Empirical example: Revealing different bottlenecks for development in Latin America

To shed some light on the complexity of development and study to what degree (a) efficiency based economic growth, (b) innovation and structural change, as well as (c) human development are overlapping factors or depict different dimen-sions in the development patterns of countries, Hartmann et al. (2010) studied the systems of development and innovation in Latin America. Taxonomies for measuring innovation systems (Godinho et al. 2004; Balzat and Pyka 2006;

UNU-MERIT and EC-JRC 2006; Fagerberg and Shrolec 2006) were combined with insights from the human capability approach (Sen 1999) as well as more mainstream economic competitiveness measures (e.g. López-Claros et al. 2006b).

This approach allowed the illustration and combination of different perspectives on development and at the same time showed how none of them alone provides a comprehensive picture of development, neither the mainstream economic empha-sis on the efficiency and openness of the economy, nor the Neo-Schumpeterian focus on knowledge and innovation, or the human development and capability focus on freedom and well-being.

A future-oriented economic structure, freedom of the actors and knowl-edge and innovation are argued to be mutually interconnected and reinforcing factors of development. The agents have to be free and need cognitive capa-bilities and economic opportunities to be able to develop as people and thereby become agents of qualitative entrepreneurship and innovation. This approach was applied to Latin American economies using a composed data set of 44

indicators of freedom, knowledge and economic structure for the period 2000–2005 (see Hartmann et al. 2010). A confirmative factor analysis using Cronbach’s alpha shows that all three factors are interrelated. Cronbach’s alpha is constructed by computing the mean of all possible split-half coefficients, which are estimated by dividing the test into two shares with a random distribution of the items and measuring the correlation between the two shares using the Spearman-Brown method (Schnell et al. 2005). Cronbach’s alpha can be formalized as presented in equation (2.1).

a 5 n

n2 1°12 as_i²

s_x² ¢

n = Number of items σi2 = Varience of item i

σx2 = Total test varience (2.1) The alpha can have a value between 0 and 1, a commonly used threshold value for acceptable reliability – i.e. the alpha value is 0.7 or higher (Hair et al. 1995).

Further information on the data and the calculation of Cronbach’s alpha can be viewed in Hartmann et al. (2010). A high level of intercorrelation between the factors freedom, knowledge and economic structure was found, with a high alpha value of 0.918. The elimination of one of the factors leads to lower, but still high alpha values (see Table 2.6). Each factor (objective class) is highly correlated with each of the other two factors (objective classes).

In the case of Latin America, then, the theoretical hypothesis that freedom, knowledge and economic structure are highly intercorrelated factors can be empirically confirmed. The high alpha values indicate that these three indicators seem to measure a common latent dimension (we may call this ‘future-oriented development’), yet the three dimensions are not the same and qualitatively require different political interventions.

In the next step, an average linkage cluster algorithm was applied to identify the similarities and dissimilarities and comparative strengths and weaknesses of twenty Latin American countries, comprising 97 per cent of the Latin American population. Cluster analysis techniques test a sample for the degree of structural commonalities between the units of analysis (Jobson 1992; Hair et al. 1995;

Backhaus et al. 2006). Its outcome is a categorization of the analysed units, so that the coherence of each cluster, as well as the heterogeneity between different Table 2.6 Interrelationship of development dimensions

Constructs Alpha, when construct Cronbach’s alpha value (development dimensions) deleted value for all three

constructs

Freedom 0.845

Economic structure 0.894 0.918

Knowledge 0.904

Source: Hartmann et al. (2010).

clusters, is maximized (Jobson 1992). For this purpose, the distance values between the countries were determined on the basis of the characteristics of each country.

In particular, squared Euclidean distances were used. The distance between the indicators of two countries i and j is calculated as follows:

d(i, j)5 a

m

k51(a_ik2a_jk)²

Here aik represents the parameter values of the characteristic k = 1,…,m for coun-try i = 1,…,n. Thus the entire quantitative data matrix is A = (aik)nxm. A hierarchical average linkage cluster algorithm is applied because it is not overly influenced by single cases and neighbours (compared with other algorithms), and it is not par-ticularly susceptible to distortions should outliers that are very different from all other cases appear (see Backhaus et al. 2006). Inter-cluster diversity is calculated as follows:

v(K, L)5 1

zKz?zLz a

i[k,j[L

d(i, j)

Both distinct classes K and L (i.e. K  L) belong to the entire classification K.

Since no analysis of a given, ex ante predetermined classification of countries is intended, an agglomerative classification is used that starts with single country clusters and entails a stepwise concentration of countries according to their degree of structural similarity. The selected clustering method yields an exhaustive as well as a disjunctive classification. This means that every country is assigned to one cluster (U_K[K K 5N), with N being the total number of analysed objects) and no country can be part of two different classes (K, L[K, K  L, so that K > L 5 ∅). To identify the optimal number of clusters (for the remaining cases), the so-called elbow criterion is applied. This measure is determined by analysing the change in the heterogeneity index over the course of the different agglomeration steps of the cluster algorithm. The elbow criterion appears when further merging steps lead to a sharp rise in the heterogeneity coefficient, i.e. a strong loss in the coherence of the different clusters and thus a strong quality reduction for the entire classification. The idea of the elbow criterion is to find the optimal cluster number that can provide the best trade-off between intra-cluster homogeneity and at the same time inter-cluster heterogeneity. Finally, to reveal the (relative) structural weaknesses, the performance of the different country clus-ters in terms of the development indicators freedom, knowledge and economy is measured by the mean square values of the corresponding item values. Each item was N (0, 1) standardized beforehand. Table 2.7 shows the resultant cluster pro-files of the eight-cluster solution.

The analysis reveals that while in some countries knowledge is the main bottleneck for future-oriented development, other countries suffer from having inefficient economic structures or large parts of their population being excluded

from economic life. For example, while Uruguay performs comparatively well in terms of human freedom and low absolute poverty levels, the economic inefficiencies and lack of future orientation of the economy hamper the overall developmental potential; or, while Mexico, Panama, and Trinidad and Tobago have quite open economies with a considerable amount of manufactured exports, advances have to be made in the dimensions of human freedom and knowledge to facilitate higher levels of social welfare. Argentina and Brazil, in contrast, show comparatively good aggregated levels of knowledge in terms of R&D expenditures and some technologically advanced sectors; however, these countries struggle with considerable social problems such as corruption, crime and high levels of inequality, which negatively affect human freedom, well-being and economic efficiency. As many Latin American authors, such as Furtado (1958, 1961), have shown, the heterogeneity of socioeconomic structures and comparative strengths and weaknesses are even more accentu-ated within national borders, on the regional and local levels, between social classes and between different economic sectors. Nevertheless, the message of the analysis is straightforward. Each of the three approaches alone (mainstream economics, human development or innovation economics) may overlook struc-tural bottlenecks preventing development in other economic or social domains.

The policy which focuses on just one of the three objective classes may not lead to qualitative change because the interrelatedness of social stability, freedom Table 2.7 Patterns of development in Latin America^a

Cluster profiles Freedom Knowledge Economic

(UNDP (Neo- structure

perspective)^b Schumpeterian and efficiency perspective)^b (mainstream

economics perspective)^b

A: Chile 1.83 1.44 1.13

B: Costa Rica 0.58 0.81 0.68

C: Uruguay 0.74 0.54 –0.12

D: Argentina, Brazil 0.14 0.81 0.18

E: Mexico, Panama,

Trinidad and Tobago 0.36 0.18 0.69

F: Colombia, El Salvador 0.17 –0.29 0.03

G: Peru, Venezuela –0.31 –0.11 –0.37

H: Dom. Republic, Ecuador –0.46 –0.61 –0.21

I: Bolivia, Guatemala, Honduras,

Nicaragua, Paraguay –0.69 –0.75 –0.64

Source: Adapted from Hartmann et al. (2010).

Notes

a Relative weaknesses of corresponding countries/cluster group are shown in bold.

b UNDP perspective’, ‘Neo-Schumpeterian perspective’ and ‘mainstream economics perspective’

added to the original table of Hartmann et al. (2010).

of the actors, innovative capacity and economic structure and dynamics may be neglected:

• focusing on the knowledge factor alone will not allow for the socioeconomic imbalances and economic inefficiencies in developing countries to be dealt with;

• concentrating only on expanding the capabilities, human rights, social choices and freedoms of all actors, the main purpose of human development goals, may underestimate the importance of (strategic) technological competitive-ness and economic efficiency; and

• concentrating purely on economic efficiency and openness neglects the importance of strategic alignment towards technological and sectoral com-petence building as well as the unfreedom and inability of large parts of the population to participate in and benefit from the innovation and development process.

However, this empirical analysis has some shortcomings that need to be explored further. First, there is even greater heterogeneity within the Latin American countries on the regional and sectoral level (Furtado 1958, 1961; Cimoli 2005;

Lopez-Claros et al. 2006a; Katz 2007). Second, we need to have a better under-standing of the micro behaviour (e.g. of entrepreneurs) and meso-structures (such as the sectoral structures and dynamics) to develop proper development policies (Dopfer et al. 2004).