Study title

We measured momentary well-being using the Experience Sampling Method (ESM) among 220 water collectors in rural Meru County, Kenya over eight weeks. Subjects reported on affect and time use at four randomly-chosen times through the day (Monday through Saturday) on a custom-designed ODK survey app, deployed on a low-cost smartphone. Subjects completed a second ODK survey each weekday evening, reporting on school attendance, study time and chores performed for each school-aged child in the household. After several weeks of baseline data, half of households were randomly chosen to receive free delivery of water to their door for four weeks, reducing water collection times to (near) zero. In-person baseline, midline and endline surveys were conducted by enumerators. The dataset “Meru ESM RCT.dta” contains (in Stata format) the merged data from the ESM exercise and the baseline, midline and endline surveys. The baseline, midline and endline survey were conducted once with each household, but each household completed multiple ESM surveys. This dataset contains 12,956 observations, so to recreate the baseline, midline and endline datasets (one row per household) one would collapse the data on phoneid. The baseline, midline and endline surveys contain some data and questions that were repeated across waves. To make variable names unique, a “_base”, “_mid” or “_end” is appended at the end of the variable name. For example, each survey contained the time that the interviewer opened the app and started the survey (start), as did the ESM survey completed by the subject. This dataset therefore contains four variables, start (the ESM surveys), start_base, start_mid, and start_end. All data was collected in ODK apps. These apps are compiled based on data in Excel spreadsheets, including variable names, questions, and answer codes. These ODK excel spreadsheets thus also serve as data dictionaries. The key unique identifier linking records is phoneid. This is an...