Iris Hui

Who is Your Preferred Neighbor? Partisan Residential Preferences and Neighborhood Satisfaction

Do people specifically seek to live among political co-partisans when they relocate? Does the partisan composition of the neighborhood affect their level of residential satisfaction? Drawing on survey data and a survey-embedded experiment, I find that people have a clear preference for co-partisans. Both Republican and Democrat identifiers prefer more co-partisans in their neighborhood. Although the preference is not the primary factor in deciding where to settle, the partisan composition of a neighborhood does affect an individual’s sense of neighborhood satisfaction. Results from a survey-embedded experiment show that respondents’ subjective satisfaction is sensitive to objective facts about their neighborhood. Respondents’ satisfaction slightly decreases when told their neighborhood has a higher presence of members of the opposite party than perceived.

Political Fit as a Component of Neighborhood Preference and Satisfaction: POLITICAL FIT AS A COMPONENT OF NEIGHBORHOOD PREFERENCE

We examine the role party identification plays in moderating peoples perception of place. Do people rely on heuristics to gauge neighborhood partisan composition? If so, those estimates may influence their perception of fit and neighborhood satisfaction. We find that in the absence of concrete, detailed information, people make quick judgments. Republicans, compared to Democrats and non–partisans, are more likely to develop impressions based on the specific location characteristics presented here. When perceived to be a political minority in an area, people are less likely to feel that they belong. In addition to conventional economic and life–cycle factors, political perceptions also affect judgments about the suitability of prospective neighborhoods.

How Do Business Owners Perceive the State Business Climate? Using Hierarchical Models to Examine the Business Climate Perceptions, State Rankings, and Tax Rates

State business climate rankings are popular and can be influential in policy making. Past academic studies have criticized rankings that are based on elusive criteria of state-level data. Breaking from such practice, the authors argue that state business climate is better conceived and measured at the individual firm level. The authors show how perceptions of business climate varies across business owners, depending on their firms’ sectors and stages of development. Using a survey of more than 3,600 small business owners, mainly in the personal and business service sectors, the authors find that many of the popular state rankings either do not associate with individual perceptions of business climate or predict in the wrong direction. The findings warn against the use of state rankings in making policies that affect small business owners. The authors also suggest a need for further study measuring the perceptions of business owners covering different sectors.

How Do Business Owners Perceive the State Business Climate?: Using Heierarchical Models to Examine Business Climate Perception and State Rankings

State business climate rankings are popular and can be influential in policymaking. Past academic studies have criticized those rankings for being based on some subjective criteria and on state-level data. However, in this article, we propose, first, that a business climate is an individual perception, and second, that a business climate is a case-specific condition depending on industries and stages of firm development. Thus, it is critical to measure the business climate at the decentralized, individual level. We employ a newly released survey of over 3,600 small business owners and conduct hierarchical models to control both individual and state variables, and to examine within and between state covariates. Regression results demonstrate that most state rankings are null even for individual perception of business climate, and in fact some rankings are negatively associated. Moreover, contrary to the conventional understanding, personal income, corporate income, and sales taxes are not reflected in the perception, but property taxes are. These findings suggest a need for fundamental reconsideration of how policymakers use business climate rankings.

Shaping the Coast with Permits: Making the State Regulatory Permitting Process Transparent with Text Mining

ABSTRACT This paper examines the California Coastal Commissions permitting process. Using several text mining techniques, including web scraping, information extraction, and supervised classification, I demonstrate how to retrieve empirical data from unstructured texts, namely public meeting agendas and staff reports. Contrary to the concern that the Commission routinely delays or rejects permitting requests, the data reveal that outright rejection of permit applications is rare. On average, eight of ten applications were approved. Single-family homes and commercial development projects were approved about 80% of the time; the rates were about 70% for seawalls and retaining walls, and 60% for land-use changes. Most applications were processed swiftly, with a median application length of 3 months. The agencys influence comes primarily from negotiating each application. Qualitative study of 50 cases pertaining to single-family home construction reveals that the agency adopts a “managed development” approach, that is, allowing development but scrupulously managing various aspects of development. These case studies illustrate how the agency interprets the broad, abstract state laws and translates the mandates into enforceable actions as permitting conditions. In areas where the state mandates conflict, particularly over development in receding shorelines, the agency has the largest leverage in creating and implementing its preferred policies. The text mining techniques demonstrated in this paper can be applied to study any governmental agency. These techniques help to extract information from a massive volume of papers and organize them into a database for analyses. The empirical data extracted from texts can significantly increase bureaucratic transparency.