Towson UniversityDepartment of Economics

Working Paper Series

Working Paper No. 2018-05

The Combined Role of Subsidyand Awareness in Uptake of

Stigmatized Products

by Vinish Shrestha and Rashesh Shrestha

November 2018

© 2018 by Author. All rights reserved. Short sections of text, not to exceed twoparagraphs, may be quoted without explicit permission provided that full credit, including© notice, is given to the source.

1

The Combined Role of Subsidy and Awareness in Uptake of

Stigmatized Products∗

Vinish Shrestha

Towson University†

Rashesh Shrestha

Economic Research Institute for ASEAN and East Asia (ERIA)‡

September 19, 2019

Abstract

Can a combination of common policy interventions better stimulate the demand for healthproducts that are widely available and well-known but whose demand may be curtailed bycultural norms? This paper studies the joint role of awareness and subsidy in increasing thedemand for sanitary pads — a product that is ubiquitous but related to social taboo in manycountries. We conduct a field experiment in Nepal in which a randomly selected group ofwomen receive menstrual health-related awareness. In addition, both treatment and controlgroups face a randomly varied effective price for sanitary pads induced through allocation ofdiscount coupons. We find that the awareness treatment shifts the demand curve to the right.Furthermore, the shift is not parallel — the impact is largest at the 50% discount level wherethe coupon redemption increases by 23 to 26 percentage points. We also find modest spillovereffects of the awareness treatment. Our results suggest that combining subsidy with awarenesscould be a more cost-effective strategy to increase take-up of health technology whose demandis constrained by social stigma.

JEL Code: I15, D12, O33, I26Key words: price subsidies, societal stigma and awareness, estimating elasticity, menstrual health

∗This paper was previously circulated as “Awareness and Demand for Sanitary Pads: Evidence from a RandomizedExperiment in Communities of Nepal”†Corresponding author: Vinish Shrestha, Towson University, Stephens Hall Room 101B, Towson, MD 21252.

Email: vshrestha@towson.edu. Phone: +1 410-704-2956.‡Email: rashesh.shrestha@eria.org. The views expressed in this paper are that of the authors. We would like to

offer our sincere gratitude to Dipasha Bista, Barsa Budhathoki, Rita Ghatani, Shanti Ghimere, Rakshya Gorkhali,Malati Gurung, Situ Kapali, Aastha Shrestha Neupane, Ranju Pandey, Sandhya Pathak, Sari Khatiwada, SaritaRegmi, Prativa Shrestha, Sapana Shrestha, and Prativa Thapa for helping us throughout the experiment phase asresearch assistants. We thank Raju Amatya and Pranish Shrestha for providing logistic guidance, without whichit would not have been possible to conduct this study. We would like to thank Anja Benshaul-Tolonen, SethGitter, Paul Glewwe and participants at the Mid-West International Development Economics Conference, and DIALConference on Development Economics for valuable comments. Also, we are thankful to Towson University forproviding us with the grant to support this study. Remaining errors are our own.

1 Introduction

Subsidy and awareness program are commonly used to increase take-up of health products in

developing countries. Subsidy can increase usage by relieving financial constraints (Ashraf et al.,

2010; Meredith et al., 2013; Dupas, 2014a). Knowledge and information can also impact demand,

especially for a new product or technology (Jalan and Somanathan, 2008; Luoto et al., 2011; Ashraf

et al., 2013), by increasing demand or changing the price elasticity of demand (Johnson and Myatt,

2006). In addition to constraints such as cost and knowledge, health behaviors in developing – and

to some extent also in developed countries – are informed by culture and social norms (for example

in cases of mental health, HIV, and menstrual health). A growing volume of work highlights the

importance of incorporating cultural aspects of the society in policy making (Ashraf et al., 2016;

Collier, 2017). So, even when the health product or service is fairly well-known, usage can be

suboptimal due to constraints imposed by societal stigma (Choudhry et al., 2016; Kaur et al.,

2018). In such cases, combining awareness and subsidy may be necessary to promote usage of

related health products or services.

In this paper, we conduct a randomized control trial in Nepal’s Nuwakot district to study the

interaction between subsidy and awareness in determining demand for sanitary pads. In rural Nepal

- and many other developing countries - menstruation is highly stigmatized.1 Despite menstrual

pads being fairly well-known and commonly available,2 women often use old cloth, cotton wool,

or rag, which to some extent could be due to the taboos surrounding the topic of menstruation.

Using unhygienic cloths can affect a girl’s psychological development by hindering social dignity

and comfort due to leakage, smell, and chafing (Sommer, 2010; Mason et al., 2013). Lack of access

to menstrual hygiene is an impediment to girl’s school attendance (Tolonen et al., 2019; Khanna,

2019). Also, usage of unhygienic cloths can increase the risk of reproductive tract infection (Phillips-

Howard et al., 2016). Thus, expanding the take-up of menstrual health products including sanitary

pads has become a priority in many developing countries.

In five villages of Nuwakot, we first conducted a baseline survey of all adult women who were

1Women are often not allowed to touch males during the time of menstruation; they are disallowed to enter kitchenand holy shrines. In our sample, approximately 55 % of women were restricted to live in shed for the duration oftheir period.

2Approximately 98% of women in our survey reported knowing about menstrual pads. Although 78% reportedhaving ever used a sanitary pad, only 42% are frequent users.

2

not pregnant to understand the prevailing practices and beliefs about menstrual hygiene. Then, to

understand the effect of price, we randomly allocated one discount coupon (valued at 10, 25, 50, 75,

or 90%) for sanitary pads that could be redeemed at the local pharmacies (“price treatment”). This

created a random variation in price faced by the consumer, which allows us to trace the demand

curve. Concurrently, we invited half of the study participants to a group awareness program in which

a locally recruited health professional provided information about women’s health, including (but

not limited to) menstrual hygiene (“awareness treatment”). By tracking the coupon redemptions,

we estimate the demand curve and the price elasticity of demand for sanitary pad for the awareness

treatment and control groups.

We find strong evidence that the awareness program increased demand for sanitary pads at

each effective price point (i.e., caused a rightward shift in demand curve), but the impact varied at

different price level (non-linear effect). Redemption rates increased as the after-discount price fell

for both awareness treatment and control groups, but the treatment group redeemed at a higher

rate even with modest discounts. The largest effect was at the discount rate of 50%. In our baseline

estimates, the awareness program increased redemption likelihood by 23-26 percentage points at

this price level. The impact is also statistically significant at 10%, 75% and 90% discounts, but the

effects pertaining to the higher discount rates reduce in magnitude when exposed to a battery of

robustness tests.

We compute price elasticities of demand for the awareness treatment and control group for

each segment of the demand curve defined by adjacent price levels. Consistent with the theory,

demand is highly elastic at the highest price and inelastic at lower prices. For example, in the

segment corresponding to discount levels between 10% and 25%, the price elasticity magnitudes

are -2.73 and -4.55 for the awareness treatment and control group respectively. Additionally, given

our experimental design, we are also able to estimate the spillover effects of awareness. The results

from spillover effects are modest but economically significant, suggesting that an increase in treated

individual by one person within a community increases coupon redemption status by 3.2 percentage

points in the control group. Our estimates are robust to possible issues arising due to partial

compliance with the awareness treatment, which we discuss thoroughly in later sections. Overall,

our study finds that awareness combined with subsidy can increase demand more effectively than

subsidy alone. This has implication for devising cost-effective strategies to increase usage of health

3

products in situations where societal stigma curtails demand.

This paper makes four main contributions to the literature. Our first contribution is to ex-

perimentally evaluate the effect of combining information and subsidy for a health product that

is associated with social stigma. Although several studies have separately evaluated the effects of

information (Jalan and Somanathan, 2008; Luby et al., 2004, 2005; Luoto et al., 2011; Dupas, 2011)

and subsidies (Ashraf et al., 2010; Meredith et al., 2013; Dupas, 2014b) on take-up of health prod-

ucts, only a few papers study their combined effects. Evaluating whether information and subsidies

are complements or substitutes, Ashraf et al. (2013) find that providing information substantially

increases the effect of price subsidies on demand. In another experiment, Meredith et al. (2013)

find that although subsidies increase quantity demanded for health products, information or an

increase in knowledge did not affect purchase decision in Kenya, Guatemala, India, and Uganda.

While the Ashraf et al. (2013) study focuses on an unfamiliar water purification technology and

Meredith et al. (2013) looks at shoes to prevent soil-transmitted helminths (STHs), we focus on

a relatively known but underused product whose demand is affected by social stigma. Given the

societal taboo attached to menstruation until this day, understanding the impact of interaction

between awareness and price subsidies in determining the demand for menstrual health products

is all the more important.

Second, in the context of the literature studying the impact of education on health behavior,

our study contributes by isolating the effect of knowledge and awareness on demand from other

correlated factors. Currently, the empirical literature on this issue uses schooling as a proxy for

knowledge (Grossman, 1972). Among these, some rely on natural experiments such as implemen-

tation of compulsory schooling laws, provision of universal education (Lleras-Muney, 2005; Gunes,

2015; Keats, 2018), and establishment of schools or colleges (Currie and Moretti, 2003; Breierova

and Duflo, 2004; Shrestha et al., 2016) for identification. However, the studies finding positive

effects between education and health are unable to identify the exact mechanism through which

education may affect health, as education often improves both knowledge and income.3 Our study

demonstrates the importance of knowledge in take-up of beneficial but infrequently used health

product.

3Several recent studies provide causal evidence regarding the positive effects of increases in income on healthoutcomes (Larrimore, 2011; Hoynes et al., 2015; Horn et al., 2017; Lenhart, 2017).

4

Third, our study is directly relevant for policy concerned with increasing usage of modern

menstrual health products in developing countries. Recently, the government of India proposed

to subsidize sanitary napkins for adolescent girls in rural areas (Garg et al., 2012). The scheme

called Yojana Ranjit, aimed at providing subsidized sanitary napkins to girls in rural areas and

those attending the district council schools, was launched in March 2018 (Joshi, 2018). Likewise,

the Indian Tax Council announced a complete reduction of tax rate on sanitary napkins from

12% to 0% to increase usage (Singh, 2018). Recently, the Nepalese government has allocated

budget to distribute free sanitary pads in government schools (Free sanitary pads for public schools,

2019). However, prevalent social norms about menstrual health may limit responsiveness to price

incentives. Our results indicate that it may be more cost-effective to provide relatively lower levels

of subsidy coupled with awareness, rather than providing high levels of subsidy (including free

distribution) but without complementary awareness programs.

Finally, interventions usually provide menstrual products to school-age girls (Oster and Thorn-

ton, 2011) and such interventions may have limited effect as the girls do not have much control

over household expenditure. Instead, by focusing on adult females who have comparatively more

decision-making power (although perhaps not a total control over their finances), we find large

effects of information campaign. Given evidence of intergenerational transmission of knowledge,

greater take-up by mothers is likely to have spillover effects on sanitary pad usage by their daugh-

ters, and help remove a significant impediment to schooling caused by poor menstrual hygiene

(Montgomery et al., 2016).

The rest of the paper is organized as follows. Section 2 provides background information to

further motivate this study and section 3 describes the experimental design of the randomized

controlled experiment. Section 4 desribes the estimation strategy, Section 5 discusses the results

and Section 6 provides robustness checks. Section 7 concludes the study.

2 Menstural Health, Price Subsidies, and Health Information

Menstrual health management (MHM) in developing countries has received greater attention from

academics and development practitioners over the last ten years and is now viewed as a global pub-

lic health challenge. The importance of improving menstural health is higher if we also consider

5

its impact on economic aspects such as human capital development and productivity. Qualita-

tive studies have repeatedly found that inadequate menstrual hygiene directly affects productivity

among girls by increasing school absenteeism (Montgomery et al., 2016; Mason et al., 2013; Grant

et al., 2013; Tegegne and Sisay, 2014). In fact, Sommer et al. (2015) trace the evolution of MHM

and find that concern with gender equality in education brought this issue to the limelight. More-

over, referring to the issue of gender gap in earnings, Ichino and Moretti (2009)’s findings suggest

that menstruation explains about 14% of wage differences by gender due to increased absenteeism

during the 28-day cycle.

A few studies have utilized randomized control trials to increase adoption of modern menstrual

health products and assess such effects on subsequent outcomes. Oster and Thornton (2011) uses

a randomized control trial in Chitwan district of Nepal to find that the effects are very small in

magnitude – girls miss 0.4 days out of 180 day school year due to menstruation, and usage of

menstrual cups did not reduce absence. One reason behind such findings could be that that study

focused on 7th and 8th graders, many of who reported that menstruation did not impede school

attendance in the baseline.4 Another reason could be poor attendance measurement, which is

usually self-reported or poorly recorded. A more recent study by Tolonen et al. (2019), based on

spot-checks, finds that provision of sanitary products reduced absenteeism in Western Kenya and

further highlights the importance of spot-checks when reducing measurement bias in absenteeism.

Phillips-Howard et al. (2016) highlight the importance of menstrual health products in reducing

risk related to sexually transmitted infections. However, existing studies do not consider the role of

societal stigma weighing down on the demand for menstrual health products when designing their

intervention.

The combination of high cost, budget constraint, and taboo surrounding menstruation leads

to poor menstrual health management (Sommer et al., 2015). Given that menstruation reduces

mobility, usage of sanitary products can enhance mobility and reduce time lost in household or

market work and from school.5 A sanitary pad serves as an input in the production of menstrual

4In Nepal, the majority of girls drop-out of school just after 5th grade. Authors’ calculation using the Nepal LivingStandard Survey 2011 suggests that among females who attended school in the past but not currently attending andof age 21 and below, 17.38% dropped out of school immediately after the 5th grade.

5Evidence suggests that a girl’s restriction to mobility increases after menarche in developing nations such as Indiaand Nepal (Jewitt and Ryley, 2014; van Eijk et al., 2016; Galli, 2017). Besides discomfort, societal taboo related tomenstruation forces a menstruating girl to stay in isolation, restricts her from visiting social arenas including religiousplaces and playgrounds.

6

health.6 The production of menstrual health also requires time input, which is a substitute for

pad quality for a given level of health. Given positive price of sanitary pads, women will trade-off

menstrual health with other consumption goods, leading to a downward sloping demand curve. In

a poor setting, they may resort to the cheapest input - reusable cloth - since the marginal benefit

of other consumption good is higher.

Previous studies have shown that lack of awareness may cause individuals to underestimate the

marginal benefit of health products. Thus, increased awareness can increase demand by providing

accurate information. A handful of studies based on randomized control trials have been conducted

to inform the benefits of health information on health behaviors. Both Cairncross et al. (2005) and

Luby et al. (2004, 2005) find that health education classes and promotion of handwashing improved

incidence of handwashing among vulnerable populations. Similarly, Jalan and Somanathan (2008)

and Madajewicz et al. (2007) find that informing the households about fecal and arsenic water

contamination, respectively, can influence households to seek a different alternative and facilitates

information regarding water purification technology. Dupas (2011) finds strong effects of providing

HIV-related information on sexual behavior. In contrast to these positive effects of information,

Meredith et al. (2013) find no evidence that information itself changed behavior to stimulate demand

for rubber sandals, used to prevent individuals from soil-transmitted helminths (STHs).

Another strand of literature has shown that subsidies can also itself induce demand for health

products and technology (Ashraf et al., 2010; Meredith et al., 2013; Dupas, 2014a). Lowering the

price of higher quality pad through subsidies has two effects. The direct effect is that it provides

an incentive to substitute towards higher quality pads, leaving more time for productive activities

through proper menstrual health maintenance. Also, a reduction in prices can lead to income effect,

which increases demand for menstrual health and health products. Both of these channels serve to

increase the quantity of high quality pads demanded. Beside these effects, subsidy can also reduce

stigma associated with menstrual health if a reduction in price also reduces utility cost due to

stigma. This can happen if subsidy increases public interaction and discussion regarding menstrual

health and promotes learning in the long run (Dupas, 2014a). But the provision of subsidy alone

might be suboptimal – combining awareness interventions with price subsidies may be necessary to

6Sanitary pads come in varying quality and price; we may conceptualize cloth/rag as the lowest quality and thecheapest among this input. We assume a one-to-one relationship between quality and price.

7

increase take-up of a stigmatized health product. Health information can influence the demand for

health products, thus determining the elasticity of demand curves for such products. For a given

reduction in price, information can change the elasticity of demand. On the one hand, information

can reduce price sensitivity by decreasing consumers’ dispersion of product valuation (Johnson and

Myatt, 2006). On the other hand, information can make demand more sensitive to price (Judd and

Riordan, 1994; Wedig and Tai-Seale, 2002).

It should be highlighted that the role of stigma is different from that of lack of awareness. While

the degree of awareness mainly affects the health production function, stigma can negatively affect

the production function and at same time induce cost in utility function. Adequate awareness

through information campaigns can essentially reduce the gap between one’s expected marginal

benefits and actual marginal benefits of better menstrual health or usage of health products, but

awareness may not be sufficient to counteract societal stigma unless it is widespread. Considering

this, door-to-door awareness may not be the most prolific form of awareness if the goal is to pro-

vide awareness and concurrently reduce menstrual health stigma. Hence, mass health information

campaign that involves the general public and promotes discussion is preferred to door-to-door

awareness campaign.

Some additional concerns are important to discuss. Sanitary pads can be characterized as a

health technology that requires repeat purchases. While subsidizing one-time-use health products

such as vaccines is uncontroversial, there are arguments against subsidizing products that require

repeat purchases such as sanitary pads. The development practitioners argue that individuals who

receive the subsidy may anchor to the subsidized price and be unwilling to pay the market price

once the subsidy runs out. Similarly, it is also argued that cost sharing through a subsidy can help

reduce wastage by making sure that products are not being distributed for free to individuals who

will not use the product. In contrast to this argument, Cohen and Dupas (2010) find that usage

rates do not vary across individuals who received the insecticide treated bed nets (ITNs) for free

compared to those individuals who paid a subsidized positive price. However, the uptake rate drops

significantly by 60 percentage points when discount rate increases by 10 percentage points from

100% (free) to 90%. The authors estimate a price elasticity of -0.37 for ITNs. Likewise, one-time

subsidy may improve take-up of such products in the long-run if there is learning, as found in the

case of anti-malarial bed nets by Dupas (2014b). Since the majority of individuals more often use

8

a cloth or rag during the time of menstruation in the sample of this study, information due to

awareness coupled with price subsidy is likely to stimulate demand of sanitary pads.

Several other properties of sanitary pads may influence its price elasticity of demand. Sanitary

pads are personal use products, which is different from a bednet (Cohen and Dupas, 2010; Tarozzi

et al., 2014) or water disinfectation (Ashraf et al., 2010) – products that may be shared among

household members. The private nature of both benefits and costs should make its demand more

responsive to price changes. However, in many developing country setting, the bargaining position

and decision-making power of women may affect adoption. The husband’s or family members’

consent may be required for the woman to make purchase decisions.

Health product adoption can also have spillover effects that are important to consider to de-

termine the true impact of an intervention and conduct accurate cost-benefit analysis of such

interventions. A meta analysis by Benjamin-Chung et al. (2017) on estimates of spillover effects

find consistent and strong impacts of reducing disease transmission, e.g. through vaccines or use of

bednets, but weaker spillovers through transmission of knowledge. In another study, Chong et al.

(2013) estimate spillover effects by studying the relationship between adoption and proportion of

friends who were treated with information. The study also finds that treatment effect is double

when there is reinforcement, i.e., a higher proportion of the treated invidiuals’ network is also

treated.

3 Experimental Design

Our experimental design is similar to interventions implemented by Ashraf et al. (2010), Cohen and

Dupas (2010), Dupas (2009), and Kremer and Miguel (2007). Given that health awareness programs

increase knowledge regarding benefits attached to health products, we should expect a higher

adoption of health products among individuals in the treatment group compared to individuals in

the control group.

3.1 Study Location and Stratification

Nepal is one of the countries where traditional norms inform ideas around menstruation. Reusable

cloth is the most common material used during menstruation (Budhathoki et al., 2018). The

9

stigma associated with menstruation is severely heightened in Nepal. Mostly, menstruating women

are prohibited from touching other household members and entering the kitchen or shrines. The

colloquial Nepali term for menstruating women roughly translates to “untouchable.” Such a cul-

tural perception makes it harder for women to manage their menstruation adequately. Chaupadi,

related to Hinduism, is practiced in some communities (mainly rural), which holds a belief that

menstruating women can cause bad luck, resulting to illness in family or harvest faliure. A form of

Chaupadi practice includes forcing a girl to sleep outside of the house in a shed (usually used to store

livestock), which exposes them to danger including deaths due to snake bites and asphyxiation. In

our sample, over 50% of the women reported being kept in shed during their period.7

The study is conducted in Nepal’s Nuwakot district. Nuwakot comprises of 61 village develop-

ment committees (VDCs) and one municipality, is located 61 kilometeres Northwest of the capital

Kathmandu. Bidur municipality, the district capital, is the most populated city with 6,270 house-

holds in 2011 (Central Bureau of Statistics, 2011). The municipality is further sub-divided into

thirteen wards (the lowest administrative unit) with population ranging between 641 and 1333. For

this study, we choose five “villages” within Bidur municipality.8 The sites were selected considering

two main aspects: 1) Need for non-urban area to assure that the usage of sanitary pad is not widely

accustomed, and 2) Areas with feasible access to transportation.9 These villages are approximately

50 minutes further away from each other (by foot). Figures 1a and 1b shows the spatial location

of Nuwakot in relation to Kathmandu, the nation’s capital. Figure 1c shows the VDCs and Bidur

municipality in Nuwakot and Figure 1d shows Bidur’s sub-divisions.

The district was highly affected by the 2015 earthquake. A post-earthquake assessment survey

conducted by the government found that almost all 77,148 households in the district were affected.10

After the earthquake, several NGOs entered the region mostly for distribution purposes. As a result,

some regions had been exposed to campaigns related to WASH (water, sanitation, and hygiene),

and more importantly menstrual hygiene. This may affect our study and we therefore took this

7This practice has recently received widespread attention in the international media (for example Lamsal, 2017;Preiss, 2017).

8In Nepal, municipalities are usually formed by combining preexisting villages. Bidur municipality was recentlyformed in March of 2017 following the constitution of Nepal (2015). Bidur’s area is 130.01 square kilometers and wasformed by combining parts of existing Bidur municipality, and peripheries of Charghare, Tupche, Gerkhu, Kalyanpur,and Khadga Bhanjyang. See http://bidurmun.gov.np/en for details.

9Out of these five areas only two have direct access to road ways.10CBS. District Profile of Nuwakot (in Nepali). Compare http://cbs.gov.np/image/data/2017/Dristrict\

%20Profile\%20of\%20Nuwakot,\%202074.pdf

10

into consideration when designing our randomization by stratifying on whether an individual was

exposed to health campaigns that included menstrual health following the 2015 earthquake, as the

only level of stratification.

The time line in Figure ?? summarizes the main events of the study sequentially. The study

involved three main phases: 1) Collection of the baseline data; 2) Awareness sessions and coupon

distribution; and 3) Coupon redemption. Next, we describe these in detail.

3.2 Baseline Survey

The first study phase involved administering a baseline survey to gather information on the following

areas: 1) Identification information, 2) Basic demographic characteristics, 3) Location specific

information, 4) Health and sanitation, and 5) Knowledge regarding menstruation.11

The questionnaire was administered by twelve female research enumerators hired locally. The

baseline survey acts as a census for this study and is conducted among all households in the

study sites. We selected one woman aged 15-50 from each household to ask questions about their

menstrual health and pregnant women were excluded from the survey. We have a total of 716

observations in the sample. The households did not have street addresses. To enable tracking of

the households for the second round, we provided a pair of same household numbers to each of the

household – one was kept by the respondent and the other was laminated and stuck on the outer

wall of the household.

The survey included several questions related to societal stigma or norms associated with men-

struation (whether an individual was banished to the shed during the period of first menstruation,

whether she is allowed into holy places, and if she is allowed to touch male members of the house-

hold). Menstruation is a delicate topic in Nepal and is frowned upon, specifically in the rural

regions of the country. The enumerators were instructed to isolate the respondent. As a result, we

got a high response rate for menstruation-related questions.

3.3 Randomization

Following the completion of baseline survey, we entered into the 2nd phase of the experiment. In this

phase, we i) randomly assigned observations to the awareness treatment group and control group,

11The survey questionnaire is reproduced in the Appendix C1.

11

ii) randomly assigned discount rates, iii) held awareness events for those in awareness treatment

group, and iv) distributed coupons to individuals in both treatment and control groups following

the timing of awareness campaigns.

3.3.1 Awareness Treatment

Using computer-based randomization, the households are allocated into either treatment or control

group after stratifying on whether an individual received menstruation-related awareness through

NGOs in the post earthquake era (pre-treatment). A section of the survey queried whether women

included in our study were exposed to WASH awareness programs, which included menstrual hy-

giene. We regard this as a crucial piece of information and use the pre-determined variable rep-

resenting exposure to campaigns involving menstrual hygiene as a stratification category while

randomizing in the second phase.12 We revisited the households that were selected for the treat-

ment group to invite them to our awareness program. The households were provided a physical

invitation card, which they had to bring to the program so that we could track attendance.

The awareness program involved four female health workers from Kathmandu and a nurse from

the Nuwakot hospital providing information about general female health and hygiene, including but

not limited to menstrual hygiene.13 Mass-awareness was preferred due to stigma associated with

menstruation. The event was organized at a local school closest to the study site. The outline of

the sub-section regarding awareness focused on menstruation is presented in Appendix section C2.

The awareness sessions also involved comprehensive discussion regarding women’s health concerns

related to pregnancy, uterine prolapse, and breast cancer. The take-up was voluntary and therefore

not universal; 65% of women who were assigned to be treated attended the awareness program.

The potential issue of non-compliance and our robustness checks is discussed in detail in section 6.

12Note that this is not the treatment the study is concerned of.13We refrained from distributing health-related pamphlets since a portion of the sample were illiterate. The detailed

content of the awareness program is provided in the Appendix C2. Two different options of awareness were availableincluding door-to-door awareness and distribution of pamphlets. We opted against the latter since a fraction ofindividuals were illiterate. The door-to-door awareness scheme was not chosen as this would have been a muchmore expensive form of treatment. However, our future work attempts to evaluate the effectiveness of door-to-doortreatment from a cost-benefit perspective.

12

3.3.2 Discount coupon

All households independent of awareness treatment are allocated to receive a discount coupon for

either 10, 25, 50, 75, or 90 percent discount for a specific brand of sanitary pad which normally

costs Rs. 70 ($0.62), for a pack of 6 pads.14 Normally, women go through about 3-4 packs per

cycle, which means the total expenditure at full price would be Rs. 210-280 ($1.85-$2.47). The

majority of individuals belong to a family with monthly income less than Rs. 24,000 (See summary

statistics in Table 1), so the expenditure on sanitary pad would represent a 3.5% of their monthly

income (Calculating at Rs 24,000 or $211.2).15

An individual was allowed to purchase up to five packs of sanitary pads using the discount, a

supply worth a little more than required for one cycle. The coupon had to be redeemed within

forty five days in two local pharmacies. These two pharmacies are selected for three main reasons.

First, these pharmacies are conveniently located for the majority of sites selected for our study.

Second, these pharmacies are located at the urban and main market place of Bidur municipality,

which attracts people from all over Bidur for business and chores. Third, these pharmacies hold

adequate stock to manage provision of sanitary pads and qualified workers to track detailed logs of

pads that were sold.

The pharmacies were reimbursed for the expected number of redemptions at the list price

of the product in advance, with a stipulation that the sales will be recorded accurately and the

final tally returned to the researchers. To assure that these pharmacies reported accurately, the

pharmacies had to collect discount coupons in addition to maintaining a log book. Research

assistants corroborated the registration in the log book by using discount coupons; in this way

the discount coupons also acted as receipts of transactions. This gives us confidence that the

pharmacies did not have an incentive to misrepresent the sales. Furthermore, since randomization

of the discount coupons was independent of the awareness treatment, we do not expect there to be

systematic misreporting of sales to households with different awareness treatment.

14Several options beside sanitary pads could be considered when it comes to selecting a menstrual health product,including tampons and menstrual cup. We select sanitary pads as an appropriate health product since the majorityof people in the sample use cloth/rag during the time of menstruation and no one in our sample is familiar withmenstrual cup. The local pharmacies around the survey areas do not carry menstrual cup. Hence, the option ofdistributing menstrual cup is undermined by its lack of availability in the market.

15On average there are about 3 girls in the household, including the mother. See Table 1 for average number ofdaughters in the household.

13

An important point to highlight is that these individuals did not know about the discount

coupons and the rates in advance. In particular, those in the awareness treatment group did not

have an incentive to attend the event due to a higher discount rate. For this group, the coupons

were distributed at the end of the awareness session. While the awareness event was taking place,

individuals in control group within a community simultaneously received their assigned discount

coupons. Figures ?? and 2 depict the timing of study activities using a flow chart.

3.4 Baseline Statistics and Randomization Check

The summary statistics for the variables from the baseline survey are presented in Table 1. Columns

(1)-(2) and (3)-(4) present the means and standard deviations for the treated and control groups,

respectively. In most of the variables, the means of treatment and control group are similar. The

majority of our sample is Hindu and the average age of a female is about 30 years. The average

schooling or the highest years of schooling is 7th grade and both mother’s and father’s schooling

level is very low (below primary level). When pertaining to menstruation-related questions, 41%

and 38% of individuals in treatment and control groups are not permitted to enter the household’s

kitchen, respectively. Both groups have similar knowledge regarding the source of menstruation

as 53% and 55% of individuals in treated and control groups answered these questions correctly.

Similarly, 73% and 75% of females in treatment and control group reported that menstruation is

a physiological issue, respectively. Likewise, over half of the women reported being kept in shed

during menstruation. An even share of households reported having ever used sanitary pads between

treatment and control groups, plus similar portion of individuals in treatment and control groups

reported using sanitary pad frequently, which is reassuring.

To formally test that the awareness treatment is randomly allocated, we regress each of the

variable on an indicator for being invited to the awareness session and control for an indicator of

whether an individual was exposed to post-earthquake campaigns regarding menstrual hygiene.16

The p-value for a test of difference between groups is shown in Column 5 of Table 1. Since not

all variables are relevant for each observation, we show the relevant number of observations in the

sixth column. Out of the 59 variables documented in Table 1, we achieved balance in 56 variables.

16As the treatment is only random conditional upon the level of stratification, we find it necessary to control forthe stratifying variable.

14

One key variable where we did not achieve balance, by pure chance, is income level. The control

group included 12 percentage points higher proportion of families with monthly income below Rs.

25,000, relative to the treatment group that had 62% of households in this income bracket. This

difference is statistically significant. Unfortunately, this is also an important variable that might

determine adoption of sanitary pad. Likewise, we find that on average household of an individual in

the treated group is more likely to have a motorbike compared to household of an average individual

in control group. However, households are similar in other categories of assets, including ownership

of land, which is one of the major sources of wealth in developing nations. We control for household

income bracket in our main estimation. Alternatively, we interact income with treatment status

to allow the effects of income to vary across treatment groups; income bracket indicator is also

interacted with usage of sanitary pad before (ever use) and variables depicting knowledge about

menstrual hygiene in auxiliary specifications. Also, an average individual in treatment group is 5

percentage points more likely to perceive that blood from menstruation is unhygienic (significant at

the 10% level). Given the relatively large number of survey variables collected, these few differences

between treatment and control groups is driven due to chance. By comparing the baseline statistics

given in Table 1, we conclude that assignment of the awareness treatment is random.

Although the awareness treatment is our main randomization of interest, we also check for

balance with respect to discount coupon assignment. We regress various baseline covariates on

interaction between awareness treatment and discount coupon level, including the discount coupon

indicators.17 The results are reported in Tables B3 and B4. In general, we find that the covariates

are balanced across both dimensions of randomization. Among estimates of 225 regressors presented

in Tables B3 and B4, 14 estimates are statistically significant (mainly at the 10% level), which is

likely by pure chance. One strong effect is found when using ever use as a regressor. Individuals

assigned 50% discount rate and in awareness treatment group are 21 percentage points less likely

to ever use sanitary pad. It can be argued that if anything, this will downward bias the estimates

for awareness treatment receiving 50% discount.18

17These specifications also control for whether an individual was exposed to menstrual health campaigns followingthe earthquake of 2015 but prior to the awareness campaign of this study, which is the level of stratification.

18This is under an assumption that people who ever used sanitary pad are more likely to redeem coupons. As arobustness test, we conduct our analysis restricted to people who reported having used sanitary pad. The discussionregarding the results from this exercise is presented in the footnote 23 of this study.

15

3.5 Sanitary pad usage at the baseline

As noted, more than half of the sample had used sanitary pads during their last menstruation,

and almost 80% had used them at least once before. Also, due to the earthquake, activities of

NGOs promoting hygiene may have contributed to uptake of sanitary pad. However, only 42%

of individuals in both treatment and control groups use sanitary pad in a regular basis. Using

our baseline data, we briefly explore the characteristics of women who reported having ever-used

sanitary pads. We regress indicator of ever used sanitary pad on age, age squared, marital status,

education, household income, and exposure to NGO’s WASH campaign following the earthquake.

The results are shown in Table 2. We find that exposure to campaigns and education are the

strongest predictors of sanitary pad use. Those who were exposed to NGO’s health awareness

campaigns had a 17 percentage points higher likelihood of using sanitary pads. Likewise, those

with tertiary education and belonging to richer households were more likely to have ever-used

sanitary pad.

4 Estimation

4.1 Impact of awareness and subsidy on demand

Our first interest is to estimate the effects of the awareness treatment on demand. As treatment was

randomized, we can estimate the impact of awareness on redemption of sanitary pads by estimating

the following equation:

Yi = α+

5∑j=2

γjDij +

5∑j=1

βj [Dij × Ti] + δXi + εi, (1)

The dependent variable, Yi, is an indicator which takes value 1 if an individual i redeemed her

coupon, otherwise the value given is 0. The explanatory variables include categories of discount Dij

(following the first summation in equation 1) that takes value in {1,2,3,4,5} for the corresponding

discount percents in {10,25,50,75,90}. Coefficients on γj show the effect of price (influenced by

subsidy) on the probability of redeeming the coupon among individuals in the control group, where

the omitted category represents individual receiving price status with 10 percent discount. For

example, γ2 evaluates the effect of receiving 25% discount coupon on the redemption rate compared

16

to individuals receiving 10% discount (the omitted category).

In the second summation in equation 1, the awareness treatment status Ti, is interacted with

discount indicators. βj (j = 1, 2, .., 5) evaluates the effect of awareness treatment on redemption

within a specific discount rate compared to the control group. For example, β1 estimates the

probability of coupon redemption of individuals in the treatment group who received 10% discount

compared to individuals in the control group with 10% discount.

Xi is a vector that comprises individual and household level control variables including in-

dicators of caste, father’s education, mother’s education, age and age squared, and income level.

Additionally, Xi also includes a variable indicating whether an individual was exposed to campaigns

regarding menstrual hygiene following the earthquake – the level of stratification (as highlighted

in Question 33 in Appendix section C1) and the baseline controls related to beliefs and practices

about menstruation such as: i) Whether an individual is confined to a shed during the time of

menstruation, ii) If she is deemed as untouchable, iii) Ever used sanitary pads, iv) Whether men-

struation blood is considered unhygienic, v) Cause for menstruation (hormones), and vi) Source of

menstruation (uterus). The estimation uses a linear probability model. We build two propositions

from specification 1.

Proposition 1: Following the law of demand, we expect the magnitude of γj to be increasing in

value as j increases.

Proposition 2: More importantly, if awareness regarding women’s menstruation and hygiene has

any effect on redemption of sanitary pads then the coefficients of βj (for j = 1, 2, .., 5) should be

positive and statistically significant at the conventional levels.

4.1.1 Elasticity calculation

To calculate the elasticity of the demand curve, we assume linearity in each segment of the demand

curve between two adjacent effective price-points created by the coupons. The four segments are

defined by discount percents (10, 25), (25, 50), (50, 75), and (75, 90). Thus for each of the

two demand curves pertaining to awareness treatment and control groups, we get four elasticity

estimates. For each segment, elasticity is defined as the ratio of proportionate change in redemption

rate and proportionate change in price. The proportions are calculated from the baseline of the

lower price (and the respective quantity demanded) of the segment. For example, for a segment

17

defined by the discount percent (10, 25),

ηT1 =(E[Yi|Di = 10, Ti]− E[Yi|Di = 25, Ti])/E[Yi|Di = 25, Ti]

(Pi(10)− Pi(25))/Pi(25)

where P (D) denotes price level associated with discount D.

This information can be directly derived from the coefficients in equation 1. For example,

consider the segment between (10, 25),

(E[Yi|Di = 10, Ti]− E[Yi|Di = 25, Ti]) =

β2 − β1 if Ti = 1

γ2 if Ti = 0

(Pi(10)− Pi(25))/Pi(25) =− 0.15/0.75

(2)

We use bootstrapped elasticity estimates obtained from 1,000 replications to test whether elas-

ticity estimates are statistically different across treatment and control groups at each line segment.

4.2 Information spillover effects

The awareness program is likely to generate positive spillovers if those in the treated group share

information with their neighbors and friends. There are two types of spillover effects. The “first

type” is when and if individuals in the awareness control group receive information regarding

women’s health through their treated neighbors. This type of spillover will most likely lead to

underestimation of the treatment effects. However, spillover effects may also occur within the

treated individuals through discussion and partnering of coupon redemption – “the second type.”

For example, a group of treated women in a community maybe more successful in persuading

women in the treated group to redeem their coupons.

It is possible to directly estimate the presence of spillover effects by using the variation in

treatment density in an area or a neighborhood. In an early contribution, Miguel and Kremer

(2004) estimate the externalities of a deworming program by looking at the effects on untreated

individuals by density of treated schools nearby. They find large externalities of deworming on

health and school participation. In contrast, also using variations in treatment density across

geographic proximities, Meredith et al. (2013) find no notable spillover effects in purchase decisions

18

of rubber sandals. We use a similar method to estimate the spillover effect of the awareness

treatment on redemption of sanitary pads. Unlike interventions that is aimed at reducing diseases,

spillovers in our case are largely informational – adoption of sanitary pads itself does not have any

direct spillovers by, say, reducing the disease burden.

In our context, exposure to treated individuals is determined at the level of a tol, roughly a

neighborhood. Tols are the lowest recognizable geographic unit in Nepal. Within the five study

villages, we have 25 tols with an average population of 240. Some of the tols are geographically

separated. To estimate the spillover effects on female i, we include the number of women in i’s

neighborhood who attended the awareness program, denoted by ITni. The random assignment

of individuals to the awareness treatment group or control group generates an exogenous source

of geographic variation in treatment density conditional upon the number of individuals in the

neighborhood. Since the assignment to treatment is random, this variable will also be random

relative to i’s unobservables. We therefore estimate the following equation.

Yi = α+5∑

j=2

γjDij +5∑

j=1

βj [Dij × Ti] + α1ITni + α2I

Tni ∗ Ti + α2Ini + δXi + εi, (3)

ITni represents the number of individuals who are treated and living within a woman i’s neigh-

borhood n and ITni is interacted with the treatment T . Additionally, we control for the number of

individuals who are living in individual i’s neighborhood who are in the sample, given by Ini. The

estimate on α1 indicates the spillover effect on the control group, whereas α2 denote the spillover on

the treatment group. The specification outlined in equation 3, which uses the exogenously deter-

mined share of treated individuals circumvents the reflection problem (Manski, 1993), and has been

used to estimate social effects (Dupas, 2014a; Miguel and Kremer, 2004; Meredith et al., 2013).

5 Results

Before discussing the regression results, we first plot the redemption rate against price – determined

by the exogenous allocation of discount coupons in Figure 3. Figure 3a shows the demand for

sanitary pads pertaining to the whole sample; the 95% confidence intervals are shown by the

dotted lines. Figure 3b plots the demand curves by the awareness treatment status, along with

19

the confidence intervals of the difference in proportion of redemption across treatment and control

groups. As expected, we see higher rate of redemption at lower effective price (higher discount

rate) in Figure 3. But we observe important differences between control and treatment groups.

Individuals who are in the treatment group have higher quantity demanded at every price point

compared to those in the control group. This suggests that awareness treatment program possibly

increased demand for sanitary pads. In general, the demand curve pertaining to the treatment

group is smoother and less responsive to prices (as the price increases) compared to the demand

for the control group.

At the highest price of Rs. 63, redemption is quite low but still larger for the awareness group.

At this price, 3% of the control group and 15% of the awareness treatment group redeem. The

95% confidence interval of the difference in proportion who redeemed at the highest price (the

lowest discount) lies above zero. Redemption rates increase sharply and equally at the next highest

price of Rs. 52.5. We see a 30 percentage point increase in redemption for both treatment and

control groups. At the third highest price level, redemption remains the same for the control group,

but continues to increase to 60% for the treatment group. Here, the 95% confidence interval of

the difference in redemption is bounded above zero. With further lowering of price, we see slight

increases in redemption rates. At the lowest price, redemption rate is 56% for the control group

and 70% for the treatment group.

Awareness seems to work in conjunction with mid-level subsidy rates. While reducing price

from Rs. 63 to Rs. 52.5 had similar effects (in percentage point terms) on redemption across two

groups, reducing the price from Rs. 52.5 to Rs. 35 had no effect on the control group but 16

percentage point effect on those receiving awareness. The treatment group’s redemption rate at

a 50% discount rate is slightly higher than the control group’s redemption at the lowest price of

Rs. 7 (90% discount rate). Further reducing the price from Rs. 17.5 to Rs. 7 had no differential

percentage point impact on redemption rates (redemption increased by 7 percentage points across

both groups). From a cost-benefit perspective, lower levels of subsidy combined with awareness can

help reduce the cost of providing health subsidies, assuming that the additional cost of delivering

the awareness program is lower than the realized savings.

Another insight that Figure 3a provides is that the valuation of sanitary pads is quite lower

than the market price plus cost involved to redeem the sanitary pads (direct + opportunity cost).

20

Even at the lowest price, the redemption rate is only 63% as shown in Figure 3a. This is consistent

with the statistic that only 42% of the sample reported using sanitary pad on a regular basis, with

cloths being a cheaper substitute.

It has to be noted that the demand curve depicted in Figure 3a may represent upper bounds due

to several reasons. Figure 3a incorporates demand schedule of people who were exposed to health

and hygiene campaigns following the earthquake, which as shown in Table 2, increases demand.

Also, it is likely that the program created a general level of excitement regarding the product,

which perhaps increased the demand for sanitary pads at the given discount rates even for people

in the control group. In contrast, some factors may have led to ambiguous effects. Particularly,

since coupons could be redeemed within 40 days, a coupon holder had enough time to compare

her coupon with neighbors and friends. At a higher discount rate this may overestimate quantity

demanded since people who otherwise would not have redeemed coupons may redeem it after

observing their neighbors receive lower discount. Similarly, in cases of lower discount rates this will

undermine quantity demanded. Next, we move to the estimation results.

5.1 Demand for Sanitary Pads

Table 3 presents the findings after estimating equation (1). The results in Column (1) is based on

the most parsimonious specification that only includes specific discount rate indicators interacted

with awareness treatment status along with the discount rate indicators, with 10 % discount coupon

as the omitted category. The results show the evidence of a downward sloping demand curve – the

redemption rate increases among people in the control group as the discount increases. Females in

the control group with 25% of discount coupon are 29 percentage points more likely to redeem their

discount coupon compared to females receiving 10% of discount. Similarly, females in the control

group receiving 90% discount coupon are 53 percentage points more likely to redeem their coupon

compared to females with 10% of discount. Across-column comparison in Table 3 indicates that

discount coupons are strong determinants of coupon redemption.19

More importantly, the results in Table 3 show that individuals who received the awareness

treatment are more likely to redeem a coupon at the same discount rate compared to females in

19Column (1) has R2 of 0.174 and R2 increases merely to 0.23 in the most comprehensive specification, shown inColumn (4).

21

control group with the respective discount coupon, across all discount rates. These results imply

that awareness is able to shift the demand curve for menstrual health products. The difference in

redemption between the treatment and the control group is the largest at the 50 percent discount

rate – females in the awareness treatment group are 26 percentage points (in Column (1)) more

likely to redeem the coupon compared to the control group. This estimate is statistically significant

at the 1 percent level. This corresponds to a large increase in quantity demanded for the treatment

group at this discount level compared to the control group as shown in Figure 3b.

Our main results are robust to inclusion of additional covariates. Column (2) in Table 3 include

household and personal characteristics such as caste indicators, father’s level of schooling, mother’s

level of schooling, age and age squared. Additionally, Column (3) includes baseline variables per-

taining to household’s attitude towards and one’s knowledge regarding menstruation. Finally,

Column (4) adds controls defining access to basic amenities such as the household’s distance to the

nearest health post, market and primary schools. The estimates are similar across four columns in

Table 3 and additional controls in Columns (2)-(4) do not affect the magnitude of estimates. This

adds to our confidence in the experimental design. Overall, these results suggest that the quan-

tity demanded is higher for the treatment group at every discount rate compared to the control

group, and the difference in redemption rate across two groups is highest at the 50% discount rate.

The price effect is a stronger determinant of redemption at the higher discount rates. At the 50%

discount rate, an individual may be relatively indifferent towards redeeming her coupon compared

to other rates; hence, awareness treatment can influence such a marginal individual to redeem her

coupon.

The next important point to note is that family income, once conditional upon a set of covariates

documented at the bottom of Table 3, poses no effect on coupon’s redemption status. This is an

important point to highlight, given some differences in baseline characteristics on family income.

Specifically, the redemption status of falling in a bracket of family income above Rs. 25,000 does

not contribute to coupon’s redemption compared to family income less than Rs. 25,000.

The results that adoption rates are quite low even for those individuals in awareness treatment

group and receiving the highest discount rate of 90% are in contrast to studies focusing on bednets

(Dupas, 2014b; Cohen and Dupas, 2010; Tarozzi et al., 2014), water purification solution (Ashraf

et al., 2010), and rubber shoe to prevent hookworm infection (Meredith et al., 2013) – for which

22

adoption rate is much higher at lower prices. As mentioned in Kremer and Miguel (2007), who also

finds fairly low levels of deworming adoption, women may have low private valuation for sanitary

pads. However, in contrast to other studies, adoption may also be significantly affected by social

stigma associated with menstruation, which can further explain relatively lower adoption rates even

at the lowest price.

Next, to ensure that differences in income between treatment and control groups are not driving

the main results, we let the effect of income (category > Rs. 25,000) vary across several covariates,

including awareness treatment status, education, ever used pad, and an indicator of correctly

stating that the source of menstruation is uterus by including interactions between income and

such variables. The results after including these interaction terms are presented in Table 4. The

coefficients on discount level indicators and the interaction terms between discount indicators and

awareness treatment are similar to the findings reported in Table 3.

To account for the correlation in the error term specific to location, we use the wild cluster

bootstrap to estimate the standard errors given the small numbers of clusters (defined by areas or

villages – five in our case), as suggested in Cameron et al. (2008). The results are presented in

Table 5. Such clustering leads to a reduction in standard error pertaining to the coefficient on the

interaction between awareness and discount rate at the 10% and 90% rates compared to Table 3;

the standard error pertaining to the 50% discount are similar.

5.2 The Elasticity Estimates

The elasticity estimates for both awareness and control groups are calculated based on the point

estimates given in Table 3, Column (4). Note that the coefficients on the respective discount rates,

shown in Table 3, are relative to people in the control group who received a discount coupon of

10% (price of Rs. 63 per pack). For example, the coefficient for the 90% discount for the control

group indicates that people with 90% discount coupons are on average about 50 percentage points

more likely to redeem their coupons compared to those who received 10% discount coupon. On

average 3% of people in the control group with a discount coupon of 10% redeemed their coupons

– this would mean that the redemption rate for people in the control group, who received a 90%

discount coupon is 53%. As such, we can construct the redemption rate for the control group at

each discount rate (price) from the regression-based estimates. Once the redemption rate for the

23

control group is established at each discount rate (price), we can estimate the redemption rate for

the treatment group based on the coefficients on the interaction between the respective discount

rate and the awareness status as shown in Table 3, Column (4).

The elasticity estimates at different price points, compared to the next lower price and the

corresponding redemption rate, are shown in Figure 4 (top panel). For example, when calculating

the elasticity at a segment of Rs. 63 (10% discount) and Rs. 52.5 (25% discount), price and quantity

demanded at Rs. 52.5 (25% discount) is taken as the relative point. The magnitude of elasticity

of demand between treatment and control groups are quite comparable and less than 1 for prices

below Rs. 63, depicting the inelastic portion of the demand curve. However, when price increases

from Rs. 52.5 to Rs. 63, the elasticity estimates for treatment and control groups are -2.73 and

-4.55, respectively, which signifies the elastic portion of the demand curve. It is interesting that

at the highest price, the elasticity estimate for the control group is twice in magnitude compared

to the treatment group. This is explained by quantity demanded shrinking close to zero for the

control group when the discount is reduced from 25% to 10% as shown in Figure 3b. The histogram

of the differences in bootstrapped elasticity estimates under the null between the treatment and

control groups are presented at the bottom of Figure 4, along with the 5th and the 95th percentiles

(in blue) and the actual elasticity estimate (in red). This figure shows that the elasticity estimates

are similar across the treatment and control groups for all calculated segments of the demand curve

except the top segment pertaining to discount levels of 10% and 25%.

From the elasticity estimates shown in Figure 4, we provide caution in averaging the estimates,

as an average merely wipes out the non-linearity in elasticity estimates on a demand curve. Given

the findings, the demand for sanitary pads is generally inelastic for a set of prices, but is elastic

at the highest price point. This is consistent with the theoretical aspect governing a downward

sloping demand curve.

5.3 Spillover Effects

As described in 4.2, we hypothesize that the mechanism governing the intensity of spillover will be

dictated by geographic distance. To test this hypothesis we count the number of treated individuals

and the total number of individuals in the sample (in treatment + control groups) at the neighbor-

hood (tol) level. Although the number of individuals living in a tol is non-random, the number of

24

individuals in the treatment group is random conditioned upon the density of the specific tol. We

use this geographic variation in the treatment density across the neighborhoods (tols) to identify

the spillover effects.

We proceed by estimating the information spillover specification given by equation 3. The

estimate on α1 and α2 depicts the spillover effect for the control and treatment groups, respectively.

The results are presented in Table 6, where Columns (1) – (4) are structured in a similar way as the

respective columns in Table 3, with an addition of the number of treated individuals, interaction

between the number of the treated individuals with the treatment status of an individual, and the

total number of individuals in the sample by neighborhood.

The results in Table 6, across all specifications, provide modest evidence of spillover effects. The

coefficient on the number of treated individuals across all specifications are positive. The results

in Columns (1) and (4) suggest that an increase in treated individual by one person within a tol

increases the probability of coupon redemption of an individual in the control group by 3.4 and 1.9

percentage points, respectively. However, the spillover estimates are imprecisely estimated. 20

6 Robustness to non-compliance

In this section, we check the robustness of our main results to partial non-compliance by the

awareness treatment group. Table B1 in the Appendix shows the number of individuals in different

randomization bins, with those in the treatment group further divided into compliers and non-

compliers.21 Overall, 65% of individuals who were invited attended the awareness session. Although

balance is maintained between individuals in the treatment group who complied and the control

group (as shown in Table 1), non-compliance can still create a sample selection problem (due to

difference in unobservables) and needs to be addressed. In the worst-case scenario, none of the

non-compliers would have redeemed, which means that the actual price elasticity of demand for

the awareness treatment group would be lower than our baseline estimate. Therefore, we use a

number of techniques to determine the size of the potential bias under various assumptions about

the behavior of non-compliers.

20The bootstrapped standard errors clustered at the neighborhood level (tol level) are presented to account forcorrelation in error terms within a neighborhood.

21Compliers are defined as those individuals who were invited to attend the awareness session and showed up.Similarly, non-compliers pertain to those who did not attend awareness sessions although they were invited.

25

First, we look at how the probability of compliance varies by baseline covariates by regressing

an indicator for attendance on baseline variables.22 Individuals who complied were 13 percentage

points more likely to have ever-used sanitary pads (See Column 6, Table B2).23 Compliers were

also slightly richer in terms of family income. To dig deeper, we graph these two variables by the

status in the experiment (control, treatment compliers, treatment non-compliers) to see how they

vary. In Figure B1a, we see that compliers were more likely to be from the middle of the income

distribution, with those in the second income category (Rs. 25,000-39,000) overrepresented relative

to the control group. However, compliers were equally likely to use sanitary pad in the baseline

compared to control group. Columns (6) and (7) in Table B2 also include discount indicators – the

coefficients on discount indicators support the assignment mechanism described in Figure 3 that

the discount level is not systematically correlated with compliance probability.

Normally, in such cases of non-compliance we could estimate intent to treat (ITT) effects.

However, due to our experimental design, individuals who were invited but did not show up for

the awareness program did not receive the discount coupon as the coupons were distributed to

the invitees after the end of the awareness session. Therefore, we do not know the redemption

status or demand for this group. The missing redemption information for some observations in the

awareness treatment group could potentially bias our estimates. If the mechanism that generated

missing values resembles that of missing at random (MAR) (Rubin, 1976), then the slope of the

demand curve and elasticity estimates would be consistently estimated using just the sample of

compliers because the missing values are not systematically related to realized levels of discount

coupons, as confirmed by the results reported in Table B2.

However, it is more plausible that the unobservable factors that determine compliance may also

determine redemption conditional upon receiving awareness, leading to a selected sample in the

22Table B5 investigates the baseline characteristics between compliers and non-compliers. We find that complierstend to belong to “other” caste, are more likely to fall in income bracket of Rs. 25,000-39,000 compared to non-compliers, live closer to college and have previously ever-used sanitary pads.

23As a simple robustness exercise, we estimate the main results by limiting the sample to individuals who reportedhaving ever used sanitary pad. Following the terminology in the Appendix A1, the assumption here is that E[ei|Ai =1, Ci = 1, Si = 1] = E[ei|Ai = 0, Si = 1] where Si indicates the history of sanitary pad use. The results are shown inAppendix Table B8. These findings are similar to our main results. Even within this restricted sample, individualsreceiving awareness are more likely to redeem sanitary pads compared to individuals in the control group with thesame discount coupon. In fact, the coefficient on the interaction between the discount of 50% and awareness is largerthan the respective coefficients presented in Table 3. This suggests that awareness may act as positive reinforcementto pre-existing preference for sanitary pads. The higher magnitude on the restricted sample could be because initiallythe portion of people who ever used sanitary pad is low in 50% awareness group.

26

awareness treatment group. Individuals who did not attend awareness despite receiving invitation

may be less likely to redeem their coupons at all discount levels because, say, health is less of a

priority at that moment. Still, we contend that estimates of slope coefficients is consistent because

allocation of discount level is still random with respect to factors determining compliance. When

calculating the slope of the demand curve, these unobservable factors will be equal in expectation

and cancel out as people who attended awareness session and received 10% discount level will be

driven by similar unobservables compared to women receiving 90% discount coupon. Thus, the

difference in redemption between individuals with higher and lower levels of discount should give

us a consistent estimate of responsiveness to price (slope of the demand curve) for the awareness

treatment group.

But the elasticity estimate, when computed based only on the group of compliers, may be biased

compared to the elasticity estimate of the whole treated group, precisely because the position of the

demand curve pertaining to the treatment group can be inaccurately estimated by using just the

sample of compliers. In essence, ignoring non-compliers can cause a parallel (presumably outward)

shift in the demand curve from its “true” position. The location of the demand curve for the

treatment group (compliers) is inherently captured by estimating equation (1). We may have

overestimated the quantity demanded at each price if women who are relatively more concerned

about their health complied with the treatment, due to selection on unobservables. This concern

is described in more detail in Appendix section A1.

Given these potential issues, we ascertain the sensitivity of our results to non-compliance fol-

lowing multiple strategies. To devise our robustness strategy, we need to consider two possible

scenarios, which can be expressed as two hypothetical questions: (1) How would have the non-

compliers behaved had they received both awareness and discount coupon? and (2) How would

have the non-compliers behaved had they received only the discount coupon but no awareness? To

examine the first scenario, we impute the values for non-compliers from the sample of compliers

within awareness treatment group. For the second scenario, our imputation technique utilizes the

information from the counterfactual awareness control group. Additionally, following the impu-

tation based on the second case, we use the intent to treat estimation method on the imputed

sample.

27

6.1 Multiple Imputation Based on Propensity Score

Next, we impute the missing redemption status by drawing from a sample of individuals who

share similar baseline characteristics to those with missing values (Lavori et al., 1995). Briefly,

the process first assigns propensity of compliance (attending or not attending awareness) based

on covariates to each individual in the awareness treatment sample. The procedure of estimating

compliance probability is described in detail in Appendix A1.1, which is based on the existing

literature (Imbens and Rubin, 2015). Then, the treatment sample is divided into two blocks by

the median value of their compliance propensity. Within each block, people who attended the

awareness session (compliers) and those who did not (non-compliers) have very similar estimated

propensity score.24

The notion here is that within each block we have individuals (compliers and non-compliers)

sharing similar values of predicted compliance probability. By using the sub-sample of compliers

in each block, we impute redemption status (0 = did not redeem, 1 = redeemed) for non-compliers

at every discount level. Within each block, we follow three main steps (the process is illustrated

in Appendix Figure A3). First, we form a new dataset by randomly drawing observations from

compliers with replacement, such that the number of draws equals the number of compliers in each

block. Second, using this bootstrapped sample, we impute the redemption status for non-compliers

at each discount level. In this step, we randomly draw a redemption value from the bootstrapped

sample at a specific discount rate, and use this to impute a missing value on redemption status for a

non-complier facing the same discount rate. This is repeated until all the missing values pertaining

to non-compliers are imputed. This process gives us an imputed data set (original compliers plus

non-compliers with imputed redemption status). The assumption here is that within each block,

the redemption behavior of non-compliers would be determined by the same data generating process

as compliers, conditional upon observables. Then, we run the regression model on this partially

imputed dataset. The procedure is repeated for 1,000 times to generate a distribution of main

estimates.

The distribution of coefficients on the interaction term between the awareness status and the

24To assess the balance between people who showed up and those who did not within each block we calculate thet-statistic for each block as defined in Section A1.1. The t-statistics for both blocks are less than 1, depicting thatthe estimated propensity score varies very little within a block.

28

respective discount rate are shown in Figure 5 for each discount rate. The red dotted lines show

the 5th and 95th percentiles of the estimates obtained after the multiple imputation and the blue

dotted line represents the estimates (of interaction between awareness and respective discount rate

on redemption status) from Table 3, Column 4. The sub-figures in Figure 5 show that the estimates

from Table 3 (Column 4), obtained by discarding the missing values on redemption status among

non-compliers, are well bounded within the 5th and 95th percentile of the distribution of estimates

obtained from the multiple imputation of missing values by using the propensity score method.

6.2 Counter-factual Experiment

As an alternative robustness exercise, we correct the bias in our estimates caused due to non-

compliance by creating a similarly selected counterfactual awareness control group. As discussed

in more detail in the Appendix section A1.2.1, we first predict the probability of compliance based

on observables from the awareness treatment group and use these estimates to predict compliance

probability for the control group had they hypothetically been invited to the awareness session.

This assumes that, since invitation was randomly allocated, the compliance behavior would be

similar among awareness treatment and control groups conditional on observables.

This exercise confirms our suspicion of positive correlation between redemption probability and

compliance probability. Only among the control group, the redemption rate is higher among those

with greater compliance probability. This is depicted in Figure 6, which shows redemption rates

by quintile bins of predicted compliance probability for the control group.

Then, we re-estimate equation (1) after dropping those individuals from the control group

who have a low predicted probability of attending the awareness session (p < 0.3) had they been

invited.25 The results from such estimation are presented in Table 7. The estimates on discount

indicators among the control group indicates a shift in the demand curve compared to the results

shown in Tables 3 and 6, which is expected if individuals who are unlikely to attend awareness

session are also less likely to redeem their coupons. The estimates on the interaction between

discount indicators and awareness indicator are similar to the estimates in Table 3 for the most

part but the estimates for higher discount rates shink. Specifically, Column (4) suggests that

25Other cut-off probability values are used as well including p = 0.25, 0.35 and 0.40. The results, although notpresented, are robust to these different cut-off values, and are available upon request.

29

people in the awareness treatment group who received discount of 10% and 50% are 16.6 and 19.7

percentage points more likely to redeem their coupon compared to people in the control group

at such respective discount levels. However, these estimates are based on a conditional sample of

individuals who are relatively more likely to attend the awareness session.

Next, in spirit of utilizing the entire sample, we replace the missing values on redemption

status among non-compliers by following the steps highlighted in Appendix sub-section A1.2.2.

In summary, we regress redemption status of the control group on their predicted compliance

probability (had they been invited), discount indicators and the interactions between the discount

indicators and predicted compliance probability. Using the estimates from such a specification, we

predict redemption status for the whole sample. Now, if the predicted redemption probability for

individuals in the awareness treatment group who did not comply is greater than 0.5, we assign

“1” as the redemption status, otherwise the value given is “0”. This process assigns redemption

status for people who did not comply in treatment group with values based on similar compliance

probability and discount rate in control group had they been invited. We estimate treatment effects

for the whole sample as in Table 3. As this process uses predicted values as regressors, bootstrapped

standard error from 750 replications are calculated. The results are shown in Table 8. The results

from this exercise are consistent with the main findings, except that there is a drop in magnitude on

the coefficient pertaining to the interaction between awareness treatment and discount indicator of

90%. The elasticity estimates obtained from this exercise are presented in Figure B4; such estimates

are similar in pattern to the estimates presented in Figure 4. However, this form of imputation

still assumes that awareness would have no effect among people in the treatment group who did

not comply had they received awareness, and hence gives a lower bound.

Using the partially imputed data described in the preceding paragraph, we further estimate

the intent to treat (ITT) effects. First, we regress whether an individual attends the awareness

session on the awareness treatment assignment from randomization in the first stage and use the

treatment assignment as an instrument. Since, the awareness treatment is randomly assigned,

this should affect coupon redemption only through compliance. We obtain the predicted values

of compliance from the first stage and estimate the second stage where discount indicators are

interacted with the predicted compliance probability (probability of attending awareness) from the

first stage. The results from this exercise is presented in Table B11. The results suggest that

30

the ITT effects are also concentrated at the 50% and the 10% discount rates for the awareness

treatment group.

7 Conclusion

Increasing the usage of modern health technology in a context of a society with ingrained tradi-

tional practices may require both awareness to promote knowledge and subsidies to reduce financial

burden. In this study, we provide experimental evidence that awareness can increase take-up of

sanitary pads at mid-level prices in a rural Nepal location, where menstruation is often stigmatized

and proper menstrual health management is still lacking. The demand for sanitary pad, a repeat-

and private-use women’s health product, is downward sloping for both awareness treatment and

control groups. The effect of awareness is strong but non-linear. Awareness shifted the demand

curve for the awareness treatment group towards the right, but its effect is mainly concentrated at

the 50 % discount level.

The results of this study informs policy by emphasizing that awareness induces the effect of

subsidies when deployed to increase up-take of health products. In this term, our results comple-

ments the findings of Ashraf et al. (2010); the authors find that information coupled with subsidies

can increase demand for an unfamiliar health product (water purification product in their case).

However, our topic of focus involves the take-up of menstrual health product, which is known to

aid menstrual health management (MHM). To the most part, due to societal stigma surrounding

menstruation, menstrual health is typically poor among women living in several developing na-

tions. Increasingly, this issue has caught attention of academics and development practitioners and

the quest for proper MHM is now regarded as a global public health challenge. Several policies

directed towards improving menstrual health has been adopted. A program by the Indian govern-

ment, launched in 2011, to provide subsidized sanitary napkins to adolescent girls were reportedly

under-subscribed (Sharma, 2018). Another scheme by the state of Maharastra also aims to increase

take-up by subsidizing the price of sanitary napkins (Joshi, 2018). It is vital to understand the

elasticity of menstrual health products and the combined role of awareness while designing such

policies.

We recognize several shortcomings and potential extensions of the study. First, only women were

31

invited to attend the awareness campaign. As the societal stigma clearly governs the treatment of

girls and women during menstruation, it is necessary that the awareness or information programs

target men as well, specifically given the patrilocal society. Second, although this study uses

the scheme of mass awareness, other types of awareness mediums — particularly, door-to-door

awareness — may have differential effects, which can be further explored. Third, while this study

focuses on the short-term effects of the program, awareness can have long-term impacts through

learning (Dupas, 2014b, 2011). These are some extensions that we look forward to incorporate in

future studies.

32

Bibliography

Ashraf, N., Bau, N., Nunn, N., and Voena, A. (2016). Bride price and female education. Technicalreport, National Bureau of Economic Research.

Ashraf, N., Berry, J., and Shapiro, J. M. (2010). Can higher prices stimulate product use? evidencefrom a field experiment in Zambia. American Economic Review, 100(5):2383–2413.

Ashraf, N., Jack, B. K., and Kamenica, E. (2013). Information and subsidies: Complements orsubstitutes? Journal of Economic Behavior & Organization, 88:133–139.

Benjamin-Chung, J., Abedin, J., Berger, D., Clark, A., Jimenez, V., Konagaya, E., Tran, D.,Arnold, B. F., Hubbard, A. E., Luby, S. P., et al. (2017). Spillover effects on health outcomes inlow-and middle-income countries: a systematic review. International Journal of Epidemiology,46(4):1251–1276.

Breierova, L. and Duflo, E. (2004). The impact of education on fertility and child mortality: Dofathers really matter less than mothers? Technical report, National bureau of economic research.

Budhathoki, S. S., Bhattachan, M., Castro-Sanchez, E., Sagtani, R. A., Rayamajhi, R. B., Rai, P.,and Sharma, G. (2018). Menstrual hygiene management among women and adolescent girls inthe aftermath of the earthquake in Nepal. BMC Women’s Health, 18(1):33.

Cairncross, S., Shordt, K., Zacharia, S., and Govindan, B. K. (2005). What causes sustainablechanges in hygiene behaviour? a cross-sectional study from kerala, india. Social science &medicine, 61(10):2212–2220.

Cameron, A. C., Gelbach, J. B., and Miller, D. L. (2008). Bootstrap-based improvements forinference with clustered errors. The Review of Economics and Statistics, 90(3):414–427.

Central Bureau of Statistics (2011). National populatio n and housing census 2011 (national report).Report, Central Bureau of Statistics, Nepal.

Chong, A., Gonzalez-Navarro, M., Karlan, D., and Valdivia, M. (2013). Effectiveness and spilloversof online sex education: Evidence from a randomized evaluation in colombian public schools.National Bureau of Economic Research Working Paper 18776.

Choudhry, F. R., Mani, V., Ming, L. C., and Khan, T. M. (2016). Beliefs and perception aboutmental health issues: a meta-synthesis. Neuropsychiatric disease and treatment, 12:2807.

Cohen, J. and Dupas, P. (2010). Free distribution or cost-sharing? evidence from a randomizedmalaria prevention experiment. The Quarterly Journal of Economics, pages 1–45.

Collier, P. (2017). Culture, politics, and economic development. Annual Review of Political Science,20(1):111–125.

Currie, J. and Moretti, E. (2003). Mother’s education and the intergenerational transmission of hu-man capital: Evidence from college openings. The Quarterly Journal of Economics, 118(4):1495–1532.

Dupas, P. (2011). Do teenagers respond to hiv risk information? evidence from a field experimentin kenya. American Economic Journal: Applied Economics, 3(1):1–34.

33

Dupas, P. (2014a). Short-run subsidies and long-run adoption of new health products: Evidencefrom a field experiment. Econometrica, 82(1):197–228.

Dupas, P. (2014b). Short-run subsidies and long-run adoption of new health products: Evidencefrom a field experiment. Econometrica, 82(1):197–228.

Efron, B. (1994). Missing data, imputation, and the bootstrap. Journal of the American StatisticalAssociation, 89(426):463–475.

Free sanitary pads for public schools (May 29, 2019). The Himalayn Times. Retrieved fromhttps://thehimalayantimes.com/kathmandu/free-sanitary-pads-for-public-schools/.

Galli, K. (2017). Menstrual health and hygiene management in nepal.

Garg, R., Goyal, S., and Gupta, S. (2012). India moves towards menstrual hygiene: subsidizedsanitary napkins for rural adolescent girls—issues and challenges. Maternal and child healthjournal, 16(4):767–774.

Grant, M., Lloyd, C., and Mensch, B. (2013). Menstruation and school absenteeism: evidence fromrural malawi. Comparative education review, 57(2):260–284.

Grossman, M. (1972). On the concept of health capital and the demand for health. Journal ofPolitical economy, 80(2):223–255.

Gunes, P. M. (2015). The role of maternal education in child health: Evidence from a compulsoryschooling law. Economics of Education Review, 47:1–16.

Horn, B. P., Maclean, J. C., and Strain, M. R. (2017). Do minimum wage increases influence workerhealth? Economic Inquiry, 55(4):1986–2007.

Hoynes, H., Miller, D., and Simon, D. (2015). Income, the earned income tax credit, and infanthealth. American Economic Journal: Economic Policy, 7(1):172–211.

Ichino, A. and Moretti, E. (2009). Biological gender differences, absenteeism, and the earnings gap.American Economic Journal: Applied Economics, 1(1):183–218.

Imbens, G. W. and Rubin, D. B. (2015). Causal inference in statistics, social, and biomedicalsciences. Cambridge University Press.

Jalan, J. and Somanathan, E. (2008). The importance of being informed: Experimental evidenceon demand for environmental quality. Journal of Development Economics, 87(1):14–28.

Jewitt, S. and Ryley, H. (2014). It’sa girl thing: Menstruation, school attendance, spatial mobilityand wider gender inequalities in kenya. Geoforum, 56:137–147.

Johnson, J. P. and Myatt, D. P. (2006). On the simple economics of advertising, marketing, andproduct design. American Economic Review, 96(3):756–784.

Joshi, A. (2018). Sanitary napkins to cost as low as 5 rupees thanks to the government of Maha-rashtra.

Judd, K. L. and Riordan, M. H. (1994). Price and quality in a new product monopoly. The Reviewof Economic Studies, 61(4):773–789.

34

Kaur, R., Kaur, K., and Kaur, R. (2018). Menstrual hygiene, management, and waste disposal:Practices and challenges faced by girls/women of developing countries. Journal of environmentaland public health, 2018.

Keats, A. (2018). Women’s schooling, fertility, and child health outcomes: Evidence from uganda’sfree primary education program. Journal of Development Economics, 135:142–159.

Khanna, M. (2019). The precocious period: Menarche, education and marriage in india. WorkingPaper.

Kremer, M. and Miguel, E. (2007). The illusion of sustainability. The Quarterly journal of eco-nomics, 122(3):1007–1065.

Lamsal, P. (2017). In Nepal, women are still banished to ’menstrual huts’ during their periods. it’stime to end this dangerous tradition.

Larrimore, J. (2011). Does a higher income have positive health effects? using the earned incometax credit to explore the income-health gradient. The Milbank Quarterly, 89(4):694–727.

Lavori, P. W., Dawson, R., and Shera, D. (1995). A multiple imputation strategy for clinical trialswith truncation of patient data. Statistics in medicine, 14(17):1913–1925.

Lenhart, O. (2017). Do higher minimum wages benefit health? evidence from the uk. Journal ofPolicy Analysis and Management, 36(4):828–852.

Lleras-Muney, A. (2005). The relationship between education and adult mortality in the unitedstates. The Review of Economic Studies, 72(1):189–221.

Luby, S. P., Agboatwalla, M., Feikin, D. R., Painter, J., Billhimer, W., Altaf, A., and Hoekstra,R. M. (2005). Effect of handwashing on child health: a randomised controlled trial. The Lancet,366(9481):225–233.

Luby, S. P., Agboatwalla, M., Painter, J., Altaf, A., Billhimer, W. L., and Hoekstra, R. M. (2004).Effect of intensive handwashing promotion on childhood diarrhea in high-risk communities inpakistan: a randomized controlled trial. Jama, 291(21):2547–2554.

Luoto, J., Levine, D., and Albert, J. (2011). Information and persuasion: achieving safe waterbehaviors in kenya.

Madajewicz, M., Pfaff, A., Van Geen, A., Graziano, J., Hussein, I., Momotaj, H., Sylvi, R., andAhsan, H. (2007). Can information alone change behavior? response to arsenic contaminationof groundwater in bangladesh. Journal of development Economics, 84(2):731–754.

Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. Thereview of economic studies, 60(3):531–542.

Mason, L., Nyothach, E., Alexander, K., Odhiambo, F. O., Eleveld, A., Vulule, J., Rheingans, R.,Laserson, K. F., Mohammed, A., and Phillips-Howard, P. A. (2013). ‘we keep it secret so noone should know’–a qualitative study to explore young schoolgirls attitudes and experiences withmenstruation in rural western kenya. PloS one, 8(11):e79132.

Meredith, J., Robinson, J., Walker, S., and Wydick, B. (2013). Keeping the doctor away: ex-perimental evidence on investment in preventative health products. Journal of DevelopmentEconomics, 105:196–210.

35

Miguel, E. and Kremer, M. (2004). Worms: identifying impacts on education and health in thepresence of treatment externalities. Econometrica, 72(1):159–217.

Montgomery, P., Hennegan, J., Dolan, C., Wu, M., Steinfield, L., and Scott, L. (2016). Menstruationand the cycle of poverty: a cluster quasi-randomised control trial of sanitary pad and pubertyeducation provision in Uganda. Plos one, 11(12):e0166122.

Oster, E. and Thornton, R. (2011). Menstruation, sanitary products, and school attendance: Evi-dence from a randomized evaluation. American Economic Journal: Applied Economics, 3(1):91–100.

Phillips-Howard, P. A., Nyothach, E., ter Kuile, F. O., Omoto, J., Wang, D., Zeh, C., Onyango, C.,Mason, L., Alexander, K. T., Odhiambo, F. O., et al. (2016). Menstrual cups and sanitary padsto reduce school attrition, and sexually transmitted and reproductive tract infections: a clusterrandomised controlled feasibility study in rural western kenya. BMJ open, 6(11):e013229.

Preiss, D. (Aug 10 2017). Law in Nepal sets penalties for forcing a woman into a menstrualshed. Retrieved from https://www.npr.org/sections/goatsandsoda/2017/08/10/542585664/law-in-nepal-sets-penalties-for-forcing-a-woman-into-a-menstrual-shed.

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3):581–592.

Sharma, N. C. (2018). Use of sanitary pads sparse despite govt schemes: studies.

Shrestha, V. et al. (2016). Can basic maternal literacy skills improve infant health outcomes?evidence from the education act in nepal. Towson University Department of Economics, pages2016–08.

Singh, K. (2018). Will India have cheaper sanitary napkins after tax exemption?

Sommer, M. (2010). Where the education system and women’s bodies collide: The social and healthimpact of girls’ experiences of menstruation and schooling in tanzania. Journal of adolescence,33(4):521–529.

Sommer, M., Hirsch, J. S., Nathanson, C., and Parker, R. G. (2015). Comfortably, safely, andwithout shame: defining menstrual hygiene management as a public health issue. AmericanJournal of Public Health, 105(7):1302–1311.

Tarozzi, A., Mahajan, A., Blackburn, B., Kopf, D., Krishnan, L., and Yoong, J. (2014). Micro-loans, insecticide-treated bednets, and malaria: evidence from a randomized controlled trial inOrissa, India. American Economic Review, 104(7):1909–41.

Tegegne, T. K. and Sisay, M. M. (2014). Menstrual hygiene management and school absenteeismamong female adolescent students in northeast ethiopia. BMC public health, 14(1):1118.

Tolonen, Anja Benshaul, G., Zulaika, E., Nyothach, C., Oduor, L., Masaon, D., T., O. K., Alexan-der, K. F., and Laserson, Phillips-Howard, P. A. (2019). Pupil absenteeism, measurement, andmenstruation: Evidence from western kenya. Working Paper, Center on Global Economic Gov-ernance, CDEP-CGEG WP No. 74.

van Eijk, A. M., Sivakami, M., Thakkar, M. B., Bauman, A., Laserson, K. F., Coates, S., andPhillips-Howard, P. A. (2016). Menstrual hygiene management among adolescent girls in india:a systematic review and meta-analysis. BMJ open, 6(3):e010290.

36

Wedig, G. J. and Tai-Seale, M. (2002). The effect of report cards on consumer choice in the healthinsurance market. Journal of health economics, 21(6):1031–1048.

37

Figures and Tables

Figure 1: Study location

(a) Country Map (b) Nuwakot

(c) Nuwakot and Bidur Municipality(d) Bidur sub-divisions

Note: Figure 1a shows the map of the country and Figure 1b shows the map of Nuwakot in relation to the capital,Kathmandu. Figure 1c shows Bidur municipality with respect to other VDCs in Nuwakot. Figure 1d shows theoutline of Bidur.Source: Figures 1a, 1b, and 1c from authors. Figure 1d: CBS. District Profile of Nuwakot.http://cbs.gov.np/image/data/2017/Dristrict\%20Profile\%20of\%20Nuwakot,\%202074.pdf

38

Figure 2: Experiment Time line: Round 2 in Detail

Second Round

Treat Control

Invitation (treatment)

Coupon Distribution(Control)

AwarenessSession

(Treatment)

Coupon Distribution(Treatment)

Redeem or No Redeem

• In the first round, the baseline census was taken in five villages in Bidur municipality based on the selection criteria. Wedistributed two sets of household specific identification numbers – one to the respondent and the other was laminatedand stuck on a wall of the house. Since the majority of households lacked a physical address, this allowed us to identifythe households in the second phase. Round 2 is described as below.

1. Each household was assigned into the treatment or control group by a computer-based randomization, stratified byexposure to awareness of health and hygiene (including menstrual health) following the earthquake. Discount couponswere randomized as well.

2. The invitations were sent out to people in the awareness treatment group to come attend the awareness session. Therewas no mentioning of discount coupons.

3. Awareness session was conducted. Simultaneously, when awareness sessions were ongoing, discount coupons were dis-tributed among people in the control group.

4. Once the awareness session was completed, the individuals who attended the awareness session received a coupon.

5. Discount coupons were randomized across both treatment and control groups.

39

(a) Redemption

(b) Redemption by Treatment and Control Groups

Figure 3: Demand for Sanitary Pads

Source: Authors’ calculations.

Note: Figures 3a and 3b plot unconditional averages of redemption by discount levels. The solidline and dotted bars in 3a represent means and 95% confidence intervals, respectively. In Figure3b, the treatment group comprises individuals who received awareness and the control group includeindividuals who did not receive awareness. The dotted bars in Figure 3b show the 95% confidenceinterval for difference in proportion of redemption between two groups at the respective price level.Sub-figures (A)-(B) are weighted by the probability of being exposed to hygiene-related awareness(including menstrual hygiene) event following the 2015 earthquake – the stratification variable.

40

Figure 4: Elasticity Estimates

Source: Authors’ calculations.

Note: The elasticity estimates are presented in the top figure based on the two closest price segmentsof the demand curve. For example, elasticity estimate at the price of Rs. 17.5 (25% discount) isrelative to the next lowest price point – Rs. 7 (90% discount). The distribution of the difference inmagnitude of respective elasticity estimates between control and treatment groups under the null (forspecific price segments) are obtained from bootstrapping with 1,000 replications and are presented inthe bottom figure. The blue lines present the values (lower and upper bounds) at the 10% level ofsignificance and the red dotted line correspond to the actual difference in elasticity estimates betweenthe awareness treatment and control groups.

41

Figure 5: Distribution of Estimates from 1,000 Replications (Propensity Score Method)

Source: Authors’ calculations.

Note: The figure shows the histogram of estimates obtained from 1,000 replications after imputingthe missing values by using the propensity score method described in section 6. The vertical reddotted lines represent the 5th and 95th percentile of the estimates. The blue dotted line representsthe coefficient on the interaction term between the respective discount rate and awareness group,obtained from Table 3, Column (4).

42

(a) Redemption by Probability of Compliance

Figure 6: Redemption rate of control group by quintiles of compliance probabilities

Source: Authors’ calculations.Note: The figure shows the redemption probability among control group by quintile of predicted complianceprobability.

43

Table 1: Balance Exercise Across Treatment and Control Groups

Variable mean (T) sd. (T) mean (C) sd. (C) p. val N

1 Hindu 0.81 0.393 0.789 0.409 0.686 581

2 Chhetri 0.097 0.297 0.113 0.317 0.606 581

3 Brahmin 0.155 0.363 0.135 0.342 0.723 581

4 Janjati 0.133 0.34 0.093 0.291 0.096 581

5 Other 0.615 0.488 0.659 0.475 0.313 581

6 Age 30.098 9.46 29.375 8.964 0.428 577

7 Marital 0.81 0.393 0.811 0.392 0.913 580

8 Girls in HH 2.128 1.206 1.991 1.225 0.157 546

9 Boys in HH 1.991 1.207 2.064 1.214 0.383 553

10 Highest Education 6.991 5.123 7.096 5.108 0.729 576

11 Father’s Education 2.956 4.941 2.623 4.671 0.402 578

12 Mother’s Education 1.476 4.327 1.541 4.401 0.983 578

13 Plan to Migrate 0.022 0.147 0.028 0.182 0.677 579

14 Employed 0.155 0.363 0.172 0.378 0.51 580

15 Husband Employed 0.934 0.358 0.902 0.297 0.317 469

16 Family Income (0-Rs. 24,999) 0.615 0.488 0.738 0.44 0.001 581

17 > Rs. 25,000 0.385 0.488 0.262 0.44 0.001 581

18 Motorbike 0.323 0.478 0.237 0.426 0.042 580

19 Car 0.022 0.147 0.031 0.174 0.531 580

20 Cycle 0.035 0.185 0.042 0.202 0.692 580

21 Television 0.761 0.427 0.76 0.428 0.969 580

22 Fridge 0.31 0.463 0.246 0.431 0.123 580

23 Internet 0.058 0.252 0.056 0.243 0.724 580

24 Number of Rooms 2.929 1.845 2.725 1.722 0.24 578

25 Toilet 0.89 0.314 0.876 0.33 0.647 482

26 Own Land 0.911 0.285 0.935 0.659 0.66 580

27 Distance Primary School 14.369 12.458 16.29 14.191 0.187 577

28 Distance College 20.264 16.329 22.986 18.802 0.128 569

29 Distance Health Post 32.699 23.368 33.659 24.649 0.609 581

30 Distance Market 22.889 18.604 26.59 35.911 0.133 579

31 Last Interaction HP. (days) 209.009 358.84 211.668 431.966 0.86 567

32 Last HP Visit (days) 239.628 410.444 218.475 415.076 0.379 512

33 Last Market Visit (days) 20 87.908 11.562 65.321 0.206 528

34 Awareness after Earthquake 0.571 0.505 0.586 0.493 0.878 581

35 Awareness Sanitation 0.344 0.476 0.355 0.479 0.829 573

36 Awareness Water 0.52 0.519 0.518 0.5 0.901 574

37 Awareness Hygiene 0.407 0.492 0.453 0.515 0.213 572

38 Awareness Faeces 0.291 0.455 0.313 0.465 0.581 571

39 Last Mestruation 2.159 0.844 2.068 0.861 0.157 581

40 Not Permitted Kitchen 0.412 0.493 0.38 0.486 0.351 581

41 Not Permitted Holy Place 0.053 0.225 0.056 0.231 0.89 579

42 Kept in Shed 0.538 0.5 0.586 0.493 0.272 578

43 Untouchable 0.342 0.476 0.335 0.473 0.865 580

44 Source Vagina 0.129 0.336 0.121 0.327 0.759 580

45 Source Bladder 0.027 0.161 0.023 0.149 0.746 580

46 Source Uterus 0.533 0.5 0.552 0.498 0.6 580

47 Source Abdomen 0.058 0.234 0.048 0.214 0.64 580

48 Source Do Not Know 0.253 0.436 0.256 0.437 0.984 580

49 Cause Pathological 0.058 0.234 0.048 0.214 0.59 580

50 Cause Curse 0.022 0.148 0.008 0.092 0.163 580

51 Cause Physiology 0.733 0.443 0.749 0.434 0.575 580

52 Cause Do Not Know 0.187 0.391 0.194 0.396 0.915 580

53 Know about Menstrual Hygiene 0.991 0.094 0.972 0.166 0.115 579

54 Blood Unhygienic? 0.698 0.46 0.624 0.485 0.092 579

55 Use Sanitary Pad 0.8 0.401 0.769 0.422 0.426 580

56 Frequently Use Pad 0.425 0.495 0.423 0.495 0.958 581

57 Use Last 0.575 0.495 0.569 0.496 0.809 581

44

Table 2: Correlates of Sanitary Pad Use in Baseline

(1) (2) (3) (4)

Received health awareness after earthquake=1 0.139*** 0.145*** 0.162*** 0.172***

(0.0371) (0.0367) (0.0370) (0.0371)

Educ Low secondary 0.153** 0.131* 0.129*

(0.0666) (0.0671) (0.0664)

Educ Secondary 0.150** 0.129** 0.120**

(0.0498) (0.0500) (0.0525)

Educ Tertiary 0.234*** 0.195*** 0.193***

(0.0525) (0.0532) (0.0582)

Income Rs. 25000-39,999 0.126** 0.115**

(0.0433) (0.0434)

Income Above Rs. 40,000 0.195** 0.175**

(0.0642) (0.0648)

Chhetri 0.0957*

(0.0539)

Brahmin 0.0286

(0.0528)

Janjati 0.0671

(0.0497)

Observations 710 705 700 700

Standard errors in parentheses

* p<0.1, ** p<0.05, *** p<0.01

Note: The dependent variable is whether an individual ever used sanitary pad. Theregression also controls for age, age squared and marital status. Robust standard errorsadjusted for heteroskedasticity are presented in parenthesis.

45

Table 3: The Effect of Awareness Program on Redemption of Coupon by Discount Percentage

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.116 0.141 0.147 0.153∗

(0.090) (0.091) (0.091) (0.091)

25 percent discount*awareness 0.109 0.078 0.079 0.075

(0.088) (0.088) (0.089) (0.089)

50 percent discount*awareness 0.260∗∗∗ 0.233∗∗∗ 0.226∗∗ 0.228∗∗

(0.088) (0.089) (0.090) (0.089)

75 percent discount*awareness 0.173∗∗ 0.160∗ 0.164∗∗ 0.172∗∗

(0.084) (0.083) (0.084) (0.083)

90 percent discount*awareness 0.153∗ 0.134 0.122 0.148∗

(0.085) (0.085) (0.087) (0.087)

25 percent discount 0.288∗∗∗ 0.294∗∗∗ 0.292∗∗∗ 0.295∗∗∗

(0.078) (0.077) (0.078) (0.078)

50 percent discount 0.296∗∗∗ 0.297∗∗∗ 0.297∗∗∗ 0.301∗∗∗

(0.076) (0.076) (0.077) (0.077)

75 percent discount 0.454∗∗∗ 0.455∗∗∗ 0.449∗∗∗ 0.442∗∗∗

(0.077) (0.077) (0.078) (0.078)

90 percent discount 0.526∗∗∗ 0.515∗∗∗ 0.511∗∗∗ 0.502∗∗∗

(0.078) (0.078) (0.079) (0.079)

Above Rs. 25,000 0.032 0.031 0.012

(0.043) (0.043) (0.044)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 571 565 563 558

R2 0.174 0.211 0.216 0.233

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, Column (1) includes awareness status after the earthquake (whether anindividual received menstrual health and hygiene awareness that comprised menstrualhealth following the earthquake but prior to the study); Column (2) adds household andpersonal control variables such as caste dummies, father’s education, mother’s education,age, and age squared. Column (3) adds variables from the baseline survey pertaining tothe household’s attitude and one’s knowledge regarding menstruation. Column (4) addsthe household’s distance to the nearest school, health care center, and the market wherepharmacies are located. Robust White’s standard errors adjusted for heteroskedasticityare presented in parenthesis.

46

Table 4: The Effect of Awareness Program on Redemption of Coupon by Discount Percentage (Including the InteractionTerms)

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.194∗∗ 0.184∗ 0.185∗ 0.188∗

(0.096) (0.107) (0.108) (0.108)

25 percent discount*awareness 0.111 0.073 0.074 0.077

(0.093) (0.106) (0.106) (0.106)

50 percent discount*awareness 0.251∗∗∗ 0.216∗∗ 0.217∗∗ 0.218∗∗

(0.091) (0.103) (0.103) (0.103)

75 percent discount*awareness 0.209∗∗ 0.181∗ 0.183∗ 0.186∗

(0.088) (0.105) (0.105) (0.105)

90 percent discount*awareness 0.199∗∗ 0.181∗ 0.182∗ 0.183∗

(0.095) (0.107) (0.107) (0.107)

25 percent discount 0.296∗∗∗ 0.301∗∗∗ 0.301∗∗∗ 0.301∗∗∗

(0.078) (0.078) (0.078) (0.078)

50 percent discount 0.303∗∗∗ 0.306∗∗∗ 0.306∗∗∗ 0.305∗∗∗

(0.077) (0.077) (0.077) (0.077)

75 percent discount 0.445∗∗∗ 0.443∗∗∗ 0.443∗∗∗ 0.442∗∗∗

(0.078) (0.078) (0.078) (0.079)

90 percent discount 0.503∗∗∗ 0.501∗∗∗ 0.500∗∗∗ 0.500∗∗∗

(0.079) (0.079) (0.079) (0.079)

Above Rs. 25,000 0.061 0.046 0.023 −0.0004

(0.057) (0.058) (0.110) (0.120)

Income*Treatment X X X X

Income*Educa X X X

Income*Ever use X X

Income*Source uterus X X

Observations 558 554 554 554

R2 0.235 0.241 0.241 0.241

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, Column (1) includes awareness status after the earthquake (whether anindividual received health and hygiene awareness that comprised menstrual health aware-ness following the earthquake but prior to the study); Column (2) includes household andpersonal control variables such as caste dummies, father’s education, mother’s education,age, and age squared. Column (3) adds variables from the baseline survey pertaining tothe household’s attitude and one’s knowledge regarding menstruation. Column (4) addstol (or area) related controls to proximity including household’s distance from the nearestmarket, school, and healthpost (in minutes by foot). Robust White’s standard errorsadjusted for heteroskedasticity are presented in parenthesis.

47

Table 5: The Effect of Awareness Program on Redemption of Coupon by Discount Percentage – Wild Cluster Bootstrap

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.116∗∗∗ 0.141∗∗∗ 0.147∗∗∗ 0.153∗∗∗

(0.038) (0.032) (0.022) (0.035)

25 percent discount*awareness 0.109 0.078 0.079 0.075

(0.093) (0.084) (0.088) (0.096)

50 percent discount*awareness 0.260∗∗∗ 0.233∗∗ 0.226∗∗ 0.228∗∗

(0.093) (0.105) (0.109) (0.089)

75 percent discount*awareness 0.173 0.160 0.164 0.172

(0.129) (0.120) (0.118) (0.107)

90 percent discount*awareness 0.153∗∗∗ 0.134∗∗∗ 0.122∗∗∗ 0.148∗∗∗

(0.049) (0.045) (0.039) (0.048)

25 percent discount 0.288∗∗∗ 0.294∗∗∗ 0.292∗∗∗ 0.295∗∗∗

(0.078) (0.086) (0.083) (0.098)

50 percent discount 0.296∗∗∗ 0.297∗∗∗ 0.297∗∗∗ 0.301∗∗∗

(0.043) (0.055) (0.057) (0.044)

75 percent discount 0.454∗∗∗ 0.455∗∗∗ 0.449∗∗∗ 0.442∗∗∗

(0.093) (0.070) (0.071) (0.061)

90 percent discount 0.526∗∗∗ 0.515∗∗∗ 0.511∗∗∗ 0.502∗∗∗

(0.035) (0.023) (0.022) (0.047)

Above Rs. 25,000 0.032 0.031 0.012

(0.057) (0.049) (0.053)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 571 565 563 558

R2 0.174 0.211 0.216 0.233

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, Column (1) includes awareness status after the earthquake (whether anindividual received menstrual health and hygiene awareness that comprised menstrualhealth following the earthquake but prior to the study); Column (2) adds household andpersonal control variables such as caste dummies, father’s education, mother’s education,age, and age squared. Column (3) adds variables from the baseline survey pertaining tothe household’s attitude and one’s knowledge regarding menstruation. Column (4) addsthe household’s distance to the nearest school, health care center, and the market wherepharmacies are located. Wild cluster bootstrap, clustered at the area level, is used toestimate the standard errors presented in parenthesis.

48

Table 6: Spillover Effects of Treatment

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.144 0.163 0.195 0.188

(0.115) (0.118) (0.119) (0.121)

25 percent discount*awareness 0.112 0.077 0.099 0.077

(0.133) (0.135) (0.139) (0.136)

50 percent discount*awareness 0.264∗∗ 0.243∗ 0.258∗∗ 0.255∗∗

(0.131) (0.130) (0.126) (0.126)

75 percent discount*awareness 0.206 0.189 0.216∗ 0.212∗

(0.135) (0.131) (0.127) (0.127)

90 percent discount*awareness 0.114 0.107 0.129 0.138

(0.136) (0.133) (0.127) (0.127)

Number of treated individuals 0.032 0.022 0.017 0.017

(0.022) (0.022) (0.022) (0.021)

Number Treated*awareness 0.015 0.012 0.014 0.014

(0.029) (0.029) (0.029) (0.028)

25 percent discount 0.300∗∗∗ 0.304∗∗∗ 0.304∗∗∗ 0.311∗∗∗

(0.106) (0.109) (0.111) (0.110)

50 percent discount 0.306∗∗∗ 0.304∗∗∗ 0.306∗∗∗ 0.303∗∗∗

(0.107) (0.107) (0.108) (0.106)

75 percent discount 0.447∗∗∗ 0.452∗∗∗ 0.447∗∗∗ 0.436∗∗∗

(0.106) (0.098) (0.096) (0.097)

90 percent discount 0.562∗∗∗ 0.544∗∗∗ 0.539∗∗∗ 0.524∗∗∗

(0.107) (0.104) (0.101) (0.100)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 571 565 563 558

R2 0.227 0.254 0.262 0.272

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, Column (1) includes awareness status after the earthquake (whether an in-dividual received health and hygiene awareness that comprised menstrual health followingthe earthquake) and the total number of individuals in the neighborhood who were sam-pled in the baseline; Column (2) adds household and personal control variables such ascaste dummies, father’s education, mother’s education, income, age, and age squared.Column (3) adds variables from the baseline survey pertaining to the household’s atti-tude and one’s knowledge regarding menstruation. Column (4) adds tol (or area) relatedcontrols to proximity including the household’s distance from the nearest market, school,and healthpost (in minutes by foot). Clustered bootstrapped standard error at the levelof geographic area (tol) are presented in parenthesis.

49

Table 7: Effect of Awareness Program on Redemption After Adjusting the Sample in the Control Group

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.127 0.150 0.157∗ 0.166∗

(0.094) (0.095) (0.094) (0.095)

25 percent discount*awareness 0.083 0.043 0.045 0.042

(0.093) (0.092) (0.093) (0.094)

50 percent discount*awareness 0.234∗∗∗ 0.202∗∗ 0.190∗∗ 0.197∗∗

(0.090) (0.093) (0.093) (0.093)

75 percent discount*awareness 0.104 0.088 0.095 0.107

(0.089) (0.088) (0.088) (0.088)

90 percent discount*awareness 0.068 0.064 0.047 0.066

(0.092) (0.092) (0.093) (0.093)

25 percent discount 0.339∗∗∗ 0.339∗∗∗ 0.338∗∗∗ 0.345∗∗∗

(0.088) (0.087) (0.087) (0.087)

50 percent discount 0.352∗∗∗ 0.342∗∗∗ 0.343∗∗∗ 0.346∗∗∗

(0.085) (0.085) (0.085) (0.085)

75 percent discount 0.536∗∗∗ 0.536∗∗∗ 0.530∗∗∗ 0.524∗∗∗

(0.088) (0.087) (0.087) (0.088)

90 percent discount 0.627∗∗∗ 0.598∗∗∗ 0.604∗∗∗ 0.602∗∗∗

(0.091) (0.090) (0.091) (0.091)

Above Rs. 25,000 0.055 0.058 0.041

(0.048) (0.048) (0.048)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 495 488 486 482

R2 0.191 0.228 0.242 0.255

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

The sample is restricted to individuals who complied to the treatment (attended aware-ness) and individuals in the control group with the counter-factual probability of attendingthe awareness program, had they been invited, greater than 0.3. Additionally, Column (1)includes awareness status after the earthquake (whether an individual received health andhygiene awareness that comprised menstrual health following the earthquake but prior tothe study); Column (2) includes household and personal control variables such as castedummies, father’s education, mother’s education, age, and age squared. Column (3) addsvariables from the baseline survey pertaining to the household’s attitude and one’s knowl-edge regarding menstruation. Column (4) adds tol (or area) related controls to proximityincluding the household’s distance from the nearest market, school, and healthpost (inminutes by foot). Robust White’s standard errors adjusted for heteroskedasticity arepresented in parenthesis.

50

Table 8: The Effect of Awareness Program on Redemption of Coupon by Discount Percentage (A Counterfactual Exper-iment)

Dependent variable:

Redemption Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.193∗∗∗ 0.188∗∗∗ 0.184∗∗∗ 0.182∗∗∗

(0.065) (0.066) (0.065) (0.067)

25 percent discount*awareness 0.069 0.038 0.040 0.040

(0.080) (0.080) (0.079) (0.079)

50 percent discount*awareness 0.202∗∗∗ 0.191∗∗ 0.181∗∗ 0.193∗∗

(0.078) (0.079) (0.080) (0.077)

75 percent discount*awareness 0.073 0.055 0.059 0.066

(0.081) (0.081) (0.082) (0.080)

90 percent discount*awareness 0.015 0.015 0.012 0.028

(0.079) (0.076) (0.078) (0.080)

25 percent discount 0.275∗∗∗ 0.283∗∗∗ 0.283∗∗∗ 0.283∗∗∗

(0.058) (0.063) (0.063) (0.064)

50 percent discount 0.304∗∗∗ 0.301∗∗∗ 0.308∗∗∗ 0.305∗∗∗

(0.058) (0.059) (0.061) (0.060)

75 percent discount 0.453∗∗∗ 0.453∗∗∗ 0.451∗∗∗ 0.443∗∗∗

(0.063) (0.061) (0.065) (0.065)

90 percent discount 0.553∗∗∗ 0.539∗∗∗ 0.541∗∗∗ 0.527∗∗∗

(0.065) (0.062) (0.066) (0.070)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 688 681 679 674

R2 0.179 0.203 0.210 0.222

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, Column (1) includes awareness status after the earthquake (whether an in-dividual received health and hygiene awareness that comprised of menstrual health aware-ness following the earthquake but prior to the study); Column (2) includes household andpersonal control variables such as caste dummies, father’s education, mother’s education,age, and age squared. Column (3) adds variables from the baseline survey pertaining tothe household’s attitude and one’s knowledge regarding menstruation. Column (4) addstol (or area) related controls to proximity including the household’s distance from thenearest market, school, and healthpost (in minutes by foot). Bootstrapped standard er-rors from 750 replications are presented in the parenthesis to account for using predictedvariable as a regressor (as described in detail in A1.2.2).

51

Appendix A

A1 Elasticity estimates under non-compliance

One concern about non-compliance to our awareness treatment is that compliers could self-selectinto receiving awareness, so that the unobservable factors affecting their redemption could be dif-ferent from non-compliers. Our data shows that women who showed up for the awareness programupon receiving invitation (compliers) were more likely to have ever used sanitary pad. Given sucha findings, we need to consider whether and how presence of non-compliers in the analysis affectour estimates.

To conceptualize the effect of non-compliance in our elasticity estimates, consider a linear de-mand for sanitary pad:

Yi = β0 + β1Hi + β2Ai + β3(Hi ×Ai) + ei (1)

whereHi denotes high discount (Hi ∈ {0, 1} for simplicity), Ai ∈ {0, 1} denotes awareness treatmentstatus, and ei is unobservables affecting demand.

To calculate the elasticity of demand for those receiving awareness, denoted by φ1, we can usethe formula:

φ1 =

(E[Yi|Hi = 1, Ai = 1]− E[Yi|Hi = 0, Ai = 1]

E[Yi|Hi = 0, Ai = 1]

)/∆P

=β0 + β1 + β2 + β3 + E[ei]− (β0 + β2 + E[ei])

β0 + β2 + E[ei]/∆P

=

(β1 + β3

β0 + β2 + E[ei]

)/∆P

(2)

The second equality utilizes the fact that E[ei|Hi = 0, Ai = 1] = E[ei|Hi = 1, Ai = 1] = E[ei]due to random allocation of discount level and awareness treatment. Similar calculation shows thatfor the control group elasticity φ0 is given by

φ0 =

(β1

β0 + E[ei]

)/∆P (3)

From the equality established in equation 2, it is clear that the impact of awareness on elasticityis due to both change in slope β3 and change in location of demand curve β2. Without loss ofgenerality, we can assume that E[ei] = 0 in a regression with a constant term, so we can calculateelasticity by running regression of redemption on discount level for the treated and control group,and estimate the coefficients. Because the elasticity formula is non-linear, we can use delta methodto calculate the standard errors. We are interested in the hypothesis that |φ1| − |φ0| < 0, i.e., theawareness reduces the price-responsiveness of demand.

The effect of non-compliance is such that we are compelled to estimate φ1 using a sample ofindividuals who chose to comply with the program. Let Ci = 1 for compliers, Ci = 0 for non-compliers. In our selected sample due to partial non-compliance, our estimates elasticity for thetreatment group, φ1 is given by

φ1 =

(β1 + β3

β0 + β2 + E[ei|Ci = 1]

)/∆P (4)

The key issue is that if E[ei|Ci = 1] 6= E[ei], it creates a bias in the elasticity estimates.Now, consider the self-selection criteria:

52

C∗i = Wiγ + ui (5)

where C∗i is latent utility of compliance, and so oberved compliance status Ci is given by

Ci = 1 if C∗i > 0

Ci = 0 if C∗i ≤ 0

Then, the estimated elasticity using the selected sample is given by

φ1 =

(β1 + β3

β0 + β2 + E[ei|ui > −Wiγ]

)/∆P (6)

Note that E[ui|Ai = 1] = E[ui|Ai = 0] due to randomization. Thus it must be the case that

E[ei|ui > −Wiγ,Ai = 1] = E[ei|ui > −Wiγ,Ai = 0] (7)

In other words, the same DGP would affect selection (attendance) among the control group in thecounterfactural scenario that they received the invitation. This allows us to impute the values oncoupon redemption among the non-compliers based on the counterfactual scenario as described inSection A1.2.

For a simple illustration, we generate 1,000 observations of a hypothetical distribution of un-observables determining redemption and compliance (ui, ei), assuming a joint-normal distributionwith mean zero and covariance 0.5. We then randomly allocate half of the observations to groupA = 0 and the other half to A = 1. Because the assignment to the group is random, the jointdistribution of unobservables should be the same as the full sample. The scatter plot by groupassignment is shown in Figure A1. While we observe the full distribution of the control group(A = 0), we only observe compliers in the awareness treatment group (A = 1). In this hypotheticalscenario, assume that compliance takes place if ui > −1.4. This is shown by the vertical line inFigure A1. Awareness can lower the threshold of redemption, as shown by the horizontal lines.Due to non-compliance, we only capture observations to the right of the vertical line for A = 1.However, the threshold that determines compliance among the control group had they been invitedas a counterfactual will be the same as for the awareness treatment group due to randomization.Hence, the distribution of non-compliers and the distribution of hypothetical non-compliers in thecontrol group had they been invited will be similar.

A1.1 Robustness exercise 1: Multiple imputation with propensity score

We note that compliance status gives us some information about likely redemption status. Thus,in the first robustness exercise, we impute redemption status of non-compliers using comparablesample of compliers. The comparison here is based on the propensity score of attending awarenesssessions given by observed covariates. To estimate the propensity score, we closely follow Imbensand Rubin (2015). First, we declare some basic covariates that are a priori viewed as importantin determining the probability of attending awareness sessions. This includes six pretreatmentcovariates: i) Education level (dummy of 0−4th grade, 5th−8th grade, and upper secondary levels),ii) Family income, iii) Indicator of whether an individual was exposed to health campaigns includingmenstrual health-hygiene following the 2015 earthquake, iv) Age, v) Whether an individual everused sanitary pad (ever use), and vi) Whether an individual was placed in a shed during the time

53

Figure A1: Illustration of non-compliance issue

Note: The figure shows hypothetical distribution of (ui, ei). The errors follow joint normal distri-bution with mean 0 and covariance of 0.5.

of menstruation (shed).In the second round, we consider caste (Brahmin, Chhetri), age squared (age sq), an indicator

if a person correctly answered the source of menstruation as uterus (source uterus), whether theperson attended any hygiene related awareness prior to the study (hygiene), an indicator represent-ing the cause of menstruation as hormones (cause hormones), and whether a person is regarded asuntouchable during menstruation (untouchable). These variables are entered linearly in the modelspecification defined in the first step one by one. For each of the specification, we calculate thelikelihood ratio statistic based on the null hypothesis that the newly added covariate in the secondround has a zero coefficient. We check whether the likelihood statistic is greater than 2; if such isthe case, the variable with the largest likelihood ratio statistic is included. This process indicatespicking untouchable as an additional covariate (with the likelihood statistic of 2.45). We repeat theprocess discussed above for the remaining five covariates; the likelihood ratio for all the remainingfive variables is less than 1. So these variables are not included in the model specification.

In the third round, we decide on the interaction terms to include in the specification. We checkfor the interactions between an indicator of ever use and the following variables: i) Education, ii)Family income, iii) Un-touchability, iv) Age, and v) An indicator defining whether an individualwas placed in the shed during menstruation (shed). We enter these terms one by one in themodel specification defined in round two and record the likelihood ratio statistic. If the largestlikelihood ratio statistic is greater than 2, we include that interaction in the model. This processleads to include interactions defined by ii) (ever use and family income) and iii) (ever use anduntouchable). Hence, this way we construct the final specification used to estimate propensityscore of attending the awareness sessions. Using the estimated propensity score we drop the control

54

units with an estimated propensity score of less than the minimum value among the treated unitsand also the treated units with greater than estimated propensity score than the maximum valueamong the control units. Figure A2 shows the linearized propensity score (lps), where estimated

ˆlps = ln(ˆe(x)

1− ˆe(x)) and ˆe(x) is the estimated propensity score.

Next, we assess the adequacy of propensity score by selecting strata with respect to the medianof the linearized propensity score and confirming within-block equality of means by calculatingthe t-statistic using tj = lt(j)−lc(j)√

S2l (j).(1/Nc(j)+1/Nt(j))

. Here, lt(j) and lc(j) are averages of block-specific

propensity scores by treatment and control groups, respectively; and S2 is the block-specific samplevariance. This allows us to pick 2 stratas (divided by the median of the linearized propensity score);all the blocks are well-balanced, with the t-statistic less than 1 (0.24 and 0.4 for the first and secondstrata, respectively). Multiple imputations within each block are carried out as described in detailin section 6.1. This process is briefly described in A3.

Figure A2: Linearized Propensity Score

55

Figure A3: Illustration of multiple imputation with propensity score

Awarenesstreatment

samplewith

missingvalues

Highcompliancepropensitysubsample

High com-pliancepropen-sity non-compliers(missing

redemption)

Highcompliancepropensitycompliers

Lowcompliancepropensitysubsample

Lowcompliancepropensitycompliers

Low com-pliancepropen-sity non-compliers(missing

redemption)

Bootstrappedsample

Bootstrappedsample

Imputeredemption

statusof high

propensitynon-

compliers

Imputeredemption

statusof low

propensitynon-

compliers

Awarenesstreatment

samplewith

imputedmissingvalues

Awarenesscontrolsample

Estimation

A1.2 Solution 2: Counterfactual experiment

A1.2.1 Adjusting the Sample in the Control Group

Alternatively, we conduct a counter-factual experiment to adjust the sample in the control groupsuch that individuals in the control group are similar to compliers in the awareness treatmentgroup. Here, we use the sample in the awareness treatment group to model the probability ofcompliance using a logistic regression. The regressors included are the highest level of education,ever used sanitary pad, indicators of family income, caste indicators, indicators of assets (motorbike,fridge), variables depicting attitude and knowledge regarding menstruation, employment status,self-reported distance to closest college, mother’s education, and the proportion of individualsattending awareness sessions within a tol among those who were invited.

We use the predicted values of compliance from this model to see how well the model performs.

56

The receiver operating characteristic curve that plots the true-positive rate against a false-positiverate is given in Figure A4. The accuracy of the model obtained in the testing sample is of 70%after picking the threshold of 0.3. We use the estimates on regressors from the model pertainingto the awareness treatment sample to calculate the predicted values of attending the awarenesssessions had people in the control group been invited to the session based on observables. Followingequation 7, it has to be noted that unobservable factors similar to the treatment group would havedriven people in the control group to decide on whether to attend the awareness session had theybeen invited, since awareness treatment and control groups are randomized. After obtaining theprobability of attending awareness for people in the control group had they been invited, we firstconsider people with probability higher than 0.3 (threshold value). In other words, we drop thoseindividuals in the control group who have low probability of attending (p < 0.3). Following thisprocedure, which is similar to trimming based on propensity score as described in Imbens andRubin (2015), we now have an adjusted sample of the control group who are similar to compliersin the awareness treatment group. We estimate the effect of awareness treatment on redemptionusing this sub-sample. This strategy particularly drops individuals in the control group who areless likely to comply had they been invited to the awareness session.

Figure A4: Receiver Operating Characteristic (ROC) curve

For clarity, the distribution of predicted compliance probabilities for the entire sample (bothtreatment and control) are shown in Figure A5. Figure A5a shows that the distribution of predictedprobability of compliance is similar across treatment and control group. This is expected due torandomization across two groups.

57

(a) Treatment and control (b) Complier and non-compliers

Figure A5: CDF of compliance probabilities

Source: Authors’ calculations.Note: Figure A5a shows the cumulative distribution of predicted compliance probabilities for control and treatmentgroup. The predicted compliance probability is estimated by running a probit regression of compliance on age,age-squared, discount level, education, family income, distance to college, prior sanitary pad use, and exposure toawareness program among the treated sample. In panel (B), the treatment group is further sub-divided intocompliers and non-compliers.

A1.2.2 Using Predicted Redemption Status to Replace Missing Values

Next, in spirit of utilizing the entire sample, we replace the missing values of redemption statusamong non-compliers in the awareness treatment group based on predicted redemption status byfollowing the steps below.

1. Predict the compliance probability by using a logistic regression model for the treatmentsample. This allows to predict compliance probability for the control group had they beeninvited.

2. Regress coupon redemption status on discount indicators, compliance probability (from Step1), and the interactions between the compliance probability and discount indicators for thecontrol sample.

3. Use estimates from Step 2 to predict redemption status for the whole sample (both treatmentand control).

4. For only the missing values on redemption status (for non-compliers), replace redemptionstatus as “1” if the predicted redemption probability is greater than 0.5, otherwise the valuegiven is “0”.

5. Estimate the effect of awareness treatment on the whole sample after the missing values onredemption status is replaced by using steps 1-4.

It should be noted that this way of replacing missing values on redemption status for the non-compliers in the treatment group still assumes that people who did not comply would not be affected

58

by the awareness treatment even if they were provided with treatment. In this sense, estimates ofawareness treatment will most likely represent a lower bound.

A1.3 Alternative Multiple Imputation

Next, we impute the missing values of coupon redemption at each discount rate for the non-compliers using the rest of the sample following the literature (Efron, 1994; Imbens and Rubin,2015). We begin by noting that since both discount rate and awareness invitation were randomized,the distribution of preference parameter will be the same for those in the treatment and controlgroup. Thus, estimating the demand equation only on the control group sample gives consistentpopulation estimates of those who never received the awareness program. Thus, crudely we canuse the behavior of control group at different discount rates to infer behavior of the missing groupat the respective discount rates (sample I). This assumes that individuals who did not attend theawareness session even though they were invited will behave in similar ways as to individuals inthe control group at respective discount rates because they were never exposed to awareness (i.e.invitation by itself has no effect if not attended).

Alternatively, we use observed outcomes from both treatment and control groups for imputationof missing values (sample II). Imputation based in this second sample depends on an assumptionthat the behavior of non-compliers is determined by a mixture of people from both treatment andcontrol groups, according to the weights. This assumes that on average 65% of the time people whodid not comply behaves like the control group and 35% of time they behave like the people in thetreatment group, at the respective discount rates. The weights are determined by the proportionof treatment group with non-missing values on redemption (compliers) with respect to the totalnumber people with non-missing values (compliers+control group).

We perform 1,000 replications from the two aforementioned samples by drawing observationsrandomly with replacement. As an example pertaining to sample I, in the first round of imputationwe randomly draw an observation of the respective discount rate, say, 10%, from sample I. Thevalue of redemption status for a non-complier is imputed by using this draw. Observations aredrawn randomly from sample I with replacement until all the missing values on redemption statusamong non-compliers are imputed. This marks the end of round 1. This process is repeated 1000times, which gives 1,000 imputed data sets. For each newly imputed dataset, we estimate theeffect of awareness on coupon redemption, which gives a distribution of estimates. Similar processis carried out for sample II, except that this sample comprises of both control group (65% of thetime) and compliers in treatment group (35% of the time).

The histogram for the point estimates reflecting the interaction between awareness and therespective coupon rate are presented in Figure B2. The histograms in bright green and red representthe distribution of estimates (non-overlapped estimates) when imputations are based on sample Iand sample II, respectively. The elasticity estimates calculated by using point estimates fromreplications are shown in Figure B3. The figures indicate a significant overlap between the estimateswhen missing values are imputed from two samples, shown by bars in dark green color. Table B9shows the mean and standard deviation of such effects portrayed by distributions in Figure B2.The results suggest that at the most conservative setting, when awareness would have absolutelyno effect among non-compliers even had they attended these sessions, people of the treatmentgroup with discount coupon of 50% would still be 14.6 percentage points more likely to redeemtheir coupons compared to people in the control group with the same discount rate. We view thisestimate as a lower bound and prefer imputations based on the propensity score and counterfactualanalysis since the imputed values on the redemption status for the non-compliers in these processare carried out by using similar group of individuals.

59

Appendix B

Figures and Tables

Figure B1: The Distribution of income and sanitary pad use by experiment status.

(a) Income distribution by experiment status (b) Santiary pad use in baseline by experiment status

Source: Authors’ calculations.Note: Compliers refers to those invited to the awareness event who showed up. Non-compliers recieved theinvitation but did not show up. Control group refers to individuals who were not invited to the awareness sessions.

60

Figure B2: The Distribution of Estimates from 1,000 Replications

Source: Authors’ calculations.

Note: The figure shows the distribution of estimates on the interaction term between treatmentand respective discount indicator obtained from 1,000 replications to impute the missing values fornon-compliers. The bright green represents the histogram of estimates obtained when imputationare carried on by using the control sample (sample I as defined in section A1.3). The red barsrepresent the histogram when imputation are based on both compliers and control group (sampleII). The dark green bars represent the overlap between estimates when imputations are carried byusing samples I and II.

61

Figure B3: The Distribution of Elasticity Estimates of Awareness Treatment Group (1,000 Replications)

Source: Authors’ calculations.

Note: The figure shows the elasticity estimates obtained from 1,000 replications after imputingthe missing values on the redemption status for the non-compliers. The elasticity estimates areobtained from equation 2. The bright green represents the histogram of estimates obtained whenimputations are carried on by using the control sample (sample I as defined in section A1.3). Thered bars represent the histogram when imputations are based on both compliers and control group(sample II). The dark green bars represent the overlap between estimates when imputations arecarried by using samples I and II.

62

Figure B4: Elasticity Estimates (Imputation from Counterfactual)

Source: Authors’ calculations.

Note: The elasticity estimates are based on the estimates reported in Table 8 (Column 4) and arecalculated using equation 2. The estimates are relative to the next lower price (and the respectivequantity). For example, elasticity at price Rs. 17.5 (discount 75%) is relative to Rs. 7 (discount 90%).

63

Table B1: The Number of Observations by Randomization and Compliance Status

Discount % No awareness Awareness-pooled Awareness-By compliance

Compliers Non-compliers

10 68 67 40 27

25 70 67 43 24

50 76 67 44 24

75 72 71 51 21

90 69 78 50 29

Note: The table shows the number of individuals by treatment combination. Approximately 65% of individuals assigned toreceive awareness actually showed up for the awareness campaign. Compliers include individuals in the treatment group whoattended the awareness session and non-compliers represent individuals who were invited but did not attend the session.

64

Table B2: Determinants of Accepting the Invitation

Dependent variable:

Invitation

(1) (2) (3) (4) (5) (6) (7)

Highest education −0.003 −0.003 −0.004 −0.004 −0.004 −0.004 −0.004

(0.004) (0.003) (0.004) (0.004) (0.004) (0.004) (0.004)

Father’s education 0.0002 0.003 0.002 0.001 0.003 0.002 0.002

(0.006) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

Mother’s education 0.009 0.006 0.005 0.005 0.005 0.006 0.006

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

Brahmin 0.033 0.027 0.029 0.024 0.022 0.021 0.019

(0.083) (0.084) (0.087) (0.088) (0.088) (0.088) (0.089)

Chhetri 0.008 0.013 0.011 0.006 −0.009 −0.018 −0.014

(0.091) (0.090) (0.093) (0.094) (0.097) (0.098) (0.099)

Age −0.008 −0.010 −0.012 −0.011 −0.012 −0.013 −0.014

(0.018) (0.018) (0.019) (0.019) (0.019) (0.019) (0.019)

Age square 0.0001 0.0001 0.0002 0.0002 0.0002 0.0002 0.0003

(0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0003)

Family income (Rs. 25-39,999) 0.144∗∗ 0.120∗ 0.124∗∗ 0.125∗∗ 0.131∗∗ 0.132∗∗

(0.060) (0.062) (0.062) (0.062) (0.062) (0.063)

Family income (Rs. 40,000-59,999) 0.090 0.074 0.090 0.073 0.081 0.079

(0.120) (0.122) (0.122) (0.122) (0.123) (0.123)

Family income (Rs. 60,000 and up) −0.291 −0.316 −0.307 −0.337 −0.313 −0.307

(0.207) (0.209) (0.210) (0.212) (0.213) (0.214)

Kept in shed 0.039 0.036 0.041 0.036 0.036

(0.060) (0.061) (0.061) (0.061) (0.061)

Untouchable −0.092 −0.086 −0.091 −0.087 −0.089

(0.057) (0.057) (0.057) (0.058) (0.058)

Ever use pad 0.133∗ 0.137∗∗ 0.137∗ 0.147∗

(0.069) (0.069) (0.071) (0.077)

Last use −0.027

(0.029)

Source uterus 0.082 0.086 0.083 0.078 0.080

(0.066) (0.067) (0.066) (0.067) (0.067)

Hygiene 0.043 0.035 0.039 0.037 0.038

(0.076) (0.076) (0.076) (0.078) (0.078)

Cause hormones −0.035 −0.029 −0.032 −0.027 −0.027

(0.072) (0.073) (0.072) (0.073) (0.073)

Distance market 0.001 0.001 0.001

(0.002) (0.002) (0.002)

25 percent discount 0.026 0.027

(0.087) (0.087)

50 percent discount 0.043 0.046

(0.087) (0.088)

75 percent discount 0.092 0.092

(0.084) (0.084)

90 percent discount −0.031 −0.029

(0.083) (0.084)

Frequently use pad −0.023

(0.064)

Observations 344 344 341 341 340 340 340

R2 0.018 0.043 0.069 0.061 0.070 0.078 0.078

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, all columns include control for awareness status after the earthquake (whether an individ-ual received sanitary related health and hygiene awareness that comprised menstrual health followingthe earthquake but prior to the study). Robust White’s standard errors adjusted for heteroskedasticityare presented in parenthesis.

65

Table B3: Balance Exercise Discount

(a) Demographic Characteristics

Dependent variable:

brahmin chhetri age educa father educa employ hus employ

(1) (2) (3) (4) (5) (6) (7)

10 percent discount*awareness −0.085 −0.024 0.977 1.623 −0.140 −0.050 0.097

(0.062) (0.055) (1.681) (1.236) (0.862) (0.082) (0.065)

25 percent discount*awareness 0.039 0.067 1.983 −0.698 −0.415 −0.030 0.039

(0.061) (0.054) (1.642) (1.218) (0.846) (0.082) (0.064)

50 percent discount*awareness 0.010 −0.001 2.423 0.518 −1.488∗ 0.159∗∗ 0.056

(0.061) (0.054) (1.637) (1.210) (0.843) (0.081) (0.065)

75 percent discount*awareness −0.053 0.003 0.429 −0.662 0.686 0.006 −0.058

(0.060) (0.053) (1.615) (1.196) (0.830) (0.080) (0.066)

90 percent discount*awareness −0.044 −0.003 −1.717 0.391 0.425 −0.039 0.046

(0.060) (0.053) (1.603) (1.184) (0.826) (0.079) (0.064)

25 percent discount −0.079 0.013 0.108 0.015 0.357 −0.064 −0.012

(0.059) (0.052) (1.582) (1.160) (0.812) (0.078) (0.061)

50 percent discount −0.064 0.059 0.206 −0.577 −0.077 −0.034 0.021

(0.057) (0.051) (1.558) (1.141) (0.799) (0.076) (0.062)

75 percent discount −0.024 0.027 0.697 0.999 −0.695 −0.051 0.089

(0.058) (0.052) (1.578) (1.161) (0.810) (0.077) (0.061)

90 percent discount 0.008 0.002 2.518 −0.160 −0.976 −0.016 0.040

(0.059) (0.052) (1.591) (1.167) (0.817) (0.078) (0.061)

Observations 697 697 692 692 694 695 560

R2 0.009 0.013 0.013 0.010 0.015 0.015 0.013

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

(b) Assets

Dependent variable:

Income TV motor bike own land rooms toilet fridge

(1) (2) (3) (4) (5) (6) (7)

10 percent discount*awareness 0.083 −0.014 0.023 0.042 0.331 −0.005 −0.040

(0.083) (0.079) (0.081) (0.095) (0.322) (0.064) (0.080)

25 percent discount*awareness 0.020 −0.107 −0.013 −0.161∗ −0.079 0.048 −0.030

(0.082) (0.078) (0.079) (0.094) (0.319) (0.065) (0.079)

50 percent discount*awareness 0.017 0.056 0.016 0.021 −0.063 −0.034 0.056

(0.081) (0.077) (0.079) (0.093) (0.315) (0.065) (0.079)

75 percent discount*awareness 0.070 −0.063 0.130∗ −0.008 −0.137 0.0003 0.041

(0.081) (0.077) (0.078) (0.093) (0.312) (0.064) (0.078)

90 percent discount*awareness 0.092 0.013 −0.015 0.006 0.135 0.018 0.041

(0.080) (0.076) (0.078) (0.092) (0.309) (0.064) (0.077)

25 percent discount −0.013 0.078 0.022 0.172∗ 0.063 0.006 −0.038

(0.079) (0.075) (0.076) (0.090) (0.303) (0.060) (0.076)

50 percent discount −0.023 −0.014 0.039 −0.030 −0.152 −0.024 −0.065

(0.077) (0.073) (0.075) (0.088) (0.298) (0.061) (0.074)

75 percent discount −0.050 0.065 −0.024 0.038 −0.025 −0.022 −0.114

(0.078) (0.074) (0.076) (0.090) (0.303) (0.062) (0.076)

90 percent discount 0.010 −0.016 0.011 −0.026 0.072 −0.064 −0.181∗∗

(0.079) (0.075) (0.076) (0.090) (0.305) (0.063) (0.076)

Observations 697 697 697 695 693 583 697

R2 0.026 0.008 0.011 0.022 0.013 0.024 0.022

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

66

Table B4: Balance Exercise Discount

(a) Stigma

Dependent variable:

kitchen holy place shed untouchable

(1) (2) (3) (4)

10 percent discount*awareness 0.112 0.037 −0.062 0.051

(0.089) (0.041) (0.091) (0.089)

25 percent discount*awareness −0.052 −0.078∗ 0.044 0.017

(0.088) (0.041) (0.089) (0.088)

50 percent discount*awareness −0.028 0.057 0.021 0.111

(0.087) (0.040) (0.088) (0.087)

75 percent discount*awareness −0.008 0.034 −0.154∗ 0.050

(0.086) (0.040) (0.088) (0.086)

90 percent discount*awareness −0.007 −0.039 −0.113 0.051

(0.086) (0.040) (0.088) (0.086)

25 percent discount 0.060 0.116∗∗∗ −0.046 −0.042

(0.084) (0.039) (0.085) (0.084)

50 percent discount 0.003 −0.002 −0.069 −0.037

(0.082) (0.038) (0.084) (0.082)

75 percent discount −0.006 −0.0004 0.090 −0.048

(0.084) (0.039) (0.085) (0.084)

90 percent discount 0.018 0.030 −0.022 −0.012

(0.084) (0.039) (0.086) (0.084)

Observations 697 695 695 696

R2 0.008 0.025 0.013 0.011

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

(b) Knowledge and Use

Dependent variable:

uterus Source unknown hormones cause n.k unhygenic frequent use ever use

(1) (2) (3) (4) (5) (6) (7)

10 percent discount*awareness 0.059 −0.018 0.097 −0.023 0.032 −0.067 −0.037

(0.091) (0.079) (0.087) (0.081) (0.074) (0.090) (0.075)

25 percent discount*awareness −0.163∗ 0.154∗∗ −0.066 0.033 0.129∗ 0.003 −0.044

(0.090) (0.078) (0.085) (0.080) (0.073) (0.088) (0.074)

50 percent discount*awareness −0.103 0.081 −0.004 0.012 −0.073 −0.098 −0.216∗∗∗

(0.089) (0.078) (0.085) (0.079) (0.072) (0.087) (0.073)

75 percent discount*awareness 0.005 0.017 −0.012 −0.028 0.070 −0.048 −0.012

(0.088) (0.077) (0.084) (0.078) (0.072) (0.087) (0.073)

90 percent discount*awareness −0.061 0.070 −0.060 0.060 0.077 0.029 0.016

(0.088) (0.077) (0.084) (0.078) (0.071) (0.086) (0.072)

25 percent discount 0.043 0.019 0.059 −0.013 −0.010 −0.008 0.040

(0.086) (0.075) (0.082) (0.076) (0.070) (0.084) (0.071)

50 percent discount 0.017 0.041 −0.046 0.070 0.127∗ 0.121 0.107

(0.084) (0.073) (0.080) (0.075) (0.069) (0.083) (0.069)

75 percent discount 0.049 0.027 0.025 0.050 0.062 0.031 0.102

(0.086) (0.074) (0.081) (0.076) (0.070) (0.084) (0.070)

90 percent discount 0.051 −0.041 0.065 −0.020 0.038 0.046 0.113

(0.086) (0.075) (0.082) (0.077) (0.070) (0.085) (0.071)

Observations 695 695 695 695 697 697 696

R2 0.014 0.040 0.023 0.031 0.034 0.019 0.046

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

67

Table B5: Balance Exercise Across Compliers versus Non-Compliers

Variable mean (T) sd. (T) mean (C) sd. (C) p. val N

1 Hindu 0.81 0.393 0.77 0.423 0.406 352

2 Chhetri 0.097 0.297 0.095 0.295 0.941 352

3 Brahmin 0.155 0.363 0.111 0.316 0.408 352

4 Janjati 0.133 0.34 0.063 0.245 0.032 352

5 Other 0.615 0.488 0.73 0.446 0.04 352

6 Age 30.098 9.46 30.112 9.134 0.992 350

7 Marital 0.81 0.393 0.754 0.432 0.173 352

8 Girls in HH 2.128 1.206 2.1 1.356 0.831 331

9 Boys in HH 1.991 1.207 1.934 1.276 0.774 332

10 Highest Education 6.991 5.123 7.452 11.55 0.606 349

11 Father’s Education 2.956 4.941 2.508 4.741 0.487 351

12 Mother’s Education 1.476 4.327 0.833 3.067 0.163 351

13 Plan to Migrate 0.022 0.147 0.048 0.214 0.193 352

14 Employed 0.155 0.363 0.208 0.71 0.35 351

15 Husband Employed 0.934 0.358 0.878 0.329 0.195 280

16 Family Income (0-Rs. 24,999) 0.602 0.491 0.73 0.446 0.012 352

17 Rs. 25,000-39,999 0.319 0.467 0.198 0.4 0.007 352

18 Rs. 40,000-60,000 0.058 0.233 0.04 0.196 0.523 352

19 >Rs. 60,000 0.009 0.094 0.032 0.176 0.101 352

20 Motorbike 0.323 0.478 0.238 0.428 0.179 352

21 Car 0.022 0.147 0.024 0.154 0.866 351

22 Cycle 0.035 0.185 0.032 0.176 0.794 352

23 Television 0.761 0.427 0.722 0.45 0.473 352

24 Fridge 0.31 0.463 0.254 0.437 0.357 352

25 Internet 0.058 0.252 0.056 0.23 0.818 352

26 Number of Rooms 2.929 1.845 2.848 1.732 0.809 350

27 Toilet 0.89 0.314 0.852 0.357 0.428 299

28 Own Land 0.911 0.285 0.928 0.405 0.881 350

29 Distance Primary School 14.369 12.458 16.111 14.185 0.229 351

30 Distance College 20.264 16.329 24.613 16.561 0.023 344

31 Distance Health Post 32.699 23.368 34.921 23.366 0.436 352

32 Distance Market 22.889 18.604 21.5 18.842 0.745 351

33 Last Interaction HP. (days) 209.009 358.84 220.113 431.02 0.969 345

34 Last HP Visit (days) 239.628 410.444 255.128 471.142 0.924 305

35 Last Market Visit (days) 20 87.908 12.571 91.52 0.498 318

36 Awareness after Earthquake 0.571 0.505 0.556 0.545 0.641 352

37 Awareness Sanitation 0.344 0.476 0.302 0.461 0.721 347

38 Awareness Water 0.52 0.519 0.444 0.499 0.592 347

39 Awareness Hygiene 0.407 0.492 0.341 0.476 0.634 347

40 Awareness Faeces 0.291 0.455 0.302 0.461 0.461 346

41 Last Mestruation 2.159 0.844 2.159 0.843 0.988 352

42 Not Permitted Kitchen 0.412 0.493 0.365 0.483 0.338 352

43 Not Permitted Holy Place 0.053 0.225 0.048 0.214 0.755 351

44 Kept in Shed 0.538 0.5 0.532 0.501 0.922 349

45 Untouchable 0.342 0.476 0.437 0.544 0.117 351

46 Source Vagina 0.129 0.336 0.168 0.375 0.269 350

47 Source Bladder 0.027 0.161 0.016 0.126 0.602 350

48 Source Uterus 0.533 0.5 0.456 0.5 0.176 350

49 Source Abdomen 0.058 0.234 0.056 0.231 0.765 350

50 Source Do Not Know 0.253 0.436 0.304 0.462 0.49 350

51 Cause Pathological 0.058 0.234 0.048 0.214 0.804 351

52 Cause Curse 0.022 0.148 0.024 0.153 0.877 351

53 Cause Physiology 0.733 0.443 0.714 0.454 0.629 351

54 Cause Do Not Know 0.187 0.391 0.214 0.412 0.53 351

55 Know about Menstrual Hygiene 0.991 0.094 0.992 0.089 0.919 350

56 Blood Unhygienic? 0.698 0.46 0.624 0.486 0.249 350

57 Use Sanitary Pad 0.8 0.401 0.698 0.461 0.058 351

58 Frequent Use Pad 0.425 0.495 0.365 0.483 0.361 352

59 Use Last 0.575 0.495 0.484 0.502 0.144 352

60 Invitation 1 0 2 0 0 35268

Table B7: Balance Exercise Across Treatment and Control Groups (Restricting to Ever Use Sanitary Pad)

Variable mean (T) sd. (T) mean (C) sd. (C) p. val N

1 Hindu 0.806 0.397 0.821 0.384 0.598 453

2 Chhetri 0.111 0.315 0.132 0.339 0.501 453

3 Brahmin 0.15 0.358 0.139 0.347 0.907 453

4 Janjati 0.117 0.322 0.084 0.278 0.17 453

5 Other 0.622 0.486 0.645 0.479 0.639 453

6 Age 28.564 8.901 28.162 8.604 0.776 450

7 Marital 0.772 0.421 0.779 0.415 0.779 452

8 Girls in HH 2.108 1.136 1.969 1.28 0.187 424

9 Boys in HH 1.982 1.242 2.038 1.212 0.587 426

10 Highest Education 7.747 4.871 7.915 4.788 0.651 450

11 Father’s Education 3.2 5.103 3.213 5.07 0.985 452

12 Mother’s Education 1.722 4.708 1.849 4.8 0.952 452

13 Plan to Migrate 0.028 0.165 0.033 0.199 0.764 451

14 Employed 0.167 0.374 0.199 0.4 0.335 452

15 Husband Employed 0.934 0.387 0.911 0.286 0.545 350

16 Family Income (0-Rs. 24,999) 0.589 0.493 0.7 0.459 0.009 453

17 Rs. 25,000-39,999 0.322 0.469 0.201 0.402 0.001 453

18 Rs. 40,000-60,000 0.067 0.25 0.073 0.261 0.791 453

19 >Rs. 60,000 0.011 0.105 0.022 0.147 0.368 453

20 Motorbike 0.333 0.484 0.267 0.443 0.218 453

21 Car 0.022 0.148 0.037 0.188 0.365 453

22 Cycle 0.044 0.207 0.048 0.213 0.879 453

23 Television 0.767 0.424 0.769 0.422 0.939 453

24 Fridge 0.333 0.473 0.264 0.441 0.167 453

25 Internet 0.061 0.24 0.066 0.263 0.528 453

26 Number of Rooms 3.05 1.897 2.809 1.759 0.232 451

27 Toilet 0.895 0.307 0.878 0.328 0.673 383

28 Own Land 0.905 0.294 0.952 0.729 0.503 452

29 Distance Primary School 14.626 12.602 15.741 13.58 0.469 449

30 Distance College 19.908 16.682 22.558 18.577 0.185 441

31 Distance Health Post 31.333 21.778 32.934 25.372 0.511 453

32 Distance Market 22.709 18.501 26.191 39.274 0.22 451

33 Last Interaction HP. (days) 174.556 300.542 199.31 406.543 0.718 446

34 Last HP Visit (days) 202.968 360.746 208.861 420.106 0.857 410

35 Last Market Visit (days) 23.39 97.95 11.16 70.797 0.14 409

36 Awareness after Earthquake 0.594 0.504 0.597 0.491 0.743 453

37 Awareness Sanitation 0.335 0.473 0.376 0.485 0.249 447

38 Awareness Water 0.54 0.511 0.529 0.5 0.79 448

39 Awareness Hygiene 0.415 0.494 0.433 0.496 0.32 446

40 Awareness Faeces 0.286 0.453 0.319 0.467 0.267 445

41 Last Mestruation 2.144 0.84 2.055 0.858 0.244 453

42 Not Permitted Kitchen 0.383 0.488 0.359 0.481 0.498 453

43 Not Permitted Holy Place 0.056 0.23 0.059 0.236 0.903 452

44 Kept in Shed 0.545 0.499 0.608 0.489 0.166 451

45 Untouchable 0.372 0.485 0.344 0.476 0.548 453

46 Source Vagina 0.133 0.341 0.125 0.331 0.778 453

47 Source Bladder 0.033 0.18 0.018 0.134 0.322 453

48 Source Uterus 0.578 0.495 0.597 0.491 0.733 453

49 Source Abdomen 0.061 0.24 0.037 0.188 0.355 453

50 Source Do Not Know 0.194 0.397 0.223 0.417 0.505 453

51 Cause Pathological 0.061 0.24 0.044 0.205 0.423 453

52 Cause Curse 0.022 0.148 0.011 0.104 0.327 453

53 Cause Physiology 0.772 0.421 0.784 0.412 0.681 453

54 Cause Do Not Know 0.144 0.353 0.161 0.368 0.718 453

55 Know about Menstrual Hygiene 0.989 0.105 0.974 0.159 0.288 452

56 Blood Unhygienic? 0.7 0.46 0.64 0.481 0.237 452

57 Use Sanitary Pad 1 0 1 0 0.234 453

58 Use Last 1.539 0.899 1.509 0.879 0.78 453

59 Know about Moon Cups 0.072 0.693 0.026 0.199 0.267 453

69

Table B8: The Effect of Awareness Program on Redemption of Coupon by Discount Percentage (Ever Used)

Dependent variable:

Redemption

(1) (2) (3) (4)

10 percent discount*awareness 0.062 0.113 0.111 0.131

(0.108) (0.106) (0.107) (0.107)

25 percent discount*awareness 0.050 0.022 0.028 0.021

(0.097) (0.096) (0.097) (0.097)

50 percent discount*awareness 0.320∗∗∗ 0.337∗∗∗ 0.304∗∗∗ 0.304∗∗∗

(0.105) (0.105) (0.106) (0.105)

75 percent discount*awareness 0.180∗∗ 0.174∗ 0.174∗ 0.192∗∗

(0.091) (0.089) (0.090) (0.090)

90 percent discount*awareness 0.075 0.060 0.056 0.083

(0.092) (0.090) (0.092) (0.092)

25 percent discount 0.362∗∗∗ 0.380∗∗∗ 0.373∗∗∗ 0.382∗∗∗

(0.092) (0.091) (0.091) (0.091)

50 percent discount 0.298∗∗∗ 0.303∗∗∗ 0.321∗∗∗ 0.330∗∗∗

(0.089) (0.089) (0.089) (0.088)

75 percent discount 0.488∗∗∗ 0.499∗∗∗ 0.497∗∗∗ 0.496∗∗∗

(0.091) (0.090) (0.090) (0.090)

90 percent discount 0.617∗∗∗ 0.622∗∗∗ 0.625∗∗∗ 0.617∗∗∗

(0.091) (0.090) (0.091) (0.091)

Above Rs. 25,000 0.052 0.021

(0.052) (0.053)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 446 443 442 437

R2 0.200 0.248 0.259 0.280

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

The sample is restricted to those individuals who reported having ever used sanitarypad in the baseline survey. Additionally, Column (1) includes awareness status afterthe earthquake (whether an individual received health and hygiene awareness that com-prised menstrual health following the earthquake but prior to the study); Column (2)includes household and personal control variables such as caste dummies, father’s edu-cation, mother’s education, age, and age squared. Column (3) adds variables from thebaseline survey pertaining to household’s attitude and one’s knowledge regarding men-struation. Column (4) adds tol (or area) related controls to proximity, including thehousehold’s distance from the nearest market, school, and healthpost (in minutes byfoot). Robust White’s standard errors adjusted for heteroskedasticity are presented inparenthesis.

70

Table B9: Means and Standard Deviations of Estimates from Multiple Imputation

Variable mean(1) sd(1) mean(2) sd(2)

1 10% discount 0.068 0.019 0.051 0.014

2 25% discount 0.068 0.038 0.057 0.036

3 50% discount 0.181 0.039 0.146 0.037

4 75% discount 0.132 0.033 0.112 0.033

5 90% discount 0.11 0.034 0.085 0.038

The table reports means and standard deviations of estimates obtained after imputation based onhistograms shown in Figure B2. mean(1) and sd(1) pertain to estimates from Sample I and mean(2)and sd(2) relates to Sample II.

71

Table B10: Interaction of Ever Used Pad with Covariates

Dependent variable:

Redemption

(1) (2) (3)

10 percent discount*awareness 0.152∗ 0.152∗ 0.177∗

(0.092) (0.092) (0.092)

25 percent discount*awareness 0.062 0.062 0.057

(0.089) (0.089) (0.090)

50 percent discount*awareness 0.204∗∗ 0.204∗∗ 0.214∗∗

(0.091) (0.091) (0.091)

75 percent discount*awareness 0.154∗ 0.154∗ 0.161∗

(0.084) (0.084) (0.084)

90 percent discount*awareness 0.132 0.132 0.161∗

(0.087) (0.087) (0.088)

25 percent discount 0.290∗∗∗ 0.290∗∗∗ 0.293∗∗∗

(0.078) (0.078) (0.078)

50 percent discount 0.316∗∗∗ 0.316∗∗∗ 0.314∗∗∗

(0.078) (0.078) (0.078)

75 percent discount 0.454∗∗∗ 0.454∗∗∗ 0.450∗∗∗

(0.079) (0.079) (0.079)

90 percent discount 0.521∗∗∗ 0.521∗∗∗ 0.503∗∗∗

(0.079) (0.079) (0.079)

Above Rs. 25,000 0.093 0.093 0.078

(0.071) (0.071) (0.072)

Family Income*Ever Used −0.057 −0.057 −0.063

(0.050) (0.050) (0.050)

HH Controls X X X

Baseline Controls X X

Distance Controls X

HH Controls*Ever Used X X X

Baseline Controls*Ever Used X X

Distance Controls*Ever Used X

Observations 559 559 554

R2 0.235 0.235 0.258

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Additionally, all columns include awareness status after the earthquake (whether an individual receivedhealth and hygiene awareness that comprised menstrual health following the earthquake but prior tothe study). Column (1) adds household and personal control variables such as caste dummies, father’seducation, mother’s education, age, and age squared and their interactions with an indicator of everused sanitary pad. Column (2) adds variables from the baseline survey pertaining to the household’sattitude and one’s knowledge regarding menstruation and their interactions with ever used. Column(3) adds the household’s distance to the nearest school, health care center, and the market wherepharmacies are located and their interaction with ever used. Robust White’s standard errors adjustedfor heteroskedasticity are presented in parenthesis.

72

Table B11: Intent to Treat (ITT) Effects

Dependent variable:

Redemption Redemption

(1) (2) (3) (4)

10 percent discount*predicted compliance 0.305∗∗ 0.297∗∗ 0.291∗∗ 0.288∗∗

(0.124) (0.124) (0.124) (0.124)

25 percent discount*predicted compliance 0.110 0.060 0.064 0.063

(0.122) (0.122) (0.122) (0.123)

50 percent discount*predicted compliance 0.319∗∗∗ 0.302∗∗ 0.285∗∗ 0.305∗∗

(0.120) (0.121) (0.122) (0.122)

75 percent discount*predicted compliance 0.116 0.087 0.093 0.105

(0.119) (0.119) (0.120) (0.119)

90 percent discount*awareness 0.024 0.023 0.018 0.045

(0.118) (0.118) (0.119) (0.119)

25 percent discount 0.277∗∗∗ 0.285∗∗∗ 0.285∗∗∗ 0.285∗∗∗

(0.078) (0.078) (0.078) (0.078)

50 percent discount 0.304∗∗∗ 0.301∗∗∗ 0.308∗∗∗ 0.305∗∗∗

(0.076) (0.077) (0.077) (0.077)

75 percent discount 0.455∗∗∗ 0.455∗∗∗ 0.452∗∗∗ 0.444∗∗∗

(0.078) (0.078) (0.079) (0.079)

90 percent discount 0.556∗∗∗ 0.541∗∗∗ 0.543∗∗∗ 0.529∗∗∗

(0.078) (0.079) (0.079) (0.079)

Above Rs. 25,000 0.092∗∗ 0.096∗∗ 0.084∗∗

(0.042) (0.042) (0.043)

HH Controls X X X

Baseline Controls X X

Distance Controls X

Observations 688 681 679 674

R2 0.179 0.203 0.210 0.222

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

In the first stage we predict the probability of compliance by using the awareness treatment assignmentas the instrument. In the second stage, using the predicted values of compliance, we obtain the intent totreat effects as shown in the Table above. Additionally, all columns include awareness status after theearthquake (whether an individual received health and hygiene awareness that comprised of menstrualhealth following the earthquake but prior to the study). Column (2) adds household and personalcontrol variables such as caste dummies, father’s education, mother’s education, age, and age squaredand their interactions with an indicator of ever used sanitary pad. Column (3) adds variables from thebaseline survey pertaining to the household’s attitude and one’s knowledge regarding menstruation.Column (4) adds the household’s distance to the nearest school, health care center, and the marketwhere pharmacies are located and their interaction with ever used. Robust White’s standard errorsadjusted for heteroskedasticity are presented in parenthesis.

73

Appendix C

C1 Survey questionnaire

Person Number .........Tol/Area Name (Basti Name)..........Household Number..........Note: All personal information collected in this survey will be kept confidential and will be used only for statisticalpurposes.

Part A. Basic Demographic Questions

1. What is your relationship to the head of the household?

a. Head of the household b. Wife c. Mother d. Daughter e. Sister f. Daughter in law g. Sister in law h.Mother in law i. Niece j. Other

2. Religion of the household.

a. Hindu b. Buddhist c. Christian d. Others

3. Caste

(please fill) (.......)

4. Age

(please fill) (.......)

5. Marital Status

a. Married b. Divorced c. Separated d. Never Married

6. If married, number of children

a. Girls (please write down the number of girls) (...)

b. Girls living with you (...)

c. Boys (please write down the number of boys) (...)

d. Boys living with you (...)

7. Location of birth

a. District b. VDC c. Municipality

8. Location of birth

a. Urban b. Rural

9. During the past 12 months, how many months did you live here?

(.......)

10. Do you plan on migrating from this location in this coming year?

(.......)

11. What is the highest level of father’s education?

(.......)

12. What is highest level of mother’s education?

(.......)

13. Did you ever go to school?

a. Yes b. No

14. What is the highest level of education you completed?

(.......)

15. Are you currently employed?

a. Yes b. No

16. If yes, your occupation

Please write 999 if housewife (.......)

Ask questions 17 and 18 for only married individuals.

74

17. Is your husband currently employed?

a. Yes b. No

18. Your husband’s occupation

(.......)

19. Monthly family income: (Note: Family income includes both husband’s and wife’s income if respondent ismarried, family income includes both mother’s and father’s income if respondent is a daughter)

a. Rs. 0 to 24,999 b. Rs. 25,000 to 39,999 c. Rs. 40,000 to 60,000 d. Rs. 60,000 to 80,000 e. Rs. >80,000

20. Do you own a piece of land?

a. Yes b. No

21. If yes (for question 20), how many acres?

(.......)

22. Do you own the following:

a. Motorbike b. Car c. Cycle d. TV e. Fridge f. Internet

23. How many rooms does your household occupy?

a. Total b. Kitchen c. Toilet d. Bed Room e. Living Room f. Total

24. Were you affected by the earthquake?

a. Yes b. No

Part B. Location (Basti) Specific Questions

25. How long does it take to get to the following facilities?

a. Primary school b. College c. Healthpost d. Local Bazaar

26. What is population of the Basti?

(.......)

27. When was the last time you visited the following (in days): a. Healthpost b. Local bazaar

Health and Sanitation

28. When did you last speak to a health personal (doctor, nurse, or a health care worker)?

a. (.......) days b. (.......) months c. (.......) years

29. How do you categorize your health?

a. Excellent b. Good c. Poor

30. Do you have difficulty doing the following:

a. Walking b. Climbing stairs c. Doing daily chores

31. Did you receive any aid after the earthquake?

a. Yes b. No

32. Did you receive any health related awareness after the earthquake? a. Yes b. No

33. If yes, what sector was is in:

a. Faeces related b. Home Sanitation related c. Hygiened. Menstrual Health and Sanitary pad related Stratification Variable

34. When was the last time you had menstruation?

a. A week ago b. 2 weeks ago c. More than 2 weeks

Stigma Against Menstruation

35. Were you allowed in the kitchen during your last menstruation? a. Yes b. No

36. Were you allowed in holy places during menstruation? a. Yes b. No

37. Were you secluded to a shed or a room during menstruation? a. Yes b. No

38. Were you considered an untouchable specially to the male members during menstruation? a. Yes b. No

75

Knowledge Regarding Menstruation

39. How do you best describe menstruation?

a. Pathological process b. Curse from god c. Physiological process d. Do not know

40. Cause of menstruation?

a. Hormones b. Caused by disease c. Curse of god d. Do not know

41. Source of menstrual blood flow.

Vagina Bladder Uterus Abdomen Do not know

42. Heard about menstruation before attaining menarche?

a. Yes b. No

43. Know about menstrual hygiene?

a. Yes b. No

44. Do you think that menstrual blood is unhygienic?

a. Yes b. No

Usage of Sanitary Items

45. Have you ever used sanitary pad during menstruation?

a. Yes b. No

46. What did you use during your last menstruation?

a. Sanitary pad b. Moon cup (menstrual cup) c. Cloth or rag d. Other (please state)

47. If answer is sanitary pad for 46, which brand did you use?

a. Stayfree b. Whisper c. Safety d. Others

48. If answer is sanitary pad for 46, where or how did you obtain sanitary pad?

a. Health worker b. Clinic c. Earthquake relief groups (NGOs) d. Purchased it oneself

49. If answer is sanitary pad for question 46, how long have you been using sanitary pad?

a. Months b. Years

50. How did you know about sanitary pad?

a. School b. Health worker c. Husband d. Daughter e. Other family members f. Awareness programs g.Other (Please explain) (.......) h. Do not know about sanitary pads

51. If answer is cloth or rag for 46, did you clean cloth with soap and water?

a. Yes b. No

52. If answer is cloth or rag for 46, did you dry cloth out in the sunlight? a. Yes b. No

53. Did you change pads or cloths more than three times a day during menstruation?

a. Yes b. No

54. Do you bathe daily with soap during menstruation?

a. Yes b. No

55. How often do you use sanitary pad?

a. Every time when having period b. Frequently c. Only when going outside (once in a while) d. Never

56. If answer is c or d for 55, why not?

a. Uncomfortable b. Parents disapprove of pads c. Cannot afford d. Lack of access (health post too faraway) e. Prefer using cloth than sanitary pad

57. Do you know about moon cups?

a. Yes b. No

58. If Yes to 57, how did you know about moon cups?

a. School b. Health worker c. Husband d. Daughter e. Other family members f. Awareness programs g.Other (Please explain) h. Do not know about moon cups

76

C2 Sanitation pad information packet/script

This section summarizes information dispensed on awareness sessions. The sessions were discussion based and experiences ofparticipants were highlighted and questions were answered by the health workers.

Cultural and Religious Beliefs on Menstruation• Menstruation – usually regarded as a sign of impurity, when bodily excretions are considered to be pollutants

• Societal Restrictions:

– Disallowed in kitchen and holy places; discouraged to participate in social gatherings

– Secluded to a shed, to avoid contact

– Considered untouchable

• CHAUPADI

– An ancient ritual based in Hinduism, where women are banished from their homes during menstruation

– Harsh superstitions attached with menstruation; e.g., if a menstruating women fetches water, wells will dry up.

– Seclusion to shed has caused deaths due to suffocation 26

– Practice of CHAUPADI is banned by Nepal’s supreme court (in 2005)

What is Menstruation?• Menstruation is a physiological phenomenon

• Shed the lining of uterus (womb)

• Blood flows from the uterus through small openings in the cervix

• Passes from vagina

• Length: 3-5 days (and can vary)

• Menstrual Cycle

– Period happens regularly; regularity signals that organs are functioning well

– Provides hormones and prepares for pregnancy

– Average period cycle is 28 days but can vary (range: 21 to 45 days)

– Helpful to track period

What Happens During Menstruation?• Rise in estrogen (female hormone), which plays a key role in health

• Ovum in ovaries start to mature; ovulation: In about 14 days (of 28 day cycle), the egg leaves ovary

• Egg travels to fallopian tube to ovaries

• Women is most likely to get pregnant during 3 days before or on the day of ovulation.

Menstrual Hygiene and Health• Risk associated with infection may be higher than normal during menstruation.

• Mainly because blood passing through vagina creates a pathway for bacteria to travel to uterus

• Insertion of unclean material into vagina can facilitate bacteria to have easier access to cervix and the uterine cavity.

• If using cloths or rags, they should be washed with clean water and soap and completely dried up.

• Several relatively new technologies:

– Sanitary pads (commonly found in pharmacy stores); Tampons; Menstrual cups (difficult to find in the market)

• How does the sanitary pad work and how to use a pad?

• Should be changed once every 3-4 hours

26https://www.cnn.com/2017/07/10/asia/nepal-menstruation-hut-deaths-outrage/index.html

77

a. Uterus: Patheghar; b. Ovary: Dimbasya; c. Vagina: Yonimarg; d. Ovule: Anda; e. Infection:Sankraman; f. Microorganism: Jibjantu; g. Urethral Opening: Mutradwar; h. Urinary Bladder:Mutrasawa

Proper Disposable of Sanitary Material• Should not be disposed along with domestic household waste

• Should be wrapped around a newspaper before disposal to lessen risk from pathogens

• Use clay incinerator for disposal if available

• Better to use biodegradable products if available in the market

78