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The Effect of Proximity to Public and Private Health Facilities on Health Outcomes: Examining the Health Care System in Udaipur District, India

Grace Inbae Kim

Presented to the Department of Economics in partial fulfillment of the requirements for a Bachelor of Arts degree with Honors

Harvard College Cambridge, Massachusetts March 11, 2010


Abstract Generating better health outcomes in low-resource settings depends on ease of access to health services and goods. I examine whether individuals in households closer to a public or private health facility exhibit better health in 100 villages of the Udaipur district in Rajasthan, India. Controlling for household socioeconomic status and quality of the staff at the nearest public health facility, children who live within 1 km of a public health facility receive one more immunization on average than children who live further away. This result supports economic theory that a robust public health sector and government subsidies are necessary for the optimal uptake of public health goods generating positive externalities. On other health outcomes examined, however, distance to either a public or private health facility has no clear or consistent effect – a result which questions the effectiveness of these facilities in generating better health. Directions for policy are to facilitate ease of access to public health facilities and improve the quality of care provided. Acknowledgments I am indebted to my advisor Amitabh Chandra, for his encouragement and patient advice, and to Mauricio Duque, for insightful comments on my drafts. I am grateful to Erica Field and Vanya Pasheva for teaching a junior seminar that required a paper, which ended up being the start of this thesis. I would like to thank the staff at the Center for Geographic Analysis at Harvard University for invaluable assistance in creating maps of the Udaipur district; Rick Townsend for Stata help; Jan Zilinsky and Elizabeth Pyjov for their edits and advice; and my family and friends for their constant support and encouragement.


Table of Contents 1.

Introduction

2.

Literature Review 2.1 Primary Health Care Systems 2.2 Effect of Distance to Health Services on Health Outcomes 2.3 Health Care System of India

3.

Data 3.1 Survey Data: Health Care Delivery in Rural Rajasthan 3.2 Setting: Udaipur District, Rajasthan, India 3.3 Child Health Measures 3.4 Adult Health Measures

4.

Methods 4.1 Measuring Distances from Households to Health Facilities 4.2 Estimation Strategy 4.3 Robustness Check

5.

Results 5.1 Summary Statistics 5.2 Child Health Outcomes 5.3 Adult Health Outcomes

6.

Conclusion

7.

References

8.

Appendix A: Figures

9.

Appendix B: Tables


1. Introduction The unpredictable and potentially catastrophic nature of disease, a lack of full insurance contracts, and the asymmetric nature of health information between physicians and patients generate a variety of inefficient outcomes: in most countries, health care is underprovided and unequally distributed. Economic theory postulates that in the presence of market failures (such as limited access to a public good like basic health care) broad-based, governmental attempts to correct them are both legitimate and desirable. In low-resource settings in the developing world, individuals may lack the knowledge and discretionary income to obtain better health. A primary health care system could be able to cater to the pressing needs of the local population. Based on this philosophy, India has a national health care system designed and developed to serve the health needs of populations in both urban and rural areas. Despite the system’s official commitment to providing free and low-cost services to the poor, public health centers are far from their Pareto frontier, due to staff absenteeism and low demand for health services. Despite the availability of supposedly free services, individuals are found to turn to private health providers, leading to proposals that the public health care system be dismantled in light of their failures in serving the population. Despite their limitations, health centers staffed by qualified people can be an invaluable tool in improving population health in targeted interventions. In this thesis, to contribute to the debate over public health clinics’ efficacy in providing basic health care services and goods, I examine the health care system of 100 villages in a district of rural India, with the majority of the population suffering


from malnutrition, illiteracy, and poverty. I estimate the effect that distance from households to the closest public and private health facilities has on health outcomes. Measures of health examined include immunization rates, self-reported symptoms and health, tuberculosis testing, prevalence of anemia, hypertension, malnutrition, and respiratory problems. Children who live closer to a public health facility have higher rates of immunization, an effect that remains consistent across different specifications and robustness checks. The results support the hypothesis that public health centers may be effective in addressing conditions that pose significant threats to population health. Distance to public health facilities has no consistent or significant effect on other health outcomes, suggesting that public health centers have a curtailed effect on improving health. 2. Literature Review 2.1 Primary Health Care Systems Two characteristic market failures exist in healthcare: the existence of large externalities in the control of many infectious diseases that are mostly addressed by standard public health interventions, and the widespread breakdown of insurance markets that leave people exposed to catastrophic financial losses (Filmer et al. 2002). To set priorities in health in the developing world, policies must address poverty and inequality and consider the practicality of implementing policies given limited administrative capabilities. A 2008 World Health Organization report touts primary health care systems as a key mechanism via which to make health more accessible and equitable. The characteristic

features

of

primary

care

systems

are person-centeredness,


comprehensiveness and integration, continuity of care, and participation of patients, families and communities (WHO 2008). A model public health system would rely on stewardship and regulation from the government and communities to strengthen accountability, ensure users have a voice in the local health system, and encourage good performance to promote population health. Patients would be guided through the health system to facilitate relationships with clinicians in preventing, detecting, and treating diseases (Glasgow et al. 1999). Tasks for public health centers include treating and preventing illnesses that pose significant threats to population health, such as infectious diseases (Gersovitz 2000). Public health systems, staffed by teams of health professional with specific and sophisticated biomedical and social skills, necessitate adequate resources and investment (Fortney 2005). Thus, financing issues trouble the public health care system, for the need to significantly scale up resources leads to calls for the introduction of user fees. However, evidence finds that the overall effect of user fees is negative since the downward-sloping demand curve is particularly steep among the poor, and the revenue collected does not generally compensate for the administrative costs. (Palmer et al. 2004). Standard public finance analysis also implies that public subsidies should fund health goods generating positive externalities, such as vaccines. This premise has further fueled much of the push to provide health goods for free or at reduced prices. One study randomly assigned some prenatal clinics to distribute insecticidetreated nets for free and others to sell them for a small fraction of the actual price (the subsidy was decreased from 100 to 87.5 percent), and found that demand for the


protective nets fell drastically (by about three fourths) when a positive price was charged (Cohen and Dupas 2008). This supports the claim that free health treatments enjoy higher rates of usage than those that must be purchased for even a nominal fee by out-of-pocket payments. Thus, the government generally operates like an NHS (National Health Service) through its Ministry of Health, by financing basic public health and other services from a limited budget (Breman et al. 2006, 231). Costbenefit analyses also support the use of government subsidies to influence private behaviors regarding health, such as protecting against disease or accounting for public externalities, to optimize health outcomes and prevent productivity losses from disease (Hammer and Gersovitz 2007). Despite their potential for good, primary care programs may have disappointing small effects because of two weak links standing between spending and increased health care. The weak links include an inability to monitor and control the behavior of public employees and competition with private markets for health care (Gersovitz and Hammer 2007). In developing countries, high staff absenteeism was noted amongst public employees, including health workers and teachers, necessitating measures to increase staff productivity for public health care systems to fully reach their potential (Chaudhury and Hammer 2003, Chaudhury et al. 2006). A public health care system must also find ways to work with the private sector, which plays a substantial role in almost all low- and middle-income countries (Breman et al. 2006, 91). Private sector may have a comparative advantage in providing treatments in demand and requiring specialized care. The public sector, on the other hand, may


be best equipped to implement preventive health interventions and address infectious diseases with large negative externalities. 2.2 Effect of Distance to Health Services on Health Outcomes The distance to a health provider may have an impact on health outcomes. An increase in distance to primary care was found to be a significant and substantial predictor of a decrease in primary care visits, and health systems can implement strategies to encourage members to use more primary care services without driving up costs (Fortney et al. 2005). Increased distance to the closest hospital was also found to increase deaths from heart attacks and unintentional injuries in California (Buchmueller et al. 2005). In rural areas, having a clinic even one additional kilometer further away could have a significant effect on people’s abilities to access health care services. Distance to the closest clinic has a strong effect on health outcomes in a developing country as well. In India, distance from the household to the health facility showed a significant inverse association with use of health facilities for inpatient treatment (Singh and Ladusingh 2009). In the Udaipur district of India, the further away a household was from a public health clinic, the more likely it was for the inhabitants to utilize private providers (Duflo and Banerjee 2005). Convenience and accessibility may be key factors in choice of health provider and utilization of health services. 2.3 Health Care System of India The organization of health care services in India extends from the national to village level. The Union Ministry of Health and Family Welfare functions at the national level, while at a more local level, a Primary Health Center (PHC) covers a


population of about 30,000. Each PHC is expected to have one medical officer, two health assistants, health workers and supporting staff. The most peripheral health institutional facility is the sub-center, led by one male and one female multi-purpose health worker. At present, one sub-center exists for about 5,000 persons (WHO 2008). In principle, sub-centers and primary health centers (PHCs) or community health centers (CHCs), which are larger than PHCs, are supposed to be open six days a week, six hours a day. Free and accessible health care is supposed to be available to anyone who chooses to use the public health care system, with the sub-centers staffed by a trained nurse (ANM) providing the first point of care, the PHCs or CHCs the next step, and the referral hospitals dealing with the most serious problems (Banerjee et al. February 2004). According to the Central Bureau of Health Intelligence of India, providers in the public sector all hold a bachelor of medicine and bachelor of surgery (MB BS) degree (equivalent to a doctor of medicine, or MD, degree in the United States) and work at public dispensaries, PHCs, or hospitals. Qualified health providers are trained at Indian government institutions to work at public health clinics). In contrast, a wide array of qualifications exists in the private sector, with training periods ranging from six months to six years. These can be separated into three broad groups: those with an MB BS degree, those with formal training in alternative medicine (Ayurvedas, homeopaths, Unani, and integrated systems doctors with degrees other than the MB BS degree), and those with little or no formal training (Banerjee et al. February 2004).


Households in India overwhelmingly favor seeking care in the private sector. Visits to a private provider account for 82 percent of all visits nationwide and 75 percent in urban samples. With no medical insurance apart from that implicit in the provision of government health care, this implies that families incur high out-ofpocket spending: The World Health Organization estimates that of total spending on health care in the country in 2000, 82 percent was out-of-pocket spending on primary and inpatient care. The phenomenon of choosing to pay private providers, rather than rely on government health services was also observed in both rural and urban areas, including Delhi. The reasons cited are perceptions of poor treatment (clinically and in terms of courtesy), high absentee rates and lack of attention among public sector providers (Chaudhury et al. 2006, Banerjee et al. May 2004). Others argue that public physicians provide worse care compared to private physicians conditional on ‘what they know,’ or their education levels (Das and Hammer 2005). The 2005 National Family Health Survey (NFHS) revealed that, in the state of Rajasthan, the main reasons for not using government health facilities were the poor quality of care (63%), lack of a nearby facility (35%), and long waiting time (17%). In addition, although the primary care system is intended to provide free services, patients must purchase medications themselves if they are not available at the clinics (Dugger 2004). The direction of the causality is also unclear – whether absenteeism causes lack of utilization, or whether the lack of utilization leads to absenteeism. If unaware of the benefits of preventative health measures like vaccinations and testing, the


public may be indifferent to or even averse to receiving such services. Since it takes effort to learn about the effectiveness of correct treatments and drug regimens, the public may desire innovative treatments that that are readily provided by an unregulated private market. Nurses and doctors in the public sector may have little incentive to go to work if they know that their prospective patients have little or no interest in what they do (Banerjee and Duflo 2009). Methods to increase the efficacy of these public health facilities include creating incentives to reduce absenteeism, improving the quality of care provided by staff, and generating demand for preventive health services. 3. Data 3.1 Survey Data: Health Care Delivery in Rural Rajasthan The data used for this analysis is a 2003 survey titled, “Health Care Delivery in Rural Rajasthan.� The survey was conducted by the MIT Poverty Action Lab, under the leadership of Esther Duflo, Abhijit Banerjee, and Angus Deaton, in collaboration with the local NGO Seva Mandir. The data was collected between January 2002 and August 2003 in 100 hamlets/villages in Udaipur district, Rajasthan. Udaipur has a large tribal population and an unusually high level of female illiteracy (at the time of the 1991 census, only 5% of women were literate in rural Udaipur). The data has four components: a village survey (100 villages); a facility survey (of all public and private facilities that serve the villages); a weekly visit to all public facilities serving the villages; and a household and individual survey, covering 5759 individuals in 1024 households. The data collected in the household survey include measures of integration in society, education, fertility history, perception of health


and subjective well being, and experience with the health system (public and private), as well as a small array of direct measures of health (hemoglobin, body temperature, blood pressure, weight and height, and a peak flow meter measurement of lung capacity) (Banerjee et al. February 2004). The dataset is a comprehensive set of all of the economic and health indicators of this population to find the determinants of health. One disadvantage is that it is purely an observational survey. Since no random experiments were conducted in these villages during that time or prior to that time, economic and health indicators are likely confounded. 3.2 Setting: Udaipur District, Rajasthan, India The primary health care system of the Udaipur district provides an ideal setting in which to examine the efficacy of government-funded primary health centers. PHCs and sub-centers service the Udaipur District, with a sub-center for every 3,600 individuals (the official target is 3,000 per sub-center). Despite the relative low density and high poverty of the population surveyed, the average household was only 1.53 kilometers from the closest public facility (Banerjee et al. February 2004). Nearly all the subcenters have an Auxiliary Nurse Midwife (ANM), who provides basic services, and those in the most disadvantaged areas have two. The ANMs have completed secondary school and 1.5 years of training. Primary Health Centers (PHC) have 5.8 medical staff, on average, including 1.5 doctors, and 87% of the Community Health Centers (CHC) has one or more specialists. The average visit to a sub-center costs the patient only Rs. 33 (approximately 39 rupees equal US$ 1), and a PHC/CHC visit costs Rs. 100 for visits that involve operations and tests.


Private practitioners are usually more expensive and have less formal training and credentials than their public health provider counterparts. Private providers range in quality and expertise. Unqualified practitioners of nontraditional medicine ("Bengali doctors") cost Rs. 105 per visit; qualified private doctors cost Rs. 179; and traditional healers (bhopas) cost Rs. 131. For the poor, each visit to a public facility costs 71 rupees, compared with 84 for visiting a private doctor. The gap is larger for the middle group, who actually spend less per visit to a public facility in absolute terms than the poor, and about 50 per cent more per visit to a private facility, but the gap is about the same size (in proportional terms) for the rich (Banerjee et al. May 2004). Besides being geographically comprehensive, the public health care system is staffed by qualified medical personnel and is cheaper than other alternatives. However, the Indian public health system is in poor condition and many people seem to utilize less of its health services than of the private sector. Patients visit the health facilities 0.51 times a month on average (once in two months). Of those visits, less than a quarter (0.12) is to a public provider, the majority of the rest are to private facilities (0.28), and the rest are to bhopas (0.11). In villages served by a facility closed more often, the poor are less likely to visit the public facilities and more likely to visit the bhopas. In the survey, the most commonly cited reason for not going to the public health clinics was “no proper treatment at government facilities,” “better treatment available elsewhere,” “I do not need to go,” “too far,” “too expensive,” “do not know where it is,” “do not know about government hospitals” (Banerjee and Duflo 2009). The poor are less likely to visit the public facilities and more likely to visit the bhopas, likely because visits to the health clinics


are frustrating. The public clinics have high staff absentee rates and unpredictable schedules, rendering their services too sporadic for individuals to rely on the public clinics (Banerjee et al. 2004). High absenteeism undermines the effectiveness of the public health clinics, perhaps more so in rural areas where population density is low. Villagers would be unwilling to travel long distances if the probability that the health care provider will be at the public health center is very low. Many seek private doctors, also known as compounders, or bhopas, for treatment, due to the lack of reliability of the government run health clinics, and choose to pay out-of-pocket for private health care rather than accept the supposedly free government services (Banerjee et al. 2008). Previous research has shown that patients tend to associate specific diseases with specific providers. In the survey, no clear pattern was found in comparing public to private facilities, except that people who have cough in blood tend to go to the public facility relatively more often. People tend to go to the bhopas for conditions considered less serious. Self-reported health is fairly high, perhaps because people may not be particularly demanding about their own healthcare and hence may under-use healthcare facilities (Banerjee and Duflo 2009). Although the district is one of the poorest in rural Rajasthan, a comprehensive health care system, staffed by qualified medical personnel, to service the village inhabitants is in place. The question remains whether the public health facilities are doing any good in for the populations in these villages. 3.3 Child Health Measures


Having access to a well-trained health provider and health interventions are crucial at the early stage of life, especially as infants are susceptible to a wide variety of infectious diseases and health issues. In the analysis, the sample is restricted to children under the age of 19 and consists of 2641 children. Health outcomes for children examined were chosen as indicators of general health and treatment: (1) the number of immunizations received (out of a total of 5 possible immunizations), (2) classification of the child’s health (10 = very good health, 1 = very bad health), (3) time the child took to squat and stand five times (in seconds), (4) whether the child was tested for tuberculosis, (5) whether the child has anemia (defined if hemoglobin count was below 10 grams per decimeter. Normal range for children is 11-13 g/dl), (6) whether the child had diarrhea in the past 30 days, (7) whether the child experienced vomiting in the past 30 days, (8) whether the child is handicapped, (9) whether the child is retarded, and (10) the number of months the child was breastfed. A variable Immunization Score was constructed to measure the number of vaccinations the child received at the time of the survey, out of a total of 5: Bacillus Calmette-GuÊrin (BCG), a vaccine against tuberculosis prepared from a strain of attenuated live bovine tuberculosis bacillus; Diphtheria-Pertussis-Tetanus (DPT), a class of three combination vaccines; a measles vaccine; Oral Polio Vaccine (OPV); and a second polio vaccine that is injected. Immunization Score is examined as a marker for the level of early child health interventions. Children can receive multiple vaccines by the age of two, which are crucial for reducing the risk of catching an infectious disease and infant mortality.


Immunizations are one of the most cost-effective health investments, with proven strategies that make it accessible to even the most hard-to-reach and vulnerable populations. Vaccines have clearly defined target groups, can be delivered effectively through outreach activities, and do not require major lifestyle changes (Hammer and Gersovitz 2000). Immunizations are health goods likely underprovided by the private sector, which would underestimate the benefits of providing a vaccine, and underutilized by individuals, who may lack the education to be aware of the benefits. The benefits from providing widespread use of vaccines include the warding off of widespread infectious diseases and their associated cost on health and productivity. The public health system can reduce barriers to immunizations, expand access, link practices and clinics to the registry, offer education to physicians and staff, and promote the message that immunizations are important, safe and effective. The Indian government provides the World Health Organization/UNICEF vaccine package, the Extended Package of Immunization (EPI), via the primary health centers. For children, the EPI includes one dose of BCG, three doses of DPT, three doses of OPV, and one dose of measles vaccine. The immunization schedule for the five vaccines are: at birth for BCG, at birth or 6-14 weeks for DPT, 9-12 months for measles, and at birth or 6-14 weeks for OPV. A child should be fully immunized by age at one year (WHO 2008). Rural populations in India are far less likely to be immunized than urban populations. One-year-olds in urban areas were 30 percent more likely to have been vaccinated against measles than children in rural areas. Health disparities also mirror


socioeconomic disparities. Measles immunization coverage among one-year-olds with mothers in the highest education level was 89.3, while with those in the lowest education, it was 41.0. Coverage amongst one-year-olds in the highest income quartile was 85.2 while for those in the lowest income quartile it was 39.9. Of the target population, 87 percent was vaccinated for BCG, 83 percent for DPT, 71 percent for measles, and 68 percent for polio. 1 Little less than half of the target population is still not vaccinated against measles and polio – diseases that are highly contagious and debilitating. In the Udaipur district, lack of immunization is a serious problem and immunization rates are vastly over-reported (Banerjee and Duflo 2009). In light of these facts, it is important to find ways to increase immunization rates. Another measure of child health that requires elaboration is the number of months the child was breastfed, which is an indication of the level of maternal health knowledge and care. Breastfeeding is one of the most effective ways to ensure child health and survival. A lack of exclusive breastfeeding during the first six months of life contributes to over a million avoidable child deaths each year (WHO 2008). 3.4 Adult Health Measures The health outcomes for adults were chosen based on their degree of prevalence in the population and treatability by a primary health care system. This analysis looks at the prevalence of the following selected health outcomes amongst the adults surveyed: (1) the number of self-reported symptoms in the past 30 days, (2) self-ranking on a health ladder (10 indicated very good health, 1 indicated very 1

From the official country statistics report in 2006, the year of the last coverage survey, from the Central Bureau of Health Intelligence of India. WHO-UNICEF coverage estimates corroborate the country estimates, though they are slightly lower: 85 percent for BCG, 81 percent for DPT, 67 percent for measles, and 62 percent for polio.


bad health), (3) the time the individual took to squat and stand five times, (4) whether the individual was tested for tuberculosis, (5) anemia (indicated by hemoglobin levels below 12 g/dl.) (6) malnutrition (a Body Mass Index (BMI) under 21 indicates inadequate food and nutrient intake), (7) respiratory problems (indicated by average peak flow meter reading below 350 ml/expiration), and (8) hypertension (indicated by systolic blood pressure/diastolic blood pressure above 140/90 mmHg). Some of the health measures examined requires elaboration. the number of self-reported symptoms refers to whether the individual answered in the affirmative to the question, “In the past 30 days, have you experienced any of the following symptoms?� The symptoms include cold symptoms, dry cough, productive cough, cough with blood, blood in spit, hot fever, diarrhea, body ache, weakness/fatigue, problems with vision, headache, backache, vomiting, trouble breathing, pain in upper abdomen, pain in lower abdomen, genital ulcers, painful urination, swelling ankles, hearing problems, skin problems, chest pain, memory loss, full paralysis, partial paralysis, night sweats, weight loss. For female respondents, the list also includes menstrual problems and white discharge. The maximum value for self-reported symptoms was 20 for males and 22 for females. Respiratory problems were indicated if average peak flow meter reading was below 350 ml/expiration, which is considered to be an indicator of respiratory difficulties for an adult taller than 1.60 meters. Peak-flow meter measurements measure lung capacity and were taken in the survey to detect asthma or other respiratory disorders such as chronic bronchitis. A higher peak flow meter indicates a better control the individual has of possible respiratory ailments (Banerjee et al.


February 2004). Whether the individual was tested for tuberculosis was a health intervention examined because TB is one of the leading causes of mortality in India, killing two persons every three minutes, or nearly 1,000 every day. The Central Bureau of Health Intelligence of India notes that the country accounts for 20% of the global incidence of cases of tuberculosis. A properly equipped primary health care system could provide life-saving interventions, including blood pressure reducing medications, antibiotics for tuberculosis, nutrition and dietary supplements. Although some illnesses may require the skills of a specialist, a primary care system can serve as a necessary baseline of care. 4. Methods 4.1 Measuring Distances from Households to Health Facilities The dataset includes Geographic Positioning System (GPS) coordinates for 159 public health facilities, 411 private health facilities, and 1019 households surveyed in the Udaipur. The coordinates were recorded in degrees minutes and converted to decimal degrees. For each household, the distances in kilometers to the closest public and private health facility were calculated using the ArcGIS software NEAR command, which utilizes the Pythagorean method. Each household was matched with the facility identification number of the closest public and private health facility, and characteristics of the corresponding public health facility were appended to the household dataset. The household data was then merged with individual-level data. The child dataset, with 2641 observations, and the adult dataset, with 2520 observations, were each merged to the household dataset by matching on


household ID. If several individuals are in the same household, each individual is a separate observation with the same household characteristics. 4.2 Estimation Strategy The econometric specification measures the effect of distance to the nearest facility on health outcomes for the sample of children and adults surveyed. The primary specification examines the effect of a gradient of distance to the nearest public health clinic on health outcomes: Health Outcomei = β0i + β1iPublic_Distance_1-2kmi + β2iPublic_Distance_23kmi + β3iPublic_Distance_3+km + ∑ βjiSocioeconomic_Controlsji + ∑ βkiPublicFacility_Controlsk where i refers to the individual; β1, β2, β3 are beta coefficients for the public facility distance variables; βjs are beta coefficients for control variables measuring the socioeconomic characteristics of individual i; and βk are beta coefficients for controls measuring the characteristics of the public facility closest to individual i. Public_Distance_1-2km, Public_Distance_2-3km, and Public_Distance_3+km are a set of dummy variables that correspond to whether the closest public facility is 1-2, 2-3, or more than 3 kilometers, respectively, from the household of the individual. A similar specification estimates the effect of distance to the nearest private facility on health outcomes: Health

Outcomei

=

β2iPrivate_Distance_2-3i βjiSocioeconomic_Controlsji

β0i

+

β1iPrivate_Distance_1-2kmi

+

β3iPrivate_Distance_3+

+

+ ∑


where i refers to the individual; β1, β2, β3 are beta coefficients for the private facility distance variables; and βjs are beta coefficients for socioeconomic characteristics of individual i. Private_Distance_1-2km, Private_Distance_2-3km, and Private_Distance_3+km are a set of dummy variables that correspond to whether the closest private facility is 1-2, 2-3, or more than 3 kilometers, respectively, from the household of the individual. The set of dummy variables specifies the gradient of distance to measure the effect of living an extra kilometer away from the nearest health facility on health outcomes. The distance ranges were chosen to be 1-2, 2-3, and greater than 3 km away because the average household is approximately 2 km away from the closest public facility. The coefficients measure the magnitude of the effect with respect to the baseline of whether the closest public or private facility is 0-1 kilometers away. For each observation, only one of the distance dummy variables is 1 and the other two are 0, or all of the distance dummy variables are 0 if the individual lives within 1 km from the nearest health facility. The coefficient on each distance dummy variable is an estimate of the effect of living within that distance from the health facility on the outcome variable, when compared to the baseline of living in a household 0-1 km away from a health facility. For all regressions, heteroskedasticity-robust standard errors are clustered at the village/hamlet level, to control for possible serial correlation. Controls for socioeconomic status are the same for both children and adults. They include (1) the level of education of the individual, (2) the natural logarithm of total monthly consumer expenditures (in rupees) for the household, (3) whether the household


owns livestock, (4) whether the household has electricity, and (5) household size. For children, education status is measured by a dummy for whether the child is in school at the time of the survey, whereas for adults, level of education is measured by a dummy for whether the individual is literate. Monthly total consumer expenditures are a proxy for the household income level of the household. Household size might affect health outcomes if a smaller family results in better care for a single child. Whether the household has electricity is a proxy for access to utilities and is likely correlated with proximity to public health facilities, which are probably close to the electricity grid. I include these control variables to possibly eliminate the risk of omitted variable bias and any spurious relationship between proximity to a clinic and health. The controls are similar to controls used in a previous examination of health status in the Udaipur district, which included the number of adults and children who skipped meals, number of assets owned, and years of completed education (Case and Deaton 2005). Controls for public health facility characteristics are included as proxies for the mode of transportation to and from the clinic, targeted populations, and the motivation and effectiveness of the staff in providing services. Four control variables are included in the final specification in order to control for public health facility characteristics that might affect the effect of distance to the nearest facility on health outcomes. The first control variable measures whether patients travel on foot (versus another mode of transportation, such as an ambulance or public transport) if they were referred to another facility. This variable acts as a proxy for whether the primary mode of transportation is walking, which would likely magnify the effect of


distance on health outcomes if the lack of motor vehicles or faster modes of travel prohibits households from seeking health services located further away. The second control variable is the number of villages the public health facility serves, as a measure of the scope of the facility’s services. The third control variable is the distance of the facility from the Public Health Center (PHC) that heads clinics. The Indian health care system is set up so that a PHC is responsible for peripheral health clinics, the sub-centers and aid-posts that comprise the majority of the public health facilities surveyed. The fourth control variable is the distance to the facility from the home of a medical officer on staff. Presumably, living closer to a facility could decrease barriers of access that might lead to staff absenteeism, and doctors who live closer may be more attentive to their patients’ needs if the time to commute to work is reduced. 4.3 Robustness Check A straightforward regression of health outcomes on the distances to the nearest facilities runs the risk of being naive because of possible confounding variables and self-selection bias. Individuals more cognizant of their own and their children’s well-being may have chosen to move closer to public health facilities to have easier access to treatments and consultations. The household’s concern for health would then be the omitted variable behind the relationship between the two factors, confounding the causal effect of the proximity to a facility on health outcomes. However, it is unlikely that households moved to be in closer proximity to the public health facilities, for the public health care system was likely built in order to be in proximity to dense clusters of households and people.


In Udaipur, 99 percent of the households that live below $1 a day own some land in addition to the land on which their house is built. Although the median landholding among the poor who own land is one hectare or less in Udaipur, most households engage in a combination of agricultural and entrepreneurial labor to make a living. The poor also tend not to migrate for long periods to earn more because of a reluctance to leave their social networks behind, which presumably act as an (informal) form of insurance (Banerjee and Duflo 2007). Because of these characteristics of the Udaipur district, households are far less likely to have moved frequently. Nonetheless, to avoid the possibility that the household may have deliberately settled in a location closer to a public health clinic, a measure to control for the length of time the household stayed in the same location is needed. Households that have not moved for an extended period of time are likely to have remained in the same location before the public health facilities were set up. Controlling for the length of stay of the household in a location would control for possible self-selection bias. Although data on duration of stay at the location is not available, there was data on the primary source of household income, whether agricultural labor or salaries and wages. 2565 children are in households engaged in agricultural labor. Restricting our sample to those households whose primary source of income comes from agricultural labor likely selects for households that had not moved since before the construction of the public health clinics, for households would be responsible for the care and maintenance of their own land. Selecting for these households reduces the possibility that the individuals may have moved to be closer


to a public health facility so that proximity to the public health facility would be correlated with preferences for health. The analysis includes a robustness check using a smaller sample of households engaged in agricultural labor to check if the restriction in sample changes the effect of distance to the health facility on health outcomes. 5. Results 5.1 Summary Statistics Table 1 presents summary statistics from the child dataset, and examines differences in socioeconomic characteristics and health outcomes across two groups. The first group lives within 2 km of a public health facility and the second group lives further away. Out of a sample size of 2641, 1270 households (approximately half) have no clinics in a 2 km radius, 822 have one, 426 have two clinics, 111 have three clinics, and 12 have four clinics. I divided the sample of 2641 children into two roughly equally sized groups to show differences in socioeconomic and health characteristics between households that live closer to a public clinic and those that live further away. Table 1 also shows the two-sample t-test for immunizations received (out of a total of 5), child characteristics, health measures, and household socioeconomic characteristics. Children in the group with at least one clinic in a two km distance are much more likely to receive any of the five vaccines covered in the dataset. This difference is statistically significant, with a p-value of essentially 0. Children living closer to a clinic are more likely to be older and be in school, take less time to squat and stand five times, and are less likely to have vomited or have a cough in the past thirty days. The difference of the health measures between


the two groups is statistically significant, but magnitude of the difference is too small to be of practical significance. Children living close to a clinic are more likely to be in households with electricity, higher monthly consumer expenditures, with more spent on a sick person, and smaller families. Table 2 compares some of the child’s characteristics across groups after restricting the sample to households engaged in agricultural labor. Those children who live close to a clinic are still much more likely to have received an additional vaccine. The households that are closer to a public health facility are more likely to have electricity, have fewer household members (a difference of .5), and more likely to eat two meals a day regularly. With the restricted sample, the difference in household characteristics across the two groups decreases, but the difference in immunization rates is still approximately the same across the two groups. Table 3 shows the summary statistics for the adult dataset. 25 percent of adults are literate, 25 percent are in households with electricity, 93 percent are in households that own livestock, and only 77 percent are in households where all household members eat at least two meals a day, 33 percent have spent more than 500 rupees on a sick person, and 95 percent have visited a health provider in the past 30 days. Of adults, 24 percent live within 1 km of a public health facility, 57 within 2 km, 70 percent within 71 percent, and up to 92 percent within 5 km. A much greater percentage, 85 percent, lives within 1 km of a private health facility. The average distance to the closest public facility is 2.3 km. Of the respondents, 96 percent are female, probably because women are far more likely to be at home at the time the


survey was conducted. The statistics indicate a population largely illiterate, rural, and poor. Table 4 shows the summary statistics for adult health measures. Selfclassification on the health ladder ranges around 6, while a rating of 10 indicates very good health. 20.7 percent of the population suffers from hypertension, while 23 percent supper from hypotension. The average BMI is 18 and 86 percent of the population suffers from low nutrition. An estimated 67 percent of the population experiences respiratory problems, and 43 percent suffer from anemia. Only 5 percent (137 out of 2520) have been tested for tuberculosis, and of those tested, 46.5 percent were diagnosed with TB. Of those diagnosed, 21.7 percent received their first diagnosis of TB in the past year and just 60.7 percent were treated for tuberculosis. 29 percent have suffered from a cough during the past 30 days, and of those, 32.8 percent are still suffering from cough. Overall, these statistics paint a fairly bleak and disheartening picture of health for adults in the Udaipur district. Table 5 shows summary statistics for the total of 177 public health facilities surveyed. Each facility serves an average of 3.4 villages, and the amount of people the facility serves ranges from 933 to 353,043. The reason is that the facilities range from subcenters, which are only responsible for 3,000 people, to community health centers, which are responsible for a larger population. 4.5 percent of the sample is community health centers, 15.2 percent are public health centers, 5.6 percent are aidposts, and 52.5 percent are subcenters. 85.8 percent address family planning, 53 percent address immunizations and vaccines, 83.9 percent address STDs and RTIs (Reproductive Tract Infections), 69.7 percent address general health, and 45.7


percent address eyes and vision. These percentage values for provision of basic health services indicate that the facilities are perhaps doing less than what is fully required of public health facilities. Factors measuring the motivation of the health care workers and ease of access were also examined. These variables are used as controls to examine a possible differential effect of workers’ motivation on the impact of the proximity to the public facilities. A doctor who has to travel further to get to work may exhibit higher absentee rates; the average distance of the medical officer’s home from the facility was 2.5 km and the maximum distance was 36 km. The average distance of the facility from the PHC that heads the clinic was 13.4 km and the longest distance was 65 km. Since clinics are to report to the PHC, being closer to a PHC may increase accountability. 15.8 percent reported that the patients travel on foot to reach the nearest facility, as opposed to public transportation or via motor vehicles. 48.3 percent reported that staff members treat patients in their own homes, signifying that nearly half of the facilities have staff willing to venture out into neighboring households to provide care. 90.5 percent report that the facility follows regular hours, but as this figure is self-reported, is likely to be an overestimate of the actual incidence. 5.2 Child Health Outcomes I examined the effect of distance to the nearest public and private health facility on child health outcomes. Table 6A corresponds to Ordinary Least Squares (OLS) and probit regressions for distances to the nearest public facility, and Table 6B corresponds to distances to the nearest private facility. The coefficients on the


distance variables can be interpreted with respect to the baseline of whether the nearest clinic is 0-1 km away. These regressions control for household socioeconomic and public facility characteristics. The further the child is away from a public facility, the fewer immunizations he or she is likely to receive (Table 6A, Column 1). The coefficient for the first row shows that the number of immunizations received decreases by an estimated 1.09 for children living 1-2 km away from the nearest health facility, 1.315 for children living 2-3 km away, and 1.325 for those living more than 3 km away, compared to a child who lives 0-1 km away from a clinic. The effect is significant and consistently increases as the distance to the nearest facility increases. The other health outcomes are not affected by the public facility distance variables in a significant or consistent manner. The time to squat and stand 5 times does increase by 86 seconds for children living 1-2 km away, and increases to 2.5 minutes for those living 2-3 km away. However, the effect decreases to 89 seconds for children living more than 3 km away, leading to an inconsistent effect of distance (Column 3). One postulation for these results is that, according to a public goods model, public health facilities have a greater responsibility to provide vaccinations than other health interventions because of the significant negative externalities associated with infectious diseases. The gradient of distance to the nearest private facilities does not have a clear significant or consistent effect on any of the health outcomes (Table 6B). Although a few of the coefficients are significant, the coefficients for the three different distance


variables show no consistent gradient of the impact of distance. Most health outcomes are unaffected by proximity to either the public or private facilities. Table 7 shows robustness checks for the health outcome for which proximity to a public health facility clinic seems to have a significant and consistent effect – Immunization Score. In a “naïve” estimate without including control variables, the estimated number of immunizations received decreases by .678 if a facility is 1-2 km away, .821 if 2-3 km away, and 1.3 if more than 3 km away, when compared to a child living within 1 km away from a facility (Column 1). These results are significant at the 5 percent level. Including household controls lowers the magnitude and significance of the coefficients, but the distance variables are still significant at the 10 percent level (Column 2). Including controls for the characteristics of the nearest public health facility, however, strengthens the effect of proximity to a clinic (Column 3). The magnitude of the decrease in immunizations for children rises from .48 to 1.054 for facilities 1-2 km away, from .67 to 1.33 for facilities 2-3 km away, and from .99 to 1.44 for facilities more than 3 km away, compared to children living 0-1 km away from a facility. These results suggest that quality and motivation of the staff does increase the impact that proximity to a clinic has on health outcomes. To address the possibility that households have may have moved to be closer to a public health facility, the same specification with the sample restricted to households engaged in agricultural labor is shown (Column 4). The magnitude in the difference of immunizations increases slightly: compared to a child living 0-1 km from a public health facility, a child received 1.1 fewer immunizations if 1-2 km away, 1.4 fewer immunizations if 2-3 km away, and 1.5 fewer immunizations if


more than 3 km away. The coefficients are significant at either the 1 or 5 percent level (Column 3). The magnitude of the coefficients does not vary significantly from the coefficients of the regression on the unrestricted sample. This result supports the assumption that households were not likely to have moved to be closer to a public health facility, and all other regressions are run on the full sample of children. Proximity to a clinic would have a less significant impact on immunization rates if children were able to receive immunizations via another channel. Since schools likely require their students to be immunized, enrollment in school would significantly increase the chance of receiving vaccinations. To test this theory, I restrict the sample to children enrolled in school, to create a control group to test the impact of distance to a public health facility on immunization rates (Column 5). The coefficients on the distance variables are smaller in magnitude and significance than those in Columns 3 and 4. These results support the hypothesis that children attending school are likely to have received required vaccinations prior to enrollment, so that the distance to the nearest clinic would have a smaller effect on immunization rates. This regression illustrates the necessity of controlling for whether the child is in school to estimate the causal effect of distance to the nearest public facility on health outcomes. As Immunization Score has a maximum value of 5 and a minimum value of 0, a tobit specification including household and facility controls was also used as a robustness check (Column 6). The coefficients and standard errors do not vary significantly from the OLS regression. The impact of the distance to the public health facility also remains the same when controlling for the distance to the nearest


private facility. Children living further than 1 km away from a public health center still receive one fewer vaccine on average, and the decrease in immunizations increases the further away the clinic is located (Column 7). Though a strong correlation between distance to a health facility and immunization rates exists, proximity to a health center does not necessarily have a causal effect on immunization rates. Public health centers may be more likely to be located in denser areas with schools, an electricity grid, running water, and other amenities. The village that the household is in may also be responsible for the provision of immunizations. With the inclusion of village fixed effects (using a dummy variable for each of the 100 villages), the coefficients on the distance variables are no longer significant (Column 8). These results suggest that perhaps the village the child resides in is driving much of the variation in immunization rates. Some villages may enjoy higher immunization rates and public health facilities in close proximity to households. I also examined the effect of distance to a public facility on the probability of the child having been administered any one of the vaccines, as additional robustness checks (Table 8). Controlling for household socioeconomic status and public health facility characteristics, the further away the nearest clinic is from the household, the smaller the probability that the child received one of the five vaccines. The differences are significant at the 5% level for all five vaccines, which are similar to the significance level of the coefficients for the distance variables in the OLS regressions for Immunization Score. Compared to children living less than 1 km away, children living 1-2 km away from the nearest facility are less likely to have


received the BCG vaccine by 59 percent, DPT vaccine by 63.7 percent, OPV by 68.4 percent, measles vaccine by 70.4 percent, and polio vaccine by 50.1 percent. Children living 2-3 km away from the nearest public health facility are even less likely to have received these vaccines. Compared to children living less than 1 km away from a facility, these children are less likely to have received the BCG vaccine by 62.9 percent, DPT by 82.5 percent, OPV by 91.2 percent, measles vaccine by 97.7 percent, and polio vaccine by 51.5 percent. The effect of living more than 3 km away from the nearest facility is not as great of a change in probabilities when going from 1-2 km to 2-3 km. However, compared to children living less than 1 km away, living more than 3 km away from the nearest facility lowers the probability of having received the BCG vaccine by 81.1 percent, DPT by 83.4 percent, OPV by 83.2 percent, measles vaccine by 90.2 percent, and polio vaccine by 50.5 percent. Tables 9A and 9B show the effect of distance to public or private facilities on whether the child is in school and the socioeconomic status of the child’s household. Children living within 1-2 km are 17.3 percent less likely to be in school than children who live within 1 km of a public health facility, and this result is significant at the 1 percent level. The effect dips for children living 2-3 km away such that they are 15 percent less likely to attend school, but children living more than 3 km away are 39 percent less likely to attend school. Households living further away from the clinic are also less likely to have electricity and more likely to have livestock. Monthly consumer expenditures decrease for households living further away from public clinics. Correlation between proximity to health facilities and socioeconomic characteristics exists, such that poorer and more agricultural households are likely to


live further away from the public clinics. However, the overall effect of distance to the nearest public or private facility is inconsistent in magnitude or significance for most of the socioeconomic characteristics. Still, some of these characteristics are included as control variables in the main specification, which removes the possibility of the child’s level of education and household characteristics driving the correlation between distances to the nearest public facility and the child immunization rates examined. 5.3 Adult Health Outcomes Table 10A corresponds to regressions estimating the effect of whether the nearest public facility falls within 1-2 or 2-3 or more than 3 km away from the household on health outcomes. Table 10B corresponds to regressions estimating the effect of whether the nearest private health facility falls within 1-2 or 2-3 or more than 3 km away from the household. All regressions control for the individual’s socioeconomic status, and regressions in Table 10A also control for characteristics of the nearest public health facility. The majority of these results do not demonstrate a significant effect of either the public or private facility distance variables. Distance to the nearest public health clinic does have a positive effect on the time it took to squat and stand 5 times, a general measure of well-being and physical ability (Column 3). The effect is small – an adult living 1-2 km away from a public health facility takes just 1.22 more seconds than one living less than 1 km away – but is significant at the 1 percent level. Compared to those living less than 1 km away from a public health facility, adults 12 km away are 26.3 percent more likely to have been tested for tuberculosis, and


those living 2-3 km away are 23.2 percent more likely (Column 4). In a public goods model, testing for tuberculosis is similar to providing immunizations, as both help prevent the spread of infectious diseases, but, unlike children, adults living closer to a public health center do not seem to be provided with more public health goods. The magnitude of the coefficients of the other health measures (self-reported symptoms, self-reported health ranking, incidence of anemia, malnutrition, respiratory problems, and hypertension) show some inverse correlation between distance to the nearest public health facilities and health outcomes. As distance to the nearest public health facility increases, self-reported health ranking decreases, prevalence of anemia increases, and rates of hypertension increase. Although the coefficients are consistent in magnitude across the distance variables, the coefficients are not significant at even the 10 percent level (Table 10B). Rates of malnutrition and respiratory problems seem unaffected by the distance to the nearest health facility. These results do not shed a positive light on the effectiveness of these public health facilities, even when they are in close vicinity to households. Living further away from a private health facility does not appear to bode worse or better health outcomes either, for the majority of the results are insignificant at even the 10 percent level. Adults living 1-2 km away from a private facility are 19.7 percent more likely to be malnourished than those living within 1 km away, at the 10 percent significance level. This finding is tempered, however, by the result that living 2-3 km away decreases the probability of malnutrition by 4.8 percent (Column 7).


Although testing for tuberculosis is a health good that generates positive externalities, proximity to public health clinics does not increase the probability of being tested for tuberculosis. Rather the effect is in the opposite direction, such that adults living further away from the nearest public health facility have a higher probability of having been tested. I examine different specifications using probit regressions for the probability of having been tested for tuberculosis (Table 11). The regressions estimate the effect of distance to the nearest public clinic without controls (Column 1), with controls for household socioeconomic status (Column 2), with controls for public health facility characteristics (Column 3), and with the sample is restricted to households engaged in agriculture (Column 4). The coefficients show no consistent effect of distance on TB testing rates. Adults living 1-2 km away from a public health facility are 45 percent more likely to be tested for tuberculosis than adults living less than 1 km away, but adults who live even further away (2-3 km) are only 33.2 percent more likely (Column 4). Proximity to private facilities does not seem to have a significant impact on rates for tuberculosis testing either, for the coefficients are insignificant, even when controlling for household socioeconomic status (Column 5-6). The effect of distance to the nearest public clinic is significant when controlling for distance to the nearest private facilities (Column 7). The coefficients on the public facility distance variables can be interpreted with respect to the baseline of having a public and private facility within 1 km. Being within 1-2 km of a public health facility is still significant at the 5 percent level, such that individuals are 25.6 percent more likely to have been tested for TB that those living within 1 km. The effect is robust when


adding public facility controls (Column 8), and when restricting the sample to households engaged in agricultural labor (Column 9). Unlike its impact on child immunization rates, proximity to the nearest public facility does not seem to have a positive effect on testing for tuberculosis. The next two tables show the impact that distance to public and private facilities has on socioeconomic characteristics for adults. Table 12A shows distance to the nearest public health facility as explanatory variables, while Table 12B shows distance to the nearest private facility as explanatory variables. Adults living further away from a public health facility are less likely to be literate (Column 1), and their parents are also less likely to have been literate (Columns 2-3). Households are also poorer (Column 4), are less likely to have electricity (Column 5), are more likely to own livestock (Column 6), and are more likely to have their primary income come from agriculture (Column 7). These results show that there is significant correlation between these socioeconomic variables and the distance to the nearest public clinics, underscoring the importance of controlling for socioeconomic characteristics when estimating the effect of distance to the nearest public facility on adult health outcomes. Table 12B shows that living further away from the nearest private facility is associated with lower rates of electricity use (Column 5), but the coefficients on distance variables vary in sign and are not consistently significant on other measures of socioeconomic status. 6. Conclusion


Proximity to health facilities in the Udaipur district has an imperceptible impact on health outcomes, except one. Children who live closer to a public health facility are more likely to be immunized, and this result is robust over many different specifications. Distance to public health facilities may be significant barriers for households seeking vaccines for their children. One policy implication is to reduce barriers of distance by improving transportation infrastructure and building more accessible roads. Proximity to a private health provider has no significant or consistent effect on child immunization rates, so the public sector may be more effective than the private sector in protecting children against infectious diseases and providing preventive health services. Controlling for the motivation of the staff of the public facilities increases the provision of immunizations by nearby public health clinics, which suggests that a higher quality staff leads to better provision of services. More incentives are needed to motivate health providers to actively promote their services to nearby households nearby. Policies to reduce the chronic absenteeism that plagues the public health facilities can also restore the public’s faith in the primary health care system and generate demand for services provided by public health providers. Areas for future research include disaggregating the effect of the distance to the nearest public health facility on child immunization rates. The inclusion of village fixed effects removes much of the significance of the coefficients on the distance variables, which suggests that the village the child resides in is a strong predictor of immunization rates. Further work is also needed to estimate the impact


of competition with health providers in the private sector on the ability of public health facilities to provide quality care. The main finding of this paper is that that proximity to a public health facility has a significant impact in improving child immunization rates, though not on other health measures, and proximity to a private health facility has no consistent or significant impact on all health outcomes examined. This result supports the hypothesis that the public sector is more effective in providing health goods that generate positive externalities, are underprovided by the private sector and targeted toward vulnerable populations like children. In theory, the public health care system of India is equipped with trained workers with ready access to resources and poised to act as gatekeepers to essential health services. In practice, ease of access in rural locales, motivation of the staff, competition with the private sector, and the nature of the goods and services provided affect the ability of public health facilities to improve health outcomes for surrounding populations.


7. References

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8. Appendix A: Figures Figure 1

Private Facilities (Green), Public Facilities (Red), and Households (Blue).

Private Health Facilities Public Health Facilities Households


Figure 2

Public Facilities (Red) and Households (Blue).

Public Health Facilities Households


Figure 3

Private Facilities (Green) and Households (Blue).

Private Health Facilities Households


Figure 4

Public Facilities (Red), Private Facilities (Green), and Density of Households


Figure 5

Households (Blue) and Density of Public Facilities


0

Child's Immunization Score (Mean) .5 1 1.5 2 2.5

Figure 6

0-1 km

1-2 km

2-3 km

3-4 km

4-5 km

Distance to the nearest Public Health Facility (km)

Figure 7

.4

Immunization Score .3351 .313

.1

Percentage of Children .2 .3

.3008

.0209

0

.0301

0

1 2 3 Number of vaccines received, out of 5

4

5


Figure 8

0 2 4

1 3

Percentage of children with 1, 2, 3, or 4 public health facilities within 2 kilometers of their home, for children who live within 2 km of at least one public facility. 60 percent have 1 clinic within a 2 km radius, 31 percent have 2 clinics, 8 percent have 3 clinics, and less than 1 percent has 4 clinics. Figure 9

0

1

Avg # of Vaccines Received 2 3

4

Avg. # Vaccines vs. # of Clinics within 2km

0

1

2

3

4

The average number of vaccines a child receives, over the number of public health facilities within 2 kilometers.


Figure 10

David Reshef, an MIT-India intern with MIT's Abdul Latif Jameel Poverty Action Lab, interviews local women in the Udaipur District of India.

Photo Credits: MIT Poverty Action


9.   Appendix B: Tables Table 1 Summary Statistics: Child Data Mean

Sample Size (N = 2641) Immunization Score BCG Vaccine DPT Vaccine Oral Polio Vaccine Measles Vaccine Polio Vaccine Child Characteristics Age Child is in school Months child was breastfed Low hemoglobin level (<10 g/dl) Time to squat and stand 5x (sec) Child's health rank* Child had diarrhea in past 30 days Child vomited in past 30 days Child had cough in past 30 days Child is retarded Child is handicapped Household Characteristics Household has Electricity Household owns Livestock Monthly Total Consumer Expenditures (rupees)

n

Mean (µ)

PF>2km 1270 1.56 0.261 0.249 0.221 0.205 0.625

P-value

1.94 0.34 0.33 0.30 0.29 0.67

PF<2km 1371 2.29 0.414 0.401 0.381 0.375 0.718

2641 2641 2641 2641 2641 2641 2441 2641 2014 1887 1155 1877 2641 2641 2403 2641 2641

7.00 0.48 20.36 0.42 6.19 6.84 0.14 0.10 0.18 0.01 0.01

7.225 0.519 20.571 0.421 6.053 6.814 0.134 0.084 0.158 0.006 0.012

6.768 0.429 20.136 0.414 6.365 6.864 0.154 0.117 0.201 0.003 0.007

0.0039 0 0.157 0.7602 0.002 0.579 0.142 0.005 0.006 0.299 0.22

2641 2641

0.17 0.94

0.199 0.931

0.132 0.94

0 0.323

2641

2480.07

2595.529

2355.418

0.005

0.32 6.58

0.341 6.464

0.296 6.715

0.014 0.003

0.74

0.733

0.737

0.811

Spent >500 rupees on a sick person 2641 Household Size 2641 Members eat two meals a day regularly 2636 * 10 = very good health, 1 = very bad health.

0 0 0 0 0 0

Note: Sample sizes for each variable may be less than 2641 because of missing or unreported observations. P-values are from two-sample t-tests. Sample includes all children surveyed.


Table 2 Summary Statistics: Child Data Sample Restricted to Children in Households in Agricultural Labor Mean

Sample Size (n = 411) Immunization Score S.E. of Immunization Score Immunization Card Child Characteristics Age (for minors, 18 or under) Child is in school Household Characteristics Household has Electricity Household owns Livestock

n

Mean (Âľ)

Min

Max

411

0

5

411

1.815 0.099 0.095

0

375 411

7.144 0.496

411 411

0.17 0.983

Monthly Total Consumer Expenditures Spent >500 rupees on a sick person Household Size

411 411 411

Members eat two meals a day regularly

411

PF>2km 243 1.539 0.118 0.062

P-value

1

PF<2km 168 2.214 0.167 0.143

1 0

18 1

7.059 0.523

7.202 0.477

0.712 0.356

0 0

1 1

0.244 0.982

0.119 0.984

0.002 0.915

20241 1 11

2643.934 0.393 6.429

2361.191 0.337 6.951

0.235 0.254 0.013

1

0.875

0.728

0.0002

2476.765 425.3 0.36 0 6.737 1 0.788

0

0.001 0.009

Note: Sample sizes for each variable may be less than 2641 because of missing or unreported observations. P-values are from two-sample t-tests.


Table 3 Summary Statistics: Adult Data Sample Size (N = 2520) Adult Characteristics % Literate % Married % Female % Mother is/was literate % Father is/was literate

Household Socioeconomic Status Household has Electricity Household owns Livestock Ln of Monthly Total Consumer Expenditures (rupees) Members eat two meals a day regularly Spent >500 rupees on a sick person Number in household aged 14 or older Number in household aged 0-13 Use of Medical Services Visited health provider in past 30 days Distance to closest public facility Distance to closest private facility Health Centers % Live within 1 km of public facility " 2 km " " 3 km " " 4 km " " 5 km " % Live within 1 km of private health facility " 2 km " " 3 km " " 4 km " " 5 km "

25.28 76.66 96.14 1.548 15.08 n

Mean (Âľ)

Min

Max

2520 2520

0.25 0.93

0 0

1 1

2519 2517 2520 2520 2520

7.70 0.77 0.33 3.77 2.38

5.30786 10.2069 0 1 0 1 1 9 0 8

2511 2505 2505

0.95 2.30 0.70

0 1 0.04378 13.5055 0 8.86433

24.25 57.30 70.95 82.82 91.59 85.16 93.13 94.33 95.36 96.07

Note: Sample sizes for each variable may be less than 2520 because of missing or unreported data.


Table 4 Summary Statistics: Adult Health Measures Sample Size (N = 2520) Adult Health Measures

n

Mean (Âľ) Min Max

Sum of self-reported symptoms in the last 30 days (Total = 22) " " for males (Total = 20) " " for females (Total = 22) Self-classified health rank (10 = very good, 1 = very bad) Prevalence of hypertension (SBP/DBP > 140/90 mmHg) Prevalence of hypotension (SBP/DBP < 110/60 mmHg) Body Mass Index (BMI) Malnutrition (BMI < 21) Peak flow (ml per expiration) Respiratory problems (Peak flow < 350 ml/expiration) Prevalence of anemia (Hemoglobin < 12 g/dl) Body temperature (in Celsius) Time to squat and stand up 5 times (s) Tested for TB Diagnosed with TB First diagnosis of TB in past year Treated for TB Cough during past 30 days Still suffering from cough Height (cm)

2520 3.77 0 22 44 4.11 0 19 1097 3.85 0 21 2080 5.96 1 10 2520 0.21 0 1 2520 0.23 0 1 2394 18.12 11 155.4 1097 0.86 0 1 2310 294.32 83 636.7 2520 0.67 0 1 2520 0.43 0 1 2389 35.82 11 65.4 1744 8.53 3 65.9 2514 0.05 0 1 129 0.47 0 1 60 0.22 0 1 60 0.62 0 1 2519 0.29 0 1 625 0.33 0 1 2400 157.44 51 211

Weight (kg)

2403

44.70

20

165

Note: Sample sizes for each variable may be less than 2520 because of missing or unreported data.


Table 5 Summary Statistics: Public Health Facilities Sample Size (N = 177) % Community Health Centers 4.52 % Public Health Centre 15.25 % Aidpost 5.65 % Subcentre 52.54 % Addresses Family Planning 85.80 % Addresses Immunizations/Vaccinations 53.09 % Addresses STDs/RTI (Reproductive Tract Infections) % Addresses General Health % Addresses Eyes/Vision

83.95 69.75 45.68

Public Health Facility Characteristics No. of villages facility serves No. of people facility serves Distance of Medical Officer's Home from Facility Distance of facility from PHC that heads clinic

n 103 138 85 103

Mean (Âľ) 3.47 15896.59 2.53 13.37

Min 1 933 0 1

Max 10 353043 36 65

Patient uses transport on foot to travel to nearest facility Staff members treat patients in their homes Facility Follows Regular Hours

177 176 21

0.16 0.48 0.90

0 0 0

1 1 1

Note: Sample sizes for each variable may be less than 177 because of missing or unreported observations.


Table 6A Effect of Proximity to a Public Health Facility on Child Health Outcomes 1 2 3 4 5 OLS OLS OLS Probit Probit

Outcome Variable Public health facility, 1-2 km

Immunization Score

Time to squat Self-reported and stand 5 Tested for Prevalence health times (s) TB of anemia

6 OLS

Months Breastfed

-1.093** (0.314) -1.315** (0.452) -1.325** (0.426) 2.042 (1.604)

-0.385 (0.421) -0.491 (0.407) -0.322 (0.461) 6.933** (2.248)

86.26+ (47.51) 150.5** (44.58) 89.36* (40.40) 1264.5** (263.7)

-0.00607 (0.00566) -0.00460 (0.00488) -0.00608 (0.00579) -0.0103+ (0.00576)

0.0567 (0.0812) 0.155+ (0.0791) -0.0612 (0.0591) 0.937* (0.461)

0.257 (1.547) 1.424 (1.058) -0.324 (0.982) 15.56* (6.390)

Controls for socioeconomic characteristics

X

X

X

X

X

X

Controls for public facility characteristics

X

X

X

X

X

X

640 0.172

473 0.004

483 0.339

480 -0.013

473 0.082

483 0.004

Public health facility, 2-3 km Public health facility, > 3 km Constant

Observations R-squared

Notes: Coefficients report results from OLS regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 6B Effect of Proximity to a Private Health Facility on Child Health Outcomes 1 2 3 4 5 OLS OLS OLS Probit Probit

Outcome Variable Private health facility, 1-2 km Private health facility, 2-3 km Private health facility, > 3 km Constant Controls for socioeconomic characteristics Observations R-squared

Immunization Score

Time to squat Self-reported and stand 5 Tested for Prevalence health times (s) TB of anemia

6 OLS

Months Breastfed

-0.395+ (0.201) 0.0435 (0.440) -0.531+ (0.310) -1.057 (0.929)

0.176 (0.295) -0.0496 (0.346) -0.560 (0.373) 5.220** (1.196)

-23.06 (29.73) 13.65 (90.38) -19.47 (31.28) 1169.2** (143.9)

-0.00671** (0.00241) 0.0166 (0.0245) -0.00813** (0.00269) -0.0920* (0.0414)

0.0346 (0.0731) 0.0181 (0.186) 0.0287 (0.0915) 0.302 (0.244)

0.437 (0.733) 0.833 (1.024) 0.870 (0.815) 21.29** (3.357)

X 2640 0.094

X 1877 0.013

X 2147 0.297

X 2181 0.005

X 1886 0.022

X 2002 0.027

Notes: Coefficients report results from OLS regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 7 Effect of Distance to Public Health Facility on Immunization Rates 1 2 3 4 5 6 7 OLS OLS OLS OLS OLS Tobit OLS Sample Further Agri. In Restricted to: Labor School Outcome Variable Immunization Score

8 OLS

Public health facility, 1-2 km

-0.678** -0.476+ -1.054** -1.121** -0.718+ -1.054** -1.031** -0.568 (0.257) (0.242) (0.331) (0.347) (0.401) (0.328) (0.334) (0.492)

Public health facility, 2-3 km

-0.821* -0.567+ -1.328* -1.407* -1.269+ -1.328** (0.327) (0.301) (0.492) (0.523) (0.633) (0.487)

-1.293* (0.487)

-1.031 (1.380)

Public health facility, > 3 km

-1.302** -0.994** -1.440** -1.478** -1.231* -1.440** (0.289) (0.266) (0.450) (0.467) (0.512) (0.446)

-1.284* (0.499)

-0.428 (1.487)

Controls for socioeconomic characteristics

X

Controls for public facility characteristics

X

X

X

X

X

X

X

X

X

X

X

X

Village Fixed Effects

X

Constant

2.808**

0.147

2.071

Observations

(0.227) 2433

(1.178) 2433

(2.101) 583

0.055

0.132

0.167

R-squared

2.800

2.799

(2.055) (2.616) 571 326 0.167

0.131

2.071

2.597

-3.623

(2.079) 583

(2.149) 583

(3.614) 583

0.174

0.342

Notes: Coefficients report results from OLS regressions. The tobit regressions have a lower limit of .0001 and upper limit of 6, and coefficients report marginal effects. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%)


Table 8 Effect of Distance to a Public Health Facility on Immunization Rates 1 2 3 4 5 Probit Probit Probit Probit Probit Outcome Variable Public health facility, 1-2 km Public health facility, 2-3 km Public health facility, > 3 km Log of Monthly Consumer Expenditures Household owns livestock Child is in school Household has electricity Household size Patient uses transport on foot to travel to nearest facility No. of villages facility serves Distance of facility from PHC that heads clinic Distance of Medical Officer's Home from Facility Constant Observations

BCG

DPT

OPV

Measles

Polio

-0.590** (0.196) -0.629* (0.272) -0.811** (0.285)

-0.637** (0.192) -0.825* (0.343) -0.834** (0.281)

-0.684** (0.210) -0.912** (0.348) -0.832** (0.274)

-0.704** (0.211) -0.977** (0.369) -0.902** (0.247)

-0.501** (0.160) -0.515* (0.211) -0.505* (0.239)

-0.0753 (0.152) 0.426 (0.392) 0.404** (0.147) 0.510+ (0.279) 0.105** (0.0386)

-0.0420 (0.157) 0.368 (0.400) 0.329* (0.140) 0.536+ (0.281) 0.109** (0.0378)

-0.0681 (0.156) 0.353 (0.428) 0.408** (0.137) 0.625* (0.285) 0.120** (0.0380)

-0.0732 (0.171) 0.301 (0.415) 0.478** (0.142) 0.472+ (0.244) 0.119** (0.0385)

-0.0909 (0.172) -0.0649 (0.220) 0.571** (0.131) -0.0299 (0.255) 0.148** (0.0414)

-0.281 (0.412) -0.0180 (0.0428)

-0.275 (0.412) -0.00988 (0.0449)

-0.351 (0.410) -0.00776 (0.0470)

-0.506 (0.344) 0.000731 (0.0433)

-0.390 (0.356) -0.0202 (0.0475)

-0.0106 (0.0157)

-0.0118 (0.0159)

-0.0169 (0.0158)

-0.0139 (0.0157)

-0.00512 (0.0138)

0.0107 (0.0257) -0.400 (1.058) 640

0.0189 (0.0263) -0.648 (1.121) 640

0.0281 (0.0279) -0.507 (1.124) 640

0.0357 (0.0291) -0.507 (1.256) 640

-0.0142 (0.0286) 0.540 (1.091) 640

Notes: Coefficients report results from OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village/hamlet level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 9A Effect of Distance to a Public Facility on Socioeconomic Characteristics 1 2 3 4 5 Probit Probit Probit OLS OLS

Outcome Variable Public facility, 1-2 km Public facility, 2-3 km Public facility, > 3 km Constant Observations R-squared

Child is in Household has Household owns school electricity livestock -0.173** (0.0628) -0.150 (0.102) -0.389** (0.0817) 0.124* (0.0556) 2626

-0.450* (0.200) -0.380 (0.273) -0.618* (0.257) -0.628** (0.171) 2626

0.424* (0.199) 0.279 (0.239) 0.260 (0.202) 1.306** (0.147) 2626

Log of Monthly Consumer Expenditures -0.162* (0.0779) -0.221* (0.102) -0.163+ (0.0850) 7.755** (0.0700) 2640 0.018

6 Probit

Spent > 500 rupees on Household Size illness -0.0960 (0.196) 0.161 (0.268) 0.244 (0.230) 6.511** (0.137) 2641 0.003

-0.260* (0.126) -0.369* (0.175) -0.209 (0.131) -0.282** (0.0961) 2626

7 Probit Primary income from agriculture 0.339 (0.318) 1.220** (0.414) 0.614+ (0.366) 1.644** (0.271) 2626

Notes: Coefficients report results from OLS regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 9B Effect of Distance to a Private Facility on Socioeconomic Characteristics 1 2 3 4 5 Probit Probit Probit OLS OLS

Outcome Variable Private facility, 1-2 km Private facility, 2-3 km Private facility, > 3 km Constant Observations R-squared

Child is in Household has Household owns school electricity livestock -0.114 (0.0946) 0.123 (0.340) -0.147 (0.195) -0.0467 (0.0382) 2626

-0.590* (0.300) -0.0671 (0.230) -0.442 (0.302) -0.914** (0.109) 2626

-0.202 (0.263) -0.715 (0.471)

1.543** (0.0844) 2489

Log of Monthly Consumer Expenditures -0.116 (0.0874) 0.0868 (0.126) -0.0156 (0.102) 7.633** (0.0358) 2640 0.002

6 Probit

Spent > 500 rupees on Household Size illness -0.0146 (0.291) -0.362 (0.506) -0.252 (0.264) 6.607** (0.107) 2641 0.000

-0.139 (0.172) 0.279 (0.400) -0.244 (0.261) -0.459** (0.0581) 2626

7 Probit Primary income from agriculture 0.588 (0.365) -0.736 (0.524) -0.213 (0.439) 2.006** (0.173) 2626

Notes: Coefficients report results from OLS regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. Observations are restricted to children 18 years old or younger. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 10A

Outcome Variable Public health facility, 1-2 km Public health facility, 2-3 km Public health facility, > 3 km Constant Controls for socioeconomic characteristics Controls for public facility characteristics Observations R-squared

Effect of Proximity to a Public Health Facility on Adult Health Outcomes 1 2 3 4 5 6 7 OLS OLS OLS Probit Probit Probit Probit SelfTime to squat Self-reported reported and stand 5 Tested for Respiratory Symptoms health times (s) TB Anemia Malnutrition Problems

8 Probit

Hypertension

0.211 (0.351) 0.262 (0.584) 0.419 (0.561) 0.309 (2.000)

-0.118 (0.297) -0.375 (0.277) -0.298 (0.352) 4.754** (1.242)

1.220** (0.390) 1.559** (0.353) 0.207 (0.282) 3.193 (3.047)

0.263* (0.119) 0.232+ (0.129) 0.0375 (0.134) -2.801** (0.679)

0.0670 (0.109) 0.125 (0.135) 0.0466 (0.129) -0.460 (0.515)

-0.0350 (0.0882) 0.0214 (0.149) 0.00270 (0.105) 2.417** (0.462)

-0.204 (0.147) -0.487** (0.155) -0.427* (0.171) 0.444 (0.815)

0.144 (0.0970) 0.196 (0.167) 0.292* (0.131) -2.005** (0.568)

X

X

X

X

X

X

X

X

X 617 0.002

X 526 0.054

X 425 0.215

X 2491

X 2496

X 2496

X 617

X 2496

Notes: Coefficients report results for OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%)


Table 10B

Outcome Variable Private health facility, 1-2 km Private health facility, 2-3 km Private health facility, > 3 km Constant Controls for socioeconomic characteristics Observations R-squared

Effect of Proximity to a Private Health Facility on Adult Health Outcomes 1 2 3 4 5 6 7 OLS OLS OLS Probit Probit Probit Probit SelfTime to squat Self-reported reported and stand 5 Tested for Respiratory Symptoms health times (s) TB Anemia Malnutrition Problems

8 Probit

Hypertension

0.160 (0.289) -0.717 (0.539) -0.271 (0.326) 3.128* (1.362)

-0.162 (0.205) 0.803* (0.310) -0.170 (0.290) 4.647** (0.902)

0.770 (0.594) -1.051** (0.383) 0.254 (0.552) 6.185** (1.667)

0.103 (0.160) -0.0824 (0.246) 0.101 (0.185) -2.592** (0.668)

-0.177 (0.146) 0.524 (0.473) -0.0958 (0.126) -0.342 (0.496)

0.197+ (0.118) -0.0484 (0.132) 0.221* (0.112) 2.393** (0.472)

-0.0629 (0.108) 0.495** (0.0851) 0.106 (0.0959) -0.427 (0.366)

-0.0174 (0.168) -0.146 (0.164) -0.00777 (0.171) -1.748** (0.551)

X 2511 0.012

X 2073 0.035

X 1739 0.104

X 2491

X 2496

X 2496

X 2496

X 2496

Notes: Coefficients report results for OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%)


Table 11 Effect of Distance to Public Health Facility on Tuberculosis Testing 1 2 3 4 5 6 7 Probit Outcome Variable Tested for TB Public health facility, 1-2 km 0.215* 0.261* 0.520* 0.452* 0.256* (0.105) (0.118) (0.218) (0.222) (0.120) Public health facility, 2-3 km 0.182 0.231+ 0.408+ 0.332 0.231+ (0.124) (0.129) (0.215) (0.224) (0.128) Public health facility, > 3 km -0.0268 0.0252 0.0720 0.000362 0.0119 (0.130) (0.134) (0.218) (0.236) (0.142) Private health facility, 1-2 km 0.106 0.103 0.0902 (0.156) (0.160) (0.159) Private health facility, 2-3 km -0.0891 -0.0817 -0.0580 (0.237) (0.245) (0.263) Private health facility, > 3 km 0.0391 0.0318 0.0922 (0.194) (0.186) (0.204) Constant -1.696** -2.802** -2.195 -1.894 -1.612** -2.596** -2.810** (0.0685) (0.679) (1.367) (1.435) (0.0522) (0.667) (0.685) Observations 2514 2506 616 595 2514 2506 2506 Controls for socioeconomic characteristics X X X X X Controls for public facility characteristics Sample restricted to households in agriculture

X

X X

8

9

0.478* 0.452+ (0.235) (0.236) 0.383 0.347 (0.238) (0.243) 0.0135 -0.0861 (0.216) (0.209) 0.326 0.310 (0.212) (0.208) 0.295 0.955 (0.806) (0.667)

-2.354 (1.443) 601 X

-2.139 (1.570) 580 X

X

X X

Notes: Coefficients report results for OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 12A

Outcome Variable

Effect of Distance to a Public Facility on Socioeconomic Characteristics 1 2 3 4 5 6 Probit Probit Probit OLS Probit Probit Log of Monthly Mother Father Consumer Household has Household Literate literate literate Expenditures electricity owns livestock

Public health facility, 1-2 km

-0.264* (0.109)

-0.919** (0.318)

-0.438** (0.127)

-0.214* (0.0980)

-0.700** (0.218)

0.608** (0.210)

-0.0692 (0.202)

0.598+ (0.339)

Public health facility, 2-3 km

-0.298+ (0.177)

-0.207 (0.441)

-0.386+ (0.205)

-0.268+ (0.136)

-0.374 (0.316)

0.465* (0.226)

-0.223 (0.271)

0.919+ (0.470)

Public health facility, > 3 km Constant

-0.282* (0.132) -0.474** (0.0989) 2497

-0.499+ (0.279) -1.868** (0.217) 2504

-0.365* (0.163) -0.766** (0.120) 2499

-0.213+ (0.115) 7.858** (0.0910) 2519 0.026

-0.687* (0.285) -0.254 (0.185) 2505

0.430+ (0.221) 1.193** (0.173) 2505

-0.0722 (0.217) 6.229** (0.136) 2520 -0.000

0.824* (0.366) 1.485** (0.298) 2505

Observations R-squared

7 OLS

8 Probit

Household size

Agriculture

Notes: Coefficients report results for OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)


Table 12B

Outcome Variable

Effect of Distance to a Private Facility on Socioeconomic Characteristics 1 2 3 4 5 6 Probit Probit Probit OLS Probit Probit Log of Monthly Mother Father Consumer Household has Household owns Literate literate literate Expenditures electricity livestock

Private health facility, 1-2 km

-0.263+ (0.146)

Private health facility, 2-3 km

0.404** (0.148)

Private health facility, > 3 km Constant

0.0272 (0.165) -0.658** (0.0588) 2497

Observations R-squared

-0.424 (0.379)

0.0721 (0.376) -2.150** (0.157) 2459

8 Probit

Agriculture

-0.236 (0.171)

-0.102 (0.0850)

-0.663* (0.284)

0.150 (0.250)

0.439 (0.412)

0.269 (0.262)

0.0656 (0.132)

-0.226 (0.209)

-0.480 (0.499)

-0.665 (0.581)

0.219 (0.195) -1.034** (0.0764) 2499

-0.0602 (0.111) 7.706** (0.0476) 2519 0.001

-0.339 (0.336) -0.615** (0.120) 2505

1.493** (0.102) 2399

0.192 (0.398) 1.885** (0.205) 2505

Notes: Coefficients report results for OLS regressions and marginal effects for probit regressions. Heteroskedasticity-robust standard errors are in brackets and are clustered at the village level. (+ Significant at 10%; * Significant at 5%; ** Significant at 1%.)

Health Outcomes  

Effect of Proximity to Public and Private Health Facilities on Health Outcomes: Examining the Health Care System in Udaipur District, India

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