freedom, growth, and the environment

Freedom, growth, and the environment

SCOTT BARRETTPaul H. Nitze School of Advanced International Studies, Johns HopkinsUniversity, 1619 Massachusetts Avenue, NW, Washington, DC20036–2213, USA. Tel: (202) 663–5761. Fax: (202) 663–5769. email: [email protected]

KATHRYN GRADDYExeter College, Oxford University

ABSTRACT. A number of recent papers have found that certain measures of pollutionworsen and later improve as income per head increases. It is widely believed that thedownhill portion of this inverted-U curve reflects an induced policy response; that, asincomes rise, citizens demand improvements in environmental quality, and that thesedemands are delivered by the political system. In this paper we find that, for a numberof pollution variables, an increase in civil and political freedoms significantly improvesenvironmental quality. For other pollution variables, however, we find that freedomshave no effect. The former finding suggests that political reforms may be as important aseconomic reforms in improving environmental quality worldwide. The latter findinghints that the observation that pollution levels fall with income, once income becomeshigh enough, may not always reflect an induced policy response.

IntroductionIs economic growth good or bad for the environment? Recent empiricalevidence points to a pattern reminiscent of the Kuznets curve: for anumber of indicators of environmental quality (but not all), economicgrowth seems to be accompanied by a deterioration in environmentalquality at low income levels and an improvement in environmental qualityat high income levels.1 This is a striking result; but, as we demonstrate inthis paper, it is also a partial result.

Environment and Development Economics 5 (2000): 433–456Copyright © 2000 Cambridge University Press

We are grateful to Gene Grossman and Alan Krueger for supplying us with theircomputer programs and data. We also thank Freedom House for making their dataavailable to us. Our research was partly funded by a MacArthur Foundation grantto the Global Environment and Trade Study. We have learned much about the topicof this paper from Partha Dasgupta, Paul Ehrlich, and Karl-Göran Mäler. JagdishBhagwati, Robert Deacon, Charles Perrings, and Todd Sandler provided helpfulcomments on an earlier draft, as did participants at the June 1998 World Congressof Environmental and Resource Economists, the NBER’s 1998 Summer Institute,and the GETS/MacArthur Foundation Workshop on Trade and Environment.

1 See the special issue of this journal, Volume 2, Part 4, October 1997. See alsoCropper and Griffiths (1994), Grossman and Krueger (1993, 1995), Hilton andLevinson (1998), Holtz-Eakin and Selden (1995), Selden and Song (1994),Schmalensee, Stoker, and Judson (1998), Shafik (1994), and World Bank (1992).

Though the estimated models are in a reduced form, and so cannot tellus why the inverted-U might exist, there is a kind of consensus on the prin-cipal mechanism at work. This is that there is an induced policy response:as nations become richer, their citizens demand that the non-materialaspects of their standard of living be improved. But if this reasoning iscorrect, then the observed levels of environmental quality will depend onmore than a nation’s prosperity. They will depend also on citizens beingable to acquire information about the quality of their environment, toassemble and organize, and to give voice to their preferences for environ-mental quality; and on governments having an incentive to satisfy thesepreferences by changing policy, perhaps the most powerful incentivebeing the desire to get elected or re-elected. In short, they will depend oncivil and political freedoms. These vary widely, however, both acrosscountries and over time. So the omission of these variables could beimportant. In this paper we re-estimate the relations estimated byGrossman and Krueger (1995; hereafter, G–K), including as explanatoryvariables certain measures of civil and political freedoms. In essence, weask a different question from the one that introduced this paper. We ask:Is more freedom good or bad for the environment?

For a number of measures of environmental quality, we find that ourfreedom variables are jointly highly significant.2 Inclusion of the freedomvariables does not affect the qualitative nature of the relationship betweenpollution and per capita income or the associated turning points; we findno evidence that the results reported by G–K are biased. But our results doshow that, for a number of different measures, environmental quality isincreasing in the extent of civil and political freedoms. In this sense, ourresults suggest that more freedom is good for the environment. Moreover,the effect is quantitatively and not just statistically significant. In the caseof sulfur dioxide the effect is especially strong: we find that a low freedomcountry, with an income level near the peak of the inverted-U, can reduceits pollution at least as much by increasing its freedoms as it can byincreasing its income per head. This is policy relevant if, as we argue, free-doms can be controlled independently of incomes.

Levels of some measures of environmental quality, however, seem not todepend on freedoms, even when the underlying relationship exhibits astrong inverted-U, and this hints that the downhill portion of the inverted-U

434 Scott Barrett and Kathryn Graddy

2 After writing this paper, we learned that Torras and Boyce (1998) also used theFreedom House data to estimate similar relationships. They use a different trans-formation of these data and a somewhat different model to determine the effect ofthe ‘distribution of power’ on pollution levels but obtain broadly similar results.Congleton (1992), Murdoch and Sandler (1997), Murdoch, Sandler, and Sargent(1997), and Fredriksson and Gaston (1998) also test for links between democracyand environmental policy. However, these papers are concerned with trans-boundary environmental damage—a very different situation than examined inthis paper. Bohn and Deacon (1997) examine the connections between investmentin natural resource industries and measures of both political stability and the typeof government (distinguishing, for example, between parliamentary and non-par-liamentary democracies). This again is a different kind of analysis using differentdata.

may not always result from an induced policy response. True, even dictator-ships have to please some constituency to remain in power (see Olson, 1993).So, even if our freedom variables were not significant, a kind of inducedpolicy response could still underly the inverted-U. But it does strike us aspeculiar that freedoms would show up as being very significant in somecases and not at all significant in others. We comment on possible reasons forthese results, and their implications for future research, later in the paper.

Before proceeding with the substance of the paper, we wish to empha-size that our results are not immune to the criticisms that have beenlevelled against the environmental Kuznets curve literature.3 Like pre-vious studies, we consider a selected number of pollutants only; ourresults imply nothing about optimality; and the models from which theyare derived ignore system-wide linkages. What we add in this paper is aperspective that has not been expressed previously. We believe that ourresults are important, but they are important only in this context.

Section 1 discusses the data used in our study, while sections 2 and 3present our econometric results for air and water pollution, respectively.Section 4 extends these results, by controlling for country fixed effects. Thefinal section of the paper discusses the implications of our work, both forthe literature on growth and environment and the literature on trade andenvironment.

1. DataWith the exception of our freedom variables, we rely entirely on the dataused by G–K. The pollution data were gathered by the Global Environ-mental Monitoring System and have two great virtues: they arecomparable across different countries; and they are direct measures ofenvironmental quality (as opposed to, say, pollution emission levels), andso are measures that citizens will have preferences over—preferences thatcan find expression at the ballot box or otherwise.4 Air quality is measuredin selected urban areas; water quality in selected river basins. The incomedata are from Summers and Heston (1991), and adjust for differences inpurchasing power. Note that these data are country averages, whereas thepollution readings are local.5 Time and physical features of the pollutionsites (like mean annual water temperature) are also included in our regres-sions as independent variables. For a more detailed description of thesedata, see Grossman and Krueger (1995).

Our measures of civil and political freedoms are constructed fromindices developed by Freedom House, which have been used widely in the

Environment and Development Economics 435

3 See in particular the policy forum in this journal, volume 1, part 1, February 1996.4 Shafik (1994) and the World Bank (1992) also examine a number of direct indi-

cators of environmental quality.5 Grossman and Krueger (1995, p. 361) note that the use of country-level GDP is

appropriate since ‘environmental standards are often set at a national level’. Thisstatement is correct, but there are also variations. For example, in the UnitedStates, some regions have not attained the standards that were set for them.Furthermore, environmental standards are not always uniform, even if they areset at the national level. In the United States, for example, regions with superiorair quality have been protected from any significant deterioration in air quality.

literature.6 The civil freedoms index reflects constraints on the freedom ofthe press, and on the rights of individuals to debate, to assemble, todemonstrate, and to form organizations, including political parties andpressure groups. The political freedoms index reflects whether a govern-ment came to power by election or by the gun, whether elections, if any,are free and fair, and whether an opposition exists and has the opportunityto take power at the consent of the electorate.

The Freedom House indices of freedoms are on a scale from 1 to 7, where7 is the lowest level of freedom. Following Barro (1996), we convert theseindices to a scale of from 0 to 1, where 0 now corresponds to the lowestlevel of freedom. Since the ranking of freedoms by Freedom House isordinal, it might seem natural to represent each of the seven indexnumbers by dummies. However, there are two reasons for not doing so:there are too few observations in some freedom categories, and the inclu-sion of many dummies would result in a loss in degrees of freedom. Thissecond reason is especially important if one believes, as we do, that currentpollution levels should depend not just on current freedoms but on therecent history of freedoms. We therefore employ two different representa-tions of the freedom data. The first enters the converted indices directly,and therefore on a linear scale. The second uses grouped dummies. In cre-ating these dummies, we again follow Barro by defining a low freedomcountry as having an index number between 0 and 0.33, and medium andhigh freedom countries as having index numbers of from 0.33 to 0.67 andfrom 0.67 to 1, respectively. The low political (civil) freedom dummy takeson a value of 1 if the country is a low political (civil) freedom country andzero otherwise. The other dummies are calculated in a similar fashion. Allfreedom variables enter our regressions as a moving average of the currentyear and the previous three years to reflect the expected lag betweenchanges in freedoms and changes in environmental quality.

For the primary regressions, we use the same basic regression model asG–K.7 Of course, one might explore alternative specifications, such asincluding income–freedom interactive terms. However, G–K include manyincome variables and we use different freedom variables. It is not obviouswhich variables should be interacted or whether different variables shouldbe constructed for this purpose. Nor is it obvious that richer specificationswould be appropriate, given the data limitations. Perhaps more import-antly, our ambition for this paper is modest. We only want to determinewhether politics can be shown to intermediate between income and pol-lution, as is so often supposed without any supporting evidence. If the linkreally does exist, it should show up in our model; and if our model canshow this while making only the smallest deviation from G–K’s, which isnow a standard in this literature, so much the better. There is, however,


6 See Barro (1996), Dasgupta (1993), Helliwell (1994), Murdoch, Sandler, andSargent (1997), and Perotti (1996).

7 As in G–K, we estimate the model using generalized least squares and employ arandom effects estimator that takes into account the unbalanced nature of thepanel (see G–K for details). Note that the peak of the inverted-U can be sensitiveto the functional form used in estimation (see Hilton and Levinson, 1998).

one specification change that does seem warranted, if only as a test forrobustness, and this is to control for fixed effects. These results arereported separately, toward the end of the paper.

2. Results for air pollutantsTable 1 presents the summary statistics for three different air pollutants. Anumber of observations follow from these. First, incomes and freedoms arepositively correlated. In particular, the high freedom countries are rich; thelow and medium freedom countries are poorer. Second, pollution levelsare negatively correlated with freedom with one small exception: sulfurdioxide concentrations are about the same for countries with low andmedium political freedoms. Finally, the correlations between freedomsand income and freedoms and pollution are very similar for both politicaland civil freedoms. This is because civil and political freedoms are veryhighly correlated. The correlation coefficients between the linear trans-formations of the civil and political freedom indices are about 0.95 for thedata corresponding to each of the three pollutants.

Table 2 presents the regressions for the three measures of air pollution


Table 1. Summary statistics by freedom indices for urban air pollutants (means and standard deviations)

Political freedoms Civil freedoms

Low Medium High Low Medium High

Sulfur dioxideMedian daily 48.81 51.53 25.95 53.66 34.08 26.07observed level (41.34) (42.93) (25.72) (41.91) (36.18) (25.63)

Income 2.60 3.82 9.69 2.65 2.85 10.23(1.24) (1.17) (4.36) (1.27) (1.61) (3.95)

No. of obs. 348. 81. 889. 305. 182. 831.

No. of countries 15. 7. 25. 11. 14. 25.

SmokeMedian daily 73.86 57.67 30.32 88.94 49.40 30.01observed level (53.39) (26.79) (36.00) (55.68) (33.11) (34.99)

Income 3.43 3.86 8.12 3.70 3.43 8.32(0.77) (1.29) (2.57) (0.50) (1.18) (2.41)

No. of obs. 94. 49. 323. 65. 90. 311.

No. of countries 5. 4. 13. 3. 6. 13.

Heavy particlesMedian daily 252.62 146.23 94.81 267.39 169.11 83.36observed level (134.32) (85.34) (90.45) (133.79) (108.20) (73.50)

Income 2.28. 3.62 11.51 2.41. 2.42 12.26(1.32) (1.20) (5.16) (1.37) (1.55) (4.49)

No. of obs. 310. 78. 621. 281. 149. 579.

No. of countries 10 6 16 10 10 16


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(here and elsewhere standard errors are shown in parentheses).Regressions 1, 4, and 7 replicate the G–K regressions with the exceptionthat freedoms data for Hong Kong are unavailable and so this territory isexcluded. The omission of Hong Kong does not affect the qualitativenature of the relationship between pollution and income but it does affectthe turning point in the case of smoke. This rises from $6,151 in the G–Kstudy to $7,286 (the turning point for sulfur dioxide rises from $4,053 in theG–K study to just $4,202; both regressions reveal that heavy particlesdecrease monotonically with income).

Regressions 2, 5, and 8 add the freedom dummies. In all three regres-sions, the freedom dummies are jointly significant at the 1 per cent level,as is income. There are, however, some surprising results. These includethe low coefficient on the low political dummy in the sulfur dioxideregression and the high and low coefficients on medium political and civilfreedoms in the heavy particles regression. However, as noted earlier, pol-itical and civil freedoms are highly correlated. Countries with high civilfreedoms also have high political freedoms, and countries with low civilfreedoms have low political freedoms. Thus, while the different definitionsof freedom may have different effects, the data indicate that they are not(and, possibly, that they cannot be) controlled separately. We thereforecombine these freedom variables for the purposes of making predictions.The combined effect of political and civil freedoms—something we calldemocratic freedoms—is striking: pollution levels are monotonicallydecreasing in the extent of democratic freedoms.

The combined effect of freedoms is shown more clearly in figures 1a, 2a,and 3a. Following G–K, these graphs were constructed by assuming thatcurrent and lagged income take on the same values.8 All other variables,excluding freedoms, take on their mean values. The low democratic free-doms curve was obtained by setting the low political and low civilfreedom dummies equal to 1 and by setting all the other freedom dummiesequal to 0. The medium and high democratic freedoms curves were calcu-lated in a similar manner. The number of the shapes in these graphsindicate the number of observations for each income level in the panel dataset.9 The vertical ranges of these graphs represent four standard deviationsin the dependent variables.

Figure 1a shows that the relationship between sulfur dioxide concen-trations and freedoms is nearly linear. This is not true for the otherpollutants. For smoke, as figure 2a shows, a move from low to mediumdemocratic freedoms has the biggest effect. For heavy particles, however,figure 3a shows that the move from medium to high democratic freedomshas the more substantial effect.

For comparison, regressions 3, 6, and 9 measure civil and political free-doms on the linear scale. Of course, the results reported above suggest


8 G–K construct the variable by multiplying GDP, GDP squared, and GDP cubed bythe sum of the estimated coefficients for current and lagged GDP.

9 Note that the rising portion of the relation for sulfur dioxide at the higher incomelevels only includes data for Canada, Switzerland, and the United States. It is forthis reason that G–K discount this portion of the relation.

that, at least for smoke and heavy particles, the linear specification is a veryrough approximation. However, it must be remembered that the dummyspecification aggregates over different raw index numbers. So both repre-sentations of freedoms—the linear form and the dummies—imposerestrictions, and neither is obviously the more superior. We note, however,that the coefficients on both the civil and political freedom variables arenegative in all three regressions.

Figures 1b, 2b, and 3b depict the relationship between income and pol-lution with the freedom variables excluded (as in G–K) and included,where in the latter case the freedom dummies are set equal to their mean


Figure 1. GEMS/AIR: Sulfur dioxide in cities

values. In all three cases, the former regression predicts a higher concen-tration of pollution than the latter at low incomes (if only very slightly) anda lower concentration of pollution than the latter at high incomes. This isto be expected. Recall that freedoms and incomes are positively correlated(the correlation coefficients between the linear freedom variables andincome range between 0.72 and 0.84 for the three data sets). Low incomecountries tend to have fewer freedoms than the average, and our regres-sions indicate that they should therefore have higher pollution levels. Highincome countries tend to have more freedoms than the average and soshould have lower pollution levels.


Figure 2. GEMS/AIR: Smoke in cities

Note,finally, that thecircles infigures1b,2band3bshowthemeanresidualfor the fitted regressions including the freedom variables for each $2,000income interval, with the size scaled to reflect the number of observations ineach interval. A comparison with G–K reveals a very close correspondence.There is thus no indication that the G–K estimates are biased.

3. Results for water pollutantsTable 3 presents the summary statistics for 11 different measures of waterpollution (but note that dissolved oxygen is a measure of water quality, notpollution). These confirm a general positive association between incomeand freedom. However, in contrast with the data for air pollutants, the


Figure 3. GEMS/AIR: Heavy particles in cities


Table 3. Summary statistics by freedom indices for water pollutants (means andstandard deviations)



Dissolved oxygenMean daily 7.72 6.90 8.60 7.75 6.92 8.88observed level (2.82) (5.13) (2.46) (2.93) (4.22) (2.43)

Income 2.51 4.07 8.60 2.51 2.97 9.92(1.49) (1.57) (5.20) (1.42) (2.03) (4.42)

No. of obs. 296. 296. 1007. 273. 466. 860.

No. of countries 22. 20. 31. 21. 21. 26.

Biological oxygen demandMean daily 3.32 7.99 7.34 3.58 7.19 7.55observed level (3.89) (19.02) (27.50) (4.43) (19.01) (29.11)

Income 2.51 4.06 6.62 2.51 2.97 8.11(1.49) (1.56) (4.69) (1.41) (2.02) (4.10)

No. of obs. 269. 293. 722. 251. 459. 574.

No. of countries 21. 18. 27. 20. 18. 23.

Chemical oxygen demandMean daily 18.36 59.32 51.14 18.92 48.56 57.86observed level (16.17) (102.72) (136.60) (16.08) (84.48) (158.14)

Income 1.97 4.54 6.00 1.95 2.99 8.00(1.21) (1.34) (4.91) (1.10) (2.14) (4.36)

No. of obs. 120. 200. 530. 123. 346. 381.

No. of countries 10. 9. 19. 9. 12. 18.

NitratesMean daily 0.88 1.21 1.79 1.04 0.84 2.07observed level (1.25) (1.47) (4.63) (1.35) (1.31) (5.12)

Income 2.67 3.70 7.97 2.65 2.31 9.79(1.61) (1.66) (5.54) (1.55) (1.89) (4.72)

No. of obs. 210. 131. 676. 187. 292. 538.

No. of countries 18. 13. 21. 15. 18. 18.

Fecal coliformsMean daily 218370. 67967. 80584. 237319. 107919. 59701.observed level (788312) (210883) (609272) (819458) (450290) (585941)

Income 2.65 4.09 8.78 2.51 3.02 10.20(1.49) (1.60) (5.36) (1.41) (2.04) (4.53)

No. of obs. 224. 227. 810. 206. 368. 687.

No. of countries 17. 14. 27. 15. 18. 23.

Total coliformsMean daily 25050.70 106294.40 203578.10 60051.24 187972.80 182737.00observed level (99607.51) (263936.40) (1059427.00) (206218.10) (901880.50) (1031001.00)

Income 2.70 4.24 4.54 2.66 2.05 6.73(1.53) (1.32) (4.68) (1.34) (1.91) (4.65)

No. of obs. 25. 82. 387. 27. 223. 244.

No. of countries 5 9 14. 7. 11. 11.


relationship between water pollution levels and freedom is not alwaysnegative. Levels of biological and chemical oxygen demand, nitrates, totalcoliforms, and cadmium generally increase with the extent of freedom.10

Table 3. Continued



LeadMean daily 0.071 0.031 0.013 0.073 0.026 0.013observed level (0.099) (0.067) (0.028) (0.098) (0.064) (0.029)

Income 2.46 4.21 11.44 2.26 4.30 11.46(1.51) (0.80) (2.92) (1.17) (0.87) (2.88)

No. of obs. 43.68 499. 44. 68. 498.

No. of countries 4. 8. 14. 5. 7. 12.

CadmiumMean daily 0.021 0.003 0.050 0.023 0.003 0.050observed level (0.063) (0.007) (0.179) (0.066) (0.007) (0.179)

Income 2.47 4.10 11.33 2.18 4.12 11.31(1.51) (1.07) (2.83) (1.20) (1.17) (2.84)

No. of obs. 43. 62. 544. 39. 64. 546.

No. of countries 5. 8. 19. 4. 9. 17.

ArsenicMean daily 0.007 0.010 0.005 0.005 0.010 0.005observed level (0.008) (0.011) (0.006) (0.006) (0.011) (0.006)

Income 2.14 4.44 11.63 1.96 4.45 11.61(0.76) (0.70) (2.94) (0.38) (0.69) (2.96)

No. of obs. 17. 49. 302. 16. 49. 303.

No. of countries 3. 4. 12. 3. 5. 11.

MercuryMean daily 0.345 0.176 0.297 0.335 0.153 0.301observed level (0.238) (0.344) (0.832) (0.261) (0.306) (0.832)

Income 2.72 4.29 11.29 3.25 4.31 11.28(1.77) (0.82) (2.81) (1.55) (0.95) (2.80)

No. of obs. 17. 62. 558. 14. 64. 559.

No. of countries 5. 8. 21. 5. 8. 19.

NickelMean daily 0.010 0.009 0.010 0.009observed level (0.010) (0.011) (0.011) (0.011)

Income 4.22 12.05 4.26 12.01(0.67) (2.58) (0.72) (2.64)

No. of obs. 14. 336. 12. 338.

No. of countries 3. 8. 3. 8.

10 For nickel, there is only one observation for low political freedoms (the Philip-pines in 1982) and no observations for low civil freedoms. Hence, we cannotinclude these dummy variables in our regression. Instead, we include the single observation for low political freedoms in the medium political freedomsdummy.

446 Scott Barrett and Kathryn GraddyT

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Freedom data were available for all the countries and years for whichG–K had water pollution data, and so we were able to replicate exactly theG–K results for these measures of water pollution. For this reason, we onlyreport here the regression results with the freedoms variables included.These are shown in tables 4 and 5.

The regressions show that freedoms do not significantly affect theoxygen regime of rivers, as measured by dissolved oxygen (DO), biologicaloxygen demand (BOD), or chemical oxygen demand (COD). The concen-tration of nitrates (NIT) is significantly affected by freedoms, but onlywhen freedoms are entered in the linear form. Even then, the coefficientson political and civil freedoms cancel each other out. An increase in demo-cratic freedoms thus has no discernible quantitative effect on nitrate levels.The income variables are jointly significant in the dissolved oxygen andnitrates regressions, however, and the estimated relationships closelyresemble those estimated by G–K: dissolved oxygen follows the U-shapedrelationship and nitrates the inverted-U. That freedoms do not affect thesepollution levels suggests that the negative relationship between pollutionand income at higher income levels may not reflect an induced policyresponse. This may seem surprising, but oxygen loss does not threatenhuman health directly and nor are nitrates especially dangerous.11

Fecal coliform is different. It is a direct health hazard. Fecal contami-nated water carries infectious diseases like cholera and typhoid and isimplicated in many cases of diarrheal disease, roundworm infection, andschistosomiasis. We should expect levels of this pollution to depend onfreedoms, and this is confirmed by our regressions. An increase infreedom, however measured (civil or political, entered either in linear formor as dummies), reduces fecal contamination (denoted FEC in tables 4 and5). Figure 4a illustrates our results, and one can confirm that, as in the caseof the various air pollution measures, the underlying relationship is nearlyidentical to the G–K result. For the same income level, countries withhigher freedoms have lower pollution readings, but increases in income doseem to be needed to bring these readings down to very low levels.

Our results for total coliform are also highly significant, but the signs onthe freedom variables are the wrong way round (see also figure 5a). G–Kalso obtain unexpected results for total coliform—results which theydescribe as ‘baffling’.12 But unlike fecal coliform, total coliforms are notnecessarily disease producing. So at one level our results should not causemuch concern. However, it is also possible that our results reflect a samplebias: the regressions for total coliform rely on a much smaller number ofobservations. To see whether sample bias may be a problem, we re-esti-

11 Nitrates may cause ‘blue baby syndrome’ in newborns, but the condition is veryrare.

12 Specifically, G–K find that total coliforms increase sharply with incomes at highincome levels. Note, however, that Shafik (1994) obtains a very similar relation-ship for measures of fecal coliform. Shafik also finds that dissolved oxygendecreases monitonically with income. These differences can probably be traced tothe use of different data, but they are nonetheless worrying. Neither Shafik norG–K comment on these different results.


Tab

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mated the relationships for both measures of coliform for a consistent setof data. The results are given in table 6 and illustrated in the figures 4b and5b. The fecal and total coliform regressions are now fairly consistent witheach other. Moreover, both the combined freedom variables and the com-bined income variables are no longer significant in the fecal coliformregressions. We conclude from this exercise that, as a result of the muchlarger data set, the fecal coliform relationships estimated in tables 4 and 5are the more reliable.

We turn finally to the results for heavy metals. Freedoms are jointly sig-


Figure 4. GEMS/WATER: Fecal coliforms in rivers


Table 6. Selected sample regressions for fecal and total coliforms (random effectsestimation). Dependent variable is 1 � log annual mean concentration

Variable Fecal coliforms Total coliforms

Income (1000s) 0.316 �0.972 �1.027 �2.064(2.519) (2.543) (2.250) (2.268)

Income squared �0.054 0.100 0.105 0.223(0.384) (0.390) (0.343) (0.347)

Income cubed 0.004 �0.002 �0.002 �0.006(0.016) (0.017) (0.015) (0.015)

Lagged income �0.316 2.009 1.495 3.690(2.872) (2.844) (2.565) (2.535)

Lagged income 0.035 �0.325 �0.256 �0.613squared (0.477) (0.470) (0.425) (0.419)

Lagged income �0.001 0.016 0.013 0.029cubed (0.022) (0.022) (0.020) (0.019)

Low political 0.010 �0.416freedom dummy (1.209) (1.078)

Low civil �0.333 �0.795freedom dummy (1.053) (0.940)

Medium political 1.267 0.829freedom dummy (0.579) (0.517)

Medium civil �0.610 �0.707freedom dummy (0.339) (0.303)

Political freedoms �0.367 �0.058(linear scale) (2.322) (2.067)

Civil freedoms 0.737 2.453(linear scale) (2.359) (2.100)

No. of observations 431. 431. 431. 431.

P-value (combined 0.181 0.945 0.106 0.048freedom variables)P-value (combined 0.730 0.450 0.135 0.203income variables)P-value (combined 0.181 0.479 0.003 0.005freedom and incomevariables)

Note: All equations include mean temperature, year and an intercept ascovariates.

nificant only in the case of arsenic (denoted ARS in tables 4 and 5). Whenfreedoms are entered in linear form, an increase in democratic freedomsreduces arsenic concentrations. When entered as dummies, arsenic levelsincrease slightly and then decrease with democratic freedoms. Theseresults are broadly consistent with the hypothesis of an induced policyresponse, but we note that these regressions rely on a small sample andmay not be reliable. Interestingly, we find that the relationship between

lead levels (LEAD) and income is not significant when freedoms areincluded as independent variables (the relationship between lead levelsand income is significant in G–K’s regression). However, the combinedeffect of income and freedom is very significant. This suggests that the gen-erally negative relationship between incomes and lead levels detected byG–K may indeed result from an induced policy response. As for the otherheavy metals, G–K found a statistically significant relationship betweenconcentrations of cadmium (CAD) and income, but the relationships fornickel (NICKEL) and mercury (MERC) were not significant.


Figure 5. GEMS/WATER: Total coliforms in rivers

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4. Fixed effectsBecause political and civil freedoms vary little within countries over time,we have tested our results for robustness by including separate countrydummy variables. Essentially, our fixed effects regressions test whetherour results might reflect omitted country characteristics like geographythat just happen to be correlated with freedoms.

As shown in table 7, we find that our main results hold up surprisinglywell. The relationship between freedom and pollution is still highly sig-nificant for SO2, heavy particles, and fecal coliforms. While the direction ofthe effect for heavy particles appears somewhat ambiguous in theseregressions, if we include only a combined low civil and political freedomsdummy, the essence of our earlier results is still supported (the coefficienton the low freedoms dummy is 33.272 with a standard error of 12.680).Similarly, while the combined political and civil freedom variables are nolonger significant at the 5 per cent level in the smoke regression, the com-bined civil freedom variables on their own are significant.

We wish to stress that the fixed effects model provides an overly strongtest of the effect of freedoms on pollution levels. A country is what it ispartly because of the political and civil institutions it embraces. Countrieswith more repressive regimes may also subsidize energy consumption orconcentrate industrial production, so that in our earlier regressions theeffects of the subsidies and planning decisions may be reflected in thefreedom variables. But of course it is partly because freedoms are low inthese countries that such subsidies and central planning decisions can besustained. For this reason, it is best to consider the random and fixedeffects models as alternatives, each with its own advantages and disad-vantages.

5. ImplicationsEffective environmental protection requires both economic reforms, asArrow et al. (1995) and the World Bank (1992) have emphasized, and—aswe have shown—political reforms. Which type of reform ought to comefirst? We cannot say, but our results do clash with a related literaturewhich is less equivocal on this question. Barro (1996) argues that politicalfreedoms in poor countries should not be promoted directly but that econ-omic freedoms should be. One reason for this is that Barro finds that anincrease in political freedoms has a small adverse effect on growth, forcountries that have already attained a medium level of freedom. We find,however, that some measures of environmental quality are monotonicallyincreasing in the extent of freedoms. If GDP per head were synonomouswith welfare, then one might still agree with Barro that political freedomsought not be promoted (ignoring, of course, the direct contribution thatfreedom makes to well-being). But we know that GDP per head is an inad-equate measure of welfare, not least because it excludes the benefits ofenvironment improvements (Dasgupta and Mäler, 1995). To the extent thatfreedoms are instrumental in correcting for market failures, they mayincrease welfare, even if at the expense of growth.

Barro’s recommendation that economic reforms should precede politicalreforms is also supported by his finding that political freedoms tend to


erode over time if they get out of line with a country’s standard of living.The implication is that freedoms cannot be controlled independently ofincomes. But Barro’s estimates only reveal that democratic freedoms tendto increase with incomes; they do not indicate that a country cannotincrease freedom by more than the average for its income level, and onecan point to a number of countries that have increased freedoms rapidly(Barro might say prematurely) without ever sliding back: in our samplealone, these include Argentina, Spain, Portugal, Greece, and Brazil, not tomention a number of the former Communist countries of Eastern Europe.As Dasgupta (1993: 116) has put it, ‘the claim that the circumstances thatmake for poverty are also those that make it necessary for governments todeny their citizens political and civil liberties is simply false’. This meansthat our results do have real implications for policy. If the environmentalKuznets curve suggests that growth need not harm the environment, ourresults show that an expansion of freedoms may lead to an improvementin environmental quality.

Data for Brazil provide an anecdotal illustration of this contention.Between 1977 and 1987, per capita income increased, decreased, and thenincreased again. It was less than 10 per cent greater at the end of the periodthan at the beginning and averaged around $4,000 over this period, a levelnear the peak of the inverted-U for the sulfur dioxide curve (figure 1a).Such small changes in income, especially near the peak of the inverted-U,should have translated into small changes in pollution levels. However,sulfur dioxide concentrations in Brazil fell by half over this period. At thesame time, freedoms increased. The raw score of political and civil free-doms fell from 4 and 5, respectively in 1977 to 2 and 2 in 1987, enough tolift Brazil from being a medium to a high freedom country. Of course, thisis only an illustration, but it supports our interpretation of the statisticalevidence that freedoms can change independently of incomes, and have ademonstrable effect on environmental quality.

This conclusion has important implications for the trade and environ-ment debate. Environmentalists and protectionists claim that weakerenvironmental standards in developing countries should be brought intoalignment with the higher standards found in industrialized countries. AsBhagwati has repeatedly emphasized, however, diversity is usually to becheered.13 Certainly, in a model in which environmental standards arechosen by governments to maximize the welfare of their citizenry, diver-sity is beneficial. The hole in this argument is that only textbookgovernments choose standards in this way. Actual standards are chosen bya political process. Of course, we know that no form of government can besure of sustaining first-best outcomes every time. However, there is reasonto believe that countries with greater civil and political freedoms will do abetter job at supplying public goods (see Olson, 1993). The point is that, tothe extent that the interests of the citizens of some countries are poorly rep-resented, the appropriate remedy is not to impose an outcome (aharmonized standard) but rather to promote a move toward greater free-doms. As Bhagwati and Srinivasan (1996: 170) put it:


13 See, for examples, Bhagwati (1993, 1996).

even if one argues that the decisions made undemocratically by a dic-tatorship or an oligarchy are vitiated, there is no reason to believe thatthe higher standards being pursued by a foreign country representingthe competitive interests of a foreign industry or labor union in anindustry are what a more democratic process would yield. The correctapproach should rather to be encourage a shift to more democraticprocedures in arriving at social and economic legislation, includingenvironmental policy. Process, not outcomes (especially outcomessought by self-serving groups elsewhere), is what we should aim at incountries that lack democratic ways.

This paper shows that the promotion of freedoms will in many casesactually lead to improvements in environmental quality. That is, pro-motion of a more democratic process is not the only ‘right’ thing to do butit will also lead to the changes in outcomes desired by the environmentaland protectionist lobbies.

The mystery in our results is why freedoms should affect some measuresof environmental quality and not others. With the exception of heavymetals and total coliforms, which may suffer from small sample problems,freedoms significantly affect environmental quality measures that relatedirectly to human health: all three of our air pollution measures in additionto fecal contamination. But they do not affect DO, BOD, COD, or nitrates.Our results for the oxygen-related measures of water quality are especiallysurprising, because sewage treatment intended to reduce fecal coliformwould tend to increase DO and reduce BOD and COD in the bargain. Theresults seem to hint that something other than an induced policy responsemay lie behind the inverted-U relationships for these measures of waterquality. The distinction is important, for if the relationship is determinedby, say, fixed factors, it may not be stable; growth in the middle incomecountries may not be accompanied by reductions in pollution, assuggested by the inverted-U.

The latter explanation is at least plausible in the case of nitrates. Toreduce nitrates in rivers would require reductions in the use of fertilisers,or more specifically in fertiliser run-off, and changes in cropping tech-niques. The richer countries have done little to regulate these activities(occasionally, nitrates are removed from drinking water). Moreover, therelease of both unused fertiliser and organic nitrogen into rivers varieswith the type of crop that is planted as well as climatic conditions. Sosomething other than an induced policy response may explain theinverted-U, at least in this case. Research that links the inverted-U to actualpolicies would seem to be badly needed.

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freedom, growth, and the environment

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