The Growth of Service Sector in
BRIIC Economies:
An Input - Output Study
Master Thesis
Maisya Farhati
S2472651
maisya.farhati@gmail.com
Supervisor: Dr. Raquel Ortega-Argilés
Co-assessor: Prof. Dr. Steven Brakman
Faculty of Economics and Business
University of Groningen
1
ABSTRACT
In recent years, there has been growing attention to service sector in the world economies. This study analyses service sector in Brazil, Russia, India, Indonesia and China (BRIIC), which are five of the largest economies in today’s developing world. I examine how the services links with overall economic activities and what drives its growth in the period 2000 - 2010. It is found that in BRIIC economies, final demand in other sectors has not enhanced services output. Furthermore, using structural decomposition analysis, this study investigates various aspects which contribute to the growth of services output in BRIIC during period 2000 - 2010, which are domestic final demand, export and changes in technology. The result suggests that in BRIIC economies, domestic final demand has been the main driver of the growth of services. The contribution of this effect exceeded more than 70% of overall effect in all economies, where domestic final demand for services contributed higher than the non-services one. Nevertheless, there is heterogeneity across economies in regard to the contribution of other effects.
2
CONTENTS
2. 1 INTRODUCTION 1.1. Background
1.2. Objectives and Added Values of Research 1.3. Research Structure 4 4 6 7 2. 2 LITERATURE REVIEW
2.1. Services and Structural Change
2.2. The Strong Linkage of Service Sector in Economy
8 8 9
2. 3 SERVICES IN BRIIC ECONOMIES 13
2. 4 DATA AND METHODS
4.1. Data Sources and Data Collection Process 4.2. Methodology
4.2.1. Introduction to Input-Output Model 4.2.2. Causative Matrix
4.2.3. Input Coefficient of Service Sector 4.2.4. Structural Decomposition Analysis
18 18 20 20 21 23 24 2. 5 RESULTS AND DISCUSSION
5.1. The Extent of Service Sector in BRIIC Economies 5.2. Services Intensity of BRIIC Economies
5.3. Sources of Growth of Service Sector in BRIIC Economies
27 27 28 29 2. 6 CONCLUSION REFERENCES APPENDIX
1. Sector Classification in NIOT 2. Typology of Structural Change 3. Services Intensity
4. Summary of Computed Left Causative Matrix
3
LIST OF TABLES
4.1 List of Service Sector 4.2 Basic Outline of NIOT
5.1 The four quadrants of the typology of structural change
5.2 Typology of Structural Change in Service Sector in BRIIC (2000-2005 and 2005-2010) 5.3 Average of service intensity between 2000-2005 and 2005-2010
5.4 Source of output growth for service sector of Brazil 2000-2010 (constant price 2005) 5.5 Source of output growth for service sector of Russia 2000-2010 (constant price 2005) 5.6 Source of output growth for service sector of India 2000-2010 (constant price 2005) 5.7 Source of output growth for service sector of Indonesia 2000-2010 (constant price 2005) 5.8 Source of output growth for service sector of China 2000-2010 (constant price 2005)
19 20 27 28 29 29 30 30 31 31 LIST OF FIGURES
2.1 The Smile Curve
3.1 Shares of BRIICS and OECD GDP to World GDP (%), 2007(Constant price 2005) 3.2 BRIIC Output Structure (%), 2000 and 2012
3.3 Services Trade Balance of BRIIC 2004 - 2010* (in billion US dollar)
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1. INTRODUCTION 1.1. Background
The structure of an economy can be identified by comparing the shares of its three main sectors— agriculture, manufacturing-industry1 and services —in the country’s total output and employment
(Soubbotina and Sheram, 2000). Three stages of economic development which can be ordered from least developed to most developed are as follows:
Factor-driven economies which is primarily based on the extraction of natural resources;
Efficiency-driven economies which is driven by industrialization and increasing scale-intensity;
Innovation-driven economies which is marked by the expansion of service sector followed by more variety, R&D, and knowledge intensity in industrial sector.
In the past, services have traditionally been defined in negative terms, not by what they are, but what they are not (i.e. tangible, durable, storable and transportable). Moreover, service sector has been considered as the residual activities of the economy after classifying agriculture and manufacturing (European Commission, 2003). In term of policy maker’s perspective, the contrast between services and manufacturing could be found especially in developing economies where manufacturing is viewed as the leading edge of modernisation and skilled job creation, as well as a fundamental source of various positive spillovers (Tybout, 2000). As the matter of fact, this phenomenon also occurred in Europe and Japan in post-World War II era. Manufacturing activities have traditionally been regarded as the central source of economic growth through realisation of economies of scale, capital intensification, and incremental innovation (Jorgenson and Timmer, 2009).
Nevertheless, currently there is ever-growing importance of services in the knowledge-based economy (Tijaja, 2013; Damuri, 2012). Moreover, developed service sector marks the innovation-driven economy. European Commission (2003) summarised the main characteristics of the service sector as follows:
Service output is generally characterised as intangible by nature. However, more specifically the output of some services activities, for instance research and software development, can be codified and traded in much the same way as physical goods. In addition, through information
1 Manufacturing and industry are sometimes classified separately and altogether. For instance, World Bank
5 technology, many services can nowadays be embedded in physical products and thus stored, transported and delivered via physical means.
The production of services, especially knowledge intensive services, requires interaction between the producer and the client. The so-called “soft technology” (e.g. qualifications, skills and expertise) becomes the focus of the interaction which has to be present on both the supply and demand side.
Knowledge-intensive services provide the intangible assets (know-how, software, organisational skills, R&D capabilities etc.) which have often become key drivers of value creation, whereas physical and financial assets tend to become commodities which are relatively less important. The transition from agriculture through industry to a services economy is said to be the common pattern of economic development of many countries (Duarte and Restuccia, 2007; Shepherd and Pasadilla, 2012). While manufacturing sector still becomes the major contribution in many developing economies in the last decades, there has been a gradual increase in the importance of service sector for economic development. In response to this trend, policy makers in developing economies, which are previously preoccupied with tangible sectors, take consideration to the rise of intangibles (Goswami et al., 2012).
Eichengreen and Gupta (2009) identify two waves of service sector growth. The first wave – which made up primarily of traditional services (i.e. lodging, meal preparation, house cleaning, beauty and barber shops) – occurred in countries with relatively low levels of per capita GDP. The second one – which comprises modern services – is seen to occur in countries with higher per capita incomes. They define ‘modern services’ as those which are receptive to the application of new information technology and increasingly tradable across borders i.e. financial services, communication, computer, technical, legal, advertising and business. In addition, Eichengreen and Gupta (2009) found that the second wave also occurred at lower income countries after 1990. Nonetheless, it is not equally evident in all economies. Such cases are most apparent in democratic countries; countries which are relatively opened to trade and countries that are relatively close to the major global financial centres, such as New York and London. Shepherd and Pasadilla (2012) argue that emerging markets are currently developing services because doing so is likely to form an important part of efforts to avoid the so-called ‘middle income trap’2. It is important as improving service sector productivity is the key to
strengthening the economy and supporting the sustained innovation needed to move to higher income status.
6 In developed economies, services frequently account for two-thirds or three-quarters of all economic activities (Shepherd and Pasadilla, 2012). Organisation for Economic Co-operation and Development (OECD) already acknowledged the need for a stronger and more dynamic service sector as there has been growing weight of services in its member economies. The growing contribution of service sector will increase labour utilisation and productivity growth (OECD, 2005).
It becomes interesting to investigate further about the second wave of service sector growth; whether the increasing role of service sector also occurs in developing economies and how the services link with other sectors. In Asia, for example, except the success story of services in India and Philippines, most of economies have relatively slower developmental change in service sector (Noland, et al., 2013).
This study examines the service sector in Brazil, Russia, India, Indonesia and China (BRIIC): how it links with the overall economic activities and what drives its growth. Using structural decomposition analysis, this study investigates various aspects which contribute to the growth of services output, which are domestic final demand, export and changes in technology. In term of export in services, it is known that developing economies still need to struggle to compete with more developed ones. Besides the fact that barriers to trade in services are more complex than barriers to trade in goods (i.e. domestic restriction and regulation), recent studies confirm that service sector performance critically depends on human capital, the quality of the telecommunication network and the quality of institutions, which are likely to be better in developed economies (Amin and Mattoo, 2006; Goswami et al., 2012; and Eichengreen and Gupta, 2009). Thus, this study will provide new findings on services through developing economies’ perspective which has different institutional settings with the developed ones.
1.2. Objectives and Added Values of the Research
This research focuses on BRIIC, which are five of the largest economies in today’s developing world. It aims to: (1) study the extent of service sector in BRIIC economies; (2) identify sectors which are services intensive in BRIIC economies; and (3) study the source of growth of service sector of BRIIC economies during period 2000 - 2010.
7 existing literatures give more emphasis on the relationship between services and GDP per capita. Some of them (Acemoglu et al., 2001; Amin and Mattoo, 2006) also relate services with institutional setting which contribute to show how it matters to the sector. Acemoglu et al. (2001) suggest that institutions matter for the level of GDP per capita at some level.
Service sector is often associated with developed economies. Since 1980, the service sector has predominated in the economic activity of the European Union, Japan and the U.S (Jorgenson and Timmer, 2009). This is in line with the finding of Amin and Mattoo (2006) which suggests that countries with better institutions have relatively larger and more dynamic service sectors. There is no doubt that institutions in developed economies are likely to be better developed compared to those in developing economies, which in turn supports the larger growth of services. Indeed there are also some developing economies which are well-known for their services such as India and Philippines, but the rest are hardly discussed (Noland et al., 2013).
By studying service sector in BRIIC economies, this research contributes to the literature in the subject by:
Analysing a set of countries – BRIIC – that is gaining importance in global economic activities.
Analysing service sector which can be considered pivotal in economy as it has strong linkage to other sectors. However, the literature that focuses on service sector is still limited compared to manufacturing and industry or economic activities in general.
Enlarging period of time. The analysis covers quite long period which is 2000 – 2010 and divided into two sub-periods (2000 – 2005 and 2005 – 2010).
Using each National Input – Output Table (NIOT), the result portrays service sector in each of the economies and thus can be compared among each other.
1.3. Research Structure
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2. LITERATURE REVIEW 2. 1 Services and Structural Change
The growing attention to service sector is often related to the process of structural transformation in the world economies. In the economic development literature, structural transformation is defined as reallocation of labour across sectors over time. It is characterised by a systematic fall in the share of employment in agriculture over time, by a steady increase in the share of employment in services and by a hump-shaped pattern for the share of employment in manufacturing (Duarte and Restuccia, 2007).
As labour and other resources move from traditional agriculture into modern economic activities, overall productivity rises and incomes expand. In other words, the key driver of development is indicated by labour flows from low-productivity activities to high-productivity activities (McMillan and Rodrik, 2011). Therefore, structural change can be categorised into productivity-enhancing structural change and productivity-reducing structural change. The first one suggests that the labours and resource move from low to high productivity sector which contributes to overall productivity growth, and vice versa for the latter. McMillan and Rodrik (2011) find that there are significant differences between structural change in Asian economies and African – Latin American economies. While Asia experiences productivity-enhancing transformation, the contrary occur in the other two regions. Since 1990, structural change has been reducing productivity growth in both Africa and Latin America. They identify three factors that help determine whether (and the extent to which) structural change contributes to overall productivity growth, which are the share of natural resources in exports, undervalued exchange rates and labour market flexibility.
9 decline as labour is reallocated from manufacturing (a high relative productivity sector) to services (a low relative productivity sector).
Jorgenson and Timmer (2009) study the patterns of structural change in advanced economies (Europe, United States and Japan) over the period 1980 – 2005. They found that in all regions, the importance of services has steadily increased as shown by the ratio of value added in services to goods production over the period from 1980 to 2005. Another finding is shown by the ratio of productivity in market services over goods production, indexed to unity in 1980. There was negative trend in services productivity in 1980s in all regions, nevertheless this trend stopped in Japan and the U.S. around 1990. Labour productivity growth in goods production was no longer higher than in market services3, and
even lagged behind in some sub-periods. The same pattern also applies to multifactor productivity. Overall, Jorgenson and Timmer (2009) conclude that the classical trichotomy among agriculture, manufacturing and services has lost most of its relevance in developed economies. Services now account for about three-quarters of value added and hours worked, and productivity growth in market services predominates over productivity growth in goods production in Japan and the U.S., although not in Europe. Moreover, the shares of skilled labour compensation and information and communications technology (ICT) in value added have increased substantially in all sectors, particularly in distribution services and in particular finance and business services. It indicates that technology is well diffused and utilised in service sector in developed economies.
With the rise of service sector, Noland et al. (2013) argue that services is necessary to maintain the growth of employment. In developing economies for instance, manufacturing sector is now maturing and productivity level has reached its peak level. Hence it attenuates the capacity of manufacturing sector to generate employment. Service sector, which tend to be more labour intensive, then becomes important to be vitalised in the developing economy. With the adoption and utilisation of ICT, services will also generate higher value added activities and boost the productivity-enhancing transformation.
2. 2 The Strong Linkage of Service Sector in the Economy
The service sector should be considered not only as it is but also as a pivotal part to boost the national competitiveness through its strong (forward) linkages with the rest of economy. The concept of forward linkages measures the inter-relationship of a given industry with the rest of the economy
3 Jorgenson and Timmer (2009) use EU KLEMS data. Non-market services including public administration,
10 through the demand side. It shows the impact to a given industry if the final demand of every other industry were to increase by one unit (OECD, 2007).
The strong linkage of service sector is in line with two great unbundlings that marked globalisation (Baldwin, 2006). The first unbundling is the breakdown of production and consumption, meaning that the locations of production and consumption are no longer required to be close to each other. This phase, which was driven by declining transportation cost, allowed spatial separation of factory and consumer. The second unbundling is the breakdown of various tasks of production. It was driven by decline of communication and information cost. There became an opportunity to offshore some routine tasks which are easily codifiable and do not need face-to-face contact. This unbundling process has led to what known today as global value chains (GVC).
According to Tijaja (2013), the service sector plays two key roles in GVC: (1) by connecting each point along the global value chains; and (2) by constituting their own global value chains. In similar view, Damuri (2012) addresses the importance of services as production sector and input. As production sector, services generates value added, creates employment and acts as exports commodity4. As
inputs, services acts as intermediate inputs to other sectors as well as provides basic socio-economic needs to population such as education and health.
Figure 2.1 The Smile Curve
Source: Baldwin et al. (2012)
The increasing role of services in GVC can also be observed by the deepening ‘smile curve’. Smile curve shows value added per stage starting from R&D and moving right down to final sales and after-sales
4 Goswami et al. (2012) discuss further about services exports in developing economies.
value added
11 services. In the past three decades, the smile curve has gone from flat (good jobs all along the chain) to U-shaped, with fabrication stages – especially final assembly – now received much lower shares of value than before (Baldwin et al., 2012). Figure 2.1 shows that the R&D, design, logistics, marketing and after-sale services – all are parts of service sector – generate more value added in the GVC. Most of services tasks in the value chain also require higher skill and knowledge compared to routine tasks in assembly and manufacturing. Based on the strong linkage and increasing value added, a well-developed service sector is important fuel for growth both directly and indirectly through spillovers to other sectors (Shepherd and Pasadilla, 2012).
In general, services products cannot be separated from most of the products from other sectors as the services products are embodied in them. There is a wide range of market services which directly affects the competitiveness of enterprises, both manufacturers and other services providers. These are collectively referred to as “business-related services”. European Commission (2003) classifies the business-related services into four major groups of activities, namely business services, distributive trades, network services and financial services.
Transportation and logistics services, for example, play crucial role in distributing manufacture products both for domestic and international market. Poor quality of service sector leads to inefficiency and high cost economy. Hence it means higher price of goods sold to consumers (Damuri, 2012). Information and communication technology is also pivotal as it plays a critical role in information flexibility, product quality and fast response which are important in overall business process (Roy et al., 2002).
Some existing studies have discussed about the linkage of services with other economic activities. Arvis et al. (2010) emphasize that the quality and competence of core logistics service providers is one of the important aspects of overall country performance. The study shows that better logistic services strongly correlate with trade outcomes in goods sectors. In addition, Arnold et al. (2010) discuss the role of service sector in broader scope. The authors find that improvements in regulation on services in India – which covers banking, telecommunications, insurance, and transport – have been an important source of productivity gains in manufacturing.
12 With regards to services role in providing basic socio-economic needs, Shepherd and Pasadilla (2011) find that less restrictive services trade policies are associated with better human development outcomes across a range of sectors. Using data of World Bank’s World Development Indicator, they find that some services directly produce outputs that are pivotal for human development such as health and education. Indirectly services also contribute as inputs into the production and distribution of goods that are necessary for human development purposes such as information and telecommunication, logistics and transportation. Therefore, a more efficient service sector should mean that such goods and services can be accessed more cost effectively and more broadly by poor people.
A number of studies that emphasise the linkage of service sector to overall economy have led some researchers to investigate whether or not the contribution of service sector has really increased in an economy. Roy et al. (2002) and Toh and Thangavelu (2013) focus on information and communication technology (ICT), which is part of growing service sector. ICT sector is considered as one of the key drivers of the ‘knowledge economy’5.
Roy et al. (2002) study the extent of informatisation in the Indian economy during the period 1983- 84 to 1989- 90 and identify the information intensive sectors. During the period, there was increased linkage between the information sector and other non-information sectors in India. This growth has been mainly driven by domestic demand expansion. Although export expansion and technological coefficient changes also have role, the effects are not too significant. Later on, the same investigation was done by Toh and Thangavelu (2013) on Singapore ICT sector. The results indicate that the ICT sector provided the key linkages for the expansion of high-value added manufacturing activities and electronics export for the Singapore economy.
The existing literature has shown the growing importance of service sector in the economic development. There is multiplicative positive effect of supporting policies on services towards other sectors due to the pivotal role of services in the economy.
1
5 Powell and Snellman (2004) define knowledge economy as production and services based on
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3. SERVICE SECTOR IN BRAZIL, RUSSIA, INDIA, INDONESIA AND CHINA (BRIIC)
The BRIICS economies – consist of Brazil, Russia, India, Indonesia, China and South Africa –represent an important set of emerging economies in today’s world. In the past two decades, the BRIICS have become more integrated with world intermediate inputs, final goods and services markets, as indicated by their growing shares in world trade, trade-to-GDP ratios and shares of exported value added (OECD, 2008).
In 2007, gross domestic product (GDP) of the Organisation for Economic Co-operation and Development (OECD) economies accounted for 14.75% of world GDP. In the same period, BRIICS – which represents around one sixth of the number of OECD economies – contribute to almost 4% of world GDP. In addition, the OECD acknowledges BRIICS, among other trade partners, as the most important economies because of its significant trade associations between the OECD and BRIICS. However, due to data limitation in the input-output data used in the analysis, this study will exclude South Africa (further the group of economies will be addressed as BRIIC).
Figure 3.1 Shares of BRIICS and OECD GDP to World GDP (%), 2007 (Constant price 2005)
Source: World Bank (2013)
As emerging economies, BRIIC also experience the pattern of transition in the economy. It can be observed through the dynamics of three main sectors: agriculture, manufacturing-industry and services. Figure 3.2 shows the output structure of BRIIC as percentage of each sector to the overall GDP in 2000 and 2012.
81.35 3.91 14.75
Rest of the world BRIICS
14
Figure 3.2 BRIIC Output Structure (%), 2000 and 2012
Source: World Development Indicator, World Bank (2013)
As the Figure 3.2 shows, in the past decade, output in agricultural sector has been the least contributor to overall output in all BRIIC economies, while manufacturing-industry and services contributed to different proportion in each of the economies. In general, output in services has grown between 2000 and 2012 in all BRIIC economies although in some of the economies the increase is not very significant. Along the two sub-periods, service sector has become dominant in Brazil, Russia and India. In Brazil, the increasing contribution of services in overall economy might be related with its economic reforms after 1990 which encompass goods and service sector. Service sector reform involves an on-going process of relaxing the participation of foreign firms in these sectors. The reform in services is important because some backbone services such as transport, logistics, financial and telecom are crucial for manufacturing activities (OECD, 2008). Similar with the case of Brazil, OECD (2008) also shows that allowing foreign direct investment in business services in Russia has resulted welfare gains in developing varieties of business services and restructuring the service sector. Russian firms received not only financial injection but also a chance to overcome the huge gap in the level of technology, organisation and management, methods of service, business culture, etc.
Moreover, India has been well known for its information and telecommunication sector. This is also the main contributor to the fast growing service sector in India. The infrastructure for services delivery (telecommunication networks) has improved dramatically, whereas that for goods delivery (roads and ports) is improving much more slowly (Goswami et al., 2012). Policy-makers in industrialized countries and in a growing number of developing countries including India see the
6 5 6 4 23 17 16 14 15 10 28 26 38 36 26 26 46 47 46 45 67 68 56 60 51 57 38 39 39 45 0 20 40 60 80 100 120 2000 2012 2000 2012 2000 2012 2000 2012 2000 2012 Brazil Russia India Indonesia China
Services
15 potential gains from using IT-based processes to enhance their access to global knowledge, markets and capital (Roy et al., 2002).
However, the domination of services is not the case in Indonesia and China. While the service sector is growing, its contribution is still less than and equal to manufacturing-industry. In 2012 for example, service sector in Indonesia and China contributed as much as 39% and 45% respectively. Those are lower than and equal to the contribution of manufacturing and industry altogether which contributed to 47% and 45% respectively in the same year.
The lower share of services compared to manufacturing-industry in Indonesian economy could partly be attributed to its strictly regulated service sector compared to its goods sector, as well as problems with services data measurement and reporting (Tijaja, 2012). Policy and institutional aspects are very critical in supporting the service sector. In response to more competitive services activities in the global world, Chinese government has recently started to balance the domination of heavy industry and manufacturing with services development. The aim is to foster more productive growth by increasing the share of activity generated by the service sector, which includes logistics, tourism, engineering, health care and information technology. Service sector becomes one of the priorities in their domestic rebalancing agenda (IMF, 2013).
Figure 3.3 Services Trade Balance of BRIIC 2004 - 2010* (in billion US dollar)
Source: OECD Factbook statistics (2013) *Data on China only available until 2009
Whereas the contribution of service sector in national economy is rising, BRIIC countries still have to struggle for competing in services trade. Figure 3.3 shows services trade balance of each BRIIC
-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 2004 2005 2006 2007 2008 2009 2010
16 economy with the rest of the world. Except for India, all other BRIIC countries have negative or deficit trade balance in services which indicates more imports than exports6. OECD (2008) suggests that the
percentage of services exports in Brazil increased in the 1990s but has partly reversed since 2000. However, despite the decline, Brazil has maintained a comparative advantage in services relative to China and Russia. However compared to the world average and India’s remarkable service sector specialisation, Brazilian services remain underdeveloped.
Figure 3.4 Exports in Services Value Added of BRIIC in 2000, 2005, 2008 and 2009 (in million US dollar)
Source: OECD-WTO TiVA Database (2013)
Nevertheless, there are some drawbacks of the calculation of gross exports because of double counting problems. It means that exported product from one country actually contains the imported products or intermediates from other countries. Therefore, it is more reliable to refer to data on trade in value added because it captures activities of various economies participating along global value chain, not only the final products7. Based on trade in value added as seen in Figure 3.4, the trend of services trade
during 2000 – 2009 has been positive. Only after 2008 it decreased which might be affected by the global financial crises. In value added terms, services exports account for 42% of exports from G20
6 Trade balance or balance of trade is the difference between a country's imports and exports. A country has a
trade deficit if it imports more than it exports and vice versa for trade surplus.
7 OECD and WTO released data on the Trade in Value Added (TiVA). The second release of the TiVA database
(May 2013) presents indicators for 57 economies (including all OECD countries, Brazil, China, India, Indonesia, Russian Federation and South Africa) covering the years 1995, 2000, 2005, 2008 and 2009 and broken down to 18 industries. Further information can be accessed through
http://www.oecd.org/sti/ind/measuringtradeinvalue-addedanoecd-wtojointinitiative.htm 0 50000 100000 150000 200000 250000 300000 350000 2000 2005 2008 2009
17 economies (all BRIIC economies are parts of G20) and more than 50% for some countries (OECD-WTO-UNCTAD, 2013).
One important contribution which distinguishes India and other BRIIC economies is its role as outsourcing and offshoring activities8 destination of many multinational companies from developed
countries. Among other developing economies, India has been the leading recipient of offshored service jobs. The growth in offshoring is closely linked to the prior development of India’s software sector and an enabling regulatory and other institutional environment (Dossani and Kenney, 2007; Peng et al., 2008). India also invests in its large educated English-speaking population and the higher education of approximately top 5% of the university eligible population (Peng et al., 2008). Those enable India to be major exporter of information technology services, business process outsourcing (BPO) and software workers. While high-value added activities were largely performed in advanced economies and low value-added ones were performed in developing economies, Martinez-Noya et al. (2011) argue that this location pattern might be changing. This is because developing countries such as India keep upgrading their technological competences and thus more technological advanced activities are being offshored to India.
However, the global financial crises occurred in 2008 put significant effects on downward trend of India’s services trade balance after that period. Financial crises in 2008 led to economic recession in developed economies include US and EU countries. Thus, wages are now more balanced between developed and developing countries because people in developed ones are willing to work with lower wages rather than losing jobs. Mărgulescu and Mărgulescu (2014) address that there has been decreasing relative wage advantage of offshoring to developing economies. For instance, in the case of IBM, cost of labour in India was 80 % less than in the U.S. in the beginning, but currently the gap reduced to 30-40 % and decreases further. India's economic growth also began slowing afterwards because of a decline in investment, driven by high interest rates, higher inflation and investor pessimism about the government's commitment to further economic reforms and about the global situation itself. Futhermore, re-shoring phenomenon has expanded whereby business operations which were previously offshored to developing economies are now gradually transferred back to its country of origin (Mărgulescu and Mărgulescu , 2014).
.
8 Martinez-Noya et al. (2011) discuss more thoroughly about outsourcing and offshoring activities of
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4. DATA AND METHODS 4.1. Data Sources and Data Collection Process
In the globalisation era, there is increasing need to have more comprehensive database which cover many countries and are comparable to each other. In order that the data can be useful for policy making and analysis, the following three aspects should be taken into account. It must: (i) be global, (ii) cover changes over time in order to evaluate past developments, and (iii) include a variety of socio-economic and environmental indicators. In addition, it is also necessary to have all data in a coherent framework, e.g. using the same industry classification (Dietzenbacher et al., 2013).
The input–output framework allows us to understand the impact of one particular sector in an integrated framework in terms of its linkages to overall economy include its own sector and other sectors (Toh and Thangavelu, 2012). For that purpose, this study uses National Input-Output Table (NIOT) of each of BRIIC countries for the year 2000, 2005 and 2010, obtained from the World Input-Output Database (WIOD)9. The WIOD provides data of 40 countries (all 27 EU countries and 13 major
other countries) and estimates for the rest of the world. All data in WIOD are obtained from official national statistics and are consistent with the National Accounts. Because it is presented in current prices, the data is converted to constant 2005 prices using CPI deflators.
The NIOT consists of 59 products which are produced and used by each of 35 sectors. This classification is based on the Statistical Classification of Economic Activities (NACE) revision 110 which
corresponds to International Standard Industrial Classification (ISIC) revision 3.11 Because this study
aims to observe the contribution of service sector to the economy, it is necessary to classify which sectors from the NIOT are categorised as services. Services discussed in this study are based on services classification of NACE revision 1, but limited to education and commercial services 12. Hence it
9 WIOD consist of World Input-Output Table (WIOT). The construction of WIOD and WIOT is described in detail
in Timmer (ed) (2012) and Dietzenbacher et al. (2013). WIOD can be accessed on http://www.wiod.org/. All data are available for free.
10 NACE Revision 1 is a 4-digit activity classification which was drawn up in 1990. It is a revision of the General
Industrial Classification of Economic Activities within the European Communities originally published by Eurostat in 1970.
11 ISIC is the United Nation system which becomes international reference classification of productive activities.
Currently majority of countries around the world adopt this classification as their national classification or refer to this in developing their national classifications. According to Eurostat (2006), NACE rev. 1 is derived from ISIC rev. 3, whereby ISIC and NACE have exactly the same items at the highest levels, but NACE is more detailed at lower levels.
12 Based on NACE rev. 1, services comprise economic activities covered by wholesale and retail trade (sector G)
19 does not include public and other community, social and personal services13. It results in 15 out of 35
industries in NIOT to be classified as service sector as presented in the Table 4.1. These sectors will then be merged as one sector in the NIOT.
Table 4.1 List of Service Sector NIOT
code Service Sector
50 Sale, Maintenance and Repair of Motor Vehicles and Motorcycles; Retail Sale of Fuel
51 Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles
52 Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods
F Construction
H Hotels and Restaurants 60 Inland Transport 61 Water Transport 62 Air Transport
63 Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies
64 Post and Telecommunications J Financial Intermediation 70 Real Estate Activities
71t74 Renting of M&Eq and Other Business Activities M Education
N Health and Social Work
Source: National Input Output Table
For the purpose of the simplification, the industries present in the NIOT will be classified using new numbering. The classification is presented in Appendix 1. It will also be helpful because the fifteen different service sectors in Table 4.1 are now merged into one sector named ‘services’ with classification number 21. Therefore, the total number of industries in NIOT reduced from 35 to 21. The
the second wave of service sector growth and the so-called ‘modern economy’ as discussed in Eichengreen and Gupta (2009).
13 However in the NIOT, health sector is presented in the same section with social work under ‘Health and Social
20 observation in this study will be divided into two sub-periods, which are 2000-2005 and 2005-2010. It will allow us to observe and compare the change of output between the two sub-periods.
4.2. Methodology
4.2.1 Introduction to Input-Output Table
According to Dietzenbacher (2012) and Timmer et al. (2012), National Input-Output Table (NIOT) is a systematic part of the National Accounts and focus on production which is represented in money terms. The table contains data of sector to sector economic transaction in an economy at one period of time14. For ease of discussion, it is assumed that each sector produces only one (unique) product.
Rows on the NIOT represent the selling sectors that deliver outputs to all sectors in the table, while the columns show the purchasing sectors that use those outputs. The column indicates the values of all intermediates, labour and capital inputs used in production.
In addition to being an intermediate, each of the products can also be delivered for final use. Final use includes domestic use (household, private or government consumption and investment) as well as exports. The final element in each row indicates the total use of each product. As a whole, a product can be imported or domestically produced. Total supply of the product in the economy is determined by domestic output plus imports. An important accounting identity in the NIOT is that total output by the domestic industry is equal to the use of output from the domestic industry such that all flows in the economic system are accounted for (Timmer et al., 2012).
Table 4.2 Basic Outline of NIOT
Industry Final use/demand
Total output 1 2 3 Domestic Export Industry 1 Z11 Z12 Z13 D1 E1 X1 2 Z21 Z22 Z23 D2 E2 X2 3 Z31 Z32 Z33 D3 E3 X3 Imports Value added Total output
Note: the yellow-shaded area shows inter-industry linkages or the use of intermediate inputs in production stages.
21 Table 4.2 shows the basic outline of NIOT. Z11 represents the intermediate produced by industry 1
which is used by industry 1 itself. Similarly, Z12 represents the intermediate produced by industry 1
which is used by industry 2. D1 and E1 are domestic final demand and exports of the product of
industry 1 respectively. Total output is represented by X whereby X1 represents total output for
industry 1. It is the sum of all intermediates and final use of an industry 1. Similar interpretation goes to other industries in the table.
The extension of NIOT is known as World Input-Output Table (WIOT). Using the same concept, in WIOT, the use of products is broken down according to their country of origin. Each product is produced either by a domestic industry or by a foreign industry. In contrast to the NIOT, where the imported products are aggregated, this information is made explicit in the WIOT. Therefore, for each of the products, we can observe whether the intermediate is domestically produced and or imported. If it is also imported, we can observe from which foreign industry the intermediate is. In the same manner, it is applied to the export of products.
This study uses NIOT instead of WIOT because it will focus on the use of services in each of BRIIC economy and how they link with other sectors. The study will also observe the proportion of the use of services for both domestic final demand and exports.
4.2.2 Causative Matrix
Firstly, this study will observe the extent of service sector in BRIIC economies in both sub-periods using causative matrix. The matrix is used to identify the impact of final demand in service sector to output of service sector itself and the impact of final demand in non-service sectors to output of service sectors. The causative matrix approach has the advantage of capturing both the direct changes in interactions and the relative changes due to the presence of other sectors (Toh and Thangavelu, 2013).
Roy et al. (2002) and Toh and Thangavelu (2013) use causative matrix to measure the extent of information sector of Indian economy (1983-84 and 1989- 90) and Singapore economy (1990-2000) respectively. According to those studies, the following causative matrix (C) will be employed:
Pt+1 = C x P (4.1)
C = Pt+1 x Pt-1 (4.1*)
C is considered as the matrix of growth rate which summarises the nature of inter-period change,
22 that the value of C is not obtained by matrix multiplication. Instead it is an element-wise multiplication of Pt+1 and Pt-1.
To implement the use of C in the input-output analysis, normalised Leontief inverse matrix L is used as transition probability matrix P. The elements of each column of the original Leontief inverse are normalised which yields matrix L. The normalisation process is done by dividing each element of the matrix by the sum of respected column so that the new column sum will equal to one. Roy et. al. (2002) address that the normalisation process standardises the change in magnitudes of the output multipliers15 and focuses the analysis upon the relative effects on one and another. Thus the model is
now written as:
Lt+1 = C · Lt (4.2)
C = Lt+1 x Lt-1 (4.2*)
Following the standardised Leontief inverse matrix L, standardised output multiplier of sector j is given as follows:
OMj = l1j + l2j +· · ·+lnj = 1,
where lij is the i − j element of matrix L.
The sum of the elements in each row of the causative matrix is interpretable as a sort of final demand multiplier which is the amount of output of one particular sector required to fulfil final demand of all sectors. When the sum is greater than one, it indicates greater contributions to output multipliers. The sector concerned experience greater output impacts when the final demands in other sectors change. Row sums less than one indicate that the impacts from the final demand changes are weakening. Negative deviations of the diagonal elements of the sectors from unity imply decrease in the relative internalisation of their own final demand output impacts (Roy et al., 2002; Toh and Thangavelu, 2013). The sectors in the causative matrix are classified according to (1) the deviation of their diagonal elements from one, with positive deviation indicating increased relative indigenisation of their own final demand output impacts and (2) the deviation from zero of the sums their respective off-diagonal elements (ODE), with positive deviation reflecting increased relative output impacts on the sector engendered by final demand in all other sectors.
15 The output required from each sector in order to produce certain amount (or to fulfil final demand) of one
23 The diagonal element of C matrix shows final demand impact of each sector to its own output while the off-diagonal elements of C matrix show final demand impact of other sectors to output of a particular sector. In this analysis, sectors will be divided into service sector and each of other non-service sectors.
4.2.3 Input Coefficient of Service Sector
This section attempts to measure the services intensity of various sectors and their changes over two sub-periods. Thus it will confirm whether the diffusion of service sector has occurred and continued to expand in other sectors of BRIIC economies.
Referring to Roy et al. (2002) and Toh and Thangavelu (2013), the intensity measure used in this study is the ratio of services intermediates used per unit of output or also known as input coefficient of service sector. Denoting this ratio for sector i by hi, we can proceed to obtain a second measure (h*i),
that accounts for both direct and indirect services requirement to produce one unit of goods by each sector, as follows.
h∗’ = h’(I −A)-1 (4.3)
where h*’is the row vector with element h*i; h’ is the row vector with element hi; and (I −A)−1 is the
standard Leontief inverse matrix.
Equation (4.3) departed from two basic types of sector intensity (Roy et al., 2002), in this case services intensity. The first type imputes productive services use, S, to total output by the economy, x. The second one imputes productive services use to final output, y, i.e.
h’x = S= h∗’y
To obtain h’, total productive services use is allocated between the producing sectors. Considering all
n producing sectors in the economy, S= ∑ . Sector S has total output value xi,so hi is defined by hi ≡
. Thus the vector h∗’ can be derived from h’ using relationships:
h’x = S (4.4)
h∗’y = S (4.5)
x = (I −A)-1 y (4.6)
24
h’(I −A)-1 y = S (4.7)
Comparing (4.5) with (4.7) gives h∗’ = h’(I −A)-1 as in equation (4.3).
4.2.4 Structural Decomposition Analysis
In order to calculate the changes or growth of services output, this study uses structural decomposition analysis (SDA). It is a well-known methodology to assess the relative importance of effects that together constitute the actual change in a certain variable of interest. In other words, the SDA quantifies the contributions of determinants of change to total change in a variable over time. For example, the total change in gross outputs between two periods could be broken down into: (1) the part associated with changes in technology (as reflected, initially, in the changes in the Leontief inverse for the economy over the period); and (2) the part related to changes in final demand over the period (both domestic final demand and export). At the next level, both Leontief inverse and final demand can be disaggregated into parts of interest (Miller and Blair, 2009).
The basic equation is as follows16:
( ) ( )
(4.8)
( ) ( ) (4.9)
( ) ( ) (4.10)
x = n-element (column) vector of gross output (or production value) in each sector,
in which each element xi in x matrix indicates gross output in sector i A = n× n matrix of intermediates,
in which each element aij in A matrix indicates the deliveries of good i (which is produced in sector i) that are sold to sector j (as an intermediate input in the production of sector j)
M = ( ) = multiplier matrix or also known as the Leontief inverse
f = n x k matrix of final demand categories,
in which each element fij in f matrix indicates the goods and services produced by sector i that are bought by category j (e.g. private consumption by the households).
25 According to Miller and Blair (2009), the alternative equations in (4.9) and (4.10) are equally valid in the sense that both are “mathematically correct”. However, it is clear that the measures in (4.9) of the individual contributions from changed technology and from changed final demands will be different from those in (4.10), except in the case where the multiplier matrix in t1 and t0 are equal (in other
words M1 = M0) and/or the final demand in t1 and t0 are equal (in other words f 1 = f 0) which are very
unlikely.
In the equivalent equations (4.9) and (4.10), the first term refers to the change in total output x caused by changes in the matrix A and the second term to the change in total output x caused by changes in final demand f. In this way we can decompose the change in the output levels. By summation we can thus decompose the change in the global output.
In addition to equation (4.9) and (4.10), there are some other alternatives of decomposition (Miller and Blair, 2009). However, among many researchers, Dietzenbacher and Los (1998) examine a wide variety of possible decompositions and conclude that using an average of results from (4.9) and (4.10) is often an acceptable approach.
As this study aims to see the role of services, the overall sector will be classified into service sector (S) and non-service sector (N). Furthermore, in order to see the different role of change in domestic final demand and export to the total output, final demand f will be decomposed into domestic final demand
d and export which is represented by e. Thus the equation (4.9) is decomposed as follows:
( ) ( ) (changes in services input) ( ) (changes in non-services input)
( ) (changes in domestic final demand for services sectors) ( ) (changes in domestic final demand for non-services sectors)
( ) (changes in export of services sectors)
( ) (changes in export of non- services sectors) (3.11) In the same way, equation (4.10) is decomposed as follows:
( ) ( ) (changes in services input) ( ) (changes in non-services input)
26 ( ) (changes in export of services sectors)
( ) (changes in export of non- services sectors) (3.12)
27 5. RESULTS AND DISCUSSION
5.1 The Extent of Service Sector in BRIIC Economies
The table in Appendix 4 presents the summary of computed left causative matrix. It shows that in all economies, the row sum corresponding to the service sector is less than one. As discussed in Data and Methods section, when the sum is greater than one, it indicates greater contributions to output multipliers. Meaning that the sector concerned experience greater output impacts when the final demands in other sectors change (for example 10% increase in final demand leads to more than 10% increase in output). On the contrary, row sums less than one indicate that the impacts from the final demand changes are weakening. Therefore, according to the result in Appendix 4, this implies that the final demand in other sectors does not have increasing impact on service sector in BRIIC economies during both period 2000 – 2005 and 2005 – 2010.
Moreover, using causative matrix C, the sectors are classified according to (i) the deviation of their diagonal elements from one, with positive deviation indicating increased relative indigenisation of their own final demand to output impacts and (ii) the deviation from zero of the sums of their respective off-diagonal elements (ODE), with positive deviation reflecting increased relative output impacts on the sector engendered by final demand in all other sectors.
Table 5.1 The four quadrants of the typology of structural change*
ODE<0 ODE>0
IV I
Cii>1
Increase in relative indigenisation of their own final demand to output impacts, but no increase in relative output impacts
engendered by final demand in all other sectors.
Increase both in final demand to output impacts by their own final demand and by final demand in all other sectors.
III II
Cii<1
No increase both in final demand to output impacts by their own final demand and by final demand in all other sectors.
No increase in relative indigenisation of their own final demand to output impacts, but increase in relative output impacts engendered by final demand in all other sectors.
Adapted from Roy et al. (2002) and Toh and Thangavelu (2013)
* Cii is row i and column i (diagonal element) of matrix C; ODE is the sum of off-diagonal element of matrix C.
28 structural change as seen in Table 5.1. The details of the classification are presented in Table 1a to 5b in the Appendix 2 with highlight on the service sector (classification number 21).
Table 5.2 Typology of Structural Change in Service Sector in BRIIC (2000-2005 and 2005-2010)
Country 2000-2005 Typology of Structural Change 2005-2010
Brazil IV IV
Russia III IV
India IV IV
Indonesia III III
China IV IV
Table 5.2 summarises typology of structural change of services in each of BRIIC economies during 2000-2005 and 2005-2010. Except for Indonesia and Russia, service sector falls in category IV in both sub-periods with positive deviation of their diagonal elements from one (Cii>1) but negative deviation
from zero of the sums of their respective off-diagonal elements (ODE<0). It implies that in most of BRIIC economies, final demand on service sector has an enhanced output impact on the service sector itself. However final demand of other sectors has no increasing output impact on the services. It indicates that there has been relatively idle utilisation of services by other sectors. The case of Russia shows a shift between two sub-periods from type III to type IV which indicates expanding final demand to output impacts by services own final demand. Whereas in the case of Indonesia, there has been relatively idle utilisation of services both by services itself and by other sectors as it remains in the type III over two sub-periods. The present analysis suggests that in term of its output effect engendered by final demand of other sectors, the service sector has not much expanded yet in BRIIC economies during the period 2000 – 2010.
5.2 Services Intensity of BRIIC Economies
29 India, from 1.5% (coke, refined petroleum and nuclear fuel) to 35.2% (electrical and optical equipment) in Indonesia, and 14.2 (agriculture) to 47.4% (public admin and defense; compulsory social security) in China.
Table 5.3 Average of service intensity between 2000-2005 and 2005-2010
Country 2000-2005 Average of Change 2005-2010 Trend
Brazil -0.0010 0.0131 Increasing
Russia 0.0301 0.0608 Increasing
India 0.0143 0.0230 Increasing
Indonesia 0.0175 0.0198 Increasing
China -0.0497 0.0394 Increasing
Table 5.3 shows the trend of average value of services intensity in BRIIC between two sub-periods. Although there is heterogeneity across economies and sectors, the results suggest that in all BRIIC economies, the average of the change of services coefficient increased in the second sub-period. It means that on average there is increasing use of services input per unit of output in the second period compared to the first sub-period. Therefore, although final demand in other sectors has not enhanced services output (as suggested by the causative matrix), there has been increase in services intensity in BRIIC economies in the period 2005 – 2010 compared to that in 2000 – 2005.
5.3 Sources of Growth of Service Sector in BRIIC Economies
Structural decomposition analysis (SDA) model is used for the purpose of identifying the sources of growth of service sector in each of BRIIC economies. Referring to Dietzenbacher and Los (1998), the result of the analysis is derived from the average of (4.9) and (4.10). Table 5.4 until Table 5.9 provide information on the contributors to the services growth, which are divided into domestic final demand effect, export effect and technical coefficient effect (effect of the change in services input). Furthermore, each of them is similarly decomposed into effect of services and non-services.
Table 5.4 Source of output growth for service sector of Brazil 2000-2010 (constant price 2005)
30 2 Export effect 12,727.12 -11.13 28,877.49 4.37
Services 1,732.63 -1.51 17,438.79 2.64
Non-services 10,994.49 -9.61 11,438.70 1.73 3 Technical coefficient effect -4,515.97 3.95 15,984.63 2.42
Services -11,471.51 10.03 29,105.56 4.40
Non-services 6,955.54 -6.08 -13,120.93 -1.99
Table 5.5 Source of output growth for service sector of Russia 2000-2010 (constant price 2005)
Russia 2000-2005 Overall Services 2005-2010 US$ million % US$ million % Change in services output 205,048.38 100.00 208,941.18 100.00 1 Domestic final demand effect 177,062.46 86.35 160,891.27 77.00 Services 159,911.03 77.99 150,548.40 72.05 Non-services 17,151.43 8.36 10,342.87 4.95 2 Export effect 13,243.48 6.46 8,097.40 3.88
Services 7,268.05 3.54 9,666.57 4.63
Non-services 5,975.44 2.91 -1,569.17 -0.75 3 Technical coefficient effect 14,742.44 7.19 39,952.51 19.12
Services 4,397.96 2.14 36,364.00 17.40
Non-services 10,344.47 5.04 3,588.52 1.72
Table 5.6 Source of output growth for service sector of India 2000-2010 (constant price 2005)
India 2000-2005 Overall Services 2005-2010 US$ million % US$ million % Change in services output 265,356.68 100.00 245,482.11 100.00 1 Domestic final demand effect 198,743.07 74.90 203,374.05 82.85 Services 172,920.52 65.17 178,233.25 72.61 Non-services 25,822.55 9.73 25,140.81 10.24 2 Export effect 50,500.10 19.03 25,249.58 10.29
Services 35,758.48 13.48 13,167.03 5.36
Non-services 14,741.62 5.56 12,082.55 4.92 3 Technical coefficient effect 16,113.51 6.07 16,858.48 6.87
Services 23,109.35 8.71 17,750.30 7.23
31
Table 5.7 Source of output growth for service sector of Indonesia 2000-2010 (constant price 2005)
Indonesia
Overall Services
2000-2005 2005-2010 US$ million % US$ million % Change in services output 19,659.33 100.00 183,120.23 100.00 1 Domestic final demand effect 17,114.64 87.06 169,073.01 92.33 Services 16,344.44 83.14 151,205.51 82.57
Non-services 770.19 3.92 17,867.50 9.76
2 Export effect -2,046.13 -10.41 4,934.60 2.69
Services 1,251.29 6.36 2,291.51 1.25
Non-services -3,297.42 -16.77 2,643.09 1.44 3 Technical coefficient effect 4,590.90 23.35 9,112.60 4.98
Services 4,083.23 20.77 5,816.63 3.18
Non-services 507.66 2.58 3,295.96 1.80
Table 5.8 Source of output growth for service sector of China 2000-2010 (constant price 2005)
China 2000-2005 Overall Services 2005-2010 US$ million % US$ million % Change in services output 906,661.00 100.00 3,003,478.00 100.00 1 Domestic final demand effect 769,778.16 84.90 2,527,689.21 84.16 Services 672,114.85 74.13 2,247,281.10 74.82 Non-services 97,663.32 10.77 280,408.11 9.34 2 Export effect 227,607.42 25.10 273,416.47 9.10 Services 102,862.78 11.35 105,724.88 3.52 Non-services 124,744.64 13.76 167,691.59 5.58 3 Technical coefficient effect -90,724.69 -10.01 202,372.74 6.74 Services -118,727.26 -13.09 113,617.05 3.78 Non-services 28,002.57 3.09 88,755.70 2.96
All BRIIC economies, except Brazil, show positive growth of services output in both sub-period 2000-2005 and 2000-2005-2010. Moreover, when compared between two sub-periods, all economies experience increase in growth of services except India which has modest decline from US$ 265,356.68 million to US$ 245,482.11 million. As for Brazil, although the growth of output is negative over first sub-period, it catches up over second sub-period with the increase from US$ -114,368.95 to US$ 660,774.08.
32 in all economies, where domestic final demand for services contributed higher than the non-services one. The result implies that growth of services output in BRIIC has been mainly driven by domestic services activities. Nevertheless, Brazil, India and Indonesia show increasing contribution of non-services domestic final demand in the second sub-period.
Another contributor to growth of services output is export, includes both services and non-services. Its contribution varied across economies. The most striking feature can be seen in the case of Indonesia wherein output growth was dampened by negative export effect in the first sub-period. Nevertheless, the export effect increased from -10.41% to 2.69% in the second sub-period. On the contrary, India and China show deteriorating effect of export between two sub-periods. In the case of India, the effect declined from 19.03% in the first sub-periods to 10.29% in the second sub-period. The decline was even dramatic in China which dropped from 25.10% in the first sub-periods to 9.10% in the second sub-period.
The decline of export contribution in the growth of services output in both economies during second sub-period might be affected by the re-shoring phenomenon which recently arises. Financial crises in 2008 has led to economic recession in developed economies include US and EU countries. This simultaneously occurred in the period when the real wage in developing economies strikingly increased. In China, for instance, the aggregate of the salaries and benefits of an average worker increased between 2000 and 2005 by 10% per year and have accelerated between 2005 and 2010 to 19% a year. Moreover, the Chinese government has set the target to increase the minimum wage by 13% per year until 2015. In India, wage rate has also grown by 10-20 % annually in the last decade (Mărgulescu and Mărgulescu , 2014).
Many multinational companies have gradually transferred and are considering to transfer their business operations back to the country of origin. Collaboration survey by AlixPartners, McKinsey and Hackett on a global scale regarding the intentions of companies to relocate manufacturing confirms it. In the period 2009 – 2011, 26% (of production capacity) of offshoring activities has been reduced (Mărgulescu and Mărgulescu , 2014). More recently, Boston Consulting Group (BCG) survey in April 2012 revealed that 37% of American manufacturing companies with annual sales above $1 billion were planning or actively considering shifting production facilities from China to America. The most common reason given was higher Chinese labour costs (The Economist, 2013).
33 nowadays closely interrelating, even co-locating, research and development, design and marketing, manufacturing and assembly close to the markets become important again. It is recognised by companies as strategy to much faster response to market shifts and to much faster innovation. Therefore, the movement of manufacturing will give consequences on services, too.
The relatively low contribution of export effect in growth of services output in BRIIC economies might relate with the challenging services export itself. Goswami et al., (2012) acknowledge that barriers to trade in services are more complex than barriers to trade in goods17. The barriers for foreign services
providers include not only explicit restrictions to entry but also on policy and regulation of domestic services. Domestic regulation is established either purely because countries differ in the choice and strictness (such as qualification or licensing requirements) or because of the purpose of merely protecting domestic providers.
Besides the effect from demand side (domestic final demand and export), the other effect which contributes to growth in services output is technical coefficient effect. Except for the first sub-period in China, the result suggests that production technology changes have positively contributed in the growth of services output associated with final demand shifts. In the second sub-period for instance, the adoption of production technology has contributed to 2.4%, 19.12%, 6.87%, 4,98% and 6.74% increase in the output of services in Brazil, Russia, India, Indonesia and China respectively.
17 Goswami et al., (2012) provides examples of the types of barriers exporters face for the four modes of supply,
