Automation and Sectoral Reallocation

Article by Dennis Hutschenreiter, Tommaso Santini, and Eugenia Vella

Illustration of a robot sitting on a scale while workers move to the other side
Original artwork by Angelica Lena

In this paper, we study the sectoral reallocation of employment due to automation in Germany. Empirical evidence by Dauth et al. (2021) shows that robot adoption has induced a shift of employment from the manufacturing to the service sector, leaving total employment unaffected. We rationalize this evidence through the lens of a two-sector, general equilibrium model with matching frictions and endogenous participation.

Few papers have studied the effect of automation on employment in a multi-sectoral model. Berg et al. (2018) argue that the inclusion of a non-automatable sector amplifies the difference between the effect of automation on low- and high-skill workers. Sachs et al. (2019) also include a non-automatable sector in an overlapping generations model. They study the possibility of one generation improving their welfare at future generations’ expense through robot adoption. To the best of our knowledge, we are the first to build a two-sector general equilibrium model with search and matching frictions to analyze the long-run impact of automation on both sectoral and aggregate employment.

We consider a representative household that decides how to allocate its members between non-participants in the labor markets, job-searchers in manufacturing, and job-searchers in services. The household also accumulates capital. On the production side, there is a representative firm in each sector. The manufacturing firm decides how many vacancies to post and how much capital to borrow from the household. Automation increases the capital intensity of the technology in the manufacturing sector. This can be motivated by the idea that some work operations, formerly performed by humans, are now executed by robots (Acemoglu and Restrepo (2018)). In the service sector, we assume for simplicity that no capital is needed, and thus the representative firm decides only the number of vacancies to post.

Two panels plot sectoral employees in Germany and employment in the model

Key takeaways

After calibrating our model to the German economy in 1994, we perform steady-state comparative statics to study the long-run impact of automation on sectoral reallocation of employment. As the stock of robots increased by 87% in Germany between 1994 to 2014, we can qualitatively compare the model economy’s reaction to an increase in the degree of automation with the sectoral employment shares we observe in Germany over time.

The right panel of Figure 1 shows the model implied values of sectoral employment levels together with total employment. A higher degree of automation, which we take as a proxy of robot adoption, increases employment in services and decreases it in manufacturing. Despite the adjustments of sectoral employment, total employment remains constant, consistently with the empirical evidence of Dauth et al. (2021). In the left panel of Figure 1, we plot the employment shares in the German economy, which we compute using data from the German Federal Statistical Office (DESTATIS). The model qualitatively replicates the observed pattern in sectoral employment.

To assess how well our model can explain the sectoral reallocation of employment in Germany, we focus on comparing two steady states. These two steady states correspond to the start and end years in the empirical analysis of Dauth et al. (2021). The model predicts a decline of 27% in the ratio of manufacturing employment to service employment, which is reasonably close to the one found in the aggregate data for the German economy, i.e., 32%.

Having shown that the model replicates the sectoral reallocation we observe in the data, we then ask the following question. What determines the extent of sectoral reallocation?

Two main parameters govern the strength of the sectoral reallocation of employment in the model: (1) the elasticity of substitution between capital and labor in the manufacturing sector α and (2) the elasticity of substitution between the outputs of the two sectors χ. Intuitively, in the first case, as α decreases, capital and labor become stronger complements in the production of the manufacturing good. As automation raises the return of capital for a given capital stock, in the long-run this leads to a higher capital stock in the steady state. The stronger the complementarity between the two inputs in manufacturing (i.e., the lower α), the higher is the relative demand for manufacturing workers. Therefore, the sectoral reallocation of employment due to automation is mitigated for lower values of α, as Figure 2 demonstrates.

Figure plots employment and degree of automation
Note: The plotted variables are normalized to zero in the initial steady state. α denotes the elasticity of substitution between capital and labor in manufacturing production. χdenotes the elasticity of substitution between the two sectoral goods.

Concerning the second parameter, we need to distinguish two different effects on production and employment in the service sector. Firstly, since automation leads to a higher accumulation of capital in the long run and, thus, to higher household wealth, this will lead to a higher demand for services, which is a normal good. Secondly, the stronger is the complementarity between the two goods in the economy, the higher is the increase in the demand for services. Consequently, a higher substitutability (i.e., a higher χ) between service and manufacturing goods mitigates the increase in the demand for services and, thus, the sectoral reallocation of employment, as Figure 2 shows.

Conclusion

To sum up, we build a general equilibrium model with an automatable and a non-automatable sector and labor market frictions that is able to rationalize the empirical evidence presented by Dauth et al. (2021) on (i) the substantial sectoral reallocation of employment and (ii) the null-effect on total employment. We show that our calibrated model can reasonably explain the empirical strength of the sectoral reallocation of labor. Furthermore, we analyze which key parameters govern the magnitude of this effect in the model.

An interesting extension of our model would be to include heterogeneous agents and capital-skill complementarity (see e.g. Dolado et al. (2021) and Santini (2021)). With that extended framework, one could study the interplay between automation, sectoral automation, and inequality. We leave this topic for future research.

References

Connect with the authors

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Dennis Hutschenreiter is a PhD candidate in the IDEA Program (UAB and Barcelona GSE).

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Tommaso Santini is a PhD candidate in the IDEA Program (UAB and Barcelona GSE)

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Eugenia Vella is a Research fellow at AUEB, ELIAMEP, and MOVE.

Opening the Black Box of Austerity: Evidence from Fiscal Consolidation Plans

Alessandro Franconi ’17 (Macroeconomic Policy and Financial Markets)

In a new LUISS Working Paper, “Opening the Black Box of Austerity: Evidence from Fiscal Consolidation Plans,” Alessandro Franconi ’17 (Macro) explores the effects of austerity measures on labour markets and on income inequality and finds evidence of a mechanism that can mitigate the size of the economic contraction.

Paper abstract

This paper explores the effects of austerity measures on labour markets and on income inequality and finds evidence of a mechanism that can mitigate the size of the economic contraction. The results indicate that: (i) Fiscal consolidation causes greater distortions for the youth, hence they deserve a special attention to avoid severe long-term economic costs. (ii) While at first glance transfers cuts seem to be ideal, a careful examination suggests that these policies can jeopardise the success of fiscal consolidation. (iii) Tax hikes, negatively affecting the productive sector, trigger frictions in the labour market that give rise to recessionary effects. (iv) Spending cuts, targeting public sector wages and employment, can endanger the capabilities of the current and future labour force. (v) Lastly, income inequality increases with tax hikes and spending cuts, whereas the muted response to transfers cuts is explained by the reaction of labour demand.

Connect with the author

Pay out emergency benefits or subsidize wages?

Opinion piece by Francesco Amodio ’10 (Economics) on Canada’s response to COVID-19 crisis

BLAIR GABLE / REUTERS

In a piece published on March 31 in the Montreal Gazette, Francesco Amodio ’10 (Economics) looks at measures taken by the Canadian government in response to the coronavirus pandemic.

Here’s an excerpt:

The measures taken by the Canadian government are in line with those taken in other countries, where governments are adopting either one or the combination of the following two approaches. In the first one, the government lets firms lay off workers, then pays out employment insurance benefits or other kind of income support transfers. The second approach focuses instead on “saving jobs,” with the government subsidizing wages in order to avoid layoffs.

Each approach has its pros and cons. In the short run, the size of wage subsidies and number of potential layoffs will determine which one is costlier. Perhaps more importantly, the two approaches differ in their medium- to long-run impacts.

Francesco Amodio ’10 is a graduate of the Barcelona GSE Master’s Program in Economics and the GPEFM PhD Program (UPF and Barcelona GSE). He is currently Assistant Professor of Economics at McGill University in Montreal, Canada. Follow him on Twitter or visit his website

Is it ethical that soccer players earn more than doctors?

Post by Nadim Elayan, current student in the Barcelona GSE’s Master Program in International Trade, Finance and Development. Follow him on Twitter @Nadim1306.


soccer3

It is unethical that 20-year-old guys with no studies whatsoever earn 20 million euros a year by just kicking a ball during one hour and a half once a week whereas doctors who have studied almost an entire decade and save human lives every day earn 500 times less.

A fair society should compensate with higher wages people who save human lives than people that entertain us during the weekends.

How can we stand by watching soccer players earning millions a year while there are people starving in the same country?

Most of us will have probably heard these sentences or similar ones regarding the large wages that soccer players earn and even that this situation is immoral or a bad incentive for kids to have a good education. But is this true? Do soccer players actually earn more than doctors?

Before analyzing the Spanish soccer labor market or discussing the ethical implications of this situation we need first to say that this is not true. The fact that we can name some players with shockingly salaries it does not mean that on average soccer players earn more than doctors, or even more than the average salary of a specific country. In order to compare professions we need a non-biased sample. We cannot look at the best soccer player in the whole history, Lionel Messi, and compare his salary with a regular doctor in Barcelona and then conclude that soccer players earn 500 times more than doctors. This situation would be the same as looking at Yao Ming, a Chinese basketball player with a height of 7.6 feet (2.29 meters) and a weight of 310 pounds (141 kg) and wrongly concluding that Chinese people are 2 feet taller (0.6 meters) and they weigh 130 pounds (59 kg) more than the average European citizen. Thus Lionel Messi is not the best representative of soccer players’ earnings terms as Yao Ming is not the best representative of the Chinese citizen in physical terms.

In Spain there are more than 700,000 professional and amateur soccer players according to the Real Federación Española de Fútbol and most of them work without a salary or earning below the minimum wage and that is why most of them need another job. There are about 500 players earning on average a wage of 1,336,250.32€ a year, the ones playing in the First Division and also about 500 players earning a wage below 200,000€ on average, the ones playing in the Second Division[1]. So in total we can count that within Spain there are only around 1000 players earning a salary way above the salary an average doctor earns, which in Spain is 64,424.66€[2] on average.

We would have also to take into account that the soccer professional life is barely higher than 10 years while the doctor’s one would be around 35 to 40 years. All this without considering the high risk of injury a soccer player faces every day that would leave him without any salary at all the rest of his life. But of course on the other hand soccer players work no more than 15 hours a week on average and therefore the wage for this .14% gets even larger if calculated per hour. So in order to compensate for the differences in professional lives we should observe that soccer players would earn at least 3.5 to 4 times more than doctors.

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Table 1. Source: OCDE and El Pais.

We can see that these 1,000 players, .14% out of the total, earn way more than doctors on average but the next group of professional players who earn the most are the ones playing in 2nd B Division. There are 2,000 players in this Division earning on average 35,000€, what is actually less than doctors, but the hourly wage would still be above, 55.55€ per hour. If we go further away focusing in the ones playing in 3rd Division their hourly wage is 14.44€ already below the doctors one.

Summing up .14% out of total soccer players earn a salary way above the doctors’ average that more than compensates their shorter professional life. The next .28% still earns a higher hourly wage than doctors’ average but not enough to compensate their shorter professional life. So the rest 99.58% of the soccer players do not earn more than the average salary of a doctor. Actually most of them do not earn anything and some of them earn a little bit if they are in the starting line-up or for each victory.

Once showed that the average soccer player does not earn more than the average doctor, not even more than the minimum wage, we will analyze the soccer labor market and try to explain why this 0.14% earn so much money.

Analyzing the soccer labor market

First we will focus on the soccer labor demand. It is extremely high for very low values of labor hired, so for the first soccer players in the First Division and even for the Second Division. It is easy to show this by noticing that European societies are willing to fill 50 to 90 thousand people stadiums more than 30 times a year at prices between 20€ and 200€ per person each game. Therefore demand is huge. But for higher levels of labor hired, so 2nd Division B, 3rd Division and following Divisions the labor demand is very low and close to 0. Usually these games are free attendance or paying a type of mandatory lottery participation.

chart

Now we can talk about the labor supply. In this case we can separate it into 2 subgroups. For simplicity we just divide the market in 2 subgroups, the first is composed by the 1000 soccer players playing in First or Second Division and the rest of the players. Even when considering perfect inelastic labor supplies we see very high wages for the first subgroup due to the high labor demand and also due to the very scarce labor supply of this type. Whereas for the second subgroup the wages are extremely low, almost 0, because of the low labor demand and the extremely high labor supply.

Ethical implications

People think constantly about soccer, they fill large stadiums paying really high prices and spend almost 100€ a year to buy the newest shirt of their team. Furthermore between 20 and 60 percent of total TV spectators watch Champions League games in prime time and even watch TV programs and listen radio programs that only talk about soccer… In conclusion people spend a large fraction of their income and time on soccer. Is it unethical that a large fraction of this cake is sent to workers via wages?

If we think that this 0.14% of total soccer players earn too much we should then say where we send this money generated by them. Would it be more ethical to let billionaire soccer teams owners to keep a larger fraction instead? In a non-profit Solow Economy, total output goes to capital and labor, therefore , high output will translate into high wages (for one club L=25, so very low). For example in F.C. Barcelona the season 2014-2015 has spent 509 million € in total and 288.9 million € of them only in players’ wages. So 56.76% of total expenditure goes to their soccer players[3].

Secondly if we speak about fairness we should want a society that creates only inequality from people who had the same life opportunities but they succeeded and managed to highlight in their respective fields and dislike inequalities coming from differences in opportunities. Since playing soccer is not expensive, all kids can play it everywhere and the best clubs, knowing this, have scouters all over the world. This makes this labor market pretty competitive, almost every kid at age 12 has at least tried once to get in F.C. Barcelona or Real Madrid through many tests these clubs organize everywhere. Thus, the kids who finally succeed must have been better than almost every kid of their age in the planet. All this combined make the differences in wages created be explained by differences in talent and effort no matter the race, family’s economic status or better education opportunities. In fact, most of the best soccer players like Pelé, Maradona, Ronaldinho, Cristiano Ronaldo, Samuel Eto’o, so also the ones earning high wages, come from very poor families.

Lastly the fact that there are people starving in a country has nothing to do with the fact that there are soccer players earning large wages because this is determined by a very large labor demand and a very scarce labor supply for this very specific First and Second Division players. The labor demand is determined by the society’s taste so we should blame this taste if we think that the amount of money generated by soccer is disproportionately huge and instead we should allocate this exact amount of time and income to fight hunger and other problems we find more relevant.


 

[1] Every team posts their wage budget annually. These values are obtained by dividing the total wage budget over the total players in that division.

[2] Source: OECD

[3] Source: fcbarcelona.com and Europa Press.


 

Breakfast seminars: food for thought

By Marlène Rump ’15, current student in the International Trade, Finance and Development master program at Barcelona GSE. Marlène is on Twitter @marleneleila.

seminars

On Wednesday, October 22, we didn’t have classes, so we decided to explore one of the numerous events on the GSE calendar. For some brain and other food, the breakfast seminar on Labour, Public and Development Economics sounded just right.

The presentations scheduled were held by two of UPF’s PhD students who are in their last year. This means they are finalizing their “job market paper”, which refers to the paper they will use as a demonstration of their skills and interests when they apply for positions.

One important purpose of the seminar is giving the students an opportunity to practice presenting and defending their work, as well as receiving improvement suggestions from fellow PhD students and professors.

Backlash: The Unintended Effects of Language Prohibition in US Schools after World War I

Vicky Fouka started the seminar with her paper on language prohibition in the US Schools after World War I. She compared two states, similar in most social aspects, one of which banned the teaching of German from the primary schools for a few years and the other, her control state, which didn’t.

The prohibition, which was implemented by the authorities in early 1920s, originated from a German-hatred which was widespread in the United States after World War I. What was promoted as an integration measure had the exact opposing effects: Vicky finds that the Germans living in the state with language prohibition deepened their cultural segregation. In comparison with the control state, they were more likely to marry a German spouse and give their first child a very German sounding name.

Editor’s note: Vicky Fouka is a graduate of the Barcelona GSE Master in Economics. See more of her research on her website.

Cultural Capital in the Labor Market: Evidence from Two Trade Liberalization Episodes

The second presentation was also about the assimilation of immigrants, however Tetyana Surovtseva conducted her analysis with modern day data. Her assumption was that if the host country of immigrants increased trade with their country of origin, these immigrants had an advantage on the labor market in trade related sectors. Her hypothesis was that if the host country of immigrants increased trade with their country of origin, these immigrants had an advantage on the labor market in trade related sectors. Her underlying premise is that immigrants have a certain “cultural capital”, other than language, which is valuable for corporations involved in trade with their country of origin.

Tetyana examined the labor market demand for Chinese and Mexican immigrants in the US after a punctual improvement of trade agreements. Her findings suggest that labor market returns to the immigrant cultural capital increase as a result of trade with the country of origin.

Editor’s note: Tetyana is also a Barcelona GSE Economics alum. More about her work is available on her job market page.

Attend some seminars! Especially if you’re thinking of doing a PhD.

For both presentations there were numerous questions which gave additional insight especially on the methods of research. We also learned that most PhD students start their final thesis three years before the end of their program.

After this experience, I can highly recommend attending the seminars. You learn about interesting economic questions and see a specific application of your econometrics classes and this in only one hour. In addition, for those who are envisaging doing a PhD, the presentations give a genuine insight of the type of research you could be conducting.