Transforming Non-Renewable Resource Economies (NREs)
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 2 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: bbattaile@worldbank.org; smishra2@imf.org

This paper provides an empirical benchmarking of growth, productivity and export patterns for developing NREs against other low and middle income developing countries, to inform policy discussions and future analytical work. There is stark heterogeneity in the association of resource sector and overall growth outcomes, by commodity and degree of dependence. Over the long term, inter-sectoral growth dynamics have been more muted for NREs than other developing countries, especially at lower incomes. Despite productivity convergence in mining, as expected, productivity growth in manufacturing and services was generally lower in NREs. Exceptions are few, in East Asia and the CIS area which experienced broad-based productivity growth. NRE product exports are more concentrated and relatively less complex, though we find increasingly diversified service export baskets. Technological progress and specialization in trade in services may offer diversification options for the future.

Abstract

This paper provides an empirical benchmarking of growth, productivity and export patterns for developing NREs against other low and middle income developing countries, to inform policy discussions and future analytical work. There is stark heterogeneity in the association of resource sector and overall growth outcomes, by commodity and degree of dependence. Over the long term, inter-sectoral growth dynamics have been more muted for NREs than other developing countries, especially at lower incomes. Despite productivity convergence in mining, as expected, productivity growth in manufacturing and services was generally lower in NREs. Exceptions are few, in East Asia and the CIS area which experienced broad-based productivity growth. NRE product exports are more concentrated and relatively less complex, though we find increasingly diversified service export baskets. Technological progress and specialization in trade in services may offer diversification options for the future.

I. Introduction and Motivation

There has been renewed focus in the literature on the role of structural transformation in economic development and growth2. This is a particularly important issue for developing non-renewable resource economies (NREs) which face unique transformation challenges3. For example, resource sectors tend to be highly capital intensive and offer limited employment opportunities to accommodate workers exiting sectors with lower average productivity, such as agriculture and informal services. In addition, NREs can face significant Dutch disease effects, including solely from a shift in demand following a resource discovery.4 Policymakers thus often seek a more balanced growth model in NREs, aiming for resource rents to fuel productivity gains in the non-resource sectors.

Impressive NRE growth during resource-driven booms can mask deeper structural issues that are critical for long term development. The underlying sources of growth and structures of production are key to the sustainability and distribution of benefits from rising national incomes. This has driven a keen interest among NRE policymakers to explore ways to promote non-resource sectors of their economies, both for growth and volatility management reasons. Performance to date has been mixed on resource booms delivering the longer term structural change consistent with sustained development and higher per capita incomes. It is also important to note global diversification patterns, which vary by income levels. More rapid diversification spurts are linked with early stages of economic development (Cadot et al., 2013). Export diversification is associated with greater macroeconomic stability, through lower vulnerability to shocks and lower terms of trade volatility (Lederman and Maloney, 2012). Overall, diversification in Low Income Countries (LICs) shows an overall shift in resources from sectors where prices are highly volatile and correlated, such as mining and agriculture, to less volatile and correlated sectors, such as manufacturing, resulting in greater stability (Koren and Tenreyro, 2007). Thus it is imperative for NRE policymakers to know how the structural change in their economies compares to other countries. However, the economic narrative of transformation (or the lack thereof) in the structure of production of NREs remains scarce.

This paper addresses this gap by taking a cross-country empirical approach to benchmarking NREs against other countries along key growth-related dimensions. In the process, the paper utilizes new and existing data on value added, drivers of sectoral output per worker, and exports. The analysis decomposes the sectoral contributions to GDP, productivity, and trade growth over time in NREs. We also play special attention to the role of services in the structure of production in NREs, given there is often increased activity in the service sectors that accompanies resource booms. Recent empirical work on the dynamics of service sector growth has helped clarify the positive relationship between the service sector share of output and per capita income. Eichengreen and Gupta (2013) identify two waves of service sector growth in their sample of mostly industrialized countries—a first wave in countries with relatively low levels of per capita GDP and a second wave in countries with higher incomes. Thus, a key question for NREs is how sustainable any service sector growth is, and how it links to other sectors of the economy, especially if it is driven largely by consumption of resource rents versus a more sustainable move to more modern sectors.

The paper is structured as follows. Section 2 takes stock of sectoral drivers to growth over the past few decades in NREs compared to other countries at similar stages of development. Section 3 takes a more microeconomic approach to growth patterns by looking at differences in productivity across sectors. Section 4 documents relative performance in the competitiveness of product and service exports. We provide concluding comments in Section 5.

II. Growth Patterns in NREs

We begin by documenting resource-led growth and the changing structure of production over the last 30 years across NREs. This section sets out to answer three questions. What is meant by “resource-led” growth? How have sectoral contributions to GDP growth shifted over time in NREs, relative to other countries? What has been the role of the service sectors in changes to the structure of production?

Globally, the sources of GDP growth have shifted toward services, now accounting for a majority of growth for low, middle and high income groups of countries. Overall, there has been a shift in economic activity out of agriculture and manufacturing, and into the service sectors. There has been a marked increase in the average share of GDP growth derived from the service sectors, from two-thirds in the 1980s to nearly three-quarters in the 2000s.

Non-renewable resource economies largely escaped the worst of the global crisis, with significantly better aggregate growth performance than the rest of the world. Over the long term, average real growth for non-renewable resource economies is roughly the same as for other countries—just over 1.5 percent per annum over the last 50 years for oil-producers—though with significantly higher volatility.5 The latter point is driven by the movement in export prices these economies depend on. Recent growth outcomes since the global crisis have not been an exception to this overall pattern. Average real GDP growth has been considerably better for NREs, both before and after the crisis, as energy export prices remained buoyant after the short-lived collapse in 2009 (Figure 1). Fiscal and current account balances also initially fared much better. However, the recent decline in commodity prices starting in 2014 has exposed the vulnerabilities of NREs. Revenues have declined sharply, and most NREs are implementing expenditure reductions in light of expected continued sluggishness of commodity demand.

Figure 1.
Figure 1.

NREs Weathered Post Global Crisis Period Better Than Other Countries

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using World Development Indicators, World Bank, 2014.Notes: NREs include all countries reported in Annex Table 1.

A. Defining “Resource-Led” Growth

What is meant by “resource-led” growth? For an oil exporting country, a possible definition is an episode of positive GDP growth when the oil sector is growing faster than other sectors. However, the growth of the value of barrels produced may be too narrow a measure to capture the full extent of the impact of producing and selling the commodity. There are goods and services that support the oil sector, and the spending of resource rents drives other parts of the economy. These indirect channels of growth from resource sectors can be difficult to quantify, thus precise measurement of “resource-led” growth is problematic. In general, resource-led growth relates to the co-movement between aggregate economic growth and growth in a sizable resource sector.

Resource booms are highly unstable and differ by commodity. Figure 2 illustrates the heterogeneous experience of the value of non-renewable commodity exports over the past 30 years. This “heat map” shows the average annual growth rate of the value of commodity exports across all countries, ranging from above 50 percent growth in red to less than -50 percent in green. Differentiation by commodity is stark. Oil and copper export values have shown high rates of growth for the majority of the period, with relatively few contractions. Iron, minerals and mining have shown more modest, yet mostly positive, growth. In contrast, uranium and gold are exported in low volumes and exhibit more erratic export growth and contraction rates. Figure 2 also shows there has been an increase in average non-renewable commodity export growth since the 2000s, relative to the two previous decades. The data also clearly shows the nearly uniform contraction in the value of exports across commodities in 2009 as the global crisis affected trade across the world.

Figure 2.
Figure 2.

Non-Renewable Resource Export Growth by Commodity

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using WITS Database. Classification based on IMF (2012).

Resource reliance is volatile over time. The heterogeneity of “resource-led” growth experiences across NREs is shown in Annex 3 Figure I, where the commodity export data shown in Figure 2 is linked with GDP time series for 30 NREs with available data. In Panel A, each annual observation of non-renewable export growth and GDP growth is represented by a box, with the size of the box indicating GDP growth and the color of the box indicating export growth. Strong GDP growth performance is clearly seen for countries like Indonesia, Botswana, and Chile with relatively large boxes consistent over time. Strong episodes of “resource-led” growth episodes are captured by consecutive years of large and red boxes. Examples of such episodes in the 2000s include Zambia (copper), Bolivia (gas) and Azerbaijan (oil). Similar to Figure 2, the universal collapse in commodity exports in 2009 is starkly apparent, though with differing effects on GDP growth across countries. Panel B presents export growth (color) with the relative importance of the non-renewable commodity exports (size), proxied by the share of export value as a percentage of GDP. This allows us to differentiate the NREs, for example into countries where export revenues from non-renewable commodities are relatively modest, such as Mexico, versus very resource dependent countries such as Gabon, Angola, Nigeria and Libya. Annex 3 Figure 1 Panel B also shows this dependence can vary dramatically over time, such as boom years in the importance of gold in Liberia in the early 1990s or volatile oil booms in Turkmenistan in the 1990s.

B. Muted Sectoral Dynamics

How have sectoral contributions to GDP growth shifted over time in NREs, relative to other countries? This section considers this question using cross-country data and focusing on our sample of 40 NREs mentioned above.

Services have become the prime driver of growth. Figure 3 Panel A shows the disaggregation of value-added shares by decade since the 1980s for 122 developing countries, as well as a breakdown by income group. Consistent with the literature, the aggregate data show an overall shift in the sources of growth from agriculture to services, with manufacturing stagnant. This overall pattern generally holds across income groups, though the levels of agriculture (services) are lower (higher) as income increases. Dynamism appears positively related to income. Larger increases in the sectoral contribution of services to gross value added are found in middle income countries. Low income countries start from a lower base of service sector gross value added, and the increase is more muted between the 1980s and 1990s. However, the gains from the 1990s to 2000s are roughly equal for all income groups (about 2 percentage points). In the context of non-renewable resources, it is useful to disaggregate industry into manufacturing and non-manufacturing, with the latter including the production of resource sectors such as oil, gas and minerals. Non-manufacturing industry has increased in low income countries, while declining in lower and upper middle income countries.

Figure 3.
Figure 3.

Shift in Sectoral Shares, by NRE Income Group

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations World Development Indicators, World Bank, 2014.

Do these overall patterns hold for NREs? Figure 3 Panel B shows a contrasting picture for this group of countries. As expected, non-manufacturing industry contributes a much larger share to GDP (30 percent on average for 2000-10) than other countries (13 percent for the same period). Agriculture and services are accordingly smaller. More surprising is the lack of dynamism of sectoral contributions for NREs. For example, the contribution of services remains low, unchanged at 40 percent for the 2000s relative to the 1980s. The shares for other activities also remain surprisingly stagnant.

These dynamics differ across income groups of NREs. For resource-dependent countries, the shares of services and non-manufacturing industry rise with income. In addition, the long-term increase in services is larger for higher income countries. Services have even slightly contracted among low income resource-rich countries in favor of non-manufacturing industry. A more nuanced picture emerges when considering growth shares by income levels rather than the income groups. Annex 3 Figure II Panel A shows shifts in sectoral shares of GDP from the early 1990s to the most recently available data using the per capita log of GDP. The lower share of agriculture for richer NREs (downward sloping fit) is in line with the pattern for non-NREs (top panel of two charts). However, industry’s share of GDP (middle panel) is generally higher than non-NREs, while services (bottom panel) are lower on average. Decomposing industry provides a clearer picture of the trend in the middle panel given this includes most of the non-renewable resource extraction is included in industry. Annex 3 Figure II Panel C shows that focusing on only manufacturing activity within industry yields a different picture. The majority of NREs have lower shares of manufacturing than other countries at equal levels of income. Thus the aggregate result for industry in Figure 3 is driven by non-manufacturing industry (related to resource sectors).

C. Traditional Services Dominate

What has been the role of the service sectors in changes to the structure of production? Using more disaggregated data on services, wholesale and retail trade is the dominant subsector for NREs (Figure 4). This finding applies for both the 1990s and 2000s. In terms of income groups, wholesale and retail trade contributes the biggest share to services for low and lower-middle income countries. However, for upper-middle income countries the high-value group of financial intermediation / real estate / renting / business activities is the most dominant sector. It is important to note this analysis includes a restricted set of NREs given data constraints. Disaggregated data on a comprehensive set of services since the 1990s is available for only a limited set of NREs.6

Figure 4.
Figure 4.

Service Sector Value Added Shares, by Income Group for NREs and Non-NREs

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using World Development Indicators, World Bank, 2014.

The relative share of traditional services declined. An alternative disaggregation of services is provided in Eichengreen and Gupta (2013), breaking services into traditional, hybrid and modern7. Using a new dataset that allows the application of this typology for 22 NREs, traditional services contribute the majority of services in resource-dependent countries, regardless of income classification. Overall the share of traditional services decreased from 25 percent of total value added in the 1990s to 20 percent in 2000s. The traditional sector drove the contraction of service sector shares from the 1990s to 2000s. Traditional shares were the largest, and its decline was the biggest for lower-middle income countries.8

There is scope for growth to be driven by modern services for some NREs. A simple regression between service shares and income for the overall sample of 22 resource-dependent countries with detailed service data does not support the two-wave growth phenomenon. Looking further into specific services subsectors, there were no inflection points for significant increases in traditional and hybrid services. The share of modern services, on the other hand, is positively (and linearly) associated with income per capita. It has a quartic relationship with income per capita at a 10 percent level of significance. Changes in the shares of hybrid and modern services were also found to have a positive linear relationship with income per capita, suggesting that some NREs at higher levels of income have been able to promote growth in higher productivity services.

III. Labor Productivity Growth

Economic growth benefits from the accumulation of endowments that put more inputs to work in the economy, as well as productivity gains that enhance the ability to turn these inputs into outputs. The latter has been shown to explain cross-country variation in measures like income per worker. Helpman (2004) finds more than 60 percent of the differences in levels and 90 percent of the differences in the growth rate of income per worker explained by differences in productivity. Hence, there has been significant attention in development economics on productivity levels and differentials going back to the dual economy modeling of Lewis.

Developing countries are generally characterized by large productivity gaps between sectors of the economy, much larger than for advanced economies. These gaps are an indication of significant allocation inefficiencies across and within sectors. In this regard, the transfer of technologies, know-how, networks, and practices are critical to improve productivity and drive long-run growth. While there has been a global convergence of manufacturing and services productivity, the diffusion of productivity in Africa and Latin America appears to be slower (see MacMillan and Rodrik, 2011; Gelb et al, 2014). For NREs, the resource curse points to a lack of improving productivity in non-resource sectors. How do productivity levels and differentials in NREs benchmark against other countries? This section takes an in-depth look at this empirical question.9

There is heterogeneity in productivity performance across NREs at similar stages of development. Annex 3 Table I presents the aggregate source of GDP growth in NREs versus non-NREs at similar stages of development. The table is split by time periods. The contribution of capital stock to GDP growth remains the main source of NRE growth. Increasing labor utilization is also a source of productivity enhancement. Emerging market NREs are absorbing more human capital and labor based economic growth. While the residual TFP growth in Emerging Market countries (EMs) remains low, in LICs is comparable to other non-NRE LICs. Annex 3 Table II provides a summary of the contribution to labor productivity growth.

Some NREs have been able to succeed in productivity gains while others are stuck in low or negative productivity changes over time. Countries such as Indonesia, Kazakhstan, Russia, Laos, Mongolia, Vietnam, and Zambia over time show increased productivity growth in the overall economy (see Annex 3 Table III). While the contribution of human capital to productivity growth remains relatively low, capital deepening has played a more important role (see Annex 3 Table IV). The adoption of new technologies that have led to transformation and modernization of the economy has played a much greater role (see Annex 3 Table V).

For given levels of service sector labor productivity, NREs income levels are higher than expected, whereas considering industry productivity their income levels are lower than the global average. In order to benchmark aggregate productivity in NREs against other nations at similar stages of development, we plot Annex 3 Figure III. In Panel A, we plot (the log of) per capita income against (the log of) labor productivity in services and industry. We plot all possible years with available data between 1960 and 2013, and highlight the sample of NREs in red. The charts can be interpreted as predicting a country’s income level based on the observed productivity level; countries above the line have income levels higher than would be predicted by their sectoral productivity level (relative to all countries in the world at similar stages of development). Panel A shows that the income levels predicted by their aggregate service labor productivity level in NREs are slightly higher than expected. In other words, service labor productivity is lower in NREs than other countries at similar stages of development. In terms of overall industrial productivity, for most years and most countries, productivity in NREs would predict a slightly higher income level. The level of aggregate industrial productivity is slightly higher in NREs than other countries at similar stages of development.10

Dominant resource sectors lead higher capital intensity for the overall economy (which in turns leaves lower labor and labor compensation intensive reallocation). NREs are moving away from labor intensive growth to capital intensive growth, however from a much lower base. The wage share of income (much like the rest of world) is also declining in NREs. However, the trend in decline in wage share in NREs is occurring at much earlier stages than other developing countries (Annex 3 Figure IV). While the share of labor compensation in GDP has been declining for rest of the world, it is declining from much lower levels in NREs. Conversely, capital compensation in GDP continues to increase in NREs compared to other developing countries, even though starting from a more capital intensive base levels.

NREs in Asia and have witnessed comparably faster productivity growth across sectors. Whereas others in Sub-Saharan Africa, Latin America, and Middle East exhibit more concentrated sources of economic growth. Convergence will require eliminating the inter-sectoral productivity differences between these groups. The average annual productivity growth between 2000 and 2012 shows that the median agriculture productivity has growth of almost 4 percent in NREs; this is almost twice that of other developing countries. Similarly, manufacturing productivity has fared better than other developing countries (primarily led by Indonesia, Vietnam, and Russia).

Convergence in fast mining productivity growth across the world masks the catch up across sectors for other NREs. The analysis compares regional growth in sub-sectoral labor productivity in NREs with non-NREs for the period 2000-12 in Tables 1 and 2. In particular, we note that NRE productivity improvements in Eastern Europe and Central Asia have been at par with productivity improvements in the last decades for non-NREs in the same regions. Productivity growth in NREs in Latin America, the Middle East and North Africa, and Sub-Saharan Africa show more mixed signs. Agriculture, manufacturing, and high-end service productivity growth for NREs in the aforementioned regions has potential for faster catch ups.

Table 1.

Productivity Growth Across Regions

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Source: authors’ calculations based on IMF (2014).
Table 2.

Accounting for Productivity Growth Across Sectors in NREs, 2000-12

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Source: authors’ calculations based on IMF (2014).

Can improvements in service labor productivity drive gains in overall labor productivity for NREs and boost per capita GDP growth? To answer this question, we run a simple econometrics exercise. The analysis uses unbalanced panel data using fixed effect regression data spanning 1960-2013 for 98 countries, controlling for initial conditions. We regress the annual growth in industrial labor productivity, controlling for initial labor productivity in that country against the growth rate of service labor productivity. The overall trend between industrial and service labor productivity has a positive and statistically significant relationship. The coefficient elasticity is presented in Annex 3 Figure V. One unit of growth in service labor productivity yields over 0.5 percent increase in industrial labor productivity. This magnitude is slightly larger for NREs. Similarly, the second panel plots the elasticity by regions for growth in service labor productivity on per capita GDP growth. Again, we note that one unit of labor productivity growth in service for NREs yields output growth to increase by 0.25 percent, a magnitude that is higher for NREs than other economies at various stages of development.11 These back of the envelope calculations demonstrate that productivity gains in services have generally had a positive impact on overall economic and productivity growth in NREs.

IV. Export Transformation

Exports from NREs have increased since the early-1990s – both as a share of GDP and as a share of world exports (Figure 5). Not surprisingly this is largely driven by oil and mining based exports. The exports of goods from NREs constituted 5.6 percent of world goods exports in 1994, and subsequently grew to around 7 percent at the turn of the century and to almost 10 percent in 2013. Service exports from NREs account for around 4 percent of world service exports in 2013. Changing global consumer demand has been a key driver for this increase, as fuel-exporting economies have been able to increase the targeting of merchandise exports away from advanced economies and more toward emerging and developing markets (see Figure 6).12

Figure 5.
Figure 5.

NRE Exports of Goods and Services

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using WDI, World Bank, 2015.
Figure 6.
Figure 6.

Destination of Merchandise Exports from Fuel Exporting Countries

(% of total merchandise exports)

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using Direction of Trade Statistics, IMF (2014).

Trade in services has grown, though in contrast to other countries import demand has far outstripped the growth in exports. NRE service exports have grown marginally in world market. Service exports grew from 3.1 percent in 2000 to around 4 percent in world service exports market in 2013. However, in net terms there is a stark difference of NRE experience compared to other countries. Figure 7 shows net services, defined by service exports / service imports, for both country groups. On average, non-NREs exported significantly more services than they imported between 2000 and 2012. NREs, in contrast, showed the opposite pattern of importing relatively more services, perhaps driven by Dutch disease effects from the spending of resource rents.13 In a similar vein, Annex 3 Figure VII Panel A displays service exports in service value added (%) to gauge tradability of services. The world in general is experiencing a boom in exporting services (relative to services being created at home). However, consumption based services are growing faster in NREs.

Figure 7.
Figure 7.

Service Exports / Service Imports

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using BPM5, IMF 2014.Note: Both axis are service exports/service imports (%).

A. Composition and Diversification

Fast-growing developing economies have often transformed their exports toward a strong manufacturing base; comparatively, NREs have room to converge in manufacturing exports.14 Aggregate statistics can mask the scale and scope of transformation in NREs. Aggregate manufacturing exports from NREs seem to be growing at par with peer economies, given from a low base (Figure 8). The aggregate share of manufactured exports from specific NREs does not seem as starkly different from other fast growing economies (see Figure 9). Countries like Suriname, Niger, and Bahrain exhibit a relatively high share of manufacturing exports. However, more examination will illustrate that for some of these economies, the transformation to manufacturing is a residual of the statistical classification system. Therefore, in Figure 10 we aggregate NREs’ manufacturing exports by communities of products. The majority of manufactured exports are related to processed oil and other resource based exports.

Figure 8.
Figure 8.

Manufacturing Export Growth

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using WITS UN COMTRADE SITC Rev.3 three-digit level.
Figure 9.
Figure 9.

Share of Manufacturing in Merchandise Export Basket for NREs

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Figure 10.
Figure 10.

Composition of NREs Manufacturing Exports

(% of NRE Exports in manufacturing)

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using WITS UN COMTRADE SITC Rev.3 three-digit level.

More than 99 percent of NREs have not diversified into manufacturing. In order to obtain a more country specific view of reallocation over in NREs, manufacturing exports are presented in more detail in Annex 3 Figure VI. The chart compares the types of the overall merchandise export basket in 1990 and 2010. The color code differentiates primary or resource based exports (light and dark grey) from more technology-skill intensive exports. The chart highlights that the majority of exports, over 90 percent NREs, are primary and resource based. With the exception of a few cases like Mexico, Vietnam, and Indonesia, almost 99 percent of exports from NREs are primary or resource based in nature. This is in contrast to non-NRE s where the share of high-tech and medium-tech manufacturing exports in total manufacturing exports has increased globally (particularly for fast growing economies in Asia).15

Within NREs, there has been a limited range of diversification success. Successful transformers like Indonesia, Vietnam, and Mexico have done better than others, because the realized growth is driven by productive capacity. These are the relatively good performers. The notion of good and bad cases can be measured more clearly from product and services that are exported. Some countries like Ecuador, Chile, Bolivia, Iran, and Kazakhstan also show some shifts into manufactured products. Looking at evidence over the past two decades, the rest aren’t so lucky. The bad cases may be countries like Niger where almost all merchandise exports are uranium (for French nuclear plants), or oil in the case of Iraq.

More and more services can now be unbundled: a single service activity can be divided into tasks completed at different geographic locations. Adam Smith famously described how the productivity of a pin factory was boosted if, instead of one worker doing all the tasks involved in making a pin, a number of workers each specialized in particular tasks and then exchanged the fruits of their labor. A similar process of specialization and exchange is under way in many service industries. As with goods, services productivity can rise because of specialization (a finer division of labor) and scale (falling unit costs of production).

The unbundling of services has opened up niches that can be exploited by developing economies as well as advanced economies (see Loungani and Mishra, 2014; Mishra et al 2011). Though measuring services trade is difficult, it appears that developing economies’ share in world service exports increased from about 14 percent in 1990 to 25 percent in 2011. Even though it is measured from a much lower base, service export growth has exceeded that from advanced economies.

NREs can trade modern services; especially in fragile events, services are traded across borders. Growth in service exports has been fast in NREs. We plot the aggregate growth in exporting services for NREs with other EMs and LICs in Annex 3 Figure VII Panel B. Service exports have grown four times since 2000.16 We index the year 2000 to 100. In particular, high value digitally traded services are growing fast from NREs. Panel C plots the growth modern and traditional service exports from NREs. The primary source of aggregate growth in NREs service exports has been led by growth in traded information, business, marketing and financial services.17

Next, we benchmark export diversification in NREs against other countries. Annex 3 Figure VIII Panel A ranks countries concentration of their merchandise exports, using the Herfindahl index based on UNCTAD SITC Rev. 3 merchandise exports. The color codes identify the NREs against other countries. A lower number implies higher diversification; higher numbers signify relatively concentrated exports. A strong majority of NREs have more concentrated merchandise exports than the world average for 2007-12, with 10 of the top 12 most concentrated export baskets in the world. Turning to services, the story is more mixed. Panel B plots a similar ranking for service export diversification. Sample coverage is more limited, and the results show both highly concentrated NREs as well as NREs with relative diversification in service exports, particularly the larger countries.

B. Quality and Complexity

Quality management and quality assurance is critical for firms to be successful in the global market. Moreover, diversification is important to create new opportunities to upgrade (see Henn, Papageorgiou, and Spatafora, 2013).18

There are pockets of high quality products from the relatively developed NREs, but overall stagnant product quality improvements. Annex 3 Figure IX Panel A displays the quality of product exports from NRE sample at the 4-digit level in 2010 with other low and middle income countries. Panel A shows the specific products where NREs have quality close to world frontier. For each product line, the dot represents the low to high bounds of export quality frontier from the group of NREs or non-NREs.19 It shows that even in some products that NREs have high export quality, there is room to improve. Panel B shows the whole range of products comparing NREs range with non-NRE range. Overall ranges of NREs product specific exports are lower than comparable EMs and LICs. Panel C plots the median export quality across all NREs products at the 4-digit level in 1980 and 2010.

A new indicator called economic complexity index (ECI), developed by Hausmann et al (2011) and Simoes and Hidalgo (2011), is based on the underlying idea that countries differ in the amount of productive knowledge they hold, and so do products. It is a holistic measure that captures a country’s productive knowledge and capabilities. The ECI combines metrics of the diversity of countries with the ubiquity of products. Countries that possess more knowledge have what it takes to produce a more diverse set of products. In other words, the amount of embedded knowledge that a country has is expressed in its productive diversity. Ubiquity is defined as the number of countries that make a product. The ubiquity of a product reveals information about the volume of knowledge that is required for its production. Complex products – those that require large productive knowledge–are less ubiquitous. Therefore, the amount of knowledge that a country has is expressed both in the diversity and ubiquity of the products that it makes.20

The overall complexity of exports from NREs has room to converge with the world frontier. Figure 11 displays the median economic complexity (for merchandise exports) measure for NREs with other regions. The complexity of NREs lags behind most countries in the world. Similarly, to document the complexity in service exports, we plot Panel B. The vertical axis is the ubiquity of a country’s service exports, and the horizontal axis is the number of services exported by a country (or diversity). We highlight the specific NREs in our sample of service trade and provide the matrix of interpretation. While Kazakhstan is moving towards a diversified country exporting unique services, others like Mexico and Ecuador are non-diversified countries exporting standard services. Chile, Indonesia, others are also non-diversified service exporters but exporting more unique services. Overall, there is potential for NREs to expand exports and increase quality, diversity, and uniqueness of products and services for world markets.

Figure 11.
Figure 11.

Economic Complexity of Merchandise Exports

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Source: Authors’ calculations using IMF BPM6 Credit Accounts, 2015.

C. Product Space and Specialization

This section builds upon the earlier analysis on the evolution and composition of NRE exports, and looks at possible implications for future exports performance and growth. We use the product space and network approach.21 In this model of structural transformation, the ‘product space’ shows the changes in the revealed comparative advantage are governed by the pattern of relatedness of products at the global level (Hidalgo et al., 2007). As countries change their export mix, there is a strong tendency to move towards products that are more closely related to ones already being produced rather than to goods that are less closely related. In order to analyze future prospects of exports and growth performance, two notional variables “path” and “density” from the product space are used.22

Capabilities to produce competitive merchandise exports are lower in NREs compared to other countries at similar stages of development. Annex 3 Figure X compares the relative probability of having a comparative advantage among two export product groups using the density measure introduced in the product space approach. Panel A compares manufacturing exports for NREs with other developing countries for 2007-11. NREs are less likely to be competitive in each income group. Panel B similarly shows the average likelihood of comparative advantage for the export of primary and resource goods. Both charts highlight that NREs across all stages of development have relatively lower probability of having comparative advantage in both resource and manufacturing exports than other countries at similar stages of development.

Many NREs only export one resource good; others are more diversified. Annex 3 Figure XI shows the export basket for specific NREs, some more successful diversifiers than others. Panels A, B and C show the 2012 exports basket for Indonesia, Chile and Syria. Resource base exports are important for these countries; however there is considerable diversification in non-resource merchandise exports. Music equipment, foot-wear, garments, data processing machines etc., have been emerging new products from Indonesia and wine, fish, and copper wires from Chile. Panels D, E and F show the export baskets for Bolivia, Iraq and Mali. Each of these countries have more than 40 percent of their exports concentrated in a single non-renewable resource. These charts show the significant heterogeneity in diversification experience, and the tremendous room for growth for transformation of export baskets to higher value added and technology content exports.

Going forward, there is potential to build new comparative advantages based on the set of current specializations.23 Figure 12 shows the product space network map of merchandise exports in 2012 for the same group of six countries.24 Two observations are noteworthy. The number of products in which these economies have comparative advantage is not very concentrated at the core (except for Indonesia and Chile). Moreover, the network exhibits heterogeneity and a core-periphery structure, as discussed above - the core of the network consists of metal products, machinery, and chemicals, whereas the periphery is formed by fishing, tropical, and cereal agriculture. Over time, the various varieties of apparels and textiles have led to comparative advantages in related products such as fabrics, leather, fashion, garment technology exports (green nodes).25 Economies like Syria are marginally diversify (even in products) and given initial capabilities have the potential to diversify more easily to sources of comparative advantage in several other products and services. Others like Iraq or Mali remain highly concentrated.

Figure 12.
Figure 12.

Product Space Representation of Selected NREs 2012

Citation: IMF Working Papers 2015, 171; 10.5089/9781513573571.001.A001

Adding a temporal analysis to the standard product space approach highlights examples where NREs have shown dynamic changes in competitiveness over time. On the basis of RCA time series data, product exports can be divided into four groups: classics, emerging, disappearing and marginal.26 Annex 3 Figure XII provides a graphical summary of NRE export baskets along these temporal groupings. Panel A shows the median and mean shares of exports in each of the four groupings, for resource-based exports and manufacturing exports, while Panels B and C provide a further breakdown by country. We rank the sample of 40 NREs by the countries’ exporting most products. It is evident excluding the top 5 from each sample leaves all the remaining economies with over 90 percent of products remaining marginal. However, there are few emerging small players. Panel D shows the economies against category of services. If the country has an emerging RCA in that service, it identifies the country and the service. Examples include Mongolia’s emerging comparative advantage in agriculture, mining, and on site processing services for green and renewable energies, Guinea’ growing diversity in exporting health care expenditure services, to architectural and engineering services, business travel service from Azerbaijan, and health related service from Algeria, Cameroon, Guyana. Chile is a growing hub of banking and financial service acquisitions across the Latin America.

Table 3.

Top 80 products with emerging comparative advantage from NREs

(ranked by product complexity)

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Source: Authors’ calculations using UN COMTRADE SITC Rev. 3 from WITS.
Table 4.

NRE Service Exports: Change in Revealed Comparative Advantage 2000-13

(average across NREs)

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Notes: The chart maps the change in revealed comparative advantage (RCA). The color of the square represents the change in RCA between 2000 and 2013. Light blue is a moderate increase and dark blue is a large increase. The number in each square indicates the 2007-11 average RCA level (where RCA>=1 implies comparative advantage in exporting that particular service).