German labor market reform and the rise of Eastern Europe: dissecting their effects on employment

Walter, Timo Article — Published Version German labor market reform and the rise of Eastern Europe: dissecting their effects on employment Empirica Provided in Cooperation with: Springer Nature Suggested Citation: Walter, Timo (2023) : German labor market reform and the rise of Eastern Europe: dissecting their effects on employment, Empirica, ISSN 1573-6911, Springer US, New York, NY, Vol. 50, Iss. 2, pp. 351-387, https://doi.org/10.1007/s10663-023-09569-w This Version is available at: https://hdl.handle.net/10419/306662 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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/ Vol.:(0123456789) Empirica (2023) 50:351–387 https://doi.org/10.1007/s10663-023-09569-w 1 3 ORIGINAL PAPER German labor market reform andtherise ofEastern Europe: dissecting their effects onemployment TimoWalter1 Accepted: 19 January 2023 / Published online: 17 February 2023 © The Author(s) 2023 Abstract From the early 1990s until 2005 the unemployment rate rose in Germany from 7.3 to 11.7%. While the unemployment rate reached its peak in 2005, it decreased steadily in the following years. The fourth stage of the German labor market reform (Hartz IV) was implemented in 2005 with the intent to cut the unemployment rate. This paper investigates the employment and welfare effects of the Hartz IV reform. Moreover, I am interested in the employment impact of German labor market reform due the rise of the East, which is the productivity increase in Germany and Eastern Europe that has fostered joint fast-growing trade. The focus lies on the national and county level (including 402 counties). As the effects on regional labor markets differ and take time, the paper builds on the dynamic and spatial trade model of Caliendo etal. (2019). I find that the Hartz IV reform is responsible for a 25% drop in unemployment, with a particular impact on eastern German counties. The rise of the East leads to an additional positive contribution to the labor market. Keywords Dynamic trade model· Labor market reform· Productivity shocks· Welfare implications· Germany· Eastern Europe JEL Classification F14· F16· F17 1 Introduction A pivotal year in the German labor market development was 2005: After the German reunification the unemployment rate grew from 7.30 to 11.4% in 1997. Followed by a phase of recovery, which was mainly driven by the “new economy”. The bursting of the dot-com bubble led to an increase of the German unemployment rate Responsible Editor: Martin Halla. * Timo Walter t.w[email protected] 1 University ofHohenheim (520E), Schloss Hohenheim 1C, 70599Stuttgart, Germany 352 Empirica (2023) 50:351–387 1 3 to its all-time high in 2005 with 11.7%. However, up to the financial crisis in 2008 the unemployment rate fell sharply to 7.8% and even to 5% in the following decade. Figure1 illustrates the development of the unemployment rate and the number of unemployed over the period 1991–2019. Hereby, the question about the cause of the strong decrease in the unemployment rate since 2005 naturally arises. At this time, an important labor market policy milestone took place in Germany. In this regard, the German Hartz reforms stand out as a potential channel for the reversal of unemployment.1 They had their focus on the restructuring of the lowwage sector in Germany. The labor market reforms were implemented between 2003 and 2005 in four stages (Hartz I–Hartz IV). Especially through the fourth stage (Hartz IV) and the introduction of the long-term unemployment benefit “Arbeitslosengeld II” (hereafter “ALG II”) on January 1st, 2005 it was hoped to cut the unemployment: On the one hand, the long-term unemployment benefit was initiated to provide a life of human dignity for all people living in Germany between the age of 15 and 65 (or 67), who are capable of working and cannot afford to satisfy their basic material needs.2 On the other hand, the long-term unemployment benefit is conditional, and the recipients are obliged to aim actively for integration into the labor market. In the case of a breach of duty, the long-term unemployment benefit is reduced by 30%, in the case of a second time by 60%, and in the case of a third time the benefit is cut all together. The long-term unemployment benefit is financed by the federal government via the Federal Employment Agency (“Bundesagentur f  u r Arbeit”), except for housing and other costs that are usually paid by municipalities and counties (§6 SGB II). Typically, the long-term unemployment benefit (“ALG II”) is paid after a person is unemployed for more than 12 months and thus is not eligible for the short-term unemployment benefit “Arbeitslosengeld I” (“ALG I”) anymore. Moreover, a person can be eligible for the long-term unemployment benefit even if the person is working, yet earns less than he needs to satisfy the basic demands. This group makes about one third of all long-term unemployment benefit (“ALG II”) recipients.3 This paper investigates the impact of the German labor market reform (Hartz IV) on the German labor market at the German county level (“Kreisebene”). For my analysis I build on the new spatial multi-country and multi-sector equilibrium model of Caliendo etal. (2019). It includes a dynamic set-up, which considers the adjustments of the labor market, as the economic and policy effects on employment differ 1 Other factors could also have contributed to the rapid fall of unemployment, e.g. wage moderation, economic improvement or the increasing flexibility of the labor market institution, see Dustmann etal. (2014). 2 According to the Second Book of the Code of Social Law (§8 SGB II), a worker is capable of working if he is able to work for at least three hours a day and not handicapped due to illness or disability. Foreigners can also receive the unemployment benefits if they live in Germany and have a valid work permit (not for the first three months), and if they are no asylum seekers, see §7 SGB II. 3 Besides those main groups there are other groups (e.g. students) which are eligible for the long-term unemployment benefit. But, as those groups are not part of the accessible workforce, I will not consider them in the analysis in more detail. 353 1 3 Empirica (2023) 50:351–387 for each sector and need time to adapt. Further, the model provides a rich theoretical framework which takes input–output linkages, labor mobility frictions, goods mobility frictions as well as spatial factors into account. In order to incorporate the German labor market reform (Hartz IV) in a dynamic general equilibrium setting I apply the extension of the basic Caliendo etal. (2019) model, as it considers the policy effects of the Social Security Disability Insurance (SSDI) program in the United States. In doing so, I borrow closely from the SSDI framework and modify the model to make it suitable for analyzing the effects of German labor market reform. I add value through my empirical and counterfactual analysis. In particular, I conduct an analysis of the Hartz IV reform’s employment and welfare effects. Thereby, I answer the question: How would German employment and welfare have evolved, if the Hartz IV reform would not have taken place? I do this by constructing first a baseline economy where the data develop as they actually did. Second, I then construct a counterfactual economy for the case of the Hartz IV reform. By taking the difference between the baseline and the counterfactual economy I am able to identify the employment and welfare impact of the Hartz IV reform. In this regard, I undertake a comprehensive study of the German labor market effect that includes the county level and covers all 402 German counties (NUTS-3 level). The time of interest of my analysis are the years between 2005 and 2014, as during that time the German labor market reforms were introduced. Hereby, I construct an input–output table for the German counties, compatible with the World Input–Output Database (WIOD). I follow the approach of Krebs and Pflüger (2018) and use the production value added data for each county. The data is obtainable from the regional statistic data (“Regionalstatistik”) of the German Federal and Regional Statistical Offices, Statistische Ämter des Bundes und der Länder (2020). It includes seven sectors, which are the sectors of interest in my analysis.4 With those seven sectors I am able to construct the input–output table on the county level. Further I include a short-term unemployment sector and a sector for the long-term unemployment (“ALG II”).5 To identify income taxes and the costs of the long-term unemployment benefit (“ALG II”) I rely on the data of the federal government budget (“Bundeshaushalt”). Employment, short-term unemployment and longterm unemployment data are provided by the Statistics of the Federal Employment Agency (“Bundesagentur f  u r Arbeit”). In order to identify the movement of households across sectors and counties, I construct a labor mobility matrix. In addition, 4 The sectors include four manufacturing and three service sectors: Agriculture and forestry, fisheries (Sector 1); production industry without construction (Sector 2); manufacturing and processing (Sector 3); construction (Sector 4); trade, transport, hotels and restaurants, information and communication (Sector 5); financial, insurance services (Sector 6); public services, education, health services (Sector 7). 5 Regarding the trade flow data, I make use of the data provided by Statistisches Bundesamt (Destatis) (2020) and the World Input–Output Database (WIOD), that includes data on 43 countries and an aggregate of the rest of the world. I combine the 56 sectors of the database into the seven sectors used in my simulation. 354 Empirica (2023) 50:351–387 1 3 I identify the probabilities of households becoming employed, short-term unemployed and long-term unemployed. My analysis shows, that without the labor market reform (Hartz IV) the German short-term unemployment would have been 0.4 percentage points larger. This is equivalent to a 385,000 decrease in the number of unemployed and explains a 25% decrease in unemployment between 2005 and 2014. Furthermore, I can demonstrate that welfare would decline as a result of the labor market reform, since the increased labor supply puts pressure on wages and thus reduces welfare. This is especially true for the East German counties. In the empirical part of this paper, I am additionally interested in the employment effects of German labor market reform through the rise of the East. That is the increase in trade between Germany and Eastern Europe due to strong productivity growth in Eastern Europe and Germany. Figure2 shows that the German imports from Eastern Europe and the German exports to Eastern Europe grew stronger since 2005 (up to the financial crisis in 2008/09), compared to the previous years. This is indicated by the fact that the actual German imports from and exports to Eastern Europe are lager since 2005 than the import and export trend (if the imports and exports of 2005 would rise as between 2004 and 2005). Dauth etal. (2014) find that the growing trade flows have led to net-employment gains in Germany, as new export opportunities economically stimulated regions with strong export-oriented sectors. Yet, other regions with sectors vulnerable to import competition experienced higher levels of unemployment triggered by the trade exposure. This led to unevenly distributed employment gains or even losses across different regions.6 Dauth etal. (2021) suggest the rising productivity in Eastern Europe Fig. 1 Development of Unemployment in Germany. Source Statistik der Bundesagentur für Arbeit (2020); Author’s own calculations 6 In addition to the rise in trade with Eastern Europe, the rising trade with China could also have impacted the labor market in Germany. Dauth etal. (2014) investigate in their paper the employment 355 1 3 Empirica (2023) 50:351–387 as a driving force behind the increasing trade flows.7 Especially through the economic transformation the Eastern European productivity levels grew substantially, and hence, could have led to more pressure on the German labor market through increasing import competition. At the same time the German productivity grew as well and could have contributed to the increase in exports to Eastern Europe. However, less is known about the precise impact of productivity on the rising trade between Germany and Eastern Europe, and henceforth on the effects on the German labor market. Depending on which effect prevails, the rise of the East can contribute to the decline in German unemployment and accelerate the effect of the Hartz IV reform. The question then becomes, how would the German labor market have reacted if the Hartz IV reform had taken place, but without the simultaneous rise of the East? In other words: How substantial would the marginal employment effects of the Hartz IV reform have been as a result of the rise of the East? My focus is on eleven Eastern European countries, which are represented in the World Input–Output Database (WIOD) (Release 2016) by Timmer et al. (2015). Namely Czech Republic, Hungary, Poland, Slovakia, Estonia, Latvia, Lithuania, Slovenia, Bulgaria, Croatia and Romania. Since productivities play a crucial part in my study, I identify the precise productivity changes on the sectoral level driving the rising trade between Germany and Eastern Europe. I calibrate for Germany the changes in productivity that corresponds to the increase of Eastern European imports. For Eastern Europe I conduct the productivity changes, which are responsible for the import increase in Germany. I calibrate the productivity changes in two steps: In the first step, I use the instrumental-variable strategy by Autor etal. (2013) to conduct the predicted import changes for Germany and also for Eastern Europe, which arise from the productivity shocks. In the second step, I apply the iteration approach of Caliendo etal. (2019) to detect the productivity changes. By iteration the productivity changes are identified, when the predicted import changes match with the model’s import changes. 7 Several other factors could also play major roles behind the rising trade flows between Germany and the Eastern European countries. Especially the trade integration of the Eastern European countries could have led to a decrease in trade cost and hence to an increasing trade flow with Germany. Particularly, the eastward enlargement of the European Union between 2004 and 2007 could have contributed to the reduction of the unemployment level in Germany. However, the precise impact on the German labor market by the trade liberalization remains unclear, as the estimation of the economic effect of the trade barrier reduction is empirically challenging, Dauth etal. (2014). effect of the so-called “China Shock” and the increase in trade with Eastern Europe. Their findings indicate that the impact of the increasing trade flows of the “China Shock” was less significant than the effects of the increase in trade with Eastern Europe. The authors argue that the reason for a smaller impact of the “China Shock” is that Germany already imported goods from other countries where China had its comparative advantage in. For example Germany imported labor intensive goods like textiles from Italy, but after the “China Shock” trade divergence took place and the source of imports to China changed. Through this trade divergence the German labor market was less impacted by the increase in import competition from China. Footnote 6 (Continued) 356 Empirica (2023) 50:351–387 1 3 I then conduct the counterfactual simulation in the following manner, closely using the approach of the SSDI model: First, I carry out a counterfactual analysis in which the rise of the East happens, but the Hartz IV reform does not occur. Second by running a counterfactual analysis in which both the rise of the East and the Hartz IV reform do not take place. The difference between the two analyses then gives the marginal effects of the Hartz IV reform through rise of the East. I observe that the rise of the East leads to an additional positive contribution to the labor market, as employment increases by 0.05%. The largest impact is in the manufacturing sector, where 40,000 additional jobs are created. My paper is based on several ideas from previous research. The approach of “dynamic hat algebra” used in my paper and developed by Caliendo etal. (2019) is based on the approach of relative changes of Dekle etal. (2008) and its “hat algebra”. Moreover, the applied Caliendo etal. (2019) model builds on the work of Eaton and Kortum (2002), Artuç etal. (2010) and Dvorkin (2014). It is linked to a strand of dynamic equilibrium models such as Artuc and McLaren (2010) and DixCarneiro (2014). Compared to the reduced form approach à la Autor etal. (2013), the general equilibrium framework of Caliendo etal. (2019) has several advantages: It allows for a comprehensive general equilibrium setting with input–output linkages and a spatial multi-country and multi-sector framework. Furthermore, it makes it possible to evaluate many different policy interventions. The general equilibrium framework enables analysis in multiple domains, e.g., employment and welfare effects in different labor markets. As Caliendo etal. (2019) point out that the Fig. 2 German Trade Development to Eastern Europe. The analysis in my study includes eleven Eastern European countries. Thus, the data on Eastern Europe (in this graph) include those same countries, namely Czech Republic, Hungary, Poland, Slovakia, Estonia, Latvia, Lithuania, Slovenia, Bulgaria, Croatia, Romania. Source World Bank (2021); Author’s own calculations 357 1 3 Empirica (2023) 50:351–387 reduced-form approach can only determine changes in workers in one labor market relative to another labor market. It is therefore more straightforward to examine aggregate welfare and employment effects in a general equilibrium framework. Muendler (2017) argues that the reduced-form approach delivers empirical results, albeit with less conclusiveness, and indicates that general equilibrium models provide clearer outcomes. Concerning the effect of the Hartz reforms several major studies have been conducted. Using different approaches and matching models the results vary from an unemployment decrease of 0.1% estimated by Launov and Wälde (2013) to 2% by Hochmuth etal. (2021) and of 2.82% by Krause and Uhlig (2012) as well as around 3% by Hartung etal. (2018). Krebs and Scheffel (2014) discover an unemployment reduction through the German labor market reforms (Hartz I to Hartz IV) by around 3%. However, they trace a 2% decrease to the impact of Hartz I to Hartz III (e.g. restructuring and increasing the number of “jobcenters” as well as the establishment of “minijobs”) and a 1% reduction to Hartz IV. I am able to show that using the comprehensive dynamic general equilibrium model that takes into account multiple sectors and counties, the impact of Hartz IV might be smaller. In addition, I complement the literature with a detailed evaluation of the labor market reform at the level of German counties. As a further effect, I include the rise of the East (increase in trade between Germany and Eastern Europe due to productivity growth in Germany and Eastern Europe), which amplifies the employment impact of the Hartz IV reform. The most recognizable work on the German trade exposure of Eastern Europe with its effect on the German labor market has been explored in a series of papers by Dauth etal. (2014, 2017, 2021). However, they do not explore the underlying fundamentals of the rising trade flows, e.g. fall of trade costs and rise in productivity. Related work has explored the effect of the “China Shock” on the U.S. labor market. Autor etal. (2013, 2014) and Acemoglu etal. (2016) suggest the productivity growth in China led to the “China Shock”, whilst Pierce and Schott (2016) demonstrate that the reduction of trade barriers, e.g. China joining the World Trade Organization (WTO) in 2001, led to the growth of Chinese trade flows. The structure of this paper is as follows: In Sect.2, I introduce a long-term unemployment state into an otherwise standard dynamic trade model à la Caliendo etal. (2019). Section 3 provides a description of the calibration of the data necessary to numerically solve the model. In Sect.4 I present my findings of the economic impact of the German labor market reform (Hartz IV) and the additional labor market effects due to the rise of the East. In Sect.5 I conclude. 2 Model The Caliendo etal. (2019) model is a dynamic version of a multi-sector, multicountry Ricardian trade model à la Eaton and Kortum (2002). It incorporates a spatial general equilibrium and allows for labor market dynamics via labor mobility. I closely follow the extension of the Social Security Disability Insurance (SSDI) model and include long-term unemployment benefits in the framework. The model further consists of many exogenous factors (fundamentals) which are constant or 358 Empirica (2023) 50:351–387 1 3 time-varying. To avoid the need to solve the fundamentals the model applies the equilibrium conditions in relative changes. Thus it includes the ”dynamic hat algebra” approach, which embeds the ”hat algebra” method of Dekle etal. (2008) in a time-varying environment. Regarding the mechanisms of the Hartz IV reform, it is argued that the Hartz IV reform has two main mechanisms that lead to a decrease in short-term unemployment via negative incentives. In the old labor market system, the unemployment benefits “Arbeitslosengeld” (60% of income) was paid for 12 and even up to 32 months. Afterwards the lower unemployment help “Arbeitslosenhilfe” (53% of income) was paid. Under the new system the “Arbeitslosengeld I” (60% of income) is only paid 12 months (in rare occasions 18 months). Hence, the unemployed would have an incentive to get a job. In addition, the long-term unemployment benefit “Arbeitslosengeld II” is much lower than the former unemployment help “Arbeitslosenhilfe” which gives an additional incentive for the unemployed to get into workforce again. In the model I focus on the effect of the “Arbeitslosengeld II” and not explicitly count for the time of the first mechanism. 2.1 Households The model consists of a world with N regions labeled as n or i and of J sectors, indexed as j or k. As the model concentrates on the labor market reform in Germany regions can be seen as German counties. In the numerical analysis the German labor market model is incorporated into the multi-country context. A competitive labor market exists in each sector j of region n. Households can either be employed and work in sector j or they can be short-term unemployed (in “sector 0”) or long-term unemployed (in “sector A”). Representative consumers in region n that are employed in sector j get the market wage wnj j and provide in turn one-unit of labor. Depending on their preferences U (C nj t) , they can choose from a consumption bundle of final local goods Cnj t . The consumption bundle consists of local consumption goods ( c nj,k t) from different sectors: Cnj t =∏ J k=1 (c nj,k t )𝜂 k , where 𝜂k is the share of final consumption of sector k and ∑J k=1 𝜂k= 1 . The households are forward looking and consider their potential future utility levels. This also includes the option of becoming shortterm unemployed and even long-term unemployed. The households decide, depending on the expected value, in what region-sector combination they want to provide their unit of labor. I apply a standard approach used in dynamic discrete choice models to solve the households’ optimization problem. A key to identify the lifetime utility plays the idiosyncratic shock 𝜖ik t , which is standardized distributed Type I Extreme Value. In this context, the idiosyncratic shock can be interpreted as additional benefits the households receive, when moving into region i and sector k (including the short-term unemployment “sector 0”). However, the households do not know the value of the idiosyncratic shock beforehand.8 The value of being employed in region n and sector j at time t is given by: 8 Here, the 2.2, 2.3 and 2.4 equations are based on the SSDI extension focusing on the German labor market reform. 365 1 3 Empirica (2023) 50:351–387 Employment Agency.21 In Table1 I provide an overview of the development of the population composition in Germany. 3.3 Probabilities In this section let us turn to the probabilities that households are changing their status between employed, short-term unemployed and long-term unemployed. The probability that an employed person of region n of sector j at time t earns less than the minimum existence wage (“in ungef  o rderter Erwerbst  a tigkeit”) and therefore is applicable for the “ALG II” benefit is given by 𝛼nj t . In this case the person receives a certain part of the benefit, till the total income is equivalent to the amount of the primary “ALG II” benefit.22 To calculate 𝛼nj t I rely on the data of the statistics of the Federal Employment Agency for the years 2007 to 2014. Hereby I consider 𝛼nj t at t for each year. The probability 𝛼nj t is calculated as the share of people in unsubsidized employment (“in ungef  o rderter Erwerbst  a tigkeit”) in terms of total employment.23 For the years 2005 and 2006 the dataset is restricted, therefore I construct the average of the years 2007 to 2014 for each sector and apply them for each sector in 2005 and 2006. The probability that a short-term unemployed person at time t is longer than 12 months unemployed and therefore enters into the status of long-term unemployment is given by 𝛿t . In order to conduct 𝛿t I use the unemployment data of the statistics of the Federal Employment Agency. I define 𝛿t as the inflow of people who are shortterm unemployed and are getting long-term unemployed compared to the total stock of short-term unemployed people at time t. As the data of inflows are only available at the national level, I consider 𝛿t to be a constant for each region-sector combination. I construct 𝛿t for the years 2005 to 2014. The inflow data as well as the stock data of short-term unemployed people are merely available for the years 2007 to 2014. For the years 2005 and 2006 only the stock data are available, for the inflow data I rely on the change rate between 2007 and 2008. I use this as a trend to construct the data for 2005 and 2006. 𝜌nA t defines the probability that a person in region n who is long-term unemployed will stay in the long-term unemployed program and will further receive the longterm unemployed benefit. My focus of interest is again on the time between 2005 and 2014 for each county. I make use of the short-term and long-term unemployment data of the statistics of the Federal Employment Agency and use the outflow of people of long-term unemployment compared to the stock of the long-term unemployed.24 However, the county data is only available for the years 2009 to 2014. For 21 As the data is only available for the years 2007 to 2014, I use the change rate of the years 2007 to 2008 for each county, and use this as an approximate to calculate the values for the years 2005 and 2006. 22 The group consists mainly of self-employed, mini-jobbers, part-time employees, but also full-time employees are applicable for the “ALG II” benefit. 23 As noted in 2.1, the equation involves also short-term unemployment. Therefore, I calculate the probability of remaining in short-term unemployment and not moving to ALGII, which is 1 −𝛼 n0 t . I do this separately for all short-term unemployment sectors. 24 In a one minus relationship. 366 Empirica (2023) 50:351–387 1 3 the years 2005 to 2008 I take the average of the years 2009 to 2014. In some cases data for sector-region combination are not available. Hence, I use the average of the previous year of the sector-region combination as an approximation. 3.4 Productivity shock As the growing productivities of the Eastern European countries and Germany are thought to be possible drivers of the “rise of the East“, they play a crucial role in my simulation. I am specifically interested in those productivity changes, which are responsible for the increasing trade flows between Germany and Eastern Europe. By applying the approach of Caliendo etal. (2019) I calibrate the productivity changes. For Germany, I conduct the changes in productivity corresponding with the rising imports into the eleven Eastern European countries. Vice versa I calibrate for each of those eleven Eastern European countries the productivity changes which cause the import increase to Germany (imports from the particular country into Germany). Moreover, I conduct for every country the productivity changes on the sectoral level. In order to attain the productivity changes two steps based on Caliendo etal. (2019) are necessary: First, I apply the instrumental-variable strategy of Autor etal. (2013) to get the predicted import changes for Germany and the eleven Eastern European countries respectively. In the second step I calibrate by iteration the productivity changes as the model;s import changes have to match with the predicted import changes. The instrumental-variable strategy of Autor etal. (2013) contains the import change from Germany (or one of the eleven Eastern European countries) by other advanced economies. At the core of the instrumental-variable strategy lies a first-stage regression: The dependent variable ΔMGER,j is the sectoral j import change in Germany for the years between 2005 and 2014, which the regression tries to predict by the explanatory variable. ΔMother,j is the sectoral change of imports by advanced countries. Following Caliendo etal. (2019) I use here Australia, Denmark, Finland, Japan and Spain as advanced economic countries and rely on the World Input Output Database (WIOD) as data source. For Germany, I find the coefficient a2 to be 3.058 (3.1) ΔMGER,j=a1+a2ΔMother,j+uj Table 1 Overview uemployment, long-term uemployment and employment sares in Germany. Source Statistik der Bundesagentur für Arbeit (2020); Author’s own calculations in % 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Unemployed 4.6% 4.2% 3.6% 3.1% 3.7% 3.3% 2.7% 2.7% 2.9% 2.7% ALG II 11.5% 10.9% 9.8% 9.0% 8.9% 8.8% 8.5% 8.2% 8.1% 8.0% Employed 84.0% 84.9% 86.6% 87.8% 87.4% 87.9% 88.8% 89.1% 89.1% 89.3% 367 1 3 Empirica (2023) 50:351–387 with a standard error of 0.022 and a high R-squared of 0.99.25 The regressions for each of the eleven Eastern European countries are similar: Where ΔMEEi,j denotes the sectoral import change for each Eastern European country i in the same time period. Likewise, ΔMother,j is the change of sectoral j imports of the advanced economies between 2005 and 2014. The results are displayed in Table2, most countries, besides Estonia and Latvia, have high R-squared values that indicate respectable prediction power. After having estimated the coefficients I use the baseline economy and the counterfactual economy26 of the model to calibrate the sectoral productivity changes for each of the eleven Eastern European countries and Germany. This is done by iteration to find the optimal productivity change of each sector and country in order that the model’s import changes matches the predicted import changes a2ΔMother,j respectively. For Germany, I find a productivity change in “agriculture and forestry, fisheries” (Sector 1) of 0.1%; in “production industry without construction” (Sector 2) of 2,8%; in “manufacturing and processing” (Sector 3) of 3.4% and in “construction” (Sector 4) of 9.4%. The findings are supported by a high correlation between the model’s import changes and the predicted import changes a2ΔMother,j of 0.998. The results of the sectoral productivity changes of the eleven Eastern European countries are displayed in the online appendix Table A.4. 3.5 Labor income tax & long‑term unemployment benefit The labor income tax 𝜏T t plays a major role in financing the long-term unemployment benefit. The tax is levied on every German labor income. The total amount of labor income tax revenue various each year. To compute the labor income tax, I rely on the data of the federal budget (“Bundeshaushalt”). Thereby 𝜏T t is composed by using the federal expenditure of the long-term unemployment benefit as a share of the total amount of income taxes. An overview of the development of the labor income tax for the years between 2005 and 2014 is provided in Table3. Besides that, it is necessary for my analysis to identify the per capita long-term unemployment benefit bA for the base year of 2005. By taking the data from statistics of the Federal Employment Agency I calculate a per capita expenditure for the recipients of 4080 Euro.27 Having identified the labor income tax and the long-term unemployment (3.2) ΔMEEi,j=a1+a2ΔMother,j+uj 25 Caliendo et al. (2019) find for the U.S. a coefficient of 1.386 with a standard error 0.033 and an R-squared of 0.99. 26 Similar to Caliendo et al. (2019), the fundamentals in the baseline economy develop as they did between 2005 and 2014 and the counterfactual economy includes the same development of fundamentals. However, the sectoral productivity changes are set in such a way that the import changes of the model are close to the predicted import changes a 2 ΔMother , j . 27 Thus, I use the total expenditure of 14.6 billion Euros (based on federal budget “Bundeshaushalt”) and divide it by 3578719 recipients which leads us to a per capita expenditure for the recipients of 4080 Euro. For calculation reasons it is in U.S. Dollar $5534. 368 Empirica (2023) 50:351–387 1 3 benefit I can endogenously determine the lump sum tax/transfer Gt charged by the German rentiers by applying the equation of the government budget. 3.6 Share ofvalue added ingross production The share of the value added in gross production by sector j of region n (countries and counties) is denoted as 𝛾nj . In order to conduct the share of the value added in gross production for the 42 countries (without Germany) of the year 2005 I rely on the value added and gross production data provided by the socio-economic accounts (WIOD 2016 Release). For each country, I aggregate the 56 sectors of the dataset to fit the seven sectors used in my analysis. Regarding the aggregate of the “Rest of the World” of the World Input–Output Database (WIOD), I take the average of the 42 countries for each of those seven sectors. For the 402 counties in Germany I set up the share of the value added in gross production by applying the data from the regional statistic and of the German Federal and State Statistics Office. Especially for the manufacturing and processing (Sector 3) and construction sector (Sector 4) the value added and the gross production is available by the regional statistic.28 For the other five sectors the value-added data is provided, however, I have no gross output data available on the county level. Therefore, I construct the value added in gross production of Germany from the socio-economic accounts for those sectors and apply those shares as a constant on the county level.29 Table 2 Coefficient Results for a2 (for Eastern European Countries). Source World Input Output Database, Release 2016; Author’s own calculations Coefficient Standard eror R-squared CZE 5.402 0.201 0.997 HUN 5.813 0.256 0.996 POL 4.154 0.413 0.982 SVK 4.892 0.351 0.986 EST 0.113 0.081 0.235 LVA 0.787 0.638 0.516 LTU 2.305 0.296 0.974 SVN 15.298 0.083 0.999 BGR 18.097 0.605 0.998 HRV 3.177 0.441 0.984 ROU 7.173 0.583 0.988 28 For 44 counties data points are missing in the manufacturing and processing sector. Thus, I take the average share of the value added in gross production of the rest of the counties and implement the average share for those 44 counties. 29 A similar approach is used in Caliendo etal. (2019). 369 1 3 Empirica (2023) 50:351–387 3.7 Share ofstructures invalue added The value-added share of structures is denoted as 𝜉n . On the country level I make use of the socio-economic accounts (WIOD 2016 Release) to construct the share of structures in value added for each of the 42 countries (Germany not included) plus the aggregate of the “rest of the world” for the year 2005. The value-added share of structures is not directly taken from the data. However, as a work-around I use the relationship of one minus the share of labor compensation in value added which gives the value-added share of structures. I apply this relationship and use the labor compensation (in millions of national currency) and the gross value added at current basic prices (in millions of national currency) to identify the value-added share of structures for each country. As there is no data available for the aggregate of the “rest of the world” I use the average of the 42 countries as an approximation for the value-added share of structures. For the German county level, I make use of the regional statistics data of the German Federal and State Statistics Office. Since no data are available for the year 2005 I rely on the closest available data of 2004 to construct the value added share of structures. I apply the same approach as above for the relationship of the share of labor compensation in value added to identify the share of value added for the structures at the county level. I construct the share of labor compensation in value added by dividing the total amount of income per person in employment by the gross domestic product per person in employment of each county. For some counties data points are missing, thus, I use the average of the other German counties as an estimate. 3.8 Dispersion ofsector productivity In my analysis 𝜃 reflects the dispersion of productivity of each sector. I rely on the values for Germany on the sector-specific productivity dispersion parameter of Aichele etal. (2014),30 which are based on the approach of Eaton and Kortum (2002) and are Fréchet distributed. For agriculture and forestry, fisheries (Sector 1) I take the average of the dispersion of productivity of the grains & crops; cattle, sheep, goats, horses; forestry; fishing sectors in Aichele etal. (2014). The same approach holds true for the production industry without construction (Sector 2), manufacturing and processing (Sector 3); construction (Sector 4) as I rely on the respective sectors of Aichele etal. (2014). Regarding the service sectors: Trade, transport, hotels and restaurants, communication (Sector 5); financial, insurance services (Sector Table 3 Development of Labor Income Tax responsible for financing ALG II. Source Bundesministerium der Finanzen (2020); Author’s own calculations 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0.076 0.129 0.098 0.087 0.098 0.098 0.078 0.061 0.063 0.066 30 Note there is also an updated version available Aichele etal. (2016). 370 Empirica (2023) 50:351–387 1 3 6); public services, education, health services (Sector 7). I consider the approach of Egger etal. (2012) which is applied in Aichele etal. (2014) and Walter (2022). Hereby 𝜃 can be considered a constant in the service sectors. Egger etal. (2012) estimate an inverse 𝜃 of 5.959. This translates in my case to a 𝜃 of 0.1678. Table4 summarizes all dispersion productivity parameters used in my paper. 3.9 Labor mobility & mobility elasticity In order to estimate the labor mobility 𝜇 for the years 2005 to 2014, I construct a matrix of counties-sector input-outflows. The matrix shows the mobility of labor across counties and sectors (including the short-term unemployed and long-term unemployed sector). The value of each element of the county-sector input-outflow matrix represents the probability that a household working in sector j of county n and will be doing work in this county-sector combination (of the element) in the following year. Hereby, I denote higher probabilities to the circumstance that the household will stay in the same sector j and the same county n in the next time period. I make further assumptions that when a household decides to move, it is more likely to move into another neighbor region but staying in the same sector. Moving to more distant regions further decreases the probability. Also changing jobs to less similar sectors (e.g. having a job in construction and moving to the financial sector is less likely) reduces the probability of the element. My assumptions are based on the findings of Dauth etal. (2021), who identify the labor mobility across the county and sector level in Germany by using the data of Integrated Labor Market Biographies (IEB) from the German Institute for Employment Research (2020). 70% of the workforce stay in the sector and do not move to a different county. Out of the remaining 30% I assume that roughly two-third stay in the same sector, but move into other counties.31 The other one third consists of the people staying in the same county, but switching work to another sector (25%), the rest being people who move to other counties and switching work. My assumptions of the 30% of worker switching jobs and/or counties are differing to the findings of Dauth etal. (2021) which find that 10% get a new job in the same sector with or without switching counties, and the other 20% changing sectors with or without switching counties. As they consider 3-digit industry and I am only considering 7 sectors, my probability to stay in the same sector is higher than the finding of Dauth etal. (2021). In order to construct the mobility for short-term unemployed and long-term unemployed people I rely on the regional statistics data and as well the statistics of the Federal Employment Agency. Applying those assumptions, I denote for each possible element of the region-sector input-outflow matrix a certain probability, by which I can construct the labor mobility matrix 𝜇 for 2005 to 2014. As an estimation for the mobility elasticity 𝜈 I adopt the result of the annual rate of 𝜈=2.02 of Caliendo etal. (2019). 31 Out of the 73% of workers staying, 52% of workers move into neighbor counties, while the other 21% move to other counties in Germany, with the same probability. 371 1 3 Empirica (2023) 50:351–387 3.10 Discount interest rate As my analysis relies on a dynamic model and considers the time change, it is necessary to identify the interest rates. In particular, 𝛽 reflects the discount interest rate. To conduct the discount factor, I rely on the long-term interest-rate data from the OECD (2020) for the years 2005 to 2014. I find an average discount factor of annually 0.9687 and apply this value in my analysis. 4 Simulation After having derived the key variables let us turn our attention to the analysis. In the following I present a short outline of the approach to conduct the simulation. Hereby the simulations build on the Caliendo etal. (2019) extension of the Social Security Disability Insurance (SSDI) program. I construct the baseline economy for the years 2005 and 2014 which consist of the development of the actual fundamentals. The baseline economy is needed to solve for the counterfactual equilibrium. In my counterfactual analysis I simulate the impact of the German labor market reform. In order to estimate the impact of the German labor market reform (in particular Hartz IV) I conduct the counterfactual economy. Specifically, I let the fundamentals develop as they did, but eliminate the parameters of the long-term unemployment benefit and cut the respective labor income tax.32 4.1 Long‑term unemployment benefit I start by focusing on the impact of long-term unemployment benefits introduced as part of labor market reforms. My counterfactual question to answer is: What would have happened to the German labor market, if the long-term unemployment benefit would have been eliminated? Through this question I can examine the labor market changes due to Hartz IV. In addition, I examine the welfare implications of labor Table 4 Dispersion of German Sector Productivity. Source Aichele etal. (2014), Egger etal. (2012); Author’s own calculations Dispersion of productivities 𝜃 Agriculture and forestry, fisheries (Sector 1) 0.3542 Manufacturing without construction (Sector 2) 0.1849 Manufacturing (Sector 3) 0.3201 Construction (Sector 4) 0.1678 Trade, transport, hotels and restaurants, communication (Sector 5) 0.1678 Financial, Insurance services (Sector 6) 0.1678 Public services, education, health services (Sector 7) 0.1678 32 In the counterfactual analysis, the agent expects to receive the long-term unemployment benefit, however, it is eliminated for the rest of the time period. Due to the elimination of the benefit, also the taxes which are financing the long-term unemployment benefit are cut. 372 Empirica (2023) 50:351–387 1 3 market reform. Figure3 shows the effect of the long-term unemployment benefits on the German unemployment between 2005 and 2014. Thus, the long-term unemployment benefit would account for an around 0.4% decrease in short-term unemployment.33 That would correspond to an approximated reduction of unemployed people by 385,000 over time. This explains a 25% decrease in unemployment between 2005 and 2014. I further find that some sectors are profiting from the introduction of the long-term unemployment benefit as for example the employment of the manufacturing sector would increase by 0.15%. I also see an increase in the service sectors: Trade and commerce increase by 0.2%, public sector by 0.2% and finance sector by 0.08%. Other sectors as for example agriculture, production and construction would decrease in the long run, see online appendix A.5. Next, I present the findings for the impact of the long-term unemployment benefit introduction on the German county level. Figure4 displays the decreasing regional short-term unemployment shares. The regional unemployment shares are more affected by the German labor market reform than counties in the west. Although, some counties, especially in Bavaria experience a stronger decline as well. However, as Fig.5 shows, the counties in the East contribute more to the decline of aggregate unemployment in Germany. Berlin has with 1.25% the highest contribution to the total reduction of Germany’s unemployment. The decrease of short-term unemployment leads to an increase of employment in other sectors. For the manufacturing sector I find an increase in the regional manufacturing share in Baden-Württemberg and Bavaria. In Mecklenburg-Western Pomerania and Brandenburg some counties experience the highest increase in the regional manufacturing share, which together with the named counties in the south contribute the largest to the aggregate increase of the manufacturing sector Fig. 3 Short-Term Unemployment Decrease due to the Long-Term Unemployment Benefit. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 33 A brief explanation of the approach to identify the labor market impact of the long-term unemployment benefit: First, I let the fundamentals develop as they did in the data. Second, I simulate the counterfactual analysis and cut the long-term unemployment benefit as well as the responsible taxes. This leads to a higher unemployment level in the counterfactual scenario. The difference between the unemployment levels of the baseline and the counterfactual scenario is the short-term unemployment effect due to the introduction of the Hartz IV reform. 373 1 3 Empirica (2023) 50:351–387 in Germany, see Figs.6 and 7. Further, I discover a shift into the trade and commerce sector as well as into the public sector. I find a similar pattern for counties in the southern part of Germany as well as in Mecklenburg-Western Pomerania and Brandenburg that contribute the largest to the employment increase in those sectors. Next, I conduct a welfare analysis of labor market reform that looks at the overall change in real wages. Figure8 shows the long-term percentage change in welfare in each county. I find that welfare falls by − 0.08% in the long run due to Hartz IV reform. Welfare is falling above all in the new federal states. Especially in the federal states of Saxony Anhalt, Saxony and Thuringia. The impact of the labor market Fig. 4 Change in Regional Unemployment Shares. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 374 Empirica (2023) 50:351–387 1 3 reform on welfare in the western German counties is small and almost negligible. However, there are some exceptions, e.g. in Neunkirchen in Saarland, where welfare falls by − 0.12%. The labor market reform has a negative impact on welfare, because although the reform employs more people, the increase in labor supply puts pressure on wages, which has a negative impact on real wages. The increase in employment does reduce production costs, which lowers overall prices. However, these effects are smaller than the decline in wages, so that welfare falls. Particularly in the counties with higher employment effects of the Hartz IV reform, there is therefore a decline in overall welfare. Looking at the sectoral level, Fig.9, the largest impact Fig. 5 Regional Contribution to total Short-Term Unemployment Decrease. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 381 1 3 Empirica (2023) 50:351–387 effect). In order to capture the full impact of labor market reform, my work builds on the dynamic spatial multi-country and multi-sector equilibrium Caliendo etal. (2019) model. I apply the model by including the structure of the long-term unemployment benefit in order to simulate the impact of the Hartz IV reform. I conduct a comprehensive study on the Hartz IV reform at the German county level, covering 402 German counties and 43 states with 7 sectors plus an unemployment sector and a long-term unemployment sector. To run the analysis, I use the World Input–Output Database (WIOD) and data from the German Federal and Regional Statistical Offices as well as the statistics from the Federal Employment Agency as the main data sources. Fig. 12 Regional Contribution to total Short-Term Unemployment Decrease. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 382 Empirica (2023) 50:351–387 1 3 My main finding is that the Hartz IV reform reduces the short-term unemployment by 0.4%. Futher I find evidence that Hartz IV reform reduces short-term unemployment in eastern German counties. The results suggest that the long-term unemployment benefit (Hartz IV) certainly had its impact on the unemployment in Germany, though other parts of the Hartz reform (e.g. restructuring the Federal Labor Institution) could have played a major role as well. Fig. 13 Changes in Regional Long-Term Unemployment Shares. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 383 1 3 Empirica (2023) 50:351–387 Moreover, I identify a modest impact of the Hartz IV reform through the rise of the East. Without the productivity growth of the rise of the East the short-term unemployment would have been 0.036% larger. Dissecting the effects on employment I find that the labor market effects caused by the German productivity shock is larger than those of Eastern Europe. Fig. 14 Regional Contribution to total Long-term Unemployment Decrease. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 384 Empirica (2023) 50:351–387 1 3 Fig. 15 Changes in Regional Manufacturing Shares. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 385 1 3 Empirica (2023) 50:351–387 Fig. 16 Regional Contribution to total Manufacturing Increase. The figures are based on the Author’s own calculations and rely on the data explained in chapter3 386 Empirica (2023) 50:351–387 1 3 Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1066302309569-w. Acknowledgements I would like to thank Benjamin Jung, Thomas Beißinger, Henning M  u hlen, Martyna Marczak, Oliver Krebs for valuable comments and suggestions. Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Acemoglu D, Autor D, Dorn D, Hanson GH, Price B (2016) Import competition and the great US employment sag of the 2000s. J Labor Econ 34(1):141–198 Aichele R, Felbermayr G, Heiland I (2016) Going deep: the trade and welfare effects of TTIP revised. Ifo-Working Paper 219 Aichele R, Felbermayr GJ, Heiland I (2014) Going deep: the trade and welfare effects of TTIP. CESifo Working Paper Series Artuç E, Chaudhuri S, McLaren J (2010) Trade shocks and labor adjustment: a structural empirical approach. Am Econ Rev 100(3):1008–45 Artuc E, McLaren J (2010) A structural empirical approach to trade shocks and labor adjustment: an application to turkey. Adjustment Costs and Adjustment Impacts of Trade Policy, World Bank, p 33 Autor D, Dorn D, Hanson GH (2013) The china syndrome: local labor market effects of import competition in the United States. Am Econ Rev 103(6):2121–68 Autor DH, Dorn D, Hanson GH, Song J (2014) Trade adjustment: worker-level evidence. Q J Econ 129(4):1799–1860 Bundesministerium der Finanzen (2020) Federal budget data, Retrieved from https:// www. bunde shaus halt. de accessed 21.05.2020 Caliendo L, Dvorkin M, Parro F (2019) Trade and labor market dynamics: general equilibrium analysis of the China trade shock. Econometrica 87(3):741–835 Costinot A, Rodríguez-Clare A (2014) Trade theory with numbers: quantifying the consequences of globalization. In: Handbook of international economics, vol 4. Elsevier, pp 197–261 Dauth W, Findeisen S, Suedekum J (2014) The rise of the East and the Far East: German labor markets and trade integration. J Eur Econ Assoc 12(6):1643–1675 Dauth W, Findeisen S, Suedekum J (2017) Trade and manufacturing jobs in Germany. Am Econ Rev 107(5):337–42 Dauth W, Findeisen S, Suedekum J (2021) Adjusting to globalization in Germany. J Labor Econ 39(1):263–302 Dekle R, Eaton J, Kortum S (2008) Global rebalancing with gravity: measuring the burden of adjustment. IMF Staff Papers 55(3):511–540 Dix-Carneiro R (2014) Trade liberalization and labor market dynamics. Econometrica 82(3):825–885 Dustmann C, Fitzenberger B, Schönberg U, Spitz-Oener A (2014) From sick man of Europe to economic superstar: Germany’s resurgent economy. J Econ Perspect 28(1):167–88 Dvorkin M (2014) Sectoral shocks, reallocation and unemployment in competitive labor markets. Technical Report, Yale University Eaton J, Kortum S (2002) Technology, geography, and trade. Econometrica 70(5):1741–1779 387 1 3 Empirica (2023) 50:351–387 Egger PH, Larch M, Staub KE (2012) Trade preferences and bilateral trade in goods and services: a structural approach. CEPR Discussion Paper No. DP9051 German Institute for Employment Research (2020) Integrated employment biographies (IEB), Retrieved from https:// www. iab. de accessed 11.06.2020 Hartung B, Jung P, Kuhn M (2018) What hides behind the German labor market miracle? Unemployment insurance reforms and labor market dynamics. CEPR Discussion Paper No. DP13328 Hochmuth B, Kohlbrecher B, Merkl C, Gartner H (2021) Hartz IV and the decline of German unemployment: a macroeconomic evaluation. J Econ Dyn Control 127:104114 Krause MU, Uhlig H (2012) Transitions in the German labor market: structure and crisis. J Monetary Econ 59(1):64–79 Krebs O, Pflüger M (2018) How deep is your love? A quantitative spatial analysis of the transatlantic trade partnership. Rev Int Econ 26(1):171–222 Krebs T, Scheffel M (2014) Labor market reform and the cost of business cycles. ZBW-Deutsche Zentralbibliothek, Kiel und Hamburg Launov A, Wälde K (2013) Estimating incentive and welfare effects of nonstationary unemployment benefits. Int Econ Rev 54(4):1159–1198 Muendler M-A (2017) Trade, technology, and prosperity: an account of evidence from a labor-market perspective. WTO Staff Working Paper Organisation for Economic Co-operation and Development (OECD) (2020) Long-term interest rates, Retrieved from https:// data. oecd. org/ inter est/ longterminter estrates. htm accessed 15.06.2020 Pierce JR, Schott PK (2016) The surprisingly swift decline of US manufacturing employment. Am Econ Rev 106(7):1632–62 Statistik der Bundesagentur für Arbeit (2020) Labour market data, Retrieved from https:// stati stik. arbei tsage ntur. de accessed 23.05.2020 Statistische Ämter des Bundes und der Länder (2020) Regional statistic data, Retrieved from https:// www. regio nalst atist ik. de accessed 20.05.2020 Statistisches Bundesamt (Destatis) (2020) Trade flow data, Retrieved from https:// www. desta tis. de accessed 06.04.2020 Timmer MP, Dietzenbacher E, Los B, Stehrer R, De Vries GJ (2015) An illustrated user guide to the world input-output database: the case of global automotive production. Rev Int Econ 23(3):575–605 Walter T (2022) Trade and welfare effects of a potential free trade agreement between Japan and the United States. Rev World Econ 158(4):1199–1230 World Bank (2021) World integrated trade solution. Retrieved from https:// wits. world bank. org accessed 23.03.2021 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.