Countries are ranked on many criteria, the results of which can have far-reaching ethical and practical implications, particularly for emerging nations seeking role models. One highly influential ranking, the World Economic Forum’s Global Competitiveness Report (GCR), has been criticized for containing multiple methodological, conceptual, and logical flaws that bias competitiveness rankings toward countries that favor neoliberalism. Using datasets not afflicted by such flaws, we examine Bergsteiner and Avery’s (J Bus Ethics 109(4):391–410, 2012) prediction that competitiveness scores of the USA and the UK are substantially overstated. Results of re-ranking 104 countries using 29 economic, environmental, and social datasets from reputable sources support this assertion, with the USA showing the greatest discrepancy on a 100-point scale between its 2013–2014 GCR score (5) and our study’s 2013 score (57), and the UK falling from GCR score 9 to 40. We explore reasons for this discrepancy, including examining the relationship between a country’s neoliberal traditions and its rankings on the indicators.
Many governments, corporations, and other institutions use data from surveys comparing national competitiveness. Accordingly, instruments for ranking countries have mushroomed in recent decades, but most suffer from serious methodological flaws and ideological bias. For example, Bergsteiner and Avery (2012) presented evidence of major methodological problems and a strong neoliberal bias in the World Economic Forum’s (WEF) Global Competitiveness Report (GCR), probably the world’s most influential ranking. The GCR’s over-reliance on ideologically driven inputs rather than evidence-based outcomes results in unrealistically high rankings for some underperforming countries, particularly those with neoliberal traditions such as the USA and the UK. This lends succor to an economic model—variously known as neoliberalism, liberal market economics, the shareholder-centered model, Anglo/USA capitalism, the Chicago School, the Washington Consensus or in its most extreme form (Stiglitz 2002) market fundamentalism—that is in disrepute in both theory (e.g., Albert 1993; Komlos 2015; Stiglitz 2013) and practice (e.g., the 2007–2009 global financial crisis). Bergsteiner and Avery (2012) postulated that removing the bias and methodological flaws would result in the key protagonists of neoliberalism, the USA and the UK, falling significantly in international competitiveness rankings. This paper tests this hypothesis by re-ranking 104 countries using outcome-only datasets unafflicted (as far as possible) by known flaws and biases and comparing the results with the 2013–2014 GCR. The GCR’s high ranking of the USA and UK is of grave, global concern because—as we will show—it pseudo-legitimizes an economic model that is economically, environmentally, and socially wanting. It supports the proposition that the aggressively promoted neoliberal shareholder-focused, short-term, and market-driven economic model must be optimal for competitiveness, and hence entitles its most powerful proponent, the USA, to use this power to make and impose concomitant rules on others. Given the poor track record of the neoliberal model, perpetuating the myth that the USA and the UK are highly competitive economies raises profound ethical concerns. Therefore, this paper addresses the following questions:
Do global country rankings change significantly by removing the GCR’s methodological flaws and ideological bias? And, if so, does this result in a substantial drop in the USA’s and the UK’s competitiveness as hypothesized by Bergsteiner and Avery (2012)?
Is there is a relationship between the extent to which a country adopts neoliberal thought and its ranking on the 29 indicators?
Before presenting our methodology, discussing the analysis and results, and acknowledging the limitations and possible future directions of our study, we describe three key problems with the GCR and some other rankings.
Among many flaws in country ranking reports generally, three major problems occur in the GCR: mixing inputs and outputs, strong ideological bias, and negating the importance of social and environmental outcomes. Given the seriousness of these three flaws, we discuss them in turn.
Major Flaw #1: Mixing Inputs (Independent Variables) and Outputs (Dependent Variables)
In their critique, Bergsteiner and Avery (2012) briefly mentioned the GCR’s mixing inputs (hypothesized drivers and enhancers of performance) and outputs (measures of actual performance). Close examination shows that just over two-thirds of the GCR indicators are input variables rather than outcomes. In order to categorize the WEF’s (2016, pp. 371–379) indicators as either inputs or outputs, we used our definitions (see below) since neither the GCR nor the literature provides a competitiveness definition that satisfactorily clarifies the distinction.
The WEF’s continued use of input indicators is scientifically inconsistent and indefensible. In its 2015–2016 GCR, the WEF (2016, p. 63) still states that it “tend[s] to exclude input measures—such as expenditures, investment regimes, and partnership models” because mixing input and output measures risks double counting, and input measures, including monetary investment, are poor indicators of quantity or quality. Curiously, in its 2016–2017 GCR, the WEF (2016, p. 54) no longer states this but says instead: “Prosperity can increase only if inputs of production are used in smarter and more efficient ways to fulfill constantly evolving human demands. Therefore we still define competitiveness as the set of institutions, policies, and factors that determine the level of productivity of an economy, which in turn determines the level of prosperity a country can achieve.” Clearly, the terms highlighted by us in italics refer to inputs, whereas the bold terms denote outcomes. The WEF’s definition of competitiveness therefore is inherently flawed, being an amalgam of two antithetical concepts, namely inputs and outputs.
This confusion is also evident in many of the phrases the GCR uses such as competitiveness drivers, enhancers, policies, and factors; enabling environments, institutions, incentives, levers, policy options, and economic agendas; keys to success; determinants of growth; ingredients for competitiveness; all of which are inputs but are not identified as such. On the outcomes side, the GCR refers to “competitiveness performance” and “broad gains in living standards.”
Mixing inputs and outputs tends to be common in country rankings. For example, the Sustainable Society Index (SSI 2014, p. 34), the Human Development Index (UN 2014, p. 25), and the Sustainable Development Goals Index (Sachs et al. 2016, p. 28) cite “school enrolment” and/or “expected years of schooling” as outputs. Notwithstanding a certain face validity, this is problematic because countries with the same level of enrollment or years of schooling can produce vastly different educational outcomes as measured, for example, by language, mathematical, and scientific literacy tests such as in the PISA studies (Program for International Student Assessment). Among other things, educational outcomes depend on inputs such as teacher quality, gender, passion, remuneration, and social standing; socioeconomic–cultural context (e.g., emphasis on cooperation rather than competition); class sizes; quality and available teaching/learning resources; curriculum quality and relevance; and access to outdoor components, all of which constitute an input called “quality of learning experience” (Heffernan 2014; Miller and McKenna 2016). It is the range of excellent educational input factors that enables Finnish children to outperform schoolchildren in other developed countries even though the latter may spend up to two additional years at school. As Friedman (1970, p. 5) pointed out, “observed facts are necessarily finite in number; possible hypotheses, infinite.” The GCR does include some questions in its opinion survey on the perceived quality of each country’s educational system. However, to expect non-expert respondents to make an informed judgment about the quality of their countries’ educational system in relation to 147 other countries is unrealistic (another methodological flaw).
Mixing inputs and outcomes, whereby inputs account for around two-thirds of GCR indicators, constitutes a fundamental error in research methodology and leads to dramatic distortions as shown below. After all, if two years of extra schooling do not produce better outcomes, it suggests problems with some other input. While examining input factors can be valuable, they need to be clearly separated from outcome variables, a basic tenet of scientific research. Mere belief that a certain action, institution, policy, or factor will deliver a desired outcome does not make it an outcome. Notably, the World Bank’s (2015, p. 82) report World Development Indicators 2015 (Economy) includes outcomes only, namely: GDP, gross savings, adjusted net savings, current account balance, central government cash surplus or deficit, central government debt, consumer price index, and broad money—all used in our analysis.
To clarify the distinction between inputs (independent variables) and outputs (dependent variables) in country rankings, we propose the following definitions:
Competitiveness enhancers Are inputs such as actions, policies, situations, and events that demonstrably enhance the level of performance of an individual, group, organization, or country. For example, government spending on education (infrastructure, personnel, resources, curricula, etc.) involves many policy and action inputs.
Competitiveness Measures outputs/outcomes, that is, the relative performance of individuals, groups, organizations, and/or countries, where an intended or unintended outcome is the result or effect that follows a policy, action, situation, or event. For example, a country’s performance on the PISA rankings is one outcome of a range of education inputs.
Major Flaw #2: Token “Inclusion” of Environmental and Social Indicators
The GCR acknowledges that well-established ideas and models that take a narrow view of economic growth and that do not take the (ir)responsible use of natural resources or social concerns into account, may not assure overall sustainability. To this end, the WEF (2014, p. 55) has introduced the notion of “sustainable competitiveness,” which it defines as “the set of institutions, policies and factors that make a nation remain productive over the longer term while ensuring social and environmental sustainability.” Once again, this definition is based on a set of inputs. Table 1 lists nine social and nine environmental indicators chosen by the WEF to evaluate countries’ social and environmental sustainability. However, the GCR 2013–2014 does not provide rankings for these 18 indicators but generates social and environmental sustainability indices (GCIs) that are used to calculate a “social sustainability-adjusted GCI” (GCI-S) and an “environmental sustainability-adjusted GCI” (GCI-E) (WEF 2014, pp. 68–69). This is done by varying the GCI a maximum of 20% above or below the GCI (WEF 2014, p. 64). In effect, this means that the WEF values the social and environmental outcomes at one-fifth of the value of the economic results. Notably, the GCR’s final competitiveness ranking is based entirely on the underlying GCI, which disadvantages countries scoring well on the GCI-S and GCI-E.
This approach underestimates the crucial role of social and environmental factors in overall country competitiveness and undermines efforts to avoid a global Tragedy of the Commons problem. In relation to climate change, the Stern Report (2007, p. vi) points out that “our actions now and over the coming decades could create risks of major disruption to economic and social activity, on a scale similar to those associated with the great wars and the economic depression of the first half of the twentieth century.” Hirschberg et al. (2007, p. 9) warn “that human society has to develop within the boundaries set by the environment, and that economy has to satisfy societal needs—not the reverse.” The Sustainable Society Index (SSI 2014, p. 14) states: “Economic wellbeing is not a goal in itself. It is a precondition to achieve human and environmental wellbeing.” Koehler (2016, p. 6) argues that for the UN’s Sustainable Development Goals to have any chance of realization “economic decisions must be made subordinate to the over-arching ethics of rights to ecological and social justice.” To downplay social and environmental considerations from competitive measures is to overvalue economic and financial outcomes at the expense of life as we know it (Gowdy 1994).
Major Flaw #3: Ideology-Driven Input Indicators
The GCR, along with some other rankings, uses input indicators that are often ideology-driven, not evidence-based, potentially misleading decision makers relying on their data even further. Ideology-burdened input issues include ease of hiring and firing, minimum wages, flexibility of wage determination, worker empowerment at the operational level, worker representation on boards, stakeholder consideration, reliance on professional management, incentive systems, importance of equity markets, tax regimes, the short-term focus of listed companies, and many others.
Ease of firing is one example that embodies all three key flaws discussed here: an input; ideology-driven; and—when widely practiced—inhibits individual, enterprise, and national performance. Consistent with past GCRs, Question 3.03 in the 2015–2016 GCR asks: “In your country, to what extent do regulations allow flexible hiring and firing of workers?” The implicit assertion here that ease of firing confers competitiveness is a false and misleading assumption on two counts. One, respondents to Question 3.03 in, say, Germany could quite reasonably indicate that at-will firing is difficult in Germany. However, this response does not imply that respondents necessarily find this a problem or prefer to be able to fire workers with “ease.” High-performing companies seldom sack their greatest asset—their highly trained staff—even where they can, as research in Germany indicates: After new German laws made firing somewhat easier, employment turnover actually decreased between 1996 and 2010 (Jung 2014).
Two, there is no evidence that making firing difficult hinders competitiveness. On the contrary, evidence shows that the projected national benefits of unregulated dismissals are illusory. For example, the IMF, a former proponent of this approach, noted: “…by triggering a wave of layoffs, reforming employment protections further weakens aggregate demand and delays economic recovery” (IMF 2016, p. 108). Similarly, forced or excessive staff turnover impacts firms negatively, as Hancock et al.’s (2013) meta-analysis of 48 studies showed, decreasing profitability and growth (Cappellari et al. 2012; Yu and Park 2006). The corporate costs of firing, particularly long-term costs, are unrecognized or discounted by promoters of easy firing and can exceed the costs of hiring (Jung 2014). So-called labor adjustment results in dismissal costs (severance pay, administrative expenses), rehiring costs (agency costs, search, recruiting, training), delays in ramping up production once business improves, reputational damage, diminished staff health and loyalty, unamortized staff training costs, loss of IP, loss of highly trained workers to competitors, and other costs.
Society also loses; considerable evidence shows (e.g., Goh et al. 2015) that a hire-and-fire culture leads to worsening health and well-being for employees, their families, and society, including increased mortality and health costs. Conversely, regulated labor markets can enhance workers’ life styles, according to a 10-country study of 370,000 Europeans conducted between 1975 and 2002 (Ochsen and Welsch 2012).
Most crucially, an analysis of the 2013–2014 GCR data done as part of this study disconfirms the GCR’s assertion that at-will firing raises competitiveness, with a non-significant correlation (rs = 0.084) between the GCR’s overall rankings and its at-will-firing survey responses. Indeed, GCR data show that European countries with highly regulated labor markets—Austria, Denmark, Finland, Germany, the Netherlands, Norway, Sweden—are typically high performers. This finding is supported by an OECD (2013) study, which shows that these high-performing countries tend to have high employment protection legislation.
“Debiasing” Country Rankings
Our study sought to identify 2013 datasets free, as far as possible, of the methodological flaws and ideological prejudices identified by Bergsteiner and Avery (2012) and others, and then compare rankings derived from these 2013 datasets with the 2013–2014 GCR rankings. However, some biases can be difficult to spot, such as omissions. One example is Credit Suisse’s (2016) Global Wealth Report, which by excluding public assets and debt data artificially inflates the performance of high debt/low public-asset countries, such as the USA.
In our search for reliable indicators, many indicators used by others were found to be flawed and hence were unusable. Table 2 draws on Cash et al.’s (2003) and Kerr et al.’s (1996) flaw-classification schemas to categorize 14 frequently occurring shortcomings in published rankings. Cash et al. (2003, p. 8086) propose that information needs to be perceived as credible, salient, and legitimate to influence evaluations of scientific advice and public responses to social issues. Here “credibility involves the scientific adequacy of the technical evidence and arguments; salience deals with the relevance of the assessment to the needs of decision makers; legitimacy reflects the perception that the production of information and technology has been respectful of stakeholders’ divergent values and beliefs, unbiased in its conduct, and fair in its treatment of opposing views and interests.” Kerr et al.’s (1996) taxonomy of biases tests whether indicators are affected by imprecision (indicator is poorly defined), commission (indicator misleads), or omission (indicator that ought to be included is omitted).
Rankings subject to flaws inevitably produce misleading assessments of countries’ actual performance. As Freedman (1985), Britt (1997), and others noted, before undertaking any analysis, the quality of basic variables, processes, and how clusters of variables interact needs addressing. Table 2 summarizes common flaws and ways in which our study minimized them.
In addition, we sought to ensure that our indicators met Hirschberg et al.’s (2007, p. 6) requirements by being measurable, quantifiable, meaningful, clear in value, clear in content, robust, reproducible, sensitive, specific, verifiable, and not involving redundancy or double counting.
Our analysis and discussion focuses substantially on 23 countries (the G23), ranked in the context of all 104 ranked countries (the G104). The G23 includes countries in the G7 (Canada, France, Germany, the UK, Italy, Japan, the USA), the BRIC countries (Brazil, Russia, India, China), all remaining Anglo countries (Ireland and New Zealand but not South Africa), some high-performing European countries (Austria, Denmark, Finland, Germany, the Netherlands, Norway, Sweden, Switzerland), as well as Thailand and Singapore. While inclusion of the G7 and BRIC countries requires no explanation, the Anglo countries are included to test the proposition implicit in Bergsteiner and Avery’s (2012) paper that the GCR overstates rankings for these countries. We excluded South Africa because the majority of the population is of indigenous descent and so is not “Anglo,” and while English is the official language of government, it is only fourth-ranked as a spoken first language of 11 official languages. Several high-performing European countries and Singapore are included given that they perform well in GCR rankings notwithstanding the GCR’s ideological bias. Thailand is included because it officially promotes a form of capitalism that is more akin to the European model than the Anglo model (Avery and Bergsteiner 2016), and together with China, India, and Singapore provides another key Asian reference point.
The 2013–2014 GCR ranks 148 countries on 117 indicators and uses data from the World Bank, its own survey questionnaires, and other sources. Unless otherwise stated, for consistency we used only World Bank datasets. Where the World Bank did not have a ranking, we used other specialized rankings. For comparability with the GCR, our target year was 2013 since data for later years were incomplete for many countries. Where data for 2013 were unavailable, we used the nearest available dataset. Where a World Bank ranking had minor indicator gaps, we augmented the data from one other additional published source. Any remaining gaps were left blank. The data were correct as at July 2017, but because some rankings are continuously updated, minor discrepancies might have arisen subsequently.
Derivation of Final Scores for the G104
The following steps were followed in deriving final scores for the countries remaining after applying procedures for overcoming flaws as described in Table 2.
When, for a given country, more than three cells for any one of the economic, environmental, or social sets of indicators were empty, that country was deleted from the sample. In total, 104 countries remained after this step.
For each country, an average was calculated for each of the three domain indicator sets (economic, environmental, and social).
These averages were then ranked (1 is best, 104 worst).
Data were not available for every G104 country on all 29 indicators. For example, only 95 countries reported on “broad money” and so were ranked from 1 to 95. To be able to compare results, all rankings were converted to “scores” on a 100-point scale.
An overall country average was derived from the three equally weighted domain averages, given that the economy, society, and the environment are interrelated. Some statisticians caution about averaging scores from the three domains or even within the domains into a single index (SSI 2014, p. 15), particularly with a significant negative relationship between social (human well-being) and environmental scores (rs = − 0.48 in our study). This is because “whenever an average is used to represent an uncertain quantity, it ends up distorting the results because it ignores the impact of the inevitable variations” (Savage 2002, p. 20). However, all country rankings do this and for comparability with the GCR we needed to aggregate the domains, while noting that the GCR—somewhat confusingly—includes some input and output social indicators on health and education in the GCI that should, more properly, form part of the GCI-S. Narrowly defined rankings can misrepresent a country’s overall wealth and performance, as is also evident in Credit Suisse’s (2016) Global Wealth Report, which excludes social and environmental indicators in its definition of national wealth.
The overall ranked averages were then again converted to scores on a 100-point scale. Thus, the final score for a country is NOT the average of the scores for the three domains, but the overall averages converted to a 100-point scale.
The overall scores for the G104 were then ranked and based on this ranking reordered for the final table in Appendix A in ESM.
To compare our results with GCR data, all GCR countries that were not also part of our set were eliminated, coincidentally leaving 100 GCR countries, which then conveniently formed a 100-point scale.
The question arises about how volatile ranking trends are, that is, are there significant shifts in countries’ ranking from one period to the next or are variations within acceptable bounds?
To examine volatility, we analyzed output data over a 20-year time span, with four reporting years (1998, 2003, 2008, 2013) for the 11 economic indicators for the G23 countries, economic indicators being the ones the GCR focuses on. Space constraints did not permit a comprehensive analysis across all G104 countries and 29 indicators. An analysis of 104 countries over 4 time periods for 41 data rows would require a 17,056-cell table.
Tables 3, 4, and 5 show definitions for the economic, environmental, and social indicators we used; provide links to the original dataset; and offer comment where necessary. Note that in Tables 3, 4, and 5, the highest (best) score is 1 and the lowest is 100, and that all indicators are outcomes.
Economic Output Indicators
Our 11 economic indicators incorporate all the World Bank’s key World Development Indicators plus additional indicators from World Bank publications and the Economist; and Adjusted Net National Income (ANNI), an indicator recommended by Stiglitz et al. (2009) in lieu of GDP. Most indicators draw heavily on World Bank definitions and explanations (see Table 3).
Note, GDP was omitted in calculating our economic indicator averages and overall country averages. Although commonly used, GDP has been widely criticized (e.g., van den Bergh 2009; SSI 2014; Stiglitz et al. 2009) as inappropriate for measuring general, or even economic, well-being. GDP overlooks certain economic factors that enrich the human economic condition and ignores others that diminish it. Even the Chief Economist of the WEF recommends not judging a country by GDP because GDP overlooks whether growth is fair, green, and improves lives (Blanke 2016). Yet, the WEF continues to use GDP.
Environmental Output Indicators
Our nine environmental indicators (see Table 4) are largely oriented toward the UN’s Sustainable Development Goals (SDGs). The actual data came from: World Bank (5 indicators), World Resources Institute (1), Living Planet Report (1), Intergovernmental Panel on Climate Change (1), and Wiedmann et al. (2013) (1).
Social Output Indicators
Social indicators are largely oriented toward the UN’s Sustainable Development Goals (SDGs), many of which involve multiple submeasures. For example, health involves submeasures of incidence of HIV, malaria, infant mortality, maternal mortality, and many others. Given the limited scope of our study and a need to balance the three indicator domains, we included nine frequently used and widely applicable social indicators that differentiate between countries. The actual data were obtained from sources shown in Table 5.
Note that country-wide factors such as corruption and crime are, among other things, an outcome of government policies (or lack thereof) and social mores (e.g., classifying drug use as a crime). However, for businesses operating in high-crime, high-corruption countries, these factors constitute an input. On the other hand, ongoing and widespread criminal behavior in and by organizations, such as IP theft or cartels, is an outcome of inappropriate organizational cultures.
Analysis and Discussion
Appendix A in ESM shows data for all G104 countries across all 29 indicators. Table 6 presents the average scores from the economic, environmental, and social domains, plus the overall score (average of domain averages) and 2013–2014 GCR scores for comparison.
In general, our results confirm Bergsteiner and Avery’s (2012) predictions that after removing known flaws and biases, the ranking of the USA would drop substantially compared with GCR rankings. Specifically, Bergsteiner and Avery predicted that the USA’s 2009–2010 GCR ranking would fall from 4th place out of 139 countries to between 30th and 60th place, which is equivalent to a score of between 21 and 43 on a 100-point scale. Using mostly 2013 data, our results show the USA scores 57 for 2013 outcome data, well below the 2013–2014 GCR score of 5 (both on a 100-point scale). Similarly, the UK’s score fell from 9 (GCR) to 40, while Canada dropped 24 points (from GCR 11 to 35). Results were mixed for the other Anglo countries. Our scores for Ireland and Australia (respectively, 28 and 21) remained somewhat similar to those in the 2013–2014 GCR (respectively, 22 and 18), with New Zealand dropping only 1 point from 15 (GCR) to 16. The relatively poor results for the USA and UK in our study need further analysis, but can basically be attributed to their poorer than generally expected economic performance (USA = 34, UK = 37) and extremely poor environmental scores (USA = 97, UK = 82).
Given popular perceptions about the USA’s economic prowess, its poor overall performance in our study must surprise, even though this has been reported long before, albeit using smaller country sample sizes. For example, a 1992 comparison of OECD countries (Wolff et al. 1992) on around 450 economic, social, and environmental measures (using very similar sources to our study) casts the USA in a similarly poor light. On roughly half the compared measures, the USA tended toward poor performance, hovered around the middle on just over a quarter of the measures, and clustered around the high end on fewer than one quarter. The overall high performers in various categories in 1992 were: Germany, Japan, Australia, and the Netherlands. Twenty-two years later, the Sustainable Society Index (SSI 2014, p. 81) ranked 151 countries on which the USA ranked 40 on human wellness, 139 on environment, and 96 on economics. This translates into a composite overall score of 60 on a 100-point scale, where 1 is best and 100 lowest, very similar to our overall score of 57 for the USA. We also note major concordance on the final rankings between Bertelsmann Stiftung’s SDG Index and Dashboards Global Report (Sachs et al. 2016) and our results, the main difference being that the UK and USA rank substantially lower in our study. An explanation for this may lie in the Bertelsmann indicators being heavily skewed toward social indicators (41 social, 9 environmental, 8 economic), whereas our domains are more balanced. Furthermore, Bertelsmann omits key economic indicators (adjusted net national income, gross national savings, government cash surplus/deficit, current account balance, government debt, foreign direct investment, broad money), and some environmental indicators, on all of which the UK and USA perform poorly.
The leading 10 countries on overall averages (Table 6) in our sample of 104 countries are Switzerland, Norway, Denmark, Austria, Sweden, Germany, Croatia, the Netherlands, Romania, and Chile. With the exception of Chile, all are European countries. The high performance of the north-European countries is consistent with results from various international rankings including the 2013–2014 (and other) GCRs. The high performance of Croatia (7), Romania (9), and Chile (10) is explainable with all three countries scoring relatively high on at least two of the three domains. These countries score significantly lower on the GCR’s ranking because their environmental and social results—respectively, 40, 20, and 54 for environment, and 21, 35, and 29 for the social domain—are not taken into account. Omitting these domains favors the USA, which scores 97 and 32, respectively, in those domains.
We found considerable variation and some surprising results within the three domains.
Norway, Singapore, Kuwait, Switzerland, United Arab Emirates, Israel, Austria, Germany, the Netherlands, and China are the 10 leading G104 countries on economic indicators (Table 6). This group consists of five north-European countries, three oil-exporting countries, two Asian countries (the smallest and largest), and Israel. Norway belongs to two groups: North-European and oil-exporting. The biggest surprises in this domain are the mediocre economic performances of the UK and the USA, which score 37 and 34, respectively, on our 100-point economic scale. For comparison, the Sustainable Society Index (SSI 2014) scores the USA lower still on economic indicators (64 out of 100). For both countries, the following specific indicators (Table 7) contribute to these low scores: gross domestic savings, unemployment, government cash deficits, current account balance, and central government debt. Emerging G23 nations, China (10), Thailand (12), and Russia (15) outperformed both the USA and UK on overall economic indicators by substantial margins. The other Anglo countries—Australia, Canada, Ireland, and New Zealand—performed comparatively better than the USA/UK, scoring 14, 23, 32, and 24, respectively.
Emerging nations tended to perform better on environmental indicators than industrialized countries (Table 6). Here, Rwanda, Bangladesh, the Philippines, Nepal, Morocco, the Dominican Republic, Sri Lanka, Tajikistan, Malawi, and Chad led the G104. Of the G23 (Table 7), emerging countries India (12), Brazil (41), Thailand (51), and China (61) outperformed all six Anglo countries—New Zealand (73), Ireland (75), UK (82), Australia (94), USA (97), and Canada (98) (Tables 6, 7). The higher scores for the emerging economies are mostly attributable to large rural populations who overall cause less environmental damage, have a lower use of energy, and higher use of renewables than highly urbanized and industrialized countries. However, emerging nations will struggle to maintain their low environmental impact as wealth grows, with Hertwich and Peters (2009) finding that nations increase their carbon footprint by 57% for each doubling of consumption.
The exceptionally poor environmental results for Australia, Canada, and the USA are difficult to explain given their countries’ vast undeveloped and/or forested acreage. Norway, a major oil exporter, stands out because it is among the worst scoring countries on energy use (score 93) but a relatively high scorer in use of renewables (score 24). The most urbanized country in the G104 is Singapore, a city-state that—unsurprisingly—has a poor environmental score of 91, which is, however, still ahead of the USA (97) and Canada (98).
Mostly North-European countries lead the G104 on social indicators: Norway, Sweden, Finland, Switzerland, Denmark, Australia, the Netherlands, Germany, Canada, and Austria (Tables 6, 7). This finding is consistent with other rankings (e.g., World Happiness Report, Sustainable Development Goals Index, Social Progress Index, Sustainable Society Index, and Human Development Report). However, once again the USA scores relatively low (32) compared with the eight European and two Anglo leaders. Interestingly, between 2007 and 2012, six G23 countries (Denmark, Ireland, Italy, Netherlands, UK, USA) showed a worsening “shared prosperity” trend for their lowest 40-percent population (see World Bank (2016s) raw data).
Why the Disparity Between the GCR and Our Study?
How can the huge differences between our study and the GCR for the two key Anglo countries—the USA and the UK—be explained? Basically, disparities arise due to methodological problems found in the GCR: exclusion of environmental and social factors, input factors accounting for two-thirds of its measures, ideological bias, excluding relevant data, including irrelevant data, conflicting competitiveness indicators, fourfold counting of one indicator, over-reliance on naïve opinions, small respondent samples to surveys, and, most importantly, the failure of neoliberalism adherents to test their ideological beliefs against outcomes. We return to this below. For similar reasons, this disparity also exists between our study and some other country rankings such as the Human Development Index (UN 2014), the Legatum Prosperity Index (Legatum Institute 2016), and IMD’s World Competitiveness Report (IMD 2016).
Volatility of Rankings
Table 8 shows that country rankings on the 11 economic indicators across four reporting periods (1998, 2003, 2008, 2013) have remained relatively stable, with the coefficient of volatility for the averages never exceeding 0.6, considerably below the accepted standard of 1.0. The implication is that the 2013–2014 GCR results are in line with previous years. This consistency is not unexpected given that national indicators tend to change slowly, except for critical events such as the Global Financial Crisis (GFC). At the individual indicator level, the most volatile indicators appear to be current account balance and foreign direct investment.
Neoliberalism and Rankings
A major issue raised in this paper is a possible link between a country’s neoliberal policies and practices, and its rankings. This part of our study is exploratory, since our primary focus was on testing countries’ performance using outcomes only, and neoliberal policies are inputs. However, it seems appropriate to examine the role of neoliberalism as a possible explanation for our findings, and this requires some reference to inputs.
Although defining the concept of neoliberalism has attracted little scholarly attention in political economy, it has come to be associated with a radical form of market fundamentalism (Boas and Gans-Morse 2009). Neoliberalism finds expression in numerous ways: a preference for small government, low taxes, minimal regulation of markets, a shareholder focus to the exclusion of a wide range of possible stakeholders, a pre-occupation with short-term outcomes, high market capitalization, flexible labor markets including the ability to fire at will and pay low wages, minimal worker unemployment support, and many others. About one-sixth of the GCR indicators are demonstrably ideologically biased toward certain so-called neoliberal input policies. The Heritage Foundation (2016a, b), a Washington-DC-based “think tank” that describes itself as “the most influential conservative group in America,” is a prominent proselytizer of this kind of evidence-poor thinking.
Flexible labor market input policies are vigorously prosecuted in the USA and UK but less so among other Anglo countries, which could explain some of the outcome differences between the Anglo countries in our study. Table 9 juxtaposes our country performance findings with five so-called success factors (SFs) propagated by the Heritage Foundation (2016a, b). The top and bottom halves of the table, respectively, show our study’s six top countries (all north-European) and the six Anglo countries. Note that the table needs to be read with great care since the data are from different sources and so the lowest numbers in columns 2, 3, 4, and 5 (numbers in bold) have the highest compliance (HC) with the neoliberal “success factors,” whereas in column 6, the highest number (in bold) is high compliance. The results fail to demonstrate that adherence to these five neoliberal SFs delivers success. Sweden eschews all five SFs, doing the opposite to what neoliberalism prescribes, and yet it scores fifth-highest in our study and 10 in the GCR. Switzerland, which has relatively low job protection but pays high unemployment benefits, is our and the GCR’s highest-scoring country overall (1), it also scores highest on SF compliance on general government expenditure (11) and tax (9.5), second-highest on market freedom (4), middling on employment protection, and least-compliant (2) with the unemployment benefits SF. The USA scores second-highest on general government expenditure and tax, middling on market freedom, and highest on employment protection and unemployment benefits. Notably, the high-performing European countries include the highest and the lowest SF compliance scores on general government expenditure and on tax, and the second-highest (4) and lowest (32) on market freedom. However, the USA is clearly most in alignment with the neoliberal SFs employment protection (0.49) and unemployment benefits (46). Unfortunately for the USA, its “good” SF scores do not translate into high performance or competitiveness.
In other words, certain neoliberal input policies (e.g., government spending and tax revenues) predict neither high nor low competitiveness, in contrast to certain labor market policies (such as employment protection and unemployment benefits). This underscores the importance of rigorous separation of input and outcome factors. The resulting highly complex, and often paradoxical, interactions between enterprise flexibility and worker security—so-called flexicurity—were investigated by Auer (2007, p. 11) who concluded that “these apparent paradoxes might be solved if one intelligently designs labor market reforms that take into account the need for stability, flexibility, and security… Omitting one of these elements—and also the social dialogue on the process side—will produce suboptimal results either for productivity, employment performance or workers’ security.” Switzerland, which has the second-highest ranking on unemployment benefit replacement rates of 51 countries (Vlandas 2012), typifies such a response. Its employers enjoy fairly high levels of flexibility in firing, but not at the expense of employees, who enjoy high unemployment benefits. The USA, which scores low on both employment protection and unemployment benefits, ranks 31 (Table 9), and so is subject to both labor market instability and worker insecurity (Auer 2007).
Table 9 therefore offers a possible insight into why the two countries most aligned with neoliberal thinking—the USA and the UK—are among the worst performers in our G23. In short, European high performers can score at the extremes of certain neoliberal policies, such as general government final consumption expenditure, tax revenue, and market freedom. However, they consistently offer more employment protection and/or unemployment benefits, which the literature links to high country performance.
We have made the case above that the GCR rankings are significantly distorted by an uncritical belief that restrictive labor regulations must impede competitiveness. In line with this thinking, the GCR (2017) identifies 15 “most problematic factor[s] for doing business.” One of these is “restrictive labor regulations.” The WEF ranked 138 countries; for 12 of them it identified “restrictive labor regulations”—which are an ideologically biased input—as their biggest problem. The countries were (GCR ranks shown in brackets): Australia (21), Austria (19), Ecuador (91), Finland (10), France (21), Netherlands (4), Oman (66), Qatar (18), Saudi Arabia (29), Singapore (2), United Arab Emirates (16), and Uruguay (73). The WEF sees no contradiction in the fact that many of these countries achieve good to excellent GCR rankings notwithstanding their strict labor regulations. It also discounts its own research, which shows that countries that score low on the neoliberal SFs, score high in its own GCR rankings. The WEF’s “most problematic factors for doing business” are themselves an interesting case of competitiveness indicator invalidity, irrelevancy, and bias. For example, 12 of the 15 indicators are inputs, most of which are subject to bias.
Based on this very brief analysis of key neoliberal input factors, it seems reasonable to suggest that the more a country deviates from certain aspects of neoliberalism, the better it will score—an avenue for future research. This finding poses an ethical dilemma for proponents of neoliberalism: Do due diligence to ascertain for themselves who is outperforming whom on outcomes—and respond accordingly—or, maintain neoliberal beliefs, frame questions accordingly, provide data that are bound to distort results, and condone whatever negative outcomes this approach entails for individuals, organizations, nations, and ultimately the planet.
Why do people perpetuate policies and practices that demonstrably deliver inferior outcomes? We suggest five reasons why the myth of the USA and UK being highly competitive persists. First, Americans appear to be unaware of the parlous state of their country. Thus, while Switzerland justifiably scores highest both overall and in the happiness ranking (Helliwell et al. 2015), the USA scores 57 overall but 13 in happiness ranking—things are perceived better by Americans than data indicate. Conversely, the famously pessimistic Germans score 6 in our study but 22 on happiness. Second, both the USA and the UK have failed to empirically test their assumptions using outcomes only. Third, they are consistently (but wrongly) ranked highly by influential bodies such as the WEF, thereby being reinforced in certain counterproductive neoliberal belief systems. Fourth, some countries are beneficiaries of significant natural, produced, and human capital, such as infrastructure, institutions, services, and skills that were developed over previous decades and centuries (e.g., Piketty 2014). These five factors raise expectations of high competitiveness and appear to make people blind to these countries’ poor actual outcomes. The WEF’s approach to country rankings is unethical in many ways, not the least being in encouraging emerging nations to emulate the unscientific and unsustainable practices of underperforming—but influential—countries.
Limitations and Further Research
To obtain an accurate picture of country competitiveness, we carefully chose indicators based on outcomes only. Extant country rankings could generally not be used because composite rankings would result in double counting; inputs and/or ideologically biased indicators would bias results; or datasets were compromised in other ways. For our study, we selected extant rankings from reputable institutions that were not biased in these ways. Nonetheless, every study has limitations:
Rankings, by definition, are based on ordinal and not information-richer interval measures. A more fine-grained, comprehensive interval-based study could be undertaken to overcome this limitation. This is a limitation that our study shares with the GCR.
The use of certain popular indicators can lead to double counting. For example, the GhG emissions indicator overlaps to some extent with the “adjusted net savings indicator.”
This study is basically a snapshot in time. A more useful, but much larger, study would involve a trend analysis of 100+ countries on 30 or more indicators at, say, 3-year intervals covering the last 50 or so years.
We were unable to subject the data to sophisticated analysis such as the aggregation methodology employed by Bertelsmann Stiftung’s SDG Index and Dashboards Global Report (Sachs et al. 2016). Future researchers should test for substitutability of input and output indicators.
Our study used 29 indicators only; however, this compares well with the GCR, which, after discounting all the inputs, biased and naïve questions, has just over 30 valid outcome measures. Nevertheless, to achieve more robust results, it may be desirable to increase the number of outcome indicators to eliminate any inadvertent bias.
Inputs need to be the subject of a separate much larger study that clearly identifies which input policies (neoliberal or not) produce which outcomes. Such a study should also include input factors that the GCRs omit, such as corporate social responsibility practices, organizational culture (e.g., cooperative vs. competitive), sustainability practices, leadership practices (e.g., top-down vs. distributed), long-term versus short-term focus, and the important role of stakeholders other than managers and shareholders (e.g., workers, customers, suppliers, and local communities). However, merely separating inputs and outcomes, while methodologically sound, remains oversimplified. For example, the United Nation’s Inclusive Wealth Report (2014), a joint initiative of several international organizations, distinguishes three kinds of inputs, so-called capital assets that contribute to wealth: natural capital, produced capital, and human capital, with care being taken as to how wealth is defined.
In short, there is a need for a robust long-term study of at least 100 countries comparing three classes of input indicators (natural, produced, and human capital) in at least three domains (economic, environmental, and social) with sufficient numbers of outcome indicators to allow meaningful and objective conclusions to be drawn about which input indicators correlate with which outcomes.
The main purpose of our study was to test the assertion that by removing technical, ideological, and other flaws in the GCR, and by adding environmental and social indicators, the competitiveness rankings of Anglo countries would drop substantially (Bergsteiner and Avery 2012), particularly for the USA and UK. This prediction was wholly supported for the USA, UK, and Canada, but less so for Ireland, which ranked only slightly worse in our study, and for Australia and New Zealand, whose scores remained about the same. Consistent with other studies, including the GCRs, we found that the champions of competitiveness are mostly north-European countries. Most of these countries are social-market economies that largely eschew neoliberal labor market positions and are characterized by strong government, regulated markets, and employee protection. This contradicts the assertion, evident in WEF publications such as its GCRs, that neoliberal policies necessarily pave the way to socioeconomic well-being. Whatever the reasons behind the high performance of the north-European countries and generally low performance of major Anglo countries, our data suggest that emerging economies should be looking to learn from the Europeans rather than from the USA or UK. Given that most of the high-performing European countries enjoy little underlying wealth in terms of natural capital (unlike Anglo countries such as Australia, Canada, and the USA), their high competitiveness is even more impressive. Research that reinforces misperceptions about the efficacy of neoliberalism helps perpetuate a system that is not only flawed, but deeply unethical in the socially divisive, environmentally destructive, and economically impoverishing outcomes it produces.
Albert, M. (1993). Capitalism against capitalism. London: Whurr.
Auer, P. (2007). Security in labour markets: Combining flexibility with security with security for decent work. Economic and Labour Market Papers 2007/12. International Labour Office.
Avery, G. C., & Bergsteiner, H. (Eds.). (2016). Sufficiency thinking: Thailand’s gift to an unsustainable world. Sydney: Allen and Unwin.
Bergsteiner, H., & Avery, G. C. (2012). When ethics are compromised by ideology: The Global Competitiveness Report. Journal of Business Ethics, 109(4), 391–410.
Blanke, J. (2016). What is GDP, and how are we misusing it? World Economic Forum. Accessible at: https://www.weforum.org/agenda/2016/04/what-is-gdp-and-how-are-we-misusing-it/.
Boas, T. C., & Gans-Morse, J. (2009). Neoliberalism: From new liberal philosophy to anti-liberal slogan. Studies in Comparative International Development, 44(2), 137–161.
Britt, D. W. (1997). A conceptual introduction to modeling: Qualitative and quantitative perspectives. Mahwah, NJ: Lawrence Erlbaum.
Cappellari, L., Dell’Aringa, C., & Leonardi, M. (2012). Temporary employment, job flows and productivity: A tale of two reforms. Economic Journal, 122(562), F188–F215.
Cash, D. W., William, C. C., Alcock, F., Dickson, N. M., Eckley, N., Guston, D. H., et al. (2003). Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences, 100(14), 8086–8091.
CIA. (2016). Distribution of family income (Gini index). World Factbook (No date). CIA. Accessible at: https://www.cia.gov/library/publications/the-world-factbook/rankorder/2172rank.html.
Credit Suisse. (2016). Global Wealth Report 2016. Accessible at: https://www.credit-suisse.com/us/en/about-us/research/research-institute/news-and-videos/articles/news-and-expertise/2016/11/en/the-global-wealth-report-2016.html.
Economist. (2008). The limits of leapfrogging. Economist 386(8566), 12–13. Accessible at: http://www.economist.com/node/10650775.
Freedman, D. A. (1985). Statistics and the scientific method. In W. Mason & S. E. Feinberg (Eds.), Cohort analysis in social research: Beyond the identification problem. New York: Springer.
Friedman, M. (1970). The methodology of positive economics, essays in positive economics—Part I. Chicago: University of Chicago Press.
Goh, J., Pfeffer, J., & Zenios, S. A. (2015). The relationship between workplace stressors and mortality and health costs in the United States. Management Science, 62(2), 608–628.
Goossens, Y., Mäkipää, A., Schepelmann, P., van de Sand, I., Kuhndt, M., & Herrndorf, M. (2007). Alternative progress indicators to Gross Domestic Product (GDP) as a means towards sustainable development. Policy Department Economic and Scientific Policy, European Parliament. Accessible at: http://www.europarl.europa.eu/RegData/etudes/etudes/join/2007/385672/IPOL-ENVI_ET(2007)385672_EN.pdf.
Gowdy, J. (1994). Coevolutionary economics: The economy, society and the environment. New York: Springer.
Hancock, J. I., Allen, D. G., Bosco, F. A., McDaniel, K. R., & Pierce, C. A. (2013). Meta-analytic review of employee turnover as a predictor of firm performance. Journal of Management, 39(3), 573–603.
Heffernan, M. (2014). A bigger prize: Why competition isn’t everything and how we do better. London: Simon & Schuster.
Helliwell, J., Layard, R., & Sachs, J. (eds.). (2015). World Happiness Report 2015. New York: Sustainable Development Solutions Network. Accessible at: www.unsdsn.org/happiness.
Heritage Foundation. (2016a). Accessible at: https://secured.heritage.org/join-heritage-parallax/beautiful?gclid=CI2_7tvF7M4CFcky0wodlS8BeQ.
Heritage Foundation. (2106b). 2016 Index of Economic Freedom: Country rankings. Accessible at: http://www.heritage.org/index/ranking.
Hertwich, E. G., & Peters, G. P. (2009). Carbon footprint of nations: A global trade-linked analysis. Environmental Science and Technology, 43(16), 6414–6420.
Hirschberg, S., Bauer, C., Burgherr, P., Dones, R., Schenler, W., Bachmann, T., et al. (2007). Environmental, economic and social criteria and indicators for sustainability assessment of energy technologies. EU Integrated Project NEEDS. Paul Scherrer Institute. Accessible at: http://www.needs-project.org/RS2b/RS2b_D3.1.pdf.
ICPR. (2016). World Prison Brief. Institute for Criminal Policy Research. Accessible at: http://www.icpr.org.uk/media/41356/world_prison_population_list_11th_edition.pdf.
IMD. (2016). IMD World Competitiveness Report 2016: Factors and criteria. Accessible at: http://www.imd.org/wcc/wcc-factors-criteria/.
IMF. (2016). World Economic Outlook. Accessible at: https://www.imf.org/external/pubs/ft/weo/2016/01/pdf/text.pdf.
Inclusive Wealth Report. (2014). United Nations Environment Programme and UN University. Accessible at: http://www.unep.org/greeneconomy/Publications/InclusiveWealthReport2014/tabid/1060943/Default.aspx.
IPCC. (2014). Climate change 2014: Synthesis report, summary for policymakers. Intergovernmental Panel on Climate Change. Accessible at: http://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_SPM.pdf.
Jung, S. (2014). Employment adjustment in German firms. Journal of Labour Market Research, 47(1), 83–106.
Kerr, N. L., MacCoun, R. J., & Kramer, G. P. (1996). Bias in judgment: Comparing individuals and groups. Psychological Review, 103(4), 687–719.
Koehler, G. (2016). Assessing the SDGs from the standpoint of eco-social policy: Using the SDGs subversively. Journal of International and Comparative Social Policy. https://doi.org/10.1080/21699763.2016.1198715.
Komlos, J. (2015). What every economics student needs to know and doesn’t get in the usual principles text. New York: M.E. Sharpe.
Legatum Institute. (2016). The Legatum Prosperity Index 2016. Accessible at: https://lif.blob.core.windows.net/lif/docs/default-source/publications/2016-legatum-prosperity-index-pdf.pdf?sfvrsn=2.
Living Planet Report. (2012). Biodiversity, biocapacity and better choices. ISBN: 9978-2-940443-37-6. Accessible at: http://www.footprintnetwork.org/content/documents/ecological_footprint_nations/ecological_per_capita.html.
Miller, J. W., & McKenna, M. C. (2016). World literacy: How countries rank and why it matters. New York: Routledge.
Ochsen, C., & Welsch, H. (2012). Who benefits from labor market institutions? Evidence from surveys of life satisfaction. Journal of Economic Psychology, 33(1), 112–124.
OECD. (2013). Strictness of employment protection—individual dismissals (regular contracts). Available at: http://stats.oecd.org/Index.aspx?DataSetCode=EPL_R>Excel download.
Piketty, T. (2014). Capital in the twenty-first century. Cambridge: Belknap Press, Harvard University Press.
Sachs, J., Schmidt-Traub, G., Kroll, C., Durand-Delacre, D., & Teksoz, K. (2016). SDG Index and Dashboards—Global Report. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN). Accessible at: http://sdgindex.org/assets/files/sdg_index_and_dashboards_compact.pdf.
Savage, S. (2002). The flaw of averages. Harvard Business Review, 80(11), 20–21.
SSI. (2014). Sustainable Society Index SSI 2014. Sustainable Society Foundation. Accessible at: http://www.ssfindex.com/ssi2014/wp-content/uploads/pdf/SSI2014.pdf.
Stern, N. H. (2007). The economics of climate change: The Stern review. Cambridge: Cambridge University Press.
Stiglitz, J. E. (2002). Globalization and its discontents. London: Penguin.
Stiglitz, J. E. (2013). The price of inequality: How today’s divided society endangers our future. New York: Norton.
Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress. CMEPSP. Accessible at: http://data.worldbank.org/indicator/NY.ADJ.NNTY.CD.
Transparency International. (2013). Corruption Perceptions Index. Accessible at: https://www.transparency.org/cpi2013/results.
UN. (2014). Human Development Report 2014. United Nations Development Programme. Accessible at: http://hdr.undp.org/sites/default/files/hdr14-report-en-1.pdf.
US Federal Reserve. (2017). Why does the Federal Reserve aim for 2 percent inflation over time? Board of Governors of the Federal Reserve System. Federal Reserve. Accessible at: https://www.federalreserve.gov/faqs/economy_14400.htm.
van den Bergh, J. C. J. M. (2009). Abolishing GDP. Peer-reviewed discussion paper. Vrije Universiteit Amsterdam, and Tinbergen Institute. Available on: http://www.tinbergen.nl/discussionpapers/07019.pdf.
Vlandas, T. (2012). World ranking in unemployment benefit replacement rates. Accessible at: http://euwelfarestates.blogspot.com.au/2012/04/world-ranking-in-unemployment-benefit.html.
WEF. (2014). Global Competitiveness Report 2013–2014. World Economic Forum. Accessible at: http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2013-14.pdf.
WEF. (2015). Global Competitiveness Report 2014–2015. World Economic Forum. Accessible at: http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2014-15.pdf.
WEF. (2016). Global Competitiveness Report 2015–2016. World Economic Forum. Accessible at: http://www3.weforum.org/docs/gcr/2015-2016/Global_Competitiveness_Report_2015-2016.pdf.
Wiedmann, T. O., Schandl, H., Lenzen, M., Moran, D., Suh, S., West, J., et al. (2013). The material footprint of nations. PNAS Early Edition. Accessible at: www.pnas.org/cgi/doi/10.1073/pnas.1220362110.
Wolff, M., Rutten, P., & Bayers, A., III. (1992). Where we stand. New York: Bantum Books.
World Bank. (2012). Tax revenue (% of GDP). Accessible at: http://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS.
World Bank. (2013). General government final consumption expenditure (% of GDP). Accessible at: http://data.worldbank.org/indicator/NE.CON.GOVT.ZS?end=2013.
World Bank. (2015). World Development Indicators 2015. Accessible at: http://openknowledge.worldbank.org/handle/10986/21634.
World Bank. (2016a). Adjusted net national income. Accessible at: http://data.worldbank.org/indicator/NY.ADJ.NNTY.CD.
World Bank. (2016b). Gross domestic savings. Accessible at: http://data.worldbank.org/indicator/NY.GDS.TOTL.ZS.
World Bank. (2016c). Inflation, consumer prices (annual %). Accessible at: http://data.worldbank.org/indicator/FP.CPI.TOTL.ZG.
World Bank. (2016d). Unemployment, total (% of total labor force). Accessible at: http://data.worldbank.org/indicator/SL.UEM.TOTL.ZS.
World Bank. (2016e). Current account balance (BoP, current US$). Accessible at: http://data.worldbank.org/indicator/BN.CAB.XOKA.CD.
World Bank. (2016f). Central government debt, total (% of GDP). Accessible at: http://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS.
World Bank. (2016g). Foreign direct investment. Accessible at: https://data.worldbank.org/indicator/BX.KLT.DINV.WD.GD.ZS.
World Bank. (2016h). Broad money (% of GDP). Accessible at: https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS.
World Bank. (2016i). Total International Reserves. Accessible at: http://data.worldbank.org/indicator/FI.RES.TOTL.CD.
World Bank. (2016j). Cash surplus/deficit (% of GDP). Accessible at: http://data.worldbank.org/indicator/GC.BAL.CASH.GD.ZS.
World Bank. (2016k). Energy use. Accessible at: http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE.
World Bank. (2016l). Renewable energy consumption (% of final total energy consumption). Accessible at: http://data.worldbank.org/indicator/EG.FEC.RNEW.ZS.
World Bank. (2016m). Energy efficiency. Accessible at: http://wdi.worldbank.org/table/3.8.
World Bank. (2016n). Adjusted net savings (current US$), including particulate emission damage. Accessible at: http://data.worldbank.org/indicator/NY.ADJ.SVNG.GN.ZS.
World Bank. (2016o). Nationally terrestrial and maritime protected areas. Accessible at: http://data.worldbank.org/indicator/ER.PTD.TOTL.ZS?end=2014&start=2013.
World Bank. (2016p). Forest area % of land area. Accessible at: http://data.worldbank.org/indicator/AG.LND.FRST.ZS.
World Bank. (2016q). Life expectancy at birth, total (years). Accessible at: http://data.worldbank.org/indicator/SP.DYN.LE00.IN.
World Bank. (2016r). Maternal mortality ratio. Accessible at: http://data.worldbank.org/indicator/SH.STA.MMRT.
World Bank. (2016s). Global database of shared prosperity. Accessible at: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity.
World Bank. (2016t). Mortality rate, under-5 (per 1,000 live births). Accessible at: http://data.worldbank.org/indicator/SH.DYN.MORT.
World Resources Institute. (2012). CAIT Climate Data Explorer: Historical emissions. Accessible at: http://www.wri.org/resources/data-sets/cait-historical-emissions-data-countries-us-states-unfccc.
Yu, G.-C., & Park, J.-S. (2006). The effect of downsizing on the financial performance and employee productivity of Korean firms. International Journal of Manpower, 27(3), 230–250.
This study was funded by Macquarie Graduate School of Management, Macquarie University (No Grant Number).
Conflict of interest
Harald Bergsteiner and Gayle C. Avery declare that they have no conflict of interest.
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Bergsteiner, H., Avery, G.C. Misleading Country Rankings Perpetuate Destructive Business Practices. J Bus Ethics 159, 863–881 (2019). https://doi.org/10.1007/s10551-018-3805-6
- Country competitiveness rankings