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Offshoring, wages and job security of temporary workers

Abstract

Temporary contracts have become an important mode of employment in many countries. We investigate the impact of offshoring on individual level wages and unemployment probabilities and pay particular attention to the question if workers with temporary contracts are affected differently than workers with permanent contracts. Data are taken from the German Socio-Economic Panel, linked with industry level data on offshoring. We do not find systematic differences between temporary and permanent workers with respect to the effects of offshoring for wages. We find, however, that offshoring increases the unemployment risk of low-skilled workers, and more so for temporary than permanent workers. Also, offshoring reduces the unemployment risk for all high- and medium-skilled workers.

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Fig. 1
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Notes

  1. Note, however, that this increase may be overstated due to a change in the statistical methodology in 2004.

  2. The WIOD is available at http://www.wiod.org/database/index.htm. See Timmer (2012) for a detailed description of the construction of the world input–output tables.

  3. We exclude workers from East Germany, as wages in the East are to a large extent shaped by the structural change of the economy that has been taking place since the fall of the wall and that most likely dominates the impact of other changing structural factors. We also exclude female workers since, as is well known, female workers have substantially different labour market outcomes than males. Hence, our sample is similar to that used in Geishecker and Görg (2008), though we have a more up-to-date time period.

  4. This approach is similar to Geishecker and Görg (2008, 2013) and Liu and Trefler (2008). However, these papers do not allow for differential effects depending on the type of employment contract and use a different offshoring measure.

  5. Imputed wages are dropped throughout the analysis. See Frick et al. (2007) for a documentation of the CNEF.

  6. A definition of the explanatory variables is given in “Appendix 3”.

  7. There are, of course, also other possibilities of employment exits in our data, e.g. workers may move into maternity leave, non-participation or new jobs. These options are not considered here. Moves into maternity leave or non-participation are set to missing (right-censored) while moves into new jobs are considered as stayers.

  8. This approach is similar to Geishecker (2008) and Bachmann and Braun (2011) who, however, only consider the period 1991–2000. Also, they do not distinguish temporary and permanent work arrangements.

  9. Calculated as the rise in offshoring (1.2 %) multiplied with the marginal effect for low-skilled temporary workers (0.00124). The average overall risk of being unemployed in period t + 1 was 3.46 % for low-skilled workers.

  10. The average overall risk of being unemployed in period t + 1 is 0.96 % for high-skilled workers.

  11. In Eq. (9), this is expresses as 1 minus the share of intermediates in total output. It can be shown that W′I LS  = diag(A′ I LS )p.

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Acknowledgments

The authors are very grateful to an anonymous referee, Bernhard Boockmann, and conference participants at the ILRR Conference on job quality at Cornell University and the Aarhus-Kiel Workshop for very helpful comments on an earlier draft. Thanks are also due to Tillmann Schwörer for help with the WIOD data. Fares Al-Hussami provided excellent research assistance. The authors gratefully acknowledge financial support through the European Commission as part of the 7th Framework Programme, Grant Agreement No. 244 552 (SERVICEGAP). Financial support from the Fritz-Thyssen Stiftung is gratefully acknowledged.

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Correspondence to Dennis Görlich.

Appendices

Appendix 1: List of manufacturing industries contained in the WIOD

No. NACE Description
1 15–16 Food, beverages and tobacco
2 17–18 Textiles and textile products
19 Leather, leather and footwear
3 20 Wood and products of wood and cork
4 21–22 Pulp, paper, paper, printing and publishing
5 23 Coke, refined petroleum and nuclear fuel
6 24 Chemicals and chemical products
7 25 Rubber and plastics
8 26 Other non-metallic mineral
9 27–28 Basic metals and fabricated metal
10 29 Machinery, nec
11 30–33 Electrical and optical equipment
12 34–35 Transport equipment
13 36–37 Manufacturing, nec; recycling
  1. Textiles (17–18) and leather (19) had to be combined for calculation of imported value added

Appendix 2: Derivation of offshoring measure: share of imported value added

Our indicator for offshoring should describe the value of imported value added, i.e. value added purchased abroad, relative to the total value of industry’s production. The difference between one and the indicator’s value should thus be the share of value which is added domestically. The indicator should also account for the cases in which imported intermediate goods (e.g. German motor imports from the Czech Republic) contain parts which were previously produced domestically (e.g. the crankshaft produced in Germany). These parts should be classified as domestic and not as foreign value added.

The indicator is derived from the WIOD. The WIOD assumes that each industry (s, r = 1, …, S) in each country (i, j, k = 1, …, L) produces a single, homogenous good, which is different from the same industry’s goods produced in other countries. Hence, there are LS different goods produced in the world.

The input–output tables of the WIOD consist of four components. (i) The intermediate inputs matrix, W, of dimension (LS × LS), describes the values of bilateral trade in intermediate goods between all countries of the world. The element in row is and column jr describes the value of intermediate goods which industry r in country j purchases from industry s in country i. (ii) The final use matrix, C, of dimension (LS × L), describes the value of all LS goods sold to final users (private households, investors, the government, and changes in inventories) in all L countries. (iii) The vector y of dimension (1 × LS) describes the value added input of each industry, and (iv) the (1 × LS) vector of total production, p, describes the total output of each industry.

An industry’s total production (p) can be formally described as

$$ \mathop p\nolimits_{is} = \sum\limits_{j = 1}^{L} {\sum\limits_{r = 1}^{S} {\mathop w\nolimits_{is,jr} } } + \sum\limits_{j = 1}^{L} {\mathop c\nolimits_{is,j} } , $$
(3)

where p is is the sum of a product’s intermediate use and final use. Note that a matrix’ cell is denoted by a small letter here. Alternatively, total production can be written as

$$ \mathop p\nolimits_{is} = \sum\limits_{j = 1}^{L} {\sum\limits_{r = 1}^{S} {\mathop w\nolimits_{jr,is} } } + \mathop y\nolimits_{is} , $$
(4)

where p is is the sum of intermediate inputs and the industry’s value added.

Equations (3) and (4) can be combined using matrix notation:

$$ {\mathbf{p}} = {\mathbf{Ap}} + {\mathbf{CI}}_{{\mathbf{L}}} $$
(5)
$$ = {\mathbf{W}}^{{\mathbf{'}}} {\mathbf{I}}_{{{\mathbf{LS}}}} + {\mathbf{y}}. $$
(6)

where p = (p 11, …, p 1S , p 21, …, p 2s , …, p L1, …, p LS ) is the (LS × 1) vector of all production values and y = (y 11, …, y 1S , y 21, …, y 2s , …, y L1, …, y LS ) is the (LS × 1) vector of value added in all industries. In Eq. (5), matrix C is multiplied by the identity vector I L in order to aggregate across all countries that consume the respective good. The (LS × LS) matrix of input coefficients, A, is constructed by dividing each element of the W matrix along the vertical by the total output of that industry:

$$ {\mathbf{\rm A}} = {\mathbf{W}} \times {\text{diag}}{\mathbf{(p}}^{{ - {\mathbf{1}}}} {\mathbf{)}}, $$
(7)

Its elements a is,jr  = w is,jr /p jr denote how many cents industry r in country j purchases from country i in order to produce one unit of output.

Matrix A contains input coefficients for a representative step in production. However, the production of many goods requires more than one production step, so that we have attribute the value added contributions of all intermediate inputs to their countries of origin as well. Ultimately, we would like to derive how many cents of value added each industry in each country contributes to one dollar of each final good. To that end, Eqs. (3) and (4) are rewritten so that the relationships between output and final use, and between output and value added can be used to derive a direct relationship between final use and value added:

$$ {\mathbf{p}} = {\mathbf{(I}}_{{{\mathbf{LS}}}} - {\mathbf{A)}}^{{ - {\mathbf{1}}}} \times {\mathbf{C}} \times {\mathbf{I}}_{{\mathbf{L}}} , $$
(8)
$$ {\mathbf{y}} = {\mathbf{[}}{\text{diag}}{\mathbf{(I}}_{{{\mathbf{LS}}}} - {\mathbf{A}}^{{\prime }} \times {\mathbf{I}}_{{\mathbf{L}}} {\mathbf{)]}} \times {\mathbf{p}}, $$
(9)
$$ {\mathbf{y}} = {\mathbf{V}} \times {\mathbf{p}}, $$
(10)

where V = diag(I LS  − A′ × I LS ).

Equation (8) describes the relationship between final use and output. Matrix B = (I LS − A)−1 is the Leontief inverse; its elements b is,jr describe by how much of the output of industry s in country i contributes to each dollar of the final good of industry r from country j.

Equation (9), which is presented in a simplified way in Eq. (10), describes the relationship between value added and output. The diagonal elements of the (LS × LS) matrix V, v ir  = y ir /p ir , denote the fraction of value added in total output of industry jr.Footnote 11 Substituting Eq. (8) into Eq. (10) results in:

$$ {\mathbf{y}} = {\mathbf{[V}} \times {\mathbf{(I}}_{{{\mathbf{LS}}}} {\mathbf{{-}A)}}^{{ - {\mathbf{1}}}} {\mathbf{]}} \times {\mathbf{C}} \times {\mathbf{I}}_{{\mathbf{L}}} , $$
(11)
$$ {\mathbf{y}} = {\mathbf{M}} \times {\mathbf{C}} \times {\mathbf{I}}_{{\mathbf{L}}} , $$
(12)

Matrix M = V × (I LS − A)−1 contains all the information needed for the estimations. It describes the contribution of value added by all industries in all countries to one dollar of final good production. Its elements m is,jr denote how many cents industry is contributes to each dollar of the final good produced by industry jr.

For our estimations, we calculate a measure of narrow offshoring, OFF, for each German industry, i.e. the share of value added in industry production, which is imported from the same industry in all other countries. Specifically, we sum over the relevant elements of M:

$$ OFF = \sum\limits_{i \ne j} {m_{is,js} } , $$

where s is the respective industry, i is the country where the imported value added stems from, and j denotes Germany.

Appendix 3: Variable definitions and summary statistics

The econometric analysis is based on the German Socio-Economic Panel (SOEP), waves 1999–2007. We use all SOEP samples for the analysis. Yearly industry level information about trade and offshoring is merged with the SOEP on basis of industry classification provided in the SOEP (NACE 1.1). Variables are defined as follows (SOEP variable names are mentioned in parentheses).

See Tables 6 and 7.

Table 6 Variable definitions
Table 7 Summary statistics

Appendix 4: Results of the random effects probit models

See Tables 8 and 9.

Table 8 Random effects probit regression on dummy for unemployment in next period, 1999–2007, linear measure for employment duration
Table 9 Random effects probit regression on dummy for unemployment in next period, 1999–2007, dummies for groups of employment duration

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Görg, H., Görlich, D. Offshoring, wages and job security of temporary workers. Rev World Econ 151, 533–554 (2015). https://doi.org/10.1007/s10290-015-0220-2

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Keywords

  • Offshoring
  • Imported value added
  • Temporary contracts
  • Wages
  • Job security

JEL Classification

  • F14
  • F16
  • J31