Dirty Pool

Green, Donald P.; Kim, Soo Yeon; Yoon, David

doi:10.1162/00208180151140630

Cited by 261 publications

(167 citation statements)

References 68 publications

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“…al. 1984, Greene 2001, Green, Kim & Yoon 2001, Beck & Katz 2001. Group specific parameters arise in hierarchical data when multiple observations are sampled from the same group, or in longitudinal or time-series cross-section panel data with repeated observations of the same individual.…”

Section: Statistical Specificationsmentioning

confidence: 99%

Productivity Effects of Information Diffusion in Networks

2007

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We examine the drivers of diffusion of information through organizations and the effects on performance. In particular, we ask: What predicts the likelihood of an individual becoming aware of a strategic piece of information, or becoming aware of it sooner? Do different types of information exhibit different diffusion patterns, and do different characteristics of social structure, relationships and individuals in turn affect access to different kinds of information? Does better access to information predict an individual's ability to complete projects or generate revenue? We hypothesize that the dual effects of content and structure jointly predict the diffusion path of information, and ultimately performance. To test our hypotheses, we characterize the social network of a medium sized executive recruiting firm using accounting data on project co-work relationships and ten months of email traffic observed over two five month periods. We identify two distinct types of information diffusing over this network -'event news' and 'discussion topics' -by their usage characteristics, and observe several thousand diffusion processes of each type of information from their original first use to their varied recipients over time. We then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it more quickly. Our results demonstrate that the diffusion of news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is influenced by demographic and network factors but not by functional relationships (e.g. prior co-work, authority) or the strength of ties. In contrast, diffusion of discussion topics, which exhibit more shallow diffusion characterized by 'back-and-forth' conversation, is heavily influenced by functional relationships and the strength of ties, as well as demographic and network factors. Discussion topics are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties, while news is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. Furthermore, we find that access to information strongly predicts the number of projects completed by each individual and the amount of revenue that person generates. The effects are economically significant, with each additional "word seen" correlated with about $70 of additional revenue generated. Our findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies and provide some of the first evidence on the economic importance of information diffusion in networks.

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Section: Statistical Specificationsmentioning

confidence: 99%

Productivity Effects of Information Diffusion in Networks

2007

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“…1993), an interesting question is what proportion of the past MIDs could have been prevented by democratic institutions. In a famous quantitative study, Oneal and Russett (2001) used data on all state dyads that existed between the period of 1886-1992 to show that democracy has a strong negative effect on the probability of the onset of a MID after controlling for various covariates, contrary to the claim by Green, Kim and Yoon (2001).…”

Section: Democracy and International Conflictmentioning

confidence: 99%

“…By regressing MID onsets on democracy and other control variables and computing the marginal effects of democracy, Oneal and Russett (2001) and Green, Kim and Yoon (2001) Since democracy here is measured by the Polity score which is a discrete scale ranging from −10 to 10, I used values from 0 to 10 as the threshold for a dyad to be defined as democratic and then calculated the probability of causal attribution for each of these coding schemes. Based on the whole period covered by the dataset (1886 to 1992, top panel), the probability of causal attribution under Assumptions 1 and 2 (or equivalently, the lower bound of this quantity under Assumption 1 alone) almost monotonically increases as we change the threshold for democracy from low to high, with a possible exception of the thresholds Figure 1.…”

Section: Democracy and International Conflictmentioning

confidence: 99%

“…One point of contention between Green, Kim and Yoon (2001) and Oneal and Russett (2001) is whether the effect of democracy had changed after 1951. The results presented in the bottom panel of Figure 2 weakly support this hypothesis, albeit in a nuanced way.…”

Section: Democracy and International Conflictmentioning

confidence: 99%

“…This assumption, unfortunately, is difficult to justify. In fact, the original issue raised by Green, Kim and Yoon (2001) was precisely the possible existence of unobservable confounders in this dataset. In the next example, I consider the situation where one can address this problem with the aid of an instrumental variable.…”

Section: Democracy and International Conflictmentioning

confidence: 99%

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Understanding the Past: Statistical Analysis of Causal Attribution

Yamamoto

2011

American J Political Sci

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Would the third-wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between non-democracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This paper proposes an alternative statistical methodology based on the widely accepted counterfactual framework of causal inference. The contribution of this paper is threefold. First, the paper clarifies differences between causal attribution and causal effects by specifying the type of research questions to which each quantity is relevant. Second, it provides a clear resolution of the long-standing methodological debate on "selection on the dependent variable." Third, the paper derives new nonparametric identification results, showing that the complier probability of causal attribution can be identified using an instrumental variable. The proposed framework is illustrated via empirical examples from three subfields of political science.

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Is globalization really good for public health?

Tausch

2015

Health Planning & Management

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Different components of the KOF Index, most notably actual capital inflows, affect public health negatively. The "decomposition" of the available data suggests that for most of the time period of the last four decades, globalization inflows even implied an aggregate deterioration of public health, quite in line with globalization critical studies. We introduce the effects of inequality on public health, widely debated in global public health research. Our annual time series for 99 countries show that globalization indeed leads to increased inequality, and this, in turn, leads to a deteriorating public health performance. In only 19 of the surveyed 99 nations with complete data (i.e., 19.1%), globalization actually preceded an improvement in the public health performance. Far from falsifying globalization critical research, our analyses show the basic weaknesses of the new "pro-globalization" literature in the public health profession. Copyright © 2015 John Wiley & Sons, Ltd.

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Dirty Pool

Cited by 261 publications

References 68 publications

Productivity Effects of Information Diffusion in Networks

Productivity Effects of Information Diffusion in Networks

Understanding the Past: Statistical Analysis of Causal Attribution

Is globalization really good for public health?

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