Choice of moderator variables

The two effect size measures will serve as the dependent variables in a meta-regression analysis. The explanatory variables, called moderator variables, are the study characteristics that are thought to be consequential. At a minimum, the meta-analyst will wish to code dummy variables for the use of different data sets (such as pollutants types in the EKC literature) and econometric modeling choices (panel vs cross section estimates, for example). However, because the number of studies is limited and most economic studies entail a unique combination of techniques, independent variables, data, time periods and other research choices, not every study characteristic can be coded and analyzed.

Nor should a researcher wish to do so. Variations due to minor modeling choices may be treated as part of the random study-to-study background (Stanley, 2001).

In the case of the EKC, the choice of the moderator variable was made after careful reading of the individual empirical studies and the information found in literature reviews on the EKC. As each primary study has to be carefully codified, the construction of a meta-analytical database is time-consuming.

We chose three types of moderator variables. The sampling variables capture the type of data used and the principal characteristics of the sample observed. The methodological variables indicate differences in econometric specification and capture the additional explanatory variables included in the model. Finally, the environmental variables consider the type of pollution examined.

3.3.1 Sampling variables

The sampling variables capture the sample size (size) and the source of the income data used (pwt).

This latter variable is justified since the income data may influence the effect size measures (see chapter, section 3.2.2). Two other variables (dvp and ldvp) control for the Borghesi (1999) and Vincent (1997), critique which says that the EKC arises from the combination of an increasing pollution-income path in developing countries and a decreasing one in developed countries. Therefore, when only high-income countries are considered, the corresponding primary studies may present more systematic EKC relationships or decreasing PIR. On the contrary, when developing countries are exclusively considered, monotonically increasing PIRs are more likely to emerge. Table 2.3 gives the precise definition of the 4 sampling moderator variables.

3.3.2 Methodological variables

The methodological variables capture the type of econometric specifications and the additional explanatory variables considered by the primary studies. An important concern was to ignore pertinent variables since the methodological differences across studies are numerous. We finally adopted 12 methodological variables controlling for the use of panel data (panel), the use of random-effect models (re), the use of first-difference estimates (diff), the inclusion of a cubic income term (cub) and the use of the logarithmic specification (log). Furthermore, the variables pop, trade, pol, educ, equ, eco and price grasp the type of additional variables the primary studies control for. Their precise definition also appears in table 2.2.

Tab. 2.2 Sampling and methodological variables

Sampling variables Mean

(standard error)

size The natural log of the sample size of the primary study 5.8

pwt Dummy variable - 1 indicates the study used Penn World Table data, 0 otherwise 0.74 dvp Dummy variable - 1 indicates the study used developed countries only, 0 otherwise 0.32 ldvp Dummy variable - 1 indicates the study used developing countries only, 0 otherwise 0.12

Methodological variables

panel Dummy variable - 1 indicates the study used panel data, 0 otherwise 0.69 re Dummy variable - 1 indicates the study used a random effect model, 0 otherwise 0.21 diff Dummy variable - 1 indicates the study used a first difference model, 0 otherwise 0.07 cub Dummy variable - 1 indicates the study included a cubic income term, 0 otherwise 0.33 log Dummy variable - 1 indicates the study used the logarithmic specification, 0 otherwise 0.41

pop Dummy variable - 1 indicates the study controlled for population size or population density, 0

otherwise 0.31

trade Dummy variable - 1 indicates the study controlled for trade policy and trade flows, 0 otherwise 0.17 pol Dummy variable - 1 indicates the study controlled for political characteristics (democratic level,

quality of institutions and efficiency of political actions), 0 otherwise 0.17 educ Dummy variable - 1 indicates the study controlled for the level of education, 0 otherwise 0.09 equ Dummy variable - 1 indicates the study controlled for equity measures (Gini index), 0 otherwise 0.08

eco Dummy variable - 1 indicates the study controlled for the economic activities of geographical areas,

0 otherwise 0.32

price Dummy variable - 1 indicates the study controlled for the price of goods and behavior closely linked to environmental quality considerations (price of energy, wood), 0 otherwise 0.06

3.3.3 Environmental variables

The environmental indicators considered by primary studies were divided into nine categories, which could be supported by the data, i.e., contained multiple observations. The two first categories capture the distinction between global stock pollution (global) and local air pollution (air). This distinction is justified since numerous studies (Ekins, 1997; Panayotou, 2000; Lieb, 2002) conclude that global pollutant indicators are more likely to follow a monotonically increasing pollution-income path. Global pollution includes CO₂ emissions, other greenhouse gas emissions, energy consumption and CFC.

Local air pollution covers the emissions of CO, NO₂, VOC, SPM and smoke. A specific category controls for SO2 concentration since numerous observations examine this particular pollutant.

Another distinction has to be made between pollutants measured through concentration levels and pollutants measured by emissions per capita. The variable conc therefore captures the environmental

indicators measured in concentration levels. Finally, the variables tox, waste, res et water control respectively for heavy metal and toxic pollution, hazardous waste, deforestation and biodiversity loss, water pollution (DBO, DCO, OD, nitrates, coliforms, access to safe drinking water and sanitation).

Note that the pollutant categories (except for the variable conc) are mutually exclusive and exhaustive.

Exhaustive means that there must be enough categories that all the observations will fall into some category. Mutually exclusive means that the categories must be distinct enough that no observations will fall into more than one category. The category water serves as the referent group (and will be excluded from the estimation) so that the effect of the other environmental variables are estimated relative to water pollutants.

Tab. 2.3 Environmental and background variables

Environmental variables Mean

(standard error) global Dummy variable - 1 indicates the pollutant has a global and long term impact, 0 otherwise 0.22

local Dummy variable - 1 indicates the pollutant is a local and short term air pollutant (heavy particles,

smoke, nitrogen oxide), 0 otherwise 0.26

so2 Dummy variable - 1 indicates the pollutant is sulfur dioxide, 0 otherwise 0.24 tox Dummy variable - 1 indicates the pollutant is toxic emissions, 0 otherwise 0.06 waste Dummy variable - 1 indicates the pollutant is hazardous waste, 0 otherwise 0.03

res Dummy variable - 1 indicates the pollutant is deforestation, park area, or biodiversity loss, 0

otherwise 0.11

water Dummy variable - 1 indicates the pollutant is biophysical oxygen demand, chemical oxygen demand, dissolved oxygen, nitrates, coliforms, access to safe drinking water and sanitation (%), otherwise 0 0.10 conc Dummy variable - 1 indicates the pollutant is measured in concentration level, 0 otherwise. 0.28

Background variable

pub Dummy variable - 1 indicates the study has been published before June 2003, 0 otherwise 0.72

A last background variable controls for published studies (pub). It has been added in order to check for the existence of a publication bias and control for systematic differences between published and unpublished studies.

The construction of pollutant categories appears to be a complex issue since some pollutants could enter several categories. We test a categorization according to environmental media (air, water, soil and landscape, energy and materials, global environment). However, the latter is unsatisfactory since it ignores the economic characteristics of pollutants. Ideally, pollutants should be grouped into categories that seize the level of damage and/or abatement costs they generate. However, actual available studies on damage and abatement costs show that these costs vary sharply from case to case. Establishing a hierarchy among pollutants seems therefore too subjective to be credible. The variables global, local and conc try however to catch part of the difference in abatement costs. It is argued that global pollutants face higher abatement costs since a drastic emission reduction requires

less energy consumption and therefore a reduction of economic activities. On the contrary, lowering concentration levels can be done at a low cost (by constructing a taller chimney, for example).

Moreover, we did not exactly replicate the pollutant categories constructed by Cavlovic et al. (2001).

The following differences are noticeable. First, energy consumption is considered a global pollutant and not a toxic emission because energy consumption is generally used as a proxy for CO2 emissions (Gandaharan and Valenzuela, 2000). Identically, as water quality is correlated with the existence of public infrastructures (sewers, purification facilities) the “access to sanitation and to safe drinking water” environmental indicators are put in the category water. The urban quality category of Cavlovic has therefore been dropped. Finally, the construction of two separate categories for urban pollutants (one grouping “smoke” and “dark matter” and the second capturing “SPM”) does not appear necessary.