Technologies that automate labour tasks do not necessarily increase unemployment. Despite widespread concerns that Artificial Intelligence (AI) will displace workers en masse, there are periods of history where productivity-enhancing technologies have actually increased employment in the affected industries. This runs counter to the simplistic notion that ‘automation causes job losses’ in the industries experiencing automation.
So, why does automation lead to employment growth in some industries at particular times, while leading to job losses at other times and in other industries?
James Bessen, an Economist from Boston University, argues that it has to do with how technology affects the nature of demand.
Employment in US manufacturing has dramatically fallen in recent decades. But for over a century, employment grew in various manufacturing industries, even in those that experienced rapid technological and productivity changes. This phenomenon is described as the ‘Inverted U’ pattern and appears to have general applicability across many manufacturing industries.
Bessen has developed a model of industry demand to explain the rise and subsequent fall of US manufacturing employment in the context of ongoing productivity growth. After gathering two centuries of time series data on US cotton textile, raw steel, and automotive manufacturing industries, Bessen’s model was able to accurately predict the rise and fall of Production Employment in these three manufacturing industries.
The Inverted U patterns of industry employment observed above are largely explained by declining price elasticities of demand. That is, the declining responsiveness of consumption to changes in price. While variations in consumer income play a role in these instances, they only account for a small portion of the total variation in per capita consumption. Nonetheless, Bessen’s model includes both price and income effects on demand, allowing the elasticities of both to change over time.
A primer on the economics of supply, demand, and elasticity
To illustrate these dynamics, consider the impacts that a new technology (for example, the automated weaving loom) can have on the supply and demand for a good in a competitive market (such as cotton cloth), with a focus on price.
When elasticity is high, a small change in price has a disproportionately large impact on the quantity demanded. For example, if a new technology improves the productivity of output, this decreases the production costs per unit of cloth. In competitive markets, the productivity savings can be passed on to consumers by lowering prices. In the example above, the price of cotton cloth per unit decreases from $4 to $3 (point A to point B). This yields a 10 unit increase in the quantity demanded. But if the price were to further decline from $3 to $2 (point B to point C), the proportional change of price required to achieve another 10 unit increase is higher. That is, to achieve a 10 unit increase in quantity demanded at point A to point B required a 25% decrease in price; whereas achieving another 10 unit increase from point B to point C requires a 33% drop in price. So, demand is becoming more ‘inelastic’ because consumers are less responsive to changes in price.
When a new productivity-enhancing technology is introduced, it reduces the production costs per unit of the good, shifting (increasing) the supply curve right. This drives down the price of the good until a new market equilibrium is met (point B above). When demand is elastic with respect to price, this indicates unmet consumer demands, so changes in price have disproportionately large impacts on quantities demanded. If the demand for the good grows fast enough, then the demand for labour will increase despite the automating technology reducing the labour required per unit of output. This can offset the labour-saving effects of automation.
If demand is inelastic, however, then price changes only yield modest changes in the quantities demanded. When productivity-enhancing technologies continue to lower the costs per unit, thus lowering the prices, consumers’ demands become satiated. Under this scenario, price declines are insufficient to raise net employment. These are the general dynamics that Bessen has observed in US manufacturing.
How automation increased (and then decreased) employment in textiles manufacturing
During the 19th century, technologies had automated 98% of the labour required to weave a yard of cloth. Yet, the number of weaving jobs actually increased for decades over this period. The basic intuition is that most consumers were priced out of the market. Many consumers had limited sets of clothes and other uses for cloth, which meant there was a lot of unmet consumer demand. As a result, the price elasticities of demand and income were high because consumers were sensitive to the price of cloth.
As automated weaving looms diffused throughout the textile industry, productivity improvements drove down the price of cloth. This increased the consumption of cloth per capita because the demand for cloth was highly elastic. To meet this consumer demand, the textile industry experienced net jobs growth, despite the labour-saving technology reducing the labour required per unit of output. Essentially, the increase in consumer demand more than offset the labour-saving effects of the automation technologies. Bessen found that although labour productivity in cotton textiles increased nearly 30-fold during the 19th century, consumption of cotton cloth increased 100-fold.
Over time, however, the ongoing productivity gains present in the textile industry drove down the price of cloth until consumer demand had become satiated. As demand became relatively inelastic, further price declines and income growth only generated anaemic increases in consumption, which failed to offset the labour-saving effects of automation.
So began the downward trajectory of textile employment, as seen in the latter parts of the Inverted U graph. The technical changes that created productivity advances would eventually lead to deindustrialisation. The elasticity of demand for price and income thus became more inelastic as cheap cloth became readily available to consumers. As a result, productivity advances later experienced depressed textile employment.
AI automation and the importance of demand
The current debate regarding the impacts of AI on jobs too often reverts to a false dichotomy. On the one hand, AI is positioned as a universal panacea that will bring about a productivity boom like that we’ve never seen; on the other, AI automation is said to pose a widespread threat to our current jobs and future prospects. Neither sides are helpful in explaining the past or predicting the future.
Bessen shows that the pace of technological change alone is not sufficient in determining the impacts of technologies on employment. It is essential to understand the nature of demand to determine whether major new technologies will increase or decrease employment in affected industries. If demand for a particular good or service is highly elastic and AI does not completely automate the production of that good or service, then technical change would create jobs rather destroy them. In this scenario, faster rates of productivity-enhancing change by AI would create faster employment growth to meet consumer demand.
Therefore, understanding the effects of AI on demand is essential to understanding its implications on employment.