Predictive models for charitable giving using machine learning techniques

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Charitable Giving

In 2005 charitable giving in the United States totaled over $260 billon, or around 1.9% of personal income, making it a significant fraction of the economy. Individual giving accounted for 77% of this total, while foundations accounted for 12%, bequests for 7%, and corporations for 5% (Giving USA, 2006). Almost 70% of US households report giving to charity. While the US typically has one of the...

Charitable Giving

The Market for Charitable Giving

T hrough good and bad economic times, charitable gifts have continued to roll in largely unabated over the past half century. In a typical year, total charitable gifts of money now exceed 2 percent of gross domestic product. Moreover, charitable giving has nearly doubled in real terms since 1990, and the number of nonprofi t organizations registered with the IRS grew by nearly 60 percent from 1...

(Un)Informed Charitable Giving∗

Evidence suggests little informed giving. To understand this behavior, we examine a model of charitable giving with costly information. We find that an individual who considers a smaller contribution is less likely to seek information, and thus the percentage of informed giving diminishes as the population grows. We also find that a direct grant to the charity exacerbates crowding-out by discou...