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Jiajun XU: Growth Identification and Facilitation Framework: A Pragmatic Approach for Promoting Economic Structural Transformation
2017-08-09
Content introduction：
Growth Identification and Facilitation Framework:
A Pragmatic Approach for Promoting Economic Structural Transformation
NSE aims to have a better understanding of the complexities and nuances of industrial policy making in an effort to make it work better. From the perspective of NSE, the effective implementation of industrial policy entails three essential elements. First, governments need to work together with private sectors to target industries compatible with the country’s latent comparative advantages. Second, governments need to identify sector-specific binding constraints in a dynamic manner whose solution may go beyond the capacity of private firms themselves. Third, governments can play a facilitating role in mitigating the binding constraints and turning the latent comparative advantages into competitive advantages in the global economy (Lin 2014).
To make the above insights implementable, the NSE has proposed a practical policy tool – the Growth Identification and Facilitation Framework (GIFF) – to help policymakers in catching-up developing countries to develop feasible and sharply focused policies in an effort to identify and unlock their latent comparative advantages to achieve structural transformation (Lin and Monga 2011). 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 comparative advantages. The GIFF offers practical development paths for enabling developing countries to follow comparative advantage in its industrial development and to tap into the potential of advantages of backwardness in industrial upgrading in an effort to achieve sustained, dynamic growth.
The application of the baseline GIFF entails six steps (Lin 2012b):
Step One: choosing the right target. The government in a developing country can identify the list of tradeable goods and services that have been produced for about 20 years in dynamically growing countries (so-called benchmark countries) with similar endowment structures and a per capita income that is about 100% to 300% higher than their own (or a similar per capital income about 20 years ago).
Step Two: removing binding constraints. The government may give priority to those which some domestic private firms have already entered spontaneously, and try to identify constraints to quality upgrading or further firm entry. Take action to remove these constraints.
Step Three: attracting global investors. In industries where no domestic firms are currently present, or only a few domestic firms are doing exports, seek foreign direct investment (FDI) from countries examined in step 1, or organize new firm incubation programs.
Step Four: scaling-up self-discoveries. Due to the fact that every country may have some unique endowments, which may produce goods valuable for the market, and some new technologies/industries might not exist 20 years ago, in addition to the industries identified in step 1, the government should also pay attention to spontaneous self-discovery by private enterprises and give support to scale up successful private innovations in new industries.
Step Five: recognizing the power of industrial parks. In countries with poor infrastructure and bad business environments, special economic zones or industrial parks may be used to overcome barriers to firm entry, attract FDI, and encourage industrial clusters.
Step Six: providing limited incentives to the right industries. The government may compensate pioneer firms identified above with: tax incentives for a limited period; direct credits for investments; and access to foreign exchange.
While the six-step method is useful to offer a ready-made practical guide accessible to policymakers, applying the GIFF is by no means a mechanical process. The above baseline GIFF has been applied to a series of country studies primarily based on desk research. The GIFF was first applied to the case of Nigeria by Justin Yifu Lin and Volker Treichel (Lin 2012a). Later the application of the GIFF has been extended to small islands economies (J. Y. Lin and Dinh 2014) and Kazakhstan (Lin and Wang 2014). The United Nations Industrial Development Organization has proactively applied the GIFF to the case of Senegal (UNIDO 2016). The pilot effort to apply the GIFF to least-developed countries (LDCs) can be found in the case of Uganda (Lin and Xu 2016) and later extend to Nepal (Xu and Hager 2017). It is worth noting that these case studies are first-cut efforts, which mainly serve the illustrative purpose. It is not intended that these case studies are used to demonstrate that the GIFF analysis has to stick to certain static methods or indicators such as revealed comparative advantages (RCA) and the ranking of export products.
Rather, GIFF analysis entails a multi-stakeholder, interactive and pragmatic approach to identifying latent comparative advantages and exploring feasible policy levers to mitigate binding constraints. It involves multiple stakeholders, including policymakers, entrepreneurs, foreign investors, international buyers, zone developers, financial institutions, researchers, and international development institutions. It is an interactive process as the reliable GIFF analysis defies a top-down approach driven by bureaucrats or experts; rather, the effective diagnosis and solution requires dynamic interactions of self-discovery among public and private sectors as well as international and local actors. It is a pragmatic approach because it rejects a one-size-fits-all blueprint and embraces a tailored trial-and-error exploration of learning and experimentation. In short, the GIFF does not stick to any specific method, but rather a set of adaptive tools for identifying latent comparative advantages or binding constraints.
To refine and enrich the GIFF, the Center for New Structural Economics (CNSE) at Peking University, where Professor Justin Yifu Lin is the Director, has been undertaking policy-focused and action-oriented projects in African countries such as Djibouti, Benin, and Nigeria. Based on solid first-hand data collection and stakeholder engagement, we help government to analyse the country’s factor endowment (‘what the country has’) and its latent comparative advantages (‘what the country can potential do well’) and propose what the government can leverage special economic zones to mitigate binding constraints on the path to economic transformation. These pilot projects can help to distil success factors and sober lessons for refining the GIFF in a dynamic learning process.
Realizing that the GIFF analysis is a multi-stakeholder, interactive and pragmatic approach can help to dispel some uninformed critiques or misunderstandings.
First, one critique of GIFF is that neither policymakers nor researchers are able to ‘pick the winners’. In other words, it is dangerous to choose the target industry by simply taking a mechanical step to select benchmark countries and benchmark sectors by using international trade data. This critique fails to understand that the first step of GIFF – choosing the right target – does not aim to precisely pin down viable sectors with latent comparative advantages in catching-up countries but rather to identify potential ‘flying geese’ from more advanced economies and then make an initial assessment on whether these flying geese can be viable in the catching-up countries.
Historical waves of industrial transfers have revealed a rule-of-thumb pattern that the GDP per capita of benchmark countries is about 100% to 300% higher than that of catching-up ones. This ratio is not something that is set in stone; rather, it serves as a safeguard against too ambitious goal-setting which often results in targeting too advanced industries in too mature industrialized economies that defy the latent comparative advantages of the catching-up countries.
After identifying the potential flying geese, it is important to take a step further to examine when and where these manufacturers decide to relocate their production line. Answering this question cannot simply rely on the numeric trade data alone; in-depth case studies are needed to uncover the firm-level decision-making process in an effort to have a better understanding of which kinds of firms are more likely to go abroad and where they are likely to go. Special attention needs to be paid to the following aspects:
- First, how will the automation mitigate the pressure of rising labour costs hence postponing the relocation decision by labour-intensive light manufacturing firms?
- Second, how does the cluster-based economic geography in the benchmark countries exacerbate the coordination problem among small-/medium-sized enterprises (SMEs) along the supply chain, compared with the vertically integrated firms?
- Third, how does the relocation decision by processing trade firms where global buyers often control the sales market differ from that by ordinary trade firms?
After identifying the potential flying geese and their likely destinations, the next step is to make an initial judgment on whether these products are in line with latent comparative advantages in the catching-up economies. Such preliminary assessment often involves a process of negation, i.e., deleting those sectors that are unlikely to be viable in destination countries. First, labour and capital are two important factor cost, which can help to detect sectors without latent comparative advantages. Sectors with latent comparative advantage are those with relatively lower factor costs but suffer from high transaction costs due to poor hard and soft infrastructure. For instance, a labour-intensive garment firm will not be viable in a country with prohibitive labour costs. Second, apart from labour and capital, it is also essential to take into account more tailored sector-specific factor endowments. For example, some fashionable products are very time sensitive, which requires timely and reliable transportation. A landlocked poor country without transportation routes in place may encounter a hard binding constraint on firm entry and scaling up at least in the short run. More often than not, private sectors have the best knowledge about the sector-specific factor endowments. So it is desirable to bring the potential flying geese, i.e., possible foreign investors, to conduct the in-depth feasibility study in the destination countries. This can help to have a more thorough understanding of the pull and push factors regarding investing a particular product in a specific geographical location.
A second critique of the GIFF analysis is that it appears to have ignored the growing trend of global value chain (GVC) in the international trade. This critique is based on the impression that the pilot country studies, primarily based on desk research, mainly rely on the UNComtrade database to come up with a list of tradeable goods and services that have been produced for about 20 years in the benchmark countries. But the UNComtrade data fails to capture the growing trend of global value chains, which often misrepresents the trade structure. Indeed, this limitation is well noted by the CNSE team. To complement the UNComtrade data (i.e., sectors the 4 or 6 digit code), the CNSE has drawn on databases on the trade in value added in order to have a better understanding of a country’s participation in GVC. More importantly, the CNSE team has further conducted firm-level surveys to delve deeper into the diagnosis of binding constraints along the value chain on the product level. This bottom-up approach complements with numeric analysis on revealed comparative advantages based on trade databases.
Last but not least, another critique of GIFF is that it is too optimistic about the power of special economic zones (SEZs). The rationale for focusing on the SEZs is that developing countries often suffer from poor overall soft and hard infrastructure. For example, the administration system is riddled with inefficiency and corruption, and the transportation is in a poor state. Given the poor initial conditions, it is difficult, if not impossible, to bring the overall nationwide environment to an acceptable level within a relatively short period of time. To break the vicious cycle, it is important to achieve a breakthrough in a demarcated area given limited resources at hand. Upon initial success, the pilot SEZs can play a catalytic role in stimulating economic reforms nationwide later on. Indeed, SEZs provide an opportunity for significant small-scale trials which can enable a process of learning and experimentation among key stakeholders including government, enterprises, zone developers, and foreign investors. Despite the huge potential of SEZs for achieving quick wins and wider economic reforms, the GIFF does not suggest that SEZs are a silver bullet. Rather, the GIFF realizes that not all SEZs deliver the promise. The success of SEZs largely depends on how it designed, run and managed. That is why the CNSE team has embarked on persistent efforts to study the success and failures of SEZs in order to have a better understand of what works and why.
Through a multi-stakeholder, interactive and pragmatic process, the GIFF is a pragmatic tool for turning a trilemma into a triple-win collaboration to facilitate the new wave of industrial transfer. It is hard to appreciate the value of GIFF unless we put it into a global perspective of industrial transfer in an era of globalization. An integral part of Kaname Akamatsu’s ‘flying geese’ model of Asian economic integration is the phenomenon through which a region as a whole becomes more economically developed through a cascading process in which a more advanced country (the ‘lead goose’) transfers capital, technology and management skills to a less developed country (a ‘follower goose’) and so facilitate their economic structural transformation (Kojima 2000). As wages have been rapidly increasing in China as average incomes rise, the pending relocation of Chinese light manufacturing presents a historical window of opportunity for catching-up countries to break into global value chains. With 85 million light manufacturing jobs, China’s upgrading to higher industries will leave a huge space for many low-income developing countries to enter a labour-intensive industrialization development phase (Lin 2012c).
Despite the tremendous opportunities for large-scale industrial transfer, however, the catching-up developing countries, African countries in particular, face basic challenges such as lack of manufacturing capability, lack of confidence of international buyers, and lack of necessary infrastructure and business environment – so-called trilemma. To overcome the above challenges, Developing country governments can take a proactive approach centering around special economic zones/industrial parks given the overall poor business environment. The strategy is to attract existing export-oriented light manufacturing firms that have the technological and managerial knowhow and the confidence of international buyers, to relocate their production to special economic zones/industrial parks in Africa. The aim is to create quick wins that produce a snowball effect, attracting foreign direct investment and domestic investment into these zones and parks and others that are inspired by them. Such success stories serve as inspiration and experience for other developing countries to kick-start their own paths to sustainable and inclusive industrialization.
In a nutshell, the GIFF is pragmatic instrument for taking a multi-stakeholder, interactive and pragmatic approach to identifying latent comparative advantages and exploring feasible policy levers to mitigate binding constraints in an effort to help developing countries to achieve economic structural transformation. Applying the GIFF involves a process of constant refinements, as it aims to tackle practical development challenges on the ground that defies any universal standardized formula. A GIFF study is not an ordinary research report that will go to the shelf. Rather, conducting such a GIFF study entails creating an integrated platform for engaging in a journey of co-discovery of latent comparative advantages and binding constraints as well as co-generation of tailored and feasible policy recommendations. The shared goal is to unleash the potential for economic structural transformation among key stakeholders including policymakers, entrepreneurs, international buyers, international organizations, and researchers in the catching-up developing countries and abroad.
References:
Kojima, Kiyoshi. 2000. “The ‘flying Geese’ Model of Asian Economic Development: Origin, Theoretical Extensions, and Regional Policy Implications.” Journal of Asian Economics 11 (4): 375–401. doi:10.1016/S1049-0078(00)00067-1.
Lin, Justin, and Celestin Monga. 2011. “Growth Identification and Facilitation?: The Role of the State in the Dynamics of Structural Change.” Development Policy Review 29 (3): 264–90.
Lin, Justin, and Jiajun Xu. 2016. “Applying the Growth Identification and Facilitation Framework to the Least Developed Countries: The Case of Uganda.” CDP Background Paper No. 32. United Nations Department of Economic and Social Affairs.
Lin, Justin Yifu. 2012a. New Structural Economics: A Framework for Rethinking Development and Policy. Washington, D.C.: The World Bank.
———. 2012b. The Quest for Prosperity: How Developing Economies Can Take Off. Princeton and Oxford: Princeton University Press.
———. 2012c. “From Flying Geese To Leading Dragons: New Opportunities and Strategies for Structural Transformation in Developing Countries.” Global Policy 3 (4): 397–409. doi:10.1111/j.1758-5899.2012.00172.x.
———. 2014. “Industrial Policy Revisited: A New Structural Economics Perspective.” China Economic Journal 7 (3): 382–96. doi:10.1080/17538963.2014.949025.
Lin, Justin Yifu, and Hinh 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 Inderjit Kaur and Nirvikar Singh, 198–229. Oxford University Press.
Lin, Justin Yifu, and Yan Wang. 2014. “Kazakhstan and Regional Integration: Joining Global Supply Chains via Growth Identification and Facilitation.”
Lin, Justin Yifu, and Jiajun Xu. 2016. “Applying the Growth Identification and Facilitation Framework to the Least Developed Countries: The Case of Uganda.” CDP Background Paper No. 32 ST/ESA/2016/CDP/32. United Nations Department of Economic and Social Affairs.
UNIDO. 2016. “Senegal: A GIFIUD Pilot towards Quick Wins in Inclusive and Sustainable Industrialization.”
Xu, Jiajun, and Sarah Hager. 2017. “Applying the Growth Identification and Facilitation Framework to Nepal.” CDP Background Paper No. 35 ST/ESA/2017/CDP/35. United Nations Department of Economic and Social Affairs.
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