How does the deployment of Artificial Intelligence (AI) in Indian firms affect labour demand and wages? Rapid advances in machine learning have spurred an intense debate about the labour market consequences of AI. Online job adverts show that demand for AI-related skills has grown almost exponentially and concurrently in several countries around the world since 2015. Yet detailed empirical evidence on the extent of AI deployment and its distributional impacts remains scarce, particularly beyond a handful of advanced economies. For low- and middle-income countries, the use cases and impacts of AI need not be the same as for advanced economies. AI could have important consequences for their development trajectory, especially for countries promoting services-led growth. In India, for example, many of the services industries that have driven structural change, productivity growth and job creation, such as Business Process Outsourcing (BPO), are highly exposed to machine learning-based automation, raising questions over the future viability of a services-led development model and its promise of promoting widespread prosperity. This project aims to fill this knowledge gap by shedding light on the labour market impacts of AI in India – the archetypical pioneer of a services-led development model. 


Rapid innovations in machine learning could reshape many jobs, raising questions about the distributional impacts of AI. While the extent of AI diffusion has been measured in high-income countries and particularly the US, there is little evidence on its deployment in low- and middle-income countries. AI could have important implications for a services-led development model, given the potential for recent advances in machine learning to automate many of the constituent tasks of many white-collar services occupations.

The researchers investigate the effects of AI in India in the predominantly urban, services sector using a new dataset of online job adverts posted from 2010 to 2019 on the country’s largest online jobs platform. The platform has an estimated market share of around 60% of online job postings in India. Researchers use the demand for AI-related skills, as observed in the text of posted job descriptions, as a proxy for AI deployment, through this, they can induce the demand for AI by studying which firms are hiring machine learning engineers, deep learning specialists and other related staff.

Using a long difference specification between 2010-12 and 2017-19, the researchers investigate the effects of growth in the demand for AI skills on the growth of non-AI job postings and wage offers at the establishment level. To isolate the impact of AI demand, rather than AI production, the project excludes AI ‘producing’ sectors from the analysis – specifically IT and education, which are responsible for the vast majority of AI patents (Klinger et al. 2020). 

The researchers exploit establishment-level variation in their workforce’s compatibility in 2010 with future capabilities of AI, as measured by the occupation-level AI exposure measure of Webb (2020). This measure captures the degree of overlap between occupations’ tasks and the tasks that patented AI technologies are designed to perform. The project then combines these occupational AI ‘shocks’ with the establishment-level occupation vacancy shares at baseline and then use the combined shift-share measure as an instrument for the demand for AI skills. The key idea behind this instrument is that firms in 2010 with a high share of workers conducting tasks that later become feasible to automate with AI, such as civil engineers or actuaries, are more likely to start deploying AI and thus hire new staff with AI skills.


Using the job adverts data, the researchers first document several patterns in AI-related hiring in India. There was a rapid take-off in demand for AI-related skills after 2016, particularly in the IT, finance and professional services industries, closely mirroring patterns found for advanced economies. AI demand increased from 0.37% of all job vacancies in 2015 to 1.03% in 2019, coinciding with an increase in demand for specific ‘deep learning’ skills, along with ‘natural language processing’ to a lesser extent.

The results show that growth in AI demand has a significant negative impact on the growth in non-AI postings and average wage offers by establishments. These negative effects on vacancy growth are most pronounced for higher-skilled professional and managerial occupations, notably engineering professionals and general and corporate managers. Using the classification of Acemoglu & Autor (2011), the researchers find that AI lowers demand for occupations that are typically non-routine task intensive, both overall and within the affected managerial and professional occupation groupings. This stands in sharp contrast to findings for computerisation and robotics, which have been shown to lower demand for routine tasks. 

Taken together, the results show sizable impacts of AI on high-skilled, non-routine, analytical work within establishments in India’s predominantly urban, white collar service sector. These effects of AI in its early years of adoption contrast with those for the computer and robot revolutions, where routine work lost out. AI jobs pay a substantial wage premium, but these opportunities are highly concentrated in certain industries, cities and large firms, providing benefits for a small group with AI skills at the expense of demand for other types of high-skilled workers. However, these displacement effects are driven by older ‘incumbent’ firms that are not AI ‘producers’.

When looking for wider effects at the district level, the researchers find little evidence of displacement. This could suggest that negative within-establishment effects are offset by growth in new startups, including those focused on AI production, or that the aggregate impacts of AI are not yet economically meaningful. Tracing the aggregate impacts of future advances in, and deployment of, AI on innovation and employment will be an important task for future research.


November 2021 - October 2023


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Firms, Farms and Labour



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