Spillover Benefits of Marketing Exclusively to Free Patients at Aravind Eye Hospital
Sachin Gupta [email protected] Omkar D. Palsule-Desai [email protected] C. Gnanasekaran [email protected] Thulasiraj Ravilla [email protected]
WP/04/016/OMQT July 2016
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Sachin Gupta is Henrietta Johnson Louis Professor of Management and Professor of Marketing at the Johnson Graduate School of Management, Cornell University, in Ithaca NY 14853, USA. Omkar D. Palsule-Desai is Associate Professor of Operations Management at the Indian Institute of Management, Indore, India. C. Gnanasekaran is Manager, Operations and HR, and Thulasiraj Ravilla is Director, Operations, and Executive Director, LAICO, at the Aravind Eye Care System, India. The authors are grateful to the Aravind Eye Care System for generously providing the data used in this paper. Comments from Vrinda Kadiyali, Ed McLaughlin, Shailendra Jain, Qiang Liu and from participants of seminars at Cornell University and the Theory and Practice in Marketing Conference are gratefully acknowledge
Abstract Spillover Benefits of Marketing Exclusively to Free Patients at
Aravind Eye Hospital
An innovative business model for delivery of health services in low and middle income countries is based on cross-subsidizing across patients. Services are offered to poor patients for free, while other patients pay competitive market prices. Driven by their social mission, organizations often focus their marketing efforts only on poor patients via outreach and education. We analyze the outreach activities (camps) of Aravind Eye
Hospital in India to learn whether these efforts produce additional spillover benefits of attracting paying patients to its hospitals. In particular, we estimate spatial and temporal (both short- and long-term) effects of camps on different types of paying patients. Based on nine years of historical data on the spatial origin of patient traffic and outreach camp locations, we find that camps have net positive effects on the number of paying patients. These effects are consistent with the camps acting as advertising for Aravind. We also find that the effects of camps are stronger for patients who pay a subsidized price than for those who pay full price, camps influence patients in a small geographic radius of six miles, and the effects persist for up to ten weeks after the camp. Camps also have long term positive effects beyond ten weeks on the number of new patients who pay full price. Further, effects become stronger with distance from the base hospital, a finding also consistent with camps acting as advertising. Our findings reinforce the viability of the cross-
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subsidization model since they identify a mechanism that creates synergy between the social mission and income generating sides of the organization.
Key words: Spatial effects, advertising, carry-over, not-for-profit, cross-subsidization, health care
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1. Introduction
Providing health care services to the poor in low and middle-income countries is a significant challenge. A number of innovative health service delivery models have been developed in the private sector to try to meet this need (see, e.g., Kim et al. 2013). One such business model involves offering services to the poor for free or at very low prices, and marketing to them via outreach activities that educate and inform patients and give them easy access. Financially this is often accomplished via cross-subsidization across patients, by offering services at higher prices to patients who can afford them, and using the resulting margins to subsidize care for the poor.
A number of organizations use cross-subsidization as a business model to provide health care services to the poor at large scale1. Examples of for-profit organizations in the private sector include CARE Hospitals
(http://www.carehospitals.com/), which provides primary care, cardiology and other specialty services in
India and subsidizes up to 70% of its patients; Narayana Hrudayalaya Hospital
(http://www.narayanahealth.org/), which performs about 12% of heart surgeries in India and subsidizes 60% of its patients, and Lumbini Eye Institute (http://www.lei.org.np/), which meets 25% of eye care needs in
Nepal and subsidizes 12% of its patients (Tung and Bennett 2014, Bhattacharya et al. 2010). In the not-for- profit arena, one of the best-known organizations is Aravind Eye Care System (http://www.aravind.org/) in
India, which practices cross-subsidization as the core of its business model (Rangan 2009).
A feature of many of these organizations is that they do not use traditional marketing techniques
(Tung and Bennett 2014). Instead, they rely on community outreach and education efforts which are primarily targeted at the poor. The use of marketing strategies tailored specifically to poor patients is necessary because of these patients’ limited purchasing power, location in underserved geographical areas,
1 The cross-subsidization model is popular not only in health care but in nonprofit organizations in general. Oster (1995) provides several examples. Jahani and West (2015) discusses enterprises that focus on “base-of-the-pyramid” markets via cross-subsidy models and why such organizations may be attractive to impact investors. Page 4 of 42
and low health literacy, and is also consistent with the social mission of these organizations. Studies have shown that despite the magnitude of the need among these populations, only a small percentage seek out health care on their own. Fletcher et al. (1999) concluded based on a field experiment in South India that
93.2% of those who could have benefited from eye treatment did not avail of the services. More recently,
Olusanya et al. (2016) found that 75% of adults in a rural community in South Western Nigeria who were in need of eye care were not utilizing eye care services. As a result, marketing to generate demand may be a critical ingredient to improving eye care.
At the same time, often these organizations devote little or no resources to marketing their services to paying patients, either because of unavailability of funds, or because they believe that marketing their services to paying patients contradicts their social mission. Interestingly, these institutions commonly face competition for these patients from other private sector providers of health care services. Given the unusual focus of marketing on customers who do not pay, or pay relatively little, an important strategic question to ask is whether these marketing efforts not only affect poor customers, but have an additional spillover impact on the demand from paying customers. Evidence of spillover would reinforce the financial viability of the cross-subsidization form of business model, since it would mean that marketing spending directed towards fulfilling the organization’s social mission also enhances its revenues. The main focus of the present paper is to theorize how such spillover marketing effects may occur, to measure and characterize them in the case of Aravind Eye Hospitals (henceforth Aravind), and provide insights into the interdependence between the income-generating and social mission sides of Aravind and similar organizations. Next we present a brief background of Aravind to set the stage, before we describe the research objectives and intended contribution of this paper.
Aravind Eye Care System
Since its inception in 1976, the Aravind Eye Care System has been engaged in its mission of “eliminating Page 5 of 42
needless blindness by providing high quality, high volume, compassionate eye care to all.” In 2014-15,
Aravind’s outpatient visits exceeded 3.5 million (Aravind Eye Care System Activity Report 2014-152). In that year over 400,000 surgeries and laser procedures were performed by Aravind eye surgeons, and over half of these surgeries were free or deeply subsidized to the patient. These statistics make Aravind one of the world’s largest and most productive eye care organizations. Underlying these numbers is an organization with a unique business model, culture, and ethos that has served as an exemplar for compassionate care around the world. Much has been written about Aravind’s business model (e.g. Rangan 2009) and operational efficiency (e.g. De Vericort and Lobo 2009). However, to our knowledge, there is little published on the marketing function of Aravind.
Aravind was founded in 1976 by Dr. G. Venkataswamy as an 11-bed hospital in Madurai in the southern Indian state of Tamil Nadu to provide care for patients with disabling cataract blindness. Cataracts are the major cause of blindness in developing countries, accounting for 41.7% of all cases in South Asia in
2010 (Jonas et al. 2014). A cataract forms as the natural lens of the eye clouds over time. Among others, poor nutrition and tropical weather are believed to be significant causes of cataract. In most cases, a cataract can be surgically removed and the eye’s natural lens replaced by an artificial one known as an Intraocular Lens
(IOL). While Aravind began as a modest venture, in the last four decades there has been dramatic growth in the organization and its service capabilities. As of 2015 Aravind has five tertiary care centers that provide specialty care, six secondary care centers that provide cataract services and specialty diagnoses, six outpatient centers that provide comprehensive eye examinations and treatment of minor ailments, and 55 primary care centers that provide comprehensive eye examinations. Additionally, Aravind conducts extensive community outreach (details on this follow), manufactures IOLs, conducts research and training, and systematically
2 http://www.aravind.org/content/downloads/aecsreport201415.pdf Page 6 of 42
shares the intellectual property of its business and technology freely with other organizations engaged in health care around the world.
A central tenet of Aravind’s business model is to be financially self-sustaining through earned revenues, with almost no reliance on grants or philanthropy. The organization has been successful in this goal by following the cross-subsidization model. Although Aravind’s mission is to serve the under-served, especially the rural poor, in order to achieve this mission it also serves a large clientele of paying patients seeking eye care. Paying patients are those who can afford to pay market rates. Patients who cannot afford to pay market rates are provided eye care for free, or at heavily subsidized prices that cover Aravind’s marginal cost. Aravind uses the profits generated from paying patients to fund care for those who cannot afford to pay. This model has not only allowed Aravind to be fully self-reliant financially, it has also generated surpluses that have funded the organization’s rapid growth and engagement in ventures such as manufacturing IOLs. In 2014-15, Aravind hospitals served almost 1.9M paying patients, 0.5M patients at deeply subsidized prices, and 1.6M free patients.
At Aravind the core services (such as surgery or comprehensive eye examinations) are essentially the same for paying and non-paying patients, in that they are provided by the same team of physicians and surgeons. Aravind does provide a differentiated service to paying patients by offering several levels of type of surgery and IOL, and choice of accommodation (features such as beds, air-conditioning, and semi-private bathrooms). All patients self-select whether they wish to be free or paying patients, and if the latter, they choose one of several price-service bundles.
An important decision that the Aravind founders had to make was where to focus the organization’s marketing efforts. Vision impairment is widespread among the poor, patients typically lack awareness and understanding of treatment options, and do not have the knowledge or financial resources to be treated.
Thus, marketing to poor patients especially in rural markets was essential for Aravind to fulfill its mission. Page 7 of 42
To reach these patients, Aravind developed an extensive community outreach program that includes eye screening camps, mobile units, and vision centers. At the same time, more affluent patients have choices in terms of providers of eye care services in southern India (examples of other private providers are Vasan Eye
Care, Sankara Nethralaya and Hindu Mission Hospital). Furthermore, these patients typically have access to information as well as the education and financial resources needed to make a considered choice. This might imply that Aravind should devote some marketing effort to create awareness of and preference for Aravind in this segment, which consists of patients who are critical for the financial sustainability of Aravind’s business. However, as a matter of principle, Aravind’s founders and current management have decided to not devote any marketing efforts directly to attract or retain this patient group.
Outreach Camps
Aravind pioneered the large-scale use of eye screening camps to reach out to the rural poor and bring to the base hospitals those who qualified for surgery. In 2014-15, almost 3,000 outreach camps screened over
560,000 patients, and the screenings resulted in almost 93,000 surgeries at base hospitals. Camps are organized with the collaboration of local community service organizations such as Lions Club, Rotary Club, community-based non-governmental organizations, hospitals, industry associations, etc., who act as
“sponsors.” The primary role of the sponsor is to set up a campsite with the necessary supporting facilities such as furniture, electricity, water, and to provide food and lodging for the medical team. A large school building is often a good venue to hold a camp. An important role of the sponsor is to undertake publicity for the camp. Aravind advises sponsors to limit promotional activities for a camp to a radius of about five miles. Promotion outside this radius is not productive since Aravind has learned from past experience that access is a significant challenge for potential camp patients. Technologies like Geographic Information
System have been in use for over two decades to identify the villages that fall within the five mile radius and the routes to take for efficient promotion of the camp in these villages. Promotional activities take the form Page 8 of 42
of distribution of handbills and posters, posting of notices on publicity boards on street corners, shop billboards and bus stops, loudspeaker announcements, and referrals through local doctors, teachers, and village leaders (Velayudhan et al. 2011). Most of the publicity is done 2-3 days before the camp date.
Aravind’s specification of an ideal location for a camp is a village or a rural town with a population of ten to twenty thousand, with easy access by surrounding areas. Dates for the camp are chosen to avoid major festivals (such as Pongal and Deepavali), elections, as well as local festivals or marriages. Most camps are held on weekends.
A very well structured process guides how a patient progresses upon arriving at a camp. The stages include patient registration, eye tests by paramedical staff, followed by an ophthalmologist’s examination. If the patient needs eye glasses, they are made available at the campsite for the patients to buy. Patients who are advised to have surgery are transported to the base hospital, either by a hired bus or by public transport, accompanied by an Aravind staff member. Since camps are attended predominantly by very poor patients,
Aravind provides all services (except glasses) at the camp for free, including transportation to the base hospital, surgery, food, post-operative medications, transportation back home, and follow-up a month later at the camp site.
Patient Segmentation
Aravind’s pricing approach leads to a clear segmentation of patients into three groups:
1) Free patients: patients who go to an Aravind outreach screening camp, and are transported to an Aravind
hospital and treated completely for free.
2) Paying patients: patients who walk-in to an Aravind hospital, choose the “paying section” of the hospital,
and are charged for each service based on a menu of product-price options. Prices for cataract surgery
range from US$100 to US$700.
3) Subsidized patients: patients who walk-in to an Aravind hospital and choose the “free section” of the Page 9 of 42
hospital. These patients are not charged any consultation fee. Patients admitted for cataract surgery pay
approximately US$12, which covers the cost of all surgical consumables, including the IOL, medications
used during the hospital stay, and post-operative eye drops for a month.
Research Goals, Contribution and Related Literature
In this paper we develop a model to measure and characterize the effects of outreach camps on the number of new walk-in (i.e. subsidized and paying) patients at Aravind. We use historical patient data over a nine-year period from one of Aravind’s tertiary care hospitals and measure both spatial and temporal effects of camps. In particular, we address the following research questions. Are there measurable spillover effects of
Aravind’s outreach camps, which are targeted to free patients, on the demand from walk-in patients? How can these effects be characterized in terms of temporal carry-over after the camp week? Do the carry-over effects persist in the long run? How far do these effects reach geographically in terms of distance from the camp, and how do the effects change with distance of the patient from the hospital? Do the effects differ between subsidized and paying patients?
Answers to these questions enable us to provide insights into a source of interdependence between the free and paying sides of the cross-subsidization business model. To our knowledge the literature has little scientific evidence on the performance of marketing activities of nonprofit organizations in general (a notable exception is Kumar et al. 2015) or marketing to the poor. In recent years there has been a lot of interest in business models to serve poor consumers, with Prahalad and Hart (2002) advocating the possibility of the “fortune at the bottom of the pyramid” via a low-price, low-margin, high-volume strategy, and others arguing against the practicality of this business model (e.g. Simanis 2012, Karnani 2007). However, the cross-subsidization model as an alternative way to serve poor customers has received less attention. Our work contributes to better understanding of this model.
When the influence of camps on walk-in patients is seen as a form of advertising (as we argue in this Page 10 of 42
paper), the effects we analyze can be related to the literature on advertising spillovers. Sahni (2016) asks the question: “Can ads cause an increase in the likelihood of the consumers purchasing products other than the advertised one?” One mechanism by which this may happen is umbrella branding. In the case of Aravind, consider the possibility that camps advertise the umbrella brand name Aravind to free patients, but spillover benefits accrue to other products in Aravind’s product-line, namely, the services bought by the subsidized and paying patients. In the context of packaged goods categories, Balachander and Ghose (2003) found that advertising of a brand extension created positive spillover effects to the parent brand. Erdem and Sun (2002) find similar effects across packaged goods categories when products share an umbrella brand.
This paper is also related to the literature on spatial models in marketing (e.g. see the review by Bradlow et al. 2005). In particular, our model specification incorporates “spatial drift,” which is the idea that model parameters are a function of an individual’s location on the map. We allow the effects of camps on walk-in patients to depend on geography in two ways. First, the effect of a camp depends on the proximity of the patient to the camp. Second, the effect of a camp is moderated by the proximity of the patient to the base hospital. We believe the latter effect is due to (unobserved) heterogeneity in awareness of Aravind, which is correlated with proximity to the hospital. We find strong evidence that the effects of camps decay as the patient moves further away from the camp, and the effects of camps increase as the patient and camp move further away from the base hospital. Our work also relates to the sizable literature on temporal carryover effects of marketing communications, in particular, advertising (see Leeflang et al. 2000 Chapter 6 for a review). In our case these effects occur for a service different from the advertised service.
The rest of the paper is organized as follows. In Section 2 we discuss a framework to conceptualize spillover effects of outreach camps. In Section 3 we describe the data and the results of preliminary, model- free analysis. In Section 4 we propose a model, and in Section 5 we provide the empirical results. In Section 6 we provide a general discussion of the findings and conclude. Page 11 of 42
2. Spillover Effects of Outreach Camps on Walk-in Patient Decisions
As noted, Aravind’s outreach camps are organized primarily in villages or rural towns in India.
These camps have a direct effect, which is to draw poor patients who need eye care. Our focus is on the indirect or spillover effects of camps on the behavior of patients who walk-in to an Aravind hospital and pay for service (i.e. either paying or subsidized patients), not those who visit a camp and are treated for free.
Using the literatures on patient behavior and the effects of marketing activities, we theorize why the behavior of walk-in patients may be affected by outreach camps. Several types of effects could occur.
First, we expect the outreach camps and the associated publicity during the 2-3 days preceding the camp to act as a form of advertising to patients who do not visit the camp3. These effects may be both informative and persuasive (Ackerberg 2003). The occurrence of a camp and associated publicity can educate patients about eye disease, inform them about the availability of eye care, remind them that they need to seek treatment for an existing eye condition, or make them aware of Aravind as a provider of eye care services. Since the quality of health care is learned mainly through experience (Nelson 1970), camps may also act as a signal of the quality of the Aravind brand, thereby raising its attractiveness.
A second possible effect of camps is that they permit “free riding” for cataract surgery by patients who, in the absence of a conveniently located camp, may have chosen to be walk-in patients, either subsidized or paying. This is a natural result of the patient self-selection based pricing strategy of Aravind.
Radhakrishnan et al. (2015) surveyed a random sample of 1271 households in one district of Tamil Nadu state, and found that 96% were willing to undergo cataract surgery if required. Among these, 57% were willing to undergo paid surgery, 31% were willing to undergo paid surgery if free surgery were not available,
3 In a very small number of cases, patients who go to a camp are referred to the base hospital and advised to go there as walk-in patients. This may happen when the patient has uncontrolled blood sugar, or there are other reasons that require that surgery be delayed. If these patients do register at the base hospital, they invariably do so as subsidized patients and not as paying patients. In our data we do not know the identities of such patients. Page 12 of 42
and 12% were willing to undergo only free surgery. This study supports the possibility that camps may
“cannibalize” subsidized and paying patient demand for Aravind. In addition to the attractive (free) price, the proximity of the camp and availability of free transport to and from the hospital is also an incentive for walk-in patients to go to a camp near them, instead of traveling on their own to the base hospital. On the other hand, paying patients are able to choose from a menu of service options (bundles of lens, surgery and comfort attributes), while free and subsidized patients have only one service option available.
Third, we expect camps to generate patient-to-patient influences in the form of word-of-mouth.
These could occur between and within the three patient segments (namely, free, subsidized, and paying patients). An example of such an effect would be that individual A, who sees the camp publicity and visits an
Aravind camp for free treatment, tells individual B, who was unaware of Aravind, about her service experience. This leads patient B to walk-in to an Aravind hospital and seek treatment but pay for service. In our model of aggregate number of patients, this kind of indirect effect of a camp on walk-in patients (i.e., patient B) is included in the overall camp effect.
A challenge we face is that given the data, our models will be able to identify only the net effect of camps on the number of walk-in patients, separately for subsidized and paying patients. However, we will be unable to separately estimate the advertising, cannibalization, and word-of-mouth effects of camps.
Nevertheless, we believe the estimated net camp effects are useful for Aravind management, and for establishing the interdependence between patient segments. In the conclusions section of the paper we discuss future research opportunities to decompose the net camp effect with more detailed, disaggregate data.
In the empirical analysis we explore the value of patients’ distance from the base hospital as a possible source of identification of positive advertising effects of camps separately from negative cannibalization effects. Ceteris paribus, we expect that patients who are far from the base hospital are likely to be less aware of Aravind as a provider of eye care. This is because the physical hospital itself serves as a Page 13 of 42
form of advertising to patients who live nearby. If so, we expect the positive advertising effect of camps to be bigger in patients markets that are farther from the base hospital. In these more distant markets, however, we also expect the cannibalization effects of camps to be larger, because of the larger cost for walk-in patients to travel to the base hospital. Changes in the net effect of camps as we move further from the base hospital may reveal the relative influence of advertising versus cannibalization.
Should we expect the advertising, cannibalization, and word-of-mouth effects of a camp to be different between subsidized and paying patients? Subsidized patients are more similar to camp patients in socio-demographic characteristics than are paying patients. As a result, they are more likely to be exposed to publicity and word-of-mouth about the camp. Thus we expect camp effects on subsidized patients to be bigger than on paying patients. However, we expect this to be the case for both, the positive effects of advertising, and the negative effects of cannibalization. Consequently we cannot predict whether the net effect of camps on subsidized patients will be larger or smaller than the effect on paying patients.
Finally, similar to the effects of other forms of marketing communications, we expect camp effects on the number of walk-in patients to carry-over into the weeks following the week in which pre-camp publicity and the camp occur. We expect carry-over because after exposure to the camp or related publicity, the decision to seek eye care may involve consulting with family members, obtaining a referral from a local eye care provider, and making arrangements to travel to the base hospital. At the same time, we expect the advertising effects of camps to decay over time due to forgetting. Consequently, in the model specification we allow for carry-over effects of camps but do not impose a structure on the shape of the carry-over.
Our focus in the discussion so far has been on the short-term effects of camps on the number of walk-in patients. Additionally, camps may have a long-run influence by building the reputation of Aravind among potential patients. Viewed in this way, outreach camps serve to create goodwill stock for Aravind in the local market, which affects the number of new walk-in patients. In Section 4 we also present a model to Page 14 of 42
measure such long-run effects.
Based on the foregoing discussion, in Figure 1 we depict how the short-run advertising and cannibalization effects of camps on the number of walk-in patients are hypothesized to occur. Both advertising and cannibalization effects are expected to be stronger closer to the time of occurrence of the camp. Note that the duration and magnitudes of effects are entirely hypothetical in this figure.
Figure 1: Hypothesized Short-run Effects of Outreach Camps on Number of Walk-in Patients
Advertising Effect
Dotted Line indicates Baseline number of walk-in patients C Time Cannibalization 1 Cannibalization 2
C : Outreach Camp and preceding 2-3 days of camp promotion Cannibalization 1: Walk-in patients who would have gone to the hospital before the camp but went to the camp instead Cannibalization 2: Walk-in patients who would have gone to the hospital after the camp but went to the camp instead
3. Data and Preliminary Analysis
We focused on Aravind’s most recently founded tertiary care hospital – Aravind, Pondicherry
(henceforth Aravind-P) – which started operations in February 2003. We focus on this hospital because reliable data on paying patients are only available for recent years, hence working with the newest hospital allowed us to get patient data from the very inception of a hospital. Aravind-P is located in the city of
Pondicherry, on the northeast coast of the southern Indian state of Tamil Nadu4. Aravind-P serves a population of about 21.6M people in five districts - Tiruvallur, Vellore, Tiruvannamalai, Villupuram and
4 Pondicherry is a city within the Union Territory of Pondicherry. A Union Territory (equivalent to a federal district in the US) is administered by the federal government and is not part of any state. In 2006, Pondicherry was officially renamed Puducherry. Page 15 of 42
Cuddalore, as well as the union territory of Pondicherry. There are several teaching hospitals, private eye hospitals and independent ophthalmologists providing eye care in this region.
Our data consist of about 1.4M new patient visits to Aravind-P from the inception of the hospital in
February 2003 till end-2014 as well as the dates and locations of outreach camps. Due to concerns about unreliability of patient data in the first three years, we use the data for the nine year period 2006 – 2014. For each patient visit we know whether the patient was a free, paying, or subsidized patient. We also know the village/town of the patient’s home, and the date of the patient’s visit to Aravind-P. In the case of free patients we know the location of the camp that the patient visited, as well as the date of the camp visit. To start we geo-coded the village/town of patients’ home locations as well as camp locations. We limit our attention to patients who live in markets in Tamil Nadu or Pondicherry. Such patients account for 100% of camp and subsidized patients, and 99.6% of paying patients. In the subsequent discussion, we refer to each unique patient-location geocode as a patient-market.
An important choice we needed to make was the level of aggregation of the data for modeling. The most disaggregate location information we have for patients is the market (village/town) in which a patient lives. Consequently we aggregate the data to the patient-market-week level to obtain the number of weekly visits by new free, paying, and subsidized patients during the period 2006-2014. There are 1,057 unique geo- codes for patient-markets in the data; each is observed for 470 weeks. In addition we observe 2,496 camps that were held in 542 unique locations during the nine-year period, and we know the week in which each camp was held5. We structure the data as balanced panels of market-weeks, separately for subsidized and paying patients.
5 The definition of a week is important. 80% of camps occur on the weekend, and publicity for the camp is done for 2-3 days before the camp. Consequently, we define a week as 7 days starting on a Monday. This allows the camp event and the publicity preceding it to occur within a single week. Page 16 of 42
Figure 2: Map of Tamil Nadu showing Aravind-Pondicherry Hospital, Camps, and Patient Locations in 2014
As noted, walk-in patients, both paying and subsidized, travel on their own to Aravind-P, the base hospital in Pondicherry. In Figure 2 we show for the year 2014 the locations of patients relative to the base hospital on a map of the Indian state of Tamil Nadu, as well as the locations of 298 camps that year. As expected, there is a heavy concentration of walk-in patients and camps close to the hospital. In Figure 3 we show for 2014 the cumulative distribution of the number of paying and subsidized patients, and of camps, in terms of distance from the base hospital. Almost 90% of walk-in customers live within 60 miles of the base hospital. Further, in our data there are very few walk-in patients who live more than 100 miles from the hospital. We also note that there are very few camps held within 10 miles of the hospital, or further than 90 miles from the hospital. The reasons are that patients who are close to the hospital can walk-in relatively easily hence camps are not needed, and patients who are very far from the hospital are difficult to serve via camps because of the travel time required for patients (who are transported from the camps to the hospital for surgery) and for Aravind-P staff.
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Figure 3: Distribution of Walk-in Patients and Camps by Distance from Aravind-Pondicherry Hospital in 2014
100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40%
30% 30% Campsof Percentage Percentage ofPatients Percentage 20% 20% 10% 10% 0% 0% < 10 < 20 < 30 < 40 < 50 < 60 < 70 < 80 < 90 < 100 >=100 Distance from Base Hospital in Miles
Subsidized Paying Camps
To obtain an initial understanding of the key phenomenon we are trying to measure, which is the effect of camps on number of walk-in patients, we conduct preliminary, model-free analysis. The analysis is conducted separately for each of the nine years in the data, and separately for paying and subsidized patients.
For each type of patient we consider only those patient-markets that had at least one patient of that kind in the nine-year period. In each year, we consider only “camp-markets,” defined as patient-markets that had at least one camp within a 3-mile radius, and disregard the other patient-markets. For each camp-market, we divide the weeks into weeks that had a camp in the immediately preceding week (call these “camp-weeks”), and weeks that did not have a camp in the immediately preceding week (call these “non-camp-weeks”). We compute the average number of walk-in patients, separately by paying and subsidized patients, in the two sets of weeks. In Table 1 we show the ratio of the average number of walk-in patients in a camp-week divided by the average number of walk-in patients in a non-camp week, separately for paying and subsidized patients.
Page 18 of 42
Table 1: Model Free Evidence of Camp Effects on Number of Walk-in Patients
Subsidized Patients Paying Patients Year Camp Number of Camp- Camp Number of Multiplier Weeks Multiplier Camp-Weeks 2006 2.43 224 1.18 316 2007 1.33 237 0.45 347 2008 3.59 181 0.57 273 2009 2.25 166 1.02 233 2010 4.63 168 0.46 228 2011 5.03 197 0.93 278 2012 1.96 201 1.17 281 2013 2.75 191 0.86 238 2014 1.77 191 0.56 258
This ratio, which we term a “camp multiplier”, ranges between 1.33 and 5.03 for subsidized patients and between 0.45 and 1.18 for paying patients. Deviations of the multiplier from 1 provide tentative evidence of camp effects; we see strong evidence in Table 1. We note that the multiplier for subsidized patients is bigger than one in each of the nine years, while the multiplier for paying patients is bigger than one in three of the nine years. Given the constraints of model-free analysis, we have limited our attention to camp effects that are within a one-week temporal lag, and a 3-mile spatial radius. Furthermore, we have not controlled for differences between markets, and seasonal (week-specific) differences. These factors will be explicitly included in the forthcoming model.
4. Model
Based on the foregoing discussion, our interest is in analyzing the number of new walk-in patients coming to
Aravind-P from each patient-market (referred to as “market” henceforth) in each week. A market is defined as a unique latitude-longitude combination that yielded at least one walk-in patient in the data. Our primary
Pay Sub model is a linear panel-data model specified separately for ymt and ymt , the number of new paying and subsidized patients respectively from market m in week t, as follows:
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ySub Sub Sub Camp_ EffectSub Sub ( 1 ) mt my t mt mt Pay Pay Pay Pay Pay ( 2 ) ymt my t Camp_ Effectmt mt
The key goal of each model is to use the variability in the number of walk-in patients across markets and across weeks to estimate the short-term effect of outreach camps, while controlling for differences between markets, years, and weeks. Each of the elements on the right hand side of the models is discussed next with reference to the model for the number of subsidized patients. Analogous interpretations apply to the model for paying patients. While these models capture short-term camp effects, later in this section we enhance these models to include long-term effects of camps.
Sub Camp_ Effectmt
This term captures the short-term effect of outreach camps on the number of subsidized patients who walk- in to Aravind-P from market m in week t. We allow for both temporal carry-over and spatial carry-over in the camp effects. To accommodate temporal carry-over we use a finite distributed lead-and-lag model6. Since the camp effect we model is the net of advertising, cannibalization, and word-of-mouth effects, we are unable to predict its magnitude or sign. Consequently we allow for complete flexibility and do not impose any structure on the carry-over effects. In addition to the week in which the camp is held and pre-camp publicity is done, we include a one-week lead effect, and 12 weeks of separate carry-over effects following the week of the camp. Both the lead effect and the choice of 12 weeks of carry-over are based on discussions with
Aravind management. Thus, a total of 14 weekly camp effects are included.
To model spatial carry-over, which is the influence of camps (both current and past) at different spatial distances on the number of walk-in patients from market m, we use approaches developed in spatial
econometrics (Anselin 1988). Let Ct be the set of all camp locations in week t. We assume that the
6 An extension of this model to infinite lags is presented subsequently in this section. Page 20 of 42
magnitude of the effect of a camp held in locationc,cCt , on the number of walk-in patients from market m depends on dist(c,m), the Euclidean distance between camp location c and market m. In general we expect that as this distance increases, the effect of the camp decreases. To represent our a priori beliefs about how spatial distance moderates the effects of camps, we define a spatial-lag function w(c,m) as follows:
w1(c,m) 1if 0 dist(c,m) 3miles,else0 w2(c,m) 1if 3 miles dist(c,m) 6miles,else0 ( 3 ) w3(c,m) 1if 6 miles dist(c,m),else0
Figure 4: Visualization of Distance of Patient-Market from Outreach Camp and from the Base Hospital
6 mi. P Hospital 3 mi. C
Circle Patient denotes radius of 26.5 miles C
C
C
This formulation essentially draws concentric circles around patient-markets to identify camps within those circles (see Figure 4). Our choice of the 6-mile radius is intended to capture the typical 5-mile area in which
Aravind camps are promoted, allowing room for the fact that our patient markets are not exact addresses but villages and towns, and hence have some fuzziness. We expect to see very small or zero effects of camps that are further than six miles from the patient market. Our choice of the 3-mile radius is to permit testing of the hypothesis that effects decay with distance.
Alternative formulations of the spatial-lag function are possible. A common formulation is to define Page 21 of 42
the spatial-lag as the inverse of the exponential of the distance, as follows:
edist(c,m)if c m w(c,m) ( 4 ) 0 otherwise
The inverse exponential has the attractive property that weights converge quickly to zero with increasing distance. We examine the robustness of our results using this formulation.
The next component of the camp effect we consider is Camp_ sizec,t , the size of the camp in location c in week t. The size of a camp is measured as the number of in-patients who attend the camp. We expect bigger camps to have bigger effects, because word-of-mouth influences generated by the camp are expected to
Sub be captured in the camp effect. Putting all these components together, the specification of Camp_ Effectmt is as follows:
q L Sub l l Camp_ Effectmt s w (c,m).Camp_ sizec,ts ( 5 ) s1 l 1 cCts
We also consider an alternative formulation of the camp effect in ( 5 ) such that we use an indicator variable for occurrence of a camp instead of the size of the camp:
q L Sub l l Camp_ Effect_ indicatormt s w (c,m).Campc,ts ( 6 ) s 1 l 1 cCts
where Campc,t is 1 if a camp was held in location c in week t, 0 otherwise. This specification disregards the information about the number of patients who attended the camp. Since we do not have a theoretical basis to choose between the two specifications, we will compare the fit of the two models to learn about the role of word-of-mouth generated by the camp in influencing walk-in patients. Note that in both ( 5 ) and ( 6 )
l s ,l 1,...,L;s 1,...,q are spatial and temporal carry-over effects of camps on the number of subsidized patients from market m in week t. In the empirical application we have set L=3, as shown in ( 3 ) and q = 12, resulting in 36 parameters that are intended to capture the short-term effects of camps. Page 22 of 42
Sub Market-Year Fixed Effects (my )
is a fixed effect for market m, m = 1, 2, …, M; year y, y = 1, 2, …, Y. We include these fixed effects in the model to control for two sets of factors: i) a large number of time-invariant unobserved differences between markets that may influence the number of walk-in patients. Particularly relevant market characteristics include socio-economic factors like population size and income, industrial development, and work and lifestyles in the region. Additionally, the fixed effects control for distance to Aravind-P hospital. ii) Since nine years is a long period of time, we allow the market effects to vary by year. This accommodates factors such as trends in cataract-related blindness and treatments, trends in awareness of the disease and of treatments, changes in market-specific competition, changes in AES infrastructure and resources, and changes in socio-demographic characteristics of patients.
Sub Weekly Fixed Effects (t )
is a fixed effect for week t, t = 1, 2, ….., 52. The number of walk-in patients demonstrates strong weekly seasonality within each year, mainly due to recurrent events like holidays, festivals, elections, and seasons.
To control for these we include fixed effects for each of the 52 weeks in the year. These effects are pooled across years.
Discussion of Model and Econometric Issues
In the model described above the use of fixed effects for market-years, and for weeks within year, allows the camp effect to be interpreted as a within-market estimate, pooled across markets. In this sense it can be viewed as a difference-in-difference estimator of the effect of camps on the number of walk-in patients.
l Sub Sub Thus, s ,l 1,...,L;s 1,...,q represent the incremental effects of a camp relative to my t , the baseline number of subsidized patients in market m, year y, and week t. In the forthcoming discussion of results we examine the variation in the camp effect by distance from the camp (that is, for l = 1, 2, 3) and by Page 23 of 42
time (that is, for s 1,...,q ).
An important econometric issue is that of the possible endogeneity of camps. Unbiased estimation of the camp effects requires strict exogeneity of camps. In other words, the assignment of camps in space and
Pay Sub time should be unrelated to contemporaneous or past outcomes of ymt and ymt . There are several ways in which endogeneity of camps may arise. One reason is that unobserved factors may affect both, the number of walk-in patients from a particular market and week, as well as Aravind’s decision to hold an outreach camp in that market and week. These factors could be market-specific (e.g. larger markets receive more camps and generate more walk-in patients), year-specific (e.g. as Aravind-P grew it held more camps and also attracted more walk-in patients), or time-specific (e.g. in a major holiday week camps are not held and fewer walk-in patients go the hospital). Since we include market-year and week-specific fixed effects, we expect these unobserved factors to be controlled for.
A second reason that might create endogeneity of camps is Aravind-P’s process for managing hospital capacity. Several hospital resources such as nursing and surgical staff are shared for the treatment of paying, subsidized and free patients. Since camps are the only demand-generating instrument directly controlled by
Aravind, the question is whether Aravind makes camp timing and location decisions to manage the total demand relative to available capacity from week to week. We interviewed Aravind management to learn about the camp planning process in detail. The process begins with an annual meeting of hospital general managers at the start of the year in which a target number of camps for the upcoming year for each hospital is determined. Each hospital then allocates this number to markets and weeks for the year. Barring exceptional circumstances, this plan is then adhered to. A regression of the weekly number of outreach camps (sum across markets) on the total number of walk-in patients (subsidized plus paying), with fixed effects to control for year and week of the year, found a significant positive coefficient for the number of
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walk-in patients. This suggests that Aravind does not reduce the number of camps in weeks of high demand from walk-in patients, thereby negating a capacity-based reason for endogeneity of camps.
Another modeling issue relates to our use of linear regression models and consequent implicit treatment of the dependent variables – the number of walk-in patients – as real-valued and continuous. In fact these variables are non-negative and integer valued, suggesting that count data models like the Poisson or
Negative Binomial Distribution (NBD) models may be more appropriate. The reason we did not use count models is that the estimation time was prohibitively high, given the large number of parameters, the large data size, and the large number of models to be estimated. We estimated NBD models for the base case specifications in addition to the linear model, and show in Appendix A a comparison of the pattern of spatial and temporal camp effects for the linear model and the NBD model for subsidized patients. We found that the results did not change substantively. Consequently we report results in this paper only for linear models.
Another robustness check we performed was the use of random effects instead of fixed effects for market- year and weeks. Once again, substantive results were unaffected.
Long-term Camp Effects on Walk-in Patients
To estimate the long-term effect of camps on the number of walk-in patients, we specify a model in which occurrence of camps contributes to the creation of a “camp stock” in neighboring patient-markets, which depreciates with time but is enhanced by new camps. Following the standard Nerlove-Arrow form (Nerlove and Arrow 1965) we assume an exponential decay process as follows:
L Sub l Camp_ Stockmt Camp_ Stockmt1 w (c,m).Campc,t ( 7 ) l 1 cCt
where Camp_ Stockmtis the camp stock in market m in week t, Campc,t is an indicator variable for the occurrence of a camp in location c in week t, wl (c,m) is a spatial-lag function as defined in ( 3 ), and
0 Sub 1 is a retention rate that captures the camp stock carried over from the previous week for Page 25 of 42
subsidized patients. An analogous specification for paying patients is not shown.
The model for the number of walk-in patients in ( 1 ) is then enhanced by adding the long-term effects of camps after the short-term effects have ended7 as follows:
Sub Sub Sub Sub Sub Sub ( 8 ) ymt my t Camp_ Effectmt Camp_ Stockmtq1 mt
Pay Pay Pay Pay Pay Pay ( 9 ) ymt my t Camp_ Effectmt Camp_ Stockmtq1 mt
7 We estimate the model with only short-term effects to determine q, the number of lags to retain, and then specify the long-term effects model such that camp stock effects occur after that point in time. Page 26 of 42
Note that in this specification, camps have unrestricted effects for q periods after the occurrence of the camp,
Sub Pay represented by the terms Camp_ Effectmt and Camp_ Effectmt as defined in ( 5 ). Thereafter, as indicated by
Sub the subscript (t-q-1) camps have a long-term effect represented by the terms Camp_ Stockmtq1 and
Pay l Camp_ Stockmtq1 . In our model, the use of L *(q 2) parameters s ,l 1,...,L;s 1,...,q affords great flexibility in estimating the effects of camps for 12 weeks after the occurrence of a camp. However, the long- term effect is restricted to follow the geometric decay incorporated in the camp stock building function in
( 7 ). It is necessary to impose some structure on the measurement of any long-term effect, and our chosen
Sub Pay specification has a long history of use for modeling long-term advertising effects. As before, my and my are
Sub Pay market-year fixed effects, and t and t are weekly fixed effects.
For both subsidized and paying patients we also estimate alternative long-term effects models in which the exponential decay process begins immediately upon occurrence of the camp, instead of q periods later. In these models the short-term effects represented by and respectively are omitted. Additionally, the camp stock variable is defined as being contemporaneous, namely,
Camp_ Stockmt instead of Camp_ Stockmtq1 .
5. Results
The model for subsidized patients in ( 1 ) is estimated on the subset of 541 markets that had at least one patient during the period 2006-2014, resulting in 254,270 observations (541 markets times 470 weeks). The model for paying patients in ( 2 ) was estimated on the subset of 782 markets that had at least one paying patient in the period 2006-2014, resulting in 367,540 observations (782 markets times 470 weeks). We discuss model validation subsequently.
Our most important finding is that camps have spillover effects on the number of walk-in patients.
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We begin by characterizing these effects in detail. Based on the parameter estimates of the model in ( 1 ), in
Figure 5 we show the effect of a camp as the incremental (over baseline) number of subsidized patients per market, assuming Camp size of 100 patients. Both temporal and spatial carry-overs are shown. In Figure 6 we show the analogous results for paying patients, using the model in ( 2 ).
Result 1: Spatial Carry-over The effect on the number of walk-in patients of a camp that is within 3 miles of the patient-market is larger than the effect of a camp that is between 3 and 6 miles from the patient-market.
The effect of camps that are more than 6 miles away from the patient market is negligible.
Discussion of Result 1: This result applies to both subsidized (Figure 5) and paying patients (Figure 6) and is based primarily on the magnitudes of the estimated effects as revealed in the patterns, rather than the statistical significance of individual effects8. The larger effect of more proximate camps is consistent with the fact that camps are publicized in a radius of about five miles around the camp, and that media used for publicizing camps such as handbills and loudspeaker announcements primarily have local reach.
8 Although a number of estimated effects of camps that are more than six miles from the patient market are statistically significant, they are close to zero in magnitude. The reason is that for any given patient-market in any given week, there is only a small number of camps that are within six miles, but a much larger number of camps that are more than six miles away. Thus the effect of camps more than six miles away is estimated with greater reliability, potentially explaining the statistical significance. In the subsequent analyses we ignore the effects of camps that are more than six miles away from the patient-market. Page 28 of 42
Figure 5: Effect of Camp on Number of Subsidized Patients per Market (assuming 100 patients per camp)
1.7 *
Distance of Camp from Patient Market
1.2 0-3 Miles 3 - 6 miles More than 6 miles
0.7
* 0.2 * * * * * * * * * * * * Incremental Number of Patients Per Week per Market per Week Per Patients ofNumber Incremental * * * * *
-0.3 -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 WEEK Number of Weeks after Camp is Held
Asterisk indicates effect is significant at p<0.05
Result 2: Temporal Carry-over in the Short-term There is a positive effect of camps on the number of walk- in patients in the week of the camp, but there is no effect in the week before the camp. The effect of a camp on the number of subsidized patients persists for up to 10 weeks after the occurrence of the camp. The effect is largest in the week immediately following the camp week. Analogous results apply to paying patients.
Discussion of Result 2: The absence of an effect in the week immediately before the camp week is consistent with the fact that publicity for the camp occurs 2-3 days before the camp, which is a period that is included in the camp week. Thus, we do not expect there to be an effect in the week before the camp week. Since walk-in patients who would have gone to the hospital in the week immediately following the camp week have the greatest incentive to go to the camp one week sooner (they have to alter the timing of their planned eye care very little), the large positive effect in the week following the camp suggests that cannibalization, if any, is small.
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Figure 6: Effect of Camp on Number of Paying Patients per Market (assuming 100 patients per camp)
0.5
*
0.4 Distance of Camp from Patient Market 0-3 Miles 3 - 6 miles More than 6 miles
0.3 * * * 0.2 * * * * *
* 0.1 * * * 0.0 * * * * * Incremental Number of Patients Per Week per Market per Week Per Patients ofNumber Incremental -0.1 -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 WEEK Number of Weeks after Camp is Held
Asterisk indicates effect is significant at p<0.05
Result 3: Advertising Larger than Cannibalization The net effect of a camp up to 6 miles away on the number of walk-in patients, summed over the lead week, camp week, and all lag weeks, is positive. This result holds separately for subsidized and paying patients. This implies that the positive effects of a camp (e.g. advertising, word-of-mouth) dominate the negative cannibalization effect.
Discussion of Result 3: By summing the statistically significant effects of camps on a patient market across the lead week, camp week, and lag weeks, we find that a camp increases the baseline number of subsidized patients by 2.78 per patient-market, and the baseline number of paying patients by 2.26 per patient-market.
Thus, the net camp effects are positive.
Result 4: Effect on Subsidized Patients versus Paying Patients The net positive effect of a camp on the number of subsidized patients is larger than the effect on the number of paying patients, in terms of both, the absolute and relative number of patients.
Discussion of Result 4: The increase in the absolute number of subsidized and paying patients was reported earlier in result 3. When measured relative to the baseline weekly number of patients in the absence of a
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camp, these effects translate to 300% for subsidized patients and 107% for paying patients. Several factors contribute to these results. On the one hand, patients who choose the “subsidized” payment option are similar in their socio-economic profile to the poor patients who avail of free services via outreach camps. By contrast, paying patients are more affluent and hence more dissimilar to camp patients. Therefore we expect the publicity for the camp to be more likely to reach subsidized patients than paying patients. This should imply that the advertising effect of a camp is larger for subsidized patients than for paying patients. On the other hand, the (negative) cannibalization effect of a camp could be larger for subsidized patients than for paying patients, for two reasons. First, subsidized patients are likely to be more resource constrained than paying patients in terms of accessing the base hospital in Pondicherry, hence they are more likely to find the camp an attractive alternative. Further, subsidized patients receive exactly the same eye care treatment that is received by free camp patients. By contrast, paying patients can choose between a variety of surgical and comfort options, which are not available if they avail of the camp. Hence the camp is a good substitute for the service sought by subsidized patients but not for paying patients. Our results show that the net outcome of these factors is that the incremental number of subsidized patients is larger than the incremental number of paying patients.
Result 5: Effects in Patient Markets More Distant from Base Hospital The net positive effect of camps in patient markets that are located over 26.5 miles (which is the median distance of patient markets) from the base-hospital in Pondicherry are considerably larger than the effects in markets that are closer. This phenomenon applies to both subsidized and paying patients.
Discussion of Result 5: The camp effect in models ( 1 ) and ( 2 ) is interacted with distance of the patient market from the Aravind-P base hospital to obtain this result. Figure 8 shows the additional number of subsidized patients due to a camp located within 6 miles of the patient market when the market is far from the base hospital, while Figure 7 shows this number for markets close to the base hospital. In markets far Page 31 of 42
from the base hospital, the baseline weekly number of subsidized patients goes up by 326.7% due to the camp, while in markets close to the hospital the equivalent increase is 64.9%. The analogous numbers for paying patients (figures are not shown for reasons of space) are 133.9% and 37.9% respectively. As discussed previously, we expect camps to have a stronger advertising effect further from the hospital because of low baseline awareness of Aravind. But we also expect camps to have a larger cannibalization effect when the market is far from the hospital. Our finding of a larger net effect of camps in markets that are further away suggests that the increase in the advertising effect outweighs increases in the cannibalization effect, if any.
Figure 7: Camp Effects on Subsidized Patients for Markets Close to Base Hospital
* 1.7 Distance of Camp from Patient Market
1.4 0-3 Miles 3 - 6 miles
Net increase relative to Baseline Weekly Number = 64.9% 1.1
0.8
0.5
* * Number of Patients Per Week Market per Week Per Patients ofNumber * 0.2 * -0.1 * *
-0.4 * -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 WEEK Number of Weeks after Camp is Held
Asterisk indicates effect is significant at p<0.10
Result 6: Effects of Size of Camp The size of the camp (number of in-patients) does not influence the number of walk-in patients over and above the occurrence of the camp. This indicates that word-of-mouth generated by patients who avail of the camp does not influence walk-in patients in the 12 week period immediately following the occurrence of the camp.
Discussion of Result 6: We estimate the model for subsidized patients in ( 1 ) using the two alternative specifications of the camp effect described in ( 5 ) and ( 6 ). These two models are non-nested but can be
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compared by specifying and estimating a comprehensive model that nests both models (the comprehensive model includes both camp size and camp indicator variables as predictors), and comparing each model against the comprehensive model (Pesaran 1974). This process is repeated for the model for paying patients.
We find that including information about camp size does not improve the fit of the model for either subsidized patients or paying patients. We should emphasize that this result should be interpreted only in relation to how the occurrence of a camp affects walk-in patients immediately after the camp, not in relation to the role of patient-to-patient influences in general. We do not examine the latter in the current study.
Figure 8: Camp Effects on Subsidized Patients for Markets Far from Base Hospital
* 1.5 Distance of Camp from Patient Market 1.3 0-3 Miles 3 - 6 miles
1.1 Net increase relative to Baseline Weekly Number = 326.7%
0.9
0.7
0.5 Number of Patients Per Week Market per Week Per Patients ofNumber * 0.3 * * * * * * 0.1 * * * -0.1 * -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 WEEK Number of Weeks after Camp is Held
Asterisk indicates effect is significant at p<0.10
Result 7: Long-term Effect of Camps The occurrence of camps has a positive long-term impact on the number of paying patients from neighboring markets. This long-term effect is 27% of the total effect of camps on the number of paying patients. Camps do not have any long-term impact on the number of subsidized patients from neighboring markets.
Discussion of Result 7: We estimate the model for subsidized patients in ( 8 ) and for paying patients in ( 9 ) using non-linear least squares. For the spatial lag function we consider a radius of 6 miles around a patient
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location, based on Result 1that camps located beyond 6 miles have a negligible effect. Further, given Result 2 that short term effects persist up to 10 weeks, we operationalize long term effects as starting in the 11th week after the occurrence of a camp. We use data for 2003-2005 to initialize the camp stock variables9. We found that when the retention rate parameters Sub and Pay were estimated simultaneously with all the other model parameters the algorithm did not converge. Consequently we estimated the models in ( 8 ) and ( 9 ) repeatedly by fixing the retention rates at several values in the range (0,1) and chose the model with the lowest Mean Square Error10.
In Figure 9 we show the Mean Square Error of models in ( 8 ) and ( 9 ) for different values of the retention rates . We find that the models fit best when and are 0.85 and 0.70 respectively. However, we find that the estimated effect of camp stock Sub is not statistically significant (p=0.50), for the selected model, while in the model for paying patients, Payis positive and statistically significant (p<0.05) for the selected model. We compute the long-term camp effect as Pay (1Pay) .
Figure 9: Model Selection for Long-Term Camp Effects Models
Subsidized Patients Paying Patients 2.484576 10.7908
2.484574 10.79078
2.484572 10.79076 2.48457 10.79074 2.484568 10.79072 2.484566 10.7907
2.484564 Mean Square Error Square Mean Mean Square Error Square Mean 2.484562 10.79068 2.48456 10.79066 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Retention Rate Retention Rate
The magnitude of the long-term camp effect can be understood relative to the total camp effect, which is defined as the sum of the short term and long term effects. We compute the short-term camp effect
9 The unreliability of data for this period noted earlier applies to patient counts and not to occurrence of camps. 10 A similar grid search approach to estimate the retention rate (sometimes called the carryover parameter) has been used by several prior studies such as Narayanan et al. (2004), Berndt et al. (1997) and Guadagni and Little (1983). Page 34 of 42
l from the same model as the sum of all statistically significant estimates of s ,l 1,...,L;s 1,...,q . The ratio of the long-term effect to the sum of the short-term and long-term effects is 27%. The absence of any long- term impact of camps on subsidized patients stands in sharp contrast to the substantial effect on paying patients. We speculate that the difference arises because in the case of paying patients camps raise the salience of the Aravind brand name, which impacts patients’ choice among alternative providers of eye care in the long run. For subsidized patients, on the other hand, camps primarily act as reminders of the need to attend to their medical need soon.
Model Validity and Robustness of Results:
The models for subsidized patients and paying patients have R2 of 0.915 and 0.949 respectively. In
Figure 10 and Figure 11 we show in-sample predictive validity of the models by market and by week, separately for subsidized and paying patients. Overall, the models fit the data very well (this is not surprising given the fixed effects that are included).
Figure 10: Predicted and Actual Number of Subsidized Patients by Market (left) and by Week (Right)
4500 1000 541 Markets 4000 900 470 Weeks 3500 800 700 3000 600 2500 500 2000 400 1500 300
1000 Actual Number of Patients ofNumber Actual Actual Number of Patients ofNumber Actual 200 500 100 0 0 0 1000 2000 3000 4000 0 200 400 600 800 1000 Predicted Number of Patients Predicted Number of Patients
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Figure 11: Predicted and Actual Number of Paying Patients by Market (left) and by Week (Right)
5000 3000 782 Markets 470 Weeks 2500 4000
2000 3000 1500 2000
1000 Actual Number of Patients ofNumber Actual 1000 ActualNumberofPatients 500
0 0 0 1000 2000 3000 4000 5000 0 500 1000 1500 2000 2500 3000 Predicted Number of Patients Predicted Number of Patients
Several alternative model specifications were estimated to assess the robustness of our results. These include: i) A Negative Binomial Model, which recognizes that the dependent variable in our models – the number
of new patients -- is a non-negative integer. See Appendix A for a discussion. ii) The linear panel model with random effects for market-year and weeks, instead of fixed effects. iii) An inverse-exponential specification for the spatial lag model, as in ( 4 ).
In all cases the substantive results remained essentially unchanged. Additionally, we estimated benchmark long-term effects models that omit the short-term effects. For subsidized patients and paying patients, the models in ( 8 ) and ( 9 ) fit the data better than the benchmark models based on F-tests (p < 0.01).
6. Discussion and Conclusions
The results in this paper are relevant to a growing number of organizations that seek to achieve a social mission and fund it via cross-subsidization across customer segments. These organizations are sometimes known as social enterprises, and may be incorporated either as for-profit or not-for-profit entities.
Examples of such organizations include Scojo India, which provides reading glasses to the poor, Dial1298, which offers ambulance services in India, and D.light, which provides solar energy solutions in developing countries globally. We focus on one of the largest such organizations in the world, Aravind Eye Hospital, Page 36 of 42
which offers the same service – cataract surgery and eye care -- at different prices to different patients. The question we ask is whether Aravind’s marketing efforts, which are focused exclusively on poor patients because of the organization’s social mission, provide spillover benefits of attracting paying patients.
Our analysis of the effects of Aravind’s outreach camps shows an overall positive impact on the number of new walk-in patients visiting an Aravind hospital. Importantly, the walk-in patients do not avail of camp services but are influenced by the occurrence of camps in the vicinity of where they live. These effects are stronger for subsidized than for paying patients. The effects occur if camps are within a six-mile radius around the location of a patient, and are larger for camps that are closer to the patient. The short run effects persist for about 10 weeks. Additionally, camps have a positive effect on the number of paying patients in the long run, and the long run effects constitute about a quarter of the total effect of camps. The effects weaken as patient locations get closer to the base hospital, probably because awareness of Aravind rises due to the physical presence of the hospital and a higher density of Aravind patients. Overall, these results suggest that camps act as a form of advertising to Aravind’s paying customers. If there are any negative cannibalization effects, they are smaller than the positive advertising effects of the camps.
Our results should be good news for Aravind and similar organizations because they imply that there are synergies between Aravind’s marketing efforts to advance its social mission of serving poor patients, and its revenue-generating business of serving paying patients to achieve financial sustainability. The findings strengthen the viability of the cross-subsidization form of business model that is central to Aravind’s success.
Our findings can be used by Aravind for logistical decisions such as management of outreach camps, and also in providing consulting to other organizations with similar business models.
Our research has stimulated several questions that are worth pursuing in follow-up work. First, separation of advertising and cannibalization effects of camps is an important issue. If in fact there is sizable cannibalization, as suggested by the survey data reported in Radhakrishnan et al. (2015), it is very important Page 37 of 42
to measure its magnitude. The data available in the current study did not permit us to separately identify cannibalization from advertising effects of camps; it may be useful to conduct field experiments to measure the rate of cannibalization. Second, measurement of social influence between patients would be both theoretically interesting and managerially useful. We expect that word-of-mouth effects occur within each of
Aravind’s three segments. But it would be especially interesting to measure such effects between patients in different segments, such as camp patients and paying patients, because these patients have different socio- economic profiles but live in close proximity. It is well known that one of the key econometric challenges in such work is differentiating between the role of social influence and that of observed and unobserved similarities between individuals. Such a study will require rich individual-level socio-economic data that could be gathered via a survey.
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Appendix A
Comparison of Linear and Negative Binomial Models
Pay Sub We specify the Negative Binomial Model as follows. Let ymt and ymt be the number of new
Paying and Subsidized patients, respectively, who walk-in to Aravind-P from market m in week t, each of which is assumed to be distributed NB. Thus, the probability mass function of each takes the following form (only one of the two variables is shown for ease of presentation; the other is analogous):
rPay k (rPay k) rPay Pay Pay Pay Pay mt Pr( ymt k| mt ,r ) Pay Pay Pay Pay Pay (r )(k 1) r mt r mt
Pay Pay where mt is the mean of the NBD and r is the over-dispersion parameter. The variance of
Pay mt Pay Pay the NBD is given as Pay 1 . As r , the NBD regression model converges to r mt
Pay the Poisson regression model. We specify , the mean of ymt as follows:
Pay Pay Pay Pay lnmt my t Camp_ Effectmt
Pay Pay Pay Pay where my is a random effect specified as my ~N(0, ) , and is a spherical matrix.
Pay Pay t is a weekly fixed effect, and Camp_ Effectmt is specified as in ( 3 ). To interpret camp effects,
l we exponentiate the coefficients s ,l 1,...,L;s 1,...,q to transform them into multipliers.
1 Thus, exp0 can be interpreted as the multiplicative factor by which the mean number of paying patients per week is estimated to increase in the week of the camp (i.e. when the lag indicator s=0) due to a camp that is within 3 miles of the patient-market (l=1). The total Page 41 of 42
q L l . multiplier effect of a camp including carry-over effects can be computed as exp s The s 1 l 1 model parameters are estimated by maximizing the likelihood.
In Figure 12 we show for subsidized patients the estimated camp multipliers from the
NBD model, in comparison with the estimated incremental number of patients from the linear model (this is reproduced from Figure 5). We see that the pattern of spatial and temporal effects between the linear and NBD models is extremely similar, lending support to our choice of using the linear model throughout because of speed of estimation.
Figure 12: Comparison of Camp Effects on Subsidized Patients from Alternative Models.
NBD Model (left, shows multiplier) and Linear Model (Right, shows incremental number)
1.9 * 1.7 * 1.8
1.7 Distance of Camp from Patient Market Distance of Camp from Patient Market 0-3 Miles 3 - 6 miles More than 6 miles 0-3 Miles 3 - 6 miles More than 6 miles 1.6 1.2
1.5
Market 1.4 0.7
1.3
* 1.2 * 0.2 * * 1.1 * * * * * * * perMarket Week Per Number Patients of Incremental * * * * * * * * * Multiplier on Baseline Number of Patients Per Week per Week Per Number Patients of Baseline on Multiplier * * * 1.0 * * * * * * 0.9 -0.3 -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 -1 CAMP 1 2 3 4 5 6 7 8 9 10 11 12 WEEK WEEK Number of Weeks after Camp is Held Number of Weeks after Camp is Held Asterisk indicates effect is significant at p<0.05 Asterisk indicates effect is significant at p<0.05
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