In response to the health threats posed by the use of solid fuel in traditional stoves, as well as concerns about deforestation, both governments and nongovernmental organizations (NGOs) have been implementing clean stove programs for several decades. This paper evaluates an improved stove program run by Gram Vikas (GV), an NGO that operates in the state of Orissa. The stove considered in this study represents a relatively inexpensive improved stove technology. It is constructed with local materials and is low-cost at roughly US$12.50. GV subsidized the stove cost by contributing stove materials (chimney), design, and a skilled mason to supervise the construction. Households were responsible for providing mud for the stove base, labor and a payment of about US$0.75, which was used both to pay the mason who assisted in building and maintaining the stoves and to contribute to a fund for stoves for any new houses built in the village. As the stove is made from locally available materials, it can be easily constructed in these remote, rural areas of India.
In addition to providing the stoves, GV conducted the standard information campaigns that NGOs run when they introduce a new program for households that have won the lottery. Specifically, during construction GV held training sessions on proper use and maintenance. Among households that received a stove in the first wave, almost 70 percent report that they attended a training session. Moreover, GV identified individuals in each village who used their stoves correctly and hired (with a small stipend) them to promote proper use and alert GV when the stoves were in need of repair. Of those who received a stove in the first wave, 62 percent report knowing who this “promoter” is, 48 percent report that they attended a meeting with the promoter, and another 47 percent state that they received a visit from the promoter to discuss stove use. In total, about 86 percent report either having GV or the promoter provide training on the stove (either through a meeting or visit).
In the summer of 2005, GV obtained permission from 42 villages to participate in the study. In a decision unrelated to the study, three villages withdrew from all GV activity. As a result, researchers added five additional villages in June 2007. Therefore, a total of 2,575 households in 44 villages participated in the study. After researchers completed the baseline survey in each village (in 2006 for the majority of villages, and in 2007 for the additional five villages), a village meeting was conducted. At each meeting, GV explained that the stoves were being built in three waves, and that the households would be randomly assigned to each wave. Next, a public lottery (monitored by the research team) was conducted to choose the first third of households in the village that would be offered a GV improved stove. GV completed the first wave of stove construction and user training between September 2006 and March 2007. After the midline survey, the second round of village meetings occurred. A lottery was conducted to choose households that would be offered a stove in the second wave of construction. From May 2009 to April 2010, the second round of stove construction and training occurred. Throughout the study, researchers conducted a series of surveys to create a panel dataset on stove use, smoke exposure, health, stove breakages and repairs, and fuel use.
Researchers collected comprehensive data on the socio-demographic characteristics of each household. This data includes household composition (size, as well as each member’s age, sex, and relationship to the head of household), demographics (education levels, caste, religion), economic indicators (assets, indebtedness), and consumption patterns. In addition, for each household member, researchers collected measures of productivity, such as employment status, time-use patterns for adults over the last 24 hours, and school enrollment and attendance for children. Through a series of surveys, researchers collected information on stove use. This included the types of stoves a household owned, meals cooked with each type of stove over the previous week, repairs and maintenance activities surrounding the stoves, and fuel expenditures (both money and time). In addition, we collected information on beliefs about the efficacy of the stoves (for example, whether they use less fuel) and on satisfaction with the stoves. To measure smoke exposure, the team measured exhaled carbon monoxide (CO) with a Micro Medical CO monitor. CO is a biomarker of recent exposure to air pollution from biomass combustion, and therefore it can be used to proxy an individual’s personal exposure to smoke from their stoves. Furthermore, it is an inexpensive way to proxy for inhalation of particulate matter, which has been shown to be an important determinant of infant mortality and life expectancy.
Researchers collected two types of health data. First, they conducted detailed health recall surveys about symptoms (coughs, colds, etc.), infant outcomes, and health expenditures. Researchers complemented these data with physical health checks for biometric measurements, such as height, weight, and arm circumference. During the physical health check, they administered spirometry tests designed to gauge respiratory health by measuring how much air the lungs can hold and how well the respiratory system can move air in and out of them. In contrast to peak flow tests, which are easier to administer, spirometry readings can be used to diagnose obstructive lung disorders (such as chronic obstructive pulmonary disease (COPD) and asthma), and also restrictive lung disorders. Further, this test is the only way to obtain measurements of lung function that are comparable across individuals. The tests were conducted using the equipment directions, as well as guidelines from the American Association for Respiratory Care. Finally, throughout the study, researchers compiled Gram Vikas’s administrative data, i.e., data on lottery participation, treatment status, and outcomes.
Most of the evidence on health improvements from reductions in indoor air pollution is based on the association between clean stove usage and health in observational data. However, those who choose to use a clean stove may generally value health more than those who do not and thus may also undertake other health investments, either of which would lead to better health. In this case, our estimated coefficients would be biased upwards. Alternatively, the improved stoves may be disproportionately used by the sick, which would cause the estimated relationships to be biased downwards. The experimental design we propose allows us to solve these endogeneity problems by comparing winners and losers from the stove lottery.
Researchers estimate the reduced form effect of winning the stove on a series of outcomes, including stove use, CO exposure, health, and other non-health stove outcomes (such as fuel use and cooking time). In their model, researchers include village survey month-year fixed effects, i.e., there are separate fixed effects for all observations from a village in a given month-year (e.g., January 2010). For CO exposure, health, and non-health stove outcomes (when possible) researchers additionally include the baseline value of the outcome to gain additional precision. Researchers also estimate how the treatment effect varies over time, as the effect of being offered a stove may change throughout time for a variety of reasons. To do that, they interact the treatment effect with a set of indicator variables for whether the observation falls within a given year after stove distribution. To scale the results, researchers estimate the effect of using any type of low-polluting stove on CO exposure using an instrumental variables strategy. Finally, the household-level equations are weighted to account for household splits and mergers. For all regression analysis, the standard errors are clustered at the household level, which is the unit at which the treatment was assigned.