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International trade and unemployment: towards an investigation of the Swiss case

Lukas mohler, simone wyss.

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Corresponding author.

Contributed equally.

Received 2017 May 15; Accepted 2017 Dec 10; Issue date 2018.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

The topic of this paper has been motivated by the rising unemployment rate of low-skilled relative to high-skilled labour in Switzerland. Between 1991 and 2014, Switzerland experienced the highest relative increase in the low-skilled unemployment rate among all OECD countries. A natural culprit for this development is “globalization” as indicated by some mass layoffs in Switzerland and as commonly voiced in public debates all over the world. Our analysis, which is based on panel data covering the years 1991 to 2008 and approximately 33,000 individuals employed in the Swiss manufacturing sector, does not, however, confirm this presumption. We do not find strong evidence for a positive relationship between import competition and (low-skilled) individuals’ likelihood of becoming unemployed.

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The online version of this article (10.1186/s41937-017-0006-7) contains supplementary material, which is available to authorized users.

Keywords: International trade, Unemployment, Low-skilled labour, Switzerland

Introduction

The relationship between international trade and employment has always been controversial. Trade economists have traditionally emphasized the efficiency-enhancing effects of international trade with no impact on total employment, at least in the medium and long term. Politicians and members of governments, in contrast, typically believe in an employment-increasing effect of international trade and often point to the numbers of jobs created by rising exports. 1 In the eyes of the public, however, international trade entails the danger of job destruction, particularly through increased imports. Trade economists agree that international trade may have distributional effects within countries. But they typically identify these effects in terms of changing factor prices: Low-skilled labour may, for example, lose ground—relatively and absolutely—in a high-income country as a result of international trade with (low-skilled) labour-abundant countries such as China or India.

In this paper, we investigate whether international trade is indeed linked to the likelihood of becoming unemployed. The focus on unemployment is motivated by our observation that the Swiss unemployment rate between low-skilled labour and high-skilled labour increased faster than that of any other OECD country between 1991 and 2014, with virtually no change in the relative wage rate between the same two groups of people. We use a representative panel data set for employees in the Swiss manufacturing sector, covering the period from 1991 to 2008, and link it to international trade data. We control for a number of individual characteristics, particularly regarding skills, age and experience, as well as industry properties. The analysis indicates that, for the Swiss economy, rising or high levels of imports do not seem to be a driving force behind the probability of becoming unemployed. Individual characteristics such as a short length of tenure, part-time employment, and low skills are, however, confirmed to be important factors that positively affect the individual’s risk of becoming unemployed.

Thus, the paper adds to the rapidly expanding literature on whether international trade is an important cause of the increase in the wage and unemployment gaps between skilled and unskilled labour that have been observed in the USA and some other countries since the 1980s. 2 We know since Stolper and Samuelson ( 1941 ) and, more generally, since Jones ( 1965 ) that trade liberalization tends to have a strong negative impact on some real factor prices and, if these are inflexible or search costs are involved, also on factor market clearing, as shown by Davis ( 1998b ), Davidson et al. ( 1999 ), and Egger and Kreickemeier ( 2008 ). Moreover, Feenstra and Hanson ( 2003 ) argue that the effects from trade in intermediate inputs may be similar to those caused by skill-biased technological change which is often made responsible for the wage gap in the US economy. Autor et al. ( 2013 ) found significant negative labour-market effects on the US economy of international trade between the USA and China and conclude: “Rising imports cause higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries” (p. 2121).

Recent trade models, which introduce some labour market frictions, as used by Brecher and Chen ( 2010 ), Davis and Harrigan ( 2011 ), Helpman and Itskhoki ( 2010 ), Helpman et al. ( 2010 ), Larch and Lechthaler ( 2011 ), Mitra and Ranjan ( 2010 ), or Ranjan ( 2012 ), imply that relative unemployment between different types of labour may be affected by trade liberalization in a variety of ways. Moreover, these models come to the conclusion that international trade may also affect the overall unemployment level in an economy—positively or negatively. 3 In empirical analyses, a negative effect of trade on overall unemployment is found by Felbermayr et al. ( 2011 ) and by Gozgor ( 2014 ) in cross-country analyses, by Hasan et al. ( 2012 ) for India and by Francis and Zheng ( 2011 ) for NAFTA. 4 Chusseau et al. ( 2010 )—in a cross-country analysis—and Horgos ( 2012 )—for Germany—show that in the case of inflexible factor prices an increase in the relative unemployment rate between skilled and unskilled labour can to some extent be linked to trade—which the former call an “inequality-unemployment trade-off”. Fugazza et al. ( 2014 ) find a positive relationship between trade and unemployment in a panel of 97 countries if countries have “a comparative advantage in sectors that have high labour market frictions” (p. 1).

Compared to the existing literature, our empirical investigation is of particular interest for three reasons. First, it focuses on a small country whose international trade reflects a large share of its domestic output. The Krugman ( 2000 ) critique that a country’s trade volume is typically too small to explain effects on different types of labour hardly applies in this case (or at least to a much lesser extent). Second, our paper’s emphasis is on the unemployment rate, and not on wages as underlined by the majority of empirical research studies. 5 This focus is in line with the recent shift in research interest among trade theorists and labour-market economists as well as with the stylized facts applying to the Swiss economy. Finally, we add to the limited literature on Switzerland in this field. The relationship between international trade and unemployment has, to our knowledge, not been analysed to date for the Swiss case. 6

The remainder of the paper is as follows. The “ Background ” section presents stylized facts that explain our research strategy. The “ Methods ” section briefly describes our research methodology. The “ Results and discussion ” section presents the main results of the econometric analysis. The “ Conclusion s” section concludes.

Past research has been motivated by an inquiry into the impact of international trade on relative wages . Feenstra ( 2010 , pp. 10), for example, describes and discusses the development of the wages of “nonproduction” relative to “production” workers in US manufacturing from 1958 to 2006. If we interpret this ratio as the relative wage rate of high-skilled to low-skilled labour, the data clearly shows that the relative wages of unskilled labour fell considerably and constantly from 1986 to 2000. This observation has been the basis for the expanding literature on trade and the wage gap in the USA that also sparked our research interest with its focus on Switzerland.

Such a development is, however, not observable for Switzerland. Using Swiss labour market panel data (Swiss Labor Force Statistic, SLFS) and the UNESCO skill classification scheme (International Standard Classification of Education, ISCED-97), 7 we calculated both the median gross wage rate of high-skilled ( W H ) and low-skilled ( W L ) labour, and the unemployment rate for the same two groups, i.e. U H and U L , for the period 1991 to 2014. Figure  1 shows that, over this period, the U L / U H rose with a compounded annual growth rate (CAGR) of 2%, whilst the W H / W L remained roughly constant with a CAGR of − 0.3%. Thus, Fig.  1 serves as a motivation to study a possible relationship between international trade and (changes in) the relative unemployment of low-skilled and high-skilled labour in the Swiss case.

Evolution of relative wages and relative unemployment in Switzerland. Source: Own calculations based on FOS ( 2008 ), Wyss ( 2010 ) FOS ( 2016a , b )

A comparison among 21 OECD countries implies that there is no other country in which U L / U H has grown as fast as in Switzerland from 1991 to 2014. 8 Figure  2 shows a CAGR of 4.8% of this ratio from 1991 to 2014 (top panel). It reveals that other countries such as South Korea or Germany also experienced a large rise in this ratio, whereas countries like the Netherlands or Belgium but also the USA or Canada demonstrate a decrease of the relative unemployment of low-skilled labour. Absolute numbers in the OECD data indicate that the Swiss U L increased from 1.2% (1991) to 8.8% (2014), whereas U H increased to a much smaller extent over this period (from 1.3 to 3.2%). Note, however, that the absolute value of the relative unemployment rate in Switzerland (2.7) is not extremely high, but rather puts the country in the middle of the reported OECD countries as shown in Fig.  2 (bottom panel). Given the strong and yet unbroken trend in the Swiss relative unemployment rate, it is of highest interest to assess whether trade may be a driving force of this development. 9

Average growth rate of relative unemployment (top panel, 1991–2014) and absolute value of relative unemployment (bottom panel, 2014) in OECD countries. Note: These are OECD countries for which data were available for the years considered. For the comparison in the top-panel, compounded average growth rates were taken. Source: Own calculations based on OECD (2007) and OECD (2015), Tables A8.4a and A5.4a, respectively

Trade theory stresses the importance of international trade in improving an economy’s allocation of resources, and not the creation of additional jobs. In a standard trade model, there is no expected link between trade liberalization and the total number of jobs in an economy. 10 The argument trade economists traditionally have put forward is that whilst more trade leads to some jobs being destroyed in the import-competing sector of an economy, new jobs are simultaneously being generated in the export sector.

An increase in unemployment is, however, compatible with the traditional trade theory if we, for example, extend a Heckscher-Ohlin type model to allow for some factor price inflexibility as shown by Davis ( 1998b ) or, adding trade in intermediate inputs, by Egger and Kreickemeier ( 2008 ). The reason is that trade typically leads to a decrease in the relative demand for low-skilled labour in a (human) capital-rich country. If the induced fall of the price of low-skilled labour—predicted by the Stolper Samuelson Theorem—is prevented by labour market rigidities, unemployment for low-skilled labour tends to rise with trade liberalization.

Recent trade models expanded in this direction allowing for a number of labour market frictions and/or using intra-industry trade models based on heterogeneous firms and job-specific rents. It turns out that, in these set-ups, trade liberalization may indeed raise unemployment of particular types of labour and affect overall unemployment in an economy. In Brecher and Chen ( 2010 ), for example, the unemployment rates of low- and high-skilled labour “often move in opposite directions” (p. 990), whereas the change of aggregate unemployment is ambiguous. Davis and Harrigan ( 2011 ) argue that, in their model, trade liberalization may destroy a considerable share of highly paid jobs without, however, necessarily affecting overall unemployment. Helpman and Itskhoki ( 2010 , p. 1100) find the surprising result that “[T]he opening to trade raises a country’s rate of unemployment if its relative labour market frictions in the differentiated sector are low.” And Hasan et al. ( 2012 , p. 269) come, based on their empirical study of India, to the conclusion: “Moreover, our industry-level analysis indicates that workers in industries experiencing greater reductions in trade protection were less likely to become unemployed, especially in net exporting industries.” 11

The focus of our paper is empirical. We seek to explain the employment status of individuals over time, i.e. whether they become unemployed or not, by changes and levels of imports and exports, controlling for various individual characteristics and industry factors. The explained variable (i.e. the individual’s status, y i ) is qualitative in nature and takes a value of 1 if an individual becomes unemployed in a certain year and 0 otherwise. The explanatory variables will be qualitative or quantitative as will be made more precise in the “ Results and discussion ” section. The econometric analysis of the relationship between the two is largely based on the linear probability model (OLS) that includes year and industry fixed effects and, for some specifications, individual fixed effects. We use this model as coefficients will be easier to interpret, but we also report the results of the analysis based on the logit model. They turn out to be qualitatively the same.

Results and discussion

We base our analysis on representative industry-panel data for the years 1991 to 2008. During this period, Switzerland established a number of bilateral agreements with trading partners—including the European Union (EU). Moreover, mutual trade liberalization between Switzerland and other countries also occurred through new membership of countries to the World Trade Organization (WTO), the EU and the European Free Trade Association (EFTA). 12 All of this implies pressure and adjustments that are typical for trade liberalizations. The question we now seek to answer is whether international trade indeed had a significant impact on the probability of (particularly low-skilled) individuals to become unemployed. If this is the case, international trade could be one reason for the increase of the relative unemployment rate for low-skilled labour described in the “ Background ” section.

Using micro data on individuals’ characteristics, we intend to assess whether an individual, who becomes unemployed, does so because of his or her particular exposure to international trade, controlling—amongst others—for skills. We present detailed summary statistics of the underlying data in the “ The data ” section and then run regressions of the change in the individual employment status on individuals’ characteristics and the trade variables in the “ Changes in employment status, individual characteristics and trade ” section. The “ Refinement of the trade variables and inclusion of individual fixed effects ” section uses a number of refined trade variables and includes individual fixed effects. The “ Sensitivity analyses ” section concludes with some sensitivity analyses.

For the industry panel data, we rely on the Swiss Labour Force Survey (SLFS). It is based on an annual and representative collection of information from Swiss residents (including foreigners, but excluding cross-border commuters) by the Swiss Federal Office of Statistics (FOS). The SLFS is in line with the methods used by the International Labour Office (ILO) which defines those individuals as unemployed who are not working, but searching for a job and ready to assume employment quickly.

This data source includes a pool of roughly 33,000 individuals over a period of 18 years (1991–2008) who were employed in the secondary sector (manufacturing) in Switzerland. As we want to attribute an industry to an individual, characterizing in which kind of industry the worker is employed, we link the SLFS data (FOS, 2009a ) on the industry two-digit SIC level with the Swiss Foreign Trade Statistics (EZV, 2009 ) and the National Account Statistics of the FOS ( 2009b ). To also characterize whether an individual works in a so-called ICT industry (i.e. an industry which displays an above-average intensity in the use of information and communication technology) or in a GAV industry (i.e. an industry which shows an above-average coverage of collectively bargained labour contracts), we also take into account the ICT-Survey of the KOF Swiss Economic Institute (KOF, 2005 ) at the Swiss Federal Institute of Technology (ETH) and the GAV-Statistics of the FOS ( 2002 ).

Summary statistics of the data used in our regressions are provided in Table  1 . The first column entitled “Change in Employment Status” is composed of individuals who are either employed during the full period of observation or indicate a change in their employment status from employment to unemployment. The second column “Employment Status” includes all individuals with a status of employed or unemployed. This leads to a maximum of 20,928 (40,875) observations of which 463 (1226) show a change in the employment status from employed to unemployed (show a status of unemployment). These observations stem from 10,242 (18,995) individuals, of which 461 (1008) show a change in their status from employed to unemployed (show at least once a status of unemployment). 13

Summary statistics of the regression data set

Source: Panel data set constructed using data from FOS ( 2009a ), EZV ( 2009 ), KOF ( 2005 ) and FOS ( 2009b ). Note that trade covariates and industry characteristics describe the industry which an individual is employed in

Our main econometric analyses will concentrate on the observations reported in the first column of Table  1 . However, we will take into account the observations in the second column in our sensitivity analysis (“ Sensitivity analyses ”). Regarding the first column, the mean year-to-year change in percentage of import (export) values in the 17 manufacturing industries considered in the analysis amounts to 6.9% (7.6%). 40.1% of the observations are linked with “GAV industries”, whereas 37.2% of the observations include individuals employed in “ICT industries”. 14 The distribution of the observed worker characteristics are reported in the bottom part of Table  1 and speak for themselves.

Changes in employment status, individual characteristics and trade

We first regress changes in the individual employment status on the individuals’ characteristics and aggregate trade variables, using the following linear probability model with time and industry fixed effects:

Note that i indexes the individual and t the year. The left-hand variable, y it , takes the value of 1 if the individual i becomes unemployed in t and was employed in t − 1, and it takes the value of 0 if the individual remains employed in t . The probability of becoming unemployed over time is explained based on a number of right-hand independent variables, starting with an individual being employed in an ICT and GAV industry, a number of socio-demographic factors (SDF) of individual i in t as well as imports ( IM ) and exports ( EX ) of the industry, in which the individual i is employed, in time t . Note that we use levels (i.e. the value) as well as changes (i.e. in percentage) for the trade covariates and also include lags. We also interact some of the variables with the individuals’ skill level (L, M, H). The results are provided in Table  2 .

Linear regressions of changes in employment status on trade variables and individual characteristics

Note: All regressions including year and industry fixed effects

Source: Panel data set constructed using data from FOS ( 2009a ), EZV ( 2009 ), KOF ( 2005 ) and FOS ( 2009b )

*p < 0.10, **p < 0.05, ***p < 0.01

We start with a base regression, leaving out all trade variables. The results are reported in the first column of Table  2 . They show that the likelihood of becoming unemployed significantly depends on the individual’s qualifications (medium and low skills) and type of contract (part-time, temporary contract). 15 In this respect, we find also a positive relationship between the individuals’ likelihood of becoming unemployed and a short or medium tenure and for foreigners (typically due to a lack of local language skills). Married and widowed employees, on the other hand, are associated with a lower probability of becoming unemployed. Note that the coefficient for employment in an ICT-intensive industry or in a GAV industry is not significantly different from zero. The size of the coefficients in Table  2 can be interpreted as follows: Compared to a high-skilled worker, a low-skilled employee bears a 1.3% higher probability of becoming unemployed.

Columns (2) to (5) include levels and changes in the trade variables ( IM , EX ), also interacted with individuals’ skill levels (low-skilled, medium-skilled). Trade levels enter the estimation in logs, whereas “trade first differences” are calculated as the rate of year-to-year changes in percentage. We also add lagged trade variables (lagged by 1 year) to allow for a more deferred adjustment process. Note that, overall, the coefficients of worker and job characteristics do not change in a qualitative manner in these different specifications, nor do the GAV and ICT coefficients (except for the low skill level as a consequence of its interaction with the trade variables). We find some evidence (on the 5% significance level) for a significant effect of import levels on the probability of becoming unemployed for low-skilled employees: A 1% higher import value is associated with a 0.017% (0.016% for lagged imports) higher probability of becoming unemployed. In other words, low-skilled individuals who work in industries characterized by relatively large contemporaneous imports may, ceteris paribus, face a slightly greater likelihood of becoming unemployed. As shown in the fourth and fifth columns of Table  2 , no significant effects are found for first differences (i.e. changes ) in import and export values: A change in imports or exports in a certain industry does not significantly affect the probability of becoming unemployed.

We further investigate the impact of trade in the next subsection by using more refined trade variables and by including individual fixed effects to take into account any unobserved individual characteristics.

Refinement of the trade variables and inclusion of individual fixed effects

We now regress changes in the individual employment status on a number of trade variables, distinguishing between imports in finished and intermediate products and between trade with the North and the South. 16 We eliminate individuals’ characteristics as well as the GAV and ICT variables as we now use individual fixed effects. 17 We continue applying the linear probability model with time fixed effects. Standard errors are clustered by industry. We start with taking trade levels (in logs) as explanatory variables and then proceed to look at the rates of changes of the same variables. The results are reported in Tables  3 and 4 .

Linear regressions of changes in employment status on trade levels using individual fixed effects

Note: All regressions including time and individual fixed effects

Linear regressions of changes in employment status on trade differences using individual fixed effects

The estimates reported in Table  3 do not lend broad support for a positive relationship between the level of imports and the risk of becoming unemployed: Most coefficients of the import-level variables are not significantly different from zero. One exception at the 1% significance level is the coefficient of the 1-year lagged imports of final products from the South (fourth column): Individuals employed in an industry characterized by a 1% higher value of imports in this category encounter a 0.008% higher probability of becoming unemployed.

The results of the analogous estimations for first differences (i.e. rates of changes) in the import and export variables in a given industry are reported in Table  4 . We neither find an unambiguous relationship between changes in imports and the risk of unemployment nor is any of the relationship significant on the 1% level. However, we find that the coefficients for a lagged increase in final as well as intermediate imports from the South are significantly different from zero (on the 5% level, fourth column). Note that the economic impact of this effect is small: A 1% increase in import value, denoted as 0.01 in the dataset, leads to an increase in the probability of becoming unemployed by 0.004%. On this background, the fact that the coefficients of intermediate export products to the South in columns (2) and (4)—0.004 and 0.007—are significantly different from zero (and positive) should not be overvalued.

Sensitivity analyses

We finally try a number of different specifications to test the robustness of our results. Detailed results of these analyses are available from the Additional file  1 to this paper (Tables OA1 to OA5).

First, we replicate the results presented in Tables  2 , 3 and 4 using the logit regression model (Additional file  1 : Tables OA2 and OA3). Regarding the results in Table  2 , the logit estimates confirm a relationship between import levels and the likelihood of low-skilled workers of becoming unemployed: Coefficients are significantly different from zero (at the 5% level) with a positive sign. Also, we can confirm sign and significance level for the individual socio-demographic variables included and reported in Table  2 . Using a logit model with fixed effects, analogously to Tables  3 and 4 , we do not find any significant effects of the trade variables, regardless of whether we use levels or first differences as explanatory variables. 18 Hence, the logit estimations lead to qualitatively identical results as the linear regression model.

Second, we use the employment status (i.e. the information whether an individual is employed (0) or unemployed (1) in period t )—instead of the change of the employment status—as the dependent variable (summary statistics can be found in the second column of Table  1 ). As a start, we replicate the estimations described in Table  2 with the new dependent variable (see Additional file  1 : Table OA4). Again, we can confirm positive coefficients regarding import levels interacted with low-skilled labour for lagged imports (significantly different from zero at the 5% level). Furthermore, we use trade levels and first differences as explanatory variables in a model with individual fixed effects and find results that are qualitatively similar to those in Tables  3 and 4 . The results for the employment status as the dependent variable are reported in Additional file  1 : Table OA5. Most coefficients are not significantly different from zero. One exception is, again, the lagged level of final imports from the South with a coefficient of 0.016 (significantly different from zero at the 1% level). However, we also find a negative coefficient for the lagged first differences of intermediate imports from the North (− 0.010, significantly different from zero at the 5% level), leaving us with an ambiguous result regarding the effect of imports on the status of employment. 19

Third, and complementary to the analyses in Tables  3 and 4 (with again the change of the employment status as the dependent variable), we use second differences of the trade variables (e.g. [ IM t   −  ( IM t −  2 )/( IM t −  2 )]) instead of first differences and 2-year lags of trade levels instead of 1-year lags. All the results including the ones from Tables  3 and 4 are reported in Additional file  1 : Table OA1. We find a negative coefficient for the second differences without lags of intermediate imports from the North (− 0.006, significantly different from zero on the 5% level) in column 14. Furthermore, a positive coefficient is found for intermediate import levels from the North lagged by 2 years (0.013, significantly different from zero on the 5% level) in column 6. All the other import coefficients are insignificantly different from zero. 20 Thus, also in these regressions, we do not find unambiguous evidence for a positive relationship between imports and the probability of becoming unemployed.

Conclusions

This paper has been sparked by the omnipresent public concern in many industrial countries that international trade through specialization and outsourcing may cause income losses and unemployment, particularly for low-skilled labour. The striking increase in the Swiss unemployment rate of low-skilled relative to high-skilled labour from 1991 to 2014—with virtually no changes of relative wages—motivated us to focus our research on the relationship between international trade and unemployment for Switzerland.

Our assessment of the Swiss case does not confirm the public concerns. The econometric analysis of a data set of roughly 30,000 workers in the Swiss manufacturing sector from 1991 to 2008, which we link with the Swiss foreign trade statistics, does not, overall, support the presumption that an increase in imports has a statistically significant (and positive) effect on the probability of individuals of becoming unemployed, irrespective of their skills. Thus, we seem to be left with other well-established factors such as the level of skills, temporary employment or the length of tenure to explain the individuals’ risk of unemployment. The startling rise in the relative unemployment rate of low-skilled labour and, at the same time, the somewhat comforting constant relative wage rate of low-skilled labour in Switzerland from 1991 to 2014 still remains to be explained. Obvious candidates to look at more carefully would, in our view, be a skill-biased technological change for the relative unemployment rate and the compositional change in immigration for the relative wage rate. 21

Our investigation therefore only offers an initial basis for a more profound analysis of the labour market effects of trade or, more generally, of globalization for Switzerland. First, the fact that we find a weak (albeit small) positive relationship between low-skilled individuals working in industries characterized by a relatively high level of imports (particularly from the South) and the probability of their becoming unemployed may indicate something that we are not able to identify, given the limited statistical power of our data set which includes only a relatively small number of individuals who became unemployed. Second, we use exports as a control variable for (changes in) demand, because increasing imports have different effects on employment if they are combined with rising exports. This presents no problem as long as the domestic markets remain relatively small, which may, even in a small country such as Switzerland, not always be the case. If compatible data were available, a more sophisticated ratio could be used such as the import penetration ratio proposed by Autor et al. ( 2014 ) for the US industries.

Third, the fact that the individuals’ characteristics could only be linked to the two-digit SIC industry level, may even out a large amount of variation within industries: An individual’s employment status may be affected by imports on a sub-industry level, which might remain unobserved on the aggregated industry level. Also, and related to this, individuals employed in large multiproduct firms may be linked to an industry which is not really relevant to their actual occupation. Thus, an analysis based on more disaggregated, possibly even firm- or establishment-level, data may challenge our results.

On the other hand, this paper’s lack of findings in support of a strong positive relationship between import competition and the risk of unemployment could also be a consequence of the relatively low unemployment rate in Switzerland and the alleged high degree of flexibility in the Swiss labour market. If individuals lose their job because of import competition, but immediately find a new one, they never become unemployed. In this regard, it is interesting to note that our analysis of six announced mass-layoff cases in Swiss manufacturing due to globalization between 2001 and 2006 revealed exactly this situation: Only one quarter of the displaced workers were, in the end, dismissed by their companies and thus became, at least for a short term, unemployed (see Wyss, 2010 ). The others swiftly found a new job in the same or in another company or industry.

Additional file

Table OA1. Linear regressions of changes in employment status on trade variables using individual fixed effects. Table OA2. Logit regressions of changes in employment status on trade variables and individual characteristics, regression coefficients. Table OA3. Logit regressions of changes in employment status on trade variables using individual fixed effects, regression coefficients. Table OA4. Linear regressions of employment status on trade variables and individual characteristics, regression coefficients. Table OA5. Linear regressions of employment status on trade variables using individual fixed effects, regression coefficients. (DOCX 54 kb)

Acknowledgements

All persons who provided feedback as well as some minor support (data, editorial) to the different versions of the paper are mentioned in the acknowledgement.

We would like to thank the co-editor, Volker Grossmann, and two anonymous referees for their extremely helpful suggestions which led to a considerable improvement of the analysis in our paper. We also thank Marius Brülhart, David Green, Douglas A. Irwin, Ronald W. Jones, Peter Kugler, Christian Rutzer and George Sheldon for helpful feedback to earlier drafts as well as Dragan Filimonovic, Lukas Hohl and Hermione Miller-Moser for data and editorial support. We also benefited from discussions at the Annual Conference of the European Trade Study Group (ETSG), the Annual Meeting of the Swiss Society of Economics and Statistics and a lunch seminar at the Department of Economics of the University of British Columbia (UBC). Simone Wyss gratefully acknowledges financial support from the WWZ-Forum and the State Secretariat for Economic Affairs (SECO) during an early stage of the research project.

Industry dummies for ICT intensity and GAV intensity

Source: Own composition based on KOF ( 2005 ) and FOS ( 2002 ). The ICT dummy equals 1 if the investment in information and communication technology is above the average of 16% of total investment. The GAV dummy equals 1 if the coverage by collective labour contracts is above the average of 36%

Authors’ contributions

LM has implemented all the regressions in the second and the final version of the paper and given input to the first and second revisions of the paper. He also contributed to the letters to the editor and the referees. RW has written the first version of the paper and re-written the paper as part of the first and second revisions. He also wrote the letters to the editor and the referees. RW and LM have been closely working together in the first and second revisions of the paper. SM has collected the data and implemented the econometric analysis for the first version of the paper. She also answered questions regarding the data and the original regressions throughout the revision process. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests

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Interestingly, this point of view is emphasized, for example, in an early document of the U.S. Department of State ( 1945 ) that formed the basis of the creation of the General Agreement on Tariffs and Trade (GATT). The title “Proposals of Expansion of World Trade and Employment” is revealing.

For early contributions see, for example, Berman et al. ( 1994 ), Borjas et al. ( 1991 ), Davis ( 1998a , 1998b ), Feenstra ( 1998 , 2010 ), Krugman ( 1995 , 2000 ), Lawrence and Slaughter ( 1993 ), Leamer ( 1998 , 2000 ) or Murphy and Welch ( 1991 ).

Whereas the overall effect on unemployment remains ambiguous or depends on parameters in these models, Dutt et al. ( 2009 ) predict a reduction in overall unemployment as a result of trade.

Moser et al. ( 2011 ) find a small (negative) effect from a reduction in the international competitiveness of firms on job flows for Germany, and more so on job creation rather than on job destruction.

See for example Feenstra and Hanson ( 1999 ), Hijzen et al. ( 2005 ) and OECD ( 2007 ) for a broad overview.

See Suarez ( 1998 ) and Müller, Marti and Nieuwkoop ( 2002 ) who focus on trade and wages. Other studies such as Sheldon ( 2007 ), Puhani ( 2003 ) and Arvanitis ( 2005 ) analyze shifts in supply and demand on the Swiss labour market, but do not explicitly investigate the effects of trade.

Note that, throughout this paper, high-skilled (H) is defined as people with tertiary education (ISCED 5-6: university, college of higher education (Fachhochschule) and school of higher education (Höhere Fachschule). Low-skilled (L) is defined as individuals with primary or lower secondary education (ISCED 1-2: mandatory education with no professional training qualification). Medium-skilled (M) is defined as individuals with upper secondary education (ISCED 3-4: professional education which, most importantly, includes completed apprenticeships).

Note that U L is defined as the unemployment rate of the 25–64-year-olds with “below upper secondary education”, whereas U H is defined as the unemployment rate of the 25–64-year-olds with “tertiary education”; see OECD ( 2007 , 2015 ).

Interestingly, South Korea shows the lowest absolute rate of relative unemployment in 2014 despite the considerable increase reported in Fig.  2 . On the other extreme, the Czech Republic shows a fall of the relative unemployment rate from 1991 to 2014, but remains the country with the highest ratio in 2014; note that, in 2014, U L ( U H ) equaled 20.7% (2.6%) for this country (see OECD ( 2015 , Table A5.4a)).

Baldwin ( 1994 , p. 73) once called the view that trade affects the number of jobs as “utter nonsense from the medium- or long-run economic perspectives”. Davidson et al. ( 1999 ) would, however, add that in a trade model with labour market frictions this is, in principle, possible, and mainly an empirical question (p. 273).

Dutt et al., 2009 , p. 33) emphasize a “fairly strong and robust empirical support (…) for the Ricardian prediction that trade openness and unemployment are negatively related across all countries”. The intuition is that trade raises productivity which increases the search effort of employees and employers that, in turn, reduces unemployment.

Note that, during this period, Switzerland or EFTA (to whom Switzerland belongs) established free trade agreements with approximately 20 countries (e.g. with Turkey (1992), Mexico (2001), South Korea (2006) and China (2014)), reached two bilateral agreements with the EU (1999, 2004) and was—through its free trade agreement (1972) and the two bilateral agreements with the EU—also affected by the enlargement of the EU by 13 new member countries in 2004, 2007 and 2013. Finally, there are approximately 30 countries (including China in 2001) that became additional members of the WTO, after its foundation in 1995 until 2008, and thus achieved improved mutual market access with Switzerland.

The deviation to all 33,000 individuals mentioned above is due to the fact that many individuals exhibit missing values in at least one of the variables of interest.

See Appendix : Table 5 regarding the assignment of individual industries. GAV stands for “Gesamt-Arbeits-Vertrag” and means collective bargaining contract; ICT stands for “Information and Communication Technology”.

Here and in the following we consider coefficients as significantly different from zero if they reach at least the 5% level.

Note that Feenstra and Hanson ( 2003 ) also base their analysis on annual changes broken down to final and intermediate imports. Anderton and Brenton ( 1999 ) differentiate between imports from industrial and low-wage countries. Based on the Swiss Trade Statistics, intermediates are defined as items in the category “raw materials”, “semi-finished products” and “intermediate goods”. An alternative definition based on input-output tables is currently not feasible as relevant statistics are not available. We also distinguish between imports from the North (industrial countries) and the South (developing countries).

Individuals remain in the same industry throughout the observed period. Hence, ICT and GAV variables are omitted when using individual fixed effects.

One may observe that the logit analysis implies a positive relationship (significantly different from zero at the 5% level) between low-skilled individuals working in GAV-industries (interacted variable) and their probability of becoming unemployed.

Also the relationship between the employment status and exports (level, change) remains ambiguous in the analysis.

Note that we also use 1-year leads of the trade variables as “placebo tests”. We refrain from showing those results in the Additional file  1 as we do not find any significant results.

For analyses of skill-biased technological change, see the seminal contributions by Berman et al. ( 1994 , 1998 ) and Krugman ( 2000 ) as well as, for an attempt to disentangle trade and technology effects, Autor et al. ( 2015 ).

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Delve into the intricate dynamics of trade and unemployment in this comprehensive guide through the world of macroeconomics. Gain a fundamental understanding of how international trade interacts with the job market and informs unemployment rates. Discover the effects and consequences of trade frictions, and evaluate varying trade policies in light of their implications on employment. This resource brings substantial insight into the interconnected realms of trade, unemployment, and macroeconomics, dissecting their critical intersections within the broader economic landscape. Moreover, it introduces an engaging exploration of globalisation's role in these phenomena, punctuated by revealing historical case studies and comparative analyses.

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Trade and Unemployment: An Overview in Macroeconomics

Trade and unemployment are two concepts deeply intertwined in the study of macroeconomics. They influence each other in many ways and forming a clear understanding of this relationship can provide valuable insights into the dynamics of economies worldwide.

Basic Understanding: Trade and Unemployment

Unemployment refers to the state of being without a job despite actively seeking work, while trade involves the exchange of goods and services, either within a country (domestic trade or internal trade) or between different countries (international trade). A close look at these definitions reveal the underlying linkage between these two concepts. Increased trade could lead to job opportunities, thereby reducing unemployment. However, this is not always the case, as the effect of trade on employment could be positive or negative depending on various factors.

Demand-pull inflation is a macroeconomic concept that describes a situation where the demand for goods and services in an economy outpaces supply, leading to increases in prices. In such a scenario, increased trade could lead to higher demand for labour, therefore reducing unemployment rates.

Suppose a country, let's call it 'Country A,' sees an inking in the demand for its goods from other nations as it is able to produce high-quality products at a reasonable cost. As a result, firms within Country A would require more labour to meet this increased demand, thereby reducing unemployment.

Core Concepts of Unemployment in International Trade

The relationship between unemployment and international trade becomes a bit more complex. Trade can affect the level of unemployment in a country depending on several factors including economic structure, labour market flexibility, and the type and composition of traded goods.

• Economic Structure : Countries focussed on manufacturing and industrial production may see a decrease in unemployment due to increase in foreign trade as they require a large workforce. However, countries focussed on services may not experience the same effect.
• Labour Market Flexibility : This refers to how easily labour can move between different sectors in a country. High labour market flexibility tends to result in lesser unemployment because workers can easily shift from sectors that are declining due to foreign trade to those that are booming.
• Type and Composition of Traded Goods : The impact of trade on unemployment can also significantly depend on the type and composition of goods being traded. For instance, if a country exports labour-intensive goods and imports capital-intensive ones, then foreign trade can reduce unemployment.

In other words, when evaluating the impact of trade on employment, it's essential to understand the structure and dynamics of the country's economy and the labour market. Furthermore, the type of goods and services traded also offer valuable insights.

Trade Equilibriums and Their Influence on Job Market

The relationship between trade and employment can be better understood with the concept of trade equilibrium. When a country reaches trade equilibrium, it's neither running a trade surplus nor a deficit, its import expenses equal export revenue. This balance can affect the job market in several ways.

Consider a scenario where a country's exports of goods and services surpass its imports (a trade surplus). This implies that the country's domestic industries are performing well, and thus there may be a high demand for labour to meet the production requirements which could reduce unemployment. However, if the country's exports decline and it ends up importing more than it exports (trade deficit), industries might lay off workers, leading to increased unemployment.

It is important to note that the impact of trade on employment is not static and changes over time as countries adapt to new technologies and shifts in global trade patterns. Thus, a full understanding of this dynamic relationship requires consistent study and analysis.

Trade Frictions and Unemployment: A Macroeconomic Analysis

A vital dimension in study of macroeconomics is understanding the impact of trade frictions on unemployment. Trade frictions, which refer to the various barriers and impediments to trade, are often a significant disruptive factor, capable of influencing labour market dynamics and, by extension, unemployment rates.

Causes and Consequences of Trade Frictions

Trade frictions can stem from numerous sources such as tariffs, quotas, trade restrictions, and even logistical challenges like inadequate infrastructure or inefficient customs procedures.

• Tariffs and Quotas : Imposed by governments to protect domestic industries from foreign competition. However, high tariffs and strict quotas can disrupt the flow of goods and services, leading to trade friction.
• Trade Restrictions : These include measures like embargos or boycotts. While they serve political or economic goals, they significantly hamper trade relations between nations.
• Inefficient Customs Procedures : These cause delays and increase costs associated with cross-border trade, thereby creating trade friction.

Trade Frictions: They refer to the various barriers and impediments to trade which can be policy-induced, like tariffs and quotas, or logistical, like inefficient customs procedures.

But what are the consequences of such frictions? The most immediate and observable impact is a reduction in the volume of trade between nations. Due to increased costs or complexities arising from these frictions, countries might find it less profitable to engage in international trade, leading to a decline in trade volumes.

A related consequence is that as trade volumes decrease, countries might turn inward and increase reliance on domestic industries. This re-orientation can lead to a change in the structure of an economy, often resulting in what's known as economic restructuring .

Impact of Trade Frictions on Unemployment

The influence of trade frictions on unemployment isn't straightforward and tends to be context-dependent. To put it simply, if trade frictions lead to limitations on imports and provoke an increase in domestic production, this might necessitate more labour thus reducing unemployment.

However, if the domestic industry isn't equipped to increase production or if the demand for domestic goods isn't strong enough to compensate for reduced trade, trade frictions might lead to stagnation and increased unemployment. This is particularly the case for economies heavily dependent on foreign trade. For instance, an export-led growth strategy would suffer tremendously under high trade friction scenarios, potentially leading to increased unemployment.

Historical Case Studies: Trade Frictions Leading to Unemployment

Historical cases offer invaluable insights into how trade frictions can lead to unemployment. For instance, consider the case of the automotive industry in the US in the 1980s. Japanese car manufacturers were outpacing their American counterparts, leading to increased imports of Japanese cars into the US. In response, the US imposed strict quotas on Japanese car imports to protect its domestic industry.

This led to a reduction in the total volume of cars being sold in the US, as Japanese manufacturers couldn't meet the high demand despite having the capacity to do so, and American manufacturers were not prepared to meet the excess demand. This mismatch led to a slowdown in the auto manufacturing industry, directly contributing to increased unemployment rates within the sector.

Another indicating case is the US-China trade war that started in 2018. This trade dispute, marked by high tariffs and trade restrictions imposed reciprocally, has had notable effects on unemployment.

Due to the tariffs, several industries in the US which relied on Chinese inputs saw their production costs rise sharply. Unable to pass on this increase in costs to consumers due to price sensitivity, many firms cut down on their workforce, thereby increasing unemployment.

These examples illustrate that trade frictions can indeed cause rises in unemployment levels. However, each situation is unique, and numerous variables come into play.

Investigating the Effects of Trade on Unemployment

Trade has a significant influence on unemployment rates across the globe. It's essential to examine this relationship closely to better comprehend the dynamics of international economies and understand the larger implications of trade policies. The effect of trade on unemployment is multifaceted and can be analysed from both positive and negative perspectives.

The Positive Effects of International Trade on Unemployment

International trade can play a vital role in reducing unemployment levels. By promoting economic activity and stimulating growth, trade can foster job creation and contribute to labour market stability. Here's how it accomplishes this:

• Increasing Demand: International trade allows countries to access larger markets, which can increase the demand for their goods and services. This elevated demand often necessitates an uptick in production levels, which can lead to job creation and subsequently lower unemployment rates.
• Fostering Economic Growth: Foreign trade can be a key driver of economic growth. By generating additional income and profits for businesses, trade can increase investment in capital and labour, thereby creating more job opportunities.
• Promoting Specialisation: International trade encourages nations to specialise in the production of goods and services they can produce more efficiently. By focusing on areas where they have a comparative advantage, countries can boost productivity, which may increase demand for specific skill sets and decrease unemployment rates within these sectors.

Additionally, international trade can lead to the transfer of technology and skills between countries. Technological advancements can result in upgraded industries which may need qualified workforce leading to a decrease in unemployment rates. However, this impact can be complex as it might also lead to job losses if certain skills become redundant due to technological upgrades.

Adverse Effects of Trade on Job Markets

While trade can bring about positive implications for unemployment, it can also produce adverse effects on job markets. Disruptions caused by global trade can lead to job displacements and increased unemployment rates.

• Structural Unemployment: International trade can result in structural changes to an economy, especially when a nation transitions from being primarily agricultural to increasingly industrial or service-oriented. These shifts may render some jobs obsolete, resulting in structural unemployment.
• Competition and Downsizing: Increased competition from international markets can make it difficult for domestic firms to compete. This pressure can lead businesses to downsize their operations or even shut down, causing job losses.
• Trade Deficits: When a country consistently imports more than it exports, it could witness a significant outflow of capital, which may in turn limit the growth of domestic industries and lead to higher unemployment.

Moreover, not all job losses can be recouped by creating new roles in other sectors. Workers who lose their jobs may not possess the skills needed for new industries or may live in areas where job opportunities are scarce. In such instances, trade can contribute to sustained high unemployment rates.

A Comparative Study: Effects of Local and International Trade on Unemployment

Focussing on the comparison between the effects of local and international trade on unemployment reveals compelling insights. Local trade, or trade within a country's borders, may not have the same impact as international trade due to distinct dynamics.

Local trade, by its very nature, limits exposure to global competitive pressures. Domestic firms primarily compete with other domestic firms. While competition exists, it's often mitigated by common regulations, standards and costs. Therefore, local trade might not have the same level of job displacement as seen in international trade. However, evolutions in local markets could still lead to some job losses as industries expand, contract or evolve.

On the other hand, international trade leads to direct competition between domestic firms and foreign counterparts. This competition can be more intense due to factors like varying costs of labour, diverse regulations and standards, and different levels of access to technologies or resources. Hence, the potential for job displacement and unemployment can be higher. However, international trade also offers opportunities for job creation that might not be accessible through local trade alone. In essence, the comparative effects of local and international trade on unemployment are contingent on numerous factors.

Causes of Trade and Unemployment: A Comprehensive Discussion

Trade and unemployment, two critical aspects of macroeconomics, are affected by a wide array of factors. From trade policies and economic conditions to globalisation and technological advancements, a variety of elements shape the intricate dynamics of trade and unemployment. Let's delve deeper into these causative factors.

Understanding the Relationship Between Trade Policies and Unemployment

Trade policies are one of the key variables that shape the landscape of international trade and have consequential impacts on unemployment levels. These policies, which can range from tariffs and import quotas to economic sanctions and export subsidies, can influence how and to what extent countries engage in international trade.

Consider tariffs , which are essentially taxes imposed on imported goods. High tariffs can make imported goods more expensive, encouraging consumers to buy domestically produced goods instead. This increased demand for local products could stimulate domestic industry and potentially increase employment. In fact, the economic model known as the Stolper-Samuelson theorem holds that import restrictions, such as tariffs, can result in higher real wages for individuals working in sectors that produce protected goods. However, this theorem assumes that resources, including labour, can freely move between sectors - an assumption that doesn't always hold in reality.

Import quotas , which directly limit the quantity of a certain good that can be imported, can have a similar impact. By reducing competition from foreign goods, import quotas allow domestic industries to sell more, which could theoretically increase employment levels in these sectors.

However, the effects of trade policies on unemployment are not always positive. Economic sanctions , for example, can severely restrict a country’s ability to trade and often lead to increases in unemployment. Similarly, export subsidies may initially boost employment in the subsidized industry. Still, they can lead to inefficiencies and long-term negative impacts on employment by allocating resources away from more productive areas of the economy.

Protectionist Policies : These are measures taken by a government to restrict or restrain international trade, often with the intent of protecting local businesses and jobs from foreign competition. Policies such as tariffs and quotas can, however, distort trade and reduce economic efficiency.

Macroeconomic Factors Influencing Trade and Unemployment

Several macroeconomic factors also feed into the relationship between trade and unemployment. These include currency exchange rates, inflation, fiscal and monetary policy, technological changes, and business cycles, to mention a few.

The exchange rate of a country's currency can have a profound impact on its international trade. When a currency is strong compared to that of its trading partners, its exports become more expensive, and imports become cheaper. This situation can lead to a decrease in demand for domestically produced goods and, consequently, a potential increase in unemployment.

Inflation also plays a part. Higher prices can make a country's exports less competitive on the international market, dampening demand for these goods and leading to lower levels of production and possible layoffs. Similarly, the economic policies of a country, including fiscal and monetary policies, can affect trade and unemployment. For instance, high interest rates can reduce domestic investment, stifery economic growth, and increase unemployment rates.

The business cycle , which refers to the fluctuations in economic activity over time, can also influence trade and unemployment. During downturns of the business cycle, demand for goods and services often declines, leading to reduced trade and increased unemployment.

Technological changes can also have significant effects on trade and unemployment. Technological advancements that improve productivity can boost a country's exports and potentially increase employment in sectors that adopt these technologies. However, technological changes can also make certain jobs obsolete, leading to increased unemployment in affected industries.

The Role of Globalisation in Trade and Unemployment

Globalisation has been a transformative force in shaping trade and unemployment patterns around the world over the past few decades. Globalisation, which can broadly be understood as the increased interconnectedness and interdependence of the world's economies, is closely intertwined with changes in trade and unemployment.

From a trade perspective, globalisation has led to a dramatic increase in international trade volumes. As barriers to trade have come down, and transport and communication technologies have evolved, goods, services, and capital now flow more freely across borders than ever before. This phenomenon has led to increased competition, industry realignment and, importantly for our discussion, changes in employment patterns.

The impact of globalisation on employment is multifaceted. New trade opportunities facilitated by globalisation can create jobs in certain sectors, contributing to a decrease in unemployment levels. However, competition from inexpensive imports can cause domestic industries to contract, leading to job losses and potential increases in unemployment.

Moreover, globalisation often precipitates shifts in the structure of an economy. Such structural shifts can cause what's known as structural unemployment – unemployment resulting from changes in the fundamental composition of the economy. For instance, increased trade might boost the service sector while diminishing the manufacturing industry in a particular country. Workers displaced from shrinking sectors might not have the skills needed for jobs in expanding sectors, leading to structural unemployment.

In summary, understanding the relationship between trade and unemployment is a complex task considering the numerous factors involved. From trade policies and economic conditions to globalisation, these aspects together determine the patterns of trade and unemployment that we observe in the macroeconomic data.

The Interplay of Trade and Unemployment in Macroeconomics

In macroeconomics, the interplay between trade and unemployment is a crucial subject of discussion. Essentially, the relationship between these two macroeconomic indicators is dynamic and complex, influenced by a myriad of variables including government policies, economic cycles, and global market trends.

Evaluating the Role of Unemployment in Trade Theory

In traditional trade theory, unemployment plays a significant role. Classical models of trade, assuming full employment, largely neglect the impacts of unemployment. But contemporary trade theories, such as New Trade Theory, recognise the intertwining nature of trade and unemployment. How unemployment affects the dynamics of trade can be analysed from a number of perspectives.

Employment levels within an economy can directly influence its trade flows. High unemployment rates can reduce a country's domestic demand, making it more dependent on exports for economic growth. Furthermore, unemployment often leads to a reduction in wages. Lower wages can increase a country's international competitiveness by reducing production costs, making its exports more attractive on the global market.

However, these impacts of unemployment on trade could also vary across industries. Sectors with high unemployment could experience an increase in exports due to lower wages, while those with low unemployment might witness a decrease in exports if wage pressures increase production costs.

The effects of unemployment on trade can also be shaped by how unemployment benefits are structured in an economy. In countries where such benefits are generous, workers might have less incentive to accept low-wage jobs, potentially increasing production costs and reducing international competitiveness. Conversely, in countries with lower unemployment benefits, the pressures on workers to accept any available job could keep wages low and boost competitiveness.

Consider a country with high unemployment benefits. Workers in this country might not readily accept low-wage jobs, holding out for higher-paying ones instead. This could result in a slower adjustment of wages and a potential increase in production costs. In such a scenario, if this country tries to export goods in an internationally competitive market, it might struggle because of its higher costs, translating to more expensive export goods. On the other hand, in a country with low unemployment benefits, workers might be compelled to accept lower-wage jobs, keeping production costs and, therefore, export prices low. This could enhance the country's international competitiveness.

Trade Policies: Implications for Job Creation and Unemployment

Trade policies are a crucial link between trade and unemployment. A range of policies, from tariffs and quotas to free trade agreements and export subsidies, can influence trade flows and employment levels simultaneously.

For instance, tariffs and quotas, forms of protectionist policies, are designed to shield domestic industries from foreign competition. By restricting imports, these policies can boost domestic production, which in turn could stimulate job creation and reduce unemployment. However, these benefits could be offset by the potential downsides. High tariffs or stringent quotas could increase costs for industries that rely on imported input goods, potentially leading to job losses in these sectors.

Consider an industry that relies on imported raw materials. When a tariff is imposed on these raw materials, their cost increases. If these additional costs can't be passed onto consumers, businesses might be forced to cut costs elsewhere, potentially leading to job losses. This demonstrates how while protectionist policies might boost employment in certain sectors, they could simultaneously lead to job losses in others.

The Balance Between Trade and Unemployment in Sustaining Economic Health

The balance between trade and unemployment plays a vital role in maintaining the health and stability of an economy. Indeed, the interaction between these two macroeconomic variables can have profound impacts on economic growth and development.

Healthy trade relations, characterised by a conducive mix of imports and exports, can stimulate economic growth by encouraging competitiveness, improving productivity, and promoting technological advancement. However, certain trade dynamics such as high trade deficits (where imports significantly outweigh exports) could lead to a drain of domestic resources, weakening economic health.

Unemployment, on the other hand, is a critical indicator of an economy's health. High unemployment rates could signal an underutilisation of the labour force, an inefficient allocation of resources and could lead to slower economic growth. Furthermore, persistent high unemployment could create social issues and exacerbate income inequality, which in turn could harm an economy’s sustainable development.

Finding a balance between trade and unemployment involves managing the fine line between the benefits of open trade (and inherent competition) and the possible detriment to domestic industries and employment. Trade liberalisation, for example, can be beneficial in boosting competitiveness and improving economic efficiency, but it could also lead to job losses in sectors exposed to international competition. Similarly, protectionist policies could preserve domestic jobs in the short term, but they might suppress economic efficiency and hinder long-term growth. Thus, balancing the imperatives of trade and employment is indeed a complex, yet vital, task in macroeconomic management.

Overall, understanding the intricate relationship between trade and unemployment is crucial for policymakers, economists, and market participants. It's a critical lens through which the health of an economy can be gauged, offering valuable insights for decision-making in economic policy, business strategy, and investment planning.

Trade and Unemployment - Key takeaways

• Trade frictions refer to obstacles preventing free trade between nations. Such frictions can lead to a decline in trade volumes.
• The impact of trade frictions on unemployment can vary. If the domestic industry can compensate for reduced trade, unemployment might decrease. However, in scenarios where the domestic industry is unable to increase production or the demand for domestic goods isn't strong enough, trade frictions might increase unemployment.
• Historical cases, including the US automotive industry in the 1980s amid increased imports of Japanese cars and the US-China trade war, demonstrate that trade frictions can lead to increased unemployment.
• International trade can have both positive and adverse effects on unemployment. It can foster job creation as it stimulates economic growth and increases demand. However, global trade can also lead to job losses due to structural changes, competition and downsizing, and trade deficits.
• The role of trade policies in shaping the relationship between trade and unemployment is key. Policies such as tariffs and import quotas can influence domestic industries and employment levels. However, measures like economic sanctions can restrict a country's trade ability, leading to increased unemployment.
• Other factors influencing trade and unemployment include macroeconomic elements such as currency exchange rates, inflation, fiscal and monetary policy, technological changes, and business cycles.
• Globalisation, understood as the increased interconnectedness of the world's economies, has led to changes in employment patterns, with new trade opportunities potentially decreasing unemployment levels but also potentially increasing them due to intensified competition.

Flashcards in Trade and Unemployment 15

Globalisation increases international trade volumes, leading to increased competition and changes in employment patterns. While it can create jobs due to new trade opportunities, it can also cause job losses due to competition from cheap imports and cause structural unemployment due to shifts in the economy.

High unemployment reduces domestic demand, making a country more dependent on exports for economic growth. It also often leads to a reduction in wages, which increases a country's international competitiveness by reducing production costs and making its exports attractive on the global market.

In macroeconomics, the relationship between trade and unemployment can be both positive and negative. Increased trade can create job opportunities, reducing unemployment. However, it can also lead to job loss, depending on economic structure, labour market flexibility, and the type and composition of traded goods.

Local trade may mitigate job displacement due to common regulations and standards. International trade opens up to global competition, leading to potential job displacement. However, it also offers job creation opportunities not accessible through local trade.

International Trade can reduce unemployment by promoting economic growth, fostering specialization, and increasing demand. It can also lead to the transfer of technology and skills between countries which can decrease unemployment rates.

A robust exchange rate makes exports expensive and imports cheaper, which can increase unemployment. Inflation can also decrease demand making exports less competitive. High interest rates can stifle economic growth. Business cycle downturns often lead to reduced trade and increased unemployment.

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• Original article
• Open access
• Published: 05 June 2018

International trade and unemployment: towards an investigation of the Swiss case

• Lukas Mohler 1 ,
• Rolf Weder 1 &
• Simone Wyss 1  

Swiss Journal of Economics and Statistics volume  154 , Article number:  10 ( 2018 ) Cite this article

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The topic of this paper has been motivated by the rising unemployment rate of low-skilled relative to high-skilled labour in Switzerland. Between 1991 and 2014, Switzerland experienced the highest relative increase in the low-skilled unemployment rate among all OECD countries. A natural culprit for this development is “globalization” as indicated by some mass layoffs in Switzerland and as commonly voiced in public debates all over the world. Our analysis, which is based on panel data covering the years 1991 to 2008 and approximately 33,000 individuals employed in the Swiss manufacturing sector, does not, however, confirm this presumption. We do not find strong evidence for a positive relationship between import competition and (low-skilled) individuals’ likelihood of becoming unemployed.

Introduction

The relationship between international trade and employment has always been controversial. Trade economists have traditionally emphasized the efficiency-enhancing effects of international trade with no impact on total employment, at least in the medium and long term. Politicians and members of governments, in contrast, typically believe in an employment-increasing effect of international trade and often point to the numbers of jobs created by rising exports. Footnote 1 In the eyes of the public, however, international trade entails the danger of job destruction, particularly through increased imports. Trade economists agree that international trade may have distributional effects within countries. But they typically identify these effects in terms of changing factor prices: Low-skilled labour may, for example, lose ground—relatively and absolutely—in a high-income country as a result of international trade with (low-skilled) labour-abundant countries such as China or India.

In this paper, we investigate whether international trade is indeed linked to the likelihood of becoming unemployed. The focus on unemployment is motivated by our observation that the Swiss unemployment rate between low-skilled labour and high-skilled labour increased faster than that of any other OECD country between 1991 and 2014, with virtually no change in the relative wage rate between the same two groups of people. We use a representative panel data set for employees in the Swiss manufacturing sector, covering the period from 1991 to 2008, and link it to international trade data. We control for a number of individual characteristics, particularly regarding skills, age and experience, as well as industry properties. The analysis indicates that, for the Swiss economy, rising or high levels of imports do not seem to be a driving force behind the probability of becoming unemployed. Individual characteristics such as a short length of tenure, part-time employment, and low skills are, however, confirmed to be important factors that positively affect the individual’s risk of becoming unemployed.

Thus, the paper adds to the rapidly expanding literature on whether international trade is an important cause of the increase in the wage and unemployment gaps between skilled and unskilled labour that have been observed in the USA and some other countries since the 1980s. Footnote 2 We know since Stolper and Samuelson ( 1941 ) and, more generally, since Jones ( 1965 ) that trade liberalization tends to have a strong negative impact on some real factor prices and, if these are inflexible or search costs are involved, also on factor market clearing, as shown by Davis ( 1998b ), Davidson et al. ( 1999 ), and Egger and Kreickemeier ( 2008 ). Moreover, Feenstra and Hanson ( 2003 ) argue that the effects from trade in intermediate inputs may be similar to those caused by skill-biased technological change which is often made responsible for the wage gap in the US economy. Autor et al. ( 2013 ) found significant negative labour-market effects on the US economy of international trade between the USA and China and conclude: “Rising imports cause higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries” (p. 2121).

Recent trade models, which introduce some labour market frictions, as used by Brecher and Chen ( 2010 ), Davis and Harrigan ( 2011 ), Helpman and Itskhoki ( 2010 ), Helpman et al. ( 2010 ), Larch and Lechthaler ( 2011 ), Mitra and Ranjan ( 2010 ), or Ranjan ( 2012 ), imply that relative unemployment between different types of labour may be affected by trade liberalization in a variety of ways. Moreover, these models come to the conclusion that international trade may also affect the overall unemployment level in an economy—positively or negatively. Footnote 3 In empirical analyses, a negative effect of trade on overall unemployment is found by Felbermayr et al. ( 2011 ) and by Gozgor ( 2014 ) in cross-country analyses, by Hasan et al. ( 2012 ) for India and by Francis and Zheng ( 2011 ) for NAFTA. Footnote 4 Chusseau et al. ( 2010 )—in a cross-country analysis—and Horgos ( 2012 )—for Germany—show that in the case of inflexible factor prices an increase in the relative unemployment rate between skilled and unskilled labour can to some extent be linked to trade—which the former call an “inequality-unemployment trade-off”. Fugazza et al. ( 2014 ) find a positive relationship between trade and unemployment in a panel of 97 countries if countries have “a comparative advantage in sectors that have high labour market frictions” (p. 1).

Compared to the existing literature, our empirical investigation is of particular interest for three reasons. First, it focuses on a small country whose international trade reflects a large share of its domestic output. The Krugman ( 2000 ) critique that a country’s trade volume is typically too small to explain effects on different types of labour hardly applies in this case (or at least to a much lesser extent). Second, our paper’s emphasis is on the unemployment rate, and not on wages as underlined by the majority of empirical research studies. Footnote 5 This focus is in line with the recent shift in research interest among trade theorists and labour-market economists as well as with the stylized facts applying to the Swiss economy. Finally, we add to the limited literature on Switzerland in this field. The relationship between international trade and unemployment has, to our knowledge, not been analysed to date for the Swiss case. Footnote 6

The remainder of the paper is as follows. The “ Background ” section presents stylized facts that explain our research strategy. The “ Methods ” section briefly describes our research methodology. The “ Results and discussion ” section presents the main results of the econometric analysis. The “ Conclusion s” section concludes.

Past research has been motivated by an inquiry into the impact of international trade on relative wages . Feenstra ( 2010 , pp. 10), for example, describes and discusses the development of the wages of “nonproduction” relative to “production” workers in US manufacturing from 1958 to 2006. If we interpret this ratio as the relative wage rate of high-skilled to low-skilled labour, the data clearly shows that the relative wages of unskilled labour fell considerably and constantly from 1986 to 2000. This observation has been the basis for the expanding literature on trade and the wage gap in the USA that also sparked our research interest with its focus on Switzerland.

Such a development is, however, not observable for Switzerland. Using Swiss labour market panel data (Swiss Labor Force Statistic, SLFS) and the UNESCO skill classification scheme (International Standard Classification of Education, ISCED-97), Footnote 7 we calculated both the median gross wage rate of high-skilled ( W H ) and low-skilled ( W L ) labour, and the unemployment rate for the same two groups, i.e. U H and U L , for the period 1991 to 2014. Figure  1 shows that, over this period, the U L / U H rose with a compounded annual growth rate (CAGR) of 2%, whilst the W H / W L remained roughly constant with a CAGR of − 0.3%. Thus, Fig.  1 serves as a motivation to study a possible relationship between international trade and (changes in) the relative unemployment of low-skilled and high-skilled labour in the Swiss case.

Evolution of relative wages and relative unemployment in Switzerland. Source: Own calculations based on FOS ( 2008 ), Wyss ( 2010 ) FOS ( 2016a , b )

A comparison among 21 OECD countries implies that there is no other country in which U L / U H has grown as fast as in Switzerland from 1991 to 2014. Footnote 8 Figure  2 shows a CAGR of 4.8% of this ratio from 1991 to 2014 (top panel). It reveals that other countries such as South Korea or Germany also experienced a large rise in this ratio, whereas countries like the Netherlands or Belgium but also the USA or Canada demonstrate a decrease of the relative unemployment of low-skilled labour. Absolute numbers in the OECD data indicate that the Swiss U L increased from 1.2% (1991) to 8.8% (2014), whereas U H increased to a much smaller extent over this period (from 1.3 to 3.2%). Note, however, that the absolute value of the relative unemployment rate in Switzerland (2.7) is not extremely high, but rather puts the country in the middle of the reported OECD countries as shown in Fig.  2 (bottom panel). Given the strong and yet unbroken trend in the Swiss relative unemployment rate, it is of highest interest to assess whether trade may be a driving force of this development. Footnote 9

Average growth rate of relative unemployment (top panel, 1991–2014) and absolute value of relative unemployment (bottom panel, 2014) in OECD countries. Note: These are OECD countries for which data were available for the years considered. For the comparison in the top-panel, compounded average growth rates were taken. Source: Own calculations based on OECD (2007) and OECD (2015), Tables A8.4a and A5.4a, respectively

Trade theory stresses the importance of international trade in improving an economy’s allocation of resources, and not the creation of additional jobs. In a standard trade model, there is no expected link between trade liberalization and the total number of jobs in an economy. Footnote 10 The argument trade economists traditionally have put forward is that whilst more trade leads to some jobs being destroyed in the import-competing sector of an economy, new jobs are simultaneously being generated in the export sector.

An increase in unemployment is, however, compatible with the traditional trade theory if we, for example, extend a Heckscher-Ohlin type model to allow for some factor price inflexibility as shown by Davis ( 1998b ) or, adding trade in intermediate inputs, by Egger and Kreickemeier ( 2008 ). The reason is that trade typically leads to a decrease in the relative demand for low-skilled labour in a (human) capital-rich country. If the induced fall of the price of low-skilled labour—predicted by the Stolper Samuelson Theorem—is prevented by labour market rigidities, unemployment for low-skilled labour tends to rise with trade liberalization.

Recent trade models expanded in this direction allowing for a number of labour market frictions and/or using intra-industry trade models based on heterogeneous firms and job-specific rents. It turns out that, in these set-ups, trade liberalization may indeed raise unemployment of particular types of labour and affect overall unemployment in an economy. In Brecher and Chen ( 2010 ), for example, the unemployment rates of low- and high-skilled labour “often move in opposite directions” (p. 990), whereas the change of aggregate unemployment is ambiguous. Davis and Harrigan ( 2011 ) argue that, in their model, trade liberalization may destroy a considerable share of highly paid jobs without, however, necessarily affecting overall unemployment. Helpman and Itskhoki ( 2010 , p. 1100) find the surprising result that “[T]he opening to trade raises a country’s rate of unemployment if its relative labour market frictions in the differentiated sector are low.” And Hasan et al. ( 2012 , p. 269) come, based on their empirical study of India, to the conclusion: “Moreover, our industry-level analysis indicates that workers in industries experiencing greater reductions in trade protection were less likely to become unemployed, especially in net exporting industries.” Footnote 11

The focus of our paper is empirical. We seek to explain the employment status of individuals over time, i.e. whether they become unemployed or not, by changes and levels of imports and exports, controlling for various individual characteristics and industry factors. The explained variable (i.e. the individual’s status, y i ) is qualitative in nature and takes a value of 1 if an individual becomes unemployed in a certain year and 0 otherwise. The explanatory variables will be qualitative or quantitative as will be made more precise in the “ Results and discussion ” section. The econometric analysis of the relationship between the two is largely based on the linear probability model (OLS) that includes year and industry fixed effects and, for some specifications, individual fixed effects. We use this model as coefficients will be easier to interpret, but we also report the results of the analysis based on the logit model. They turn out to be qualitatively the same.

Results and discussion

We base our analysis on representative industry-panel data for the years 1991 to 2008. During this period, Switzerland established a number of bilateral agreements with trading partners—including the European Union (EU). Moreover, mutual trade liberalization between Switzerland and other countries also occurred through new membership of countries to the World Trade Organization (WTO), the EU and the European Free Trade Association (EFTA). Footnote 12 All of this implies pressure and adjustments that are typical for trade liberalizations. The question we now seek to answer is whether international trade indeed had a significant impact on the probability of (particularly low-skilled) individuals to become unemployed. If this is the case, international trade could be one reason for the increase of the relative unemployment rate for low-skilled labour described in the “ Background ” section.

Using micro data on individuals’ characteristics, we intend to assess whether an individual, who becomes unemployed, does so because of his or her particular exposure to international trade, controlling—amongst others—for skills. We present detailed summary statistics of the underlying data in the “ The data ” section and then run regressions of the change in the individual employment status on individuals’ characteristics and the trade variables in the “ Changes in employment status, individual characteristics and trade ” section. The “ Refinement of the trade variables and inclusion of individual fixed effects ” section uses a number of refined trade variables and includes individual fixed effects. The “ Sensitivity analyses ” section concludes with some sensitivity analyses.

For the industry panel data, we rely on the Swiss Labour Force Survey (SLFS). It is based on an annual and representative collection of information from Swiss residents (including foreigners, but excluding cross-border commuters) by the Swiss Federal Office of Statistics (FOS). The SLFS is in line with the methods used by the International Labour Office (ILO) which defines those individuals as unemployed who are not working, but searching for a job and ready to assume employment quickly.

This data source includes a pool of roughly 33,000 individuals over a period of 18 years (1991–2008) who were employed in the secondary sector (manufacturing) in Switzerland. As we want to attribute an industry to an individual, characterizing in which kind of industry the worker is employed, we link the SLFS data (FOS, 2009a ) on the industry two-digit SIC level with the Swiss Foreign Trade Statistics (EZV, 2009 ) and the National Account Statistics of the FOS ( 2009b ). To also characterize whether an individual works in a so-called ICT industry (i.e. an industry which displays an above-average intensity in the use of information and communication technology) or in a GAV industry (i.e. an industry which shows an above-average coverage of collectively bargained labour contracts), we also take into account the ICT-Survey of the KOF Swiss Economic Institute (KOF, 2005 ) at the Swiss Federal Institute of Technology (ETH) and the GAV-Statistics of the FOS ( 2002 ).

Summary statistics of the data used in our regressions are provided in Table  1 . The first column entitled “Change in Employment Status” is composed of individuals who are either employed during the full period of observation or indicate a change in their employment status from employment to unemployment. The second column “Employment Status” includes all individuals with a status of employed or unemployed. This leads to a maximum of 20,928 (40,875) observations of which 463 (1226) show a change in the employment status from employed to unemployed (show a status of unemployment). These observations stem from 10,242 (18,995) individuals, of which 461 (1008) show a change in their status from employed to unemployed (show at least once a status of unemployment). Footnote 13

Our main econometric analyses will concentrate on the observations reported in the first column of Table  1 . However, we will take into account the observations in the second column in our sensitivity analysis (“ Sensitivity analyses ”). Regarding the first column, the mean year-to-year change in percentage of import (export) values in the 17 manufacturing industries considered in the analysis amounts to 6.9% (7.6%). 40.1% of the observations are linked with “GAV industries”, whereas 37.2% of the observations include individuals employed in “ICT industries”. Footnote 14 The distribution of the observed worker characteristics are reported in the bottom part of Table  1 and speak for themselves.

Changes in employment status, individual characteristics and trade

We first regress changes in the individual employment status on the individuals’ characteristics and aggregate trade variables, using the following linear probability model with time and industry fixed effects:

Note that i indexes the individual and t the year. The left-hand variable, y it , takes the value of 1 if the individual i becomes unemployed in t and was employed in t − 1, and it takes the value of 0 if the individual remains employed in t . The probability of becoming unemployed over time is explained based on a number of right-hand independent variables, starting with an individual being employed in an ICT and GAV industry, a number of socio-demographic factors (SDF) of individual i in t as well as imports ( IM ) and exports ( EX ) of the industry, in which the individual i is employed, in time t . Note that we use levels (i.e. the value) as well as changes (i.e. in percentage) for the trade covariates and also include lags. We also interact some of the variables with the individuals’ skill level (L, M, H). The results are provided in Table  2 .

We start with a base regression, leaving out all trade variables. The results are reported in the first column of Table  2 . They show that the likelihood of becoming unemployed significantly depends on the individual’s qualifications (medium and low skills) and type of contract (part-time, temporary contract). Footnote 15 In this respect, we find also a positive relationship between the individuals’ likelihood of becoming unemployed and a short or medium tenure and for foreigners (typically due to a lack of local language skills). Married and widowed employees, on the other hand, are associated with a lower probability of becoming unemployed. Note that the coefficient for employment in an ICT-intensive industry or in a GAV industry is not significantly different from zero. The size of the coefficients in Table  2 can be interpreted as follows: Compared to a high-skilled worker, a low-skilled employee bears a 1.3% higher probability of becoming unemployed.

Columns (2) to (5) include levels and changes in the trade variables ( IM , EX ), also interacted with individuals’ skill levels (low-skilled, medium-skilled). Trade levels enter the estimation in logs, whereas “trade first differences” are calculated as the rate of year-to-year changes in percentage. We also add lagged trade variables (lagged by 1 year) to allow for a more deferred adjustment process. Note that, overall, the coefficients of worker and job characteristics do not change in a qualitative manner in these different specifications, nor do the GAV and ICT coefficients (except for the low skill level as a consequence of its interaction with the trade variables). We find some evidence (on the 5% significance level) for a significant effect of import levels on the probability of becoming unemployed for low-skilled employees: A 1% higher import value is associated with a 0.017% (0.016% for lagged imports) higher probability of becoming unemployed. In other words, low-skilled individuals who work in industries characterized by relatively large contemporaneous imports may, ceteris paribus, face a slightly greater likelihood of becoming unemployed. As shown in the fourth and fifth columns of Table  2 , no significant effects are found for first differences (i.e. changes ) in import and export values: A change in imports or exports in a certain industry does not significantly affect the probability of becoming unemployed.

We further investigate the impact of trade in the next subsection by using more refined trade variables and by including individual fixed effects to take into account any unobserved individual characteristics.

Refinement of the trade variables and inclusion of individual fixed effects

We now regress changes in the individual employment status on a number of trade variables, distinguishing between imports in finished and intermediate products and between trade with the North and the South. Footnote 16 We eliminate individuals’ characteristics as well as the GAV and ICT variables as we now use individual fixed effects. Footnote 17 We continue applying the linear probability model with time fixed effects. Standard errors are clustered by industry. We start with taking trade levels (in logs) as explanatory variables and then proceed to look at the rates of changes of the same variables. The results are reported in Tables  3 and 4 .

The estimates reported in Table  3 do not lend broad support for a positive relationship between the level of imports and the risk of becoming unemployed: Most coefficients of the import-level variables are not significantly different from zero. One exception at the 1% significance level is the coefficient of the 1-year lagged imports of final products from the South (fourth column): Individuals employed in an industry characterized by a 1% higher value of imports in this category encounter a 0.008% higher probability of becoming unemployed.

The results of the analogous estimations for first differences (i.e. rates of changes) in the import and export variables in a given industry are reported in Table  4 . We neither find an unambiguous relationship between changes in imports and the risk of unemployment nor is any of the relationship significant on the 1% level. However, we find that the coefficients for a lagged increase in final as well as intermediate imports from the South are significantly different from zero (on the 5% level, fourth column). Note that the economic impact of this effect is small: A 1% increase in import value, denoted as 0.01 in the dataset, leads to an increase in the probability of becoming unemployed by 0.004%. On this background, the fact that the coefficients of intermediate export products to the South in columns (2) and (4)—0.004 and 0.007—are significantly different from zero (and positive) should not be overvalued.

Sensitivity analyses

We finally try a number of different specifications to test the robustness of our results. Detailed results of these analyses are available from the Additional file  1 to this paper (Tables OA1 to OA5).

First, we replicate the results presented in Tables  2 , 3 and 4 using the logit regression model (Additional file  1 : Tables OA2 and OA3). Regarding the results in Table  2 , the logit estimates confirm a relationship between import levels and the likelihood of low-skilled workers of becoming unemployed: Coefficients are significantly different from zero (at the 5% level) with a positive sign. Also, we can confirm sign and significance level for the individual socio-demographic variables included and reported in Table  2 . Using a logit model with fixed effects, analogously to Tables  3 and 4 , we do not find any significant effects of the trade variables, regardless of whether we use levels or first differences as explanatory variables. Footnote 18 Hence, the logit estimations lead to qualitatively identical results as the linear regression model.

Second, we use the employment status (i.e. the information whether an individual is employed (0) or unemployed (1) in period t )—instead of the change of the employment status—as the dependent variable (summary statistics can be found in the second column of Table  1 ). As a start, we replicate the estimations described in Table  2 with the new dependent variable (see Additional file  1 : Table OA4). Again, we can confirm positive coefficients regarding import levels interacted with low-skilled labour for lagged imports (significantly different from zero at the 5% level). Furthermore, we use trade levels and first differences as explanatory variables in a model with individual fixed effects and find results that are qualitatively similar to those in Tables  3 and 4 . The results for the employment status as the dependent variable are reported in Additional file  1 : Table OA5. Most coefficients are not significantly different from zero. One exception is, again, the lagged level of final imports from the South with a coefficient of 0.016 (significantly different from zero at the 1% level). However, we also find a negative coefficient for the lagged first differences of intermediate imports from the North (− 0.010, significantly different from zero at the 5% level), leaving us with an ambiguous result regarding the effect of imports on the status of employment. Footnote 19

Third, and complementary to the analyses in Tables  3 and 4 (with again the change of the employment status as the dependent variable), we use second differences of the trade variables (e.g. [ IM t   −  ( IM t −  2 )/( IM t −  2 )]) instead of first differences and 2-year lags of trade levels instead of 1-year lags. All the results including the ones from Tables  3 and 4 are reported in Additional file  1 : Table OA1. We find a negative coefficient for the second differences without lags of intermediate imports from the North (− 0.006, significantly different from zero on the 5% level) in column 14. Furthermore, a positive coefficient is found for intermediate import levels from the North lagged by 2 years (0.013, significantly different from zero on the 5% level) in column 6. All the other import coefficients are insignificantly different from zero. Footnote 20 Thus, also in these regressions, we do not find unambiguous evidence for a positive relationship between imports and the probability of becoming unemployed.

Conclusions

This paper has been sparked by the omnipresent public concern in many industrial countries that international trade through specialization and outsourcing may cause income losses and unemployment, particularly for low-skilled labour. The striking increase in the Swiss unemployment rate of low-skilled relative to high-skilled labour from 1991 to 2014—with virtually no changes of relative wages—motivated us to focus our research on the relationship between international trade and unemployment for Switzerland.

Our assessment of the Swiss case does not confirm the public concerns. The econometric analysis of a data set of roughly 30,000 workers in the Swiss manufacturing sector from 1991 to 2008, which we link with the Swiss foreign trade statistics, does not, overall, support the presumption that an increase in imports has a statistically significant (and positive) effect on the probability of individuals of becoming unemployed, irrespective of their skills. Thus, we seem to be left with other well-established factors such as the level of skills, temporary employment or the length of tenure to explain the individuals’ risk of unemployment. The startling rise in the relative unemployment rate of low-skilled labour and, at the same time, the somewhat comforting constant relative wage rate of low-skilled labour in Switzerland from 1991 to 2014 still remains to be explained. Obvious candidates to look at more carefully would, in our view, be a skill-biased technological change for the relative unemployment rate and the compositional change in immigration for the relative wage rate. Footnote 21

Our investigation therefore only offers an initial basis for a more profound analysis of the labour market effects of trade or, more generally, of globalization for Switzerland. First, the fact that we find a weak (albeit small) positive relationship between low-skilled individuals working in industries characterized by a relatively high level of imports (particularly from the South) and the probability of their becoming unemployed may indicate something that we are not able to identify, given the limited statistical power of our data set which includes only a relatively small number of individuals who became unemployed. Second, we use exports as a control variable for (changes in) demand, because increasing imports have different effects on employment if they are combined with rising exports. This presents no problem as long as the domestic markets remain relatively small, which may, even in a small country such as Switzerland, not always be the case. If compatible data were available, a more sophisticated ratio could be used such as the import penetration ratio proposed by Autor et al. ( 2014 ) for the US industries.

Third, the fact that the individuals’ characteristics could only be linked to the two-digit SIC industry level, may even out a large amount of variation within industries: An individual’s employment status may be affected by imports on a sub-industry level, which might remain unobserved on the aggregated industry level. Also, and related to this, individuals employed in large multiproduct firms may be linked to an industry which is not really relevant to their actual occupation. Thus, an analysis based on more disaggregated, possibly even firm- or establishment-level, data may challenge our results.

On the other hand, this paper’s lack of findings in support of a strong positive relationship between import competition and the risk of unemployment could also be a consequence of the relatively low unemployment rate in Switzerland and the alleged high degree of flexibility in the Swiss labour market. If individuals lose their job because of import competition, but immediately find a new one, they never become unemployed. In this regard, it is interesting to note that our analysis of six announced mass-layoff cases in Swiss manufacturing due to globalization between 2001 and 2006 revealed exactly this situation: Only one quarter of the displaced workers were, in the end, dismissed by their companies and thus became, at least for a short term, unemployed (see Wyss, 2010 ). The others swiftly found a new job in the same or in another company or industry.

Interestingly, this point of view is emphasized, for example, in an early document of the U.S. Department of State ( 1945 ) that formed the basis of the creation of the General Agreement on Tariffs and Trade (GATT). The title “Proposals of Expansion of World Trade and Employment” is revealing.

For early contributions see, for example, Berman et al. ( 1994 ), Borjas et al. ( 1991 ), Davis ( 1998a , 1998b ), Feenstra ( 1998 , 2010 ), Krugman ( 1995 , 2000 ), Lawrence and Slaughter ( 1993 ), Leamer ( 1998 , 2000 ) or Murphy and Welch ( 1991 ).

Whereas the overall effect on unemployment remains ambiguous or depends on parameters in these models, Dutt et al. ( 2009 ) predict a reduction in overall unemployment as a result of trade.

Moser et al. ( 2011 ) find a small (negative) effect from a reduction in the international competitiveness of firms on job flows for Germany, and more so on job creation rather than on job destruction.

See for example Feenstra and Hanson ( 1999 ), Hijzen et al. ( 2005 ) and OECD ( 2007 ) for a broad overview.

See Suarez ( 1998 ) and Müller, Marti and Nieuwkoop ( 2002 ) who focus on trade and wages. Other studies such as Sheldon ( 2007 ), Puhani ( 2003 ) and Arvanitis ( 2005 ) analyze shifts in supply and demand on the Swiss labour market, but do not explicitly investigate the effects of trade.

Note that, throughout this paper, high-skilled (H) is defined as people with tertiary education (ISCED 5-6: university, college of higher education (Fachhochschule) and school of higher education (Höhere Fachschule). Low-skilled (L) is defined as individuals with primary or lower secondary education (ISCED 1-2: mandatory education with no professional training qualification). Medium-skilled (M) is defined as individuals with upper secondary education (ISCED 3-4: professional education which, most importantly, includes completed apprenticeships).

Note that U L is defined as the unemployment rate of the 25–64-year-olds with “below upper secondary education”, whereas U H is defined as the unemployment rate of the 25–64-year-olds with “tertiary education”; see OECD ( 2007 , 2015 ).

Interestingly, South Korea shows the lowest absolute rate of relative unemployment in 2014 despite the considerable increase reported in Fig.  2 . On the other extreme, the Czech Republic shows a fall of the relative unemployment rate from 1991 to 2014, but remains the country with the highest ratio in 2014; note that, in 2014, U L ( U H ) equaled 20.7% (2.6%) for this country (see OECD ( 2015 , Table A5.4a)).

Baldwin ( 1994 , p. 73) once called the view that trade affects the number of jobs as “utter nonsense from the medium- or long-run economic perspectives”. Davidson et al. ( 1999 ) would, however, add that in a trade model with labour market frictions this is, in principle, possible, and mainly an empirical question (p. 273).

Dutt et al., 2009 , p. 33) emphasize a “fairly strong and robust empirical support (…) for the Ricardian prediction that trade openness and unemployment are negatively related across all countries”. The intuition is that trade raises productivity which increases the search effort of employees and employers that, in turn, reduces unemployment.

Note that, during this period, Switzerland or EFTA (to whom Switzerland belongs) established free trade agreements with approximately 20 countries (e.g. with Turkey (1992), Mexico (2001), South Korea (2006) and China (2014)), reached two bilateral agreements with the EU (1999, 2004) and was—through its free trade agreement (1972) and the two bilateral agreements with the EU—also affected by the enlargement of the EU by 13 new member countries in 2004, 2007 and 2013. Finally, there are approximately 30 countries (including China in 2001) that became additional members of the WTO, after its foundation in 1995 until 2008, and thus achieved improved mutual market access with Switzerland.

The deviation to all 33,000 individuals mentioned above is due to the fact that many individuals exhibit missing values in at least one of the variables of interest.

See Appendix : Table 5 regarding the assignment of individual industries. GAV stands for “Gesamt-Arbeits-Vertrag” and means collective bargaining contract; ICT stands for “Information and Communication Technology”.

Here and in the following we consider coefficients as significantly different from zero if they reach at least the 5% level.

Note that Feenstra and Hanson ( 2003 ) also base their analysis on annual changes broken down to final and intermediate imports. Anderton and Brenton ( 1999 ) differentiate between imports from industrial and low-wage countries. Based on the Swiss Trade Statistics, intermediates are defined as items in the category “raw materials”, “semi-finished products” and “intermediate goods”. An alternative definition based on input-output tables is currently not feasible as relevant statistics are not available. We also distinguish between imports from the North (industrial countries) and the South (developing countries).

Individuals remain in the same industry throughout the observed period. Hence, ICT and GAV variables are omitted when using individual fixed effects.

One may observe that the logit analysis implies a positive relationship (significantly different from zero at the 5% level) between low-skilled individuals working in GAV-industries (interacted variable) and their probability of becoming unemployed.

Also the relationship between the employment status and exports (level, change) remains ambiguous in the analysis.

Note that we also use 1-year leads of the trade variables as “placebo tests”. We refrain from showing those results in the Additional file  1 as we do not find any significant results.

For analyses of skill-biased technological change, see the seminal contributions by Berman et al. ( 1994 , 1998 ) and Krugman ( 2000 ) as well as, for an attempt to disentangle trade and technology effects, Autor et al. ( 2015 ).

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Acknowledgements

All persons who provided feedback as well as some minor support (data, editorial) to the different versions of the paper are mentioned in the acknowledgement.

We would like to thank the co-editor, Volker Grossmann, and two anonymous referees for their extremely helpful suggestions which led to a considerable improvement of the analysis in our paper. We also thank Marius Brülhart, David Green, Douglas A. Irwin, Ronald W. Jones, Peter Kugler, Christian Rutzer and George Sheldon for helpful feedback to earlier drafts as well as Dragan Filimonovic, Lukas Hohl and Hermione Miller-Moser for data and editorial support. We also benefited from discussions at the Annual Conference of the European Trade Study Group (ETSG), the Annual Meeting of the Swiss Society of Economics and Statistics and a lunch seminar at the Department of Economics of the University of British Columbia (UBC). Simone Wyss gratefully acknowledges financial support from the WWZ-Forum and the State Secretariat for Economic Affairs (SECO) during an early stage of the research project.

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LM has implemented all the regressions in the second and the final version of the paper and given input to the first and second revisions of the paper. He also contributed to the letters to the editor and the referees. RW has written the first version of the paper and re-written the paper as part of the first and second revisions. He also wrote the letters to the editor and the referees. RW and LM have been closely working together in the first and second revisions of the paper. SM has collected the data and implemented the econometric analysis for the first version of the paper. She also answered questions regarding the data and the original regressions throughout the revision process. All authors read and approved the final manuscript.

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Additional file 1:.

Table OA1. Linear regressions of changes in employment status on trade variables using individual fixed effects. Table OA2. Logit regressions of changes in employment status on trade variables and individual characteristics, regression coefficients. Table OA3. Logit regressions of changes in employment status on trade variables using individual fixed effects, regression coefficients. Table OA4. Linear regressions of employment status on trade variables and individual characteristics, regression coefficients. Table OA5. Linear regressions of employment status on trade variables using individual fixed effects, regression coefficients. (DOCX 54 kb)

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Mohler, L., Weder, R. & Wyss, S. International trade and unemployment: towards an investigation of the Swiss case. Swiss J Economics Statistics 154 , 10 (2018). https://doi.org/10.1186/s41937-017-0006-7

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Received : 15 May 2017

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DOI : https://doi.org/10.1186/s41937-017-0006-7

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• International trade
• Unemployment
• Low-skilled labour
• Switzerland

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