Data Set

Our empirical approach copes with the above problems by using a panel set of subjective data for a large sample of OECD and non-OECD countries of very different income levels. To assess environmental regulation and its effects we use data from two differentExecutive Opinion Surveysregularly conducted by the World Economic Forum (WEF, 2000-2006) and by the Institute of Manage-ment DevelopManage-ment (IMD, 2000-2006), respectively. Both surveys are carried out annually on a global scale to capture the opinions of business leaders on issues for which hard data is scarce or non-existent. Between 2000 and 2006 the coverage ranges between a total number of 47 and 125 of the most important low, middle and high income countries.¹

Luckily, both surveys include specific questions on the effects of environ-mental regulation on competitiveness, which provides us with the relevant country-specific measures that we want to explain by national framework con-ditions. It is important to understand that these variables assess theeffectsof environmental regulation, notcompetitivenessitself. Using perceptions of the effects of environmental regulation as dependent variables resolves one of the critical issues for the econometrician: the responsibility of correctly attributing causation is shifted to the numerous surveyed experts. As a drawback of this subjective approach, the magnitude of the change in competitiveness cannot be quantified in absolute terms. The empirics remain instead limited to inferences on the direction of influence.²

The fact of having two ratings of the regulatory impact gives us the oppor-tunity to analyze their differences. As can be seen in Figure 3.3, both variables are highly correlated, yet there is substantial additional variation requiring explanation. Although similar at first glance, once the wording of the ques-tionnaires is more carefully examined, we find that one survey question refers explicitly to the country-specific long-term consequences from induced inno-vation, while the negative phrasing of the other captures the (absence of an) immediate negative impact of complying with environmental regulation (see

1According to the WEF, the 125 countries covered by the survey in 2006 represented 98.1 per-cent of the world’s Gross Domestic Product.

2Please see also the next section for a discussion of the ordinal nature of the measurement scale.

Figure 3.3: Competitiveness effects of environmental regulation

Table 3.1: Descriptive Statistics

Variable Obs. Mean SD Min Max

SHRT 351 6.10 .85 3.86 8.43 LON G 537 4.39 .61 2.00 6.10 ST RN 662 4.10 1.26 1.20 6.80 F LEX 318 4.07 .57 2.70 5.70 N F RC 662 3.90 1.02 1.30 6.55 ICAP 662 2.91 1.23 1.28 6.66

CERT 748 658.60 2094.22 0 23466

IN CM 980 12.37 13.88 0.44 78.50

Table 3.2: Description of Variables

Competitiveness effect measures

Variable Survey question Number of observations

2000 2001 2002 2003 2004 2005 2006

SHRT

Environmental laws and compliance do not hinder the competitiveness of businesses.

(Index from 0 to 10)

47 49 49 51 51 51 53

LON G

Complying with environmental standards in your country ...(1 = significantly reduces competitiveness, 7 = helps long-term competitiveness by encouraging improvements in products and processes.)

59 75 80 102 104 117

Environmental regulation measures

Variable Survey question WEF reference

2000 2001 2002 2003 2004 2005 2006

ST RN How stringent is your country’s

environmental regulation?(1 = lax compared to most other countries, 7 = among the world’s most stringent.)

12.06 11.06 11.05 11.05 10.01 9.01 10.01

N F RC

Environmental regulations in your country are ...(1 = confusing and frequently changing, 7 = transparent and stable.)

12.10 11.10 11.09 11.08

Environmental regulations in your country are ...1 = confusing and enforced erratically, 7 = stable and enforced consistently and fairly.)

10.02 9.02 10.02

Environmental regulations in your country are ...(1 = not enforced or enforced erratically, 7 = enforced consistently and fairly.)

12.12 11.12 11.11 11.10

F LEX

Environmental regulations in your country

...(1 = offer no options for achieving compliance, 7 = are

flexible and offer many options for achieving compliance.)

12.11 11.11 11.10 11.09

Environmental regulations in your country are achieved through ...(1 = adversarial and legal means, 7 = government-business cooperation and voluntary corporate action.)

11.14 11.13 11.12 10.06

Number of observations 59 75 80 102 104 117 125

Non-regulatory framework measures

Variable Computation of Index Number of observations

2000 2001 2002 2003 2004 2005 2006

ICAP

Innovation Subindex of the Growth Competitiveness Index

= 1/4 innovation survey data (Technological readiness, Firm-level absorption, Company spending on research and development, University/industry collaboration) + 3/4 innovation hard data (Utility patents, Tertiary enrollment)

59 74 80 102 104 117 125

QU AL

Relative ISO 14001 Certification Intensity

= zero-skewness log transformation of the national deviation from the global certification intensity at the year

124 125 126 132 129 133 133

IN CM Per capita income

in thousand current international dollars 140 140 140 140 140 140 140

Table 3.2). Interpreting the latter as assessment of the competitiveness effects in short-term (SHRT) and the former as an assessment of the competitiveness effects in a longer-term perspective (LON G), we obtain novel information on the dynamical patterns of the regulatory impact.

The characteristics of national environmental regulation are measured by the average survey responses to questions regarding the relative stringency of environmental regulation (ST RN); the transparency, fairness, consistency and stability of environmental regulation enforcement (N F RC); and the openness of environmental regulation for achieving compliance in a flexible and non-adversarial way (F LEX). An important complication is related to changes in the WEF Expert Opinion Survey during the observation period. Some relevant survey questions on environmental regulations have been removed, merged with other questions, or slightly rephrased between panel waves.³ An overview of the data that was used to assess regulation is given in Table 3.2.

As an indicator of innovation capability (ICAP), we use theInnovation Sub-indexof the WEF, which is composed of both hard and survey data, reflecting many different aspects of the national innovation system, including technolog-ical, financial, educational and relational issues (see Table 3.2).⁴

We construct an indicator for environmental quality infrastructure (QU AL) using counts of environmental management system certifications according to ISO 14001 (CERT) that are available from theISO Survey of Certifications (ISO, 2011). Given the strong systemic interdependencies between quality in-stitutions, the extensive use of management system certifications indicates the overall functioning of quality infrastructure elements.⁵ There are some com-plications with regard to this measure that have to be considered. First, we

3The two survey questions on the stability and on the consistency, which are supposed to capture the proper enforcement of environmental regulation, underwent several slight changes and were eventually succeeded by a combined question comprising elements of both concepts. To obtain a more consistent time series, we merged the two variables by calculating their mean scores for the years before 2004. After having verified the high interrelatedness of the two variables capturing the flexibility of environmental regulation, we applied the same merging procedure to the survey questions on the openness towards different implementation options and on the extent of government-business cooperation, when both where available for the same year.

4Innovation capacity is defined as a country’s potential to produce a stream of commercially relevant innovations. Distinct from purely scientific or technical achievements, it focuses on the economic application of new technology. It aims to measure the fundamental conditions that create the environment for innovation in a particular location, the common innovation infrastructure.

5Due to their broad diffusion and general applicability for all types of organization, indus-try sectors and geographical regions, certifications of management systems according to ISO 9001 have for instance been used by Fagerberg and Shrolec (2008) as proxies for ‘production qual-ity/standards’.

have to correct for the size of the economy, which we do by calculating certi-fication intensities based on GDP as denominator. Second, we want to correct for the natural positive time trend from the global diffusion of ISO 14001 over the years. Therefore we take the deviation of the national certification intensity from the global certification intensity of the respective year to obtain a country-specific measure of quality infrastructure. Third, because the distribution of this measure is highly skewed, we apply a zero-skewness log transformation (see Table 3.2).

Since the so-called affluence hypothesis (Diekmann and Franzen, 1999;

Franzen, 2003; Franzen and Meyer, 2010) postulates an increasing subjective valuation of environmental quality to derive from higher income, which could change the competitiveness effect of environmental regulation, we will control for a country’s per capita income (IN CM) as available from the World Bank when evaluating the effects of regulatory and non-regulatory framework con-ditions.