Rising inequality isn’t driving mass public support for redistribution: Charlotte Cavaillé’s ‘Fair Enough? explains why not.

Rising inequality isn’t driving mass public support for redistribution: Charlotte Cavaillé’s ‘Fair Enough? explains why not.

In the past, excessive economic inequality has ended… badly. As Charlotte Cavaillé points out in her new book that studies the public’s reaction to rising inequality, “only mass warfare, a state collapse, or catastrophic plagues have significantly altered the distribution of income and wealth.” Will this time be different?

Through income redistribution, democratic and political institutions today have a clear mechanism to peacefully address income inequality if voters demand it. Still, as highlighted by Cavaille in Fair Enough?: Support for Redistribution in the Age of Inequality (Cambridge University Press), greater wealth and income inequality are not leading to greater demand for an egalitarian policy response as many would expect.

Cavaillé reports there is little evidence of rising support for redistribution, especially among the worse off. Consider public opinion in the two Western countries with the sharpest increase in income inequality: In Great Britain, public support for redistribution is decreasing, and in the United States, the gap between the attitudes of low-income and high-income voters is narrowing. What, asks Cavaillé, can we conclude about public opinion’s role as a countervailing force to rising inequality?

Based on Cavaillé’s doctoral work, Fair Enough? introduces a framework for studying mass attitudes toward redistributive social policies. Cavaillé shows that these attitudes are shaped by at least two motives: material self-interest and fairness concerns. People support policies that would increase their own expected income. On the other hand, they also support policies that, if implemented, “would move the status quo closer to what is prescribed by shared norms of fairness.” Material interest comes most into play when policies have large material consequences, according to Cavaillé, but in a world of high uncertainty and low personal stakes, considerations of fairness trump considerations about one’s personal pocketbook.

How fair is it for some to make a lot more money than others? How fair is it for some to receive more benefits than they pay in taxes? Cavaillé emphasizes two norms of fairness that come into play when we think about such questions: proportionality, where rewards are proportional to effort and merit, and reciprocity, where groups provide basic security to members that cooperatively contribute. Policy disagreement arises because people hold different empirical beliefs regarding how well the status quo aligns with what these norms of fairness prescribe.

With fairness reasoning in the picture, Cavaillé writes, “baseline expectations are turned on their heads: Countries that are more likely to experience an increase in income inequality are also those least likely to interpret this growth as unfair.”

Should we expect growing support for redistribution to be a driving force behind policy change in the future? A change in aggregate fairness beliefs, Cavaillé argues, will require a perfect storm: a discursive shock that repeatedly exposes people to critiques of the status quo as unfair on the one hand, and a large subset of individuals whose own individual experience predispose them to accept these claims as true on the other. Policy changes in postindustrial democracies are possible, Cavaillé concludes– but they are unlikely to be in response to a pro-redistribution shift in public opinion.

Charlotte CavailléCharlotte Cavaillé is an assistant professor of public policy at the University of Michigan’s Gerald R. Ford School of Public Policy and an affiliate of the Center for Political Studies at the Institute for Social Research. Her dissertation, on which ‘Fair Enough’ is based, received the 2016 Mancur Olson Best Dissertation Award.

Tevah Platt and Charlotte Cavaillé contributed to the development of this post.

Winners and Losers:
 The Psychology of Attitudes Toward 
Foreign Trade

Post developed by Katherine Pearson and Diana Mutz

Foreign trade is a complex issue, but the public still has strong opinions about the issue. Diana Mutz demonstrated that social psychology can help to understand attitudes about trade when she delivered the 2019 Miller Converse lecture. A recording of her talk “Winners and Losers: The Psychology of Attitudes Toward Foreign Trade” is available below.

Most people rely on small-scale social experiences to understand large-scale interactions such as international trade. From this understanding, people tend to embrace beliefs about trade that are not necessarily accurate. For example, folk beliefs suggest that impersonal transactions are more dangerous than personal ones, that trade is zero-sum, and that trade “deficits” mean that a country is losing more jobs as a result of imports than it gains due to exports. These beliefs are inaccurate, yet understandable, generalizations from the world of face-to-face social exchange.

Contrary to popular wisdom, trade preferences do not reflect people’s economic self-interest. Mutz demonstrates that, surprisingly, these attitudes are not influenced by a person’s occupation, industry of employment, community job loss, geographic location, or individual job loss. Instead, perceptions of what is in the collective economic interest determine attitudes toward trade. Coverage of trade in the media has a large influence on these perceptions. Media coverage of foreign trade was mostly negative until 2016. As media coverage of trade has become more balanced since 2016, support for trade has also increased.

Politicians from all parties have been unwilling to champion trade when running for office because foreign trade is seen as a political liability in the United States. As the world economy changes, Mutz asserts that leaders will need to advocate for trade and for safeguards against its negative effects. She cautions that it’s unhelpful to leave the public out of that conversation altogether as has been common in the past.

For an additional perspective, Mutz compares attitudes about trade in the United States and Canada. She finds that attitudes about trade in the two countries are different due to differing attitudes toward competition. Americans value competition more, and believe in the fairness of unequal outcomes. In the U.S., nationalism reduces support for foreign trade, but in Canada the opposite is true. Canadians who hold the strongest beliefs about national superiority want to promote more trade and immigration.

Differing perspectives on trade in these countries can be explained by variation in two different types of ingroup favoritism. First, Americans in Mutz’s studies systematically preferred trade agreements in which their fellow Americans benefited more than trading partners. In fact, there was no level of job benefits to foreign countries that would justify the loss of even a single American job. This was not the case among Canadians. In addition, Americans demonstrated their competitive attitudes toward trade by demonstrating greater support for trade agreements that not only benefit their country but also disadvantage the trading partner. Canadians, in contrast, preferred the kind of “win-win” trade agreement that economists suggest benefits all countries involved.

Attitudes about race drive attitudes about trade and Mutz finds that the reverse may also be true. In a study that asked respondents to select which students should be admitted to college, participants who had just watched an ad against foreign trade were less supportive of admitting Asian-American students, as well as students from Asia.

Mutz concludes that, while many of these results are distressing, attitudes remain malleable. Efforts to change opinions toward trade that emphasize similarity and shared values are more effective than efforts emphasizing pocketbook gains. Since 2016, her data shows that there has been an increase in support for foreign trade and a realization that it comes with benefits as well as negative consequences.

The Political Economy of Data Production

Post developed by Catherine Allen-West, Charles Crabtree and Andrew Kerner

ICYMI (In Case You Missed It), the following work was presented at the 2017 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “The IMF and the Political Economy of GDP Data Production” was a part of the session “Economic Growth and Contraction: Causes, Consequences, and Measurement” on Sunday, September 3, 2017.

Political economists often theorize about relationships between politics and macroeconomics in the developing world; specifically, which political or social structures promote economic growth, or wealth, or economic openness, and conversely, how those economic outcomes affect politics. Answering these questions often requires some reference to macroeconomic statistics. However, recent work has questioned these data’s accuracy and objectivity. An under-explored aspect of these data’s limitations is their instability over time. Macroeconomic data is frequently revised ex post, or after the fact, and as such one could ask the same question of (ostensibly) the same data, and get different answers depending on when the question was asked.

We set out to explore the political economy of data production by examining a newly available dataset of ex post revisions to World Development Indicators (WDI) data.[1]  Ex post revisions occur when newly available information changes national statistical offices’ beliefs about the nature of the economy. Those revisions extend into the past, effectively rewriting history and, in the process, providing a reasonable proxy for the inaccuracy of the initial reports. These revisions affect a wide swath of data, but we focus on Gross Domestic Product (GDP) and GDP-derived measures, like GDP per capita and GDP growth. GDP revisions are common—most GDP data available for download at the WDI are different now than they were at the time of its initial release. Normally these changes are subtle; other times they are substantial enough to condemn prior data releases as misleading.

We use these revisions to answer two related questions. First, how sensitive are political-economy relationships to GDP revisions? Should researchers worry about revisions-driven instability in the state of political-economic knowledge? We show that they should. To illustrate, we subject a simple, bivariate statistical relationships between democracy and growth to re-estimation using alternative versions of the “same” data. The democracy-growth relationship has been a topic of sufficient interest in economics and political science that instability in this relationship should give us reason for pause. Seen in this light our estimates are worrisome. As we show in Figure 1 below, our estimates are unstable across different “observation years” and further, they are unstable in ways that suggest that initial estimates were biased. Rather than simply a diminution of standard errors as more heavily revised data are introduced (which is what we would expect to see if revisions simply reduced random “noise in the data”), the estimated coefficients for Democracy change substantially across models estimated with different revisions of the same country-year GDP growth data.

Figure 1: GDP Growth ~ Democracy

Note: Figure 1 displays the relationship between GDP Growth and Democracy using the results from 21 different regression models. Plotted points represent parameter estimates, thick bars represent 90 percent confidence intervals, and thin bars represent 95 percent confidence intervals. Each point is labeled with the revision year used. The left side of the plot contains results from models estimated using the 2000-2004 data series, while the right side of the plot contains results from models estimated using the 1995-1999 data series. See paper for more details.

This finding anticipates our second question: Given the likelihood that GDP revision are non-random, what accounts for ex post revisions? What does the “political economy” of revisions look like? We show using Kolmogorov-Smirnov tests (see Figure 2) and random forest models (see Figure 3) that the International Monetary Fund (IMF) influences the magnitude of revisions for GDP and GDP-related measures. That is not entirely surprising. Our suspicion that the IMF would have such an effect is a straightforward recognition of its well-publicized efforts to provide financial and human resources to the national statistical offices of the countries in which it works. What we have “uncovered” in this exercise is simply one consequence of the IMF doing precisely what it has publically said it is doing. But this finding’s (retrospective) obviousness does not diminish its importance. Consider the empirical challenges that this presents. Political economists often ask if the IMF affects the way economies functions, but the IMF’s independent effect on the way economies are measured substantially complicates our ability to know if it does. And it doesn’t just complicate our ability to know if the IMF’s policies affect the economy, it complicates our ability to know if anything correlated with IMF participation affects the economy. Many important things correlate with IMF participation, including, for example, democracy, a country’s relationship with the UN, and whether or not a country is an ally of the United States.

Figure 2: Distributions of GDP Growth Changes

Note: Figure 2 presents compares the distributions of GDP growth revisions for years with and without IMF programs. The y-axis indicates the height of the density function and the x-axis indicates the magnitude of GDP growth revisions in percentages points. The solid green line denotes country years with an IMF program, while the dashed black line denotes countries years without a program. See paper for more details.

Figure 3: Predictors of GDP Growth Revisions

Note: Figure 3 presents the results from a random forest model that examines the predictors of GDP growth revisions. The vertical axis ranks variables according to their importance for predicting GDP Growth Changes. The horizontal axis displays estimates of permutation accuracy for each variable, calculated as the difference in mean squared error between a model that is fitted using the observed values for a measure and a model that is fitted using random (but realistic) values for the same measure. This measure is then scaled to represent the percentage increase in mean square error caused by permuting the values of the variable. Positive values indicate that the variables increase the predictive performance of the model, while negative values indicate that the variables decrease the predictive performance of the variables. See paper for more details.

Of course, politics likely affects the way the economy is measured in a variety of ways that have nothing to do with the IMF. Our random forest analysis suggests that democracy might also have an effect, for example, as might public sector corruption, and it is not hard to tell a plausible post hoc story for why that might be. But our aim is not to provide a comprehensive picture of the political economy of data production, but simply to show that it exists, and that it exists in a manner that should alert us to its importance. Taking seriously the political provenance of ostensibly apolitical data is an important (and, we believe, interesting) step towards refining the state of political economy knowledge.

[1] The raw data used in this paper are available at http://databank.worldbank.org/data/reports.aspx?source=WDI-Archives. To facilitate researcher use of this data, we will make it available in an R package, revisions. This package will contain long- and wide-format data sets.

Andrew Kerner is an Assistant Professor in the Political Science department at the University of Michigan, and a faculty associate at the Center For Political Studies.

Charles Crabtree is a PhD student in the Department of Political Science at the University of Michigan.