Income Inequality, Average Income, and Domestic Violence

Income Inequality, Average Income, and Domestic ViolenceAuthor(s): Erich WeedeSource: The Journal of Conflict Resolution, Vol. 25, No. 4 (Dec., 1981), pp. 639-654Published by: Sage Publications, Inc.Stable URL: http://www.jstor.org/stable/173913 .

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Income Inequality, Average Income, and Domestic Violence

ERICH WEEDE Department of Sociology University of Cologne, West Germany

Previous research has attempted to explain instability and violence by discontent or relative deprivation. Income inequality presumably is one of the background conditions of relative deprivation. In previous cross-national research, positive as well as zero relationships between inequality and violence have been reported. Relying on a somewhat broader personal or household inequality data base, no significant relationship between inequality and violence could be detected. As in previous work, the violence-reducing impact of high average incomes turned out to be fairly strong. What is new, however, is that higher average incomes are related not only to less deaths from political violence but also very much to a higher armed attacks to deaths ratio-that is, to less fatalities per armed attack. This may be due to comparative underreporting of armed attacks in low-income countries; therefore it might be best to rely on deaths from political violence as the only indicator of violence in future cross-national research.

This article has a very modest aim: to find out whether there is a stronger relationship between average income and collective violence than between income inequality and collective violence. In doing so, I shall first outline the theoretical background of the problem and argue that at best only a very tentative answer can be given at present. Second, in attempting to find this tentative answer, I have made a disturbing observation concerning collective violence data which constitutes the new finding of this study. Third, I will not resist the temptation to

AUTHOR'S NOTE: I appreciate the assistance of Wolfgang Jagodzinski and Horst Tiefenbach in producing this paper, which was previously presented at the Munich Conference of the Peach Science Society (International), on August 25-26, 1980. The interesting comments by Charles Taylor at the Conference as well as the suggestions by Bruce Russett and an anonymous reviewer are gratefully acknowledged.

JOURNAL OF CONFLICT RESOLUTION, Vol. 25 No. 4, December 1981 639-654

? 1981 Sage Publications, Inc.

639



640 JOURNAL OF CONFLICT RESOLUTION

"solve" the data problems by recourse to speculation and to provide an answer to my original question on the basis of this and other evidence.

THEORETICAL BACKGROUND

It has been persuasively argued for example, Davies, 1962; Feiera- bend and Feierabend, 1966; Gurr, 1968, 1970; Huntington, 1968) and vigorously disputed (Tilly, 1975, 1978) that discontent, or relative deprivation, is a major determinant of political protest and violence. Unfortunately, positive and negative evidence roughly balance each other, at least at the cross-national level of analysis, and uncomfortable ambiguity prevails (Snyder, 1978; Weede, 1975, 1977).

While this article is unlikely to overcome the near-deadlock on this topic, it may shed light on some aspects of the larger problem. Much of the cross-national and quantitative research has tried to explain conflict and violence by want-get or frustration ratios. Wants were sometimes related to social mobilization, and capabilities for fulfillment were related to economic development. While this is not the research strategy pursued in this paper, the idea is accepted that economic development or increasing average wealth reduces the incidence of violence.

An alternative line of argument links income inequality to discontent, and thereby to protest violence. If income shares are grossly unequal, some get much less than others, and they are likely to resent this. Plausible as this sounds, such a gross proposition glosses over at least two major problems.

First, while it may be in the interest of those who are relatively deprived to do something about it, it is by no means clear that they are likely to resort to collective violence. If there is a reasonable chance for upward mobility, potential challengers to the existing social order and size distribution of income might prefer to change their position within the social structure rather than try to change the social structure itself (Dahrendorf, 1972; Huntington, 1968; Rogowski and Wasserspring, 1971). Moreover, political action to improve the size distribution of income aims at a collective good. Whether groups can and do organize and mobilize for collective action must not automatically be assumed (Oberschall, 1973; Olson, 1965; Rogowski and Wasserspring, 1971; Tilly, 1978). Finally, even where mobility channels are blocked and challengers organize for political action, a power imbalance between status quo forces and challengers may repress or neutralize the protest potential (Dahrendorf, 1972; Korpi, 1974; Tily, 1978).



Weede / INCOME INEQUALITY, A VERA GE INCOME, VIOLENCE 641

Second, one may even dispute whether income inequality is directly and invariably linked to discontent and relative deprivation. In Gurr's (1970) conceptualization of relative deprivation, just deserts are important. Only if there is a discrepancy between what people get and what they feel rightfully entitled to is there relative deprivation. As long as an untouchable street sweeper in Calcutta accepts his low status and extremely meager income as deserved (that is, earned by bad deeds in a previous life), he will not feel deprived relative to his standards ofjustice and expectations, however absolutely deprived he may seem by Western standards.

In order to test relative deprivation approaches to political conflict and violence properly, we need more than inequality data. We need indicators of people's standards of justice and legitimacy, as well as indicators of their opportunities to move up the social ladder or to organize for collective action, to mobilize resources, and to prevail in conflicts of interest. There have been some heroic attempts to produce such data (for example, in Gurr, 1968, or Gurr and Duvall, 1973), but I remain less than convinced (for some criticism, see Weede, 1975, or Snyder, 1978)-nor is Gurr (1980: 148) satisfied any longer with previous efforts, including his own. Moreover, despite their obvious relevance for the assessment of relative deprivation, personal or household income inequality did not play a major role in such efforts, nor could they for lack of acceptable data, until the early 1970s.

The major function of the theoretical discussion above is to outline the limitations of my article. It does not even attempt to contribute to a solution of these major problems. Its aim is much more modest: to establish the presence or absence of cross-national relationships among income inequality, average income, and violence.

PREVIOUS STUDIES ON INEQUALITY AND VIOLENCE

There has been little research done on this topic, but enough to produce incompatible results. The most plausible and clear-cut results come from the earliest piece of research. According to Russett (1964), there is a positive correlation between inequality of land ownership and some measures of political violence. Moreover, the explanatory power improved, as it should, after the percentage of the labor force in agriculture was introduced into the equation. The more rural a society is, the more overall income inequality results from inequality in land tenure.



642 JO URNA L OF CONFLIC T RESOL UTION

Parvin's (1973) results permit two interpretations. First, if one prefers ordinary, unweighted regressions, inequality seems to contribute to less instead of to more political unrest. Second, if one weighs nations according to population size, one gets the expected sign, but the effect becomes insignificant. Since Parvin (1973) used sectoral instead of household or personal income distribution data, and since sectoral data necessarily underestimate inequality, his results must be treated with caution. Nevertheless, it is disquieting to learn that sectoral income inequality is either unrelated or negatively related to political unrest.

Nagel (1974) himself qualifies his cross-national evidence as incon- clusive. He looked for a curvilinear relationship between inequality and violence but could not find one. Nor is there any clear indication for a linear relation in his results. Nagel (1976: 315) could at least reconfirm Russett's (1964) earlier finding that Gini land tenure contributes to more deaths from political violence, particularly if weighted by labor force in agriculture.

Whereas earlier research used land or sectoral inequality data as proxies or substitutes for personal or household data, Sigelman and Simpson (1977) were the first to have access to personal income data for 49 nations in the mid-1960s. Relying on the theorizing of Nagel (1974) and others, they investigated the possibility of a curvilinear as well as linear relationship between Gini personal income inequality (from Paukert, 1973) and Hibb's (1973) internal war index. They provide no support for a curvilinear relationship but some support for a linear one. If control variables are introduced (the most important of which is GNP per capita after log transformation), however, inequality loses some of its predictive power. Therefore, Sigelman and Simpson (1977: 124) "conclude with Parvin that the overall level of societal well-being is a more critical determinant of political violence than is income inequality."

A recent study by Hardy (1979) agrees with Sigelman and Simpson (1977) on the violence-reducing impact of economic development, but it fails to replicate any significant effects of income inequality on violence. Moreover, Hardy (1979) claims that much earlier research is defective from a methodological point of view-an issue to be taken up below.

DATA AND ANALYSIS

To investigate relationships among average income, income inequality, and collective violence, one needs indicators for those concepts. My




single indicator of average income is GNP per capita in 1965 (GNPC). There are two indicators of collective violence: armed attacks and deaths from political violence 1963-1967.1 As the number of violent events or deaths is likely to be higher in more populous nations, population in 1965 is needed as a control variable. The data source for all these indicators is the second World Handbook of Political and Social Indicators (Taylor and Hudson, 1972: 102-115, 295-298, 314- 321). Inequality data come from two different sources (Paukert, 1973: 114-115; Ahluwalia, 1974: 8-9) and refer to either personal or household income shares. Top 20% income shares are preferred over Gini as an indicator of inequality because of their availability in both data sets. Moreover, the correlation between top 20% income shares and Gini is 0.96 for Paukert's data. Unfortunately, there is much more spread in the reference years for the inequality data than elsewhere. In general, Paukert's reference years tend to be somewhat earlier than Ahluwalia's. All independent and non-Communist nations for which either source provides inequality data are included,2 for a total of 66 countries.

Before we can proceed to straightforward OLS estimation of regressions of collective violence on population, GNPC, and income inequality, a few technical issues merit discussion. First, there is the problem of measurement error. Since I am reasonably confident about the reliability and validity of population and GNPC data, single indicators may suffice. Inequality data are judged as less reliable by their compilers (Paukert, 1973; Ahluwalia, 1974; World Bank, 1979). There- fore, all regressions to be reported below appear twice, once with Paukert's and once with Ahluwalia's top 20% income shares. If the results are stable over inequality data, one can certainly place more confidence in them than if they were not. For identical reasons, I decided to use two different dependent variables: armed attacks and deaths from political violence. According to previous factor analytic research (see Hibbs, 1973; Weede, 1978; Eberwein et al., 1979), both of them load highly on the same factor. If I had relied on factor scores or some other

1. Because of the extremely skewed distribution and the low loadings on violence in some factor analytic studies (see Eberwein et al., 1979; Weede, 1978), assassinations have not been included in this study.

2. Although population, wealth, violence, and even inequality data are available for some Communist countries, I preferred not to include them for two reasons: First, income inequality is a much less important dimension of inequality in Communist nations than in capitalist or Third World societies. In Communist countries privileges and, indeed, even consumption opportunities depend as much on political standing as on income. Second, repression is so much more institutionalized in Communist countries than elsewhere that collective violence against authorities is less likely. Moreover, it is less likely to be reported if it occurs.




indexing procedure, I could not have compared regressions of armed attacks with those of deaths from political violence on identical predictors. There would then be no information on the robustness of the findings.

A second technical issue concerns outliers and the skewness of distributions. Except for inequality, this constitutes a major problem for all variables. In order to mitigate against skewness and outlier problems, population and GNPC data were replaced by their natural logarithms, as were armed attacks and deaths from political violence after adding 1 (since the logarithm of zero is undefined).

A third technical issue concerns the monotonicity of relationships between variables. A nonmonotonic relationship between levels of economic development, as indicated by GNPC or energy consumption per capita, is well established for the economic development-inequality side of the triangle (Ahluwalia, 1974; Chenery and Syrquin, 1975; Kuznets, 1963; Paukert, 1973; Weede, 1980). Neither least nor fully developed industrial societies are characterized by very unequal distributions of income, but those somewhere in the middle are. Such intermediate levels of development and extremely unequal size distributions of income are typical of many Latin American societies. But this nonmonotonic relationship between independent variables need not concern us here. While Nagel (1974) and Sigelman and Simpson (1977) looked for nonmonotonic relationships, they confined their inquiry to the inequality-conflict side of our triangle of variables. They found no evidence for a nonmonotonic relationship there. Nor did I.

The functional form of the relationship between economic development and conflict or instability has been much in dispute from the beginning of cross-national research (Feierabend and Feierabend, 1966). Most researchers-including the above-mentioned cross-national studies on the inequality-violence relationship, as well as some of my own previous work (Weede, 1978)-simply assumed a monotonic or even linear relationship between economic development and instability. However, there is some evidence (Russett et al., 1964: 306-307; Hibbs, 1973: 27-30) for a nonmonotonic relationship. Relying on second-order polynomial regression, I investigated a variety of nonmonotonic as well as monotonic specifications of the GNPC-violence relationship. It may suffice to say that I found none that is superior to the regression of logged violence events on logged GNPC, at least for my data set.3

3. Actually, I found one nonmonotonic relationship that reported a minimum of deaths from political violence at 1962 U.S. dollars (in 1965). This U-type of curvilinearity




The last technical issue to be discussed concerns ratio variables.4 According to Hardy (1979) quoting Schuessler (1973), one should regress violence variables on GNP and population instead of GNPC and population in order to avoid ratio variables. According to my reading of Schuessler's (1973) paper, the methodologist tosses the ball back into the court of the theoretically concerned scientist by insisting that one should clarify whether propositions refer to component variables or to ratio variables, and to proceed accordingly. Since I cannot think of meaningful indicators of wealth or economic development which are not standardized for population, I disagree with Hardy (1979) about what should be done.

An inconsistent approach to standardization namely acceptance of GNP per capita but rejection of standardization for violence-looks least defensible at first. Nevertheless, there is some logic behind such a procedure. If one nation is twice as populous as another, it needs twice as high a GNP in order to be called equally rich or developed. However, can it be argued with equal confidence that if a nation is twice as populous as another, it can be called equally violent only if there are twice as many armed attacks or deaths from political violence? Or should we empirically find out instead how violence is related to population? To me, the latter approach looks much more promising. That is why I follow the procedure of Sigelman and Simpson (1977) rather than Hardy (1979). I standardize GNP by population beforethe regression but enter population as an additional independent variable into the equations accounting for attacks or deaths from political violence. While this is the approach whose results will be reported most often in this study, calculations based on different decisions concerning ratio variables have also been made and will occasionally be reported.

Table I reports regressions of violence variables on logged population, logged GNPC, and top 20% income shares.5 These regressions

is not compatible with the inverted U reported elsewhere. Moreover, omission of the United States is all that is needed to make the nonmonotonic relationship disappear. Fitting polynomials to single observations is not a meaningful exercise.

4. There are three possibilities of using or avoiding ratio variables. First, one may enter per capita measures of income and violence into the regression (as did Parvin, 1973). Second, one may standardize income by population before the regression but violence within it by using population as an independent variable (as did Hibbs, 1973, or Sigelman and Simpson, 1977). Third, one need not standardize any variable by population before the regression-that is, one can use GNP as an income term (as did Hardy, 1979; see also Table 2).

5. Following Parvin's (1973: 287) example, I also estimated regression equations in which the inequality data have been logarithmically transformed. By and large, however, such a procedure had little effect on the central concerns of this article.




TABLE 1

Regressions of Violence on Population, GNPC, and Income Inequalitya

LN (ATTA(CKS+ 1) LN (I)EATrHiS+ 1)

LNPOPULATION 0.78 0.83 0.7 3 0.72 (.00 0.00 0.00 0.00 0.59 0.63 0.40 0.37

LNGNPC -0.26 -0.22 -1.11 --1.11 0.20 0.23 0.00 0.00

--0.16 -0.14 --0.48 -0.47

TOP20 % income share 0.028 - 0.048 -

(PAUKI RT) 0.29 0.18 0.13 0.17

TOP20 % income share - 0.061 -- 0.05 (AHLUWALIA) 0.01 0.12

0.34 0.19

constant -4.44 -7.04 0.46 0.45

N 52 58 52 58

adjusted R2 0.30 0.34 0.35 0.32

a. (I xcept for the last three rows) first-cell entries are unstandardized regression coefficients, second-cell entries arc significance levels of regression coefficients, and third-cell entries are standardized regression coefficients.

account for about one-third of the variance in violence. All of them report strong population effects.6 But there is a fair amount of disagreement on more important matters. If violence is operationalized by armed attacks, GNPC has no significant effect-not even at the 10% level. Whether the top 20% income shares matter for the explanation of armed attacks depends on the inequality data set chosen. If violence is operationalized by deaths from political violence, GNPC has a strong effect, stronger even than population, but inequality always is insignificant even at the 10% level. This is not what one would have expected from knowing the correlation among the two indicators of either inequality (0. 71) or violence (0.79). Least of all, one would have guessed

6. My use of causal language as in "effects" should not be taken for more than a matter of habit and convenience. Certainly, my results cannot establish causality. In my view, even better results could not do so. While regression or correlation coefficients can never prove causal relationships some of them may be compatible or incompatible with certain causal propositions.



Weede / INCOME INEQ UA LITY, A VERA GE INCOME, VIOLENCE 647

TABLE 2

Regressions of Violence on Population, GNP, and Income Inequalitya

LN (ATTACKS+ 1) LN (DEATHS+ 1)

LNPOPULATION 1.04 1.05 1.83 1.82 0.00 0.00 0.00 0.00 0.79 0.79 1.00 0.95

LNGINP -0.26 --0.22 -1.11 -1.11 0.20 0.23 0.00 0.00

--0.27 --0.23 -0.80 -0.79

TOP20 % income share 0.028 - 0.048 -- (PAUKI RT) 0.29 0.18

0.13 0.17

TOP20 %o income share - 0.061 - 0.05 (AHLUWALIA) 0.01 0.12

0.34 0.19

constant ---6.26 --8.54 -7.18 -7.20

N 52 58 52 58

adjusted R2 0.30 0.34 0.35 0.32

a. See note to Table 1.

that the strongest and most visible difference in Table I arises from the choice of violence indicators. They correlate with each other better than do the inequality indicators. Moreover, if the random measurement error assumption were true, measurement error in the dependent variable would do less damage than elsewhere.'

Table 2 presents regressions that fulfill Hardy's (1979) requirement of avoiding ratio variables. That is why LNGNPC has been replaced by LNGNP.8 While regression coefficients differ from those of Table 1, the major conclusions are reinforced: Whether GNPC or GNP has any impact on violence depends on the violence indicator looked at. Estimates of inequality effects are identical in both tables. Moreover, significance levels of LNGNPC in Table I and of LNGNP in Table 2 are identical. Thus Hardy's (1979) criticism of ratio variables is plainly irrelevant to the substantive research problem at hand.

7. This is so because different degrees of measurement error in independent variables may distort their relatives contributions or even rank ordering (Namboodiri et al., 1975: 545).

8. GNP data come from Taylor and Hudson (1972: 306-312).




SOME OBSERVATIONS ON THE DOMESTIC VIOLENCE MEASUREMENT

As logged armed attacks and logged deaths from political violence produce such different results concerning the average income-violence relationship, the question of which of them might be the better indicator is obviously important. Careful inspection of a scattergram for the relationship between logged attacks and logged deaths provides some cues to the problem. By and large, North Atlantic societies as well as Japan and Israel are characterized by relatively more attacks than is to be expected from their number of deaths, whereas most African and Asian societies are characterized by few armed attacks compared to their number of deaths. The Latin American pattern is less straightforward and somewhere in between. Or, if one computes the simple difference between LNATTACKS and LNDEATHS-which, of course corresponds to a ratio at the raw score level-it correlates 0.64 with LNGNPC.9

How are these observations to be explained? One may argue that wealthy nations are actually characterized by a high attacks/fatalities ratio because their superior medical facilities improve the chances of survival for victims of political violence, or that dissidents in wealthy lands prefer nonlethal forms of violence, or that the high attacks/fatalities ratios in rich and mostly Western societies reflect reality less well than the reporting habits of the New York Times and other sources on which Taylor and Hudson (1972) relied. An armed attack without lethal effects in Chicago, Belfast, or Bonn might more likely be reported than a similar event somewhere in Africa or Asia.

While there may be something to both of these lines of thought, I am strongly inclined to believe that reporting bias is responsible for the different attacks/ fatalities ratios in more or less developed nations. And I am inclined to believe that there is less reporting bias for deaths than for armed attacks. If people get killed for political purposes, that constitutes news wherever it happens. Danzger (1975) demonstrates that even within the United States, media sensitivity varies from place to place. Most probably this holds a fortiori in cross-national analysis. Snyder and Kelly (1977) add that media sensitivity interacts with type of event, and that more intensive or lethal events are more likely to be

9. At the Munich conference, Charles Taylor made the comment that it might well be something different from average income, although related to it, that affects the armed attacks to deaths ratio. Transportation networks or political characteristics of societies may be more relevant.




reported. If this is so, then LNDEATHS should be more valid than LNATTACKS.'0

If my speculative conclusions about variation in measurement error for armed attacks and deaths from political violence should be true, then one should not construct indices of violence based on both of them, whether by factor analysis or by any other scoring technique. Such procedures in essence amount to producing a weighted sum of armed attacks and violence. In my opinion, such an index is likely to transfer bias from armed attacks to the index. The higher the weight of armed attacks and the lower the weight of deaths, the more this should be expected.

RECONSIDERING INCOME INEQUALITY, AVERAGE INCOME, AND VIOLENCE

Having argued that deaths from political violence is probably the best measure of domestic conflict available, one may conclude that Tables 1 and 2 should be interpreted as demonstrating sizable and negative effects of average income on violence but negligible and insignificant ones for inequality. However, this conclusion may be somewhat premature. While I have discussed that data quality issue at some length for violence, I have considered it much less of a problem for inequality. " A crude and simple attempt to reduce measurement error in inequality data is to average Paukert's (1973) and Ahluwalia's (1974) income shares but to retain single values from one source where necessary in order to maximize the number of cases. Or one may work on the assumption that either one of the sources might be superior, but

10. One should not place too much confidence in this reasoning. Azar et al. (1972) demonstrate that even violent events in Middle Eastern international relations have gone unreported. There is little reason to believe that domestic conflict data are better than international conflict data.

1 1. Originally, I relied on LISREL and confirmatory factor analysis (Joereskog, 1973) to handle the measurement error problem. Simpler methods used here produce similar conclusions. An interesting attempt to improve available inequality data or to estimate them, if not otherwise available, has been proposed by Russett et al. (1981). Since they estimate inequality from health and population patterns as well as from average income data, however, I would hesitate to use them together with average income for finding out whether average income or inequality is more strongly related to violence. In the Russett et al. (1981) estimates, there might be less inequality variation, independent of average income, due to their estimation procedure than in conventional inequality data. Of course, this does not preclude the use of such estimates for many other purposes.




TABLEL 3

Regressions of Violence on Population, GNPC, and Income lnequalitya

LN(ATTACKS+1) LN(D}EATHS+1)

LNPOPULATION 0.67 0.66 0.68 0.58 0.58 0.58 0.00 0.00 0.00 0.01 0.01 0.01 0.51 0.51 0.52 0.30 0.30 0.30

LNGNPC --0.32 -0.34 0.31 -1.31 -1.32 -1.31 0.08 0.06 0.09 0.00 0.00 0.00

--0.20 -0.21 -0.19 -0.54 -0.54 -0.54

TOP20 % income share 0.017 - - 0.0063 -- - (AVE RAG} ) 0.34 0.81

0.11 0.03

TOP20 '%o income share - 0.0098 - -- 0.0039 -- (PAUKERT) missing 0.58 0.88 values trom Ahluwvalia 0.06 0.02

TOP20 % income share - -- 0.022 - - 0.0078 (AHILUWALIA) missing 0.20 0.75 values from Paukert 0.14 0.034

constant -2.44 --1.85 -2.82 5.44 5.63 5.31

N 66 66 66 66 66 66

adjusted R2 0.26 0.26 0.27 0.34 0.34 0.34

a. See note to Table 1.

complement it with data from the other where necessary, again to maximize the number of cases. The results of doing so are reported in Table 3.

Looking at the LNDEATHS equations first, the obvious population effect is strongly confirmed, and the negative impact of average incomes is confirmed even more strongly. Whether one assumes Paukert's data to be better than Ahluwalia's, or the reverse, or that both are equally good, matters little. Once population and average income are controlled, income inequality does not contribute to deaths from political violence.

As some readers might not be ready to accept my skepticism about the comparative lack of good data on armed attacks, regressions accounting for them have also been presented. The obvious population effect is still observed. Average income at least remains a borderline predictor with significance levels between 5 and 10%, with a standard-




ized weight of 0.2. While inequality is slightly more strongly related to armed attacks than to deaths, inequality effects never achieve even 10% significance in the largest possible sample.

CONCLUSION

To summarize my conclusions: (1) Income inequality does not seem to affect domestic violence if population and average income effects are controlled but other possible determinants of violence are not controlled. (2) High average income is strongly related to less violence, in particular to less deaths from such violence. (3) As the armed attacks to deaths ratio is so much higher for high-income nations than for low- income nations, one may suspect that there is serious underreporting of armed attacks in poor nations. Therefore, my recommendation for researchers on domestic violence is to rely exclusively on logged deaths from political violence. Reliance on Hibbs's (1973: 15) famous internal war index, however, can do little harm because it correlates 0.97 with logged deaths from political violence. For the same reason, Hibbs' index also cannot be considered a great improvement over the deaths indicator.

Summarizing the entire series of quantitative studies relating personal or household income inequality to domestic violence, Sigelman and Simpson (1977), Hardy (1979), and I- all agree that high average incomes are more strongly related to less violence than is less income inequality. In producing at least a significant relationship between inequality and violence, Sigelman and Simpson (1977) stand alone. While I agree with Hardy (1979) in not observing a significant inequality-violence relationship, in contrast I maintain that the ratio variable issue is not that important in our context. Therefore, previous work still deserves to be taken seriously.

My results certainly do not give much plausibility to relative deprivation explanations of cross-national variations in violence. Although Gallup (1976) provides some evidence on the average income- reported happiness relationship across nations that helps to interpret the observed relationship between average income and less violence, one still would have predicted a stronger relationship between inequality and violence than between average income and less violence from relative deprivation theory. If income inequality had had the stronger effect, one would have concluded that most people choose reference groups within their societies rather than outside, that most relative deprivations arise from domestic rather than international compari-




sons. But it is not as easy as this to conciliate actual findings reported here and a relative deprivation explanation of political violence.

While my results cannot add support to a relative deprivation explanation of violence, they certainly cannot falsify it, either. The evidence is much too weak and tentative for that: The just deserts dimension of relative deprivation is not reflected in income inequality. It is hard to imagine how relative deprivation can be adequately assessed without recourse to survey data. Nor are mobility opportunities, resource mobilization of contending classes, or power relationships included in this study, despite their obvious relevance to the explanation of violence.

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Documents

Income Inequality, Average Income, and Domestic Violence