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The global economy remains unable to find the path towards recovery. According to the latest IMF estimates, global growth declined for the fifth consecutive year in 2015. And in 2016 and beyond, activity will remain weak, with global growth forecasts around 3%. While after the unravel of the global financial crisis advanced economies were largely responsible for the subdued recovery, today the blame is jointly shared by advanced and emerging economies, particularly in Latin America. The latter are being particularly affected by the rebalancing process in China, the fall in commodity prices and the gradual tightening of financial conditions in the United States, generating a mix of factors leading to lackluster growth in emerging markets. As a result, 2015 marked the first time in 15 years that emerging economies´ growth - excluding China- slipped below that of developed world. These subdued growth trends are evident in Latin America, which not only has moved away from the high rates of growth seen during the 2000s, but also facing recurrent downward revisions to growth forecasts, which could be signaling lower potential growth rates going forward.
A challenging aspect of growth in emerging markets has been the lack of progress in productivity. The phenomenal growth that much of the emerging world, especially in Latin America, posted during the 2000s was largely based on factor contribution, with productivity having a relatively marginal role. As Figure 1 illustrates, with the exception of some Asian countries such as China, India and Indonesia (OECD, 2014), most emerging countries made little progress in reducing their productivity gap with advanced economies (using the United States as a reference). This divergence is not only surprising in light of the sustained wave of growth that the emerging world registered during the last decade, but also because it is in disagreement with conventional theories of technological catch-up (Solow, 1956; Grossman and Helpman, 1991; Romer, 1990). The poor productivity record of most emerging countries, notably in Latin America, is also behind the middle-income trap, i.e. the long-lasting slowdown in growth that many countries face when they approach middle levels of per capita income. On this regard, Latin America has proven to be a fertile ground for this development trap. While several countries in the region were already in the middle income range as early as 1950, only a handful of Latin American nations have transitioned to the high income group (e.g., Chile, Uruguay, Trinidad & Tobago and a few Caribbean countries). Against this trend, the major economies in the region remain in the trap for several decades now.
The lack of an adequate pool of skilled workers prevents new growth engines to happen in emerging economies, due to a ‘misallocation of talent’ (Agénor, Canuto, and Jelenic, 2012) and low-quality education (Hanushek and Woessmann, 2009), thus hindering the overcoming of the middle-income trap. The incidence of the middle-income trap is traced to the difficulty of adjusting the economy to the sources of growth that become more important after reaching middle-income levels (Eichengreen, Park and Shin, 2011; Agénor, Canuto, and Jelenic, 2012; Felipe, Abdon and Kumar, 2012; Aiyar et al., 2013; for a more nuanced view Ye and Robertson, 2016). Moreover, several characteristics of the contemporary economy -from globalization to technological progress- made industry productivity more dependent on a broad, complex and difficult-to-achieve set of skills. The increased complexity of skills required to succeed in today’s global economy also impacts the ability of formal education systems to provide timely solutions. In an evaluation of the incidence of skills gaps in Latin America, Schwalje (2011) concludes that regional education systems fail to create the skills necessary to facilitate development and productive diversification. Other analyses (Cunningham and Villasenor, 2014) argue that this detachment between formal education and business skill demands arises from the latter being increasingly geared towards non-cognitive skills, which are not usually included in formal education curricula.
Latin America stands out as the region where a larger percentage of firms face difficulties to find the skills they need, according to World Bank’s Enterprise Surveys. Figure 2 shows that 36% percent of surveyed firms in Latin America declare an inadequately educated workforce to be a major constraint for their performances. This share is significantly above the average for the world´s sample (20.9%) and more than double those for regions with distinct development trajectories as South Asia, OECD economies, and East Europe and Central Asia. This adds to a recent report by Manpower (2015) that reveales significant shortages of trade workers, sales representatives, engineers and production technicians. Six Latin American countries ranked in the top 16 countries where employers declare having the most difficulties filling jobs: Peru (68%), Brazil (61%), Mexico (54%), Colombia (47%), Costa Rica (46%) and Panama (46%).
In a recent paper (Melguizo and Perea, 2016), we use World Bank’s Enterprise Surveys to further scrutinize skills demand satisfaction in key emerging economies regions (Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, Latin America, South Asia). The survey targets formal, non-agricultural private firms, provides information about firms’ characteristics (e.g., size, growth), and on the business environment in which the firm operates (e.g. infrastructure, informality, skills, e). With regards to skills, the survey offers a proxy of the skills gap through the following question: “Is an inadequately educated workforce no obstacle, a minor obstacle, a moderate obstacle, a major obstacle, or a very severe obstacle to the current operations of this establishment?”. To assess the size of skill gaps, we use two alternative estimation methods: first, a “collapsed” logit across the various response categories, which allows employing a standard logit; second, and in order to differentiate more clearly between response levels, a generalised ordered logit (Fu, 1998; Maddala, 1983). This method relaxes the “proportional odds” assumption of the standard ordered logit, therefore allowing for the coefficients of explanatory variables to change between levels of the dependent variable without the need for restructuring the data (Liu and Koirala, 2012). In so doing, it allows the exploitation of all the information included in the original dependent variable.
Our results confirm that Latin American firms encounter the greatest problems to satisfy their demand for skills, after controlling for the development stage of the country, the skill intensity of the firm and the industry where it operates (Table 1). In terms of odds ratios, Latin American firms are up to 13 times more likely to face performance problems derived from skills deficits than firms located in East Asia-Pacific (i.e., the reference group used across the different specifications). For a firm operating in Europe and Central Asia, this same odds ratio vis-à-vis East Asia-Pacific is 9 times, while for Africa and South Asia they are 3.9 and 4.3, respectively. By and large, firms in East Asia-Pacific are the least exposed to underperform due to inadequate skills.
Among sectors, Motor Vehicles industry shows the most acute skill gaps. The probability for firms in this sector to face skill-led performance problems is up to 1.6 time that of Other Manufacturing industries. At a close distance, the same probability for Machinery is 1.3. With these industries being characterized by high sophistication and knowledge intensity, our results suggest the challenge that a scant pool of skills poses for many developing countries to diversify into activities deemed more beneficial for development and industrial upgrading. It is no coincidence that some public-private associations are being developed in car and machinery industries through dual training schemes.
Usual caveats apply, mainly arising from limitations of our dataset. There is a growing consensus on the relevance of socio-emotional and high-order cognitive skills rather than the basic cognitive or technical ones. Unfortunately, we cannot test these hypotheses, as information on the type of skills demanded is not available. Another potentially important limitation of the dataset is the absence of the informal sector, which in many of the countries under study comprise the bulk of economic activity. In all, reaching a more comprehensive coverage in terms of skills and firm types should be a priority for developing further the empirical literature on this topic.
In any case, this analysis confirms the need for an urgent policy response to skills mismatches, particularly in Latin America. Labor and skills mismatches stand as key impediments for the development of the formal economy. Therefore, education policies should be significantly revised, including vocational education and training. A more active participation and co-ordination with the private sector and across different government bodies, as provided by the OECD Skills Strategy, is very important, since it can offer guidance on current and future business demands and provide training directly in the workplace. Rigorous evaluation mechanisms should be implemented, as these reported for Latin America in Alaimo et al (2015), to identify what works better for firms; but also for workers, particularly in terms of earnings quality and labour market security. Finally, more and better data is needed, including forward looking demands.
Table 1: Estimation results (selected specifications)
Note: significant coefficients (at least 95%) are represented in bold letters.
Agénor, R., O. Canuto and M. Jelenic (2012), “Avoiding middle-income growth traps”, Vox EU, 1 December 2012.
Alaimo, V., M. Bosch, D.S. Kaplan, C. Pagés and L. Ripani (2015), Jobs for growth. Washington DC, Inter-American Development Bank.
Aiyar, S., R. Duval, D. Puy, Y. Wu and L. Zhang (2013), “Growth Slowdowns and the Middle-Income Trap”, IMF Working Paper 13/71.
Cunningham, W. and P. Villasenor, 2014 (2014), “Employer voices, employer demands, and implications for public skills development policy”, Policy Research Working Paper Series 6853, World Bank.
Eichengreen, B., D. Park and K. Shin (2011), “When Fast Growing Economies Slow Down: International Evidence and Implications for China”, NBER Working Paper, 16919, National Bureau of Economic Research, Cambridge, MA.
Felipe, J., A. Abdon and U. Kumar (2012), “Tracking the Middle Income Trap: What is It, Who is in It, and Why?” Levy Economics Institute of Bard College, Working Paper 715.
Fu, V. (1998), “Estimating Generalized Ordered Logit Models”, Stata Technical Bulletin, 44, 27-30.
Grossman, G. and E. Helpmann (1991), “Trade, knowledge spillovers and growth”, European Economic Review, 35 (3), 517–526.
Hanushek, E. and L. Woessmann (2009), “Poor student learning explains the Latin American growth puzzle”, Vox EU, August 14 2009.
Liu, X. and H. Koirala (2012), “Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models to Estimate Educational Data”, Journal of Modern Applied Statistical Methods, 11:1.
Maddala, J. (1983), Limited Dependent and Qualitative Variables in Econometrics, Cambridge, Cambridge University Press.
Melguizo, A. and J.R. Perea (2016), “Mind the skills gap! Regional and industry patterns in emerging economies”. Working Paper 329. OECD Development Centre.
OECD (2014), Perspectives on Global Development 2014: Boosting Productivity to Meet the Middle-Income Challenge, OECD Publishing, Paris.
Romer, P. (1990), “Endogenous technological change”, Journal of Political Economy, 98 (5), 71–102.
Schalje, W. (2011), “The Prevalence and Impact of Skills Gaps on Latin America and the Caribbean”. MPRA Paper 30247.
Solow, R. (1956), “A contribution to the theory of economic growth”, The Quarterly Journal of Economics, 70(1), 65–94.
L. Ye and P.E. Robertson (2016), “Identifying prisoners of the middle-income trap”, Vox EU February 1 2016.