Tech wars: Distributional consequences of global tech rivalry

• Published date: 01 September 2024
• Author: Rod Tyers,Yixiao Zhou
• Date: 01 September 2024
• DOI: http://doi.org/10.1111/asej.12335

ORIGINAL ARTICLE

Tech wars: Distributional consequences of global

tech rivalry

Rod Tyers

1

| Yixiao Zhou

2

1

Business School, University of Western Australia, Perth, Australia

2

Crawford School of Public Policy, The Australian National University, Canberra, Australia

Correspondence

Rod Tyers, Business School, University of Western Australia, Perth, Australia.

Email: rod.tyers@uwa.edu.au

Abstract

International competition over sophisticated tech is a modern feature of great power rivalry.

Yet the automation delivered by this tech is a key source of rising inequality. While policy

motivations stem primarily from great power political and defense competition, the automa-

tion has consequences for wider economic performance. We examine global economic conse-

quences, using a six-region global macro model with multiple households, under Rawlsian,

Benthamite, capital friendly, or GDP maximizing policy criteria. Tech drives are shown to

deliver higher capital returns and more growth, and therefore to represent dominant strategies

under all but a Rawlsian criterion, despite their exacerbation of inequality and low-skilled pov-

erty. We then consider Gini-reducing fiscal interventions. These are shown to have few interna-

tional spill-over effects and to be domestically attractive only under the Rawlsian criterion.

KEYWORDS

automation, global modeling, income distribution, taxes, tech rivalry, transfers

JEL CLASSIFICATION

C68, D33, F21, F42, O33

1|INTRODUCTION

International competition over the most sophisticated tech has heated up

recently, as it approaches the forefront of great power rivalry.

1

Although the

1

For very recent writings on the tech war, see, for example, Miller (2022) and BBC (2023).

DOI: 10.1111/asej.12335

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License,

which permits use, distribution and reproduction in any medium, provided the original work is properly cited and

is not used for commercial purposes.

© 2024 The Author(s). Asian Economic Journal published by East Asian Economic Association and John Wiley &

Sons Australia, Ltd.

Received: 30 November 2023; Accepted: 26 June 2024

Asian Econ. J. 2024;38:289–340. wileyonlinelibrary.com/journal/asej 289

tech race is important for strategic interaction between great powers, it also has

consequences for overall economic performance and, most concerningly, domes-

tic inequality. The modern prominence of tech developments notwithstanding,

since the early 2000s there has been a decline in investment as a share of GDP in

the advanced economies, one consequence of which has been a slowdown in the

growth rate of total factor productivity (TFP).

2

This apparent contradiction is

the “Solow paradox.”

3

The deterioration in overall economic performance in the advanced econo-

mies has accompanied a steady decline in the domestic share of the low-skilled

in value added that extends to the transitional economies.

4

Explanations posited

for this are numerous. They include: (1) the surge in labor intensive imports

from low wage economies (Autor et al., 2013; Tyers, 2015,2016); (2) the rise of

intellectual property products as components of intangible capital (Koh

et al., 2016); (3) the interaction between IT development and the diminution of

competition within IT-intensive oligopolies (Moazed and Johnson, 2016); and

(4) the wider displacement of workers by increasingly intelligent machines

(Acemoglu and Autor, 2011; Acemoglu and Restrepo, 2020; Autor, 2016).

Indeed, the latter effect has been the subject of detailed assessments at the insti-

tutional level (World Bank, 2017).

Here, we focus on the implications of tech as a source of economic expan-

sion that embodies bias in favor of capital and against low-skill labor, incorpo-

rating into our analysis fiscal responses that constrain the expanding

inequality.

5

We use a six-region global model to examine changes due to tech

shocks on region-specific economic performance and inequality.

6

We abstract

from the detailed interactions, most especially within labor markets, that stem

from major tech shocks, since these are reviewed extensively in the literature

already cited. Because TFP performance has been poor in the OECD, while

shifts in bias against the low-skilled have continued, we characterize the auto-

mation that stems from tech shocks as a continuation of the recent pattern of

changes in factor shares. We bear in mind that TFP neutral shocks that simply

enhance the capital share at the expense of the labor share are nonetheless

growth-enhancing by virtue of their effects on capital returns and investment.

2

See Eichengreen et al. (2017) and Taylor and Tyers (2017).

3

This stems from Robert Solow’s comment in his 1987 New York Times Book Review article: “…what everyone

feels to have been a technological revolution, a drastic change in our productive lives, has been accompanied

everywhere, including Japan, by a slowing-down of productivity growth, not by a step up. You can see the

computer age everywhere but in the productivity statistics.”

4

See Bloom et al. (2018).

5

The analysis by Tyers and Zhou (2022) uses a closed, single country model and thus ignores consequences for

trade and investment between regions. Here we embed the same behavior in a global model so that these

consequences are accounted for.

6

While alternative perspectives on this change come under the general headings, “automation,”“robotics,”“AI,”

“digitalization,”and “computerization”, we refer to the collective of technical changes that save labor by using

more composites of skill and capital as “automation”and so treat automation as change that causes the share of

low-skill labor in total factor income to decline and the capital share to rise.

290 TYERS and ZHOU

To achieve this characterization, we employ a global macro model on six

regions. While numerous models of the global economy exist, this model is spe-

cial for our purpose in that (1) for each region it includes three separate house-

holds: low-skill, skilled and capital-owning, (2) it embodies a technology

specification that allows the separation of changes in factor bias from the neu-

tral components that simply raise TFP, (3) it represents both financial and trade

interactions between regions, and (4) it includes the full raft of monetary and fis-

cal interventions by governments and central banks.

7

We can thereby project

the global economy over decades under pure factor bias shocks that nonetheless

stimulate growth by raising current and expected capital returns and increasing

saving by virtue of income concentration, but which continue to reduce the low-

skill share of value added. Initial experiments examine whether, and under what

criteria, such automation is domestically beneficial when regions are economic

rivals. These suggest that joining the tech race, and thereby fostering further

domestic automation, is a dominant strategy under all but a Rawlsian criterion

that considers only the welfare of low-skill households.

8

Once low-skill worker displacement is significant, however, compensatory,

and hence inequality constraining, policy interventions become attractive for

preserving political stability. Such policies include labor market interventions

such as minimum wages and trade policy, the latter offering protection to low-

skill intensive industries. Least distortionary, however, are fiscal transfers. While

debate continues as to the best approach to addressing inequality via fiscal pol-

icy, with many favoring the universal basic income (UBI), the compensatory

policy regime we consider is a generalized “earned income tax credit”(EITC)

arrangement financed by additional taxation. This concept addresses inequality

while at the same time preserving strong macro performance.

9

In our results,

however, in part because additional taxes are required to finance it, this seem-

ingly sensible option emerges as marginally negative, at least under a Bentham-

ite criterion, which adds pecuniary welfare measures across households. It is

also marginally negative under a simple real GDP maximizing criterion. Not

surprisingly, however, EITC compensation is favored under the Rawlsian crite-

rion and rejected under a capital income criterion.

7

Our three-household types represent a simplification relative to the more complex, heterogeneous agent, models

have been recently applied to the linking of inequality to macro policy, such as those detailed by Kaplan et al.

(2023). Given the regional diversity indicated by Alvaredo et al. (2017), our approach is practical in the global

context, in the presence of multiple, differing, regions, and it more readily accommodates the simple technique we

use to separate neutral and biased technical change.

8

That automation is, in part, a policy choice as suggested by China’s immense public investments in it (State

Council, 2015).

9

Our earlier work (Tyers and Zhou, 2022) supports the EITC. The boundaries of the debate between the EITC,

the universal basic income (UBI) and minimum wages are spelled out clearly by Burkhauser and Corinth (2021).

What emerges is that no one of these approaches is sufficient to solve both welfare and inequality problems, but

the incentive to work is a critical element of the EITC.

ASIAN ECONOMIC JOURNAL 291