Gender, time-use, and energy expenditures in rural communities in India and Nepal

The Distribution of Effort: Physical Activity, Gender Roles, and Bargaining Power in an Agrarian Setting

“…However, some studies have measured physical activity levels of rural populations in developing countries and find that overall levels of physical activity are higher in low-income countries than in high-income countries (Benefice, Garnier and Ndiaye 2001;Assah et al 2011;Christensen et al 2012). Closely related to this study is the work by Zanello, Srinivasan and Nkegbe (2017), Picchioni et al (2020) and Srinivasan et al (2020), who pilot the use of accelerometers to capture energy expenditure in rural communities in Ghana, India and Nepal. These studies, however, relied on smaller sample sizes of approximately 40 individuals (i.e.…”

Oil palm and gendered time use: A mixed-methods case study from West Kalimantan, Indonesia

“…Without such methods it is impossible to observe the true effect of oil palm adoption on livelihood changes or the coping strategies employed to cope with time scarcity. Recent research has shown the promise of new and innovative approaches to time use research (e.g., accelerometers and GPS) ( Picchioni et al, 2020 ; Srinivasan et al, 2020 ). Application of these methods in conjunction With full-accounting time use recall methods could reveal the effects of agrarian and landscape change on energy expenditure and physical exertion.…”

“…This approach is preferable to modelling absolute times as it reflects the inherent trade-offs between activities. We tailored a Fractional Multinomial Logit (FML) model developed by Mullahy (2015) and used by Picchioni et al (2020) in the context of time-use studies with co-variates relevant to the local context. The methodology allows for the calculation of marginal effects that can be interpreted as trade-offs between time allocated in activties, keeping the daily allocation of time constrained to 24-hours in a day.…”

Positioning household surveys for the next decade

“…Research has demonstrated the extent and econometric implications of non-classical measurement errors in self-reported survey data on a range of topics, while also documenting the accuracy, feasibility, and cost implications of adopting direct measurement tools, such as GPS technology for plot area measurement and outline capture [79][80][81][82], crop cutting for crop yield estimation [83][84][85][86], high-frequency phone survey data collection for measuring household agricultural labor inputs [87,88], DNA fingerprinting for crop variety identification [89,90], physical activity trackers (i.e. accelerometers) for informing the measurement and analysis of labor productivity, effort, and poverty [91][92][93][94], smartphone applications for time use measurement, recording social interactions between respondents and interviewers, or real-time travel patterns [95][96][97][98][99], lowcost testing kits for the rapid measurement of water quality [100], and "web scraping" for automating the collection of prices for selected internet retailers, as opposed to relying exclusively on survey operations for the Consumer Price Index (CPI) [101].…”