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Odis Johnson Jr., University of Maryland; 2012-012

Philip N. Cohen, University of Maryland; 2012-013

Caryn N. Bell, University of Maryland; Tina K. Sacks, University of California Berkeley; Courtney S. Thomas Tobin, University of California Los Angeles; Roland J. Thorpe, Jr. Johns Hopkins University; 2019-004

Caryn N. Bell University of Maryland: Jessica L. Owens-Young American University: 2019-005

Reeve Vanneman, University of Maryland; 2019-009

The past three decades have seen the advent of major transformations in the Indian economy. The economy has achieved average growth rates of 5–9%, education has risen sharply for both men and women, fertility rates have declined, and infrastructure facilities, particularly access to electricity, cooking gas and piped water, have improved. All these factors are expected to reduce the demand for women’s time spent in domestic chores and increase their opportunities for paid work. Paradoxically, however, the National Sample Surveys document a substantial decline in women’s work participation rates (WPRs), particularly for rural women. Optimistic interpretation of these trends suggests that increasing prosperity accounts for women’s labour force withdrawal. For young women, rising school and college enrolment is incompatible with demands of the workforce. For both young and older women, rising prosperity allows for withdrawal from economic activities to focus on domestic duties. Pessimistic interpretations of these trends suggest that it is absence of suitable jobs rather than women’s withdrawal from the labour force that accounts for declining female work participation. A third explanation focuses on increasing measurement errors in work participation data from the National Sample Surveys. This paper examines these diverse explanations using data from National Sample Surveys and India Human Development Surveys for 2004–2005 and 2011–2012 and finds that: (1) Decline in rural women’s work participation recorded by National Sample Surveys may be overstated; (2) supply factors explain a relatively small proportion of the decline in women’s work participation rates; (3) public policies such as improvement and transportation facilities and MGNREGS that enhance work opportunities for women are associated with increased participation by women in the work force.

The use of solid cooking fuels—wood, straw, crop residue, and cow-dung cakes—is associated with higher levels of environmental pollution and health burden. However, even in an era when incomes have grown and poverty has declined, the proportion of Indian households using clean cooking fuels such as kerosene or Liquefied Petroleum Gas (LPG) has increased only slightly. Even among the wealthiest quintile, only about 40 percent of the households rely solely on clean fuel. Since the chores of cooking and collection of fuel remain primarily the domain of women, we argue that intra-household gender inequalities play an important role in shaping the household decision to invest in clean fuel. Analyses using data from the India Human Development Survey (IHDS), a panel survey of over 41,000 households conducted in two waves in 2004-05 and 2011–12, respectively, show that women's access to salaried work and control over household expenditure decisions is associated with the use of clean fuel.

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.

Numerous surveys link interview data to administrative records, conditional on respondent consent, in order to explore new and innovative research questions. Optimizing the linkage consent rate is a critical step toward realizing the scientific advantages of record linkage and minimizing the risk of linkage consent bias. Linkage consent rates have been shown to be particularly sensitive to certain design features, such as where the consent question is placed in the questionnaire and how the question is framed. However, the interaction of these design features and their relative contributions to the linkage consent rate have never been jointly studied, raising the practical question of which design feature (or combination of features) should be prioritized from a consent rate perspective. We address this knowledge gap by reporting the results of a placement and framing experiment embedded within separate telephone and Web surveys. We find a significant interaction between placement and framing of the linkage consent question on the consent rate. The effect of placement was larger than the effect of framing in both surveys, and the effect of framing was only evident in the Web survey when the consent question was placed at the end of the questionnaire. Both design features had negligible impact on linkage consent bias for a series of administrative variables available for consenters and non-consenters. We conclude this research note with guidance on the optimal administration of the linkage consent question.

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to reveal such information. Although previous research has shown that digital trace data can accurately predict sociodemographic characteristics, little is known about the potentials of such data to predict sensitive outcomes. Against this background, we investigate in this article whether we can accurately predict voting behavior, which is considered personal sensitive information in Germany and subject to strict privacy regulations. Using records of web browsing and mobile device usage of about 2,000 online users eligible to vote in the 2017 German federal election combined with survey data from the same individuals, we find that online activities do not predict (self-reported) voting well in this population. These findings add to the debate about users’ limited control over (inaccurate) personal information flows.