Introduction

The current stage of global economic development is characterised by pervasive automation of production processes and widespread adoption of industrial robotics. This trend, while driving growth in total labour productivity (LP), simultaneously highlights the contradiction between technological efficiency and social stability. The history of industrial revolutions demonstrates that every leap in productivity is followed by structural transformation of employment, which carries the risk of displacing human labour.

Due to a number of objective reasons, Russia embarked on robotisation of its economy with a delay compared to many developed countries. However, this process has now entered a phase of active intensification. Within the framework of the national project “Means of production and automation”, the federal initiative “Development of industrial robotics and production automation” is being implemented. Its goal is for Russia to achieve 25th place in the global robotisation density ranking by 2030, with a target value of no less than 145 units per 10,000 workers. The implementation of these plans will inevitably lead to accelerated growth in labour productivity and, consequently, to workforce displacement, which will require systemic solutions for retraining and reemploying workers in new jobs. In this regard, the problem of assessing the scale of the forthcoming labour displacement in Russia and optimising this process over time is becoming increasingly relevant.

The article aims to evaluate the level of risk associated with the planned robotisation and the acceptability of the expected scale of workforce displacement.

To achieve this purpose, the following tasks should be addressed:

— to distribute the total workforce displacement over a five–year period;

— to establish the dependence of the labour market and the robotisation campaign on the projected economic growth rates;

— to construct three most illustrative scenarios for the development of the Russian economy.

The assessments are based on the econometric model previously developed by the authors. The novelty of the study lies in the formulation of the most socially acceptable scenarios for robotisation of the Russian industry, while accounting for redistribution of the workforce displaced across sectors of the country’s economy. The economic aspects of the national economy robotisation were addressed earlier in [Balatsky, Ekimova, 2026] and are deliberately not discussed in the present article.

Technological sovereignty vs unemployment

The current geopolitical situation requires Russia to rapidly restore its technological sovereignty, the core of which is the manufacturing industry and its planned robotisation. However, the faster and the more extensive automation of production in Russia is, the higher the growth rates of labour productivity will be, and the greater the mass of displaced workers, who, ceteris paribus, will enhance the potential for unemployment. As a consequence, Russia’s key challenge is to find a balance between enhancing technological efficiency and ensuring social protection of citizens. These two objectives are inherently contradictory, which gives rise to a rather delicate task to rationally balance them without clear bias towards either side. In this context, it is essential to obtain quantitative assessments of the scale of the social threat in the form of potential unemployment that may arise during the automation of industrial production. Moreover, it is necessary to identify in advance those sectors of the economy that will be able to absorb the displaced workers.

The target of achieving robotisation density of 145 units per 10,000 workers, as set out in the federal initiative “Development of industrial robotics and production automation”, implies a fivefold increase in this macroeconomic indicator within five years. By any standards, this represents a substantial growth rate that carries the risk of massive displacement of the employed. Looking ahead, the authors’ calculations have established an international technological boundary (ITB) for robotisation density at 514 units per 10,000 people, beyond which further increases no longer raise total LP. This suggests that both LP growth and workforce displacement will continue after 2030 [Balatsky, Ekimova, 2026]. The robotisation parameters are presented in Table 1. At the same time, social tensions in the labour market will substantially depend on the future economic growth rates and the time horizon over which the reemployment of the displaced workers can be managed.

Table 1. Parameters of robotisation density in Russia’s manufacturing industry

Source: International Federation of Robotics (IFR). https://ifr.org/wr–industrial–robots/; The national project “Means of production and automation”, http://government.ru/rugovclassifier/928/about/ (In Russ.); [Balatsky, Ekimova, 2026].

Table 1 allows for a more precise formulation of the research purpose, namely to assess the scale of workforce displacement during the implementation of the robotisation programme, progressing from the initial level (29 units per 10,000 workers) to the target level (145 units per 10,000 workers), broken down by forecast years, with a subsequent transition to the ITB of 514 units per 10,000 workers.

Social implications of robotisation

Technological advances in robotics and artificial intelligence are transforming established models of production and employment. In contemporary scientific literature, robotisation processes are viewed as an objective reality and a significant driver of socioeconomic dynamics, the consequences of which need to be systemically analysed [Kotov, 2025]. In this regard, academic community is prioritising the study of the multifaceted effects of this phenomenon.

When assessing the economic effects of robotisation, researchers largely concur on its stimulating impact on GDP and labour productivity [Graetz, Michaels, 2018; Acemoglu, Restrepo, 2019; Anisimov et al., 2025; Romanova, Igishev, 2025]. At the same time, the effects of technological transformation are distributed unevenly: statistically significant impacts are observed only in industries with high robotisation density, such as automotive manufacturing, metalworking, plastics production, pharmaceuticals, and the chemical industry [Acemoglu, Restrepo, 2017; Jungmittag, Pesole, 2019; Kromann et al., 2020; Acemoglu, Lelarge, Restrepo, 2020]. Furthermore, the positive shifts are most pronounced at the level of small and medium–sized enterprises [Starovatova, 2023; Fedyunina, Gorodnyi, Simachev, 2023] and predominantly in developing nations [Schneider, 2024].

Despite a certain consensus among researchers regarding the economic effects of robotisation, its social dimension remains a subject of ongoing debate. Economists emphasise the risks of labour market polarisation [Acemoglu, Restrepo, 2019; Kotov, 2025], the potential displacement of low–skilled workers [Schlikhter, 2020; Gimpelson, Kapelyushnikov, 2022], the exacerbation of socioeconomic inequality [Frey, Osborne, 2017], and the emergence of ethical challenges [Bezotechestvo, 2024; Lei, Kim, 2024; Saenko et al., 2025]. These concerns contrast sharply with unambiguous macroeconomic benefits.

Such studies are centred around the problem of how robotisation affects the labour market. Despite a substantial body of research on this issue, no consensus has yet been reached within the academic community. Moreover, empirical studies that differ in their methodology and the set of examined factors frequently yield contradictory conclusions: some authors characterise robotisation as a factor leading to job cut, while others interpret it as a driver of employment growth.

For instance, a number of studies have found that a one–robot increase per 1,000 workers in the United States between 1993 and 2007 contributed to the country’s GDP growth by approximately 0.13 percentage points, while simultaneously decreasing the employment rate by 0.34 percentage points [Acemoglu, Restrepo, 2017; 2019]. Similar findings were obtained from the analysis of the French economy for 2010–2015: a 20 % increase in the number of robots correlated with a decrease in the employment rate by 3.2 percentage points [Acemoglu, Lelarge, Restrepo, 2020]. A reduction in the share of labour costs in the income structure has been documented in numerous sources [Dauth et al., 2017; Autor, Salomons, 2018; Carbonero, Ernst, Weber, 2018; Blanas, Gancia, Lee, 2019]. In addition, some researchers have highlighted negative trends related to job cuts [Zemtsov, 2017; Urunov, Rodina, 2018].

Against this backdrop, a number of studies point to the opposite trend, whereby robotisation results in an increase in employment. For instance, Gregory, Salomons and Zierahn [2019] confirm the positive impact of robotisation on employment dynamics in the European Union over the period of 1999–2010, stressing its constructive role as a tool for preserving and transforming employment under global competition. Similar conclusions are drawn in the study by Autor and Salomons [2018]: an analysis of the EU data for 1970–2007 shows that, despite the decline in employment in some industries, technological progress exerted an overall positive effect on aggregate employment through increased demand in related sectors and higher final consumption.

A study of the Japanese economy covering 1978–2017 [Adachi, Kawaguchi, Saito, 2020] presents additional data for analysis. The authors indicate that the reduction in the cost of robotics and the simultaneous increase in the robot fleet by 1 percentage point contributed to a 0.28 % increase in employment at the macro level. At the level of local labour markets, one additional robot per 1,000 workers increased employment by 2.2 %, which was accompanied by a fall in the average number of hours worked and a rise in hourly wage. This signifies a qualitative transformation of the workforce, rather than its simple displacement.

Despite the existing discrepancies in empirical estimates, most contemporary studies underscore the dual nature of automation: it acts not so much as a mechanical factor of job reduction, but rather as a driver of structural transformation in the labour market, where the displacement effect, which reduces the share of labour in value added, is counterbalanced by the reinstatement effect that generates new tasks and competencies falling beyond the capabilities of machines [Acemoglu, Restrepo, 2019].

In particular, a study of 1994–2014 data from Germany displays that the introduction of industrial robots does lead to job displacement in manufacturing; however, this negative effect is fully offset by the employment growth in the services sector. The authors emphasise that labour market institutions are the key factor underlying cross–country differences in the implications of automation: in Germany, where worker protection is stronger than in the United States, the displacement effect is significantly weaker, and the reallocation of labour is smoother. This allows concluding that robotisation does not necessarily cause higher unemployment, but it does require the adaptation of educational trajectories and institutional support for the transition of workers between sectors and the tasks they perform [Dauth et al., 2021].

While evaluating robotisation risks, McKinsey’s analysts project that by 2030 up to one third of US workers could be displaced, along with 400 to 800 million people worldwide. The consequences of automation will primarily affect occupations involving physical labour in predictable environments (assembly, fast food, warehousing), administration activities and data processing, as well as routine operations governed by clear algorithms. At the same time, specialists whose work requires deep expertise, creativity, or high levels of social intelligence are expected to maintain or even strengthen their positions [1].

Similar conclusions are drawn by Frey and Osborne [2017], who argue that approximately 47 % of jobs in the United States will fall into the high–risk zone of automation in the coming decades. Moreover, according to the authors, the probability of a profession being automated is inversely proportional to the level of wages and education: those positions that require creative and social intelligence (generating new ideas, negotiation, persuasion, and caregiving) as well as complex motor skills in unstructured environments are the most protected from automation, while professions in transport, logistics, office support, and manufacturing, where tasks are performed using clear algorithms and under predictable conditions, are most vulnerable to automation.

Data from the World Economic Forum indicate a more balanced dynamic when viewed in a broader perspective: by 2030, despite the fact that technological changes may lead to the elimination of 92 million jobs, 170 million new positions are expected to be created simultaneously. This will result in a net increase in employment of 78 million jobs [2].

Modern literature identifies the intensification of social inequality as one of the most significant consequences of robotisation, which emerges at all levels of social organisation – from the micro level (the individual worker) to the meso level (regional labour markets) and the macro level (national economies).

At the macro level, despite the net growth in total employment driven by final demand effects and cross–industry linkages, a steady decline in the share of labour in national income is observed [Autor, Salomons, 2018; Lei, Kim, 2024]. This points to a redistribution of goods from workers to capital owners, a trend that has been particularly pronounced since the 1980s [Elsby, Hobijn, Sahin, 2013; Karabarbounis, Neiman, 2014; Dao et al., 2017]. At the meso level, this manifests itself in the structural transformation of the economy: industries with high LP growth are reducing their share in total employment, while indirect effects fail to offset the decline in the share of labour within those industries themselves [Autor, Salomons, 2018]. At the micro level, automation deepens the gap between workers – those with skills complementary to new technologies and those whose professional functions are reduced to routine, easily algorithmized operations [Frey, Osborne, 2017]; particularly vulnerable social groups also include workers with lower levels of education, women, young specialists, and residents of peripheral regions, whose occupations are most susceptible to displacement by new technologies [Acemoglu, Restrepo, 2017; Clifton, Glasmeier, Gray, 2020].

Thus, social inequality intensifying under the influence of robotisation constitutes a persistent trend registered in studies of developed economies. However, projecting these findings onto the Russian environment requires careful consideration of specific institutional and structural characteristics of the national labour market. In Russia, the impact of robotisation is mediated by a number of restraining and transforming factors: first, the low level of robotisation density (29 robots per 10,000 workers compared to the world average of 177), which preserves the share of low–skilled workforce and slows the structural transformation of employment [Zemtsov, 2017; Gurlev, 2020]; second, the high adaptability of the Russian labour market, where cost adjustments occur primarily through wage flexibility rather than quantitative changes in employment levels, thereby disguising the potential displacement effects of automation [Gimpelson, Kapeliushnikov, 2022]; third, the regional asymmetry in technology adoption, which widens the gap between innovation centres and peripheral territories [Fedyunina, Gorodnyi, Simachev, 2023].

As a result, unlike countries with pronounced employment polarisation, the risks of robotisation in Russia are concentrated not so much on massive workforce displacement but on the erosion of job quality, LP stagnation, and deepening functional inequality between workers who have access to new technologies and those who are still engaged in the sphere of routine and low–skilled labour [Schlikhter, 2020; Arkhipova, Melnikova, 2022]. This necessitates the implementation of a coordinated policy in the fields of education, retraining, and support for technological diffusion to ensure the inclusive nature of digital transformation of the Russian economy.

Given the above, it can be concluded that there is currently no macroeconomic assessment of workforce displacement in Russia caused by the planned robotisation of the economy. The subsequent sections of this article will be addressing precisely this issue.

Robotisation scenarios in Russia: In search of a compromise

In our previous work [Balatsky, Ekimova, 2026], we constructed an econometric dependence of labour productivity on robotisation density, which is universal for a group of 20 countries:

   (1)

n = 120; R² = 0.60; DW = 1.88; A = 1.42 %; ВР = 1.53; p–value = 0.46,

where t is the observation period (year); i is the country index; P is total labour productivity; R is robotisation density in the manufacturing industry; L is the number of the employed in the national economy; n is the number of observations; DW is the Durbin–Watson statistic; А is the approximation error; BP is the Breusch–Pagan statistic; p–value is the significance level achieved. The model passes all statistical tests and can be used for analytical calculations.

Using dependence (1) for the Russian economy allows estimating the expected labour productivity growth, which, in turn, using formula (2), makes it possible to determine the scale of workforce displacement according to the future dynamics of economic growth:

         (2)

where ξ is the growth rate of the national economy’s LP, possible due to massive robotisation of industry; μ is the employment growth rate (workforce displacement) due to massive robotisation of the economy; λ is the expected growth rate of GDP.

The proposed computational procedure allows obtaining aggregated data on possible workforce displacement when implementing the scenarios in Table 1 under the assumption that λ = 0 (Table 2; in the right column, the end date is replaced with an arbitrary point in time, where the international technological boundary is reached, beyond which further robotisation of industry does not provide an increase in LP).

Table 2. Parameters of total workforce displacement in Russia under the implementation of the manufacturing industry robotisation programme

Source: The Federal State Statistics Service (Rosstat) data. https://rosstat.gov.ru/opendata/7708234640–employees2024. (In Russ.); [Balatsky, Ekimova, 2026].

It is worth noting that Table 2 presents interval data over extended time periods in order to convey the overall potential of the anticipated social shifts. However, to construct standard development scenarios, it is necessary to switch to average annual growth rates:

                                                                                                               (3)

where μ is the aggregate growth rate of labour productivity; μ*is the average annual growth rate of labour productivity; T is the time interval of the forecast scenario.

Thus, the resulting annual rate of workforce displacement μ** accounting for the projected annual economic growth rate λ* can be estimated as follows:

     (4)

where μ** is the growth rate of workforce displacement considering the production growth rate.

For small growth rate values, applied calculations can be performed using the following simplified formula:

              (5)

Even a cursory analysis of the data from Table 2 demonstrates that Russia may face a serious threat to its labour market and a significant increase in unemployment if the planned industrial robotisation programme is implemented. Moreover, if robotisation is pursued to its logical conclusion and reaches the ITB, more than one–third of the current number of the employed (∆L = –25 million workers) will have to be reemployed. The above leads to the main conclusion: when implementing industrial robotisation, the labour market cannot be left to its own devices. In other words, robotisation of the economy must be accompanied by social programmes for redistribution and reemployment of the displaced workers.

The programmes currently administered in Russia are primarily proactive and focus mainly on training specialists to work with robots. Examples include the programme for training specialists in industrial robotics run by “RT–Techpriemka” of the Rostec State Corporation [3], as well as training under the programmes “Specialist in production robotisation” and “Specialist in artificial intelligence systems” offered by the Financial University under the Government of the Russian Federation within the federal project “Active employment promotion measures” of the national project “Personnel” [4]. However, the issues of reactive support and reemployment of workers displaced by these very technologies remain beyond the scope of state measures, which may create the risk of technological unemployment in the future.

This regulatory oversight is partly due not only to the currently stable situation in the labour market, but also to the setting of some kind of records: in August 2025, the unemployment rate fell to 2.1 %, the lowest level recorded since the beginning of observations in 1991 [5]. Nevertheless, the launch of large–scale production automation may radically alter this situation.

Some developed countries have already responded to the new emerging threat by stimulating targeted programmes designed to address the problem of technological unemployment. For instance, Singapore is placing emphasis on large–scale support for middle–aged citizens through the comprehensive SkillsFuture Level–Up Programme, under which educational centres assume responsibility for career counselling and assistance in job placement for their graduates [6]. France utilises the mechanism of individual learning accounts – Compte Personnel de Formation, – which enables workers to accumulate training entitlements throughout their careers and independently choose courses for professional retraining or qualification upgrading [7]. Germany is developing a package of measures that includes the expansion of state–funded retraining programmes, the introduction of transitional allowances for workers in affected industries, and the promotion of flexible employment forms [8]. South Korea has created a unified digital platform Employment24, which integrates all employment services and uses artificial intelligence to provide personalised recommendations, thereby making the support system as accessible and effective as possible [9].

As noted above, the end date in the right column in Table 2 has been replaced with an arbitrary point in time corresponding to the attainment of the ITB. However, given that the target robotisation density of 145 units per 10,000 workers is to be achieved within six years, keeping the same pace of production automation in the future implies reaching the ITB in the subsequent seven years, by 2037. Consequently, industrial robotisation in Russia can be completed approximately by 2038. From the standpoint of international standards, this represents a rather sluggish pace of adopting new technologies. For comparison, China increased its robotisation density nearly tenfold over eight years (2025–2033), raising it from 49 to 470 units per 10,000 workers and coming very close to the ITB [Balatsky, Ekimova, 2026]. In this sense, the Russian regulator has opted for a rather moderate pace of modernisation of the national economy.

Although the aggregated figures for workforce displacement in Table 2 are a cause for concern, there are two mitigating factors: the first one is the possibility of stretching the process over time and, if necessary, prolonging it (formula (3)), and the second one is maintaining high GDP growth rates (formula (4)). In this case, it opens the possibility to design scenarios that differ significantly in the degree of social tension – and it is precisely what constitutes the scientific intrigue of the problem under study.

For a constructive assessment of Russia’s development prospects under robotisation, three most illustrative scenarios should be compared, which would differ significantly in their exogenous characteristics. For example, economic growth in Russia has always been highly uneven, and after the start of the Special Military Operation (SVO), as expected, it has become even more unstable: GDP growth rates in 2023–2025 amounted to 4.1, 4.9, and 1.0 %, respectively [10]. Thus, high non–stationarity is characteristic of Russia’s actual economic growth dynamics and should be reflected in the designed scenarios. At the same time, the introduction of robotic systems is likely to proceed in a uniform manner due to stable funding allocated for these purposes. Hence, the scenario calculations will combine relatively stationary trajectories of robotisation and LP growth with nonstationary trajectories of GDP growth.

The first of the aforementioned mitigating factors is related to stretching over time the processes of robotisation, LP growth, and workforce displacement. Based on the robotisation programme presented in Table 1, the total displacement of workers over the period of 2024–2030, amounting to 16 % of the employed population under conditions of dynamic uniformity of the process, would correspond to the annual displacement rate of μ* = –2.7 %. It is easy to see that this value is comparable to the economic growth rates. Accordingly, the second mitigating factor is associated with the intensity of production growth. For example, in 2024, a 4.9 % economic growth in Russia fully absorbed the displacement of 2.7 % of workers, whereas in 2025, GDP growth of 1.0 % implied that 1.7 % of workers would join the ranks of the unemployed. These examples fully illustrate both the challenges and the opportunities for relatively painless workforce displacement resulting from the systemic robotisation of production.

In light of the above, it is advisable to examine three traditional scenarios for the development of the Russian economy over the period of 2026–2030: pessimistic, neutral, and optimistic. The initial data for these scenarios embrace the parameters set out in the official strategic planning document “Scenario conditions for the functioning of the Russian Federation’s economy and key parameters for the forecast of the Russian Federation’s socioeconomic development for 2026 and the planned period of 2027 and 2028” [11]. This document envisages two scenarios: a baseline scenario, which describes the most probable development course of the Russian economy, and a conservative scenario, based on the assumption that economic conditions will deteriorate.

The parameters of the baseline scenario, which in this context serves as the optimistic development option, are primarily driven by the growth of domestic demand (both consumer and investment) and correspond to the target values established in the Decree of the President of the Russian Federation no. 309 of May 7, 2024 “On the national development goals of the Russian Federation for the period up to 2030 and for the perspective up to 2036” [12] and in the “Forecast of the Russian Federation’s socioeconomic development for the period up to 2036” prepared by the Ministry of Economic Development of the Russian Federation [13] that envisages stabilisation of GDP growth rates at the level of 3 %. The final GDP growth indicators for this scenario are presented in Table 3.

Table 3. Parameters of the projected development scenarios for the Russian economy

The conservative scenario, which can be considered neutral, is based on the assumption that unfavourable circumstances, if realised, will produce a cumulative effect due to the intensification of both external and internal factors hindering the growth of the Russian economy. External risks include escalation of trade restrictions, stagnation of the global economy, and tightening of the sanction regime; among internal risks there is declining investment activity and domestic demand. These trends taken together determine a slowdown in economic growth dynamics compared to the baseline trajectory with a gradual return to target values (Table 3). Thus, this scenario assumes a deterioration relative to the baseline option, yet it remains cautiously optimistic.

The pessimistic scenario is represented by the inertial development path of the Russian economy: according to estimates by the Institute of Economic Forecasting of the Russian Academy of Sciences, GDP growth rates in 2026–2030 will not exceed 1.7% [14]. This scenario, therefore, assumes uniformly sluggish growth over the next five years (Table 3). However, even such a trajectory does not imply an outright crisis, depression or economic contraction.

For all the three scenarios, we will assume uniform robotisation of industry over the years, and consequently, uniform annual workforce displacement at the annual rate of μ* = –2.7% of its 2024 value. Depending on GDP growth rates, this figure is adjusted using formula (4) to obtain the target indicator p**, from which the annual absolute number of the “new” unemployed (∆L*) is derived.

The calculation results and data from Table 3 allow drawing a number of important conclusions.

First, the inertial scenario poses serious social challenges for Russia. The expected economic growth rate is insufficient to absorb the displaced workers, and approximately 750,000 people will enter the labour market annually with uncertain employment prospects. The lack of economic activity in the system threatens to leave some of these workers chronically unemployed. The main danger of this scenario is the accumulation of the unemployed: if only half of the displaced workers are reemployed annually, then by 2030 the number of additional unemployed people created due to the large–scale robotisation of the economy will reach 1.9 million, and the overall unemployment rate will amount to 6.4%, which would be a negative record for Russia. Therefore, the inertial scenario should be added with serious efforts aimed at redistributing the surplus labour force.

Second, the conservative scenario also entails social problems for Russia, but only in the first two years, with almost the entire social burden falling on the first year. If the mass of the displaced workers is not redistributed, the unemployment rate may reach 3.7% as early as 2026. In 2027, the pressure on the labour market is expected to be three times lower; however, even this one–third of a million people are not easy to reemploy effectively. The following three years will be an obvious reserve for solving these problems, when the employment sector may expand and a certain labour shortage may emerge, easily replenished by those who have lost their jobs in the previous year or two.

Third, the baseline scenario is relatively suitable for the country from the standpoint of social problems – only a minor challenge for the labour market will be observed in the first year. In all subsequent years, employment will be expanding, although this process will be of a rather moderate nature, and there is no question of any noticeable increase in the labour market. Thus, even the most favourable baseline scenario sets a trajectory for labour market dynamics that is close to stationary. In this sense, the baseline scenario is primarily focused on maintaining the global status quo in the Russian labour market. The core value of this scenario lies in the implementation of the planned programme of economic robotisation without detriment to the labour market.

These conclusions, in turn, allow formulating two fundamental theses regarding the robotisation of the Russian economy. First, the Russian economy is capable of withstanding even a more revolutionary technological modernisation of industry, but only on the precondition of a radical increase in the country’s economic activity. Second, by adopting the programme for the robotisation of the country’s manufacturing, the government has essentially left itself no choice and must take any measures, even extraordinary ones, to ensure active economic growth. Put it another way, sustaining intensive economic growth over the next five years represents an existential challenge for Russia; otherwise, improving technological equipment in Russian production will be accompanied by an overload of the labour market and impoverishment of a significant portion of the population. Failing this, the robotisation programme will have to be curtailed. Naturally, such courses of events are highly undesirable.

The results obtained reignite the traditional discussion unfolding in Russia over recent years concerning the role of the Bank of Russia in curbing economic activity. In fact, there is already a scientific and public consensus that it is unacceptable to further maintain a high key rate that renders economic growth almost impossible [15] [Ilyin, Morev, 2026]. If in the coming years the mega–regulator continues the inflation targeting policy it pursued in 2025, the country will at best follow the inertial scenario, and at worst may enter a recessionary regime. Should a decline in production occur, it will either generate a social crisis in the country or lead to the complete cancellation of industrial robotisation and modernisation of the national economy. This represents the main macroeconomic alternative facing Russia.

These findings allow us to address an important and still unresolved issue of creating a special governing body in the country that would coordinate the actions of the Bank of Russia, the RF Ministry of Finance, and the RF Ministry of Economic Development, and possibly the RF Ministry of Industry and Trade [Polterovich, 2018]. The robotisation programme expands the functional scope of the proposed agency by adding the RF Ministry of Labour that is responsible for the state of the country’s labour market to the Bank of Russia and the aforementioned ministries. At present, such coordination of different federal regulators is not underway and not yet envisaged. It is not implausible that automation of industry may become the very organisational driver that will jump–start this issue.

Projected structural shifts

When projecting the process of workforce displacement, it is essential to understand what sectors of the economy the released workers can be redeployed to – both independently and with additional retraining. To that end, it is necessary to identify what sectors of the national economy act as drivers in absorbing workforce. Determining these sectoral drivers can reside in two fundamental considerations. The first relates to reports on sectoral employment dynamics, which will make it possible to identify the fast–growing segments of the economy. The second is grounded in the proposition that the current stage of human evolution is associated with the emergence of the leisure industry on an unprecedented scale compared to previous historical periods. In the coming decades, this industry will absorb large masses of the workforce.

The proposed approach implies examining key industry drivers of engaging the employed. These include industries that experienced sustained growth during 2020–2024 (Table 4).

Interestingly enough, manufacturing industries that are planned for active robotisation display the highest dynamism and the greatest need for workforce. This is largely due to the sector’s renaissance that has begun after 2014. However, the demand for its products remains consistently high, which explains the sector’s growing workforce. Construction and transport have traditionally absorbed large numbers of workers, while in recent years particular dynamism was characteristic of hospitality and public catering enterprises; the sector of administration and related support services is also expanding. A modest but stable demand for labour is generated by healthcare and social services, as well as culture, sports, leisure, and entertainment. The combined annual absorption capacity of these seven driver industries amounts to approximately 700 thousand workers, which is somewhat lower than the volume of workforce displacement under the inertial scenario. This indicates that this scenario is clearly problematic, and any shift in the Russian economy towards it is fraught with chronic social problems. The conservative scenario is likely to generate noticeable staffing problems in the first forecast year, but then the surplus of workers can be relatively easily redistributed across the driver industries. As for the baseline scenario, the workforce displacement it generates in the first year is largely redistributed within the manufacturing industry itself, with only a small additional outflow to other sectors.

Table 4. Driver industries for workforce absorption in Russia

Source: Rosstat data. https://rosstat.gov.ru/opendata/7708234640-employees2024. (In Russ.)

An important feature of the forthcoming robotisation is that two opposing labour flows collide within the manufacturing industry: additional demand for labour due to the industry’s expanding business activity, and workers displaced by robotic systems. Which of these flows will prevail depends entirely on the economic development scenario, that is, on the GDP growth rate.

So, we can hypothesise that the sphere of culture, sports, leisure, and entertainment will see a rapid expansion. Even in the absence of a robotisation trend in the economy, this sector has been steadily expanding. As automation of production accelerates, the leisure sector will also evolve and absorb additional workforce.

Issues for debate

The above presents the most generalised picture of possible social implications of robotisation in the Russian industry; however, it does not reflect a number of aspects that need to be further clarified.

First, the use of model (1), built on data from 20 countries, to forecast the situation in Russia implies that the structural parameters of different states are comparable. This assumption can be considered quite legitimate, since the countries covered in the calculations are comparable to Russia in terms of LP and are at a similar stage of technological development, although they are ahead of it in terms of robotisation density; the size of Russia’s labour market is captured by the employment variable. Adding Russia to the original sample proved impossible due to the lack of retrospective data for the country. Computational experiments with the model showed that removing one or two countries from the sample has virtually no effect on its parameters. Thus, the impact of economic robotisation on LP can be regarded as a relatively universal process for developed countries, which allows its extrapolation to the Russian Federation.

Second, extrapolating the international econometric relationship to Russia does not presuppose high accuracy by definition. This is due to the specific features of the Russian labour market, which is characterised by considerable flexibility, that is, growth in LP can end in a reduction (or stagnation) of wages and an increase in part–time employment. Such a development cannot be ruled out; however, it is unlikely that the flexibility of the labour market will be able to fully offset the powerful trend towards large–scale robotisation of the manufacturing industry. In this sense, the obtained figures provide fairly realistic estimates of the scale of the anticipated phenomenon.

Third, there exists a potential mismatch between old and new jobs available to the displaced workers. On the one hand, a new job in another industry may offer a lower pay packet; on the other hand, it may require skills and competencies – even at a lower qualification level – that an individual does not possess. This problem is partially alleviated by the fact that it is less skilled workers that are displaced in the first place, making their transition to less technologically intensive sectors less painful. However, a complete solution to this issue through establishing a personnel retraining system falls within the responsibility of the RF Ministry of Labour. A detailed discussion of this matter lies beyond the scope of the present article.

Fourth, all the scenarios assume a uniform process of industrial robotisation and a uniform annual workforce displacement (2.7 %), which is an obvious simplification. In real practice, the automation process may accelerate or decelerate under the influence of institutional and market factors, which will distort the annual parameters of the scenario forecasts. Predicting possible irregularities in robotisation is not feasible, which justifies the adopted simplification. Nevertheless, this does not hinder an understanding of potential risks and challenges. In essence, the total displacement of workers by year will be adjusted for the acceleration/deceleration coefficient of robotisation, while the overall scenario trajectories will remain largely unchanged.

Fifth, Russia is characterised by enormous economic and social heterogeneity of its territory. Consequently, the total number of displaced workers may affect different regions to a widely varying degree. To more fully understand the level of tension in regional labour markets, it is necessary to identify where manufacturing enterprises are concentrated and, accordingly, where the maximum workforce displacement will occur. At the same time, it is essential to determine whether there are sectoral drivers in these regions that are capable of absorbing the displaced workers. For example, in 20 Russian regions (such as the Vologda, Tula, Kaluga, Lipetsk, and Novgorod oblasts, etc.), the share of manufacturing in gross value added ranges between 25 and 50 %. It is precisely these territories that will be most heavily affected by robotisation and workforce displacement. Conversely, in 29 regions (such as the Chukotka autonomous okrug, republics of Tyva and Yakutia, Magadan oblast, etc.), this share is below 10 %, and it is not worth expecting significant movements in the labour market here. More active economic growth in industrial regions should cushion this unevenness. In any case, the intensity of workforce redistribution across the Russian territory will vary substantially, and primary attention should be concentrated on the country’s industrial regions. However, a more detailed discussion of this aspect goes beyond the scope of this article and may serve as a basis for further development of the raised issues.

Thus, the obtained quantitative estimates are largely conditional, and we are fully aware of this circumstance. Nevertheless, even considering this, the overall picture of the forthcoming changes appears to be quite relevant and informative.

Conclusion: Moving towards a new society

The article has quantitatively assessed social consequences of the planned large–scale robotisation of the manufacturing industry in Russia. Based on the previously developed econometric relationship, we have estimated the total volume of workforce displacement in the Russian economy by 2030, amounting to 16 % of the employed population, or 11.4 million people. This will be the social outcome of the federal initiative “Development of industrial robotics and production automation”, which stipulates that robotisation density in Russia is expected to attain 145 units per 10,000 workers by 2030.

The obtained workforce displacement figures may appear extremely high; however, based on the labour redistribution model, it has been demonstrated that stretching the displacement process over time can streamline it and reduce the annual displacement of workers to 2.7 % (1.8–1.9 million people), which looks far less threatening. Nevertheless, these figures are also integrated into one or another scenario of national economic development, which yields markedly different outcomes. The article examines three scenarios – the baseline (optimistic), conservative (neutral), and inertial (pessimistic) – that differ in expected economic growth rates. Our calculations prove that the inertial scenario is truly alarming, as it entails a large annual displacement of personnel (approximately 740 thousand workers), which would be almost impossible to redistribute effectively across economic sectors. Significant problems under the conservative and baseline scenarios are projected only within the first two years or solely in the first year of the forecast period, respectively.

The displaced workforce will be primarily distributed across seven driver industries, where employment has grown steadily in the previous years, namely manufacturing, construction, transportation, hospitality and catering, administration and related additional services, healthcare and social services, culture, sports, leisure, and entertainment. Together, these industries could absorb approximately 700,000 people annually.

The overall conclusion is that maintaining intensive economic growth of even 3 % over the next five years will pose an existential challenge for Russia; otherwise, improving the technological capabilities of Russian manufacturing will be accompanied by an overburdened labour market and the impoverishment of a significant portion of the population. Thus, it has been concluded that it is necessary to set up a special governing body in the country to coordinate the actions of the Bank of Russia, the Ministry of Finance, the Ministry of Economic Development, the Ministry of Industry and Trade, and the Ministry of Labour. Otherwise, sluggish economic growth will undermine the planned large–scale robotisation of the national economy.

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