Based on the rankings of the global competitiveness index and the fragile states index, this paper aims to suggest alternative approaches to shed some light on the effectiveness of rankings in helping emerging economies improve their competitiveness from an institutional standpoint.
The statistical analysis consisted of a two-stage analysis; the first stage consisted of constructing an updated Alternative Institutional Quality Index (AIQI), intending to design a comparative measure between dimensions over time. The second stage consisted of evidencing the structure of each of the observed dimensions' variance to evidence the existing changes or gaps of the AIQI and its components. The authors incorporated the Kruskas–Wallis (KW) model to test the results.
This paper demonstrates that the analyzed countries generally maintain their competitive position, even though changes in their scores are reflected. This makes invisible the development and progress factors generated by the countries that are mainly found with low scores and only reflect stable structures that allow them to maintain their position.
The current study has a limitation because it concentrated on a few selected indicators based on the literature review. The limitations of this research may be overlooked in the future by adding additional variables and observations. The paper could be improved by including intra- and inter-regional approaches to control based on the occurrence of specific circumstances (i.e. informal institutions, economic development or factor endowments).
The paper contributes to the applicable measurement of competitiveness and its structural change over time.
This paper proposed an alternative and simple methodology to assess the evolution of the competitiveness indicators; this methodology could be used to measure structural changes at different levels, which may be an input for the design and implementation of policies to foster competitiveness.
1. Introduction
Hardly has an economic concept been so much at the forefront of policymakers' concerns in recent years as competitiveness. This growing interest may be due in part to their recognition that all countries must contend with higher economic efficiency standards derived from accelerated globalization of goods and services, as well as the external shocks generated by the recent crisis.
Since the early 1980s, many articles and studies on competitiveness have been published. In addition to the extensive academic literature, two major global rankings have been developed and are widely used to assess competitiveness., the IMD World Competitiveness Yearbook (WCY) published by the Institute for Management Development and the Global Competitiveness Index (GCI) issued by the World Economic Forum.
The WCY has been published since 1989 and covers 63 countries. It benchmarks the performance of the economies based on more than 330 criteria measuring different facets of competitiveness (Institute for Management Development, 2022). The GCI was introduced in 2004 for measuring national competitiveness, taking into account the microeconomic and macroeconomic foundations of national competitiveness; it covers more than 130 countries and measures 12 pillars (Sala-I-Martin et al., 2007).
Global competitiveness rankings have frequently been the unifying theme of a large number of government action programs; this covers a wide range of activities, such as promoting the technological adaptation of companies, consolidating the bases of regional economic development, promoting the networking of SMEs and developing activities considered strategic for national economic growth.
The diversity of national approaches is determined by what countries consider to be the risks to avoid and the opportunities to seize, as well as the nature of their “chronic problems” and “critical resources.” Their assessments of whether their economic performance is commensurate with their scientific and technological potential become critical.
In many countries, government action continues to be motivated by the desire to improve in the rankings. Countries largely lack an alternative conceptual framework for analyzing the relationship between business competitiveness and national economic performance. Moreover, it is somewhat surprising that the academic debate on competitiveness focuses on the absence of a widely accepted theory of the origins of competitiveness (Anca, 2012; Bhawsar and Chattopadhyay, 2015; Buitrago R and Barbosa Camargo, 2021; Olczyk, 2016).
The aim of this paper is essentially to suggest an alternative approaches to shed some light on the effectiveness of rankings in helping emerging economies improve their competitiveness.
2. Competitiveness approaches
The literature review reveals a plethora of approaches to competitiveness; in addition to the two published rankings mentioned previously, numerous published and unpublished rankings are prepared by governments, consultants and research institutions, all of which feed an insatiable appetite for benchmarking competitive performance and providing strategic guidance.
Academic approaches to competitiveness can be broadly classified into two large groups based on the level of analysis, country and firm-level. Table 1 summarizes these approaches as published in Buitrago R and Barbosa Camargo (2021).
It also seems to be a consensus in the literature that a cornerstone to competitiveness is the quality of the institutions (Ervits and Zmuda, 2018; Guerrieri and Meliciani, 2004; Hollingsworth, 2000; Ingram and Silverman, 2000; Jaffe et al., 1993; Moon et al., 1998; Peng et al., 2008; Porter, 1990; Porter and Linde, 1995; Rodriguez et al., 2005; Soete, 1987; Tobey, 1990; Wan and Hoskisson, 2003). The concept of institutional quality has also been discussed in the literature as the basis of economic transformation (Acemoglu et al., 2001, 2002, Acemoglu, 2003, Acemoglu et al., 2005; Acemoglu and Johnson, 2005; Lane, 2014).
3. Competitiveness levels, goals and outcomes
The construct competitiveness is applied to individuals, firms, industrial sectors and countries (Buitrago R and Barbosa Camargo, 2021). Using the same concepts and methods to measure competitiveness at all levels of analysis can create complications due to the oversimplification of the phenomena. The idea of competitiveness is not the same for a country and a firm – to take just the two main levels of analysis – their objectives and the nature of competition differ (Krugman, 1991, 1994; Pedersen, 2010).
It should be noted that the fundamental objectives of competitiveness vary depending on whether it is a company or a country. While the main aim of a company is to survive and gain a foothold in the international competitive environment (Moon et al., 1998), the primary objective of a country – which is not concerned with survival – is to improve living standards and welfare (OECD, 1992; Porter et al., 2002). Different actors (companies or countries) will rank these objectives differently depending on their strategic goals; a company may prefer to increase its market share rather than maximize profits; a government may prioritize attracting foreign direct investment rather than reducing inequality. Consequently, what should the basis of competitiveness be?
The aforementioned rankings comparatively measure national competitiveness based on the results achieved by each country (Carvalho et al., 2020; Moirangthem and Nag, 2021; Tahir and Tahir, 2019). Results cannot be quantified in isolation because they are contingent on the methods used and the context in which they were achieved. A competitiveness assessment based solely on obtained results provides ex-post information and does not indicate a country's potential capacity to accomplish its objectives (ex-ante review).
Because national competitiveness is primarily defined as the capacity to generate economic growth and welfare, governments focus their efforts on promoting all the factors contributing to the economy's performance. It is critical to understand how to measure competitiveness and the factors that influence it to determine aspects of improving to increase competitiveness (Lall, 2001).
If competitiveness analysis is valid, competitiveness rankings can be used to benchmark national performance. Rankings can assist policymakers in assessing their economies' shortcomings, much like technical benchmarking assists firms in assessing themselves against competitors and developing appropriate strategies. The peril of the rankings relies on the perception they generate; rankings are used to evaluate resource allocation (i.e. local and foreign investment, aid, public expenditure); it could create perverse incentives to follow an unreachable goal or to remain unchanged.
Institutional conditions have reclaimed analytical relevance in recent years, as evidenced by the (World Economic Forum, 2018) Global Competitiveness Report, which asks, “Are institutions still important?” (p. 12), highlighting the critical importance of an adequate institutional framework for international competition. Countries with strong and inclusive institutions are likely to ensure efficient factor allocation, encourage investment activities to improve performance, reduce uncertainty, promote equitable distribution of private and social benefits and facilitate economic agent interaction. On the contrary, countries with weak institutions frequently face a range of economic difficulties, including low investment flows, slow economic growth and low per capita income (Acemoglu et al., 2001; Hall and Jones, 1999; Knack and Keefer, 1995; Mauro, 1995; Rodrik et al., 2004).
Graham and Naim (1998) identified three types of institutional functions. The first is the formulation of rules and legislation. Legislative, ministries, municipal councils and related agencies all fall under this category. The second category of the institutional role is that of enforcing and administering rules and laws. Tribunals, boards, control and regulatory bodies are all involved in this. Thirdly, the institution is responsible for the provision of public services. These are the institutions responsible for ensuring the provision of various public goods and services.
There are many explanations for institutional quality that could be classified into three categories for analysis Graham and Naim (1998):
Resource conditions: those are about the quantity, quality and distribution of available resources.
Political conditions: co-optation, corruption and politicization of resource allocation.
Systemic conditions: these are those that pertain to the clarity with which long-term goals are defined, the concentration of power in economic agents and external state intervention.
Our study considered these conditions to evaluate the national competitiveness outcomes from an institutional standpoint.
4. Methodology
The comparative analysis of economies based on rankings has become a fundamental instrument for comparative monitoring of the progress and effects of different public policies in a territory. However, these comparative forms have become a kind of stereotyped pseudo-technicism, which does not allow us to observe the efforts generated by economies with limited capacities, and which highlight the progress of economies with ample resources, representing them as “winners”.
This dynamic has led to the maintenance of imbalances among economies, driven mainly by “isomorphic” recommendations, which are not in line with the endogenous dynamics of each economy, creating an unbalanced allocation of opportunities. Hence, a reflection is required on how to rethink cohesion and regional development policies, from their concession to their implementation, by means of instruments and tools that address internal needs and cohesion between economies.
The purpose of this study is to examine the period 2007–2017 in 48 emerging and frontier economies (Table 2), for this selection we did a cross-validation of four different classification sources (International Monetary Fund, 2020; Morgan Stanley Capital International, 2020; Standard and Poors, 2020). There is no recognized definition for emerging economies; however, according to the sources consulted, they have in common that they are economies with sustained growth and stability, capable of producing high-value-added goods, participating in global commerce and integrating their financial markets. Additionally, these economies had experienced institutional transformations deploying transparent rules of the game that apply equally to all market participants; however, there is still a lag.
To carry out a comparative analysis of the evolution of institutional quality determinants in emerging countries, we categorized indicators from the GCI and the Fragile States Index ( Appendix 2) (Fund for Peace, 2019) into three dimensions to identify the institutional conditions to compete:
policy;
resources; and
systemic, the composition of each dimension is shown in Table 3.
A descriptive, longitudinal design study was conducted for the 48 emerging economies. The statistical analysis consisted of a three-stage analysis; the first stage consisted of the construction of an Alternative Institutional Quality Index (AIQI), through the implementation of the statistical method, principal component analysis (PCA), whose main objective is to synthesize a set of variables, recognizing the dynamics of the variance of each indicator. This allows to reduce the information to a set of smaller variables, called components. The first component captures the maximum amount of variance observed and will represent each dimension analyzed in this study.
By implementing this procedure in each of the observed years, a standard measurement is established, which will allow comparing them over time; however, to facilitate direct interpretation, a scaling process was implemented, so that their scores oscillate between 0 and 1.
The second stage consisted of comparing the structure of the variance of each of the observed dimensions, to analyze possible changes in the distribution of the observed countries. By means of these statistical tests, it will be possible to show whether, despite the efforts generated individually by each country, their structural behavior has been maintained.
Finally, to determine whether there are statistically significant differences between the groups of countries, based on regions, with respect to the AIQI, this study incorporated the Kruskas–Wallis (KW) model.
4.1 Alternative institutional quality index
For the construction of the AIQI, we build a dataset of 30 indicators for 48 emerging economies from 2007 to 2017. The first step consisted of the aggregation of the dimensions using the multivariate statistical methodology, Principal component analysis (PCA). One of PCA's main objectives is constructing new variables trying to lose as little information as possible by constructing factors representing a proportion of the information collected, being the first factor that guarantees the best representation of the data.
Therefore, for the construction of each of the dimensions observed, only the first standardized factor is taken. The following formula is applied using its eigenvectors, which act as weighting factors within the new scores obtained.
where:
λi = Vector variable i;
xi = Variable i;
= Average variable i; and
σi = Standard deviation variable i.
In addition, based on the new scores obtained, they are rescaled using the max-min function:
Finally, to obtain the AIQI, an average of the scores previously obtained for each dimension is taken. The complete results and rankings of the AIQI are in the Tables A1 and A2 in the Appendix 1.
4.2 Gap analysis
As we mentioned before, the rankings become a benchmark for all the economies; benchmarking and other assessments naturally lead to gap analysis. Once the general expectation of a country's performance is established, the expectation can be compared to the top current level of performance; this comparison is transformed into a gap analysis.
For the analysis of the evolution of the proposed dimensions (political, resources and systemic), we used a box diagram, which allowed us to visually compare the progress and distribution of the scores obtained in each dimension of the economies observed. To interpret these results, it is helpful to keep in mind that the larger the box, the greater the dispersion in the scores obtained, which translates into a larger gap between emerging economies. In the opposite case, the smaller the box, the smaller the dispersion in the scores, which commonly usually occurs when the countries are more homogeneous or when the leader is closer to the others. The tails represent how the top or bottom 25% of the countries are distributed.
To test the statistical changes in the structure of the AIQI and its determinants, the statistical tests of variance ratio and the nonparametric Wilcoxon–Mann–Whitney test were used to compare the annual evolution. Also, we rely on both tests' statistical value to quantitatively assess the impact and direction of the gap. If the variance ratio is greater than 1, the gap widens; if it is less than 1, the gap narrows, and if it is statistically equal to 1, the gap is maintained. The Wilcoxon test evaluates two related samples, and if it is statistically significant, it indicates that the countries' behavior is statistically similar.
5. Findings and discussion
In this section, we show the results of the AIQI in the period 2007 – 2017; we aim to evidence the behavior of the emerging economies regarding the institutional conditions to improve competitiveness.
5.1 Political dimension
Figure 1 shows the annual box plots of the countries' score concerning the political dimension between 2007 and 2017. Observing the evolutionary behavior of the gaps between countries in this dimension, it is evident that, since 2007, it has presented structural changes in terms of closing political gaps.
When comparing the variance's behavior concerning the previous year, it is evident that from 2007 to 2017, there are no statistically significant changes in its behavior. And when looking to observe whether there are structural changes in the variance concerning future periods, it is evident that from 2008 to 2017, the variance structure has remained the same.
When comparing the average behavior concerning the previous year, it is evident that from 2008 to 2017, there are no statistically significant changes in its behavior. See Table 4.
However, when analyzing the moments in which the structural changes in the average behavior occurred, it became evident that two periods were reflected ahead for the years 2008, 2009, 2013 and 2014. For the years 2008 and 2009, these differences were negative, i.e. 50% of the countries presented a deterioration in the scores obtained. And for the periods 2013 and 2014, an improvement in the scores was observed.
For the periods 2010, 2011, and 2012, the behavior is statistically equal until 2016, when there is a structural change. See Table 5.
5.2 Resources dimension
The annual behavior of the scores obtained in the Resources dimension by the countries between 2007 and 2017 is shown in the box plots in Figure 2.
In the Resources dimension, it is important to note that the increase in the gap corresponds mainly to the improvement performance of leading countries, which generates a distancing effect with the other countries.
When comparing the behavior of the variance of a given year compared to the immediately preceding year, it is evident that from 2007 to 2017, there are no statistically significant changes in its behavior. Likewise, when looking to observe if there are structural changes in the variance for future periods, it is evident that the structure of the variance has been maintained in all periods.
Furthermore, if the average behavior is compared to the previous year, it is evident that from 2007 to 2017, there are no statistically significant changes in its behavior. See Table 6.
When analyzing the moments in which the structural changes in average behavior occur, it was reflected that for the years 2007 and 2010, the changes happen to six periods ahead, as in 2007. It is noteworthy that for the years 2007, 2008, 2009, 2010 and 2011 these differences were negative, i.e. most countries presented a decline in the scores obtained. In the periods 2012, 2013, 2014 and 2016 an improvement in the scores obtained in the dimension was observed; see Table 7.
5.3 Systemic dimension
Figure 3 shows the annual box plots of the countries' scores for the Systemic dimension between 2007 and 2017.
The observed evolution of the behavior of the gaps between countries for the systemic dimension shows that, since 2007, it has presented structural changes in terms of closing systemic gaps, where over the past five years, there has been a greater distance in the scores obtained, indicating a smaller distance between countries in this dimension. The increase in terms of gaps is attributed to the improvement in scores in leading countries, which generates a distancing effect with the other countries.
Comparing the behavior of the variance of a year with the previous year, it is evident that from 2007 to 2017, there are no statistically significant changes in its behavior, furthermore, seeking to observe whether there are structural changes in the variance for future periods, it is evident that the structure of the variance has been maintained throughout the observation window.
Similarly, if we compare the average behavior with the previous year, it is evident that from 2008 to 2017, there are no statistically significant changes in its behavior.
On the other hand, when analyzing the moments in which the structural changes in average behavior occur, it is evident that in 2007 they are reflected in all subsequent periods. In 2012, 2013 and 2015, these differences were negative, i.e. 50% of the countries presented a deterioration in the scores obtained. There were no years with general improvements in the scores obtained for the following periods.
For the periods 2008, 2009, 2010 and 2011 the behavior is statistically the same, until 2012 when a structural change occurs, as shown in Table 9.
5.4 Alternative institutional quality index
The annual box plots of the score obtained in the Institutional Index by the countries concerning the Policy dimension between 2007 and 2017 are observed in Figure 4.
Analyzing the staggered behavior of the territorial gaps for the AIQI, it can be seen that, since 2007, there have been structural changes in terms of closing the gaps in the index, almost every year there has been a smaller gap in the scores obtained, indicating a greater closeness of most of the emerging countries. However, it should be noted that this reduction in the gap corresponds essentially to improvements in the scores obtained by the leading countries, which has generated an effect of closeness to the other countries, also attributable to the scores obtained by the countries with greater opportunities for improvement.
Contrasting the behavior of the variance of each year to the previous year, it is shown that from 2007 to 2017, there are no statistically significant changes in its behavior; furthermore, when wanting to observe if there are indeed structural changes in the variance for future periods, it is evident that the structure of the variance has been maintained from 2007 to 2017.
Likewise, when comparing the average behavior of a year to the previous year, it is identified that from 2007 to 2017, there are no statistically significant changes in this.
Similarly, when analyzing these structural changes in average performance could occur, it is evident that, as for the immediately consecutive periods, there are no significant changes from one year to the next. It is highlighted that for the years 2009, 2010 and 2011; these differences were negative, i.e. 50% of the countries presented a deterioration in the scores obtained in these years; an improvement was identified in 2013 and 2014. For the periods 2008, 2012 and 2015 the behavior is statistically equal.
5.5 Kruskas–Wallis test
To determine whether there are statistically significant differences between the groups of countries, based on regions, with respect to the competitiveness indicator, this study incorporated the KW model (Bagui and Bagui, 2004; Wei, 1981). The groups of countries analyzed were the same as those covered by this research described in Table 2. The relevance of this analysis consists in determining the explained variance with respect to the total variances of quadratic ranges, in relation to phenomena to which non-parametric tests would be applied.
The general model applied is based on:
where:
N = number of units of study, 48 in this case; and
= Sum of quadratic Ranges of each group under study, each of them divided by the number of units observed.
The results of the application of the KW test for each of the years: 2007, 2012 and 2017, in relation to the calculated KW values were:
In each of these cases, a total of 4 degrees of freedom was used, since there were five groups. The tabulated KW values were:
with 5% error or goodness of fit: 9.488; and
with 1% error: 13,277.
When comparing the calculated KW values with the tabulated ones for each year, it is evident that in all the years analyzed – 2007, 2012 and 2017 – the calculated values did not exceed the tabulated ones, even with a 5% error. Therefore, we confirm that there are no statistically significant differences between the groups of countries in terms of the AIQI.
6. Conclusions
By examining the structural dynamics of the indexes through the PCA methodology, it was possible to identify possible exogenous problems of the information and thus preserve the structure of the variance that best explains the behavior of the countries observed in each of the pillars. This allowed the evolution of the scores during the observation window to be evidenced and compared through standardization and rescaling.
Although the scores obtained by the studied countries vary over time, when comparing whether changes are identified in the structure of the existing gap, it is evident that it has remained constant in most of the observed periods, which means that the impact of global policies and recommendations has not generated changes in the way these countries relate to the scores obtained.
Likewise, when comparing the average behavior of the countries, it is evident that it has remained constant, which implies that at least 50% of the analyzed countries have maintained their relative scores during the observation window.
These two findings allow us to observe that the countries generally maintain their position, even though changes in their scores are reflected. This makes invisible the development and progress factors generated by the countries that are mainly found with low scores and only reflect stable structures that allow them to maintain their position.
A critical factor to consider is the speed with which the various results are obtained. Interpreting the “speed of reaction” factor can be tricky, even when the objectives are similar; countries do not always start from the same location and may take different routes to fulfill the goals. As a result, it is difficult to comprehend the nature of the issues at stake if competitiveness is viewed as a quantifiable macroeconomic variable with a well-defined causal origin.
The definition of policies based on the rankings may lead to a misdirected actions at national and regional levels. As explained in this work, even if the common goal is to generate growth and welfare, countries are externally and internally diverse, making no sense to pursue standardization of the path to reach that goal.
A continuous quest to look like the best in the ranking can distract attention from the actual requirements and vocations of the different economies. The benchmark is helpful if it is taken as a reference, not as a goal; isolating disturbances in the indicators may help understand the needed structural change to reach each country's competitiveness ultimate goal.
Finally, a more comprehensive analysis must consider the market failures that impair competitive capability, most notably the evolution of dynamic comparative advantage. Competitiveness strategies must determine which failures are addressable through policy and whether the government concerned possesses the capacity to implement such policy.
7. Contributions and limitations
This study adds to the existing body of knowledge in a variety of ways. First, it demonstrates that the analyzed countries generally maintain their competitive position, even though changes in their scores are reflected. Second, it highlights the importance of conducting a more in-depth analysis of the conditions that promote national competitiveness. Third, it demonstrates the perils of using global rankings as a benchmark and not as a reference. Finally, it demonstrates the application of a novel method for assessing structural changes in emerging economies' competitiveness positions.
The current study has restrictions because it’s concentrated on a few selected indicators based on the literature review. The limitations of this research may be overlooked in the future by adding additional variables and observations. In addition, the article could be improved by including intra- and inter-regional approaches to control based on the occurrence of specific circumstances (i.e. informal institutions, economic development or factor endowments).
References
Appendix 1
Appendix 2. Fragile states index
“The Fragile States Index (FSI) produced by The Fund for Peace (FFP), is a critical tool in highlighting not only the normal pressures that all states experience but also in identifying when those pressures are outweighing a states’ capacity to manage those pressures. By highlighting pertinent vulnerabilities that contribute to the risk of state fragility, the Index – and the social science framework and the data analysis tools upon which it is built – makes political risk assessment and early warning of conflict accessible to policy-makers and the public at large.
The strength of the FSI is its ability to distill millions of pieces of information into a form that is relevant as well as easily digestible and informative. Daily, FFP collects thousands of reports and information from around the world, detailing the existing social, economic and political pressures faced by each of the 178 countries that we analyze.” (Fund for Peace, 2019)
“Practical application: the fragile states index analytical process
Though at the ground level, the CAST framework is applied using various practices such as individual incident reporting and observation by field monitors, the sheer volume of data to be analyzed at an international level required a different approach. To that end, technology was employed to enable researchers to process large volumes of data to perform the national level assessments that feed in to the FSI.
Based on CAST’s comprehensive social science approach, data from three main streams – pre-existing quantitative data sets, content analysis, and qualitative expert analysis – is triangulated and subjected to critical review to obtain final scores for the Index.” (Fund for Peace, 2019)
