations. Patents with male lead inventors tend to undercite past patents with female lead inventors, while patent examiners of both genders appear to be more even-handed in the citations they add to patent applications. For female inventors, market-based measures of patent value load significantly on the citation counts that would be predicted by C-TEXT, but do not load significantly on actual forward citations. The under-recognition of female-authored patents likely has implications for the allocation of talent in the economy. |
| JEL: | C13 J16 J24 J71 O30 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31592&r=tid |
| By: | E. Mark Curtis; Layla O'Kane; R. Jisung Park |
| Abstract: | Using micro-data representing over 130 million online work profiles, we explore transitions into and out of jobs most likely to be affected by a transition away from carbon-intensive production technologies. Exploiting detailed textual data on job title, firm name, occupation, and industry to focus on workers employed in carbon-intensive (“dirty”) and non-carbon-intensive (“green”) jobs, we find that the rate of transition from dirty to green jobs is rising rapidly, increasing ten-fold over the period 2005-2021 including a significant uptick in EV-related jobs in recent years. Overall however, fewer than 1 percent of all workers who leave a dirty job appear to transition to a green job. We find that the persistence of employment within dirty industries varies enormously across local labor markets; in some states, over half of all transitions out of dirty jobs are into other dirty jobs. Older workers and those without a college education appear less likely to make transitions to green jobs, and more likely to transition to other dirty jobs, other jobs, or non-employment. When accounting for the fact that green jobs tend to have later start dates, it appears that green and dirty jobs have roughly comparable job durations. |
| JEL: | J01 Q0 Q4 Q5 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31539&r=tid |
| By: | David Card; Jesse Rothstein; Moises Yi |
| Abstract: | We revisit the estimation of industry wage differentials using linked employer-employee data from the U.S. LEHD program. Building on recent advances in the measurement of employer wage premiums, we define the industry wage effect as the employment-weighted average workplace premium in that industry. We show that cross-sectional estimates of industry differentials overstate the pay premiums due to unmeasured worker heterogeneity. Conversely, estimates based on industry movers understate the true premiums, due to unmeasured heterogeneity in pay premiums within industries. Industry movers who switch to higher-premium industries tend to leave firms in the origin sector that pay above-average premiums and move to firms in the destination sector with below-average premiums (and vice versa), attenuating the measured industry effects. Our preferred estimates reveal substantial heterogeneity in narrowly-defined industry premiums, with a standard deviation of 12%. On average, workers in higher-paying industries have higher observed and unobserved skills, widening between-industry wage inequality. There are also small but systematic differences in industry premiums across cities, with a wider distribution of pay premiums and more worker sorting in cities with more high-premium firms and high-skilled workers. |
| JEL: | J31 J62 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31588&r=tid |
| By: | Meißner, Leonie; Peterson, Sonja; Semrau, Finn Ole |
| Abstract: | In a race against excessive global warming, the world must accelerate the development and adoption of environmental innovations (EIs). EIs are crucial in decarbonizing the economy and meeting the netzero targets. In this literature review, we delve into the role of governments in promoting EIs across stages of maturity and the likeliness of such support to reduce emissions and mitigation costs. Various theoretical justifications, such as knowledge externalities, dynamic increasing returns, path dependency and incomplete information, highlight the necessity to promote EI through governmental Research and Development (R&D) support. While emission pricing remains the most cost-efficient climate policy, it fails as a stand-alone instrument to sufficiently encourage EI. Accordingly, the optimal approach is a policy mix complementing emission pricing with governmental R&D support. The theoretical finding is backed by empirical studies on the development and deployment of renewable energies, which also show that investment in R&D can effectively reduce emissions and mitigation costs. By combining theoretical and empirical research, the review concludes by examining two pivotal policy actions aimed at accelerating the take-off of EIs: The US Inflation Reduction Act and the European Green New Deal Industrial Plan. We evaluate their specific aspects and limitations to effectively and efficiently contribute to decarbonization. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwkwp:2254&r=tid |
| By: | Oliver Falck; Akash Kaura |
| Abstract: | For all its perennial focus on traditional industries, Germany has done a remarkable job in greening its manufacturing Green skills are quickly gaining prominence Automotive manufacturing is leading the way Germany is still a hotbed of innovation, but cannot afford to become complacent |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:econpb:_55&r=tid |
| By: | Xiang Hui; Oren Reshef; Luofeng Zhou |
| Abstract: | Generative Artificial Intelligence (AI) holds the potential to either complement knowledge workers by increasing their productivity or substitute them entirely. We examine the short-term effects of the recent release of the large language model (LLM), ChatGPT, on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based, generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that in the short term generative AI reduces overall demand for knowledge workers of all types, and may have the potential to narrow gaps among workers. |
| Keywords: | generative AI, large language model (LLM), online labor market |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10601&r=tid |
| By: | Gmyrek, Pawel,; Berg, Janine,; Bescond, David, |
| Abstract: | This study assesses the potential global exposure of occupations to Generative AI, particularly GPT-4. It predicts that the overwhelming effect of the technology will be to augment occupations, rather than to automate them. The greatest impact is likely to be in high and upper-middle income countries due to a higher share of employment in clerical occupations. As clerical jobs are an important source of female employment, the effects are highly gendered. Insights from this study underline the need for proactive policies that focus on job quality, ensure fair transitions, and that are based on dialogue and adequate regulation. |
| Keywords: | artificial intelligence, employment opportunity, working conditions, women workers, access to information technology, impact evaluation |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995324892702676&r=tid |