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IS LABOUR THE FALL GUY OF A FINANCIAL-LED GLOBALISATION?
A CROSS-COUNTRY INQUIRY ON GLOBALISATION, FINANCIALISATION AND EMPLOYMENT AT THE INDUSTRY LEVEL
Cédric Durand
CEPN - Paris13/CNRS, 99 av. Jean-Baptiste Clément, 93430 Villetaneuse
Sébastien Miroudot*
OECD1, 2 rue André-Pascal, 75775 Paris Cedex 16
Abstract: This paper looks at the relationship between globalisation, employment and financialisation at the industry level by providing an analytical framework describing three modes of insertion into global production (fragmentation of production, global competition and domestic positioning) and four industry dynamics (decline, predation, rent-seeking and expansion). This analytical framework is then tested econometrically in a panel of 38 countries and 30 industries over the period 1995-2009. While regressions pooling all industries appear inconclusive, there is some evidence of contrasted outcomes across industries when they are grouped into the categories proposed in the theoretical framework. In some sectors, the productivity gains from offshoring seem to have benefited employment and the expansion of production through investment while in others they have led to rents and a higher share of the gross operating surplus going to shareholders and financial institutions (i.e. financialisation). The debate on offshoring and trade would benefit from a better understanding of the mechanisms that explain these differences among industries.
Keywords: globalisation, offshoring, financialisation, employment, industry-level.
* The authors are writing in a personal capacity. The views expressed are theirs only, and do not reflect in any way those of the OECD Secretariat or the member countries of the OECD.
JEL codes: F16, F66
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Is labour the fall guy of financial-led globalisation? A cross-country inquiry on globalisation, financialisation and employment at the industry level"
N° 2014-1
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1. Introduction
How does globalisation affect employment? There has been a huge amount of research and
controversies dedicated to the relationship between offshoring and jobs. Some authors (Amiti and
Wei 2005; Cohen 2006; Mankiw and Swagel 2006) have argued that the direct effects on
employment are rather limited and that they are more than compensated by the productivity
enhancing effects of trade liberalisation. At the firm (or plant) level, numerous studies have shown
that an intensification of import competition in manufacturing industries leads to the exit of the least
productive firms, while increasing the productivity of remaining firms (Pavcnik 2002; Amiti and
Konings 2007; Fernandes 2007; De Loecker 2011; Hayakawa et al. 2012). Some evidence has also
been collected at the industry level (Ferreira and Rossi 2003; Hijzen and Swaim 2007; Foster-
McGregor et al. 2013).
However, the overall impact of the globalisation of production on the economy is still debated. In a
famous paper, Samuelson (2004) has shown that when a large developed economy trades with a
large developing country, the Ricardian theory does not guarantee in each region that the net gains
of the winners will be greater than the net losses of the losers. It has also been argued that
globalisation may foster at the same time higher manufacturing productivity and de-industrialisation
in developed countries because labour-intensive manufacturing activities have been offshored and
there is a higher demand for services (Kollmeyer 2009). Moreover, McMillan and Rodrik (2011)
suggest that in some developing economies the productivity-enhancing effects of trade liberalisation
may have produced growth-reducing structural change, displacing workers in even lower-
productivity activities.
Despite this huge amount of research, the role of financialisation in explaining the impact of
globalisation on employment has been less studied. Financialisation can be defined as an increase in
the share of profit (at the firm or industry level) dedicated to financial payments at the expense of
investment and employment. To our knowledge, only the work conducted by Deborah Winckler and
Will Milberg has pointed out financialisation as a possible leak between the gains from offshoring
and employment in high-income countries (Milberg 2008; Milberg and Winckler 2010; Milberg et
Winkler 2013). Following their original insights, we propose in this study to broaden the argument
empirically and analytically. More specifically, we develop an analytical framework to jointly
explore the co-determinants of financialisation and employment at the industry level, according to
their exposure to international trade and offshoring. We then test this framework with data for 30
manufacturing and services industries over the period 1995-2009 in a panel of 38 developed and
emerging economies representing about 87% of world GDP.
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The aim of the paper is to bring new insights on differences across industries to introduce new
elements in the policy debate surrounding globalisation and employment. Most of the literature has
not been able to capture the roots of public concerns related to the destructive socio-economic
effects of globalisation in high-income countries. What our contribution suggests is that it is not
globalisation per se that produces negative welfare outcomes, but its association in some industries
with the financialisation of non-financial firms.
The paper is organised as follows. Section 2 explains our analytical framework with four industry
dynamics (decline, predation, rent-seeking and expansion) and three modes of insertion into global
production (fragmentation of production, global competition and domestic positioning). Section 3
describes the data and the econometric methodology to test the hypothesis made in Section 2.
Section 4 presents the main econometric results with regressions on labour demand, the investment
to profit ratio (IPR) and the categories of our analytical framework. Section 5 discusses these results
in the context of the analytical framework and its underlying assumptions. Section 6 concludes.
2. Analytical framework
In order to investigate the globalisation-employment-financialisation nexus, we propose an
analytical framework that will allow us to identify the diversity in industry dynamics and the variety
of outcomes across countries. Due to data limitations and the complexity of the relationships
involved, we limit our analysis to the impact of globalisation on employment and do not consider
important related issues such as wages and inequalities.
Following the literature (Boyer 2000; Lazonick and O’Sullivan 2000; Stockhammer 2004; Krippner
2005; Aglietta and Berrebi 2007; Orhangazi 2008), financialisation can be described as a divorce
between profits and accumulation. Financialisation has been defined as a shift in the use of profits
with an increase in the acquisition of financial assets and/or a higher share of profits distributed to
shareholders and lending institutions at the expense of fixed capital formation. The ratio of
reinvested earnings can thus be used as a proxy for financialisation and measured by the ratio of
fixed investment (I) to profit (P), both being taken as a share of value added.
Financialisation, i.e. a reduction in the fixed investment share to profit share ratio, is occurring (F+)
between and when
. It may result from an increase in profits and/or a diminution in
fixed investment relatively to value added. There is no financialisation (F-) when
.
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The share of profit not dedicated to investment is used for the remuneration of capital, including
several types of assets that can be regarded as immaterial investment, such as technology payments.
This can be an issue in our analysis, especially since measuring this knowledge-based capital is very
difficult (Plihon and Mouhoub 2009; OECD 2013). However, it should be pointed out that most of
knowledge-related expenditures are captured in national accounts data either as services
intermediate inputs (for example R&D services, especially when R&D is outsourced) or as are part
of the definition of fixed investment (I). Softwares, for example, should be included in fixed
investment according to the 2008 System of National Accounts. Therefore, the bias should be
limited and in our empirical analysis we include skill and technology variables to take it into
account.
We define employment simply as the number of hours worked. In a context of global under-
employment, such measure better reflects the dynamic of the labour market because, contrary to the
number of employee, it is not affected by the changing importance of part-time jobs. (E+) means an
increase in the number of hours worked; (E-) a reduction.
2.1 Typologies
The starting point in our analysis is the definition of four types of industry dynamics based on the
change in employment and financialisation (i.e. the investment to profit ratio). These four categories
are described in Table 1.
The combination of growing employment and financialisation is associated with Rent-seeking. It
concerns expanding industries in terms of employment where firms manage to maintain their
competitive position while giving priority to the retribution of shareholders and/or financial
operations rather than to investments.
The combination of employment improvement and no financialisation corresponds to a dynamic of
Expansion. Firms expand their activity and dedicate the gains to higher investment and/or to wage
increase.
Predation results from a combination of diminishing employment and financialisation: facing a
lower demand for their products or strong productivity gains, firms choose not to invest but rather
to maintain or expand their payments to shareholders and/or to sustain their profitability thanks to
financial operations.
Finally, Decline is associated with diminishing employment and no financialisation. While the
industry faces adverse market conditions, profits are squeezed by the diminution of productivity and
the rigidity of wages and employment.
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F + F -
Employment + Rent-seeking Expansion
Employment - Predation Decline
Table 1: Industry dynamics according to the evolution of employment and financialisation
To assess the involvement of industries in global production networks, we also create a typology
with three globalisation dynamics. Fragmentation corresponds to a significant increase in
offshoring. We define offshoring here as the increase in the use of foreign inputs in domestic
industries, following Feenstra and Hanson (1999). Fragmentation can also be regarded as part of
vertical trade where countries specialise in a stage of production and further process a good (or
service) imported as an input and shipped to another destination, either to be further processed or to
be consumed (Hummels et al. 2001). This vertical segmentation is the basis for the recent literature
on global value chains (Bair 2009).
Global competition is characterised by the prevalence of imports of final products in the industry,
pointing to an intense international competition on this market segment. We measure it through a
significant increase in foreign value added in domestic final demand which is not explained by the
offshoring of inputs but rather by imports of final goods and services. This captures traditional trade
in final products but also another form of offshoring, which is the offshoring of the assembly of
goods. When goods are assembled off-shore, traditional measures of offshoring looking only at the
use of foreign inputs do not increase but this can also lead to increased foreign value added in
domestic consumption.
Finally, Domestic positioning is the situation in which industries experience neither a significant
increase in the offshoring of inputs, nor an increase in the foreign goods and services sold in the
domestic market (imports of value added). While there can be some overlap between the
fragmentation of production and global competition (when foreign value added in domestic
consumption increases both through imports of inputs and final products), domestic positioning is a
distinct category. Industries which are relatively isolated from the global economy vertically (trade
in inputs) as well as horizontally (global competition) are mostly domestically positioned.
Table 2 summarizes the typology and indicates the threshold we use to distinguish between
domestic positioning, fragmentation and global competition dynamics for the period of our study.
Data and indicators are presented in detail in the section 3.
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Domestic positioning Change in the share of imported intermediate inputs in total
intermediate use < 4%
Change in domestic VA in foreign final demand and in foreign VA
in domestic final demand < 5%
Fragmentation of production Change in the share of imported intermediate inputs in total
intermediate use > 4%
Global competition Change in the share of imported intermediate inputs in total
intermediate use < 4 %
Change in domestic VA in foreign final demand and in foreign VA
in domestic final demand > 5%
Table 2 Globalisation dynamics and thresholds for the indicators used (Changes between 1995/97
average and 2007/09 average )
2.2. Hypotheses
Using the two typologies described in the previous sub-section, we make some assumptions on how
we expect industries to fit in these different categories based on their economic characteristics.
Because there are important differences between developed countries and emerging economies, our
hypotheses distinguish between the two and we do the same in the empirical section of the paper.
Hypothesis 1: Financialisation and employment are negatively related to
competition intensity
Financialisation occurs when, relatively to their value added, firms are able to preserve or increase
their profits while limiting or diminishing their investment. On the one hand, this implies that
immediate competitive pressures are weak. Otherwise, their profits would have diminished as the
value or the volume of their sales decreases. On the other hand, from a supply-side perspective,
limited investment suggests a lack of credible competitive threat in the near future. Indeed, such a
contestability of markets (Baumol 1982) could foster investment in order to improve the efficiency
and the quality of production processes and product innovation.
Along the same line of argument, employment at the sectoral level is supposed to be negatively
related to competition. One expect a reduction in the workforce as some firms are eliminated –
diminishing the number of employees at the industry level – and/or have to reduce prices through
labour-saving investments, drastic cost-cutting policies and possibly the reduction of the scope of
their activities through outsourcing. On the contrary, without competitive pressures, firms will be
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less keen to reduce employment and will consider an increase in employment as long as they are
able to maintain or improve their margins.
Competition intensity results from (i) the tradability of products and (ii) the level of barriers to
entry. Traditionally, such barriers result from sunk costs and the presence of scales economies.
However, there is mounting evidence of increasing competition in capital intensive core industries
as catching-up by developing countries has added excess producing capacities. This suggests that
knowledge intensity could be a better proxy for protection against competitive pressure.
As pointed out in Figure 1, one would thus expect both financialisation and employment to expand
primarily in activities characterised by low tradability and/or sectors which are knowledge
intensive.
Hypothesis 2: Global competition is the dominant form in activities where
technologies and know-how are widely diffused, while fragmentation is
relatively more important for knowledge intensive industries. Both of them
decrease in relevance when tradability of the products lowers
Tradability of goods and services is a key determinant of global competition and fragmentation;
non-tradable products are supposed to be mostly produced domestically, although some inputs
could be imported. Global competition plays a dominant role in sectors where barriers to entry are
low and, especially, where technologies and know-how are widely diffused. This does not preclude
possibilities of offshoring. However, fragmentation is supposed to be a more distinctive feature of
sectors where firms benefit from barriers to entry and could increase their mark-up thanks to lower
input prices.
Figure 1 summarizes hypotheses 1 and 2.
Figure 1: Financialisation, employment and globalization in the light of the determinants of
competition
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Hypotheses 1 and 2 can explain two dynamics in our analytical framework: “rent seeking” (E+ ;
F+) where competition is weak due to low tradability and knowledge intensity and “decline” (E- ;
F-) where competition is strong due to high tradability and commonality of technology. As we will
further see in the discussion of our empirical results, knowledge intensity and technology
commonality are two different notions and should not be used interchangeably. Knowledge is not
only involved in technology but also in other intangible assets for which skills are important.
What is not explained on Figure 1 are the other socio-economic dynamics, “expansion” (E+ ; F-)
and “predation” (E- ; F+), as well as the importance of uneven development across countries. We
need complementary hypotheses to deal with this.
KNOWLEDGE INTENSITY
HIG
H T
RA
DA
BIL
ITY
COMMONALITY OF TECHNOLOGY
LO
W T
RA
DA
BIL
ITY
E+ F+
E- F-
do
me
sti
c c
en
teri
ng
fragmentation
global competition
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Hypothesis 3: Late comers in global production are less prone to financialisation
than core countries
For similar industries, investment opportunities are higher in emerging countries for two reasons.
First, more than in high-income economies, there are some cheap and unexploited resources, in
particular labour. Second, these economies can benefit from late-comer advantages: they have the
possibility to rapidly catch-up with industries in more advanced economies by leap-frogging to the
most efficient techniques of production and are not constrained by the disadvantages of the front-
runners - what Thornstein Veblen (1915) called “systemic obsolescence” - in terms of old-fashioned
social and technical standards and material infrastructures.
This is almost a tautology to say that catching-up necessitates important investments and all
development policies throughout history have insisted on investment (Gerschenkron 1962;
Hirschman 1988; Amsden 1989; Wade 2004; Huang 2010). Consequently, industries in catching-up
countries are more prone to “expansion” (E+; F-) than to any other category, for any level of
knowledge intensity or tradability and for all globalisation dynamics.
Hypothesis 4: The combination of fragmentation and financialisation in high-income
economies is adverse to employment
Taking into account the case of tradable “knowledge intensive” products, the relationship between
financialisation and employment is ambivalent in high-income economies. Indeed, financialisation
and growing or stable employment could occur as long as feeble competition intensity allows firms
to preserve their profits while they are neither constrained to invest, nor to diminish the workforce.
Fragmentation could also be compatible with employment stability or growth of offshoring if (i) it
allows firms to improve their competitiveness and (ii) these gains from lower input prices translate
into expanding market shares and/or in higher profits and higher capacity investment.
However, as suggested my Milberg and Winkler (2013), the combination of offshoring and
financialisation in oligopolistic markets of high-income economies should be adverse to
employment, as mark-up gains from cheaper inputs neither translate into lower prices and higher
sales, nor into investment-led expansion, but rather into higher payments to financial markets. As a
consequence, we assume that offshoring by industries in high-income economies will favour
“predation” (E- ; F+) rather than “expansion” (E+; F-).
To put it in a nutshell, our hypotheses suggest that the main socio-economic dynamic for industries
in emerging countries is “expansion” (E+; F-). For high-income economies, three dominant
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configurations are expected depending on the tradability of industry products and their knowledge
intensity (Table 3): “rent-seeking” (E+;F+) is associated with domestic positioning for industries
with less tradable products, although “expansion” (E+; F-) is also possible ; for industries producing
highly tradable products, “predation” (E-;F+) is expected to go along with offshoring and
knowledge intensity, while “decline” (E-; F-) is expected to be associated with global competition
and technology commonality.
Knowledge intensity Technology commonality
Low tradability RENT SEEKING (E+; F+) / EXPANSION (E+ ; F-)
domestic centering
High tradability PREDATION (E-; F+) offshoring
DECLINE (E-; F-)
global competition
Table 3: Expected socio-economic tendencies and globalization trends according to sectoral
characteristics in high income economies
3. Econometrics
In order to test the analytical framework described in Section 2, we provide in the rest of the paper
an econometric analysis at the country and industry level of the relationship between globalisation,
financialisation and employment over the period 1995-2009. Our strategy is the following: first we
run panel regressions where all industries and countries are pooled together, as it was done in
previous papers investigating the impact of offshoring on employment. There is not a lot of
empirical work on the financialisation phenomenon, but we also provide a regression testing its
prevalence. We will show that such regressions are mostly inconclusive and that different dynamics
operate across industries. In order to capture this and to test more specifically our hypotheses
described in Section 2, we switch to an alternative methodology based on probit regressions where
we look at the probability for each industry to be in one of the categories we identified, both in
terms of their insertion into global production and their outcome with respect to employment and
financialisation. Because we believe different industries are associated with different market
structures, different types of products and different types of global value chains, we regard the
relationship between globalisation, financialisation and employment as a classification problem,
where the variables discussed in the previous Section (such as tradability or technology intensity)
are factors that influence the probability of each industry to be in one of the categories we
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described. At the micro level, firm strategies are the determinants of employment, financialisation
and offshoring. This is why we can regard these categories as the aggregation of choices made by
firms on the basis of their socio-economic environment.
3.1 Data
The empirical analysis relies on the World Input Output Database described in Timmer et al. (2012),
which is a set of world input-output tables (WIOTs) covering 40 countries and 35 industries over
the years 1995-2011. This dataset is particularly relevant for a comparison across countries and
industries as it is based on harmonised national accounts. In addition, WIOTs cover all inter-country
inter-industry transactions and are the ideal tool to understand global production, with indicators
going beyond the traditional measures of offshoring.
Moreover, the WIOD database includes tables in previous year prices for 1995-2009, allowing the
comparison of output and value added over time in real terms and the calculation of chain price
indexes for gross output and the cost of inputs. There are also socio-economic accounts with data on
labour, capital and investment for 1995-2009. To take advantage of these data, we limit our dataset
to this period. We keep all countries, except two very small European Union economies (Cyprus
and Malta). The dataset thus covers 38 countries, which are mostly high income countries
(Australia, Austria, Canada, Belgium, Bulgaria, the Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Hungary, Japan, Korea, Ireland, Italy, Latvia, Lithuania, Luxembourg,
the Netherlands, Poland, Portugal, Romania, the Slovak Republic, Slovenia, Spain, Sweden, the
United Kingdom and the United States) but also includes 8 emerging economies (Brazil, China,
Mexico, India, Indonesia, Russia, Chinese Taipei and Turkey).
From the world input-output tables (WIOTs), we collect information at the industry level on output,
value added and the use of intermediate inputs. We calculate offshoring indexes following Feenstra
and Jensen (2012) as the share of foreign intermediate inputs in total intermediate use (for a given
industry). Building on the work of the OECD and WTO on trade in value added1, we also calculate
the foreign content in domestic final demand (imports of value added) and the domestic content in
foreign final demand (exports of value added). These flows are obtained by multiplying a vector of
value added by industry and country by the Leontief inverse of the WIOTs and then by a vector of
final demand. For each country, we obtain a matrix that describes the origin of the value added in
final demand and summing the foreign and domestic elements, we can decompose final demand
into domestic and foreign value added.
1 See //oe.cd/tiva for documents describing the concepts we refer to in this section and more details on the methodology.
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From the WIOD socio-economic accounts, we have directly for each country and industry: the
number of hours worked (our employment variable), the labour compensation, the gross operating
surplus (calculated as value added minus labour compensation), investment (gross fixed capital
formation) and a real capital stock.
The WIOD industry classification is based on NACE Rev. 1 and we use Eurostat definitions for the
technology and knowledge intensity of these sectors. We associate each industry with a technology
level from 1 (low tech industries) to 4 (high tech industries) with 2 corresponding to mid-low tech
industries and 3 to mid-high tech industries. Knowledge intensive services sectors are either mid-
low tech (2), mid-high tech (3) or high tech (4). We drop from the dataset 5 sectors that are mostly
non-market services: ‘public administration and defence’, ‘education’, ‘health and social work’,
‘other community, social and personal services’ and ‘private households with employed persons’.
These activities are not part of global production networks and less relevant for our analysis. Inputs
coming from these industries are however included in all our calculations. The list of industries and
their technology level can be found in Table A1 in the Annex. We have also included the average
share of high skill employment in this Table to highlight that the technology level of industries and
their share of high skill jobs are not strongly correlated.
3.2 Regressions on employment and the investment profit ratio
We first run the traditional labour demand regressions found in the offshoring literature,
distinguishing between an unconditional labour demand and a conditional labour demand (Hijzen
and Swaim, 2007). Using the same WIOD dataset, Foster-McGregor et al. (2013) have shown that
offshoring has an overall neutral or slightly positive effect on employment. The variable in their
results is not always significant and our own analysis will confirm its lack of robustness
We estimate two functions at the industry level, pooling observations for all years and countries.
First the unconditional (or capital-constrained) labour demand:
(1)
where is the demand for labour in country i, industry j at year t, is the price of variable
inputs (the ratio of the hourly wage to the price of intermediate inputs), is the capital stock,
the price of output (relative to the price of intermediate inputs), and is a set of demand
shifters that includes an offshoring index but also value-added trade flows, industry mark-ups –the
price-cost margin, which is a proxy for the degree of competition- and the share of high skill labour
in employment. Rather than adding fixed effects to control for unobserved time invariant factors
influencing labour demand, we estimate the function in difference. We use a 5-year difference to
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allow a sufficient number of years for labour to adjust.2 This type of estimation is also better to
capture the dynamic (the evolution over time) of employment and how it is affected by the change
in the independent variables. We add year fixed effects for unobserved time-varying heterogeneity.
Equation (1) measures the overall impact of the fragmentation of production on employment.
However, this total impact is the result of two opposing effects. On the one hand, offshoring is
expected to decrease employment because less labour is required in the industry to produce the
same output (the technology effect). On the other hand, as companies are more productive through
offshoring, the output of the industry can be higher with more jobs created (the scale effect). The
impact of offshoring on industry employment is therefore ambiguous, depending on the effect that
dominates. In order to assess the respective importance of the scale and technology effects, a second
labour demand function is estimated where gross output ( ) is added as an independent variable
(instead of the price of output relative to the price of intermediate inputs), so that all other
coefficients are estimated for a given level of output. This conditional labour demand measures the
technology effect of offshoring:
(2)
Results of the estimation of the unconditional and conditional labour demand are presented in Table
1. We run the regressions for all countries and then separately for developed countries and emerging
economies. For the whole dataset, the main variables are significant and have the expected sign.
Higher wages (relative to the price of inputs) are associated with lower employment both in the
conditional and unconditional labour demand. An increase in the capital stock also decreases
employment for a given level of output (substitution effect) but in the unconditional labour demand,
a higher capital stock is associated with more output and more employment (positive coefficient).
Output is very significant and positively correlated with employment in the conditional demand, as
one can expect. The ratio of the price of output to the price of intermediate inputs is also significant
and positive in the unconditional labour demand.
When looking at the demand shifters, the offshoring index is positive and weakly significant in the
conditional labour demand and not significant in the unconditional function. We would expect the
opposite sign for the conditional labour demand, as the theory predicts that for the same level of
output there should be less employment when part of the production is offshored. But our result is
in line with other papers that find no clear relationship between the offshoring of inputs and
employment. The foreign value added in domestic final demand is also not significant in the
regression. Interestingly, the most significant demand shifter is on the export side, when looking at
2 Hijzen and Swaim (2007) also use 5-year differences, while Foster-McGregor et al. (2013) have first differences. It
partly explains why our results are different despite using the same dataset.
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the domestic value added in foreign final demand (‘exports of value-added’). There is a negative
and significant coefficient both in the conditional and unconditional labour demand function,
suggesting that the most competitive sectors in terms of exports of value added are the ones limiting
employment the most. The other variable which is very significant is the industry mark-up, a proxy
for the profit of companies which also points to a lack of competition. Higher mark-ups are
associated with less employment, an illustration of the potential ‘financialization’ related to
offshoring.
Conditional Unconditional
All Developed Emerging All Developed Emerging
(1) (2) (3) (4) (5) (6)
Wage/Price of inputs -0.590*** -0.524*** -0.690*** -0.579*** -0.515*** -0.696***
(0.054) (0.047) (0.066) (0.054) (0.047) (0.068)
Capital stock -0.125** 0.003 -0.298*** 0.053** 0.137*** -0.156***
(0.048) (0.036) (0.046) (0.021) (0.023) (0.030)
Output 0.330*** 0.404*** 0.241***
(0.063) (0.073) (0.043)
Price of output/price of inputs 0.026 0.007 0.254***
(0.036) (0.035) (0.048)
Offshoring 0.026* 0.013 -0.022 0.010 0.007 -0.031*
(0.010) (0.012) (0.015) (0.010) (0.014) (0.015)
VA imports 0.023 0.032 -0.023 0.019 0.029 -0.035
(0.016) (0.017) (0.022) (0.020) (0.024) (0.022)
VA exports -0.151*** -0.137*** -0.221*** -0.257*** -0.264*** -0.263***
(0.019) (0.020) (0.026) (0.053) (0.065) (0.030)
Mark-up -0.064*** -0.062*** -0.095*** -0.064*** -0.059*** -0.105***
(0.006) (0.006) (0.018) (0.007) (0.007) (0.018)
High skill intensity -0.009 0.004 -0.110** -0.010 0.006 -0.115**
(0.010) (0.005) (0.036) (0.010) (0.005) (0.036)
Observations 10,752 8,378 2,374 10,752 8,378 2,374
R-squared 0.57 0.54 0.72 0.51 0.44 0.72
Adj. R-squared 0.57 0.54 0.72 0.51 0.44 0.72
Table 4 Regression results for the conditional and unconditional labour demand
Robust standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Regressions in fifth difference. Constant and fixed effects not
reported.
When estimating the functions separately for developed and emerging economies, the results are not
too different but elasticities can differ. For example, the elasticity of labour demand to wages is
higher in emerging economies. One clearcut difference is with respect to skill intensity. The variable
is not significant for developed countries but significant and negative for emerging economies. Skill
intensity is clearly labour-saving in developing countries but skill-biased technological change may
explain why the variable is not significant in developed countries.
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When pooling all industries, there is not much we can learn from the econometric analysis. The
average effect of offshoring is not robust and the scale versus technology effect does not seem to be
supported by the data. This is why we have developed an alternative theoretical framework, looking
at how the above variables are likely to lead to different outcomes in different industries.
But before presenting this alternative methodology, we would like to further test the financialisation
assumption, by running a panel regression on the investment to profit ratio (IPR). This variable
takes a higher value when more of the gross operating surplus is devoted to investment as opposed
to the remuneration of shareholders. Financialisation occurs when the IPR goes down. Assuming
that the demand for capital going to investment is a function of the industry mark-up, the output and
the previous stock of capital, we estimate the following model:
(3)
We include in the estimation the same demand shifters as in equations (1) and (2). We add country,
industry and year fixed effects to account for unobserved variables. Since our independent variable
is a ratio and capital is expected to adjust faster than labour, we do not rely on an estimation in
difference this time, but still include the lag of the capital stock to capture the dynamic of
investment. Results of the regressions are presented in Table 5 for all countries and for developed
and emerging economies separately.
Table 5 Regression results for the investment to profit ratio
(1) (2) (3)
All Developed Emerging
Countries countries Economies
Mark-up -0.782*** -0.720*** -0.778***
(0.009) (0.009) (0.021)
Output -0.408*** -0.554*** -0.720***
(0.014) (0.010) (0.017)
Stock of capital (t-1) 0.464*** 0.560*** 0.736***
(0.012) (0.011) (0.014)
Offshoring index 0.005 -0.027 -0.054
(0.018) (0.016) (0.029)
Foreign VA in domestic FD -0.099*** -0.062*** -0.006
(0.018) (0.015) (0.024)
Domestic VA in foreign FD 0.076*** -0.059*** -0.034**
(0.012) (0.007) (0.012)
Share of high skill 0.021 0.054*** 0.126***
(0.013) (0.012) (0.015)
Observations 15,430 12,099 3,331
R-squared 0.65 0.59 0.65
Adj. R-squared 0.64 0.59 0.65
Constant and fixed effects not reported. Robust standard errors in parentheses.
*** p<0.001, ** p<0.01, * p<0.05
16
Because we have fixed effects in all the dimensions of the panel, the R-squared is reasonably high
but the main variables of the model explain more than 40% of the variation in the data. The industry
mark-up is strongly correlated with lower levels of IPR, indicating that industries with high profits
are less likely to have higher levels of investment. Output is also negatively correlated with the IPR,
as more productive capacity in an industry leads (in the short term) to lower investment. The lag of
the stock of capital is positively correlated with the IPR, suggesting that past investment is
associated with more investment as a share of gross operating surplus (we have a similar result if
we introduce in equation (3) the lag of investment).
As we observed with the labour demand function, there is no significant relationship between the
IPR and the offshoring index, even when limiting the data sample to developed economies. But
interestingly, another variable measuring the insertion in globalisation is significant, the foreign
value added in domestic final demand. The more an industry is ‘globalised’, importing value added
either through the use of foreign inputs (offshoring of inputs) or imports of final goods and services
(the other side of offshoring), the lower the IPR and thus the higher the ‘financialisation’. This
regression is another starting point for looking further at the relationship between employment,
financialisation and globalisation, but still does not capture what makes the outcome different
across industries.
3.3 Understanding industry dynamics using probit regressions
To test the theoretical framework described in Section 2 with an alternative methodology, we run a
series of probit regressions on the 3 different types of insertion into the global economy previously
identified: fragmentation of production (offshoring of inputs), global competition (higher foreign
value added in domestic final demand explained by imports of final goods and services) and
domestic positioning (for industries where no fragmentation or global competition occurs).
The probit models are the following (omitting subscripts for the country, industry and year):
(4)
(5)
(6)
where is the variation of the number of hours worked (over the last year), IPR is the
investment to profit ratio previously described, Mk is the industry mark-up, Skill is the skill
17
intensity (share of high skill employment) and technology is a factor variable representing the
technology level of the industry (from 1 =low tech to 4=high tech).
We pool all industries, countries and year and estimate simple probit regressions. Since our
dependent variable does not reflect a choice, we don’t run a multinomial probit.
We use a similar methodology to test the role of globalisation variables in explaining the evolution
of employment and financialisation. The typology described in Section 2 is used to look at the
probability for each industry/country to be in one of the four categories: decline, predation, rent-
seeking or expansion.
The probit models are:
=14 + (7)
=14 + (8)
=14 + (9)
=14 + (10)
Table A2 in the Annex shows the distribution of industries for developed and emerging countries
across all the categories. Results for the globalisation dynamics are presented in Table 6 below
where we report only the marginal effect of each variable in the different models. We run
independently regressions for developed and emerging economies.
Table 6 indicates a negative relation to employment in sectors where fragmentation plays a key role
in both developed and emerging economies, suggesting that foreign labour is substituted for
domestic labour through offshoring. On the contrary, a relative insulation from fragmentation and
global competition is associated with a positive correlation for domestically positioned sectors.
Interestingly, in sectors characterised by an intensification of global competition, we observe
different signs in the relation to employment: while in developed economies the impact on
employment is negative, there is no significant relationship for emerging economies.
Financialisation is associated with fragmentation in developed economies (diminishing IPR), which
points to the possibility that gains from cheaper imported inputs may be transferred to financial
markets in a context where their mark-up tend to diminish. This is in contrast with industries under
pressure of global competition where a negative mark-up trend is not associated with
financialisation, firms of developed and emerging economies not being able to increase the share of
their profits allocated to financial markets. Among developing economies, financialisation occurs
18
only in domestically positioned sectors where mark-us are on the rise, a correlation which is not
confirmed for developed countries.
Global competition is associated with low-tech industries among developed economies but not in
emerging countries, related to the difference in the level of development of these two groups.
Interestingly, among developed economies, fragmentation is associated with lower skills but also
with more mid-high technology, pointing to the possibility for some industries to retain a wide
range of employment in some segments relatively insulated from competition on final products.
Higher skills and technology are typically associated with domestic positioning, which reflects the
rapid expansion of business services and telecoms industries.
(1) (2) (3) (4) (5) (6)
Fragmentation competition domestic fragmentation competition domestic
VARIABLES Developed developed developed Emerging emerging Emerging
Employment -0.012*** -0.025*** 0.037*** -0.022*** -0.004 0.028***
(0.002) (0.002) (0.002) (0.004) (0.004) (0.004)
IPR -0.003** 0.002* 0.001 0.001 0.012* -0.041***
(0.001) (0.001) (0.001) (0.005) (0.006) (0.010)
Mark-up -0.039*** -0.131*** 0.105*** -0.036*** -0.430*** 0.186***
(0.008) (0.012) (0.006) (0.009) (0.030) (0.013)
Skill intensity -0.578*** 0.061 0.474*** 0.155* -0.256*** 0.189**
(0.040) (0.038) (0.033) (0.067) (0.077) (0.073)
2.tech -0.036*** -0.147*** 0.193*** -0.061*** -0.056** 0.137***
(0.011) (0.010) (0.009) (0.018) (0.019) (0.020)
3.tech 0.039** -0.082*** 0.045*** 0.034 0.108*** -0.137***
(0.012) (0.011) (0.010) (0.022) (0.022) (0.021)
4.tech -0.078*** -0.036* 0.115*** -0.040 -0.028 0.101**
(0.016) (0.016) (0.014) (0.027) (0.030) (0.031)
Observations 13,458 13,458 13,458 3,570 3,570 3,570
Table 6 Probit estimations – Globalisation dynamic - Average marginal effects
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05
dy/dx for technology levels is the discrete change from the base level (low tech industries = 1)
Tables 7 and 8 present the results for the socio-economic dynamic, respectively for developed countries and
emerging economies. Because the offshoring index and the foreign value added in domestic final demand
can be collinear, these variables are in separate regressions.
Only expansion –i.e. an increase in both the IPR ratio and employment - is associated with higher mark-ups.
As expected, decline and predation are both associated with offshoring and foreign VA. One can also note the
19
strong weight of foreign VA in the case of decline and the fact that the relationship between offshoring and
decline is more robust than in the case of predation. Offshoring thus appears to be associated to an increase
in competitive pressures which sometimes can allow firms to increase their financialisation. Interestingly,
offshoring is also associated with rent-seeking – i.e. an expansion of both employment and financialisation.
In this case, as in predation, offshoring could be considered to be at the origin of the possibility for firms to
increase their payments to financial markets and institutions.
As expected, low skills intensity and low tech levels are associated with decline, whereas rent-seeking is
associated with higher skills and higher technology. Decline occurs where technology is common. On the
contrary, firms manage to increase both employment and financialisation when their knowledge intensive
production is protected from competition.
Regarding predation and expansion, there are interesting opposite signs with respect to skill intensity and
technology. One way to interpret them would be to think about industries such as business services, telecoms
services or electrical and optical equipment. As they expand, they increase their workforce to take care of
more standardised activities which require fewer skills. In the case of predation, the logic could be one of
specialisation in the higher-end segments of mature industries.
(1) (2) (3) (4) (5) (6) (7) (8)
decline decline predation Predation Rentseeking rentseeking expansion expansion
VARIABLES developed developed developed Developed Developed developed developed developed
Mark-up -0.112*** -0.078*** -0.076*** -0.060*** -0.038*** -0.052*** 0.147*** 0.123***
(0.013) (0.013) (0.009) (0.009) (0.008) (0.008) (0.008) (0.008)
Offshoring 0.098*** 0.066** 0.056** -0.187***
(0.022) (0.023) (0.020) (0.022)
Foreign VA 0.166*** 0.107*** -0.060*** -0.186***
(0.015) (0.015) (0.013) (0.015)
Exported VA -0.002 -0.009** -0.001 -0.003 -0.000 -0.000 0.001 0.002
(0.002) (0.003) (0.001) (0.002) (0.000) (0.000) (0.001) (0.001)
Skill intensity -0.221*** -0.200*** 0.100** 0.121*** 0.202*** 0.166*** -0.105** -0.137***
(0.037) (0.037) (0.036) (0.036) (0.031) (0.032) (0.035) (0.035)
2.tech -0.173*** -0.146*** -0.258*** -0.241*** 0.169*** 0.160*** 0.273*** 0.240***
(0.010) (0.010) (0.010) (0.010) (0.008) (0.008) (0.009) (0.009)
3.tech -0.105*** -0.107*** -0.187*** -0.187*** 0.141*** 0.147*** 0.154*** 0.160***
(0.011) (0.011) (0.011) (0.011) (0.009) (0.009) (0.009) (0.010)
4.tech -0.295*** -0.285*** -0.119*** -0.110*** 0.230*** 0.230*** 0.183*** 0.174***
(0.011) (0.010) (0.015) (0.015) (0.014) (0.014) (0.014) (0.014)
Observations 13,468 13,468 13,468 13,468 13,468 13,468 13,468 13,468
Table 7 Probit estimations for developed countries – Socio-economic dynamic - Average marginal
effects
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05
dy/dx for technology levels is the discrete change from the base level (low tech industries = 1)
For emerging economies, the coefficients tend to be similar to the ones observed for high-income
countries. There are, however, two interesting differences to report. First, rent-seeking tends to be
negatively correlated with offshoring, which could be explained by the fact that there is not much
20
room for gains from cheaper inputs from lower wage countries in the case of emerging economies.
The second difference concerns the role of exports of domestic value-added. While the variable is
mostly not significant for developed economies, we observe on the contrary some weak but still
significant coefficients in the group of emerging countries. While rent-seeking is associated with a
lower specialisation in exports, there is a positive link between the expansion of industries and
export activities.
(1) (2) (3) (4) (5) (6) (7) (8)
decline decline predation Predation Rentseeking rentseeking expansion expansion
VARIABLES emerging emerging emerging Emerging Emerging emerging emerging emerging
Mark-up -0.094*** -0.073*** -0.041*** -0.038*** -0.020** -0.023** 0.080*** 0.070***
(0.022) (0.021) (0.011) (0.011) (0.007) (0.008) (0.013) (0.012)
Offshoring 0.217*** 0.279*** -0.181** -0.405***
(0.051) (0.039) (0.062) (0.073)
Foreign VA 0.256*** 0.114*** -0.113** -0.282***
(0.030) (0.026) (0.038) (0.044)
Exported VA 0.048*** -0.005 -0.022 -0.033* -0.062** -0.046* 0.017 0.064**
(0.013) (0.015) (0.012) (0.014) (0.019) (0.021) (0.020) (0.023)
Skill intensity -0.816*** -0.855*** 0.415*** 0.437*** 0.189** 0.173** -0.328*** -0.352***
(0.087) (0.087) (0.042) (0.042) (0.057) (0.058) (0.079) (0.079)
2.tech -0.176*** -0.163*** 0.031* 0.047*** 0.066*** 0.061*** 0.127*** 0.110***
(0.017) (0.017) (0.014) (0.014) (0.015) (0.015) (0.022) (0.022)
3.tech -0.133*** -0.130*** 0.016 0.017 0.173*** 0.174*** -0.013 -0.015
(0.019) (0.019) (0.015) (0.014) (0.019) (0.019) (0.024) (0.024)
4.tech -0.258*** -0.256*** -0.048** -0.040** 0.421*** 0.416*** -0.067* -0.080*
(0.019) (0.018) (0.015) (0.015) (0.030) (0.030) (0.032) (0.032)
Observations 3,570 3,570 3,570 3,570 3,570 3,570 3,570 3,570
Table 8 Probit estimations for emerging economies – Socio-economic dynamic - Average marginal
effects
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05
dy/dx for technology levels is the discrete change from the base level (low tech industries = 1)
4. Discussion
In this section we discuss our initial hypotheses in light of the econometric results and descriptive
statistics presented in the Annex.
When looking at how industries are distributed across the categories introduced in the analytical
framework (Table A2), there is already some evidence supporting our third hypothesis, which is
about marked differences between developing and developed economies. Expansion is the dominant
socioeconomic dynamics among developing economies (47,9%) and financialisation is a limited
phenomenon concerning about 33% of the industries. In developed economies, 48% of the
industries are under financialisation and employment is declining for 51% of them, against 30,2% in
21
emerging economies. Expansion (28.1%) ranks first, as in emerging economies, but not very far
from predation (27,1%), followed by decline (23, 9%) and rent-seeking (20,9%).
With respect to the relevance of the employment-globalisation-financialisation nexus, finding a
negative relationship between mark-ups and employment in the labour demand estimation seems to
contradict our first hypothesis that protection from competition is favourable to employment.
However, a positive link between mark-ups and financialisation (diminishing IPR), is in line with
the other part of this first hypothesis, which suggests a trade-off between financialisation and
employment. We can see here some support for our general argument that a consistent assessment
of the impact of globalisation on labour requires to take into account the financialisation
phenomenon.
The results of the econometric analysis also support the other argument we made about the lack of
robustness of regressions trying to establish a generic relationship between offshoring and
employment. This may be partly related to the definition and measurement of offshoring, as
suggested by the different results we have for an offshoring index based on the use of foreign inputs
versus more sophisticated value-added measures of the offshoring of production. But the main
result of the analysis is that it is important to look at the specificities of industries, in particular
along the dimensions of tradability, competition and skill or technology intensity.
Our empirical results tend to support the idea that global competition –only for developed
economies- and fragmentation are adverse to employment at the industry level, contrary to domestic
positioning. However, one needs to distinguish between fragmentation which is associated with
financialisation and globalisation where there is no financialisation. This result is in line with our
fourth hypothesis and is relevant for both developing and developed economies. It is supported by
the probit analysis of socio-economic dynamics that shows a positive relationship between
predation and offshoring, which means that gains offshoring could be used to maintain payments to
financial markets and institutions at the expense of employment. Interestingly, in developed
countries, rent-seeking is also associated with offshoring which also suggests a positive relationship
between offshoring and financialisation but in a context where limited competition allows firms to
expand employment.
As expected, the dynamic of expansion, i.e. an increase in both employment and the IPR ratio, is at
the opposite of predation: it occurs (in developed countries) only when the mark-up is increasing
and in the context of diminishing offshoring and diminishing imports of foreign VA.
Finally, decline is associated with both offshoring and increased foreign VA, as firms facing an
intensification of competition (diminishing mark-up) are not able to increase the share of their
22
profits dedicated to financial markets but have to squeeze employment and try to diminish their
input costs thanks to offshoring.
Skills and technology indicators also show a good fit to the analytical framework as shown on
Figure 2 below. Decline is associated with technological commonality and low skills. At the
opposite side of the Figure, rent-seeking is associated with higher skills and higher technology.
However, there seems to be a more subtle relationship for the predation dynamic, where there is a
positive relationship with skills and negative relationship with technology. This suggests that in
some mature industries, firms focus on the high quality end of the spectrum of products thanks to
highly qualified workers. The case of expansion is less surprising: new medium to high-tech
expanding industries enlarge their workforce and in doing so recruit less skilled workers than
before.
Figure 2. An empirical test of Figure 1 (all industries and countries)
Note: Tradability is the average of the offshoring index and the foreign value in domestic final demand (to capture both
the tradability of inputs and final products). The technology intensity is as defined in Table A.1. Values for each
category are the unweighted average across all industries and countries.
5. Conclusion
From the above analysis, there are two important implications regarding the relationship between
globalisation and employment. First, it is important to take into account differences across
industries and the fact that in a non-competitive environment or in markets where technology
matters, the gains from offshoring do not automatically lead to higher levels of employment and
23
investment. This is also expected as offshoring is about specialisation and specialisation implies that
some industries are declining while others expand their output. In the context of global value
chains, however, we could expect specialisation to be more within industries than across industries.
Therefore, there are other mechanisms to take into account, such as financialisation. The first
conclusion is that industries matter and that future research on offshoring and employment should
focus on differences across industries. We refer to industries because this is the statistical unit used
in national accounts but we should rather refer to global value chains in the context of the
international fragmentation of production.
The second conclusion of the paper is, however, that using such industry-level data, we cannot find
strong econometric relationships between globalisation, employment and financialisation, and this
is why we used as our main methodology probit regressions that highlight some differences in the
probability for each industry of going into one direction or the other. More analysis at the firm level
(or the global value chain level) would be needed to bring more robust evidence. The aggregate
analysis presented in this paper should be regarded as a starting point.
Nevertheless, we believe such analysis can bring more concrete policy implications in the debate on
the globalisation of production by focusing on how policies can encourage firms to use the gains
from offshoring to increase employment and investment.
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7. Annexes
Table A1. List of industries, technology intensity and share of high skill employment
Code Industry Technology intensity High skill intensity
1 Agriculture, forestry and fishing Low tech 0.01
2 Mining and quarrying Low tech 0.05
3 Food, beverages and tobacco Low tech 0.07
4 Textiles and textile products Low tech 0.04
5 Leather and footwear Low tech 0.03
6 Wood and products of wood Low tech 0.04
7 Pulp, paper, printing & publishing Low tech 0.12
16 Manufacturing nec & recycling Low tech 0.05
8 Coke, refined petroleum and nuclear fuel Med-low tech 0.13
10 Rubber and plastics Med-low tech 0.07
11 Other non-metallic mineral Med-low tech 0.04
12 Basic and fabricated metal Med-low tech 0.09
17 Electricity, gas and water supply Med-low tech 0.16
18 Construction Med-low tech 0.06
19 Sale and Repair of motor vehicles Med-low tech 0.14
20 Wholesale trade Med-low tech 0.13
21 Retail trade Med-low tech 0.10
22 Hotels and Restaurants Med-low tech 0.08
26 Auxiliary transport activities Med-low tech 0.11
9 Chemicals and chemical products Med-high tech 0.14
13 Machinery nec Med-high tech 0.10
15 Transport equipment Med-high tech 0.14
23 Inland Transport Med-high tech 0.07
24 Water transport Med-high tech 0.11
25 Air transport Med-high tech 0.19
28 Financial intermediation Med-high tech 0.38
29 Real estate activities Med-high tech 0.32
14 Electrical and optical equipment High tech 0.14
27 Post and telecommunications High tech 0.21
30 Other Business Activities High tech 0.38
Not incl. Public administration
Not incl. Education
Not incl. Health and social work
Not incl. Other community and social services
Not incl. Private households with employed persons
26
Table A2. Number of observations in each category
Developed countries
Decline Predation Rent-seeking Expansion Total
Fragmentation 102 117 60 91 370
Competition 78 84 48 68 278
Domestic 35 42 80 93 250
Total 215 243 188 252 898
% 23,9 27,1 20,9 28,1 100
Emerging economies
Decline Predation Rent-seeking Expansion Total
Fragmentation 12 9 12 28 61
Competition 19 10 18 39 86
Domestic 15 7 22 47 91
Total 46 26 52 114 238
% 19,3 10,9 21,8 47,9 100
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