Job creation and job destruction in Turkey

Ayhan, Sinem H.; Lehmann, Hartmut; Pelek, Selin Article — Published Version Job creation and job destruction in Turkey Eurasian Economic Review Provided in Cooperation with: Springer Nature Suggested Citation: Ayhan, Sinem H.; Lehmann, Hartmut; Pelek, Selin (2025) : Job creation and job destruction in Turkey, Eurasian Economic Review, ISSN 2147-429X, Springer International Publishing, Cham, Vol. 15, Iss. 3, pp. 741-773, https://doi.org/10.1007/s40822-025-00321-2 This Version is available at: https://hdl.handle.net/10419/330406 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ORIGINAL PAPER Eurasian Economic Review (2025) 15:741–773 https://doi.org/10.1007/s40822-025-00321-2 Abstract This paper analyzes Turkey’s labor market dynamics from 2006 to 2021 through job flow analysis of administrative data from all non-financial firms registered with social security institutions. We specifically examine how firm characteristics influence employment dynamics over business cycles, with our analysis covering key events such as the 2008 global recession, the 2018 currency crisis, and the initial phase of the COVID-19 pandemic. Our results indicate a highly dynamic labor market, with gross job reallocation rates ranging from 34 to 44%, comparable to those of other emerging economies but higher than rates in Anglo-Saxon and transition countries. The high rates of excess job reallocation in Turkey suggest substantial reshuffling within the job structure, especially in the construction sector, where job creation persistence is notably low. A similar pattern is observed among low-technology and low-to-medium-technology manufacturing firms, which exhibit higher reallocation rates. Micro-firms are the primary drivers of job creation and destruction, with these rates declining as firms grow larger or older. While exposure to international competition appears relevant, with import intensity negatively associated with job creation and positively with job destruction, no systematic link is found between export intensity and job reallocation rates. Keywords Job creation · Job destruction · Firm characteristics · Administrative data · Turkey JEL E24 · J08 · J23 · J63 · L25 · L26 Received: 10 November 2024 / Revised: 19 March 2025 / Accepted: 8 May 2025 / Published online: 6 June 2025 © The Author(s) 2025 Job creation and job destruction in Turkey Sinem H.Ayhan1· HartmutLehmann2· SelinPelek3 Extended author information available on the last page of the article 1 3 Eurasian Economic Review (2025) 15:741–773 1 Introduction Job reallocation through job creation and destruction by firms is crucial for enhancing economic efficiency and growth by optimizing resource allocation and boosting productivity. Productivity improvements are driven by two primary factors: job creation by efficient firms and job destruction concentrated in less efficient ones, as noted by Roberts and Tybout (1997). While the relationship between resource reallocation and productivity gains is well-established in the literature,1 the broader dynamics of labor market adjustments—particularly in response to economic cycles—remain underexplored, especially outside industrialized countries. The job creation and destruction rates, their responsiveness to macroeconomic fluctuations, and their link to unemployment trends are key aspects that warrant deeper investigation. This study aims to extend the understanding of job reallocation dynamics in emerging economies, with a particular focus on Turkey, which the IMF (2023) classifies as an emerging and middle-income country. Turkey presents an interesting case due to its economic volatility, characterized by periods of rapid growth followed by sharp downturns. These economic fluctuations have significant implications for labor market adjustments, making Turkey an ideal setting for analyzing job flows over business cycles.2 Using an administrative dataset covering all nonfinancial private enterprises from 2006 to 2021, this study examines how job flow dynamics interact with business cycles across different firm attributes and how firm characteristics—such as size, age, technology level, and exposure to international trade—shape employment dynamics and job reallocation patterns. Empirical evidence from developing countries —including Argentina, Chile, Colombia, Mexico, and Morocco— suggests that job turnover rates are remarkably high, often exceeding those observed in the U.S., whose labor market is often considered a benchmark due to its flexible and vibrant nature (Cho et al., 2017; Flórez et al., 2021; Haltiwanger et al., 2014; Roberts & Tybout, 1997). There is also some evidence on transition economies pertaining to the first decade of transition, which consistently proposes a lower degree of job reallocation compared to industrialized countries (see, e.g., Faggio & Konings, 2003; Konings et al., 1996, Konings et al., 2003; Acquisti & Lehmann, 2000; Brown & Earle, 2002). However, existing studies often do not emphasize the cyclical nature of job flows and how they relate to broader economic conditions, such as unemployment fluctuations during recessions and recoveries3. This study aims to partially fill that gap. Our study contributes to the literature on job flows in several ways. First, unlike most existing studies, which focus primarily on manufacturing —a sector generally less dynamic— our analysis covers all non-financial sectors, providing a more comprehensive picture of job reallocation in the economy. Hijzen et al. (2010) show that 1 See, e.g., Roberts and Tybout (1997) for an overview of the early pertinent literature. A more recent study by Goswami (2022) examines the relationship between productivity growth and job reallocation in India. 2 See Ayhan et al. (2023), the discussion paper version for further details about the Turkish economy. 3 Goswami and Paul (2024) highlight the same point and examine job reallocations in India. Unlike our paper, they focus solely on the manufacturing sector and show that, from the global financial crisis onward, job creation declined while job destruction remained constant. 1 3 742 Eurasian Economic Review (2025) 15:741–773 including services significantly increases estimated job reallocation rates in the U.K., highlighting the importance of broader sectoral coverage. Second, by leveraging a long time span, we analyze labor market dynamics during both stable periods and economic shocks, including the 2008 global financial crisis, the 2018 domestic currency crisis, and the initial phase of the COVID-19 pandemic. Third, while prior research primarily focuses on firm size and, in the case of transition economies, ownership type, our study incorporates a wider range of firm attributes, including trade exposure and technology level, two key factors that shape job flows but have hardly been explored. Our estimates of job flows underscore the existence of a dynamic labor market in Turkey, with annual gross job reallocation rates ranging from 34% in 2007 to 44% in 2021. These figures align with rates in developing countries like Chile, Colombia, and Morocco, surpass those in Anglo-Saxon economies, and are substantially higher than in transition countries. Our findings also underscore the strong cyclicality of job reallocation in Turkey, with job destruction rising sharply during downturns and job creation rebounding in recovery phases. During both the 2008 and 2018 recessions, unemployment rose by approximately 3% points, yet the persistence of job flows responded differently in each case. The 2018 crisis exhibited a stronger counter-cyclical pattern, with job creation persistence dropping to its lowest recorded level and job destruction persistence peaking, signalling a prolonged labor market adjustment. Similarly, our analysis of sectoral job flows reveals strong pro-cyclical employment patterns, with job destruction rates peaking in recessions and job creation accelerating during recoveries. The magnitude of these shifts, however, varies across industries, with the construction sector experiencing extreme volatility while the services sector shows relatively milder fluctuations. The mining sector, in contrast, exhibits lower sensitivity to business cycles, underscoring the heterogeneous nature of job reallocation across sectors. The sectoral composition of job flows further reveals differential impacts of recessions: while the 2008 crisis primarily affected manufacturing, the 2018 crisis had a more pronounced effect on construction and energy-intensive industries, which are more vulnerable to domestic financial instability. Moreover, our results highlight the role of firm attributes in job flow dynamics. The existing literature, primarily from developed Anglo-Saxon economies such as Canada, the U.S., and the U.K., documents an inverse relationship between firm size and net employment growth (e.g., Baldwin et al., 1998; Haltiwanger et al., 2014; Heyman et al., 2019; Hijzen et al., 2010; Lawless, 2014; Neumark et al., 2011). However, Davis et al. (1996) argue that large firms, rather than small ones, contribute the most to job creation and the durability of created jobs. Studies on developing countries further confirm that job flows are higher for small firms (e.g., Flórez et al., 2021; Goswami & Paul, 2024; Ma et al., 2015; Arouri et al., 2016). A parallel strand of research examines how business cycles interact with job flow dynamics across firm attributes. Evidence suggests that recessions disproportionately impact young and small firms, yet start-ups continue to contribute positively to net employment growth (Criscuolo et al., 2014; Goswami & Paul, 2024). Large firms, on the other hand, exhibit a strong negative correlation between net job creation and aggregate unemployment, a relationship much weaker for smaller firms (Moscarini & Postel-Vinay, 2012). Job reallocation also appears to be pro-cyclical in high-income 1 3 743 Eurasian Economic Review (2025) 15:741–773 countries, with high-wage firms expanding more in upturns (Hijzen et al. 2024) and low-productivity, low-wage firms shedding more jobs in downturns (Bertheau et al. 2020). In developing economies, smaller establishments are more sensitive to economic fluctuations, as seen in Brazil (Cravo, 2011), while in Spain, the relationship between firm size and job stability strengthened during the financial crisis (Nagore García & van Soest, 2017). In line with the broader literature, we also find that small firms exhibit higher job turnover rates, even when accounting for age and other factors. However, the Turkish context reveals a critical distinction: Micro-firms drive employment growth but also face lower job persistence, reflecting the instability of employment in smaller enterprises. Similarly, low-technology industries contribute most to job creation and job destruction in Turkish manufacturing, yet their high excess job reallocation rates relative to those of high-technology firms suggest a very fragile employment structure characterized by low productivity and high employment volatility. Our results further highlight how firm characteristics influence resilience to economic shocks. Firms with higher trade exposure exhibit greater post-crisis recovery, likely benefiting from international demand diversification, whereas domestically focused firms experience sharper declines in job creation and stronger increases in job destruction during downturns. Similarly, high-tech firms display greater stability in job flow rates compared to medium-tech and low-tech firms, which suffer more significant employment losses during recessions. These patterns suggest that external market integration and technological capacity provide crucial buffers against macroeconomic volatility. Unlike the existing literature, we do not find a distinction in response to economic shocks across firm sizes, as all size categories exhibit a common cyclical pattern. However, consistent with earlier studies, start-ups appear to be affected differently, experiencing a sharper increase in job destruction rates ahead of the 2018 downturn and a much faster and more pronounced rebound in job creation rates following the global financial crisis. By integrating firm characteristics with business cycle dynamics, this study provides a more comprehensive understanding of how job flows evolve over time in an emerging economy. Our findings suggest that the persistence of job creation and destruction is not only driven by firm attributes but is also deeply intertwined with macroeconomic fluctuations, reinforcing the need for policies that enhance labor market resilience during economic downturns. The remainder of the paper is organized as follows: The next section presents our data sources and discusses their strengths and weaknesses, followed by a presentation of the job flow measures that are standard in the literature. Section 3 presents our results, starting off with the job flows for the entire economy, then turning to outcomes by region, and finally honing in on a battery of results linked to firm attributes. Section 4 discusses the persistence in the jobs created and the jobs destroyed in the Turkish economy. The final section provides some conclusions. 1 3 744 Eurasian Economic Review (2025) 15:741–773 2 Data and measures 2.1 Data The empirical analysis utilizes a sixteen-year panel dataset of all nonfinancial private firms and their employees registered in the administrative records of the Turkish economy. The database is constructed by the Ministry of Science, Industry, and Technology (MoSIT), which compiles administrative datasets from various public institutions. These different data sources have been integrated into the Entrepreneur Information System (EIS), resulting in a panel covering the years 2006–2021 (MoSIT, 2023). The main data source for tracking job flows is the Business Registers, focusing on the enterprise as the unit of observation. Following the common practice in the literature, we exclude the self-employed, namely one-person enterprises from the analysis given that they are not considered a firm that by definition grows and contracts by the hiring and firing of dependent workers (Burgess et al., 2000; Flórez et al., 2021; Hijzen et al., 2010). With an approximately one-million increase from 2006, the number of firms in our sample reaches 2.1 million by 2021.4 The firm-level data includes yearly information on the year of establishment geographical location (NUTS1 level), and economic sector (4-digit ISIC and NACE rev 2), as well as quarterly information on the number of employees. The EIS database combines basic firm characteristics with information on the technology level from the MoSIT and foreign trade statements from the Ministry of Trade. The technology information, reported only for the manufacturing sector, delineates four categories of low-, medium-low, medium-high, and high-tech firms based on the ISIC Rev.3 classification (OECD, 2011). The foreign trade statements provide information on the type, quantity, and value of imports and exports. However, this information refers to trade in goods only. The EIS database is one of the pioneering datasets, not only among emerging economies but also in industrialized countries. Its richness, extensive panel duration, and comprehensive coverage of the entire economy and all enterprises make it unique. Nevertheless, it also has some limitations. First, the EIS only provides information on registered firms. An exit from the data may be either due to firm closure or because the firm continues its operations without registering with the social security system (Acar et al., 2019).5 The implication of dropping out of the panel for a worker is to become non-employed (i.e., unemployed, or inactive) or to transit into informal (i.e., unregistered) employment. The share of informal employment in Turkey is not negligible. As of December 2021, about 28% of total salaried employment is informal and the ratio is recorded as 18.3% in the non-agricultural sector (TURKSTAT, 2022b). Since there is no information on informal employment in the EIS data, when there is a movement from formal to informal employment, a shortcut 4 See Table 1 in the discussion paper version for a more detailed discussion of the dataset. 5 Firm informality is not as widespread as labor informality in Turkey. Merely 4% of firms are estimated to be unregistered, which is significantly lower when compared to the approximately 30% of informal employment (Gulek, 2022). 1 3 745 Eurasian Economic Review (2025) 15:741–773 approach would be to assume a transition into non-employment. Considering the relatively high share of informal employment, this approach is likely to result in a downward bias in job-to-job transitions (Akgündüz et al., 2019). On the other hand, Tansel and Acar (2017) examine labor market transitions in Turkey using the Survey of Income and Living Conditions (SILC) panel data set. They document a substantially low transition probability from formal to informal employment (3 to 5%) when compared to the probability of 7 to 14% flowing into non-employment and about 80 to 90% of staying in formal employment across the four-year panel period that they have at their disposal.6 Another limitation of the EIS data is that it does not cover employees in the public sector. Given the possibility of finding a job in the public sector, this limitation constitutes a potential source of upward bias in transitions between employment and non-employment as in the case of informal workers. Using the 2018–2021 SILC panel data, we examine the probabilities of job-to-job transitions between private and public employment. Only 4% of public employees reported being in a different job in the previous year. Additionally, transition rates from public to private sectors (and vice versa) are found to be extremely low with less than 2%. Given these findings, the downward bias in job-to-job mobility due to flows into public employment is not expected to be a worrisome issue for the current analysis.7 2.2 Measures of job flows Following Davis and Haltiwanger (1992) the net employment growth rate of a firm between t-1 and t is defined as: g it = n it −n it− 1 1 / 2( n it + n it−1) (1) where nit refers to the employment of firm i at time t. Dividing the employment change by average employment constrains the growth rate git to the interval [-2, 2], where − 2 refers to firm exit and 2 to firm entry. The rate of job creation (destruction) is defined as the employment-weighted sum of all positive (negative) net growth rates in a group j under investigation, where j can relate to the economic sector, region, firm size category, firm age group, or the entire economy. The employment weight is simply the share of firm i in total employment of the group j , equal to the ratio of: 6 Using the recent waves of the same SILC panel dataset, we calculate the probability of moving from formal to informal employment. For the years 2016–2021, this probability ranged between 3 and 5%, confirming the findings of Tansel and Acar (2017). 7 In line with our findings, Akgündüz et al. (2019) seek to comprehend the extent of bias in the EIS data by examining annual job-to-job transitions. They utilize the 2016 Household Labor Force Survey, which contains retrospective information on labor market status for current and previous years, and document that only 5% of public sector employees and 19% of private sector employees were employed in a different job. Notably, the majority of job-to-job transitions in the Turkish labor market happen within the private sector, with about four-fifths of these transitions occurring between two wage-earner jobs. 1 3 746 Eurasian Economic Review (2025) 15:741–773 x it Xjt = n it +n it− 1 ∑i∈Ijt (nit +nit − 1) where Ijt is the set of firms in a group j at time t. Adhering to the original notation by Davis and Haltiwanger (1992) we can write the job creation rate POSjt and job destruction rate NEGjt in group j at time t as: POS jt = ∑ i ∈ I+ jt ( xit X jt ) .git and NEGjt = ∑ i ∈ I− jt ( xit X jt ) . | git | (2) where I+ and I− are the subsets of expanding/entering and contracting/exiting firms, respectively, and the job destruction rate is expressed in absolute value. The gross job reallocation rate GROSSjt is defined as the sum POSjt +NEGjt , while the net change of employment also known as the net reallocation rate NETjt is given by the difference POSjt −NEGjt . The gross and net job reallocation rates can be deemed as upper and lower bounds of the worker reallocation rate required to accommodate job reallocation (Davis & Haltiwanger, 1992; Hijzen et al., 2010). In addition, we introduce the excess job reallocation rate, EXCESSjt = GROSSjt −|NETjt|, to measure the number of job reallocations in excess of the amount required to accommodate net employment growth. Finally, we are interested in the persistence of jobs created and of jobs destroyed. As defined by Davis and Haltiwanger (1992), the rate of one-year persistence in job creation is the fraction of newly created jobs in year t that continue to be present in year t + 1. The two-year persistence rate is the fraction of newly created jobs in year t that are present in both year t + 1 and year t + 2. We can analogously define persistence rates of job creation with the time horizon going further into the future. The oneyear persistence rate for job destruction is defined as the fraction of newly destroyed jobs in year t that do not reappear in year t + 1. The two-year persistence rate for job destruction is defined as the fraction of newly destroyed jobs in year t that do not reappear in year t + 1 and year t + 2. Again, we can analogously extend this definition to years further into the future. Adhering to the notation by Hijzen et al. (2010), the persistence rate ( p ) is calculated as pi,x =(ni,t+x−ni,t−1)/(ni,t −ni,t−1) , where x stands for the length of persistence in years and spans the set {1,2, ... , 5}. The persistence rates of destroyed jobs can shed light on the extent to which job destruction leads to shortor long-term joblessness, while the persistence rates of created jobs can indicate whether the placement of workers into new jobs is permanent or transient (Davis & Haltiwanger, 1992). 3 Results 3.1 Job flows in the entire economy Table 1 displays annual rates of job creation (POS) and job destruction (NEG), net employment growth (NET), job reallocation (GROSS), and the excess job realloca1 3 747 Eurasian Economic Review (2025) 15:741–773 tion (EXCESS) for the entire economy during the period 2006–2021. The annual rates presented in the table are based on the fourth quarter-to-fourth quarter employment changes that underlie the annual job creation and destruction measures while Figure A1 in the online appendix shows annual job flow rates based on other quarters. The annual job creation rate ranges between 14% and 23% of total employment, while the job destruction rate exhibits greater variability, fluctuating between 15% and 27% across the years. It is noteworthy that both firm entry and exit contribute significantly to job reallocations. Approximately 30% of firms experience no growth, yet around one-fourth of net employment growth can be attributed to the effects of firm entry and exit (see Figures A2-A4 in the appendix). Annual flow rates do not capture intra-year transitions. We, therefore, also analyze job creation and destruction rates on a quarterly basis, as shown in Fig. 1.8 The original quarterly flow rates, calculated based on the net change in employment from one quarter to the next, are approximately half the magnitude of their annual counterparts. Since the cumulative quarterly flow rates over a year exceed the annual rate, this suggests that some round-tripping occurs within the yearly span. The higher frequency of quarterly data results in greater fluctuations and a more pronounced seasonality pattern, particularly in the job creation rate. We consistently observe peaks in the job 8 In Fig. 1, the smoothed solid lines represent seasonally adjusted job creation and job destruction rates, using a four-quarter centered moving average (to seasonally adjust, e.g., the quarterly job creation rate we use the formula: JC adj t=JC t− 2+JC t− 1+JC t +JC t +1 4) , while the dashed lines show the original series. The shaded areas indicate the recession periods during the 2008 global financial crisis and the 2018 local currency crisis. Table 1 Annual job flow rates Job creation Job destruction POS Entry Expans. NEG Exit Contr. GROSS NET EXCESS (1) (2) (3) (4) (5) (6) (7) (8) (9) 2007 0,193 0,062 0,131 0,204 0,073 0,131 0,397 -0,011 0,386 2008 0,215 0,074 0,140 0,191 0,078 0,114 0,406 0,024 0,382 2009 0,186 0,064 0,123 0,206 0,083 0,123 0,392 -0,019 0,373 2010 0,233 0,079 0,154 0,157 0,065 0,092 0,390 0,076 0,314 2011 0,234 0,079 0,155 0,156 0,065 0,091 0,390 0,078 0,313 2012 0,229 0,083 0,146 0,179 0,074 0,105 0,408 0,050 0,358 2013 0,210 0,071 0,139 0,196 0,081 0,115 0,406 0,014 0,392 2014 0,211 0,072 0,139 0,196 0,082 0,115 0,408 0,015 0,393 2015 0,230 0,079 0,150 0,187 0,076 0,111 0,417 0,043 0,374 2016 0,181 0,062 0,119 0,223 0,090 0,133 0,403 -0,042 0,361 2017 0,214 0,089 0,124 0,173 0,066 0,108 0,387 0,040 0,346 2018 0,173 0,061 0,112 0,268 0,111 0,157 0,441 -0,094 0,347 2019 0,143 0,066 0,078 0,253 0,112 0,141 0,396 -0,110 0,287 2020 0,215 0,073 0,142 0,145 0,058 0,086 0,360 0,070 0,289 2021 0,176 0,035 0,141 0,161 0,066 0,094 0,337 0,015 0,322 Mean 0,203 0,070 0,133 0,193 0,079 0,114 0,396 0,010 0,349 Note: The annual job flow rates are computed based on the fourth quarter-to-fourth quarter change in net employment. Figure A1 in the appendix depicts annual flow rates based on all four quarters. Expans. and Contr. are abbreviations for ‘expansion’ and ‘contraction’, respectively. 1 3 748 Eurasian Economic Review (2025) 15:741–773 and de Kok (2014). Adhering to the size classification of the EIS database, we define ‘micro firms’ as those employing less than 10, ‘small firms’ as those with 10 to 49 employees, ‘medium firms’ as those with 50 to 249 employees, and ‘large firms’ as those employing 250 and more workers. Small and Medium Enterprises (SMEs) in Turkey are important contributors to employment and job creation (Başçı & Durucan, 2017; Dalgıç & Fazlıoğlu, 2021; Dogan et al., 2017, 2024). This finding is in line with the evidence in many other developing as well as developed countries (e.g., Ayyagari et al., 2011; Cho et al., 2017; Díaz-Moreno & Galdón-Sánchez, 2000; Ma et al., 2015; Robu, 2013).16 SMEs account for 99.7% of all enterprises in Turkey and provide 71% of total employment (TURKSTAT, 2022a). Similarly, in our analysis sample, the vast majority of the firms are of micro-scale (91%), while they only make up 22% of the total employment, as presented in Table 4. Figure 3 depicts an inverse relationship between job reallocation rates and firm size. Strikingly, the job creation rate of micro-firms is more than double the one for small firms and about four times as large as the rate for large firms. The job destruction rate also decreases monotonically with size, but the differences across size categories are less pronounced than is the case for job creation. One point worth noting is the cyclical pattern common to all size categories. There is a reduction in the job creation rate and an accompanying increase in the job destruction rate right after the contraction periods of the 2008 crisis and more recently in 2019. Given that firms have no employees prior to entering the market, jobs generated by new entrants contribute predominantly to the job count of small firms. Therefore, the contribution of the micro firms to job creation would be overstated by considering job flow rates, as reported in Appendix Table A5. To provide a more accurate picture, we calculate the shares of job creation and job destruction and decompose them into their subcomponents. Table 4 shows that micro firms account for a much greater proportion of job creation (43.5%) and job destruction (30%) than larger-size 16 A few studies present contradictory findings, indicating that larger firms may be more effective net job creators compared to smaller firms. For example, Esaku (2022) and Kerr et al. (2014) suggest that large firms outperform small and medium-sized enterprises in job creation in South Africa. Table 4 Shares of job creation and destruction by firm size Firms Employ. Job creation Job destruction (%) (%) Total Entry Expans. Total Exit Contr. 1–2 71,68 6,68 25,05 85,96 12,60 9,41 24,27 3,30 3–9 19,31 15,33 18,42 7,62 20,63 20,42 26,77 17,81 10–19 4,58 10,11 9,96 2,04 11,58 12,33 12,38 12,31 20–49 2,93 14,66 12,18 1,45 14,38 15,85 13,12 16,97 50–99 0,77 8,72 7,59 0,91 8,96 9,08 6,57 10,11 100–249 0,48 11,95 9,05 1,05 10,69 11,12 7,16 12,75 250–499 0,15 8,28 5,49 0,52 6,50 7,18 3,90 8,54 500+ 0,11 24,28 12,25 0,45 14,67 14,61 5,82 18,22 Note: Firm size refers to the average previous year’s firm size. Entries indicate percent shares, and columns add up to 100. Expans. and Contr. are abbreviations for ‘expansion’ and ‘contraction’, respectively 1 3 755 Eurasian Economic Review (2025) 15:741–773 categories. This large share of created jobs primarily stems from new entries, with 94% of all entries being associated with micro firms. Even among expanding firms, micro firms contribute a higher share (33%) to job creation than their larger counterparts. For small and medium-sized firms, on the other hand, approximately 26% and 20% of jobs created are generated by the expansion of existing firms. A comparable trend is observed in terms of job destruction. More than 50% of exits are attributed to micro firms, and the contribution of larger size categories decreases uniformly. However, the share of micro firms in job destruction due to contraction (21%) is less Fig. 3 Rates of job creation and destruction by firm size 1 3 756 Eurasian Economic Review (2025) 15:741–773 evident compared to other size categories. Together, small, and medium-sized firms make up the majority of contraction (52%), while large-scale firms (employing over 250 workers), constituting a third of the workforce, contribute around 18% to job creation and 22% to job destruction, surpassing the contribution by medium-scale firms. This evidence is in line with the one found in Colombia (Flórez et al., 2021), the UK (Hijzen et al., 2010) and the cross-country study of Haltiwanger et al. (2014), which all document a more important role of small and large firms in job turnover, relative to medium firms. 3.5 The role of firm age Recent studies emphasize the importance of young firms in job turnover (Esaku, 2022; Haltiwanger, 2015; Lawless, 2014; Liu, 2024). Ayyagari et al. (2011) suggest that small, young firms have higher job creation rates, while Brummund and Connolly (2019), Esaku (2022), Ma et al. (2015) and Rijkers et al. (2014) emphasize the crucial role of young firms in net job creation for Brazil, Kenya, China, and Tunisia, respectively. Similarly, Masso et al. (2005) report comparable results for Estonia, attributing it to a favorable institutional environment and low start-up costs. Our findings align with this evidence from other developing countries. As presented in Table 5, three-fourths of firms, with nearly 70% of total employment, are comprised of firms that are up to 10 years old. New-entry firms (i.e., aged 0–1 years) have the highest rate of job creation. The job destruction of this group is also the largest but with a smaller distance to other age categories. The job turnover rate declines monotonically as firms get older, and firms become more destructive than creative, which translates into a falling negative employment growth rate. We also observe a distinctive pattern among new-entry firms in response to economic shocks, with a sharper increase in job destruction rates ahead of the 2018 downturn and a much faster and more pronounced rebound in job creation rates following the global financial crisis, as Figure A7 in the online appendix attests. The disproportionately high contribution of micro-firms to job creation, as discussed in the previous section, comes mainly because of new entrants. In our sample, new-entry firms that are of micro-scale account for 21% of the total number of firms, Table 5 Annual job flow rates by firm age Firms Employ. POS NEG GROSS NET EXCESS (%) (%) 0–1 22,57 17,12 0,359 0,238 0,597 0,122 0,475 2–5 30,59 29,04 0,213 0,218 0,431 -0,005 0,426 6–10 20,27 22,81 0,162 0,175 0,336 -0,013 0,323 11–15 11,48 14,75 0,140 0,148 0,288 -0,008 0,281 16–20 6,24 7,64 0,133 0,148 0,282 -0,015 0,267 21–25 3,51 4,25 0,120 0,143 0,264 -0,023 0,241 26+ 5,35 4,39 0,121 0,154 0,275 -0,033 0,242 Note: The annual job flow rates are computed based on fourth quarter-to-fourth quarter change in net employment. The age groups indicated in the first column are expressed in years 1 3 757 Eurasian Economic Review (2025) 15:741–773 while 46% correspond to young incumbent (aged 2 to 10 years old) micro firms (see Table A6 in the online appendix).17 While there is abundant evidence of the negative relationship between firm size and job reallocation, to which we contribute here, relatively little is known about the extent to which firm-age accounts for this relationship. Hence, we regress firm-level net employment growth rates git , as formulated in Eq. 1, on firm size, age, and their interaction. Table 6 shows the regression results, where we use the average firm size (i.e., number of employees) over the previous four quarters rather than the current or initial firm size to avoid the regression to the mean fallacy. Firm size is expressed in natural logarithms, while firm age is measured as a categorical variable: firms aged 0–1 serve as the baseline category, followed by those aged 2–5, 6–10, and above 10 years old. We include firm size in natural logarithms and four categories of firm age as dummies (columns 1 and 2), as well as their interactions (columns 3 to 5). Addi17 We further examine the combined contribution of firm size and age to employment dynamics through the joint distribution of job flow shares across size and age categories. Young small firms up to 10 years old and up to 50 employees account for more than a third of total job creation. The proportion of the same group is about 16% of the job destruction. Although less prominent than the young small firm group, older and bigger firms (i.e., aged above 10 years and with more than 50 employees) still account for an important share of job destruction compared to their contribution to job creation (see Table A7 in the online appendix). Table 6 Regression results. Net employment growth rate of a firm (1) (2) (3) (4) (5) Weighted ln (firm size) -0.006*** -0.004*** -0.072*** -0.070*** -0.018*** (0.002) (0.002) (0.002) (0.004) (0.005) Firm age 2–5 -0.217*** -0.299*** -0.297*** -0.162*** (0.001) (0.002) (0.003) (0.003) Firm age 6–10 -0.214*** -0.293*** -0.294*** -0.165*** (0.001) (0.002) (0.002) (0.003) Firm age 11+ -0.210*** -0.286*** -0.289*** -0.163*** (0.001) (0.002) (0.002) (0.003) Age 2–5 * ln(size) 0.086*** 0.085*** 0.022*** (0.003) (0.003) (0.004) Age 6–10 * ln(size) 0.082*** 0.081*** 0.022*** (0.002) (0.003) (0.004) Age 11+ * ln(size) 0.079*** 0.079*** 0.021*** (0.002) (0.003) (0.003) Constant 0.023*** 0.198*** 0.262*** 0.378*** 0.224*** (0.003) (0.004) (0.004) (0.007) (0.006) Dummies: Sector No No No Yes Yes Region No No No Yes Yes Year*Quarter No No No Yes Yes Observations 68,407,523 67,594,950 67,594,950 67,594,880 67,594,880 Note: The baseline category for firm age is 0–1 years old. The specifications include 16 sector dummies, 12 NUT1-level regional dummies, and 64 year-quarter dummies. Robust standard errors clustered at two-digit sector level in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 1 3 758 Eurasian Economic Review (2025) 15:741–773 tionally, we incorporate sector, region, and year-quarter dummies (column 4), and apply employment weights in the regression estimation (column 5). The results in Table 6 confirm the expected negative association of firm size with net employment growth (column 1), even after controlling for firm age (column 2). The size-age interaction terms allow for size effects to differ depending on the lifespan of the firm. The estimated coefficients on size and age are robust to the inclusion of sector, region, and time-fixed effects. The use of employment weights in the regression has a discernible impact, as it reduces the coefficient estimates substantially while still retaining statistical significance (column 5). The size effect is notably negative and significant for the baseline group of firms aged 0–1. Essentially, smaller new firms experience relatively faster growth and larger new entrants contribute less to the overall employment growth (or shrink more) relative to the more established larger firms. When considering older age groups, the size effects are calculated by combining the coefficient related to size and its interaction term. For example, the actual size effect for firms aged 2–5 is the combination of the size effect for the baseline category (new entrants) and its interaction with the corresponding age group, i.e., -0.018 + 0.022 (see column 5). Consequently, in comparison to start-ups, the actual size effect for older age categories is positive, although this effect is substantially mitigated by the negative size effect seen in the baseline category.18 3.6 Importance of foreign trade in job reallocation Another key factor influencing employment dynamics is a firm’s engagement in international trade, often viewed as an indicator of productivity and a potential driver of job reallocation, as exporting firms tend to be more productive according to the trade literature (Ha & Tran, 2017; Kien & Heo, 2009; Konings et al., 2003; Ma et al., 2015). To examine this, we quantify firms’ involvement in international trade by distinguishing between imports and exports, as well as through an aggregate measure of foreign trade —simply the sum of imports and exports. We construct import and export intensity measures by dividing the total value of imports and exports for each sector by Gross Domestic Product (GDP) and categorize sectors into quintiles based on their level of import or export intensity. Similarly, we create overall foreign trade intensity quintiles by dividing the sum of imports and exports by GDP. We find a positive association between trade intensity and employment size. As shown in the first column of Table 7, nearly 40% of employment is concentrated in the highest quintile of importing firms, while the lowest quintile constitutes roughly 10% of total employment. A comparable pattern, albeit within a narrower range, is evident in exporting firms. In proportion to their employment shares, we observe that firms in the top quintile make the most significant contribution to job creation and destruction, exceeding the contribution of the bottom quintile by more than twice. Likewise, the proportionate contribution of job creation and destruction, reflected in percentage 18 The results hold in a specification augmented with foreign trade intensity as a control variable and based on a sample with complete trade data, where the sample size drops significantly — ranging from approximately 1.1 to 1.4 million observations (see Table A.8 in the online appendix). 1 3 759 Eurasian Economic Review (2025) 15:741–773 Table 7 Regression results. Job creation and destruction rates by import and export intensity Employment (%) Job creation Job destruction (1) (2) (3) (4) (5) (6) (7) Import share quintile: q1 (baseline) 9.79 q2 11.04 -0.025*** -0.011** -0.011** 0.035*** 0.017*** 0.017*** (0.006) (0.005) (0.005) (0.007) (0.005) (0.005) q3 15.93 -0.021** -0.010* -0.011* 0.036*** 0.024*** 0.024*** (0.008) (0.006) (0.006) (0.009) (0.007) (0.007) q4 24.67 -0.026*** -0.017** -0.018** 0.043*** 0.035*** 0.036*** (0.010) (0.008) (0.008) (0.011) (0.008) (0.008) q5 38.58 -0.023 -0.025** -0.027** 0.046*** 0.049*** 0.050*** (0.014) (0.012) (0.012) (0.014) (0.011) (0.011) Export share quintile: q1 (baseline) 15.01 q2 13.74 -0.023*** -0.010* -0.010** 0.021*** 0.013** 0.013** (0.007) (0.005) (0.005) (0.007) (0.005) (0.005) q3 15.88 -0.030*** -0.010 -0.010 0.016* 0.003 0.003 (0.009) (0.007) (0.006) (0.009) (0.007) (0.007) q4 21.36 -0.029*** -0.001 -0.001 0.017* -0.005 -0.005 (0.009) (0.008) (0.008) (0.010) (0.008) (0.008) q5 34.00 -0.019 0.013 0.014 0.000 -0.025** -0.026** (0.012) (0.011) (0.010) (0.013) (0.012) (0.011) Dummies: Year Yes Yes Yes Yes Yes Yes Sector No Yes Yes No Yes Yes Region No No Yes No No Yes Observations 15,754,215 15,754,215 15,753,783 15,754,215 15,754,215 15,753,783 Note: The specifications include 99 two-digit sector dummies, 12 NUT1-level regional dummies, and 16-year dummies. Quintiles are computed based on the ratio of import or export to GDP. The first quintile is used as a baseline category. All specifications make use of the employment weights described in Sect. 2.2. Clustered standard errors at sector level in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 1 3 760 Eurasian Economic Review (2025) 15:741–773 shares, increases from the first to the top quintile (Table A9.1 in the online appendix). Naturally, when using the aggregate measure of trade intensity, we also observe a clear upward trend in employment shares, as well as job creation and destruction shares, across quintiles (see Tables A9.2 and A.10 in the online appendix). On the other hand, when we focus on job flow rates rather than shares, we observe an inverse relationship with trade intensity. However, as explained in subsection 3.2, this would be misleading due to differences in initial employment levels across groups. Nevertheless, examining the rates of job creation and destruction over time provides valuable insights into how recessions differentially affect firms based on their levels of trade integration. As shown in Fig. 4, the trends in job creation and destruction across quintiles of foreign trade intensity indicate that recessions disproportionately impact firms with lower trade exposure. During both the 2008 global financial crisis and the 2018 domestic currency crisis, lower-trade firms experienced sharper declines in job creation and greater increases in job destruction, reflecting their higher sensitivity to contractions in domestic demand. However, the differential effect of trade intensity on job destruction appears more pronounced during the domestic currency crisis. Specifically, job creation rates fell by 30.6% for high-trade firms (top quintile) and by 38.4% for low-trade firms (bottom quintile) during the 2018 crisis, whereas the decline was similar (around 20%) for both groups during the 2008 recession. Job destruction rates, on the other hand, increased more sharply among high-trade firms between 2018 and 2019, rising by 38.7% (from 0.130 to 0.170), compared to a 22.8% increase between 2008 and 2009. Recovery patterns also differed notably: After the 2018 recession, job creation among high-trade firms rebounded strongly, recovering to 120% of its lowest 2019 level, whereas low-trade firms recovered only to approximately 44% of their pre-crisis level, signaling greater post-crisis resilience for firms with higher trade intensity. These findings suggest that firms with greater exposure to international trade benefit more significantly from economic recoveries, likely due to demand diversification across international markets. Next, we examine whether the observed correlation between a firm’s trade intensity and its potential for employment growth holds within a regression framework with controls for sector, region, and year dummies. Following Hijzen et al. (2010) we estimate the impact of import and export intensities on job creation using the following regression: g+ it = β0 +∑5 q=2β1IMPqj +∑5 q=2β2EXPqj + γt + δj + θr + ϵit (3) We define the variable g+ it to be equal to git for firms that are expanding or entering with git >0 and zero otherwise, where the growth rate git is calculated as in Eq. 1. IMPqj and EXPqj refer to quintile (q) dummies for import and export share of sector j, respectively. The variables γt , δj , and θr indicate year, sector, and region dummies. Job destruction results can be obtained in a similar manner by defining the dependent variable g− it as equal to git for contracting or exiting firms with git <0 and zero for all other firms. 1 3 761 Eurasian Economic Review (2025) 15:741–773 In all specifications, we use the employment weights described in Sect. 2.2. The regression results, shown in columns 2 to 7 in Table 7, suggest a negative relationship between import intensity and job creation while the former is found to be positively associated with job destruction. There is a steady increase in the relationship as moving from low to higher quintiles. On the other hand, we do not find such a systematic relationship between export intensity and job reallocation rates. However, we do find that firms with high export intensity have substantially less job destruction than firms Fig. 4 Rates of job creation and destruction by foreign trade intensity quintiles across years 1 3 762 Eurasian Economic Review (2025) 15:741–773 with less export intensity. While Jenkins (2004), provides supportive evidence of the negative impact of increased import competition on employment in Vietnam, we believe that further exploration of trade intensity data and research on its relationship with job flows are necessary to fully understand these puzzling estimates. This is particularly important given the divergence from the observed increase in job creation and destruction shares as trade intensity moves from lower to higher quintiles, as shown in Table A8. To further investigate the role of overall exposure to international competition in explaining job flows, we extend our analysis by combining import and export intensities into a single aggregate measure of trade intensity. However, the mixed patterns previously observed with export intensity persist for job creation rates also when imports and exports are aggregated. In contrast, job destruction clearly exhibits a positive and increasing pattern across quintiles, similar to that observed separately for import intensity. This relationship is statistically significant from the third to the fifth quintile in all specifications. This analysis highlights the importance of disaggregating imports and exports, as aggregation may mask distinct patterns associated with each. Therefore, we would like to stress the disaggregated results. However, we also provide the aggregate trade intensity analysis in the online appendix for completeness (see Tables A9.2 and A.10). 3.7 The role of technology Finally, we explore the variation in employment dynamics based on the technology intensity of industries within the manufacturing sector since technology levels are available only for this sector. Technology levels are determined by the direct research and development (R&D) intensity of industries.19 As presented in Table 8, the majority of manufacturing firms, averaging around 57%, fall within the low-tech industries category. These firms collectively employ about 52% of the manufacturing workforce. Conversely, firms in high-tech industries constitute less than 1% of all firms, contributing to a mere 2.4% of the employment in manufacturing. Although the share of firms in low-tech industries has slightly decreased by about 3% points over the past 16 years, this does not indicate a shift towards high-tech development. The decline in low-tech industries’ share has been compensated by an increase in 19 For the classification of manufacturing industries into categories based on R&D intensities, see Table A11 in the online appendix. Table 8 Annual job flow rates by technology level Firms (%) Employment (%) POS NEG GROSS NET EXCESS Average 2006 2021 Average 2006 2021 Low 56,48 57,92 54,83 51,61 53,00 50,08 0,133 0,136 0,268 -0,003 0,265 Low-med 30,82 29,49 31,19 27,13 26,48 27,08 0,143 0,133 0,276 0,010 0,266 High-med 12,05 11,98 13,18 18,84 18,11 19,90 0,116 0,092 0,208 0,024 0,185 High 0,65 0,61 0,8 2,42 2,41 2,93 0,107 0,069 0,175 0,038 0,137 Note: The annual job flow rates are computed based on fourth quarter-to-fourth quarter change in net employment. The technology level is reported only for the manufacturing industry and based on the OECD ISIC Rev.3 classification 1 3 763 Eurasian Economic Review (2025) 15:741–773 the share of low-medium and high-medium technology firms. In addition, only firms in low-tech industries exhibit a negative net employment growth rate, unlike higher technology firms where job creation outpaces job destruction. A fall in the excess rate is also evident as we move from low to high-tech firms. This suggests that jobs within the low-tech segment of manufacturing, which presumably are less productive, are less stable and more susceptible to reshuffling. This aligns with Nagore García and Van Soest (2017), who find that jobs in high-tech firms are more stable than other new jobs in Spain, both before and during the great recession. Lastly, we examine how job creation and destruction dynamics evolve over time across different technology levels of firms. Similar to the differential impact of recessions on highversus low-trade firms (see Fig. 4), a disparity also emerges when analyzing job flow rates by technology level over time. As shown in Fig. 5, hightech firms exhibit stronger resilience during recessions, with job flow rates remaining relatively stable. Notably, medium-tech firms experienced the sharpest contraction in job creation and a substantial spike in job destruction, indicating heightened vulnerability to cyclical downturns. Although low-tech firms also show a decline in job creation rates and an increase in job destruction rates, the effects appear more muted. Since technology-level data is only available for the manufacturing sector, which was the most affected sector by the 2008 crisis, we observe that the impact of the global recession was more pronounced on job creation than the local currency crisis. For example, job creation in high-medium tech firms, which experienced the largest change compared to other technology levels, fell by approximately 39% between 2008 and 2009, whereas the decline was 21% during the 2018 recession. In contrast, the largest increase in job destruction occurred between 2017 and 2018, rising by approximately 52% for medium-high tech firms, compared to a 32% increase in the 2008 crisis. These findings highlight the heightened vulnerability of mediumto low-tech firms, likely due to their reliance on labor-intensive production and limited access to financial buffers during downturns. 4 Persistence in job creation and job destruction In this section, we first examine the persistence of job creation and destruction across years for the entire economy and then analyze the persistence rate by firm characteristics. Table 9 indicates a one-year persistence rate of, on average, 59% for jobs created and 75% for jobs destroyed, which decreases steadily as moving away from the reference year of the initial employment change. The average five-year persistence rate for newly created and newly destroyed jobs is 29% and 41%, respectively. 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TUIK Haber Bülteni. h t t p s : / / d a t a . t u i k . g o v . t r / B u l t e n / I n d e x ? p = I s g u c u - I s t a t i s t i k l e r i - A r a l i k - 2 0 2 1 - 4 5 6 4 2 TURKSTAT (2023). Household labor force Statistics—TURSTAT news bulletin. Turkish Statistical Institute (TURKSTAT). h t t p s : / / d a t a . t u i k . g o v . t r / B u l t e n / I n d e x ? p = I s g u c u - I s t a t i s t i k l e r i - 2 0 2 2 - 4 9 3 9 0 # : ~ : t e x t = 2 0 2 2 % 2 0 y ı l ı n d a % 2 0 4 % 2 0 m i l y o n % 2 0 8 6 6 , k i ş i % 2 0 h i z m e t % 2 0 s e k t ö r ü n d e % 2 0 i s t i h d a m % 2 0 e d i l d i Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Authors and Affiliations Sinem H.Ayhan1· HartmutLehmann2· SelinPelek3 Sinem H. Ayhan [email protected] Hartmut Lehmann [email protected] Selin Pelek [email protected] 1 Leibniz-Institute for East and Southeast European Studies (IOS) & Institute of Labor Economics (IZA), Landshuter Str. 4, 93047 Regensburg, Germany 2 IOS, University of Bologna & IZA, Landshuter Str. 4, 93047 Regensburg, Germany 3 Galatasaray University & GIAM, Ciragan Cad. No:36, Ortaköy, Istanbul 34349, Türkiye 1 3 773