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.


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.


Connect with the authors


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


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


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

The Asymmetric Unemployment Response of Natives and Foreigners to Migration Shocks

Working Paper by Nicolò Maffei Faccioli (Macro ’15 and IDEA) and Eugenia Vella (Sheffield)

What is the macroeconomic impact of migration in the second-largest destination for migrants after the United States? 

In this paper, we uncover new evidence on the macroeconomic effects of net migration shocks in Germany using monthly data from 2006 to 2019 and a variety of identification strategies in a structural vector autoregression (SVAR). In addition, we use quarterly data in a mixed-frequency SVAR.

While a large literature has analyzed the impact of immigration on employment and wages using disaggregate data, the migration literature in the context of macroeconometric models is still limited due to a lack of data at high frequency. Interestingly, such data is available for Germany. The Federal Statistical Office (Destatis) has been collecting monthly data on the arrivals of foreigners by country of origin on the basis of population registers at the municipal level since 2006. The figure below shows the net migration rate by origin. 


Key takeaways

Migration shocks are persistently expansionary, increasing industrial production, per capita GDP, investment, net exports and tax revenue. 

Our analysis disentangles the positive effect on inflation of job-related migration from OECD countries from the negative effect of migration (including refugees) from less advanced economies. In the former case, a demand effect seems prevalent while in the latter case, where migration is predominantly low-skilled and often political in nature (including refugees), a supply effect prevails.

In the labor market, migration shocks boost job openings and hourly wages. Unemployment falls for natives, driving a decline in total unemployment, while it rises for foreigners (see figure below). Interestingly, migration shocks (blue area in the first row) play a relevant role in explaining fluctuations in industrial production and unemployment of both natives and foreigners, despite the bulk of these being explained by other shocks (red area in the first row), like business cycle and domestic labor supply.


We also shed light on the employment and participation responses for natives and foreigners. Taken together, our results highlight a job-creation effect for natives and a job-competition effect for foreigners.


The COVID-19 recession may trigger an increase in migration flows and exacerbate xenophobic sentiments around the world. This paper contributes to a better understanding of the migration effects in the labor market and the macroeconomy, which is crucial for migration policy design and to curb the rise in xenophobic movements. 

Connect with the authors

About the Barcelona GSE Master’s Program in Macroeconomic Policy and Financial Markets

Bundling, information and two-sided platform competition – Job Market Paper

authorThe following job market paper summary was contributed by Keke Sun (IDEA). Keke is a job market candidate at UAB. Her research interests include Industrial Organization, Venture Capital Markets and Innovation.

Two-sided markets are economic platforms that connect two interdependent groups of users together and enable certain interactions between these two groups of users. The main characteristic of two-sided markets is the indirect network externalities, meaning that one group user’s benefits of joining one platform depends on the number of users of the other group on the same platform. My job market paper studies the impact of pure bundling and the level of consumer information on two-sided platform competition.

The Story

This paper is motivated by the casual observations from the smartphone operating system industry. The operating system (OS) platform connects consumer and application developers, the major competitors are Android by Google and iOS by Apple. Apple also has its amazing in-house handset, iPhone, it bundles the handset with the OS platform.


The Main Results

The leverage theory has established that, in standard one-sided market, if a firm can commit to pure bundling, when consumers have homogeneous valuation of the bundling product, pure bundling reduces equilibrium profits for all firms. Therefore, bundling is usually adopted to deter entry or lead to foreclosure (see Whinston (1990) and Carlton and Waldman (2002) ). However, in a two-sided market, if a platform could commit to aggressive pricing on one side and gain a larger market share. Hence, it becomes more valuable to the users on the other side. I show that, in the presence of asymmetric network externalities, when consumers have homogeneous valuation of the bundling handset, bundling may emerge as a profitable strategy when platforms engage in “divide-and-conquer” strategy: subsidizing the low externality side (consumers) for participation and making profits on the high-externality side (developers). That is, when the benefits of attracting one extra consumer are very strong, committing to aggressive pricing can be profitable without inducing the exit of the rival.

This paper also studies the impact of the level of consumer information on platform competition and the emergence of the bundling decision. Most literature on two-sided markets assumes that all agents have full information about prices and others’ preferences; therefore, can perfectly predict others’ participation decision (see Rochet and Tirole (2003), Caillaud and Jullien (2003), and Armstrong (2006) etc.). Following Hagiu and Halaburda (2014), I use the setting of a hybrid scenario in which some consumers are informed about developer subscription prices and hold responsive expectations about developer participation, while the remaining consumers are uninformed and hold passive expectations. This setting should be a good fit of a situation where information may be less than perfect for some users on different sides of the platform. For instance, some consumers don’t know how much Apple or Google charges the developers for listing applications. Information intensifies price competition with or without bundling. Bundling is more effective in stimulating consumer demand the larger proportion of informed consumers, but bundling is less likely to emerge as the fraction of informed consumers increases.

Strategy and Policy Implications

From a strategy perspective, this paper shows that both platforms have incentives to affect consumers’ knowledge regarding developer subscription prices. Without bundling, both platforms have incentives to withhold the information because consumer information intensifies price competition on both sides. However, when bundling does occur, the two platforms may have different attitudes towards consumer information. The bundling platform prefers a high level of consumer information because bundling is more effective to stimulate consumer demand. The competing platform wishes to withhold the information as it gets worse off as the level of consumer information increases. This paper also shows that when the network externalities are strong, it is more profitable for the platforms to be more aggressive.

From a public policy perspective, this paper provides recommendations concern bundling and information disclosure. Due to the existence of (positive) network externalities, consumer surplus increases with the number of developers on the same platform. Bundling does not only affect consumer subscription prices, but also affects the perceived quality of platforms as it affects developer participation. It has shown that pure bundling improves consumer welfare mainly because it offers a lower subscription price and more application variety to the majority of consumers. For the same reason, even when bundling implements second-degree price discrimination, bundling still improves consumer welfare. Also, information disclosure unambiguously improves consumer surplus by lowering subscription prices on both sides of the platform and improving developer participation. Thus, information disclosure should be encouraged or mandated for consumer’s sake.

Paper abstract and download available on Keke’s website