June 30th, 2020

Rooftops

DEVELOPMENT AND SOCIAL POLICY

Brazil's Bolsa Familia is widely credited with lifting more than 20 million people out of extreme poverty, making it a global model for anti-poverty initiatives. Developed as part of a broader theory of equitable development, it serves as the basis for ongoing efforts to expand the social welfare system for the country’s poor and working class.

In a 2017 book, economist LENA LAVINAS takes a critical approach to Brazilian social policy. Examining the relationship between social policy and financial markets, Levinas argues that, despite its successes, the strategy of "social developmentalism" in Brazil unwittingly entrenched both unequal growth and the stagnation of social protection.

From the book:

"The twenty-first century seemed poised to pluck Brazil from its history of underdevelopment. After suffering through two decades (1980–2003) of low growth and considerable macroeconomic instability, Brazil—in step with the rest of Latin America—was ready to begin a series of rosy years. In the new developmental strategy, the missing link on the way to social cohesion, so the argument went, would emerge with the advent of mass consumption. In Brazil, as in the rest of Latin America, the core impediment to the expansion of a mass consumption society resided (above all else) in the absence of mechanisms for boosting consumption in the context of low productivity and the persistent oversupply of labor.

Performance in terms of the provision of public facilities has not tracked remotely close to the vitality of the market. It does, however, reveal welfare inequities that the market obscures. Through this prism, the upward social mobility observed in Brazil in the years spanning 2003–2014 failed to even come close to promoting a true expansion of the country’s middle classes. Social policy served as collateral to access financial markets through credit. In Brazil, the market has universalized access to color TVs and fridges among those in the lowest income quintile. Treated water, however, to say nothing of adequate sanitation, remains a luxury, the province of few."

Link to the publisher's page.

  • A 2018 by Lavinas details one of the book's arguments—"the collateralization of social policy." Link. And a 2013 paper by Lavinas examines the broad adoption of conditional cash transfer schemes throughout Latin America. Link.
  • In a 2014 paper, Michael McCarthy examines union attempts to control pension fund investment. Link. Another paper by Natascha van der Zwan on the financial politics of occupational pensions. Link. See also: McCarthy's book Dismantling Solidarity, on these same themes. Link.
  • Marie Gottschalk's book The Shadow Welfare State examines the American "private-sector safety net." Link. See also: Frank R. Dobbin's 1992 paper "The Origins of Private Social Insurance: Public Policy and Fringe Benefits in America, 1920-1950." Link.
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September 9th, 2019

Original & Forgery

MULTIPLY EFFECT

The difficulties of causal reasoning and race

While the thorny ethical questions dogging the development and implementation of algorithmic decision systems touch on all manner of social phenomena, arguably the most widely discussed is that of racial discrimination. The watershed moment for the algorithmic ethics conversation was ProPublica's 2016 article on the COMPAS risk-scoring algorithm, and a huge number of ensuing papers in computer science, law, and related disciplines attempt to grapple with the question of algorithmic fairness by thinking through the role of race and discrimination in decision systems.

In a paper from earlier this year, ISSA KOHLER-HAUSMAN of Yale Law School examines the way that race and racial discrimination are conceived of in law and the social sciences. Challenging the premises of an array of research across disciplines, Kolher-Hausmann argues for both a reassessment of the basis of reasoning about discrimination, and a new approach grounded in a social constructivist view of race.

From the paper:

"This Article argues that animating the most common approaches to detecting discrimination in both law and social science is a model of discrimination that is, well, wrong. I term this model the 'counterfactual causal model' of race discrimination. Discrimination, on this account, is detected by measuring the 'treatment effect of race,' where treatment is conceptualized as manipulating the raced status of otherwise identical units (e.g., a person, a neighborhood, a school). Discrimination is present when an adverse outcome occurs in the world in which a unit is 'treated' by being raced—for example, black—and not in the world in which the otherwise identical unit is 'treated' by being, for example, raced white. The counterfactual model has the allure of precision and the security of seemingly obvious divisions or natural facts.

Currently, many courts, experts, and commentators approach detecting discrimination as an exercise measuring the counterfactual causal effect of race-qua-treatment, looking for complex methods to strip away confounding variables to get at a solid state of race and race alone. But what we are arguing about when we argue about whether or not statistical evidence provides proof of discrimination is precisely what we mean by the concept DISCRIMINATION."

Link to the article. And stay tuned for a forthcoming post on the Phenomenal World by JFI fellow Lily Hu that grapples with these themes.

  • For an example of the logic Kohler-Hausmann is writing against, see Edmund S. Phelps' 1972 paper "The Statistical Theory of Racism and Sexism." Link.
  • A recent paper deals with the issue of causal reasoning in an epidemiological study: "If causation must be defined by intervention, and interventions on race and the whole of SeS are vague or impractical, how is one to frame discussions of causation as they relate to this and other vital issues?" Link.
  • From Kohler-Hausmann's footnotes, two excellent works informing her approach: first, the canonical book Racecraft by Karen Fields and Barbara Fields; second, a 2000 article by Tukufu Zuberi, "Decracializing Social Statistics: Problems in the Quantification of Race." Link to the first, link to the second.
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