Data For Progress Working Paper #2: Why a Universal Basic Income Is Better Than Subsidies of Low-Wage Work
By Maximilian Kasy
What Kasy Proposes:
Kasy puts forward a proposal to expand the Earned Income Tax Credit (EITC) into a universal basic income. A hypothetical universal (or unconditional) basic income (UBI), which is an unconditional cash payment to every person, is also shown. In Kasy’s proposal, individuals with no income receive the highest net benefit, and as incomes increase a larger portion of the UBI is taxed back, decreasing the net benefit.
Kasy argues there are a number of reasons why a UBI is preferable to an EITC. First, the EITC and other subsidies for low-wage work cause people to work more than they otherwise would, since there is an incentive to increase work to maximize EITC benefits. As people work more, this ultimately increases the amount of money the government needs to pay out. According to Kasy, this is less efficient than simply offering unconditional transfers to everyone.
Second, subsidizing low-wage work depresses wages by essentially allowing employers to pay less than a livable wage, so EITC-type benefits are at least in part a transfer to employers, rather than workers. On the other hand, unconditional transfers actually improve workers’ bargaining power by giving them the leeway to refuse work. By its very nature, the EITC work requirement excludes those who are unable to work or unable to find a job, which limits coverage in addition to distorting incentives.
Maximilian Kasy is an Associate Professor of Economics at Harvard.
Data for Progress Research Note #3: Studying Latent Opinion
By Anne Marie Whitsell and Kevin Reuning
What They Find:
In this research note, Whitesell and Reuning use latent opinion modelling to analyze Data for Progress/YouGov Blue polling. Read their blog post explaining the method here.
Data For Progress Research Note #2: (Not) Fake News? Navigating Competing Claims Regarding Status Threat and Trump Support
By Jon Green, Sean McElwee, Meredith Conroy and Colin McAuliffe
What We Find:
In this research note, we analyze a recent critique of Mutz (2018). In her article, Mutz finds “status threat” to predict support for Trump in 2016. The criticism argues that “status threat” is ill-defined and incorrectly measured and that Mutz misspecified her models. We explore each claim in turn.
The choice to include immigration, trade, China, terrorism, and isolationism attitudes as status threat, not a material interest, as is done in Mutz (2018) is defensible. Morgan (2018) does not provide sufficient reasons for rejecting this categorization.
The modeling choices made in (Mutz 2018) are defensible.
Morgan’s concerns regarding causality are also defensible.
Attitudes about immigration were a key determinant in the 2016 election outcome.
Data For Progress Research Note #1: Senator Ideology and Engagement Rates on Twitter
By Jon Green, Ph.D. Student, Political Science, Ohio State University
What Jon Finds:
As part of Data for Progress’s continuing analysis of how politicians and the public interact on Twitter, Jon investigates the relationship between ideology and engagement rates. Jon finds that more ideological senators receive more likes and retweets per tweet they send – but that these trends are largely driven by a handful of senators with particularly strong ideologies and particularly high engagement rates.
Data For Progress Working Paper #1: Justifying Military Force: Racial Attitudes, Race, and Gender
By Jon Green, Ph.D. Student, Political Science, Ohio State University
What Jon Finds:
This working paper, from Data for Progress co-founder Jon Green (@_Jon_Green) explores a battery of questions included in the 2016 Cooperative Congressional Election Survey (CCES) asking respondents if they would endorse sending US troops abroad to do each of six different things: ensure the supply of oil, destroy a terrorist camp, intervene to stop a genocide or civil war, assist the spread of democracy, aid allies who were under attack from a foreign nation, and help the United Nations uphold international law.
Jon finds that there are notable variations in endorsement of these justifications by race and gender, but these differences are not consistent across all justifications. Women of color are far less supportive of military intervention to destroy a terrorist camp than white men, but are slightly more supportive of military intervention to uphold international law.
In addition, Jon uses a measure academics call “acceptance of racism” to model support for intervention. The acceptance of racism measure includes whether a respondent agrees that white people have advantages and agrees that racial problems are rare, and can be taken as an indicator of respondents’ comfort with systemic racial inequalities. He finds strong associations between acceptance of racism and support for some forms of colonialist interventionism, such as support for taking oil, after controlling for partisanship, ideology, demographic indicators, and military salience.
Why It Matters:
Jon’s paper adds important context to support for the use of military force, and the ways that attitudes about race shape support for different interventions.
For instance, since Trump is considering pulling the United States out of the Iran deal, we can examine the ways that racism affects attitudes towards Iran. According the 2012 American National Election Studies survey, which was fielded before the Iran Deal, stereotypes about Muslims are associated with more support for military intervention. People who believed that the word “violent” describes Muslims “extremely well” supported bombing Iran’s nuclear development sites 46 percent to 24 percent (the rest said they neither favored nor opposed). Among people who said the word violent describes Muslims “not at all well,” only 21 percent supported bombing nuclear development sites, with 44 percent opposed.
People who stereotype Muslims are less likely to support diplomacy. Among people who said the word “violent” described Muslims “extremely” well, 55 percent favored the sort of diplomatic talks that led to the Iran Deal (18 percent opposed), while among those who believed the word “violent” described Muslims “not at all well,” 71 percent favored diplomacy and only 5 percent opposed. As Trump considers undermining the Iran Deal and pursuing other actions that could lead to escalating our involvement in the Middle East, Jon’s paper gives us insights into how the reasons for using military force we consider legitimate are associated with our comfort with systemic racial inequalities.