African American Inequality in the United States

Janice H. Hammond; A. Kamau  Massey; Mayra G. Garza

Background Note | HBS Case Collection | September 2019 (Revised June 2020)

African American Inequality in the United States

by Janice H. Hammond, A. Kamau Massey and Mayra G. Garza

Abstract

This note describes how historical and on-going policies and practices that discriminate against African Americans led to present-day inequality. Topics include slavery, segregation, Jim Crow laws, “black codes,” and policies and practices relating to criminal justice, housing, and education.

Keywords: African Americans; justice; slavery; discrimination; Race; Equality and Inequality; Prejudice and Bias; Policy; History; United States;

Language: English

Format: Print

33 pages

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Citation:

Hammond, Janice H., A. Kamau Massey, and Mayra G. Garza. "African American Inequality in the United States." Harvard Business School Background Note 620-046, September 2019. (Revised June 2020.)

The Web-based beer game is an exercise that demonstrates supply channel dynamics. Simulates the flow of material and information in a simplified channel of beer production and distribution, focusing on the linkages between a beer manufacturer, its distributors, a wholesaler, and a retailer. Students are organized into teams of four, each assigned to place orders and manage inventory for one firm in the channel. In this simplified channel, orders are the only information allowed to flow among channel participants. The 90-minute exercise is played on a web-based computer simulation tool developed at the Harvard Business School. The program has a parameter page that allows the instructor to easily vary information and transportation lead times, information availability, demand, and so on. The instructor can work with students to understand the nature of the problems they experience, identify the causes, and discuss possible remedies.

Case | HBS Case Collection | October 2014 (Revised August 2018)

Caesars Entertainment

Janice H. Hammond and Aldo Sesia

This case describes the introduction of a regression analysis model for forecasting guest arrivals to Caesars Palace hotel in Las Vegas, Nevada. The company will use the forecast to staff the front desk in the hotel. The staff is unionized and the company has little flexibility to change staffing levels on a short-term basis. The case is set in the context of industry overcapacity and lower customer demand.

The case describes several models that could be used to forecast guest arrivals, including a moving average technique and a multiple regression model. The multiple regression model includes over 40 independent variables, including dummy variables (e.g., to represent day of week, month, year, holidays, paydays) as well as continuous variables to represent customer segment and average daily room rate. The case contains tables showing the output of the regression model, and compares the fit of the moving average and regression models. The case allows students to understand how such a model is developed within an organization and to evaluate the models presented. Students may work with a data file with several years of historical data or they may work with the model description and output results in the case.

Keywords: Forecasting; staffing; gaming; gaming industry; hotel industry; Decision Making; Forecasting and Prediction; Human Resources; Selection and Staffing; Entertainment; Games, Gaming, and Gambling; Operations; Service Delivery; Service Operations; Accommodations Industry; Travel Industry; Tourism Industry; Food and Beverage Industry; Las Vegas;