Peru - Impact of Health Insurance on Access Use and Health StatusFinal 17 May 09

Impact of Health Insurance on Access to Health Services, Health Services Use, and Health Status in the Developing World

Case Study from Peru

Financed by

Global Health Financing Initiative The Brookings Institution

By

February 28, 2009

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Abstract This report examines the impact that Peru’s 8-year old Integral Health Insurance (SIS) has had on access to health services and out-of-pocket spending by its beneficiaries. It uses data from the Demographic and Health Survey (DHS), which contains two cross sectional samples, one for the year 2000 and another for the period 2004-2008 (with a sample spread over 5 years). It also uses data from the National Household Survey (ENAHO) consisting of a panel collected over the period 2002-2006. We implement a set of models describing the demand for and out-of-pocket spending on health care, as a function of health insurance status and other cofactors. To test and control for the potential endogeneity between the decision to enroll into a health insurance scheme and demand, we use a bivariate probit model, where the primary equation describes the probability of demand as a function of insurance affiliation and other cofactors.

We find that SIS reduces in an important way the likelihood that those insured will have to spend money out-of-pocket for health care. There is also evidence, albeit weak, that SIS reduces the chances that individuals who obtain health care will have to incur catastrophic out-of-pocket expenditures. There is no evidence, however, that SIS reduces out-of-pocket spending among those that do have to pay for care, although it may be attributable to the small sample size. There is no evidence either that SIS’s beneficiaries increase their consumption of health services over time, as they become familiar with the insurer’s benefits and procedures. SIS increases utilization for a variety of services, both preventive and curative. The biggest impact on utilization or curative services occurs in the case of formal treatment for diarrhea and acute respiratory infections for children under 5. A positive impact on utilization that is nearly as large also occurs for all other curative treatments and individuals, although that effect is greater for children. This may be a consequence of the greater emphasis that SIS places to the provision of child care. Among preventive services, the biggest positive impact of SIS on use is for immunizations, followed by growth monitoring. For a few but important services targeted to women, SIS coverage does not have an impact on use. Such is the case for pap smears and institutional deliveries.

From our analysis it is clear that SIS has achieved important gains for its beneficiaries, in terms of lower out-of-pocket spending and higher utilization of services. The important policy question to ask in this regard is whether these gains are worth the extra money that the government has spent on SIS. We estimate that SIS spends annually S/. 60 on each of its beneficiaries, or roughly 60 percent of what MINSA spends for each Peruvian. The question, then, is whether these additional resources are well spent to produce the observed increases in utilization and the reduction in out-of-pocket spending among SIS beneficiaries. Further refining this kind of cost-effectiveness analysis is beyond the scope of this report. However, it should be carried out to shed light on the convenience of further operating and expending SIS.

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Table of Contents 1 Introduction.........................................................................................................................................................1

1.1 Background ..................................................................................................................................... 1 1.2 Goals and objectives......................................................................................................................... 1 1.3 Structure of the report ....................................................................................................................... 1

2 Description of the Peruvian Health Sector........................................................................................................2 2.1 Overview......................................................................................................................................... 2 2.2 Health social security (EsSalud) ........................................................................................................ 3 2.3 EPS system...................................................................................................................................... 4 2.4 Integral Health Insurance (SIS).......................................................................................................... 5 2.5 Peru in comparison with selected LAC countries................................................................................. 5 2.6 Literature review of the impact of health insurance in Peru .................................................................. 8

3 Methodology ........................................................................................................................................................8 3.1 Sources of information...................................................................................................................... 8 3.2 Hypotheses .................................................................................................................................... 10 3.3 Empirical specifications.................................................................................................................. 10 3.4 Control variables ............................................................................................................................ 11 3.5 Control populations ........................................................................................................................ 12 3.6 Panel data: ENAHO 2002-2006....................................................................................................... 13

4 Results ................................................................................................................................................................13 4.1 Health insurance population coverage .............................................................................................. 13 4.2 ENAHO panel sample .................................................................................................................... 16 4.3 Profiles of the insured and uninsured populations.............................................................................. 16 4.4 Impact of SEG/SMI and SIS health insurances.................................................................................. 18

4.4.1 Probability of being fully immunized (children aged 18-59 months)..............................................18 4.4.2 Probability of receiving pap-smear exam in last 5 years (women 15-49)........................................19 4.4.3 Probability of having delivery attended by a skilled health personnel (women 15-49)...................20 4.4.4 Mean percentage of growth controls attended (children under 5) ...................................................20 4.4.5 Probability of being formally treated for diarrhea (children under 5) .............................................22 4.4.6 Probability of being formally treated for ARI (children under 5)....................................................22 4.4.7 Probability of seeking curative health care for symptoms, illnesses or relapses in last 4 weeks.....23 4.4.8 Probability of spending a positive amount among those receiving formal care in last 4 weeks......24 4.4.9 Amount spent by those with positive spending ...............................................................................25 4.4.10 Probability of spending on health more than 30 percent of total household expenditure, excluding

subsistence needs (extreme poverty line) ........................................................................................25 4.4.11 Impact of prolonged exposure to health insurance ..........................................................................26

4.5 Impact of EsSalud health insurance.................................................................................................. 27 5 Summary and conclusions................................................................................................................................29

6 Bibliography ......................................................................................................................................................32

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List of Figures Figure 1 General scheme of the health sector in Peru ............................................................................................2 Figure 2 Distribution of EsSalud’s insured population, 2005.................................................................................4 Figure 4 Health insurance population coverage, 2006..........................................................................................14 Figure 5 Population coverage of health insurance according to ENAHO (2002-2006) and DHS (2000,

2004-2007) surveys (95% CI shown).....................................................................................................14 Figure 6 Number of SIS beneficiaries, according to SIS administrative data and survey data.............................15 Figure 7 Regional distribution of ENAHO panel and non panel samples ............................................................16 Figure 8 Age and gender structure of health insurance beneficiaries, 2006 .........................................................17 Figure 9 Socioeconomic distribution of health insurance beneficiaries, 2006 .....................................................17 Figure 10 Predicted probability of receiving a pap-smear exam, by health insurance status and age ....................19 Figure 11 Predicted percentage of growth controls attended, by health insurance status and education

of the mother ..........................................................................................................................................21 Figure 12 Predicted probability of seeking curative health care, by health insurance status and age,

2004........................................................................................................................................................24

List of Tables Table 1 Comparison of health workers in three LAC countries ................................................................................6 Table 2 Comparison of seven LAC countries, 2005 and 2006 ..................................................................................7 Table 3 Sample sizes of ENAHO and DHS surveys (number of individuals interviewed) .......................................9 Table 4 Dependent variables and control variables .................................................................................................12 Table 5 Health insurance population coverage by region, 2006 ..............................................................................15 Table 6 SIS service production, 2002-2007.............................................................................................................15 Table 7 Impact of SEG/SMI and SIS on probability of being fully immunized (children aged 18-59

months) .......................................................................................................................................................19 Table 8 Impact of SEG/SMI and SIS on probability of receiving pap-smear exam in the last 5 years

(women 15-49) ...........................................................................................................................................20 Table 9 Impact of SEG/SMI and SIS on probability of having delivery attended by a skilled health

personnel (women 15-49) ...........................................................................................................................20 Table 10 Impact of SEG/SMI and SIS on mean percentage of growth controls attended (children under

5).................................................................................................................................................................22 Table 11 Impact of SEG/SMI and SIS on probability of being formally treated for diarrhea (children

under 5).......................................................................................................................................................22 Table 12 Impact of SEG/SMI and SIS on probability of being formally treated for ARI (children under

5).................................................................................................................................................................23 Table 13 Impact of SEG/SMI and SIS on probability of seeking curative health care for symptoms,

illnesses or relapses in last 4 weeks (all ages) ............................................................................................24 Table 14 Impact of SIS on probability of spending a positive amount among those receiving formal care

in last 4 weeks (all ages) .............................................................................................................................25 Table 15 Impact of SIS on amount spent by those with positive spending, in Soles (all ages) .................................25 Table 16 Impact of SIS on probability of spending on health > 30% of total household expenditure,

excluding subsistence needs (extreme poverty line)...................................................................................26 Table 17 Demand model results replacing health insurance dummy with four time of exposure variables ..............27 Table 18 Utilization of health services, by type of health insurance, 2005 and 2006................................................28 Table 19 Monthly out-of-pocket expenses on selected ambulatory services, by quintile and type of health

insurance, 2006...........................................................................................................................................29 Table 20 Summary of impact indicators of SEG/SMI and SIS health insurances (%) ..............................................30

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1 Introduction 1.1 Background

The policy relevance of the impact of health insurance on health status, access and utilization of health services in developing countries is compelling. First, practically all developing countries in Latin America and the Caribbean (LAC) and Asia are implementing health insurance schemes targeted to the poor. Second, the experience in developed countries shows that health insurance has a positive impact on the health status of populations and on the performance of health systems. In particular, health insurance reduces morbidity and mortality and improves access to health services (Hadley, 2002).

This paper is a country case for the study Impact of Health Insurance on Access to Health Services, Health Services Use, and Health Status in the Developing World. The Brookings Institution’s Global Health Financing Initiative selected Bitrán & Associates to study the case of Peru. We concentrate our study on two of the main health insurances in Peru: the publicly-subsidized health insurance known as SIS (Seguro Integral de Salud) and the health social security (EsSalud). We study the impact that these two health insurances have had on three variables, namely health status, access to health services and out-of-pocket expenditures. We use household survey data from the National Household Surveys (Encuesta Nacional de Hogares –ENAHO) and the Demographic and Health Surveys (DHS).

1.2 Goals and objectives

The main goal of this research is to analyze the impact of two health insurances in Peru −SIS and EsSalud− on health status, access to health services and out-of-pocket expenditures, and to provide supporting evidence for the implementation of basic benefits packages targeted to the poor as well as coverage expansion in the developing world.

The specific objectives are the following:

1. Present methods that address the common estimation problems, mainly endogeneity. 2. Answer the following research questions

• Does health insurance improve health status? • Does health insurance increase the likelihood of seeking care? • Does health insurance increase the likelihood of receiving care? • Does health insurance reduce out-of-pocket expenditures? • Does health insurance have a relatively more important impact on health care seeking

behavior and utilization among the poor? 3. Compare results between models that properly deal with endogeneity and models that do not. 4. Draw lessons for the use of similar household data in other countries. 5. Draw policy implications for developing countries with similar health social security sectors

and basic benefits package targeted to the poor.

1.3 Structure of the report

Chapter 2 presents a general description of the Peruvian health sector, health social security (EsSalud) and Integral Health Insurance (SIS), and a literature review of studies of the impact of health insurance in Peru. Chapter 3 presents the methodology to estimate impact. Chapter 4 presents the results. Chapter 5 concludes.

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2 Description of the Peruvian Health Sector Health insurance can take many forms and so it is important to understand the context in which

they function. In this case study of Peru we concentrate in two insurance schemes: health social security and the publicly subsidized health insurance targeted to the poor. This section begins with a general overview of the Peruvian health sector, and then describes in detail the two selected insurance schemes. The last section briefly presents the how these schemes compare to others in LAC countries.

2.1 Overview

Peru has a mixed health system that includes a health social security, a public sector, a private sector and a National Police and Armed Forces sector. Figure 1 shows a general scheme of Peru’s health sector with its financial and health service flows.

Figure 1 General scheme of the health sector in Peru Central Government

General and specific taxes

Health budget

EsSalud providers

MINSA beneficiaries

(entire population)

SIS beneficiaries (poor, women, children and other specific

groups)

Ministry of Health

(MINSA)

Social Security

(EsSalud)

Private Providers

Private insurance companies

Integral Health Insurance

(SIS)

MINSA providers

Health Providing Institutions

(EPS)

National Police and Armed Forces

(members, workers and their families

Voluntary: 25% of 9% of salary

National Police and Armed Forces

National Police and Armed

Forces providers

EsSalud beneficiaries (formal sector workers and their families)

Copay‐ments

Non poor population

Premiums Copay‐ments

9% of salary

Semi‐subsidized premiums

Financial FlowHealth services

EPS provider network

Source: Authors.

The financing sources for the health system are: the Ministry of Health (MINSA); el Health Social Security (EsSalud);1 the Health Providing Institutions (Entidades Prestadoras de Salud −EPS) system; the Integral Health Insurance (Seguro Integral de Salud –SIS); private health insurance companies, households and donors. The sector’s main source of financing is the private sector. The central government’s tax revenues allocated to the health sector is the second largest source. A third financing source is the premiums individuals and employers pay to private insurance companies, EsSalud

1 Until 1996 EsSalud was the Peruvian Social Security Institution (IPSS).

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and EPS. The EPS is a system designed to complement EsSalud by covering mainly low complexity care (see section 2.3).

The main insurers in the system are: EsSalud (including EPS); SIS; and private insurance companies. The MOH is a financing agency, not an insurer and covers, by law, the entire population. In 2005, EsSalud covered approximately 17% of the population (Portocarrero et al, 2007). In 2005, SIS covered 15% of the population, of which two thirds are in the two poorest income quintiles (Portocarrero et al, 2007). The National Police and Armed Forces cover approximately 1.6% of the population, while the private sector covers 1.7%.

Health service providers in the system are: MOH providers (all levels); EsSalud providers (all levels); private providers (all levels); EPS (mainly low complexity providers), and National Police and Armed Forces providers. In principle, the MOH covers all health services, but in practice, MOH population faces rationing through waiting times and copayments. The private sector offers different health plans with copayments, deductibles and ceilings.

We concentrate our analysis on EsSalud and SIS over the other insurance schemes because they represent most of the population with health insurance coverage. We present a short description of EsSalud and SIS below.

2.2 Health social security (EsSalud)

EsSalud’s beneficiaries are mainly formal sector workers and their families. EsSalud has three types of affiliates: regular, retired, elective, and subsidized (see Figure 2). Regular affiliates are formal sector workers and their employers make a mandatory income-based contribution of 9% to EsSalud. Retired affiliates make a pension-based contribution of 4% to EsSalud. Subsidized affiliates are agrarian sector workers, fishermen and home workers.2

EsSalud has an explicit health service “minimum package” (Plan Mínimo de Atención3) of 752 diagnoses, which makes it a very comprehensive package. The package includes:

Preventive care, promotional care and curative care; Welfare and social promotion services; Subsidies for temporal disability and maternity; and Burial services.

The comprehensive benefits package of EsSalud is the same for all affiliates except elective affiliates, who have a reduced benefits package, and must pay a premium based on the health plan they choose. Voluntary affiliates of EsSalud may also be voluntary affiliates to EPS. In fact, anyone can buy health insurance from EsSalud and EPS. Yet, only in the case of formal workers does the EPS substitute services covered by EsSalud.4

2 The subsidized scheme was implemented in phases: for fishermen it began in 1997; for agrarian sector workers in 2002; and for home workers in 2005. 3 Decreto Supremo N° 009-97-SA Reglamento de la Ley de Modernización de la Seguridad Social de Salud, 1997, Anexo 2, Plan Mínimo de Atenciones. 4 ENAHO and DHS surveys do not allow us to identify what type of affiliates the individual is. The survey only asks whether they have insurance.

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Figure 2 Distribution of EsSalud’s insured population, 2005

Regular, 75%

Retired, 17%

Elective, 3%

Subsidized (Agrarian,

Fishermen, Home workers, and others), 5%

Source: EsSalud’s Institutional Annual Report, 2005

EsSalud must provide services to cover at least this package through its provider network or using other health service providers.

2.3 EPS system

In 1997, the government created the EPS system to alleviate the demand for low complexity care in EsSalud provider network.5,6 The EPS system covers mainly low complexity care services, although some high complexity care may be included in the health plan. EPS are public, private or mixed companies that offer care from their own low complexity providers, from contracted private sector providers, or both, under the regulation of the Superintendence of EPS (SEPS). Formal sector workers and employers choose to affiliate to EPS to receive primary care from private sector providers, in an attempt of receiving better and faster care.

Until April 2008, there were four EPSs in the system. By the end of 2007, 325 health service providers had contracts with EPS health plans, including: hospitals, specialized institutes, medical centers, and medical doctors’ offices among others. By the end of 2007 there were almost 800,000 affiliates an beneficiaries in the EPS system.

Employers have the option of assigning one fourth of the EsSalud contribution to EPS. The process is that an employer offers its workers the choice of affiliating them to an EPS, elections are held to choose an EPS. Employers then negotiate with EPS the health benefits plan for its workers. As in the case of EsSalud, EPS offer elective health plans with the associated premium.

EPS offer health plans that cover, at a minimum, Simple Tier Health Problems,7 although they may also include some Complex Tier Health Problems.8 In the case of dependent workers, all care not covered by the EPS is covered by EsSalud. The health plans may include copayments with a maximum of

5 Ley de Modernización de la Seguridad Social en Salud N° 26790 (17 mayo, 1997). 6 The creation of the EPS system included the Superintendence of EPS (Superintendencia de Entidades Prestadoras de Salud −SEPS). 7 The Simple Tier Health Problems (Enfermedades de Capa Simple) are those considered common and frequent, and may be addressed through ambulatory care or one day surgery. 8 The Complex Tier Health Problems (Enfermedades de Capa Compleja), are those considered complex and infrequent, and that require greater drug care, equipment, hospitalization and long treatments.

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2% of the affiliate’s income for ambulatory care and a maximum of 10% for hospitalization. Furthermore, the plan must include workplace accident coverage and work-related health problems coverage when required, and it may not exclude care for preexisting health problems.

Finally, there are two main differences between EsSalud and EPS: (1) services covered; and (2) contract tools. EsSalud covers health care services for all complexity levels as well as economic and social subsidies, while EPS’s cover low complexity level care. On the other hand, EPS’s manage their contracts with copayments, limits and exclusions, and reinsurance, while EsSalud cannot use these tools.

2.4 Integral Health Insurance (SIS)

SIS began in 2001 by merging of two health insurance programs: the health insurance for children in public schools (Seguro Escolar Gratuito) and the maternal and child health insurance (Seguro Materno Infantil). It is a targeted, fully subsidized health insurance. SIS is a decentralized agency under MINSA and that finances an explicit benefits package for its affiliates. The Ministry of Economics and Finance (Ministerio de Economía y Finanzas −MEF) allocates resources directly to SIS from general taxes.

The population eligible to become a SIS beneficiary is explicitly defined and targeted. Individuals must go to the nearest health facility and request affiliation by presenting their national identification card. SIS representatives then apply the Socio-Economic Evaluation Form (or Ficha de Evaluación Socio Económica) to determine if they are extreme poor, poor, or non poor. Affiliation is automatic if categorized as poor or extreme poor, and a contract is signed by the beneficiary and the SIS representative. SIS expanded its target population to include all poor families and has also incorporated a semi-subsidized plan for families that can pay a small premium. The semi-subsidized insurance offers the same benefits package but with a monthly premium payment that varies between S/.10.00 (US$3.50) to S/.30.00 (US$10.00) depending on their income and whether it’s an individual or a family. This semi-subsidized insurance was implemented more recently. SIS has also increased its coverage to other specific groups like motorcycle taxi drivers, victims of political violence, and victims’ families of the La Cantuta Case.9

SIS offers a benefits package includes preventive care, ambulatory care and selected surgeries for 23 health problems. Its beneficiaries seek care from MINSA’s health service provider network and do not make any copayments, since SIS offers full financial coverage. In turn, SIS pays MINSA providers for these interventions –based on a price list previously agreed upon with MINSA– through a fee for service mechanism to cover only variable costs of care.10

SIS administrative data shows that the number of affiliates in January 2008 relative to January 2007 increased by 104% (from 3.6 million to 7.3 million) (see www.sis.gob.pe). In June 2007, SIS affiliation covered approximately 33.1% of its target population (based on ENAHO 2004 incidence of poverty). The current government has pushed SIS to increase its efforts in affiliating poor and extremely poor families.

2.5 Peru in comparison with selected LAC countries

We selected six countries in LAC to compare with Peru based on whether their health social security sectors are similar to the one in Peru, or if they have health insurance programs similar to SIS. LAC countries that have similar health social security systems are: Bolivia, Paraguay, Dominican

9 The La Cantuta Case took place in Peru in 1992 during the presidency of Alberto Fujimori. A university professor and nine students from Lima’s La Cantuta University were abducted and “disappeared” by a military squad. 10 SIS is considering using a capitation payment mechanism to providers in the near future. Currently the fee is expected to cover only the variable costs of care. Notice that MINSA providers have an incentive to help SIS affiliate more people, since MINSA providers receive an “extra” payment for the care provided to SIS beneficiaries above what they generally receive which is based on historic budgets.

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Republic, El Salvador, Guatemala, Honduras and Nicaragua. Bolivia and Argentina have insurance schemes similar to SIS.

Table 2 shows some comparative data for Peru and the selected countries: Argentina, Bolivia, Chile, Dominican Republic, El Salvador and Guatemala. Peru is a medium size country in terms of population and it has similar demographics as other countries in the region. The population growth rate in Peru is 1.1% and most of the population lives in urban areas (73%). About one quarter of the population is women in fertile age, and 10.4% are children under 5 years of age.

Bolivia has the Universal Mother and Child Insurance (Seguro Universal Materno Infantil –SUMI) that started in 2003. SUMI is part of the government’s poverty reduction strategy and aims at reducing maternal and child mortality and morbidity. It covers 500 services for children under the age of 5 and for pregnant women including 6 months after delivery. This insurance is free of charge for beneficiaries and it is mandatory. Public and social security health providers as well as contracted private providers must provide the services included in SUMI.

Argentina has the Birth Plan (Plan Nacer) which is a national program for pregnant women and children under the age of six who do not have health social security or prepaid plans. Women and children only have to register and services are provided through the public providers network. The plan covers the following services that are free of charge for beneficiaries: pregnancy test, prenatal care, dental exam, blood and urine exams, immunizations, two sonograms, neonatal care, information dissemination, well baby care, and referrals.

In comparison to other LAC countries, Peru has one of the lowest health expenditures as percentage of GDP in the region. Government expenditure on health is 57% of total expenditure on health. This is also very similar to the other countries, where government expenditure fluctuates between 38% in Guatemala and 63% in Bolivia. This shows the high importance of the public sector in the region. Finally, this table also shows the role of health social security; as a percentage of general government expenditure, health social security fluctuates between 23% in Dominican Republic to 76% in Bolivia.

Finally, the last group of indicators in Table 2 shows that −with the exception of Chile− maternal and child mortality rates are high in the region.

Table 1 compares health worker supply data in Peru, Chile and Bolivia. Although the total number of health workers in Peru is large in comparison to Bolivia and Chile, when considered relative to population, sizes are very similar. The availability of doctors across these three countries is very similar, fluctuating between 10.9 and 12.2 physicians per 10,000 people.

Table 1 Comparison of health workers in three LAC countries Area Bolivia Peru Chile

Supply of professionals Health workers: 40,000. Professionals 39%, technicians 5%, aides 24%, administrators 17%, service workers 15%.

Health workers: 140,000. Doctors 17%, nurses 16%, midwives 3%, dentists, 7%, technicians and aides 36%, administrative staff 16%

Health workers: 69,000. Doctors 13%, paramedics 33%, administrators 32%, non-professionals (including some nurses and midwives) 16%, dentists 2%, others 4%.

Number of core health workers (WHO)

Year: 2001. Physicians 10,329. Nurses 18,535. Dentist 5,997



Density per 10.000 population of core health workers (WHO)

Year: 2001. Physicians 12.2. Nurses 21.9. Dentist 7.1

Year: 1999. Physicians 11.7. Nurses 6.7. Dentist 1.1

Year: 2003. Physicians 10.9. Nurses 6.3. Dentist 4.3.

Source: Modified from Urcullo et al 2008.

Most health systems in LAC region have a public sector that finances and provides health care to the poor and uninsured. Within the public sector there are targeted programs or insurance schemes similar to SIS that cover selected health care services to mothers and children. For example, Bolivia has SUMI and Argentina has Birth Plan.

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Table 2 Comparison of seven LAC countries, 2005 and 2006

Indicator Bolivia Guatemala El

Salvador Dominican

Republic Peru Argentina Chile Demographic data (2006) a, b Population (in millions) total 9.4 13.0 6.8 9.6 27.6 39.1 16.5 Population annual growth rate (%) 1.9 2.5 1.4 1.5 1.1 1.0 1.0 Population in urban areas (%) 65 48 60 68 73 90 88 Women in fertile age (15-49 years of age, % total population) 24.7 23.9 26.6 25.9 26.4 25.1 26.9 Children under 5 years of age (% total population) 13.6 16.0 11.7 11.7 10.4 8.6 7.6 Gross Domestic Product (GDP) (2006) c GDP (current US$) (billions) 11.5 30.2 18.7 31.9 92.3 214.2 146.4 GDP growth (annual %) 4.8 5.3 4.2 10.7 7.7 8.5 4.3 GDP per capita (current US$) 1,224 2,320 2,759 3,316 3,346 5,475 8,894 Health Expenditures (2006) a Total expenditure on health as percentage of gross domestic product 6.6 5.3 7 6 4.3 10.1 5.3 Per capita government expenditure on health (PPP int. $) 128 98 227 140 171 758 367 Per capita government expenditure on health at average exchange rate (US$) 50 54 112 70 83 251 249 Per capita total expenditure on health (PPP int. $) 204 259 387 449 300 1665 697 Per capita total expenditure on health at average exchange rate (US$) 79 144 191 223 145 551 473 Health financing (2006) a General government expenditure on health as percentage of total government expenditure 11.6 14.7 15.6 9.5 13.1 14.2 14.1 General government expenditure on health as percentage of total expenditure on health 62.8 37.7 58.7 31.2 57.1 45.5 52.7 Social security expenditure on health as percentage of general government expenditure on health 75.8 45.4 47.7 22.7 41.8 58.5 67.2 Out-of-pocket expenditure as percentage of private expenditure on health 81 91.4 90.3 79.5 77.5 43.8 54.8 External resources for health as percentage of total expenditure on health 3.6 1.3 3.1 1.7 1.6 0.1 0.1 Private expenditure on health as percentage of total expenditure on health 37.2 62.3 41.3 68.8 42.9 54.5 47.3 Private prepaid plans as percentage of private expenditure on health 10.2 3.1 9.7 14.4 19 51.1 45.1 Health indicators a Maternal mortality ratio (per 100 000 live births) (2005) 290 290 170 150 240 77 16 Infant mortality rate (per 1 000 live births) (2006) 50 31 22 25 21 14 8 Life expectancy at birth (years) (2006) 66 68 71 70 73 75 78 Sources: a WHO World Health Statistics 2008; b Economic Commission for Latin America and the Caribbean (ECLAC); c World Bank World Development Indicators 2008.

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2.6 Literature review of the impact of health insurance in Peru

There are very few studies on the impact of insurance in Peru. In fact, there is only one study available which addresses the question of the impact of health insurance on utilization (Jaramillo and Parodi, 2004). We review this study below. The remaining literature consists of studies on descriptive analysis and equity analysis of the health system. No literature was found that studies the impact of social security health insurance in Peru.

Jaramillo and Parodi (2004) used the Living Standards Survey 2000 (Encuesta de Niveles de Vida–ENNIV) and the DHS 2000 to perform three analyses on the impact of the school-aged children’s health insurance (Seguro Escolar Gratuito−SEG) and the mother-child health insurance (Seguro Materno Infantil−SMI): (1) affiliation by socioeconomic levels; (2) targeting errors; and (3) access to health services.

The authors perform three tests of endogeneity of the affiliation to SEG and SMI relative to the decision to seek care, and argue that neither insurance scheme appears to be endogenous, although the argument for SEG not being endogenous is stronger than for SMI. In particular, SEG affiliation is based on public school enrollment while SMI affiliation is by choice. One of the three endogeneity tests for SMI indicated some level of endogeneity in the affiliation variable.

Jaramillo and Parodi (2004) find a positive impact of SEG on health service seeking behavior and a positive impact of SMI on utilization of prenatal care visits. SEG increases the probability of seeking care on average by 17% given there is a self-reported health problem, while SMI increases the utilization of prenatal care visits by a little over 8%.

Impact results of both insurance schemes differ by income quintiles. In the case of SEG, there is a positive impact on health care seeking behavior on all income quintiles, with the exception of first income quintile (poorest). Furthermore, there is no statistically significant difference in utilization between children with SEG and those without SEG coverage among the first quintile, which suggests that this group faces other access barriers not under study.

In the case of SMI, only the fifth income quintile shows a positive and statistically significant impact on having at least four prenatal care visits. This result may be a consequence that the analysis was done shortly after its implementation.

The lack of papers that study the impact of health insurance in Peru means that our study will fill in an important gap in the literature for the Peruvian health sector. Although the Jaramillo and Parodi (2004) study provides important insights regarding the impact of insurance, it does not consider the case of the health social security sector. Furthermore, since we will analyze a panel data we provide a new approach to study the effects of health insurance on health care seeking behavior in Peru and to better address the endogeneity of the health insurance affiliation variable.

3 Methodology 3.1 Sources of information

This study uses two household surveys: the DHS surveys in 2000 and 2004-2008 (ongoing), and the ENAHO panel survey 2002-2006. The DHS surveys started in 1986, and have since been replicated approximately every 5 years. The DHS 2000 data was collected over a period of 3 months, covering 130,770 individuals. In 2004 the survey design changed, and the interviews were spread over a 5-year period, from 2004 to 2008 (see Table 3). At the time of this study, only a preliminary dataset was available, covering years 2004-2007 for household and women data and years 2004-2005 for children data. Also, the preliminary dataset did not include the key variable necessary to match children records with their insurance status in the household roster. We had to rely on age and gender variables to identify

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and match children’s insurance status. This matching was not possible in households where two children had the same age and gender, and the cases had to be dropped.

The ENAHO has traditionally been an annual repeated cross-section survey. However, in 2003, the National Institute of Statistics (Instituto Nacional de Estadísticas -INE) decided to implement a panel subsample for follow up every year. The 2003 panel subsample, consisting of 6,500 households, was extracted from the 2002 sample. To date, we have data available until 2006, so we could build a 5-year panel database from 2002 to 2006. Unlike the DHS, the ENAHO maintains a similar sample size each year. However, the panel subsample (individuals followed over the five-year period) represents only 26-30 percent of the complete ENAHO sample. Both surveys overlap in three years: from 2004 to 2006.

Table 3 Sample sizes of ENAHO and DHS surveys (number of individuals interviewed) Survey\Year 2000 2001 2002 2003 2004 2005 2006 2007 ENAHO complete sample 82,761 82,509 86,132 85,586 88,627 ENAHO panel sample 24,148 22,978 23,652 22,719 23,327 DHS 130,770 27,712 28,402 29,823 29,160 Source: ENAHO and DHS.

The DHS includes self-reported health shock variables, namely child Acute Respiratory

Infections (ARI) and diarrhea in the last 2 weeks. The survey then captures information on the demand for health services to treat these problems, but does not capture information on out-of-pocket expenditures. It also includes information on the use of preventive health services, such as prenatal control, assisted deliveries, growth controls, vaccinations, etc.

The ENAHO health shock variables are also self-reported, but less specific than in the DHS. The severity or diagnosis of illnesses or accident is not captured, which makes it harder to trace the impact of specific interventions covered by the health insurances. Also, the use of preventive services is asked in a too short period of time (3 months), which is insufficient to determine the result of preventive activities requiring longer periods of exposure. For example, immunization occurs during the first two years of life, prenatal controls occur during the span of several months, and growth controls do so during several years. However, the ENAHO adds information on out-of-pocket expenditures for health services.

Both surveys include different socioeconomic level variables. The DHS uses the Wealth Index, which is a combination of many household variables, namely dwelling characteristics, durable goods and assets. The ENAHO has information on income, consumption and assets.

Health insurance enrollment information is similar in both surveys. In the DHS household roster, the types of health insurance of each household member are registered as follows:

Question 1: Does [NAME] have health insurance? Question 2: (If yes, ) what type of health insurance (EsSalud/IPSS, Police and Armed Forces,

SIS, EPS, Private insurance)? Up to three insurance plans may be registered for each individual.

Question 3: Is [NAME] the main beneficiary of any of these health insurance plans?

In the ENAHO, the questions are similar:

Question 1: Which health insurance system are you enrolled in (EsSalud/IPSS, Police and Armed Forces, SIS, EPS, Private insurance, University insurance, Private school insurance, Other, Not insured)? More than one option may be registered.

Question 2: Who pays the contributions for enrollment in…?

10

3.2 Hypotheses

People affiliate to health insurance with the final goal of improving their health. So, the first obvious hypothesis is: Individuals with health insurance have better health compared to the uninsured. Yet, we also know that health insurance per se does not improve health, and that instead it does so through consumption of medical care. Since the neither the DHS 2004-2008 or ENAHO provide information on health status (they provide information on self-reported health shocks instead), we explore the impact of health insurance on consumption of medical care for the following interventions with proven efficacy, available from the DHS:

Child immunizations Preventive services to detect cancer (pap-smear exam) Deliveries attended by a skilled health personnel Child growth controls Treatment of diarrhea and ARI cases

Health insurance has a direct impact on demand for medical care. Thus, the second hypothesis is: Individuals with health insurance are more likely to seek formal care. The analysis of the impact of health insurance on the consumption of the preventive and curative services listed above allows testing part of this hypothesis, but is restricted to those services listed. Thus, we use the ENAHO survey, which captures information on any the demand for health care for any type of illness or accident.

There are important differences between the individuals affiliated to EsSalud and those affiliated to SIS. For example, SIS is targeted to the poor, while EsSalud is for dependent workers. To the extent that poor individuals are more likely to fall ill and more likely to face accessibility problems than non poor individuals, the impact of health insurance should be greater for the poor.

Even if health insurance increases the likelihood of seeking care, it does not necessarily guarantee that the individual will actually receive care. The utilization of care depends on the demand for and supply of health care. SIS uses MINSA’s provider network, while EsSalud uses its own provider network. The third hypothesis is: Individuals with health insurance are more likely to receive care.

Finally, health insurance protects individuals from financial costs of using medical care. As mentioned earlier, EsSalud and SIS both cover all costs associated with health care, although SIS covers a limited number of services. The fourth hypothesis is that: Individuals with health insurance spend less on medical care than the uninsured.

3.3 Empirical specifications

To analyze the impact of SIS, and its predecessor SEG/SMI, we implement a set of models describing demand and spending in health, as a function of health insurance status and other cofactors. In all specifications, we are interested in the coefficients related to insurance affiliation. To test and control for the potential endogeneity between the decision to enroll into a health insurance scheme and demand, we use a bivariate probit model, where the primary equation describes the probability of demand as a function of insurance affiliation and other cofactors (Waters, 1999; Jaramillo and Parodi, 2004):

( )

( )

( ) ondistributi normal edstandardiz:sticscharacteri observed exogenous ofvector :

naffiliatio insurancehealth ofindicator :demand variabledependent :

:)(1Pr

⋅Φ

+Φ==

XIY

whereXIY βα

11

And the second equation, also called reduce form equation, describes the probability of affiliating to health insurance:

( )

( )

insurancehealth for variablesgidentifyin ofvector :sticscharacteri observed exogenous ofvector :

naffiliatio insurancehealth variabledependent ::

)(1Pr

ZXI

whereZXI βα +Φ==

Vector Z includes a set of one or more identifying variables for health insurance, which fulfill two basic conditions: having significant impact on the probability of affiliating to health insurance, and having no significant impact on demand. We explored several candidate identifying variables in both surveys, including location and household head characteristics. Although many of these variables were significant determinants of health insurance, only one met the condition of having no impact on the dependent variable in the primary equation: lagged health insurance status in the ENAHO panel. Thus, we were unable to test and control for endogeneity in the DHS survey.

In the ENAHO panel, we implemented two endogeneity tests (Waters, 1999). First, we assessed the statistical significance of the rho value in the bivariate probit model, which indicates the presence of correlation between the error term in the demand equation and health insurance. If there is a correlation, then health insurance must be determined by unobservable factors which also determine demand, generating endogeneity. The second endogeneity test consists in inserting a variable with the predicted values of the reduced form equation in the right-hand side of the demand equation (in addition to the actual health insurance status variable). Endogeneity appears if the variable with the predicted values results statistically significant in the demand equation. When endogeneity tests positive, the bivariate probit model delivers an estimate of the impact of SIS which corrects endogeneity. When endogeneity tests negative, we use a univariate probit model for the primary demand equation only. In the case of the DHS survey, where we were unable to test endogeneity, we implemented a logit estimation of the primary demand equation.

To model spending, we implemented a two-stage model. In the first stage, we use a probit regression to model the probability of spending any positive amount, conditional on having utilized health services. In the second stage, we keep only the sample of those spending positive amounts when utilizing health services, and run an OLS regression to model the amount spent (logarithmically transformed).

3.4 Control variables

Table 4 shows the model specification for each dependent variable, indicating which control variables were included in the regressions. To explore whether the effects of health insurance vary between certain population groups, we included interactions terms between the health insurance dummy and the following variables: gender, age, years of education (of patient or patient’s mother, depending on the age of the patient), and location.

12

Table 4 Dependent variables and control variables DHS ENAHO

Prob

abilit

y of b

eing f

ully i

mmun

ized

(child

ren a

ged 1

8-59

mon

ths)

Prob

abilit

y of r

eceiv

ing pa

p-sm

ear

exam

in la

st 5 y

ears

(wom

en 15

-49)

Prob

abilit

y of h

aving

deliv

ery a

ssist

ed

by a

docto

r (wo

men 1

5-49

)

Perce

ntage

of ch

ild gr

owth

contr

ol sc

hedu

le co

mplet

ed (c

hildr

en un

der 5

)

Prob

abilit

y of b

eing f

orma

lly tr

eated

for

diarrh

ea (c

hildr

en un

der 5

)

Prob

abilit

y of b

eing f

orma

lly tr

eated

for

ARI (

child

ren u

nder

5)

Prob

abilit

y of s

eekin

g cur

ative

healt

h ca

re fo

r sym

ptoms

, illne

sses

or

relap

ses i

n las

t 4 w

eeks

(all a

ges)

Prob

abilit

y of s

pend

ing a

posit

ive

amou

nt am

ong t

hose

rece

iving

form

al ca

re in

last

4 wee

ks (a

ll age

s)

Amou

nt sp

ent b

y tho

se w

ith po

sitive

sp

endin

g (all

ages

)

Prob

abilit

y of s

pend

ing on

healt

h mor

e tha

n 30 p

erce

nt of

total

hous

ehold

ex

pend

iture

, exc

luding

subs

isten

ce

need

s (ex

treme

pove

rty lin

e)

Quintile 1 Omitted Quintile 2 x x x x x x x x x x Quintile 3 x x x x x x x x x x Quintile 4 x x x x x x x x x x Quintile 5 x x x x x x x x x x

Sex (Female = 1) xx xx xx xx xx xx xx xx Age xx xx xx xx xx xx xx xx xx Age squared (x100) x x x x Years of education xx xx xx xx xx xx Married/concubinate x x x x x x

Female household head x x x x x x x x x x Age of mother xx xx xx xx Years of education of mother xx xx xx xx Mother is married/concubinate x x x x Number of children under 5 x x x x x x x x x x Owner of dwelling Omitted x x x x

Lives in urban area xx xx xx xx xx xx xx xx xx xx

Lives in coast region x x x x x x x x x x Lives in mountains region x x x x x x x x x x Lives in Lima region Omitted x x x x Lives in higher jungle x x x x x x Lives in lower jungle x x x x x x

Omitted

Health post in community Not available x x: indicates variable included in the regression xx: indicates variable included in the regression, plus a term to capture its interaction with the health insurance dummy Source: Authors.

3.5 Control populations

The ideal control population for SIS is one identical in all regards to the current SIS beneficiary population (same socioeconomic level, health risk, health seeking preferences, etc.), but without health insurance. Since both populations are identical, any differences in demand or spending must be the consequence of health insurance. We may control for those cofactors for which we have data (socioeconomic level and health risk), but there are unobservable factors (health seeking preferences) that could make the control population different. When these unobservable factors impact on demand, the difference in demand between the treatment and control no longer reflects the impact of health insurance only, but also includes the impact of the unobservable factors. This is equivalent to endogeneity, and

13

correcting endogeneity means eliminating the impact of the unobservable factors to leave the impact of health insurance only.

The first step in selecting a control population is to keep only individuals who are eligible to SIS. That means not having insurance (I = 1), and being classified as poor by a specially designed proxy means test. Unfortunately, we cannot replicate the proxy means test because the algorithm is not available, but we may use consumption per capita to estimate poverty status. The poverty line used in the proxy means test is also not available, so we used the official poverty line. The official poverty line seems lower than the cut-off point used by SIS, because we found that a large percentage of SIS beneficiaries were non poor according to it. Instead, we opted for taking the 3 bottom quintiles, which include 85 percent of SIS beneficiaries.

The second step is to control for the available cofactors by including them in the regression models, and testing for any remaining unobserved selection bias in the form of endogeneity.

3.6 Panel data: ENAHO 2002-2006

A characteristic of SIS is that the majority of its beneficiaries are affiliated when they seek health care at MOH facilities. Certain individuals who declared to seek care in the last 4 weeks (Y = 1), and declared being affiliated to SIS (I = 1) may actually not have known about SIS before seeking care, and it would be incorrect to assume that the decision to seek care was influenced by SIS. That person’s actual insurance status should be uninsured (I = 0). To ensure that the insured population in our sample included only beneficiaries who were actually affiliated prior to seeking health care, we used the panel to confirm the health insurance status in the previous year. Thus, individuals with I = 1 in both the current and previous year were kept, and individuals with I = 1 in the current year, but I = 0 in the previous year, were dropped. Individuals with I = 0 in both the current and previous year were kept as control population, and individuals with I = 0 in the current year, but I = 1 in the previous year, were dropped.

Since we used the lagged health insurance in this manner to verify health insurance status prior to the decision of seeking health care, we took the health insurance status two years before (or double-lagged health insurance) as the identifying variable in the reduced form equation to test and control for endogeneity. This leaves us with three effective years for the analysis, from 2004 to 2006.

4 Results 4.1 Health insurance population coverage

EsSalud and SIS are the health insurers with highest population coverage in Peru, and together cover approximately one-third of the population (Figure 3). An additional 4 percent of the population is covered by other types of health insurance, including the armed forces and police, private insurance plans, university and private school insurances. The overlap (persons with more than one type of health insurance) between EsSalud and SIS, or between SIS and other health insurances, is negligible. However, about five percent of EsSalud beneficiaries overlap with other types of health insurance.

14

The ENAHO and DHS surveys provide statistically equal coverage estimates, because in 2004, 2005 and 2006 (where both surveys overlap), the ENAHO estimate’s confidence intervals (CI) are contained by the DHS estimate’s CI (Figure 4). The ENAHO population coverage estimates are more precise, because their CI are smaller and their sample sizes larger.

SIS beneficiaries are concentrated in areas with higher informal labor and poverty, like the Mountains and Jungle. The presence of SIS in these areas helps to close the gap in EsSalud population coverage in these regions (Table 5).

Figure 3 Health insurance population coverage, 2006

No insurance

63%

SIS

17%

Other

4%

EsSalud

18%

No insurance

63%

SIS

16%

EsSalud

18%

Other

4%

ENAHO

ENDES

Source: ENAHO and DHS.

Figure 4 Population coverage of health insurance according to ENAHO (2002-2006) and DHS (2000, 2004-2007) surveys (95% CI shown)

0%

5%

10%

15%

20%

25%

2000 2001 2002 2003 2004 2005 2006 2007

Pop

ulat

ion

cove

red

by E

sSal

ud ENDES ENAHO

0%

5%

10%

15%

20%

25%

2000 2001 2002 2003 2004 2005 2006 2007

Pop

ulat

ion

cove

red

by S

IS

0%

10%

20%

30%

40%

50%

2000 2001 2002 2003 2004 2005 2006 2007

Pop

ulat

ion

wit

h an

y ty

pe o

f ins

uran

ce

EsSalud

SIS

Any type of health insurance


15

Table 5 Health insurance population coverage by region, 2006

Region EsSalud SIS Any health insurance

Metropolitan Lima 27.5% 6.1% 41.1% Coast 22.4% 13.4% 37.8% Mountains 11.5% 22.7% 35.2% Jungle 10.0% 27.5% 38.4% Total 18.4% 16.4% 37.9% Source: ENAHO.

Between 2003 and 2004, the ENAHO shows a sharp drop in SIS population coverage, of unclear origin. We explored SIS administrative reports of beneficiary population, and found an entirely different situation, with the SIS population practically doubling between 2002 and 2005, and then dropping in 2006-2007 (Figure 5). However, we concluded that SIS administrative reports contained inconsistencies, and that survey data was more reliable.

Figure 5 Number of SIS beneficiaries, according to SIS administrative data and survey data

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

2002 2003 2004 2005 2006 2007

Num

ber

of S

IS b

enef

icia

ries

SIS Administrative data (1) Survey data (2)

SourceSource: (1) SIS Statistical Report, December each year. (2) ENAHO 2002-2006; DHS 2007.

To test the reliability of SIS administrative data, we calculated yearly service utilization rates (total number of services produced divided by the total number of beneficiaries), and found that utilization rates dropped from 2.6 to 1.4 services per year between 2002 and 2005, and increased to 3.1 services per year in 2006 and 2007; an unlikely behavior (see Table 6). The reason for this behavior is the inconsistency between the reported number of beneficiaries and total service production: the reported number of beneficiaries increases and then decreases, while total service production decreases and then increases (following a similar trend as the number of beneficiaries measured through survey data).

Table 6 SIS service production, 2002-2007

Year Total service production

Yearly service utilization rate

2002 15,170,607 2.6 2003 18,603,827 2.5 2004 13,068,769 1.4 2005 14,915,217 1.4 2006 17,430,217 1.7 2007 21,537,406 3.1 Source: SIS Statistical Report, December each year.

16

Because SIS administrative reports are inconsistent, we were unable to find a clear explanation for the drop in affiliation in 2004. One possibility is that SIS, during its early stages, absorbed the beneficiary pool from the maternal-child and school insurance programs. SIS coverage runs out after one calendar year and people must affiliate again after one year. Furthermore, individuals generally affiliate when they require care.

EsSalud beneficiaries are concentrated in the coastal region, with more than half living in Metropolitan Lima. The percentage of population covered by EsSalud is highest in Metropolitan Lima, reflecting the higher formal employment rate in this region. Regions with less formal labor, like the Mountains or Jungle, have lower EsSalud affiliation rates. Combining results from both surveys, we may conclude that the coverage rate of EsSalud has remained relatively constant over the period 2000-2007.

4.2 ENAHO panel sample

The ENAHO sample contains a panel subsample, representing approximately 26-30% of the complete sample. We found that households in the panel subsample were not selected with equal probability in each region. Households living in the Mountains were oversampled with respect to households in the Coast (Figure 6). Unfortunately, the ENAHO documentation does not indicate how the panel sample was selected, and our inquiries with the National Statistics Institute of Peru were unsuccessful at clarifying the nature of the problem.

Figure 6 Regional distribution of ENAHO panel and non panel samples

0%

20%

40%

60%

80%

100%

ENA

HO

Non

Pan

el

ENA

HO

Pan

el

ENA

HO

Non

Pan

el

ENA

HO

Pan

el

ENA

HO

Non

Pan

el

ENA

HO

Pan

el

ENA

HO

Non

Pan

el

ENA

HO

Pan

el

ENA

HO

Non

Pan

el

ENA

HO

Pan

el

2002 2003 2004 2005 2006

Year/Sample

Per

cent

age

of p

opul

atio

n by

reg

ion

Coast Mountains Jungle Source: ENAHO 2002-2006.

Since the panel sample over represents the Mountains and under represents the Coast, population coverage estimates of health insurance are biased. For example, EsSalud coverage results in 15% (instead of 18%, as indicated by the complete sample), and SIS coverage results in 21% (instead of 17%).

We were unable to correct the sample weights of the panel subsample in order to produce unbiased estimates, because the actual panel selection procedure is unclear. It could be that oversampling the Mountains region was a deliberate part of the procedure, but merely a side-effect, of for example, oversampling rural areas. Although the econometric analyses in this study use of the panel subsample, we opted for carrying out the descriptive analyses in this section with the complete ENAHO sample that best represents the regions.

4.3 Profiles of the insured and uninsured populations

This section examines the demographic and socioeconomic differences between the insured -to EsSalud or SIS- and the uninsured, which may be linked to health service utilization and spending

17

differences. Identifying these differences is important, in order to not confound the possible effects of health insurance on demand and spending.

SIS beneficiaries are mainly poor families, with no age or gender restriction. Survey data shows that the age structure of SIS beneficiaries is much younger than the general population and the uninsured. SIS beneficiaries are mainly children, because its benefit package is focused on maternal-child interventions (see Figure 7). We also observe that SIS beneficiaries are concentrated in the poorer quintiles, which compensates their lack of EsSalud coverage (see Figure 8).

Figure 7 Age and gender structure of health insurance beneficiaries, 2006 ENAHO DHS

2%7%

10%

3%8%

23%

16% 14%

27%

9%

0% 2%

9%

24%

11%

4%11%

26%

10%

2%7%

31%

9%3%

9%

21%16% 15%

29%

5%

0%

3%9%

29%

12%

4%11%

25%

9%

31%

40%

30%

20%

10%

0%

10%

20%

30%

40%

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

Uninsured EsSalud only SIS only Other health

insurance(s)

Total

Health insurance

Per

cent

age

in e

ach

age

grou

p

Females Males

2%7%

11%

3%10%

23%

15% 15%

29%

7%

0%4%

8%

25%

12%

5%11%

25%

10%

2%

8%

31 %

9%

3%

9%

21%

15%15%

30%

4 %

0%

3%

6%

30%

12%

5%

11%

25%

9%

29%

40%

30%

20%

10%

0%

10%

20%

30%

40%

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

0-4 5- 15-

50+

Uninsured EsSalud only SIS only Other health

insurance(s)

Total

Health insurance

Per

cent

age

in e

ach

age

grou

p

Females Males


Figure 8 Socioeconomic distribution of health insurance beneficiaries, 2006 ENAHO DHS

0%

20%

40%

60%

80%

100%

Poorest 2 3 4 Richest

Quintile of per capita consumption

Per

cent

age

in e

ach

quin

tile

Uninsured EsSalud only SIS only Other health insurance(s)

0%

20%

40%

60%

80%

100%

Poorest 2 3 4 Richest

Wealth index quintile

Per

cent

age

in e

ach

quin

tile

Uninsured EsSalud only SIS only Other health insurance(s)


EsSalud beneficiaries are mainly formal sector workers and their families, with no age or gender restriction. The ENAHO and DHS surveys show that EsSalud beneficiaries have a slightly older age structure than the general population and the uninsured. The surveys also show that EsSalud beneficiaries are concentrated in the richer quintiles of per capita consumption, while the uninsured are distributed more homogeneously (except for the richest quintile, which has a lower percentage of uninsured than the rest).

18

4.4 Impact of SEG/SMI and SIS health insurances

This section presents descriptive statistics and econometric estimation results for the impact of SEG/SMI and SIS health insurances on the following indicators:

Probability of being fully immunized (children aged 18-59 months) Probability of receiving pap-smear exam in last 5 years (women 15-49) Probability of having delivery attended by a skilled health personnel (women 15-49) Mean percentage of growth control schedule completed (children under 5) Probability of being formally treated for diarrhea (children under 5) Probability of being formally treated for ARI (children under 5) Probability of seeking curative health care for symptoms, illnesses or relapses in last 4 weeks Probability of spending a positive amount among those receiving formal care in last 4 weeks Amount spent by those with positive spending Probability of spending on health more than 30 percent of total household expenditure,

excluding subsistence needs (extreme poverty line) The impact on demand and spending of prolonged exposure to health insurance.

The first six indicators are obtained from the DHS survey, and allow us to measure the impact of SEG/SMI in 2000 and SIS in 2004. The last five indicators are obtained from the ENAHO survey, and allow to test and control for endogeneity.

The descriptive statistics include the mean value of each indicator separately for the insured and uninsured populations, and by quintile (only the three lowest quintiles are shown, as the sample is restricted to the population eligible for SIS). The econometric results include the effect of health insurance, measured as the variation in the predicted mean value of the dependent variable when changing the value of the health insurance dummy from 0 to 1. When dealing with probabilities and percentages, the effect of health insurance is expressed in percent points; for spending, it is expressed as percent variation. The statistical significance of the health insurance effect is defined as the lowest p-value among the coefficients associated with the health insurance dummy and its interactions.

4.4.1 Probability of being fully immunized (children aged 18-59 months)

Health insurance has a positive impact on the probability of being fully immunized among children aged 18-59 months. Table 7 shows both descriptive statistics and results of a logit regression. The descriptive statistics show that the insured population has a higher coverage of full immunization in children than the uninsured, but eligible, population. This happens for both SEG/SMI and SIS health insurances, although the latter shows a greater difference between insured and uninsured populations. The logit regression, which controls for other factors that affect the immunization coverage besides health insurance, confirms that the effect of health insurance is positive: SEG/SMI health insurance increases the probability of being fully immunized in 4 percent points, and SIS does so in 14 percent point.

We sought for possible interactions effects between the health insurance variable and the child’s gender, the mother’s age and education, and the household’s urban/rural location, but none of the interaction terms was significant.

The effects of both health insurances throughout the income distribution are different. The effect of SEG/SMI decreases gradually with income, and is statistically significant only in quintile 1. In contrast, the effect of SIS increases steadily with income, and is statistically significant in all quintiles gender, age of mother, education of mother, rural/urban location. Although this might appear as an undesirable characteristic for SIS (one would expect the effect of health insurance to be strongest among the poorest, and not the opposite), the effect of SIS is larger than the effect of SEG/SMI even in quintile 1.

19

Table 7 Impact of SEG/SMI and SIS on probability of being fully immunized (children aged 18-59 months)

Health Insurance, Year SEG/SMI Health Insurance,

2000 SIS Health Insurance,

2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 67% 67% 68% 64% 65% 62% 68% 66% Among uninsured but eligible population 62% 60% 62% 65% 50% 51% 50% 49% Among total population 63% 61% 63% 65% 60% 59% 62% 57% Logit model No. of observations 5,554 2,550 1,837 1,167 1,908 847 641 420 Pseudo R2 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.08 Predicted mean of dependent variable Simulation with HI = 1 64% 65% 65% 63% 65% 62% 67% 71% Simulation with HI = 0 60% 57% 61% 64% 51% 52% 52% 49% Effect of HI on dependent variable (percent points) a +4 *** +7 *** +4 -1 +14 *** +10 *** +14 *** +22 *** (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on DHS 2000 and 2004 datasets.

4.4.2 Probability of receiving pap-smear exam in last 5 years (women 15-49)

The percentage of women receiving pap-smear exams in the last 5 years is considerably lower among the insured population (7 percent in SEG/SMI and 30 percent in SIS) than among the uninsured, but eligible, population (38~41 percent). However, the low utilization rate of the insured population is caused by factors other than health insurance, such as age, education and marital status. We control for these factors with a logit regression, and show that the effect of health insurance is actually a positive one. As Table 8 shows, SEG/SMI increases the probability of receiving a pap-smear exam by 7 percent points, and SIS does so by 3 percent points.

We found a statistically significant interaction effect between SIS and age. As Figure 9 shows, the probability of receiving a pap-smear exam always increases with age, but does so faster with SIS health insurance. Thus, there is a positive effect of SIS, which is lowest among young women, and highest among older ones.

Figure 9 Predicted probability of receiving a pap-smear exam, by health insurance status and age

.2

.4

.6

.8

1

for H

ad p

apsm

ear l

ast 5

y

10 20 30 40 50 60Age

ins_SIS=0 ins_SIS=1Has SIS health insurance

Pre

dict

ed P

roba

bilit

ies

Source: Authors based on DHS 2000 and 2004 datasets. Ages 50+ extrapolated from ages 15-49.

The impact in SEG/SMI is larger than in SIS, and is more consistent throughout the income distribution. The effect of health insurance is statistically significant in all regressions carried out separately for each quintile in the case of SEG/SMI, but not in the case of SIS.

20

Table 8 Impact of SEG/SMI and SIS on probability of receiving pap-smear exam in the last 5 years (women 15-49)



2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 7% 14% 7% 5% 30% 29% 38% 18% Among uninsured but eligible population 44% 39% 44% 46% 38% 33% 38% 42% Among total population 41% 38% 42% 43% 38% 32% 38% 41% Logit model No. of observations 12,096 3,228 4,407 4,461 6,006 1,591 2,313 2,102 Pseudo R2 0.11 0.05 0.11 0.16 0.13 0.06 0.12 0.20 Predicted mean of dependent variable Simulation with HI = 1 50% 47% 49% 59% 41% 35% 46% 41% Simulation with HI = 0 43% 40% 43% 44% 38% 34% 37% 41% Effect of HI on dependent variable (percent points) a +7 *** +7 *** +6 *** +16 *** +3 * +1 +9 0 (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on DHS 2000 and 2004 datasets.

4.4.3 Probability of having delivery attended by a skilled health personnel (women 15-49)

Results show no effect of either SEG/SMI or SIS on the probability of having deliveries attended by a skilled health personnel. Although the insured population shows lower rates of assisted deliveries than the uninsured, these are caused by other factors, namely income, location, education, gender of the household head, marital status, and the total number of children living in the household. When controlling for these factors, we find no statistically significant impact of health insurance (see Table 9). We also sought for possible interactions effects between the health insurance variable and the woman’s age and education, and the household’s urban/rural location, but none of the interaction terms was significant.

Table 9 Impact of SEG/SMI and SIS on probability of having delivery attended by a skilled health personnel (women 15-49)



2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 30% 14% 46% 57% 49% 26% 61% 100% † Among uninsured but eligible population 41% 15% 41% 76% 56% 26% 63% 88% Among total population 41% 15% 41% 76% 55% 26% 62% 89% Logit model No. of observations 7,500 3,253 2,573 1,674 2,918 1,210 1,031 Pseudo R2 0.27 0.09 0.17 0.12 0.24 0.04 0.12 Predicted mean of dependent variable Simulation with HI = 1 47% 19% 54% 77% 59% 29% 66% Simulation with HI = 0 43% 18% 44% 78% 56% 29% 63% Effect of HI on dependent variable (percent points) a +4 +1 +10 0 +3 -1 +3

Not enough cases with

HI = 1

(a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). † Statistic calculated with less than 40 observations. Source: Authors based on DHS 2000 and 2004 datasets.

4.4.4 Mean percentage of growth controls attended (children under 5)

Health insurance has a positive impact on the compliance of the child growth control schedule. The descriptive statistics in Table 10 indicate that SIS children receive more growth controls than the uninsured. On average, the former attended to 54 percent of their child growth controls, while the latter attended to 44 percent. The difference in favor of SIS children is observed across the three bottom quintiles. In contrast, the descriptive statistics do not show the same clear differences in favor SEG/SMI

21

children, because the effect of SEG/SMI is confounded by other variables, particularly the age of children and the education of the mother. When controlling for these other variables, the positive impact of both health insurances appears: SEG/SMI increases the percentage of child growth controls attended by 7 percent points, and SIS does so by 9 percent points.

We found a statistically significant interaction effect between SIS and the education of the mother. As Figure 10 shows, the predicted number of growth controls increases with the education of the mother for both SIS and uninsured children, reflecting the main effect of the education variable. Also, the predicted number of growth controls is always higher for SIS children, reflecting the main effect of the health insurance variable. The interaction between both variables produces the differences in slope between SIS and uninsured populations, so that the effect of SIS among children of uneducated women is twice the effect among children of women with 15 years of education. Thus, SIS health insurance tends to equalize the condition of women with different degrees of education. Without SIS, poor education lowers the predicted number of growth controls considerably more than with SIS.

Figure 10 Predicted percentage of growth controls attended, by health insurance status and education of the mother

.35

.4

.45

.5

.55

.6

for P

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e of

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ld g

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0 5 10 15Years of education of mother

ins_SIS=0 ins_SIS=1Has SIS health insurance

Pre

dict

ed V

alue

s

Source: Authors based on DHS 2000 and 2004 datasets.

The effect of health insurance shows consistent, and statistically significant, values in all the regressions carried out for each quintile separately. The effect tends to be higher in the lower quintiles, particularly in the case of SIS.

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Table 10 Impact of SEG/SMI and SIS on mean percentage of growth controls attended (children under 5)



2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 41% 40% 44% 41% 54% 50% 55% 60% Among uninsured but eligible population 39% 33% 41% 45% 44% 38% 43% 50% Among total population 39% 34% 42% 44% 50% 47% 51% 56% OLS model No. of observations 8,060 3,762 2,640 1,658 3,140 1,405 1,066 669 Adjusted R2 0.11 0.09 0.06 0.11 0.12 0.15 0.10 0.08 Predicted mean of dependent variable Simulation with HI = 1 44% 41% 45% 50% 54% 50% 54% 59% Simulation with HI = 0 37% 32% 40% 43% 45% 37% 46% 52% Effect of HI on dependent variable (percent points) a +7 *** +9 *** +6 *** +7 *** +9 *** +13 *** +8 *** +6 *** (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on DHS 2000 and 2004 datasets.

4.4.5 Probability of being formally treated for diarrhea (children under 5)

Health insurance has a positive impact on the probability of receiving formal treatment for diarrhea. Formal treatments for ARI and diarrhea include medical consultations at any public health facility or private clinic. They also include visits to private doctors or home visits by private doctors. The following are not considered formal treatments: consultations with traditional practitioners, purchasing of drugs, oral rehydration solutions (ORS), etc. at pharmacies, drugstores or Botiquines Populares, and any other type of home treatment without the assistance of a medical doctor. As Table 11 shows, SEG/SMI increases the probability by 16 percent points, and SIS does so by 20 percent points. For both health insurances, the effect is statistically significant in the two bottom quintiles only. We sought for possible interactions effects between the health insurance variable and the child’s gender and age, the mother’s age and education, and the household’s urban/rural location, but none of the interaction terms was significant.

Table 11 Impact of SEG/SMI and SIS on probability of being formally treated for diarrhea (children under 5)



2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 43% 44% 49% 33% 50% 51% 50% 45% Among uninsured but eligible population 34% 31% 35% 38% 29% 31% 25% 29% Among total population 36% 34% 37% 38% 43% 46% 43% 37% Logit model No. of observations 1,527 725 501 301 601 282 198 121 Pseudo R2 0.02 0.03 0.06 0.04 0.04 0.07 0.05 0.05 Predicted mean of dependent variable Simulation with HI = 1 50% 46% 59% 49% 50% 54% 49% 41% Simulation with HI = 0 35% 32% 36% 37% 30% 26% 33% 31% Effect of HI on dependent variable (percent points) a +16 *** +13 *** +23 *** +11 +20 *** +28 *** +15 * +11 (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on DHS 2000 and 2004 datasets.

4.4.6 Probability of being formally treated for ARI (children under 5)

Health insurance has a positive impact on the probability of receiving formal treatment for ARI. As Table 12 shows, SEG/SMI increases the probability by 15 percent points, and SIS does so by 23

23

percent points. Unlike diarrhea treatment, the effect is statistically significant in all quintiles, but no clear distributional effects are observed. We also found no significant interaction effects.

Table 12 Impact of SEG/SMI and SIS on probability of being formally treated for ARI (children under 5)



2004 Subpopulation All Quintile 1 Quintile 2 Quintile 3 All Quintile 1 Quintile 2 Quintile 3 Observed mean of dependent variable Among population with HI 58% 55% 61% 58% 71% 76% 67% 64% Among uninsured but eligible population 50% 46% 49% 58% 52% 45% 53% 59% Among total population 51% 48% 51% 58% 65% 69% 63% 62% Logit model No. of observations 1,608 768 524 316 655 328 222 105 Pseudo R2 0.03 0.03 0.04 0.05 0.05 0.07 0.08 0.13 Predicted mean of dependent variable Simulation with HI = 1 65% 57% 69% 71% 73% 74% 75% 67% Simulation with HI = 0 50% 47% 48% 58% 51% 51% 51% 43% Effect of HI on dependent variable (percent points) a +15 *** +11 ** +22 *** +13 * +23 *** +24 *** +24 *** +24 ** (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on DHS 2000 and 2004 datasets.

4.4.7 Probability of seeking curative health care for symptoms, illnesses or relapses in last 4 weeks

This is indicator is calculated using the ENAHO 2002-2006 panel survey. As in the previous analysis, the sample is restricted to the bottom 3 quintiles. The detailed description of the indicator is the probability of seeking curative health care, with a doctor or other qualified health professional, for symptoms, illnesses or chronic disease relapses in the last 4 weeks, excluding accidents, which we will to refer to as demand. The information reported in the ENAHO shows that utilization and demand are equivalent; i.e. there is no unmet demand.

The ENAHO survey allowed us to test the potential endogeneity between demand and health insurance. As Table 13 shows, in 2004 and 2005 the two tests reject endogeneity, as the rho value is not statistically significant, and the significance level of the predicted health insurance variable when inserted in the demand equation is greater than 0.1. In 2004 and 2005 the univariate probit regression shows a positive impact of SIS. The absence of endogeneity indicates that the probability of a sick person affiliating to SIS is independent of their preference to demand care. In 2006, endogeneity appears (a negative rho value, which is statistically significant) and the bivariate probit regression, which corrects endogeneity, also shows a positive impact of SIS. The appearance of endogeneity, in the form of a negative selection effect, could imply that not only SIS increases demand, ceteris paribus, but that individuals with the tendency to demand start to disaffiliate from SIS. Watters found a similar phenomenon in Ecuador, where the poor quality of services provided by health insurance encouraged sick individuals to disaffiliate and seek care elsewhere. However, in the case of SIS households are able to remain affiliated at no cost, so there is no reason why sick individuals would tend to disaffiliate, even if unsatisfied with SIS-covered health services. Thus, we conclude that the negative selection effect observed in 2006 is caused by an error in the statistical test, and that the most likely true scenario is the absence of endogeneity, like in 2004 and 2005.

We found an interaction effect between health insurance and age, which is reasonable since this indicator pooled individuals of all ages, unlike the indicators in the DHS survey. As Figure 11 shows, the effect of SIS is higher among children, because the basic treatments included in its benefit package are more associated to pediatric ailments.

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Figure 11 Predicted probability of seeking curative health care, by health insurance status and age, 2004

0

0.2

0.4

0.6

0.8

0 20 40 60 80

Age (years)

Pre

dict

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roba

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for s

ough

t for

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e

ins_SIS_sure=0 ins_SIS_sure=0

Note: Dotted lines show 95% confidence interval. Source: Authors based on DHS 2000 and 2004 datasets.

Table 13 Impact of SEG/SMI and SIS on probability of seeking curative health care for symptoms, illnesses or relapses in last 4 weeks (all ages) Year 2004 2005 2006 Observed mean of dependent variable Among population with HI 50% 42% 44% Among uninsured but eligible population 16% 16% 16% Among total population 24% 22% 21% Endogeneity tests Value of rho in bivariate probit a -0.204 -0.143 -0.312 ** Significance of predicted HI in primary eq. 0.196 0.424 0.114 Model results Type of model probit probit biprobit No. of observations 2,174 1,912 2,008 Pseudo R2 0.13 0.08 n/a Predicted mean of dependent variable Simulation with HI = 1 33% 21% 7% Simulation with HI = 0 14% 15% 1% Effect of HI on dependent variable (percent points) a 19 *** 6 *** 6 *** (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on ENAHO 2002-2006 panel dataset.

4.4.8 Probability of spending a positive amount among those receiving formal care in last 4 weeks

The impact of SIS on the probability of spending any amount when receiving care is very high. As shown in Table 14, spending a positive amount is the most likely outcome for the uninsured, while the opposite is true of SIS beneficiaries. No signs of endogeneity are found in this model.

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Table 14 Impact of SIS on probability of spending a positive amount among those receiving formal care in last 4 weeks (all ages) Year 2004 2005 2006 Observed mean of dependent variable Among population with HI 12% 12% 17% Among uninsured but eligible population 86% 92% 89% Among total population 49% 57% 59% Endogeneity tests Value of rho in bivariate probit (a) 0.577 n/a 0.286 Significance of predicted HI in primary eq. 0.721 0.126 0.204 Model results Type of model probit probit probit No. of observations 542 399 421 Pseudo R2 0.58 0.54 0.48 Predicted mean of dependent variable Simulation with HI = 1 13% 10% 21% Simulation with HI = 0 86% 92% 88% Effect of HI on dependent variable (percent points) a -73 *** -81 *** -67 *** Note: In 2005, bivariate probit convergence not achieved. (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on ENAHO 2002-2006 panel dataset.

4.4.9 Amount spent by those with positive spending

Results from this model are inconclusive (see Table 15). The impact of SIS on the amount spent is not statistically significant, but sample sizes are small due to the fact that so few SIS beneficiaries actually spent any amount out-of-pocket.

Table 15 Impact of SIS on amount spent by those with positive spending, in Soles (all ages) Year 2004 2005 2006 Observed mean of dependent variable Among population with HI 25.9 21.4 24.4 Among uninsured but eligible population 30.9 37.0 34.4 Among total population 29.2 34.3 31.9 Endogeneity tests Significance of predicted HI in primary eq. 0.713 0.589 0.673 Log-OLS model No. of observations 246 244 262 Adjusted R2 0.10 0.10 0.13 Predicted mean of dependent variable Simulation with HI = 1 16.7 26.7 21.8 Simulation with HI = 0 19.2 19.7 20.1 Effect of HI on dependent variable (percent points) a -13% 36% 9% (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on ENAHO 2002-2006 panel dataset.

4.4.10 Probability of spending on health more than 30 percent of total household expenditure, excluding subsistence needs (extreme poverty line)

The effect of SIS on reducing high amounts of spending (above 30 percent of total household expenditure) is only statistically significant in 2005. As seen in the model for amount spent by those with positive spending, there are few observations with positive spending, and this lack of information could explain the low statistical significance of the health insurance coefficient.

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Table 16 Impact of SIS on probability of spending on health > 30% of total household expenditure, excluding subsistence needs (extreme poverty line) Year 2004 2005 2006 Observed mean of dependent variable Among population with HI 5% 5% 3% Among uninsured but eligible population 10% 11% 9% Among total population 8% 9% 8% Model results Type of model probit probit probit No. of observations 4,431 4,090 4,223 Pseudo R2 0.07 0.08 0.09 Predicted mean of dependent variable Simulation with HI = 1 8% 7% 6% Simulation with HI = 0 9% 10% 8% Effect of HI on dependent variable (percent points) a -1 -3 ** -2 Note: In 2005, bivariate probit convergence not achieved. (a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors based on ENAHO 2002-2006 panel dataset.

4.4.11 Impact of prolonged exposure to health insurance

An interesting question is whether the positive impacts of insurance on demand and spending are instantaneous, by immediately providing access to health services with little financial barriers? Or does part of the impacts appear in the longer term, requiring learning and experience from users? The ENAHO panel allows us assess whether individuals that have been affiliated to SIS longer receive greater benefits than others. We developed two modified versions of the models of demand and probability of spending, where we replaced the health insurance dummy by a continuous variable, ranging from 1 to 4, indicating the number of years continually affiliated to SIS (version 1), or four dummies, the first dummy equal to 1 if the individual had been affiliated to SIS during one year and 0 otherwise, the second dummy equal to 1 if the individual had been affiliated to SIS during two years and 0 otherwise, and so on (version 2). The first version of the model was restricted to a subsample of insured individuals only (which is why the continuous variable ranges from 1 to 4 years, instead of 0 to 4). The second version included both insured and uninsured populations, where insured individuals have one of the four dummies set to 1, and uninsured individuals have all four dummies set to 0.

Results show no long term effects of health insurance. In the first version of the demand model, the coefficient of the variable indicating the number of years affiliated to SIS was not statistically significant. Thus, among insured individuals the number of years of affiliation does appear to affect demand. In the second version, all four dummies were statistically significant, indicating that having health insurance has a positive impact on demand for individuals with 1, 2, 3 or 4 years of exposure (see Table 17). However, the coefficients do not increase with time of exposure, indicating that the impact is not stronger for individuals with longer exposure. Results for the probability of spending model are similar in that no effect of exposure appears.

This finding applies to the demand for curative health care for symptoms, illnesses or relapses in the last 4 weeks. To see if chronic patients in particular require a prolonged learning process to take full advantage of health insurance, we ran a separate regression keeping only chronic illness relapses. We did not find any long-term effect of health insurance.

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Table 17 Demand model results replacing health insurance dummy with four time of exposure variables No. of observations 5,791 Pseudo R2 0.06 Coefficient of health insurance for 1 year 1.58 *** Coefficient of health insurance for 2 years 1.69 *** Coefficient of health insurance for 3 years 1.12 *** Coefficient of health insurance for 4 years 1.40 *** Source: Authors based on ENAHO 2002-2006 panel dataset.

4.5 Impact of EsSalud health insurance

This section presents descriptive statistics for the impact of EsSalud health insurance on demand and spending indicators. EsSalud’s benefit package is comprehensive, and covers the total cost of practically all health interventions. Thus, we could expect to see higher demand in all types of health services. Table 18 compares several health service demand indicators, between the uninsured and EsSalud beneficiaries. EsSalud presents higher utilization rates for several -but not all- types of services. We found statistically significant differences (through T-tests of equality of proportions) for the following types of services:

Formal ambulatory health care for (mostly acute) health problems reported in the last 4 weeks. Hospitalizations in the last 12 months, mostly in EsSalud hospitals (see Annex). Pap-smear and breast exams in the last 5 years Doctor-assisted deliveries Full immunization of children aged 18 to 59 months Current use of family planning methods Formal treatment for child diarrhea and ARI in the last 4 weeks. As expected, the main places

of treatment are EsSalud facilities.

Since we are not controlling for the variables used in the regression analyses, the differences between EsSalud and SIS indicators do not necessarily reflect the differences between EsSalud and SIS performance. For example, the higher utilization of child growth controls among SIS beneficiaries probably appears because SIS beneficiaries are much younger than EsSalud and the general population. Another indicator –the probability of seeking formal care in the last 4 weeks- is less affected by demographic variables, and shows practically no difference between SIS and EsSalud.

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Table 18 Utilization of health services, by type of health insurance, 2005 and 2006 Indicator Uninsured EsSalud only SIS only Other Total ENAHO, 2006 Presence of chronic disease(s) 22.7% 32.7% 10.1% 25.9% 22.5% Population with health problem(s) in last 4 weeks 52.3% 53.0% 51.3% 43.4% 51.9% Hospitalized in last 12 months 3.4% 6.7% 2.6% 6.7% 4.0% Sought formal care (with health professional) in last 4 weeks 19.5% 45.3% 46.8% 46.9% 29.4% Utilization of child growth control in last 3 months 1.5% 3.1% 18.5% 2.8% 4.6% Utilization of family planning in last 3 months 4.3% 3.4% 0.5% 1.5% 3.4% Utilization of vaccines in last 3 months 17.8% 16.4% 38.0% 18.6% 20.8% Utilization of child iron supplements in last 3 months 0.4% 0.7% 4.1% 0.7% 1.1% Utilization of disease prevention in last 3 months 1.2% 2.3% 1.6% 2.6% 1.5% DHS, 2005 Pap-smear last 5 years 38.7% 62.6% 29.3% 66.4% 43.9% Breast exam last 5 years 16.1% 42.3% 11.0% 43.9% 21.4% Number of prenatal visits None 8.3% 2.1% 11.5% 0.6% 7.2% 1 to 4 36.7% 19.6% 34.8% 9.0% 32.9% 5 or more 55.0% 78.4% 53.7% 90.5% 59.8% Person providing assistance at delivery (among women with births) No assistance 0.3% 0.0% 1.3% 0.0% 0.3% Doctor 35.5% 69.5% 20.9% 92.8% 42.1% Midwife 30.1% 25.2% 29.9% 7.2% 28.5% Nurse, promoter or fieldworker 2.7% 0.5% 8.8% 0.0% 2.6% Comadrona or partera 16.2% 2.5% 21.8% 0.0% 13.8% Other 15.2% 2.4% 17.3% 0.0% 12.8% Children aged 18-59 months fully immunized 47.5% 64.7% 64.7% 70.7% 58.8% Children under 5 with diarrhea in last 4 weeks 16.3% 9.5% 18.1% 9.8% 15.9% Sought formal treatment for diarrhea 27.5% 44.2% 46.1% 37.4% 39.2% Place of treatment Traditional healer, pharmacy, at home or with friend 37.4% 2.6% 15.8% 29.9% 21.8% Community health worker 0.7% 0.0% 0.8% 0.0% 0.7% MINSA facility 48.9% 13.1% 78.4% 0.0% 62.1% EsSalud facility 0.0% 44.4% 0.0% 0.0% 3.5% Army or private facility 0.0% 0.0% 0.0% 0.0% 0.0% Other type of health facility 13.0% 37.0% 1.4% 70.1% 9.5% Treated in more than one type of health 0.0% 2.9% 3.6% 0.0% 2.4% Children under 5 with ARI in last 4 weeks 16.7% 18.3% 20.4% 11.9% 18.5% Sought formal treatment for ARI 54.7% 70.4% 69.0% 90.2% 65.4% Place of treatment Traditional healer, pharmacy, at home or with friend 21.6% 11.8% 8.9% 10.5% 13.1% Community health worker 0.0% 0.0% 0.7% 0.0% 0.4% MINSA facility 45.0% 23.4% 88.0% 7.1% 62.7% EsSalud facility 0.0% 34.2% 0.0% 0.0% 5.3% Army or private facility 3.8% 0.0% 0.0% 0.0% 1.1% Other type of health facility 20.5% 21.8% 2.2% 49.7% 12.2% Treated in more than one type of health facility 9.1% 8.9% 0.3% 32.6% 5.3% Current use of modern family planning methods 30.4% 40.1% 5.3% 39.5% 31.1% Number of growth controls (children 1- 4 years) 0 10.0% 2.8% 5.7% 0.0% 6.4% 1 8.3% 2.8% 4.8% 2.2% 5.6% 2 5.1% 4.4% 5.3% 2.1% 4.9% 3 6.7% 3.8% 8.0% 3.0% 6.7% 4 29.6% 33.3% 31.5% 14.9% 30.4% 5 40.4% 52.9% 44.7% 77.9% 45.9% Source: ENAHO and DHS.

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The ENAHO survey shows that positive out-of-pocket expenditures –among those who utilized health services- are dependent on the type of health insurance and socioeconomic level. Regardless of the type of health insurance, the poor always tend to spend less than the rich. Whether health insurance has an impact on total health expenditure is not clear, because the differences between EsSalud/SIS beneficiaries and the uninsured vary across quintiles.

Table 19 Monthly out-of-pocket expenses on selected ambulatory services, by quintile and type of health insurance, 2006 Type of health insurance

Uninsured EsSalud

only SIS only Other health insurance(s) Total

In Soles Quintile 1 12.7 22.2 7.1 25.7 12.2 Quintile 2 17.0 22.2 15.8 22.6 17.4 Quintile 3 22.4 25.9 16.6 24.2 22.4 Quintile 4 31.6 28.2 24.8 30.5 30.5 Quintile 5 48.0 74.8 89.3 51.6 57.8 Total 28.2 48.0 21.0 44.7 32.3 Source: ENAHO 2006.

5 Summary and conclusions SIS reduces in an important way the likelihood that those insured will have to spend money out of

pocket for health care. There is also evidence, although somewhat weak (because it occurs in only one of the three periods studied) that SIS reduces the chances that individuals who obtain health care will have to incur catastrophic out-of-pocket expenditures. There is no evidence, however, that SIS reduces out-of-pocket spending among those that do have to pay for care, although the lack of evidence may be attributable to the small sample size of those individuals who do have to incur an out-of-pocket spending. Indeed, only about one on seven SIS beneficiaries who obtain care has to pay for it directly. There is no evidence either that SIS’s beneficiaries increase their consumption of health services over time, as they become familiar with the insurer’s benefits and procedures.

SIS increases utilization for a variety of services, both preventive and curative (Table 20). The biggest impact on utilization or curative services occurs in the case of formal treatment for diarrhea and acute respiratory infections for children under 5. A positive impact on utilization that is nearly as large also occurs for all other curative treatments and individuals, although that effect is greater for children. This may be a consequence of the greater emphasis that SIS places to the provision of child care. Among preventive services, the biggest positive impact of SIS on use is for immunizations, followed by growth monitoring.

For a few but important services targeted to women, SIS coverage does not have an impact on use. Such is the case for pap smears and institutional deliveries.

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Table 20 Summary of impact indicators of SEG/SMI and SIS health insurances (%) Impact of SEG/SMI Impact of SIS

Indicator Baseline Value

Pro-poor effect

Baseline Value

Pro-poor effect

Probability of being fully immunized (children aged 18-59 months)

62 +4 ***

Yes 50 +14 ***

No

Probability of receiving pap-smear exam in last 5 years (women 15-49)

44 +7 ***

No 38 +3 * No

Probability of having delivery attended by a skilled health personnel (women 15-49)

41 +4 No 56 +3 No

Mean percentage of growth control schedule completed (children under 5)

39 +7 ***

No 44 +9 ***

Yes

Probability of being formally treated for diarrhea (children under 5)

34 +16 ***

No 29 +20 ***

Yes

Probability of being formally treated for ARI (children under 5) 50 +15 ***

No 52 +23 ***

No

Probability of seeking curative health care for symptoms, illnesses or relapses in last 4 weeks

n/a 16 +6~ +19 ***

n/a

Probability of spending a positive amount among those receiving formal care in last 4 weeks

n/a 86~92 -67~ -81 ***

n/a

Amount spent by those with positive spending n/a S/. 31-34

-13~ +36 n/a

Probability of spending on health more than 30 percent of total household expenditure, excluding subsistence needs (extreme poverty line)

n/a 9-11 -1~ -3 **

n/a

(a) Significance levels (* p < 0.1; ** p < 0.05; *** p < 0.01). Source: Authors, based on DHS and ENDES surveys.

In sum, SIS seems to improve access to care and reduce the financial burden associated to health care for its beneficiaries for several services. The key policy question associated to this finding is whether these positive effects justify the amount of resources spent by SIS.

But, as noted above, SIS has failed to bring about increases in use for a few but important selected services (maternal). Assessing the causes of this partial failure should be a priority for SIS managers.

It is clear that SIS has achieved important gains for its beneficiaries, in terms of lower out-of-pocket spending and higher utilization of services. The important policy question to ask in this regard is whether these gains are worth the extra money that the government has spent on SIS. Equivalently, one could ask whether the government could have achieved at least these gains by increasing the regular budget of the Ministry of Health to expand service provision, instead of creating and operating a separate agency such as SIS. Some rough calculations can be performed to address this question using budgetary figures reported for MINSA and SIS. In 2007 total per capita health spending in Peru was S/. 429, of which 27 percent, or S/. 116 was spending by MINSA and SIS together. Subtracting from this amount the per capita budget allocated to SIS (S/. 282 million spent on 4.7 million beneficiaries), one obtains a per capita spending for MINSA of S/. 105. Thus SIS spend on each of its beneficiaries S/. 60, or roughly 60 percent of what MINSA spends for each Peruvian. The question, then, is whether these additional resources are well spent to produce the observed increases in utilization and the reduction in out-of-pocket spending observed among SIS beneficiaries. Further refining this kind of analysis, which is a sort of cost-effectiveness analysis, is beyond the scope of this report. However, it should be carried out to shed light on the convenience of further operating and expending SIS.

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SIS also has a higher impact than SEG/SMI, in part due to the fact that the target population of SIS has worse health indicators than the target population of SEG/SMI. However, the maternal services have not improved with respect to SEG/SMI.

Finally, we analyzed how the impact of SIS varies by income quintile. We considered the effect of SIS to be pro-poor when the impact was greatest in quintile 1 and lowest in quintile 3, and not pro-poor otherwise. For example, the effect of SIS on the probability being fully immunized is larger in quintile 3 than in quintile 1, and is thus not considered pro-poor; the effect on the probability of seeking care for ARI is constant in every quintile, and is also not considered pro-poor; the effect on growth controls, however, is highest in quintile 1 and lowest in quintile 3, and is thus considered to be pro-poor. Although all three bottom quintiles are considered poor and eligible for SIS, it is desirable that the benefits of SIS reach the poor progressively, or at least proportionally, with their income. Cases where most of the effect reaches the relatively less poor instead of the extreme poor (like in the case of immunizations) reflect coverage inequalities that should be addressed.

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