"Analytic work begins with material provided by our vision of things, and this vision is ideological almost by definition. It embodies the picture of things as we see them, and wherever there is any possible motive for wishing to see them in a given rather than another light, the way in which we see things can hardly be distinguished from the nay in which we wish to see them. The more honest and naive our vision is, the more dangerous it is to the eventual emergence of anything for which general validity can be claimed. The inference for the social sciences is obvious, antl it is not even true that he who hates a social system will form an objectively more correct vision of it than he who loves it. For love distorts indeed, but hate distorts still more." JOSEPH SCHUMPETER T H E PROBLEM'CONOMIC anthropology, a major sub-area of anthropological inquiry, E is plagued by a serious communication gap between its practitioners. Since the impact on the field of the writings of Karl Polanyi and his followers, a clear-cut dichotomy has emerged between scholars who maintain that "formal" economic theory is applicable to the analysis of "primitive" and "peasant" economies and those who believe that it is limited in application to the market-oriented, price-governed economic systems of industrial economies.2 Prior to the publication of Trade and Markel in [he Rarly Empires (Polanyi, et al., 1957), economic anthropology (to the extent that it dealt with behavioral theory as opposed to being exclusively concerned with material goods and/or subsistence technology) represented a single field of inquiry, with the majority of its practitioners believing that formal economic theory could contribute to anthropology. However, after the publication of this substantivist magnwm opus, the field underwent ;i bifurcation into two discrete spheres of discourse. Although several attempts have heen made by various scholars to provoke a meaningful dialogue with the substnntivists, their critiques have failed to elicit any such exchnnge of views:' Thus the field is presently characterized by a "split-level" dialogue in which the proponents on the two dominant views of economics-in-anthropology are t;tllting past one another and are operating within separate spheres of discourse. Many anthropologists are still apparently unfamiliar with the scope antl content of the critiques of substantivist economics, while substantivist views continue to find expression in the literature without manifesting any noticeable concessions to the arguments of their critics (e.g., Dalton 1961; Dalton 1962; Bohannan and Dalton 1965a, Uohannan 1963; Dalton 1964). The present critique is intended to supplement its predecessors by elaborating on the thesis that the substantivists' intransigency concerning the cross-cultural applicabil- 32.3

Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

Media-based event data—i.e., data comprised from reporting by media outlets—are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly out performs current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.

Spatial/spatiotemporal interdependence—that is, that outcomes, actions or choices of some unit-times depend on those of other unit-times—is substantively important and empirically ubiquitous in binary outcomes of interest across the social sciences. Estimating and interpreting binary-outcome models that incorporate such spatial/spatiotemporal dynamics directly is difficult and rarely attempted, however. This article explains the inferential challenges posed by spatiotemporal interdependence in binary-outcome models and recent advances in their estimation. Monte Carlo simulations compare the performance of one of these consistent and asymptotically efficient methods (maximum simulated likelihood, using recursive importance sampling) to estimation strategies naïve about (inter-) dependence. Finally, it shows how to calculate, in terms of probabilities of outcomes, the estimated spatial/spatiotemporal effects of (and response paths to) hypotheticals of substantive interest. It illustrates with an application to civil war in Sub-Saharan Africa.

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Instruments based on realizations of the endogenous variable in other units -for instance, regional or global weighted averages -are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditions required for valid instruments. First, when cross-unit dependence exists in the endogenous predictor, other cross-unit relationships -e.g., spillovers and interdependence -likely exist as well and risk violations of the exclusion restriction. Second, spatial instruments produce simultaneity in the first-stage equation, as left-hand side outcomes are included as right-hand side predictors. Because the instrument and the endogenous variable are simultaneously determined, the exclusion restriction is, necessarily and by construction, violated. Taken together, these concerns lead us to conclude that spatial instruments are rarely, if ever, valid. * Thanks to Vincent Arel-Bundock and Michael Ward for their helpful comments. All remaining errors are ours alone. Authors are listed in alphabetical order, equal authorship is implied.

Most agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE) estimator, which retains the complete sample by providing finite estimates of the fixed effects for each unit. We explore the small sample performance of PML-FE versus common alternatives via Monte Carlo simulations, evaluating the accuracy of both parameter and effects estimates. Finally, we illustrate our method with a model of civil war onset.