R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 57 r-economy.com Online ISSN 2412-0731 Original Paper © Savin, I.V., Letyagin, D.K., 2022 doi https://doi.org/10.15826/recon.2022.8.1.005 UDC 331.5 JEL D40, L16, L50 Estimating the role of labor resources reallocation between sectors on the growth of aggregate labor productivity in the Russian economy I.V. Savin1, 2 , D.K. Letyagin1 1 Ural Federal University, Ekaterinburg, Russia; ivan.savin@uab.cat 2 Autonomous University of Barcelona, Barcelona, Spain ABSTRACT Relevance. Economic growth can be achieved in two different ways: through technological improvements and reallocation of market shares from less to more productive units. Despite the significant research literature on innovation in Russia, the literature on market selection, especially at the sectoral level, is rela- tively scarce. This is the research gap that this study aims to address. Research objective. The article assesses how labor resource reallocation between sectors has influenced the dynamics of aggregate labor productivity in the Rus- sian economy over the past two decades. Data and methods. For this purpose, the growth of aggregate labor productivity was decomposed into the growth of productivity within the sectors themselves and the reallocation of labor resources between them. This allowed us to conduct a quantitative estimation of the role of market selection at the sectoral level. For our study, we used data from Rosstat (from 2002 to 2018) and the World In- put-Output Database (from 2000 to 2014). Results. For Rosstat data, the ratio of the effect of changes in labor productivity and labor resource reallocation by sector on total labor productivity over the period was 0.71/0.29, and for WIOD data it was 0.44/0.56. This indicates that labor resources are more likely to be reallocated to related sectors (e.g. between manufacturing industries). Conclusions. The results suggest that there is competitive market selection at the sectoral level and that labor has generally been reallocated to more productive sectors of the economy, contributing significantly to the growth of aggregate pro- ductivity in the economy. Our study shows the sectors of the economy where this reallocation has taken place, which may help to determine where this process is successful and where it needs additional stimulation. KEYWORDS competition, competitive selection, labor productivity, productivity growth, resource allocation, structural change, decomposition, value added AWKNOWLEDGEMENTS This research was supported by grant No. 19-18-00262, “Modeling of Balanced Technological and Socio-Economic Development of Russian Regions», from the Russian Science Foundation. FOR CITATION Savin, I.V., & Letyagin, D.K. (2022). Estimating the role of labor resources reallocation between sectors on the growth of aggregate labor productivity in the Russian economy. R-economy, 8(1), 57–67. doi: 10.15826/recon.2022.8.1.005 Оценка роли перетока трудовых ресурсов между секторами на рост совокупной производительности труда в российской экономике И.В. Савин1, 2 , Д.К. Летягин1 1 Уральский федеральный университет, Екатеринбург, Россия; email: ivan.savin@uab.cat 2 Автономный университет Барселоны, Барселона, Испания АННОТАЦИЯ Актуальность. Экономический рост может быть достигнут двумя раз- личными способами: за счет технологических усовершенствований и пе- рераспределения доли рынка от менее производительных единиц к более производительным. Несмотря на значительный объем исследовательской литературы по инновациям в России, литература по выбору рынка, осо- бенно на отраслевом уровне, относительно скудна. На устранение данного пробела и направлено данное исследование. Цель исследования. В статье оценивается как переток трудовых ресурсов между секторами влиял на динамику совокупной производительности труда в российской экономике за последние два десятилетия. КЛЮЧЕВЫЕ СЛОВА конкуренция, конкурентный отбор, производительность труда, рост производительности, распределение ресурсов, структурные изменения, декомпозиция, добавленная стоимость https://doi.org/10.15826/recon.2022.8.1.005 https://doi.org/10.15826/recon.2022.8.1.005 mailto:ivan.savin@uab.cat mailto:ivan.savin@uab.cat 58 r-economy.com R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 Online ISSN 2412-0731 Introduction One of the key determinants of economic de- velopment is labor productivity. Countries that have been able to move from the category of de- veloping to developed economies are those that have been able to diversify their economies by re- directing resources from low-productivity sectors of the economy to more productive ones. The idea of this paper is to examine how the reallocation of labor between sectors of the economy has influ- enced labor productivity growth in Russia. Numerous papers have been written on the impact of structural change on labor productiv- ity in sectors of the economy (Bessonov, 2004; Gimpelson et al., 2014; Savin et al., 2020; Mc- Millan and Rodrick, 2011; Savin, 2021; Tang and Wang, 2004; Timmer et al., 2014). One of the earliest articles to discuss labor shifts be- Данные и методы. С этой целью была осуществлена декомпозиция роста совокупной производительности труда на рост производительности вну- три самих секторов и переток трудовых ресурсов между ними. Для прове- дения исследования нами были использованы данные Росстата (с 2002 по 2018 год) и Всемирной базы данных «затраты-выпуск» (с 2000 по 2014 год). Результаты. По данным Росстата соотношение влияния изменений про- изводительности труда и перетока трудовых ресурсов по секторам на совокупную производительность труда за указанный период составило 0,71/0,29, а для данных WIOD – 0,44/0,56. Это указывает на то что трудо- вые ресурсы более склонны перераспределяться в смежные сектора (на- пример, между отраслями обрабатывающего производства). Выводы. Полученные результаты свидетельствуют о наличии конкурент- ного отбора на уровне секторов экономики, а также о том, что трудовые ресурсы в целом перераспределялись в более производительные отрасли экономики внося весомый вклад в рост совокупной производительности труда в экономике. Наше исследование оценивает в какие именно сектора экономики это перераспределение происходило, что может помочь опре- делить, где данный процесс успешен, а где этот процесс нуждается в сти- мулировании. БЛАГОДАРНОСТИ Исследование выполнено при поддержке гранта РНФ (проект №19–18–00262 «Моделирование сбалансированного технологического и социально- экономического развития российских регионов»). ДЛЯ ЦИТИРОВАНИЯ Savin, I.V., & Letyagin, D.K. (2022). Estimating the role of labor resources reallocation between sectors on the growth of aggregate labor productivity in the Russian economy. R-economy, 8(1), 57–67. doi: 10.15826/recon.2022.8.1.005 评估行业间劳动力资源流动对俄罗斯经济中总劳动生产率的作用 萨文1, 2 ，莱蒂亚金1 1乌拉尔联邦大学，叶卡捷琳堡，俄罗斯；邮箱：ivan.savin@uab.cat 2巴塞罗那自治大学，巴塞罗那，西班牙 摘要 现实性：经济增长可以通过两种不同的方式实现：技术改进；将市场份 额从生产力较低的单位转移到生产力更高的单位。尽管俄罗斯有大量关 于创新的研究，但关于市场选择，尤其是在行业层面的学术研究相对稀 缺。本研究旨在填补这一空白。 研究目标：本文评估了在过去的20年里，行业间劳动力资源的流动是如 何动态影响俄罗斯经济中总劳动生产率的。 数据和方法：因此，总劳动生产率的增长被分解为各行业内部的生产率 增长和行业之间的劳动力资源流动。为了进行这项研究，我们使用了来 自俄罗斯联邦国家统计局（2002年至2018年）和世界投入产出数据库 （2000年至2014年）的数据。 研究结果：根据俄罗斯联邦国家统计局的数据，在此期间，各行业的劳 动生产率和劳动力资源流动对总劳动生产率的影响比率为0.71/0.29，而 世界投入产出数据库比率为0,44/0,56。这表明劳动力资源更有可能重新 分配到相似行业（例如，制造业之间）。 结论：结果显示，在行业层面存在竞争性选择，劳动力通常流动到更有 生产力的部门，这大大促进了经济总生产力的增长。我们的研究准确评 估了这种劳动力流动发生在哪些经济部门。这有助于确定这一过程在哪 些方面是成功的，以及在哪些方面需要优化。 关键词 竞争力，竞争淘汰，劳动生产 率，生产率增长，资源分配， 结构变化，分解，增加值 供引用 Savin, I.V., & Letyagin, D.K. (2022). Estimating the role of labor resources reallocation between sectors on the growth of aggregate labor productivity in the Russian economy. R-economy, 8(1), 57–67. doi: 10.15826/recon.2022.8.1.005 https://doi.org/10.15826/recon.2022.8.1.005 mailto:ivan.savin@uab.cat R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 59 r-economy.com Online ISSN 2412-0731 tween economic sectors was written by Denison (1962), who found that significant job cuts in the agricultural sector of the economy and resource shifts to other sectors can significantly increase aggregate labor productivity and accelerate countries’ development. Most of the literature on the effects of structural change on economic growth (Pasinetti, 1981; McMillan et al., 2014; Mironov and Konovalova, 2019) also emphasize that as resources are shifted from agriculture to modern and more productive sectors, economies grow and expand. The key factor that separates successful economies from laggards is the speed of these structural changes. Russia is a country in transition which has great heterogeneity in labor productivity between different sectors. This feature is characteristic of many developing countries in Eastern Europe, Asia and Africa. Typically, the economies of such countries have high productivity in one or more sectors of the economy (e.g., natural resource ex- traction), while others remain at the same level of development or progress very slowly. At the same time, the difference in productivity between in- dividual firms and entire sectors is much smaller in developed economies than in developing ones (McMillan et al., 2014; Dosi et al., 2015; Savin, 2020). What makes this heterogeneity in resource allocation special is that it has the potential to be an important engine of growth. When labor and other resources shift from less productive to more productive activities, the economy grows even if the sectors themselves do not gain in productivity. This situation is described by the “Simpson para- dox” (Simpson, 1951), which has previously been discussed in terms of GDP growth (Ma, 2015) and energy consumption (Gross, 2012). For example, one-third to one-half of the lag in total factor pro- ductivity in countries such as India and China compared to the United States could be reduced if the inequality between the outsider and leader sectors in productivity were eliminated (Bartels- man et al., 2006; Hsieh and Klenow, 2009). There are two factors that contribute to the growth in aggregate labor productivity: increases in productivity within sectors of the economy (the so-called “within-effect”) and the flow of labor from less productive sectors to more productive ones (the so-called “between-effect”). The latter is also called the “competitive selection” factor (Savin, 2020; Savin et al., 2019; Simachev et al., 2018). If the between-effect turns out to be posi- tive, we can conclude that there is competition be- tween industries for labor resources, as more pro- ductive industries increase their share by taking employees from less productive industries (Savin et al., 2020). The first way of increasing labor productivity is more often seen in economically advanced countries because their economies are sufficiently balanced, and reallocation of resourc- es does not increase productivity. However, real- location of resources due to competitive selection can increase productivity in developing countries with stronger heterogeneity between the sec- tors. Such an effect is positive for the economy as a  whole, as it increases both aggregate producti- vity and smooths out the inequalities between its individual sectors. This effect is also referred to in research literature as “structural change” (McMil- lan et al., 2014). Labor productivity refers to the amount of value added per worker. Aggregate labor produc- tivity is a measure of labor productivity for the economy as a whole. Competitive market selec- tion is the process of competition between indi- vidual economic actors for market share (Savin et al. 2019, 2020), when the strongest and most adaptable firms in an industry survive and grow. The term was coined by an analogy with Charles Darwin’s theory of evolutionary selection, and in economics it traditionally refers to the expansion of the market share of the most productive and efficient firms (Metcalfe, 1994). In this research we study the influence of competitive selection between economic sectors for labor resources and labor productivity in dif- ferent sectors on the change of aggregate labor productivity in Russian economy. By competitive selection we mean that economic sectors are to various degrees attractive for labor resources, and as workers migrate to more productive sectors of the economy, productivity of the whole economy increases. This study has the following objectives: first, to conduct a quantitative assessment of the role of competitive selection on the growth of aggre- gate labor productivity, reflecting the flow of labor resources between the sectors of the economy, in Russia; and second, to identify the sectors of the economy where labor resources were predomi- nantly reallocated in the period 2002–2018. This paper is organized as follows: Section 2 deals with the data and methods of analysis; Sec- tion 3 describes the decomposition of labor pro- ductivity growth, and Section 4 presents our con- clusions. https://doi.org/10.15826/recon.2022.8.1.005 60 r-economy.com R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 Online ISSN 2412-0731 Methods There are many approaches to decomposition of productivity in research literature (Baily et al., 1992; Olley and Pakes, 1996; Cantner et al., 2019). More common, however, are the approaches presented in Foster (2001) and Griliches and Regev (1995). Savin et al. (2019) show that the methods proposed by Griliches and Regev (1995) and Foster (2001) are essentially equivalent. Both approaches are distinguished by their analytical simplicity as well as the ability to compare the re- sults to those obtained by many other researchers using the same approaches. To conduct the decomposition of labor pro- ductivity, we apply the approach proposed by Grili- ches and Regev (1995), which has subsequently been used by many economists including McMil- lan et al. (2014), Dosi et al. (2015), Cantner et al. (2019), Foramitti et al. (2021a), Foramitti et al. (2021b), and Mundt et al. (2021). We preferred this method over alternatives as we can later compare our results with those of McMillan et al. (2014). First, formula (1) calculates the total labor pro- ductivity of economy j over time t as a weighted sum of labor productivity for all sectors of the economy: ∈ Π = π∑, , , ,j t i t i t i j r (1) where ri, t is a measure of the share of sector i in time t (measured by the number of employees employed in the sector); πi, t is a measure of labor productivity for sector i in time t. The decomposition of the change in the ag- gregate index is calculated by using formula (2): ∈ ∈ ∆Π = ∆ π + ∆π∑ ∑, , , , , ,j t i t i t i t i t i j i j r r (2) where ∈ ∆ π∑ , , i t i t i j r is the variable characterizing the redistribution of  labor between sectors of the economy (“be- tween” effect); ∈ ∆π∑ , , i t i t i j r is the result of changes in productivity at the lev- el of the sectors of economy themselves (“within” effect). The upper line above the variable denotes the average value for two consecutive years; delta (∆) is the measure of the difference between the two years (subtract from the value for year t + 1 the value for year t). Finally, in order to compare the results obtained for two different data sets more conveniently, we calculate the proportion of between- and within-effects by normalizing their sum to unity as shown in formulae (3–4): , , , , i t i t t i j j t t r between ∈ ∆π = ∆Π ∑ ∑ ∑ (3) , , , . i t i t t i j j t t r within ∈ ∆ π = ∆Π ∑ ∑ ∑ (4) At this point it is worth mentioning the pre- viously published studies which conducted the de- composition of labor productivity for the Russian economy. There was a study on competitive selec- tion and efficiency which showed that for firms op- erating in Russia the between-effect is on average 8%, while everything else can be explained by the productivity growth in the firms themselves (Savin et al., 2020). Similar estimates were previously ob- tained for a subsample of firms from the Ural Fed- eral District (Savin et al., 2019). Savin et al. (2020) conclude that the role of competitive selection for large firms is much lower than for small firms be- cause small and medium-sized firms are less secure and the competition among large firms should be encouraged within the economy. However, it is worth noting that both studies investigating the ef- fectiveness of competitive selection in Russia only cover industrial firms from 2006 to 2017. For our study a different time period was cho- sen: from 2002 to 2018. Moreover, we are looking at all the sectors of the Russian economy (accor- ding to the OKVED2 classifier). We investigate competition not between enterprises, but between the entire sectors of the economy. We use decom- position to estimate the redistribution of resources between sectors of the economy and to measure the between- and within- effects. Since the study by Voskoboynikov and Gimpelson (2015) is the most relevant to our analysis, further in this paper we are going to compare our results with theirs. Data In the course of our work, we used two sets of data from different sources. The first data set was obtained from the database of the Federal State Statistics Service (“Rosstat”1) and contains information on gross value added, employment, 1 https://rosstat.gov.ru/ https://doi.org/10.15826/recon.2022.8.1.005 https://rosstat.gov.ru/ R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 61 r-economy.com Online ISSN 2412-0731 depreciation, and output in 13 economic sectors for the period from 2002 to 2018. The sectors used are agriculture, hunting and forestry, and fishing; mining; manufacturing; electricity, gas, and wa- ter production and distribution; construction; wholesale and retail trade; hotels and restaurants; transportation and communications; financial ac- tivities; real estate, rental, and service operations; public administration and military security; com- pulsory social security; education; health care and provision. All figures for value added as well as labor productivity were converted to constant prices in USD in 2005 prices using producer price indices as deflators2. In order to assess the robustness of our results, we also use as an alternative data source the World Input-Output Database (WIOD3), which contains more detailed information on 33 sectors of the Russian economy from 2000 to 20144. Thus, the manufacturing sector in Rosstat is broken down in the WIOD into 24 subsectors. The data come from the latest available 2016 edition and supple- mentary socioeconomic accounts (WIOD SEA), which provides information on annual trade flows of intermediate goods, the amount of goods and services sold to final consumers, total gross out- 2 Investing, https://ru.investing.com/ 3 https://www.rug.nl/ggdc/valuechain/wiod, release 2016. 4 The global input-output database covers 56 sectors of the economy, but contains non-zero values for Russia for 33 sectors: Crop and livestock production, Mining, Food pro- duction, Clothing production, Timber production, Paper and paper products production, Coke production and production of petroleum products, Manufacture of chemical products, Manufacture of rubber and plastic products, Manufacture of other non-metallic mineral products, Manufacture of base metals, Manufacture of computers, Manufacture of machin- ery and equipment n.e.c., Manufacture of automobiles, Man- ufacture of furniture, Electricity, Construction, Retail trade, Wholesale trade, Land transport, Water transport, Air trans- port, Warehouse services, Accommodation and catering ser- vices, Telecommunications, Financial services, Operations with real estate, Administrative and support activities, Public administration and defense, Education, Human health and so- cial work, Other service activities. Therefore, in the future, we will analyze only these 33 industries. put, value added, and employment. All these data are in U.S. dollars and adjusted for inflation using national price indexes with a base year of 2010. Using a more disaggregated WIOD database, we will thus be able to get an estimate of labor reallocation not only between the large sectors such as agriculture and manufacturing, but also between the industries within manufacturing that vary widely in their level of productivity. This, in turn, will provide a more accurate estimate of the effect of competitive selection. Table 1 presents descriptive statistics for the sectors of the Russian economy and thus allows the reader to form their first impression of the data which we will work with. This table shows that industries grow at an average rate of 2% per year (the median is 4%, indicating negative val- ues in a number of sectors). The high value of the standard deviation of value-added growth (0.22) indicates significant heterogeneity in the growth rates between sectors of the Russian economy. Looking at this table, we can conclude how unevenly labor productivity is distributed across different sectors of the economy. The standard deviation of the logarithm of labor productivity is 0.74. This means that  an industry where labor productivity is by one standard deviation above the mean is four to five times more productive than an industry where labour productivity is by one standard deviation below that level (e1.5 = 4.5). If we consider the WIOD data instead of the Ross- tat data, the spread is even larger, which can easily be explained by the fact that a more detailed divi- sion of the economy into subsectors increases the difference between its most and least productive industries. All this clearly shows the high heterogeneity of labor productivity between sectors in the Rus- sian economy which we discussed earlier. In the future we are planning to assess how this hetero- geneity led to the overflow of labor resources be- tween the sectors. Table 1 Descriptive statistics of the data used Labor productivity Value-added growth Number of observations, in units Average value, in USD Median, in USD Standard deviation, in logarithm Number of observations, in units Average value, in USD Median, in USD Standard deviation, in logarithm Data Rosstat 221 21525.6 13995.99 0.74 208 0.020 0.04 0.22 Data WIOD 495 18997.9 11578.38 0.87 462 0.016 0.04 0.16 Own calculations based on Rosstat data https://rosstat.gov.ru/ and WIOD https://www.rug.nl/ggdc/valuechain/long-run- wiod?lang=en (accessed on 13.03.2021). https://doi.org/10.15826/recon.2022.8.1.005 https://ru.investing.com/ https://www.rug.nl/ggdc/valuechain/wiod https://rosstat.gov.ru/ https://www.rug.nl/ggdc/valuechain/long-run-wiod?lang=en.(accessed https://www.rug.nl/ggdc/valuechain/long-run-wiod?lang=en.(accessed 62 r-economy.com R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 Online ISSN 2412-0731 It is worth noting that many studies (de Vries et al. 2015; McMillan et al., 2014) show that high heterogeneity in labor productivity across sectors is a sign of a developing (but not yet developed) economy. They are the highest for the poorest countries and tend to decrease because of sus- tained economic growth and development. Based on these results, it can be argued that Russia can be classified as a still developing economy. Following Dosi et al. (2015), we measure la- bor productivity as the amount of value added per employee, where value added, in turn, is defined as revenue minus production and sales costs ex- cluding labor costs. Results Applying the decomposition described in equations (1-4), we produced the results presen- ted in Table 2. The analysis based on Rosstat data shows that the within-effect in the Russian econo- my prevails. Its share is approximately 71% against 29% for the between-effect. This suggests that the growth of the economy is caused to a greater ex- tent not by the reallocation of resources from one sector to another but by the growth in productivi- ty in the sectors themselves. Nevertheless, the role of competitive selection in the growth of aggregate labor productivity is positive, which is good news, especially in view of the more modest (and some- times close to zero) values obtained for firm-le- vel data (Savin et al., 2020). It is worth noting that Voskoboynikov and Gimpelson investigating the data that are similar to ours but for an earlier pe- riod (1995–2012) came to similar conclusions (in their study, the share of between-effects was about 23%). This indicates that in the later period, the contribution of labor reallocation to the growth in aggregate labor productivity increased slightly. Moreover, using the more disaggregated WIOD data, the total share for the between-effect becomes larger than for the within-effect, indi- cating that in the Russian economy the growth of aggregate labor productivity is still largely due to the reallocation of labor resources from low-pro- ductive activities to more productive ones. The difference in the results obtained by using dif- ferent data sources can be explained by the fact that one sector of the economy from the Rosstat database is divided into several smaller sectors in the WIOD database. Thus, using the WIOD data, we can better estimate the flow of labor between sectors of the economy. Indeed, a person who used to work in metal production is more likely to move to a job in metal production than in mining or in the financial sector. This can be explained by the fact that the above transition will require a different set of knowledge and skills as well as work experience, which is difficult to obtain even by undergoing special training and advanced trai- ning. From this we can conclude that a more ac- curate assessment of labor reallocation on changes in aggregate productivity requires deeper sectoral detail in order to get a more accurate estimate of competitive selection. Regardless of the level of detail of the sec- toral classifier, the results obtained in Table 2 indicate that the Russian economy showed a posi- tive dynamic of structural change in terms of re- allocation of labor resources from less to more productive sectors. Previously, McMillan et al. (2014) showed that while most countries in Afri- ca and Latin America over the period 1990–2005 exhibited a negative between-effect, indicating a  negative structural change, only Asian coun- tries have managed to consistently achieve ef- fective reallocation of labor to more productive sectors. Our estimates place Russia in the latter group of countries. There are several findings worth noting. First, the negative value of the between-effect can be in- terpreted as an indicator of the overall inefficien- cy of the economy: labor is transferred from more efficient sectors of the economy to less productive ones. Second, for some years a negative sign of the within-effect can be observed, which indicates a decrease in labor productivity in the sector of the economy itself. In some years such a sign can be explained by a sharp fall of the national currency against the U.S. dollar. This interpretation is also true for the shares of these two effects, but only when the sum of the absolute values is positive. Otherwise (for example, 2009 example for both databases) the interpretation of the signs of the shares is reversed (e.g. in 2009 the share of the within-effect was close to one, but in fact its con- tribution was negative). To take a closer look at where the labor force was flowing from and to where, in Table 3 we cal- culated the ratios of employment in 2018 to the same figure in 2002 for all the 13 major sectors of the economy as well as the absolute change in the number of employed over the same period. We use Rosstat data here rather than the WIOD to get a more general picture of labor shifts among the major 13 sectors of the economy. Similar results can be obtained for the 33 sectors of WIOD. https://doi.org/10.15826/recon.2022.8.1.005 R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 63 r-economy.com Online ISSN 2412-0731 Table 2 Results of total labor productivity decomposition Year Rosstat WIOD Within- effect Between- effect Share of within effect Share of between effect Within- effect Between- effect Share of within effect Share of between effect 2001 – – – – –140 160 –6.68 7.68 2002 – – – – –240 10 1.06 –0.06 2003 843 –5.69 1.01 –0.01 450 -20 1.06 –0.06 2004 1364 16.61 0.99 0.01 1010 100 0.91 0.09 2005 764 55.95 0.93 0.07 530 –070 1.16 –0.16 2006 1481 47.03 0.97 0.03 960 120 0.89 0.11 2007 1962 60.59 0.97 0.03 1390 170 0.89 0.11 2008 1038 92.97 0.92 0.08 2200 –140 1.07 –0.07 2009 –4944 –56 0.99 0.01 –3820 250 1.07 –0.07 2010 1099 24 0.98 0.02 760 –210 1.38 –0.38 2011 257 76.99 0.77 0.23 870 90 0.91 0.09 2012 10152 65 0.99 0.01 –500 180 1.56 –0.56 2013 –540 84 1.18 –0.18 –450 420 17.04 –16.04 2014 –4256 46.52 1.01 –0.01 –2040 150 1.08 –0.08 2015 –8058.2 11.25 1.00 0.00 – – – – 2016 32.01 14.09 0.69 0.31 – – – – 2017 791 29.13 0.96 0.04 – – – – 2018 –570 9.36 1.02 –0.02 – – – – Total 1416.48 572.86 0.71 0.29 980 1230 0.44 0.56 Source: Own calculations based on data from Rosstat and WIOD. Table 3 Changes in the amount of labor used in economic sectors from 2002 till 2018 Share in the total amount of labor used in the economy in 2002, % Share in the total amount of labor used in the economy in 2018, % Absolute change in the amount of labor used Agriculture, hunting and forestry, fishing 13.20 7.32 –3412562.00 Mining and quarrying 1.84 1.69 –21103.00 Manufacturing 19.11 14.92 –2015144.00 Production and distribution of electricity, gas and water 2.99 3.47 452949.00 Construction 7.05 9.47 1932901.00 Wholesale and retail trade 15.65 20.26 3777382.00 Hotels and restaurants 1.70 2.55 646203.00 Transportation and communications 8.09 10.11 1702070.00 Financial activities 1.13 2.05 670665.00 Real estate operations, renting and services 7.77 8.11 558955.00 Public administration and military security; compulsory social security 4.97 5.41 511493.00 Education 9.55 8.09 –581601.00 Health care and social services 6.95 6.53 6536.00 Source: Own calculations based on Rosstat data. We can see that the largest outflows were ob- served in agriculture and manufacturing. While the former is a natural process associated with the automation of production and characteristic of most transition economies, the latter is rather an unpleasant signal for the structure of the Rus- sian economy given the large role of manufac- turing in the creation of value added. The largest inflow of labor resources, in turn, was observed in construction, wholesale and retail trade as well as transport and communications. Construction and transport are sectors with relatively high la- bor productivity and it is a good signal to the Rus- sian economy. Figure 1 shows the more detailed dynamics of employment in these sectors of the economy. https://doi.org/10.15826/recon.2022.8.1.005 64 r-economy.com R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 Online ISSN 2412-0731 Figure 1. Dynamics of the number of employed labor in various sectors: (a) agriculture, (b) manufacturing, (c) construction, (d) wholesale and retail trade, and (e) transportation and communications. The number of employed (people) is shown vertically, the years are shown horizontally. Source: Our own calculations are based on Rosstat data. Accessed on 18.03.2021. 0 2000000 4000000 6000000 8000000 10000000 2000 2005 2010 2015 2020 0 5000000 10000000 15000000 2000 2005 2010 2015 2020 0 2000000 4000000 6000000 8000000 2000 2005 2010 2015 2020 0 5000000 10000000 15000000 2000 2005 2010 2015 2020 0 2000000 4000000 6000000 8000000 2000 2005 2010 2015 2020 (а) (b) (c) (d) (e) This result can be interpreted in different ways. On the one hand, the outflow of resources from manufacturing can hardly be called a posi- tive trend for the Russian economy. On the other hand, the inflow of resources in transport and con- struction is a positive trend. Interestingly, mining has lost labor resources, while sectors such as fi- nancial activity and hotel business have increased. Overall, the resulting picture differs from the one obtained earlier by Voskoboynikov and Gimpel- son (2015) for 1995–2012, where the labor real- location was into manufacturing. Thus, we found that the role of competitive market selection for labor productivity growth has increased some- what in Russia in recent years, but predominantly this reallocation occurs not in (but rather from) manufacturing but in construction, transport, and trade. This suggests that we should consider how to stop the outflow of labor from manufacturing by creating innovative directions in production and encouraging domestic enterprises to expand their market share both in the domestic market and by exporting their goods abroad (Savin and Winker, 2009; Savin and Winker, 2012). https://doi.org/10.15826/recon.2022.8.1.005 R-ECONOMY, 2022, 8(1), 57–67 doi: 10.15826/recon.2022.8.1.005 65 r-economy.com Online ISSN 2412-0731 Conclusions Labor productivity varies widely across sec- tors in the Russian economy. This indicates the potential for economic growth through the re- allocation of labor from less productive sectors to more productive ones as well as the potential for productivity growth within the sectors them- selves. We assessed the role of these two factors in changing the aggregate productivity of the Rus- sian economy. To test the reliability of the results obtained, a decomposition was carried out on two data sets: Rosstat and WIOD. The results of the decomposition lead us to a conclusion about the presence of competitive se- lection in the sectors of the economy, which indi- cates positive structural changes and the flow of re- sources from less to more productive sectors. For the Rosstat data, the ratio of the effect of changes in labor productivity and labor resource spillovers by sector on aggregate labor productivity over the period was 0.71/0.29, and for the WIOD data it was 0.44/0.56. This indicates that labor resources are more likely to be reallocated to related sec- tors (e.g., between manufacturing industries). It was found that as the granularity of sectors in the sample increases (from 13 to 33), the effect of re- source spillovers begins to dominate the economy over productivity growth within the sectors them- selves. 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(2004). Sources of aggregate labour productivity growth in Canada and the United States. Canadian Journal of Economics. 37(2), 421–444. doi: 10.1111/j.0008-4085.2004.00009.x Timmer, M., de Vries, G.J., & De Vries, K. (2015). Patterns of structural change in developing countries. Routledge. doi: 10.1257/9780203387061 Voskoboynikov, I., & Gimpelson, V. (2015). Productivity growth, structural change and infor- mality: the case of Russia. Voprosy Ekonomiki, 11, 30–61. (In Russ.) doi: 10.32609/0042-8736-2015- 11-30-61 Information about the authors Ivan V. Savin – Professor at the Department of Economics, Graduate School of Economics and Management, Ural Federal University (19 Mira Str., 620002 Ekaterinburg, Russia); Researcher, Institute of Environmental Science and Technology, Autonomous University of Barcelona (ICTA-ICP Building (Z), UAB Campus, Cerdanyola del Vallès, 08193 Barcelona, Spain); e-mail: ivan.savin@uab.cat Denis K. Letyagin – MA student at the Department of Economics, Graduate School of Econom- ics and Management, Ural Federal University (19 Mira Str., 620002 Ekaterinburg, Russia); e-mail: denletyagin@gmail.com ARTICLE INFO: received January 23, 2022; accepted March 18, 2022 Информация об авторах Савин Иван Валерьевич – PhD, профессор, кафедра экономики, Институт эконо- мики и  управления, Уральский федеральный университет (Россия, 620002, Екатеринбург, ул.  Мира,  19); научный сотрудник, Институт экологических наук и технологий, Автоном- ный университет Барселоны (Испания, 08193 Серданьола-дель-Вальес, ICTA-ICP); e-mail: ivan.savin@uab.cat Летягин Денис Константинович – магистр экономики, кафедра экономики, Уральский федеральный университет (Россия, 620002, Екатеринбург, ул. Мира, 19); e-mail: denletyagin@ gmail.com ИНФОРМАЦИЯ О СТАТЬЕ: дата поступления 23 января 2022 г.; дата принятия к печати 18 марта 2022 г. 作者信息 萨文·伊万·瓦列里耶维奇——在读博士，教授，经济系，经济管理学院，乌拉尔联邦 大学（俄罗斯，邮编: 620002, 叶卡捷琳堡，米拉街19号）；科研人员，环境科学与技术学 院，巴塞罗那自治大学（西班牙，邮编：08193，萨尔达尼奥拉-德尔巴列斯，ICTA-ICP研 究中心）；邮箱：ivan.savin@uab.cat. 莱蒂亚金·丹尼斯·康斯坦丁诺维奇——经济系硕士，经济系，乌拉尔联邦大学（俄罗 斯，邮编: 620002, 叶卡捷琳堡，米拉街19号）；邮箱：denletyagin@gmail.com. https://doi.org/10.15826/recon.2022.8.1.005 https://doi.org/10.1111/j.0008-4085.2004.00009.x https://doi.org/10.1257/9780203387061 https://doi.org/10.32609/0042-8736-2015-11-30-61 https://doi.org/10.32609/0042-8736-2015-11-30-61 mailto:ivan.savin@uab.cat mailto:denletyagin@gmail.com mailto:ivan.savin@uab.cat mailto:denletyagin@gmail.com mailto:denletyagin@gmail.com mailto:ivan.savin@uab.cat mailto:denletyagin@gmail.com