Beyond Competitiveness: Is Productivity Everything?

doi:10.21203/rs.3.rs-4017971/v1

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Beyond Competitiveness: Is Productivity Everything?

https://doi.org/10.21203/rs.3.rs-4017971/v1

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Our study examines whether productivity is a better predictor of economic growth than the Competitiveness Index (GCI). Our empirical findings suggest the following. First, we find that ranking countries by productivity matches the GCI results. Second, the correlation between all 12 sub-pillars of GCI, and the overall GCI and productivity yield similar results. Third, using a panel dataset for 104 countries over 2007–2019, our panel VAR (PVAR) results indicate that productivity has a stronger prediction power for both real GDP growth, as well as for the inequality-adjusted GDP growth compared to the GCI. Finally, using the residuals of a regression of productivity on GCI, we explore the drivers of the differences between productivity and GCI. We find that higher values of rule of law, property rights, economic freedom and political stability explain why advanced economies are positioned higher in their level of productivity than in the GCI ranking.

JEL codes: C33; E24; E61; O47

Competitiveness

Productivity

GCI

inequality-adjusted GDP growth

Panel VAR

Understanding why some nations are poor while others are rich has been one of the most dominant questions preoccupying economists and policy makers for decades (Porter, 1990; Olson, 1996; Hibbs and Olsson, 2004; Acemoglou and Robinson, 2013). As emphasized by the Nobel laureate economist Robert Lucas, once one starts to think about questions like this, it is hard to think about anything else (Lucas, 1988). By the same token, as a possible explanation for the cross-country variation in growth, the cross-country variation in competitiveness has also been at the epicentre of interest of international investors, traders, independent analysts and policy makers for a long time, ongoing conceptual and methodological disputes notwithstanding. Despite methodological caveats, measuring a nation’s competitiveness position has become even more significant in the rapidly changing global economic landscape induced by information and communication technology and globalization (Lall, 2001).

The annual global competitiveness index (GCI) of the World Economic Forum (WEF) has played a pivotal role in reducing the size of asymmetric information regarding the relative competitiveness of a large number of countries and their competitiveness over time. In this sense, the WEF fills an important gap in our understanding of countries’ relative performances and risks, hence serving as an important “national rating agency”. Reflecting on such valuable role, the WEF has enjoyed prominent international attention, and its annual gathering in Davos, Switzerland, attracts global political and industry leaders and investors from all around the world, generating enormous media coverage. In this regard, it should be recognized that the WEF’s GCI has substantially contributed to pinpointing the relative strengths and weaknesses of countries based on a broader set of indicators, with the aim of encouraging policy makers to reframe policy priorities and to navigate the direction of investment and trade flows.

As emphasized by Kitson et al. (2004), “economists and experts everywhere have elevated ‘competitiveness’ to the status of a natural law of modern capitalist economy”, so that the impact of the GCI rankings on government policies has also been vividly observable, as reflected in the European Commission’s 1993 White Paper on Growth, Competitiveness, and Employment (Krugman, 1996) and the UK government’s White Paper on Competitiveness (Lall, 2001; Kitson et al., 2004). While the new rankings are a disappointment to some governments, they are a source of euphoria and celebration for other governments (Xia et al., 2012). Reflecting on the GCI, a number of governments have introduced policies and reform agendas to improve the target ranking positions or scores (Ketels, 2016).

However, the GCI has also encountered disputes over possible inaccuracies and the often difficult-to-understand construction of the ranking of countries’ competitiveness based on indicators that are, largely, intertwined, hence making it difficult to disentangle individual effects from joint effects. Some criticize the GCI of the WEF for lacking rigor and clarity and for the use of qualitative instead of quantitative data (Lall, 2001). Others criticize it for its lack of theoretical foundations since competitiveness is a concept that emerges from business and has little to do with macroeconomic theory (Krugman, 1996). Other critiques have emphasized the weakness of the GCI in terms of its statistical skeleton for compiling data (OECD, 2008, Ketels, 2016). In this regard, Bowen and Moesen (2011) argue that since the WEF aggregates various hard and soft (country-level survey) primitive data into a unified composite indicator, it could potentially offer biased country rankings by rewarding some countries while penalizing others (Ülengin et al., 2011). The GCI is also subject to criticism for its use of the same fixed weights applied to every country (or group of countries), which creates a bias that favours countries that score high on the “technology” sub-dimension of the index. Another more technical issue is the aggregation of individual indicators into a single synthetic indicator (OECD, 2008). The weights and specific functions for this aggregation are often viewed as standing on weak methodological foundations (Bowen and Moesen, 2011 and Ketels, 2015; 2016). Based on principal component analysis, Adamkiewicz (2019) showed that there are caveats regarding the current method used by the WEF to rank countries’ performance and proposed how some of those caveats could be minimized.

When it comes to policy implications, critiques are raised regarding two potential problems associated with the usefulness of the GCI (Xia et al., 2012). The first limitation is associated with the stability of the country’ rankings, where significant variability over a short period of time has been observed. The second and even more significant drawback is associated with the failure of the GCI to serve as a robust predictor of short-term and long-term economic growth. It should be recognized, however, that the GCI has improved significantly from its early periods of publications and that the current GCI, although still far from being broadly acceptable, fundamentally reflects most of the critiques.

Against this background, this study extends the debate by answering the following key questions. First, if the link between national competitiveness and productivity is proven to exist, will productivity ultimately be more informative in explaining cross-country variation in economic performance than the currently used national competitiveness index? Second, do countries achieve steady position in global competitiveness without achieving higher level of productivity? Our assumption is that, given the controversies surrounding the World Economic Forum’s (WEF) global competitiveness index (GCI), productivity is considered more accurate and less ambiguous metric relative to competitiveness in measuring countries’ relative international performance. This is important because the ranking of countries based on GCI has been criticized mainly due to its statistical skeleton, inclusion of too many variables that are significantly intertwined, and mainly due to the instability of countries ranking over time, hence its inadequacy to predict future performance.

Using data for 104 advanced and emerging economies, we find that positive shocks to both productivity and GCI have a positive effect on both real GDP growth and growth of real GDP adjusted for income inequality. However, productivity shocks seem to have more pronounced effects when compared to GCI shocks. Furthermore, we find that, while a strong linear relationship exists between productivity and GCI for low-income and middle-income economies, this relationship becomes non-linear for high-income countries. For high-income countries, growth in productivity does not seem to be associated with growing GCI. Thus, we find some evidence for ‘productivity beyond competitiveness’. By regressing the residuals of the regression of productivity on GCI on several country-level characteristics, we study the drivers of the observed differences between productivity and GCI. We find that higher level of property rights, rule of law, economic freedom and political stability seem to explain the productivity beyond competitiveness – that is, why some countries have higher productivity than their level of international competitiveness would warrant for.

The remainder of this paper is organized as follows. In section 2, we provide review of the related literature. In Section 3, we discuss the methodology and the data. In Section 4, we present our empirical findings, while in Section 5 we conclude this study.

There is wide-ranging recognition that competitiveness is not only a driver of countries’ economic growth but also a question of survival in an increasingly interconnected world. The discussion surrounding competitiveness boils down to three related areas: First, what exactly is competitiveness, and what are the most relevant indicators for measuring nations’ competitiveness? Second, how does one compare the competitiveness of countries that are heterogeneous in their social, economic, cultural and institutional parameters and that possess a heterogeneous set of natural endowments? This is important because nations’ different stages of development imply that the hierarchy and priority of the key factors that affect their absolute and relative competitiveness should differ at various phases of their economic development.

These and similar questions have been addressed by both theoretical as well as empirical studies. Porter’s (1990) influential work ‘The Competitive Advantage of Nations’, which distinguishes competitive advantage from comparative advantage, in line with his national diamond, signals a departure from the neoclassical version of comparative advantage. In recognition of the prevalence of such heterogeneity, the World Economic Forum classifies countries into three groups based on their sources of economic development, measured in terms of real GDP per capita: factor-driven, efficiency-driven and innovation-driven groups. Moreover, the GCI splits countries into five groups according to their phase of economic development, measured in terms of GDP per capita.

The third line of debate revolves around the concept of competitiveness on a national (macroeconomic) level, which is different from competitiveness on a microeconomic (firm) level. Competitiveness is somewhat precisely defined on the firm level and reflects firms’ abilities to produce goods and services and to compete with other firms under a free market framework. As pointed out by Porter (1990), while competitiveness on a national level is difficult to define, the concept is straightforward on the firm level, where firms compete among each other in terms of market share in a particular industry or territory and the payoffs from competitiveness can be measured in terms of profitability and rate of return. Thus, competition should ensure that only more competitive firms survive, while less competitive firms should exit the market (Krugman, 1996; Martin, 2003; Lall, 2001; and Schuller and Lidbom, 2009). In this line, Krugman (1994, 1996) argues that competition on the firm level differs from competition on the national level in various ways. First, rivals can push less competitive firms out of business, while this is not the case for competition on the country level. Second, there is no complementarity in competition among firms, since the success of one firm leads to the failure of rivals, while such a zero-sum game is not the case on the macroeconomic (country) level.

In contrast, competitiveness on a macroeconomic (country) level has been defined rather precariously, and there is still an ongoing debate in academic circles and a clear call to refine the concept. Krugman (1994), who perceives national competitiveness as a “dangerous obsession”, argues that if competitiveness has any meaning, then it is a “funny” way of saying “productivity” but has very little to do with international competition. Krugman has been consistent in his disappointment regarding the use of ‘competitiveness’ to mean that countries compete just like companies, which he describes as “a poor metaphor” (Krugman, 1996). This line of argument has been recognized by the GCI 2008 report, which states that “many nations can improve their prosperity if they can improve productivity. Improving productivity will raise the value of goods produced and improve local incomes, expanding the global pool of demand to be met”.

From a different perspective, the Organization for Economic Cooperation and Development (OECD, 1992) defines a country’s competitiveness as “the degree to which a country can, under free and fair market conditions, produce goods and services which meet the test of international markets, while simultaneously maintaining and expanding the real incomes of its people in the long term.” The broad OECD definition sheds some light on the complexity of determining the competitiveness of countries. The WEF does not simplify the concept either, as it defines its competitiveness index as “a set of institutions, policies, and factors that determine the level of productivity of a country, conditions of public institutions and technical conditions” (Blanke et al., 2005; Schwab and Sala-I-Martin, 2015).

From a broader perspective, competitiveness is defined as the “ability of a country (region, location) to deliver the beyond GDP goals for its citizens today and tomorrow” (Aigingerand Vogel, 2015). In this respect, Delgado et al. (2012) stated that “these different views of competitiveness have confused the public and scholarly dialogue, and have obscured the development of an integrated framework to explain causes of cross-country differences in economic performance”. Indeed, this shows the difficult circumstances under which the ranking of the competitiveness of countries and its dynamics over time emerges. As emphasized by Farrugia (2002), “The value of a competitiveness index depends on the rigor of the underlying analytical framework and the methodology for making the rankings. However, it is difficult to gauge competitiveness by any single or even defined set of variables” (see also, Høyland et al., 2012).

As noted above, competitiveness at the national level has received fierce opposition from various perspectives, and there is a growing body of literature that favours the concept of productivity as a more meaningful metric for cross-country comparisons. Following our earlier discussion, there is a “whiff” of productivity in nearly every definition of competitiveness on the national level to the extent that the two concepts are at times used interchangeably to mean the same thing. However, the broader body of the current literature considers them to be distinguishable, possible overlapping in their fundamental drivers notwithstanding. As argued in Gelb et al. (2019), while government policies and regulations play an important role in shaping the business environment, such policies ultimately trickle down to serve as drivers of labor productivity. However, some argue that productivity is a measure of the internal strength of companies, while competitiveness is a relative measure of how firms perform compared to their rivals (Oral and Chabchoub, 1997).

As Fagerberg et al. (2007) emphasized, competitiveness is a relative rather than an absolute term, and it is the cross-country variation in competitiveness that matters more than any absolute value. Their analysis rooted in the Schumpeterian spirit suggests that competitiveness is multifaceted and can take different forms, such as price, technological and capacity, and demand competitiveness. Their findings based on data of 90 countries during 1980–2002 seem to suggest that, while price competitiveness has played a smaller role, other types of competitiveness have played a favourable impact on the growth and development of developing countries. Similarly, Kouamé and Tapsoba (2019) emphasized the role of structural reforms, mainly real sector reforms, in productivity and competitiveness on the microeconomic level.

The key question that follows the debate is, is productivity everything? Productivity is generally understood as the efficiency with which inputs are translated into some final output. Hence, it is considered a reasonable metric of welfare and living standards. In his seminal work, Porter (1990) sparked the debate on the competitiveness of nations. Porter argued that competitiveness on the national level is rather ambiguous and that the focus should be on the determinants of productivity and its dynamics over time because “the only meaningful concept of competitiveness at the national level is national productivity”.

The Nobel laureate economist Paul Krugman is often cited for his famous statement on productivity: “Productivity isn’t everything, but in the long run it is almost everything”. Krugman (1994) argues that a country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker. Kohler (2006) also favours the productivity approach to national competitiveness and relates productivity to comparative advantage in trade theory and to total factor productivity in modern growth theory. According to Kohler (2006), “A country’s welfare is determined by its absolute level of productivity and not by some international competitiveness rankings”. Kohler (2006) further argues that the paradigm of international competitiveness is potentially misleading and that policy makers should instead formulate policies to enhance productivity.

OECD (2001), which itself defines productivity as the ratio of the volume of output to the volume of input used, recognizes the existence of various metrics of productivity, such as labor, capital and total factor productivity. As emphasized by Syverson (2011) and van Ark (2014), however, labor productivity is the most common metric and is generally considered the main variable for cross-country variation in income per capita. This is also acknowledged by the OECD (2015), which argues that “the large differences in income per capita observed across countries mostly reflect differences in labor productivity”. The study also forecasts productivity “to be the main driver of economic growth and well-being over the next 50 years”.

As argued in Dehesa (2007), the assumption that a country’s competitiveness is the product of lower prices and costs is inaccurate and unjustified. Kaldor (1978), cited in Dehesa (2007), showed the absence of any meaningful correlation between reductions in the relative costs and prices of an economy and its market share in international markets (known as the “Kaldor Paradox”). It is also believed that countries compete with low prices and costs just because their productivity level is low. As underscored by Krugman, “to say that a country can be able to combine developing country wages with developed country productivity is an oxymoron” (Krugman 1996). In the long run, it is believed that a country’s external competitiveness is primarily measured by its productivity. As emphasized by Dehesa (2007), “a country’s level of productivity determines—in the long term and in the final instance—not only its relative level of competitiveness but also its potential GDP growth, its real wage levels, and its well-being”.

Productivity has been widely recognized as a driver of the standard of living and many market economies have established a council for productivity. The EU set up the national productivity boards in 2016, and most of the member countries of the EU have established such councils. The US has had the National Productivity Council for a long time. In this regard, there is a strong body of literature that favours the use of productivity as a more reasonable measure of international competitiveness. Nonetheless, a recent decline in labor productivity in advanced economies seems to serve as a warning signal about the sustainability of labor productivity as a long-term source of higher living standards. Studies confirm that a decline in labor productivity leads to a slower pace of technological development and innovation (Cowen, 2011; Gordon, 2012 and 2016; Nakamura et al., 2019). Some suggest total factor productivity as superior proxy to labor productivity for various reasons (van Ark, 2014). In line with the Solow growth framework (Solow, 1957), living standard is determined by technological progress, which is detected as a residual effect after accounting for the contribution of other inputs. As emphasized by van Ark (2014, p. 3), “the main shortcoming of the labor productivity metric, however, is that it cannot distinguish between the gains from more equipment per worker and the gains from more efficiency or better technology use per worker”. Along this line, we use total factor productivity as a measure of productivity.

In the following section, we describe our dataset and empirical approach.

3.1 Data

We use annual data on 104 advanced and emerging economies, over the years 2007–2019. The data on Global Competitiveness Index (GCI) as well as its sub-pillars, comes from the World Economic Forum (WEF)’s database and annual reports. For our measure of productivity, we use the total factor productivity (TFP).[2] The data on TFP is taken from Penn World Tables. As the primary aim of our empirical methodology is to examine whether productivity is a better predictor of economic growth compared to the Global Competitiveness Index, our primary variable of interest is the growth rate of real GDP. Following Baxa and Paulus (2020), we express this variable as the first difference of the logarithm of real GDP (in PPP) per capita. However, as real GDP growth might be unequally distributed, and considering the recent criticism of using real GDP as the primary proxy for economic performance, we also use the inequality-adjusted GDP growth as an alternative proxy for robustness purposes. For our measure of income inequality, we use the Gini coefficient. To calculate the inequality-adjusted GDP, we invert the Gini coefficient[3] and multiply it with GDP (PPP) per capita – so that more weight is given to countries where income is more equally distributed.

Furthermore, we fill our regressions with additional control variables that are commonly used in empirical studies that investigate the response of real GDP (Loayza and Raddatz, 2006; Towbin and Weber, 2013). These include the exchange rate, inflation and investments. We use nominal effective exchange rate (NEER) as our measure of exchange rate, the logarithm of consumer price index (CPI) as our measure of inflation, while we express investments as a share of nominal GDP. The data on Gini coefficient comes from the Standardized World Income Inequality (SWIID) and the World Income Inequality (WIID) databases. The data on the remaining control variables is taken from the World Bank’s World Development Indicators database. The data on macroeconomic policy variables are also taken from the World Bank database.[4]

3.2. Empirical Methodology

The main objective of our empirical methodology is to investigate whether productivity could be a better predictor of real GDP growth compared to competitiveness, the latter being proxied by GCI. To that end, we use the panel Vector Autoregression (PVAR) model to examine whether positive shocks to productivity increase real output more than positive shocks to GCI. The reduced PVAR model that we use has the following generalized form:

$${y}_{it}=\mu +\sum _{j=1}^{p}{{\Pi }}_{j}{y}_{i,t-j}+{\epsilon }_{it}$$

where ${y}_{it}$ is the vector of endogenous variables for country i in time t, $\mu$ stands for the constant, ${\epsilon }_{it}$is the error term, which also includes the country fixed effects, while ${{\Pi }}_{j}$ represents the parameters to be estimated. In our baseline estimations, the vector of endogenous variables includes the following variables: total factor productivity or global competitiveness index, exchange rate, inflation, investments, real GDP.[5] All the endogenous variables enter our regressions in their first differences. Finally, p represents the number of lags. The number of lags (1) is selected in line with Leroy and Lucotte (2015) by taking the average number of lags suggested by the information criteria in country-specific VARs.

In line with Horvath and Voslarova (2017), we use the procedure of Love and Zicchino (2006) to estimate our PVAR. By introducing fixed effects in our regressions, this approach enables us to control for unobserved time-invariant heterogeneity among the countries included in our sample. Love and Zicchino (2006) apply the Helmert procedure to address well-known Nickell bias (Nickell, 1981) caused by the introduction of fixed effects in dynamic panels. The Helmert procedure is based on the use of forward mean differencing to remove the fixed effects – the lagged regressors are then used as instruments and the parameters are estimated with the generalized method of moments. Next, we use the coefficients from the PVAR to compute the orthogonalized cumulative impulse response functions (IRFs). Cholesky decomposition is used to obtain the IRFs. Monte Carlo simulations with 50 replications are used to compute the 90% confidence intervals.

Next, we also aim to empirically explore the potential differences between productivity and GCI. To this end, we first simply regress productivity on GCI:

$${Prod}_{jit}= {\alpha }_{0}+ {\alpha }_{1}{GCI}_{jit}+ {\mu }_{jit}$$

where Prod stands for productivity, GCI stands for the Global Competitiveness Index and $\mu$ are the standard errors in region j, country i and time t. Next, we regress the residuals from regression (2) on several potential drivers of the differences between productivity and GCI:

$$\widehat{{\mu }_{ji}}= \beta + \gamma \stackrel{-}{{X}_{ji}} + {\rho }_{j} + {v}_{ji}+ \stackrel{-}{{ϵ}_{ji}}$$

where $\widehat{\mu }$ are the residuals from Eq. (2), X is the vector of control variables, ${\rho }_{j}$ are world region fixed effects, while ${v}_{ji}$ are country fixed effects. The Eq. (3) is estimated with the between effects (BE) estimator, so that we can focus on studying the cross-sectional differences between countries – as opposed to merely studying the cross-time differences (i.e., within developments) with the standard fixed effects (FE) estimator. The vector of control variables primarily includes various policy-related variables[6], which we hypothesize could be the drivers of the differences between productivity and GCI.

In the following section, we first outline the results of a simple preliminary analysis. Next, we discuss our baseline results.

4.1 Preliminary Analysis

The results of our initial analysis are based on simple descriptive statistics and scatter plots. Nevertheless, these results provide some first interesting findings that help us motivate further our empirical analysis. First, in Fig. 1 we plot the relationship of GCI and total factor productivity for 118 countries using annual data over the period 1997 to 2019. Figure 1 indicates that there exists a high correlation between GCI and productivity. However, this high correlation seems to hold only at lower levels of productivity/GCI – as productivity increases beyond certain level, further increases in productivity do not seem to be accompanied by increases in GCI. Thus, this simple motivational exercise seems to indicate the presence of a ‘productivity beyond competitiveness’ phenomenon – a notion that we also address in our baseline empirical analysis.

[Please insert Fig. 1 around here]

Subsequently, we plot each of the 12 pillars of the GCI with both overall GCI score and productivity (Figs. 2–5). The plots reveal that the correlation between the scores of each GCI pillar and both overall GCI score and productivity remains very high.

[Please insert Figs. 2–5 around here]

Finally, we also present world heatmaps of GCI and productivity for the year 2017 in Fig. 6. Overall, we observe that the countries with the highest ranking based on the GCI are nearly identical with the countries with the highest level of total factor productivity.

[Please insert Fig. 6 around here]

4.2 Baseline Results

Next, we move on to outline the results of our baseline regressions. First, we use the PVAR model to estimate the effect of productivity and GCI shocks. This approach enables us to assess whether the GCI like productivity incapsulates all the important country characteristics that are crucial for economic growth. Consequently, we aim to compare the responses of real GDP to productivity and GCI shocks. We report the IRFs from these estimations in Figs. 7–12. Figure 7 presents the response of real GDP to shock in the Global Competitiveness Index (GCI). Figure 8 presents response of real GDP to a productivity shock (TFP expressed in current prices) and Fig. 9 presents response of real GDP to shock to the total factor productivity expressed in constant prices.

[Please insert Figs. 7–9 around here]

The reported results reveal that, while the shock to the GCI leads to an increase in real GDP, the increase is smaller in magnitude compared to the increase in real GDP induced by shocks to both of our measures of productivity. In particular, a one standard deviation increase in Global Competitiveness Index (GCI) leads to an increase in real GDP growth by approximately 0.7%. However, this increase is smaller in magnitude compared to the increase in real GDP induced by a one standard deviation increase in total factor productivity (both real and nominal) – which is more than twice as large. This difference in responses is both economically and statistically significant. Thus, it seems that the GCI does not capture all the important country-characteristics that lead to higher productivity and higher economic growth.

Nevertheless, there is an increasing body of empirical literature that underlines the importance of inclusive economic growth. As a result, we next study the responses of growth in real GDP adjusted for income inequality to the productivity and GCI shocks. Figure 10 reports the response of real GDP adjusted for income inequality to a shock to the GCI. Figure 11 presents the response of real GDP adjusted for income inequality to a shock to the total factor productivity (in current prices), and Fig. 12 presents the response of real GDP adjusted for income inequality to a shock to the total factor productivity (in constant prices). The results for real GDP adjusted for income inequality yield the same conclusions. However, the growth in real GDP adjusted for income inequality seems to be more responsive to both productivity and GCI shocks.

[Please insert Figs. 10–12 around here]

In the next step of our empirical analysis, we also investigate the drivers of the differences between productivity and GCI. This analysis is motivated by Fig. 1, which do seem to show that the strongly positive relationship between the productivity and GCI disappears after the level of productivity exceeds a certain threshold. To investigate this hypothesis of ‘productivity beyond competitiveness’, we first simply regress productivity on the GCI. Next, we regress the residuals from this regression on some of the macroeconomic policy variables, such as rule of law, property rights, economic freedom and political stability, which we assume might be the drivers of productivity beyond competitiveness, and also considered in previous studies (see for instance, Feng, 1997; Haggard et al., 2008; Nissan and Niroomand, 2008; Compton et al., 2011; Syverson, 2011; Aisen and Veiga, 2013; Banks, 2015; Noha and Loreto, 2020; Emara and Rebolledo, 2021). We report the results of these regressions in Table 1. We find that higher levels of rule of law, property rights, economic freedom and political stability explain the observed phenomenon of ‘productivity beyond competitiveness’, as the coefficients of these variables are positive and statistically significant – indicating a positive effect of these variables on productivity beyond competitiveness. To address collinearity issues, we estimate regressions by including the policy variables one-by-one. Consequently, our results indicate that the GCI does not seem to give sufficient weight to these factors, which still seem to be disproportionally important for productivity and by extension for economic growth. Furthermore, somewhat surprisingly, Asian countries seem to have disproportionately low productivity given their high GCI ranking. Moreover, in contrast to our expectations, financial development beyond the level that would be implied by the stage of economic development, seems to have a negative effect on productivity beyond competitiveness. That is, it seems that the GCI gives too much weight to financial system variables, which seem not to be that important for productivity.

Table 1

Determinants of the ‘Productivity beyond Competitiveness’
	(1)	(2)	(3)	(4)	(5)
	Residual of Productivity vis-à-vis GCI
Financial Development - Misalignment	-0.140	-0.271**	-0.144	-0.251**	-0.182*
	(0.109)	(0.108)	(0.105)	(0.111)	(0.107)
High-tech exports (% of manufacturing exports)	-0.000	-0.002	-0.001	-0.002	-0.001
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Rule of law		0.067***
		(0.018)
Political stability			0.056***
			(0.020)
Property rights				0.002***
				(0.001)
Economic freedom					0.005***
					(0.002)
Asia Dummy	-0.159***	-0.162***	-0.164***	-0.152***	-0.156***
	(0.058)	(0.054)	(0.056)	(0.056)	(0.056)
Europe Dummy	-0.008	-0.061	-0.038	-0.042	-0.019
	(0.041)	(0.041)	(0.042)	(0.042)	(0.041)
Africa Dummy	-0.034	-0.011	-0.022	-0.009	-0.010
	(0.049)	(0.046)	(0.047)	(0.048)	(0.048)
MENA Dummy	0.080	0.050	0.101*	0.061	0.075
	(0.058)	(0.055)	(0.056)	(0.056)	(0.056)
Constant	0.022	0.037	0.032	-0.084*	-0.291**
	(0.037)	(0.035)	(0.036)	(0.050)	(0.116)
Observations	1,158	1,158	1,158	1,157	1,157
R-squared	0.214	0.316	0.273	0.283	0.276
Countries	104	104	104	103	103

[Please insert Table 1 around here]

In this study, we empirically assess whether productivity is a superior predictor of economic growth when compared to World Economic Forum’s (WEF) Global Competitiveness Index (GCI), thus providing a number of insights to the ongoing debate regarding ranking of countries based on the Global Competitiveness Indexed that is compiled by the World Economic Forum.

Our results seem to suggest a number of interesting contributions to the ongoing debate regarding ranking of countries based on the Global Competitiveness Index that is compiled by the World Economic Forum. First, there is a high correlation between the GCI and total factor productivity. Second, the scatter plots of each GCI pillar against both the overall GCI and productivity seem to yield strikingly similar results. However, this strong correlation between the productivity and GCI seems to be driven by low-income and middle-income economies. For high-income economies, the growing productivity does not seem to be accompanied by growing GCI scores. Thus, we find some evidence for the phenomenon of ‘productivity beyond competitiveness’. This finding is further reinforced by the results of our empirical analysis. Our results obtained from the panel VAR (PVAR) show that, while both GCI and productivity seem to induce GDP growth, the impact of total factor productivity is larger in magnitude compared to the impact of GCI. Lastly, we also empirically explore the drivers of the observed differences between countries’ productivity and GCI levels. Interestingly, we find that higher levels of economic freedom, property rights, political stability and the rule of law explain the phenomenon of productivity beyond competitiveness. That is, these factors explain, why for high-income economies, the increasing productivity is not reflected in increasing GCI. Given, the increasing critiques against the conceptual framework of competitiveness on the macroeconomic level and its statistical skeleton, total factor productivity could be eventually a more straightforward metric for global country ranking.

Author Contribution

M.W.T. conceptualization, N.A. and B.F. data collection. All authors performed data analysis, wrote and reviewed the manuscript.

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2. In alternative specifications, we use TFP expressed in current prices, as well as TFP expressed in constant prices.

3. So that its values range from 0 to 1, with 0 representing perfect inequality and 1 representing perfect equality.

4. The data that support the findings of this study are available from the corresponding author upon reasonable request.

5. In this ordering, we place the exchange rate after productivity/GCI – as we argue that these two measures, which reflect economy’s future prospects, might first influence the exchange rate, which itself is often assumed to be driven by economy’s future prospects vis-à-vis the rest of the world (Engel and West, 2005). We place the inflation directly behind the exchange rate, as, owing to J-curve, inflation is likely to be influenced by exchange rate before real GDP. However, in a robustness check, we verify the robustness of our results to different orderings of the endogenous variables.

6. These variables include economic freedom, financial development, rule of law and property rights, and political stability.

No competing interests reported.

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Beyond Competitiveness: Is Productivity Everything?

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1. Introduction

2. Literature Review

3. Data and Methodology

3.1 Data

3.2. Empirical Methodology

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4.1 Preliminary Analysis

4.2 Baseline Results

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