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Matteo Lanzafame discusses the impact of demographic change on labor productivity growth. The analysis covers a panel of 90 advanced and emerging economies over 1961–2018 and produces three main findings.

Image courtesy of interviewee. November 29, 2023

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The study investigates the effects of demographic change on labor productivity growth, examining both emerging and advanced economies. It discusses the concept of demographic transition, highlighting how reductions in birth rates and increased life expectancy lead to a higher proportion of the working-age population, boosting productivity in emerging economies—a phenomenon referred to as the "demographic dividend."

In contrast, advanced economies face challenges associated with aging populations, leading to a shrinking workforce and decreased productivity growth. The paper identifies several ways demographic changes impact labor productivity, including declines in physical and human capital accumulation, slower innovation rates, and lower overall productivity levels of an aging workforce.

The authors conduct an empirical analysis focusing on labor productivity growth (as opposed to GDP growth) to better understand these dynamics. They also explore the role of automation, finding that increased automation—measured by the ratio of robots per 1,000 employees—can mitigate the negative impacts of demographic aging on productivity in advanced economies.

Key findings include:

1. **Aging and Young Dependency Effects**: Population aging negatively affects labor productivity growth significantly.
2. **Emerging vs. Advanced Economies**: The impact of demographic changes is more pronounced in emerging economies compared to advanced ones, possibly due to differing adaptations in technology and policy in response to demographic shifts.
3. **Role of Automation**: Automation plays a crucial role in buffering the impact of demographic changes on productivity, although it highlights a gap where only a few economies (like Japan and Germany) have sufficient automation to neutralize these effects statistically.

Consequently, the study suggests policy implications, advocating for measures to adapt labor systems, promote lifelong learning, and enhance automation to address the challenges posed by demographic changes. Overall, technological progress, particularly automation, is highlighted as vital for reducing the adverse effects of demographic shifts on economic growth.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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So the study focuses on, the effects of,

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demographic change in general on,

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labor productivity growth.

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Um, this is related to a significant literature

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that connects, the features

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of what's is called the demographic transition to,

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long-term growth.

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And if you look for example, at emerging economies,

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this is really very much connected to

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what people call the demographic dividends.

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So the idea that demographic transition, starting from,

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you know, situation in which birth rates are still high,

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but then life expectancy, starts to increase

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because, mortality rates fall.

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This creates an increase in working age population.

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And so the labor force,

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and so the number of people who are available for work,

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and the bottom line is that everything else constant,

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this boosts, growth in, in emerging economies.

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And, you know, if you look at Asian economies, for example,

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which were successful in increasing the growth rates.

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And so you see these growth accelerations,

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they are really very much connected

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to the demographic dividend that I was describing before.

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And then if you look at advanced economies,

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you can see the process working in reverse.

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Uh, you see population aging

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because, the, the birth rates have been,

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falling for some time.

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And so the working age population shrinks

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and the number of people available for work as well.

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And, and this reduces, yeah, growth in, in,

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advanced economies.

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So some emerging economies are already transitioning

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towards, older societies as well.

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So we, we expect to see similar effects in, in,

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these emerging economies, too.

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But this is the main focus of let's say,

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the early literature in, in this field.

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A second element that,

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our paper really focuses on is the impact

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of demographic change on labor productivity growth.

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And there are several channels

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by which this, this can happen.

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Um, demographic change, for example,

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in energy population can affect the accumulation

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of physical capital in an economy.

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And this is because all the populations usually diss save.

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So that setting rates, in the,

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in the aggregated economy, falls

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because, you know, people retire.

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So they want to maintain a similar level of consumption.

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They have to use the settings that they accumulated

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before in, in their working lives.

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So this reduces everything else.

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All stands, physical capital accumulation.

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At the same time, human capital accumulation falls again,

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people retire, become older.

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They are not in formal education anymore.

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They don't, you know, learn by doing

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what they're working, they are not in training, et cetera.

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And there is also evidence in the literature

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that an older population, leads to a slower rate

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of innovation, in, in the economy.

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And so the accumulation of knowledge,

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and of course, I mean, if you think of, you know,

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an older workforce, probably the, the efforts

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or the the, productivity of an older worker

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is going to be lower with respect to a younger worker that,

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you know, is more energetic and so on.

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So this is really what our paper focuses on.

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We, we, undertake an empirical investigation

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of the relationship between demographic change

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and labor productivity growth.

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And then we zoom in on the role of automation

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or the role of robots in, in the production process.

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There is a lot of literature in, in, this field.

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Um, I would say our paper is very much connected to,

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some of the cross.

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Ours is a cross country analysis.

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So it's related to,

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the cross-country empirical investigation

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that you find already in the literature.

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One deviation from the typical study this field is

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that the typical study really focuses on, GDP growth

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for per capita GDP growth.

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So that's the dependent variable,

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in, in these studies.

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And because, as I was saying,

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before, the effects

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of demographic change can work essentially via these two

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channels, either affecting, working age population,

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and so labor force goals or affecting productivity goals.

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So if you've just focus on GDP

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or per capita GDP,

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you cannot really disentangle properly

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these, these two effects.

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So what we do is really focusing just on,

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productivity goals.

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Um, and so try

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to see whether we can really find robust estimates

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for the impacts on, on these elements only.

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Um, the second part of the literature that is related

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to our work is, the recent contributions

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that look at the impacts of robotization, of automation, of,

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of, production processes, essentially.

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And there are, in particular a couple of contributions by,

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Dar MLU and risk triple in 2017 and 2021.

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And from these two papers, basically there are two messages.

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The first one is that, they look at the relationship

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between, population aging and per capita GDP,

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and they cannot find a significant relationship,

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a statistically significant relationship.

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So they argued that this is

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because, in particular advanced economies which are

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already, further ahead in,

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in the demographic transition.

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These economies have already adopted,

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adjustments in the terms in, in, in terms of, you know,

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policy reforms

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and in terms of, technological innovations.

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And then in the second paper,

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they look in particular at the relationship

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between population aging

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and the adoption of automation techniques.

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And they find that there is a significant relationship.

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So if we put the two together,

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then we should find a significant relationship between,

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automation and the impact

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of demographic change on labor productivity growth.

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So this is the second hypothesis

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that we investigate empirically in, in our paper.

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And then a third contribution, I would say

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Is, is methodological. So

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we use a panel bar with exogenous, variables.

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Uh, it's not entirely new because there is a paper by Axon

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and others in 2019 could do, could do the same thing.

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And this technique is useful

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because it allows you to control for the kind

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of endogenous mechanisms that, that I was mentioning before.

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And at the same time to distinguish between, you know,

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short term impact and a long-term impacts.

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The main difference between our paper and, and Axo

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and others is that they just focus on a small sample

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of advanced economies, so ECD economies,

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whereas our paper focuses on a, on a large sample of,

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advanced and emerging economies.

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So as I was saying, we have an unbalanced panel

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of 90 economies, 35 advanced economies,

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55 emerging economies.

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So it's a pretty balanced in terms of the representation

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of economies, panel.

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And we have annual data over 1961

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to 2018.

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Although the time period changes,

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depending on the specification of,

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of the model in particular, when we stand the model

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to introduce, data on, on robots,

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then the time series is shorter and stops in 2015.

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Um, the data that we use, so the model specification is,

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is pretty standard and very simple.

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So our dependent variable is labor productivity growth,

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and we have a very simple growth model

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where labor productivity growth depends on the accumulation

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of physical capital, human capital and,

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and knowledge, which we proxy with the

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economic complexity index.

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Um, specification is such that we treat the,

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two, demographic shares.

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Um, so the share of the young dependents,

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people age from zero to 19,

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and the share of elderly dependents are all dependents.

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Uh, people age from 60, 60 and beyond, essentially.

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So we treat these two shares as being exogenous.

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And this is because, you know, demographics

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and in particular fertility,

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decisions have really determined, a long time ago.

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Uh, so they are not dependent on, you know,

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what's happening today.

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So connectivity,

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and at the same time, the frequency of, of, you know,

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variation of these two demographic shares is where, to me,

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low, very low with respect to the annual,

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variation in the, in, sorry,

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the variation in our annual variable.

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So for these reasons, three, these, these two shares are,

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exogenous.

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And so what we are capturing really is, is causality from,

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demographic change to labor productivity

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and the other endogenous variables in the, in the panel bar,

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rather than just a simple correlation.

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Um, so this is the, the general setup.

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And as I was saying, after, initially, using this,

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this simple model for our analysis, we extend the model

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to introduce a proxy for, automation

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of the production process.

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And this proxy is given by the ratio of the number

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of robots, per 1000 employees.

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And we use that to investigate the question

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of whether automation has an impact on, the relationship

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between demographic change and labor productivity.

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I would say that we have three main, results,

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in the paper.

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Uh, the first one is that we find robust

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and critical evidence that, population aging,

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but also increased in, in the number of, in the share

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of, of the young dependents,

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has a ne negative impact on,

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on labor productivity growth.

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So the, the implication of this is essentially that

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a shrinking working age population share, sorry, share

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of workers in the economy reduces labor relativity rules.

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And this is, robust to, to different specifications

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and is a very, significant results.

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Uh, to give you an idea, we find that in the long run,

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many increase of 1% in the share

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of the old dependents.

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So again, people age, 60

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and beyond reduces labor productivity growth

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by 0.67 percentage points.

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So it's a significant influx, that, that we fund.

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The second result is that in the second part

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of the analysis, what we do is, you know, using the fact

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that we have a large sample, so we split the sample

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and look at the, the same relationship

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and the same, estimations,

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for advanced economies and emerging economies.

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Again, just trying to explain the idea that

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whether you are at the beginning

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or further apart in the demographic transition,

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that's gonna have an impact on the relationship.

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And, you know, emerging economies are an average much

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younger than than a bounce.

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So we do that, we still find exactly the same results,

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for two demographic shares.

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So whether you are an a bounce economy

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or an emerging economy demographic,

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change is still important in terms of shaping the,

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the dynamics of labor productivity growth.

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But the interesting result that we find is that the,

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the coefficient terms, so the impact of aging is bigger

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in emerging economies.

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And you know, it, it could be, thought of

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as seen like the counterintuitive results,

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because again, emerging economies are in general much

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younger than advanced economies.

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But if you think back, to the contributions of asog

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and restrict that I mentioned

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before, this is very much in line with what we say.

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Uh, bounce economies are already, you know,

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older populations are older,

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and so you have already situations in which, you know,

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policy changes to say pension systems

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or, labor markets and so on.

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But these are already in place.

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And at the same time, firms in,

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in advanced economies have already adopted,

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technological innovations to essentially cushion this,

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this impact of aging on,

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on production and, and productivity.

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And if you look at the data on robots, for example, you find

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that, in a bunch economies, the number of robots per

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for thousand employees, the proxy that I was measuring

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before, it's much higher than, than with emerging economies.

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So all of this really suggests

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that we should be looking into the input of, of automation,

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and that's our third main, results.

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So we find that automation does matter

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and that, you know, the increase

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of robots square 1000 employees reduces the impact

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to particular population aging on,

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on labor productivity fields.

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Um, however, in in our panel

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and in our sample, so when we include robots,

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our sample, our time series stops in 2015.

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So one thing that we do is looking at whether, in 2015,

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so the latest data, we still see a significant impacts of,

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of the demographic change

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and population aging on productivity, even

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considering, automation.

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And that is the case, the impact is still significant.

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There are only three economies in 2015,

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which had enough robots per 1000 employees

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to essentially, reduce the impact of aging, to,

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to zero at least, statistically.

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And these three economies were Japan, Germany, and,

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and the Republic of Korea.

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So you need to have, in 2015, at least I think,

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around eight robots per 1000 employees to, to,

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to have a not statistically significantly impact engine

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on connectivity growth.

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So I would say there are at least a couple of,

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policy implications.

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So the first one is that, again, we find significant impacts

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of our population agent, in particular on,

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labor connectivity growth.

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So that's already a message.

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I would say that, something from, from a policy point

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of view needs to be done.

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And there are several things that have been done already,

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again, in advance, economies in particular.

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So, you know, changes to, as I was mentioned

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before, the pension system

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or incentives, for people to remain longer in the,

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in the labor force or, to increase participation rates,

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for example, by, by women

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and other, groups, which have lower participation.

14:06.415 --> 14:08.715
So even to, remove

14:08.735 --> 14:11.635
or relax constraints on, on, immigration.

14:11.775 --> 14:13.835
So that migration, can,

14:13.835 --> 14:15.515
can help increase the native force.

14:16.095 --> 14:18.635
Um, however, this really impacts, only

14:19.205 --> 14:21.755
let's say the first channel that I mentioned before, right?

14:21.775 --> 14:25.475
So the direct link between, demographic change

14:25.475 --> 14:26.795
and working age population and,

14:26.855 --> 14:29.155
and labor force, if you want this

14:29.155 --> 14:31.275
to have an effect on labor productivity as well,

14:31.275 --> 14:34.675
then you need to combine these measures with, say,

14:35.035 --> 14:38.555
lifelong learning programs or on the job training and so on.

14:38.855 --> 14:40.915
So to make sure that, that people

14:41.015 --> 14:42.355
who remain in the labor force

14:42.415 --> 14:45.675
or who join the labor force, from outside, say migrants,

14:46.095 --> 14:49.235
become as productive as if not more productive as,

14:49.255 --> 14:51.725
as the average or the average in the, in the labor force.

14:52.745 --> 14:55.885
Um, and then the second message I would say is that

14:57.095 --> 14:59.275
in the long term, I mean this is, this is what,

14:59.615 --> 15:01.035
you know, growth theory

15:01.215 --> 15:04.235
or economic theory in general tells us in the long run,

15:04.295 --> 15:05.915
it is technological progress,

15:05.915 --> 15:09.235
and it is productivity that drives, you know, the growth

15:09.655 --> 15:12.195
of, of living standards or per capita GDP

15:12.195 --> 15:14.035
or living standards more, more generally.

15:14.655 --> 15:18.715
And what our, results suggest is that in, in this area

15:19.135 --> 15:20.595
of technological innovation,

15:20.595 --> 15:23.795
technological pro progress automation can, can really help

15:24.335 --> 15:26.275
in, in reducing the, the impacts of,

15:26.605 --> 15:29.995
let's call it unfathomable, demographic change.

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