Suchergebnisse

Consists of eight comparative studies drawn from history, archeology, economics, economic history, geography, and political science. The studies cover a spectrum of approaches, ranging from a non-quantitative narrative style in the early chapters to quantitative statistical analyses in the later chapters. The studies range from a simple two-way comparison of Haiti and the Dominican Republic, which share the island of Hispaniola, to comparisons of 81 Pacific islands and 233 areas of India. The societies discussed are contemporary ones, literate societies of recent centuries, and non-literate past societies. Geographically, they include the United States, Mexico, Brazil, western Europe, tropical Africa, India, Siberia, Australia, New Zealand, and other Pacific islands

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties.

Political scientists often attempt to exploit natural experiments to estimate causal effects. We explore how variation in geography can be exploited as a natural experiment and review several assumptions under which geographic natural experiments yield valid causal estimates. In particular, we focus on cases where a geographic or administrative boundary splits units into treated and control areas. The different identification assumptions we consider suggest testable implications, which we use to establish their plausibility. Our methods are illustrated with an original study of whether ballot initiatives increase turnout in Wisconsin and Ohio, which illustrates the strengths and weaknesses of causal inferences based on geographic natural experiments.

The paper poses organizational case studies as a provocative way of addressing a long-standing controversy within the social sciences between the subjectivist and objectivist schools of thought. Whereas organizational case studies are customarily conducted as a form of subjectivist research, they may, in addition, be conducted so as to fit the conceptions of objectivist research as well. The paper explains how to achieve this result by conducting case studies as a form of natural experiment. The paper uses organizational case studies in this way to reveal the intersection which exists between the subjectivist and objectivist schools of thought. Organizational case studies which fall in the intersection constitute, in themselves, refutations to the alleged incompatibility between the two schools of thought, and exemplify how a rapprochement can be forged between them. The paper uses an actual organizational case study, Kanter's Men and Women of the Corporation, to illustrate these points.

A natural experiment is a real-world situation that generates as-if random or haphazard assignment to treatment. Geographic or administrative boundaries can be exploited as natural experiments to construct treated and control groups. Previous research has demonstrated that matching can help enhance these designs by reducing imbalances on observed covariates. An important limitation of this empirical approach, however, is that the results are inherently local. While the treated and control groups may be quite similar to each other, they could be substantially different from the target population of interest (e.g., a country). We propose a simple design inspired by the idea of template matching to construct generalizable geographic natural experiments. By matching our treated and control groups to a template (i.e., the target population), we obtain groups that are similar to the target population of interest and to each other, which can increase both the internal and external validity of the study.

Although there are compelling theoretical reasons to believe that unequal political representation in a legislature leads to an unequal distribution of funds, testing such theories empirically is challenging because it is difficult to separate the effects of representation from the effects of either population levels or changes. We leverage the natural experiment generated by infrequent and discrete census apportionment cycles to estimate the distributional effects of malapportionment in the U.S. House of Representatives. We find that changes in representation cause changes in the distribution of federal outlays to the states. Our method is exportable to any democratic system in which reapportionments are regular, infrequent, and nonstrategic.