Regulating Bracero Migration: How National, Regional, and Local Political Considerations Shaped the Bracero Program

This article examines how federal, state, and municipal governments administered the migrant worker selection process in the states of Guanajuato, Jalisco, and Michoacán during the initial phase of the Bracero Program, a bilateral initiative that allowed Mexican men to work in the United States as seasonal contract farmworkers. It argues that multiple political factors—such as the activities of groups that opposed the ruling Partido Revolucionario Institucional, organized labor conflicts, and the need to respond to natural disasters—influenced how officials allocated contracts, which rural workers were deemed eligible or ineligible to migrate, and which individual rural workers ultimately received contracts. The article shows that federal authorities delegated increased administrative responsibilities to state and municipal governments as the Bracero Program progressed, which in turn allowed regional and local officials to exercise considerable influence during selection periods.

PrivCrowd: A Secure Blockchain-Based Crowdsourcing Framework with Fine-Grained Worker Selection

Blockchain-based crowdsourcing systems can mitigate some known limitations of the centralized crowdsourcing platform, such as single point of failure and Sybil attacks. However, blockchain-based crowdsourcing systems still endure the issues of privacy and security. Participants’ sensitive information (e.g., identity, address, and expertise) have the risk of privacy disclosure. Sensitive crowdsourcing tasks such as location-based data collection and labeling images including faces also need privacy-preserving. Moreover, current work fails to balance the anonymity and public auditing of workers. In this paper, we present a secure blockchain-based crowdsourcing framework with fine-grained worker selection, named PrivCrowd which exploits a functional encryption scheme to protect the data privacy of tasks and to select workers by matching the attributes. In PrivCrowd, requesters and workers can achieve both exchange and evaluation fairness by calling smart contracts. Solutions collection also can be done in a secure, sound, and noninteractive way. Experiment results show the feasibility, usability, and efficiency of PrivCrowd.