Is the Global Competitiveness Index by the World Economic Forum a reliable tool for the design of labor market policies? Evidence from Latin American countries

Using indexes to rank the competitiveness of countries has gained astounding popularity. Policymakers closely monitor these indexes to design economic and social policies. Favorable rankings are used to consolidate the country’s strengths, while poor ones are used to adjust the country’s economic policies to achieve sustained growth. The reliability of competitiveness indexes as a public policy tool depends on their ability to capture the country’s growth potential. We argue that these indexes are not always constructed with a representative survey sample. In such cases, competitiveness indexes fail in capturing the country’s growth potential and thus lead to misleading policy recommendations.

The Global Competitiveness Index (GCI), developed by the World Economic Forum (WEF), has been used as a standard measure of a country’s competitiveness. It is a composite index of 12 competitiveness pillars and is constructed from statistical data and survey questions to local business executives. Mostly based on subjective data, the labor market pillar assesses the amount of flexibility that exists in national labor legislation on issues concerning dismissal protection, severance pay, and wage determination. Countries that have less protective legislation score better on the index. Despite the significant experience of Latin American countries with labor market flexibilization policies, business executives perceive the labor market as rigid. This article discusses the accuracy of the GCI for policy purposes in the labor market area by an analysis of its subjective component. The interest is centered on Peru, Colombia, the Dominican Republic, Mexico, and Brazil, the set of countries that have built entire competitiveness systems based on the GCI.

The literature on the GCI can be classified into studies that examine its role in macroeconomic indicators and studies that analyze its use as a policy tool. Research in the former assumes that the GCI measures exactly what the WEF intends to measure, i.e., competitiveness, and thus draws conclusions assuming that the GCI correctly represents reality. A series of recent studies has indicated that the GCI influences economic growth (Amar and Hamdi, 2012; Kordalska and Olczyk, 2015; Dadgar et al., 2018; WEF, 2018b; Kalim et al., 2019; Nogueira and Madaleno, 2021a,b), exports (WEF, 2017; Siddiqui, 2020), and capital flows (Athari et al., 2018).

Evidence that debates the claim that the GCI influences economic growth is limited. See, for instance, Petrylé (2017) and Xia et al. (2012).

Our paper is related to the branch of the literature that, through an analysis of the GCI’s methodology, questions the use of the GCI as a policy tool. Lall (2001) pointed out several analytical, methodological, and quantitative problems of the GCI. In the same line, Aleksynska and Cazes (2016) have raised concerns on the suitability of the GCI to compare countries and their regulations over time, given significant variation of components not only across but also within the indicators, whereas Djogo and Stanisic (2016) highlight that the GCI fails to measure countries’ current competitiveness as it ignores political integration and recent economic and financial crises. Another group of studies questions the use of business leaders’ opinions as primary data and highlights limitations such as the cultural bias inherent to survey responses (Zinnes et al., 2001), the lack of representation of other national groups (Rosenau, 2003; Kaufmann and Kraay, 2008; Soto-Rodriguez and Maiz-Vazquez, 2016), the executive’s orientation in favor of or against the government (Kaufmann et al., 2004), and the ideological bias that rewards some policies rather than good outcomes (Bergsteiner and Avery, 2012, 2019).

Taken altogether, these studies illustrate the limitations of the GCI with a broad approach. We, instead, focus on the set of Latin American and the Caribbean (LAC) countries where the GCI plays a key role in the design of policy and, moreover, on a specific area of the GCI. This approach allows us to add a source of potential bias that has been, to our knowledge, ignored in the literature: the lack of representativeness of the subjective data used to construct the GCI. This deficiency, combined with inadequacies in the questionnaire, shapes our conclusion that labor market reforms in LAC countries are largely based on an index that prioritizes opinions and expectations of a particular business sector. Thus, we argue that the GCI should be used with caution or, at best, simply avoided in policy design.

We put forward our analysis in three parts. First, we evidence that around half of the GCI indicators are constructed with subjective data and that the labor market area presents the highest share of the subjective component, with 67% of indicators based on the opinions of business leaders. Competitiveness in LAC economies, according to the GCI, has been disappointing over the years, and the labor market pillar is one of the areas that deteriorated the most. This context leads us to focus on the set of countries in the region where the GCI plays a prominent role in the design of competitiveness policies: Peru, Colombia, the Dominican Republic, Mexico, and Brazil. The selected countries rely on the GCI to build entire competitiveness systems and formally organize their institutions for competitiveness.

The second part of our analysis discusses the performance of the selected countries in the labor market area, at both an aggregate and a disaggregated level. Here, we show that the indicators with the worst performance come from the subjective component. We highlight that this translates into the business executives’ perception of the labor market as rigid, which contrasts with the significant experience of flexibilization policies undertaken in the region. The last part of our analysis addresses methodological issues in the subjective component of the GCI. We reveal that the survey sample in the GCI is not representative of the business structure in LAC countries. While around 80% of business executives surveyed represent large firms, most firms (99.5%) in the region are micro-, small-, and medium-sized enterprises (MSMEs), with almost nine out of 10 classified as microenterprises (Organisation for Economic Co-operation and Development [OECD] et al., 2019). This is followed by a discussion on how repetitive and unclear the questions are and on the inability of the respondents to always provide an informed answer that allows comparison across countries.

The remainder of the paper is set out as follows. Sections 2 and 3 introduce the GCI and discuss its role in policy design in Latin America. In Section 4, we elaborate on the GCI’s labor market pillar and discuss the performance of the selected economies in this area. Section 5 sets forth a methodological critique of the subjective component of the GCI in the labor market pillar, which addresses the problems of bias in both the survey sample and the questionnaire. Lastly, Section 6 concludes the paper.

Since 1979, the WEF has published yearly Global Competitiveness Reports to provide insight into a country’s economic environment and the country’s ability to achieve sustained levels of prosperity and growth. Since 2006, these reports rank countries by their competitiveness measured by the GCI.

The WEF defines competitiveness as “the set of institutions, policies, and factors that determine the level of productivity of a country” (WEF, 2018b, p. 43).

The GCI combines 98 indicators into 12 pillars, organized into four categories: i) business dynamism, ii) human capital, iii) markets, and iv) innovation capacity. Each indicator’s value is converted into a score ranging from “0” to “100” using a min–max normalization approach.

The score of a country c in indicator i is calculated as scorei,c=valuei,e−wpifrontieri−wpi×100 scor{e_{i,c}} = \left( {{{valu{e_{i,e}} - w{p_i}} \over {frontie{r_i} - w{p_i}}}} \right) \times 100 , where value_i,c is the raw value of country c for indicator i, wp_i (worst performance) is the lowest acceptable value for indicator i, and frontier_i is the best possible outcome. Depending on the indicator, this may be a policy target or aspiration, the maximum possible value, or a number derived from statistical analysis of the distribution (90th or 95th percentile). If a value is below the worst performance, its score is zero; if a value is above the frontier value, its score is capped at 100.

The overall GCI score is the simple average of the 12 pillar scores and can be interpreted as the distance to the ideal frontier of productivity, which is normalized at a score of 100.

The indicators used in the GCI are a mix of hard data obtained from various international organizations (the World Bank, the International Monetary Fund, and the United Nations Educational, Scientific, and Cultural Organization, among others) and soft data collected via the global Executive Opinion Survey (henceforth, the Survey) conducted by the WEF’s partner institutions in the participating countries. In 2018, the partner institutes in the LAC countries were composed of universities (45%), research organizations (20%), government agencies or competitiveness councils (20%), and business associations (10%).

The partner institutes were classified according to the information provided on their websites. We did not find the website of the Center for Intellectual Capital and Competitiveness, a Mexican partner institute. This institution represents 5% of the partner institutes in LAC in 2018.

Table 1 presents, for each pillar, the distribution of indicators by the data source.

As can be seen in Table 1, 45% of the indicators come from the Survey, and the remaining ones come from the statistics of international institutions. Out of the 12 main pillars of competitiveness, five contain more subjective data than data obtained from international organizations. The Labor Market and Skills pillars present the highest share of subjective data (67%). On the other hand, three pillars are partially based on the Survey, and only four come exclusively from objective data.

The use of the GCI in Latin America’s competitiveness agenda is related to the competitiveness challenges in the region. Figure 1 illustrates the region’s progress in narrowing gaps with global leaders in the period 2007–2017.

GCI scores for 2018 and 2019 have not been included because they were calculated using the GCI 4.0 methodology, which is not comparable with that of previous years. See Figure A1 in Appendix for the evolution of the GCI score in the period 2017–2019, with the 2017 scores calculated backcast. Trends observed in the period 2007–2017 (Figure 1) persist in 2018 and 2019.

Figure 1

Over the period 2007–2017, the average GCI score in the LAC region is below the global average. Moreover, the distance between the highest-scored LAC country and the best global performer is larger than the distance between the lowest-scored LAC country and the worst global performer. This suggests that the LAC region is, on average, closer to the least competitive economies than to those most competitive. Indeed, Latin America is home to two of the 10 least competitive economies, and its competitiveness performance exhibits wide dispersion.

For instance, Chile's score (70.3, 33^rd) is almost twice that of Haiti (36.5, 138^th) (WEF, 2019).

An investigation of the progress of different factors and institutions that matter for future growth provides a focal point for the discussion of the role that the GCI plays in policy design. Figure 2 provides a snapshot of the evolution of competitiveness in the different GCI pillars.

Figure 2

After a decade of competitiveness monitoring in the LAC countries, many of the fundamentals of competitiveness and growth have worsened in the region. Figure 2 shows that institutions, financial market development, goods, and labor markets have deteriorated in the region. Although on average, infrastructure, macroeconomic environment, health, and higher education have marginally improved, the distance between the best and the worst in the region continues to increase. On the other hand, the pillars that clearly show an improvement are market size and technological readiness.

As they integrate into the global economy, policymakers in LAC countries emphasized the need to incorporate the private sector into a comprehensive competitiveness strategy. The LAC countries created the National Competitiveness Council and Commission (NCC) as a public–private partnership body, with the government represented by ministers of key sectors and the private sector represented by business executives appointed by the President. The NCC is in charge of identifying and promoting actions and reforms that would strengthen productivity and competitiveness. To that end, the NCC relies on competitiveness indexes to benchmark the country’s progress in the competitiveness ranking.

Peru, Colombia, and the Dominican Republic have adopted the GCI to build entire competitiveness systems and formally organize their institutions for competitiveness. The NCC in Peru implemented the National Competitiveness Policy, which frames the strategy that the country will follow until 2030 to achieve sustained growth.

Supreme Decree No.345-2018-EF.

Mexico and Brazil, on the other hand, use the GCI data as input to construct their own competitiveness indexes. These countries restrict the comparison to the set of countries that share their economic and social perspectives and focus on the variables that are more directly related to the reality of the set of countries chosen.

Mexico uses (a) the National Competitiveness Index by the National Institute of Statistics, Geography and Informatics and (b) the International Index for Competitiveness by the Mexican Competitiveness Institute. The former is entirely based on hard data mostly coming from national sources. The latter is constructed with the GCI data. The National Confederation of Industry in Brazil uses the GCI data to construct a competitiveness index presented in the yearly Brazil Competitiveness Reports.

While mechanisms permitting private sector representatives to voice their concerns and opinions have been formally established in the LAC countries for a long time, the competitiveness systems in Colombia and Peru have brought private participation in policymaking to a different level. The private sector in these countries created the Private Council on Competitiveness (PCC) as an articulator and interlocutor between the public sector, the private sector, academia, and other organizations interested in promoting competitiveness. The PCC publishes a yearly report that analyzes the performance of these countries in each of the GCI pillars. Based on this diagnosis, the report proposes solutions that could advance Colombia and Peru’s competitiveness. The PCC is a member of the NCC’s Board of Directors in Colombia and Peru and is one of the institutions that administer the Survey in Colombia. Thus, the PCC not only allows the interest of the private sector to be voiced but also assigns a direct role to the private sector in policy design.

An examination of the PCC composition in Peru and Colombia provides an initial insight into any bias toward particular economic groups in policy design. Currently, the PCCs in Peru and Colombia are made up, respectively, of 18 and 23 national and multinational corporate groups or companies.

Information retrieved from https://compite.com.co/el-cpc/miembros/ and https://www.compite.pe/somos/ on July 06, 2022.

Additional institutional arrangements that evidence the role of the GCI in policymaking for the LAC are the regional indexes and the Competitiveness Labs, both based on the GCI framework. The regional index in Colombia, designed by the PCC in partnership with the academic sector, replicates the role that the GCI plays at a national level and has become a tool for local governments and private sector organizations to monitor their performance. The Competitiveness Lab seeks to identify the region’s main competitiveness gaps and produce policy recommendations. In 2013, the WEF launched its first regional Competitiveness Lab focused on the LAC. The Lab identified that skills, technological readiness, and innovation explained the productivity lag in LAC countries vis-à-vis advanced economies (WEF, 2015a). Given its finding, the Latin America Competitiveness Lab has been creating actionable agendas for public–private collaboration to enhance innovation and skills in the region. Colombia, Mexico, and Brazil have incorporated these plans into their national competitiveness agendas as part of a broader strategy to boost competitiveness and improve the business environment.

See WEF (2016b) for Mexico, WEF (2016a) for Colombia, and WEF (2018a) for Brazil.

The rationale of the WEF’s labor market pillar is that well-functioning labor markets foster productivity through (i) flexibility, which allows matching workers with the most suitable jobs for their skills, and (ii) talent management, which develops the workers’ talent to reach their full potential. According to the WEF, a well-functioning labor market materializes in a flexicurity system, in which the labor market is characterized by high flexibility combined with workers’ protection, gender parity for labor market participation, and a welfare state through active labor market policies (WEF, 2018b).

In its yearly reports, the WEF highlights that flexible labor markets represent a healthy feature for the business environment. In particular, efficiency and flexibility in the labor market are crucial for ensuring that workers are allocated to their best use in the economy. Among the main indicators to measure flexibility in the labor market, the WEF points out the following: cooperation in employer–employee relations; the flexibility that employers have in hiring, firing, and determining their workers’ wages; the extent to which pay is related to worker productivity; and whether there is equal treatment of women and men in the labor market. On the other hand, aspects that frame the extent of workers’ rights in an economy include civil rights, the right to bargain collectively, the right to strike, the right to associate freely, and the right to due process (WEF, 2018b).

Table 2 illustrates the scores and positions of the countries under analysis in the labor market pillar of the GCI, as well as in each of its 12 indicators. The table also shows the regional scores.

Table 2 indicates that labor market performance remains one of the biggest challenges for the LAC region. The region’s average score is 55.3, marginally more than halfway to the frontier. Overall, the LAC region ranks in Position 4 among the seven regions. While the LAC region, on average, is the second-best regional performer in ease of hiring foreign labor and internal labor mobility, it is the worst or second-worst regional performer in active labor market policies, the flexibility of wage determination, hiring and firing practices, redundancy costs, cooperation in labor–employer relations, and pay and productivity.

The GCI scores depicted in Table 2 paint a mostly negative picture of the selected countries’ labor markets. While their overall labor market performance lies around the regional average score, the selected countries are among the bottom 35 globally in at least two labor market indicators. Peru has the lowest number of indicators (n=2) performing among the worst 35 in the world, whereas Brazil has the highest number of indicators (n=7) performing in the same range.

A small number of indicators in the bottom 35 should not be interpreted as a better performer. For instance, the two indicators that make Peru belong to the bottom 35 globally, make Peru belong also to the bottom 15 globally.

Table 2 also illustrates that the selected countries excel in some indicators. Yet, there is not a specific area of the labor market in which all countries stand out. For instance, Peru is <30 points below the competitiveness frontier in the wage determination flexibility indicator (76.3, 16th), while the Dominican Republic is among the top globally in female labor force participation (100, 3rd) and internal labor mobility (68, 17th). Conversely, the selected countries share labor market rigidities in terms of hiring and firing practices and active labor market policies. The selected countries are among the bottom 40 globally in these indicators, with Peru (31.5, 128th) and Brazil (20.8, 138th) being the worst performers in hiring and firing practices, and Peru (17.9, 126th) and the Dominican Republic (19, 120th) being the worst performers in active labor market policies.

A closer look at the performance of indicators according to the data source reveals a heterogeneous pattern. The selected countries rank worse on indicators collected from the Survey. That is, the labor market is more burdensome when the performance is measured by the opinions of business leaders than by statistical data. While this can be a result of the indicators’ performance and not of the indicators’ data source, the fact that business leaders perceive the labor market as rigid is at odds with the significant experience of Latin America in implementing labor market reforms mostly oriented to deregulate the labor market and to support productive units to formalize.

Though not exhaustively, Table 3 lists the main efforts undertaken for formalization in the selected countries. We have classified the policies into three main components: regulatory environment, incentives to formalize employment, and the boosting of enterprise registration and creation.

Table 3 reveals that, with few exceptions, the selected countries have implemented policies consistent with WEF’s mandates.

In Brazil and the Dominican Republic, the reforms towards the end of the 1980s and early 1990s produced more protective labor regulations. In 2002, the Peruvian Supreme Court ruled in favor of the right to reinstatement of workers who were dismissed unfairly.

An analysis of the evolution of the GCI scores in the labor market pillar for the selected economies further illustrates the disconnection between the policies implemented and the perception of business representatives regarding the labor market as rigid. Figure 3 depicts the evolution of the labor market performance according to the GCI in a subset of years when formalization policies were implemented.

Due to data limitations and GCI's methodology comparability, we restrict this analysis to the period 2007–2017.

Figure 3

Except for Peru, the overall performance of the labor market according to the GCI has decreased over time. The declining trend in the labor market performance seems to be driven by the trend of the business leaders’ perceptions. Specifically, the rate of decrease in the subjective labor market performance is between 1.82 and 3.12 times that in the overall performance, with Brazil and Colombia presenting the smallest and largest proportions, respectively.

Table A1 in Appendix shows the GCI scores over time.

Another interesting feature of the role that subjective data play in the GCI scores is that, for most years, the labor market pillar in Peru, Brazil, and Colombia has performed better when objective indicators are included in the GCI scores. However, the pattern observed in the Dominican Republic and Mexico tells a different history. Thus, we cannot draw any conclusion on whether subjective data improve or deteriorate the GCI labor market scores in the selected countries.

Overall, the evidence presented above indicates that the labor market flexibilization reforms are not properly reflected in the labor market GCI scores, particularly when performance is measured exclusively with opinion data. This suggests that the Survey respondents either are not aware of the measures taken or are benefiting from them. On the one hand, most policies have been implemented to ease MSEs’ transition to formality; then, if small units of production are not adequately represented in the Survey, these policies are not necessarily known to the Survey respondents.

On the other hand, reductions in the cost of formalization have been the underlying premise behind the policies implemented in the selected economies. As Table 3 shows, a series of legislative initiatives sought to simplify procedures for registering companies and reduce the cost of operating formally. These measures resulted in lower costs of hiring and firing, as well as a lower tax burden for small units of production (Chacaltana, 2016). Thus, if the Survey respondents represent the firms operating with the most protected and regulated systems, that business sector could benefit from policies aimed at reducing formalization costs. Evidence of the positive relationship between the use of short-term contracts and firm size in Colombia and Peru supports the view that large firms advocate for the use of flexible forms of employment.

See Castellani et al. (2020) and Jaramillo and Campos (2019).

The Survey captures the opinions of business leaders around the world on a broad range of topics for which statistics are unreliable, outdated, or nonexistent for many countries. Respondents evaluate these aspects on a scale of “1” (worst) to “7” (best). The questionnaire is the same across countries.

The literature agrees on two preconditions for reliable survey results.

See Aleksynska and Cazes (2016) and Lall (2001).

This article has discussed the accuracy of the GCI for policy purposes in Latin America through an analysis of its subjective component. The region provides an ideal scenario for that purpose because some countries have built entire competitiveness systems based on the GCI to advance in competitiveness. We focus on the labor market area as LAC policymakers have identified formalization as a primary policy objective that would lead to competitive economies. Consistent with the WEF’s mandate, for a long time, the LAC countries have combated informality with reductions in the cost of formalization. A series of legislative initiatives strove to simplify procedures for registering companies and for reducing the costs of operating formally.

Despite the region’s ample experience in formalization policies, business representatives’ perceptions of the labor market as rigid persist. The fact that the labor market flexibilization reforms are not properly reflected in the labor market GCI scores suggests that the Survey respondents either are not aware of the measures adopted or are benefiting from them, which leads to examination of the methodology of the GCI subjective component. We have shown that the Survey sample in the GCI is not representative of the business structure in the selected economies, particularly that of Peru. The Survey sample consists mainly of large firms, whereas MSEs are the cornerstone of the productive structure in Latin America, accounting for >95% of firms in the countries of study. Moreover, these countries have a firm landscape oriented to microenterprises, which comprise around 98% of businesses. In contrast, the Survey assigns the same likelihood to a microenterprise and a firm with, say, 200 workers, to be selected in the Survey sample.

We have further shown that the Survey provides qualitative information on elusive concepts. Business executives do not always have the same benchmark to compare countries, and it is not clear how a respondent answers questions about topics he/she does not know. In the same line, some concepts are counted more than once in the Survey, which implies that the indicators are biased toward the overrepresented concepts. In light of the methodological issues revealed in this paper, we conclude that the GCI is not sufficiently sound to deliver coherent policy advice.