Empirical exploration of the effects of economic diversification on human development

4.5 Empirical exploration of the effects of economic diversification on human development

This section introduces the promising new field of empirical research on the interrelations between human development and economic diversification. There are many different taxonomies and methodologies available with which to meas-ure the well-being and economic diversification of countries. The subsequent cross-sectional analysis compares the impact of different export diversification measures on human development and economic growth. Interestingly even this rather simple approach leads to quite robust results. Of course, much further empirical work can and needs to be done, using for instance panel data, applying network analysis techniques and controlling for several factors influencing the results. But one essential contribution of the analysis described below has been to open up an enormous range of interesting areas for analysis and in-depth insights into the relations between structural change and human welfare. This promises to provide us with a better understanding of the complex relations between economic and human development.

4.5.1 Measurement

This section discusses the variables and the methodologies that were applied. From around the start of the twenty-first century, there has been an increased focus on different taxonomies with which to measure human capabilities, well-being and social progress. The composed indicators range from objective measures of well-being and deprivation, such as the HDI (UNDP 1990) or the MPI (Alkire and Foster 2007; UNDP 2010), to more subjective measures of well-being, such as surveys of happiness and life satisfaction. New taxonomies tend to be multidimensional and combine several elements of both objective indicators such as life expectancy and health, and relatively subjective indicators such as community and family life or work-time balance, which might vary across cultures (e.g. Economist Intelligence Unit 2005; Stiglitz et al. 2009; Hall et al. 2010). For the sake of simplicity, we choose in this study the HDI as dependent variable because it the most commonly discussed and most broadly accepted and available indicator for human capabilities and well-being. It is certainly not a comprehensive indicator for all the different elements constituting human well-being and freedom, but at least it considers three basic elements which most people around the world would agree to be vital, namely income, health and education (UNDP 1990).

Regarding the measurement of economic diversification, great advances have been made using export and employment data (e.g. Funke and Ruhwedel 2001;

Hidalgo et al. 2007; Frenken et al. 2007; Saviotti and Frenken 2008; Hausmann and Hidalgo 2010). To calculate different dimensions of economic diversification (related and unrelated variety), we use export data for the year 2000 from a NBER dataset created by Feenstra et al. (2005). The dataset contains the exports of virtually all countries in the world to all other countries, distinguishing between 772 product categories at the 4-digit level of the Standard International Trade Classification (SITC-4). Export data is used because of its broad availability and relatively good comparability. Naturally, there might be some bias, in that larger countries tend to be more diversified (e.g. India in contrast to Lebanon); however, the study also applies the methodology suggested by Hidalgo et al. (2007) and Hausmann and Hidalgo (2010) to handle this problem by considering revealed comparative advan-tages above certain thresholds. The results also show that larger countries in terms of population, such as China and India, do not necessarily show a higher level of diversification than smaller countries, such as Belgium or Switzerland.

Based on the export data we calculate different proxy indicators for the eco-nomic diversity of countries; namely entropy, HHI, the number of revealed comparative advantages and the product ubiquity. Each of these measures consid-ers different dimensions of divconsid-ersity, such as the variety, balance and quality of the economic sectors in which the economies are able to reach a level of competi-tiveness and comparative advantage allowing them to export these goods.

• Entropy places a higher value on smaller sectors, measures both variety and balance, and allows for the differentiation between unrelated, semi-related and related variety (Frenken et al. 2007; Saviotti and Frenken 2008). The entropy H can be calculated as follows:

H5 a

n i51

p_ilog₂a1 p_ib,

where pi stands for the share of a given sector i in the total exports of a country.

The value of entropy grows along with an increase in the number of sectors and with the evenness of the distribution of the share of the total exports (Saviotti and Frenken 2008). An essential advantage of the entropy measure is that a hierarchical decomposition of the contribution of each sectoral level (e.g. 1- to 6-digit level in the SITC system) on the overall diversity can be made (Frenken 2007). Entropy values at different digit levels can be intro-duced into a regression analysis without necessarily leading to collinearity problems (Jacquemin and Berry 1979). This allows unrelated, semi-related and related variety to be distinguished by measuring the level of variety on different levels of sectoral aggregations (Frenken et al. 2007). In our empiri-cal application, the different types of variety are proxied by the entropies on the 1-digit, 2-digit and 4-digit level, respectively.

• The Hirschman-Herfindahl Index (HHI) places a higher weighting on larger sectors and basically measures concentration and balance of the sectors.

HHI5 a_i ° X_i a^NjX_j¢

2

The value of the HHI ranges between 0 and 1, where 1 supposes an absolute concentration of the exports x in one product sector i. Hence, the lower the value, the more balanced and less concentrated the sectors are.

• The number of revealed comparative advantages and the ubiquity of the exports (Hidalgo et al. 2007; Hidalgo and Hausmann 2009; Hausmann and Hidalgo 2010; Balassa 1965) are indicators which measure the amount and ubiquity/quality of export diversification. The revealed comparative advan-tage (RCA) measures whether a country c exports more of product i, as a share of its total exports, than other countries. It is calculated as follows:

RCA5

x(c, i)>aix(c, i)

a^c x(c, i)>a^{c, i}x(c, i)

If the RCA is higher than 1, country c has a comparative advantage in the export of the product i. If it is lower than 1, then the country has a compara-tive disadvantage. Furthermore, the empirical analysis calculates the average ubiquity of the products i exported by country c by using the method intro-duced by Hidalgo and Hausmann (2009).

k_{c, N} 5 1

k_{c, 0} a_i M_ci k_{i, N21}

where kc stands for the observed level of diversification of the exports of a country and ki for the ubiquity of a product, or in other words, the number of countries who export product i. Mci represents an adjacency matrix which measures the RCAs for each country (rows) in the 772 product categories (columns). Further information is available in Hidalgo and Hausmann (2009).

4.5.2 Results

A set of simple linear regression models are used to analyse the impact of the different types of diversification on human development and on gross domestic product at purchasing power parity per capital (GDP PPP per capita), respectively.

The available data on exports, human development and GDP PPP per capita for the year 2000 allows for the analysis of a comprehensive set of 121 countries, ranging from countries with very low to very high human development and from highly concentrated to very diversified export portfolios. The dependent variables of the cross-sectional analysis are the HDI and GDP PPP per capita for the year 2000;

and the explanatory variables are the entropies on the 4-, 2- and 1-digit level, the HHI, the number of RCAs and the average product ubiquity. This allows us to compare and plot 16 different simple linear regressions. The method is simple but provides robust results. First, economic diversification has a highly significant positive effect on both GDP and human development, independent of the diversi-fication indicator applied (see Table 4.2). The effect is so strong that, regardless of whether the measurement focuses on variety, balance, disparity or quality at the 1-, 2- or 4-digit levels, export diversification always plays a significant role in the explanation of the GDP and the human development of a country.

Second, it is striking that economic diversification explains more of the vari-ance in the HDI than in mere economic income (see Table 4.3). The determination Table 4.2 Empirical effects of economic diversification on human development and GDP Effects of different types of Human development GDP PPP per capita

diversity on HDI and GDP^a in 2000 in 2000

Simple linear regressions Stand. T Sig Stand. T Sig

N = 121 countries Coeff. Coeff.

Beta Beta

Entropy at the 4-digit SITC level 0.692 10.459 0.000 0.484 6.042 0.000 2-digit SITC level 0.648 9.285 0.000 0.453 5.545 0.000 1-digit SITC level 0.531 6.830 0.000 0.302 3.456 0.001 HHI at the 4-digit SITC level 0.538 6.954 0.000 0.279 3.166 0.002 2-digit SITC level 0.543 7.048 0.000 0.315 3.624 0.000 1-digit SITC level 0.455 5.571 0.000 0.230 2.579 0.011 RCAs No. RCA > 1 at the 0.637 9.004 0.000 0.524 6.712 0.000

4-digit level

Average ubiquity 0.584 7.839 0.000 0.388 4.598 0.000 Source: Human development data and charts (2013), Feenstra et al. (2005).

Notea Diversity measures based on Feenstra et al. (2005) export data from 2000, at the 1-, 2- and 4-digit level of the Standard Industrial Trade Classification (SITC).

coefficient (R²) is significantly higher for all the simple linear models explaining human development.

The high variance in the relation between economic diversification and economic growth can also be observed in Figure 4.3, Figure 4.4 and Figure 4.5. It is important to note that these results imply that the significant positive effect of diversification on human development does not just result from the fact that the HDI includes income. Economic diversification is a better predictor of human development than income taken alone. As such, economic diversification must also be positively related with other components of human development, such as education and life expectancy (which constitute the other two components of the HDI). Several rea-sons have been highlighted in the theoretical section of this chapter, including:

(a) it results in a better distribution of power within an economy; (b) the requirement of productive capabilities positively affects human development such as infrastruc-ture, institutions, health and education; and (c) it gives more occupational choices.

The implications for human development policy are straightforward: qualita-tive economic diversification is not only crucial for sustained economic growth, but appears to be even more important for human development. Accordingly, proper economic policy can substantially contribute to human development. Now the question arises: what are the impacts of different types of diversification and thus, which type(s) of diversification policy should be promoted? In an attempt to address these questions, this study contrasts the effects of unrelated, semi-related and semi-related variety on economic and human development. Figure 4.3, Figure 4.4. and Figure 4.5 plot (a) the related variety of countries at the 4-digit level (measured by Shannon entropy, Shannon 1948) (Figure 4.3), (b) the semi-related variety at the 2-digit level (Figure 4.4) and (c) unsemi-related variety at the 1-digit level (Figure 4.5) against the HDI and GDP per capita. Trendlines are added to the figures to show general tendencies in the described relations. A set of interesting observations can be made for further qualitative and empirical explo-ration. Whereas unrelated variety seems to have a marginally increasing positive effect on human development, related variety has – as predicted in theory sec-tion – a marginally decreasing positive effect on human development. In contrast, Table 4.3 Explanatory power of economic diversificationa for human development^a Coefficients of determination (R²) Human development GDP in 2000

N = 121 countries in 2000 R² R²

Entropy at the 4-digit SITC level 0.479 0.235

2-digit SITC level 0.420 0.205

1-digit SITC level 0.282 0.091

1- HHI at the 4-digit SITC level 0.289 0.078

2-digit SITC level 0.294 0.099

1-digit SITC level 0.207 0.053

RCAs No. RCA > 1 at 0.405 0.275

the 4-digit level

Average ubiquity 0.341 0.151

Source: Human development data and charts (2013), Feenstra et al. (2005).

Notea Diversity measures based on Feenstra et al. (2005) export data from 2000, at the 1-, 2- and 4-digit level of the Standard Industrial Trade Classification (SITC).

NOR

Relation between Related Variety and GDP per capita in 2000

ALB

Figure 4.3 The effects of related variety on human development and GDP^a Source: Human Development Index in 2000 (UNDP 2010). SITC export data in 2000 (Feenstra et al. 2005).

Notea The country codes are based on ISO 3166. Related variety is measured by the Shannon entropy on the 4-digit SITC level subtracted by the Shannon entropy on the 2-digit SITC level. The trendlines show a general tendency of the described relations.

ALB

Relation between Semi-Related Variety and GDP per capita in 2000

ALB

Figure 4.4 The effects of semi-related variety on human development and GDP^a Source: Human Development Index in 2000 (UNDP 2010). SITC export data in 2000 (Feenstra et al. 2005).

Notea The country codes are based on ISO 3166. Semi-related variety is measured by the Shannon entropy on the 2-digit SITC level subtracted by the Shannon entropy on the 1-digit SITC level.

The trendlines show a general tendency of the described relations.

ALB

Relation between Unrelated Variety and GDP per capita in 2000

ALB

Figure 4.5 The effects of unrelated variety on human development and GDP^a Source: Human Development Index in 2000 (UNDP 2010). SITC export data in 2000 (Feenstra et al. 2005).

Notea The country codes are based on ISO 3166. Unrelated variety is measured by the Shannon entropy on the 1-digit SITC level. The trendlines show a general tendency of the described relations.

marginally increasing positive returns of diversification for GDP can be observed in all three types of measured diversity.

As predicted in the theoretical section, economic diversity indeed seems to have a marginally increasing positive effect on GDP ( f´>0, f ˝>0). However, quite a high level of variance is evident. Some resource and oil-rich countries (such as Kuwait, Argentina and Norway) achieve very high levels of income despite comparatively low economic diversity values; in contrast, some large develop-ing countries (such as India and Pakistan) have low levels of average income but relatively high levels of economic variety. Nevertheless, a general tendency of marginally increasing returns of economic diversification on GDP can be observed, in line with theoretical approaches highlighting cumulative effects, increasing returns and recombinant growth (Myrdal 1957; Jacobs 1969; Romer 1986; Weitzman 1998).

Recombinant growth, however, seems to be realizable and gain full power only at high levels of economic diversity. One key reason for this seems to be the need to fill the gaps in the productive capabilities between different sectors before advanced recombinant growth can fully set in (Hidalgo et al. 2007). Gaps in the product space and structural heterogeneity can prevent learning and interactive innovation between the sectors and can also imply very strong differences in pro-ductivity and income generation (e.g. Furtado 1961; Katz 2007; ECLAC 2008).

This also partially explains the tendency of increasing positive returns of unrelated economic variety on human development. However, while virtually all countries with a high level of unrelated variety also have a medium to high level of human development, it is not the same picture when GDP is considered. In addition, as predicted theoretically, marginally decreasing positive effects of related economic variety on human development can be observed, whereas the effect on GDP con-tinues to have a marginally increasing tendency. The empirical results sustain the theoretical analysis and show the need to further explore the different effects of economic diversification on GDP and on human development. The core research hypothesis for further empirical proof and refinement in more advanced econo-metric studies as well as qualitative case studies can be summarized as follows.

1 Economic diversification has a positive effect on both human and economic development.

2 Whereas unrelated variety has a marginally increasing positive effect on human development (f´>0, f ˝>0), related variety has a marginally decreasing positive effect on human development (f´>0, f ˝<0).

3 Both unrelated and related economic diversification have a marginally increasing positive effect on economic growth (f´>0, f ˝>0).

4 Economic diversification is even more essential for human development than for income per capita, because it demands human capabilities and tends to distribute the economic and political power.

This study has been a modest preliminary attempt to explore the empirical rela-tions between economic diversification and human development. Naturally, these

hypotheses must be confirmed and further studied within systematic empirical work, using also panel data, introducing control variables and distinguishing between different dimensions of human development and types of economic diversification. In addition, the positive effects between economic diversification and human development certainly go in both directions. While diversification provides more choices and increases the demand for higher levels of human capabilities, human development is essential for the productive capabilities of a country to innovate and diversify. The strengths and directions of the effects, dependent on different types of economic diversification and dimensions of human development, must be further explored. Nevertheless, the main purpose to show possibilities for new studies and insights of the complex relations between structural change and human development has been achieved.

4.6 Chapter conclusion

This chapter has shown that economic diversification has multiple positive, negative, complex and changing effects on human development which deeply affect human development across space and over time. The data now available to researchers, along with interdisciplinary research approaches, however, enables more comprehensive examination of these complex effects. This in turn provides new insights allowing for a democratic debate and policy measures to make struc-tural change and economic diversity work for human development.

The diverse theoretical effects outlined above can be studied at national, regional or individual level, using a varied set of diversity measures. This pro-vides societies with many potential benefits, as it can contribute to a new understanding of socioeconomic development and help policy makers to foster economic and human development simultaneously. Studying the effects of eco-nomic diversification on the choices and capabilities of people opens up a large number of further promising possibilities for more comprehensively considering the relations between human and economic development. These include panel analysis, or triangulating with other data sources (e.g. employment data, different indicators of well-being and life standards, polynomial functions). A variety of new possibilities for theoretical and applied research in welfare economics and complexity research can be opened up. However, the main aim and value of the theoretical and empirical research on the effects of economic diversification on human development can provide the civil society, companies and policy-makers with new insights on how to simultaneously foster economic and human develop-ment in their regions and countries.

We have seen that a future-oriented policy to fostering individuals’ capa-bilities and choices goes hand in hand with an industrial policy promoting adequate economic diversification. Governments should foster different types of diversification – for instance, related or unrelated variety growth – according to their productive structure at a given point in time. To design proper innovation and development policies, a fruitful mix of selection and variation processes has to be found. At lower complexity levels, countries need to foster

endogenous capability upgrading and diversification evolution, which allow for systemic feedbacks. This is similar to the idea of development push strate-gies (e.g. Rosenstein-Rodan 1943; Nurkse 1953; Hirschman 1958). At higher levels of complexity, the emphasis of policy design should increasingly shift towards proper selection mechanisms within complexity, focusing less on fos-tering the quantity of further consumption and employment choices, and more on the quality of choices and their impact on the well-being of people.

Increasing the number of choices exponentially does not necessarily lead to more freedom and well-being, and can even have negative effects due to rising costs in decision processes. In countries with both higher and lower complexity and productive capabilities, the focus on short and medium-term related variety growth should be evaluated against the long-term welfare effects of unrelated variety growth. It seems that long-term unrelated variety growth deserves major attention, because it distributes the economic and political power within countries and leads to more democratic regimes with more choices for people. However, this does not mean randomly diversifying into all possible product areas. Instead, the endogenous exploration of local, regional and national productive capabili-ties has to be emphasized to promote competitive diversification which in turn promotes both economic growth and social welfare simultaneously. A final factor is that to promote qualitative diversification, prolific knowledge exchange,

Increasing the number of choices exponentially does not necessarily lead to more freedom and well-being, and can even have negative effects due to rising costs in decision processes. In countries with both higher and lower complexity and productive capabilities, the focus on short and medium-term related variety growth should be evaluated against the long-term welfare effects of unrelated variety growth. It seems that long-term unrelated variety growth deserves major attention, because it distributes the economic and political power within countries and leads to more democratic regimes with more choices for people. However, this does not mean randomly diversifying into all possible product areas. Instead, the endogenous exploration of local, regional and national productive capabili-ties has to be emphasized to promote competitive diversification which in turn promotes both economic growth and social welfare simultaneously. A final factor is that to promote qualitative diversification, prolific knowledge exchange,

Im Dokument Economic Complexity and Human Development (Seite 106-117)