Klaus Bernhard Michel

Académie Universitaire Wallonie-Bruxelles

Université libre de Bruxelles

Solvay Brussels School of Economics and Management

Thesis presented in order to obtain the degree of PhD in Economics and

Management sciences.

Klaus Bernhard Michel

Thesis carried out under the direction of: François Rycx

Jury :

Mr Jozef Konings (KU Leuven)

Mrs Carine Peeters (Université libre de Bruxelles)

Mr Robert Plasman (Université libre de Bruxelles)

Mr Glenn Rayp (Universiteit Gent)

Academic year 2013-2014

Economic and Environmental Causes and Consequences of Offshoring:

An Empirical Assessment

3 Acknowledgements

I gratefully acknowledge having received support from a great number of people in the course of this research work. I would like to thank all of them and mention a few in particular.

First and foremost, I am deeply indebted to my employer, the Belgian Federal Planning Bureau (FPB), for allowing me to combine this PhD research work with my day-to-day work at the office. The FPB has given me the possibility to re-use my working papers in this volume, to present them at international conferences and to submit them for publication in peer-reviewed journals. In that respect, I am particularly grateful to Henri Bogaert, the Commissioner, Joost Verlinden, the head of the Sectoral Division, and Bart Hertveldt, his deputy and the head of the Input-Output Team.

Second, I am also deeply indebted to my supervisor François Rycx. His support has been crucial and he has helped me improve so many aspects of this research work. He has been a great coach for making my work attain academic standards.

Third, I would also like to thank the members of the jury, Jozef Konings, Carine Peeters, Robert Plasman and Glenn Rayp for their guidance and valuable comments that have greatly contributed to improving the quality of the work presented here.

Fourth, I am grateful for the support I have received from several of my colleagues at the FPB: Luc Avonds, Bernadette Biatour, Geert Bryon, Michel Dumont, Caroline Hambÿe, Koen Hendrickx, Chantal Kegels, Igor Lebrun, Bart Van den Cruyce and Guy Vandille.

5

Table of contents

4.1.Theory and labour demand equation 39

4.2.Review of the relevant literature 42

4.3.Descriptive statistics 45

4.4.Results 47

4.5.Discussion 54

5. Productivity gains and spillovers from offshoring... 56

5.1.Review of the relevant literature 59

5.2.Estimation strategy and offshoring spillovers 62

5.3.Results 69

5.4.Discussion 77

6. Offshoring and the skill structure of labour demand ... 79

6.1.Review of the relevant literature 80

6.2.Trends in employment by skill category 83

6.3.Model specification 86

6.4.Results 91

6 7. Does offshoring contribute to reducing domestic air emissions? ... 104

7.1.Review of the relevant literature 107

7.2.Trends in air emissions 111

7.3.Decomposition analysis 114

7.4.Results 120

7.5.Discussion 131

8. Is offshoring driven by air emissions? ... 132

8.1.Review of the relevant literature 135

7

List of abbreviations

2sls Two-stage least squares

AEA Air Emission Accounts

ACID Composite emission indicator for acidifying gases

BEET Balance of emissions embodied in trade

Belspo Belgian Science Policy

CEEC Central and Eastern European countries

CPA Standard Classification of Products by Activity in the European Community

EEA European Environmental Agency

FDI Foreign direct investment

FE Fixed effects estimation

FPB Federal Planning Bureau

GHG Composite emission indicator for greenhouse gases

GMM Generalised method of moments

GMM-DIF Difference GMM

GMM-SYS Systems GMM

HAC Heteroskedasticity and autocorrelation consistent

ICT Information and Communications Technology

IDA Index decomposition analysis

INA Institute for the National Accounts

8 ISCED International Standard Classification of Education

ISUR Iterated seemingly unrelated regression

NA National Accounts

NACE Statistical Classification of Economic Activities in the European Community

NAFTA North American Free Trade Agreement

NBB National Bank of Belgium

OECD Organisation for Economic Cooperation and Development

OLS Ordinary least squares

PHE Pollution haven effect

PHH Pollution haven hypothesis

SAM Social Accounting Matrix

SDA Structural decomposition analysis

SUT Supply-and-Use Tables

TFP Total factor productivity

9

1.

Introduction

Over the last couple of decades, the nature of globalisation and international trade has changed. Production processes have become more and more fragmented: they are divided into ever smaller parts considered as separate activities or production stages, which are then spread over various locations in different countries. In this way, truly global value chains have emerged, where goods are shipped across multiple borders in the course of the production process before the final product is completed and sold to final consumers in the destination market. This stands in sharp contrast to the traditional view of international trade as the exchange of final goods that are the outcome of an integrated production process in a single country. In other words, as Grossman and Rossi-Hansberg (2006) put it in their title: “it’s not wine for cloth anymore”.

10 Thus, as emphasized in Baldwin (2011), the fall in coordination costs through ICT developments has enabled firms to carry out production stages abroad in a cost efficient way. In particular, it has become possible for multinationals to combine their advanced technological know-how with lower wages in developing economies. This implies that intermediate inputs of equivalent quality can be obtained abroad at a lower cost. As a consequence, the sourcing of intermediates from foreign suppliers has become increasingly important boosting trade in intermediates.1 _{In terms of the spatial}

organisation of production, such foreign sourcing is a major feature of the breakup and internationalisation of production processes or value chains, and it is generally referred to as offshoring. Figure 1.1 Offshoring Location Domestic Foreign Ownership Affiliated (in-house) Domestic in-house sourcing Foreign in-house sourcing Non-affiliated (outsourcing) Domestic outsourcing Foreign outsourcing

Source: adapted from GAO (2004).

In the definition of offshoring, it is the location dimension rather than the ownership dimension that matters. It reflects the idea of foreign sourcing of intermediates

1 _{For an early account of this, see Hummels et al. (2001), for a more recent one, see Johnson}

11 from a supplier that may be affiliated (in-house foreign sourcing) or non-affiliated (foreign outsourcing). This is illustrated in the traditional way in Figure 1.1, which depicts the sourcing decision along the two dimensions (ownership and location), and where offshoring corresponds to the two cells shaded in grey.

A major aspect of offshoring is that it is not restricted to manufacturing but also encompasses service activities. In other words, both materials and service inputs into the production process are increasingly sourced from foreign suppliers. A typical example for the former is imported parts and components used in car assembly. Such materials offshoring is not really new: it occurred already before the start of the “second unbundling”. Nevertheless, it has been further boosted by the fall in coordination costs. Regarding the offshoring of services, ICT developments in the 90s have played an important role by fostering the tradability of many services, in particular business services. Typical examples are accounting or call centre services, for which foreign sourcing has become possible in the course of the “second unbundling” and is nowadays very common.

For measuring offshoring, it has become standard to use the share of imported intermediates in total intermediates. This is the measure pioneered by Feenstra and Hanson (1996 and 1999). It is mostly calculated at the industry-level with data from input-output tables (IOT) and it illustrates the importance of foreign sourced intermediates in the production process. Moreover, it is also frequently taken as a proxy measure for the shift of activities abroad. The underlying idea is that the output of the shifted activity is then imported to the initial host country.2

2 This is traditionally referred to as relocation. However, as argued in Fontagné and Peeters

12 OECD (2010) provides comparable figures on the Feenstra and Hanson measure for a large panel of countries based on a set of harmonised IOT for member countries and major non-member countries. Figure 1.2 gives the ranking of countries in 2005 in terms of the share of imported intermediates – of both goods and services – in total intermediates. Small open economies have generally higher shares. The weighted OECD average stands at 16%. Belgium is 7th in the ranking with a share of 34%.3 The largest part of the share is due to materials offshoring. Moreover, Figure 1.3 shows that offshoring has been on the rise between 1995 and 2005 for most countries. For Belgium, the increase in the share of imported intermediates in total intermediates amounts to 4%, which is approximately equal to the OECD average. Globally, most of the increase comes from service offshoring.

Figure 1.2 Offshoring by country in 2005

Source: OECD (2010)

sourcing and organisation strategies of multinational firms. The offshoring measure will be discussed in greater detail – including its shortcomings – in chapter 3.

3 _{The value for Belgium reported in OECD (2010) is globally in line with what we find based}

on more detailed national data, see below.

13 Figure 1.3 Change in offshoring by country between 1995 and 2005

Source: OECD (2010)

From a theoretical point of view, the change in the nature of globalisation calls for new models or at least for adapting old models so as to produce testable predictions regarding its consequences. While the wider public in developed countries mostly perceives offshoring as a threat to jobs and well-being, the dominant view among trade economists is that offshoring – or fragmentation as it was initially referred to in the theoretical literature – “is just a particular, to some extent modern, manifestation of trade” (Deardorff, 2006, p.1057). Hence, classic trade theory can be used to look at how offshoring affects production patterns, factor prices and welfare. Typically, offshoring is modelled as trade in intermediates, e.g. Jones and Kierzkowski (2001), or more recently as trade in tasks, e.g. Grossman and Rossi-Hansberg (2008).

Regarding welfare at the country-level, Bhagwati et al. (2004) express the trade model based view that offshoring “leads to gains from trade and increases in national income, with the caveats that are standard in this literature” (p.112). The main caveat in

14 this respect is the terms-of-trade effect of offshoring, which may reverse gains from trade. In their comprehensive extension of the Hekscher-Ohlin model to incorporate trade in tasks, Baldwin and Robert-Nicoud (2010) show that when moving from a trade in goods only equilibrium to an equilibrium with trade in tasks, gains from trade depend on the effect on the terms-of-trade. Moreover, the literature has highlighted specific circumstances in which offshoring proves harmful for a country, e.g. Kohler (2004) shows that in a specific factors model offshoring may lead to welfare losses for a developed country when offshoring is considered as a discrete event, i.e. the offshored activity is performed either entirely at home or entirely abroad.

Factor price effects of trade determine the gains from or losses due to offshoring for specific groups. This has been the main focus of the theoretical literature on offshoring. In a standard Hekscher-Ohlin model extended to include trade in intermediates, the real return of the factor that is used intensively in an offshored production stage will fall (Jones and Kierzkowski, 2001). This is basically as predicted by the Stolper-Samuelson theorem. So, if labour-intensive production stages are offshored, then this can be expected to depress real wages or alternatively lower employment in a more rigid labour market. The same type of reasoning can be applied to wages of different skill categories of workers, i.e. offshoring low-skill intensive production stages will reduce wages or employment of low-skilled workers.

15 Kierzkowski, 2001) or productivity effect (Grossman and Rossi-Hansberg, 2006) that can more than compensate the Stolper-Samuelson effect described above. Grossman and Rossi-Hansberg (2006) define a case in which offshoring of low-skilled intensive production stages will actually raise wages of low-skilled workers. Their argument is that, in the same way as for technological progress, the increase in productivity through offshoring provides an incentive for expanding production, which will in turn raise labour demand for skilled workers. Hence, the overall impact on labour demand for low-skilled workers may be positive. Baldwin and Robert-Nicoud (2010) clearly define the conditions for this to occur. They extend a Hekscher-Ohlin model à la Trefler (1993) to incorporate trade in tasks. This corresponds to the possibility for a home country to have intermediates produced abroad at local factor prices making use of its own superior technology. They call this the “shadow migration transformation”, i.e. offshoring works as if foreign workers were to migrate to the home country to be employed in a particular production stage and still be paid their original wage. Then, offshoring comes down to a cost saving operation that amounts to the difference between the cost of performing the offshored production stage at home or in the foreign country. Baldwin and Robert-Nicoud (2010) show that in this framework a trade in tasks analogue of the Stolper-Samuelson theorem holds. From this, they derive that wages – or alternatively wages of low-skilled workers – in the home country rise “if and only if the cost saving is sufficiently greater in the labour-intensive (or low-skill intensive) sector” (p.21).

16 offshoring really gives rise to productivity gains and whether offshoring worsens the relative labour market position of low-skilled workers. For these empirical investigations, detailed industry-level data for Belgium are used. They come from a time series of constant price supply-and-use tables (SUT) and other sources that are all described in chapter 2. With these data, the above-mentioned standard offshoring measure can be calculated as explained and illustrated in chapter 3.

The first empirical analysis is presented in chapter 4. It consists in estimating a standard labour demand equation derived from a neoclassical production function framework and augmented with the offshoring intensity measures. The aim is to determine whether offshoring has reduced industry-level employment or whether job creation due to the above-mentioned productivity effect has been big enough to compensate for direct job losses. Previous papers have mostly failed to find evidence of a negative employment impact at the industry-level (e.g. Amiti and Wei, 2005 and 2006; Falk and Wolfmayr, 2005; Cadarso et al., 2008). Compared to these analyses, chapter 4 introduces an offshoring intensity measure computed with constant price data and extends the analysis to market service industries with a sample that is more detailed than in papers that have previously examined market service industries (Amiti and Wei, 2005; Falk and Wolfmayr, 2008).

17 from offshoring (e.g. Amiti and Wei, 2009; Ito and Tanaka, 2010), but there are a few that do not do so (Daveri and Jona-Lasinio, 2008). The main contribution of chapter 5 to the literature is to extend the standard estimation framework so as to also include indirect productivity gains from offshoring that come about through intermediate input purchases. This amounts to examining the possibility of forward and backward spillovers from offshoring. Moreover, productivity gains from offshoring are also estimated for market service industries, which has been done in only one previous paper (Criscuolo and Leaver, 2005).

low-18 skilled employment share is specifically examined. Only Crino (2012) has done so before.

However, the effect of offshoring for developed countries is not necessarily restricted to economic consequences. Offshoring may also influence environmental outcomes. This has not yet been investigated specifically for offshoring, but there is a vast literature on how trade influences pollution levels, in particular for air emissions. On the one hand, analyses of the balance of emissions embodied in trade (BEET) illustrate how trade alters the inter-country distribution of emissions by allowing to dissociate production from consumption (e.g. Ahmad and Wyckoff, 2003; Wiedmann et al., 2007; Peters and Hertwich, 2008). On the other hand, a parallel strand of the literature has attempted to measure to what extent trade contributes to changing emissions over time. Since the pioneering work by Grossman and Krueger (1993), it has become standard to consider that trade affects the change in a country’s emissions through three channels: first, by raising output (scale effect), second, by changing the composition of output (composition effect), and, third, by modifying production techniques (technique effect). Antweiler et al. (2001) develop a theoretical model where these three effects occur as a result of an increase in trade openness of a country. The overall impact of an increase in trade openness on pollution depends on whether the country exports or imports pollution-intensive goods and on its policy stance regarding environmental damage. According to the results of their econometric estimations, the overall effect on pollution

concentrations4 is positive, i.e. greater trade openness lowers pollution concentrations.

However, most authors apply decomposition analysis – either index decomposition or structural decomposition – to address this question, but fail to find evidence of a large

19 contribution of trade to the fall in emissions (e.g. Kander and Lindmark, 2006; Levinson, 2009; Grether et al., 2010). Chapter 7 presents a decomposition of domestic emissions that is specifically geared towards the identification of the contribution of replacing domestic intermediates by foreign intermediates to changes in emissions. This basically comes down to measuring the contribution of offshoring, which is novel compared to the existing literature. In order to apply the decomposition analysis for Belgium, data from the SUT are combined with data for three composite air emission indicators from the Belgian air emission accounts (AEA).

20 can be estimated. They provide evidence regarding a PHE for imports of intermediates. The estimations in chapter 8 rely on data from the time series of SUT and emissions data from the AEA for three composite indicators.

21

2.

Data sources

The data used for the empirical analysis presented here come from different sources. They are all industry-level data compatible with the national accounts (NA). We first present the economic data followed by the environmental data.

2.1. Economic data

The main source for the economic data is a time series of supply-and-use tables (SUT) for Belgium in current and constant prices. SUT are industry-by-product tables where industry-level output and intermediate consumption as well as total imports and final demand by category (private and public consumption, investment and exports) are split into product categories. At the product-level, total supply must be equal to total use. SUT are an extension of the NA and they are the basis for the derivation of symmetric industry-by-industry or product-by-product IOT (Eurostat, 2008). For Belgium, initial tables in current prices are constructed by the National Bank of Belgium (NBB) on behalf of the Institute for the National Accounts (INA). At the most detailed level of disaggregation, they cover approximately 120 industries in NACE Rev.1.1 and 320 product categories in CPA2002.5 We will refer to this as the SUT classification. These tables are then revised and updated at the Federal Planning Bureau (FPB) so as to respect a common vintage of the NA, which makes them comparable across years. Moreover, margins and taxes minus subsidies on products are subtracted from the use table at purchasers’s prices so as to obtain SUT at basic prices. These tables have then been

5 _{NACE is the Statistical Classification of Economic Activities in the European Community and}

22 deflated with separate product-level price indices for domestic output and imports. The methodology is summarised in Avonds et al. (2007 and 2012).

Over the past ten years, several versions of this SUT database have been produced at the FPB. The first version dates from 2007 (Avonds et al., 2007). The tables are compatible with the 2007 vintage of the NA and the years 1995-2003 are covered. Constant prices are of the year 2000. This is the database that was used in the analysis of total industry-level employment in chapter 4. In subsequent years, the database has been updated so as to extend the time span and incorporate more recent NA revisions. In the second version dating from 2008, SUT for the year 2004 were added. This version was used for the productivity analysis in chapter 5. Finally, for the third and most recent version dating from 2012, the entire time series underwent a revision and the time span was extended to include the years 2005-2007. Hence, the tables of this version are compatible with the 2010 vintage of the NA and cover the period 1995-2007. Constant prices are of the year 2005. This version has been used for looking at labour demand by skill category in chapter 6 and for the environmental analyses in chapters 7 and 8.6

The first and foremost use of the data from the SUT made here is for computing the offshoring intensities as described in the next chapter. This requires data on imported intermediate inputs, which are indeed available from the use tables of imports that are part of the SUT database. For IO reference years (1995, 2000 and 2005), these use tables of imports have been constructed according to the specific methodology described in van den Cruyce (2004). It is based on data from the extended structural business surveys and

6 _{Due to the use of several versions of the SUT database in the various empirical analyses}

23 trade statistics at the level of the firm. These two sources are used to identify imports of intermediates for each firm in the sample, which are then aggregated to the industry-level. For each of the non-reference years, the use table of imports is computed by applying shares from the reference-year tables to the use table (for domestic production and imports) and adjusting the results to import totals by product.7 Moreover, as explained in greater detail in the next chapter, imported intermediates are disaggregated by country of origin according to a proportional method based on detailed import data from the NBB, cross-tabulated by product category and country of origin.

For the estimations and calculations below, industry-level data for several other variables are required. These are output, value added, total energy, materials and service inputs, and compensation of employees, i.e. wages, and they are also drawn from the SUT. The latter variable is further disaggregated by ISCED8 _{level of educational}

attainment based on survey data (Dumont, 2008).

Finally, industry-level data for three additional variables come from other sources. First, data on industry-level employment – in hours for total employment and in numbers for employment by ISCED level of educational attainment – comes from the Social Accounting Matrix (SAM) assembled at the FPB (Bresseleers et al., 2007). Second, the capital stock by industry is computed based on detailed investment data from the NBB. The data is cross-tabulated by industry and product category allowing for a split into ICT and non-ICT capital. Details on the methodology are provided in Biatour et al. (2007) and Michel (2010 and 2011). Third, the industry-level R&D capital stock is calculated with

24 industry-level R&D expenditure data made available by the Belgian Science Policy (belspo). Methodological aspects are discussed in Biatour et al. (2011).

Table 2.1 Data sources

Variable Name Unit Data source Splits References Y output € (mio) Harmonised SUT

(FPB1_{) based on data} from INA2

Avonds et al. (2007), Avonds et al. (2012) VA value-added € (mio) Harmonised SUT

(FPB1_{) based on data} from INA2

Avonds et al. (2007), Avonds et al. (2012) K capital stock € (mio) Own calculations

based on detailed investment data from NBB3

ICT and non-ICT capital Biatour et al. (2007), Michel (2010, 2011) L labour workers, hours worked Social Accounting matrix (SAM – FPB1₎ By level of education (high, medium, low) for the number of workers

Bresseleers et al. (2007) E,M,S energy,

materials, services inputs

€ (mio) Harmonised SUT (FPB1_{) based on data} from INA2

Domestic and imported (by region based on detailed trade data from NBB3₎

Van den Cruyce (2004), Avonds et al. (2007), Avonds et al. (2012) W Labour

compensation

€ Own calculation based on harmonised SUT (FPB1_{) and on data} INA2

By level of education (high, medium, low)

Avonds et al. (2012), Dumont (2008)

R&D R&D stock € (mio) Own calculations based on R&D expenditure data from BSP4

Biatour et al. (2011)

Remarks: 1 Federal Planning Bureau, 2 Institute for the National Accounts, 3 National Bank of Belgium, 4 Belgian Science Policy (belspo)

25 the level of aggregation, the breakdown used for estimations in chapters 4-6 corresponds to the SUT classification, whereas the breakdown is limited to NACE Rev.1.1 2-digit in chapters 7 and 8 so as to match the environmental data (see below). Codes and descriptions of these classifications are given in Tables 2.2 and 2.3 respectively.

Table 2.2 – List of NACE Rev.1.1 2-digit manufacturing industries

Code Description Shorthand

15 Manufacture of food products and beverages Food 16 Manufacture of tobacco products Tobacco

17 Manufacture of textiles Textile

18 Manufacture of wearing apparel; dressing and dyeing of fur Apparel 19 Tanning and dressing of leather; manufacture of luggage, handbags, and footwear Leather 20 Manufacture of wood and of products of wood and cork, except furniture Wood 21 Manufacture of pulp, paper and paper products Paper 22 Publishing, printing and reproduction of recorded media Printing 23 Manufacture of coke, refined petroleum products and nuclear fuel Fuel 24 Manufacture of chemicals and chemical products Chemicals 25 Manufacture of rubber and plastic products Rubber 26 Manufacture of other non-metallic mineral products Mineral 27 Manufacture of basic metals Basic metal 28 Manufacture of fabricated metal products, except machinery and equipment Fabricated metal 29 Manufacture of machinery and equipment n.e.c. Machinery 30 Manufacture of office machinery and computers Computers 31 Manufacture of electrical machinery and apparatus n.e.c. Electrical 32 Manufacture of radio, television and communication equipment and apparatus Communication 33 Manufacture of medical, precision and optical instruments, watches and clocks Instruments 34 Manufacture of motor vehicles, trailers and semi-trailers Motor vehicles 35 Manufacture of other transport equipment Transport equipment 36 Manufacture of furniture; manufacturing n.e.c. Furniture

26 Table 2.3 List of industries of the SUT-classification, code and description

14A Mining and quarrying of stone, sand, clay and chemical and fertilizer materials, production of salt, and other mining and quarrying n.e.c.

15A Production, processing and preserving of meat and meat products 15B Processing and preserving of fish and fish products

15C Processing and preserving of fruit and vegetables 15D Manufacture of vegetable and animal oils and fats 15E Manufacture of dairy products

15F Manufacture of grain mill products, starches and starch products 15G Manufacture of prepared animal feeds

15H Manufacture of bread, fresh pastry goods, rusks and biscuits 15I Manufacture of sugar, chocolate and sugar confectionery

15J Manufacture of noodles and similar farinaceous products, processing of tea, coffee and food products n.e.c. 15K Manufacture of beverages except mineral waters and soft drinks

15L Production of mineral waters and soft drinks 16A Manufacture of tobacco products

17A Preparation and spinning of textile fibres, weaving and finishing of textiles

17B Manufacture of made-up textile articles, except apparel, other textiles, and knitted and crocheted fabrics 18A Manufacture of wearing apparel; dressing and dyeing of fur

19A Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and footwear 20A Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw 21A Manufacture of pulp, paper and paper products

22A Publishing

22B Printing and service activities related to printing, reproduction of recorded media 23A Manufacture of coke, refined petroleum products and nuclear fuel

24A Manufacture of basic chemicals

24B Manufacture of pesticides and other agro-chemical products

24C Manufacture of paints, varnishes and similar coatings, printing ink and mastics 24D Manufacture of pharmaceuticals, medicinal chemicals and botanical products

24E Manufacture of soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations 24F Manufacture of other chemical products

24G Manufacture of man-made fibres 25A Manufacture of rubber products 25B Manufacture of plastic products 26A Manufacture of glass and glass products 26B Manufacture of ceramic products 26C Manufacture of cement, lime and plaster

26D Manufacture of articles of concrete, plaster and cement; cutting, shaping and finishing of stone; manufacture of other non-metallic mineral products

27A Manufacture of basic iron and steel and of ferro-alloys and tubes

27B Other first processing of iron and steel; manufacture of non-ferrous metals; casting of metals

28A Manufacture of structural metal products, tanks, reservoirs, containers of metal, central heating radiators, boilers and steam generators; forging, pressing, stamping and roll forming of metal

28B Treatment and coating of metals; general mechanical engineering

28C Manufacture of cutlery, tools, general hardware and other fabricated metal products

29A Manufacture of machinery for the production and use of mechanical power, except aircraft and vehicle engines 29B Manufacture of other general purpose machinery

29C Manufacture of agricultural and forestry machinery and of machine tools 29D Manufacture of domestic appliances

30A Manufacture of office machinery and computers

31A Manufacture of electric motors, generators and transformers, of electricity distribution and control apparatus, and of insulated wire and cable

31B Manufacture of accumulators, batteries, lamps, lighting equipment and electrical equipment 32A Manufacture of radio, television and communication equipment and apparatus

33A Manufacture of medical, precision and optical instruments, watches and clocks 34A Manufacture of motor vehicles

27

36A Manufacture of furniture

36B Manufacture of jewellery and related articles

36C Manufacture of musical instruments, sports goods, games and toys; miscellaneous manufacturing 37A Recycling

45A Site preparation

45B General construction of buildings and civil engineer works; erection of roof covering and frames 45C Construction of motorways, roads, airfields, sports facilities and water projects; other construction work 45D Building installation

45E Building completion; renting of construction or demolition equipment with operator 50A Sale, maintenance and repair of motor vehicles and motorcycles, parts and accessories 50B Retail sale of automotive fuel

51A Wholesale trade and commission trade, except of motor vehicles and motorcycles

52A Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods 55A Hotels and other provision of short-stay accommodation

55B Restaurants, bars, canteens and catering 60A Transport via railways

60B Other scheduled passenger land transport; taxi operation; other land passenger transport 60C Freight transport by road; transport via pipelines

61A Sea and coastal water transport 61B Inland water transport

62A Air transport

63A Activities of travel agencies and tour operators; tourist assistance activities n.e.c.

63B Cargo handling and storage, other supporting transport activities; activities of other transport agencies 64A Post and courier activities

64B Telecommunications

65A Financial intermediation, except insurance and pension funding 66A Insurance and pension funding, except compulsory social security 67A Activities auxiliary to financial intermediation

70A Real estate activities

71A Renting of automobiles and other transport equipment

71B Renting of machinery and equipment and personal and household goods 72A Computer and related activities

73A Research and development

74A Legal activities, accounting activities; market research and public opinion polling

74B Business and management consultancy activities; management activities of holding companies 74C Architectural and engineering activities and related technical consultancy

74D Advertising

74E Labour recruitment and provision of personnel

74F Investigation and security activities; industrial cleaning; miscellaneous business activities n.e.c. 80A Education (market sector)

85A Human health activities 85B Veterinary activities 85C Social work activities

91A Activities of membership organisations

92A Motion picture and video activities; radio and television activities 92B Other entertainment activities

92C News agency activities and other cultural activities 92D Sporting and other recreational activities

93A Other service activities n.e.c.

28 2.2. Environmental data

The environmental data used here are data on pollutant air emissions from the Air Emission Accounts (AEA) for Belgium that are compiled by the FPB as an environmental satellite account of the NA. They contain data on 15 types of pollutant air emissions that are listed in Table 2.4.

Table 2.4 – Types of air emissions in the Belgian AEA

Name Symbol Unit (evaluation)

Methane CH4 Tonnes

Nitrous oxide N2O Tonnes

Nitrogen oxides NOx Tonnes (NO2 equivalent)

Carbon monoxide CO Tonnes

Carbon dioxide CO2 Thousands of tonnes Sulphur oxydes SOx Tonnes (SO2 equivalent)

Ammonia NH3 Tonnes

Non-Methane Volatile Organic Compounds NMVOC Tonnes

Particulate matter PM2.5 et PM10 Tonnes (mass equivalent of filter measurements) Hydrofluorocarbons HFC Tonnes (CO2 equivalent)

Perfluorocarbons PFCs Tonnes (CO2 equivalent) Sulphur hexafluoride SF6 Tonnes (CO2 equivalent) Chlorofluorocarbons CFC Tonnes (CO2 equivalent) Hydrochlorofluorocarbons HCFC Tonnes (CO2 equivalent) Source: Eurostat (2009) and Janssen and Vandille (2011)

Methods of compilation and data for the AEA are described in Janssen and Vandille (2011). Compatibility with the NA is provided for by the method. The industry coverage is 2-digit in NACE Rev.1.1. For the purpose of the analysis presented here, data for the years 1995-2007 have been used. The majority of the 15 types of air emissions have been aggregated into three standard composite indices according to the type of environmental damage they cause (Janssen and Vandille, 2011). They are defined as follows.

 The greenhouse gas index (tonnes of CO2-equivalents):

29

 The acidification indicator (tonnes of H+-equivalents):

ACID = 0.03125 * SO2 + 0.021739 * NOx + 0.058824 * NH3

 The tropospheric ozone forming potential indicator (tonnes of NMVOC equivalents):

TOFP = 1.22 * NOx + NMCOV + 0.11 * CO + 0.014 * CH4

30

3.

Measuring offshoring

The standard measure of offshoring pioneered by Feenstra and Hanson (1996) has become widely used over the past fifteen years. It is an intensity measure that amounts to calculating the industry-level share of imported intermediates in total non-energy inputs. A distinction can be made according to the types of intermediates that are sourced from abroad. We refer to offshoring of parts and components entering manufacturing processes as materials offshoring, e.g. integrated electronic circuits used in computer assembly or lenses used in the production of optical instruments. But, as mentioned earlier, business services such as bookkeeping or payroll services may nowadays also be offshored. During the last couple of decades, such business services have become increasingly tradable due to ICT developments and service trade liberalisation. This has made it easier to source them from abroad. Following Amiti and Wei (2005), we call this business services offshoring.

For industry i and year t, materials offshoring om can be written as

𝑜𝑚𝑖𝑡=

∑𝐽′𝑗=1𝐼𝑖𝑗𝑡𝑚

𝐼𝑖𝑡𝑛𝑒

where Im stands for imported intermediate inputs, Ine for non-energy intermediate inputs and j is the product index covering products from 1 to J’ that are taken to represent manufactured goods, i.e. materials.

Again for industry i and year t, business services offshoring os can be written as

𝑜𝑠𝑖𝑡 =

∑𝐽𝑗=𝐽′₊₁𝐼𝑖𝑗𝑡𝑚

𝐼_𝑖𝑡𝑛𝑒

31 As an alternative, a normalisation by output rather than total non-energy intermediate inputs has also been used in the literature (Ekholm and Hakkala, 2006;

Geishecker, 2006).9 This can be written as 𝑜𝑚_𝑖𝑡𝑦 =∑ 𝐼𝑖𝑗𝑡𝑚

𝐽′ 𝑗=1 𝑌_𝑖𝑡 and 𝑜𝑠𝑖𝑡 𝑦₌∑𝐽𝑗=𝐽′+1𝐼𝑖𝑗𝑡𝑚 𝑌_𝑖𝑡 where om y _and

osy stand for offshoring normalized by output and Y represents output. We have used this alternative measure in the analysis of total industry-level employment in chapter 4, whereas the subsequent chapters are based on the standard measure with a normalisation by non-energy intermediate inputs.10

Moreover, some authors opt for a more restrictive measure called ‘narrow offshoring’ initially defined by Feenstra and Hanson (1999).11 _{It takes only imported}

intermediate inputs from the same industry into account. However, we prefer the broader offshoring measure defined above since we believe that the scope of foreign sourcing is not necessarily limited to core activities and since it allows for a distinction between materials and business services offshoring for all industries.

The offshoring intensities are also frequently interpreted as an indicator for relocation, i.e. the shift of activities abroad. This is owed to the fact that direct measures of relocation are scarce and that in many circumstances relocation entails imports of the output of the shifted activity. It must nonetheless be emphasized that the offshoring intensities computed as the share of foreign sourced intermediates are an indirect and imperfect measure of the shift abroad of activities as they do not cover all possible cases. The main omissions are that they do not take into account cases where the final stage of the production process is shifted abroad, and cases where an activity is shifted abroad

9 _{Some authors also divide by value added, e.g. Hijzen et al. (2005).}

32 without giving rise to imports, e.g. output that used to be exported and, subsequent to relocation, is sold directly to local customers abroad or exported to a third country. Both lead to an underestimation of the extent of relocation.12 Furthermore, regarding service offshoring, there are many organisational forms through which the relocation of service activities may be implemented, e.g. electronic transmission of the results of the service activity or temporary movement of workers. The service trade based intensity measure used here does not cover all of these organisational forms and therefore also likely underestimates the real extent of the shift abroad of service activities.

However, there are also imports of intermediates that do not result from relocation, and hence lead to an overestimation of the extent of relocation. This is the case for certain inputs that are imported but have actually never been produced in the home country. Such imports correspond to foreign sourcing, i.e. are included in the offshoring intensity indicator, but do not correspond to cases of relocation. The first and foremost examples for this are raw material and energy inputs. For these product categories, the overestimation is avoided as they are not included in the offshoring intensity measures defined above. However, certain specialised manufacturing inputs, e.g. microprocessors in the assembly of electronic devices or specific paints in car manufacturing, may also be imported without ever having been produced locally.

Overall, following OECD (2007a), it is more likely that the standard industry-level offshoring intensity measures defined above underestimate than overestimate the real extent of relocation. Hence, econometric results on the impact on employment and

12 _{The underestimation may also be due to what may be referred to as sequential relocation. This}

33 productivity – when significant – should rather be interpreted as lower bound estimates of the real impact.

In the literature, both om and os are usually computed with data from SUT or IOT. However, imports of intermediates are mostly obtained in a proportional way given that use tables of imports containing detailed data on imported intermediates by industry and product are not available.13 This is done by multiplying industry i’s total purchases of product j (as intermediate) with the share of imports in the total supply (imports and domestic output) of product j. This amounts to a rather restrictive assumption as it implies an identical import share for product j in all uses, i.e. in all industries that purchase this product as an intermediate input and in final demand for that product.

Here, we compute om and os with data drawn from the time series of constant price SUT for Belgium that is described in chapter 2. As explained above, this database contains use tables of imports calculated according to a specific methodology. Hence, we are able to avoid the proportionality assumption.

Regarding the product detail in the SUT, materials offshoring covers all imports of intermediate manufacturing goods of the CPA categories 15-37 (except for energy products) and business services offshoring encompasses all imports of intermediates belonging to the CPA categories 72-74.

Since the classical offshoring scenario consists in the shift of a production stage from a high-wage to a low-wage country, it is worthwhile trying to split the imports of intermediates by country of origin so as to identify those coming from low-wage

13 _{There are some exceptions to this rule of a proportional calculation of imported intermediates.}

34 countries. Egger and Egger (2003) were the first to suggest such a split of the offshoring intensity. However, in the SUT no information is available on the country of origin of imported intermediate inputs. Therefore, just like all other contributions to this literature, we rely on a proportional method combining the data on offshoring from the SUT with data on Belgian imports broken down by country of origin. According to this proportional method, computing the amount of imported intermediates by industry i from country c implies multiplying the amount of imported intermediates for each product by the share of country c in total imports of that product. Hence, we write:

𝑜𝑚_𝑐𝑖𝑡= ∑ 𝑀_𝑀𝑗𝑐𝑡 𝑗𝑡 𝐼𝑖𝑗𝑡 𝑚 𝐽′ 𝑗=1 𝐼_𝑖𝑡𝑛𝑒 𝑜𝑠_𝑐𝑖𝑡 = ∑ 𝑀_𝑀𝑗𝑐𝑡 𝑗𝑡 𝐼𝑖𝑗𝑡 𝑚 𝐽 𝑗=𝐽′₊₁ 𝐼_𝑖𝑡𝑛𝑒

where om_c and os_c stand for materials and business services offshoring intensities to country c, Mj is total imports of materials or business services and Mjc is imports of

materials or business services from country c.

Table 3.1 Materials and business services offshoring, total and by region of origin, constant prices

Materials offshoring Business services offshoring 1995 2007 avg gr std dev 1995 2007 avg gr std dev Manufacturing Total 35.68 38.33 0.6 17.50 0.71 1.94 8.7 1.76 OECD 32.57 32.13 -0.1 15.80 0.68 1.77 8.4 1.64 CEEC 0.55 1.95 11.2 1.15 0.02 0.06 11.9 0.04 ASIA 0.88 1.82 6.2 1.99 0.01 0.03 16.5 0.03 Market services Total 4.88 7.50 3.6 6.59 3.20 5.71 4.9 4.21 OECD 4.51 6.42 3.0 6.06 3.05 5.23 4.6 3.93 CEEC 0.05 0.31 17.3 0.24 0.07 0.19 8.9 0.09 ASIA 0.19 0.48 8.0 0.42 0.03 0.09 11.0 0.06 Legend: avg gr: average annual growth rate; std dev: standard deviation.

35 For manufactured goods, i.e. materials, the data on imports by country of origin at the 5-digit CPA-level come from merchandise trade statistics, while for business services imports, we use balance of payments data by country of origin for the categories ‘computer and information services’ and ‘miscellaneous business, professional and technical services’.14 _{We calculate offshoring intensities for three regions: OECD, which}

includes 22 OECD member states15, CEEC, which corresponds to ten Central and Eastern European countries16_{, and ASIA, which includes eight newly industrialised economies of}

Asia as well as China and India17. Together, those three groups account for more than 90% of Belgian imports. Both CEEC and ASIA contain typical offshoring destinations.

Figure 3.1 Offshoring intensity by industry in 2007

Remarks: Sum of materials and business services offshoring in % on the y-axis; industries of the SUT classification (see Table 2.3) from left to right on the y-axis.

14 _{The source for all import data for Belgium is the National Bank of Belgium (NBB).}

15 _{Austria, Australia, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland,}

Italy, Japan, Luxemburg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the UK and the US. These countries plus Turkey were the OECD member states by the middle of the 1970’s.

16 _{Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the}

Slovak Republic and Slovenia.

17 _{China, Hong Kong, India, Indonesia, Malaysia, the Philippines, Singapore, South Korea,}

Thailand and Taiwan.

36 Results for materials and business services offshoring intensities, total and by region, are shown in Table 3.1 for both manufacturing and market service industries.18

They are based on data from the most recent, third, version of the time series of SUT for Belgium and cover the years 1995 to 2007.19 Starting from a high level of 35.7% in 1995, the intensity of materials offshoring in manufacturing grows relatively slowly to reach 38.3% in 2007. Business services offshoring is at a much lower level, but grows relatively fast from 0.7% in 1995 to 1.9% in 2007. In the market service industries, the materials offshoring intensity rises from 4.5% in 1995 to 6.4% in 2007. Business services offshoring again stands at a lower level – 3.2% in 1995 – but grows faster to reach 5.7% in 2007. The main features in the figures for the regional offshoring intensities are the same for manufacturing and market services. Offshoring to OECD countries largely dominates for both materials and business services. Especially for the latter, offshoring to CEE and Asian countries is still very small during the period considered here. Nonetheless, it stands out from Table 3 that between 1995 and 2007 offshoring to Asian and CEE countries grows fastest for both materials and business services. Furthermore, Figure 3.1 gives a flavor of the cross-industry variation in the offshoring intensity for materials and business services in 2007.

Finally, the possibility of computing volume measures of offshoring based on separate prices for domestic output and imports is particularly important since value

18 _{In terms of the split between manufacturing and market service industries, it should be noted}

that we have included construction industries in manufacturing (see Table 2.3).

19 _{As explained above, offshoring intensities from the third version of the SUT database have}

37 measures tend to underestimate the extent of offshoring. Indeed, activities are generally being offshored in order to make cost savings, i.e. because imported intermediates are cheaper than domestically produced intermediates. Then, the growth in the offshoring intensity in value terms can be expected to be biased downwards. This is exactly what we find when comparing offshoring intensities in current and constant prices from the corresponding SUT as illustrated by their average growth rates in the manufacturing sector shown in Table 3.2.

Table 3.2 Average annual growth rates of current and constant price materials and business services offshoring over 1995-2007

Current prices Constant prices

Materials offshoring 0.30% 0.60%

38

4.

The effect of offshoring on total industry-level employment

This chapter addresses the issue of offshoring and its impact on labour demand for Belgium considering that it is a typical example of a small open economy, for which barriers to offshoring are low. In that case, offshoring of labour-intensive production stages will put downward pressure on wages and employment. In order to determine the impact of offshoring on industry-level employment, we rely on the offshoring-augmented neoclassical labour demand framework that is used in most of the literature. As in Amiti and Wei (2006) and Cadarso et al. (2008), the labour demand equation is estimated using both static and dynamic panel data techniques. Manufacturing and service industries are separated for the estimations and manufacturing is further split into high-tech and low-tech industries. To the best of our knowledge, there are only two papers that have estimated the impact of offshoring on labour demand in service industries (Amiti and Wei, 2005; Falk and Wolfmayr, 2008). Compared to these estimations, our sample size of 35 industries for market services is significantly greater. Finally, our estimations always include both materials and business services offshoring.

The data for the estimations come from the first version of the SUT database, which covers the years 1995-2003 (see chapter 2). The NA vintage of the data is 2007 and the base year for deflation is 2000. Computing offshoring intensities in constant prices is new with respect to previous contributions to the literature, which generally rely on measures in current prices based on input-output tables of different NA vintages.

20 _{A standalone version of this chapter has been published as Michel, B. and F. Rycx (2012),}

39 This chapter is organised as follows. The labour demand equation is derived in section 4.1 and the relevant empirical literature is reviewed in section 4.2. Section 4.3 contains descriptive statistics. Estimation results are presented in section 4.4 and discussed in section 4.5.

4.1. Theory and labour demand equation

The motivation for firms to engage into offshoring comes from the cost arbitrages they make, i.e. they try to locate each stage of the production process where factor prices make this most profitable. The classical scenario is that they take advantage of lower labour costs in developing economies if the gains outweigh the extra coordination costs of the more complex production process and the extra transport costs for shipping intermediate goods to downstream stages of the production process.

40 also be influenced by the degree of rigidity of the labour market and a country’s terms of trade that may be altered by offshoring.

It has become standard in the literature to evaluate the employment impact of offshoring in a neoclassical labour demand framework.21 This may be extended to take offshoring into account. We specify a log-linear labour demand equation taking the prices of labour and capital (w and r) as well as output (Y) into account.22 Letting L denote employment and subscripts i and t respectively industry and years, this is:

𝑙𝑛𝐿𝑖𝑡 = 𝛼 + 𝛽1𝑙𝑛𝑤𝑖𝑡+ 𝛽2𝑙𝑛𝑟𝑖𝑡+ 𝛾𝑙𝑛𝑌𝑖𝑡 (4.1)

Theory predicts β1, the own-price elasticity of labour, to be negative, whereas β2,

the cross-price elasticity with respect to capital should be positive. The income elasticity of labour demand, γ, is also expected to be positive.

To the extent that offshoring is measured through imported intermediate inputs, it may be treated as an extra factor of production whose price will have an impact on labour demand. This reflects the idea that offshoring represents foreign labour services that are a substitute for domestic labour services.23 _{The elasticity of labour demand with respect to}

the price of imported intermediates is then expected to be positive. When import prices for intermediate inputs fall, i.e. when offshoring becomes relatively cheaper, then this should depress labour demand. However, since data on the price of imported intermediates are difficult to come by, Amiti and Wei (2005, p.329) suggest to use the offshoring intensity as an “inverse proxy”. Implicitly, this rests on the assumption of a negative own-price elasticity of the volume of imported intermediates for a given level of

21 _{For this framework, see Hamermesh (1993).}

22 _{In a log-linear equation, parameters can be interpreted as elasticities.}

23 _{Hence, the price of imported intermediate inputs may enter the log-linear labour demand}

41 output. It is nonetheless worth mentioning that this inverse relationship is less clear-cut for the value measure of the offshoring intensity used in the literature because of the price effect, which casts some doubts on whether it is a good inverse proxy. But this should be solved by using a volume measure as we have done here. In the end, the argument comes down to the same as what most authors do to measure the impact of offshoring on labour demand: augment the labour demand equation by one or more variables that measure offshoring. In our case, these are the regional offshoring variables om_c and os_c defined in the previous chapter for OECD, CEEC and ASIA.

𝑙𝑛𝐿_𝑖𝑡 = 𝛼 + 𝛽₁𝑙𝑛𝑤_𝑖𝑡+ 𝛽₂𝑙𝑛𝑟_𝑖𝑡+ 𝛾𝑙𝑛𝑌_𝑖𝑡 + ∑ (𝜃_1𝑐𝑙𝑛𝑜𝑚_𝑐_𝑖𝑡+ 𝜃_2𝑐𝑙𝑛𝑜𝑠_𝑐_𝑖𝑡)

𝑐 (4.2)

Controlling for output Y implies that the scale of the production may not change in response to offshoring, i.e. feedback from offshoring to labour demand through increased production is eliminated from a conditional labour demand equation such as (2). We would then predict a negative employment impact of offshoring, i.e. θ1k and θ2k <0.24

Equation (4.2) defines static labour demand. It can easily be transformed to become testable by adding time and industry dummies αt and εi as well as a disturbance term uit.

To capture lagged effects, we also include first order lags of the explanatory variables. Moreover, the price of capital – the rental rate – rit is dropped on the assumption that for

capital “all firms face the same price, which […] is some function of time” (Amiti and

24 _{Amiti and Wei (2005, 2006) as well as OECD (2007a, 2007b) also specify an unconditional}

labour demand equation by controlling for output price instead of output volume. In such a setting output may be increased in response to productivity gains through offshoring and lead to enhanced labour demand. Hence, the parameters θ1k and θ2k are not expected to be negative

42 Wei, 2005, p.330). In other words, rit is taken to be part of the time dummies αt. The

testable form of equation (4.2) then becomes:

𝑙𝑛𝐿𝑖𝑡 = 𝛼 + 𝛽1𝑙𝑛𝑤𝑖𝑡+ 𝛽2𝑙𝑛𝑤𝑖𝑡−1+ 𝛾1𝑙𝑛𝑌𝑖𝑡 + 𝛾2𝑙𝑛𝑌𝑖𝑡−1

+ ∑(𝜃1𝑐𝑙𝑛𝑜𝑚_𝑐𝑖𝑡+ 𝜃2𝑐𝑙𝑛𝑜𝑚_𝑐𝑖𝑡−1+ 𝜃3𝑐𝑙𝑛𝑜𝑠_𝑐𝑖𝑡+ 𝜃3𝑐𝑙𝑛𝑜𝑠_𝑐𝑖𝑡−1) + 𝜀𝑖 + 𝑢𝑖𝑡 𝑐

(4.3)

The aim is to bring this equation to the data at the industry-level to estimate the sign and magnitude of the θ parameters, which reflect the impact of offshoring.

4.2. Review of the relevant literature

We are aware of seven papers that have estimated the sign and magnitude of the total employment impact of offshoring with industry-level data.25 Four of them – Falk and Wolfmayr (2005, 2008) and OECD (2007a, 2007b) – examine a panel of respectively EU and OECD countries, while the three other ones – Amiti and Wei (2005, 2006) and Cadarso et al. (2008) – focus on a single country – respectively the UK, the US, and Spain. All rely on a labour demand framework similar to the one derived above.

The data, the econometric methodology, the features of the offshoring variable and the econometric results of these papers are summarised in Tables 4.1, 4.2 and 4.3. There are major differences in the datasets regarding the industry detail: in the cross-country studies the data are pooled over the countries in the sample. Moreover, only three papers present data on service industries with a low level of industry detail. Another important difference is the way of measuring the dependent variable, i.e. employment. Only Cadarso et al. (2008) use data in hours. A static conditional labour demand including wage and output as controls is the rule, but unconditional or dynamic labour demand equations are also specified in some of the papers. To some extent, this is linked to the

43 available data. While the cross-country studies estimate cross-sections in five-year differences by ordinary least squares due to a lack of data for intermediate years, the single country studies are based on various more robust panel data methods.

Table 4.1 Summary of data and econometric methodology in studies on total employment impact of offshoring

Period Country/ region

Industry detail

Labour demand Depend. variabled

Controls Econometric methodology Falk and Wolfmayr

(2005)

1995-2000a _{EU7 144 manuf.} (pooled)

Cond., static FT+PT Wage, output LD Falk and Wolfmayr

(2008)

1995-2000a _{EU5 105 manuf.} 100 serv. (pooled)

Cond., static FT+PT Wage, output LD OECD (2007a) 1995-2000a _{OECD12 266 manuf.}

(pooled) Cond. & uncond., static FTE and FT+PT Wage, output, output price, invest. deflator LD OECD (2007b) 1995-2000a _{OECD17 182 manuf.}

58 serv. (pooled) Cond. & uncond., static nae _{Wage, output,} output price, capital stock, R&D intensity LD

Amiti and Wei (2005) 1995-2001 UK 69 manuf. 9 serv.

Cond. & uncond., static & dynamic nae _{Wage, output}f_, output price, output price LD, FD and FEg Amiti and Wei (2006) 1992-2000 US 96 manuf.b _{Cond. &}

uncond., static & dynamic

nae _{Wage, output,} output price, import share, hi-tech capital

LD, FD, FE, IV and GMM Cadarso et al. (2008) 1993-2002 Spain 93 manuf.c _Cond.,

dynamic

hours Wage, output FD, FE and DPD

Legend: EU7 = Austria, Denmark, Finland, Germany, Italy, the Netherlands and Sweden; EU5 = Austria, Finland, Germany, Italy and the Netherlands; OECD12 = Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Norway, South Korea, Sweden and the United States; OECD17 = Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Italy, the Netherlands, Norway, Portugal, Spain, Sweden, the United Kingdom and the United States; FT+PT: total number of full-time and part-time employed; FTE: full-time equivalents; LD: long differences; FD: first differences; FE: fixed effects; IV: instrumental variables; GMM: generalised method of moments; DPD: dynamic panel data methods (both difference and systems GMM). Remarks: a: no data for intermediate years; b: 450 industries for some estimations; c: separate data on the offshoring

intensity is available for only 26 more aggregated industries; d: total industry employment; e: no information is provided on how total industry employment is measured; f: nominal output for service industries; g: also includes a specification with a lagged dependent variable among the explanatory variables, but no information is provided on whether this is estimated using a GMM-technique.

44 show that the impact may even be positive. When the offshoring intensity is split by region, it is rather offshoring to low-wage countries that has a negative impact while offshoring to high-wage countries is not significant. Overall, the results depend to a large extent on whether an autoregressive term is included.26 Regarding service industries, the evidence on the employment impact of offshoring is scarce. Hindered by the lack of reliable data on service industries, only two papers examine this explicitly though with rather small datasets. They find a negative or non-significant coefficient on their services offshoring variable. Finally, even when the coefficients of the offshoring variables are found to be significant, they are generally small, i.e. none of these estimations reveals evidence of massive job losses due to offshoring.

Table 4.2 Summary of offshoring measures in studies on total employment impact of offshoring

Source - Calculation Denominator Value/ Volume Narrow/ Broad Materials offshoring Services offshoring Regional data Falk and Wolfmayr (2005) Imported use

tables

Output Current prices

narrow yes no yesg Falk and Wolfmayr (2008) Imported use

tables

Output Current prices

bothc _{yes yes}e

yesh OECD (2007a) Imputed Total intermediate

inputsa

Current prices

broad yes yesf

no OECD (2007b) Imputed Value-added Current

prices

bothd _{yes yes}f _no Amiti and Wei (2005) Imputed Total intermediate

inputsa

Current prices

broad yes yesf

no Amiti and Wei (2006) Imputed Total intermediate

inputsa

Current prices

broad yes yesf

no Cadarso et al. (2008) Imported use

tables

Output Deflatedb _{narrow yes no yes}i Remarks: a: excluding energy inputs; b: no information is provided on how this measure has been deflated; c: broad

offshoring only for services; d: includes ‘narrow offshoring’ and ‘difference offshoring’, which sum to ‘broad offshoring’; e: total services offshoring and business services offshoring; f: business services offshoring only; g: distinction between low-wage countries (CEEC and Asian countries) and high-wage countries; h: for materials offshoring, distinction between high-wage countries, CEEC, and China and East Asian countries, and for services offshoring, distinction between high-wage and low-wage countries; i: for enlarged region of CEEC only.

26 _{This is, of course, not possible in the long difference specifications of the cross-country}

45 Table 4.3 Summary of estimated coefficients for offshoring variables in studies on total

employment impact of offshoring

Manufacturing industries Service industries

Materials offshoring Services offshoring Materials offshoring Services offshoring

Falk and Wolfmayr (2005) ns/-b _{x x x}

Falk and Wolfmayr (2008) ns ns x ns/-i

OECD (2007a) - - x x

OECD (2007b)a _-/nsc _-/nsf _{x x}

Amiti and Wei (2005) ns + -h _-h

Amiti and Wei (2006) ns/+d _ns/-g _{x x}

Cadarso et al. (2008) ns/-e _{x x x}

Legend: ns: not significant; +: positive; -: negative; x: not estimated.

Remarks: a: labour demand equations augmented by ‘narrow offshoring’ and ‘difference offshoring’ are also estimated for all industries, i.e. without a distinction between manufacturing and service industries, where ‘narrow offshoring’ is negative significant and ‘difference offshoring’ not significant in conditional labour demand and ‘difference offshoring’ is positive significant and ‘narrow offshoring’ not significant in unconditional labour demand; b: negative only for offshoring to low-wage countries for less skill-intensive industries; c: not significant for broad offshoring and in the unconditional labour demand specification; d: positive, but very small in some specifications; e: negative only for offshoring to CEEC for medium high-tech industries; f: not significant in the unconditional labour demand specification; g: negative only in a few specifications; h: small sample size for service industries; i: only total services offshoring considered, negative coefficient only for offshoring to low-wage countries.

4.3. Descriptive statistics

The main data source for the estimations is the first version of the SUT database described above.27 Offshoring intensities are computed as imported intermediate materials or business services divided by output (see chapter 3). Descriptive statistics for offshoring are reported in Table 4.4. Trends in offshoring intensities are globally in line with what has been discussed in chapter 3 for the most recent version of the database.

27 _{Note that the industry breakdown here is 58 manufacturing and 35 market services industries,}