social structure and energy efficiency: a preliminary cross-national analysis

Human Ecology, VoL 6, No. 2, 1978

Social Structure and Energy Efficiency" A Preliminary Cross-National Analysis'

Frederick H. But te l 2

This study questions the assumption that there is an immutably positive relationship between per capita GNP and per capita energy consumption among human societies. A ratio o f per capita GNP to per capita energy consumption ($U.S./kg coal equivalent) is proposed as a measure o f energy efficiency for a cross-national analysis o f 118 worm nation-states and a subset of 25 developed market economies. This ratio is found to vary considerably, between 0.19 and 9.80. A review o f literature suggests possible relationships among several sociodemographic characteristics o f nations and levels o f efficiency with which energy is converted into goods and services. Among the total sample, level o f production (measured in terms o f per capita GNP) bears a substantial inverse association with energy efficiency. When per capita GNP is hem constant, agricultural share of gross domestic product and percentage o f labor force in agriculture continue to be positively associated with energy efficiency among the total sample. Variables measuring defense expenditure, urbanization, and population density exhibit somewhat smaller multivariate relationships with energy efficiency when per capita GNP is controlled, i.e., these variables have significant multivariate parameters, but are less closely related to energy efficiency than level of production and agricultural composition o f the economy and labor force. Agricultural composition o f the economy and labor force is the major predictor of energy efficiency among the subset o f 25 developed market economies. The results suggest that among the developed industrial societies level o f production is less important than the composition o f production activities in determining aggregate energy efficiency.

KEY WORDS: multivariate analysis; energy efficiency; urbanization; GNP; GDP.

t This paper was originally presented at the annual meeting of the North Central Sociological Association, Pittsburgh, Pennsylvania, May 1977. This research was supported by funds from the Michigan Agricultural Experiment Station, the Ohio Agricultural Research and Development Center, and the National Research Council, National Academy of Sciences.

2Department of Rural Sociology, Cornell University, Ithaca, New York 14853.

145

0300-7839 /78 /0600-0145505 .00 /0 �9 1978 Plenum Publishing Corporation

146 Buttel

INTRODUCTION

Sociologists, political scientists, economists, and other social scientists have for some years conducted cross-national analyses of phenomena such as political violence, welfare and related public expenditures, economic inequality, and so forth. It is interesting to note that these studies have typically used per capita gross national product (GNP) and per capita energy consumption inter- changeably as indicators of "level of societal development," "level of industrialization," "division of labor," "standard of living," and other concepts (see, for example, Wilensky, 1975; Jackman, 1974, 1975 ; Hibbs, 1973 ; Rubinson, 1976). Indeed, it has been reported that per capita GNP and per capita energy consumption are quite closely related (Sawyer, 1967; but see Mazur and Rosa, 1974; Schipper, 1976), since substantial amounts of inanimate energy subsidies are required to achieve industrialization and mass consumption (Cottrell, 1955).

However, Makhijani and Lichtenberg (1972) have suggested that the ac- cepted high correlation between GNP per capita and energy consumption per capita among world nations be reconsidered. Citing a U. S. government study, Makhijani and Lichtenberg report that eight industrial countries with nearly identical standards of living (as measured by per capita GNPs within 10% of each other; the United Kingdom, Australia, West Germany, Denmark, Norway, France, Belgium, and New Zealand) exhibited major variations in the consumption of energy. Anderson (1976: 54) has also noted disparities in the efficiency with which nations convert energy into useful production and consumer goods. Anderson reports that the U.S. consumes twice as much inanimate energy on a per capita basis as does Sweden, while Sweden's per capita GNP is only slightly lower than that of American society (see Hardesty et al., 1971; Schipper and Lichtenberg, 1976;Biswas, 1974 for related evidence).

These arguments point toward the utility of recognizing that nations of the world systematically vary in their efficiencies of converting energy into goods and services. The importance of establishing the major sociodemographic sources of these variations is patent, since a better understanding of the social factors underlying such efficiency might well enhance the efforts of public policy makers or social movements (such as the environmental movement) that seek to influence energy policy. Nevertheless, it will become apparent that many of the variables or factors that account for variation in energy efficiency among nations of the world are - at least at the present time - rather permanent parameters of most nations' social structures and are unlikely to be changed in the near future without radical shifts in political-economic organization.

In this study a ratio of per capita GNP/per capita energy consumption is formulated as a dependent measure of a nation's efficiency of energy conversion. This study then examines the bivariate and multivariate associations of several social structural characteristics of world nation-states with energy efficiency

Social Structure and Energy Efficiency 147

among a sample of 118 world nations and a subsample of 25 developed market economies. The study concludes by summarizing the empirical results, detailing the major implications of the findings, and suggesting cautious interpretation of such cross-national analysis.

FRAMEWORKFOR ANALYSIS

This port ion of the article is devoted to detailing the rationale for examin- ing the possible relationships between each of several independent variables and the dependent measure of efficiency of energy conversion. In doing so, the independent variables are divided into two clusters: (1) variables systematically related to level of societal development, and (2) variables not clearly connected to level of development. This is done to sensitize the reader to the magnitude of the interrelationships among certain variables-and to anticipate an inevitable problem --multicollinearity - these relationships create for regression analysis.

It is useful to indicate at the outset what is meant by "energy efficiency. ' '3 As noted in the preliminary discussion of the measurement of this concept, energy efficiency here pertains to the efficiency with which societies convert commercial energy resources - generally inanimate energy - into socially useful production and consumer goods. Thus a society which requires a large amount of energy to produce a given level of per capita GNP is considered to be less efficient than a nation that uses a relatively small amount of energy to produce this same amount of GNP per capita [see below for a discussion of measurement procedures and problems; see also, Biswas (1974) for discussion of a related concept] .

Independent Variables Associated with Level of Societal Development

A number of variables that would appear to exhibit possible relationships with energy efficiency are highly correlated with level of societal development as measured, for example, by per capita GNP (see Table I1). Despite these high intercorrelations, it is argued that each given independent variable quite possibly might explain variance in energy efficiency over and above the direct effects of other variables in this cluster.

31 refer to the dependent variable as "energy efficiency" because of the way I chose to ope- rationally define it (i.e., as GNP/kg coal equivalent energy consumption). "Energy efficiency" is taken to be the inverse of "energy intensity," although I recognize that both concepts are more typically applied to disaggregated clusters of activities (e.g., end-use activities) than to the aggregate GNP performance of a national economy.

148 Buttel

Gross National Product Per Capita

Gross national product per capita (hereafter, per capita GNP) is probably the most frequently employed indicator of level of societal development. This indicator is taken to be a measure of, following Schwartz (1975), "level of production," although it is becoming increasingly apparent that level of production is only one aspect of societal development (Havens, 1972; Daly, 1973).

Although a cross-national analysis of energy efficiency was suggested by studies showing that nations with similar - and rather large - GNPs have quite different rates of energy consumption, it is argued here that level of production is likely to be the major parameter of world nations that influences their effl- ciencies of energy conversion. For example, Cottrell (1955) pointed out over 20 years ago that in order for a nation or its elite to increase production, geo- metrically increasing energy inputs are required to: (1) accumulate the necessary quantity of energy "converters," i.e., machines, tools, and so on, and (2) provide a sufficient quantity of energy to operate these relatively inefficient high-energy converters (see also Koenig and Edens, 1975: 27). High levels of per capita GNP requre proportionately larger amounts of inanimate energy inputs (Odum, 1971). While societies tend to develop certain efficiencies after beginning to em- ploy a given type of high-energy converters (e.g., by developing "better" internal combustion engines), it appears that high-production societies face inherent energetic constraints in their attempts to increase the overall efficiency of energy conversion and consumption. Per capita GNP is thus hypothesized to be inversely related to energy efficiency.

Percentage of GDP From the Agricultural Sector

Percentage of GDP from the agricultural sector (hereafter, agricultural GDP) 4 is dearly inversely related to GNP per capita, since nonagricultural production is closely linked to high levels of overall production of goods and services in a society. Agricultural GDP thus is an inverse indicator of the diversification of the economic system beyond primary, or agricultural, production.

The fact that agricultural GDP and per capita GNP probably bear a substantial inverse relationship does not mean that the relationship is perfect. Agriculture is the basic production sector in which it is possible to retain the use of animate energy sources (Odum, 1971: chapter 4), and societies at similar, even "advanced," levels of development seem to vary a fair amount in their

4 GDP refers to gross domestic product, which is computed somewhat differently than gross nationa~ product. GDP refers to production activity within a given national geographic boundary, regardless of the residence or the nationality of the owners of the productive property. In estimating GNP, income accruing to residents of a country from their property or labor used abroad is added, while income paid to foreign residents is subtracted.


division of labor beyond primary production (see Makhijani and Lichtenberg, 1971). It is suggested here that at a given level of development and production, the less the diversification of production beyond the agricultural sector, the greater will be the efficiency of energy conversion in a given society. This is argued to be the case because, ceteribus paribus, diversification of production outside of the agricultural sector likely implies: (1) high energy inputs for the (industrial) production in other sectors of the economy, and (2) reliance on "industrial," or more energy-intensive and labor-saving, agricultural techniques so that the remaining farm population can produce sufficient food for their urban counterparts. The import of energy inputs into industrial production has been discussed earlier, while much has been written on the "inefficiencies" engendered by industrial agriculture (Pimentel et al., 1973; Cottrell, 1955; Odum, 1971; Steinhart and Steinhart, 1974). The results of both would seem to entail substitution of inanimate for animate energy, and it is thus hypothesized that agricultural GDP will be positively related to cross-national patterns of energy efficiency.

Percentage of(Male) Labor Force in Agriculture

Percentage of labor force in agriculture is taken here to be an indicator of division of labor outside the agrarian sector and, as such, is intimately related to the diversification variable just discussed. Labor force composition is likewise relevant to energy efficiency in a fashion similar to diversification of production; i.e., the implications of generally energy-intensive nonagricultural production, as well as increased energy intensity in the agricultural sector. To a large extent, then, the relationships of agricultural GDP and percentage of labor force in agriculture to energy efficiency are likely to be consonant - that is, substantially positive. Nevertheless, labor force composition with respect to agriculture is of particular interest because of its potential relevance to public policy. For example, within some limits, governments may decide to either help provide for the retention of workers in the agricultural sector, or hasten the exodus of labor from agriculture via policies such as encouraging mechanization or favoring the commercial stratum of farmers.

Urbanization

Urbanization, a further variable associated with level of societal development, refers to the extent to which people reside in large population concentrations in a given society. Urbanization is generally thought of as being a prerequi- site for industrial production, since these production techniques require central locations for the work process, as well as for distribution and consumption (see, for example, Hawley, 1971: chapter 14). Again, however, societies at various

150 Buttel

levels of development exhibit considerable divergence in the extent to which they are urbanized, and these residual variations independent of per capita GNP are of interest in accounting for energy efficiency of nations.

A number of social and ecological scientists have argued that the concentration of people in space requires increasing inanimate energy subsidies (Odum and Odum, 1976; Odum, 1971; Koenig and Edens, 1975). Urbanization entails increasing insulation of human societies from animate sources of energy, since residents of large urban concentrations are essentially unable to tap into natural flows of energy. Moreover, concentration of the population in cities entails concentration of energy flows in urban areas. Concomitant concentration of energy requires additional energy subsidies in order to "solve" the problems- e.g., waste disposal, water purif icat ion- that result from concentrating people and energy (Marquis, 1968). It is thus hypothesized that the extent of urbanization in a society is inversely related to efficiency of inanimate energy conversion.

Independent Variables Not Directly Associated with Level of Development

Military Expenditure

Critics of energy/materials waste in the United States have often pointed to the American military establishment as a major source of much of this waste (Melman, 1965; Anderson, 1976; Pirages and Ehrlich, 1974:151-156). As Heller (1972: 21) notes, for example, "Military and space e f fo r t s . . , are voracious consumers of energy and materials." It is thus anticipated that societies which allocate a high proportion of their wealth to defense expenditures will be less energy efficient than societies that spend a small percentage of their GNP on the military. To examine this notion measures of the magnitude of military expenditures are employed on both a per capita and a percentage of GNP basis.

Size of Territory

Social scientists have become interested in the effects of size and spatial structure on resource utilization -p rompted , in part, by research showing that size or scale of production activities tends to result in a monotonic increase in per unit energy requirements (Tummala and Connor, 1973). Schumacher (1973: Chapter 5) has speculated that these same effects of size or scale might pertain to nations as well. Social demographers and human ecologists (for example, Hawley, 1971: chapter 6) suggest that size of territory for a society or a city increases the amount of specialization, large-scale organization, and centralization that can be supported, and large territorial size might well compel centrali-


zation to achieve a minimal level of societal coordination. From another vantage point, large systems tend to be more difficult to decentralize than small systems, given that decentralization facilitates energy and resource conservation (Schum- acher, 1973: chapter 5). It is thus hypothesized that increasing size of territory necessitates increasing energy subsidies in order to provide for a given level of centralization and coordinat ion- that size is inversely related to energy efficiency (see also Biswas, 1974: 13).

Population Density

The possible relationships of population density on energy efficiency are probably complex ones, since they might well be mediated, in part, through urbanization and division of labor. High population density has been observed to correspond with "excessive" urbanization (Hawley, 1971: chapter 13), which, if a previous hypothesis is correct, would reduce energy efficiency, s However, it has also been noted (Cottrell, 1955: 134-170) that high population densities re- tard the industrialization of agriculture and division of labor, thereby enhancing energy efficiency. While the latter point may have some validity, there is also evidence that nations with high population densities are increasingly turning to high-energy agricultural inputs (petro-fertilizers, pesticides) to enhance food production, even though their division of labor remains constant (Brown, 1974: 10). On balance it would appear that population densities -especially at high levels such as in Taiwan or India - compel nations to compensate with increased inanimate energy subsidies. This seems to be particularly the case while petroleum- based energy was quite inexpensive (Carton, 1974). Therefore, it is predicted that population density is inversely related to the efficiency of inanimate energy conversion in world nations.

DATA AND METHOD

Sample

The sample utilized in this study is essentially that of world nation-states (circa 1965). All data were taken from Taylor and Hudson's (1972) World Handbook of Political and Social Indicators. Taylor and Hudson report data for a total of 136 nation-states, and 18 nation-states were eliminated from my sample for one of two reasons: (1) 11 nations were dependent territories in 1965,

s"Overurbanization" is a concept some demographers have developed with reference to underdeveloped nations that have excessively large urban populations and correspondingly high rates of unemployment in urban centers (Hawley, 1971).

152 Buttel

meaning that military expenditure data were inappropriate and unavailable, and (2) seven nations had missing data for computation of the dependent variable. The final sample, then, consisted of 118 world nation-states, and the data reported herein for all variables are for the period of approximately 1965.

I have also examined relationships among a subsample of 25 developed market economies. This classification is based on that reported by the United Nations (1973) and includes Western Europe, South Africa, Canada, United States, Oceania, Japan, and Israel.

Energy Efficiency: Meaning and Measurement

The dependent variable- efficiency of the conversion of inanimate energy into useful goods and services - was measured as a ratio of GNP to kilograms of coal-equivalent energy consumption. This variable, then, purports to measure the dollar equivalent of production of goods and services per kilogram of coal-equivalent energy consumption.

This ratio and its converse are hardly novel measures, and in fact they have been employed frequently enough (see, for example, Biswas, 1974; Darmstadter e t al., 1971) to stimulate a critical literature (see Berndt and Wood, 1974, 1975; Schipper, 1976; Schipper and Lichtenberg, 1976; Schipper and Darmstadter, 1976). I recognize that a GNP-energy ratio disguises national differences in cli- mate, industrial mix, and major historical circumstances (e.g., the expensiveness of energy) that have had decisive impacts on industrial structures, and other factors that relate to aggregate societal energy efficiency-intensity. Nevertheless, I feel it is useful to explore the broad social structural parameters - specifically, urbanization - that contribute to aggregate energy efficiency. Detailed disaggre- gation of energy efficiency (see, for example, Schipper and Lichtenberg, 1976; Hirst and Moyers, 1973) can tell us, for example, that the U. S. transportation mix is inefficient, but can provide only limited insight into the developmental- structural parameters that fostered such an inefficient transportation mix in the first instance. It should be kept in mind, however, that this dependent measure is a very rough, approximate indicator of aggregate energy efficiency (the inverse of intensity with respect to the activity of producing dollar equivalents of GNP). The measure thus has nothing to say about the energy intensities of various end uses of energy and their relative impacts on aggregate societal energy efficiency.

Social scientists are also aware of the measurement problems involved with both the numerator and denominator of this dependent variable. GNP has a number of problems as a measure of the amount of goods and services - let alone "social welfare" - in a society (Heller, 1972). Nevertheless, it appears to be the most appropriate measure in light of its widespread availability among most world


nation-states. 6 The energy consumption data used herein refer to apparent "gross inland consumption of commercial fuels and water power" (Taylor and Hudson, 1972:291 ; see also Schwartz, 1975: 195-198). In addition to the gene- ral problems of data availabil i ty- particularly among Third World nations - gross inland consumption of commercial energy resources contains a number o f possible sources of error. Noncommercial sources o f energy (e.g., wind and solar power) are excluded, and not all energy resources sold in a given nation during a given year are consumed as energy. But these problems appear to be minor, since most energy utilized in world nations is commercially supplied (Schwartz, 1975: 195-198), and the great bulk of energy resources is disposed of as fuel (as opposed to being processed into petrochemicals, for example). These diffi- culties notwithstanding, the ratio of per capita GNP to per capita energy consumption appears to be an applicable measure of energy efficiency on a cross- national basis, although the reader should be aware of the conceptual limitations inherent in this aggregate ratio, as well as the conceptual-measurement problems in both the numerator and denominator of the measure.

The score for each nation on the dependent variable is given in Table I. These data correspond to the $U.S. o f GNP generated in a given society for each kilogram coal equivalent of apparent gross inland commercial energy consumption. The scores range from 0.19, for Trinidad and Tobago, to 9.80, for Yemen. The United States (0.39) and other industrialized societies are well represented among the bottom one-fourth o f world nations in terms of energy efficiency (see Table I). Because the scores are substantially skewed, the data reported in Table I were transformed into their natural logarithms to better approximate the normal distribution required for regression analysis.

Measurement: Independent Variables

GNP per capita, agricultural GDP (agricultural share of gross domestic product), percentage of male labor force in agriculture, defense expenditures as percentage of GNP, defense expenditures per capita, urbanization (percentage of population living in cities of 100,000 or larger, 1960), size of territory, and population density (persons per km 2) were all taken from Taylor and Hudson (1972), as noted. Agricultural GDP and percentage of labor force in agriculture had some missing data (16.9 and 18.6%, respectively, o f all data cases), while there were no missing data for the other six variables. Missing data cases for agri-

6One further problem engendered by the use of a GNP/energy consumption ratio is that GNP appears on both sides of the regression equation when GNP per capita is employed as an independent variable. This problem is particularly acute in the case of large errors in measurement; these measurement errors appear in precisely the same fashion on both sides of the regression equation - possibly leading to the inflation of the coefficient of deter- mination and the test statistic for the independent variable, GNP per capita.

154 Buttel

Table I. Energy Efficiency a

Afghanistan Albania Algeria Argentina Australia Austria Belgium Bolivia Brazil Bulgaria Burma Burundi Cambodia Cameroon Canada Central African Rep. Ceylon Chad Chile China (People's Rep.) Colombia Congo-BrazzaviUe Congo-Kinshasa Costa Rica Cuba Cyprus Czechoslovakia Dahomey Denmark Dominican Republic East Germany Ecuador E1 Salvador Ethiopia Finland France Gabon Ghana Greece Guatemala Guinea Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Ivory Coast Jamaica Japan

3.32 1.05 0.74 0.57 0.42 0.49 0.38 0.89 0.77 0.32 1.51 3.67 3.02 1.80 0.32 2.37 1.26 4.80 0.52 0.24 0.53 0.99 1.11 1.35 0.41 0.76 0.28 2.33 0.51 1.37 0.23 1.02 1.61 4.50 0.65 0.65 1.17 2.74 0.88 1.75 0.74 2.24 1.46 0.39 0.62 0.59 0.89 0.64 0.40 0.43 0.64 0.62 1.65 0.56 0.48

Jordan 0.88 Kenya 0.73 Kuwait 0.28 Laos 2.02 Lebanon 0.59 Liberia 0.77 Libya 1.67 Luxembourg 0.43 Malagasy Republic 2.14 Malawi 1.12 Malaysia 0.86 Mali 3.10 Mauritania 2.52 Mexico 0.47 Morocco 1.28 Nepal 9.13 Netherlands 0.48 New Zealand 0.78 Nicaragua 1.47 Niger 5.69 Nigeria 1.91 Norway 0.53 Pakistan 1.21 Panama 0.38 Paraguay 1.73 Peru 0.62 Philippines 0.77 Poland 0.28 Portugal 0.78 Rhodesia 0.37 Romania 0.38 Rwanda 3.33 Saudi Arabia 0.72 Senegal 1.34 Sierra Leone 2.26 Somalia 2.22 South Africa 0.22 South Korea 0.24 South Vietnam 1.37 Soviet Union 0.38 Spain 0.55 Sudan 1.45 Sweden 0.57 Switzerland 0.87 Syria 0.70 Taiwan 0.35 Tanzania 1.29 Thailand 1.17 Togo 2.32 Trinidad andTobago 0.19 Tunisia 1.07 Turkey 0.81 Uganda 2.07 United Arab Republic 0.53 United Kingdom 0.35


Table I. Continued

United States 0.39 West Germany 0.45 Upper Volta 5.30 Yemen 9.80

�9 Uruguay 0 . 6 3 Yugoslavia 0.38 Venezuela 0.30 Zambia 0.44

aExpressed in dollars GNP (U.S.) generated for each kilogram of coal-equivalent gross inland consumption of commercial sources of energy, 118 nation-states, ckca 1965. Source: Taylor and Hudson (1975). These data were computed by calculating:

GNP per capita (in $U.S.) Apparent gross inland consumption of commercial energy sources per capita (in kilograms of coal equivalent)

cultural GDP and percentage of labor force in agriculture were eliminated from all statistical computations. It should be emphasized that countries having missing data for agricultural GDP and percentage of male labor force in agriculture tended overwhelmingly to be underdeveloped nations, and their removal from the sample necessarily entails some bias in that sample. This problem, however, is less prominent among the subsample of 25 developed market economies.

Seven independent variables (all with the exception of percentage of male labor force in agriculture) had distributions that were moderately or highly skewed. A natural logarithmic transformation was therefore performed on each of these seven independent variables.

RESULTS

Table II reports zero-order correlation coefficients for the relationships among the variables of this study. These data provide preliminary support for the hypothesis that GNP per capita, urbanization, defense expenditures per capita and as a percentage of GNP, and population density are inversely related to energy efficiency among the total sample of 118 world nation-states. Also as predicted, agricultural GDP and percentage of labor force in agriculture are positively correlated with energy efficiency. Size of territory, however, proves to be unrelated to the efficiency with which nations convert energy into goods and services. Of the eight independent variables, GNP per capita is most closely associated with the dependent variable (r = -0.678), again as anticipated. Three other independent var iables- agricultural GDP, urbanization, and defense expenditures per capita - exhibit zero-order correlation coefficients larger than an absolute value of 0.60.

The correlation coefficients in Table II also suggest that five independent variables - those argued to be associated with, or components of, level of societal development - have rather high intercorrelations. GNP per capita is correlated

Tab

le I

I. Z

ero

-Ord

er C

orr

elat

ion

Mat

rix

, 11

8 W

orl

d N

atio

n-S

tate

s, C

irca

196

5

XI

X2

X3

X4

Xs

X~

X7

Xs

X9

0".

X 1

= G

NP

per

cap

ita

X 2

= A

gri

cult

ura

l %

of

GD

P

-83

3a

-

X a

= P

erce

nta

ge

of

lab

or

in a

gri

cult

ure

-

741

86

4

- X

4 =

Urb

aniz

atio

n

66

1

-4 7

1 -5

09

-

X s

= D

efen

se e

xp

end

itu

res

per

cap

ita

80

1

-71

3

-63

6

65

8

X 6

= D

efen

se e

xp

end

itu

res

as %

of

GN

P

111

-13

7

157

25

8

X~

= T

erri

tori

al s

ize

-14

9

100

155

071

X a

= P

op

ula

tio

n d

ensi

ty

18

6

-15

3

-17

5

24

0

X 9

= E

ner

gy

eff

icie

ncy

-6

78

65

5

51

7

-61

5

aDec

imal

s o

mit

ted

. C

orr

elat

ion

s si

gnif

ican

t at

th

e .0

5 le

vel

are

in i

tali

cs.

65

0

- 04

8 2

75

-

20

5

30

9

-56

9

-63

4

-2O

2

OO

4 -2

67

Tab

le I

II.

Zer

o-O

rder

Co

rrel

atio

n M

atri

x,

25 D

evel

op

ed M

ark

et E

con

om

ies,

Cir

ca 1

965

X~

X2

X3

X4

Xs

X6

X7

X s

X9

X 1

= G

NP

per

cap

ita

X~

= A

gri

cult

ura

l %

of

GD

P

-59

9a

X

~ =

Per

cen

tag

e o

f la

bo

r in

ag

ricu

ltu

re

-60

4

84

9

- X

4 =

Urb

aniz

atio

n

005

-12

7

-15

1

- X

s =

Def

ense

ex

pen

dit

ure

s p

er c

apit

a 3

94

-6

39

-5

89

32

0 X

~ =

Def

ense

ex

pen

dit

ure

s as

% o

f G

NP

07

9 -4

19

05

7 3

60

X

7 =

Ter

rito

rial

siz

e 13

0 -1

06

-0

06

5

51

X

s =

Po

pu

lati

on

den

sity

-2

81

-1

82

-2

55

-0

02

X

9 =

En

erg

y e

ffic

ien

cy

-08

7

51

7

40

7

-08

3

aDec

imal

s o

mit

ted

. C

orr

elat

ion

s si

gnif

ican

t at

th

e .0

5 le

vel

are

in i

tali

cs.

79

5

- 22

6 19

5 -

193

158

-59

4

-09

4

02

2

-32

6

120


in excess of + 0.65 with agricultural GDP, percentage of labor force in agriculture, urbanization, and defense expenditures per capita, for example. These substantial intercorrelations indicate that multicollinearity will be a problem for subsequent regression analysis, since it is difficult to establish the direct effects of correlated independent variables; the parameters calculated tend to be quite unstable and have large standard errors (Blalock, 1964: 88-90). These correlations among the developed market economies are similar, although somewhat attenuated (see Table III). Nevertheless, the reduced sample size here (N = 25) prevents the lower intercorrelations from contributing to more stable parameter estimates among this subsample.

The correlation matrix among the developed market economies exhibits some major differences with that of the total sample -especially with respect to the consistent attenuation of all relationships. In part, this is because the developed market economies are very similar to one another; i.e., there is only a restricted variance in their structural properties for one to explain.

However, despite these attenuated correlations, there is overall consonance in Tables II and III. Agricultural GDP and percentage of labor force in agriculture retain their bivariate associations with energy efficiency among the developed market economies (r = 0.517 and 0.407, respectively). Territorial size is inversely associated with energy efficiency, as originally anticipated (r = -0.326).

GNP per capita, however, fails to exhibit a substantial inverse correlation with energy efficiency among the developed market economies (r =-0 .087) . This may be due in part to the fact that high per capita GNP is the principal criterion the U.N. uses to define the developed market economies category - that is, the classification entails a considerable amount of bias with respect to the dependent measure. Nevertheless, if we accept the fact that among developed market economies there is only a minimal association between per capita GNP and energy efficiency, the obvious conclusion is that once societies reach a given level of industrial development, the overall level of production does not strongly influence the aggregate efficiency with which energy is converted into goods and services. This is not to say that there is no variance in energy efficiency among the 25 developed societies. Indeed, they exhibit considerable variation in energy efficiency (between $0.22 and $0.88 GNP/kg coal-equivalent energy consumption), and this variation is best accounted for by the agricultural composition of the economy and the labor force.

Having explored the bivariate relations of several possible antecedent variables to energy efficiency, it is useful to further examine such relationships at the multivariate level. This effort, however, is constrained in at least five ways by the nature of the data base. First, as noted, multicollinearity presents severe parameter estimation problems. Second, the lack of a comprehensive model - and the measurement of the full range of variables implicated in such a model - limits one's ability to fully detail the direct, indirect, and total effects of each variable on energy efficiency (see Alwin and Hauser, 1975 for a fuller explica- tion of this terminology). For example, a given variable might prove to have no

158 Buttel

direct effect (i.e., a nonsignificant product-moment correlation coefficient or beta-weight), but still have an important indirect effect on the dependent variable. in question by virtue of its impact on an intermediate variable in a causal chain. Also, one must question whether the cross-national data available are suf- ficiently accurate to allow precise parameter estimation for more than two or three independent variables. The small sample size is a related constraint. Lastly, the data reported here are cross-sectional in nature, and social change inferences made from cross-sectional data should be regarded as tenuous.

With these constraints in mind, I did a multivariate analysis based on estimating parameters by controlling for a series of important independent variables. I used three independent variables - per capita GNP, agricultural GDP, and percentage of male labor force in agriculture - for this task, using only one control variable at a time in order to minimize the problems discussed above.

Table IV reports such data (standardized partial regression coefficients) for the total sample. The primary control variable of interest here is per capita GNP, due to its dominant association with energy efficiency among the total sample. Defense expenditures per capita, urbanization, population density, and agricultural composition of the economy and labor force retain their explanatory power at the multivariate level with per capita GNP held constant, while the standardized partial regression coefficient for defense expenditures as a percentage of GNP no longer bears a significant relationship to energy efficiency. It is useful to note, in addition, that territorial size begins to exhibit the inverse relationship with energy efficiency anticipated above, although the magnitude of the relationship is small. This relationship is perhaps confounded at the bivariate level by, for example, the inverse relationship of territorial size and per capita GNP( = -0.149; see Table II).

When agricultural GDP is controlled for, the standardized partial regression coefficients for other independent variables remain approximately the same as when per capita GNP was held constant. Controlling for percentage of labor force in agriculture shows some interesting results, however, principally because percentage of labor force in agriculture seems to exert causal dominance when entered into the same equation with per capita GNP (b* = 0.460 and -0.224, respectively). Again, the significance of these results is difficult to gauge because of the limitations of the data base. Nevertheless, one can preliminarily suggest that the agricultural sector of world nations appears to be as important to this larger subset as was apparent above with respect to the developed market economies. It should be noted that any attempt to determine the direct effects of agricultural GDP and percentage of labor force in agriculture is fruitless because of their understandably high intercorrelation.

Table V presents equivalent data for the subsample of 25 developed market economies. The results are very similar to the bivariate data described above, with agricultural composition of the economy and labor force being the major variables accounting for variance in energy efficiency. Territorial size continues to have a sizeable negative standardized partial regression coefficient when either

Tab

le I

V.

Rel

atio

nsh

ips

Bet

wee

n S

elec

ted

Ind

epen

den

t V

aria

bles

an

d E

ner

gy

Eff

icie

ncy,

Con

trol

ling

for

Gro

ss N

atio

nal

Pro

du

ct P

er C

apit

a, f

orA

gri

cult

ura

l Sha

re o

f G

ross

Do

mes

tic

Pro

du

ct,

and

fo

r P

erce

ntag

e o

f M

ale

Lab

or

Fo

rce

in A

gric

ultu

re,

118

Nat

ion-

Sta

tes,

Cir

ca 1

965

=t

ca

Ind

epen

den

t yar

iabl

e

Sta

nd

ard

ized

par

tial

re

gres

sion

coe

ffic

ient

s,

cont

roU

ing

for

gros

s n

atio

nal

pro

du

ct p

er c

apit

a

Sta

nd

ard

ized

par

tial

re

gres

sion

coe

ffic

ient

s,

con

tro

llin

g f

or

agri

cult

ural

sh

are

of

gros

s d

om

esti

c p

rod

uct

Sta

nd

ard

ized

par

tial

re

gres

sion

coe

ffic

ient

s,

cont

roll

ing

for

per

cen

tag

e o

f la

bor

forc

e in

agr

icul

ture

r ~o

Def

ense

ex

pen

dit

ure

s pe

r ca

pita

-0

.25

5 a

-0

.28

7 a

-0

.10

3

Def

ense

ex

pen

dit

ure

s as

%

of

GN

P

-0.1

28

-0

.34

7 a

-0

.28

7 a

U

rban

izat

ion

-0.2

97

a -0

.40

3a

' -0

.24

3a

Ter

rito

rial

siz

e -0

.10

0

-0.1

00

-0

.09

9

Pop

ulat

ion

den

sity

-0

.14

6 a

-0

.11

6

-0.1

06

G

ross

nat

iona

l p

rod

uct

pe

r ca

pita

-

_0

.34

9 a

_

0.2

24

a

Agr

icul

tura

l sh

are

of

gros

s d

om

esti

c p

rod

uct

0

.36

4a

- _

b

Per

cent

age

of

mal

e la

bor

forc

e in

agr

icul

ture

0.

460

a _

b

_

r

aln

dic

ates

th

at t

he

un

stan

dar

diz

ed p

aram

eter

est

imat

e is

at

leas

t tw

ice

as l

arge

as

the

stan

dard

err

or.

bThe

se c

oeff

icie

nts

are

no

t re

po

rted

sin

ce m

ulti

coll

inea

rity

pro

hib

its

the

calc

ulat

ion

of

stab

le p

aram

eter

est

imat

es.

Tab

le V

. R

elat

ions

hips

Bet

wee

n S

elec

ted

Inde

pend

ent

Var

iabl

es a

nd E

nerg

y E

ffic

ienc

y, C

ontr

olli

ng f

or G

ross

Nat

iona

l P

rodu

ct P

er C

apit

a, f

or A

gric

ultu

ral

Sha

re o

f G

ross

Dom

esti

c P

rodu

ct,

and

for

Per

cent

age

of M

ale

Lab

or F

orce

in

Agr

icul

ture

, 25

Dev

elop

ed M

arke

t E

cono

mie

s, C

irca

196

5

Inde

pend

ent

vari

able

Sta

ndar

dize

d pa

rtia

l re

gres

sion

coe

ffic

ient

s,

cont

roll

ing

for

gros

s na

tion

al p

rodu

ct p

er c

apit

a

Sta

ndar

dize

d pa

rtia

l re

gres

sion

coe

ffic

ient

s,

cont

roll

ing

for

agri

cult

ural

sh

are

of g

ross

dom

esti

c pr

oduc

t

Sta

ndar

dize

d pa

rtia

l re

gres

sion

coe

ffic

ient

s,

cont

roll

ing

for

perc

enta

ge o

f m

ale

labo

r for

ce in

agr

icul

ture

Def

ense

exp

endi

ture

s pe

r ca

pita

-0

.07

0

0.22

3 0.

593

a

Def

ense

exp

endi

ture

s as

%

of

GN

P

0.02

9 0.

054

0.37

7 U

rban

izat

ion

-0.0

83

-0

.02

2

-0.0

00

T

erri

tori

al s

ize

-0.3

20

-0

.32

3

-0.2

33

P

opul

atio

n de

nsit

y -0

.10

4

0.24

0 0.

254

Gro

ss n

atio

nal

prod

uct

per

capi

ta

- 0.

250

0.29

1

Agr

icul

tura

l sha

re o

f gr

oss

dom

esti

c pr

oduc

t 0.

725

a _

_b

P

erce

ntag

e of

mal

e la

bor

forc

e in

agr

icul

ture

0.

562a

_

b

alnd

icat

es t

hat

the

unst

anda

rdiz

ed p

aram

eter

est

imat

e is

at

leas

t tw

ice

as l

arge

as

the

stan

dard

err

or.

bThe

se c

oeff

icie

nts

are

not

repo

rted

bec

ause

mul

tieo

llin

eari

ty p

rohi

bits

the

cal

cula

tion

of

stab

le p

aram

eter

est

imat

es.


of the control variables is held constant. Keeping in mind the limited relevance

of significance tests to cross-national enumerations, the coefficient for territorial size consistently falls just below significance at the conventional .05 level. 7 No independent variable has a statistically significant standardized partial regression coefficient when agricultural GDP and percentage of male labor force in agriculture are held constant, wi th the one exception of per capita defense expenditures when percentage of male labor force in agriculture is controlled. Nevertheless, Table III indicates an intercorrelation o f these two variables at approximate ly r = 0.60, casting some doubt on the validity of this large multivariate parameter, particularly because it is so large and in a direction opposite

from that of my hypothesis and previous evidence. ~

DISCUSSION

It is apparent that despite the fairly substantial relationship of per capita GNP and per capita energy consumption among world nations, there are major variations among these nations in terms o f the efficiency or rate of converting energy into the product ion o f good and services. Moreover, these variations in energy efficiency are substantially accounted for by social structural and demo- graphic characteristics. GNP per capita - employed as an indicator of level of product ion - and agricultural composit ion o f the economy and labor force had

major effects on energy efficiency - inverse and positive, respectively. The most consistent aspect of the analysis across the total sample and the

subsample of developed market economies was the relationship of agricultural GDP and percentage of male labor force in agriculture to energy efficiency. While per capita GNP appeared to be the major predictor of energy efficiency at the bivariate level among the total sample, per capita GNP had virtually no relation with energy efficiency among the developed societies. I suggest that these data indicate that while the total level of product ion has obvious import across the developed and underdeveloped societies, composit ion - rather than level - of product ion is o f major import to the developed societies. 9 These results high-

7It should be noted that tests of significance are not altogether appropriate here, since the sample essentially is the universe of all nation-states. However, tests of significance are employed primarily for their heuristic value in evaluating statistical relationships.

8 An analysis of regression residuals for bivariate relationships among the total sample showed a tendency for the given model to have the best fit at low to moderate levels of energy efficiency, while the fit was poorer at the upper extremes. Since these high energy efficiency scores pertain almost exclusively to underdeveloped nations, the problem of measurement error is patent. The fit for comparable models among the developed market economies showed no such tendency for poor fit at high levels of energy efficiency.

9 This is not to discount the empirical importance of curbing levels of nonagricultural production in order to enhance energy efficiency. Or to put the matter somewhat differently, the small relation of per capita GNP to energy efficiency among developed societies should not be employed in defense of the notion that unfettered economic expansion in the United States or other developed societies is compatible with energy conservation and

162 Buttel

light the importance of the agricultural sector among urbanized, industrial societies in the Western world.

The other independent variables had less dramatic and/or consistent impacts on energy efficiency. Among the total sample, urbanization and magnitude of defense expenditures had substantial inverse impacts on energy efficiency, although neither variable was important among the developed societies. Likewise, territorial size was inversely related to energy efficiency among the developed market economies, but had only a meager relationship (in the same direction, however) among the total sample.

It is painfully obvious, however, that these parameters of nation-states which bear relationships with energy efficiency are not easily a l t e r ed - in the interest of energy efficiency, or any other sociopolitical-economic goal. Perhaps the most promising area in which more effort might be expended to enhance energy efficiency in the advanced societies is that of taking steps to revitalize (and demechanize) the rural sector. Repopulation of rural communities would conceivably reduce the incentives for highly mechanized, high-energy agriculture (because of a larger supply of labor), along with reducing the concentration of energy flows and congestion in (and the energetic inefficiencies of) cities, as noted above. This is of course not to dismiss the manifold problems that would be involved in such a transition (Schumacher, 1973). The political-economic parameters of such a "de-developmental" shift are also unclear (compare Anderson, 1976: chapter 11 ; Renschaw, 1976: part 3).

It is important to note in conclusion, however, that the present study has a number of major limitations. Cross-sectional, cross-national analysis presents problems of explanation and causal inference, in addition to inherent problems of imprecision of measurement, availability of data, small sample size, and so on (see, for example, Mazur and Rosa, 1974). For example, specifying a variable such as level of production does not provide any concrete notion of the factors accounting for, or laws of motion underlying, historical changes in societies' levels of production and how these changes might be related to subsequent efficiencies of energy conversion. Also, a level of production variable leaves open the question of distribution of production within a given society and how inequalities might be related to resource scarcity (Morrison, 1976; Schnai- berg, 1975). The dependent variable of this study - energy efficiency - suffers from previously discussed conceptual and empirical problems. It is obvious, then, that this study must be regarded as preliminary and suggestive in nature. The re-

provision of energy supplies in the future. In fact, the substantial inverse associations between per capita GNP, and agricultural GDP and percentage of labor force in agriculture (see Table III; r = -0.599 and -0.604, respectively), suggest that per capita GNP does have an inverse indirect effect on energy efficiency among the developed economies through its impact on the composition of production and the labor force in relation to agriculture. Unfortunately, the data available to me are inadequate to provide a thorough test of this notion.


suits must be interpreted with caution. For example, one must avoid glorifying the underdeveloped nations because o f their comparative et:ficiency o f energy conversion and recognize that in many cases this "eff iciency" is achieved only at the expense of massive human misery and suffering. In any event, it does appear that this research can shed light on the broad parameters which constrain and shape societies' uti l ization of energy resources, as well as help illuminate some paths to be taken to help human societies bet ter husband their natural

resources.

ACKNOWLEDGMENTS

Denton E. Morrison, Lee Schipper, Otis Dudley Duncan, Allan Mazur, Christopher K. Vanderpool, and Frederick Frankena provided helpful comments on previous drafts of the manuscript. Olivia Mejorado, Judith Davinich, Linda Buttel, and Susan Roggelin provided invaluable research assistance.

REFERENCES

Alwin, D. F., and Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review 40: 37-47.

Anderson, C. H. (1976). The Sociology of Survival: Social Problems of Growth. Dorsey, Homewood, Ill.

Berndt, E. R., and Wood, D. O. (1974). An economic interpretation of the energy-GNP ratio. In Macrakis, M. S. (ed.), Energy: Demand, Conservation, and Institutional Pro- blems, MIT Press, Cambridge, Mass. pp. 21-30.

Berndt, E. R., and Wood, D. O. (1975). Technology, prices, and the derived demand for energy. The Review of Economics and Statistics 52: 259-268.

Biswas, A. E. (1974). Energy and the Environment. Planning and Finance Service, Environ- ment Canada, Ottawa.

Blalock, H. M., Jr. (1964). Causal Inferences in Nonexperimental Research. University of North Carolina Press, Chapel Hill, N.C.

Brown, L., with Eckholm, E. P. (1974). By Bread Alone. Praeger, New York. Catton, W. R., Jr. (1974). Depending on ghosts. Humboldt Journal of Social Relations 2:

45-49. Cottrell, F. (1955). Energy and Society. McGraw-Hill, New York. Daly, H. E. (1973). The steady-state economy: Toward a political economy of biophysical

equilibrium and moral growth. In Daly, H. E. (ed.), Toward a Steady-State Economy, W. H. Freeman, San Francisco, pp. 149-174.

Darmstadter, J., Teitelbam, P. D., and Palach, J. (1971). Energy in the World Economy. A Statistical Review of Trends in Output, Trade, and Consumption Since 1925. Johns Hopkins University Press, B'altimore.

Hardesty, J., Clement, N. C., and Jenks, C. E. (1971). The political economy of environmental destruction. In Johnson, W. A., and Hardesty, J. (eds.), Economic Growth vs. the Environment, Wadsworth, Belmont, Calif., pp. 85-106.

Havens, A. E. (1972). Methodological issues in the study of development. Sociologia Ruralis 12: 252-272.

Hawley, A. H. (1971). Urban Society: An EcologicalApproach. Ronald, New York. Heller, W. (1972). Coming to terms with growth and the environment. In Schurr, S. H. (ed.),

Energy, Economic Growth, and the Environment, Johns Hopkins University Press, Baltimore, pp. 3-29.

164 Buttel

Hibbs, D. A., Jr. (1973). Mass Political Violence: A Cross-National Causal Analysis. Wiley- Interscience, New York.

Hirst, E., and Moyers, J. C. (1973). Efficiency of energy use in the United States. Science 179: 1299-1304.

Jackman, R. W. (1974). Political democracy and social equality: A comparative analysis. American Sociological Review 39: 29-45.

Jackman, R. W. (1975). Politics and Social Equality: A Comparative Analysis. Wiley-Inter- science, New York.

Koenig, H. E., and Edens, T. C. (1975). Energy, ecology, and economics: Elements of a thermodynamically-based economy. Report DMRE-71-1, Design and Management of Rural Ecosystems, Michigan State University, East Lansing.

Makhijani, A. B., and Lichtenberg, A. J. (1971). An assessment of energy and materials utilization in the U.S. Memo No. M-310, Energy Resources Laboratory, University of California, Berkeley.

Makhijani, A. B., and Lichtenberg, A. J. (1972). Energy and well-being. Environment 14: 10-18.

Marquis, S. (1968). Ecosystems, societies, and cities. American Behavioral Scientist 2:11-15. Mazur, A., and Rosa, E. (1974). Energy and life-style. Science 186: 607-610. Melman, S. (1965). Our Depleted Society. Holt, Rinehart and Winston, New York. Morrison, D. E. (1976). Growth, environment, equity, scarcity. Social Science Quarterly

57: 292-306. Odum, H. T. (1971). Environment, Power and Society. Wiley-lnterscience, New York. Odum, H. T., and Odum, E. C. (1976). Energy Basis for Man and Nature. Mc-Graw-Hill,

New York. Pimentel, D., Hurd, L. E., Belloti, A. C., Forster, M. J., Oka, I. N., Sholes, O. D., and Whit-

man, R. J. (1973). Food production and the energy crisis. Science 182: 443-449. Pirages, D. C., and Ehrlich, P. R. (1974). Ark 11." Social Response to Environmental Im-

peratives. W. H. Freeman, San Francisco. Renschaw, E. F. (1976). The End of Progress: Adjusting to a No-Growth Economy. Dux-

bury, North Scituate, Mass. Rubinson, R. (1976). The world-economy and the distribution of income within states: A

cross-national study. American Sociological Review 41: 638-659. Sawyer, J. (1967). Dimensions of nations: Size, wealth, and politics. American Journal of

Sociology 73: 145-172. Schipper, L. (1976). Towards more productive energy utilization. AnnualReview o f Energy

I: 455-517. Schipper, L., and Darmstadter, J. (1976). What is conservation? Paper prepared for the De-

mand and Conservation Panel Report, Committee on Nuclear and Alternative Energy Systems, National Research Council, National Academy of Sciences, Washington, D.C.

Schipper, L., and Lichtenberg, A. J. (1976). Efficient energy use and well-being. Science 194: 1001-1013.

Schnaiberg, A. (1975). Social syntheses of the societal-environmental dialectic: The role of distributional impacts. Social Science Quarterly 56: 5-20.

Schumacher, E. F. (1973). Small is Beautiful: Economics as i f People Mattered. Harper, New York.

Schwartz, T. P. (1975). Societal energy consumption: An evolutionary theory and a preliminary empirical analysis. Unpublished PhD dissertation, Department of Sociology, University of North Carolina, Chapel Hill, N. C.

Steinhart, J. S., and Steinhart, C. E. (1974). Energy use in the U.S. food system. Science 184: 307-315.

Taylor, C. L., and Hudson, M. C. (1972). World Handbook of Political and Social Indicators. Yale University Press, New Haven, Conn.

Tummala, R., and Connor, L. (1973). Mass-energy based economic models. 1EEE Transac- tions o f Systems, Man, and Cybernetics SMC-3: 548-555.

United Nations (1973). United Nations World Energy Supplies, 1968-1971. United Nations, New York.

Wilensky, H. L. (1975). The Welfare State and Equality." Structural and Ideological Roots o f Public Expenditures. University of California Press, Berkeley.

social structure and energy efficiency: a preliminary cross-national analysis

Documents