Zero-inflated Ordered Probit Model Research Articles

A categorical modeling approach to analyzing the impacts of the Lacey Act 2008 amendment on Chinese companies’ export cost and the implications on their sourcing behaviors

The United States (US) Lacey Act 2008 amendment (LAA) is a timber legality regulation that requires US importers to monitor and minimize the risk of illegally harvested wood products within their supply chains. This paper empirically examines the effect of the LAA on Chinese companies’ export costs to the US. The study uses 138 responses from two surveys in Shanghai, China, in 2013, i.e., 5 years after the LAA was implemented. Given the high proportion of zero export increase indicated by the Chinese companies, a zero-inflated ordered probit model was used to model Chinese companies’ export cost increases to the US. The research results demonstrate that pre-LAA raw material sourcing patterns are primary indicators of the respondents’ export cost increase to the US as a result of the LAA. From the results, it can be inferred that log and lumber importers from suspect regions are taking additional measures, by changing their procurement practices, to ensure the legality of their raw material, which is adding to their cost structure. The results also indicate that smaller companies, given their flexibility with raw material procurement, were less likely to experience a post-LAA cost increase relative to their larger counterparts.

Exploring the covariates of sport participation for health: an analysis of males and females in England

Increasing sport participation has been identified as a key vehicle for achieving World Health Organisation guidelines for health-enhancing physical activity (HEPA). A policy challenge is to promote this changed behaviour. Making use of the Active People Survey, a zero-inflated ordered probit model is used to identify the covariates of sport participation for males and females with respect to the related decisions to either participate in sport or not, and to participate at either lower or higher than desired intensity. To inform current policy emphases, the Active Places Survey is also used to examine the causal impact of facility provision on these behaviours. Results indicate that females are more likely to adopt less intensive activity even with time available. Families could place most constraints on females, but also reduce the intensity of male participation. Education has a greater association with higher intensity female activity. The largest effects are identified for sport club membership and facility satisfaction. Both of these affect male intensity of activity most. The main policy challenges facing sport in contributing to HEPA are shown to involve retaining male intensity of participation when ageing and encouraging greater female intensity of participation, with more appropriate matching of facilities.

Modeling Two Types of Peace

A growing body of applied research on political violence employs split-population models to address problems of zero inflation in conflict event counts and related binary dependent variables. Nevertheless, conflict researchers typically use standard ordered probit models to study discrete ordered dependent variables characterized by excessive zeros (e.g., levels of conflict). This study familiarizes conflict scholars with a recently proposed split-population model—the zero-inflated ordered probit (ZiOP) model—that explicitly addresses the econometric challenges that researchers face when using a “zero-inflated” ordered dependent variable. We show that the ZiOP model provides more than an econometric fix: it provides substantively rich information about the heterogeneous pool of “peace” observations that exist in zero-inflated ordinal variables that measure violent conflict. We demonstrate the usefulness of the model through Monte Carlo experiments and replications of published work and also show that the substantive effects of covariates derived from the ZiOP model can reveal nonmonotonic relationships between these covariates and one’s conflict probabilities of interest.

Investigating the influence of curbs on single-vehicle crash injury severity utilizing zero-inflated ordered probit models

The severity of traffic-related injuries has been studied by many researchers in recent decades. However, previous research has seldom accounted for the effects of curbed outside shoulders on traffic-related injury severity. This study applies the zero-inflated ordered probit (ZIOP) model to evaluate the influences of curbed outside shoulders, speed limit change, as well as other traditional factors on the injury severity of single-vehicle crashes. Crash data from 2003 to 2007 in the Illinois Highway Safety Database were employed in this study.The ZIOP model assumes that injury severity comes from two distinct sources: injury propensity and injury severity when this crash falls into the injury prone category. The modeling results show that on one hand, single-vehicle crashes that occurring on roadways with curbed outside shoulders are more likely to be injury prone. On the other hand, the existence of a curb decreases the likelihood of severe injury if the crash was in the injury prone category. As a result, the marginal effect analysis implies that the presence of curbs is associated with a higher likelihood of no injury and minor injury involved crashes, but a lower likelihood of incapacitating injury and fatality involved crashes. In addition, in the presence of curbed outside shoulders, the change of speed limit adds no significant impact to the injury severity of single-vehicle crashes.Moreover, the modeling results also highlight some interesting effects caused by vehicle type, light and weather conditions, and drivers’ characteristics, as well as crash type and location. Through a comprehensive evaluation of the modeling results, the authors find that the ZIOP model performs well relative to the traditional ordered probit (OP) model, and can serve as an alternative in future studies of crash injury severity.

Information Politics Versus Organizational Incentives: When Are Amnesty International's ‘‘Naming and Shaming’’ Reports Biased?1

Hill, Daniel W., Will H. Moore, and Bumba Mukherjee. (2013) Information Politics Versus Organizational Incentives: When Are Amnesty International's ‘‘Naming and Shaming’’ Reports Biased? International Studies Quarterly, doi: 10.1111/isqu.12022 © 2013 International Studies Association ‘‘Information politics’’ INGOs such as Amnesty International have incentives to maintain their credibility by carefully vetting information about rights abuses committed by governments. But they are also strategic actors that may inflate allegations of abuse to fulfill organizational imperatives. This raises an intriguing question: When are INGOs more likely to exaggerate their allegations? In answer to this question, we argue that news media reporting pressures INGOs to comment for organizational reasons, even if the information available to them is poor. On the other hand, higher numbers of domestic human rights NGOs increase the quality of available information, and INGOs will find more credible information provided about states as the winning coalition to the selectorate rises. Yet, an incentive to exaggerate allegations under certain conditions does not imply that INGOs will always do so. Indeed, there exists significant observed variation in INGO reports about government abuse. To test our hypotheses, we employ a zero-inflated ordered probit model with correlated errors that permits us to model an unobservable probability (the probability that the INGO exaggerates its allegations) and correct for potential bias. Results provide support for our hypotheses, and suggest that Amnesty International adheres to its credibility criterion, rarely succumbing to incentives to exaggerate abuse.

영 과잉 순서적 프로빗 모형을 이용한 한국인의 음주자료에 대한 베이지안 분석

영 과잉 순서적 프로빗 모형을 이용한 한국인의 음주자료에 대한 베이지안 분석

The Zero-Inflated ordered probit approach to modelling sports participation

This paper investigates the determinants of the frequency and the probability of sports participation in Spain by applying the Zero-Inflated Ordered Probit (ZIOP) model. The ZIOP model allows to account for excessive zero observations and differentiate between genuine ‘non-participants’ and individuals who did not participate at a time but would do so if the circumstances were different. The results show that the decision to participate in sports or not and the frequency of sports participation are driven by different factors.

New estimates of the demand for physical and mental health treatment

Consumers' price responsiveness is central to US health-care reform proposals, but the best available estimates are now more than 25 years old. We estimate health-care demands by calculating expected end-of-year prices and incorporating them into a zero-inflated ordered probit model applied to several overlapping panels of data from 1996 to 2003. Results from our correlated random effects specification indicate that the price responsiveness of ambulatory mental health treatment has decreased substantially and is now slightly lower than physical health treatment. This suggests that concerns over moral hazard alone do not warrant less generous coverage for mental health. However, prescription drug demand is more price elastic.

A zero-inflated ordered probit model, with an application to modelling tobacco consumption

Data for discrete ordered dependent variables are often characterised by “excessive” zero observations which may relate to two distinct data generating processes. Traditional ordered probit models have limited capacity in explaining this preponderance of zero observations. We propose a zero-inflated ordered probit model using a double-hurdle combination of a split probit model and an ordered probit model. Monte Carlo results show favourable performance in finite samples. The model is applied to a consumer choice problem of tobacco consumption indicating that policy recommendations could be misleading if the splitting process is ignored.