balancing environmental sustainability with economic · pdf filebalancing sustainability and...

Thomas Tribunella, PhD., 209 Rich Hall, School of Business, State University of New York at Oswego, Oswego, NY 13126, [email protected], 315.312.2544; Barry Friedman, PhD., 247 Rich Hall, School of Business, State University of New York at Oswego, Oswego, NY 13126, Süleyman S ah University , Istanbul, Turkey, barry. [email protected], 315.312.6381; Pamela Cox, PhD., 257 Rich Hall, School of Business, State University of New York at Oswego, Oswego, NY 13126, [email protected], 315.312.2532

Balancing Environmental Sustainability with Economic

ProductivityThomas Tribunella, PhD, Barry Friedman, PhD,

and Pamela Cox, PhD

A well-developed body of literature has detected positive effects of technology investments on economic productivity as well as economic freedom on productivity. We contribute to this literature by studying the joint effects of a nation’s technological achievement and economic freedom on environmental sustainability balanced with economic productivity. Using a sample of over 100 countries, we fi nd that economic freedom does not enhance the effect of technology on economic productivity and environmental sustainability. In fact, we fi nd that the standalone effect of technology is nearly as large as its interactive effect with freedom when applied to a combined measure of environmental sustainability and economic productivity.

Key Words: Economic Sustainability, Productivity, Technology, Economic Freedom

Acknowledgements: An early version of this manuscript was presented at the Business Research Consortium of Western New York, 2010 Conference. We thank the reviewers and participants for their insightful comments which have been incorporated into this updated version of the paper.

INTRODUCTION

Economic productivity is considered a key indicator of national success. A country’s productivity is often determined by its Gross Domestic

184 THE BRC ACADEMY JOURNAL OF BUSINESS

Product per Capita (GDPC) (levels or percent changes) and is used as proxies to measure economic prosperity. Because of its importance, con-siderable research has been directed toward determining factors infl u-encing economic productivity. This literature, inspired by Solow (1956), spans half a century and hundreds of publications. A recent offshoot, appearing in the Technology Management and Information Systems (IS) literature, seeks to assess the effect of technology on productivity. Surely, this research has been encouraged by the advent of the ecom-merce and the digital economy. The conclusion from these studies is that investments in technology have a positive impact on productivity.

However, recent concerns about the environment and climate change have prompted the view that productivity must be balance with environ-mental sustainability. Even though nations still seek prosperity they must balance it with the long-term health of the ecosystem. The important question being address is: How does a nation balance its investments in capitalism, technology, and productivity with environmental concerns?

Accordingly, we study the impact of economic freedom on the relation-ships between technology, economic growth, and environmental sustain-ability. We use a sample of more than 100 nations. We argue that a climate of economic freedom allows various entities (individuals, partnerships, corporations, organizations, governments) the fl exibility and incentives to harness the positive effects of technology. Not only would greater invest-ments be made in technology but these investments will have a greater possibility of increasing productivity in an environmentally responsible way. As a result, we expect technology when used in an environment of economic freedom, to have a positive effect on productivity and sustain-ability. We test this proposition and fi nd results that are not consistent with our expectations. We report results indicating a signifi cantly posi-tive association with technology’s effect on environmentally sustainable productivity. We note that economic freedom does not affect sustainable productivity when technology is introduced into the equation.

In the next section of the paper, we review the relevant literature that we draw upon. This is followed by our research models includ-ing a description of our sample in the Data and Methodology section.

185Balancing Sustainability and Growth

We report the outcomes of our statistical tests in the Results section and discuss the fi ndings from our investigation. In closing, we outline the contributions of this study, the limitations of the research, suggest future possible investigations, and then we draw our fi nal conclusions.

BACKGROUND AND LITERATURE REVIEW

Productivity, Technology and Environmental SustainabilityThere is a signifi cant amount of literature associating technology with environmental sustainability. This is understandable since substantial technological breakthroughs must be made before many eco-friendly alternatives are cost-competitive with traditional products. For example, much research and development is needed to replace the burning of fos-sil fuels with greener alternatives. Therefore, investment in technology is a critical component the solving the sustainability problem.

Many consider the relationship between environmental sustainability and economic productivity as a tradeoff between governmental regula-tion and private property. The problem is how to balance societal benefi t of a clean environment with the advantages of economic productivity and growth such as employment opportunities and prosperity. Framing the debate this way, environmental sustainability becomes a zero sum game. Gains in sustainability only come at the cost of more intrusive government regulations and taxes with the accompanying losses in private property and freedom. However, some authors believe that the environment- competitiveness debate has been framed incorrectly (Por-ter and Linde 1995).

The view of the economy and environment in a fi xed world contrib-utes to the notion that ecological and economic goals are diametrically opposed to each other. In this static world, environmental regulation inev-itably raises costs and reduces the competitiveness of companies striv-ing to succeed in global markets where some countries do not regulate polluters. However, the reality is that both the economy and the environ-ment are interactive and changes in technology investments can benefi t both the economy and the environment. Accordingly, there may be an


economic paradigm that produces synergy between environmental and economic goals. The interactive view searches for win-win transactions that improve environmental sustainability and economic productivity. Competitiveness arises from superior productivity, either in terms of lower costs than rivals or the ability to offer higher quality products. For example reducing waste, ineffi ciency and energy use may lower the costs of production and increases competitiveness (Porter and Linde 1995). This also contributes to environmental sustainability.

Therefore, as nations invest and innovate they produce more output with less inputs and waste. Porter and Linde’s (1995) analysis may have some empirical support with the Environmental Kuznets Curve (Gross-man and Krueger 1995). The original Kuznets Curve addressed the issues of economic growth and the distribution of income (Kuznets 1955). The curve was modifi ed to plot productivity with environmental pollution. The Environmental Kuznets Curve (EKC) is an inverted u-shaped curve, it posits that as productivity increases pollution decreases. Accordingly, as a nation reaches a threshold of productivity, approximately $8,000 in GDP per capita, the nation begins investing in technology which drives down pollution and increases the sustainability score (Grossman and Krueger 1995).

Technology and Economic ProductivityThere is an extensive body of literature related to technology and eco-nomic productivity (Dedrick, Gurbaxani and Kraemer, 2003; Indjikian and Siegel, 2005; and Merville, Kraemer and Gurbaxani, 2004). The majority of this literature focuses on information technology. Accord-ingly, this focus is driven by the rapid computerization of many business processes and the advent of the World Wide Web. Independent variables in the literature have refl ected investments in computer hardware, soft-ware, Internet and communication technologies. Using various methodol-ogies, the research has described a strong positive empirical relationship between technology and productivity. For example, Dedrick, Gurbaxani and Kraemer (2003) categorize these studies based on the aggregation level of data: fi rm-level, industry-level, and country-level.


Early research focused on whether or not technology produced productivity and increased economic growth. The evidence from the 1980s was predominantly negative, (e.g. Roach 1987; Strassman 1985). This is in contrast to the studies from the 1990s and later indicating a stong positive relationship between technology and productivity (e.g., Brynjolfsson and Hitt, 1996; Jorgenson and Stiroh, 2000; Lichtenberg, 1995). The so-called ‘productivity paradox’ (Solow, 1987) found in earlier studies has been ascribed to several explanations. Perhaps early technology investments were too small and insignifi cant to produce a measurable and positive effect (Oliner and Sichel, 1994). These invest-ments needed meet a minimum threshold of value before they could affect productivity (Osei-Bryson and Ko, 2004). Another explanation posited that the learning curve associated with technology causes a time-lag between the investment and the productivity (Dedrick, Gurbaxani and Kraemer, 2003).

Furthermore, the literature has asserted that other variables contributing to organizational performance may have been overlooked in evaluating IT infl uences on productivity (Devaraj and Kohli, 2000). For example, studies have suggested that organizational factors such as type of IT, management, workplace practices, change initiatives, organizational struc-ture, culture, employee training, and fi nancial conditions may affect IT and productivity (Dedrick, Gurbaxani and Kraemer, 2003). Additionally, environmental factors such as industry competitiveness, regulation, the macro environment, level of development, public policies, cultural fac-tors, education, and IT infrastructure are important factors infl uencing the value of IT (Merville, Kraemer and Gurbaxani, 2004). Finally, the productivity benefi ts of IT may expand beyond the value chain of the company making the IT investments. Accordingly, part of these benefi ts may be captured by the broader supply chain such as suppliers, strategic partners, and customers (Bresnahan, 1986; Hitt and Brynjolfsson, 1996).

Since country level studies are pertinent to our research, we will discuss some of these studies. Many of the country level studies are recent being published in the mid-1990s to today, use univariate tests associating technology investments with economic productivity, and


focus on developed countries. The association between technology and productivity is signifi cant and positive in developed economies (Daveri, 2000; Lee, Gholami, and Tong, 2005; Oliner and Sichel, 2000; Pook and Pence, 2004). Then again, this link is not signifi cant in many developing countries as shown by most of the studies in this area (Dewan and Krae-mer, 2000; Lee, Gholami, and Tong, 2005).

It appears that a certain threshold of technology or infrastruc-ture investment is required before an association between technology and pro ductivity can be detected in developing countries. Complemen-tary, macro-economic and public policy factors can modify the impact of technology on productivity. For instance, Shih, Kraemer and Dedrick (2007) posit that factors such as openness to trade can affect the level of technological invest ments. Other authors (Mbarika, Byrd, and Ray-mond 2002) have suggested that macro-level factors including policy, economic, fi nancial, technological, political, and geographical are key determinants in the level of IT and telecommunications growth in Least Developed Countries (LDC).

Economic Freedom and ProductivityWhile our study contributes to the literature linking technology to sustainable productivity, it is infl uenced by a long literature stream link-ing freedom and productivity. This literature stream has its foundation in the ‘national economic productivity’ literature in mainstream econom-ics (Solow, 1956). Perhaps the most intuitive literature is the produc-tion function approach that relates inputs such as labor and capital to production output. Economic freedom and technology investments are considered to be modifi ers of this relationship.

Economic freedom is the measure to which a free-market economy exists as opposed to a socialist (government controlled) economy. The components of economic freedom are: an environment favoring a voluntary exchange, free competition, protection of persons and property, and a limited degree of interventionism in the form of government ownership, regulations, and taxes (Gwartney and Lawson, 2002; Berggren, 2003).


Economic freedom can be differentiated from civil and political freedoms. Civil freedom includes elements such as the freedom of the press, the free-dom of association, religion, and speech. Political freedom involves open participation in the political process, elections that are competitive, and governments that are relatively corruption-free (Gwartney and Lawson, 2002). Civil and political freedoms may create environments where indi-viduals and societies can express themselves and enjoy many liberties, economic freedom has the potential to directly affect economic activity. We focus on economic freedom rather than political freedom because we are more interested in the policies that directly affect sustainable economic productivity.

Studies using various research methods have examined country level data to determine whether economic productivity is correlated with indi-cators of economic freedom. In general, most studies have found that the level of economic freedom exerts a positive and signifi cant association on economic productivity and growth (Goldsmith, 1997; Ali and Crain, 2002; Vega-Gordillo and Alvarez-Arce, 2003). In addition, a number of studies that have used changes in economic freedom as an independent variable and have concluded that the change in economic freedom is also positively and signifi cantly correlated to growth and productivity (Dawson, 1998; Gwartney, Holcombe, and Lawson, 2004).

It is conceivable that not all dimensions of economic freedom impact economic growth. The index of economic freedom used in most studies (e.g., Gwartney and Lawson, 2002) covers various aspects of freedom such as size of government, legal structure and property rights, access to sound money, freedom of exchange and regulation of business. Carlsson and Lundstrom (2002) decompose the various aspects of economic free-dom and fi nd that the most signifi cant effects are associated with legal structure and freedom of exchange. These results, especially those per-taining to legal structure, are an excellent complement to the well-known ‘law and fi nance’ literature where a key result is that country level inves-tor protection enhances corporate value (La porta, Lopez-De-Silanes, Shleifer, and Vishny, 2002).


CONTRIBUTIONS OF THIS STUDY

Our study makes three contributions to the literature. First, we create a composite dependent variable that combines two factors in a single mea-sure. This composite variable combines the Environmental Sustainability Index (ESI) with economic productivity at the nation level (GDPC). These two variables are multiplied and then the nations are ranked from lowest (1) to highest (143). We call this new variable the Environmentally Sus-tainable Productivity (ESP) Rank. The ESP Rank of all countries used in this study is included in Appendix A.

Moreover, we are proposing a model that considers the economic and environmental needs of a nation. A model that sustains the environment while signifi cantly hurting the economy is not realistic. Furthermore, some economic activity directed at increasing effi ciency may lead to a more sustainable environment (Porter and Linde, 1995). A perfect world would be both clean and prosperous. We propose a model to extend the literature with an empirical analysis by introducing fi ve regression mod-els that test the association of individual, combined and joint impacts of Technology (Tech) and economic Freedom (Free) on the Environmen-tally Sustainable Productivity (ESP) rank.

Second, we build on recent efforts by Archibugi and Coco (2004; 2005) to measure the technological capability of a country. We use the ArCo index compiled by these authors to more comprehensively mea-sure the technological prowess of a country. Although the ArCo index and the more traditional IT measurements are highly correlated, we feel that our study allows us to make broader inferences about technology.

Third, we assess the impact of economic freedom, particularly as a vari-able that modifi es the effect of technology on sustainable productivity. We draw on and complement the research that uses country level data to exam-ine the links between freedom, technology, productivity and environmental sustainability (Grossman and Krueger, 1995; Robin, Tribunella and N’Da, 2009; Tribunella and Friedman, 2010). In summary, we are contributing to the literature by creating the ESP rank and extending the literature by empir-ically measuring the effects of Technology and Economic Freedom on ESP.


DATA AND METHODOLOGY

Explanation of the DataOne of the dependent variables in our study is GDP per capita adjusted for purchasing power parity and expressed in the US currency (GDPC). GDP per capita is a measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. GDP is a primary indicator of a nation’s economic perfor-mance. It is calculated by adding the value of all fi nal goods and services produced in the country during the year. The per capita GDP is useful when comparing one country to another because it shows their relative economic performance. An increase in per capita GDP signals growth in the economy and tends to translate as increased productivity. Per capita GDP is also used as an indicator of the standard of living. We use this as a proxy for economic productivity. We collected values for this variable for the year 2005 from the Nation Master online database (www.nation-master.com) which sourced the data from the World Development Indi-cators Database. The database covers 169 nations from 1820 to 2005.

Our next variable is the Environmental Sustainability Index. The Yale Center for Environmental Law and Policy (YCELP) and the Cen-ter for International Earth Science Information Network (CIESIN) of Columbia University, in collaboration with the World Economic Forum and the Joint Research Centre of the European Commission collects and computes the Environmental Sustainability Index (SEDAC, 2010). Benchmarking National Environmental Stewardship (Environmental Performance Measurement Project 2010) contains a detailed description of the statistical methodology used to compute the ESI. The components and indicators in the ESI are listed in Table 1.

Our independent variables are technology and economic freedom. Our measure of technology is the Indicator of Technological Capabili-ties for Developed and Developing Countries (Tech). The indicator was compiled by Archibugi and Coco (2004). Values range from 0 (lowest capability) to 1 (highest capability). We use this index because it com-prehensively covers most of the countries in the world with a ranking of


162 countries in 1990 and 2000. It is one of the most widely used and detailed indexes. Furthermore, the authors use publicly available data and explain their methodology very clearly. The scale has three major dimensions and eight sub-Indexes of the Indicator of Technological Capabilities for Developed and Developing Countries which are listed in Table 2. The literature suggested that a higher value of productivity does not occur immediately after technology investments. Research sup-ports the idea of a delayed effect. Authors suggest (Dedrick, Gurbaxani and Kraemer, 2003) a lagged effect of IT on productivity due to an IT learning curve. Hence we used a technology indicator from 2000 while the rest of the data is from 2005.

5 Components 21 IndicatorsEnvironmental Systems 1. Air Quality

2. Biodiversity3. Land 4. Water Quality5. Water Quantity

Reducing Environmental Stresses 6. Reducing Air Pollution7. Reducing Ecosystem Stresses8. Reducing Population Growth9. Reducing Waste & Consumption Pressures10. Reducing Water Stress11. Natural Resource Management

Reducing Human Vulnerability 12. Environmental Health13. Basic Human Sustenance14. Reducing Environment-Related Natural

Disaster VulnerabilitySocial and Institutional Capacity 15. Environmental Governance

16. Eco-Effi ciency17. Private Sector Responsiveness18. Science and Technology

Global Stewardship 19. Participation in International Collaborative Efforts

20. Greenhouse Gas Emissions21. Reducing Trans-boundary Environmental

Pressures

TABLE 1. Environmental Sustainability Index.


Creation of Technology Technology Infrastructure Development of Human Skills

• Patents• Scientifi c Articles

• Internet Penetration• Telephone Penetration• Electricity Consumption

• Tertiary Science and Engineering Enrolment

• Mean Years of Schooling• Literacy Rate

TABLE 2. The ArCo Indicator.

Our economic freedom variable is the Economic Freedom of the World (Free) indicator by Gwartney and Lawson (2005) from the Fraser Institute. The EFW index has been compiled since 1970 and the data is publicly available at www.freetheworld.com. The EFW for a country is measured on a ten point scale with 10 denoting the highest level of economic freedom. EFW contains fi ve major areas: Size of government, legal structure and property rights, sound money policies, freedom to exchange, and business regulations. Each major area has several com-ponents explained in Table 3. We use this index because it comprehen-sively covers most of the countries in the world with information on 123 countries in 2005. It is also one of the most widely recognized and detailed indexes of economic freedom. The index has been stable over time and has been used in several published papers (Cole, 2003; Heitger, 2004; Mbaku, 2003; Vega-Gordillo and Alvarez-Arce, 2003).

Method and Regression ModelsWe performed linear regression and stepwise multiple-regression analy-ses on the above data. The following fi ve models (A-E) are employed in the research.

Model A: ESP = β0 + β1TechModel B: ESP = β0 + β1FreeModel C: ESP = β0 + β1Tech + β2 FreeModel D: ESP = β0 + β1Tech*FreeModel E: ESP = β0 + β1Tech + β2 Free + β3Tech*Free


Where:ESP = Environmentally Sustainable Productivity RankTech = Technology CapabilityFree = Economic Freedom

5 Areas Components

Size of Government Government ExpendituresTax RatesTransfer PaymentsSubsidies

Legal Structure and Property Rights

Judicial independenceImpartial courtsProtection of intellectual propertyMilitary interference in rule of law and political

processIntegrity of the legal system

Access to Sound Money Growth of the money supplyInfl ation variabilityRecent infl ation rateFreedom to own foreign currency

Freedom to Exchange with Foreigners

Tariffs and taxes on international tradeRegulatory trade barriersActual size of trade sectorDifference between offi cial exchange rate and black

market rateInternational capital market controls

Regulation of Credit, Labor, and Business

Credit Market RegulationsLabor Market Regulations • Impact of minimum wage • Wages set by centralized collective bargaining • Unemployment Benefi ts • Use of conscripts to obtain military personnel • Business Regulations • Price controls • Time to start a new business • Irregular payments such as permits, licenses

assessments

TABLE 3. Areas and Components of the Economic Freedom of the World Index.


Model A is the basic model relating productivity and sustainability to technology that has been extensively studied in the literature without productivity in the model. Model B is the basic model relating produc-tivity and sustainability to freedom; this model is once again fairly well researched without sustainability in the model. An interesting aspect of our paper is related to models C and D. Model C assesses the separate effects of both technology and freedom on productivity and sustainability. Model D adds further complexity by also considering the interaction between technology and freedom in their impact on productivity and sustainability. The purpose of Model D is to measure the interaction of the independent variables (IVs) which can be represented in a regression equation by the cross-product term of the two linear variables. In the context of simple linear regression an interaction describes a situation in which the simulta-neous infl uence of two IVs, on a third dependent variable, is not additive (equal to the addition of the IV coeffi cients). In equation D we are testing to see if the interaction between the explanatory variables Tech and Free has an effect on the dependent variable. Finally, model E combines C and D in the same equation. In equation E we are testing to see if the interac-tion between the explanatory variables Tech and Free has been modifi ed by their relationship with each other. All the models combine productivity and sustainability into a composite dependent variable which is not found in the literature to the best of our knowledge.

RESULTS

Descriptive Statistics and CorrelationsTable 4 reports descriptive statistics on the four key dependent and independent variables: sustainability (ESI), technology (Tech), freedom (Free) and productivity (GDPC). Tech ranges from .031 to .870, and has a mean and median of 0.313 and 0.334 respectively. Free ranges from 3.22 to 8.54 with mean and median values of 6.54 and 6.39 respectively. GDPC ranges in value from 600 to 49,700 and has a mean and median of 11,095 and 5,900 respectively. We note the presence of skewness in GDPC: this is caused by a few countries having extremely high values


thereby increasing the mean. ESI ranges from 29.2 to 75.1, and has a mean and median of 49.3 and 49.9 respectively.

Table 5 reports correlations between model variables. We note signifi -cant correlations between each of the independent variables technology and freedom and the dependent variable productivity (GDPC). We note that the correlation between Tech and GDPC is 0.880, and that the cor-relation between Free and GDPC is .709.

Table 5 also indicates a strong relationship between technology and freedom. Higher technology investments appear to be associated with higher levels of freedom. For instance, the correlation between Tech and Free is .676. A practical concern is that the high correlation creates problems of independent variables that are associated with each other in the regression tests noted below. However, the correlation is below .700 which is just enough to avoid the problem of multicollinearity.

Regression Analysis and Models of Sustainable ProductivityFigures 1, 2, and 3 display the scatter plots, best fi t lines, and regression equations for models A, B and D. We note that that the residuals are independently distributed around the regression lines and hence the problems of autocorrelation and serial correlation are not present. This would indicate the models studied in this paper were good candidates for regression analysis.

Variable ESI GDPC Tech FreeMin 29.2 600 .03 3.22

Max 75.1 49,700 .87 8.54

Median 49.9 11,095 .33 6.39

Mean 49.3 5,900 .31 6.54

Observations 143 143 14 110

Std. Deviation 8.5 11,931 .19 1.06

TABLE 4. Environmental Stability Index (ESI), GDP per capita (GDPC), Technology Index (Tech), and Freedom Index (Free) Descriptive Statistics.


TABLE 5. Correlations Matrix for All Variables.

ESI GDPC ESI*GDPC ESP Tech Free Free*TechESI 1.00

GDPC .42*** 1.00

ESI*GDPC .56*** .97*** 1.00

ESP Rank .51*** .87*** .85*** 1.00

Tech .47*** .88*** .87*** .88*** 1.00

Free .33*** .70*** .69*** .69*** .67*** 1.00

Free*Tech .45*** .91*** .91*** .87*** .98*** .77*** 1.00

*p � .05, ** p � .01, *** p � .001

Table 6 reports the main results of the study. All models are statis-tically signifi cant: the F-statistics for all models have p-values (not reported) of less than 1%. Model A is a regression of ESP rank on Tech. Consistent with results reported in the literature, we fi nd high R-squares (.85) as well as a signifi cant coeffi cient for Tech (t-statistic of 24.42). We also fi nd strong results with Model B that uses Free as the independent variable (IV): the coeffi cient has a t-statistic of 10.42. However, the R2 of Model B is lower (.50). Model C uses Tech as well as Free as inde-pendent variables. Here both coeffi cients are signifi cant (t-statistics of 16.59 and 3.25 respectively) but the R2 for model C (.86) is only slightly higher than the R2 for model A.

Table 6 Model D indicates another interesting result. Here we test for the explanatory value of an interacting variable. Tech*Free is the product of Tech and Free. The coeffi cient of Tech*Free is signifi cant and positive with a t-statistic of 22.28. In this case the results suggest that the interaction between the independent variables Tech and Free is not as infl uential as Tech alone on the dependent variable ESP. In contrast, the coeffi cient for Tech*Free in model E is negative (-19.76). In model E we conducted a regression with Tech, Free and the interaction variable. The negative coeffi cient of the interaction variable is likely a statistical


FIGURE 1. Linear Regression Line Fit and Equation for Model A.

y = 146.46x + 1.8118

0

50

100

150

0.000 0.200 0.400 0.600 0.800 1.000

ESP

Tech

Tech Line Fit Plot

ESP

Predicted ESP

FIGURE 2. Linear Regression Line Fit and Equation for Model B.

y = 21.122x - 79.535

-50

0

50

100

150

3.00 4.00 5.00 6.00 7.00 8.00 9.00

ESP

Free

Free Line Fit Plot

ESP

Predicted ESP

FIGURE 3. Linear Regression Line Fit and Equation for Model D.

y = 17.469x + 12.053

0

20

40

60

80

100

120

140

0.000 2.000 4.000 6.000 8.000

ESP

Free*Tech

Free*Tech Line Fit Plot

ESP

Predicted ESP


artifact (spurious fi nding) that may be due to multicolinearity between the interaction variable and the two original IVs.

DISCUSSION OF DATA AND RESULTS

Before turning to our main results, we note a few patterns in the key independent variables. The rapid evolution of electronic commerce in the second half of the 90’s resulted in its adoption by organizations and the general population worldwide. As a consequence, various investments were made in IT infrastructure in general and Internet technologies in particular, mainly in developed countries but also in developing nations. These investments may explain why the technology variable has such a high score in many countries. Other explanations for the high value of technology include Y2K related IT investments and deregulation. As with technology, economic freedom is also increasing in many countries. A plausible explanation may be the sweeping political changes across the world (e.g., dismantling of the Soviet Union); the resulting political freedom may have led to economic freedom. An alternative explanation for higher values of economic freedom is the success of global institu-tions such as the WTO (World Trade Organization) in instilling values of economic freedom and prosperity.

Model1A B C D E

β t β t β t β t β tTech 146.46 24.42** NA 129.34 16.59** NA 265.50 6.75**

Free NA 21.12 10.42** 4.75 3.25** NA 11.34 4.87**

Tech*Free NA NA NA 17.47 22.28** �19.76 �3.52**F 596.20 108.63 329.77 496.56 247.45

Adj. R2 .85 .50 .86 .82 .87

TABLE 6. Regression Analyses ESP Rank Regressed on Independent Variables: standardized Beta Coeffi cients and t-statistics for Each Model (N = 109).

1 ESP Rank is the dependent variable* p � .05, ** p � .01, *** p � .001


Second, we fi nd a signifi cant correlation between the technology and freedom variables. Although possible, we do not assume or infer a causal relationship between these two variables. It is possible that the high correlation between technology and freedom is caused by the effect of other variables (e.g., education levels, political freedom) not stud-ied in this research. Third, consistent with expectations the data suggest that the gain in productivity observed may be attributable in part to IT investments made by corporations as well as governments to embrace e-commerce and to face the Y2K issue.

In this part of the paper, we discuss the results of our research models. Econometrics involves the application of statistical methods to economic data so as to endow economic theories with empirical content (Kohler, 2002). In this paper we suggest that the Environmentally Sustainable Productivity (ESP) Rank is positively associated with increases in Tech-nology (Tech) and Economic Freedom (Free). Using regression analysis on 2005 data we fi nd strong evidence for this theory and three plausible models. From an econometric point of view we can say that, all else being equal:

Model A (R2 = .85): ESP Rank = 1.81 + 146.46FreeModel C (R2 = .86): ESP Rank = –22.27 + 129.34Free + 4.75TechModel D (R2 =.82): ESP Rank = 12.05 + 17.47Free(Tech)

We fi nd signifi cant relationships between technology and sustainable productivity as well as between freedom and sustainable productivity. These results arise from long literature streams in the information sys-tems and economics fi elds respectively. We report correlations as well as regression coeffi cients consistent with prior studies (Robin, Tribunella and N’Da, 2009) and report the results in Tables 4 and 5 respectively. What is perhaps novel about our results is the use of a more compre-hensive variable for technology, the ArCo variable. Also, we are able to compare the relative importance of technology and economic free-dom as infl uencers of sustainable productivity. We fi nd that technology


is a more important variable. In Table 5, model A (using technology as the independent variable) has a higher R-squares than model B (using economic freedom). The relative importance of technology is also con-fi rmed with model C where both technology and economic freedom are used as independent variables: we note a marginally higher R2 compared to model A. Thus, the addition of freedom in model C as an explanatory variable does not appear to add much beyond the use of technology.

A result of our study is that freedom modifi es the effect of technology on productivity to a small degree. Model D adds the interaction variable indicating how freedom does not greatly modify the effect of technol-ogy on environmentally sustainable productivity (ESP). Since the coeffi -cient in Model D is an interaction variable, this makes it more diffi cult to predict the consequences of changing the value of Tech, since it interacts with Free. Decision makers might be frustrated by this information since both variables are hard to control with fi scal policy. When comparing Model D with Model A we fi nd that R2 decreases from .85 to .82. This is counter-intuitive, one would think that economic freedom would provide the incentive to develop higher levels of technologies that would even-tually lead to a more sustainable environment. We note that this vari-able has a signifi cantly positive coeffi cient but the overall adjusted R2 goes down when compared to model A (technology alone as a predictor). This indicates that countries with higher levels of freedom do not have a greater link between technology and sustainable productivity. This is not consistent with Gompert (1998) who argues that there is synergy between IT and economic freedom. Arguably, the marginal effect of IT invest-ments is greater when companies in a country have the freedom to cre-atively obtain value from it. However, freedom does not appear to have much infl uence on productivity through technology. Interestingly, the regression models support the proposition that technology has signifi cant positives associations with environmentally sustainable productivity. An explanation for this result is that high ESI countries may limit economic freedom through environmental regulation. We know of no other study indicating these results but acknowledge that most of the results are not


surprising. What is surprising is that freedom does not add to the positive association between technology and sustainable productivity.

LIMITATIONS, FUTURE RESEARCH, AND CONCLUSIONS

Research LimitationsNo research is perfect and this study also has its limitations. Because we utilized cross-sectional associations, we cannot make strong statements about causality. Furthermore, our research design could be subject to the problem of correlated independent variables that were omitted from the analysis. Nevertheless, our results are reasonable and consistent with expectations set forth in related studies. Another limitation of this study concerns the technique employed for data analysis. Instead of using linear multiple-regression analysis, we could have adopted different techniques such as nonlinear regression, hypothesis testing or structural equations modeling. Additionally, this study relies on data collected in 2000 and 2005. Using data from different time periods may have yielded differ-ent results. Finally, our policy focus in the current study is on economic freedom. We realize that culture, political freedom and economic free-dom may be synergistic, especially in developing countries.

Future ResearchFirst, the analysis should be replicated using a more recent measure of environmental sustainability. The last ESI update was in 2004/05. In addition to updating the data, researchers could vary the independent variables. We realize that politics, tradition and culture have a signifi -cant role to play. We argue that historical factors concerning a country’s societal and political landscape may predestine its policies. These fac-tors should be entered as independent regression variables to study their affect. Furthermore, the multifaceted variables such as the ESI and Eco-nomic Freedom could be disaggregated into their individual elements to see which components are the most infl uential. Finally, this study was conducted at the nation level therefore it could be replicated at the fi rm level to see if the results remain consistent.


CONCLUSION

The information economy and technology have changed the world dramatically during the last few decades. This has stimulated inter-est in determining whether technology investment has had a positive economic impact on productivity. A major concern has been whether nations experience economic as well as environmental benefi ts as a consequence of investments in technology. A well-developed stream of research has addressed this concern and other related issues. We contrib-ute to this literature by exploring the role played by economic freedom in the relationship between technology, productivity and sustainability. Our principal fi nding is that technology creates economic productivity and sustainability in countries, but higher levels of economic freedom adds only a small marginal improvement to this association. Therefore, we also validate the important role played by technology in creating economic productivity and sustainability. While both technology and economic freedom produce economic productivity, we fi nd that technol-ogy is the far more dominant factor when sustainability is introduced as a dependent variable.


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Country ESI GDPC ESP Tech Free

Norway 73.4 46,300 143 .726 7.33

Iceland 70.8 38,000 142 .670 7.70

Ireland 59.2 44,500 141 .564 8.13

Finland 75.1 33,500 140 .830 7.67

United States 52.9 43,800 139 .757 8.54

Sweden 71.7 32,200 138 .870 7.40

Canada 64.4 35,700 137 .755 8.02

United Arab Em. 44.6 49,700 136 .395 7.70

Austria 62.7 34,700 135 .615 7.51

Switzerland 63.7 34,000 134 .799 8.24

Denmark 58.2 37,100 133 .700 7.63

Australia 61.0 33,300 132 .686 8.00

Japan 57.3 33,100 131 .720 7.30

Germany 56.9 31,900 130 .680 7.54

Netherlands 53.7 32,100 129 .690 8.10

France 55.2 31,200 128 .600 7.50

United Kingdom 50.2 31,800 127 .679 8.35

New Zealand 60.9 26,200 126 .650 8.20

Italy 50.1 30,200 125 .526 7.08

Belgium 44.4 33,000 124 .640 7.48

Israel 50.9 26,800 123 .763 6.81

Slovenia 57.5 23,400 122 .498 6.10

Spain 48.8 27,400 121 .513 7.30

Greece 50.1 24,000 120 .490 6.85

Estonia 58.2 20,300 119 .470 7.10

Portugal 54.2 19,800 118 .445 7.38

South Korea 43.0 24,500 117 .601 6.70

APPENDIX A. Countries by Descending ESP Rank.

(continued on next page)



Czech Rep. 46.6 22,000 116 .474 6.99

Taiwan 32.7 29,600 115 .662 7.20

Latvia 60.4 16,000 114 .440 6.82

Slovakia 52.8 18,200 113 .485 5.84

Argentina 62.7 15,200 112 .426 7.20

Hungary 52.0 17,600 111 .467 6.66

Lithuania 58.9 15,300 110 .407 6.48

Kuwait 36.6 23,100 109 .403 7.26

Croatia 59.5 13,400 108 .414 5.87

Uruguay 71.8 10,900 107 .416 6.80

Costa Rica 59.6 12,500 106 .360 7.30

Trinidad & Tob. 36.3 19,800 105 .380 7.19

Malaysia 54.0 12,800 104 .370 6.69

Oman 47.9 14,400 103 .300 7.60

Russia 56.1 12,200 102 .480 4.73

Chile 53.6 12,600 101 .424 7.50

Poland 45.0 14,400 100 .464 5.72

South Africa 46.2 13,300 99 .372 6.76

Botswana 55.9 10,900 98 .254 7.28

Brazil 62.2 8,800 97 .329 5.78

Bulgaria 50.0 10,700 96 .450 5.51

Saudi Arabia 37.8 13,800 95 .327

Libya 42.3 12,300 94 .312

Colombia 58.9 8,600 93 .331 5.61

Mexico 46.2 10,700 92 .358 6.25

Panama 57.7 8,200 91 .382 7.37

Tunisia 51.8 8,900 90 .287 6.27

APPENDIX A. (continued )



Thailand 49.7 9,200 89 .342 6.64

Kazakhstan 48.6 9,400 88 .380

Gabon 61.7 7,100 87 .230 5.24

Belarus 52.8 8,100 86 .430

Namibia 56.7 7,500 85 .217 6.90

Turkey 46.6 9,100 84 .346 5.75

Romania 46.2 9,100 83 .393 4.85

Peru 60.4 6,600 82 .345 6.94

Macedonia 47.2 8,300 81 .300

Dominican Rep 43.7 8,400 80 .308 6.71

Algeria 46.0 7,600 79 .277 4.00

Ukraine 44.7 7,800 78 .418 4.47

Venezuela 48.1 7,200 77 .370 5.76

Iran 39.8 8,700 76 .313 5.21

Azerbaijan 45.4 7,500 75 .338

Albania 58.8 5,700 74 .251 5.54

Guyana 62.9 4,900 73 .271 7.25

Armenia 53.2 5,700 72 .327

China 38.6 7,800 71 .306 5.28

Paraguay 59.7 4,800 70 .323 6.30

Turkmenistan 33.1 8,500 69 .289

Jordan 47.8 5,100 68 .340 7.29

Lebanon 40.5 5,900 67 .370

Ecuador 52.4 4,500 66 .319 5.33

Sri Lanka 48.5 4,700 65 .280 5.96

Guatemala 44.0 5,000 64 .234 6.31

El Salvador 43.8 4,900 63 .311 7.21





Cuba 52.3 4,100 62 .321

Philippines 42.3 5,000 61 .322 6.96

Jamaica 44.7 4,700 60 .347 7.00

Morocco 44.8 4,600 59 .217 6.33

Georgia 51.5 3,800 58 .380

Angola 42.9 4,500 57 .107

Indonesia 48.8 3,900 56 .265 5.90

Egypt 44.0 4,200 55 .269 6.68

Bolivia 59.5 3,100 54 .304 6.70

Syria 43.8 4,100 53 .282 5.11

India 45.2 3,800 52 .225 6.06

Nicaragua 50.2 3,100 51 .238 6.52

PN Guinea 55.2 2,700 50 .145 5.85

Honduras 47.4 3,100 49 .258 6.34

Ghana 52.8 2,700 48 .203 5.70

Cambodia 50.1 2,700 47 .096

Viet Nam 42.3 3,100 46 .239

Cameroon 52.5 2,400 45 .192 5.53

Mauritania 42.6 2,600 44 .111

Mongolia 50.0 2,100 43 .197

Pakistan 39.9 2,600 42 .191 5.03

Moldova 51.2 2,000 41 .396

Kyrgyzstan 48.4 2,100 40 .306

Bangladesh 44.1 2,300 39 .123

Guinea 48.1 2,100 38 .079

Gambia 50.0 2,000 37 .123

Uganda 51.3 1,900 36 .133 6.56





Iraq 33.6 2,900 35 .246

Senegal 51.1 1,800 34 .151 5.69

Zimbabwe 41.2 2,100 33 .279 4.83

Sudan 35.9 2,400 32 .140

Côte d’lvoire 47.3 1,800 31 .136 5.90

Togo 44.5 1,700 30 .145 4.84

Congo 53.8 1,400 29 .207 4.77

Bhutan 53.5 1,400 28 .103

Rwanda 44.8 1,600 27 .113 5.27

Nepal 47.7 1,500 26 .121 5.86

Central Afr Rep 58.7 1,200 25 .110 5.02

Mali 53.7 1,300 24 .066 5.74

Uzbekistan 34.4 2,000 23 .319

Nigeria 45.4 1,500 22 .142 5.30

Chad 45.0 1,500 21 .071 5.60

Mozambique 44.8 1,500 20 .098

Haiti 34.8 1,800 19 .129 6.37

Burkina Faso 45.7 1,300 18 .050

Kenya 45.3 1,200 17 .204 6.59

Myanmar 52.8 1,027 16 .179 3.22

North Korea 29.2 1,800 15 .187

Benin 47.5 1,100 14 .113 5.84

Zambia 51.1 1,000 13 .240 6.74

Tajikistan 38.6 1,300 12 .356

Madagascar 50.2 900 11 .116 5.28

Niger 45.0 1,000 10 .031 5.52

Guinea-Bissau 48.6 900 9 .076 3.83




Tanzania 50.3 800 8 .155 6.16

Liberia 43.4 900 6 .095

Sierra Leone 43.4 900 6 .075 4.91

Ethiopia 37.9 1,000 5 .067

Yemen 37.3 1,000 4 .140

Dem Rep Congo 44.1 700 3 .125

Malawi 49.3 600 2 .134 4.81

Burundi 40.0 700 1 .078 5.61


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