Smart home cameras raise privacy concerns in part because they frequently collect data not only about the primary users who deployed them but also other parties -- who may be targets of intentional surveillance or incidental bystanders. Domestic employees working in smart homes must navigate a complex situation that blends privacy and social norms for homes, workplaces, and caregiving.
This paper presents findings from 25 semi-structured interviews with domestic childcare workers in the U.S. about smart home cameras, focusing on how privacy considerations interact with the dynamics of their employer-employee relationships. We show how participants’ views on camera data collection, and their desire and ability to set conditions on data use and sharing, were affected by power differentials and norms about who should control information flows in a given context. Participants’ attitudes about employers’ cameras often hinged on how employers used the data; whether participants viewed camera use as likely to reinforce negative tendencies in the employer-employee relationship; and how camera use and disclosure might reflect existing relationship tendencies.
We also suggest technical and social interventions to mitigate the adverse effects of power imbalances on domestic employees’ privacy and individual agency.
more »
« less
How Much Should we Trust Estimates of Firm Effects and Worker Sorting?
Many studies use matched employer-employee data to estimate a statistical model of earnings determination with worker and firm fixed effects. Estimates based on this model have produced influential yet controversial conclusions. The objective of this paper is to assess the sensitivity of these conclusions to the biases that arise because of limited mobility of workers across firms. We use employer-employee data from the US and several European countries while taking advantage of both fixed-effects and random-effects methods for bias-correction. We find that limited mobility bias is severe and that bias-correction is important.
more »
« less
- Award ID(s):
- 1851808
- NSF-PAR ID:
- 10380591
- Date Published:
- Journal Name:
- Journal of Labor Economics
- ISSN:
- 0734-306X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Extant research on the gender pay gap suggests that men and women who do the same work for the same employer receive similar pay, so that processes sorting people into jobs are thought to account for the vast majority of the pay gap. Data that can identify women and men who do the same work for the same employer are rare, and research informing this crucial aspect of gender differences in pay is several decades old and from a limited number of countries. Here, using recent linked employer–employee data from 15 countries, we show that the processes sorting people into different jobs account for substantially less of the gender pay differences than was previously believed and that within-job pay differences remain consequential.
-
null (Ed.)Abstract Workplace wellness programs cover over 50 million U.S. workers and are intended to reduce medical spending, increase productivity, and improve well-being. Yet limited evidence exists to support these claims. We designed and implemented a comprehensive workplace wellness program for a large employer and randomly assigned program eligibility and financial incentives at the individual level for nearly 5,000 employees. We find strong patterns of selection: during the year prior to the intervention, program participants had lower medical expenditures and healthier behaviors than nonparticipants. The program persistently increased health screening rates, but we do not find significant causal effects of treatment on total medical expenditures, other health behaviors, employee productivity, or self-reported health status after more than two years. Our 95% confidence intervals rule out 84% of previous estimates on medical spending and absenteeism.more » « less
-
null (Ed.)Abstract Safely managed waste reuse may be a sustainable way to protect human health and livelihoods in agrarian-based countries without adequate sewerage. The safe recovery and reuse of fecal sludge-derived fertilizer (FSF) has become an important policy discussion in low-income economies as a way to manage urban sanitation to benefit peri-urban agriculture. But what drives the user acceptance of composted fecal sludge? We develop a preference-ranking model to understand the attributes of FSF that contribute to its acceptance in Karnataka, India. We use this traditionally economic modeling method to uncover cultural practices and power disparities underlying the waste economy. We model farmowners and farmworkers separately, as the choice to use FSF as an employer versus as an employee is fundamentally different. We find that farmers who are willing to use FSF prefer to conceal its origins from their workers and from their own caste group. This is particularly the case for caste-adhering, vegetarian farmowners. We find that workers are open to using FSF if its attributes resemble cow manure, which they are comfortable handling. The waste economy in rural India remains shaped by caste hierarchies and practices, but these remain unacknowledged in policies promoting sustainable ‘business’ models for safe reuse. Current efforts under consideration toward formalizing the reuse sector should explicitly acknowledge caste practices in the waste economy, or they may perpetuate the size and scope of the caste-based informal sector.more » « less
-
more » « less
Abstract Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1086/720009
