metadata: authors: 0: author: Liu,Yan-000529044
count: World
docna: 0: docna: Generative AI : Catalyst for Growth or
            Harbinger of Premature De-Professionalization ?
docty: Policy Research Working Paper
abstracts: cdata!: This paper presents a multi-sector
            growth model to elucidate the general equilibrium effects of
            generative artificial intelligence on economic growth,
            structural transformation, and international production
            specialization. Using parameters from the literature, the
            paper employs simulations to quantify the impacts of
            artificial intelligence across various scenarios. The paper
            introduces a crucial distinction between high-skill, highly
            digitalized, tradable services and low-skill, less
            digitalized, less-tradable services. The model&#x2019;s
            key propositions align with empirical evidence, and the
            simulations yield novel and sobering predictions. Unless
            artificial intelligence achieves widespread cross-sector
            adoption and catalyzes paradigm-shifting innovations that
            fundamentally reshape consumer preferences, its growth
            benefits may be limited. Conversely, its disruptive impact
            on labor markets could be profound. This paper highlights
            the risk of &#x201c;premature
            de-professionalization&#x201d;, where artificial
            intelligence likely shrinks the space for countries to
            generate well-paid jobs in high-skill services. The analysis
            portends that developing countries failing to adopt
            artificial intelligence swiftly risk entrapment as commodity
            exporters, potentially facing massive youth underemployment,
            diminishing social mobility, and stagnating or even
            declining living standards. The paper also discusses
            artificial intelligence&#x2019;s broader implications on
            inequality, exploring multiple channels through which it may
            exacerbate or mitigate economic disparities.
display_title: Generative AI : Catalyst for Growth
            or Harbinger of Premature De-Professionalization ?
listing_relative_url: /research/2024/09/34392231/generative-ai-catalyst-growth-or-harbinger-premature-de-professionalization
topicv3: ICT Innovation and Transformation m1326189 2399,Digital Economy Strategy m1327584 2345
publishtoextweb_dt: 2024-09-17T00:00:00Z
dois: 10.1596/1813-9450-10915
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 62
author_keys: 0: author_key: 1676956
model_extractions: Policy Research Working Paper 10915

# Generative AI

Catalyst for Growth or Harbinger of Premature De-Professionalization?

_Yan Liu_ Digital Development Global Practice September 2024
Policy Research Working Paper 10915

# **Abstract**

This paper presents a multi-sector growth model to elucidate the general equilibrium effects of generative artificial intelligence on economic growth, structural transformation, and international production specialization. Using parameters from the literature, the paper employs simulations to quantify the impacts of artificial intelligence across various scenarios. The paper introduces a crucial distinction between high-skill, highly digitalized, tradable services and low-skill, less digitalized, less-tradable services. The model’s key propositions align with empirical evidence, and the simulations yield novel and sobering predictions. Unless artificial intelligence achieves widespread cross-sector adoption and catalyzes paradigm-shifting innovations that fundamentally reshape consumer preferences, its growth benefits may be limited. Conversely, its disruptive impact on labor markets could be profound. This paper highlights - the risk of “premature de-professionalization”, where arti ficial intelligence likely shrinks the space for countries to generate well-paid jobs in high-skill services. The analysis portends that developing countries failing to adopt artificial intelligence swiftly risk entrapment as commodity exporters, potentially facing massive youth underemployment, diminishing social mobility, and stagnating or even declining living standards. The paper also discusses artificial intelligence’s broader implications on inequality, exploring multiple channels through which it may exacerbate or mitigate economic disparities.

This paper is a product of the Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at yanliu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Generative AI: Catalyst for Growth or Harbinger of Premature De-Professionalization?

World Bank

**Keywords:** Artificial Intelligence, Structural Transformation, Trade, Economic Growth, Inequality **JEL Codes:** O14, F16, F63

> ∗Email: yanliu@worldbank.org. I am grateful for excellent comments from Stephane Straub, Estefania Belen Vergara Cobos, and He Wang. All remaining errors are my own.
# **1 Introduction**

Artificial Intelligence (AI), particularly recent advances in generative AI, has sparked intense debate about its potential impact on economic growth, labor markets, and global trade patterns. Estimates and projections of AI’s economic effects vary widely. Goldman Sachs (2023) predicts that generative AI could increase global GDP by 7%, equivalent to US$ 7 trillion over a decade. Acemoglu 2024 provides a more conservative estimate, predicting a GDP increase of only 0.9% to 1.1% over the next decade. D. Autor 2024 argues that generative AI offers a unique opportunity to expand the middle class by enabling middle-skill workers to perform higher-stake decision-making tasks currently reserved for elite experts. In contrast, Frey and Osborne 2024 suggests that by lowering barriers to entry in cognitive occupations, generative AI will increase competition, eventually driving down wages and causing significant labor market disruptions. If generative AI tools commodify expertise and reduce the returns to specialized skills, individuals may be less incentivized to acquire advanced expertise, leading to further downsides to productivity and wages (Capraro et al. 2024).

While discourse on AI’s economic impact predominantly focuses on advanced economies, its influence on developing countries and global production patterns remains uncertain and profound. Korinek and Stiglitz 2021 warn that AI could worsen the terms of trade for developing countries by eroding their laborcost advantage, potentially leading to further impoverishment. Conversely, AI might reduce geographical and language barriers, shrink human capital gaps, and increase outsourcing of cognitive tasks to these nations, potentially diminishing income disparities with wealthier countries. However, this optimistic outcome hinges on rapid AI adoption in developing countries—a prospect hampered by low labor costs and significant barriers such as poor infrastructure, education, and regulatory frameworks.

Given AI’s varied impacts across industries and countries, a nuanced analysis is required. This paper examines generative AI’s potential effects on economic growth, labor markets, and inequality through the lens of structural transformation and global production specialization, aiming to offer insights into how AI may reshape economies and societies worldwide.

Structural transformation—the reallocation of economic activities across sectors—is integral to economic growth. Decades ago, economists noted a decline in the share of agriculture in both employment and output, a temporary rise in manufacturing, and a long-term shift towards services (Kuznets 1957).

Multi-sector growth models offer unique advantages for studying AI’s impact. The dominant growth models in economics have historically been single-sector constructs (Solow 1956; Romer 1986; Lucas Jr 1988; Aghion and Howitt 1992; Grossman and Helpman 1991). These models have proved powerful 1
and appealing, though they are not without limitations. Such models emphasized the importance of capital, labor, education, and innovation in driving growth, but overlook the differences and intricate inter-linkages between sectors. Consequently, single-sector models fall short in elucidating how an economy’s compositional structure relates to its growth trajectory. The economic complexity theory (Hidalgo and Hausmann 2009) shows that a country’s industrial composition and specialization patterns strongly influence its growth prospects. Understanding the complex interplay between growth and structural transformation is essential for formulating policies that enable sustainable development at scale (Gollin and Kaboski 2023).

I construct a simple multi-sector growth model to conceptualize the distinct channels through which AI influences growth and structural transformation. The model builds on recent structural transformation frameworks (Rodrik 2016; Comin, Lashkari, and Mestieri 2021; Matsuyama 2019) and incorporates nonhomothetic preferences, where income elasticities of demand vary across sectors. The economy has four sectors: agriculture (A), manufacturing (M), high-skill services (Sh), and low-skill services (Sl). Labor is the only factor of production. The model initially assumes a closed economy, later extending to an open economy context. It also explores various model extensions, transitioning from exogenous to endogenous growth and incorporating inter-sectoral productivity linkages. The model’s propositions are strongly supported by empirical evidence.

High-skill services include information and communication technology (ICT) services, finance and insurance, and professional, scientific, and technical services. The term ”high-skill” serves as a shorthand for sectors characterized by high levels of skill, income, tradability, and digitalization. These industries significantly differ from other service sectors across all four dimensions. Based on data from the U.S. and China, these three industries exhibit the highest average earnings, ICT service input intensity, trade intensity, and the largest share of employees with advanced degrees (master’s or higher). Other services are classified as low-skill services. Although education and healthcare also have a high proportion of employees with advanced degrees, they are considered low-skill due to their low tradability and digitalization.

AI affects growth and structural transformation through three distinct channels:

1. Demand: AI creates radically new products and shifts consumer preferences. It reshapes utility functions by altering income elasticities for certain sectors and changing the elasticity of substitution across sectors.

2. Supply: AI affects relative prices across sectors. While previous technologies primarily affected the goods-producing sector and routine tasks, generative AI targets high-skill services, particularly 2
cognitive and creative tasks (Eloundou et al. 2023; Gmyrek, Berg, and Bescond 2023).

3. International production specialization: AI changes comparative advantages among countries. Nations that adopt generative AI early can enhance their comparative advantage in high-skill services. Additionally, generative AI has been shown to improve the productivity of less-skilled, lessexperienced workers (Brynjolfsson, Li, and Raymond 2023; Noy and W. Zhang 2023; Peng et al. 2023; Dell’Acqua et al. 2023), potentially enabling developing countries to compete in high-skill services and replace more expensive labor in developed nations.

Using model parameters sourced from the literature, the paper quantifies AI’s potential impact through simulations. I explore three scenarios regarding AI’s influence:

1. Short-term/pessimistic: AI exclusively increases labor productivity growth in high-skill services.

2. Medium-term/neutral: AI enhances labor productivity growth across all four sectors, with the most substantial increase observed in high-skill services.

3. Long-term/optimistic: AI not only boosts labor productivity in all sectors but also catalyzes the creation of revolutionary products, fundamentally altering societal preferences.

The simulations reveal several novel and sobering findings:

1. The recent rise in high-skill services employment share in many countries will eventually stagnate or decline, following a hump-shaped curve similar to manufacturing. Although higher incomes increase demand for high-skill services, advances in AI will reduce the need for white collar workers, shifting job concentration to low-skill services.

2. AI will further limit the potential for creating quality jobs in the high-skill services sector, particularly in developing countries. Similar to premature de-industrialization, AI will cause ”premature de-professionalization”, where high-skill services employment peak earlier and at lower income levels.

3. Small open economies face a critical juncture with AI adoption. Failure or delay in embracing AI technologies risks eroding existing comparative advantages in high-skill services and manufacturing, or impeding the development of such advantages. These countries may consequently find themselves trapped as commodity exporters, with employment heavily concentrated in agriculture and lowskill services. In contrast, successful and timely AI adoption could catalyze the development of comparative advantages in manufacturing or high-skill service sector.

3
4. Unless AI is widely adopted across sectors and drives transformative innovations that permanently shift consumer preferences, its growth benefits will likely be underwhelming. Even with a 50% increase in labor productivity growth in high-skill services, real income sees only a modest initial boost of 0.046 percentage points, which gradually diminishes over time. After 100 years, real income is just 2.4% higher than without AI. If AI enhances productivity across all four sectors, real income at year 100 is 19% higher. In the most optimistic scenario, real income at year 100 is 28% higher than the baseline.

The paper then addresses the limitations of the model and considers how generative AI might influence growth and inequality more broadly. The model’s exclusion of capital overlooks a key aspect of AI’s impact: automation, job displacement, and the declining labor income share. Additionally, by omitting intermediate inputs, the model may underestimate the demand for high-skill services. The assumption of homogeneous, freely mobile labor across sectors, which leads to equalized incomes, diverges from reality and provides little insight into income inequality. Generative AI could lower entry barriers for certain high-skill service jobs, potentially driving down average wages and reducing income inequality across industries and occupations. However, it may also influence incentives for education and skill acquisition. If AI shrinks the share of high-skill, well-paid jobs and depresses wages in these fields, individuals may become less motivated to invest in higher education, which could hinder long-term growth and limit social mobility.

The paper contributes to the growing body of literature on the economic impacts of AI in several ways.

First, to the best of my knowledge, it is the first study to analyze the effects of generative AI through the lens of structural transformation and international production specialization. Unlike many existing studies that focus on occupational exposure based on current tasks, this model incorporates demandside factors, rooted in non-homothetic preferences across sectors. This broader approach captures AI’s potential to reshape task content and create new occupations, offering a more holistic view at the macroeconomic level.

Second, this paper advances traditional structural transformation models by introducing a critical distinction between high-skill, highly digitalized, tradable services and low-skill, less digitalized, nontradable services. This differentiation yields deeper insights into the uneven impact of AI across service sectors and occupations, as well as its implications for economic growth and inequality.

Third, alongside non-homothetic preferences, this study explores a new demand-side channel through which AI may drive growth: shifting consumer preferences. One scenario models AI as a catalyst for 4
game-changing new products and industries, permanently altering income elasticity in certain sectors and the elasticity of substitution across sectors. Combined with the channel of international production specialization, this approach offers rich insights into AI’s varied economic impacts across different types of countries.

Fourth, the paper uses simulations to illuminate the general equilibrium effects of AI on economic growth and labor markets under various scenarios, quantifying the scale of these effects. Consistent with Acemoglu 2024, the results suggest that AI’s short- to medium-term growth impact will likely be modest.

Notably, this paper is the first to predict AI’s potential to reduce opportunities for well-paid jobs in high-skill services. This could be particularly harmful for late AI adopters and developing countries, a phenomenon I dub as ”premature de-professionalization”. It underscores the diminishing prospects for developing economies to foster high-skill service employment, cautioning that those slow to adopt AI risk being relegated to commodity exporters, facing large scale youth underemployment, reduced social mobility, and potential declines in living standards.

The rest of the paper is organized as follows: Section 2 presents stylized facts about structural transformation, the divergence between high- and low-skill services, and AI’s disproportionate effects on high-skill services. Section 3 introduces the four-sector growth model and derives several empirically supported propositions. Section 4 simulates the baseline model using parameters from the literature and quantifies AI’s potential impact through three channels across three scenarios. Section 5 discusses model limitations and the broader implications of AI for growth and inequality. Section 6 concludes.

# **2 Stylized Facts**

## **2.1 Structural transformation**

### **Demand side driver: Different income elasticities across sectors**

Differences in income elasticities of demand across sectors are a key driver of structural transformation. Economists have long studied systematic variations in the sectoral composition of demand as income changes, a concept explored through Engel curves. Typically, services exhibit higher income elasticities than agricultural and manufactured goods (1). As a result, non-homothetic preferences across sectors can explain the shift in consumer spending from food and manufacturing to services, observable in both real and nominal terms (Kongsamut, Rebelo, and Xie 2001; Foellmi and Zweim ̈uller 2008; Boppart 2014; Comin, Lashkari, and Mestieri 2021).

5
<!-- Start of picture text --> Education . vevevevteeeevsresesese deceeetereeecerereeesteretstered@eereeersn@beess<br>Entertainment fees, admissions, reading |r<br>Furniture and fixtures |-- 0 Hes ° vos<br>Entertainment equipment and subscription TV Jor Op<br>Appliances, phones, computers with associated services | Orr<br>0 Re) 1 1.5 2<br>© Income elasticity: low<br>Income elasticity: high<br><!-- End of picture text -->
<!-- Start of picture text --> All Countries HIC UMC LMC+LIC<br>4 4 4 4<br>#3 £3 #3 #3<br>> 2 2 2<br>32 532 32 532<br>g8gg<br>.. - mg<br>2333<br>34s 3 4s<br>1111<br>00 0 0<br>mA M ms si mA M Mish si mma M Ms @s mma M Mish @/sI<br><!-- End of picture text -->
<!-- Start of picture text --> High income countries<br>Chile Denmark Spain France<br>80 80 80 80<br>60 60 60 60<br>40200 ee Tdee 40204TM0 ao = 40]20Oo,2>«ESne 4020of *eee<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>>A M -® Sh SI >A M -® Sh sl A M -=® Sh sl >A M -® Sh sl<br>United Kingdom Italy Japan Korea, Rep.<br>68 0 4060 ~, 68 0 4060 ee<br>40 > 407 ~~,<br>een 0 0 0<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>A M -® Sh sl A M -® Sh sl +A M -® Sh sl A M -® Sh sl<br>Netherlands Sweden Taiwan, China United States<br>80 80 60 80<br>60 60 ~. 60<br>40 40 40 a 40<br>o,"--- 04 Bo 0 0<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>A M -= Sh sl +A M -= Sh sl +A M -= Sh sl +A M -= Sh sl<br><!-- End of picture text -->
<!-- Start of picture text --> Upper middle income countries<br>Argentina Brazil Botswana China<br>80 60 80 80<br>6040 0 60 60<br>20 came 40 eee mene 40 te<br>0 — 2 0 2 0 eae 2 0 a<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>A M -= Sh SI A M -= Sh sl >A M -= Sh sl >A M -® Sh sl<br>Colombia Costa Rica Indonesia Mexico<br>60 60 80 60<br>4020 Neer 4020 a eee— “20 ooneveten, 4020 rm wetecanaasy<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>+A M -® Sh sl -A M -= Sh sl +A M -= Sh sl -A M -=® Sh sl<br>Peru Thailand Venezuela, RB South Africa<br>6040 6080 6080 60 ~<br>40 ~. 40<br>20 me «40 ee<br>eee 20 20 qe spoptpeanemmeEES 20<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>- A M -= Sh SI se A M -m Sh SI >A M -= Sh sl - A M -= Sh SI<br><!-- End of picture text -->
<!-- Start of picture text --> Lower middle and low income countries<br>Bolivia Egypt, Arab Rep. Ethiopia Ghana<br>60804020 ~ “a, 604020 ~~,—~_, 1008060400 LEE 604020 aerate tad<br>eee 0 ee 0 TS 0 [ieeead<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>A M -= Sh SI A M -= Sh sl A M -= Sh S| >A M -® Sh sl<br>India Kenya Malawi Nigeria<br>608040 wens 604080 oo 1006040 a 8040 Noo<br>20 20 20 20<br>0 el 0 re 0 _ STINT 0 al<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>+A M -® Sh sl -A M -= Sh sl +A M -® Sh sl -A M -=® Sh sl<br>Philippines Senegal Tanzania Zambia<br>60 80 100 80 ee<br>40 Pen: 60 i 80 me 60<br>Sen) 60<br>20 40 40 40<br>20 20 20<br>0 —————— 0 a 0 ee 0 ee<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year Year Year<br>eA M -= Sh SI se A M -m Sh SI -A M -® Sh SI - A M -= Sh SI<br><!-- End of picture text -->
Rising income inequality within industries, especially among different service industries, further underscores the need to distinguish various types of services in economic research. Card, Rothstein, and Yi 2024 have documented significant heterogeneity in industry wage premiums in the US, showing that workers in higher-paying industries possess higher observed and unobserved skills, thus widening betweenindustry wage inequality. Haltiwanger, Hyatt, and Spletzer n.d. found that most of the rise in earnings inequality in the US is due to increasing between-industry inequality, with the disparity particularly pronounced in the service sector.

High-skill, highly digitalized, tradable service industries, such as information and communication technology (ICT) services, financial services, and professional and business services, are pulling further ahead of other service industries. In 2022, the average annual earnings in the information, finance and insurance, and professional service sectors exceeded US$120,000 in the US, placing them among the top five highest-earning industries. In China, ICT services, finance and insurance, and professional services were the top three highest-earning industries in 2022. In both countries, the highest and lowest earning industries are in the service sector, with ICT services being the highest paying and accommodation and food services the lowest (Figure 6).

This growing disparity in earnings across industries and occupations has led to significant differences in returns to education across majors, thereby influencing the supply of skills. Median and average earnings vary greatly by students’ college majors, even among similar students ( Altonji, Blom, and Meghir 2012; Andrews, Imberman, and Lovenheim 2017; Andrews and Stange 2019; Arcidiacono, Hotz, and Kang 2012; Hamermesh and Donald 2008). Indeed, the mean earnings differences across majors are at least as large as the earnings gap between high school and college graduates. Recently, engineering and computer science majors have yielded the highest returns for students, followed by business, health, and STEM fields (L. Zhang, Liu, and Hu 2024). Consequently, students have increasingly abandoned humanities in favor of fields like computer science and business. The share of tertiary graduates majoring in ICT programs has risen in most countries between 2016 and 2022, and numerous certificate programs, coding bootcamps, and online platforms have emerged to meet the surging demand. On Coursera, computer science and business are the most popular course categories and degree programs.

11
Accommodation and food services Agriculture, forestry, fishing, and hunting Retail trade Other services, except government Educational services Administrative and waste management services Arts, entertainment, and recreation Transportation and warehousing Health care and social assistance Construction Real estate and rental and leasing Manufacturing Wholesale trade Mining Professional, scientific, and technical services Utilities Finance and insurance Management of companies and enterprises Information 0 50,000 100000 150000 Average annual earnings 2022 (USD) <!-- Start of picture text --> Accommodation and food services<br>Agriculture, forestry, fishing, and hunting<br>Services to households<br>Administrative and waste management services<br>Construction<br>Real estate<br>Manufacturing<br>Leasing and business services<br>Transportation and warehousing<br>Wholesale and retail<br>Public administration<br>Education<br>Arts, entertainment, and recreation<br>Mining<br>Utilities<br>Health care and social assistance<br>Professional, scientific, and technical services<br>Finance and insurance<br>ICT services<br>0 10,000 20,000 30,000 40,000<br>Average annual earnings 2022 (USD)<br><!-- End of picture text -->
<!-- Start of picture text --> Agriculture, Forestry, Fishing and Hunting<br>Accommodation and Food Services<br>Transportation and Warehousing<br>Construction<br>Retail Trade<br>Mining, Quarrying, and Oil and Gas Extraction<br>Administrative and Waste Management Services<br>Manufacturing<br>Wholesale Trade<br>Real Estate and Rental and Leasing<br>Other Services (except Public Administration)<br>Arts, Entertainment, and Recreation<br>Utilities<br>Finance and Insurance<br>Government<br>Health Care and Social Assistance<br>Information<br>Management of Companies and Enterprises<br>Professional, Scientific, and Technical Services<br>Educational Services<br>0 10 20 30 40<br>Employment with at least Master degree (%)<br><!-- End of picture text --> Healthcare and social assistance focrrrterre ret nn tee recttee nest cestrenticss Arts, entertainment and recreation fosters ic eer terete cs teccecenccse cer necancerceaescccesece tees scientific, and technical SErViCES joer erreterereter etree reece ten ernest tcne ett tnce te catices Professional, scientific, and technical SErViCES joer <!-- Start of picture text --> 2 4 8 16 32 64 30<br>IT services input intensity (%)<br><!-- End of picture text -->
Construction Human health and social work activities Real estate activities Other service activities Utilities and waste management Education Accommodation and food service activities Agriculture, forestry and fishing Arts, entertainment and recreation Financial and insurance activities Professional, scientific and technical activities Administrative and support service activities Information and communication Wholesale and retail trade Transportation and storage Manufacturing Mining and quarrying 0 10 20 30 Export intensity 2020
<!-- Start of picture text --> Agriculture, Forestry, Fishing and Hunting<br>Accommodation and Food Services<br>Construction<br>Mining, Quarrying, and Oil and Gas Extraction<br>Transportation and Warehousing<br>Manufacturing<br>Other Services (except Public Administration)<br>Health Care and Social Assistance<br>Utilities<br>Administrative Waste Management Services<br>Government<br>Real Estate and Rental and Leasing<br>Retail Trade<br>Arts, Entertainment, and Recreation<br>Educational Services<br>Wholesale Trade<br>Professional, Scientific, and Technical Services<br>Information<br>Management of Companies and Enterprises<br>Finance and Insurance<br>0 1 2 3 4 5<br>Exposure to generative Al<br><!-- End of picture text -->
# **3.1 Closed economy**

Suppose there are four sectors in the economy: (1) agriculture (A), (2) manufacturing (M), (3) lowskill, less-digitalized, non-tradable services (Sl), which includes utilities, construction, transportation and warehousing, retail and wholesale, accommodation and food services, recreational and personal services, healthcare, education, administrative support, real estate and rental, and (4) high skill, highly digitalized, tradable services (Sh), including professional, scientific, and technical services, finance and insurance, and ICT services. Labor is the single non-tradable factor of production.

## **Demand**

The economy has a representative consumer that lives indefinitely. The consumer’s aggregate consumption (or welfare) at time t is denoted by _Ct_ , which combines four sectoral goods: _CAt_ , _CMt_ , _CSht_ , and _CSlt_ . The individual only cares about current consumption and spends all the income at time t on consumption.

The representative consumer has the implicit utility function: _σ_ is the elasticity of substitution across sectors, and _εi_ is the income elasticity for good _i_ . Ω _i_ is a time invariant constant for good _i_ . _i ∈{A, M, Sl, Sh}_ . If _εi_ = 1 for all ı, equation 1 becomes the standard homothetic CES, which is directly explicitly additive. By letting _εi_ depend on _i_ , this class of utility functions, no longer directly explicitly additive but still directly implicitly additive, allows the income elasticity to differ across sectors, while keeping the constant elasticity of substitution across sectors, _σ_ , as a separate parameter. This makes it possible to control for the income elasticity differences without affecting the price elasticity, which helps to isolate the role of income elasticity differences (Matsuyama 2019).

As in standard CES utility functions, when _σ<_ 1, the four sectoral outputs are gross substitutes. When _σ >_ 1, they are gross complements. In this model, we assume 0 _<σ<_ 1. If _εi ≥ σ_ for any i, real income _Ct_ is strictly increasing with _Cit_ .

The consumer chooses _Cit, i ∈{A, M, Sh, Sl}_ , to maximize _Ct_ , subject to the budget constraint: the total expenditure, _Et_ =<sup>�</sup> _i_<sup>_PitCit_,mustbelessthanorequaltotheindividual’stotalwageincome,</sup><sup>_wt_.</sup> 16
_Pt_ is the aggregate price level at time t.

Demand for the products of each sector can be derived: Consumption of good i strictly decreases with its relative price, and strictly increases with real income. And the model assumes income elasticity for the four sectors satisfy the following: This assumption draws upon various estimates of sectoral income elasticities from existing literature (Comin, Lashkari, and Mestieri 2021; Young 2013; Aguiar and Bils 2015). As incomes rise, consumer expenditure patterns tend to shift from goods to services, reflecting a growing preference for experiences over material possessions. Food exhibits the lowest income elasticity, as even the wealthiest individuals have finite nutritional needs. Similarly, manufactured goods have a limited income elasticity, as the utility gained from owning multiple TVs, smartphones, or cars diminishes beyond a certain point.

The challenge lies in estimating the income elasticity for high skill services. First, a major portion of financial services, ICT services, professional, scientific, and technical services primarily cater to businesses rather than individual consumers, making it difficult to derive income elasticity from household expenditure surveys. As an economy’s real income level increases, businesses become more sophisticated, driving up demand for these services to enhance productivity. Second, households often consume such services indirectly through other products or services, making it challenging to isolate and accurately measure the expenditure on these services. Third, some of these services like legal and accounting services are purchased infrequently, such expenses often fall out of the regular expenditures that surveys typically focus on. Fourth, some digital services could be accessed for free or through non-monetary means (e.g., data sharing), which is not captured in traditional expenditure surveys.

By far the highest income elasticity is observed in non-tradable, low-skill services including housing, education, healthcare, tourism, and personal services. Aguiar and M. Bils (2015) estimated that income elasticity in US households is 0.4-0.5 for food, 1-1.5 for manufactured goods, 1.2-1.3 for entertainment equipment and television subscription, 1.65 for entertainment fees, admissions, and reading, 1.6-1.8 for childcare, and 1.6-1.9 for education, as shown in Section 2, Figure 1. These results corroborate the model assumption in Equation 4.

Equation 3 yields the below: consumption of good _i_ relative to good _j_ decreases with relative price 17
and increases with the relative income elasticity. As real income increases, consumption of goods with the highest income elasticity increases disproportionately.

# **Supply**

Labor is the only input in production. There is a fixed one unit of labor in each period, allocated across the four sectors to produce the four kinds of goods: _LAt_ + _LMt_ + _LSlt_ + _LSht_ = 1. Supply for each sector is determined by the following production function, which has constant return to scale: _Yit_ denotes the quantity of output for sector _i_ at time _t_ . _Xit_ reflects labor productivity in sector _i_ . Technology is initially assumed to be exogenous. While sectoral technology growth can be endogenous, the potential for labor productivity growth in each sector is largely determined by the inherent characteristics of the sector. For instance, the production of largely homogenous goods, such as in agriculture and manufacturing, can be significantly enhanced through the use of machinery and economies of scale, leading to substantial labor productivity growth. In contrast, low-skill, non-tradable services are often customized based on unique contexts, require face-to-face interactions, rely on unstructured physical movement or manual dexterity, making them difficult to automate or produce in large volumes. Baumol’s classic example illustrates this point: it still takes a string quartet the same amount of time to play a piece today as it did when Mozart wrote it. Therefore, there are valid reasons to assume that potential labor productivity growth across sectors is exogenous. I also demonstrate that moving from exogenous to endogenous growth does not affect equilibrium pathways later in this section.

Labor productivity in each sector _Xit_ grows at a constant rate over time. Let productivity growth rate in each sector be _τi_ . Initial productivity level is normalized to one _Xi_ 0 = 1. Then productivity for each sector at time t can be written as _Xit_ = _e_<sup>_τit_</sup> . Sectoral productivity growth satisfies the following: productivity grows the fastest in the agriculture sector, followed by manufacturing, and high skill services, and grows the slowest in the low-skill service sector.

_Lit_ is labor input for sector _i_ at time _t_ . All the revenue will be paid to the individual as wage, and 18
wage rate is the same across sectors.

# **Equilibrium**

There are four market-clearing conditions for the four sectors: consumption must equal production in each sector: _Cit_ = _Yit_ .

Total nominal income of the economy at time _t_ is the value of all goods produced (always equal to total expenditure, as the economy has no savings), which equals the sum of labor income from each sector: Let the share of nominal output for good i be _ωit_ , the model implies that sectoral nominal output share will equal employment share: _Yt_ is the same as _Ct_ , denoting real income.

The rest of the paper use sectoral employment/expenditure/nominal output share interchangeably, or simply refer to it as sectoral share.

Under the given technology, demand and supply in the four sectors will pin down price and output in each sector. As the model can only determine relative prices, let agricultural goods be the numeraire, _PAt ≡_ 1.

Solving the equilibrium yields the following:

**Proposition 1** _Output/consumption in each sector is determined by two separate forces: the supply side force and the demand side force. Production/consumption in each sector grows monotonously over time._

The supply side force (price effect, _Xit_<sup>_σ_in equation 10) increases output/consumption through produc-</sup> tivity growth. The demand side force (income effect, _Yt_<sup>_εi−σ_</sup> in equation 10) increases output/consumption through higher demand resulting from real income growth.

The agricultural sector exhibits a unique dynamic: its income elasticity equals the elasticity of substitution ( _εA_ = _σ_ ), output/consumption is only influenced by the supply side force.

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Changes in the relative quantity of output/consumption between sectors i and j, _Y_<sup>_<u>Y</u>_</sup> _jt_<sup>_<u>it</u>_,isdetermined</sup> by: (1) difference in sectoral productivity growth rate and time, _σ_ ( _τi − τj_ ) _t_ ; and (2) difference in sectoral income elasticities and real income growth, ( _εi − εj_ ) ∆ _logYt_ .

**Proposition 2** _Relative price between sector i and j is inverse to their productivity levels. Sectors with higher productivity have lower relative prices._

Relative prices are only driven by supply-side forces in the form of differential rates of productivity growth. As agricultural product is set as the numeraire and agriculture sector has the highest productivity growth, prices for all other sectors will grow monotonously over time:

**Proposition 3** _For non-agricultural sectors, sectoral employment/expenditure/nominal output share monotonously increases with real income/welfare, and monotonously decreases with productivity._

_Employment/expenditure/nominal output share of agriculture monotonously decreases over time. For both manufacturing and high-skill tradable service sectors, employment share exhibits an inverted U- pattern over time. Share of low-skill, nontradable services rises monotonously._ The dynamics of sectoral employment share can be derived: In the agriculture sector, where _εA_ = _σ_ , the employment share decreases at a constant rate over time, as ∆ _logLAt_ = ( _σ −_ 1) _τA<_ 0.

For other sectors, the first item (income effect) is positive but decreasing over time, while the second term (price effect) is negative and constant. Given the sectoral income elasticity assumption in 4 and sectoral productivity growth assumption in 7, both manufacturing and high-skill service sectors exhibit 20
an inverted U-pattern in employment share over time. Conversely, the employment share in low-skill, nontradable services rises monotonously.

Higher labor productivity in any sector reduces its employment share. For manufacturing and highskill services, increased labor productivity leads to an earlier, lower peak in employment share, reached at a lower real income level.

This model explains the phenomenon of premature de-industrialization in developing countries, even without direct trade shocks. As these countries industrialize, they can leverage cutting-edge manufacturing technology embodied in imported machinery and equipment. The resulting higher productivity means fewer workers are needed to produce the required output at any given income level.

Furthermore, Proposition 3 predicts a shrinking capacity for job creation in the high-skill service sector and a premature decline in its employment share in developing countries. As total labor input is fixed: The real income growth rate can be derived below: Real income growth is determined by four key factors: sectoral composition, sectoral productivity growth, sectoral income elasticity, and the elasticity of substitution.

**Proposition 4** _As an economy transitions from sectors with high productivity growth to those with low productivity growth, its real income growth rate decreases. In the long run, the growth rate is constrained by the sector exhibiting the slowest technological progress._

Equation 15 demonstrates that higher productivity growth in any sector invariably increases real income growth in each period. The agriculture sector, assumed to have the highest labor productivity growth and the lowest income elasticity, contributes significantly to real income growth when it has a higher initial employment share. In the long term, the employment share in the low-skill service sector approaches one, and the real income growth rate converges to<sup><u>(</u></sup> (<sup>1</sup> _ε_<sup>_−_</sup> _Sl_<sup>_σ_</sup> _−_<sup><u>)</u></sup><sup>_τ_</sup> _σ_<sup>_<u>Sl</u>_</sup> )<sup>.</sup> This convergence highlights the importance of productivity growth in the low-skill service sector for sustaining long-term economic growth.

**From exogenous to endogenous growth**

21
The model can be extended by relaxing the assumption of exogenous and constant productivity growth in each sector. Following Matsuyama 2019, sectoral productivity growth is assumed to be proportional to the employment share in that sector. This assumption is intuitive: as a sector becomes more important in an economy, more research and development efforts are directed towards it. Productivity growth is proportional to sectoral employment share rather than sectoral consumption share (which are equal in a closed economy), as an economy needs to maintain a critical mass of employment in the sector to transmit the tacit knowledge required for production. Under this assumption: Changes in sectoral employment and nominal output share become: All four propositions from the exogenous growth model remain valid. The agriculture sector’s employment share continues to decrease monotonically over time, albeit no longer at a fixed rate. The rate of decrease gradually declines and converges to zero as the agricultural employment share shrinks.

For manufacturing and high-skill services, the first term (supply-side force through productivity growth) is negative and follows a U-shaped pattern, while the second term (demand-side force through higher real income) is positive and decreasing. The net growth starts positive, decreases to below zero, then increases and gradually converges to zero. Employment share in both sectors first increases and then decreases.

For the low-skill service sector, the first term is negative and decreasing, while the second term is positive and decreasing. The net growth remains positive and eventually converges to zero. Real income growth is given by: As the economy transitions from agriculture-dominated to low-skill services-dominated, the denominator increases over time. If _ηSl < ηA_ , the numerator decreases over time, resulting in positive but decreasing real income growth.

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# **3.2 Open economy**

Now consider a global economy comprising multiple countries of varying sizes at different stages of development. Each country has a representative consumer with identical preferences defined by equation 1, and consumption in each sector is determined by equation 3. Production in sector i of country j is given by _Yit_<sup>_j_=</sup><sup>_X_</sup> _it_<sup>_jLj_</sup> _it_<sup>,wherecountriesdifferintheirtechnologylevelsandproductivitygrowthrates</sup> across sectors.

Agriculture, manufacturing, and high-skill services are tradable, eliminating the need for domestic supply to equal domestic demand. Global demand and supply determine prices for each type of tradable good, with the price of agricultural goods normalized to one. For simplicity, assume zero trade costs. The relationship between consumption and production can be expressed as: where _λ_<sup>_j_</sup> _it_<sup>_>_1 indicates that country j is a net importer of goods in sector i at time t, and</sup><sup>_λj_</sup> _it_<sup>_<_1 denotes</sup> that country j is a net exporter.

In any country j producing all four types of goods simultaneously, the wage rate must be equal across sectors, and the marginal revenue product of labor should be consistent across sectors: _w_ = _PAXA_ = _PM XM_ = _PShXSh_ = _PSlXSl_ . This condition ensures labor market equilibrium within each country, as workers would otherwise shift to sectors offering higher wages.

This section examines two extreme cases, as outlined in Rodrik 2016: the large country case and the small country case. In the large country case, prices are determined endogenously by domestic economic developments, with net trade flows acting as exogenous shocks. In the small country case, the economy is a price taker in world markets due to its limited size.

For the large country case, changes in sectoral employment share are given by: Economic growth is determined by: 23
Trade balance requires that expenditure on imported goods equals income earned from exports:

**Proposition 5** _In a large open economy:_

_1. A positive (negative) productivity shock in sector i leads to lower (higher) employment and nominal output share in that sector, and higher (lower) real income._ _2. A positive trade shock increasing net exports in sector i (_ ∆ _logλit<_ 0 _) increases employment and nominal output share in that sector while reducing them in at least one other tradable sector. A negative trade shock has the opposite effect._ _3. If a trade shock shifts employment from high to low productivity growth sectors, real income growth slows in each period. Conversely, if it shifts employment from low to high productivity growth sectors, real income growth accelerates in each period._ A positive trade shock that increases net exports in sector i has an effect similar to a higher relative price of good i, shifting consumption away from that sector.

**Proposition 6** _In a small open economy:_

_1. A positive productivity shock in a tradable sector attracts more labor to that sector, increasing exports and purchasing power for other tradable goods, thus raising welfare/real income. Conversely, a negative productivity shock reduces labor in the affected sector, increases reliance on imports, and decreases real income._ _2. An increase in the global price of tradable sector i reduces domestic consumption while increasing production, leading to higher net exports in sector i and increased net imports in at least one other tradable sector._ _3. A decrease in the global price of tradable sector i increases consumption while reducing employment in that sector, resulting in higher net imports and increased net exports in at least one other tradable sector._ In the small country case, prices for tradable goods are exogenous. Consumption across the four sectors at a given real income level is largely determined by global prices (Equation 24).

24
In an open economy, a small country j has a comparative advantage in producing good i if its theoretical domestic price _Pit_<sup>_j_=</sup><sup>_X_</sup> _XAt_<sup>_j_</sup> _it_<sup>_j_isbelowtheglobalprice</sup><sup>_Pit_.Thisleadstolaborreallocationfrom</sup> other tradable sectors to sector i to capitalize on the higher global price, resulting in exports of surplus production and imports of other tradable goods. The term ”theoretical domestic price” is used because the actual domestic price must equal the global price in the absence of trade costs.

Conversely, if the theoretical domestic price exceeds the global price for good i, country j has a comparative disadvantage. In this case, country j will import good i and shift labor from sector i to the sector with the highest comparative advantage.

Consider a small open economy with a comparative advantage in agriculture. This economy allocates labor only to two sectors: agriculture and nontradable low-skill services. The equal wage rate condition implies: _PAtXAt_ = _XAt_ = _PSltXSlt_ = _PtCt_ .

Trade balance requires that expenditure on imported manufactured goods and high-skill services equals income from agricultural net exports: For low-skill nontradable services, consumption and production must be equal: Solving these equations yields: An increase in global prices for manufacturing or high-skill services reduces the real income of the agriculture exporter, as it can import fewer of these goods. Conversely, a decrease in these global prices raises the country’s real income. Higher domestic productivity in agriculture or low-skill services unambiguously increases the real income level.

This simple model illustrates how global price changes and domestic productivity improvements affect a small open economy specializing in agriculture, highlighting the importance of terms of trade and sectoral productivity in determining overall economic well-being.

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# **3.3 Model validation and AI impact channels**

Most of the six model propositions are intuitive and have been observed for an extended period. Rigorous empirical analysis, presented in Appendix A, strongly supports these propositions. The analysis draws upon multiple datasets covering the past seven decades, providing robust evidence for the model’s validity.

The model demonstrates how technology influences relative prices, nominal and real outputs, sectoral employment shares, as well as real income levels and growth trajectories. It also highlights the distinct effects of technology on large and small open economies through trade. Given that generative AI primarily enhances productivity in high-skill services, the model predicts the following:

- Impact on real income: If AI’s productivity gains are confined to the high-skill service sector, the aggregate growth effect will be limited, as this sector represents a relatively small share of total output and employment. However, AI’s growth impact will be significantly greater if it drives productivity increases across all sectors.

- Impact on sectoral composition: AI’s effect on sectoral employment shares is ambiguous, contingent on its impact on real income and sectoral productivity. In general, AI tends to accelerate the shift toward low-skill services. If its productivity impact is most pronounced in high-skill services, as evidence suggests, it will result in an earlier and lower peak in high-skill services employment at a lower real income level. Similar to the phenomenon of premature deindustrialization, generative AI could lead to ”premature de-professionalization” in many developing countries.

- Impact on comparative advantage and trade: In large open economies, AI is unlikely to alter comparative advantage, as productivity changes are offset by corresponding shifts in relative prices. Over time, AI is expected to reduce employment in high-skill services and drive down global relative prices in this sector. For small open economies, AI presents an opportunity to gain comparative advantage in high-skill services, increasing production, exports, and employment in this sector.

The analysis above assumes that AI does not alter consumer preferences. In the following section, I use model parameters drawn from the literature and empirical evidence to quantify AI’s impact under various scenarios. Beyond exploring different productivity boost scenarios, I introduce a more ambitious scenario in which AI creates entirely new products and industries, leading to a permanent shift in consumer preferences.

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# **4 Simulating AI’s Impact**

This section begins by simulating economic growth and structural transformation patterns in both closed and open economy settings as a baseline. All simulations assume exogenous growth. Appendix B provides additional simulations using varied productivity growth parameters, as well as endogenous growth models with and without inter-sectoral productivity spillovers. The simulated growth and structural transformation patterns closely align with trends observed in the stylized facts, further validating the model’s credibility.

## **4.1 Model parameters and baseline**

### **Closed economy baseline**

First consider a closed economy with exogenous productivity growth. Model parameters are shown in Table 1. The elasticity of substitution is assumed to be 0.5 based on Comin, Lashkari, and Mestieri 2021. Income elasticities across the four sectors are also based on Comin, Lashkari, and Mestieri 2021 and Aguiar and Bils 2015. Labor productivity growth rate is 8% in agriculture (A), 4% in manufacturing (M), 2% in high-skill services (Sh), and 1% in low-skill services (Sl). The sector constant Ω _i_ is 0.9 for A, 0.05 for M, 0.01 for Sh, and 0.04 for Sl. These values correspond to the initial employment share.

|El|asticity of Sub|stitution: _σ_ =|0_._5|
|---|---|---|---|
||Income|Elasticity||
|_εA_ = 0.5|_εM_ = 1.4|_εSh_ = 1.6|_εSl_ = 1.8|
||Labor Produ|ctivity Growt|h|
|_τA_ = 8%|_τM_ = 4%|_τSh_ = 2%|_τSl_ = 1%|
||Sector|constant||
|Ω_A_ = 0.9|Ω_M_ = 0.05|Ω_Sh_ = 0.01|Ω_Sl_ = 0.04|

Table 1: Baseline Model Parameters The simulated real income, sectoral employment and nominal output share over 100 years (T=100) is shown in Figure 11. As discussed previously, real income growth declines over time and gradually converge to<sup><u>(1</u></sup> _ε_<sup>_−_</sup> _Sl_<sup>_σ_</sup> _−_<sup><u>)</u></sup><sup>_τ_</sup> _σ_<sup>_<u>Sl</u>_</sup> = 0 _._ 38%.

Agriculture employment share drops dramatically from 90% at t=0 to around 2% at t=100. Manufacturing share peaks at 17% at t=28 (when real income is 7.4) and decreases to 8% at t=100. High-skill services share ascends from 1% at t=0 to a peak of 8% at t=60 (when real income is 11.4), and gradually declines to 7% at t=100. Low-skill services share grows from 4% in the beginning to 83% at t=100. Real income (or welfare) starts at 1 and reaches 15 at t=100. Real income, which can be interpreted as 27
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demand for novel products, and international trade. The first two mechanisms affect model parameters, while the third is simulated in the open economy model.

I examine three distinct scenarios:

1. AI exclusively increases labor productivity growth in the high-skill service sector. This scenario is the most plausible in the short term, given that finance, information and communication technology (ICT), and professional services demonstrate the highest occupational exposure to generative AI (Figure 10).

2. AI enhances labor productivity growth across all four sectors, with the most substantial increase observed in high-skill services. This scenario is likely to transpire in the medium to long term, as generative AI becomes widely integrated into machinery, robotics, and production processes across sectors, enabling more sophisticated interactions with the physical world.

3. AI not only boosts labor productivity in all sectors but also catalyzes the creation of revolutionary products and industries, leading to a permanent increase in the income elasticity for high-skill services and higher elasticity of substitution between broad sectors. This scenario envisions a transformative future where AI fundamentally alters societal preferences and consumption patterns. Imagine a world where:

- The boundaries between physical and virtual realities blur, with individuals spending an increasing portion of their income on immersive metaverse experiences and hyper-realistic AIgenerated entertainment.

- Every household is equipped with versatile, AI-powered robots that handle a wide array of tasks, from intricate housework to personalized healthcare and eldercare.

- AI-driven personal assistants evolve into indispensable life companions, offering not just task management but also emotional support and intellectual stimulation.

- Novel AI-human collaborative professions emerge, redefining traditional notions of work and creativity.

- AI-engineered sustainable technologies revolutionize energy consumption and environmental preservation, creating new economic sectors centered on eco-friendly innovations.

This scenario, while highly speculative, may only materialize in the longer term if AI capabilities continue to advance exponentially and reshape the fabric of society and the economy.

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For simplicity, the simulation in this section is based on the exogenous growth model. Results from the endogenous growth model yield very similar outcomes.

# **AI’s impact: Closed economy**

I begin by examining a closed economy setting. The parameters for the three generative AI scenarios are as follows:

- Baseline: _εSh_ = 1 _._ 6

- High-skill services (Sh) shock: 50% increase in _τSh_

- All-sector shock: 50% increase in _τSh_ , 5% increase in _τA_ , 10% increase in _τM_ , 40% increase in _τSl_

- All-sector + demand shock: 50% increase in _τSh_ , 5% increase in _τA_ , 10% increase in _τM_ , 40% increase in _τSl_ , _εSh_ increases from 1.6 to 1.7, _σ_ increases from 0.5 to 0.6

Figure 14 illustrates the impact of generative AI on sectoral employment share under different scenarios. Subfigure (a) depicts changes over time, while subfigure (b) shows changes relative to real income.

In the scenario where generative AI only boosts productivity growth by 50% in high-skill services, agricultural employment share remains the same as in the baseline model in each period. Manufacturing and low-skill services experience slightly higher employment shares in each period and at the same income level. High-skill services employment share peaks earlier but at a significantly lower level, with this peak occurring at a lower real income level. Overall real income growth surpasses the baseline scenario in each period, albeit the increase is very modest.

When generative AI boosts productivity growth across all sectors, agriculture share declines more rapidly, manufacturing share peaks earlier at a higher level, with this peak occurring at a higher real income level. High-skill services share is lower than the baseline but higher than in the high-skill services shock scenario. Real income at each period exceeds both the baseline and high-skill services shock scenarios.

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These findings highlight the varying potentials of AI to influence economic growth, with the most substantial impacts emerging from widespread adoption and innovation across multiple sectors.

# **AI impact: Open economy**

In the context of a large open economy that exports an increasing share of high-skill services it produces, advances in AI are poised to propel real income growth. Initially, exports can sustain employment in the high-skill service sector. However, this growth trajectory faces two inevitable constraints: (1) global demand growth will eventually plateau; (2) the continuous enhancement of AI capabilities exerts downward pressure on high-skill services employment. The relentless progression of AI technology will ultimately precipitate a decline in high-skill services employment, unless AI fundamentally alters consumer preferences as described in scenario 3. Even in this optimistic scenario, where AI spawns new markets and shifts consumption patterns, the high-skill services employment share will eventually peak and decrease, albeit at a more gradual pace. The magnitude of the growth impact in the large open economy mirrors that of the closed economy model. This suggests that the domestic structural changes driven by AI have a more profound influence on economic growth than the expansion of export markets for AI-enhanced services.

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<!-- Start of picture text --> A M<br>1 2<br>BIN 15 oo<br>6Sa<br>~Ay /<br>0 Sbbbenentetenesseneseneeet 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>t t<br>Sh Sl<br>15 8 eee<br>05 aa 4 wea<br>7 2 a<br>04+. 04°<br>0 20 40 60 80 100 0 20 40 60 80 100<br>t t<br>—— Baseline Sh shock ------ All-sec shock All-sec + demand shock<br><!-- End of picture text --> <!-- Start of picture text --> A M<br>12<br>8_— 15 Ps =.-<br>—s | VA iar -<br>0 5 10 15 20 0 5 10 15 20<br>Real income/welfare Real income/welfare<br>Sh Sl<br>0 . a 20 - =<br>0 5 10 15 20 0 5 10 15 20<br>Real income/welfare Real income/welfare<br>—— Baseline Sh shock ------ All-sec shock All-sec + demand shock<br><!-- End of picture text -->
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skill services. Instead, it just need to satisfy the following condition: _Pit_<sup>_D_=</sup><sup>_X_</sup> _XAt_<sup>_D_</sup> _it_<sup>_D≤P G_</sup> _it_<sup>=</sup><sup>_X_</sup> _XAt_<sup>_G_</sup> _it_<sup>_G_,wherei</sup> represents either the manufacturing or high-skill service sector.

# **5 AI’s Broader Implications**

The model and simulations presented thus far have illuminated the complex interplay among AI, economic growth, and sector composition, highlighting AI’s differential impact on various types of countries under diverse scenarios. A country’s industrial specialization pattern not only shapes its growth prospects (Hidalgo and Hausmann 2009) but also profoundly influences inequality. Next I discuss the limitations of this simple model and how AI may affect growth and inequality more broadly.

Firstly, the model’s omission of capital overlooks a crucial dimension of AI’s economic impact. AI is likely to further enrich capital owners at the expense of workers, exacerbating a trend already observed in many advanced economies. Digital transformation, automation, and rising industry concentration have contributed to falling labor income shares (D. Autor et al. 2017; Karabarbounis and Neiman 2014; Eden and Gaggl 2019). AI technologies tend to concentrate economic rents among a small cadre of superstar firms, leveraging network effects, economies of scale and scope, and control of core assets to generate significant first-mover advantages (Azoulay, Krieger, and Nagaraj 2024). The adoption of digital technologies and AI often favors large companies. This scale dependence in IT demand further accentuates the rise in concentration and the labor share decline (Lashkari, Bauer, and Boussard 2024). As AI capabilities expand by leaps and bounds, a broader spectrum of jobs could face automation or heightened competition due to lower entry barriers, potentially widening the chasm between labor and capital owners (Frey and Osborne 2024).

Secondly, the model’s exclusion of intermediate inputs in production may underestimate the demand for high-skill services. Many high-skill services, such as business software and professional and technical services, are integral to business production. AI breakthroughs are likely to catalyze business investment in the technology and complementary factors, potentially bolstering employment in high-skill services for a period. Some futurists have long speculated that technological progress will ultimately liberate humanity from mundane labor, leaving only creative pursuits - art, science, entrepreneurship - as viable occupations. The McKinsey Global Institute (2023) projects that generative AI will augment workers in high-skill services, forecasting significant employment growth in STEM fields, business and legal professions, and creative industries between 2022 and 2030.

However, the perpetual growth of high-skill services employment share seems implausible and has 38
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hundred employees despite serving hundreds of millions of users.

Third, the model’s assumption of zero trade costs oversimplifies reality. AI is poised to reduce trade costs by bridging language and cultural barriers, potentially stimulating global trade and investment in specific sectors. Developing countries with comparative advantages in manufacturing and high-skill services that successfully adopt AI stand to benefit significantly. Generative AI may catalyze a new wave of services outsourcing and offshoring, creating opportunities for some developing nations, albeit with limited job creation potential.

Fourth, the model’s assumption of homogeneous, freely mobile labor across sectors, resulting in equalized income, diverges from reality and fails to offer insights on inequality. Substantial wage disparities exist across industries, with between-industry income inequality emerging as a major driver of broader inequality (Eckert, Ganapati, Walsh, et al. 2019; Haltiwanger, Hyatt, and Spletzer 2023). Individual intentionally sort into different sectors and occupations based on their characteristics. Agricultural workers often lack the education and skills necessary for transitioning to manufacturing or high-skill services. Similarly, manufacturing and low-skill service workers face significant barriers to entering high-skill services. Geographical constraints further impede cross-sector job mobility, particularly for rural residents with limited local job diversity. Generative AI may lower entry barriers for certain high-skill service jobs, potentially suppressing average wages and reducing inter-occupational income inequality.

Recent studies on the distributional effects of generative AI within occupations yield mixed results. Some research indicates that newer, less skilled workers benefit more from generative AI (Brynjolfsson, Li, and Raymond 2023; Noy and W. Zhang 2023; Peng et al. 2023; Dell’Acqua et al. 2023). Conversely, other evidence suggests that AI and ChatGPT have significantly reduced translation tasks and earnings, with more pronounced negative effects on low-skill workers performing low-value tasks (Yilmaz, Naumovska, and Aggarwal 2023; Liu, Deng, and Monahan 2024). While generative AI may enable mediocre white-collar workers to undercut their highly skilled peers, potentially reducing within-occupation income inequality, the increased interchangeability of workers could suppress wages across entire occupations (Capraro et al. 2024; Frey and Osborne 2024).

Sixth, the model does not account for the effect of inequality on growth. Historical precedent suggests that digital technologies have fostered the rise of superstar firms, cities, and nations, exacerbating inequality. Workers and firms lacking adequate digital infrastructure, training, and access to generative AI tools may struggle to capitalize on productivity gains, widening the gap with better-resourced competitors or colleagues. If generative AI automates a broad spectrum of existing jobs without creating commensurate high-skill, well-paid new occupations, it may precipitate a downward shift in the labor market: skilled 40
workers may be pushed into lower-paid, simpler tasks, while the lowest-skilled workers risk unemployment altogether (Acemoglu and D. Autor 2011). In a scenario where generative AI amplifies inequality and triggers social unrest, sustaining economic growth becomes increasingly challenging (Aghion, Caroli, and Garcia-Penalosa 1999).

Additionally, generative AI will affect individuals’ incentives for acquiring education and skills. Traditionally, high-skill, well-paid jobs have demanded specialized expertise, with the promise of higher income and more fulfilling careers motivating individuals to invest in college degrees and advanced training. However, technological advancements continuously reshape the landscape of valuable skills, rendering some expertise obsolete while creating demand for new competencies.

The current competition for scarce generative AI talent exemplifies this shift, with annual compensation packages reaching a million dollars for top professionals<sup>1</sup> . Simultaneously, expertise in other fields is being devalued. If AI leads to a decline in the proportion of high-skill, well-paid jobs in the long term, individuals may be discouraged from investing in higher education due to diminished prospects of securing rewarding employment (Capraro et al. 2024).

Evidence suggests this trend is already underway. In the United States, the percentage of high school graduates immediately enrolling in college has decreased from 69% in 2018 to 62% in 2022<sup>2</sup> . Moreover, parents and students are increasingly questioning the value of a college degree, with approximately half of U.S. adults believing that a four-year degree is less important for obtaining a well-paying job today than it was two decades ago.<sup>3</sup> This paper underscores risk of ”premature de-professionalization” - the potential contraction of opportunities for well-paid jobs in high-skill services within developing countries. This could potentially lead to widespread underemployment, reduced social mobility, and disillusionment among younger generations. While economists have historically argued that technological advancements create more jobs than they eliminate, this optimism may not hold in the AI era. Acemoglu and Restrepo 2019 has documented an acceleration in automation coupled with a deceleration in the creation of new tasks. Digital technologies and AI have eliminated millions of middle-skill, middle-income routine jobs while generating low-barrier, poorly-paid, precarious positions in low-skill services and the gig economy. In developing countries, the current high employment shares in agriculture and low-skill services mask underemployment on a grand scale, as these sectors often serve as last resorts for those unable to secure more promising opportunities.

> 1The Fight for AI Talent: Pay Million-Dollar Packages and Buy Whole Teams. https://www.wsj.com/tech/ai/the-fightfor-ai-talent-pay-million-dollar-packages-and-buy-whole-teams-c370de2b

> 2Immediate College Enrollment Rate, https://nces.ed.gov/programs/coe/indicator/cpa

> 3Roughly half US adults say it’s less important to have a four-year college degree today in order to get a well-paying job than it was 20 years ago. https://www.pewresearch.org/social-trends/2024/05/23/is-college-worth-it-2/

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As automation and AI challenge the traditionally successful manufacturing-led development strategy, some developing nations are pivoting towards services-led growth. However, if AI advancements ultimately reduce the employment share in high-skill, tradable services as the model predicts, developing countries face a narrow window of opportunity. They must swiftly adopt AI to establish and entrench their comparative advantage in this sector, outpacing their peers. Late adopters will likely confront intensified competition, elevated automation risks, and a diminishing pool of high-skill service jobs. Consequently, these economies risk becoming trapped as commodity exporters, further widening the global economic and social divide.

# **6 Conclusion**

This paper presents a multi-sector growth model that dissects the general equilibrium impact of AI on economic growth, structural transformation, and labor markets across various scenarios. The model propositions not only align with empirical evidence but also sound a stark warning: AI will shrink the space for countries to generate well-paid jobs in high-skill services.

The simulations deliver a sobering message: unless AI can be widely adopted across sectors and spark truly paradigm-shifting innovations that fundamentally alter consumer preferences, its growth benefits are likely to be limited. In contrast, its disruptive impact on labor markets threatens to be deep and far-reaching. This grim outlook is compounded by the well-documented premature de-industrialization plaguing developing countries. Now, even a services-led growth strategy is becoming increasingly elusive, thwarted by relentless technological progress and non-homothetic preferences that favor less-tradable, predominantly low-skill services.

The paper highlights the urgency for developing countries to embrace AI and cultivate a comparative advantage in more complex and growth-enhancing sectors. Currently the world is still in the nascent stages of AI development and deployment, creating a temporary expansion in demand for high-skill service jobs. Generative AI’s ability to bridge language and cultural barriers promises to reduce trade costs and spark a new wave of services outsourcing and offshoring. However, this opportunity will not persist indefinitely.

The stakes for developing countries are immense. Those that fail to swiftly adopt AI risk being ensnared in a commodity exporter trap, condemning their youth to a future blighted by massive underemployment, dwindling social mobility, and potentially declining living standards.

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