The Nature of technology, structuraltransformation, and the nature of work
1 Introduction
Technology is a fickle thing. During the century after 1870, rapidly advancing, labour-saving
technology pulled workers into the manufacturing sector. This didn’t fundamentally change the natureof work – most people were still working with their hands – but it changed what they were making
and where. During the same period, labour’s share of GDP was stable, and income inequality was
falling. And then the correlations flipped.
During the half century following 1970, rapidly advancing, labour-saving technology pushed workers
out of manufacturing. It also deeply transformed the nature of work for many. Most workers switched
to service-sector jobs that involve less ‘hand work’ and more ‘head work’. At the same time, labour’sshare of GDP fell, and inequality rose (see the charts).
How could technology have such radically different effects during these two different eras?

This paper, which is very much in draft form, has two goals. The first is to crystallise – in aparsimonious general equilibrium setting – a simple accounting of the differences between the jobstechnology link from 1870-1970, and the link from 1970-present. The account rests on the assertion
that that outcomes were radically different because the technological shocks were radically different.
The pre-1970 developments were driven by mechanisation; the post-1970 outcomes were driven bycomputerisation.
Mechanisation favoured the making of things. The resulting automation directly boosted the
productivity of people who worked with their hands. It helped people who worked with their heads,
but only indirectly because it was a technology of things, not thoughts. It created masses of new1

industrial jobs. Moreover, since most people back then worked with their hands, the more-manualthan-mental aspect of the mechanisation lowered inequality by boosting the relative productivity of‘hand workers’ versus ‘head workers’. The 1970s technological impulse did just the opposite.
Computerisation provided better tools for those who work with their heads, but better replacements
for those who work with their hands. Creating better replacements for factory workers – robots andthe like – was a massive push-factor that emptied factories even as industrial output continued to rise.
The better tools for brain workers, by contrast, was a massive pull-factor into the service sector. It
created millions of new service-sector and professional jobs – many of them in occupations that werepreviously unimageable. From the social cohesion point of view, the new technology was divisive.
Since the ‘head workers’ were already better off than the ‘hand workers’, a technology whichfavoured brain over brawn favoured the few who were already favoured, while disfavouring the many
who weren’t.
The second goal is to contribute to the ‘future of work’ discussion by using general-equilibrium
insights to address how the nature of future technology will affect the nature of future work. While wecannot know the names of future jobs, general equilibrium considerations allow us to think about thenature of those jobs. Here again, the main point of departure is the assertion is future technology islike to be very different than computerisation, or mechanisation.
The recent commercialisation of Artificial Intelligence (AI) – most notably in the form of machinelearning – is bringing a radically different automation to the workplace. In a nutshell, AI-trained
computer models are allowing ‘software robots’ to ‘think’ in ways they never could before. Machinelearning combine with big data has given computers a brand-new range of cognitive skills and someof these are useful in workplaces, especially offices. Machine learning, in other words, is providing
better substitutes for ‘head workers’ – or at least when it comes to certain tasks that previously could
only be done by humans. This could have big effects for a very simple reason. 80-90% of theworkforce is in the service sector.
Automation of factories will surely continue, but since such a small share of the 21st century
workforce is in factories, the impact on office work is what will really affect the nature of work forsocieties as a whole. The key point is that many ‘thinking tasks’ are being automated – a range oftasks that used to require humans can now done by ‘white collar robots’ such as Robotic Process
Automation systems, virtual assistants, and sophisticated AI platforms like IBM’s Watson. This waveof technology is, in other words, is providing better substitutes for people who worked with theirheads because it was a technology of cognition, not construction. This is likely to bring about
important changes.
Until recently, most white-collar, service-sector, and professional jobs were shielded from automation
by humans’ cogitative monopoly. Computers couldn’t think, so jobs that required any type of thinking– be it teaching nuclear physics, arranging flowers, or anything in between – required a human.
Automation was a threat to people who did things with their hands, not their heads. Indeed, in many
empirical studies, routine-ness is still used as a proxy for automation. Machine learning is changing
this.
Plan of paper. The paper is organised in three substantive sections in addition to the introduction andconcluding remarks. The first section looks at implications of mechanisation, the second looks atcomputerisation, and the third uses the same framework to look at the potential impact of AI.
2

Literature review (incomplete). The first two sections could be viewed as working in the structuraltransformation literature. Here some key papers are including Matsuyama (1992, 2009), Hémous, andOlsen (2014), Caselli and Coleman (2001), Ngai and Pissarides (2007), Herrendorf, Schmitz, andTeixeira (2012), Michaels, Rauch, and Redding (2012), , Bustos, Garber, and Ponticelli (2017),
McMillan, Rodrik, and Sepulveda (2017), Eckert and Peters (2018), Karádi and Koren (2018), andSotelo (2018). See Herrendorf, Rogerson and Valentinyi (2014) for an overview.
Matsuyama (2009) for a very succinct modelling of the two traditional drivers of structural changethat Acemoglu and Guerrieri (2008) call “demand-side” (non-homothetic preferences) and “supplyside” (biased technical progress) explanations. Ngai and Pissarides (2007) refer to them as the “utilitybased” and “technological” explanations, respectively.
In terms of modelling of the technological differences between mechanisation and computerisation,
the key references are various papers by Acemoglu and co-authors, especially Acemoglu andRestrepo (2016) who distinguish ‘enabling technologies’ (factor augmentation) and ‘replacing
technologies’ (factor replacing). Acemoglu and Autor (2010) provide a clear, and empirically motived
discussion of the discussion of the differences and economic mechanism at play.
These articles build out from the very extensive empirical literature on the Skill-Biased Technical
Change (SBTC) hypothesis posited by, inter alia, Freeman (1976), Katz and Murphy (1992), andAutor et al. (1998, 2008), and tested empirically by, for example, Katz and Murphy (1992), Autor
Katz and Krueger (1998), and Autor, Katz and Kearney (2008), Hemous and Olsen (2014). The
hypothesis is that a burst of new technology caused a rise in the demand for highly skilled workers
that outstripped the supply and thus created inequality.
A related set of contributions in the trade theory literature concerns the impact of technology on
wages and outputs. This work last garnered substantial attention during the late 20th century when theissue was whether the deleterious developments in rich nation labour markets were due to ‘skill biased
technological progress’ or globalisation. Most notable was the robust Krugman-Leamer exchange ofviews, e.g. Leamer (1994, 1996) and Krugman (1995). Other noteworthy contributions were made by
Wood (1994), Jones and Ruffin (2007), and Xu (2000).

2 Technology as machines helping manual workers
A worker with an electric drill can do the work of, say, three workers using hand drills. At least since
Robert Solow’s famous work on growth in the 1950s, economists tend to think of technologicalprogress in this way – machines helping humans do more (this is also called the ‘factor augmenting’view).
From a partial equilibrium perspective, this sort of innovation cuts both ways when it comes to jobs
(Ngai and Pissarides 2007). Mechanization means that the same pile of work can be done with fewer
workers. All else equal, that makes technology into a job-destroying force. But the implied costsavings means lower prices and thus more sales (a higher pile of work) and this tends to maketechnology into a job-creating force. There is, in a sense, a footrace between the height of the pile andefficiency of workers. When the productivity-production footrace is won by the piling-raising side –technology acted as a ‘pull factor’ – pulling workers into the sector. Where the efficiency-side wins, it
is a ‘push factor’, pushing workers out of the sector.
3This partial equilibrium view misses some critical elements. If technology is a push factor, where do
the displaced workers go? If it’s a pull factor, where do they come from? This is where generalequilibrium (GE) analysis becomes important. Standard GE insights, however, are difficult to apply
since GE insights link price and endowment shocks to wages, and outputs – not jobs. There is,however, an intellectual sleight-of-hand – first demonstrated by Jones (1965) – that gets past this.
A few moments reflection reveals that under the machines-helping-humans conceptualisation ofprogress, one worker with a better drill can be thought of as three workers using the old technology.
This reflection – the Jones’ Equivalence –reduces a new problem – what will better robots do to
output? – to a problem previously solved – what happens to output when factor endowments change?
The solution to the ‘problem previously solved’ has a name – the Rybczynski theorem. It says that anincrease in the supply of one factor tends to expand the output of the sector that in intensive in its use.
Moreover, the expansion is more than proportional, and other sectors tend to contract.
A slight twist on this result gives us the jobs impact. As it turns out, the employment impact turns on
the magnification aspect of Rybczynski. A tech shock that is Jones-equivalent to an expansion of the
supply of A-workers will expand output of the A-intensive sector more than proportionally. Thismeans it will create new jobs for A-workers in the A-intense sector since the output expansion morethan compensates for the efficiency gain. This provides insights about the nature and sector of futurejobs – even if it does not tell the names of the new jobs.
This GE insight may seem counterintuitive from the partial equilibrium angle. After all, it says thatthe new jobs, the jobs of the future, will be relative intensive in the use of the type of labour thatrobots are replacing today. And the new jobs will appear in sectors that are relatively intensive in thetype of labour with which robots are competing most directly. The insight, by contrast, is obvious
from the general equilibrium perspective. If an economy gets more unskilled workers, the unskilledintensive sector must expand to absorb them all. Since this expansion will require some skilledworkers – and these need to come from somewhere – the skill-intensive sector must shrink.
This section presents a minimalist framework that permits crystallisation of the general equilibrium
insights. It offers an economically logic accounting for why jobs were created fastest for workers who
were most directly concerned with the progress and in sectors where these workers were used
intensively. In short, it is an accounting of how mechanisation led to an expansion of the
manufacturing sector. The model presents a version of the Rybczynski analysis in Baldwin, Haalandand Venables (2019); it is simplified in terms of the technology but set in an imperfectly competitive
setting that allows us to talk about new firms, new products, and new jobs.

2.1 The basic GE model – Helpman‐Krugman meets Jones
The economy has two productive factors (LA and LB) and two sectors (1 and 2) both of which aremarked by Dixit-Stiglitz monopolistic competition. Preferences are CES within sectors and CobbDouglas across sectors with 𝜇 as the expenditure share on sector-2 varieties.
Production of each variety is marked by increasing returns defined by homothetic, linear cost𝑤 𝑎
𝐹 ; 𝑖 1,2. Here the 𝑤 𝑗 𝐴, 𝐵 are wages,
𝑤 𝑎𝑎𝑥functions: 𝑐 𝑤 , 𝑤 , 𝑥
the 𝑎 𝑗 𝐴, 𝐵, 𝑖 1,2 are the unit, labour-input requirements, and 𝑎 and 𝐹 are the variable- andfixed-costs parameters. Sector-1 production is intensive in ‘manual’ tasks and sector-2 is intensive in‘thinking’ tasks; A-workers are abundant in manual ‘skills’ (M-skills) and B-workers are abundant in4‘thinking’ skills (T-skills). The factor-intensity assumption, written using the factor-intensity ratios,
𝜆 , so A-workers are used intensive in sector 1 while B-workers are used
𝜆 ≡ 𝑎 ⁄𝑎 , is 𝜆intensively in sector 2.
This section simplifies by assuming A-workers have only M-skills, and B-workers have only T-skills,
so there is a perfect congruence between worker-type and sector skill-intensity. The regularity
Λ 𝜆 , where Λ is thecondition that ensures firms operate in both sectors in equilibrium is 𝜆endowment ratio.
As per Dixit-Stiglitz monopolistic competition, each firm produces a unique variety, and optimally𝑤 𝑎𝑤 𝑎𝑎 ⁄ 1 1/𝜎 , where 𝜎 isprices it as a mark-up over marginal costs, i.e. 𝑝
the sector-i CES elasticity of substitution. Choosing units such that 𝑎 ⁄ 1 1/𝜎
1, and solving
the free entry conditions, the equilibrium scale of firms, 𝑥̅ , and prices, 𝑝 , are 𝑝
𝑤 𝑎
𝑤 𝑎 ,
𝑥̅
𝐹 𝜎 ; 𝑖 1,2. With this, the closed economy equilibrium is characterised by employment,
pricing, and market-clearing conditions, namely:1
𝒑

𝑨𝒘,

𝑨′ 𝑿,

𝑳

𝑝

𝜇𝑋 ⁄ 1

𝜇 𝑋

𝑿≡

𝑎
𝑋
,𝑨 ≡ 𝑎𝑋𝑎𝑎2

where the prime indicates the transpose and:
𝑤
𝒘≡ 𝑤

,

𝒑≡

1
,
𝑝

𝑳≡

𝐿
𝐿

,

, 𝑋 ≡ 𝑛 𝑥̅Here 𝑛 is the mass of products/firms in sector-i.
Clarity is served by adding a few assumptions that unclutter the formulas in this and subsequent
sections by eliminating asymmetries that are unrelated to factor intensity. The number of A and B
𝐿 ≡ 𝐿), expenditure shares are equal (𝜇 1/2), and the endowment ratio isworkers is equal (𝐿such that Λ𝜆𝜆 ⁄2. Solving for the number of firms/products and the number of jobs per
sector per labour type yields:
𝑛

𝑎 𝐿
𝑎 𝑎

𝑎 𝐿
𝑎 𝑎

1
,
𝐹𝜎

𝑛

𝑎 𝐿
𝑎 𝑎

𝑎 𝐿
𝑎 𝑎

1
,
𝐹𝜎

𝐽

𝑎 𝑛 𝐹𝜎3where 𝐽 is the number of jobs for i-type workers in sector j.
Observe how these solutions for the n’s, and thus output and jobs, are determined entirely by
employment considerations. This narrows the focus to the analysis to the pure, GE effects; the 𝑛 ’s,
outputs, and thus jobs, respond to neither relative prices nor relative wages. As a result, the classic,
structural-transformation considerations of demand elasticities and factor substitutability (Ngai and
Pissarides 2007) are left aside. This is intentional and it is meant to clarify the GE mechanisms ofaction.

2.2 Conceptualising technology – machines helping humans do more
Technological progress is conceptualised as reductions in the unit labour-input requirements (the𝑎 ’s). The Jones’ equivalences between such factor-augmenting tech shocks and endowment and1

All section 1 varieties always have the same price so there is no ambiguity as to the numeraire, 𝑝.
5price shocks are simple.2 Technical progress means a fall in some or all of the 𝑎 ’s. The vector oflabour savings, 𝑺𝑸 , and vector of cost savings, 𝑺𝑪 , are:
𝑺𝑸 ≡

𝑆
𝑆

𝑨𝑺

𝑆
𝑋
, 𝑺𝑪 ≡𝑋𝑆

𝑨 𝒘

where 𝑨′ ≡

𝑑𝑎
𝑑𝑎

𝑑𝑎
𝑑𝑎

4

and all elements of the 𝑨 and the 𝑺’s are positive.
With these, the employment condition becomes 𝑳 𝑺𝑸 𝑨 ′𝑿, so we can view any tech shock as anendowment shock, and thus use the Rybczynski insights to address the jobs question. The pricing
equations become: 𝒑 𝑺𝑪 𝑨𝒘, so the Stolper-Samuelson insight speak directly to tech-wages
questions.
Direct calculation using (3) and (4) yields:
Result 1: Rybczynski theorem with a tech twist: Technical shocks impact output as follows:
𝑆 ), creates more
1) Sign effects: Any tech shock that saves more A- than B-labour (i.e. 𝑆
new firms/products in the A-intense sector than in the B-intense sector (i.e. 𝑛𝑛 ). The𝑆 .
opposite holds for 𝑆
a. This means that only the skill-bias of the tech shock matters for the sign of therelative output effect; the tech shock’s sectoral composition is irrelevant.
2) Magnification effects:
a. For pure skill-biased shocks (e.g. 𝑎
𝑎 , 𝑗 𝐴, 𝐵), the standard Rybczynski
effect applies without modification. The ‘own sector’ 𝑛 expands more thanproportionally, where ‘own sector’ means the sector intensive in the use of the factor
experiencing the productivity gain. The other sector contracts.
b. For pure sector shocks (e.g. 𝑎𝑎 , i=1,2), the affected sector’s output expandsin the same proportion as the labour-savings shock, but the other sector’s output is𝑎
0 yields 𝑋
𝑎 ,𝑋0).
unchanged (e.g. 𝑎i. This result is obvious since a sector-specific tech shock expands theendowment in exactly the same proportions as the affected sector uses
factors, so all the saved factors are employed in the progressing sector.
𝑎 , 𝑗 𝐴, 𝐵, 𝑗 1,2), all sectors expand inc. For Hicks Neutral shocks (i.e. 𝑎
proportion to the shock.
Sector- and factor-specific shocks are a mixture of 2a) and 2b). See Appendix for the general analysis.
One sector- and factor-specific shock that is particularly relevant to the discussion is a skill-specificand sector-specific advance that boosts A-worker productivity in the A-intense sector 1. Plainly,𝑆0, so the result is an output expansion of sector 1 than is more than proportionate to the𝑆tech shock, i.e. 𝑛
about jobs?

𝑋

a

0

𝑋

𝑛 . Result 1 tells us what happens to output, but what

As it turns out, the magnification effect is critical. If jobs are to be created in a sector, the number of
new firms has to expand more than proportionally compared to the labour-saving shock. To wit:
2This strict correspondence between technical change and endowment changes was introduced
formally in Jones (1965 Section IX).
6

Result 2a: Rybczynski theorem with a jobs twist: The jobs impact of tech-shocks are marked by:
1) For pure skill-biased shocks, technical progress that raises productivity only of, say, Aworkers creates new jobs in the sector that uses A-workers most intensely (sector 1); it
destroys jobs in the other sector. NB:
a. These new jobs involve producing new, previously unknown products, so we cannot
know the names of the new jobs but we can know the sector in which they willappear.
b. The job creation extends to both types of workers in sector 1.
c. The shock destroys jobs for both types of workers in sector 2 since it leads to exit ofsector-2 firms and elimination of their products from the market.
Progress biased towards B-workers has the opposite effect.
2) For pure sector shocks technical change that raises productivity A- and B-workers equally
but only in one sector produces no job creation or destruction in either sector.
3) For Hicks Neutral shocks, technical change that raises productivity A- and B-workers
equally and in both sectors produce no job creation or destruction in either sector.
See the appendix for details.
A similar exercise using the Stolper-Samuelson theorem shows that real wage effects depend upon the
sector in which the technical progress occurs, not the factor. This is exactly the opposite of the jobs
impact where it was the factor that mattered, not the sector. In particular, pure sector-biased
productivity advances raise the real wage of workers used intensively in the advancing sector; theother type of workers lose. A productivity gain that saves the same amount in both sectors, in the
𝑆 , will raise real wages in both sectors.
sense that 𝑆
Result 2b: Stolper-Samuelson theorem with a tech twist:
1) For pure skill-biased shocks, technical progress that raises productivity only of, say, only A𝑆 , and negative otherwise. Given the imposed symmetry,
workers is non-negative if 𝑆
this holds, so A-workers gain. B-workers lose as per the usual Stolper-Samuelson logic.
Progress biased towards B-workers has the opposite effect.
2) For pure sector shocks technical change that raises productivity A- and B-workers equally
but only in one sector produces a real wage rise for the type of labour used intensively in theadvancing sector; the real wage of the other type of labour falls.
3) For Hicks Neutral shocks, technical change that raises productivity A- and B-workers
equally and in both sectors produce raises all real wages proportionally.
See the appendix for details.
With these results in hand, we turn to the discussion of how biased technological progress could
account for the first grand structure transformation and the inequality results that followed it.

2.3 An interpretation of the results
If we think of manufacturing as sector 1 and A-workers as manual workers, the great technologicalinnovations of the 18th and early 19th centuries were mechanisation of manufacturing. In the modelthat corresponds to very lopsided reduction in the amount of labour needed to produce any
manufactured good, namely 𝑎 . To be concrete, and to clarify the economic logic, assume that the
only thing mechanisation did was reduce 𝑎 .

7

What Result 2 tells us is that the shock will create jobs for A-workers in manufacturing and destroyjobs for manual workers in the other industry. The result is that the share of workers in manufacturing
would rise. For a partial equilibrium perspective, it might seem odd that workers were moving into thesector where automation was advancing fastest, but GE considerations – totally apart from the classic
price-lowering-demand-expanding effects – link automation with job creation. As shall become clear
when we contrast this with a different type of technology in the next section, this result is entwinedwith the nature of technology shock.

2.3.1 Technology and the nature of work
During the shift of workers into factories in the 1870-1970 period, people had little idea of the namesof the new jobs that would appear in the manufacturing sector. After all, all sort of products involving
electricity, lighting, telecommunications, internal combustion engines, and industrial chemistry where
invented well into the structural transformation. The light bulb, the radio, and the electric fan, just to
take a few examples, were invented in invented in 1879, 1899, and 1882 (respectively). This isexactly how it happens in the model. The expansion of sector 1 involves the introduction ofpreviously unknown varieties.
But while the names of the new jobs were unknowable, GE considerations shed light on the nature of
the new jobs. Since the technology was sparing of manual labour, the new jobs had to be manuallabour intensive. Moreover, they had to be in the sector that used manual labour intensively.

3 Technology as better a substitute for manual workers
and better tools for brain workers
The technology in the previous section was conceptualised in a standard way – sometimes called the‘factor augmenting’ technical progress. This approach makes strong (implicit) assumptions about therole of humans in production. By channelling all technology improvements through human capacities,
this approach makes it impossible, or at least very awkward, to take the human out of the production
process. That is exactly why we got the somewhat counterintuitive result that the technology wasinevitably job-creating in the sector where it advanced fastest. But in the 21st century is this still theright way to conceptualise technology?
There is an argument that the nature of technological advances changed qualitatively sometimearound 1970 with the advent of computerisation – what some call the ‘third industrial revolution’.
Industrial applications of computers and telecommunications developed gradually from WWII,
eventually reached into factories from the 1960s with numerical control systems. The year 1973
provides a convenient starting date for the ‘services transformation’ since that is the year that Texas
Instrument employees Gary Boone and Michael Cochran patented the first ‘computer on a chip’. Thiswas revolutionary.
Putting a computer on a chip made earlier approaches to building computers obsolete; before,
computers were built up from racks of circuit boards. By combining on a single thumb-nail sized
device, the ‘brain’ (central processing unit, or CPU), digital memory, and circuits to handle inputs andoutputs, the computer-on-a-chip reduced the cost and improved reliability – all while reducing powerusage and thus overheating problems. And sticking a computer-on-a-chip into a robot arm allowed
many repetitive mechanical tasks to be automated. Furthermore, the same robot could be quickly
reprogrammed to do other tasks when the time came so the capital expenditure could be limited.
8This was a watershed in the history of automation. It gave machines cognitive capacities that they
never had. True, it was only the ‘thinking slow’ capacities – to use Kahneman’s term for logical,
conscious thinking – but it allowed a disassociation of machines and human that was previously
unheard of; robots, for example, could weld together some cars parts without continuous humanintervention. In short, the technology created better substitutes for humans who worked their hands.
Note that the automation focused on routine tasks (thinking slow) since these were the ones thathumans were most successful at turning into computer code. Given the 20th century programming
technology, ‘think fast’ tasks – like natural language processing, computer vision, computer reading,and the like – were beyond the scope of automation. Humans were still needed for these thinking-fasttasks.
What follows is a minimalist framework to clarify the economic mechanisms engaged by this very
different form of technological progress. In short, it is an accounting of how computerisation led to acontraction of employment in the manufacturing sector and expansion of employment in the service
sector. In the terminology of the seminal work by Acemoglu and Restrepo (2016), this section
considers worker replacing technological progress.

3.1 A Ricardo‐Roy model of robots and downgrade unemployment
The economy in this section closely resembles that of the previous section with a few complications
and a few simplifications. There are two sectors, two types of skills, and two types of workers.
The first complication is allowing workers to have mixed skill sets. A-type workers are still relatively
well-endowed with M-skills, and B-workers are still relatively well endowed with T-skills, but theyeach have some of both. A key assumption is that workers can only have one job and thus can sell theskills to only one firm.
To simplify the expressions, A-workers have 2 units of M and 1 unit of T while B-workers have thereverse (2 units of T and 1 unit of M), and factor intensity differences are taken to be extreme andpolar opposites, namely sector-2 varieties are produced with only T-skills (one unit of T per sector-2firm), while sector-1 varieties are made with only M-skills (one unit of M per sector-1 firm). Given
the perfect identification of workers and skills in the previous model, this is equivalent to assuming𝑎1, 𝑎𝑎0, but note that the worker-skill equivalence is broken here, so it is best𝑎to think in skill-space rather than worker-space.
Monopolistic competition now links prices to skill-rewards, which we denote with 𝑣 , 𝑖
𝑝

𝑣

1,

𝑝

𝑣 ,

𝑥̅

𝐹 𝜎; 𝑖𝑀, 𝑇, i.e.:

1,2𝑖

The model is first solved for static technology, lining it up so most variables are unity initially.

3.1.1 Pre‐robot equilibrium
Analysis begins with the worker’s employment choices. Workers go to the sector that affords them
the highest wage. If they work in the M-intensive sector, their T-skills will be useless; if they work in
the T-intensive sector, their M-skills will be useless. Plainly, B-types get jobs in sector 2 if and only if
𝑣 . A-types work in sector 1 if and only if 2𝑣
𝑣 . Assortative matching (A and B types
2𝑣
work in sector 1 and 2, respectively) occurs when:
2𝑣
𝑣
𝑣 /2
11
9

Once we know perfect sorting occurs, the rest of the equilibrium is straightforward. Workers’ sectoral
choice determines the inputs and thus the outputs of sectors 1 and 2. These, in turn, determine therelative price of goods and thus the 𝑣 ’s. The equilibrium is closed by noting the rewards to skills
determine workers’ choice of sectors. Specifically, with sorting:𝑋since 𝐿

𝐿

𝑀

2𝐿,

𝑋

𝑇

2𝐿

𝑖𝑖

𝐿. The numeraire choice, and the market-clearing and pricing conditions imply:
𝑣

𝑋
𝑋

𝑝

𝑀
,
𝑇

𝑝

𝑣

1,

𝑤

2𝑣 , 𝑤2𝑣

12where 𝑀, 𝑇 denote the supply of skills actually employed in equilibrium. The expressions for wages
follow from workers’ skill sets and sector choices. Using the symmetry:
𝑝

𝑝

𝑣

𝑣

𝑤
2

𝑤
2

𝜔
2

𝑤
2

1

Note that a third of the economy’s supply if each skill is unemployed in equilibrium; this ‘skills
unemployment’ is essential to the analysis.

3.2 Computerisation: Industrial robots performing manual tasks
Technology is conceptualised as arriving in the form of industrial robots. For simplicity, robots appear
exogenously, costlessly, and to be concrete, they are owned by B-type workers (who are now both
workers and robot owners). Robots can supply only M-skills, so this is an extreme form skill-biased
and sector-biased technological progress. Robot-generated M-skills are perfect substitutes for the Mskills of humans. The number of robots is fixed at 𝑁 but we consider the impact of the robots
becoming more proficient in the sense of providing more M-skills per robot, specifically:
𝑀

𝐴 𝑁 ,

𝑀

2𝐿

𝑀

#

where 𝐴0 is a factor-augmenting type of technology for robots (𝑑𝐴
0 means better robots).
The second expression holds in the sorting equilibrium. Note that we remain in the two-skill, twosector setting even though there are three factors.

3.2.1 A three‐phase impact
The fact that the sorting condition, i.e., 2𝑣𝑣
𝑣 /2, involves strict inequalities alerts us thatthe evolution of the economy will be marked by phases. In phase I, 2𝑣𝑣
𝑣 /2, continues tohold even as the rising supply of M-skills depresses its relative reward, thus making A-types closer toindifferent to working in the two sectors. At some point, 𝑣 will have risen sufficiently to push 2𝑣𝑣
𝑣 /2. This happens when the robot supply of M-skills reaches 𝑀 where 𝑣
2, i.e.:
2
so 𝑀

2𝐿

𝑀
2𝐿

2𝐿. When 𝑀 reaches this point, phase II starts.

10

3.2.2 Blue collars turn white
Phase II is marked by A-workers shifting into sector 2. The rate of the shift is such that the output ofboth sectors rises in tandem so there is no change in prices or rewards to skills during phase II. Direct
calculation shows that the number of switchers (i.e. of A-type workers who have switched into their
non-comparative advantage sector), denoted as 𝐿 , is:3
𝐿

𝑀

2𝐿

,

4

𝑅

2𝐿

𝑣 ,
Once all A-workers have changed sectors, phase III begins, and 𝑣 continues its rise with 2𝑣
so all workers strictly prefer working in sector 2. This happens when 𝑀 is sufficiently high to ensure
2 without any humans in sector 1. Labelling this threshold as 𝑀 :
𝑣
2

𝑅
2𝐿

𝐿

6𝐿. During phase III, the relative price of sector 2 products rises along with the real wages
so 𝑅
for both types of workers. To summarise:
Result 3 (jobs effects): Technological progress which is sector- and factor- specific destroys jobs forworkers in the sector experiencing the progress. New jobs involved in the production of new products
are created in the other sector.
Notice the stark difference from the impact of factor-augmenting technology in the previous section.
Robots are a form of sector- and factor-specific technological advance but here the progress drives
human out of the sector. Under the factor-augmenting technological conceptualisation, such progress
pulled humans into the sector. Figure 1 illustrates the shift of workers and relative reward to the robotcompeting skill. The level of M-skills provided by robots, 𝑀 , is a proxy for the size of thetechnology shock.

3

The expression is the 𝐿that keeps 𝑣

2, i.e. 2

for 𝑀

𝑀 ,𝐿

0.
11

Figure 1: Three-phase structural adjustment to robotic technical progress
Sector‐2
employment
share

𝑣 /𝑣

1

1

½

½

Phase I
𝑀𝑖

Phase II
2𝐿

Phase III
𝑀

6𝐿

Phase I

𝑀

Phase II

𝑀𝑖

2𝐿

Phase III
𝑀

6𝐿

𝑀

3.2.3 Real wages, incomes, inequality, and ‘downgrade unemployment’
In all phases, the reward to M-skills is constant in terms of the numeraire (𝑣 =1), but in phase III
only robots earn 𝑣 . The reward to T-skills, 𝑣 , rises as the price of sector-2 products rises in phase I
and phase III. In real terms, A-workers start with real wages, 𝜔
2, and proceed to see their realearnings fall as the relative price of sector-2 products rises (the price index equals 𝑣 / ). Bworkers start with 𝜔
2, and see their real wages rise in phases I and III since 𝑤
2𝑣 , rises
4
faster than goods prices. Specifically:
𝜔 ,&

2
, 𝜔 ,
√𝑣

𝑣 ,

𝜔

2 𝑣

2𝐿 and 𝑅
using subscripts to indicate phases in an obvious notation. Observe that at both 𝑅
6𝐿, 𝜔2√2. These findings are reflected in Figure 2 and Result 4:√2, and 𝜔
Result 4 (welfare effects): The workers who are well-endowed with the skills that robots areproviding (A-workers) are at first harmed by the technology while other workers are helped.
However, once A-workers have moved fully out of the sector and are thus no longer directlycompeting with robots, further advances in robot proficiency are beneficial to all types of workers.
Eventually, A-types see their real incomes rise above the pre-robot level.

4

The expressions follow from 𝜔 , &

√

, 𝜔 ,

√

, 𝜔

√

,

12

Figure 2: Real wage evolution over the three phases

Units ofnumeraire
𝜔
2
𝜔

2

2𝐿

𝑀
6𝐿

12𝐿

The value added that accrues to robot owners is simple to calculate. Robots provide only M-skills andthese are, at any point in time, both valuable and limited, so perfectly competitive firms pay robot
1. As theowners the same per-unit price of M-skills as they would pay to a human, namely 𝑣
quantity of M-skills provided per robot rises steadily, the own-product wage rises, but the real value
falls. As a consequence, in real terms, robot owners gain in line with A-workers.
In terms of GDP shares, the robots’ share rises from zero to ½ during phases 1 and 2, but then stays at
half due to the Cobb-Douglas preferences. Obviously, this means labour’s share of GDP drops from
100% to 50% over the same timeframe. Wage and income inequality also rise.
With two income groups, the Gini coefficient equals the high-income group’s share of GDP minus its
𝑠 , where 𝑠 and 𝑠 are the B-workers’ share of GDP and population
share of population: 𝐺 𝑠
respectively. Having allocated robot ownership to B-workers arbitrarily, B-workers’ GDP sharedepends upon real wages and the reward paid to robot-owner. Due to the Cobb-Douglas preferences,half of national expenditure is devoted to sector-1 varieties and half to sector-2 varieties. With zero
pure profits, this expenditure translates into factor payments, so the factors active in each sector gethalf the GDP in all phases. At the start of phase I, the Gini is zero since robots are getting nothing andwages are equal, but as robots start producing and earning more income inequality rises steadily due
to both wage inequality (as discussed above) and due to the uneven distribution of robot income.
From the beginning of phase III onward, robots get half of GDP, and workers divide the rest with Bworkers getting twice as much as A-workers. Thus the GDP share going to B-workers, 𝑠 , is 5/6,
leaving 𝑠 equal to 1/6, while the shares are population are both ½. The Gini at the start of phases I
and III are thus:
𝐺𝑖𝑛𝑖

0,

𝐺𝑖𝑛𝑖

1⁄3

The Gini measure of income inequality rises steadily from the beginning of phase I till the start ofphase III where it plateaus.
13Redistribution of robot income could fully redress the rising inequality, since robots produce half ofGDP. Consider the calculation for phase III where the Gini has stabilised at 1/3rd. Since B-workers get
twice as much wage income as A-workers from sector 2, income equality could be restored byallocating 2/3rds of robot income to A-workers. This, in short, is an economy where robot incomes
would be a means of avoiding the inequality aspects of technological progress. To summarise:
Result 5 (Rising inequality and the nature of future work):
1) Income inequality as measured by the Gini coefficient rises as robots get more productive;2) Redistribution of robot income could avert any rise in inequality.
A-workers change sectors optimally, but by phase III none of them are using the skills in which they
have an intrinsic ‘comparative advantage’. For the A-type workers, the shift is essentially adowngrading of jobs – at least in terms of utilising their talents. They do not become unemployed, butthey are unable to take advantage of their M-skills in their new jobs.
Note also that by phase III, robots have taken over all M-tasks in the economy – thus fundamentallytransforming the nature of work.
Result 6 (nature of work and downgrading):
At the economy wide level, advancing robot technology changes the nature of work. From phase three
onward, robots do what they can; humans do what robots cannot do. Better robots create no
unemployment in terms of the number of workers with jobs, but it leads to the unemployment of the Mskills of all workers. This is not a new development for B-workers, but for A-workers this could beviewed as ‘downgrade unemployment’.
Proposition 7 (labour share of GDP): Throughout phase one, labour share of GDP falls from 100%
to 50%, and stays there from the beginning of phase III onwards.
Stepping outside the model, Phase II can be thought of as the de-industrialisation of advanced
economies since the 1970s, namely the shift of jobs from factories to offices.

3.3 An interpretation of the shift of jobs to offices
If we think of sector 1 as manufacturing and A-workers as blue-collar workers, and sector 2 as theservice sector and B-workers as white collar workers, this stylised account of the impact of robots
provides a suggestive accounting of why technology had such different effects in the century before
1970 and the half century after 1970. Labour saving technology in manufacturing created jobs when it
was labour-augmented but destroyed jobs when it was labour substituting.

3.3.1 Technology and the nature of work redux
When computerisation arose in the 18th century and accelerated at the end of the 19th century, people
knew that technology would destroy many jobs. New jobs would, and were created, but at the time,
people could not know the names of the jobs, or what sort of things workers would be making.
Some early visionaries talked about the post-industrial society, especially Bell (1974). He pointed outthat by 1973, half the US workforce was in services, and he predicted that this expansion would
continue and spread abroad since computerization was enhancing the productivity of service workers.
Again, something like GE reasoning was being used to predict the nature of future work even if Bell14

had no idea of the particulars of the services that future workers would be producing. But the nature of
the GE insights is quite different.
The GE implications for the nature of work in the previous section – where machine-helping-humans
technology made farmers into factory workers – was based on the constraint that all the workers got
reemployed. Rybczynski logic guided our thinking as to the factor-intensity of the new jobs andsectors in which they would appear.
In this sector – where robots-replacing-humans technology made factory workers into service workers– the GE hints for the nature of work are radically different. Here the nature of future work ispredicted by a process of elimination; humans do what robots can’t do. In this simple model, allmanual tasks are eventually taken over by robots; the only tasks left for humans to do was thinking
tasks. The GE insight then is that the new jobs would be intensive in the task that robots couldn’t do.
In short, GE insights applicable to the ‘blue collars turn white’ transformation and robot technology
are radically different than they were in the ‘farm to factory’ transformation driven by humanaugmenting technology.
The nature of robots, however, is changing rapidly. They are starting to acquire the ability to provide
thinking skills. This will surely displace many human jobs. The next section looks at what GE insights
can tell us about where the new jobs will appear and what they will be like.

4 AI as a better substitute for brain workers and better
tools for manual workers
Just as computerisation can be thought of as a technological watershed in the 1970s, Machine
Learning can be viewed as a watershed from about 2016. A convenient signpost for this second
technological watershed is the reversal of Moravec’s Paradox.
In the late stone ages of AI, 1988 to be specific, AI-pioneer Hans Moravec wrote, "it is comparatively
easy to make computers exhibit adult level performance on intelligence tests or playing checkers, anddifficult or impossible to give them the skills of a one-year-old when it comes to perception andmobility." 5 Until recently, computers were good at the hard stuff but bad at the easy stuff. The deep
reason was programming.
Humans taught computers to do things with computer programs. These explained, step-by-step, what
the computer should do. This meant that before we could teach computers to think, we had tounderstand how we think, step-by-step. The paradox arose since, as another early hero of AI, Marvin
Minsky put it, "we're least aware of what our minds do best." We understand how we do arithmetic,
algebra, and archery; we haven’t a clue as to how we recognize a cat, or keep our balance whenrunning over hill and dale. Machine learning solved the paradox by skipping the programming.
With machine learning, the computer (the ‘machine’ part) estimates a very large statistical model of
how to guess a solution to a particular problem (the ‘learning’ part). Thanks to advances in computing
power, and access to big data, computers trained by machine learning routinely achieve human-level
performance on specific thinking tasks - like recognizing faces or translating Chinese into English. In
5Moravec, Hans (1988), Mind Children, Harvard University Press.

15

essence, such software are ‘white-collar robots’, where the white collar part refers to the workers
replaced by the tech.
This section explores the impact of technology when technology is providing better substitutes forthinking skills.

4.1 Machine Learning: Algorithms performing thinking tasks
The economy considered here is exactly as in the previous section, and the analysis starts from thephase III situation (where all humans are working in sector 2). The single addition is related to thedevelopment of commercially viable AI. Computer models, trained with AI algorithms (such as ‘deep
learning’, neural networks, or similar), are able to provide thinking skills that are perfect substitutes
with human-provided thinking skills. Thus, the total supply of T-skills in the economy is now:
𝑇

𝐴 𝑁,

𝑇

3𝐿

𝑅

#

where 𝑇 is the robot-supplied T-skills, and, with a slight abuse of the technical terms, the robot
providers of T-skills are labelled ‘algorithms’ with the number of algorithms denoted as 𝑁 . The
supply of AI-generated thinking skills is 𝑁 times the effectiveness of each algorithm, 𝐴 . As before,
the technological progress is costless and exogenous, and these ‘white collar robots’ are owned by Bworkers.

4.1.1 White collars turn blue
The introduction of AI shifts the economy into a new phase, phase IV. Assuming algorithms get better
faster than industrial robots, 𝑇 rises relative to 𝑀, and this drives up sector-2 production relative tosector-1. The attendant relative output shift lowers 𝑝 and thus 𝑣 . At first (i.e. during phase IV), theprogress of AI has no impact on the allocation of jobs since 𝑣
2 and all workers earn more insector 2 (recall the analysis opens during phase III from the previous model).
1) where A-workers start switching
At some point, 𝑣 is driven back down to a level (namely 𝑣
back to sector-1 jobs. The threshold for this shift between phase IV and V is defined by:
1

𝑀
3𝐿

𝑇

so phase V starts when the quantity of AI-generated T-skills reaches 𝑇
𝑀
3𝐿. As happened inphase II, the flow of A-workers from sector-2 to sector-1 proceeds at a pace that keeps 𝑣
1. Whenall A-workers (who have 2 units of M-skills to sell) are in sector-1, the economy is back at a situationof assortive matching and a new phase, phase VI starts. Here 𝑣
𝑝 is:
𝑣

2𝐿
2𝐿

𝑀
𝑇

Thus the threshold between phases V and phase VI, is defined by𝑀
𝑇Assuming AI productivity continues to advance faster than industry robot productivity, 𝑣 willcontinue to decline. Once 𝑣, B-workers, who have only 1 unit of M-skills to sell, start to switch

to sector-1 as well. This is the start of phase VII, the threshold for which is defined by
16

2𝐿
2𝐿

1
2
so phase VII begins once 𝑇
so 𝑣

2 𝐿

𝑀
𝑇

𝑀 . Again, the reallocation of jobs flattens AI’s impact on 𝑣 ,while B-workers are transitioning to sector 2. The final phase, phase VIII, begins once all

B-workers have made the transition. From this point, the price of sector-2 varieties continues to fall,
1, as always (by choice of numeraire). Phase VIII starts when 𝑇
bringing 𝑣 down with it, but 𝑣
surpasses:
3𝐿

1

𝑀
𝑇

so phase VIII is triggered when 𝑇 exceeds 𝑇

3𝐿

𝑀 . The phases are illustrated in Figure 3.

Figure 3: Phases of structural adjustment to AI

𝑣 /𝑣
2

1
½

Phase
IV

Phase
V

Phase
VI

Phase
VIII

Phase
VII

Phase
VIII

𝑇

𝑇

𝑇

Phase
VII

𝑇

Sector‐1 job
share
1

½

Phase
IV

Phase
V

Phase
VI

𝑇
In terms of real wages, A-workers and B-workers are harmed by the drop in 𝑣 during phase IV since
their wage falls faster than prices. The owners of AI, by contrast, win during this phase since theyearn 𝑣 for each unit of T-skills produced by their algorithms.
17

In phase V, when A-workers are moving to sector 1, the wages are flat along with prices so only AIowners gain. At the end of this phase, each worker is in his or her comparative advantage sector and2,
the equilibrium shifts to phase VI. Here A-workers benefit since their wages are pinned at 2𝑣
but the price index continues to fall. B-workers continue to lose at the same pace as in phase IV. Fromthe start of phase VII, B-workers start to join A-workers in sector 1 even though they can only sell
one unit of M-skills rather than 2 units of T-skills. The shifting of workers freezes prices and wages,
so again only AI-owners gain. A- and B-workers neither gain nor lose. Once the shift to sector 1 iscomplete, phase VIII begins and both types of workers gain.

4.1.2 Inequality and labour share of GDP
The rising wage inequality that was associated with the rise of industrial robots is reversed by the riseof AI-trained algorithms in phase V. That is, both A- and B-wages equal unity in terms of thenumeraire in phase V. Beyond that, AI drives inequality back up but this time in favour of thepreviously disfavoured A-workers since now B-workers are experiencing the downgrade
unemployment. By phase VIII, A-workers earn twice as much as B-workers since they have twice asmuch M to sell.

5 Concluding remarks
There is an argument that the economy is on the cusp of a third grand transformation. The first was allabout goods. It shifted people from one type of manual work (agriculture) to another (manufacturing).
The second destroyed jobs for those who worked mostly with their hands (in factories) while creatingjobs for those who mostly worked with their heads (in offices). Today’s technological impulse isdoing a bit of this, but with so few workers left in factories, the extra capabilities that industrial robots
are getting from machine learning constitute more of a continuation than a transformation. The realtransformative impact will be felt in the service sector. But what will the future jobs be like?
The first part of any likely answer is an admission of ignorance and caution. Automation displaced
jobs in the nineteenth and twentieth centuries. Human creativity invented “needs” that we did noteven know we needed. That’s why many of us today work in jobs that would sound very strange toCharles Dickens in nineteenth-century London. The jobs were created in service sectors since they
were the sectors that were shielded from automation. The same will almost surely happen again today.
Jobs will appear in sheltered sectors. But what sort of jobs will these be?
We cannot know what new jobs will be, but by studying the competitive advantage of AI, we can sayquite a bit about what sheltered jobs will look like in the future – a point I discuss at length in mybook (Baldwin 2019). By studying the things that AI-trained robots can already do well, we canpredict that the jobs that survive competition from AI and the new jobs that will be created are thosethat stress humanity’s great advantages. Algorithms have not been very successful at acquiring social
intelligence, emotional intelligence, creativity, innovativeness, or the ability to deal with unknown
situations. Experts estimate that it will take something like fifty years for AI to attain top-level humanperformance in social skills that are useful in the workplace, like social and emotional reasoning,
coordination with many people, acting in emotionally appropriate ways, and social and emotional
sensing.
This suggests that the most human skills will be sheltered from AI competition for many years. The
implication is as simple as it is profound. Humanity will be important in most of the jobs of the future.
18

That is the good news. The bad news is that millions, perhaps hundreds of millions of white-collar andprofessional workers will have to change jobs and perhaps retrain.
If the correct figure turns out to be in the millions and it is spread over a couple of decades, thetransition is unlikely to trigger a politically significant backlash. But it the correct figure is in thehundreds of millions and is spread over a single decade, the economic and social upheaval could leadto a backlash of the type we saw in the 1800s and 1900s.
This may sound alarmist, but to put in context, I close with an extended quote taken from a speech
given by the US labour minister (Secretary of Labor) James J Davis in 1927 – just few years for thegreatest upheaval of all – the Great Depression – and the greatest backlashes of all – the rise ofcommunism, fascism, and New Deal capitalism.
Excerpt from a 1927 speech by US Sectary of Labor, James Davis:“Every day sees the perfection of some new mechanical miracle that enables one man to
do better and more quickly what many men used to do. In the past six years especially,
our progress in the lavish use of power and then harnessing that power to high speed
productivity machinery has been tremendous. Nothing like it has ever been seen on earth.
We must, I think, soon begin to think a little less of our wonderful machines and a little
more of our wonderful American workers, the alternative being that we may havediscontent on our hands.
Understand me, I am not an alarmist. If you take the long view there’s nothing in sightthat gives us great concern. I am no more concerned over the men once needed to blowbottles then I am over the seamstress that we once were afraid would starve when thesewing machine came in. In the end, every device that lightens human toil and increases
production is a boon to humanity. It is only the period of adjustment, when machines turnworkers out of their old jobs and into new ones, that we must learn to handle them so as
to reduce distress to the minimum.
We must ever go on, fearlessly scrapping all methods and old machines as fast as we findthem obsolete. But we cannot afford the human and business waste of scrapping men. In
former times the man suddenly displaced by a machine was left to his fate.
The new invention we need is a way of caring for this fellow made temporarily jobless. In
this enlightened day we want him to go on earning, buying, consuming, adding his bit to
the national wealth in the form of product and wages.”
If only President Coolidge had listened to his minister!

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