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This chapter uses the historian’s method of micro-history to rethink the significance of the Supreme Court decision Muller v. Oregon (1908). Muller is typically considered a labor law decision permitting the regulation of women’s work hours. However, this chapter argues that through particular attention to the specific context in which the labor dispute took place – the laundry industry in Portland, Oregon – the Muller decision and underlying conflict should be understood as not only about sex-based labor rights but also about how the labor of laundry specifically involved race-based discrimination. This chapter investigates the most important conflicts behind the Muller decision, namely the entangled histories of white laundresses’ labor and labor activism in Portland, as well as the labor of their competitors – Chinese laundrymen. In so doing, this chapter offers an intersectional reading of Muller that incorporates regulations on Chinese laundries and places the decision in conversation with a long line of anti-Chinese laundry legislation on the West Coast, including that at issue in Yick Wo v. Hopkins (1886).

The purpose of this paper is to confirm empirically the existence of a US apparel import intermediary (AII) identity crisis, and to provide a detailed descriptive profile of AIIs, differentiating them from apparel firms not primarily engaged in importing activities.

A survey study was conducted using a national sample of US AIIs. Based on these firms' executives' responses, a firm identity issue was analyzed and a detailed profile of these firms' business characteristics was developed, using frequency comparisons.

The study confirmed that US AIIs are currently experiencing an identity crisis, as nearly half of the study respondents misclassified themselves as apparel manufacturers or other business types, suggesting a significant distortion in US Economic Census data. The study also provided a descriptive profile of US AIIs, including geographic location and other business operation characteristics.

Three fourths of the survey respondents were located in the state of New York. Whether most US AIIs truly reside in New York cannot be known with certainty. Generalization of the study findings to a greater population should be cautious.

Confirmation of an AII identity crisis suggests both aggregate and individual firm‐level impacts on import activities. The study offers a new term, “intermediary”, to replace the US Census Bureau term “wholesaler” to accurately reflect the industry's transformation.

The study provides the first empirical support for a US AII identity crisis. The detailed profile of US AIIs offers industry data not available prior to this study.

This paper seeks to provide researchers and librarians with an overview of the US Census Bureau's American Community Survey (ACS), with a specific focus on practical issues that users must face when choosing and using ACS datasets.

Each of the following issues are explored subsequent to a general overview of the ACS: choosing among census datasets from different census programs, interpreting and choosing between the different ACS period estimates, selecting census geography, understanding and recalculating margins of error, and accessing the data. Samples of ACS tables and formulas for creating derived estimates are used to illustrate how to interpret and work with the data.

The ACS datasets are fundamentally different from the decennial census as they are period estimates created from rolling sample surveys. The ACS has a steeper learning curve; this complexity is due in part to the number of choices users must make between datasets, but the primary challenge is learning how to understand and work with estimates as opposed to population counts.

While other papers have discussed the benefits and challenges of the ACS, this paper is structured around the practical issues that researchers must face when using it. Special consideration is given to calculating derived estimates using spreadsheet formulas, as this is a key task that many users will need to perform and spreadsheets are the most likely tool users will employ to manipulate the data.

Recent federal legislation and numerous public policy debates have relied heavily on estimates of the number of police agencies and police officers in the USA. Historically, these estimates have been problematic, varying tremendously over time, across different sources, and using different methodologies. Currently, the two main sources of agency‐level data for estimating these numbers are the FBI’s Uniform Crime Reports and the Census Bureau’s Law Enforcement Directory Survey. While there is a great deal of overlap between these two databases, each contains thousands of departments not listed in the other. Also, among those departments listed in one or more of these databases, there is tremendous variation in the number of police officers recorded. While some of the disparity can be explained by banal differences in counting and record‐keeping methods, much is rooted in differing definitions of what constitutes a “police officer” and a “police agency”. In this study, we closely examine both databases in an effort to account for the differences between them. In addition, we introduce a new data source derived from the records of the Justice Department’s Office of Community Oriented Policing Services (COPS). Based on a thorough exploration of all three databases, we discuss the impact of their differences on criminal justice policy and police research. We first develop our own estimate of the number of police departments and police officers in the USA that differs substantially from other current estimates. We then estimate the number of police officers that the COPS office and future evaluators should use as the baseline for measuring the Clinton Administration’s success at adding 100,000 officers to the streets of America. Finally, we offer a modest set of recommendations for achieving greater uniformity across separate police agency databases.

In the United States, many arguments for mass transport often focus on its benefits to the environment or in terms of congestion relief. This chapter instead looks at direct micro-scale and meso-scale economic and social benefits by identifying occupations, industries, and demographic groups that are both growing and can benefit from transit. In using this form of local-scale identification of unique economic circumstances, government planners, elected officials, and academics can create a practical, GIS-focused approach to increase the utility and acceptance of mass transportation in the United States.

Using a GIS-based approach with US Census and US Labor Department data, the chapter focuses on local growth sectors in the economy, then identifying their transit-usage patterns. Additionally some GIS are used to identify concentration areas of both occupations and transit usage, along with areas of likely employment for those individuals. Locally this creates “micro-climates” or hot spots for favorable views and usage of transport. These micro-climates can be duplicated in other similar areas to increase utilization, and therefore return on investment, of public transportation.

For denser areas, affluent downtowns, and areas which are accessible to hospitals and universities, the primary growth engines of the current US labor market, transit has a positive correlated relationship. Additionally, recent immigrant groups, particularly those with limited English speaking ability, have also shown a correlated growth in transit usage. By identifying these areas with GIS, transit can better align its services to needs and improve it return on investment.

The United States has had a fifty-year-long negative view of public transport outside of major urban areas. By identifying groups with positive correlated use of transit, the return on investment and public perception of its use and environmental sustainability can mesh with land use planning and perceived quality of the service. In so doing, transit use may be encouraged.

The United States is faced with very high petrol prices relative to its history, and somewhat lesser incomes among its sub-age-30 work cohorts. This has encouraged density. However, in order to provide for this increased demand, there must be an increase in both supply and politically perceived value in its investment. This chapter seeks to be an early pragmatic model in valuing public transport at a local level.

The past century and a quarter can be divided into three successive eras for homeownership policy characterization. For the first four decades, the federal government pursued a laissez-faire policy that left housing issues to the individual states and private markets. For the next six decades, the federal government implemented a policy created as part of the Roosevelt New Deal program. Finally, the Clinton administration discarded the New Deal policy in favor of a more aggressive policy that has continued to the present day. The purpose of this study is to compare the performance of the respective policies.

The study introduces two metrics. The first metric, based on government homeownership rate data, enables comparison of the laissez-faire and New Deal policies. The second metric, based on financial frictions in the mortgage market, enables comparison of the New Deal and Clinton policies.

Analysis based on the first metric suggests the New Deal policy was successful in meeting its macroeconomic objectives and was more effective overall than the laissez-faire policy. Analysis based on the second metric suggests the New Deal policy was also more successful in both respects than the Clinton policy.

The findings suggest that the Clinton homeownership policy was the primary driver behind the recent US housing crisis and that vulnerability in the secondary mortgage market created by the Clinton policy represents systemic housing market risk.

The study introduces simple analytical tools to address problems related to systemic risk in the US housing and housing finance markets due to homeownership policy.

The purpose of this paper is to assess whether additional information about a community’s level of social capital can help to better predict a return rate from that area, in order to better target resources to improve mail-in responses.

Two-sample two-stage least squares is used to apply determinants of six different measures of social capital from the Social Capital Community Benchmark Survey to observations in the Decennial Census (DC) which are then aggregated to the census tract level. The probability of a census tract having a high level of each social capital measure is estimated. Multivariate regression is used to identify the importance of high community social capital for predicting census mail-in return rates.

The analysis reveals that a higher level of trust contributes the most to increasing return rates and a high level of political activism decreases return rates. Additionally, higher levels of sociability contribute negatively to DC return rates, which is consistent with sociability being linked to a more insular (i.e. family and friends) focus.

While contributing statistically significantly to the predictability of census tract response rates, the cost of acquiring measures of social capital for each census tract may not to be viewed worth the gain in predictive power.

Higher levels of trust contribute positively to survey participation, suggesting that any social, economic or political environment that diminishes trust will undercut civic engagement. Political activism and (insular) sociability decrease participation.

This paper combines non-public and public data to obtain measures of social capital along more dimensions than are typically studied, and finds that not all types of social capital are related to feelings of social integration in the same way.

This article seeks to demonstrate a technique for using a Geographic Information System (GIS) to analyze US Census data to better understand potential library users and improve library service planning.

A GIS was used to link variables such as age, race, income, and education from the 2000 US Census with service area maps of two proposed branch libraries. Thematic maps were created for each of the census variables to display demographic information about potential library users within a three‐mile radius of the proposed libraries.

The GIS maps and their associated attribute data enhanced the ability to analyze and compare the demographics of potential users in the two library areas and identify significant differences. The data on age, race, education and income for residents in the two areas were combined with known library use indicators to help plan library services with the potential to attract different populations in the local community.

Provides practical information about downloading US Census data into a GIS to be able to present demographic data about potential library users both visually and quantitatively.