We argue that sociological analyses of inequality could benefit from engaging the literatures on decision-making. In turn, a sociological focus on how contexts and structural constraints influence the outcomes of decisions and the strategies social groups can use in pursuit of their goals could inform our understanding of decision-making. We consider a simple two-class model of income and the need of capitalists and workers to mobilize resources to influence the adaptive landscape that shapes responses to decisions. We then examine the implications of the rational actor model and the heuristics and biases literature for class-based decision-making. We consider the importance of altruism in mobilizing collective action, and we offer some evidence that altruism is most common in the middle ranges of income and that altruism is a major influence on support for redistributive policies. These results, while tentative, suggest the value of having scholars of development and inequality engage with the literatures on decision-making.

Inequality is a defining problem for scholarship on development. In mainstream economics after World War II “development” became roughly synonymous with “growth in income per capita” (Arndt 1981). The interest in such growth was motivated by the substantial inequalities in per capita affluence across and within nations. These inequalities, and in particular high rates of poverty, entrained ethical as well as geopolitical concerns about the ability of capitalism to reduce poverty and inequality. So, in some sense, most of the sociology of development literature is about inequality.1 

Inequality has been a concern in environmental scholarship at least since the 1970s (Bean v. Southwestern Waste Management Corp.1979; Bullard 1983,Burch 1976; Hare 1970; United Church of Christ Commission for Racial Justice 1987; U.S. General Accounting Office 1983). This has led to a rich and powerful literature demonstrating how environmental risks are stratified by race, ethnicity, social class, and gender and how communities have organized to resist these injustices (Brown, Morello-Frosch, and Zavestoski 2011; Brulle and Pellow 2006; Downey 2015; Mohai, Pellow, and Roberts 2009). Recently, a growing body of research is examining how inequality influences both stress on the environment and the ability of societies to produce human well-being and achieve sustainability (Baek and Gweisah 2013; Boyce 1994; Jorgenson 2015, 2016; Jorgenson, Dietz, and Kelly 2017; Jorgenson, Schor, and Huang 2017; Jorgenson et al. 2015, 2016; Knight, Schor, and Jorgenson 2017; Mikkelson, Gonzalez, and Peterson 2007; Torras and Boyce 1998). From both of these traditions concern with inequality has become a part of the larger discourse on sustainability and is strongly reflected in the U.N.'s Sustainable Development Goals (United Nations General Assembly 2015).

Here we will attempt to make a small contribution to these vast literatures by offering some reflections on how research on decision-making may help inform our understanding of the dynamics of inequality. Our approach draws on a key idea in structural human ecology: that context matters (Dietz 2013). To adequately understand inequality we have to be clear about how context shapes decisions and how context in turn is shaped by decisions—an evolutionary perspective (McLaughlin 2012a; McLaughlin and Dietz 2008). That is, efforts to reduce inequality are conducted on a socially constructed adaptive landscape. In particular, the intersection of class, gender identity, race/ethnicity, and ability/disability create opportunities and constraints even as these categories are constructed and transformed by agency (Crenshaw 1989).

The literatures on decision-making and on inequality are each vast and we cannot claim to survey them.2 Our task will be modest: to offer some initial thoughts on why more attention to decision-making and inequality could be of benefit to both streams of research. Work on decision-making brings to research on inequality a sense of how agency plays out in context. Work on inequality brings to research on decision-making a strong sense of how context—in particular, position in the social structure—shapes decisions, and their outcomes, and the cultural evolution of strategies. We will begin by clarifying the aspect of inequality that will be our focus for exploring decision-making: class differences in income. We will then offer a conceptual model of how individuals might make strategic decisions based on their class position. While acknowledging the importance of categories beyond class, and especially their intersections, we focus on class as an example. We consider what three major traditions of research on decision-making—the rational actor model, the heuristics and biases literature, and the social psychological literature that engages values—might offer in understanding the dynamics of class and inequality. Since the values literature has been least deployed in understanding decision-making and inequality, we offer a small empirical example focusing on altruism and the problem of collective action. We conclude by suggesting some directions for further research.

SPECIFYING INEQUALITY

Inequality in what? Most analyses of inequality focus on income, and we will as well. Theoretically, we might prefer to focus on inequality in wealth, but since data on wealth are much harder to obtain than data on income, most empirical work examines income. From the perspective of sustainability and for broad cross-society comparisons, it is useful to take human well-being as a key criterion for analysis (Dietz 2015b).3 The literature cited above documents the adverse effects of inequality on sustainability, and in particular on well-being relative to environmental stress. Further investigations of the link of inequality to life satisfaction and other measures of well-being are certainly warranted. There is also increasing attention to multidimensional approaches to inequality and poverty, drawing on Nussbaum and Sen's capabilities argument and deploying rather sophisticated quantitative methods (Aaberge and Brandolini 2015; Kakwani and Silber 2008; Nussbaum and Sen 1993; Nussbaum and Glover 1995). All of this warrants further attention. But for the rest of this paper we will focus on income while noting the need for further work on wealth, well-being, and sustainability.

Inequality across what units? Alderson and Pandian (2018) offer an insightful analysis of the dynamics of inequality across two key units of analysis: households and nations. But while these are the focus of most analytical work on inequality, and appropriately so, it is useful to note that there are several other units of analysis that need to be considered for both ethical and analytical reasons.

Note that while Alderson and Pandian follow convention in framing their analysis in terms of inequality across individuals, most of the data we have are based on households as a unit of analysis, and do not allow us to examine inequality within households. There can be substantial inequality in access to resources within households, which can have huge effects on the well-being of women, children, and the elderly (Bennett 2013; Himmelweit et. al 2013; Kulic and Dotti Sani 2017). This does not diminish the importance of focusing on inequality between households, since the household is the basic unit for production of well-being for most people. But we need to be attentive to within-household access to resources. As with analyses of wealth, progress is limited by the dearth of high-quality data sets that allow us to look inside the household.

Sociologists will of course be attentive to three other levels of aggregation in looking at inequality. One is how access to resources varies across attributes that are used to mark social distinctions, such as gender identity, race/ethnicity, ability/disability, and language, as well as their intersections. Sociologists will also attend to within-country regional differences in income and wealth, such as rural versus urban and core versus periphery. And of course class and inequality have been central concerns in sociology since its founding (Wright 2005, 2015). We will focus on class and conceptualize processes that bring agency and decision-making into the dynamics of inequality by class.

Inequality by what measure? Most work on inequality follows one of two strategies. The first is to focus on an aggregate measure of inequality that compares the distribution of income for everyone to a hypothetical ideal distribution—typically complete equality, with an equal income for every household or nation. The Gini index is the dominant measure of deviation from a totally equitable distribution. The second approach, and the most useful conceptualization for understanding agency, decisions, and class, is to examine the share of income or wealth controlled by the richest and the poorest segments of a population and the difference between the share of income going to the working class and that going to the capitalist class.4 

AGENCY AND CLASS

Structuralist theories, whether neoclassical or Marxist, tend to view changes in inequality as rather inexorable outcomes of the dynamics of capitalism at particular stages of its development. In doing so, they pay little attention to decision-making and agency, assuming in the case of neoclassical theory that all actors are self-interested maximizers, and in the case of most Marxist theories that at least capitalists are. So, for example, the Kuznets curve, in which income inequality first increases with economic growth and then declines, is usually attributed to a shift from rural agricultural employment to urban industrial employment and eventually the emergence of a welfare state.5 These structuralist analyses are evolutionist in the sense that a teleological process unfolds following relatively predictable stages (Dietz, Burns, and Buttel 1990). Decision-making is simple maximization: capitalists move investments to where the rate of return is highest; workers seek the best jobs they can manage given the human resources they have and the constraints they face.6 

Some versions of development theory, inspired by Parsonian structural functionalism and its emphasis on norms, argued that a major obstacle to economic growth was a “traditional” psychology that discouraged risk-taking, entrepreneurship, and the sort of decision calculus expected under the rational actor model (e.g. Lewis 1966; Rostow 1960). There is a strong flavor of “blaming the victim” in these analyses, and a tendency to ignore the context in which individuals make decisions and thus the risks, constraints, and opportunities they face. Particularly striking is the lack of attention to the many dimensions of discrimination faced by social groups that are disproportionally poor. Overall, this literature seems to suggest there are two prevalent decision-making modes. One is traditional and an obstacle to economic growth; the other is the rational actor model associated with modernity, which is assumed to facilitate growth and, by implication, the alleviation of poverty.

An advantage of an evolutionary approach is that it simultaneously engages both agency and structure (Dietz and Burns 1992; McLaughlin 2012a, 2012b). Individuals make decisions, and the outcomes of those decisions depend substantially on how social structure shapes opportunities, constraints, and risks. In turn, the outcomes of decisions influence future actions as actors observe what works and what does not in achieving their goals, leading to changes in culture over time—cultural evolution (Richerson and Boyd 2005; Richerson, Boyd, and Paciotti 2002). But individuals can also act to change the institutional landscape, and that in turn changes the response to decisions (Dietz and Burns 1992; McLaughlin and Dietz 2008). Indeed, one can reasonably define power as the ability to shape the adaptive landscape so as to change the outcomes of decisions.

Class

We follow Wright's (2000) approach and argue that a simple two-class model may be instructive in thinking about the dynamics of inequality in a way that allows scope for agency and decision-making. By “class” we mean the predominate way an individual or a household uses the resources they control to generate income in the market—the household production function (Dietz 2015b). Direct use of ecosystem services and labor outside the market are of tremendous importance in all societies and in some regions may be as important for well-being as income from the market for many households (e.g. Yang et al. 2015). But since we want to focus on inequality in market societies, we will leave those important elaborations aside.

Capitalists are of course those who deploy financial resources and ownership of manufactured and natural resources to generate further wealth. Markets make manufactured, natural, and human resources fungible, so the challenge for the capitalist is how to most effectively deploy their wealth to maximize return. As Wright (2000) argues, a key part of the strategy for capitalists is not just mobilizing manufactured and natural resources and labor but also engaging in efforts to change the adaptive landscape by shaping institutions, policies, norms, and new technology to favor their interests. Of course, workers will do the same.

Workers are those who sell their human resources, having limited amounts of natural and manufactured resources and wealth. Their challenge is to find the most productive way to allocate their time to maximize their well-being, including time allocated to labor in the market in exchange for income. We note that one feature of the contemporary U.S. appears to be not only an increase in inequality but an increase in the amount of time workers allocate to working in the market versus to non-market activities that might also contribute to well-being (Schor 2008). In a sense, this is a hidden cost of it becoming more difficult to generate adequate income from labor, requiring increasing commitments of time to labor in the market relative to other activities.

Classical Marxist theory differentiated workers from the lumpenproletariat. This distinction has been important in the analysis of the struggles of minorities, including African Americans and colonized peoples (Fanon and Farrington 1969; Mills 2014). However, for the analyses we pursue here, it is not necessary to separate the poor and chronically economically marginal from workers. To a first approximation, the poor and workers differ primarily in their degree of success in deploying human resources to obtain income. While some individuals and households remain in poverty for much of their lives, for many in the U.S. poverty is episodic. For example, in U.S. households from 2009 to 2011, three-quarters of those that were in poverty at some point in the period stayed there for less than a year, and only 4% stayed in poverty for the full three years (Edwards 2014). Individuals and households often move back and forth between being poor and being working-class, that is, between having limited income and having adequate work and income (Cellini, McKernan, and Ratcliffe 2008; Morduch and Siwicki 2017).

For this analysis we don't consider small business owners (including farmers), managers, or the “new class” of those employed by government or nonprofits as classes distinct from workers. While there may be many nuances they face in generating well-being from the resources they control, for the most part they are faced with the same problem as workers: needing to allocate time to labor to generate income. Some small business owners control an enterprise large enough that they are making most of the income from the ownership of those assets rather than from working at a job that they “own.” Similarly, there is some threshold beyond which a farmer shifts from being a worker on their own farm (who may also have employees) to being an owner allocating fungible resources. The degree to which white-collar workers have interests that coincide with that of their firm, rather than being workers, seems to have declined with the higher turnover in those positions and the rise of the “gig economy.” At the same time, efforts to downsize government and privatize education and other social services may reduce the degree to which workers in those sectors constitute a “new” class outside the market.

We strongly emphasize that this is a simple model meant to be heuristic. It is undoubtedly wrong in some respects and needs to be more nuanced. But it is useful to think through the links between strategy and decision-making and class.

CLASS AND THE COMMONS

A reasonable strategy for a profit-seeking capitalist would then be to minimize the costs of human and natural resources and to maximize the profit obtained from owning manufactured and financial resources. Reducing the costs of human resources can be accomplished in several ways. First, as Braverman (1974) noted, manufactured resources can be substituted for labor, including deskilling of jobs so there are more workers capable of doing them. Second, work can be moved to areas with lower labor costs (other regions within the country, or other countries). That of course requires policies, particularly international trade policies, that facilitate such “offshoring,” as well as the technology that makes a global supply chain feasible. Third, the ability of workers to organize and bargain collectively can be reduced. Reducing the costs of natural resources can also be accomplished in three ways that parallel the strategies for reducing labor costs. First, new technologies can be deployed to reduce the costs of the natural resources needed, either by using less (e.g. energy efficiency) or by making their production cheaper (e.g. hydraulic fracturing for gas production). Second, resources can be obtained from regions where they are less costly, because of greater supply or less regulation. Third, regulation that reduces impact on the environment but raises costs can be opposed or rolled back.

In some sense, workers face much the same challenge. At one level, they will want to allocate their resources, primarily their time, so as to directly increase their well-being. But because their overwhelming source of income is via labor in the market, and because in contemporary U.S. society income at least above a moderate floor is essential for well-being, dividing time between market labor and other sources of well-being is central to their decision process. Of course, workers can also engage in processes to change the adaptive landscape to make their labor more valuable. But here a critical issue arises that motivates much of our analysis.

Changing the adaptive landscape by altering norms, laws and regulations, and technologies is a problem of collective action—a commons problem (Dietz, Ostrom, and Stern 2003; Olson 1965; Ostrom et al. 2002). If I am a worker and I allocate none of my time or other resources to political efforts to reduce unemployment and raise wages, I still benefit from lower unemployment and higher wages if they appear. If I am a capitalist who will benefit from a policy regime that ignores the costs of climate change and thus of using fossil fuels, I benefit from that policy regime whether or not I did anything to maintain it. So, if all workers or capitalists are narrowly self-interested, policies to reduce unemployment will not be supported, and there will be no effective efforts to block a carbon tax.

However, the first-order prediction from narrow self-interest is not wholly accurate. The world is filled with successful efforts to manage the commons and provide collective goods, and there are myriad ways to overcome the problem of free riders, who benefit but do not contribute. While a core principle of commons research is that solutions to the free-rider problem have to be designed in a context-sensitive way, two general points are useful in thinking about the problem of class and inequality. First, the larger the group that has to contribute to commons management or the provision of a collective good, the more difficult it can be to prevent free riding, although the relationship can be complex (Ostrom 2005; Yang et al. 2013). Second, not all people are narrowly self-interested. Most are to some degree self-interested, but concern with the well-being of other humans is an important motivation for many (Dietz 2015a; Fehr and Fischbacher 2003; Pilliavin and Charng 1990; Simmons 1991; Stich, Doris, and Roedder 2010). So while some individuals will be inclined to free ride and not contribute to the collective good unless sanctioned, others will be motivated to contribute.

If we think about the problem of commons as a frame for decision-making by class, a sharp distinction becomes clear. Suppose a worker makes $30,000 a year. Suppose further that she can convince 29 friends who have the same income to join her in donating 0.1% of their income, or $30, to a political effort to reduce unemployment or increase wages or provide other benefits to workers. She has raised $900. Suppose a capitalist with a net worth of $1 billion and an annual income of $50 million convinces the same number of friends to devote the same share of income to fight unions or a carbon tax. Their pool of funds is $1.5 million. Even if only a single billionaire contributes, thus avoiding the coordination problem with peers, the funds available are $50,000, 55 times as much as the workers have raised. Put differently, our worker would have to organize over 1,600 other workers to match the resources that a single billionaire can muster. Both workers and capitalists face a problem of the commons, but the problem is far more severe for workers. Thus, altruism is particularly important for efforts to organize to achieve working-class goals.

DECISION-MAKING AND ITS IMPLICATIONS

There are three major schools of research on decision-making: the rational actor model, the heuristics and biases approach, and social psychological approaches. Of the social psychological theories, for reasons we will elaborate in the next section, the most relevant for our discussion is values-beliefs-norms theory. In this section we discuss the rational actor model and the heuristics and biases literature that has emerged in response to it.

The Rational Actor Model

Von Neumann and Morgenstern's (1944) model of self-interested utility maximization has been enormously influential for both normative and descriptive analyses (see also Jaeger et al. 2001). Indeed, one can think of much of contemporary microeconomics as careful and thoughtful extensions of and corrections to the descriptive model, generating a complex and subtle framework.7 

As a normative model, the rational actor model, instantiated in subjective expected utility, is a clear guide for how our hypothetical self-interested capitalist can achieve his or her ends. It is the core of much of the curriculum in business schools, where the problems of maximizing profit subject to constraints are both researched and taught. So, capitalists have access to an elaborate machinery to achieve their ends.

In theory, workers can also deploy the rational actor model to maximize the utility they obtain by allocating their resources. The microeconomic theory of labor markets and consumer decision-making assumes, for the most part, that this is exactly what workers are doing. Further, most statements of the problem of the commons and of collective action begin with an assessment of the outcomes that will obtain if everyone follows the rational actor model. However, many scholars point out that the computational burden of the subjective expected utility calculus is overwhelmingly challenging for most people (Kahneman and Tversky 1979; Simon 1955, 1972; Tversky 1972). For each possible decision, all outcomes must be identified, along with the stream of costs and benefits that each decision will produce over time. Those costs and benefits have to be assessed in a common metric (nearly always money), future costs and benefits discounted to present value, and uncertainty taken into consideration using probabilities. The information needed and the computations required are daunting.

The Heuristics and Biases Literature

Given the difficulties of enacting the computations of the rational actor model, it is clear that we make most decisions using series of shortcuts, typically labeled “heuristics and biases” (Bruch and Feinberg 2017; Kahneman, Slovic, and Tversky 1982). Rather than looking at all options for a decision, we might satisfice by sorting on a few key characteristics, reducing the set of alternatives to a modest number, even if that means we may miss the “best” one. We tend to assign probabilities not based on an unbiased assessment of frequency of occurrence but based on how prominent or cognitively available an event is or how much we dislike, dread or fear it. We give heavy weight to information that is consistent with what we already know and ignore evidence that doesn't fit a pattern we have developed, rather than altering the pattern to accommodate new data. Thus, our assimilation of new information is biased toward reinforcing existing beliefs. Further, we tend to associate with those who hold views similar to our own, reducing the amount of novel information we receive (Henry and Vollan 2014). Overall, we can think of two decision-making systems at play: one fast, the other slow (Kahneman 2011). The slow system is what the rational actor model describes. But many, perhaps most, decisions are made via the fast system, in which we deploy a few heuristics to make a decision, without deploying the complexity of the rational actor model.

These heuristics and biases seem to be used by everyone, except professionals who have been explicitly trained to avoid them (and then only in special settings). In most circumstances they are useful tools that allow us to make reasonable decisions quickly. In some sense, they can be seen as a response to the time and information costs of the complex calculations of the rational actor model. However, in some circumstances, rather than being roughly right, they can be badly misleading. And they can be easily manipulated. Cialdini (2007 [1984]) has argued that much of advertising and political campaigning are built around exploiting these heuristics and biases in our decision-making (see also Dietz 2011).

In general, individual capitalists are as subject to these foibles as anyone else. However, they, and the firms that make resource allocation decisions for them, have the advantage of hiring professionals who can deploy tools that instantiate the full calculus of rational decision-making and avoid the mistakes that can come from heuristics and biases. Our argument is not that capitalists are especially brilliant in making decisions but that they have the resources to put in place mechanisms that enable careful, instrumentally rational decision-making. Of course, there are innumerable examples of capitalists who ignore advisers. And with rapid social, economic, and technological change, the careful calculations do not always accurately predict the future—the political economy is a nonlinear complex adaptive system that can be chaotic, in the formal sense of nearly impossible to predict (Arthur 1999, 2014). But overall, capitalists have the ability to use the machinery of the rational actor model to maximize profits.

In contrast, workers usually do not have the resources to hire professionals to instantiate the rational actor model to maximize their utility or return on investment. While for some decisions, such as a major purchase, they may work through the rational actor model logic, this is a costly process, and an unfamiliar one. So, workers are much more likely to be influenced by the foibles and suboptimal decision methods that come with heuristics and biases.

For those under financial stress the problems of decision-making are worse in several ways (Haushofer and Fehr 2014). First, it is reasonable for them to be highly risk-averse even if that is costly in the long run. For the affluent, the loss of a significant fraction of income is manageable, and risking it is sensible if the odds are low and a substantial gain is likely. But for the financially strapped, a loss may be a tipping point that results in loss of a car needed for work, homelessness, or other catastrophic change (Desmond 2012). Thus, risk aversion, while it may in the long run be a drag on gaining wealth, is essential to survival in the short run. A large literature from both developed and developing nations has documented this risk aversion. There is also evidence that, in contrast to risk aversion, perceived inequality may lead to heightened risk-taking in an effort to “catch up” (Payne, Brown-Iannuzzi, and Hannay 2017a, 2017b; van Hoorn 2017). Second, many daily tasks become more complicated with a lower income. This leaves less time and mental energy for decision-making and the research that is required for optimizing choices (Mani et al. 2013). Third, many of the poor are also members of communities that have faced systematic discrimination and lack of access to key resources, such as credit and loans, available to the dominant groups. Generations of experience with these problems can lead to ways of handling risk that deviate from the rational actor calculus but are finely tuned to the social context of those groups (Rivers and Arvai 2007; Stack 1975).

Household energy efficiency provides a useful example of how decision-making constrained by context can exacerbate inequality. At least since the 1980s, the equity costs of household energy consumption have been clear (Cramer et al. 1985). Most U.S. households could save money while reducing their environmental impact by adopting more efficient technology and practices, and such changes are among the highest-return investments a household can make (Dietz et al. 2009). For low-income households, the financial returns can be substantial, and more efficient heating and cooling could also directly improve well-being. But the obstacles to improved efficiency can be understood by thinking about decision-making in context. First, many low-income households are renters who have no control of the stock of household appliances nor the weatherization of their dwelling, while landlords who do not pay energy bills have no incentive to reduce them. Second, most Americans misunderstand household energy use in systematic ways, underestimating the energy used by the most profligate practices and appliances and overestimating the impacts of the least energy-intensive (Attari et al. 2010). This means that actions will often be misdirected unless scarce time is spent investigating the best actions to take. Third, many programs to subsidize energy efficiency via weatherization or purchase of more efficient appliances are tied to tax and other rebates that distance the rebate from the time of purchase and may require extensive paperwork; again this puts the less affluent at a disadvantage. On a more positive note, consideration of how context constrains decisions helps identify obstacles to energy efficiency, and such insights can be used to design more effective, context-sensitive programs, including programs targeted at low-income and disadvantaged communities (Stern et. al 2010; Vandenbergh et al. 2011)

Capitalists handle the limits of their personal decision-making ability by putting in place organizational mechanisms to make rational analyses. This can also happen for workers, through collective mechanisms. Unions and nonprofits manage pension funds to take complex investment decisions out the hands of individual workers. Social Security and Medicare provide a basic insurance system managed by government agencies that saves individuals from having to make complex calculations about how to fund long-term insurance for income and medical care. But these programs require collective action, and the free-rider problem referred to above obtains.

Historically, the rational actor model dominated the analysis of decisions that are consequential for development, especially efforts to obtain income and wealth. However, the heuristics and biases literature, often relabeled “behavioral economics,” is making inroads as scholars attempt to understand how decisions are actually made (Datta and Mullainathan 2014; Jäntti, Kanbur, and Pirttilä 2014). We endorse the suggestion of Bruch and Feinberg (2017) that sociologists should engage with this approach. On the one hand, the heuristics and biases approach moves from the strong assumptions of the rational actor model that have troubled most development sociologists and offers a more nuanced way of conceptualizing decision-making. On the other hand, most work on heuristics and biases is insufficiently attentive to context and in particular variation in the historical experience of various social groups, and how those experiences have influenced the evolution of adaptive strategies and decision-making styles. Sociologists’ attention to context, intersectionality, and differences across groups could help reformulate research on decision-making in ways that make it more useful for understanding inequality.

ALTRUISM AND INCOME

Social psychological work on environmental decision-making is centrally concerned with commons problems of the sort faced by workers in organizing for the collective good. So, not surprisingly, one of the key themes in that work is the contrast between self-interest and altruism. Values-beliefs-norms theory, one of the social psychological approaches most commonly used for modelling environmental decisions, focuses on this contrast (Dietz 2015c; Steg 2016; Steg and de Groot 2012; Stern, Dietz, and Kalof 1993). While the entire theory is about altruism, we will simplify by focusing only on values, since that is the core of the theory. Values-beliefs-norms theory conjectures that environmental decisions are driven by values, and in particular by self-interest, altruism toward other humans, and altruism toward other species and the environment itself. For our purposes, we will focus on altruism toward other humans, which we will call simply altruism.

Given the arguments we have made about the importance of collective action for workers compared to capitalists, we can speculate that altruism may be more prevalent among workers than among capitalists. There is some evidence consistent with this assertion. In British data, Manstead (2018) finds greater altruism among the working class than among the affluent. There is some evidence that students at the most elite law schools are more focused on economic efficiency and less on equality than students at less elite law schools (Fisman et al. 2015). In one of the few studies to survey the very wealthy, Page, Bartels, and Seawright (2013) found they were far less supportive of redistributive policies.

We emphasize that we are looking for general tendencies. There will be huge variation in values within any group: some capitalists will certainly be altruistic and some workers narrowly self-interested (Dietz 2005). And while values are generally considered fairly stable over the life course, they may certainly change in some circumstances. But it may be illuminating to consider some empirical results.

While the work on values in environmental decision-making is substantial and links to an even larger literature on values overall, there has been relatively little analysis of how values, and in particular affluence, are influenced by position in the social structure. Here we present a very preliminary analysis to explore these issues.

Our data are from a Qualtrics survey in April 2017 of adults residing in the continental U.S. The survey was conducted with an online panel of respondents developed by Qualtrics. Our sample had quotas for gender and race, and as a result our sample matches the gender and race distribution of the U.S. adult population. However, it is slightly older (U.S. Census Bureau 2016), more educated (Ryan and Bauman 2017), and more liberal (Saad 2016). Because of small numbers of individuals who reported a race other than Black or White, we have restricted our working sample to those two groups, resulting in a sample size of 970. To limit self-selection bias of respondents particularly interested in the topics of the survey, the solicitation for participation the survey was labelled “Let Us Know About Your Attitudes on Various Social Issues.” All participants were paid the equivalent of the federal minimum wage of $7.25 for their participation. On average, participants took 26 minutes to complete the survey, for $3.14 in compensation. The survey protocol was approved by the Institutional Review Board of our home institutions.

The survey included the most commonly used measure of altruistic values (Dietz 2015c). The stem for the items asked the degree to which a statement represented a “guiding principle in your life,” with a 1–5 response scale from “not at all” to “extremely important.” The altruism items were “social justice, correcting injustice, care for the weak”; “a world at peace, free of war and conflict”; and “equality, equal opportunity for all.” Note that these items tap a very general sense of altruism—in some of the literature the underlying values are labeled “benevolence” or “universalism” (Schwartz 2015). Chronbach's alpha for the scale, based on the average across the three items, was 0.801.

Our survey does not have measures of class per se, but we will use income as a surrogate, since our arguments about the ability to mobilize for collective action are couched in terms of income. Income was measured on a five-point scale, and we recoded to the midpoint of the range of each scale value to create a continuous variable. We used both linear and quadratic income terms to capture potential nonlinear effects. The model for altruism also controls for whether or not the respondent was male, was Black, was Hispanic (which was not mutually exclusive with race), for educational attainment, and for age cohort groups (those under 35, roughly the Millennials; those between 36 and 50, roughly Generation X; those between 51 and 70, roughly Baby Boomers; and those over 70, capturing all pre-Baby Boomers). We use those over 70 as the base category for comparisons (the “left out” category), so its coefficient in the models below is zero by constraint. Variance inflation factors indicate no serious collinearity except between the linear and quadratic income terms. All analyses were conducted using Stata 15.1.

We estimate three models using ordinary least squares regression (Table 1). The first model predicts altruism based on income and the other variables. This is a direct test of the conjectured relationship between altruism and class, using income as a surrogate for class. The second model and third models use altruism, income, and the other variables to predict support for two redistributive policies: government spending to reduce poverty and to alleviate homelessness. The response options on these three items are 1, too much; 2, about right; and 3, too little, so high scores indicate support for redistributive polices. A substantial literature examines support for redistributive polices, and there is a literature on the impact of altruistic values on political decisions (Ashok, Kuziemko, and Washington 2015; Caprara et. al 2017; Schwartz et al. 2014). Schwartz et al. (2014) have shown a relationship between altruism and concerns with equality. But, to the best of our knowledge, this is the first analysis of the effects of altruism on support for redistributive policies. The model controls for income and the other demographic variables, so we are estimating the net effects of altruism, controlling for position in the social structure. That is, we are treating altruism as an intervening or mediating variable between position in the social structure and policy support.

TABLE 1.

Effect of income on altruism (N = 970)

AltruismSupport for poverty reduction programsSupport for homelessness programs
Variable Unstandardized regression coefficient 
Income, linear 0.014** −0.003 −0.005 
Income, quadratica −0.100** 0.000 0.000 
Black 0.407** 0.320** 0.225** 
Hispanic 0.252** 0.039 0.086 
Male −0.255** −0.036 −0.060 
Education −0.013 −0.012 −0.018 
Age under 35 0.268* −0.047 −0.012 
Age 35–50 0.016** −0.002 −0.001 
Age 51–70 −0.065 −0.002 0.026 
Age over 70 0+ 0+ 0+ 
Altruism – 0.394** 0.352** 
Intercept 2.861** 0.963** 1.311** 
 R2 = 0.087 R2 = 0.207 R2 = 0.214 
AltruismSupport for poverty reduction programsSupport for homelessness programs
Variable Unstandardized regression coefficient 
Income, linear 0.014** −0.003 −0.005 
Income, quadratica −0.100** 0.000 0.000 
Black 0.407** 0.320** 0.225** 
Hispanic 0.252** 0.039 0.086 
Male −0.255** −0.036 −0.060 
Education −0.013 −0.012 −0.018 
Age under 35 0.268* −0.047 −0.012 
Age 35–50 0.016** −0.002 −0.001 
Age 51–70 −0.065 −0.002 0.026 
Age over 70 0+ 0+ 0+ 
Altruism – 0.394** 0.352** 
Intercept 2.861** 0.963** 1.311** 
 R2 = 0.087 R2 = 0.207 R2 = 0.214 

*p < 0.05, **p < 0.01.

+Constrained to zero. aEstimated coefficient multiplied by 1000 for readability

The first column in Table 1 shows the effect of income on altruism, controlling for the other variables. The effect is significant and nonlinear—an inverted U-shaped curve. Net of the control variables, altruism appears to peak at an income of about $69,000.8 The decline as income moves higher is consistent with our conjecture about altruism being less important for the wealthy than for those of more modest means. The decline of altruism at lower incomes was not anticipated but perhaps reflects a need to marshal resources toward the self and family. We note that women, Blacks, and Hispanics are more altruistic than men, Whites, and non-Hispanics, respectively. This is consistent with arguments that groups that face oppression often develop altruistic values (Stern, Dietz, and Kalof 1993). Age/cohort has a significant overall effect (F3,960 = 13.63, p < 0.01). In particular, it appears that the two more recent cohorts (those under 50) are more altruistic than later cohorts. Several studies show similar results regarding age/cohort and support for redistributive policies (Ashok, Kuziemko, and Washington 2015; Busemeyer, Goerres, and Weschle 2009). With cross-section data we cannot disentangle the life cycle and cohort effects. The life cycle effect might occur because as individuals move to retirement, they become less dependent on income from labor in the market and more dependent on both transfer payments and investments, and thus their status as workers is somewhat attenuated. The cohort effect might arise because Generation X and Millennials are facing considerable economic difficulties compared to older cohorts, and, following our general argument, this may lead them to higher altruism (Carter 2014; Ferri-Reed 2013; Furman 2014; Xu et al. 2015).

In the second and third columns of the table we find that, not surprisingly, altruism is the strongest predictor of support for poverty reduction and programs to reduce homelessness, and net of altruism only race has a significant effect, with Blacks more supportive. This suggests that altruism may be a major intervening variable between position in the social structure, especially class/ income and support for redistribution. (We obtain the same substantive results using ordered probit for these two dependent variables.)

Our results are consistent with a large literature suggesting that those who benefit from the system via higher incomes are most likely to justify the system and be less supportive of redistributive policies (e.g. Fong 2001; Jost, Banaji, and Nosek 2004; Newman, Johnston, and Lown 2015). What we have added to this discussion is the argument that a general concern with the well-being of others, altruism, is a key intervening variable between support for redistributive policies and position in the social structure, which we have tapped with income as a surrogate for class, as well as with gender, race/ethnicity, education, and age cohort. We find that altruism has a curvilinear relationship to income, with altruism most prevalent in the intermediate range of income and lower in the least and most affluent in our sample.

We emphasize that these results are meant only for explication of the idea of exploring differences in values across social classes. The limits of the analysis are sharp—we have measured only income and not class per se, do not have information on economic circumstances beyond self-reported income, have limited information on the most affluent, and are using only a single measure of altruism and only two measures of support for redistributive policies. Nonetheless, the results are consistent with our conjectures and suggest the value of further explorations of the role of altruism in inequality and the ability to organize to reshape the adaptive landscape.

NEXT STEPS

Our major argument is that examinations of inequality could benefit by taking more explicit account of decision-making and in particular how different contexts may shape both different approaches to decision-making and different responses to decisions. The rational actor model implicitly or explicitly underpins most thinking about inequality. But critiques of and extensions to it are becoming much more influential in the literature. For example, there is a growing body of work using experiments to study how people make decisions in response to alternative institutional arrangements to manage commons dilemmas (Anderies et al. 2011; Lopez et. al 2012; Ostrom, Gardner, and Walker 1994, 1996; Pottete, Janssen, and Ostrom 2010). Such experiments have also been usefully deployed to examine the influence of engagement with market-based institutions on individual decision-making in societies where markets are not as dominant as they are in much of the contemporary world (Henrich et al. 2001, 2004, 2005, 2012).

Less attention has been paid to the relationship between values, especially altruism, and the social bases of inequality. Our simple model of class dynamics suggests that workers face a much more difficult challenge in overcoming collective action problems than do capitalists, so altruism is more important in political mobilization for workers than for capitalists. Our preliminary empirical results are consistent with this conjecture.

One important direction for further research is to refine our conceptualization of altruism and link it more closely to identity. We have used only a very broad measure of altruism, one that captures concerns with humans overall without reference to any groups with whom a decision-maker might especially identify. Given that inequality is a global problem, and that the processes that generate inequality in a nation and even in a community have global reach, this is not inappropriate. But theoretical work on cultural dynamics demonstrates that the broader the scope of altruism—the larger the group of people with whom a decision-maker is concerned—the harder it is for altruistic values to persist and spread (Richerson and Boyd 2005). In turn, it is easier to maintain altruism within groups that share identity. However, such solidarity, especially if driven by symbolic markers of identity that are not closely related to class, can focus the scope of concern on groups defined by those identity markers rather than by shared economic interests. This can weaken not only general altruism but inclinations to cooperate within class lines. That is, to the extent that identity is not defined by class but by other factors, the difficulties in organizing for workers may be exacerbated. Of course, using categories of race/ethnicity, gender identity, and region to hide class interests has often been a political strategy to disrupt class-based solidarity and weaken efforts at redistribution (for reviews see Bullock 2017; Bullock and Reppond 2018). For example, willingness to donate funds to alleviate lead contamination in water increase with altruism but are strongly negatively influenced by the belief that minorities receive unfair advantage in contemporary society (Dietz et al. 2018). Strategies that divide class interests are part of the processes shaping an adaptive landscape that constrains the decisions of oppressed groups within a society. While our example here focuses on class and decision-making as a basis of inequality, further work must explore a broader set of categories and their intersections.

We view this essay as an invitation for sociologists of development and others concerned with inequality to engage with the literatures on decision-making. Considering decisions and the constraints on them can lead to useful insights into the processes of stratification and the ways in which power manifests in society. And the strong emphasis on power, inequality, and the constraints on decision-making that are at the heart of the sociology of development will in turn help refine our understanding of decision-making and agency.

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NOTES

NOTES
Dietz's work on this project was funded in part by Michigan AgBio Research.
1.
As an aside, debates about inequality, and whether social actions can remediate it, can be found in foundational arguments in American sociology; compare for example the views of William Graham Sumner (1883a, 1883b), Lester Ward (1884), and W. E. B. Du Bois (Du Bois 1898; Du Bois and Eaton 1899).
2.
Mullan (2017) offers an insightful synthesis.
3.
There remains some controversy about the relationship between well-being and income (Easterlin 1974, 2015; Tay, Kuykendall, and Diener 2015). Some recent analyses suggest that when little relationship is found between growth in income and increased life satisfaction it may be because growth in income is accompanied by growth in inequality and inequality adversely effects well-being (Mikucka, Sarracino, and Dubrow 2017; Oishi and Kesebir 2015).
4.
Atkinson (1970), among others, has noted that we are often most concerned about the lack of resources of the least affluent and offered an index that in essence weights the Gini coefficient to reflect the kinds of inequality of most concern. It can be viewed as a middle ground between looking at the entire distribution and focusing only on specific groups.
5.
Kuznets (1955, 1979) himself was cautious about whether the historical trajectory he observed in Britain and the United States would apply elsewhere.
6.
We use the term “human resources” rather than the more common “human capital” because “capital” implies the desire to invest a resource to obtain a profit (Dietz 2015b). We thank Roy Sablosky for noting that “human resources” is also used as a term within the discourse of capitalist management and there implies that such resources are fungible with and sometimes substitutable for other resources.
7.
The content of graduate training in microeconomics has changed tremendously in recent decades. One of the standard texts, for example, introduces the heuristics and biases approach in the first chapter, and game theory occupies about 10% of the discussion (Mas-Colell, Whinston, and Green 1995).
8.
At the suggestion of a reviewer, we used dummy variables for the income categories instead of the linear and quadratic terms. The effect of the four resulting dummy variables is significant (F4,958 = 3.12, p = 0.014). Consistent with the quadratic model, the dummy variable for the category of income between $50,000 and $75,000 had the strongest positive effect on altruism, while the smallest predicted value for altruism was associated with the most affluent group, those with incomes of $100,000 and over.