1
Sana El Harbi (Corresponding author)
University of Sousse, and URFQ,
Sousse, Tunisie
E-mail : [email protected]
Gilles Grolleau
Montpellier SupAgro and LAMETA, UMR1135
2, place Pierre Viala, Bât. 26,
34060 Montpellier Cedex 1, France.
E-mail : [email protected]
Are People Living in Countries Emitting Less CO2 Happier?
Abstract
In this paper we investigate whether people living in countries emitting less per capita CO2
are happier. We begin by considering a panel data approach to address this question. The
results we obtain in this framework imply that there is no influence of CO2 emissions on
happiness even for the developed countries. However, the multicollinearity problem coupled
with this surprising result prompted us to consider a more appropriate technique. We use then
the partial least square regression (PLS). In this case when considering the whole sample, per
capita CO2 emissions have no effect on the average happiness in countries. However, when
considering separately the two sub-samples of developing and developed countries, the PLS
results show that per capita CO2 emissions affect the happiness in developed countries, but
have no perceptible influence on the happiness in developing countries. This difference of the
effect of CO2 emissions on happiness coupled with the importance of revenue can constitute a
contentious issue between developing and developed countries.
2
1. Introduction
Since decades, economists and psychologists studied the relationship between wealth and
happiness. They have generally concluded that ‘wealth increases human happiness when it
lifts people out of abject poverty and into the middle class but that it does little to increase
happiness thereafter’ (Gilbert, 2006; Layard, 2005). While the impact of several other factors
on happiness such as democracy, inflation or personal characteristics has been examined1,
very little is know about how polluting emissions of a given country affect the happiness of its
inhabitants.
The purpose of this paper is to fill this gap by exploring empirically whether countries with
lower per capita CO2 emissions experience are happier after controlling for other more
conventional factors. At the individual level, Rehdanz and Maddison (2008) found that in
Germany, even when controlling for a range of other factors higher local air pollution and
noise levels significantly diminish subjective well-being. Except Welsch (2002, 2006) who
found, among other results that nitrogen dioxide pollution has a statistical significant and
negative impact on overall happiness, empirical evidence is scarce. We investigate the
robustness of Welsch’s findings (2002, 2006) over a larger and more diversified sample of
countries including developing and developed countries (N=28). Indeed, the Welsch sample
included only developed countries (N=10). Nevertheless, the advantage of considering more
countries is somewhat offset by stronger limitations on the pollution type we can study.
Indeed, availability constraints on other pollutants data lead us to investigate CO2 emissions
for which reliable time series are available. In doing so, we follow several previous studies
(e.g., Hoffmann et al., 2005 and references therein) which argued that CO2 could be used as a
valid proxy for pollution. As the primary greenhouse gas responsible for global warming,
regulation of CO2 emissions has been an important inter-governmental issue. A ‘comparison
between the top 20 polluters of the world based on CO2 and SO2 emission in 2000 finds 15
countries being in both lists. Further, the correlation coefficient between three major
pollutants (CO2 with NO and SO2) among 111 countries in 1990 were 0.9529 and 0.9536
respectively. The high level of correlation between the three pollutants provides sufficient
evidence to show that the use of CO2 as a proxy to measure pollution levels is valid
(Hoffmann et al., 2005).
1
Interestingly, the effects of weather, climate, noise and other characteristics on happiness have also been
investigated and have been found to exert a significant influence on reported life satisfaction (Rehdanz and
Maddison, 2005; Brereton et al. 2008; Weinhold, 2009).
3
The remainder of this paper is organized as follows. Data are described in section 2. Section 3
presents our empirical analysis and section 4 discusses our results. Section 5 concludes.
2. Data
Our sample includes 28 countries (13 developing and 15 developed) over 9 years covering the
period from 2000 to 2008. Several environmental indicators exist for OECD countries
(nitrogen dioxide, lead emissions, sulfur dioxide, carbon dioxide, etc.) but this is not the case
for developing countries. Our willingness to investigate a larger set of countries and the
severe limitations on data lead us to consider a unique type of pollution, i.e., per capita CO2
emissions per year, expressed in metric tons. Per capita CO2 emissions mainly indicate the
emissions from fossil-fuel burning, cement manufacture and gas flaring. The choice of per
capita emissions is justified by the fact that it is deemed to be an adequate proxy for
environmental quality for nations with similar population densities and environmental
regulations.
The world database of happiness2 offers different measures of happiness drawn from surveys
on representative sample of population. Given that happiness is fundamentally an individual
issue, the average happiness is about the happiness in a country and not of a country. The
most complete happiness database available is the four step verbal life satisfaction where
individuals are asked to respond to the following question: How satisfied are you with the life
you lead?”. Responses are scaled from (1): not satisfied at all to (4): very satisfied.
In addition to our per capita CO2 emissions variable, we introduce some other explanatory
variables. While there are several candidates, i.e., factors that are likely to affect happiness,
our choice of additional explanatory variables is dictated by related literature, conditioned by
data availability for the considered sample and technical constraints namely in terms of
collinearity. Concretely, we included the three following variables in our empirical
investigation:
• Income (GDP per capita): while there are numerous discussions on how income
affects happiness, there is a strong consensus among social scientists that income is a
major determinant of happiness (e.g., Frey and Stutzer, 2002)
2
Veenhoven,
R., World
Database
of
Happiness,
Erasmus
University
Rotterdam.
Available at http://worlddatabaseofhappiness.eur.nl/hap_nat/nat_fp.php Assessed at March, 21, 2010.
4
• Inflation rate (INFL) which captures the purchasing power of individuals. As
intuitively expected, earlier studies found that inflation affects negatively subjective
well being (e.g., Di Tella et al., 2003; Wolfers, 2003, Graham and Pettinato, 2001;
Alesina et al., 2004)
• Share of exports in total GDP (EXP) which captures the trade openness which is
supposed to boost the happiness level (e.g., Ming-Chang, 2009).
The source, definition and descriptive statistics of variables are briefly presented in table 1.
Table 1. Definition of the variables and descriptive statistics (N=226)
Variable and acronyms
Happiness level (H)
Source
World database on
Descriptive statistics
Mean
SD
Min
Max
2.914
0.395
1.86
3.64
0.835
26.172
Happiness
Per capita CO2 emissions
World Bank .World
(metric tons per capita)
development
(Poll)
indicators
Income per capita (in natural FMI, World
logarithm) (Y)
8.043
4.876
9.285
1.094
6.913
10.936
46.581
29.955
9.54
181.313
4.549
7.731
-1.319
96.1
Economic Outlook
Share of export in total GDP World Bank Data
(EXP)
Indicators
Inflation rate (INFL)
FMI, World Economic
Outlook
As GDP per capita is expressed in thousands of dollars while the endogenous variable
(happiness) is a four step measure, then, for reasons of data adequacy, we have carried out the
regressions by considering the logarithmic transformation of this independent variable.
3. Empirical strategy
In order to investigate the relationship between CO2 emissions and happiness in nations, we
at first glance use of a panel data approach. We estimate then the following equation:
5
H it = α i + κ t + β 0 + β 1 Pollit + β 2 Y it + β 3 Z it + uit (1)
Where Hit is the average happiness in a country i (i = 1, 2,…, N) at period t (t = 1, 2,…,T). In
order to capture the effect of polluting emissions we consider per capita CO2
emissions, Pollit . The α i and κ t represent country and year specific effects, and the uit are
the idiosyncratic errors. The Yit is the natural log of per capita GDP at time t in country i.
Eq. (1) also includes Z, a vector of additional explanatory variables, namely the inflation rate,
and the share of export in total GDP.
4. Results and discussion
The basic model in Eq. (1) was estimated using the well known fixed effect and random effect
models. Welsch (2002) observes that it is difficult to isolate the effect of pollution because of
high correlation between income and pollution. Our data display this problem too. The
correlation matrix indicates a correlation coefficient between income per capita and CO2
emissions per capita around 0.68 . Generally, to deal with the collinearity problem, one can
either increase the sample size or eliminate the correlated variables. Nevertheless these
strategies are not appropriate here. Indeed, given the theoretical importance of income in
explaining the happiness in nations, the option of eliminating it is simply not envisageable.
The sample extension alternative is also impossible regarding the data availability (see section
2). Though, As long as it is not perfect, multicollinearity does not actually bias results, it
rather provides large standard errors in the related independent variables and instable
estimated coefficients. Nevertheless, conscious of the multicollinearity problem we begin by
estimating Eq (1) with classical panel data tools, hoping that this problem will be mitigated by
our data size.
The regression results are presented in Table 2.
Table2. Empirical results on the whole sample (N=226)
H1
H2
H3
Random
Fixed
Random
Fixed
Random
Fixed
effect
effect
effect
effect
effect
effect
Poll
-0.0019
0.0062
-0.009
0.0022
-0.0122
-0.00513
GDP
0.3358***
0.745***
0.3333***
0.702***
0.304***
0.574 ***
Exp
0.0023*
0.015
0.003***
0.0039 **
Infl
-
-
-0.0081***
-0.0079***
6
Intercept
-0.20072
-4.122***
-0.236
-3.76***
0.0413
-2.5916***
R2 overall
0.5415
0.5422
0.5448
0.5492
0.537
0.5520
R2 within
0.2162
0.2185
0.2055
0.2184
0.3231
0.3470
R2 between
0.5728
0.5730
0.5702
0.5761
0.5423
0.5652
F tests
28.09
-
27.43
23.91
(0.000)
(0.000)
(p-value)
(0.000)
Hausman
20.05
16.52
13.81
(P-value)
(0.000)
(0.0009)
(0.0079)
226
226
Number of
226
observations
*, ** and *** stand for significance at the 10, 5 and 1 percent level, respectively
Model H1 expresses the average happiness in nations as a function of CO2 emissions per
capita and GDP per capita. In this case, the GDP per capita is significant with the positive
expected sign that is when the GDP per capita increases, happiness increases too. However,
CO2 emissions per capita do not seem to affect happiness in nations. Models (H2) and (H3)
include additional explanatory variables. In all these models, the coefficient values have
remained relatively stable.
The pollution variable has the expected sign but non-significant in H2 and H3 too. By
contrast, income, inflation and exports coefficients are found to be significant with the
expected signs. Although all these variables are significant, their effect on happiness is largely
differentiated: the income per capita has by far the larger effect on the happiness in nations.
The fact that we do not find any significant influence of CO2 emissions per capita on
happiness is intriguing, especially regarding the results of Welsch (2006) who found that air
pollution significantly explains the inter-country and inter-temporal differences in subjective
well being. Indeed, this author found that individuals are willing to pay for the improvement
of air quality. He found that for the period 1990-1997, valuation of air quality improvements
amounts to almost $900 per capita per year for the nitrogen dioxide and more than $1400 per
capita per year in the case of lead emissions. Moreover, it is widely admitted that citizens of
developed countries are more and more concerned by environmental issues. These
considerations encourage us to refine our empirical analysis. Consequently, we analyze
whether per capita CO2 emissions affect happiness by considering separately developing and
developed countries.
7
The descriptive statistics and correlation matrix related to these sub-samples are provided in
the appendix (A.2-A.5).
Table3. Empirical results on developed countries (N=131)
H1
H2
H3
Random
Fixed
Random
Fixed
Random
Fixed
effect
effect
effect
effect
effect
Effect
Poll
0.0033
0.0041
0.0013
0.009
0.00035
0.0018
GDP
0.553***
0.614***
0.536***
0.603***
0.494***
0.5767***
Exp
-
-
0.0007
0.00044
0.00135
0.0011
Infl
-
-
-
-0.012*
-0.0134*
-2.54**
-3.16***
-2.387**
-3.058*
-1.945*
-2.78*
R2 overall
0.311
0.3114
0.3281
0.321
0.3185
0.3144
R2 within
0.199
0.199
0.1992
0.200
0.220
0.221
R2 between
0.27
0.2705
0.2903
0.2819
0.2798
0.274
-
128.89
-
122.41
-
111.34
Intercept
F tests
(p-value)
(0.000)
(0.000)
(0.000)
Hausman
-0.98
1.47
0.4
(P-value)
.
(0.689)
(0.98)
131
131
131
Number
of
observations
*, ** and *** stand for significance at the 10, 5 and 1 percent level, respectively
Surprisingly, even when considering the developed countries, the effect of a polluted air on
the reported happiness remains non significant. Obviously, this is inconsistent with Welsh’s
(2006) results. However, export coefficient turns out to be not statistically significant. This is
probably due to the fact that these developed economies are already open and that the
differences in the share of exports in overall GDP are negligible between countries and across
years. This is supported by the very small standard deviation indicated in the descriptive
statistics (see appendix A.3). As expected, inflation is found to have a negative and significant
effect on the average happiness in nations. This finding provides support for inflationcontrolling policies, all other things being equal.
In the case of developing countries The random effect model displays a significant negative
effect of Poll on the happiness, however the Hausman test strongly supports the fixed effect
model where, like the developed countries case, per capita CO2 emissions coefficient is not
8
significant. Meanwhile, the GDP per capita remains the most important factor that influences
positively the average happiness. (Table 4)
Table 4. Empirical results (developing countries)
H1
H2
H3
Random
Fixed
Random
Fixed
Random
Fixed
effect
effect
effect
effect
effect
Effect
Poll
-0.351
0.0422
0.035
0.042
-0.049*
0.001
GDP
0.442***
0.7289***
0.38***
0.637***
0.34***
0.4723***
-0.00025
-0.0038
-0.0028
0.0093**
-0.0077***
-0.0085***
Exp
Infl
Intercept
-0.823
-3.645***
0.322
-3.031*
0.054478
-1.646
R2 overall
0.344
0.2401
0.2638
0.0965
0.257
0.086
R2 within
0.201
0.226
0.1912
0.2301
0.338
0.39
R2 between
0.419
0.2663
0.3860
0.1388
0.2905
0.087
F tests
(p-value)
10.67
7.66
9.5
(0.0000)
(0.000)
(0.00)
Hausman
8.56
14.71
19.14
(P-value)
(0.013)
(0.002)
(0.000)
89
89
89
Number of
observations
*, ** and *** stand for significance at the 10, 5 and 1 percent level, respectively
As expected, inflation has a negative significant effect on happiness. The results regarding
openness are not robust over specifications and we cannot draw any clear cut conclusions.
One very plausible explanation of the discrepancy of our results compared to Welsh (2002)
results can be due to the pollutant choice. Welsh (who considered Nitrogen, Particles, and
lead) argues that the positive association he obtained between happiness and environmental
quality is due to the fact that the countries of the sample he considered are “on the downward
-sloping part of the Environmental Kuznets curve”.
Whereas numerous panel and cross-country studies provided evidence in favor of
Environmental Kuznets curve (EKC) in some countries and for several pollutants (Grossman
and Krueger, 1991; Shafik,1994), there were also some evidence against this hypothesis for
9
a number of measures of environmental degradation (Rothman and de Bruyn, 1998). 3 Indeed,
several studies have challenged the robustness of estimated environmental Kuznets curve
relationships namely for per capita CO2 emissions (Harbaugh et al., 2002, Millimet et al.
2003, Perman and Stern, 2003).
More precisely, if we examine how the per capita CO2 emissions is related to the income (see
table 5) we found that our per capita CO2 emissions is significantly positively related to the
level of prosperity4. And this is true either for the entire sample or for our two sub-samples. In
other words, the countries considered in this paper are, contrary to those considered by Welsh
(2002), “still” on the upward – sloping part of the environmental Kuznets curve. Note also
that this positive correlation is particularly strong and highly significant.
Table 5: Results of the regression of per capita emissions on GDP
Entire Sample
Developped countries
Developping countries
GDP
2.63***
5.33***
1.55***
intercept
-12.98***
-43.69***
-7.67***
R overall
0.51
0.48
0.34
*, ** and *** stand for significance at the 10, 5 and 1 percent level, respectively
This encourages us to sacrifice the richness of the panel data structure to use specific
techniques that can better deal with the multicollinearity problem. Hence we will use in the
following the Partial Least Square Regression.
3
More precisely the application of EKC analyses to greenhouse gas emissions, such as carbon dioxide (CO2),
has raised several important questions. First, some empirical studies have estimated an inverted-U shape of per
capita CO2 emissions with respect to per capita income, but with a peak in this function occurring well outside
the range of incomes in the studies’ samples.
4
We also regressed the percapita emissions on GDP and square GDP (results can be obtained under request).
The obtained results show the non existence of Kuznet like curve.
10
5- Partial least square results
5-1 Partial Least Square (PLS) regression
PLS regression proposed by Wold et al (1984), is a technique that combines features from
factorial analysis and multiple regression. It is particularly valuable when the degree of
freedom is very small (it can be also used even when the number of predictors is larger than
the observation number) or when the explanatory variables display a high degree of
collinearity.
The goal of PLS regression is to predict Y from a set of exogenous variables X and to
describe their common structure.
It determines components from X that are also relevant for Y. More precisely, this regression
seeks to find a set of components that performs a simultaneous decomposition of X and Y
with the constraint that these components explain as much as possible the covariance between
X and Y5. After having determined the components, the PLS regression technique implies the
decomposition of X to predict Y.
To appreciate the overall model the following statistics (among others) are generally used:
• R2X(cum): the cumulative fraction of the variantion of X explained by the
components.
• R2Y (cum): the cumulative fraction of the variation of the Y explained explained by
the component
• Q2 (cum): the cumulative predicted fraction of the variation of X.
We have run the PLS regression with the software SIMCA-P, which provided the PLS
components and coefficients as well as several plots in different plans (t1/t2; t1/t3, w1/w2
..ect). For the sake of presentation simplicity, we will examine in the following w*(c1)/w*(c2)
plan which is described by Tenenhaus (1998) as a privileged tool that allows the interpretation
of components t1/t2 related to their ability to predict Y.6 This plot is obtained by the
superposition of the loadings (w1/w2) and (c1/c2)7, it discribes the relationship between the
endogenous and exogenous variables .
5-2 Developped countries
Graph (1) shows that all our considered variables in this case are positively correlated with the
first loading w1(c1). However, happiness, GDP per capita and inflation are positively
correlated with the second component whereas per capita CO2 emissions and export are
5
Let’s define t : the matrix of scores of X. U the score matrix of Y. The component ci are the weights
representing the correlation between Y and t. the matrix W represents the correlation between X and U.
6
The other plots and lists are available under request.
7 All our components are significant according the cross validation rule.
11
negatively correlated with this second loading this negative correlation is much more
important for the variable POLL. This configuration allows us to suspect a possible negative
relationship between the happiness in nations and per capita emissions.
Developed countries
w*c[1]/w*c[2] plan
0.60
X
Y
GDP
INFL
Happiness
w*c[2]
0.40
0.20
0.00
-0.20
Exp
-0.40
POLL
0.20
0.30
0.40
0.50
0.60
0.70
w *c[1]
Graph 1
The PLS regression coefficients (graph 2) which measure the contribution of each exogenous
variable to the construction of the variable Y give support to this contention. Indeed, the PLS
equation (expression 2) for the considered developed countries is as follows:
)
H = 10.8+0.719GDP+0.32INF+0.04Exp-0.126POll (2)
0.60
0.40
0.20
Var ID (Primary)
R2X(cum)=0.877 ,R2Y (cum)=0.4 , Q2 (cum)=0.368.
Exp
POLL
INFL
0.00
GDP
CoeffCS[3](Happiness)
Developped countries , coefficients plot
12
Graph ( 2 )
The VIP values reflect the importance of terms in the model both with respect to y (its
correlation with the responses) and with respect to X (in the projection). Terms with large VIP
(larger than 1) are the most relevant for explaining Y.
The VIP values of all our considered variables are greater or close to one except for the
inflation variable. (see table 6)
Variable
VIP
GDP
1.31
Poll
1.025
Exp
0.92
INFL
0.52
Table ( 6 ) the VIP values
This result suggests that prosperity is the most influential variable on happiness. That is when
income increased happiness increased too. However per capitaCO2 emissions have a
negative, but small, impact on happiness.
5-3 PLS Results for Developping countries
13
Developping countries
w*c[1]/w*c[2] plan
X
Y
GDP
0.20
w*c[2]
0.00
happiness
INFL
-0.20
-0.40
POll
-0.60
-0.80
EXP
-0.50
-0.40
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
w *c[1]
Graph 3.
In the w*(c1)/w*(c2), (graph 3 )and contrarily to the other considered variables, GDP and H
have the same position regarding the two components i.e they are positively correlated to both
of the two components. EXP is negatively correlated to the two considered components
whereas the situation of par capita CO2 emissions is more ambiguous in that it is as happiness
positively correlated with the first loading but negatively correlated with the second.
Developping countries,
Coefficients plot
0.20
0.00
Var ID (Primary)
R2X(cum)=0.733 ,R2Y (cum)=0.404. , Q2 (cum)=0.365
Graph (4)
Variable
VIP
GDP
1.436
Poll
0.514
GDP
POll
INFL
-0.20
EXP
CoeffCS[2](happiness)
0.40
14
EXP
0.801
INFL
1.01
Table (7 ) the VIP values
The PLS regression coefficients (equation 3) are represented in graph (4).
)
H = 7.81+0.45GDP-0.26INF-0.33EXP+0.0039Poll ( 3 )
Obviously, GDP is the most influential variable to predict happiness (see graph 4). Moreover
the VIP values (see table 7) indicate that only GDP and INF are the most relevant to explain
happiness. Per capita CO2 emissions variable do not affect happiness.
5-4 The overall sample
Developping and Development countries
w*c[1]/w*c[1] plan
X
Y
GDP
0.50
w*c[2]
happiness
INFL
0.00
POLL
-0.50
Exp
-0.40
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
w *c[1]
Graph 5
The points pattern in the plan w*(c1), w*(c2) for the overall sample (graph 5) look very
similar to the developping countries case, execpt for the export variable. The PLS equation is
indicated in the expression ( 4 )
)
H = 7.37+0.66GDP-0.048INF+0.007Exp+0.092Poll ( 4 )
The PLS regression for the overall sample (see graph 6 ) confirms the primordial importance
of the economic prosperity in explaining the happiness in countries. The coefficients of the
other variables are by far of a minor importance. The VIP table (table 8) indicates that only
the variables GDP and POLL have VIP values greater than one. They are the most relevant in
explaining happiness.
Notice the positive effect of per capita CO2 pollution that is when per capita emissions
increase, the happiness increases too.
15
Developping and Developped countries
coeficients plot
CoeffCS[3](happiness)
0.60
0.50
0.40
0.30
0.20
0.10
GDP
POLL
Exp
INFL
0.00
Var ID (Primary)
R2X(cum)=0.895. ,R2Y (cum)=0. 0.552. , Q2 (cum)= 0.543
Graph 6
Variable
VIP
GDP
1.41
Poll
1.0049
Exp
0.64
INFL
0.749
Table (8 ) the VIP values
5-5 Discussion of the PLS results
Unequivocally, the major determinant of happiness in nations is economic prosperity that
prevails in a country. But our PLS results also show that per capita emissions also affect
happiness too. More precisely, it
• decreases the happiness in developed nations but
•
increases the happiness in the overall considered countries and
•
seems to be irrelevant for the developing countries.
However, the influence of the openness on happiness is questionable. Indeed, all VIPs values
for either the overall sample or developed or developing countries are less than one for the
variable EXP. However, for the developing countries inflation is found to be relevant with
the expected negative impact on happiness.
16
6. Conclusion
We analysed whether per capita CO2 emissions affect the average happiness in countries. Our
results suggest that per capita CO2 emissions effect on happiness depends on the development
stage of countries. Moreover, even in developed countries the effect of per capita income on
happiness is largely more important than any other influence.
Our results may help understanding the modest outcomes of the Copenhagen summit. Indeed,
beyond technical disputes, our study suggests that the discrepancy between developed and
developing countries may originate from the benefits in expected happiness that each party
anticipates. Indeed, because the happiness in countries is by far more influenced by income
than by any other dimension, developed countries are not willing to forgo economic growth
for lower polluting emissions. Developing countries where air quality has no detectible effect
on their happiness are reluctant to accept any environmental deal unless it can proved that this
arrangement will increase their income per capita. Our happiness approach suggests that the
failure of the Copenhagen summit may be explained by this bottleneck situation. It is possible
that an equilibrium point will be reached in the future, when the green values will be more
rooted in both advanced and less advanced economies. However, the problem is that
meanwhile irreversible changes threatening humankind could occur before reaching this
equilibrium point.
Lastly, a promising extension of our study would be to investigate the robustness of our
results by using other reliable proxies for pollution and environmental quality in a larger
number of countries, encompassing several environmental dimensions and over a sufficient
period. Fortunately several efforts are currently deployed to fill these gaps. Rather than
replacing other analyses, we believe that studies using happiness in countries can usefully
complement them and provide new insights to policymakers.
17
References
Alesina. A., Di Tella, R., MacCulloch, R. , 2004. Inequality and happiness: Are Europeans
and Americans different. Journal of Public Economics. 88, 2009–2042.
Brereton, F., Clinch JP., Ferreira, S. , 2008. Happiness, Geography, and the Environment.
Ecological Economics. 65, 386-396.
Di Tella, R., MacCulloch, RJ., Oswald, A., 2003. The macroeconomics of happiness. The
Review of Economics and Statistics. 85 ,4 , 809–827.
Frey BS and Stutzer, A.,2002. Happiness and economics, University Press, Princeton, USA.
Gilbert, D.,2006. Stumbling on Happiness, Knopf, New York, USA.
Graham, C., Pettinato, S., 2001.
Happiness, markets and democracy: Latin America in
comparative perspective. Journal of Happiness Studies. 2, 237–268.
Grossman, G. M. & Krueger, A. B. 1991. 'Environmental impacts of a North American Free
Trade Agreement'. NBER Working Paper Series, 3914.
Hoffmann, R.; Lee, CG., Ramasamy, B., Yeung, M., 2005. FDI and Pollution: a Granger
Causality Test Using Panel Data, Journal of International Development. 17, 311-317.
Harbaugh, W.T., A. Levinson, and D.M. Wilson. 2002. Reexamining the Empirical Evidence
for an Environmental Kuznets Curve. Review of Economics and Statistics 84(3): 541-551.
Layard, R. , 2005. Happiness: Lessons from a new science. Allen Lane, London, Great
Britain.
Millimet, D.L., J.A. List, and T. Stengos. 2003. The Environmental Kuznets Curve: Real
Progress or Misspecified Models? Review of Economics and Statistics 85(4): 1038-1047.
Ming-Chang ,T., 2009. Market Openness Transition Economies and Subjective Well-being.
Journal of Happiness Studies. 10, 523-539
Perman, R. and D.I. Stern. 2003. Evidence from Panel Unit Root and Cointegration Tests
that the Environmental Kuznets Curve Does Not Exist. Australian Journal of Agricultural and
Resource Economics 47(3): 325-347.
Rehdanz, K., Maddison., D., 2008. Local environmental quality and life-satisfaction in
Germany. Ecological Economics. 64 ,787-797.
Rothman, D. S. & de Bruyn, S. M., 1998. 'Probing into the environmental kuznets curve
hypothesis', Ecological Economics, 25, 143-145.
Shafik, N., 1994. 'Economic development and environmental quality: An econometric
analysis', Oxford economic papers, 46, 757-773.
Tenenhaus, M., 1998. La régression PLS. Paris: Technip.
18
Welsch H., 2002. Preferences over prosperity and pollution: Environmental valuation based
on happiness studies. Kyklos. 55, 473–494.
Welsch H., 2006. Environment and happiness: Valuation of air pollution using life
satisfaction data. Ecological Economics. 58, 801–813.
Weinhold, D., 2008. How big a problem is noise pollution? A brief happiness analysis
by a perturbable economist" LSE Working Paper.
Wold, S., Ruhe, A., Wold, H., & Dunn, W. J.,1984. The Collinearity Problem in Linear
Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses, 5(3), 735743. SIAM. Retrieved from http://link.aip.org/link/?SCE/5/735/1
Wolfers, J., 2003. Is business cycle volatility costly. Evidence from surveys of subjective
well-being. International Finance. 6,1–26.
19
Appendix
A.1 Correlation matrix (developing and developed countries)
Poll
GDP
Exp
Poll
1
GDP
0.68
1
Exp
0.47
0.19
1
Inf
-0.26
-0.42
-0.053
A. 2 Descriptive statistics (developing countries)
Variables
Mean
S.D.
Min
Max
H
2.63
0.336
1.86
3.32
Poll
10.513
1.184
8.417
13.321
GDP
8.256
0.698
6.913
9.5462
Exp
42.35
18.396
10.89
85.38
Infl
7.136
10.736
-1.319
96.1
A.3 Descriptive statistics (developed countries)
Variables
Mean
SD
Min
Max
3.125
0.28
2.46
3.46
Poll
10.62578
4.69
5.59
26.17
GDP
10.162
0.356
9.305
10.936
Exp
50.057
36.549
9.54
181.31
Infl
2.307
1.134
-0.887
5.254
Exp
Inf
H
A.4 Correlation matrix (developed countries)
Poll
GDP
Poll
1
GDP
0.69
1
Exp
0.51
0.41
1
Inf
0.12
-0.27
0.22
1
20
A.5 Correlation matrix (developing countries)
Poll
GDP
Exp
Poll
1
GDP
0.53
1
Exp
0.65
0.27
1
Inf
-0.17
-0.27
-0.10