365
      ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No.  3  |  2017  |  365-414
THE EFFECTS OF OUTSOURCING AND 
OUTWARD FDI ON SKILL STRUCTURE 
IN SLOVENIA: EVIDENCE ON MATCHED 
FIRM-EMPLOYEE DATA
MOJCA LINDIČ
1
 Accepted: June 12, 2017
 Received: October 10, 2016
ABSTRACT: This paper studies the effect of outsourcing and outward FDI on firms’ skill struc-
ture. Its main contributions consist of studying changes in the skill structure that can be as-
sociated with outsourcing and outward FDI to high- and low-income countries, and including 
a new dimension when defining skills, which also controls for occupational classification of 
workers. The analysis employs a matched employer-employee dataset for Slovenian manu-
facturing and service firms between 1997 and 2010. The results indicate that outward FDI 
to high- and low-income countries has a positive impact on the skill share in manufacturing 
firms. The results also show that in the case of some occupational groups firms prefer to employ 
more educated individuals.
Keywords: skill structure, FDI, outsourcing, skill differentiation, manufacturing and service firms.
JEL classification: F14, F16
DOI: 10.15458/85451.40
1. INTRODUCTION
Globalisation has changed the world dramatically in the most recent decades, with trade 
liberalisation and technology improvements leading to lower trade barriers and to the 
drop of transportation and communication costs (IMF, 2013). In line with these changes, 
transnational companies change and adjust the structure and organisation of their value 
added activities, where outward foreign direct investment (FDI) and outsourcing are among 
their main methods of strategic positioning. Forecasts on increasing internationalisation 
specify that firms will carry out even more of their activities outside of their enterprises in 
the future; for instance by increasing FDI flows, or by increasing foreign affiliate activity 
(UNCTAD, 2013). Studies argue that outward FDI and outsourcing activities are likely to 
depend upon several determinants in the future (see for example Baldwin, 2013). First, 
increasing developments in coordination and communication technology would make the 
supply chains more complex in terms of the number of stages and countries involved. On 
the other hand, improvements in information technology would combine different stages 
of production and thus lead to less complex supply chains. Widening or narrowing the 
1 This paper was prepared when Mojca Lindič was employed at the University of Ljubljana, Faculty of Eco-
nomics, Ljubljana, Slovenia, e-mail: mojca.lindic@ef.uni-lj.si

366 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
wage gap between developed and developing countries is also expected to have a significant 
impact. Following the “new trade theory” , the growth of developing countries would increase 
the overall trade, while increasing wages and income convergence of emerging markets 
would cause the flying geese pattern. Finally, an important factor will also be transportation 
costs, especially oil prices. Increasing oil prices would influence the geography of supply 
chains, which would consequently become more regional (Baldwin, 2013).
Although empirical studies confirm a significant impact of outward FDI and outsourcing 
on the skill structure of firms in developed countries, they do not control for both factors 
in a single model. On average, studies conclude that through increased import competition 
from low-wage countries, which transfers labour demand towards more skilled workers 
(Feenstra & Hanson, 1996), outward FDI and outsourcing have a positive impact on the 
employment of skilled labour (see for example Mion & Zhu, 2013, Hijzen, Görg, & Hine, 
2005, Strauss-Kahn, 2003, Egger & Egger, 2003, and Feenstra & Hanson, 1996). Positive 
impact on the skill structure of firms in developed countries is a consequence of relocating 
the unskilled labour-intensive production to countries abundant with unskilled labour, 
whereas high-technology stages of productions continue to be produced in developed 
countries (Hijzen, Görg, & Hine, 2005). In addition, analysing Slovenian data, Drenkovska 
and Redek (2015) find that internationalization offers firms access to more developed and 
competitive markets, which in turn has a positive impact on Slovenian exporters.
The paper is motivated to analyse the following goals. The first aim is to measure the 
effect of outward FDI and outsourcing on the skill structure of firms. To the best of my 
knowledge, previous empirical models controlled for only one of the measures at a time 
and on average confirmed a positive impact of outward FDI or outsourcing on the skilled 
labour in developed countries. Not controlling for both factors in one model might lead to 
the missing variable bias. In addition, it is important to account for both factors since their 
combined effect is expected to increase in the future (see for example UNCTAD, 2013 and 
Baldwin, 2013). In addition, a new dimension is added for defining skills, which controls 
also for the occupational classification of workers. It is important to take this into account 
since workers gain their skills not only by formal education but also through various forms 
of vocational trainings and during their work career. The final aim is to control for the 
income level of outward FDI and outsourcing destinations, since previous studies indicate 
shifts of unskilled-intensive parts of production to countries abundant with relatively less-
skilled labour. As a result, one would expect outsourcing from high-income countries and 
outward FDI to low-income countries to have a positive impact on the relative employment 
of skilled workers in the home country. The reasoning behind is that outsourcing from 
high-income countries enables firms to have access to technologically more advanced 
intermediate inputs, which in turn demand the employment of highly skilled workers. 
On the other hand, outward FDI to low-income countries might shift some of the more 
manually-intensive parts of production abroad and keep the high value added departments 
in the home country (as for example research, sales, marketing, finance, etc.).
The analysis employs a matched firm-employee panel dataset for Slovenian firms in the 
period from 1997 to 2010. Outsourcing is defined as the ratio between firm’s value of 

367 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
intermediate imports and its value of total material costs, while a dummy variable controls 
for a presence of outward FDI in a firm. Results of the basic model indicate to a positive 
impact of outward FDI on the relative employment of tertiary educated workers in 
manufacturing firms, and to a positive impact of outsourcing on the relative employment 
of skilled workers in service firms. The results confirm it is important to account for 
differential effect of the two measures in one model, as their impact on the skill structure 
of firms differs. In addition, outward FDI to low- and high-income countries shows a 
positive impact on the relative employment of skilled labour in manufacturing firms. In 
service firms, results are not statistically significant. Finally, results show that the impact of 
educational level differs between occupational groups, indicating that firms give different 
emphasis on educational level when employing different occupational groups.
The remainder of the paper is organised as follows. In the next section, a brief summary 
of the relevant literature is given. Section three introduces the methodology used, whereas 
section four describes the data and presents descriptive statistics. The empirical analysis 
and discussion of results are included in section five. The last section summarises and 
provides conclusions.
2. LITERATURE REVIEW
First are discussed theoretical models which explore the effects of outsourcing and outward 
FDI on the labour demand. Since there is not a unique definition of outsourcing and 
outward FDI/offshoring in the literature, the terms will be used interchangeably throughout 
the literature review, following the terminology used by the authors of the studies.
Grossman and Rossi-Hansberg (2008) study the impacts of the falling offshoring costs 
on factor prices in the home country by differentiating between trade in goods, which is 
the conventional meaning of trade, and trade in tasks, which relates to adding a value to 
goods in different locations. In the model, offshoring influences the firms’ performance 
positively by allowing them to hire some factors abroad at a lower price. On the other hand, 
offshoring also brings costs as the monitoring and management of workers is hindered 
due to long distances. The model also shows that trade in tasks gives rise to shared gains 
for all domestic factors, including skilled workers (Grossman & Rossi-Hansberg, 2008).
In another theoretical paper, Egger and Egger (2003) focus on a small country case, 
treated as home country, which has a possibility to outsource a low-skilled part of its 
production to low-wage foreign countries. In a competitive labour market framework, 
outsourcing increases relative wages of high-skilled labour, while it does not affect relative 
employment. However, in a unionised framework, outsourcing increases both, relative 
wages and the relative employment of high-skilled labour in the home country.
Compared to rather scarce theoretical analyses on the effects of outsourcing and 
offshoring on the labour market, empirical studies are more abundant. Feenstra and 
Hanson (1996, 1999) analyse the impact of outsourcing in the United States. The results 

368 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
of their earlier paper point to an increase in the relative demand for skilled labour due 
to increased outsourcing (Feenstra & Hanson, 1996). In their later paper, Feenstra and 
Hanson (1999) conclude that outsourcing impacts the relative wages of non-production 
workers positively (Feenstra & Hanson, 1999).
Amiti and Wei (2005a, 2005b) explore the effects of service outsourcing and offshoring 
in the UK and US, respectively. For the UK market, the authors find that job growth 
and outsourcing are not negatively correlated at the sectoral level (Amiti & Wei, 2005a), 
whereas for the US market, the effect of offshoring on employment differs according to the 
disaggregation of industries. When industries are finely disaggregated, the results point to 
a negative effect. On the other hand, when industries are defined on a broader level, the 
negative effect disappears. This leads to a conclusion that, although offshoring might affect 
employment negatively within industries, dismissed workers renew their employment in 
other growing industries (Amiti & Wei, 2005b).
Hijzen, Görg, and Hine (2005) also examine the effects of outsourcing on the UK labour 
market. The results indicate that outsourcing affects the demand for unskilled labour 
negatively and together with technological change leads to changes in the skill structure 
of manufacturing industries (Hijzen, Görg, & Hine, 2005). Parallel conclusions on the 
negative effect of increased outsourcing on the relative employment of unskilled workers 
were made by Strauss-Kahn (2003) for French manufacturing industries. In addition, 
Egger and Egger (2003) indicate that outsourcing increases the relative employment of 
high-skilled labour in Austria as a consequence of trade liberalisation in the Central and 
Eastern Europe.
While Michel and Rycx (2009) find no major influence of materials or business 
services offshoring on the employment in Belgian firms, they highlight the importance 
of distinguishing between manufacturing and service industries. Traditionally, only 
manufacturing industries were related to offshoring, since their products are easily 
tradable. However, improvements in information and communication technologies had 
a significantly positive impact on offshoring in service industries (Michel & Rycx, 2009). 
This was confirmed also by De Backer and Yamano (2012), who compare the increase of 
offshoring in different countries. Although offshoring increased in the observed period 
from 1995 to 2005 in both, manufacturing and service industries, the increase was on 
average bigger in the latter. Importing intermediates from abroad is however on average 
still more important in manufacturing industries (De Backer & Yamano, 2012). Similar 
conclusions were made by Horgos (2006) using German data, inferring that outsourcing 
is concentrated in high-skilled manufacturing industries, while the highest increase in 
outsourcing is visible in service industries (Horgos, 2006).
A noteworthy restraint of empirical studies, presented in previous paragraphs, is in the type 
of data used. The studies used data, disaggregated only at the industry level and therefore 
could not control for firm-specific and individual-specific characteristics that may have an 
impact on the skill structure of firms. Moreover, identifying the labour demand curve is 
more challenging when using industry-level data (Hijzen & Swaim, 2010).

369 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Konings and Murphy (2006) evaluate the substitution of workers between parents and 
their affiliates in European multinational enterprises. Contrary to the common belief, 
their results indicate employment relocations between parent firms and their affiliates, 
both based in the North EU, but they find no significant employment flows between the 
parent and affiliates, based in the South EU, and Central and Eastern Europe (Konings & 
Murphy, 2006).
In another study using firm-level data, Biscourp and Kramarz (2007) differentiate between 
two types of imports – imports of finished goods and imports of intermediate goods, which 
they define as offshoring. They find a strong and negative correlation between imports 
and job destruction, where this impact is especially strong for imports of finished goods, 
imports from low-wage countries, and for larger firms (Biscourp & Kramarz, 2007).
Focusing on trade liberalisation in China after its accession to the World Trade 
Organisation, Bloom, Draca, and Van Reenen (2011) find that the impact on labour 
demand varied across firms. Specifically, although the increased Chinese competition 
did not affect labour demand in high-tech firms, it decreased in low-tech firms (Bloom, 
Draca, & Van Reenen, 2011). Mion and Zhu (2013) analysed the effects of increased 
Chinese imports on Belgian manufacturing firms and the Belgian labour market, by 
differentiating between imports of final and intermediate goods. Authors find that the 
increased Chinese competition hurt firms in low-tech industries, while on the whole, the 
increased competition was followed by reduces in firm employment growth, and upgrades 
in skill structure (Mion & Zhu, 2013).
Furthermore, Lo T urco and Maggioni (2012) studied the effects of offshoring on the labour 
demand in Italian manufacturing firms by controlling also for the origin of countries. The 
authors emphasise it is important to differentiate between high- and low-income countries, 
since different origins of outsourcing can point to a different performance level of firms. 
Their results show that importing intermediates from high-income countries does not 
affect employment, while the effects on the employment are negative when importing 
intermediates from low-income countries.
To sum up, the findings of presented studies show that liberalising trade with developing 
countries brings opportunities for cost reductions and technology improvements in the 
developed countries, while on the other hand it also presents threats to labour markets. 
However, the majority of studies conclude this threat is not large and is usually concentrated 
on the low-skilled employees. Also important is the emphasis made in several papers 
(see for example De Backer & Yamano, 2012, Michel & Rycx, 2009, Horgos, 2006) on the 
significance of differentiating between manufacturing and service industries, as well as the 
importance of using firm-level data (Hijzen & Swaim, 2010).
This paper contributes to the abovementioned studies in several ways. First, to the extent 
of my knowledge, previous analyses defined skills only by looking at workers’ educational 
attainment or by differentiating between production and non-production workers. 
However, since skills can be acquired through employment and experience, and not only 

370 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
through formal education, it is important to take into account alternative dimensions when 
defining skills. This study defines skills also by the occupational classification of workers 
and makes separate analyses for occupational groups that define skills. Second, while the 
bulk of analyses were concentrated on the effects of only outward FDI or only outsourcing, 
it is important to study differential effect of the two by controlling for both factors in one 
model, and to avoid the missing variable bias problem. More precisely, previous empirical 
models, which accounted for only one of the factors in their model, showed that outward 
FDI and outsourcing both have a positive impact on the labour demand. Finally, I also 
control for the income level of source markets of both, outsourcing and outward FDI, in 
order to control for the potential differences of partner’s performance. While Lo Turco 
and Maggioni (2012) also differentiated between outsourcing from high- and low-income 
countries, they did not account for outward FDI, skilled workers or service firms in their 
analysis.
3. METHODOLOGY
As aforementioned, since definitions of outsourcing and offshoring/outward FDI 
vary significantly across different studies, this section first presents the definitions of 
outsourcing and outward FDI, used in this analysis. The framework and specification of 
the basic and extended models are presented next.
Feenstra and Hanson (1996) define outsourcing as the import of intermediate inputs 
by domestic firms, whereas in their more recent paper (Feenstra & Hanson, 1999), they 
introduce two measures of outsourcing. First is the ratio between imported intermediate 
inputs, relative to the total expenditure of non-energy intermediates in each industry, 
and the second is defined as inputs that are purchased from the same two-digit Standard 
Industrial Classification (SIC) industry as the good being produced (Feenstra & Hanson, 
1999). Many of the papers follow these definitions and methodology. Similar definition 
for outsourcing is also used in the reports of IMF (2013) and UNCTAD (2013), which 
define outsourcing as purchasing intermediates from another firm, rather than producing 
them within the firm. Taking into account these definitions, in this analysis, outsourcing 
is defined as the ratio between the value of non-energy intermediate imports and the value 
of total material costs of a firm i in year t:
       (1)
where intermediate imports are defined according to the assigned Broad Economic 
Categories (BEC) codes. Under BEC classification, goods can be classified in three 
categories; capital, intermediate, and consumption goods.
Besides estimating the effects of outsourcing, this analysis also takes into account the 
effects of outward FDI. For the latter, I again follow the definition of IMF (2013) and 
UNCTAD (2013) which define offshoring either as a process of relocating a part of or 
  
outsourcing and outward FDI, used in this analysis. The framework and specification of 
the basic and extended models are presented next. 
 
Feenstra and Hanson (1996) define outsourcing as the import of intermediate inputs by 
domestic firms, whereas in their more recent paper (Feenstra & Hanson, 1999), they 
introduce two measures of outsourcing. First is the ratio between imported intermediate 
inputs, relative to the total expenditure of non-energy intermediates in each industry, and 
the second is defined as inputs that are purchased from the same two-digit Standard 
Industrial Classification (SIC) industry as the good being produced (Feenstra & Hanson, 
1999). Many of the papers follow these definitions and methodology. Similar definition for 
outsourcing is also used in the reports of IMF (2013) and UNCTAD (2013), which define 
outsourcing as purchasing intermediates from another firm, rather than producing them 
within the firm. Taking into account these definitions, in this analysis, outsourcing is 
defined as the ratio between the value of non-energy intermediate imports and the value of 
total material costs of a firm i in year t: 
 
   
it
it
it
sts co material Total
imports te Intermedia
g Outsourcin =    (1) 
 
where intermediate imports are defined according to the assigned Broad Economic 
Categories (BEC) codes. Under BEC classification, goods can be classified in three 
categories; capital, intermediate, and consumption goods. 
 
Besides estimating the effects of outsourcing, this analysis also takes into account the 
effects of outward FDI. For the latter, I again follow the definition of IMF (2013) and 
UNCTAD (2013) which define offshoring either as a process of relocating a part of or all 
the activities to another firm, located overseas, or as foreign direct investments. To 
estimate the effect of outward FDI, I take into account the dataset from the Bank of 
Slovenia, which comprises information on the FDI flows for every Slovenian firm. This 
dataset also gathers information on the volume of the FDI and the destination country of 
the investment. Since the information on the volume and the type of FDI is available only 
for 2007 onwards, whereas the information on the presence of firm’s FDI flows, if the 
share of equity of the Slovenian firm in a foreign enterprise is at least 10%, is available for 
the entire observation period, this variable was formed as a dummy variable.  
 
Definitions of the outsourcing and outward FDI therefore take into account only foreign 
flows. The weaknesses of the abovementioned definitions are mainly the consequence of 
data limitations. More precisely, since firms can buy intermediate goods also from 
domestic firms, it would be important to include this information to the analysis, should it 
be available. To control for this, this study used a proxy in the form of domestic cost level, 
calculated as the difference between the total level of material costs and imported 
intermediates. In addition, since not all FDI flows affect firms’ skill share, definition of 
outward FDI should also include information on the type and volume of the FDI flows in 
order to make a more comprehensive measure on its effect on the skill share. 
Unfortunately, this data is available only for the recent years. It is also important to 
mention that the outward FDI dummy can also reflect relocating its assembly to a foreign 
subsidiary or obtain inputs from the subsidiary. The former would increase the skill share 
in a Slovenian firm, especially if the outward FDI is present in a country with cheaper 
labour. To control for this, it is important to take into account also the destination country 
of the outward FDI. On the other hand, importing inputs from the subsidiary might 

371 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
all the activities to another firm, located overseas, or as foreign direct investments. To 
estimate the effect of outward FDI, I take into account the dataset from the Bank of 
Slovenia, which comprises information on the FDI flows for every Slovenian firm. This 
dataset also gathers information on the volume of the FDI and the destination country of 
the investment. Since the information on the volume and the type of FDI is available only 
for 2007 onwards, whereas the information on the presence of firm’ s FDI flows, if the share 
of equity of the Slovenian firm in a foreign enterprise is at least 10%, is available for the 
entire observation period, this variable was formed as a dummy variable. 
Definitions of the outsourcing and outward FDI therefore take into account only foreign 
flows. The weaknesses of the abovementioned definitions are mainly the consequence 
of data limitations. More precisely, since firms can buy intermediate goods also from 
domestic firms, it would be important to include this information to the analysis, should 
it be available. To control for this, this study used a proxy in the form of domestic cost 
level, calculated as the difference between the total level of material costs and imported 
intermediates. In addition, since not all FDI flows affect firms’ skill share, definition of 
outward FDI should also include information on the type and volume of the FDI flows in 
order to make a more comprehensive measure on its effect on the skill share. Unfortunately, 
this data is available only for the recent years. It is also important to mention that the 
outward FDI dummy can also reflect relocating its assembly to a foreign subsidiary or 
obtain inputs from the subsidiary. The former would increase the skill share in a Slovenian 
firm, especially if the outward FDI is present in a country with cheaper labour. To control 
for this, it is important to take into account also the destination country of the outward 
FDI. On the other hand, importing inputs from the subsidiary might interlink with firms’ 
outsourcing. Therefore, in future studies, it would be interesting to analyse the share of 
inputs from countries, where firms have outward FDI, and compare this with the share of 
inputs from countries, where firms do not have outward FDI.
3.1 Framework and specification of the basic model
The empirical model mainly follows the theoretical framework, introduced by Hummels 
et al. (2014). The production function of a firm i in year t is defined as:
       
(2)
where the dependent variable, Y
it
, is the output, A
it
 is productivity, K
it
 is capital, H
it
 is 
skilled labour, and C
it
 is a composite input, consisting of domestic and foreign inputs. 
The latter relate to outsourcing and/or outward FDI activities, and the former relate to 
unskilled labour and domestic inputs. As presented in the literature review, outward FDI 
and outsourcing activities have distinct impacts on skilled and unskilled labour, where the 
impact on the skilled labour is on average positive, while the impact on the unskilled labour 
is on average negative (see for example Hummels et al., 2014, Mion & Zhu, 2013, Hijzen, 
Görg, & Hine, 2005, Strauss-Kahn, 2003, Egger & Egger, 2003, Feenstra & Hanson, 1996). 
  
interlink with firms’ outsourcing. Therefore, in future studies, it would be interesting to 
analyse the share of inputs from countries, where firms have outward FDI, and compare 
this with the share of inputs from countries, where firms do not have outward FDI. 
 
3.1 Framework and specification of the basic model 
 
The empirical model mainly follows the theoretical framework, introduced by Hummels et 
al. (2014). The production function of a firm i in year t is defined as: 
 
     ),,(
it it it it it
C H K f AY =    (2) 
 
where the dependent variable, Y
it
, is the output, A
it
 is productivity, K
it
 is capital, H
it
 is 
skilled labour, and C
it
 is a composite input, consisting of domestic and foreign inputs. The 
latter relate to outsourcing and/or outward FDI activities, and the former relate to unskilled 
labour and domestic inputs. As presented in the literature review, outward FDI and 
outsourcing activities have distinct impacts on skilled and unskilled labour, where the 
impact on the skilled labour is on average positive, while the impact on the unskilled 
labour is on average negative (see for example Hummels et al., 2014, Mion & Zhu, 2013, 
Hijzen, Görg, & Hine, 2005, Strauss-Kahn, 2003, Egger & Egger, 2003, Feenstra & 
Hanson, 1996). Since both factors affect the labour demand of firms, the model of 
Hummels et al. (2014) is extended by including also domestic inputs and outward FDI into 
the model. Due to the abovementioned data limitations of outward FDI and outsourcing’s 
definitions, it was not possible to find an appropriate measure for both factors that would 
consider all flows and is therefore not entirely consistent with the presented model. It 
would be interesting to take this into account in the future studies, when data limitations 
are resolved. 
 
To implement the theoretical model in the data, the equation (2) is rearranged and P
it
 
introduced as a reduced-form of the demand for firm i’s products. Furthermore, 
international activities of firms are separated into outward FDI (FDI
it
) and outsourcing 
(Out
it
), and domestic inputs into unskilled labour (L
it
) and domestic costs (DC
it
). 
Furthermore, following Hummels et al. (2014), the logarithm of the average wage level 
(W
it-s
), and the logarithm of the value of exports (X
it-s
) in firm i and year t are added to the 
model. The latter is introduced in order to capture time varying shocks to demand for 
firms’ output. A detailed derivation of the model is enclosed in the Appendix. 
 
After rearranging, the empirical model becomes: 
 
 Skill_share
it 
= ß
0
 + ß
1
Out
it
 + ß
2
FDI
it
 + ß
3
X
it
 + ß
4
A
it
 + ß
5
K
it
 + 
+ ß
6
W
it
 + ß
7
DC
it
 + Time
t
 + Ind
t 
+ ε
it
, 
(3) 
 
where the dependent variable Skill_share
it
 is the logarithm of the ratio between skilled 
employees and the total number of employees in firm i and year t. Similarly to Hummels et 
al. (2014), skilled workers in the first part of the analysis are defined as tertiary educated 
workers, i.e. if they attain some form of college degree, which is normally at least 14 years 
of school attainment in Slovenia. As already explained, outsourcing (Out
it
) is defined as the 
share of intermediate imports in the total material costs, and outward FDI (FDI
it
) as the 
dummy variable, controlling for the outward FDI. Other explanatory variables are the 
following: X
it
 is a logarithm of the value of exports, A
it
 is a measure of productivity, K
it
 is a 
logarithm of capital per employee, W
it
 is a logarithm of the average annual wage level, and 

372 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Since both factors affect the labour demand of firms, the model of Hummels et al. (2014) 
is extended by including also domestic inputs and outward FDI into the model. Due to 
the abovementioned data limitations of outward FDI and outsourcing’s definitions, it was 
not possible to find an appropriate measure for both factors that would consider all flows 
and is therefore not entirely consistent with the presented model. It would be interesting 
to take this into account in the future studies, when data limitations are resolved.
To implement the theoretical model in the data, the equation (2) is rearranged and 
P
it
 introduced as a reduced-form of the demand for firm i’s products. Furthermore, 
international activities of firms are separated into outward FDI (FDI
it
) and outsourcing 
(Out
it
), and domestic inputs into unskilled labour (L
it
) and domestic costs (DC
it
). 
Furthermore, following Hummels et al. (2014), the logarithm of the average wage level 
(W
it-s
), and the logarithm of the value of exports (X
it-s
) in firm i and year t are added to 
the model. The latter is introduced in order to capture time varying shocks to demand for 
firms’ output. A detailed derivation of the model is enclosed in the Appendix.
After rearranging, the empirical model becomes:
        (3)
where the dependent variable Skill_share
it
 is the logarithm of the ratio between skilled 
employees and the total number of employees in firm i and year t. Similarly to Hummels 
et al. (2014), skilled workers in the first part of the analysis are defined as tertiary educated 
workers, i.e. if they attain some form of college degree, which is normally at least 14 years 
of school attainment in Slovenia. As already explained, outsourcing (Out
it
) is defined as 
the share of intermediate imports in the total material costs, and outward FDI (FDI
it
) as 
the dummy variable, controlling for the outward FDI. Other explanatory variables are 
the following: X
it
 is a logarithm of the value of exports, A
it
 is a measure of productivity, 
K
it
 is a logarithm of capital per employee, W
it
 is a logarithm of the average annual wage 
level, and DC
it
 is a logarithm of the domestic cost level in firm i and year t. Domestic 
cost level (DC
it
) is calculated as the difference between the total level of material costs 
and imported intermediates. To increase the sensitivity of results, two different measures 
of productivity (A
it
) are used; value added per employee and total factor productivity. 
The total factor productivity is calculated using the proposed method of Levinsohn and 
Petrin (2003). Levinsohn and Petrin (2003) extend the model of Olley and Pakes (1996) 
by substituting investments with intermediate inputs when estimating the production 
function. The authors argue one of the main benefits of this procedure is data driven as 
the procedure can be used also for firms with zero investments, while another advantage is 
the result of intermediate inputs being more responsive to the total productivity term than 
investments (see for example Levinsohn & Petrin, 2003, Petrin, Poi & Levinsohn, 2004). 
Both measures – the Levinsohn-Petrin measured total factor productivity and the value 
added – have been for example used in Damijan, Konings, and Polanec (2014). Finally, 
variable Time
t
 controls for year specific effects and Ind
t
 denotes industry dummy variables 
(2-digit NACE rev. 1 industries).
  
interlink with firms’ outsourcing. Therefore, in future studies, it would be interesting to 
analyse the share of inputs from countries, where firms have outward FDI, and compare 
this with the share of inputs from countries, where firms do not have outward FDI. 
 
3.1 Framework and specification of the basic model 
 
The empirical model mainly follows the theoretical framework, introduced by Hummels et 
al. (2014). The production function of a firm i in year t is defined as: 
 
     ),,(
it it it it it
C H K f AY =    (2) 
 
where the dependent variable, Y
it
, is the output, A
it
 is productivity, K
it
 is capital, H
it
 is 
skilled labour, and C
it
 is a composite input, consisting of domestic and foreign inputs. The 
latter relate to outsourcing and/or outward FDI activities, and the former relate to unskilled 
labour and domestic inputs. As presented in the literature review, outward FDI and 
outsourcing activities have distinct impacts on skilled and unskilled labour, where the 
impact on the skilled labour is on average positive, while the impact on the unskilled 
labour is on average negative (see for example Hummels et al., 2014, Mion & Zhu, 2013, 
Hijzen, Görg, & Hine, 2005, Strauss-Kahn, 2003, Egger & Egger, 2003, Feenstra & 
Hanson, 1996). Since both factors affect the labour demand of firms, the model of 
Hummels et al. (2014) is extended by including also domestic inputs and outward FDI into 
the model. Due to the abovementioned data limitations of outward FDI and outsourcing’s 
definitions, it was not possible to find an appropriate measure for both factors that would 
consider all flows and is therefore not entirely consistent with the presented model. It 
would be interesting to take this into account in the future studies, when data limitations 
are resolved. 
 
To implement the theoretical model in the data, the equation (2) is rearranged and P
it
 
introduced as a reduced-form of the demand for firm i’s products. Furthermore, 
international activities of firms are separated into outward FDI (FDI
it
) and outsourcing 
(Out
it
), and domestic inputs into unskilled labour (L
it
) and domestic costs (DC
it
). 
Furthermore, following Hummels et al. (2014), the logarithm of the average wage level 
(W
it-s
), and the logarithm of the value of exports (X
it-s
) in firm i and year t are added to the 
model. The latter is introduced in order to capture time varying shocks to demand for 
firms’ output. A detailed derivation of the model is enclosed in the Appendix. 
 
After rearranging, the empirical model becomes: 
 
 Skill_share
it 
= ß
0
 + ß
1
Out
it
 + ß
2
FDI
it
 + ß
3
X
it
 + ß
4
A
it
 + ß
5
K
it
 + 
+ ß
6
W
it
 + ß
7
DC
it
 + Time
t
 + Ind
t 
+ ε
it
, 
(3) 
 
where the dependent variable Skill_share
it
 is the logarithm of the ratio between skilled 
employees and the total number of employees in firm i and year t. Similarly to Hummels et 
al. (2014), skilled workers in the first part of the analysis are defined as tertiary educated 
workers, i.e. if they attain some form of college degree, which is normally at least 14 years 
of school attainment in Slovenia. As already explained, outsourcing (Out
it
) is defined as the 
share of intermediate imports in the total material costs, and outward FDI (FDI
it
) as the 
dummy variable, controlling for the outward FDI. Other explanatory variables are the 
following: X
it
 is a logarithm of the value of exports, A
it
 is a measure of productivity, K
it
 is a 
logarithm of capital per employee, W
it
 is a logarithm of the average annual wage level, and 

373 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Following Hummels et al. (2014), outsourcing, exports, and levels of domestic costs are not 
scaled by firm size in order to enhance the explanatory value of the model. More precisely, 
changes in firm size might be a consequence of the changes in these variables. Instead, the 
model has been estimated with and without firm size as one of the explanatory variables.
It would be convenient to include also other control variables that have an important effect 
on the skill structure of firms, as for example information on the R&D expenditures and 
the number of patents. However, since this data is not available or is incomplete, there 
exists a missing variable bias, which would be important to be taken into account in the 
future studies.
Finally, the models are estimated with methods for panel data analysis; the pooled ordinary 
least squares, fixed effects, and random effects. The three basic panel methods were used 
in order to test the sensitivity of the results, while the methods were applied following the 
procedures suggested by Cameron and Trivedi (2009). Following Hummels et al. (2014), 
standard errors were clustered at firm levels. Due to cluster-robust standard errors and an 
unbalanced panel dataset, a robust version of the Hausman test was needed in order to 
compare the models (Cameron & Trivedi, 2009). In accordance, the method proposed by 
Schaffer and Stillman (2010) was applied, while the Sargan-Hansen test is reported in the 
tables with regression results.
3.2 Extensions of the model
The formation of the extended model is based on the model, presented in the previous 
subsection. First, the model is extended by differentiating between outsourcing from 
high- and low-income countries, and outward FDI to high- and low-income countries. 
Countries are classified as high- or low-income according to the definitions, made by the 
World Bank, where the low-income, lower-middle-income and upper-middle-income 
economies for a particular year are assigned as low-income countries, and high-income 
economies as high-income countries (WB, 2015).
The extended model, controlling for outsourcing from low- and high-income countries, 
and outward FDI to low- and high-income countries is the following:
        (4)
where Out_high
it
 is outsourcing from high-income countries, FDI_high
it
 is an interaction 
term between outward FDI and a dummy variable, controlling for high-income countries, 
and High
it
 denotes a dummy variable, controlling for high-income countries. Since firms 
could import only a fraction of their inputs from high-income countries, the variable Out_
high
it
 was restricted so that the definition covers only firms that import more than a half of 
their intermediate inputs, in terms of value, from high-income countries. The rest of the 
  
 
 Skill_share
it 
= ß
0
 + ß
1
Out
it
 + ß
2
Out_high
it
 +ß
3
FDI
it
 + ß
4
FDI_high
it
 + 
                   + ß
5
High
it
 + ß
6
X
it
 + ß
7
A
it
 + ß
8
K
it
 + ß
9
W
it
 + ß
10
DC
it
 +  
                             + Time
t
 + Ind
t 
+ ε
it
, 
(4) 
 
where Out_high
it
 is outsourcing from high-income countries, FDI_high
it
 is an interaction 
term between outward FDI and a dummy variable, controlling for high-income countries, 
and High
it
 denotes a dummy variable, controlling for high-income countries. Since firms 
could import only a fraction of their inputs from high-income countries, the variable 
Out_high
it
 was restricted so that the definition covers only firms that import more than a 
half of their intermediate inputs, in terms of value, from high-income countries. The rest of 
the model in the expression (4) follows the basic model (3). As aforementioned, the 
presented literature suggests that outsourcing from high-income countries and outward FDI 
to low-income countries would increase firms’ skill share. The coefficients ß
1
 and ß
2
 
reflect the impact on firms’ skill structure if firms outsource the majority of their 
intermediate inputs from high-income countries, while the impact of outward FDI to low-
income countries on firms’ skill structure is reflected in the coefficient ß
3
. 
 
Subsequently, the extensions of the model also include occupational level when defining 
skills, in order to take into account also the nature of the tasks and duties of workers’ jobs. 
Four different skill levels could be applied to ten major groups of occupations, which are 
classified by the International Labour Organization (ILO). The setting of the present paper 
takes into account a version of the International Standard Classification of Occupations 
(ISCO), the ISCO-88 classification. The top two skill levels, 3 and 4, with the skill level 4 
being the highest, relate to tertiary education and correspond to three major groups: 
“Managers” (skill levels 3 and 4), “Professionals” (skill level 4) and “Technicians” (skill 
level 3) (ILO, 2012, Elias & Birch, 1994). These three major groups of occupations define 
skilled workers in the extended model. “Managers” include legislators, senior officials and 
managers, whose main tasks consist of determining, formulating and supervising the 
implementation of government policies, laws and public regulations, or planning, directing 
and coordinating the policies and activities of enterprises, organisations, or departments. 
“Professionals” work in the fields of physical, life or social sciences, or humanities and are 
responsible for increasing the existing stock of knowledge, finding solutions to the 
problems by applying scientific and artistic concepts and theories, and transferring their 
knowledge onto others. Finally, “Technicians” include technicians and associate 
professionals who have technical knowledge and experience in the fields of physical, life 
or social sciences, or humanities. Their main tasks include carrying out technical work and 
teaching at particular educational levels, related to the abovementioned fields (ILO, 2014). 
 
4. Data and descriptive statistics 
 
By combining different databases, a rich firm-level and employee-level panel dataset for 
Slovenian firms was obtained, covering the period from 1997 to 2010. The dataset 
comprises information on the balance sheet data and income statements of Slovenian firms, 
their export and import activities (i.e. value of exports and imports, type of exported and 
imported goods, and destination of exports and imports), characteristics of employees (i.e. 
gender, age, gross wage, educational level, and occupational level), and information on the 
foreign direct investments of Slovenian firms. The latter gathers information on the FDI 
flows for a particular Slovenian firm. The dataset links the following databases: personal 
income-tax data, transaction-level data on exports and imports of goods, Statistical 

374 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
model in the expression (4) follows the basic model (3). As aforementioned, the presented 
literature suggests that outsourcing from high-income countries and outward FDI to low-
income countries would increase firms’ skill share. The coefficients ß
1
 and ß
2
 reflect the 
impact on firms’ skill structure if firms outsource the majority of their intermediate inputs 
from high-income countries, while the impact of outward FDI to low-income countries 
on firms’ skill structure is reflected in the coefficient ß
3
.
Subsequently, the extensions of the model also include occupational level when defining 
skills, in order to take into account also the nature of the tasks and duties of workers’ jobs. 
Four different skill levels could be applied to ten major groups of occupations, which are 
classified by the International Labour Organization (ILO). The setting of the present paper 
takes into account a version of the International Standard Classification of Occupations 
(ISCO), the ISCO-88 classification. The top two skill levels, 3 and 4, with the skill level 
4 being the highest, relate to tertiary education and correspond to three major groups: 
“Managers” (skill levels 3 and 4), “Professionals” (skill level 4) and “Technicians” (skill 
level 3) (ILO, 2012, Elias & Birch, 1994). These three major groups of occupations define 
skilled workers in the extended model. “Managers” include legislators, senior officials 
and managers, whose main tasks consist of determining, formulating and supervising the 
implementation of government policies, laws and public regulations, or planning, directing 
and coordinating the policies and activities of enterprises, organisations, or departments. 
“Professionals” work in the fields of physical, life or social sciences, or humanities and 
are responsible for increasing the existing stock of knowledge, finding solutions to the 
problems by applying scientific and artistic concepts and theories, and transferring 
their knowledge onto others. Finally, “Technicians” include technicians and associate 
professionals who have technical knowledge and experience in the fields of physical, life or 
social sciences, or humanities. Their main tasks include carrying out technical work and 
teaching at particular educational levels, related to the abovementioned fields (ILO, 2014).
4. DATA AND DESCRIPTIVE STATISTICS
By combining different databases, a rich firm-level and employee-level panel dataset 
for Slovenian firms was obtained, covering the period from 1997 to 2010. The dataset 
comprises information on the balance sheet data and income statements of Slovenian 
firms, their export and import activities (i.e. value of exports and imports, type of exported 
and imported goods, and destination of exports and imports), characteristics of employees 
(i.e. gender, age, gross wage, educational level, and occupational level), and information 
on the foreign direct investments of Slovenian firms. The latter gathers information on 
the FDI flows for a particular Slovenian firm. The dataset links the following databases: 
personal income-tax data, transaction-level data on exports and imports of goods, 
Statistical Registry of Employees, firm-level accounting data, and FDI, and was provided 
by the Statistical office of the Republic of Slovenia (SORS), the Tax Authorities of Slovenia 
(TARS), the Bank of Slovenia, and the Agency of the Republic of Slovenia for Public Legal 
Records and Related Services (AJPES).

375 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
As a small and open economy, Slovenia presents an interesting country for empirical 
analysis. During the observation period, Slovenia was exposed to increasing international 
competition due to EU accession and multilateral liberalisation (Zajc Kejžar & Ponikvar, 
2014), while at the end of the observation period, Slovenia experienced the economic 
downturn.
After observing vast differences between manufacturing and service firms (Table 1), and 
taking into account the aforementioned emphasis on the importance of separating the 
analysis for manufacturing and service firms (see for example De Backer & Y amano, 2012, 
Michel & Rycx, 2009, Horgos, 2006), the empirical analysis was carried out independently 
for the two types of firms. Manufacturing firms on average employ a higher total number 
of employees and tertiary educated employees, compared to service firms. Furthermore, 
especially in the more recent years, manufacturing firms on average employ slightly older 
employees than service firms, where age can be considered as a proxy for the experience of 
employees (Zoghi, 2010). When comparing the average annual gross wages for the recent 
years, manufacturing firms on average pay their employees lower wages than service 
firms. However, when comparing the average wages of tertiary educated employees, 
manufacturing firms pay higher average wages than service firms. Manufacturing firms 
on average also have lower skill shares than service firms. The latter differences in the 
average gross wages and the skill shares could be the outcome of a different occupational 
and educational structure of employees in manufacturing and service firms, which will 
be presented in one of the upcoming paragraphs. For brevity, in the following tables, the 
descriptive statistics for the first half of the treated period is presented with a four-year 
gap, while the descriptive statistics for the recent years is complete.

376 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table 1. Characteristics of Slovenian manufacturing and service firms
Total
Ye ar 1998 2002 2006 2007 2008 2009 2010
Employment 18.1 17.3 15.5 15.2 14.2 13.0 12.2
Employment of 
tertiary educated
2.1 2.3 2.4 2.5 2.4 2.4 2.4
Skill share 21.7 23.2 25.0 25.4 25.9 27.0 28.1
Age 36.3 38.2 39.3 39.5 39.8 40.2 40.5
Gross wage 5,139 8,002 10,625 11,311 11,850 11,941 12,260
Gross wage of 
tertiary educated
8,696 12,804 16,132 16,993 17,993 17,752 17,703
Number of firms 25,216 27,064 30,908 32,799 35,833 36,814 37,882
Manufacturing firms
Year 1998 2002 2006 2007 2008 2009 2010
Employment 41.4 38.9 35.0 34.4 31.8 27.8 26.6
Employment of 
tertiary educated
3.9 4.2 4.5 4.6 4.4 4.3 4.3
Skill share 14.5 14.6 15.9 16.1 16.3 17.2 18.0
Age 36.2 37.9 39.7 39.9 40.4 40.8 41.2
Gross wage 5,048 7,658 10,320 11,066 11,664 11,547 11,962
Gross wage of 
tertiary educated
9,785 14,154 17,397 18,267 19,232 18,868 18,808
Number of firms 5,411 5,750 6,140 6,318 6,696 6,746 6,798
Service firms
Year 1998 2002 2006 2007 2008 2009 2010
Employment 9.7 9.9 9.6 9.6 9.3 8.9 8.5
Employment of 
tertiary educated
1.4 1.7 1.9 2.0 2.0 2.0 2.0
Skill share 25.1 27.4 30.1 30.9 31.7 32.9 34.1
Age 36.3 38.3 39.4 39.6 40.0 40.3 40.6
Gross wage 5,229 8,257 11,036 11,779 12,426 12,511 12,791
Gross wage of 
tertiary educated
8,260 12,363 15,723 16,557 17,565 17,379 17,351
Number of firms 18,037 19,047 21,527 22,729 24,773 25,647 26,495
Note: Explanations of the variables are as follows: Employment: mean number of employees; Employment of 
tertiary educated: mean number of tertiary educated employees; Skill share: the average of the share of the tertiary 
educated; Age: mean age of employees; Gross wage: mean annual gross wage in €; Gross wage of tertiary educated: 
mean annual gross wage of tertiary educated employees in €; Number of firms: number of observations.
Source: SORS, author’s calculations

377 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
In addition, I also make a comparison between outward FDI and outsourcing firms (Table 
2). Both types of firms are bigger in size, compared to an average firm in Table 1. This 
confirms previous findings of Wagner (2011), whose study indicates a self-selection of 
firms into outward FDI and finds that these firms are larger, more productive and more 
human capital intensive. The average age of employees in outward FDI and outsourcing 
firms is also slightly higher than in the average firm. Finally, the average gross wages 
and gross wages of tertiary educated employees are above the average, where the highest 
average is in the FDI firms. Checking the overlap between outward FDI and outsourcing 
by transforming the outsourcing variable into a dummy variable and performing the cross-
tabulation between the two dummy variables shows that less than 10% of outsourcing firms 
also make use of the outward FDI, and vice versa. The table is enclosed in the Appendix.
A separate analysis was done also for firms that make use of outward FDI to high-income 
countries and outsource the majority of their intermediate inputs from high-income 
countries. The descriptive statistics for these firms show that they on average pay higher 
wages than the average FDI and outsourcing firms. Furthermore, while firms that make 
use of outward FDI to high-income countries are on average bigger than the average FDI 
firm, firms that outsource from high-income countries are on average smaller than the 
average outsourcing firm. The table is enclosed in the Appendix. Different characteristics 
of FDI and outsourcing firms, and the finding that the overlap between both types of firms 
is less than 10% confirm the importance of taking into account both factors, outsourcing 
and outward FDI, in one model.

378 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table 2. Characteristics of Slovenian firms which use outward FDI and outsource
FDI firms
Ye ar 1998 2002 2006 2007 2008 2009 2010
Employment 301.9 207.3 194.3 185.6 175.0 163.6 161.3
Employment of 
tertiary educated
35.1 27.9 32.1 32.1 31.9 32.6 34.0
Skill share 22.5 25.7 31.8 32.9 34.5 35.4 37.6
Age 38.9 39.1 40.1 40.1 40.4 41.0 41.5
Gross wage 8,384 11,988 16,934 18,187 19,325 19,453 20,081
Gross wage of 
tertiary educated
15,215 19,729 24,759 26,282 27,707 27,380 27,487
Number of firms 474 831 895 943 994 957 894
Outsourcing firms
Ye ar 1998 2002 2006 2007 2008 2009 2010
Employment 56.9 56.2 64.7 57.0 55.4 48.7 48.4
Employment of 
tertiary educated
5.9 6.5 8.7 8.2 8.2 8.1 8.4
Skill share 23.0 22.2 24.4 25.0 25.1 26.4 27.4
Age 36.6 38.4 39.2 39.5 39.9 40.4 40.6
Gross wage 5,930 9,277 13,391 14,474 15,587 15,622 16,333
Gross wage of 
tertiary educated
9,867 14,863 19,995 20,758 22,416 22,092 22,370
Number of firms 2,524 2,820 2,430 2,619 2,695 2,473 2,486
Note: Explanations of the variables are as follows: Outsourcing firms: firms that import intermediate products; 
FDI firms: firms that engage in outward FDI; Employment: mean number of employees; Employment of tertiary 
educated: mean number of tertiary educated employees; Skill share: the average of the share of the tertiary 
educated; Age: mean age of employees; Gross wage: mean annual gross wage in €; Gross wage of tertiary educated: 
mean annual gross wage of tertiary educated employees in €; Number of firms: number of observations.
Source: SORS, author’s calculations
Next, the occupational structure of manufacturing and service firms is compared by using 
ISCO-88 classification (Figure 1). In manufacturing firms, the share of Machinery workers 
has been decreasing through the period, but it is still the highest among all occupational 
groups. On the other hand, the share of Craft workers has been increasing through the 
period, but remained second. The third largest share in manufacturing firms belongs to 
Technicians, while the fourth and fifth largest shares appertain to Elementary occupations 
and Clerks, respectively. In service firms, on the other hand, Service workers occupy the 
largest share and the share remains steady throughout the observed period. The second 
largest share in service firms belongs to Technicians, while the third to Clerks. Among 
other occupational groups, Elementary occupations represent the fourth largest share and 
Machinery workers the fifth. Since the shares of Agricultural and Army workers represent 

379 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
only a minor part of the total shares in both, manufacturing and service firms, they were 
excluded from further empirical analysis (description of all major occupational groups is 
included in the Appendix).
Figure 1. Occupational structure of manufacturing and service firms in Slovenia 
Source: SORS, author’s calculations
Table 3 presents the descriptive statistics of the three major groups of occupations 
that define skilled workers in the extended model; i.e. Managers, Professionals, and 
Technicians. The table combines results for both, manufacturing and service firms. 
Professionals represent the highest share of tertiary educated among all groups, followed 
by Managers and Technicians. Looking at the total average in the observed period, 88.7 % 
of Professionals, 55.1 % of Managers, and 27.4 % of Technicians were tertiary educated. 
This allocation of shares is consistent with the ISCO-88 classes of skill levels, presented 
in the methodological part. Taking into account the average age of employees, Managers 
are on average the oldest among all occupational groups, Professionals were on average 
a bit older than the average worker in the first years of the observational period, while 
in the recent years, they are a bit younger than the average. In contrast, Technicians are 
the youngest of the three groups and compared to the total population of employees. 
Managers earn the highest gross wages among all occupational groups, followed by 
Professionals. Technicians also have above average wages, in parallel to the total average 
numbers. However, since Professionals and Managers present more than a half of all 
tertiary educated workers and earn the highest wages, tertiary educated Technicians earn 
below the average gross wages, when taking into account only tertiary educated workers.
  
excluded from further empirical analysis (description of all major occupational groups is 
included in the Appendix). 
 
Figure 1. Occupational structure of manufacturing and service firms in Slovenia  
 
Source: SORS, author’s calculations 
 
Table 3 presents the descriptive statistics of the three major groups of occupations that 
define skilled workers in the extended model; i.e. Managers, Professionals, and 
Technicians. The table combines results for both, manufacturing and service firms. 
Professionals represent the highest share of tertiary educated among all groups, followed 
by Managers and Technicians. Looking at the total average in the observed period, 88.7 % 
of Professionals, 55.1 % of Managers, and 27.4 % of Technicians were tertiary educated. 
This allocation of shares is consistent with the ISCO-88 classes of skill levels, presented in 
the methodological part. Taking into account the average age of employees, Managers are 
on average the oldest among all occupational groups, Professionals were on average a bit 
older than the average worker in the first years of the observational period, while in the 
recent years, they are a bit younger than the average. In contrast, Technicians are the 
youngest of the three groups and compared to the total population of employees. Managers 
earn the highest gross wages among all occupational groups, followed by Professionals. 
Technicians also have above average wages, in parallel to the total average numbers. 
However, since Professionals and Managers present more than a half of all tertiary 
educated workers and earn the highest wages, tertiary educated Technicians earn below the 
average gross wages, when taking into account only tertiary educated workers. 
 
  

380 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table 3. Characteristics of employees in skilled occupations
Managers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total 
employment
5.6 5.5 6.1 6.1 6.3 6.8 7.0
Share in the 
tertiary educated
25.2 23.4 22.6 21.8 21.7 21.8 21.1
Age 41.5 43.0 43.7 43.7 43.6 43.9 43.9
Gross wage 8,972 14,125 18,436 19,530 20,398 20,231 20,301
Gross wage of 
tertiary educated
12,859 18,988 24,080 25,517 26,755 26,237 26,092
Professionals
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total 
employment
3.9 4.5 5.7 6.0 6.2 7.0 7.8
Share in the 
tertiary educated
31.2 31.6 33.9 34.1 34.2 34.6 34.8
Age 38.4 38.5 38.6 38.7 39.0 39.2 39.4
Gross wage 9,756 14,707 17,803 18,642 19,792 19,750 19,503
Gross wage of 
tertiary educated
10,277 15,411 18,455 19,462 20,732 20,750 20,622
Technicians
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total 
employment
15.6 16.2 16.8 16.7 16.5 16.9 16.9
Share in the 
tertiary educated
32.2 31.7 30.9 30.7 30.1 29.3 29.1
Age 35.7 37.5 38.6 38.7 39.1 39.5 39.9
Gross wage 6,113 9,389 12,166 12,980 13,891 13,970 14,246
Gross wage of 
tertiary educated
8,377 12,484 15,190 16,077 17,127 16,993 17,006
Note. Explanations of the variables are as follows: Share in the total employment: share of a particular occupational 
group in the total employment (in %); Share in the tertiary educated: share of a particular occupational group in 
the total number of tertiary educated employees (in %); Age: mean age of a particular occupational group; Gross 
wage: mean annual gross wage of a particular occupational group in €; Gross wage of tertiary educated: mean 
annual gross wage of tertiary educated in a particular occupational group in €.
Source: SORS, author’s calculations

381 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
The descriptive statistics of other occupational groups (included in the Appendix) reveal 
that other groups present only a minor share in the group of tertiary educated workers. 
The highest share in the total employment is on average presented by Machinery workers, 
followed by Craft workers, Elementary workers, Service workers, and Clerical workers, 
while the highest earners among these groups are on average Clerical workers, followed 
by Machinery workers, Craft workers, Service workers and Elementary workers. This 
distribution of occupations is also the reason for higher average wages and higher average 
skill shares in service firms.
5. EMPIRICAL ANALYSIS
What is here called a basic model measures the effect of outsourcing and outward FDI on 
the skill structure of firms. Later, the first extension of the model differentiates between 
outsourcing from high- and low-income countries, and outward FDI to high- and low-
income countries, while in the second extension an alternative definition of skilled 
employees is introduced, taking into account information on the occupational level of 
employees.
5.1 Basic model
The basic model analyses the effect of outsourcing and outward FDI on the skill structure 
in Slovenian firms. As introduced in the methodology part, the models were estimated 
with the pooled ordinary least squares, and with fixed effects and random effects models. 
In the basic model, tertiary educated workers are defined as skilled.

382 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table 4. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms (observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI
0.147*** 0.063** 0.092*** 0.315*** 0.002 0.071**
[4.19] [2.28] [3.53] [7.88] [0.08] [2.31]
Outsourcing
-32.55*** 1.111 -0.64 34.500** 11.820* 17.180***
[-4.51] [0.20] [-0.13] [2.15] [1.93] [2.72]
log(capital per emp)
0.014 0.013 0.016* -0.008 0.000 0.001
[1.23] [1.39] [1.92] [-1.08] [0.080] [0.21]
log(tfp)
0.027* -0.020* -0.021* 0.046*** -0.067*** -0.043***
[1.89] [-1.67] [-1.95] [4.74] [-8.34] [-6.10]
log(export value)
-0.002 0.000 0.000 -0.016*** -0.002 -0.006***
[-0.89] [0.08] [0.19] [-4.85] [-0.68] [-2.69]
log(gross wage)
0.619*** 0.244*** 0.295*** 0.643*** 0.155*** 0.260***
[15.6] [8.86] [10.8] [27.0] [10.5] [17.1]
log(domestic costs)
0.172*** 0.051*** 0.101*** 0.126*** 0.088*** 0.099***
[13.6] [3.49] [9.40] [14.6] [8.48] [13.6]
Constant
-6.172*** -0.675 -2.446*** -6.060*** -0.600* -2.036***
[-11.7] [-1.17] [-4.49] [-20.4] [-1.77] [-8.11]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations
41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within)
0.068 0.064 0.035 0.027
R-squared (between)
0.097 0.167 0.059 0.209
R-squared (overall)
0.182
0.158
0.201
0.192 0.175
0.219
Sargan-Hansen statistics 503.727***     1,359.569***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated workers are defined as skilled. The explanation of variables: FDI: dummy 
variable, controlling for the outward FDI; Outsourcing: share of intermediate imports in the total material 
costs; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the total 
factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross wage): 
logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. *** 
p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

383 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Taking into account only the results of the most preferred model, according to the Sargan-
Hansen test, i.e. the fixed effects, outward FDI shows a positive impact on the share of 
skilled workers in manufacturing firms, while outsourcing shows a positive impact on 
firms’ skill share in service firms (Table 4).
To control the robustness of the results, the models were estimated by adding firm size as 
one of the control variables. In addition, value added was substituted for the total factor 
productivity. Robustness checks confirm results from the basic model for manufacturing 
firms, while the correlation between outsourcing and firms’ skill share is no longer 
statistically significant when controlling also for firms’ size. The robustness checks are 
included in the Appendix.
5.2 Extensions of the model
In order to obtain new information, two extensions of the basic model have been made. 
The first extension differentiates between outsourcing from high- and low-income 
countries and outward FDI to high- and low-income countries. The second extension 
includes information on the occupational level of workers when defining skills. 
5.2.1 Differentiation between high- and low-income countries
Differentiating between outsourcing from high- and low-income countries and outward 
FDI to high- and low-income countries makes possible to estimate whether a particular 
type of source country of outward FDI and outsourcing has a more significant impact on 
the skill structure of domestic firms.
Results indicate that the effect of outsourcing and outward FDI on the share of high skilled 
employees is different for different source countries, especially when including also the 
alternative definition of skills, which will be presented in the subsequent subsection. 
Again, according to the Sargan-Hansen statistics, the most preferred results are obtained 
with the fixed effects method, so the following conclusions concentrate on the results of 
this method. For manufacturing firms, FDI to low-income countries shows a statistically 
significant positive impact on the share of skilled employees. The insignificant coefficient 
of the variable FDI_high implies that FDI to high-income countries does not have a 
stronger impact on the relative employment of skilled employees than FDI to low-income 
countries. In order to test if the sum between the coefficient FDI, which measures the 
impact of outward FDI to low-income countries on firms’ skill share, and interaction term, 
i.e. FDI_high, is statistically significantly different from zero, the Wald test was applied 
(the p-value of the F-test was 0.056). On the other hand, the impacts of outsourcing and 
outward FDI on firms’ skill structure are statistically insignificant in service firms.

384 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table 5. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, differentiating between high- and low-income countries 
(observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.196*** 0.059** 0.088*** 0.313*** 0.006 0.072**
[5.01] [2.01] [3.18] [7.04] [0.17] [2.27]
FDI_high -0.176*** 0.023 0.019 0.012 -0.025 -0.012
[-3.46] [0.57] [0.50] [0.17] [-0.43] [-0.20]
Outsourcing -19.510 33.190 27.720 59.230** 18.110 26.880**
[-0.49] [0.80] [0.78] [2.00] [1.53] [2.14]
Outsourcing_
high
-16.330 -37.140 -33.370 -42.910 -9.236 -15.86
[-0.40] [-0.89] [-0.93] [-1.19] [-0.81] [-1.28]
High 0.029 0.032* 0.032* 0.014 0.005 0.018
[0.85] [1.82] [1.95] [0.38] [0.25] [0.98]
log(capital 
per emp)
0.014 0.013 0.016* -0.008 0.000 0.001
[1.24] [1.39] [1.93] [-1.09] [0.08] [0.19]
log(tfp) 0.026* -0.020* -0.020* 0.046*** -0.067*** -0.043***
[1.83] [-1.67] [-1.95] [4.72] [-8.34] [-6.10]
log(export 
value)
-0.002 0.000 0.000 -0.016*** -0.001 -0.006***
[-0.91] [0.083] [0.20] [-4.83] [-0.68] [-2.67]
log(gross 
wage)
0.618*** 0.244*** 0.295*** 0.643*** 0.155*** 0.260***
[15.6] [8.85] [10.8] [27.1] [10.5] [17.1]
log(domestic 
costs)
0.172*** 0.050*** 0.100*** 0.126*** 0.088*** 0.099***
[13.4] [3.43] [9.26] [14.6] [8.48] [13.6]
Constant -6.176*** -0.657 -2.425*** -6.054*** -0.600* -2.034***
[-11.6] [-1.14] [-4.45] [-20.3] [-1.77] [-8.10]

385 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Industry 
effects
Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared 
(within)
0.068 0.064 0.035 0.027
R-squared (between) 0.097 0.166 0.059 0.209
R-squared 
(overall)
0.183 0.099 0.158 0.192 0.061 0.175
Sargan-Hansen statistics 517.471***     1,371.458***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated workers are defined as skilled. The explanation of variables: FDI: dummy 
variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for the outward FDI to high-
income countries; Outsourcing: share of intermediate imports in the total material costs; Outsourcing_high: share 
of intermediate imports from high-income countries in the total material costs if a firm imports the majority 
of its intermediate products from high-income countries; High: dummy variable, controlling for high-income 
countries; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the total 
factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross wage): 
logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. *** 
p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations
5.2.2 Alternative definition of skills
In the final extension of the model, the information on the occupational level of workers 
is added to the definition of skills, which was to this point defined by reaching a tertiary 
level of education. As in the previous section, a differentiation between high- and low-
income countries has been made. The most preferred method, according to the Sargan-
Hansen test, is again the fixed effects, so the following conclusions relate to the results of 
this method.
When defining skilled employees only by their occupational level; i.e. when they are 
classified as Managers, Professionals, or Technicians, the results are not statistically 
significant anymore for manufacturing firms (Table 6). Similarly as when defining skills 
only by the educational level, the results for service firms are not statistically significant.
Next, to further exploit the advantages of taking into account information on occupational 
level, the effect of outward FDI and outsourcing on firms’ skill share is estimated using 
disaggregated data for each of the three major skilled occupational groups. The following 
table presents only the results of the most preferred method according to the Sargan-
Hansen test (i.e. the fixed effects), while the results of all methods are included in the 
Appendix.

386 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
When defining skills only by the occupational level and disaggregating the data by 
the three major skilled occupational groups (Table 7), results for manufacturing firms 
show a statistically significant impact of outsourcing only for the relative employment 
of Technicians. The results point to a positive impact of outsourcing from low-income 
countries on the relative employment of Technicians, while the statistically significant 
and negative coefficient of the variable, controlling for outsourcing from high-income 
countries, implies that outsourcing from high-income countries has a weaker but still 
positive impact on the relative employment of Technicians. In order to test if the sum 
between the coefficient Outsourcing, which measures the impact of outsourcing from 
low-income countries on the relative employment of Technicians, and interaction term, 
i.e. Outsourcing_high, is statistically significantly different from zero, the Wald test was 
applied (the p-value of the F-test was 0.035). In service firms, outward FDI to low-income 
countries shows a statistically significant positive impact on the relative employment of 
Professionals. The insignificant coefficient of the variable FDI_high implies that FDI to 
high-income countries does not have a stronger impact on the relative employment of 
Professionals than FDI to low-income countries. The Wald test indicates outward FDI 
has a positive impact on the relative employment of Professionals in service firms (the 
p-value of the F-test between the variables FDI and FDI_high was 0.000). In addition, 
similarly as for manufacturing firms, the results for service firms show a positive impact 
of outsourcing from low- and high-income countries on the relative employment of 
Technicians (the p-value of the F-test between the two variables was 0.001).

387 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table 6. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, using occupational classification for defining skills 
(observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.153*** 0.022 0.037* 0.132*** 0.014 0.033
[5.04] [1.08] [1.85] [4.19] [0.59] [1.44]
FDI_high -0.001 0.004 0.006 0.086 -0.064 -0.052
[-0.03] [0.17] [0.25] [1.32] [-1.05] [-0.85]
Outsourcing 18.390 9.327 11.240 37.49*** 6.797 13.40***
[0.69] [0.45] [0.66] [2.86] [1.51] [2.85]
Outsourcing_high -64.63** -10.290 -15.370 -27.73* -2.318 -6.16
[-2.24] [-0.49] [-0.89] [-1.75] [-0.43] [-1.08]
High 0.150*** 0.000 0.020 0.153*** 0.029* 0.058***
[4.52] [0.005] [1.07] [6.65] [1.93] [4.21]
log(capital per emp) 0.004 0.019*** 0.018*** -0.012** 0.002 -0.002
[0.45] [2.59] [2.74] [-2.18] [0.47] [-0.55]
log(tfp) 0.138*** 0.030*** 0.043*** 0.146*** 0.017*** 0.046***
[11.6] [2.75] [4.40] [19.4] [2.63] [8.00]
log(export value) -0.008*** -0.003* -0.004*** -0.010*** 0.000 -0.004**
[-4.22] [-1.80] [-3.04] [-3.69] [0.079] [-2.15]
log(gross wage) 0.454*** 0.229*** 0.260*** 0.353*** 0.157*** 0.203***
[13.1] [8.68] [9.90] [21.4] [11.0] [14.8]
log(domestic costs) 0.041*** -0.024* -0.006 0.000 0.012 0.007
[3.69] [-1.88] [-0.61] [0.028] [1.43] [1.09]
Constant -2.250*** 1.500*** -0.606* -0.857*** 1.620*** 0.746***
[-5.84] [3.82] [-1.79] [-4.14] [6.93] [3.30]

388 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.026 0.024 0.012 0.009
R-squared (between) 0.084 0.145 0.131 0.212
R-squared (overall) 0.176 0.091 0.150 0.195 0.110 0.185
Sargan-Hansen statistics 608.733***     1,003.023***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where Managers, Professionals, and Technicians in ISCO-88 classification are defined as skilled. 
The explanation of variables: FDI: dummy variable, controlling for the outward FDI; FDI_high: dummy variable, 
controlling for the outward FDI to high-income countries; Outsourcing: share of intermediate imports in the 
total material costs; Outsourcing_high: share of intermediate imports from high-income countries in the total 
material costs if a firm imports the majority of its intermediate products from high-income countries; High: 
dummy variable, controlling for high-income countries; log(capital per emp): logarithm of the capital per 
employee in a firm; log(tfp): logarithm of the total factor productivity per employee in a firm; log(export value): 
logarithm of the value of exports; log(gross wage): logarithm of the average annual gross wage level; log(domestic 
costs): logarithm of the domestic cost level. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the 
analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

389 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table 7. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, for the major skilled occupational groups (observation 
period: 1997-2010)
  Manufacturing firms Service firms
  Managers Professionals Technicians Managers Professionals Technicians
FDI -0.013 0.049 -0.014 -0.022 0.161*** -0.032
[-0.47] [1.26] [-0.53] [-0.54] [4.06] [-0.90]
FDI_high 0.031 0.068 -0.041 0.042 0.007 -0.067
[0.72] [1.33] [-1.32] [0.58] [0.10] [-1.09]
Outsourcing -8.522 9.717 79.35** 3.162 -3.324 37.180**
[-0.47] [0.18] [2.40] [0.36] [-0.68] [2.29]
Outsourc-
ing_high
3.103 -12.38 -67.09** -10.32 -1.262 4.780
[0.17] [-0.23] [-2.02] [-1.28] [-0.22] [0.27]
High 0.022 0.014 0.031 0.006 0.028** 0.065***
[1.02] [0.74] [1.21] [0.32] [1.99] [3.00]
Industry 
effects
Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observa-
tions
41,751 41,751 41,751 87,613 87,613 87,613
R-squared 
(within)
0.029 0.053 0.024 0.016 0.028 0.035
R-squared 
(between)
0.030 0.186 0.126 0.030 0.084 0.084
R-squared 
(overall)
0.040 0.176 0.103 0.026 0.093 0.091
Sargan-
Hansen 
statistics
268.051*** 504.516*** 487.702*** 250.856*** 796.516*** 584.975***
Note. Econometric method: FE: fixed effects. The dependent variable is defined as the logarithm of the ratio between 
skilled employees and the total number of employees, where Managers, Professionals, and Technicians in ISCO-88 
classification are defined as skilled. The explanation of variables: FDI: dummy variable, controlling for the outward 
FDI; FDI_high: dummy variable, controlling for the outward FDI to high-income countries; Outsourcing: share 
of intermediate imports in the total material costs; Outsourcing_high: share of intermediate imports from high-
income countries in the total material costs if a firm imports the majority of its intermediate products from high-
income countries; High: dummy variable, controlling for high-income countries. Control variables used: logarithm 
of the capital per employee in a firm, logarithm of the total factor productivity per employee in a firm, logarithm 
of the value of exports, logarithm of the average annual gross wage level, logarithm of the domestic cost level. *** 
p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

390 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Finally, both dimensions for defining skills are combined, defining workers as skilled if 
they meet both criteria; i.e. if they attain tertiary education and are classified as Managers, 
Professionals, or Technicians. Equally as when defining skills by only occupational level, 
the results from the Table 8 for the most preferred method, i.e. the fixed effects, are not 
statistically significant. When dividing the analysis into the three major occupational groups 
(T able 9), the results for manufacturing and service firms show that outsourcing from low- 
and high-income countries has a positive impact on the relative employment of tertiary 
educated Technicians, where outsourcing from high-income countries has a positive but 
weaker impact than outsourcing from low-income countries. For manufacturing firms, 
the p-value of the F-test between the two variables was 0.069, whereas for service firms, 
it was 0.014. In addition, outward FDI to low- and high-income countries has a positive 
impact on the relative employment of tertiary educated Technicians in manufacturing 
firms (the p-value of the F-test between the two variables was 0.026) and also on the 
relative employment of tertiary educated Professionals in service firms (the p-value of the 
F-test between the two variables was 0.000).
These results suggest that the effect of educational level is not common, but it instead 
differs between different occupational groups, indicating that firms give different weight 
on educational attainment when employing individuals with different occupations. In 
manufacturing firms, outward FDI to high- and low-income countries does not have 
a positive impact on the relative employment of Technicians but only on the relative 
employment of tertiary educated Technicians. This result indicates that FDI firms 
would rather employ more educated Technicians. The finding is especially interesting 
as, taking into account descriptive statistics, less than a third of Technicians in the 
sample are tertiary educated, which means that a higher educational level gives them an 
opportunity to differentiate from the peers. Furthermore, following the hypothesis made 
in the introduction, one would expect that outsourcing from high-income countries 
would increase firms’ skill share due to importing more technologically advanced inputs. 
However, results show that outsourcing from low-income countries has a positive impact 
on the relative employment of Technicians in both, manufacturing and service firms, and 
that the impact of outsourcing from low-income countries is on average stronger than 
the impact of outsourcing from high-income countries. The latter result is not entirely 
consistent with aforementioned hypothesis. But, this finding might also indicate that 
outsourcing from high-income countries might not be a sufficient proxy for importing 
technologically more advanced inputs, which would be important to take into account in 
the future analyses.

391 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table 8: The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, using educational level and occupational classification 
for defining skills (observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.220*** 0.036 0.071** 0.364*** 0.026 0.092***
[5.39] [1.21] [2.53] [7.72] [0.77] [2.82]
FDI_high -0.135** 0.025 0.027 0.073 -0.042 -0.018
[-2.56] [0.68] [0.75] [1.02] [-0.71] [-0.31]
Outsourcing -26.840 17.480 14.800 71.24** 18.57 27.54**
[-0.65] [0.44] [0.43] [2.23] [1.47] [2.10]
Outsourcing_
high
-7.378 -17.230 -16.000 -52.92 -11.68 -16.79
[-0.18] [-0.45] [-0.47] [-1.42] [-0.95] [-1.30]
High 0.186*** 0.041** 0.060*** 0.184*** 0.057*** 0.095***
[4.73] [1.96] [2.95] [5.69] [3.14] [5.71]
log(capital per 
emp)
0.002 0.011 0.013 -0.008 0.003 0.003
[0.14] [1.26] [1.62] [-1.03] [0.51] [0.55]
log(tfp) 0.029** -0.007 -0.010 0.068*** -0.049*** -0.024***
[2.08] [-0.59] [-0.93] [6.87] [-6.41] [-3.47]
log(export 
value)
0.001 0.001 0.002 -0.005 0.000 -0.002
[0.45] [0.62] [1.03] [-1.42] [0.16] [-0.89]
log(gross 
wage)
0.640*** 0.246*** 0.297*** 0.674*** 0.168*** 0.273***
[16.4] [9.41] [11.3] [27.3] [11.3] [17.6]
log(domestic 
costs)
0.168*** 0.044*** 0.096*** 0.110*** 0.076*** 0.085***
[13.0] [3.15] [9.16] [12.3] [7.39] [11.5]
Constant -6.439*** -0.648 -3.164*** -6.425*** -0.801** -2.355***
[-12.1] [-1.32] [-9.98] [-21.6] [-2.45] [-9.38]

392 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Industry 
effects
Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared 
(within)
0.061 0.057 0.031 0.024
R-squared 
(between)
0.111 0.180 0.085 0.234
R-squared 
(overall)
0.196 0.111 0.173 0.212 0.078 0.195
Sargan-Hansen statistics 542.808***     1,611.500***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated Managers, Professionals, and Technicians in ISCO-88 classification are 
defined as skilled. The explanation of variables: FDI : dummy variable, controlling for the outward FDI; FDI_high: 
dummy variable, controlling for the outward FDI to high-income countries; Outsourcing: share of intermediate 
imports in the total material costs; Outsourcing_high: share of intermediate imports from high-income countries 
in the total material costs if a firm imports the majority of its intermediate products from high-income countries; 
High: dummy variable, controlling for high-income countries; log(capital per emp): logarithm of the capital per 
employee in a firm; log(tfp): logarithm of the total factor productivity per employee in a firm; log(export value): 
logarithm of the value of exports; log(gross wage): logarithm of the average annual gross wage level; log(domestic 
costs): logarithm of the domestic cost level. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the 
analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

393 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table 9. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, for the major skilled occupational groups (observation 
period: 1997-2010, only tertiary educated)
  Manufacturing firms Service firms
  Managers Professionals Technicians Managers Professionals Technicians
FDI
0.015 0.036 0.089*** -0.026 0.145*** 0.070
[0.50] [0.99] [2.70] [-0.66] [3.69] [1.62]
FDI_high
-0.004 0.075 -0.036 0.021 0.046 -0.091
[-0.086] [1.51] [-0.81] [0.33] [0.72] [-1.16]
Outsourcing
6.473 25.900 57.590** 2.340 -1.409 41.350**
[0.44] [0.57] [2.14] [0.28] [-0.31] [2.43]
Outsourc-
ing_high -6.381 -29.770 -52.080* 1.138 -5.204 -26.810*
[-0.43] [-0.67] [-1.95] [0.13] [-0.95] [-1.71]
High
0.024 0.017 0.045** 0.017 0.031** 0.048***
[1.35] [1.03] [2.37] [1.08] [2.35] [2.80]
Industry 
effects
Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observa-
tions 41,751 41,751 41,751 87,613 87,613 87,613
R-squared 
(within) 0.021 0.055 0.058 0.011 0.030 0.037
R-squared 
(between) 0.013 0.181 0.139 0.060 0.082 0.064
R-squared 
(overall) 0.012 0.175 0.133 0.047 0.091 0.076
Sargan-
Hansen 
statistics
693.487*** 586.418*** 380.796*** 569.012*** 791.558*** 563.509***
Note. Econometric method: FE: fixed effects. The dependent variable is defined as the logarithm of the ratio between 
skilled employees and the total number of employees, where tertiary educated Managers, Professionals, and T echnicians 
in ISCO-88 classification are defined as skilled. The explanation of variables: FDI: dummy variable, controlling for the 
outward FDI; FDI_high: dummy variable, controlling for the outward FDI to high-income countries; Outsourcing: 
share of intermediate imports in the total material costs; Outsourcing_high: share of intermediate imports from high-
income countries in the total material costs if a firm imports the majority of its intermediate products from high-
income countries; High: dummy variable, controlling for high-income countries. Control variables used: logarithm of 
the capital per employee in a firm, logarithm of the total factor productivity per employee in a firm, logarithm of the 
value of exports, logarithm of the average annual gross wage level, logarithm of the domestic cost level. *** p<0.01, ** 
p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

394 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
6. CONCLUSION
This paper studies the impact of outward FDI and outsourcing on the relative employment 
of skilled employees in Slovenian manufacturing and service firms. Using a matched 
firm-employee level dataset for the period from 1997 to 2010, the study contributes to 
the previous studies in several ways. First, it incorporates both measures of strategic 
positioning of firms into one model with the purpose of measuring differential effect 
of outward FDI and outsourcing on the skill structure of firms. In addition, study 
differentiates between outsourcing from high- and low-income countries, and outward 
FDI to high- and low-income countries. Finally, different dimensions when defining skills 
are taken into account in order to increase the explanatory value of the model. The basic 
model uses a conventional definition of skills, defining workers as skilled when they attain 
tertiary education. However, since workers develop additional knowledge and expertise 
after entering employment, taking into account solely the level of formal education 
when defining skills ignores the knowledge acquired during the course of employment. 
Consequently, three major occupational groups define workers as skilled in the model 
extensions; Managers, Professionals, and Technicians.
The main findings of the analysis are the following. First, outward FDI has a positive impact 
on firms’ skill share in manufacturing firms, while outsourcing has a positive impact on 
firms’ skill share in service firms. Second, when controlling also for the income level of 
destination countries, the impact of outsourcing in service firms is no longer positive. On 
the other hand, in manufacturing firms, outward FDI to low- and high-income countries 
have a positive impact on firms’ skill share. These results confirm the hypothesis about the 
expected positive effect of outward FDI to low-income countries, while the hypothesis for 
the expected positive effect of outsourcing from high-income countries is not confirmed. 
Finally, using workers’ occupational level for defining skills increases the explanatory 
power of the model. Results for manufacturing firms show that outward FDI to high- 
and low-income countries have a positive impact on the relative employment of tertiary 
educated Technicians, while the result for Technicians in general is not significant. This 
result indicates that firms differentiate between more and less educated individuals within 
the same occupational group. In addition, this holds only in the case of Technicians, the 
group with the lowest share of tertiary educated among the three skilled occupation groups, 
indicating that a Technician can best differentiate from his or her peers by obtaining a 
higher level of education. Furthermore, the results also show that outsourcing from low-
income countries has a stronger impact on the relative employment of tertiary educated 
Technicians than outsourcing from high-income countries in both, manufacturing 
and service firms. This result might be the consequence of a different skill structure of 
outsourcing firms, which was already indicated in descriptive statistics. Namely, the 
share of tertiary educated workers in outsourcing firms is lower than in FDI firms, which 
might be a sign that outsourcing firms have different preferences for employing workers 
with different occupational attainment compared to FDI firms. In the future analyses, it 
would therefore be interesting to control also for reverse causality between the dependent 
and control variables. In addition, this result in general confirms the hypothesis, which 

395 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
assumed that outsourcing from high-income countries increases firms’ skill share. The 
reasoning behind the hypothesis was that outsourcing from high-income countries is a 
proxy for importing more technologically advanced inputs. In order to fully analyse this 
hypothesis, it would be important in the future studies to instead of controlling only for 
the income level of source countries to control also for the type of imported inputs; i.e. 
high-technology versus low-technology inputs. The results in this paper might indicate 
that controlling solely for the income level of source countries in the case of outsourcing 
might not be a sufficient proxy for the technology level of inputs.
Results also bring policy implications as they confirm how important it is for the 
governments to provide competitive educational system that would equip individuals with 
skills and knowledge, which would they later use while working. Finally, paper’s results 
might also encourage individuals to invest in acquiring additional skills and increase their 
educational level in order to increase their employability.
With the aim of deepening the results of the empirical model, it would be useful to include 
additional measures for the outward FDI, especially since outward FDI proved to have 
an important impact on the skill structure of firms. These measures would for instance 
include the performance indicators of foreign partners that are in control of domestic 
firms, their employment structure, their value added, their value of the foreign investment, 
etc. Some of the information is already included in the dataset, but only for the recent 
years. Therefore, by extending the observation period, the new information might bring 
additional contributions to the results. In addition, although this study uses additional 
dimension when defining skills, it would be interesting to control also for other variables 
(e.g. the period of employment), and personal characteristics of workers (e.g. gender, 
length of pursuing the studies, social status of the family, etc.) in order to fully control for 
the informal education of workers. Finally, since previous empirical studies confirmed 
the importance of analysing not only the impact of outward FDI and/or outsourcing on 
skills, but rather its impact on offshorable and non-offshorable occupations, it would be 
interesting to broaden the empirical analysis also to other occupational groups.
In the future studies, it would also be interesting to test the self-selection of firms into 
outward FDI and outsourcing, as many studies confirmed the self-selection of more 
productive firms into trading activities (see for example Damijan and Kostevc, 2015; 
Wagner, 2011; and Melitz 2003). By estimating data on German manufacturing firms, 
Wagner (2011) confirms there is a self-selection of firms into offshoring. The analysis 
concludes these firms are larger, more productive and more human capital intensive. 
Studying the Spanish manufacturing firms, Damijan and Kostevc (2015) confirm that 
more productive firms self-select into importing. By importing, firms get an access to 
new, cheaper and/or better-quality products, which decrease firms’ variable costs and 
enable greater investing in innovations. Furthermore, studying the Belgian firm-level 
data, Amiti, Itskhoki and Konings (2014) confirm that more productive firms import a 
larger share of their inputs from abroad, which additionally increases their productivity. 
Although the current paper controls for several firm-characteristic variables, it is limited 
in controlling fully also for the self-selection of firms into outward FDI and outsourcing. 

396 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Therefore, it would be interesting to analyse also the reverse causality between firms’ skill 
structure, and outward FDI and outsourcing.
Finally, it would also be important to take into account in the future studies the recent 
changes in the economic environment and especially the impacts of various structural 
reforms. However, since Slovenia passed a labour market reform only in 2013, it would 
be necessary to extend the observation period in order to fully capture its effects. Using 
Italian survey data, Presbitero, Richiardi, and Amighini (2012) empirically analyse 
whether the increased usage of more flexible types of employment can be a substitute to 
offshoring, and whether it also additionally increases delocalization. The results show 
that a higher share of temporary workers reduces the likelihood of offshoring in the 
future. However, the results no longer hold after controlling also for reverse causality 
and spurious correlation.
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usual caveat applies.

400 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE  30 
 
 
Appendices 
 
Appendix A: Derivation of the model 
 
First, consider the following production function for firm i in year t: 
 
 Y
it
 = A
it
 K
it
α
 H
it
ß
 C
it
1–α–ß
 (A1) 
 
As already mentioned, the dependent variable, Y
it
, is output, A
it
 is productivity, K
it
 is 
capital, H
it
 is skilled labour, and C
it
 is composite input, consisting of domestic and foreign 
inputs (
it it it
F DC += ), where the latter relate to outsourcing and/or outward FDI activities, 
whereas the former relate to domestic costs and unskilled labour. 
 
As in Hummels et al. (2014), I introduce P
it
 as a reduced-form of the demand for firm i’s 
products and determine the demand for skilled labour of firm i in year t, by taking 
derivatives of the equation (A1): 
 
 P
it
 (∂Y
it
 / ∂H
it
) = P
it
 A
it
 K
it
α
 ßH
it
ß–1
 C
it
1–α–ß
 (A2) 
 
First, foreign inputs of firms, F
it
, are separated into outsourcing (Out
it
) and outward FDI 
(FDI
it
) activities (
it it it
FDI Out F += ), while the domestic inputs of firms, D
it
, are separated 
into unskilled labour (L
it
) and domestic costs (DC
it
); (
it it it
DC LD += ). Next, I take 
logarithms of the equation (A2) and get the following: 
 
 
0) l n ( )1 ( ln ) 1( ln ln ln =+++ −−+ − + + +
it it it it it it it it
DC L FDI Out H KA P βα β βα (A3) 
 
To implement equation (A3) in the data, the equation is first rearranged so that the variable 
of interest is the skilled labour: 
 
 
) l n ( )1 ( ln ln ln ln ) 1(
it it it it it it it it
DC L FDI Out KA P H +++− − +++=−− βα α ββ
 
(A4) 
 
The variables are divided by the total number of employees in a firm. However, as already 
explained, following Hummels et al. (2014), I do not scale outsourcing, exports and levels 
of domestic costs by firm size in order to enhance the explanatory value of the model. 
Furthermore, as in Hummels et al. (2014), the logarithm of the value of exports (X
it
) is 
introduced to capture time varying shocks to the demand of firms’ output (P
it
), and the 
logarithm of the average wage level in firm i and year t (W
it
). 
 
Finally, the observed model is the following: 
 
 Skill_share
it 
= ß
0
 + ß
1
Out
it
 + ß
2
FDI
it
 + ß
3
X
it
 + ß
4
A
it
 + ß
5
K
it
 + 
+ ß
6
W
it
 + ß
7
DC
it
 + Time
t
 + Ind
t 
+ ε
it
 
(A5) 
APPENDICES
Appendix A: Derivation of the model

401 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Appendix B: Description of ISCO-88 major occupational groups
ISCO-88 classification arranges occupations in ten major groups. The first major group, 
“Managers” , includes legislators, senior officials and managers, whose main tasks consist of 
determining, formulating and supervising the implementation of government policies, laws 
and public regulations, or planning, directing and coordinating the policies and activities 
of enterprises, organisations or departments. The next major group are “Professionals” who 
work in the fields of physical, life or social sciences, or humanities. They are responsible for 
increasing the existing stock of knowledge, finding solutions to the problems by applying 
scientific and artistic concepts and theories, and transferring their knowledge onto others. 
Another major group, “T echnicians” , includes technicians and associate professionals who 
have technical knowledge and experience in the fields of physical, life or social sciences, 
or humanities. Their main tasks include carrying out technical work and teaching at 
particular educational levels, related with the abovementioned fields. Furthermore, the 
group “Clerks” includes occupations which possess the knowledge and skills of organising, 
storing, computing and retrieving information. Their main tasks are performing secretarial 
duties, operating different office machines, recording and computing numerical data, and 
performing various customer-oriented clerical duties. The group “Service workers” covers 
service, shop, and market sales workers whose main tasks consist of providing personal 
and protective services, and selling goods in shops or at markets. In addition, the group 
“ Agricultural workers” consists of skilled agricultural and fishery workers, who produce 
farm, forestry and fishery products, and sell them to purchasers, marketing organisations 
or at markets. Next, the group “Craft workers” includes craft and other related trade 
workers, whose main tasks include extracting raw materials, constructing buildings 
and other structures, and making various products and handicraft goods. Moreover, the 
group “Machine operators” includes plant and machine operators and assemblers who 
operate and monitor large scale, and often highly automated, industrial machinery and 
equipment. “Elementary occupations” combine occupations the main tasks of which in 
general include simple and routine tasks by using the hand-held tools and in some cases 
considerable physical effort. Finally, the group “ Armed forces” includes individuals, who 
are serving in the armed forces on a voluntary or compulsory basis and are restricted to 
accept civilian employment (ILO, 2014).

402 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Appendix C: Complementary tables
Table C1. Cross-tabulation between outward FDI and outsourcing dummy variables
      FDI
Total 
(Outsource)
      No Ye s  
Outsource
No Row percentage   98.0% 2.0% 100.0%
  Column percentage 91.8% 68.7% 91.2%
Ye s Row percentage   90.8% 9.2% 100.0%
  Column percentage 8.2% 31.3% 8.8%
Total (FDI)   Row percentage   97.4% 2.6% 100.0%
    Column percentage 100.0% 100.0% 100.0%
Note. Outsource is a dummy variable, controlling for firms that import intermediate products from abroad; FDI 
is a dummy variable, controlling for firms that engage in the outward FDI. Pearson’s chi-squared test: 7,000 (Pr 
= 0.000).
Source: SORS, author’s calculations

403 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C2. Characteristics of Slovenian firms which use outward FDI to and outsource 
from high-income countries
Firms, using outward FDI to high-income countries
Ye ar 1998 2002 2006 2007 2008 2009 2010
Employment 509.4 421.3 407.8 309.1 214.2 200.4 199.3
Employment of 
tertiary educated
56.3 55.0 63.5 59.1 39.6 40.1 42.1
Skill share 23.83 25.49 31.46 32.6 34.69 35.85 37.5
Age 39.5 39.5 40.1 40.1 40.6 41.2 41.7
Gross wage 8,930 12,782 17,741 20,128 19,547 19,894 20,481
Gross wage of 
tertiary educated
15,835 21,712 26,789 29,254 27,963 27,881 28,004
Number of firms 143 172 198 197 207 194 195
Firms, outsourcing from high-income countries
Ye ar 1998 2002 2006 2007 2008 2009 2010
Employment 27.8 27.2 34.2 35.3 33.6 30.3 29.6
Employment of 
tertiary educated 2.4 3.0 4.2 4.6 4.5 4.8 4.8
Skill share 22.8 22.4 25.1 25.8 25.6 26.9 27.9
Age 35.9 38.1 39.0 39.4 39.9 40.4 40.5
Gross wage 5,787 9,256 13,987 14,884 15,897 16,167 16,936
Gross wage of 
tertiary educated 9,106 14,139 20,092 20,677 22,361 22,156 22,437
Number of firms 1,495 1,782 1,345 1,649 1,942 1,839 1,797
Note. The explanations of variables are as follows: Outsourcing firms: firms that import intermediate products; 
FDI firms: firms that engage in the outward FDI; Employment: mean number of employees; Employment of 
tertiary educated: mean number of tertiary educated employees; Skill share: the average of the share of the tertiary 
educated; Age: mean age of employees; Gross wage: mean annual gross wage in €; Gross wage of tertiary educated: 
mean annual gross wage of tertiary educated employees in €; Number of firms: number of observations.
Source: SORS, author’s calculations

404 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table C3. Characteristics of employees in unskilled occupations
Clerical workers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total employment 12.0 10.5 9.7 9.5 9.3 9.5 9.4
Share in the tertiary educated 4.5 4.7 5.9 6.3 6.4 6.6 6.8
Age 35.0 37.1 38.7 38.8 39.3 39.7 39.9
Gross wage 5,203 8,126 10,451 11,064 11,622 11,730 12,044
Gross wage of tertiary educated 7,357 11,217 12,981 13,593 14,419 14,482 14,626
Service workers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total employment 11.1 11.3 11.2 11.4 11.5 12.1 12.3
Share in the tertiary educated 1.3 1.6 2.3 2.8 3.0 3.3 3.6
Age 33.0 35.0 36.4 36.7 37.2 37.6 38.0
Gross wage 4,225 6,369 8,286 8,862 9,365 9,458 9,859
Gross wage of tertiary educated 6,698 9,654 11,006 11,411 12,295 12,286 12,766
Craft workers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total employment 15.8 16.8 17.8 18.0 18.2 17.9 17.4
Share in the tertiary educated 0.4 0.7 1.3 1.4 1.5 1.6 1.7
Age 35.0 36.6 38.2 38.3 38.6 39.1 39.6
Gross wage 4,553 6,827 9,054 9,615 10,218 10,244 10,631
Gross wage of tertiary educated 6,689 10,606 13,804 14,393 15,984 15,680 15,744
Machinery workers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total employment 27.8 23.1 19.0 18.2 17.5 15.8 15.5
Share in the tertiary educated 4.9 5.9 2.5 2.3 2.4 2.1 2.0
Age 35.3 37.4 39.4 39.7 40.1 40.6 41.2
Gross wage 4,582 7,120 9,468 10,154 10,601 10,529 11,241
Gross wage of tertiary educated 6,210 8,512 12,522 13,744 14,427 14,645 16,352
Elementary workers
Ye ar 1998 2002 2006 2007 2008 2009 2010
Share in the total employment 7.2 11.2 13.1 13.5 13.8 13.4 13.1
Share in the tertiary educated 0.1 0.2 0.5 0.5 0.6 0.6 0.7
Age 35.8 36.5 37.9 38.1 38.5 39.2 40.0
Gross wage 3,664 5,400 7,031 7,483 7,700 7,815 8,385
Gross wage of tertiary educated 4,942 6,463 8,340 8,702 9,619 9,334 9,571
Note. The explanations of variables are as follows: Share in the total employment: share of a particular occupational 
group in the total employment (in %); Share in the tertiary educated: share of a particular occupational group in 
the total number of tertiary educated employees (in %); Age: mean age of a particular occupational group; Gross 
wage: mean gross annual wage of a particular occupational group in €; Gross wage of tertiary educated: mean 
gross annual wage of tertiary educated employees in €.
Source: SORS, author’s calculations

405 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C4. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, robustness checks: include firm size as explanatory 
variable (observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.158*** 0.063** 0.090*** 0.285*** -0.019 0.042
[4.52] [2.26] [3.43] [7.08] [-0.57] [1.38]
Outsourcing -29.46*** 0.114 -2.032 32.280** 6.616 12.270**
[-4.13] [0.020] [-0.41] [1.99] [1.20] [2.11]
log(capital per emp) 0.009 0.016 0.019** -0.005 0.012** 0.0104**
[0.81] [1.64] [2.26] [-0.68] [2.07] [2.05]
log(tfp) -0.031 -0.005 -0.001 0.126*** 0.003 0.033***
[-1.39] [-0.43] [-0.098] [8.90] [0.40] [4.11]
log(export value) -0.001 0.000 0.000 -0.017*** -0.001 -0.006***
[-0.59] [0.08] [0.08] [-5.19] [-0.55] [-2.75]
log(gross wage) 0.638*** 0.240*** 0.290*** 0.608*** 0.132*** 0.234***
[15.7] [8.63] [10.5] [25.3] [9.02] [15.6]
log(domestic costs) 0.204*** 0.039** 0.0849*** 0.080*** 0.026** 0.034***
[11.2] [2.30] [5.90] [6.38] [2.37] [3.88]
log(employment) -0.081*** 0.032 0.039* 0.130*** 0.197*** 0.188***
[-2.72] [1.16] [1.77] [6.36] [9.45] [11.8]
Constant -6.354*** -0.606 -2.363*** -5.638*** -0.105 -1.482***
[-11.9] [-1.04] [-4.19] [-18.0] [-0.31] [-5.78]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.068 0.064 0.039 0.031
R-squared (between) 0.098 0.165 0.060 0.205
R-squared (overall) 0.099 0.156 0.200 0.056 0.168 0.216
Sargan-Hansen statistics 503.784***     1,384.700***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total 
number of employees, where tertiary educated workers are defined as skilled. The explanation of variables: 
FDI: dummy variable, controlling for the outward FDI; Outsourcing: share of intermediate imports in the total 
material costs; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the 
total factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross 
wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level; 
log(employment): logarithm of the number of employees. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in 
brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

406 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table C5. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms; robustness checks: exchange total factor productivity 
for value added (observation period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.136*** 0.063** 0.094*** 0.302*** 0.012 0.082***
[3.87] [2.30] [3.62] [7.54] [0.37] [2.68]
Outsourcing -34.140*** 1.577 0.012 32.730** 12.580** 17.290***
[-4.65] [0.29] [0.0024] [2.03] [1.98] [2.69]
log(capital per emp) 0.015 0.013 0.016* -0.022*** 0.003 0.001
[1.30] [1.40] [1.90] [-2.84] [0.52] [0.14]
log(value added per emp) 0.013 -0.014 -0.012 0.112*** -0.039*** -0.006
[0.60] [-1.08] [-0.97] [8.27] [-4.50] [-0.77]
log(export value) -0.003 0.000 0.001 -0.017*** -0.002 -0.006***
[-1.18] [0.098] [0.32] [-4.99] [-0.77] [-2.74]
log(gross wage) 0.629*** 0.243*** 0.294*** 0.613*** 0.153*** 0.255***
[15.5] [8.83] [10.7] [25.7] [10.4] [16.8]
log(domestic costs) 0.157*** 0.057*** 0.108*** 0.100*** 0.098*** 0.105***
[15.4] [3.80] [10.4] [12.4] [9.21] [13.9]
Constant -5.924*** -0.780 -2.608*** -5.909*** -0.937*** -2.348***
[-11.4] [-1.37] [-4.87] [-20.7] [-2.74] [-9.52]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,795 41,795 41,795 87,621 87,621 87,621
R-squared (within) 0.068 0.064 0.033 0.025
R-squared (between) 0.097 0.167 0.067 0.213
R-squared (overall) 0.183 0.100 0.160 0.193 0.068 0.179
Sargan-Hansen statistics 1,694.361***   1,464.491***
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated workers are defined as skilled. The explanation of variables: FDI: dummy 
variable, controlling for the outward FDI; Outsourcing: share of intermediate imports in the total material costs; 
log(capital per emp): logarithm of the capital per employee in a firm; log(value added per emp): logarithm of 
the value added per employee in a firm; log(export value): logarithm of the value of exports; log(gross wage): 
logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. *** 
p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

407 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C6. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms, robustness checks: include firm size as explanatory 
variable; robustness checks: exchange total factor productivity for value added (observation 
period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.159*** 0.063** 0.090*** 0.280*** -0.019 0.043
[4.52] [2.26] [3.44] [6.96] [-0.57] [1.38]
Outsourcing -29.63*0** -0.007 -2.150 32.310** 6.641 12.300**
[-4.15] [-0.0012] [-0.43] [2.00] [1.21] [2.11]
log(capital per emp) 0.013 0.016* 0.019** -0.022*** 0.012** 0.006
[1.09] [1.72] [2.26] [-2.90] [2.01] [1.19]
log(value added per emp) -0.020 -0.003 0.001 0.127*** 0.003 0.0319***
[-0.91] [-0.26] [0.10] [8.92] [0.30] [4.00]
log(export value) -0.001 0.000 0.000 -0.017*** -0.001 -0.006***
[-0.57] [0.083] [0.11] [-5.16] [-0.55] [-2.74]
log(gross wage) 0.634*** 0.240*** 0.290*** 0.607*** 0.132*** 0.234***
[15.6] [8.62] [10.5] [25.3] [9.03] [15.6]
log(domestic costs) 0.201*** 0.038** 0.083*** 0.078*** 0.026** 0.035***
[11.1] [2.22] [5.81] [6.44] [2.39] [3.93]
log(employment) -0.055*** 0.037 0.041** 0.037** 0.195*** 0.164***
[-2.80] [1.56] [2.23] [2.50] [10.5] [12.0]
Constant -6.364*** -0.594 -2.351*** -5.632*** -0.104 -1.485***
[-12.0] [-1.02] [-4.17] [-18.1] [-0.31] [-5.79]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,795 41,795 41,795 87,621 87,621 87,621
R-squared (within) 0.069 0.065 0.039 0.031
R-squared (between) 0.099 0.165 0.060 0.205
R-squared (overall) 0.184 0.100 0.157 0.193 0.056 0.168
Sargan-Hansen statistics 1,693.178***   1,571.077***
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated workers are defined as skilled. The explanation of variables: FDI: dummy 
variable, controlling for the outward FDI; Outsourcing: share of intermediate imports in the total material costs; 
log(capital per emp): logarithm of the capital per employee in a firm; log(value added per emp): logarithm of the 
value added per employee; log(export value): logarithm of the value of exports; log(gross wage): logarithm of 
the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level; log(employment): 
logarithm of the number of employees. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis 
used cluster-robust standard errors.
Source: SORS, author’s calculations

408 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table C7. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Managers” (observation 
period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.153*** -0.013 -0.007 0.075 -0.022 -0.0244
[4.04] [-0.47] [-0.26] [1.37] [-0.54] [-0.64]
FDI_high 0.077 0.031 0.033 0.094 0.042 0.045
[1.56] [0.72] [0.81] [0.88] [0.58] [0.65]
Outsourcing 39.38 -8.522 -4.528 5.631 3.162 3.072
[1.18] [-0.47] [-0.27] [0.20] [0.36] [0.33]
Outsourcing_high -66.26* 3.103 -4.694 -22.73 -10.32 -9.253
[-1.87] [0.17] [-0.27] [-0.67] [-1.28] [-1.06]
High 0.0898** 0.022 0.031 0.083** 0.006 0.016
[2.20] [1.02] [1.48] [2.57] [0.32] [0.91]
log(capital per emp) 0.024** 0.023** 0.020** -0.029*** 0.010* 0.001
[2.16] [2.53] [2.55] [-3.78] [1.78] [0.14]
log(tfp) 0.148*** 0.040*** 0.057*** 0.136*** 0.029*** 0.048***
[10.1] [3.16] [5.02] [13.3] [3.58] [6.70]
log(export value) -0.001 0.001 -0.001 -0.011*** -0.005* -0.007***
[-0.46] [0.34] [-0.30] [-2.95] [-1.89] [-3.02]
log(gross wage) 0.400*** 0.257*** 0.272*** 0.300*** 0.233*** 0.248***
[10.2] [8.72] [9.52] [14.3] [13.4] [15.4]
log(domestic costs) -0.088*** -0.050*** -0.065*** -0.074*** -0.013 -0.035***
[-6.65] [-3.48] [-5.73] [-8.28] [-1.25] [-4.50]
Constant -0.868* -0.074 -1.152** 0.251 0.111 0.127
[-1.79] [-0.18] [-2.05] [0.78] [0.39] [0.51]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.029 0.027 0.016 0.014
R-squared (between) 0.030 0.055 0.030 0.049
R-squared (overall) 0.087 0.040 0.067 0.053 0.026 0.046
Sargan-Hansen statistics 268.051***     250.856***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where Managers in ISCO-88 classification are defined as skilled. The explanation of variables: FDI : 
dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for the outward FDI 
to high-income countries; Outsourcing: share of intermediate imports in the total material costs; Outsourcing_
high: share of intermediate imports from high-income countries in the total material costs if a firm imports the 
majority of its intermediate products from high-income countries; High: dummy variable, controlling for high-
income countries; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the 
total factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross 
wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. 
*** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

409 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C8. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Professionals” (observation 
period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.267*** 0.049 0.101*** 0.373*** 0.161*** 0.223***
[5.85] [1.26] [2.86] [6.72] [4.06] [5.97]
FDI_high 0.040 0.068 0.0886* 0.166* 0.007 0.046
[0.59] [1.33] [1.82] [1.67] [0.10] [0.70]
Outsourcing -20.98 9.717 11.01 -17.82* -3.324 -5.842
[-0.64] [0.18] [0.25] [-1.81] [-0.68] [-1.22]
Outsourcing_high 6.365 -12.38 -13.12 -17.76 -1.262 -2.706
[0.19] [-0.23] [-0.30] [-1.42] [-0.22] [-0.47]
High 0.009 0.014 0.016 0.056*** 0.028** 0.047***
[0.32] [0.74] [0.94] [2.84] [1.99] [3.81]
log(capital per emp) -0.017** 0.004 0.002 0.004 -0.007* -0.001
[-2.26] [0.50] [0.37] [0.91] [-1.91] [-0.40]
log(tfp) -0.070*** -0.041*** -0.055*** -0.084*** -0.068*** -0.067***
[-7.35] [-4.33] [-7.17] [-11.6] [-11.6] [-13.6]
log(export value) 0.004** 0.002 0.004** 0.007*** 0.005** 0.005***
[2.01] [1.03] [2.46] [2.73] [2.07] [2.96]
log(gross wage) 0.361*** 0.096*** 0.141*** 0.366*** 0.048*** 0.121***
[13.6] [5.51] [8.56] [22.8] [4.89] [13.1]
log(domestic costs) 0.147*** 0.078*** 0.121*** 0.079*** 0.080*** 0.083***
[16.5] [6.47] [15.4] [12.7] [9.77] [15.5]
Constant -4.273*** -0.849** -2.673*** -3.497*** -1.100*** -1.741***
[-9.36] [-2.12] [-11.8] [-17.7] [-3.98] [-12.8]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.053 0.049 0.028 0.022
R-squared (between) 0.186 0.244 0.084 0.222
R-squared (overall) 0.242 0.176 0.227 0.214 0.093 0.203
Sargan-Hansen statistics 504.516***     796.516***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number of 
employees, where Professionals in ISCO-88 classification are defined as skilled. The explanation of variables: FDI : 
dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for the outward FDI 
to high-income countries; Outsourcing: share of intermediate imports in the total material costs; Outsourcing_
high: share of intermediate imports from high-income countries in the total material costs if a firm imports the 
majority of its intermediate products from high-income countries; High: dummy variable, controlling for high-
income countries; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the 
total factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross 
wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. 
*** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

410 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table C9. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Technicians” (observation 
period: 1997-2010)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI -0.027 -0.014 -0.002 0.024 -0.032 0.009
[-0.68] [-0.53] [-0.091] [0.46] [-0.90] [0.27]
FDI_high -0.304*** -0.041 -0.069** -0.210** -0.067 -0.060
[-5.47] [-1.32] [-2.28] [-2.17] [-1.09] [-1.07]
Outsourcing -43.280 79.35** 57.860 59.580*** 37.180** 46.050***
[-0.90] [2.40] [1.60] [2.77] [2.29] [2.90]
Outsourcing_high -0.257 -67.09** -49.910 2.928 4.780 1.549
[-0.01] [-2.02] [-1.37] [0.11] [0.27] [0.087]
High 0.192*** 0.031 0.055** 0.206*** 0.065*** 0.103***
[4.77] [1.21] [2.25] [6.61] [3.00] [5.29]
log(capital per emp) -0.002 0.007 0.010 0.006 -0.024*** -0.017***
[-0.14] [0.68] [1.12] [0.81] [-3.56] [-2.99]
log(tfp) -0.038*** -0.083*** -0.082*** -0.102*** -0.132*** -0.124***
[-2.74] [-6.26] [-7.19] [-10.1] [-14.1] [-15.8]
log(export value) -0.007*** -0.002 -0.003 -0.002 0.003 -0.002
[-2.93] [-0.89] [-1.62] [-0.62] [0.97] [-0.69]
log(gross wage) 0.325*** 0.070*** 0.114*** 0.255*** -0.010 0.052***
[10.1] [2.66] [4.65] [13.1] [-0.61] [3.77]
log(domestic costs) 0.215*** 0.105*** 0.158*** 0.241*** 0.188*** 0.222***
[17.2] [6.34] [13.6] [28.7] [15.7] [28.2]
Constant -5.234*** -0.608 -1.883*** -5.096*** -0.839** -2.143***
[-9.39] [-0.97] [-3.86] [-19.4] [-2.42] [-10.5]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.024 0.022 0.035 0.030
R-squared (between) 0.126 0.185 0.084 0.166
R-squared (overall) 0.176 0.103 0.154 0.170 0.091 0.158
Sargan-Hansen statistics 487.702***     584.975***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number of 
employees, where Technicians in ISCO-88 classification are defined as skilled. The explanation of variables: FDI : 
dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for the outward FDI 
to high-income countries; Outsourcing: share of intermediate imports in the total material costs; Outsourcing_
high: share of intermediate imports from high-income countries in the total material costs if a firm imports the 
majority of its intermediate products from high-income countries; High: dummy variable, controlling for high-
income countries; log(capital per emp): logarithm of the capital per employee in a firm; log(tfp): logarithm of the 
total factor productivity per employee in a firm; log(export value): logarithm of the value of exports; log(gross 
wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm of the domestic cost level. 
*** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-robust standard errors.
Source: SORS, author’s calculations

411 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C10. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Managers” (observation 
period: 1997-2010, only tertiary educated)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.172*** 0.015 0.038 0.296*** -0.026 0.012
[4.19] [0.50] [1.38] [5.36] [-0.66] [0.31]
FDI_high -0.058 -0.004 0.001 0.028 0.021 0.031
[-1.02] [-0.086] [0.013] [0.27] [0.33] [0.51]
Outsourcing -1.731 6.473 6.051 39.070 2.340 7.559
[-0.054] [0.44] [0.44] [1.48] [0.28] [0.87]
Outsourcing_high -16.01 -6.381 -6.625 -30.540 1.138 -1.760
[-0.49] [-0.43] [-0.47] [-0.90] [0.13] [-0.19]
High 0.117*** 0.024 0.032* 0.098*** 0.017 0.037**
[3.40] [1.35] [1.90] [3.25] [1.08] [2.53]
log(capital per emp) 0.005 0.015** 0.015** -0.019** 0.001 -0.002
[0.47] [2.00] [2.15] [-2.57] [0.19] [-0.41]
log(tfp) 0.037*** 0.025** 0.022** 0.090*** -0.006 0.011*
[2.75] [2.14] [2.17] [9.63] [-0.88] [1.82]
log(export value) 0.004 0.003 0.004* -0.008** -0.003 -0.005***
[1.63] [1.51] [1.89] [-2.37] [-1.56] [-2.62]
log(gross wage) 0.432*** 0.164*** 0.193*** 0.440*** 0.154*** 0.206***
[13.0] [7.63] [9.15] [21.4] [11.9] [16.1]
log(domestic costs) 0.037*** -0.016 0.011 0.031*** 0.025*** 0.024***
[3.16] [-1.30] [1.14] [3.69] [2.80] [3.64]
Constant -3.544*** -0.203 -1.520*** -3.761*** -0.790*** -1.367***
[-7.24] [-0.52] [-5.24] [-12.1] [-2.89] [-6.15]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.021 0.018 0.011 0.009
R-squared (between) 0.013 0.062 0.060 0.111
R-squared (overall) 0.075 0.012 0.055 0.098 0.047 0.089
Sargan-Hansen statistics 693.487***     569.012***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number 
of employees, where tertiary educated Managers in ISCO-88 classification are defined as skilled. The explanation 
of variables: FDI: dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for 
the outward FDI to high-income countries; Outsourcing: share of intermediate imports in the total material 
costs; Outsourcing_high: share of intermediate imports from high-income countries in the total material costs 
if a firm imports the majority of its intermediate products from high-income countries; High: dummy variable, 
controlling for high-income countries; log(capital per emp): logarithm of the capital per employee in a firm; 
log(tfp): logarithm of the total factor productivity per employee in a firm; log(export value): logarithm of the 
value of exports; log(gross wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm 
of the domestic cost level. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-
robust standard errors.
Source: SORS, author’s calculations

412 ECONOMIC AND BUSINESS REVIEW  |  VOL. 19  |  No. 3  |  2017
Table C11. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Professionals” (observation 
period: 1997-2010, only tertiary educated)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.288*** 0.036 0.093*** 0.368*** 0.145*** 0.204***
[6.37] [0.99] [2.80] [6.65] [3.69] [5.53]
FDI_high 0.053 0.075 0.101** 0.174* 0.046 0.075
[0.78] [1.51] [2.13] [1.74] [0.72] [1.21]
Outsourcing -21.370 25.900 22.980 -13.730 -1.409 -3.239
[-0.69] [0.57] [0.60] [-1.47] [-0.31] [-0.76]
Outsourcing_high 7.226 -29.770 -25.640 -20.260* -5.204 -6.498
[0.23] [-0.67] [-0.68] [-1.69] [-0.95] [-1.17]
High 0.020 0.017 0.020 0.060*** 0.031** 0.048***
[0.78] [1.03] [1.30] [3.17] [2.35] [4.12]
log(capital per emp) -0.017** 0.006 0.005 0.001 -0.008** -0.002
[-2.27] [0.86] [0.84] [0.27] [-2.11] [-0.70]
log(tfp) -0.063*** -0.034*** -0.048*** -0.080*** -0.062*** -0.061***
[-7.05] [-3.77] [-6.63] [-11.7] [-11.2] [-13.1]
log(export value) 0.005** 0.001 0.004** 0.006*** 0.005** 0.005***
[2.43] [0.81] [2.54] [2.71] [2.12] [3.06]
log(gross wage) 0.343*** 0.090*** 0.134*** 0.366*** 0.051*** 0.122***
[13.5] [5.57] [8.72] [23.1] [5.58] [13.9]
log(domestic costs) 0.139*** 0.069*** 0.112*** 0.073*** 0.073*** 0.076***
[16.2] [6.43] [15.8] [12.0] [9.32] [14.7]
Constant -4.049*** -0.741** -2.598*** -3.391*** -1.063*** -1.727***
[-9.06] [-2.01] [-12.4] [-17.7] [-3.92] [-13.3]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.055 0.050 0.030 0.023
R-squared (between) 0.181 0.248 0.082 0.218
R-squared (overall) 0.251 0.175 0.234 0.213 0.091 0.201
Sargan-Hansen statistics 586.418***     791.558***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number of 
employees, where tertiary educated Professionals in ISCO-88 classification are defined as skilled. The explanation 
of variables: FDI: dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for 
the outward FDI to high-income countries; Outsourcing: share of intermediate imports in the total material 
costs; Outsourcing_high: share of intermediate imports from high-income countries in the total material costs 
if a firm imports the majority of its intermediate products from high-income countries; High: dummy variable, 
controlling for high-income countries; log(capital per emp): logarithm of the capital per employee in a firm; 
log(tfp): logarithm of the total factor productivity per employee in a firm; log(export value): logarithm of the 
value of exports; log(gross wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm 
of the domestic cost level. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-
robust standard errors.
Source: SORS, author’s calculations

413 M. LINDIČ  |  THE EFFECTS OF OUTSOURCING AND OUTWARD FDI ON SKILL STRUCTURE IN SLOVENIA
Table C12. The effect of outsourcing and outward FDI on the skill share in Slovenian 
manufacturing and service firms for the occupational group “Technicians” (observation 
period: 1997-2010, only tertiary educated)
  Manufacturing firms Service firms
 
Pooled 
OLS
FE RE
Pooled 
OLS
FE RE
FDI 0.214*** 0.089*** 0.130*** 0.340*** 0.070 0.156***
[4.83] [2.70] [4.33] [6.70] [1.62] [4.13]
FDI_high -0.210*** -0.036 -0.048 0.000 -0.091 -0.030
[-3.62] [-0.81] [-1.16] [-0.00] [-1.16] [-0.43]
Outsourcing 19.890 57.590** 50.200* 51.680** 41.350** 45.940***
[0.63] [2.14] [1.94] [1.98] [2.43] [2.77]
Outsourcing_high -38.790 -52.080* -47.520* -24.660 -26.810* -27.120*
[-1.22] [-1.95] [-1.85] [-0.88] [-1.71] [-1.73]
High 0.123*** 0.045** 0.063*** 0.092*** 0.048*** 0.073***
[4.46] [2.37] [3.59] [3.65] [2.80] [4.85]
log(capital per emp) 0.003 -0.014 -0.004 0.002 -0.004 -0.001
[0.36] [-1.51] [-0.52] [0.39] [-0.76] [-0.24]
log(tfp) -0.019* -0.046*** -0.048*** -0.063*** -0.081*** -0.077***
[-1.72] [-4.48] [-5.77] [-7.83] [-10.9] [-13.1]
log(export value) -0.001 -0.002 -0.001 0.003 0.001 0.001
[-0.33] [-0.97] [-0.60] [0.91] [0.49] [0.27]
log(gross wage) 0.300*** 0.109*** 0.147*** 0.341*** 0.035*** 0.122***
[11.3] [5.62] [8.40] [18.7] [2.90] [11.1]
log(domestic costs) 0.177*** 0.0745*** 0.126*** 0.170*** 0.118*** 0.146***
[18.5] [5.69] [15.1] [23.5] [12.0] [24.7]
Constant -5.280*** -1.487*** -2.795*** -5.222*** -1.348*** -2.627***
[-13.8] [-3.16] [-11.4] [-22.5] [-4.79] [-16.9]
Industry effects Ye s Ye s Ye s Ye s Ye s Ye s
Year effects Ye s Ye s Ye s Ye s Ye s Ye s
Observations 41,751 41,751 41,751 87,613 87,613 87,613
R-squared (within) 0.058 0.055 0.037 0.032
R-squared (between) 0.139 0.199 0.064 0.144
R-squared (overall) 0.194 0.133 0.181 0.152 0.076 0.142
Sargan-Hansen statistics 380.796***     563.509***  
Note. Econometric methods: Pooled OLS: pooled ordinary least squares; FE: fixed effects; RE: random effects. 
The dependent variable is defined as the logarithm of the ratio between skilled employees and the total number of 
employees, where tertiary educated Technicians in ISCO-88 classification are defined as skilled. The explanation 
of variables: FDI: dummy variable, controlling for the outward FDI; FDI_high: dummy variable, controlling for 
the outward FDI to high-income countries; Outsourcing: share of intermediate imports in the total material 
costs; Outsourcing_high: share of intermediate imports from high-income countries in the total material costs 
if a firm imports the majority of its intermediate products from high-income countries; High: dummy variable, 
controlling for high-income countries; log(capital per emp): logarithm of the capital per employee in a firm; 
log(tfp): logarithm of the total factor productivity per employee in a firm; log(export value): logarithm of the 
value of exports; log(gross wage): logarithm of the average annual gross wage level; log(domestic costs): logarithm 
of the domestic cost level. *** p<0.01, ** p<0.05, * p<0.1, robust t-statistics in brackets, the analysis used cluster-
robust standard errors.
Source: SORS, author’s calculations