The Economic Boosters of the Emerging Markets

Education and Technology: economic boosters in Emerging Markets

By Luz Angela Serrano

Today more and more the emerging market economies are focus of analysis due to their economic capacity and progress. In this sense, this blog analyzes the effect of education on high technology exports across emerging markets particularly in the middle income economies in 2010. I argue that Emerging Markets with high levels of education are more likely to present high technology exports levels. Thus, the question proposed is: how has education affected emerging market economies’ growth in terms of high added value exports.

First of all, it is important to understand that low-income and middle-income economies are collectively referred to as developing economies or emerging markets according to the World Bank and the International Monetary Fund classification. The importance of studying emerging markets is based on the fact that these countries constitute approximately 80% of the global population, and represent about 20% of the world's economies (World Bank). Therefore, it has been recognized that emerging markets should take advantage of their favorable positions to rebuild policy buffers. Low and middle income countries should strengthen fiscal governance to make productive use of their natural and financial resources. In consequence, in all cases fiscal policy in emerging markets incorporates measures aimed at increasing productivity and long term growth.

Traditional theory and thinking focus on development as a transition from agricultural to industrial production, with manufacturing being the prime engine of growth. This is in line with the existing literature on how sophistication of goods exports affects growth. Hausmann et al (2007) have shown that it is not the specialization alone, but the sophistication of goods exports that matters for growth[1]. Additionally, reviewing the literature, I found that different studies have shown that innovation and technology transfer are the key drivers of economic growth in today’s world Economy. Indeed numerous studies have exposed that differences in per capita income depend on education and technological transfer. Therefore, education is a complement of technological advancement as part of the development growth. History has shown that countries tend to invest first in education and this fact allows me to think that technological exports occur after achieving certain level of education.

So, even though there is evidence of causal effect of education on economic growth in technological terms, there are not studies focusing on the effect of education on technological exports as an indicator of growth. Therefore, this blog aims to fill the gap in terms of analyzing the effect of education on the high technological exports as a measurement of exports diversification in middle income economies, arguing that appropriate economic policy in developing economies should concentrate on strengthening these processes throughout the country and easing the flow of information and technology between the main players – innovators, companies, state agencies and financial institutions[2]. One way to achieve this change is increasing the level of investment in education. The analysis excludes the low-income economies even though they are part of the emerging markets because it is likely that these economies do not have the ability to integrate into the world economy with exports of technology.

In order to response to the before mentioned question, I used data from the World Bank related to World development indicators and from the Quality of Governance Institute corresponding to 101 countries under the middle income economy category. Within this classification, the World Bank identifies 54 countries under the Upper-middle- income group and 47 countries under the Lower-middle-Income group. The internal categorization (Low and high) in the middle income economies is based on the GNI per capita of each country. In addition, to measure the effect of education on export diversification, the model uses High-technology exports with high R&D intensity as a percentage of manufactured exports. Regarding the primary explanatory variable I use the average schooling years in the total population aged 25 and over. Also, I incorporate others alternative explanatory variables such as urban infrastructure, population measured by the labor force (economic active people over 15), exports of goods and services (%GDP), economic income and some measures of trade and global integration (for more details see table 2). I include these additional explanatory variables because I suspect they may cause an effect on high technology exports and education in middle income countries. I recognize limitations since this model may contain variables that are correlated with other variables in the model such as GDP and exports of goods and services as a % of GDP. Finally, to estimate the effect of education on export diversification I use a linear regression model with transformation of some predictor variables.

The figure A presents the effect of education on the high-technology exports level among middle income economies. The results show that on average every additional year of schooling is associated with 38% increase in technology export level. However, the results are not homogeneous for all middle income countries; there are outliers’ countries such as China, Malaysia and Costa Rica, which are within the category of upper middle income economy that experiment advanced levels of education and larger levels of technology exports in regards to the average trend.

Moreover, table 1 presents the estimated effects of education on high technology exports. Model 1 shows the unconditional effects, and model 2 and 3 present conditional effects. Focusing on model 2, I find first that the effect of education is positive and linear on exports diversification. On average, and controlling for the labor force effect; the addition of one year on education in schooling is associate with 43% increase on technology exports among emerging markets. The effect is statistically significant in both cases (p<0.05). Model 3 incorporates more explanatory variables, but; unfortunately the results show that the effect of population (labor force) is the only one statistically significant. On average,  1% increase in the labor force is associated with 48% increase on technology exports.

Table 1. Determinants of High Technology Exports

DV: Technology Exports (log)	(1)	(2)	(3)
Avg. Schooling	0.38**	0.43***	0.17
	(3.14)	(4.13)	(1.13)
Labor force(Log)		0.42***	0.48***
		(3.92)	(4.01)
Exports of goods & serv. (Log)			0.66
			(1.50)
Improved water source (Log)			4.35
			(1.03)
GDP per capita (Log)			-0.24
			(-0.70)
Economic Globalization			0.027
			(1.29)
Constant	-0.83	-7.80***	-28.9
	(-1.21)	(-4.16)	(-1.57)
R-squared	0.17	0.38	0.32
Observations	44	44	39

Note: OLS estimates with t-stats in parentheses. ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001

Source: World Bank World Development Indicators and Quality of Governance.

In summary, filling the gap in terms of analyzing the effect of education on the high technological exports as a measurement of exports diversification in middle income economies, I find that education causes a positive effect on technology exports. Basically, an increase in years of schooling is associated with an increase in export diversification (measured by technology exports). Thus, emerging markets or developing countries with middle income economy should concentrate efforts on strengthening the education investment as an alternative to diversify added value exportation in order to achieve the ultimate goal of increasing a sustainable economic growth. In fact, countries should continue to build on specialization and sophistication in service activities as a potential route to economic growth and it is likely that technology exports are the appropriate approach to achieve economic growth as China has done. 

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Note: The following table presents a detailed summary statistics used in the analysis.

Table 2. Summary Statistics

Variable	Mean	Std. Dev.	Min.	Max.	Description
High-technology exports	1.46	1.29	-0.9	4.0	% of manufactured exports
Avg. Schooling	5.60	1.62	2.2	8.8	Average schooling years in total population  >25
Exports of goods & serv.	3.48	0.51	2.4	4.5	% of GDP
Labor force	16.04	1.69	12.7	20.5	economically active population >15
Improved water source	4.56	0.05	4.4	4.6	% of urban population with access
GDP per capita	8.48	0.69	6.4	9.7	Gross domestic product in current US dollars
Economic Globalization	57.79	10.65	36.6	81.9	actual flows of trade and investments, and restrictions on trade and capital
Observations	39

References:

• Hausmann, Ricardo, Jason Hwang, and Dani Rodrik (2007), “What You Export Matters”, Journal of Economic Growth, 12(1):1–25.
• Lall, Sanjaya, John Weiss, Jinkang Zhang (2005), “The Sophistication of Exports: A New Measure of Product Characteristics”, Queen Elizabeth House Working Paper Number 123.
• Gurbiel, Roman (2002).  On Economic Growth: The Central and Eastern Europe Experience. Warsaw School of Economics. Center of International Production Cooperation. 

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[1] See also Lall et al (2005) on production sophistication.

[2]  Gurbiel, Roman (2002).