JOBS WORKING PAPER Issue No. 82 Diversification Paths: What Can Mongolia Learn from the Export Trends of Other Resource- Dependent Countries? Gordon Betcherman and Mohammad Muaz Jalil DIVERSIFICATION PATHS: WHAT CAN MONGOLIA LEARN FROM THE EXPORT TRENDS OF OTHER RESOURCE-DEPENDENT COUNTRIES? Gordon Betcherman and Mohammad Muaz Jalil May 2024 © 2024 International Bank for Reconstruction and Development / The World Bank. 1818 H Street NW, Washington, DC 20433, USA. Telephone: 202-473-1000; Internet: www.worldbank.org. Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http://creativecommons.org/licenses/by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution—Please cite the work as follows: Gordon Betcherman and Mohammad Muaz Jalil. 2024. “More Vibrant and Inclusive Labor Markets for Economic Recovery and Diversification”. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO. Translations—If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations—If you create an adaptation of this work, please add the following disclaimer along with the attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank. Third-party content—The World Bank does not necessarily own each component of the content contained within the work. The World Bank therefore does not warrant that the use of any third-party-owned individual component or part contained in the work will not infringe on the rights of those third parties. The risk of claims resulting from such infringement rests solely with you. If you wish to re-use a component of the work, it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner. Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. Images: © World Bank. Further permission required for reuse. 1 MONGOLIA JOBS DIAGNOSTIC: More Vibrant and Inclusive Labor Markets for Economic Recovery and Diversification Background Paper: Diversification Paths: What Can Mongolia Learn from the Export Trends of Other Resource-Dependent Countries? Gordon Betcherman 1 and Mohammad Muaz Jalil2 May 2024 2 Contents Acknowledgements......................................................................................................................... 4 Abbrevia�ons .................................................................................................................................. 4 Abstract ........................................................................................................................................... 5 1. Introduc�on ................................................................................................................................ 6 2. Diversifica�on in Resource-Rich Countries ................................................................................. 7 2.1 Mongolia’s Resource Dependency ........................................................................................ 7 2.2 Diversifica�on in Resource-Dependent Countries ................................................................ 9 2.3 Structural Change, Premature Deindustrializa�on, and Services ....................................... 10 3. The GIFF Methodology for Analyzing Diversifica�on Prospects ............................................... 12 3.1 The GIFF Methodology........................................................................................................ 12 3.2 Iden�fying Comparator Countries ...................................................................................... 13 3.3 Analysis of Export Trends of Resource-Rich Comparators .................................................. 16 4. Export Baskets of Mongolia and Its Resource-Rich Comparators ............................................. 17 4.1 Mongolia’s Export Trends.................................................................................................... 18 4.2 Export Trends for Aspira�onal Comparators....................................................................... 19 4.3 Export Trends for High Aspira�onal Comparators .............................................................. 24 5. Discussion.................................................................................................................................. 26 References..................................................................................................................................... 31 3 Acknowledgements This paper was prepared by Gordon Betcherman (Consultant) and Mohammad Muaz Jalil (PhD Candidate, University of Otawa) as a background paper for the Mongolia Jobs Diagnos�c. The paper benefited from feedback from Yang Huang (Senior Economist), Natalia Millan (Economist), and Mongolmaa Norjinlkham (Senior Social Protec�on Specialist). The publica�on of this paper and the Mongolia Jobs Diagnos�c has been made possible through a grant from the Korea-World Bank Partnership Facility (KWPF). Abbrevia�ons BPM Balance of Payments and the International Investment Position Manual FDI foreign direct investment GCI Global Compe��veness Index GDP gross domes�c product GIFF Growth Iden�fica�on and Facilita�on Framework HDI Human Development Index IMF Interna�onal Monetary Fund NEET not in employment, educa�on, or training SDR special drawing right 4 Abstract Mongolia has benefited from its mineral wealth, but diversifica�on is now a major priority. But to what extent is diversifica�on possible, and what would it look like? This paper adopts the Growth Iden�fica�on and Facilita�on Framework methodology to examine the extent to which other resource-rich countries at a higher level of development than Mongolia have diversified the products and services they export. The literature on diversifica�on underscores the challenges resource-dependent economies face in diversifying, and this is supported by our analysis. We find that the export baskets of the six resource-rich comparator countries studied tended to be even more concentrated on resources in 2019 than they had been in 2001. There was litle evidence of new industrial products emerging as important exports, and services remained a small part of total trade. However, in most of the countries, exports of some high-produc�vity and tradable services had grown significantly, even if they s�ll accounted for rela�vely small shares of overall trade. Like the other countries included in the study, Mongolia faces challenges in diversifying out of mining, especially in terms of new manufacturing industries where its remoteness and small popula�on are significant constraints. However, the experiences of other resource-rich countries, as well as Mongolia’s endowments, suggest that high-produc�vity and tradable services, such as business services and telecommunica�ons, computer, and informa�on services, may hold promise for future growth and diversifica�on. 5 1. Introduc�on This paper has been prepared as part of the World Bank’s Mongolia Jobs Diagnos�c (Betcherman et al. 2022). The Jobs Diagnos�c involves an analysis of the Mongolian labor market, including the evolu�on of labor supply and demand as well as relevant policies and ins�tu�ons, to provide evidence to support the crea�on of more and beter jobs in the future. Based on this analysis, the Jobs Diagnos�c provides policy recommenda�ons to address various job challenges, including the crea�on of new jobs in a more diversified economy. Mongolia’s economy has relied heavily on the mining sector for growth, trade, and investment. Although mining has contributed in a major way to the country’s economic development and poverty reduc�on record over the past two decades, the prospects for con�nuing along this resource-dependent path are not favorable. Environmental concerns, the expected decline in demand from export des�na�ons (especially coal to China), and the economic vola�lity caused by resource dependency are crea�ng impetus for a new economic model where different sectors drive economic development and job crea�on. The importance of economic diversifica�on has been highlighted in the government’s strategy, including the country’s development plan, Vision 2050 (Government of Mongolia 2020). Diversifica�on was also a theme of the latest World Bank Country Economic Memorandum (World Bank 2020). Diversifica�on is a challenge for many developing countries, especially those that have relied on resources. These countries o�en have low levels of economic complexity, which means that they have a limited number of assets and capabili�es that are transferable across sectors (Hidalgo and Hausmann 2009; Lashitew, Ross, and Werker 2020). This characteriza�on applies to Mongolia, which has a low economic complexity ra�ng, crea�ng challenges for future economic diversifica�on (Tudela-Pye and Meroto 2023). One framework for analyzing diversifica�on possibili�es is the Growth Iden�fica�on and Facilita�on Framework (GIFF) developed by Lin and colleagues (e.g., Lin and Monga 2010; Lin and Xu 2016). The GIFF offers a guide to poten�al growth sectors in a target country by analyzing the evolu�on of exports in comparator countries. The logic of this framework is that countries with similar endowments—and especially those that are slightly more developed than the target country—can provide insights into products and services where the target country may have natural compara�ve advantages. The objec�ve of this paper is to use a modified and par�al GIFF approach to iden�fy poten�al growth sectors for Mongolia. The methodology we use is to analyze export trends for a set of countries that we have selected as useful comparators for Mongolia. A key part of this analysis is to iden�fy appropriate comparators. To do this, we generally follow the approach used in the GIFF methodology to iden�fy comparator countries. This selec�on process considers a number of economic and human capital variables to iden�fy poten�al countries. These countries are then further assessed in terms of endowment similari�es with Mongolia to arrive at a final list. In our approach, we have three groups of comparators: 6 “standard,” “aspira�onal,” and “high aspira�onal.” We analyze exports in 2001 and 2019 for the aspira�onal and high aspira�onal comparators that are resource rich to gain insights into the extent and nature of their diversifica�on. We find that the export baskets of the six resource-rich comparator countries studied tended to be even more concentrated on resources in 2019 than they had been in 2001. There was litle evidence of new industrial products emerging as important exports, and services remained a small part of total trade. However, in most of the countries, exports of some high-produc�vity and tradable services had grown significantly, even if they s�ll accounted for rela�vely small shares of overall trade. Mongolia’s export trends have been similar to those of the comparator countries, and it faces challenges in diversifying out of mining. However, the experiences of other resource-rich countries, as well as Mongolia’s endowments, suggest that high-produc�vity and tradable services, such as business services and telecommunica�ons, computer, and informa�on services, may hold promise for future growth and diversifica�on. In the next sec�on, we review some of the relevant literature on diversifica�on, par�cularly in resource-dependent countries. In sec�on 3, the par�al GIFF methodology that we use is described. Then, in sec�on 4, the methodology is applied by analyzing data on exports for Mongolia and selected comparator countries. In the concluding sec�on, the results are discussed, and implica�ons are drawn for future job crea�on possibili�es for Mongolia. 2. Diversifica�on in Resource-Rich Countries 2.1 Mongolia’s Resource Dependency Mongolia’s economy has been heavily dependent on resources, especially the mining of coal, gold, and copper. Mining has been the major contributor to economic growth, especially during the 2003–13 period, when the country’s economic performance benefited significantly from large-scale mining investments. Figure 1 summarizes the contribu�on mining has made to aggregate gross domes�c product (GDP) and the extent to which mining has dominated exports, government revenue, and foreign direct investment. The figure also highlights how much this dependence on mineral resources increased during the 2000–19 period. 7 Figure 1: Mongolia’s Growing Economic Dependence on the Mining Sector 2000 2019 89% 73% 42% 44% 24% 26% 11% 5% Mineral sector/GDP Mineral exports/total Mineral FDI in mineral exports revenue/total sector/total FDI revenue Source: World Bank (2020), based on Na�onal Sta�s�cs Office, Ministry of Finance, Bank of Mongolia, and World Bank es�mates. Note: FDI = foreign direct investment; GDP = gross domes�c product. The mining-dependent economy has been beneficial in many respects. Average annual GDP growth was about 7 percent during the first two decades of the century. With solid economic growth, poverty has declined, especially prior to 2015, and inequality has remained stable and moderate. However, there are concerns related to this resource-driven economy. Growth is closely linked to commodity prices, and their vola�lity has translated into considerable instability in the Mongolian economy. The growth has been primarily due to capital investment and natural resource deple�on rather than produc�vity improvements that are necessary for prosperity over the long run. The resources generated by mining have not been used to strengthen the intangible capital that is increasingly important for economic development. These issues are discussed in detail in the World Bank’s last Country Economic Memorandum (World Bank 2020). Furthermore, environmental considera�ons add to the concerns about the structure of the Mongolian economy going forward. The dependence on coal, in par�cular, will be a problem with Mongolia’s commitment to a green transi�on and similar commitments that are being made by trade partners, most importantly China. In response to such concerns, Vision 2050, Mongolia’s long-term development plan, sets out diversifica�on as an important objec�ve. The plan iden�fies various products and services as priori�es for crea�ng a more diversified economy, including mineral processing, food processing, wool and cashmere, tourism, crea�ve industries, and informa�on technology (Government of Mongolia 2020). 8 2.2 Diversifica�on in Resource-Dependent Countries Diversifica�on is a key aspect of economic development. It is part of a broader process of structural change whereby produc�ve inputs are reallocated from low- to high-produc�vity ac�vi�es, resul�ng in increases in the value of what a country produces and in the incomes of its popula�on. As Hausmann and his colleagues (e.g., Hidalgo and Hausmann 2009) have demonstrated, specializing in increasingly more complex products is strongly correlated with na�onal income. Yet it can be challenging for countries to diversify into new produc�ve ac�vi�es. Developing different and more complex products (or services) depends on acquiring new capabili�es (i.e., know-how). But where do these new capabili�es come from? Generally, the expecta�on has been that they will be closely related to exis�ng capabili�es. In other words, movement across the “product space” would be path dependent. Diversifica�on has been seen as more challenging in developing countries than in developed countries, which have a broader and more sophis�cated range of capabili�es and, thus, the capacity to make “longer leaps” across the product space. In developing countries, the general goal is to leverage capabili�es they already have to expand into more complex products that are a “short leap” from exis�ng ones (Balland et al. 2022). Developing countries face addi�onal challenges in diversifying into new products because many of these countries are commodity or resource dependent, as is the case in Mongolia. Resource- rich countries typically face more hurdles in diversifying than other countries (Lashitew, Ross, and Werker 2021). Because they have relied on extrac�on of natural resources, these countries usually have not developed a wide range of capabili�es to move into more complex products, especially far across the product space. Resource-rich countries can diversify in two ways (Joya 2018). One is resource-based diversifica�on, where minerals or other resources are not only extracted but now are also processed domes�cally. The other is broad-based diversifica�on, with new ac�vi�es that are not necessarily connected to the resource sector. It is true that conven�onal views on diversifica�on suggest that only the former is possible given the specialized and limited compara�ve advantages of resource-dependent countries. However, this view is now being challenged more seriously. Compara�ve advantage may not only be determined by factor endowments but also by country-specific features such as history, ins�tu�ons, and geography (Ahmadov 2014; Joya 2018). There are examples of countries that have diversified into areas unrelated to their ini�al resource specializa�on (World Bank Group 2019). Indeed, recent empirical work by the United Na�ons Industrial Development Organiza�on found that, even in developing countries, there was an unexpectedly high incidence of new exports unrelated to the country’s ini�al export basket (Coniglio et al. 2021). 9 2.3 Structural Change, Premature Deindustrializa�on, and Services The conven�onal path of structural change has been from agriculture to modern industrial and service ac�vi�es. As the structural transforma�on proceeds, the objec�ve is to move up the value-added ladder by specializing, or diversifying, within industry and eventually services. For developing countries, low-cost manufacturing has tradi�onally been the star�ng point for this process of structural upgrading. This has been exemplified by the experience of several East Asian and Southeast Asian countries. However, there are now doubts about whether recent changes in technology and trade are limi�ng future opportuni�es in manufacturing for developing countries where the dominant asset is abundant low-wage labor. Rodrik (2016) has referred to this as “premature deindustrializa�on.” He has noted that, compared to the experience of today’s developed countries, the share of manufacturing in the economy now peaks at lower levels and at far lower levels of income. This raises the ques�on of where growth and job crea�on will come from if the conven�onal path is now much less likely. For the most part, the literature on structural change and diversifica�on is only now adjus�ng to this new reality. Indeed, the economic complexity literature and the GIFF, which we will turn to in the next sec�on, s�ll focus on the possibili�es for specializa�on in goods. The methodologies and the data tend to be limited to goods (especially goods exports) without including services. Tradi�onally, there has not been much interest in service-led development. Services were not tradable, and there were limited prospects for produc�vity gains in the sector. However, because of technological advances, neither is necessarily s�ll the case for many services (Nayyar, Hallward- Dreimer, and Davies 2021). Services value added as a share of trade has been rising (albeit o�en embedded in goods). Among lower-middle-income countries, produc�vity in services has been growing more rapidly than in industry or agriculture (figure 2). Given these developments, along with premature deindustrializa�on, services must be more central to structural change and diversifica�on (Rodrik 2021). 10 Figure 2: Trends in Labor Produc�vity by Sector, Lower-Middle-Income Countries, 1990–2020 Source: Nayyar, Hallward-Dreimer, and Davies 2021. The service sector is very diverse (box 1). It includes public sector and private sector ac�vi�es. Some services are tradable, whereas others are less so. Technological change opportuni�es vary across the different service industries, and, accordingly, so do possibili�es for produc�vity growth. Box 1: The Diversity of Service Industries Tradi�onally, economists have seen services as an undifferen�ated sector—a residual of ac�vi�es that do not fit into the primary sector or industry. However, the sector includes a wide range of ac�vi�es that differ significantly in terms of, for example, tradability, technological intensity, linkages with other sectors, the rela�onship between producer and consumer, labor intensity, and skill requirements. Nayyar, Hallward-Dreimer, and Davies (2021) organize the service sector into four categories: • Global innovator services, including professional, scien�fic, and technical services; informa�on and communica�on technology services; and financial services • Low-skill tradable services, including transporta�on and warehousing, accommoda�on and food, and wholesale trade • Skill-intensive social services, including educa�on and health • Low-skill domestic services, including retail trade; entertainment, arts, and recrea�on services; administra�on and support services; and other community and personal services There may be litle doubt that service industries will account for the bulk of job crea�on in the future, as they have in the recent past. However, how growth is distributed across these four categories will determine the quality of jobs that countries are able to create. To this point, job crea�on in developing countries has been mostly concentrated in low-skill services. Their challenge will be to acquire the capabili�es to develop services that generate beter jobs, specifically in global innovator services and skill-intensive social services. 11 Services will play an increasingly important role in structural change and diversifica�on, but the key ques�on will be which types of services grow. 3. The GIFF Methodology for Analyzing Diversifica�on Prospects We now turn to the prac�cal ques�on of how to iden�fy sectors that have poten�al for crea�ng future growth and jobs in Mongolia. The GIFF, developed by Lin and colleagues, offers a systema�c approach for doing this. In this sec�on, the GIFF methodology is explained, focusing on the parts of the overall methodology that will be applied to the Mongolia case. 3.1 The GIFF Methodology The GIFF was developed as a systema�c way to iden�fy natural compara�ve advantages for countries as a prelude to developing a strategy for upgrading and growth. In essence, it is a tool for opera�onalizing the new structural economics approach to development (Lin 2010). The GIFF is designed to help policymakers in catching-up developing countries to develop feasible and sharply focused policies in an effort to iden�fy and unlock their latent compara�ve advantage to achieve structural transforma�on. At the heart of the GIFF is the principle that developing countries should not focus on what they do not have but what they do have in an effort to unleash their latent compara�ve advantages (Lin and Xu 2016, 3). The GIFF has been applied to several countries in Sub-Saharan Africa and Asia (e.g., Lin and Dinh 2014; Lin and Wang 2014; Lin and Xu 2016; Sichoongwe, Kaonga, and Hapompwe 2021; Xu and Hager 2017). The GIFF is a prac�cal instrument that can be applied by policy makers to guide structural change and diversifica�on strategies. Fully implemen�ng the framework involves the six steps below, which include analysis (step 1) and policy ac�ons (steps 2–6). 1. Choosing the right target (i.e., picking comparator countries and analyzing diversifica�on possibili�es based on the export paterns of these countries) 2. Iden�fying and removing binding constraints for these goods and services 3. Atrac�ng global investors 4. Scaling up self-discovery 5. Recognizing the power of industrial parks/special economic zones 6. Providing limited incen�ves to the right industries In this paper, we only apply the first step of the GIFF methodology to Mongolia. This involves, first, selec�ng comparator countries and, second, analyzing their export trends to iden�fy poten�al sources of diversifica�on for the target country. 12 3.2 Iden�fying Comparator Countries Our methodology for selec�ng comparator countries generally follows the approach used in the GIFF. However, some adapta�ons have been made to reflect differences in the purposes of using comparator countries. In the GIFF studies, comparators provide examples of development paths that could lead to economic growth and diversifica�on in the target country. For this purpose, comparator countries should be slightly ahead of the target country in terms of development, should be growing dynamically, and should have factor endowments similar to the target country. In this paper, we also use comparators to iden�fy poten�al growth sectors, so the criteria used in the GIFF studies are appropriate for that purpose. However, in another study, we use comparators to benchmark Mongolia’s labor market performance regionally and interna�onally (see Betcherman and Jalil 2023). For that purpose, it is important to have some comparator countries that are similar to Mongolia in terms of level of development, as well as endowments. 3 The methodology we have used is summarized in figure 3. Figure 3: Process for Selec�ng Comparator Countries Note: GDP = gross domes�c product. Step 1: Shortlis�ng Countries Based on GDP per Capita In the GIFF studies, Lin and his colleagues select comparators with similar endowments to the target country that are slightly ahead in terms of economic development. To iden�fy poten�al comparators that are slightly more developed, the GIFF studies use two indicators: a GDP per capita about 100–300 percent higher than the target country or a per capita income from 20 years ago that is similar to the target country’s current per capita income (Lin and Xu 2016). 13 We use the current GDP per capita approach and select three groups of poten�al comparators: • Standard comparators, with a GDP per capita of 100–150 percent of Mongolia’s; these countries are used for benchmarking Mongolia’s labor market performance, as reported in Betcherman and Jalil (2023) • Aspirational comparators, with a GDP per capita of 150–250 percent of Mongolia’s; this group includes the countries that are primarily used for iden�fying poten�al sources of growth and diversifica�on for Mongolia, which is the focus of this paper • High aspirational comparators, with a GDP per capita of greater than 250 percent of Mongolia’s; these countries serve to provide a longer-term vision for Mongolia’s diversifica�on Using 2019 GDP per capita levels, we iden�fied 31 poten�al countries in the standard category, 25 in the aspira�onal category, and 43 in the high aspira�onal category. 4 Step 2: Endowment Factors and Sta�s�cal Analysis To illustrate the GIFF approach, Lin and Xu (2016), in their analysis of Uganda, created a list of countries with GDP per capita between 100 and 300 percent of the target country. They then determined comparator countries in two stages. First, they removed slow-growing countries. Second, they retained countries with similar endowment factors as Uganda, including geography (landlocked countries), resource richness, labor abundance, and manufacturing value added. Regarding the growth condi�on, we excluded countries that had below 3 percent annual average growth (2000–19) for the standard and aspira�onal categories and below 2 percent for the high aspira�onal group. For the remaining countries, we carried out further analysis, using quan�ta�ve and qualita�ve indicators, to capture factor endowments similar to Mongolia. These indicators are listed in table 1. Table 1: Indicators Used for Selec�ng Poten�al Comparators Variables Rationale Manufacturing value added Industry performance, also used by Lin and Xu (2016) Trade (% of GDP) Export orientation Global Competitiveness Index Overall economic competitiveness Population density To select countries with similar low-density profile as Mongolia Human Development Index To identify human capital level Human Capital Index Unemployment, youth total (% of Performance in reducing youth unemployment; also used by Lin and total labor force, ages 15–24) Xu (2016) Export Diversification Index (2014) Manufacturing quality and export diversification Manufacturing quality 14 Variables Rationale Additional qualitative factors Countries that are transition economies (e.g., eastern European countries); similar geography (e.g., landlocked: Central Asian countries) Note: GDP = gross domes�c product. For each of the quan�ta�ve variables, an analysis was undertaken of the latest levels and also trends over the 2000–19 period. Step 3: Integra�ng Qualita�ve Informa�on The selec�on of comparator countries was finalized a�er consulta�on with World Bank colleagues and others knowledgeable about Mongolia. The final selec�on is shown in table 2. This list was also compared with the Country Economic Memorandum list of comparator countries (World Bank 2020), and it turns out there was some overlap: 8 of the 12 countries used as comparators in that report were also on our list. Table 2: Comparator Countries Selected STANDARD COMPARATORS Thailanda High manufacturing value added; high on human capital but, unlike Mongolia, low on youth unemployment Vietnam a High youth population but low NEET and very diversified export/manufacturing sector despite lower per capita income than Mongolia Colombiab Resource rich and primary commodity exports; high human capital Azerbaijana Transition economy; resource rich Armeniab Transition economy; landlocked ASPIRATIONAL COMPARATORS Malaysiab Diversified export, including electrical/industrial machinery; resource/primary commodities/agricultural products still account for 20% of export Russia b More diversified manufacturing sector; oil/energy/resource rich; low population density Kazakhstanb Resource rich; similar geography Chileb Resource/mineral rich; globally competitive economy; high human capital; diversified export; high unemployment among youth HIGH ASPIRATIONAL COMPARATORS South Successful example of structural transformation; strong manufacturing/export base Korea Australiab Resource rich; low population density Canadab Resource rich; low population density Estonia Transition economy; successful economic transformation focusing on service rather than industry Note: NEET = not in employment, educa�on, or training. a. Countries referred to in World Bank (2020, 26, 49). b. Iden�fied as comparator countries in World Bank (2020, 48). 15 3.3 Analysis of Export Trends of Resource-Rich Comparators The logic of the GIFF methodology is that countries with similar endowments and that are slightly more developed than the target country can offer insights into sectors where the target country may have natural compara�ve advantages. This is done by analyzing what the export mix has been for these comparator countries and how their export basket has evolved over �me. As noted earlier in this sec�on, the analysis reported in this paper is only the first step in a full implementa�on of the GIFF methodology. In a full implementa�on, once poten�al growth products have been iden�fied based on the export baskets of comparators, the next step would be to make an ini�al judgment on whether these products are in line with the target country’s compara�ve advantages. 5 This can include an assessment of labor and capital cost structures as well as other endowments that can determine the feasibility of diversifying into a specific product. Once general and sector-specific constraints have been considered, the GIFF then turns to how investment, self-discovery, and incen�ves can transform the poten�al products into a concrete plan for growth and diversifica�on. Our analysis of exports includes the four aspira�onal comparators we have selected: Malaysia, Russia, Kazakhstan, and Chile. The aspira�onal group meets the condi�on of being somewhat more developed than Mongolia, as measured by GDP per capita. As of 2019, the GDP per capita in each of these countries was slightly more than double Mongolia’s (table 3). The higher development level of the aspira�onal countries is also indicated by their scores on the Global Compe��veness Index (GCI) and the Human Development Index (HDI) rela�ve to Mongolia. They also ranked higher in terms of economic complexity, although only Malaysia had a rela�vely high rank on this index. The aspira�onal countries also had fairly strong economic performance over the 2000–19 period, with annual GDP growth rates ranging from 3.7 percent to 6.5 percent. In terms of endowments, all four countries are rich in resources, which is a defining characteris�c of Mongolia. Three other important characteris�cs of Mongolia are its very low popula�on density, transi�on economy, and landlocked geography. Both Russia and Kazakhstan have low popula�on density and are transi�on economies, but Malaysia and Chile do not. Only Kazakhstan is landlocked (table 3). Although the analysis focuses on the exports of the aspira�onal comparators, we also look at export trends of the two high aspira�onal countries that are resource rich: Australia and Canada. These countries are much more developed than Mongolia, as illustrated by their GDP per capita and GCI and HDI scores (table 3). However, they both have low popula�on density levels, like Mongolia, in addi�on to their natural wealth. Australia’s economic complexity is quite low; although Canada’s is higher, it is not among the global leaders in this category. These rela�vely low economic complexity rankings may suggest a high degree of resource dependency, but Australia and Canada have been included in the analysis for poten�al insights into where diversifica�on might occur for Mongolia farther down the road. 16 Table 3: Selected Characteris�cs: Mongolia and Resource-Rich Comparator Countries GDP per Annual capita Economic GDP relative to GCI rank, HDI, complexity Population Transition Landlocked Country growth Mongolia 2017–2018 2019 ranking, densitya country geography rate (%), (%), 2019 2000–2019 2019 Aspirational Malaysia 230 5.1 23 0.79 23 100 No No Russia 220 3.8 38 0.81 54 9 Yes No Kazakhstan 214 6.5 57 0.79 79 7 Yes Yes Chile 204 3.7 33 0.83 73 26 No No High aspirational Australia 403 2.9 21 0.93 86 3 No No Canada 397 2.7 14 0.91 36 4 No No Mongolia 100 6.8 101 0.72 112 2 Yes Yes Sources: World Bank, World Economic Forum, United Na�ons Development Programme, and Harvard Growth Lab. Note: GCI = Global Compe��veness Index; GDP = gross domes�c product; HDI = Human Development Index. a. Popula�on density refers to the number of people per square kilometer. The export data for goods used in our analysis are based on United Na�ons Comtrade sta�s�cs and, in some cases, draw on published data by na�onal sta�s�cal offices. These data are classified according to the Harmonized System codes, using six-digit, four-digit, and two-digit classifica�ons (Mendoza 2021). For this research, we rely on the two-digit product classifica�on and, to a lesser extent, the four-digit classifica�on, covering the period between 2001 and 2019. The trade data on services are based on the Balance of Payments and the International Investment Position Manual (BPM) collected by the Interna�onal Monetary Fund (IMF). The BPM has undergone several itera�ons. The latest version, BPM6, has more granular classifica�ons of services than earlier versions (IMF 2009). To maximize data comparability, the data on services were collected between 2005 (the earliest available data using BPM6) and 2019. We present service exports at the BPM1 level, which is the highest level of aggrega�on. 4. Export Baskets of Mongolia and Its Resource-Rich Comparators In this sec�on, we analyze the export trends for the aspira�onal comparators and the high aspira�onal comparators. We begin with export trends for Mongolia. Export values for all countries are reported in current U.S. dollars. 6 The objec�ve of this analysis is to iden�fy the extent to which these countries have diversified their exports beyond resources. The data cover the 2001–19 period for goods exports and 2005– 19 for service exports. To consider the extent of diversifica�on in each country, we look at how 17 export shares of individual products and services have changed and what the growth rates have been for exports of individual products and services. The tables included in this sec�on include products and services that accounted for at least 3 percent of the country’s exports (as a share of goods or services) in 2019. 4.1 Mongolia’s Export Trends Mongolia has experienced rapid growth in trade volumes in the first two decades of the century. Goods exports were only slightly more than $500 million in 2001, but they had grown in nominal terms to over $7.6 billion by 2019 (table 4). However, the goods export basket became even more concentrated in resources over the period. This is due to the tremendous growth in mineral fuels, oils, and bituminous substances, which accounted for only 1 percent of goods exports in 2001 but 46 percent in 2019. This category is dominated by coal, which was not exported in 2001 but had exports valued at over $3 billion in 2019. Mongolia’s other major export—ores, slag, and ash— increased its share of goods exports from 29 percent to 35 percent over the period. By far the most important product in this category is copper ore, which amounted to $1.8 billion in 2019. Over 80 percent of Mongolia’s goods exports in 2019 were in the two mining categories (product codes 26 and 27), with coal and copper accoun�ng for 64 percent of all goods exports. To further illustrate the growing dominance of resource exports in Mongolia, table 4 also includes three nonresource categories—wool, yarn, and woven fabric and two apparel and clothing groups—that were significant export products in 2001 but not in 2019. Table 4: Major Goods Exports, Mongolia, 2001–2019 Exported value, Exported value, 2019 Share, Share, 2001–19 Code Product label 2001 (US$, 2001 (%) 2019 (%) change (%) (US$, thousands) thousands) TOTAL All products 523,223 7,619,754 100 100 1,356 Mineral fuels, mineral oils, and 27 products of their distillation; 3,838 3,485,104 1 46 90,705 bituminous substances; mineral ... 26 Ores, slag, and ash 153,197 2,686,199 29 35 1,653 Natural or cultured pearls, precious 71 or semiprecious stones, precious 75,433 419,076 14 6 456 metals, metals clad... Wool, fine, or coarse animal hair; 51 67,568 374,568 13 5 454 horsehair yarn; and woven fabric Articles of apparel and clothing 61 29,204 50,316 6 1 72 accessories, knitted or crocheted Raw hides and skins (other than fur 41 57,389 13,455 11 0 -77 skins) and leather Articles of apparel and clothing 62 73,079 6,935 14 0 -91 accessories, not knitted or crocheted Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. Services accounted for 14 percent of total trade in 2019 and have been growing at a much slower rate than goods exports. Travel, which consists primarily of tourism, remains the most important service export, as it was in 2005 (table 5). Transport services, while s�ll important, have been 18 growing more slowly than other services. Other business services have become a much more important export over the period, though the volume is quite small. Trade flows of telecommunica�ons, computer, and informa�on services remain very small. Table 5: Major Service Exports, Mongolia, 2005–2019 Exported Exported value, value, 2019 Share, Share, 2005–19 Code Service label 2005 (US$, (US$, 2005 (%) 2019 (%) change (%) thousands) thousands) S All services 414,000 1,233,000 100 100 198 4 Travel 177,000 513,000 43 42 190 3 Transport 199,000 389,000 48 32 95 10 Other business services 10,000 226,000 2 18 2,160 Telecommunications, computer, and 9 information services 15,000 50,000 4 4 233 5 Construction 1,000 39,000 0 3 3,800 Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. 4.2 Export Trends for Aspira�onal Comparators 4.2.1 Chile Resource-based products, predominantly copper, were Chile’s most important goods exports in 2001, and they became s�ll more important over the next two decades. Ores, slag, and ash accounted for 14 percent of goods exports at the beginning of the period, and that share had roughly doubled by 2019 (table 6). Copper ore exports have accounted for well over 80 percent of this category, reaching nearly 90 percent in 2019. The other major export category is copper and ar�cles thereof, which largely consists of copper alloys. This category lost some share of total goods exports between 2001 and 2019 but was s�ll the second most important by far. When fish, fruits and nuts, wood pulp, and wood are considered, the dominance of resource-based exports (mineral and nonmineral) becomes even more apparent. 19 Table 6: Major Goods Exports, Chile, 2001–2019 Exported Exported value, 2001 value, 2019 Share, 2001 2001–19 Code Product label Share, 2019 (%) (US$, (US$, (%) change (%) thousands) thousands) TOTAL All products 1,874,5415 69,145,962 100 100 269 26 Ores, slag, and ash 2,595,826 20,020,938 14 29 671 74 Copper and articles thereof 4,844,018 14,946,321 26 22 209 Fish and crustaceans; mollusks 03 1,409,842 5,781,495 8 8 310 and other aquatic invertebrates Edible fruit and nuts; peel of citrus 08 1,277,396 5,777,233 7 8 352 fruit or melons Pulp of wood or of other fibrous 47 cellulosic material; recovered 1,068,184 2,725,994 6 4 155 (waste and scrap) paper or ... Wood and articles of wood; wood 44 1,158,295 2,320,994 6 3 100 charcoal Inorganic chemicals; organic or 28 inorganic compounds of precious 382,119 2,116,789 2 3 454 metals, of rare-earth metals, ... 22 Beverages, spirits, and vinegar 663,035 1,958,950 4 3 195 Commodities not elsewhere 99 444,342 1,917,082 2 3 331 specified Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. Services accounted for 12 percent of Chile’s total exports in 2019, and service exports have been growing very slowly. They increased in nominal terms by only 32 percent over the 2005–19 period (table 7). Exports in the major service category, transport, declined by 28 percent over the period. Although there was some growth in other services included in the table, export volumes remained low. Overall, services are contribu�ng very litle to the diversifica�on of Chile’s export basket. Table 7: Major Service Exports, Chile, 2005–2019 Exported Exported value, value, 2019 Share, 2005 Share, 2019 2005–19 change Code Service label 2005 (US$, (US$, (%) (%) (%) thousands) thousands) S All services 6,992,000 9,259,000 100 100 32 3 Transport 4,260,000 3,079,000 61 33 -28 10 Other business services 1,116,000 2,515,000 16 27 125 4 Travel 1,002,000 2,279,000 14 25 127 Telecommunications, computer, 234,000 413,000 9 and information services 3 4 76 6 Insurance and pension services 153,000 325,000 2 4 112 SN Services not allocated 130,000 320,000 2 3 146 Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. 20 4.2.2 Kazakhstan Oil exports have been dominant in Kazakhstan, and the dependence on oil increased over the 2001–19 period. The mineral fuels category represented two-thirds of the country’s goods exports in 2019, up from 56 percent at the beginning of the period. Oil accounts for over 80 percent of the exports in this category. The fastest-growing major export category over the 2001– 19 period was ores, slag, and ash, with the dominant product being copper ore. There is litle evidence of diversifica�on away from resources in Kazakhstan’s goods exports; in fact, the one significant industrial export in 2001 was iron and steel, and this category’s share halved over the period (table 8). Table 8: Major Goods Exports, Kazakhstan, 2001–2019 Exported Exported value, 2001 value, 2019 Share, 2001 2001–19 Code Product label Share, 2019 (%) (US$, (US$, (%) change (%) thousands) thousands) TOTAL All products 8,485,515 57,722,942 100 100 580 Mineral fuels, mineral oils, and products of their distillation; 27 4,757,677 38,717,325 56 67 714 bituminous substances; mineral ... 72 Iron and steel 1,008,207 3,473,438 12 6 245 26 Ores, slag, and ash 221,909 2,727,064 3 5 1,129 74 Copper and articles thereof 704,127 2,619,526 8 5 272 Inorganic chemicals; organic or 28 inorganic compounds of precious 271,800 2,213,704 3 4 714 metals, of rare earth metals, ... Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. Service exports have been growing in Kazakhstan, but they only accounted for 12 percent of exports in 2019. There has been litle change in the composi�on of service exports over the 2005– 19 period, with transport and travel accoun�ng for over 80 percent in both years (table 9). Table 9: Major Service Exports, Kazakhstan, 2005–2019 Exported Exported value, 2005 value, 2019 Share, 2005 2005–19 Code Service label Share, 2019 (%) (US$, (US$, (%) change (%) thousands) thousands) S All services 2,087,000 7,754,000 100 100 272 3 Transport 1,024,000 3,974,000 49 51 288 4 Travel 701,000 2,456,000 34 32 250 10 Other business services 179,000 527,000 9 7 194 Government goods and services 87,000 278,000 12 n.e.c. 4 4 220 Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. Note: n.e.c. = not elsewhere classified 21 4.2.3 Russia Russia is another example of a country that has been resource dependent, with litle sign of export diversifica�on. Russia’s export basket is concentrated in a few commodi�es; only four product categories accounted for at least 4 percent of goods exports (table 10). Mineral fuels dominate exports. This was the case in 2019 when that category accounted for 52 percent of goods exports, the same share it had in 2001. However, the composi�on of mineral fuel exports changed over the period. In 2001, oil accounted for 63 percent of these exports and gas 33 percent, but in 2019, oil accounted for 85 percent. There are a few industrial products that do export to some degree, such as iron and steel, machinery, and electrical machinery; however, their share of goods exports is small (4 percent, 2 percent, and 1 percent, respec�vely, in 2019) and declined between 2001 and 2019. Table 10: Major Goods Exports, Russia, 2001–2019 Exported Exported value, value, 2019 Share, Share, 2001–19 Code Product label 2001 (US$, (US$, 2001 (%) 2019 (%) change (%) thousands) thousands) TOTAL All products 99,868,397 422,777,167 100 100 323 Mineral fuels, mineral oils, and products of 27 their distillation; bituminous substances; 51,860,985 220,845,173 52 52 326 mineral... 99 Commodities not elsewhere specified 12,356,012 55,265,424 12 13 347 72 Iron and steel 5,547,426 18,140,726 6 4 227 Natural or cultured pearls, precious or 71 semiprecious stones, precious metals, 1,137,864 15,258,928 1 4 1,241 metals clad... Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. Service exports in Russia grew slightly more quickly than goods exports over the 2005–19 period (118 percent versus 75 percent) but s�ll only accounted for 13 percent of all exports in 2019. In Russia, like Kazakhstan, the composi�on of service exports changed very litle over the period, with transport, other business services, and travel maintaining rela�vely constant shares of services trade through the period (table 11). The one excep�on was telecommunica�ons, computer, and informa�on services, which grew rapidly over the period. 22 Table 11: Major Service Exports, Russia, 2005–2019 Exported value, Exported 2005 value, 2019 Share, Share, 2005–19 Code Service label (US$, (US$, 2005 (%) 2019 (%) change (%) thousands) thousands) S All services 28,845,000 62,786,000 100 100 118 3 Transport 9,125,000 21,481,000 32 34 135 10 Other business services 5,792,000 12,994,000 20 21 124 4 Travel 5,870,000 10,961,000 20 17 87 Telecommunications, computer, and 1,041,000 5,489,000 4 9 427 9 information services 5 Construction 3,313,000 4,786,000 11 8 44 2 Maintenance and repair services n.e.c. 917,000 1,900,000 3 3 107 Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. Note: n.e.c. = not elsewhere classified 4.2.4 Malaysia Of the four aspira�onal comparators, Malaysia exhibits the most diversified export basket, with the least reliance on resources. Two of the three largest export categories are industrial products: electrical machinery and machinery. Most of the specific products within these categories are related to electronics. The major commodi�es being exported are mineral fuels (primarily oil) and animal and vegetable fats and oils (primarily palm oil). Although Malaysia’s major exports are industrial products, there is litle evidence of further diversifica�on away from resources over the 2001–19 period (table 12). Table 12: Major Goods Exports, Malaysia, 2001–2019 Exported Exported value, 2001 value, 2019 Share, 2001 Share, 2019 2001–19 Code Product label (US$, (US$, (%) (%) change (%) thousands) thousands) TOTAL All products 88,004,108 238,161,125 100 100 171 Electrical machinery and equipment and parts thereof; 85 33,567,770 81,965,042 38 34 144 sound recorders and reproducers, television... Mineral fuels, mineral oils, and 27 products of their distillation; 8,554,204 34,479,307 10 14 303 bituminous substances; mineral... Machinery, mechanical 84 appliances, nuclear reactors, 19,270,447 21,772,846 22 9 13 boilers; parts thereof Animal or vegetable fats and oils 15 and their cleavage products; 3,090,495 11,469,172 4 5 271 prepared edible fats; animal... Optical, photographic, cinematographic, measuring, 90 1,912,751 10,065,997 2 4 426 checking, precision, medical or surgical... 39 Plastics and articles thereof 1,847,219 9,594,622 2 4 419 40 Rubber and articles thereof 1,700,810 7,107,004 2 3 318 Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. 23 Services represented 15 percent of all Malaysia’s exports in 2019, up slightly from 12 percent in 2005. Travel accounts for almost half of this total (table 13). Growth in service exports has been strongest in other business services; telecommunica�ons, computer, and informa�on services; and manufacturing services on physical inputs owned by others. 7 Table 13: Major Service Exports, Malaysia, 2005–2019 Exported Exported Share, value, 2005 value, 2019 Share, Code Service label 2005 2005–19 change (%) (US$, (US$, 2019 (%) (%) thousands) thousands) S All services 19,750,000 41,089,000 100 100 108 4 Travel 8,846,000 19,828,000 45 48 124 10 Other business services 2,773,000 7,075,000 14 17 155 3 Transport 4,056,000 5,240,000 21 13 29 Telecommunications, computer, 9 and information services 1,050,000 2,996,000 5 7 185 Manufacturing services on 1 physical inputs owned by others 2,000 2,869,000 0 7 143,350 Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. 4.3 Export Trends for High Aspira�onal Comparators 4.3.1 Australia Despite being a high-income country, Australia’s export basket is dominated by resources (table 14). In 2019, mineral fuels and ores, slag, and ash accounted for 62 percent of all goods exports. The main product in the former category is coal, and iron accounts for the bulk of exports in the later. Both categories increased their share significantly over the period. The increase in the export of ores was par�cularly striking. This was especially the case for iron exports. Manufactured goods represent a small part of Australia’s exports. The main manufacturing categories are machinery, pharmaceu�cals, and electrical machinery, but none accounts for as much as 2 percent of total goods exports, and exports in each grew at a slower rate between 2001 and 2019 than all exports. Table 14: Major Goods Exports, Australia, 2001–2019 Exported Exported Share, Share, value, 2001 value, 2019 2001–19 Code Product label 2001 2019 (US$, (US$, change (%) (%) (%) thousands) thousands) TOTAL All products 63,288,189 272,579,608 100 100 331 Mineral fuels, mineral oils, and products 27 of their distillation; bituminous 13,142,434 88,880,849 21 33 576 substances; mineral... 26 Ores, slag, and ash 5,132,594 78,792,909 8 29 1,435 Natural or cultured pearls, precious or 71 semiprecious stones, precious metals, 3,116,828 18,057,454 5 7 479 metals clad... 02 Meat and edible meat offal 3,251,447 11,573,586 5 4 256 Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. 24 Services account for a larger share of total exports in Australia than in the standard comparator countries. In 2019, this share was 20 percent in Australia compared to 12–15 percent in the standard comparators. Travel is the major service export (table 15). Exports in other business services; financial services; and telecommunica�ons, computer, and informa�on services all grew faster than services as a whole. Table 15: Major Service Exports, Australia, 2005–2019 Exported Exported value, 2005 value, 2019 Share, Share, 2005–19 Code Service label (US$, (US$, 2005 (%) 2019 (%) change (%) thousands) thousands) S All services 32,227,000 70,951,000 100 100 120 4 Travel 18,423,000 45,709,000 57 64 148 10 Other business services 3,686,000 8,440,000 11 12 129 3 Transport 5,631,000 5,515,000 17 8 -2 7 Financial services 1,289,000 3,701,000 4 5 187 Telecommunications, computer, and 9 1,258,000 3,596,000 4 5 186 information services Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. 4.3.2 Canada Resources are also important in Canada’s export basket, although the dependency is not as great as it is in Australia. Mineral fuels, specifically oil and gas, was the most important export category in 2019, supplan�ng vehicles, which were the top export in 2001 (table 16). A�er these two categories, Canada’s goods exports include a range of resource, agricultural, and industrial products. Nevertheless, there is litle evidence of the overall export basket shi�ing away from resources into industrial goods. The fastest-growing export categories between 2001 and 2019 were semiprecious metals and ores, along with pharmaceu�cal products. Table 16: Major Goods Exports, Canada, 2001–2019 Exported Exported Share, value, 2001 value, 2019 Share, 2001–19 change Code Product label 2001 (US$, (US$, 2019 (%) (%) (%) thousands) thousands) TOTAL All products 261,058,775 446,562,311 100 100 71 Mineral fuels, mineral oils, and products of 27 their distillation; bituminous substances; 37,342,350 98,433,244 14 22 164 mineral... Vehicles other than railway or tramway rolling 87 53,075,965 61,438,011 20 14 16 stock, and parts and accessories thereof Machinery, mechanical appliances, nuclear 84 22,420,204 34,773,897 9 8 55 reactors, boilers; parts thereof 99 Commodities not elsewhere specified 15,841,200 21,889,905 6 5 38 Natural or cultured pearls, precious or 71 semiprecious stones, precious metals, metals 2,655,076 21,294,078 1 5 702 clad... Source: Interna�onal Trade Centre, based on United Na�ons Comtrade data. 25 As was the case with Australia, service exports are more important in Canada’s total export basket than they are in the standard comparator countries. In Canada, services represented 20 percent of all exports in 2019, and service exports grew at a faster rate than goods exports between 2005 and 2019. Canada exports a diverse group of services, including travel as well as other business services. Table 17: Major Service Exports, Canada, 2005–2019 Exported value, Exported value, 2005 Share, Share, 2005–19 Code Service label 2019 (US$, (US$, thousands) 2005 (%) 2019 (%) change (%) thousands) S All services 61,506,000 112,771,000 100 100 83 10 Other business services 18,357,000 31,563,000 30 28 72 4 Travel 14,912,000 29,776,000 24 26 100 3 Transport 10,626,000 14,004,000 17 12 32 Telecommunications, 9 computer, and information 5,331,000 11,939,000 9 11 124 services 7 Financial services 3,338,000 9,259,000 5 8 177 Charges for the use of 8 2,872,000 6,786,000 5 6 136 intellectual property n.e.c. Personal, cultural, and 11 2,245,000 4,314,000 4 4 92 recreational services Source: Interna�onal Trade Centre, based on Interna�onal Monetary Fund data. Note: n.e.c. = not elsewhere classified 5. Discussion The six comparator countries included in this analysis are all rich in resources, par�cularly oil and gas, coal, and minerals. The experience of resource-rich countries is par�cularly relevant for Mongolia, a country that has been resource dependent but now is facing the challenge of diversifying into other areas. By analyzing export trends in these comparator countries that are all more developed than Mongolia, this paper has observed the extent to which these countries have been able to diversify their exports away from the resources on which they have tradi�onally depended. The economic literature has iden�fied the challenges resource-rich countries face in diversifying their economies. Although there is some ques�oning of the conven�onal view now, dependence on oil and gas, minerals, and other natural resources is associated with limited capabili�es to jump to other products or services. There are not many products or services that require similar capabili�es as resource extrac�on. The export trends of the comparator countries are consistent with this theory. There is litle evidence of any significant diversifica�on, at least when exports are used as the indicator. In fact, the export baskets of these countries tended to be more concentrated in resources in 2019 than they were in 2001. Before accep�ng this resource persistence theory, we analyzed whether our results could be due to commodity price trends; for example, par�cularly high commodity prices in 2019 could have 26 resulted in a high value for resource exports in that year which could explain our findings. However, as figure 4 indicates, according to the IMF commodity price index, which covers primary commodi�es and energy, commodity prices were not par�cularly high in 2019, and they were approximately the same in real terms as they had been in 2001. Figure 4: Indices of Primary Commodity Prices, 2000–2023 Source: Interna�onal Monetary Fund. Note: The figure combines indices of nonfuel primary commodity prices and energy prices. Real prices are deflated by the U.S. Consumer Price Index. SDR = special drawing right. Three main observa�ons are evident from our analysis of export trends, which are summarized in table 18. 27 Table 18: Summary of Export Trends in Comparator Countries Country Major exports in 2001 Major exports in Observa�ons on diversifica�on (% share of goods 2019 (% share of exports) goods exports) Chile Mineral processing, Ore mining, especially • Dominance of mining and mineral processing; in especially copper (26) copper (29) fact, concentra�on greater in 2019, due to more Aspirational mining exports comparator Ore mining, especially Mineral processing, copper (14) especially copper (22) • Other significant goods exports are all resource- Resource based intensive • Service exports 12% of total, growing slowly Kazakhstan Oil and gas (56) Oil and gas (67) • Very high and increasing concentra�on on oil and gas. Aspirational Iron and steel (12) Iron and steel (6) comparator • Major industry export, iron and steel, has declining Copper refining (8) Copper refining (5) share Resource intensive • Service exports 12% of total, transport and travel main exports Russia Oil and gas (52) Oil and gas (52) • Litle change in export mix, with dominance of oil and gas Aspirational Commodi�es not Commodi�es not comparator elsewhere classified (12) elsewhere classified • No major industrial exports, with largest—iron and (13) steel—declining share Resource Iron and steel (6) intensive Iron and steel (4) • Service exports 13% of total; transport, other business services, and travel are main exports Malaysia Electrical/electronic Electrical/electronic • Goods exports oriented to manufacturing though machinery/equipment machinery/equipment declining share Aspirational (38) (34) comparator • Small evidence of diversifica�on in manufacturing Machinery (22) Oil and gas (14) Partial • Service exports 15% of total, about half in travel; resource Oil and gas (10) Machinery (9) growth in global innova�ve services intensive Australia Mineral fuels, especially Mineral fuels, • Dominance of resource exports increased over coal (21) especially coal (33) period High aspirational Ore mining, especially Ore mining, especially • Manufacturing exports are small share comparator lead (8) lead (29) • Services account for 20% of all exports; travel is main Resource service export; some growth in global innova�ve intensive services, but s�ll small export category Canada Vehicles (20) Mineral fuels (22) • Some diversifica�on outside resources, but oil and gas increased share High Mineral fuels (14) Vehicles (14) aspirational • Major manufacturing exports grew more slowly Machinery (9) Machinery (8) comparator • Service exports 20% of total exports; travel, Resource transport, and global innovator services are major intensive service exports First, the resources that had been the countries’ major exports in 2001 generally accounted for an even larger share of exports in 2019. This was true of copper in Chile and oil and gas in 28 Kazakhstan, and in Russia, oil and gas con�nued to account for over half of goods exports. This was even the case in the two high aspira�onal comparators. In Australia, coal and lead mining increased their share of goods exports, and the same was true for oil and gas exports in Canada. Second, the export data offer litle evidence of diversifica�on into industrial products over the period covered. In the first place, at the beginning of the period, there were few industrial products that accounted for at least 3 percent of goods exports. With the excep�on of Malaysia and Canada, those products that did exceed this threshold were examples of resource-based and not broad-based (i.e., unrelated to natural resources) diversifica�on. The most prominent examples were copper alloys in Chile and iron and steel in Kazakhstan. In each of these cases, exports grew more slowly than overall goods exports, and their share of goods exports declined over the period. Malaysia and Canada had more diversified goods exports than the other countries. Moreover, the industrial products that have had significant export volumes in these countries—electrical machinery and machinery in Malaysia and vehicles and machinery in Canada—are not closely linked to resources. However, in both countries, the export shares of these products decreased between 2001 and 2019, and no new industrial products became high-volume exports in either country over the period. Third, although services need to be important in economic diversifica�on going forward, they remain a small part of total exports. Among the aspira�onal countries, services only account for 12–15 percent of all exports, and this share remained largely unchanged over the period of our analysis. Tradable services are more important in Australia and Canada, where they account for about 20 percent of all exports. Ideally, growth is most desirable in global innovator services that are tradable, and technologically and skills-intensive, and can thus be sources of produc�vity growth and high-wage jobs. In this respect, there have been some posi�ve developments in the comparator countries. Although lower-skill services such as transport and travel remain major service exports, several examples of global innovator services have become more significant. Not unexpectedly, this is most evident in Canada, where other business services; 8 telecommunica�ons, computer, and informa�on services; and financial services are all significant service exports. But there is also evidence of global innovator service exports growing in some of the aspira�onal comparators. Other business services are an increasingly important export for Chile, Russia, and Malaysia, as are telecommunica�ons, computer, and informa�on services in the later two. What does our analysis mean for Mongolia? Diversifica�on into nonresource goods will be challenging based on the experience of other resource-dependent countries included in the analysis. Indeed, Mongolia’s export paterns are similar to the other countries we have analyzed. Goods exports are dominated by two commodi�es, coal and copper, and their share increased over the 2001–19 period. No industrial products have emerged as major exporters, and trade in services remains small. However, while no new industrial products became important sources of exports, there are some that, while s�ll small, did grow rapidly. Copper refining and meat prepara�on are the best 29 examples. These are connected to a country’s primary sector products and are “short leaps,” to use the language of economic complexity. Indeed, processed product exports, such as meat, wool, and cashmere, are included in the Vision 2050 diversifica�on strategy. Currently, processed products account for a very small share of exports, but some—most notably, meat prepara�on— have been growing rapidly. Longer leaps into manufacturing goods that have driven structural change in some other East Asian economies are less likely in Mongolia because of its remoteness and small popula�on. Services may offer more promising prospects based on evidence from comparator countries as well as Mongolia’s endowments. Travel, which primarily consists of tourism, remains the most important service export. Tourism is iden�fied in the Vision 2050 strategy as a priority sector for future growth. In addi�on, some global innovator services demonstrate poten�al. Other business services grew rapidly, and telecommunica�ons, computer, and informa�on services – the fourth- largest category – is flagged for its growth poten�al in Vision 2050. Mongolia’s remoteness is less of a constraint for services such as these, where connec�vity comes from technology. Moreover, Mongolia’s youth are reasonably well-educated. COVID has resulted in the rapid growth of digitaliza�on worldwide, which could make this a promising �me for Mongolia to leverage its human capital to expand technology-intensive services. Major investments in the hard and so� infrastructure necessary for global innovator services such as business services and telecommunica�ons, computers, and informa�on services will be essen�al. Mongolia’s resources can generate the capital for these investments if they are used strategically for economic growth and diversifica�on. 30 References Ahmadov, A. 2014. “Blocking the Pathway out of the Resource Curse: What Hinders Diversifica�on in Resource-Rich Developing Countries?” Global Economic Governance Working Paper 2014/98, Global Economic Governance Programme, Blavatnik School of Government, University of Oxford, Oxford, UK. htps://www.geg.ox.ac.uk/publica�on/geg- wp-201498-blocking-pathway-out-resource-curse-what-hinders-diversifica�on-resource. Balland, P.A., T. Broekel, D. Diodato, E. Giuliani, R. Hausmann, N. O'Clery, and D. Rigby. 2022. “Reprint of the New Paradigm of Economic Complexity.” Research Policy 51 (8): 1–11. https://doi.org/10.1016/j.respol.2022.104568. Betcherman, G., and M. Jalil. 2023. “Benchmarking Labor Market Indicators for Mongolia.” Background Paper for the Mongolia Jobs Diagnos�c, World Bank, Washington, DC. Betcherman, G., Y. Huang, N. Millan, and M. Norjinlkham. 2022. Mongolia Jobs Diagnostic: More Vibrant and Inclusive Labor Markets for Economic Recovery and Diversification— Synthesis Report. Washington, DC: World Bank Group. http://documents.worldbank.org/curated/en/099030001132333606/P17445302d04700790 adb8066bc652052a1. Coniglio, N., D. Vurchio, N. Cantore, and M. Clara. 2021. “On the Evolu�on of Compara�ve Advantage: Path-Dependent versus Path-Defying Changes.” Journal of International Economics 133 (November): 103522. htps://doi.org/10.1016/j.jinteco.2021.103522. Government of Mongolia. 2020. Vision 2050: Long-Term Development Policy for Mongolia. Ulaanbaatar: Government of Mongolia. Hidalgo, C., and R. Hausmann. 2009. “The Building Blocks of Economic Complexity.” Proceedings of the National Academy of Sciences of the United States of America 106 (26): 10570–75. htps://doi.org/10.1073/pnas.0900943106. IMF (Interna�onal Monetary Fund). 2009. Balance of Payments and International Investment Position Manual. 6th ed. Washington, DC: IMF. htps://www.imf.org/external/pubs/�/bop/2007/pdf/bpm6.pdf. Joya, O. 2018. “How Should Resource-Rich Countries Diversify? Es�ma�ng Forward-Linkage Effects of Mining on Produc�vity Growth.” Economics of Transition and Institutional Change 27 (2): 457–73. htps://doi.org/10.1111/ecot.12191.   Lashitew, A.A., M.L. Ross, and E. Werker. 2021. “What Drives Successful Economic Diversifica�on in Resource-Rich Countries?” World Bank Research Observer 36 (2): 164–96. htps://doi.org/10.1093/wbro/lkaa001. Lin, J.Y. 2010. New Structural Economics: A Framework for Rethinking Development. Policy Research Working Paper 5197, World Bank, Washington, DC. htp://hdl.handle.net/10986/19919. 31 Lin, J.Y., and H.T. Dinh. 2014. “The New Structural Economics and Strategies for Sustained Economic Development in the Pacific Island Countries.” In Oxford Handbook of the Economics and the Pacific Rim, edited by I.N. Kaur and N. Singh, 198–229. Oxford: Oxford University Press. htps://doi.org/10.1093/oxfordhb/9780199751990.013.009. Lin, J.Y., and C. Monga. 2010. “The Growth Report and New Structural Economics.” Policy Research Working Paper 5336, World Bank, Washington, DC. htps://doi.org/10.1596/1813- 9450-5336. Lin, J.Y., and Y. Wang. 2014. “Kazakhstan and Regional Integra�on: Joining Global Supply Chains via Growth Iden�fica�on and Facilita�on.” Working Paper. Lin, J.Y., and J. Xu. 2016. “Applying the Growth Iden�fica�on and Facilita�on Framework to the Least Developed Countries: The Case of Uganda.” Commitee for Development Planning Background Paper 32, Department of Economics and Social Affairs, United Na�ons, New York. htps://www.un.org/development/desa/dpad/wp- content/uploads/sites/45/publica�on/CDP-bp-2016-32.pdf. Mendoza, A. 2021. “Harmonized Commodity Descrip�on and Coding Systems (HS).” United Na�ons Sta�s�cs Wiki, May 12. htps://unstats.un.org/wiki/pages/viewpage.ac�on?pageId=87426301. Nayyar, G., M. Hallward-Dreimer, and E. Davies. 2021. At Your Service? The Promise of Services- Led Development. Washington, DC: World Bank. htp://hdl.handle.net/10986/35599. Rodrik, D. 2016. “Premature Deindustrializa�on.” Journal of Economic Growth 21 (March): 1–33. htps://doi.org/10.1007/s10887-015-9122-3. ———. 2021. “The Metamorphosis of Growth Policy.” Project Syndicate, October 11. htps://www.project-syndicate.org/commentary/new-growth-policies-for-developing- countries-by-dani-rodrik-2021-10. Sichoongwe, K., O. Kaonga, and C. Hapompwe. 2021. “Applying the Growth Iden�fica�on and Facilita�on Framework: The Case of Zambia.” Innovation 2 (4): 73–83. htps://doi.org/10.11648/j.innov.20210204.14. Tudela-Pye, J., and D. Meroto. 2023. “Macroeconomic Diagnos�cs.” Background Paper for the Mongolia Jobs Diagnos�c, World Bank, Washington, DC. World Bank. 2020. Minds and Mines: Leveraging Natural Wealth to Invest in People and Institutions. Mongolia Country Economic Memorandum. Ulaanbaatar: World Bank. htps://openknowledge.worldbank.org/handle/10986/34551. World Bank Group. 2019. “Economic Diversifica�on: Lessons from Prac�ce.” In Aid for Trade at a Glance 2019: Economic Diversification and Empowerment, edited by the Organisa�on for Economic Co-opera�on and Development and World Trade Organiza�on, 135–60. Paris: OECD. htps://doi.org/10.1787/18ea27d8-en. 32 Xu, J. 2017. “Growth Iden�fica�on and Facilita�on Framework: A Pragma�c Approach for Promo�ng Economic Structural Transforma�on.” Center for New Structural Economics at Peking University, August 9. htps://www.nse.pku.edu.cn/en/ywsy/news/245659.htm. Xu, J., and S. Hager. 2017. “Applying the Growth Iden�fica�on and Facilita�on Framework to Nepal.” Commitee for Development Planning Background Paper 35, Department of Economic and Social Affairs, United Na�ons, New York. htps://www.un.org/development/desa/dpad/wp- content/uploads/sites/45/publica�on/CDP-bp-2017-35.pdf. 1 Professor Emeritus, School of Interna�onal Development and Global Studies, University of Otawa, Otawa, Canada. 2 PhD Candidate, School of Interna�onal Development and Global Studies, University of Otawa, Otawa, Canada. 3 These comparators are also used in the Mongolia Jobs Diagnos�c (Betcherman et al. 2022) and some of the background research papers for the Jobs Diagnos�c. 4 It should be noted that for all indicators used in the selec�on of comparators, we did not include data a�er 2019 because of poten�al COVID-19 effects. 5 See Xu 2017. 6 Export values are es�mated by Comtrade based on data received from na�onal central banks, which are in local currency; Comtrade then uses an average annual exchange rate to convert local currency to U.S. dollars. A similar process applies to the services trade data. 7 This category includes services for processing goods either in the repor�ng country or abroad. The Interna�onal Trade Centre notes that the trade valua�on for this service changed in 2010 and advises cau�on in interpre�ng the data. 8 This category includes research and development, professional consul�ng, and technical and trade-related services. 33 Most Recent Jobs Working Papers: 79. Heading Towards 1.5ºC – Impacts on Labor Demand in Selected Countries (2024) Ulrike Lehr and Hector Pollitt. 78. Shaping Better Jobs Policies through Measurement: Findings from a Pilot Program to Estimate Indirect Jobs (2023) Theresa Osborne and Jose Manuel Romero 77. Occupational Choice and Energy Access – Electricity For More and Better Jobs (2023) Ulrike Lehr 76. Measuring Ex Ante Jobs Outcome of the Bangladesh Livestock and Dairy Development Project. (2023) Mansur Ahmed, FNU Jonaed and NazmulHoque. 75. Jobs, Food and Greening: Exploring Implications of the Green Transition for Jobs in The Agri-Food System. (2023) Gianluigi Nico and Luc Christiaensen. 74. Who Is Most Vulnerable to the Transition Away from Coal? Ruda Śląska Residents’ Preferences towards Jobs and Land Repurposing. (2023) 73. Does Agricultural Intensification Pay? (2023) Ghislain Aihounton and Luc Christiaensen. 72. Cost-Effectiveness of Jobs Projects in Conflict and Forced Displacement Contexts—Annexes. (2022) Virginia Barberis, Laura Brouwer, Jan Von Der Goltz, Timothy Hobden, Mira Saidi, Kirsten Schuettler and Karin Seyfert. 72. Cost-Effectiveness of Jobs Projects in Conflict and Forced Displacement Contexts. (2022) Virginia Barberis, Laura Brouwer, Jan Von Der Goltz, Timothy Hobden, Mira Saidi, Kirsten Schuettler and Karin Seyfert. 71. Towards a Just Coal Transition Labor Market Challenges and People’s Perspectives from Wielkopolska. (2022) Luc Christiaensen, Céline Ferré, Maddalena Honorati, Tomasz Janusz Gajderowicz and Sylwia Michalina Wrona. Click here for full Jobs Paper Series Address: 1776 G St, NW, Washington, DC 20006 Website: http://www.worldbank.org/en/topic/jobsanddevelopment Twitter: @WBG_Jobs Blog: https://blogs.worldbank.org/jobs/