metadata: authors: 0: author: Anand,Rahul
1: author: Lundstrom, Susanna
2: author: Mishra,Saurabh
count: World
docna: 0: docna: Service export sophistication and economic growth
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Judicial System Reform,Industrial Economics,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,International Trade and Trade Rules,Economic Growth,Macroeconomic Management,Business Cycles and Stabilization Policies
teratopic: Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Communities and Human Settlements
abstracts: cdata!: Can increasing sophistication in service
            exports lead to economic growth? Although services were
            historically produced primarily for domestic consumption,
            they are gradually becoming more productive, tradable and
            unbundled. The authors construct an index of "service
            exports sophistication" to document this phenomenon.
            Panel data estimations indicate a positive association
            between growth in per capita income and higher
            sophistication of service exports. The results also suggest
            that this phenomenon is growing in importance over time.
            Considering the limits of traditional industrialization in
            igniting global growth, the results provide an alternative channel.
repnme: repnme: Service export sophistication and economic growth
display_title: Service export sophistication and
            economic growth
listing_relative_url: /research/2011/03/13947629/service-export-sophistication-economic-growth
topicv3: Justice m1326106 840,Economic Growth m1326241 471,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Balance of Payments m1326965 441
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Communities and Human Settlements
no_of_pages: 39
author_keys: 0: author_key: 1039381
1: author_key: 1013160
model_extractions: WPS5606 Policy Research Working Paper 5606 Service Export Sophistication and Economic Growth _Saurabh Mishra Susanna Lundstrom Rahul Anand_ The World Bank South Asia Region Economic Policy and Poverty March 2011
Policy Research Working Paper 5606

# **Abstract**

Can increasing sophistication in service exports lead to economic growth? Although services were historically produced primarily for domestic consumption, they are gradually becoming more productive, tradable and unbundled. The authors construct an index of “service exports sophistication” to document this phenomenon. Panel data estimations indicate a positive association between growth in per capita income and higher sophistication of service exports. The results also suggest that this phenomenon is growing in importance over time. Considering the limits of traditional industrialization in igniting global growth, the results provide an alternative channel.

This paper is a product of the Economic Policy and Poverty, South Asia Poverty Reduction and Economic Management. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at saurabhthemishra@gmail.com, slundstrom@worldbank.org and ranand@imf.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Service Export Sophistication and Economic Growth**

_By Saurabh Mishra, Susanna Lundstrom and Rahul Anand_<sup>_*_</sup>

**Keywords:** services, specialization, productivity, growth, globalization **JEL Classification:** F01, O4

_* Mishra_ : The World Bank (e-mail: <u>saurabhthemishra@gmail.com);</u> _Lundstrom_ : The World Bank, (e-mail: <u>slundstrom@worldbank.org);</u> _Anand_ : International Monetary Fund, (e-mail: ranand@imf.org); We are grateful for valuable comments from Ricardo Hausmann, Olivier Blanchard, Justin Yifu Lin, Kalpana Kochhar, Brian Pinto, Ejaz Ghani, Lakshmi Iyer, Philippe Aghion, Poonam Gupta, Deepak Bhattasali, John Lincoln Newman, Israel Osorio-Rodarte, Shahid Yusuf, Norman Loayza, Aaditya Mattoo, Francis Rowe, Juan Sebastian Saez, Arti Grover, Bartlomiej Kaminski, Saranyan Krishnan, Nikola Spatafora, Will Martin, and T.G. Srinivasan. The paper gained from discussions with seminar participants at the World Bank. The views expressed in this paper are those of the authors, and do not necessarily represent those of the World Bank group or International Monetary Fund or its executive directors. Any remaining errors are solely ours.
_“Ever since Adam Smith and David Ricardo, economists have explained and extolled the gains in living standards that derive from international trade. Those arguments are just as valid for trade in services as for trade in goods.”_ Alan Blinder, 2006

# **1. Introduction**

What drives growth has been a perennial question of enquiry in economic literature, and recent rapid growth of China and India has rekindled this debate. These two countries have taken two different routes to achieve growth rates of nearly 7 percent per annum. While China has followed a more traditional manufacturing led growth strategy, India‘s growth has been driven by growth in the services sector. The Indian experience has led researchers to challenge the conventional notion that industrialization is the only plausible route to rapid economic development (Ghani and Kharas, 2010; Felipe et al., 2007; UNCTAD, 2003). Services have grown as a share of the world‘s GDP in the last decade accounting for 70 percent of global GDP, and service exports in developing countries have almost tripled between 1997 and 2007.<sup>1</sup> Even though manufacturing continues to be a dominant driver of growth, recent developments suggest that we must include the service sector in the debate. Services are no longer exclusively an input for trade in goods but have become a "final export" for direct consumption. In view of these changes in the nature of services and their growing importance, the objective of this paper is to examine whether growth in service exports and its sophistication can provide an additional route for rapid economic growth in developing countries. Sophistication has a multitude of interpretations but it broadly aims to capture the productivity level associated with a country‘s production, empirically mirrored in exports data. We develop a new index of ―service exports sophistication‖ and examine its relation with growth.

As countries develop, they undergo structural transformation. Traditional theories have focused on growth as transition from agricultural to industrial production, with manufacturing being the prime ―engine of growth‖ (Kaldor 1966, 1967). This has led to a large body of empirical research that has focused on manufacturing export led growth and related industrial policy. This was further motivated by the growth experience of the East Asian Tigers (Rodrik, 1999, 2004; Hausmann et al., 2005; Pack et al., 2003, 2006; UNIDO 2009; Schott, Peter, 2008).

However, as per capita income increases, most countries witness a rising share of services in the total output. The exact cause of this shift and its implications for future growth is still not very well researched. The negligence of services as drivers of growth in the policy and research debate stems from the notion that services are associated with low productivity and are merely inputs in the production of goods. However, the mid 1990s saw two seemingly separate but related developments. First was the revolution in information and communication technology (ICT) and, second, rapid growth in the global forces often referred to as the 3Ts – technology, transportability, and tradability – with the advent of internet age. Both events had a profound impact on the nature, productivity, and tradability of services (Ghani and Kharas, 2010; Baumol,

> 1 The global value cross-border services exports in 2007 was $3.3 trillion, (20 percent of total world trade). However, the share of services rises to almost 50 percent if transactions are measured in terms of direct and indirect value added content—that is if measured in terms of processing of imported components into final products for export (Hubert Escaith 2008). Adding the sales of services by foreign affiliates of multinational firms, then the value of trade in services rises further. Data for fifteen OECD countries puts the value of such sales at some $1.5 trillion in 2007 (WTO 2009, Hoekman et al., 2010).

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1967).<sup>2</sup> This has resulted in rapid growth of what can be called _modern impersonal progressive services_ , such as communication, banking, insurance, business-related services, remote access services, transcribing medical records, call centers, education, etc. These services differ significantly from the _traditional personal services,_ which demand face-to-face interaction. A rising number of services can now be stored and traded digitally, and are not subject to many of the trade barriers that physical exports have to overcome. They have become similar to manufacturing goods in the sense that they benefit from technological advancement, and their costs depend on economies of scale, agglomeration, networks, and division of labor. More importantly, these sophisticated services mostly require digital labor mobility that provides an opportunity for relatively innovative, high-tech job creation in low and middle-income economies.

Service activities have not only become tradable, but also unbundled due to these changes. A single service task or an activity in the global supply chain can now be fragmented and done separately at different geographical locations. Fragmentation in service exports activities has also provided prospects for specialization, which did not exist previously. Factors such as relatively low transportation costs (sometimes even negligible), less susceptibility to trade barriers, lower capital intensity, and a mix of regulations may drive competitiveness amongst service exporting countries. This in turn may drive specialization and sophistication in service exports.

There are two aspects of this new channel to growth – specialization within service exports, and the sophistication of service exports. Whereas, increasing trade volumes in niche products or services fuel growth, the more interesting question is whether growth can be achieved by improving the sophistication of service exports. This is in line with the existing literature on how sophistication of goods exports affects growth. Hausmann, Hwang and Rodrik (2007, hereinafter referred to as HHR) have shown that it is not the specialization alone, but the sophistication of goods export that matter for growth. In order to examine this phenomenon for service exports, we create an index, which we call ―service exports sophistication‖. A major contribution of this paper is to bring service exports channel of growth to the fore of discussion on the drivers of growth, and to show that it may be an alternative route for developing countries. Even though many researchers have looked at service led growth, to the best of our knowledge this is a first attempt at exploring service exports sophistication as a possible driver of growth. Another contribution of this paper is to develop a dynamic index of service exports sophistication (subject to the constraints on data, which we discuss in subsequent sections), which can be used to explore these questions in detail, and to understand more country-specific growth patterns.

Our results show that exports‘ ―quality‖ in services is positively associated with growth performance. The results hold even after controlling for income per capita, skills, the size of domestic service sector, goods sophistication, financial development, rule of law, and country time invariant factors. The results also suggest that this phenomenon is growing in importance over time. This may have important implications for countries that are stuck in a ―middle-income trap,‖ such as Malaysia, Vietnam, etc., or for countries that wish to sustain their rapid growth,

> 2 Triplett and Bosworth (2004 find that the services sector accounted for over 70 percent of the post-1995 surge in labor productivity in the United States.

3
such as India, Sri Lanka, etc.<sup>3</sup> It also highlights a need to refocus of policy debate on drivers of growth. Countries could potentially benefit by adopting policies that increase the value addition in service exports; improve productivity, eliminate obstacles to increasing sophistication in niche service activities; and promote export performance. Service exports and increasing its sophistication may be an additional channel for sustained high growth. We highlight a need to refocus policy debate on drivers of growth. Learning from this phenomenon in development policy practice will depend on country specific factors and is an area of our future work.

The paper starts with a literature review in Section 2, and we motivate our analysis by presenting some stylized facts in Section 3. In Section 4, we construct a dynamic service export sophistication index (Service EXPY) and present some descriptive statistics about both service EXPY and its PRODY components. Section 5 presents econometric model and discusses the results. Section 6 concludes with future research and policy considerations.

# **2. Literature Review**

Trade in services has been an area of interest for very long, dating back to the works of Adam Smith, David Ricardo, and Karl Marx, who considered services as distinct from goods when defining labor productivity. Classical economists thought of services as inputs to agriculture and industry, and as such did not devote much attention to services trade or to services as drivers of growth. However, technological changes and globalization in the last decade have changed the traditional notions about services, and the way economists have looked at it. Traditionally, services as a sector were thought exclusively for domestic consumption requiring face-to-face transactions such as eating in restaurants, haircuts, and loans from a bank (Baumol, 1967). However with the technological changes, services have acquired the characteristics of goods and have become tradable (Bhagwati, 1984). Baumol (1985) has classified these services as modern impersonal progressive services, which can be thought of as the modern service exports such as financial services, insurance, business processing, and computer information services. Ghani and Kharas (2010) have argued that technology, tradability, and transportability have transformed the dynamism of service exports, as they can be produced and stored and traded in binary code globally, and unlike goods these high-productivity modern services are no longer restricted by time and space. Blinder (2006) has referred to this phenomenon of globalization of services as the ―tip of the iceberg.‖ Growing importance of services in GDP growth and increased tradability of services have led to a spurt in research on various aspects of services, ranging from trade liberalization to outsourcing of services. Initial works have focused on job losses and labor market outcomes of globalization of services especially in the USA (Kletzer, 2008; Kletzer and Jensen, 2008, 2005; Mann, 2006; Panagariya et al., 2005; Mann, 2005). Realizing the importance of services in growth, a body of research has developed where researchers have explored how particular aspects of services affect growth. Mattoo, Rathindran and Subramanian (2007) have examined the openness in financial and telecommunication services to demonstrate that it is an important driver of long run

> 3 A middle income trap is when countries are squeezed between low cost producers in developing countries and high end producers in advanced economies.

4
economic growth. Fixler and Siegel (2004) have examined the specific services exports and productivity gains from outsourcing.

India‘s distinctive service led growth pattern has compelled researchers to explore the relationship between services and growth in greater detail. However, most of the work has tried to explain as to why India embarked on this unique growth path. India‘s growth pattern might have roots in its history of investment in tertiary education, telecommunication policy with a concoction of global economic environment, domestic regulations, soft infrastructure, English language heritage and democratic society that paved the way for service led growth strategy (Kochhar et al, 2006; Broadberry et al, 2008; Goopta; Eichengreen and Gupta, 2009, 2010). Other influential works include comparison of growth patterns of China and India, and growth accounting shows that the growth in the India‘s total factor productivity is coming from productivity in services (see Bosworth, Collins and Virmani, 2007; Bosworth et al., 2008).

However, most of these researches have focused on country specific and regional factors behind growth in services and their implications for growth. The role of service exports and its sophistication has not received much attention in this literature. Works of Lall, Weiss, and Zhang (2005) and Hausmann, Hwang and Rodrik (2007), are among the first to explore these questions. They have built their argument on the premise that the exports associated with higher average income are more sophisticated. However, they have focused solely on the goods sector. HHR have developed an index of goods export sophistication (EXPY) and shown that it is not the amount of exports but the sophistication of exports that matters for growth. They have also found that this index is a good predictor of subsequent growth. They have argued that rich countries are those that have latched on to ―rich-country products,‖ while countries that continue to produce ―poor country goods‖ remain poor.

As services have grown in importance and their exports are increasing rapidly, in this paper we extend the idea of HHR. We construct a dynamic service exports sophistication index, and use it in panel growth regression to examine if service exports sophistication is associated with growth.

# **3. Stylized Facts**

We motivate our analysis by presenting some stylized facts about the growing importance of services and the changing nature of services (services becoming more productive, tradable, and fragmented). Figure 1 plots GDP growth (controlling for initial income level and initial level of service value added as a share of GDP) for 125 countries over 2000-07 against service value added growth.<sup>4</sup> From the figure it appears that countries with higher service output growth experienced higher overall economic growth. From this we take a closer look at the growing importance of services in growth; increasing productivity of services; increasing tradability of services ; and evolution of services from traditional to modern.

> 4 The horizontal axis controls for initial income per capita and initial level of service value added (% of GDP). The coefficient of both service and manufacturing growth is statistically significant at the 5 percent level.

5
# **Figure 1. GDP growth and service output growth, 2000-2007**

<!-- Start of picture text --> 0 5 10 15<br>Average service output growth<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations. Note: Y axis is GDP growth controlling for initial income per capita and initial service value added (% of GDP).

# **3.1. Growing importance of services**

In Figure 2 we show the sectoral contribution of different sectors in GDP growth. Sectoral contribution of services to GDP growth has risen for most countries in the world. Also, for some developing countries, services have become more important than industry in driving the GDP growth. Some developing countries have gone for service led growth at an earlier stage of economic development contrary to what is suggested by the traditional theories. Table A1 presents the sectoral shares and growth rates in GDP for the world and different regions. Figure A1 plots the contribution of sectors in GDP growth for the world and the developing economies. Services are a major contributor in global growth and their share in global production has increased considerably. For example, the contribution of service sector to growth in 2000-07 versus 1990-99 increased by two percent for developing countries (see Figure 3).

6
# **3.2. Services have become more productive**

Revolution in ICT technologies has made services more productive. In some countries, the real sources of growth in output per worker have emerged in services than manufacturing. Table 1 shows output per worker and total factor productivity growth sectorally over the period of 19902006 for India and China. Both countries have experienced relatively high TFP growth in services. However, India‘s growth experience has been leveraged by service TFP growth whereas China‘s in Industry. The two countries have taken different growth paths, and India‘s growth experience has shown that over 7 percent annual growth can be achieved by a developing country on the foundations of service productivity growth. With lack of disaggregated data on employment by sectors for developing countries, we plot the service output per worker for selected advanced economies in Figure 4 which suggests a rising trend.

**Table 1. Sources of Growth in Output per Worker in India and China: Total Economy and Major Sectors, 1990-2006,(average annual percentage change).**

||Total|Economy|Agri|culture|Ind|ustry|Ser<br>|vices|
|---|---|---|---|---|---|---|---|---|
||1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|
|**India +**|||||||||
|Output Per Worker|4.1|4.5|1.3|1.4|3.3|2.7|4.9|4.6|
|TFP|1.8|2.1|0.7|0.9|0.6|1.6|3.1|1.9|
|**China***|||||||||
|Output per Worker|8.9|9.0|4.6|4.2|10.2|10.4|4.7|6.0|
|TFP|4.5|4.4|1.8|2.2|6.9|6.4|1.1|1.8|

_Source_ : + India from Bosworth, Maertens (2010). * China from Bosworth, Collins (2006). Note: Latest time period for China is 2004.

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**Figure 5. Services are gradually becoming tradable (Service exports as a share of service value addition, 1980 - 2007)**

<!-- Start of picture text --> 11<br>10<br>9<br>8<br>7<br>6<br>World Developing countries Advanced countries<br>5<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations.

**Figure 6. Index of goods exports/industry value added and service exports/service value added**

<!-- Start of picture text -->
1.3<br>1.25<br>1.2<br>1.15<br>1.1<br>1.05<br>1<br>Goods Services<br>0.95<br>2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text -->

_Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations Note: 2000 is indexed to 1.

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# **3.4. Evolution of services from traditional to modern**

The 3T‘s have changed the very nature of services from traditional to modern. These modern services (such as financial services, information processing services etc.), which are tradable and impersonal, take advantage of ICT, globalization, and scale of economies; and benefit from higher productivity growth. Also, there is ample scope for traditional service (such as tourism, education, musicians, entertainment production media etc. which require face to face contact) to absorb productivity gains, knowledge spillovers, tradability and fragmentation from ICT. In the absence of disaggregated national accounts data on services, we use the IMF Balance of Payments service exports data to classify service exports into modern and traditional service exports. Figure 7 uses available categories in Balance of Payments and shows that modern services are growing faster after the turn of the century. Figure A2 confirms that measurable modern services are growing faster than traditional service exports only after 2000.<sup>6</sup>

**Figure 7. Average annual growth in Modern and Traditional Services, World**

<!-- Start of picture text --> 20<br>18<br>1990-99 2000-08<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Modern Traditional<br><!-- End of picture text --> _Source_ : Balance of Payments, IMF, 2010 and authors‘ calculations.

Notes: Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational services.

> 6 Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational services.

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# **4. Data and Construction of Service Export Sophistication**

We build upon the HHR framework, which uses a good export sophistication measure _Goods EXPY_ as a proxy for the most productive set of goods the country can produce at a given time. They argue that export data is the best way to reveal this production frontier as we can expect countries to export those goods in which it is most productive. Along the same reasoning and as described below we will construct a _Service EXPY_ to proxy the service production frontier of a country, by and large following the methodology developed by HHR.

We start by constructing so-called _PRODY_ ‘s for each category of goods exports, reflecting the income/productivity level associated with each good, and we do the same for each category of services. _PRODYj_ is the income value associated with the service _j_ , and is constructed by using the service export ( _x_ ) share of a country _i_ in world‘s export of service _j_ , divided by the sum of shares of _j_ in world exports of _j_ across all countries exporting that service.<sup>7</sup> These ratios are multiplied by the exporting countries‘ respective per capita income level ( _Y_ ) and the result is summed up across all countries. In other words, the _PRODY_ becomes the weighted average of per capita GDPs, where the weights represent the revealed comparative advantage in service _j_ for each country.<sup>8</sup> _PRODY_ s are constructed for each service category, for each year of available data, and are by construction the same for all countries.

_EXPY_ is then the weighted income value of services exported by a country, computed as the sum of _PRODY_ s using as weights the share of the particular service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics which has data available for over 190 countries from 1990 to 2007. Due to data being reported inconsistently on service exports, we end up with a sample of approximately 100 countries 1990 to 2007. GDP per capita data is taken from the World Bank Indicators database. Before describing the service EXPY we would like to discuss two of its characteristics that differ from preexisting goods index – the need for dynamic PRODYs and the high level of aggregation.

First, the _Goods EXPY_ were constructed using _static PRODY_ s (the _PRODY_ for each good is held constant at the average value 1999-2001). This means that any increase in _Goods EXPY_ measures a country‘s jump from a low _PRODY_ product to a high _PRODY_ product (that is the share of high _PRODY_ good in the export basket increased). However, in the case of _Service_

> _xij_ 7 _X i_ is hence the value-added share of commodity j in the country‘s overall export basket.

> 8 The rational for using revealed comparative advantages as weights is to control for country size when ranking the services.

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_EXPY_ we will use what we call _dynamic PRODY_ s, i.e. the _PRODY_ values of a certain service export may vary year to year. Hence, an increase i _n dynamic EXPY_ can be due to (i) an increase in the _PRODY_ of a service and/or (ii) an increase in the share of high _PRODY_ products in the export basket. This is important for two reasons. First, it gives us an opportunity to capture the effect of changes in PRODY on EXPY. We do not only want to measure the fact that countries are getting more and more engaged in higher value service export, but also that the service exports themselves are increasing in sophistication due to improvements in ICT and 3T‘s. In fact many richer and high-skilled countries are engaged in service exports boosting the _PRODY_ s of some service export categories.<sup>9</sup> Secondly, since the data is much less disaggregated for services, using _static PRODY_ fails to capture the movements to higher _PRODY_ service exports that are only sub-categories of the broader categories captured by the Balance of Payments data.

A second characteristic, and just mentioned, is that service export data is not collected in the same detailed manner as goods export data. This means we will end up with only ten broad categories of service exports. They are different enough to make the analysis interesting, but it of course limits the extent to which we can understand the mechanisms in play. For example, if a country – within the same service export category - move from a sub-category of services with presumed low _PRODY_ , to a sub-category of services with presumed high _PRODY_ that would not show up at all in the _static EXPY_ . As mentioned, the _dynamic EXPY_ would however capture this as it allows for the _PRODY_ s to change over time. Unfortunately though, even when comparing the _static EXPY_ and the _dynamic EXPY_ , due to the high aggregation of data it would be impossible to know if the increase in the _dynamic EXPY_ was created by a higher share of high _PRODY_ services in the basket or an increase in the _PRODY_ s themselves as the _static EXPY_ fails to capture changes in the subcategories of the export basket.

Table A3 shows that not only the mean _service EXPY_ has increased over the years but also the standard deviation of _service EXPY_ , i.e. countries are becoming increasingly diverse in their value of _service EXPY_ , This indicates that the potential _service EXPY_ , proxy for the ―potential service production frontier‖, has increased and some countries have started to benefit from this higher potential.

The _PRODY_ s for each service category over time is presented in Table 2. The left column present the traditional services (transportation, travel, communication and construction), while the right column present the modern services (insurance, financial, computer & information, royalties & license fees, other business services, and personal, cultural & recreational services). In general, the _PRODY_ of the modern services are higher and their growth has been stronger, despite the higher initial levels. Our focus herein is on commercial service exports; therefore we do not include government services in our sample of measurement of _Service EXPY_ (see Annex for a more details description of what is measured within each category).

Finally we would like to highlight limitations to the interpretation of service exports and _Service EXPY_ due to the way data is registered. First, the level of aggregation due to broad categories already mentioned is sometimes exacerbated by the further aggregation in what the countries

> 9 One potential problem with the _dynamic PRODY_ is that as developing countries starts getting engaged in the high _PRODY_ services, the _PRODY_ s themselves decreases due to the lower income of developing countries. However, at this point, the share of service exports of high _PRODY_ value from developing countries is still too small to have an overall impact. The impact of a high _PRODY_ service in the export basket of a small country can be significant for that country‘s _EXPY_ , but the high _PRODY_ export from that small country will not have an impact on the overall _PRODY_ of that service, in the same sense that a small firm is a price taker in a large market.

13
report. Secondly, even though there have been no productivity improvements in a particular service that a country export, they may still register and increased _PRODY_ for that export if the broader category it belongs to have made productivity improvements.<sup>10</sup>

**Table 2. Global PRODY for service exports**

|**Global PRODY by**<br>Transportation|**sub-service**<br>Travel|**s for the World**<br>Communication|Construction|Insurance|Financial|Computer &<br>Information|Royalties &<br>License Fees<br>|Other business<br>services|Personal,<br>cultural and<br>recreational<br>services|
|---|---|---|---|---|---|---|---|---|---|
|1990<br>7420|7372|7343|13073|6906|20649|16414|10040|7456|18054|
|1991<br>7936|7489|7307|16415|6956|17560|16073|7424|7342|16303|
|1992<br>7981|7193|6801|17275|7817|19216|14403|8656|7648|12058|
|1993<br>8164|7311|4934|14453|7823|17603|14644|8999|7856|6394|
|1994<br>8255|7542|5241|13066|8278|14871|11120|9407|8299|8695|
|1995<br>9214|7691|5937|12780|11222|21643|16844|17054|8698|11607|
|1996<br>9135|7828|6255|12840|11448|22384|17131|15034|8920|12783|
|1997<br>9263|7943|6174|8980|11498|23841|18015|11576|9107|13953|
|1998<br>9800|7684|6346|10104|11201|22416|22789|12153|9710|12344|
|1999<br>10319|7951|6503|10210|11075|23612|22433|14409|10013|13216|
|2000<br>10516|8082|6553|10422|10856|24409|19852|14973|10763|14282|
|2001<br>11213|8408|6552|9695|10496|25160|18058|15318|11132|14613|
|2002<br>11559|8696|6467|9185|13842|24862|17906|14828|11812|13442|
|2003<br>11870|8769|6926|8232|15187|22486|17961|14670|12725|13615|
|2004<br>12656|9086|7932|9494|14894|23871|19150|12611|13175|14491|
|2005<br>12575|9312|9205|11402|15725|25444|19226|14610|13940|15374|
|2006<br>12779|9653|9522|12691|14316|28546|19090|12488|15557|16490|
|2007<br>12751|9986|7518|12593|13721|31167|19129|18158|16191|16192|
|Mean<br>10,189|8,222|6,862|11,828|11,292|22,763|17,791|12,912|10,575|13,550|
|_Source_: Authors‘ calculatio<br>Standard Deviation<br>1,870|ns.<br>846|1,172|2,541|2,885|3,925|2,770|3,047|2,784|2,792|

_Source_ : Authors‘ calculations.

Our aim is to highlight these issues of measurement as the next generation of growth is being fueled by services and academics and policymakers should to be aware of these issues when they think about new strategies for economic development. While we acknowledge that the data on service exports is far from perfect, we would still argue that it is good enough to get a first picture of service export sophistication dynamics and its relation to growth, which is where we turn our attention now.

Figure 8 shows the evolution of _Service EXPY_ over time for selected countries. The following two figures show log _Service EXPY_ against log GDP per capita. The relationship is positive. In order to look at the evolution of service export sophistication over time, we contrast the level correlation in 1992 and 2007 in Figures 9 and 10 respectively. For both time periods, the scales have been kept constant as we want to bring the attention to the shifting paradigm of service exports globally. India and China started at similar income levels in 1992 but India jumped up by 2007. In accordance to Kaldor‘s law, as the world gets richer, services become an important driver of the global economy. In Figure 11 we show S _ervice EXPY_ against _Goods EXPY_ . The

> 10 For example, India‘s export of computer information services from 1990 till later in 1990‘s was 0. With the ICT boom, India‘s share of Computer & Information services rose to 0.43. This is almost half of Indian service exports. With PRODY for Computer & Information services (CIS) rising faster, a higher share of exporting CIS resulted in India‘s Service EXPY to match that of the UK by 2007 even though its income per capita was substantially lower.  Another example is Saudi Arabia. The PRODY of the category other business services (OBS), which include management consulting, legal services, accounting, auditing and other professional and miscellaneous services is relatively high.  Saudi Arabia has reported all of its services exports since 1990 to 2007 under OBS hence its share is 1. As the Global PRODY increased for OBS, Saudi Arabia‘s Service EXPY also rose and by 2007 its EXPY was at par with the United States.

14
chart draws attention to relative low variance in _Service EXPY_ versus _Goods EXPY_ . In the next section, we discuss our empirical methodology for testing the relation between growth and S _ervice EXPY_ .

**Figure 8. Service EXPY for selected countries**

<!-- Start of picture text --> 18000<br>Brazil China<br>16000 India Ireland<br>Singapore Sri Lanka<br>14000<br>United States Indonesia<br>12000 Paraguay<br>10000<br>8000<br>6000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source_ : Authors‘ calculations.

15
# **Figure 9. Commercial Service EXPY against GDP per capita, 1993-95**

**Figure 10. Commercial Service EXPY against GDP per capita, 2005-07**

_Source_ : Authors‘ calculation. Note: Color shows details about the region code, size of bubble shows population size.

16
**Figure 11. Service Export Sophistication & Goods Export Sophistication**

<!-- Start of picture text --> LUX<br>IRL<br>IND GBRCHE<br>MLI CIV BEN GHA GABPNG TZACMRJAMGUYBWABLZMNGECUARMHNDLSOGMBCOGPERPRYCHLGTMLKAALBSENSLEMUSMARKGZSLVPAN ISLTUN COLJORMOZ ARGBGRAUSEGYZAFROMGRCLVARUSBRAURYTURVENLTUBOLIDNHRV CRICYPESTNORPRTPOLITATHACHNESPMEXSVNNLDAUTFRAPHLUSAMYSHUNSWESAUKORFINJPNSGP<br>SDN NAMKEN<br>NIC<br>NPL<br>PAK<br>RWA<br>BGD<br>BDI<br>45°<br>8 8.5 9 9.5 10<br>ln Goods EXPY 2005<br>10<br>9.5<br>9<br>8.5<br>8<br>7.5<br><!-- End of picture text --> _Source_ : Authors‘ calculations.

# **5. Empirical Methodology and Results**

We now turn to the analysis of the relationship between the ―dynamic commercial service EXPY‖, referred to as _Service EXPY_ ( _sEXPY_ ) and constructed as described in the previous section, and economic growth. We first present the results of the cross sectional analysis followed by various dynamic panel estimation techniques to capture the time series dimensions in the evolution of service export sophistication and its impact on growth. Since we are interested in investigating the empirical validity of a phenomenon that started in the mid nineties and its nature is changing rapidly, we choose three year panel for our analysis. The data includes up to 103 countries over the time span of 1990-2007. Choice of the time period is guided by two considerations- first, the consistent reporting of service exports in the Balance of Payments only started in the late 1980‘s and second, we are interested in capturing a phenomenon which gained momentum only in the 90s. We control for the standard determinants of growth in our reduced form regressions and include initial level of income per capita to control for the convergence effects.

The choice of explanatory variables in these growth regressions is never straightforward. A survey of empirical methodology by Durlauf et al (2005) acknowledged 43 distinct growth 17
theories and at least 145 proposed regressors as proxies. We turn to the standard growth literature to guide us in this regard and focus on a subset of well accepted growth determinants in our analysis (see annex tables and data description for details).

The baseline empirical growth model consists, in addition to the variable of interest, of four determinants of economic growth - initial income level, rates of physical and human capital accumulation, trade openness and institutional quality. We use data on income per capita from WDI. To account for differences in human capital accumulation we use latest available data on years of schooling from Barro and Lee (2010).<sup>11</sup> We use M2 (as percent of GDP) as a proxy for financial development from WDI. Finally, we use a measure of Rule of Law from World Governance indicators.<sup>12</sup> We use dynamic panel regressions based on three year panel data for each country. The regression specification is the following: where is the natural logarithm of GDP per capita, is the level of GDP per capita at the beginning of each three year period, is the dynamic service export sophistication measure, is the set of relevant control variables for growth determinants, represents time dummies, stands for country fixed effects and is the error term.

Results of fixed effect and System GMM regression for three year periods are presented in Table 3.<sup>13</sup> The convergence effect, human capital, and service EXPY seem to matter for growth only in the last time period (2005-07). We add various controls for growth to our baseline regression for the period. Introduction of the controls does not change the significance of the co-efficient on _SE_ .<sup>14</sup>

> 11 Neoclassical growth model, since the seminal work of Lucas (1988), the concept of capital since is usually broadened from physical capital to include human capital.

> 12 For robustness checks we also tried institutionalized democracy measure from Integrated Network on Social Conflict Research which has a value between 0 and 10. This is a composite index which includes information on degree of participatory democracy, the existence of institutionalized constraints on the exercise of power by the executive, and other aspects of plural democracy, such as the rule of law, systems of checks and balances, and freedom of the press. Our results do not change.

> 13 By using shorter time spans we are left with a sample of approximately 100 countries by 2007. We have excluded few countries where the data on service exports was reported haphazardly for a limited time period; hence we only keep reliable data for panel estimations. 14 Initial, lagged levels are used to address potential endogeniety problems (see for example Barro and Lee (1994))

18
# **Table 3. Service EXPY and growth: 3 year panel, fixed effect and System GMM, 1990-2007**

(dependent variable – GDP per capita growth)

||FE<br>|||||System G<br>|MM<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|log lag initial GDP/cap|-0.03***|-0.03***|-0.04***|-0.04***|-0.04***|0.002|0.004|0.008|0.01|0.007|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|**log lag initial service EXPY**|**0.05*****|**0.04*****|**0.04*** **|**0.03*** **|**0.03*****|**0.06*****|**0.05*** **|**0.07*****|**0.07****|**0.08****|
||**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.01)**|**(0.02)**|**(0.03)**|**(0.04)**|
|log human capital||0.05***|0.03|0.01|0.01||-0.005|-0.01|-0.02|-0.02|
|||(0.02)|(0.02)|(0.02)|(0.02)||(0.07)|(0.06)|(0.05)|(0.04)|
|log financial development|||0.02***|0.01*|0.01*|||-0.004|-0.008|-0.01|
||||(0.008)|(0.008)|(0.008)|||(0.02)|(0.03)|(0.03)|
|Trade (% of GDP)||||0.04***|0.04***||||-0.002|-0.004|
|||||(0.01)|(0.01)||||(0.01)|(0.01)|
|Rule of Law|||||-0.006|||||0.01|
||||||(0.007)|||||(0.03)|
|Constant|-0.23***|-0.15*|-0.18*|-0.22**|-0.22*|-0.58***|-0.49***|-0.71***|-0.75**|* -0.78***|
||(0.08)|(0.09)|(0.10)|(0.10)|(0.10)|(0.16)|(0.16)|(0.20)|(0.25)|(0.21)|
|R-squared|0.08|0.09|0.14|0.20|0.20||||||
|observations|428|421|375|370|370|428|421|375|370|370|
|Countries|108|106|95|95|95|108|106|95|95|95|
|Rho|0.80|0.71|0.81|0.86|0.87||||||
|Hansen J-statistic|||||||||||
|(p) value||||||0.24|0.32|0.49|0.38|0.23|
|Second-order serial|||||||||||
|correlation (p-value)||||||0.002|0.002|0.001|0.002|0.002|
|Note: All equations include<br>Number of Instruments|period dum|mies. Fi|xed effe|cts inclu|de dum|mies for c<br>7|ountries<br>10|. GMM<br>10|is the<br>10|Blundell-<br>12|

<mark>Note: All equations include period dummies. Fixed effects include dummies for countries. GMM is the Blundell-</mark> Bond System-GMM estimator using lagged growth rates and levels as instruments. The GMM estimation also uses log population and log area as additional instruments. * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. Robust t-statistics are in parentheses.

As noted earlier a key constraint of the fixed effects regressions is that growth and export sophistication may be endogenous.<sup>15</sup> However, the results in the second panel of Table 3 show that dynamic _Service EXPY_ matter for growth even when we control for endogeneity using a version of the Blundell-Bond system GMM estimator.

Table 4 presents the results of fixed effect and System GMM with robustness checks. First, we control for the size of the domestic service sector in the economy. Results are shown in columns (1-2) of Table 4. As evident, even after controlling for the size of the domestic service sector, _Service EXPY_ continues to have a significant and positive association with economic growth. Second, we control for the sophistication of goods export (in column 3-4 of Table 4). Again, our

> 15 This is a key concern as witnessed in standard OLS regressions. The system GMM addresses this issue.

19
results go through.<sup>16</sup> One of the orthodoxy in service led growth is that it is an India specific story. Therefore, in column 5-6 of Table 4, we drop India, and as the results holds we can confirm that there are other sophisticated service exporters driving the results. Moreover, after controlling for initial income per capita, skills, financial development, polity, time invariant factors, export sophistication in services is a good indicator of growth performance globally.

In order to further exploit the time varying effect of sophistication in service exports and growth, we repeat the earlier exercise for the time period 1999-2007. Results are shown in Table 5. The coefficient on _sEXPY_ is significant under all specifications and the size of the coefficient is by in large bigger.<sup>17</sup> The fixed effect panel estimation controls for time-invariant country specific characteristics and influence out the impact of _Service EXPY_ within countries. The coefficient on _Service EXPY_ in the first panel is positive and statistically significant, implying that higher dynamic export sophistication is associated with higher growth in GDP per capita. Adding growth determinants (in Panel I and Panel II) does not take away the explanatory power of _Service EXPY_ , and it remains positively related to GDP per capita growth.<sup>18</sup> We are in particular interested in whether service exports sophistication can provide an additional route to high economic growth in low-income countries. Therefore, in Table 6 we conduct sensitivity test by restricting our sample of countries to low and middle income countries (with income per capita in PPP terms below $10,000). The results are presented in Table 6. As previously, we use the above progression of adding growth determinants and robustness check controls. The results from fixed effect and System GMM show that _sEXPY_ remains positively associated with growth. Again, controlling for initial _Goods EXPY_ and the size of domestic service sector does not change the results. The regression results confirm the dynamism in export quality of services, and provide a statistical indication of the positive relation between service quality of exports and growth.

> 16 The System GMM but not our fixed effect in our specification predicts goods EXPY to be non-significant.Tthis result has also been documented in other studies (see Weiss, 2010 ). In our case, this could also exist due to the high multicollinearity between service sophistication and goods sophistication.

> 17 We further try unconventional growth regression using annual panel data estimations to have a bigger sample and understand the evolution of service EXPY and growth. We conduct sensitivity tests by restricting the sample to period after 1998, 1999 and so on. As the period gets restricted to more and more recent years, the coefficient on Service EXPY becomes bigger (Results not shown in the paper).

> 18 Since the trade statistics in services do not capture the inputs and processes under services, at best our estimation is on the lower side. If these were included we believe that the growth elasticity of service export sophistication could be substantially higher.

20
# **Table 4. Service EXPY and growth: 3 year panel data regressions, 1990-2007**

**(Controlling for Size of Service sector and Goods export sophistication)** (dependent variable – GDP per capita growth)

||FE|System GMM|FE|System GMM|FE|System GMM|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|log lag initial GDP/cap|-0.04***|0.009|-0.04***|-0.05***|-0.05***|-0.05***|
||(0.01)|(0.04)|(0.01)|(0.01)|(0.01)|(0.02)|
|**log lag initial service EXPY**|**0.03*****|**0.10***|**0.03****|**0.05***|**0.03*****|**0.06****|
||**(0.01)**|**(0.05)**|**(0.01)**|**(0.03)**|**(0.01)**|**(0.03)**|
|log human capital|0.0003|-0.02|-0.01|0.02|-0.02|0.03|
||(0.02)|(0.06)|(0.02)|(0.03)|(0.02)|(0.03)|
|log financial development|0.01**|-0.01|0.01|0.03|0.01|0.03*|
||(0.009)|(0.04)|(0.008)|(0.01)|(0.009)|(0.01)|
|Trade (% of GDP)|0.04***|-0.006|0.04***|0.01|0.04***|0.007|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Rule of Law|-0.009|0.02|-0.006|0.03***|-0.01|0.04***|
||(0.007)|(0.04)|(0.007)|(0.01)|(0.008)|(0.01)|
|log service value added (% of GDP)|0.02*|-0.06|0.02*|0.002|0.02*|-0.009|
||(0.01)|(0.10)|(0.009)|(0.05)|(0.01)|(0.05)|
|log lag initial goods EXPY|||0.02**|0.009|0.01**|0.007|
||||(0.009)|(0.01)|(0.009)|(0.01)|
|Drop India|No|No|No|No|Yes|Yes|
|Constant|-0.27**|-0.66|-0.28***|-0.38|-0.36|-0.38|
||(0.12)|(0.46)|(0.11)|(0.26)|(0.13)|(0.27)|
|R-squared|0.21||0.22||0.24||
|observations|357|357|362|349|345|345|
|Countries|93|93|95|93|92|92|
|Rho|0.90||0.90||0.92||
|Hansen J-statistic|||||||
|(p) value||0.54||0.40||0.27|
|Second-order serial|||||||
|correlation (p-value)||0.02||0.004||0.005|
|Note: All equations include period<br>Number of Instruments|dummies.|Fixed effects in<br>12|clude dumm|ies for countrie<br>38|s. GMM is|the Blundell-<br>38|

<mark>Note: All equations include period dummies. Fixed effects include dummies for countries. GMM is the Blundell-</mark> Bond System-GMM estimator using lagged growth rates and levels as instruments. The GMM estimation also uses log population and log area as additional instruments. * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. Robust t-statistics are given in parentheses.

21
|(12)<br>(13)<br>(14)|0.003<br>0.006<br>-0.07<br>(0.002)<br>(0.04)<br>(0.03)<br>**0.09****<br>**0.10***<br>**0.06***<br>**(0.04)**<br>**(0.05)**<br>**(0.03)**<br>-0.01<br>-0.01<br>0.06<br>(0.04)<br>(0.06)<br>(0.05)<br>-0.01<br>-0.01<br>0.03<br>(0.03)<br>(0.04)<br>(0.02)<br>-0.009<br>-0.01<br>0.006<br>(0.01)<br>(0.01)<br>(0.01)<br>0.01<br>0.02<br>0.05**<br>(0.03)<br>(0.04)<br>(0.02)<br>-0.07<br>0.03<br>(0.10)<br>(0.06)<br>0.005<br>(0.02)<br>* -0.87***<br>-0.64<br>-0.38<br>(0.22)<br>(0.44)<br>(0.31)<br>277<br>267<br>263<br>94<br>92<br>92<br>0.26<br>0.58<br>0.275<br>0.002<br>0.02<br>0.007<br>12<br>12<br>38<br>and levels as log population<br>as additional instruments.|
|---|---|
|(10)<br>(11)<br>**nd System GMM**|-0.001<br>0.01<br>(0.02)<br>(0.02)<br> **0.06*****<br>**0.07****<br>**(0.01)**<br>**(0.03)**<br>0.006<br>-0.02<br>(0.05)<br>(0.05)<br>0.003<br>-0.008<br>(0.02)<br>(0.03)<br>-0.007<br>(0.01)<br>* -0.63*** -0.75**<br>(0.18)<br>(0.25)<br>281<br>277<br>95<br>94<br>0.40<br>0.40<br>0.002<br>0.002<br>10<br>10<br>g lagged growth rates<br>pulation and log area|
|System GMM<br>(8)<br>(9)<br>**year panel fixed effect a**<br>**07)**<br>P per capita growth)|-0.007<br>-0.003<br>(0.01)<br>(0.02)<br>**0.07*****<br>**0.05*****<br>**(0.01)**<br>**(0.01)**<br>0.01<br>(0.06)<br>-0.67***<br>-0.47**<br>(0.16)<br>(0.150<br>320<br>315<br>108<br>106<br>0.37<br>0.319<br>0.002<br>0.002<br>7<br>10<br>tem-GMM estimator usin<br>stimation also uses log po<br>s.|
|(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>**Table 5. Dynamic service EXPY and growth: 3**<br>**(1999-20**<br>(dependent variable – GD|-0.06***<br>-0.06*** -0.06*** -0.06*** -0.06*** -0.07***<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>**0.07*****<br>**0.08*** 0.06*** 0.06*** 0.06*** 0.07*****<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>0.08***<br>0.08*** 0.05*<br>0.05*<br>0.04<br>0.02<br>(0.03)<br>(0.03)<br>(0.03)<br>(0.03)<br>(0.03)<br>(0.03)<br>0.008<br>0.006<br>0.005<br>0.008<br>0.005<br>(0.009)<br>(0.009)<br>(0.009)<br>(0.01)<br>(0.01)<br>0.05*** 0.05*** 0.05*** 0.05***<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>0.004*** 0.003<br>0.002<br>(0.009)<br>(0.009)<br>(0.009)<br>0.002<br>0.005<br>(0.01)<br>(0.01)<br>0.01<br>(0.009)<br>-0.32***<br>-0.40*** -0.43*** -0.43<br>-0.42*** -0.51***<br>(0.11)<br>(0.12)<br>(0.11)<br>(0.11)<br>(0.14)<br>(0.14)<br>0.24<br>0.27<br>0.35<br>0.35<br>0.35<br>0.38<br>315<br>281<br>277<br>277<br>367<br>263<br>106<br>95<br>94<br>94<br>92<br>92<br>0.92<br>0.91<br>0.93<br>0.93<br>0.93<br>0.95<br>ects include dummies for countries. GMM is the Blundell-Bond Sys<br>10% level ** Significant at 5% level *** instruments. The GMM e<br>* Significant at 1% level. Robust t-statistics are given in parenthese|
|FE<br>(1)|-0.06***<br>(0.01)<br>**0.10*****<br>**(0.01)**<br>log financial development<br>Trade (% of GDP)<br>Rule of Law<br>log service value added (% of GDP)<br>log lag initial goods EXPY<br>-0.41***<br>(0.10)<br>R-squared<br>0.21<br>320<br>Countries<br>108<br>Rho<br>0.95<br>Number of Instruments<br>Note: All equations include period dummies. Fixed eff<br>and log area as additional instruments. * Significant at<br>* Significant at 10% level ** Significant at 5% level **<br>(p) value<br>Second-order serial<br>correlation (p-value)<br>log lag initial GDP/cap<br>**log lag initial service EXPY**<br>log human capital<br>Constant<br>observations<br>Hansen J-statistic|

22
|(14)<br>(15)<br>0.02<br>-0.03**<br>(0.10)<br>(0.01)<br>**0.21****<br>**0.05***<br>**(0.09)**<br>**(0.03)**<br>-0.01<br>0.02<br>(0.11)<br>(0.02)<br>-0.09<br>0.01<br>(0.07)<br>(0.01)<br>-0.04<br>0.004<br>(0.04)<br>(0.01)<br>0.01<br>0.01<br>(0.07)<br>(0.01)<br>0.17<br>0.05<br>(0.18)<br>(0.03)<br>0.01<br>(0.01)<br>*<br>-2.27***<br>-0.69***<br>(0.83)<br>(0.22)<br>221<br>213<br>71<br>70<br>0.88<br>0.13<br>0.05<br>0.04<br>12<br>38<br>as log population<br>nal instruments.|
|---|
|(13)<br>0.10<br>(0.10)<br>*** 0.17**<br>**(0.11)**<br>-0.10<br>(0.13)<br>-0.07<br>(0.06)<br>-0.03<br>(0.04)<br>0.006<br>(0.08)<br>** -1.98**<br>(0.65)<br>224<br>71<br>0.77<br>0.07<br>12<br>s and levels<br>as additio<br>|
|(12)<br>0.07<br>(0.06)<br>*****<br>**0.16****<br>**(0.05)**<br>-0.04<br>(0.11)<br>-0.05<br>(0.04)<br>-0.02<br>(0.02)<br>** -1.73*<br>(0.51)<br>224<br>71<br>0.56<br>0.07<br>10<br>growth rate<br>nd log area<br>**em GMM**|
|(11)<br>0.003<br>(0.04)<br><br>**0.08****<br>**(0.03)**<br>0.04<br>(0.07)<br>0.003<br>(0.02)<br>* -0.92*<br>(0.43)<br>228<br>72<br>0.07<br>0.05<br>10<br>g lagged<br>pulation a<br>**and Syst**<br>**,000)**|
|GMM<br>(10)<br>0.008<br>(0.03)<br>**0.09*****<br>**(0.02)**<br>0.03<br>(0.05)<br><br>-0.97**<br>(0.25)<br>228<br>72<br>0.05<br>0.03<br>10<br>timator usin<br>uses log po<br>**xed effect**<br>**ow US $10**<br>rowth)|
|System<br>(9)<br>0.01<br>(0.01)<br>**0.08*****<br>**(0.02)**<br>-0.87***<br>(0.26)<br>233<br>74<br>0.07<br>0.02<br>7<br>em-GMM es<br>timation also<br>.<br>**ear panel fi**<br>**r capita bel**<br>per capita g|
|(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>-0.007<br>-0.01<br>-0.01<br>-0.02<br>-0.02<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br><br>**0.03*** 0.03*****<br>**0.03*** 0.03*****<br>**0.04*****<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>**(0.01)**<br>-0.007<br>-0.01<br>-0.01<br>-0.01<br>-0.02<br>(0.02)<br>(0.02)<br>(0.02)<br>(0.02)<br>(0.02)<br>0.007<br>-0.001<br>-0.001<br>0.0001<br>-0.002<br>(0.009)<br>(0.009)<br>(0.009)<br>(0.009)<br>(0.01)<br>0.03***<br>0.03*** 0.03***<br>0.04***<br>(0.01)<br>(0.01)<br>(0.01)<br>(0.01)<br>-0.006<br>-0.006<br>-0.009<br>(0.007)<br>(0.007)<br>(0.008)<br>0.009<br>0.01<br>(0.01)<br>(0.01)<br>0.007<br>(0.008)<br>*<br>-0.28*** -0.30*** -0.30*** -0.31*** -0.40***<br>(0.11)<br>(0.11)<br>(0.11)<br>(0.11)<br>(0.12)<br>0.11<br>0.19<br>0.19<br>0.19<br>0.23<br>228<br>224<br>224<br>221<br>213<br>72<br>71<br>71<br>71<br>70<br>0.69<br>0.84<br>0.84<br>0.85<br>0.90<br>dummies for countries. GMM is the Blundell-Bond Syst<br>* Significant at 5% level *** instruments. The GMM es<br>nt at 1% level. Robust t-statistics are given in parentheses<br>**Table 6. Dynamic service EXPY and growth: 3 y**<br>**(Sample: Countries with income pe**<br>(dependent variable – GDP|
|(2)<br>-0.001<br>(0.01)<br>**0.03*****<br>**(0.01)**<br>-0.006<br>(0.02)<br><br>-0.32**<br>(0.10)<br>0.10<br>228<br>72<br>0.63<br>ects include<br>10% level *<br>* Significa|
|FE<br>(1)<br>-0.004<br>(0.01)<br>**0.03*****<br>**(0.01)**<br>-0.29***<br>(0.08)<br>0.10<br>233<br>74<br>0.62<br>ies. Fixed eff<br>Significant at<br>at 5% level **|
|log financial development<br>Trade (% of GDP)<br>Rule of Law<br>log service value added (% of GDP)<br>log lag initial goods EXPY<br>R-squared<br>Countries<br>Rho<br>Number of Instruments<br>Note: All equations include period dumm<br>and log area as additional instruments. *<br>* Significant at 10% level ** Significant<br>(p) value<br>Second-order serial<br>correlation (p-value)<br>log lag initial GDP/cap<br>**log lag initial service EXPY**<br>log human capital<br>Constant<br>observations<br>Hansen J-statistic|

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# **6. Concluding Remarks**

Technology has changed the very nature of the production frontier of services and in particular service exports, which has resulted in a rapid increase in the service exports and growing share of services in GDP growth. Our analysis suggests that increasing sophistication of services is positively related to growth. We specifically look at what countries export rather than how much and the results show that service exports sophistication is positively related to growth, even when controlling for a number of variables across different samples. Though we have not proved causality beyond doubt, there is an indication for a new channel for growth that may be of significance for developing countries, and especially countries that are trying to get out of the middle-income trap.

The distinctiveness of increasing service export sophistication and growth is twofold: i) traditional service activities gain in productivity from technology, transportability and tradability, and ii) there is a host of new service activities that have emerged (due to unbundling and new technological innovation). Thus, thinking about export led growth, diversification, the models of globalization and growth should include specialization and sophistication of services as well. Including services in growth considerations does not imply neglecting manufacturing exports and its benefits, just that services can be an additional channel for promoting high growth. Increasing sophistication in service exports has important implications for countries that are stuck in a ―middle-income trap,‖ such as Malaysia, Vietnam, etc., or for countries that wish to sustain their rapid growth, such as India, Sri Lanka, etc. Further, re-igniting growth in United States and G-20 economies will partially rely on innovation and competitiveness in sophisticated service exporting firms. It also offers a prospective route for growth in Africa. In the global rebalancing, service exports might be an imperative channel either through the lens of IndiaChina, South-South Trade, Advanced-Emerging trade clusters or possible new trade networks.

As outlined above, to have a better understanding of how service sophistication affects growth, we need a better and more disaggregated data on service exports, treating them at par with data on goods exports. Considering that service exports sophistication is positively associated with economic growth, our paper set out the future research agenda. What are the determinants of sophistication of service exports and how do they differ from the determinants of good exports sophistication? What are the dynamics in the product space of different service activities? What structural, institutional and economic policies in country-specific context create value added within service exports, foster service productivity growth and export performance, i.e., what are the channels through which service export sophistication affects growth? What is the relation between local trade restrictions and global trade agreements and export of sophisticated services? What are countries doing to prevent sophistication in service exports? Is this nature of service growth inclusive?

There are many examples, but the Indian experience has shown that even when approximately 1 percent of its population is engaged in high productivity service exports, it has contributed to overall high growth. Various factors may drive competitiveness amongst countries exporting services, driving specialization and sophistication in service exports. Countries should continue to build on specialization, but also extend this idea to sophistication in service activities as a potential route to economic growth.

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