Cyrus Samii

School desegregation might have induced unintended behavioral responses of white families as well as state and local governments. This paper examines these responses and is the first to study the effects of desegregation on the finances of school districts. Desegregation induced white flight from blacker to whiter public school districts and to private schools, but the local property tax base and local revenue were not adversely affected. The state legislature directed significant new funding to districts where whites were particularly affected by desegregation. Desegregation therefore appears to have achieved its intended goal of improving resources available in schools that black attended.

From an interesting new paper by Sarah Reber in The Review of Economics and Statistics on desegregation achieving its goals via an unintended concentration of public resources (link).

A Fine Theorem blog (link) points to an interesting new paper on “Pre-Industrial Inequality” by Branko Milanovic, Peter H. Lindert, and Jeffrey G. Williamson forthcoming in The Economic Journal (link). The paper defines a new measure of income inequality called the “extraction ratio.” The extraction ratio is the ratio of the measured Gini coefficient to the “maximum possible Gini coefficient” that would obtain were all available societal surplus to be in the hands of a vanishingly small elite. The motivation for the extraction ratio measure is as follows: Consider a society divided into a poor and rich class, and for illustration suppose that the poor each earn P per year and the rich each earn R (that is, incomes are constant within classes) with P << R. If P is fixed to subsistence level, then the surplus that the rich enjoy relative to the poor (R-P) is a linear function of total income in society. As such, levels of inequality as measured by, say, the Gini coefficient are also a function total income. Two societies may exhibit class stratification that is similarly reprehensible in that in both cases, only the rich enjoy any surplus income over the subsistence level, but they may differ greatly in their Gini measures; or, two societies may have the same Gini coefficient, but differ in whether the poor obtain any surplus over subsistence. The extraction ratio allows one to distinguish these cases. The cases would seem to imply very different political circumstances, with a higher extraction ratio being associated with a more exploitative society, intuitively. The authors find that while the distribution of Gini coefficients does not differ so much between the pre-industrial and post-industrial ages, extraction ratios tended to be quite a bit higher in the pre-industrial age. Many studies attempt to correlate inequality as measured by the Gini coefficient to political outcomes, with very mixed results. It would be interesting to see if this new measure produces different insights.

The Economist View blog posts a letter from economist Steve Ziliak (link) describing a case due to be argued before the Supreme Court next week on whether “drug manufacturers and other companies [should] be required to report the adverse effect of a product on users, if the effect is not statistically significantly different from zero at the 5% level.” Briefs presented before the court commenting on the case are here (link). The blog post also links to this page, as well as to some of Ziliak’s writing on significance testing.

This short essay argues that the following criteria should be used to judge whether an analytical economic model is good or not:

1. parsimony, viz., minimal assumptions and parameters, to reduce risk of overfitting. This would seem to be the essence of modeling, right?
2. tractability.
3. conceptual insightfulness, which in the authors’ characterization bears some resemblance to Lakatos’s axiom that a scientific theory should produce “novel facts”.
4. generalizability.
5. falsifiability.
6. empirical consistency.
7. predictive precision, which is a necessary complement to falsifiability and empirical consistency: a model that makes vague predictions may hold up against the data, but a more useful model might be one that makes sharp predictions that are only slightly off from the data.

The authors acknowledge that these criteria may conflict, forcing trade-offs. Special tensions would seem to arise between parsimony/tractability and falsifiability/empirical consistency/predictive precision.

In their discussion, the authors claim that economic models should not be judged on whether they satisfy optimization axioms. They wish to create space for models that allow a separation between the normative preferences of agents and the actions that they ultimately take—the separation may be due to non-voluntary errors, biases, or emotions. Abandoning optimization axioms means that behavior does not immediately reveal preferences, which complicates normative analysis. The authors accept this, claiming that instead, we should specify models that incorporate parameters capturing non-voluntary processes, and then use data to identify “latent” preferences after conditioning on estimates of these parameters.

Full reference: Gabaix, Xavier, and David I. Laibson. 2008. “The Seven Properties of Good Models.” In The Foundations of Positive and Normative Economics, ed. Andrew Caplin and Andrew Schotter, 292–99. New York: Oxford University Press.

Ungated link: http://bit.ly/eL88IB

From a new working paper on “The Political Economy of Deforestation in the Tropics” by Robin Burgess, Matthew Hansen, Benjamin Olken, Peter Potapov, and Stefanie Sieber (link),

Logging of tropical forests accounts for almost one-fi…fth of greenhouse gas emissions worldwide, significantly degrades rural livelihoods and threatens some of the world’s most diverse ecosystems. This paper demonstrates that local-level political economy substantially affects the rate of tropical deforestation in Indonesia. Using a novel MODIS satellite-based dataset that tracks annual changes in forest cover over an 8-year period, we fi…nd three main results. First, we show that local governments engage in Cournot competition with one another in determining how much wood to extract from their forests, so that increasing numbers of political jurisdictions leads to increased logging. Second, we demonstrate the existence of ““political logging cycles,” where illegal logging increases dramatically in the years leading up to local elections. Third, we show that, for local government officials, logging and other sources of rents are short-run substitutes, but that this a¤ect disappears over time as the political equilibrium shifts. The results document substantial deviations from optimal logging practices and demonstrate how the economics of corruption can drive natural resource extraction.

There’s lots to like about the paper, including a well-identified causal story. (They were lucky that others had already done most of the leg-work needed to demonstrate this.) It is also a timely contribution, as Indonesia is one of the pilot cases for the new global REDD initiative to deal with green house gas build up through forest protection “carbon credits” (link). This kind of “diagnostic” research can determine intervention points that should be targeted by future programs aiming to promote forest conservation. It’s already a long paper, but their case would be strengthened if they provided some narrative accounts that demonstrated the plausibility of their interpretation of the data.