Adverse Selection, Moral Hazard, and Income Effect in Health Insurance

Adverse Selection, Moral Hazard, and Income Effect in Health Insurance: The Case of Ghana Samuel Amponsah

Abstract This study tests for the presence of adverse selection, moral hazard, and income effect in the context of the market for public health insurance in sub-Saharan Africa (SSA) using the Ghanaian market as a case study. After controlling for selection bias, I find that the likelihood of purchasing health insurance increases with health risk, which suggests evidence of adverse selection. This result indicates that the insured who had ex-ante risk use more health care services than the uninsured. Moreover, I find a positive correlation between income and health insurance coverage. Taken together, the overall marginal effect of the income dummies does not show widespread social exclusion of the poor from the National Health Insurance Scheme (NHIS), as has been perceived by many Ghanaians as well as previous studies. The coefficients on the NHIS indicate increases in health care consumption. I do not observe changes in the behavior of the insured households that may lead to higher health care service usage, which suggests the observed ex-post moral hazard cannot be considered as inefficient moral hazard. JEL: I11, D82, G22, C21 Keywords: Ghana health insurance, adverse selection, moral hazard, income effect

I．Introduction Although adverse selection, moral hazard, and income effect are three of the main predictions of insurance theory, empirical papers find mixed evidence of their existence. Yet these three issues continue to occupy center stage in the health economics literature because of their potential policy implications. Theoretical literature on health insurance predicts that asymmetric information in health insurance may produce inefficient outcomes due to adverse selection and moral hazard (see, Arrow 1963; Pauly 1968; Akerlof 1970; Zeckhauser 1970; Spence and Zeckhauser1971; Rothschild and Stiglitz 1976; Wilson 1980). Pauly (1968) shows that moral hazard may exist in health insurance markets because insured individuals do not bear the full cost of health care expenditures. Similarly, Rothschild and Stiglitz (1976) develop a model in which adverse selection is present because individuals have private information about their risk status.They show that those with high risk and thus higher expected expenditures will self-select into insurance in order to cover their costs. According to Einav, Finkelstein and Levin (2010) adverse selection in insurance markets is

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経済研究　東京国際大学大学院経済学研究科　第 14 号　2013 年 3 月

commonly used as shorthand for a situtation in which high-risk individuals self-select into more generous coverage. However, a number of recent studies have provided mixed empirical evidence on the predictions of theoretical models of insurance market under asymmetric information (de Meza and Webb 2001; Finkelstein and McGarry 2006; Fang, Keane and Silerman 2008; Buchmueller et al. 2008; Cutler, Finkelstein and McGarry 2008; Bolhaar, Lindeboom and van der Klaauw 2008). These new studies suggest a possible advantageous selection. One potential explanation for this puzzle is that individuals who are risk averse may invest in preventive activities and at the same time demand more insurance. With regard to income effect, empirical evidence on health insurance supports the positive effect of income on insurance coverage and shows that there are two income effects that can arise in health insurance markets. One, which is the focus of this study, arises from the level of the health insurance premium relative to household income (Bundorf, Herring and Pauly 2005). The other, which is not the focus of the paper, arises from any (net) income effects on the demand for medical care.1) While these three issues have been discussed in the health insurance literature, the discussion has concentrated more on developed countries. In the context of sub-Saharan Africa (SSA), studies on adverse selection, income effect, and moral hazard in health insurance are scant. Two possible exceptions are Kirigia et al. (2005), who investigate the demand for health insurance coverage among South African women using data from the South African Health Inequalities Survey, and Asante and Aikins (2008), who examined the relationship between wealth and National Health Insurance Scheme (NHIS) enrollment in two districts of Ghana. The former paper estimated a logit model in which the health insurance coverage was specified as a function of individual demographics and health status characteristics, while the latter employed test means method to analyze the differences between insured and uninsured households. Unfortunately, both studies failed to address the relationship between health risk and health insurance coverage, as discussed in the traditional adverse selection models. These studies were also silent on ex-post moral hazard issues. Since 1987, health care financing in SSA has been undergoing a series of major reforms. One major reason behind these reforms is the need to meet the Millennium Development Goal targets for health gains and poverty reduction. Given the catastrophic effect of user fees policies and out-ofpocket (OOP) payments on health care utilization (especially on the poor households) in this region over the years, governments, non-governmental organizations (NGOs), and communities have recognized the relevance of social health protection systems as one way of breaking the vicious circle of poverty and illness.2) This has led to the materialization of a new wave of community-based national health insurance schemes in many SSA countries. Presently, community-based national health insurance schemes exist in the following SSA countries; Benin, Burkina Faso, Cameroon, Ghana, Ivory Coast, Mali, Nigeria, Rwanda, Senegal, and Tanzania. Therefore, it is worthwhile to investigate the following questions in the case of SSA: 1) What are the effects of health risk on health insurance purchase decision (adverse selection)? 2) What are the effects of income on health insurance purchase decision (income effect)? 3) Does the purchase of health insurance change health care seeking behavior of households (moral hazard)? This paper contributes to the literature on the relationship between health risk and health insurance choice (adverse selection), income and health insurace choice (income effect), as well as


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Adverse Selection, Moral Hazard, and Income Effect in Health Insurance: The Case of Ghana

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the effect of health insurance on health care utilization (ex-post moral hazard). I use both a crosssection and pooled cross-section data in my investigation thus allowing me to estimate the inherent health risk of each individual household, and examine its effect on health insurance purchase decison. I also use three different measures of health care services that make it possible for me to investigate the robustness of the effects of health insurance on different health care services. Third, I use the generalized method of moments (GMM) estimation methodology that provides consistent and efficient estimates in the presences of heteroskedasticity to model the demand for health care. The rest of the paper is organized as follows. The section immediately following this introduction provides detailed background information on the Ghana health insurance system. Section III specifies the empirical models and estimation methods for the empirical analysis, while Section IV describes the data and the sample as well as the summary statistics. Section V presents the empirical results and evaluates their implications. Section VI concludes.

II．Institutional Settings A．The Ghana Health Insurance System This section explains the institutional peculiarities of the Ghana health insurance system, paying more attention to the incentive set by National Health Insurance Scheme (NHIS), which may be the driving force for health care utilization. The National Health Insurance Act (NHIA) was passed in August 2003 (Government of Ghana 2003). One basic characteristic of the Act is the legal obligation for each resident in Ghana other than members of the Armed Forces and the Police Service, to enroll in the NHIS or other health insurance plan. At the current moment, the NHIS is the only major health insurance plan operating in the country. Despite the compulsory health insurance mandate, enrolment into the NHIS or any other health insurance scheme is actually voluntary because there is no law that enforces enrolment and no one is automatically enrolled. Currently, about 145 district mutual health insurance schemes exist under the umbrella of the NHIS. The NHIS is public health insurance based on the principle of solidarity and is financed by 2.5 percent health insurance levy on selected goods and services, 2.5 percent of workers (mainly formal sector) social security contributions and health insurance premium (paid by informal adults)3) The scheme charges annual premiums that range between a minimum of GH¢7.2 and a maximum of GH¢48 per adult member. The premiums do not vary much among the various district schemes, but registration fee for photo ID cards and administrative expenses do vary. It is important to note that the scheme has premium exemptions for the following groups of people; children under the age of 18 years, adults of age 70 years or more, all pregnant women (this started in 2008), those classified as indigent, and Social Security and National Insurance Trust (SSNIT) contributors, as well as SSNIT pensioners. In general, the NHIS has defined benefit package of health services that the scheme covers. The package is fairly extensive and purports to cover about 95 percent of health problems (diseases) in Ghana. However, highly specialized care such as dialysis for chronic renal failure and organ transplant are not covered. There are no deductibles and no co-payments for health care services.


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III．Empirical Models and Estimation Methods A．Model for Adverse Selection and Income Effect Two of the goals of this paper are to examine the presence of adverse selection and income effect in the Ghanaian health insurance market. In order to do that, I assess empirically the relationship between health insurance coverage and an individual household＇s health risk, and income. Following Hurd and McGarry (1997) and Bundorf, Levin and Mahoney (2008), I estimate the following model: （1）

Yij = α ＋βQ ij ＋δX ij ＋ηR ij＋εij ,

where Yij denote the health insurance status of household i with insurance choice j (Yij takes on a value of one if the individual household is insured and zero otherwise). Observable household characteristics have been seperated into income quintile Qij and other household characteristics Xij. Income quintile Qij was used as dummy variables to capture the effect of income on NHIS coverage. In the empirical estimation, as elements of Xij in household characteristics, I include education, age, sex, and household size.4) In addition, I include three dummy variables for children under the age of 18, adults aged 70 or older, and pension fund membership. As explained in Section II, these groups have health insurance premium exemptions, which means a household with a member from any of these groups is more likely to join the scheme than other households. In order to include the effect of household behavior, I added two more dummy variables that account for hospital admission, as well as alcohol and tobacco uses. Moreover, I included the number of days of reported loss of activities due to illness. Furthermore, I included in X ij other control variables that may have effect on the dependent variable. For example, the sources of lighting and water, as well as type of dwelling may have an effect on both health and the decision to purchase health insurance. Xij also include a dummy variable for government employee, because in those districts where there are more public sector employee, greater number of households are more likely to purchase health insurance. The demand for health insurance in Ghana may differ from region to region, partly because of the differences in income among different households and partly because there are differences in the location of health insurance offices, and access to health care facilities. For the purposes of accounting for these disparities, I included in Xij a set of regional dummies. Einav, Finkelstein and Levin (2010) shows the interdependence of demand (self-select) for health insurance and insurer costs, and the possibility that households have private information relevant for insurer cost. Because of this interdependency I use household health risk to proxy for information households might have had in self-selecting. Rij is a set of health risk dummies that captures private information about health status, that, in turn, may affect choice behavior. Thus, Rij is part of unobservable household characteristics. Rij in Equation (1) is categorized into five different groups (i.e., RISK 1 to RISK 5), because the relationship between health risk and insurance coverage


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is usually non-linear (Bundorf, Herring and Pauly 2005). The health risk variable was obtained by using the method of propensity score matching (PSM) for the pooled (GLSSIV and GLSSV) data set. Since the GLSSIV was conducted in 1998/99 when NHIS was not available, our estimated health risk variable (i.e., estimated propensity score) is considered to be a clean ex-ante health risk variable.5) I estimate Equation (1) by logistic regression. In the logit model, health insurance premium is excluded because a measure of price (health insurance premium) is unavailable for the uninsured households. Similar approach has been used by Kirigia et al. (2005). The estimated results are presented in Table 2, while a detailed explanation is provided at the empirical results section. B．Model for Moral Hazard Next, I explore the presence of moral hazard in the GLSS data. In order to accomplish this task, I have to consider the relationship between health insurance coverage and demand for health care, as well as changes in household behavior. As such, I need data on some outcome variable D, such as the number of times insured households used health care services. Given individual household data on health care usage, I test for the presence of moral hazard by estimating the following econometric model: （2）

Dij = γ＋ψYij＋λX ij＋υij ,

where the dependent variable, Dij, represents demand for health care and I use three different variables; number of health practitioner visits, number of doctor consultations, and number of hospital admission nights in the past two weeks preceding the survey. Ψ is the coefficient of interest on the health insurance variable (Yij), defined in Equation (1). It measures the extent to which NHIS coverage increases health care utilization. Xij represents household characteristics, λ is a vector of coefficients on these controls, and υij is an error term. I estimate Equation (2) using GMM, and the instrumental variables used are shown in Table A3.

IV．Data The empirical analysis relies on two data sets, the GLSSIV and GLSSV conducted by the Ghana Statistic Service (GSS) in 1998/99 and 2005/06, respectively. The Ghana Living Standard Survey (GLSS) is a nationally representative multi-purpose survey of households in Ghana.6） The GLSSIV data is based on questionnaires to 26,411 respondents out of 5,998 households. On the other hand, the GLSSV data include interviews with 37,128 individuals from 8,867 households. Section 3 of both surveys provides information on health including condition of illness, usage, expenditure, and others. What was not covered in the GLSSIV was information on individual health insurance status. However, Section 3F of the GLSSV provides this information. This is important, because the indicator of health insurance coverage is the dependent variable that I analyze in Equation (1), and is also the key variable used to analyze the relationship between health insurance


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coverage and health care usage. The two data sets are particularly well suited for studying adverse selection and moral hazard in health insurance, since, they contain detailed information about the past and present health status of household members. A．Propensity Score Matching and Sample Selection The GLSSV reports whether the respondent is covered by the NHIS or not. In the empirical analysis insured households are defined as those whose head or other member is a registered member of the NHIS. Given that our aim is to study adverse selection in health insurance, our sample from the GLSSV should be at least similar to those who reported illness in the GLSSIV. Thus, in order to deal with sample selection bias and reverse causation problems, PSM method was employed to select our sample from the two data sets.7) I estimate the propensity score for illness and used it for the PSM. Although the GLSSV has information on insured individuals, these individuals were unable to provide information on their insured health expenditures, because these expenditures are unknown to them. The choice for PSM and estimation of the health risk variable was motivated by the fact that the NHIS covers all the health costs of insured individuals whose sickness fall within the prescribed benefit package of the scheme. As a result of the PSM, 5,703 households from the GLSSIV and 8,190 households from the GLSSV who were on the common support region were selected for further analysis.8) However, since GLSSIV has no information on health insurance coverage, the analysis that follows covers only the 8,190 households. The next section prodives detailed descriptive statistics of the variables used for the analysis based on the matched sample. B．Descriptive Statistics Table 1 summarizes the descriptive statistics for households matched from the GLSSV. Results for the full sample are presented in the first column, and are decomposed into insured and uninsured households in columns 2 and 3, respectively. The sample mean health insurance coverage rate for the survey period is about 20 percent. When adjusted by the GLSSV sampling weights, this mean value implies about 4.3 million individuals from 1.1 million households were receiving NHIS coverage in 2006. These are quite close to the aggregate numbers on NHIS tabulated administratively by the National Health Insurance Authority. Since PSM was used to select our sample, the figures in the table compare household characteristics that are similar to pre-NHIS household characteristics for the insured and uninsured. I find no statistically significant differences in income of the two groups. I have provided the mean and standardized values of income. For families under this study, we standardize income to be mean zero, standard deviation one. The standardization is performed using the entire 8,190 sample from the GLSSV. The insured households have incomes that are 0.21 standard deviations above the national average. The uninsured households, on the other hand, have incomes that are only 0.02 standard deviations below the national average. Concerning adverse selection and moral hazard, on the one hand, the summarized statistics point


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to the difficulty in identifying the presence of adverse selection based on the household health risk variable without a formal model of health insurance choice. On the other hand, the statistics on health characteristics show that households who choose health insurance have higher values for health care service usage. I also find no significant differences in other household characteristics provided. As expected from PSM, these mean figures confirm that the selected households are comparable, and that there are no selection bias between the insured and uninsured that might otherwise affect the estimates of NHIS impact.

V．Empirical Results A．Effect of Health Risk on Health Insurance Choice (Adverse Selection) Table 2 presents the parameter estimates from the logit specification of the household＇s health insurance purchase decision. The first column refers to the logit coefficient estimates, the third to odds ratio estimates and the fourth to marginal effect estimates. The marginal effects are presented to facilitate the interpretation of the magnitude of the estimates. The demand estimates indicate that enrollment into the NHIS on the basis of health risk is high. In analyzing the presence of adverse selection I included in the model a set of health risk dummies. The omitted category is the dummy variable for the 1st health risk category. I find a monotonic relationship between health risk and health insurance coverage. Households with higher health risk are more likely to enroll in the NHIS than those with lower health risk. The coefficient on the 5th dummy is statistically different from zero at the 1 percent level, those on the 3th and 4th dummies are statistically significant at the 5 percent level, while the coefficient on the 2nd dummy variable is statistically significant at the 10 percent level. Overall, the results suggest the presence of adverse selection in the NHIS. As the odds ratio estimates in the table illustrate, compared to the reference group, households in the 3rd, 4th and 5th risk categories are about 30, 51 and 70 percent, respectively, more likely to have health insurance coverage. The results are consistent with findings from other studies that have found that health risk has positive relationship with health insurance coverage. Much of the existing literature finds evidence of higher health risk household members selecting into more generous health insurance plans (e.g., Bundorf, Levin and Mahoney 2008, Cutler and Zeckhauser 2000). The presence of adverse selection in the NHIS is not surprising because the health insurance premiums faced by households are not risk rated; therefore, those with higher health risk have higher incentive to obtain health insurance coverage. B．Effect of Income on Health Insurance Choice As the first 4 rows of Table 2 illustrate, the logit estimates indicate substantial income effect on health insurance purchase decision. Among the selected sample, we find a positive correlation between income quintile and health insurance purchase decision. Relative to the reference group of


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Table 1　Summary Statistics of Variables Mean 152.42 0.03

All

SD 334.78 1.04

Insured Mean SD 210.46 418.65 0.21 1.30

Uninsured Mean SD 137.45 307.75 0.02 0.95

Income (Ghana Cedi) Income (standardized) Income Quintile First quintile 0.19 0.39 0.12 0.32 0.20 0.40 Second quintile 0.19 0.40 0.15 0.36 0.20 0.40 Third quintile 0.20 0.40 0.20 0.40 0.20 0.40 Fourth quintile 0.21 0.41 0.22 0.41 0.21 0.40 Fifth quintile 0.21 0.41 0.31 0.46 0.19 0.39 Demographics HH size (persons) 4.09 2.62 4.02 2.63 3.99 2.62 45.15 15.73 Head＇s age (years) 45.09 15.73 44.84 15.76 Female 0.30 0.46 0.30 0.46 0.30 0.46 Pension 0.08 0.27 0.08 0.28 0.07 0.26 Member (aged 70 or older) 0.12 0.32 0.18 0.38 0.10 0.30 Member (below 18 years) 0.67 0.47 0.70 0.46 0.66 0.47 Number of lost activity days per capita 0.96 2.68 1.02 2.70 0.94 2.67 Poverty status 0.27 0.45 0.19 0.39 0.29 0.45 Education (head’s highest grade completed) Illitrate 0.32 0.47 0.31 0.46 0.32 0.47 Primary 0.13 0.34 0.13 0.34 0.13 0.34 Junior high/middle school 0.39 0.49 0.39 0.49 0.39 0.49 0.08 0.27 0.09 0.29 0.08 0.27 Senior high school Tertiary 0.08 0.27 0.08 0.27 0.08 0.27 Health Characteristics Propensity score of illness 0.53 0.12 0.54 0.11 0.53 0.12 Number of health practitioner visits per capita 0.52 0.84 0.65 0.88 0.49 0.82 Number of doctor consultations per capita 0.20 0.49 0.32 0.60 0.17 0.46 Reported hospital admission 0.16 0.37 0.21 0.41 0.15 0.54 Number of admission nights per capita 0.20 0.55 0.26 0.57 0.19 0.54 0.18 0.38 0.20 0.40 Expenditure on alcohol or tobacco 0.20 0.40 Health risk (quintile of sickness propensity score) Risk 1 0.28 0.45 0.27 0.45 0.29 0.45 Risk 2 0.25 0.43 0.24 0.43 0.25 0.43 Risk 3 0.21 0.41 0.21 0.41 0.22 0.41 Risk 4 0.17 0.38 0.18 0.38 0.17 0.37 Risk 5 0.09 0.28 0.09 0.29 0.08 0.28 Regional Dummies Western 0.10 0.30 0.10 0.32 0.11 0.31 Central 0.10 0.30 0.08 0.27 0.11 0.31 Greater Accra 0.16 0.37 0.13 0.34 0.17 0.37 Volta 0.08 0.27 0.04 0.19 0.09 0.28 Eastern 0.14 0.5 0.15 0.36 0.13 0.34 Ashanti 0.18 0.38 0.22 0.42 0.16 0.37 Brong Ahafo 0.09 0.29 0.16 0.36 0.07 0.26 0.09 0.28 0.10 0.30 0.09 0.28 Northern Upper East 0.04 0.19 0.01 0.11 0.04 0.20 Upper West 0.02 0.15 0.01 0.11 0.03 0.16 Instruments Source of HH energy is electricity 0.40 0.50 0.68 0.47 0.44 0.50 Source of water is indoor plumbing 0.16 0.36 0.23 0.42 0.14 0.34 Head is government employee 0.07 0.26 0.17 0.38 0.05 0.21 HH dwelling is a bungalow or flat 0.54 0.50 0.66 0.47 0.51 0.50 Returned migrant 0.27 0.45 0.29 0.46 0.27 0.44 In-migrant 0.63 0.48 0.64 0.48 0.63 0.48 Notes：This table shows the means for household observations in the analysis sample for GLSSV after the PSM. Means are shown for all households, insured households, and uninsured households. The sample size are 8,190, 1,645, and 6,545,respectively. HH = household. Number of lost activity days, number of health practitioner visits, number of doctor consultations, and number of admission nights are in per capita terms.


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Table 2 The Effect of Income and Heath Risk on Health Insurance Coverage Coefficient Income Quintile (Third quintile = 1) First quintile Second quintile Fourth quintile Fifth quintile Demographics HH size Head＇s age Age square Female Pension Member (aged 70 or older) Member (below 18 years) Number of lost activity days per capita Reported hospital admission Expenditure on alcohol or tobacco Education level (Illiterate = 1) Primary school Junior high/middle school Senior high school Tertiary Health risk (Risk 1 = 1) Risk 2 Risk 3 Risk 4 Risk 5 Instruments Source of HH energy is electricity Source of water is indoor plumbing Head is government employee HH dwelling is a bungalow or flat Regional Dummies (Ashanti = 1) Western Central Greater Accra Volta Eastern Brong Ahafo Northern Upper East Upper West Intercept

Robust Std. Error

Odds Ratio

Marginal Effect

0.377＊＊＊ 0.316＊＊＊ 0.090 0.248＊＊

0.105 0.098 0.094 0.098

0.685 0.729 0.913 1.281

－ 0.048 † － 0.048 † － 0.012 † 0.035 †

0.014 0.006 0.228 0.093 0.030 0.923＊＊＊ 0.062 0.011 0.170＊＊ 0.003

0.016 0.010 0.475 0.075 0.129 0.085 0.104 0.011 0.078 0.092

1.014 0.994 1.334 0.910 1.030 2.517 1.064 0.989 1.186 0.997

0.002 － 0.001 0.039 － 0.013 † 0.004 † 0.156 † 0.008 † － 0.001 † 0.024 † － 0.001 †

0.062 0.049 0.118 0.048

0.108 0.088 0.133 0.137

0.940 0.952 1.126 1.049

－ 0.008 † － 0.007 † 0.017 † 0.007 †

0.171＊ 0.264＊＊ 0.412＊＊ 0.529＊＊＊

0.102 0.115 0.130 0.164

1.186 1.302 1.510 1.697

0.024 † 0.038 † 0.061 † 0.083 †

0.975＊＊＊ 0.273＊＊ 1.413＊＊＊ 0.312＊＊＊

0.075 0.099 0.105 0.067

2.652 1.314 4.108 1.366

0.136 † 0.040 † 0.270 † 0.042 †

0.309＊＊ 0.467＊＊＊ 0.895＊＊＊ 0.766＊＊＊ 0.002 0.709＊＊＊ 0.305＊＊ 0.909＊＊＊ 0.636＊＊＊ 3.447＊＊

0.118 0.136 0.151 0.123 0.115 0.105 0.122 0.178 0.177 1.637

0.733 0.627 0.408 0.465 0.998 2.032 1.357 0.403 0.529 0.032

0.039 † 0.056 † 0.086 † 0.094 † 0.000 † 0.116 † 0.045 † 0.095 † 0.071 †

Notes: This table presents the estimated coefficient, odds ratio and the marginal effect of each income quintile and healthrisk category relative to the third income quintile (Q3) and the first health risk category（category 1), respectively, from alogistic regression of health insurance coverage as a function of income, health risk, and control variables. The marginal effects are the sample average of the change in probability given a change from income quintile (3) or risk category (1) tothe indicated income quintile or risk category. † is for discrete change of dummy variable from 0 to 1. ＊＊＊ Significant at the 1 percent level. ＊＊＊


Significant at the 5 percent level. Significant at the 10 percent level.

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the third quintile, households in the fifth quintile are about 28 percent more likely to enroll in the NHIS, while those in the first and second quintiles are 31 and 27 percent less likely to enroll in the scheme, respectively. Compared to the 3rd quintile, a negative but insignificant correlation was observed for the fourth quintile. The findings confirm empirical results reported in Ghana and South Africa, which high lighted the strong correlation between income and health insurance purchase decision. Indeed, Asante and Aikins (2008), using a single cross-section household survey data of two rural districts in Ghana, find that there is a positive correlation between income and NHIS enrollment. Similar result was also found by Kirigia et al. (2005), who showed that about 90 percent of South African women earning 7,600 Rand and above per month are more likely to purchase health insurance as compared to 6.3 percent of women in the income range of 1-950 Rand. What makes this study different from these two previous studies is that PSM was used to select our sample. Thus, it was possible to deal with the inherent problem of sample selection bias, which makes the estimated results more efficient. I further considered the difference in enrollment between the extremely poor and poor households (i.e., households in the first and second quintile). The estimated marginal effects shown in Table 2 indicate that on average the poor and extremely poor households both register about 0.048 less adult members than the non-poor. This means that if households in the 3rd quintile register 1,000 members, each of these two group of households will register 48 members less. This finding does not show widespread social exclusion of the poor from the NHIS, as has been perceived by many Ghanaians and previous studies that failed to address sample selection bias. C．Effect of Health Insurance on Health Care Utilization (Moral Hazard) This section presents the results of the effect of health insurance on health care utilization. Before presenting the empirical results, an explanation on the model selection and its relevant tests are provided. 1．MODEL SELECTION To test the endogeneity of health insurance status, Wu-Hausman and Durbin-Wu-Hausman (DWH) specification tests were conducted for each regression. The DWH test indicates the existence of endogeneity, which suggests that using the maximum likelihood (ML) estimation method for our data will result in inconsistent parameter estimates. Therefore, a further consideration was to select either instrumental variables (IV) or GMM estimator. The Pagan and Hall＇s test for heteroskedasticity was conducted to select the appropriate estimation method. The results of the test rejected the null hypothesis of homoscedasticity at the 1 percent level (Table 3), which indicates that GMM estimator is preferable to model the demand for health care. I also conducted a number of tests to examine the relevance, validity, and orthogonality of the instruments used. Table 4 presents the test results. The first two statistics used are the Shea partial R square and the partial R square from the first-stage regression. They are not low enough to flag a weak-instruments problem. Also, the F-statistics test of weak instruments has near zero p-value for the endogenous plan choice. These F-statistics are well above the rule-of-thumb benchmark of an


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Table 3　Endogeneity and Homoskedasticity Tests Health Practitioner Visits Statistics

Doctor Consultations

Hospital Admissions

P-value

Statistics

P-value

Statistics

P-value

Panel A. Endogeneity test Wu-Hausman

8.63

0.003

88.62

0.000

76.02

0.000

Durbin-Wu-Hausman

8.64

0.003

87.95

0.000

75.56

0.000

0.000

491.66

0.000

99.94

0.000

Panel B. Homoskedasticitity test Pagan-Hall

615.93

Table 4　Test of Instruments Health Practitioner Visits

Doctor Visits

Hospital Admissions

Panel A. Diagnosis of weak instruments Partial R2

0.09

0.09

0.09

Shea Partial R2

0.09

0.09

0.09

F-statistics test KP Wald rk F statistic Anderson-Rubin Wald

157.74＊＊＊ 4.41

＊＊

156.54＊＊＊

208.01＊＊＊

＊＊＊

23.22＊＊＊

27.01

Panel B. Diagnosis of Valid Instruments Hansen＇s Statistic (p-value)

0.82

0.61

0.36

C-statistic (p-value)

0.84

0.42

0.26

Notes: See Table A3 at the Appendix for the instruments used. 　　＊＊＊ Significant at the 1 percent level.

F-statistic value of 10 (Staiger and Stock 1997), which means none of the instruments used in the models seems to be weak instrument. The validity of the instruments was examined using Hansens J-statistic test of over identifying restrictions. The test results listed in Table 4 show that the instruments are valid. The orthogonality condition of the instruments was tested using the C-statistic. The p-values of the test suggest instruments used are exogenous. All the empirical statistics described above led us to conclude that the selected instruments were appropriate enough to estimate the demand models using the GMM estimator. 2．EMPIRICAL ESTIMATION RESULTS OF EFFECT OF HEALTH INSURANCE ON HEALTHCARE UTILIZATION (EX-POST MORAL HAZARD) The estimation results for the three health care utilization models are presented in this section and the analyses will focus on the incentive effect of the health insurance variable (NHIS) and other characteristics such as individual behaviors that may lead to greater health service usage after the take-up of health insurance. The estimated coefficients from the health care utilization models are reported in Table 5. Column 1 reports the results of GMM estimation for health practitioner visits. Columns 2 and 3 report similar GMM results restricted to medical doctor consultations and hospital admissions, respectively. Starting with the effect of health insurance on health care utilization, the results show that after


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Table 5　Effect of Health Insurance on Health Care Utilization Health Practitioner Visits


Hospital Admission Nights

Coefficient Robust SE



Health Insurance Health insurance status

0.289＊＊＊

0.073

0.508＊＊＊

0.052

0.007＊＊

0.004

0.004＊

0.002

0.004

0.018＊＊＊

0.003

0.434＊＊＊

0.056

Demographics Income (log) Household size

0.037

＊＊＊

0.007＊＊

0.003 0.003

Head＇s age

0.007＊＊

0.003

Age square

0.316＊＊

0.134

0.186＊＊

0.084

0.049

0.095

0.019

0.006

0.013

0.002

0.015

0.018

0.021

Female

0.056

Member (aged 70 or older)

0.043

＊＊

＊＊＊

0.003＊

0.003

0.002

0.001

0.029

0.070＊＊＊

0.017

0.016

0.081＊＊＊

0.010

0.103＊＊＊

0.053＊＊＊

0.004

0.020＊＊＊

0.002

Member (below 18 years)

0.242

Lost activity days

0.177＊＊＊

0.007

Alcohol or tobacco

0.077＊＊＊

0.022

0.018

0.057

0.001

0.018

Poverty status

0.157＊＊＊

0.024

0.075＊＊＊

0.014

0.044＊＊

0.021 0.033

0.011 0.003

Education Level of Head Primary school

0.046

0.032

0.011

0.019

0.004

Junior secondary

0.078＊＊＊

0.024

0.017

0.016

0.013

0.018

Senior high school

0.015＊＊

0.035

0.033

0.024

0.007

0.029

Tertiary

0.094＊＊

0.035

0.002

0.023

0.036

0.025

0.179＊＊＊

0.033

0.018

0.025

0.005

0.025

Central

0.128＊＊＊

0.030

0.019

0.022

0.036

0.029

Greater Accra

0.057＊＊

0.026

0.046＊＊

0.021

0.038

0.024

Volta

0.003

Regional Dummy Western

0.037

0.001

0.023

0.041

0.026

Eastern

0.067＊

0.036

0.031

0.022

0.048＊＊

0.024

Brong Ahafo

0.222＊＊＊

0.038

0.111＊＊＊

0.023

0.053＊＊

0.026

0.038

0.087＊＊＊

0.025

0.050＊

0.029

0.009

0.026

0.023

0.029

Northern

0.049

Upper East

0.228＊＊＊

0.053

Upper West

0.000

0.046

Intercept

0.961

0.460

0.052＊＊ 0.728

＊＊

0.028 0.560

0.127＊＊＊＊＊＊

0.110

0.029 0.325

Note: Lost activity days is the number of lost activity days per capita, and alcohol or tobacco = 1 if the household had expenditure on alcohol and tobacco. 　

Significant at the 1 percent level. Significant at the 5 percent level. 　　　＊ Significant at the 10 percent level. ＊＊＊

　　

＊＊

controlling for endogeneity, the estimated coefficients on the insurance dummy are positive and statistically significant across all the three health care utilization categories. The estimates suggest that on average, a household with health insurance coverage would have approximately 30 percent increase on health practitioner visits, 51 percent increase on doctor consultations, and 43 percent increase on hospital admission nights. The positive and significant coefficients of the health insurance variable on health careutilization indicate the presence of ex-post moral hazard. Since those who have


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higher health risk tend to join health insurance, those who join health are more likely to use more health services. Therefore, these empirical findings confirm the positive correlation between the demand for health insurance and household health risk observed in the health insurance demand model. For a clear interpretation of the positive correlation between health insurance and health care utilization (i.e., whether they are efficient or inefficient moral hazard), I now consider the effect of some behaviors that may lead to greater health care usage. In this case, I considered the effect of expenditure on tobacco and alcohol dummy variable on health care utilization. The coefficients on this variable are negative for all the three dependent variables analyzed, but significant at the 1 percent level for health practitioner visits only. Thus, being a smoker or engaging in alcohol use reduces the probability of visiting a health practitioner. Considering the negative effect of this variable on health care utilization, it is plausible to conclude that the positive correlation between health insurance and health care utilization is not due to inefficient moral hazard.9) But rather, the increases in health services usage are due to the fact that insured households have taken advantage of the reduction in the user price of these services (or free services) under insurance to use more of the services. In order to examine the effectiveness of the premium exemption policy, dummy variables for children below 18 years, adults aged 70 or older, as well as poverty status were included in the model. These three variables had different effect on health care utilization. While the coefficients on the dummy variable for children under the age of 18 years were positive and significant at the 1 percent level across all the three equations, those on adults aged 70 or older observed a negative and significant effect in the model for doctor consultations. However, for health practitioner visits as well as hospital admission nights, the coefficient on the aged dummy was insignificant, which suggests that the exemption policy for the aged had no effect on these health care services. The dummy variables for poverty status were negative and statistically significant at the 1 percent level in the models for Health Practitioner Visits and Doctor Consultations, while negative and statistically significant at the 5 percent level in the model for Hospital Admission Nights. These results reveal that as of 2006, the health insurance exemption policy for the poor was less effective. Our estimates indicate 0.16 percentage point decrease in health practitioner visits, 0.08 percentage point decrease in doctor consultations, and 0.04 percentage point decrease in hospital admission nights. These results suggest that poor household members are less likely to seek medical attention in the event of illness or injury. 3．OTHER FACTORS THAT INFLUENCE THE DECISION TO PURCHASE HEALTH INSURANCE In Sections V.A and V.B, the empirical results of the effect of health risk and the effect of income on health insurance purchase decision were discussed. While these two variables are statistically significant in the household decision to purchase health insurance, there are other factors in the model that have equal level of significance worth mentioning. In this section I discuss briefly the results of these variables. The results presented in Table 2 show that households headed by public sector employees are more likely to join the NHIS. In addition, the results indicate that the type of dwelling and source of


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lighting were significant at the 1 percent level and had the expected signs. Households living in a bungalow, apartment or a flat were about 37 percent more likely to purchase health insurance, while those with electricity as the main source of lighting are about three times more likely to enroll in the NHIS. Moreover, the source of water dummy was positive and significant at the 5 percent level.

VI．Conclusion In this paper, I investigated the presence of adverse selection and moral hazard, as well as income effect in the Ghanaian health sector. In this market, the NHIS is the only major source of health insurance and the premium is not risk rated. In such a situation, the theory predicts the presence of adverse selection (i.e., those with higher health risk will self-select into the health insurance scheme). On the effect of income on health insurance demand, after controlling for sampling bias, I find that income is positively correlated with health insurance coverage. Taken together, the overall marginal effect of the income dummies does not show widespread social exclusion of the poor from the NHIS, as has been perceived by many Ghanaians as well as previous studies. Notwithstanding, I find evidence that the exemption policies for the indigent and children less than 18 years outlined in the NHIA are not functioning effectively. I therefore recommend that policymakers and the NHIS authority should consider these issues and take steps to address the anomalies. Since well-targeted subsidies for the poorest can preserve the incentives for a viable management of the schemes and help to achieve optimal health care access for all. The finding of a positive and significant correlation between health insurance coverage and health risk indicates that the insured who had ex-ante health risk are more likely to purchase health insurance than the uninsured. This suggests that the initial NHIS enrollment was driven at least by the expected health risk of the household, a result that has potential effect on the future expenditure of the NHIS, which is consistent with the interpretation of adverse selection (Bundorf, Herring and Pauly 2005). Concerning moral hazard, my main result is that the introduction of health insurance caused health care service usage rates of the insured households to rise. I find that households with health insurance coverage are more likely to visit a health practitioner, see a medical doctor, or stay in hospital overnight. Studies on health insurance in the tradition of Pauly (1968) have argued that an increase in health care consumption that is induced by insurance could lead to inefficiency (moral hazard) as a result of changes in the behavior of the insured individuals. However, our finding of insignificant correlation between expenditure on alcohol and tobacco and health insurance coverage, as well as a negative correlation between expenditure on alcohol and tobacco and health practitioner visits suggest that those households who spend on alcohol and tobacco are less likely to use health practitioner services. I do not observe changes in the behavior of the insured households that may lead to higher health care service usage, which suggests the observed ex-post moral hazard cannot be considered as inefficient moral hazard. The findings regarding adverse selection highlight that even in the context of developing countries, private information about health risk plays important role in the purchase of health insurance, which


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may largely shift health care expenditures from households to the health insurance scheme. Whether the increases in household health care consumption can improve household welfare remains an open question for future research under different framework.

Notes 　1）Interested readers should refer to Nyman and Maude-Griffin (2001). 　2）Social Health Protection (SHP) system aims to ensure that the entire population has equitable access to essential health care services at affordable prices. They are based on solidarity and mutual aid. One example of the SHP instrument for the informal sector employment is the use of a community-based health insurance scheme or mutual health organization to finance health care services (Wietler 2010). 　3）The National Health Insurance Authority defines informal adult as members aged between 18 and 69 years who are not members of the Social Security and National Insurance Trust (SSNIT). 　4）For the justification to include these variables, see Hurd and McGarry (1997), Bundorf, Herring and Pauly (2005), or Fang, Keane and Silerman (2008). 　5）See the appendix for more on the estimation of the health risk variable. 　6）Household, according to the definition of GSS, consists of a person or group of related or unrelated persons, who live together in the same housing unit, who acknowledge one adult male or female as the head of the household, who share the same housekeeping and cooking arrangements. 　7）Sample selection bias is defined as a stastitical sample of a population in which all participants are not equally balanced or objectively represented. This may be due to a non-random sample of a population, or the behavior of the units being sampled, including nonresponse on survey questions. Reverse causation means that outcome Y might exert a causal effect on an explanatory varibale X, in addition to (or instead of) the effect of X on Y. See Heckman (1999) or Dowd and Town (2002). 　8）See Figure A1 in the appendix. 　9）See also Table 2

References Akerlof, A. George. (1970),“The Market for Lemons: Quality Uncertainty and the Market Mechanism.” Quarterly Journal Economics, Vol. 84(3): 488–500. Arrow, Kenneth. (1963),“Uncertainty and the Welfare Economics of Medical Care.”American Economic Review, Vol. 53(5): 941–973. Asante, Felix, and Moses Aikins. (2008),“Does the NHIS Cover the Poor?”Institute of Statistical Social and Economics Research (ISSER) and School of Public Health, University of Ghana, Legon, Accra. http://www. moh-ghana.org/moh/docs/NHIS (accessed 25 April 2009). Bolhaar, Jonneke, Maarten Lindeboom, and Bas van der Klaauw. (2008),“A Dynamic Analysis of the Demand for Health Insurance and Health Care.”The Institute of the Study of Labor (IZA) Discussion Paper 3698. Buchmueller, Thomas C., Denzil Fiebig, and Glenn Jones. (2008),“Advantageous selection in private health insurance: The case of Australia.”Center for Health Economics and Evalution (CHERE), Working Paper 2008/2. Bundorf, Kate M., Brdley Herring, and Mark Pauly. (2005),“Health Risk, Income and Employment-Based Health Insurance.”National Bureau of Economic Research (NBER) Working Paper 11677. Bundorf, M. K., D. J. Levin, and N. Mahoney. (2008),“Pricing Matching and Efficiency in Health Plan Choice.” National Bureau of Economic Research (NBER) Working Paper 14153. Cutler, David M., Amy Finkelstein, and Kathleen McGarry. (2008),“Preference Heterogeneity in Insurance Markets: Explaining a Puzzle.”American Economic Review Papers and Proccedings, Vol. 98(2): 157–162.


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Cutler, D. M., and R. Zeckhauser. (2000),“The Anatomy of Health Insurance.”In Handbook of Health Economics, Vol. 1a. , ed. A. J. Culyer and J. P. Newhouse, 563–643. Amsterdam; New York; Oxford: Elsevier, North-Holland. Dehejia, Rajeev H., and Sadek Wahba. (2002),“Propensity Score-Matching Methods for Nonexperimental Causal Studies.”The Review of Economics and Statistics, Vol. 84(1): 151–161. de Meza, David, and David C. Webb. (2001),“Advantageous Selection in Insurance Markets.”RAND Journal Economics, Vol. 32(2): 249–262. Dowd, Bryan, and Robert Town. (2002),“Does X Really Cause Y?”AcademyHealth, Washington, D. C. Einav, Liran, Amy Finkelstein, and Jonathan Levin. (2010),“Beyond Testing: Empirical Models of Insurance Markets.”Annual Review of Economics, Vol. 2: 311–336. DOI: 10.1146/annurev.economics. 050708.143254. Fang, Hanming, Micheal P. Keane, and Dan Silerman. (2008),“Sources of Advantageous Selection: Evidence from the Medigap Insurance Market.”Journal of Political Economy, Vol. 116(2): 303–350. Finkelstein, Amy, and Kathleen McGarry. (2006),“Multiple Dimensions of Private Information: Evidence from the Long-Term Care Insurance.”American Economic Review, Vol. 96(4): 938–958. Government of Ghana. (2003),“The National Health Insurance Act 650 of 2003.” Heckman, J. James. (1999),“Causal Parameters and Policy Analysis in Econometrics: A Twentieth Century Retrospective.”The Quarterly Journal of Economics, Vol. 115(1): 45–97. Heckman, J. James., Hidehiko Ichimura, and Petra Todd. (1998),“Matching as an Econometric Evaluation Estimator.”The Review of Economic Studies, Vol. 65(2): 261–294. Hurd, Mihael D., and Kathleen McGarry. (1997),“Medical insurance and the use of health care services by the elderly.”Journal of Health Economics, Vol. 16(2): 129–154. Kirigia, Joses M., Luis G. Sambo, Benjamin Nganda, Germano M. Mwabu, Rufaro Chatora, and Takondwa Mwase. (2005),“Determinants of health insurance ownership among South African women.”BMC Health Service Research, Vol. 5(1): 17. DOI: 10.1186/1472-6963-5-17. Nyman, John A., and Roland Maude-Griffin. (2001),“The welfare economics of moral hazard.”International Journal of Health Finance and Economics, Vol. 1(1): 23–42. Pauly, Mark V. (1968),“The Economics of Moral Hazard.”American Economic Review, Vol. 58(3): 531–537. Rothschild, Michael, and Joseph E. Stiglitz. (1976),“Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information.”Quarterly Journal of Economics, Vol. 90(4): 629–649. Smith, Jeffrey A., and Petra E. Todd. (2005),“Does Matching Overcome LaLonde＇s Critique of Nonexperimental Estimators?”Journal of Econometrics, Vol. 125(1-2): 305–353. Spence, Michael, and Richard Zeckhauser. (1971),“Insurance, Information and Individual Action.”American Economic Review, Vol. 61(2): 380–387. Staiger, Douglas, and James H. Stock. (1997),“Instrumental Variables Regression with Weak Instruments.” Econometrica, Vol. 65(3): 557–586. Wietler, Katharina. (2010),“Mutual Health Organizations in sub-Sahara Africa - Opportunities and Challenges.” Technische Zusammenarbeit (GTZ) GmbH Discussion Papers on Social Protection. www.gtz.de/de/ dokumente/gtz2010-en-mutual-health-organisations.pdf. Wilson, Charles A. (1980),“The Nature of Equilibrium in Markets with Adverse Selection.”Bell Journal of Economics, Vol. 11(1): 130–180. Zeckhauser, Richard. (1970),“Medical Insurance: a Case Study of the Tradeoff between Risk Spreading and Appropriate Incentives.”Journal of Economic Theory, Vol. 2(1): 10–26.

Appendix: Health Risk Measure, Propensity Score Matching, and Matching Results

PSM method has been accepted in the field of economics as one of the ideal methods to be


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employed when one wants to evaluate the impact of a social program. This framework has been widely used in evaluating labor market policies (for example, Dehejia and Wahba 2002; Heckman and Ichimura 1998), but empirical examples can also be found in other fields of study. It is widely used because it has the potential of addressing the possible occurrence of sample selection bias. Matching is a method of sampling from a large group of potential controls (non-participants) with the aim of selecting a subset of the control sample that has similar observable characteristics to those in the treated group (participants). A1 Estimating Propensity Score: Matching Algorithm When estimating the PSM, two choices have to be made. The first one concerns the model to be used for the estimation and the second one has to do with the variables to be included in this model. Normally, a logit or probit function is used for this purpose, given that treatment is typically dichotomous. For example, in this paper, the interest is in the propensity score for reported illness for the two survey years. Thus, the treatment assignment indicator equals one for the treated group (those who reported sick) and zero for the control group. The preference for logit or probit model over linear probability models derives from the well-known shortcomings of the linear probability model, especially the unlikeliness of the functional form when the response variable is highly skewed and have predictions that are outside the [0, 1] bounds of probabilities. In this study, the logit model was used because the logit distribution has more density mass in the bounds. Concerning the second choice, the matching strategy to be employed depends on the conditional independence assumption (CIA), which requires that the outcome variable(s) must be independent of treatment conditional on the propensity score. In most cases, there will be no comprehensive list of clear relevant variables that will assure that the matched comparison group will provide an unbiased impact estimate. Since our interest is to estimate the propensity score for illness, and later use it for matching, an obvious set of factors that have to be considered in the PSM model are those that affect the quality of health. Moreover, consideration should also be given to factors that affect self-selection, such as one＇s distance from the location of a health facility, and other demographic factors. Presumably, factors that may affect health in Ghana include the household expenditure, the dwelling, the source of water, and the source of lighting of the households. For the self-selection variable, I considered location of the household (i.e., urban or rural locality). Other demographic variables included in the model are the household size, head＇s education, sex of household head and sector of employment of household head (public or private). For the convenience of the reader, the results of the estimated parameters of the logit regression are reported in Table A1. After the propensity scores have been estimated, matching algorithms have to be chosen (see Smith and Todd (2005) for technical details). For this analysis, kernel matching estimator was used. Kernel matching is a non-parametric matching estimator that compares the outcome of each treated person to a weighted average of the outcomes of the control group, with the highest weight being place on those with scores closest to the treated individuals.


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Table A1　Logit Results For Probability of Sickness Coefficient Robust

Standard Error

Expenditure (log)

0.392＊＊＊

0.026

Household size

0.150＊＊＊

0.010

＊＊＊

Female

0.215

0.034

Age

0.002＊＊

0.001

Primary school

0.295＊＊＊

0.580

Junior high/middle school

0.189＊＊＊

0.056

Senior high school

0.016

0.093

Tertiary

0.116

0.098

Education level (Illiterate = 1)

Locality (Rural Coastal = 1) 0.456＊＊＊

0.080

Other Urban

0.142＊＊

0.063

Rural Forest

0.280＊＊

0.060

Accra

Rural Savannah

＊＊

0.201

0.664

Instruments Source of HH energy is electricity

0.112＊＊＊

0.049

Source of water is indoor plumbing

0.502＊＊＊

0.056

Head is government employee

0.111＊

0.067

＊＊＊

HH dwelling is a bungalow or flat

0.043

0.038

Intercept

2.529＊＊＊

0.177

Observations

14.301

Notes: The table reports coefficients of logit estimation from pooled GLSSIV and GLSSV data set. The dependent variable is an indicator equal to one if a household member reported illness or injury in either of the survey years. 　　

Significant at the 1 percent level. Significant at the percent 5 level. 　　　　＊ Significant at the 10 percent level. ＊＊＊

　　　＊＊

A2 Matching Results I use kernel matching with normal density and bandwidth of 0.02. As explained in Section IV, the GLSSIV has sample size of 5,998 households, while the GLSSV has sample size of 8,867 households. Of the 8,867 households, 564 were excluded due to missing information on some variables used in the analysis, leaving a sample size of 14,301 for the PSM. After the matching, with the‘minima and maxima’comparison I excluded all observations lying outside the region of common support. In all, 408 households were excluded. Figure A1 provides a snap shot of the distribution of the propensity score for the treated and control groups. The horizontal axis of the figure displays the propensity scores indexed from the lowest to the highest and the vertical axis depicts the density. The distribution of the propensity score suggests that the densities of the propensity scores are more similar after matching. As shown in the figure, the tails of the histogram shows that there are clear and sizable differences in the


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— 19 —

Figure A1 Histogram of Propensity Score minima and maxima of the propensity score density for the treatment and the control groups, as such all observations where the propensity score is smaller than the minimum or larger than the maximum in the groups were deleted. From the analysis of common support, about 3 percent of the observations were classified as off-support. Aside using the distribution of the propensity score to assess the quality of matching, a balancing test was conducted to check whether the propensity score adequately balanced covariates in both groups. The basic idea of the balancing test is to check if there are significant differences in covariate mean values for both groups. A rejection of the test for any of the covariates means matching on the propensity score was not successful and remedial measures have to be taken. Table A2 reveals that before matching, there were large differences in the covariates of both groups. Almost all the p-values of the covariate mean values were statistically significant. Clearly, after the matching, the differences are no longer statistically significant, which suggests that the PSM helped to reduce the bias associated with observable characteristics.


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Table A2　T-Test After Matching Before Matching

After Matching

Treated

Control

Difference

P-value

Treated

Control

Difference

P-value

4.732

4.736

0.004

0.834

4.740

4.749

0.008

0.592

Expenditure (log) Household size

4.467

3.977

0.490

0.000

4.258

4.189

0.069

0.100

Male

0.775

0.656

0.118

0.000

0.761

0.762

0.000

0.950

Age

46.005

45.066

0.939

0.000

45.790

45.862

0.072

0.773

0.000

0.024

0.202

0.202

0.000

0.966

Education level (Illiterate = 1) Illitrate

0.195

0.219

Primary school

0.474

0.409

0.066

0.000

0.482

0.485

0.003

0.666

Junior high

0.250

0.257

0.007

0.332

0.241

0.237

0.004

0.543

Senior high School

0.043

0.062

0.019

0.000

0.039

0.039

0.000

0.995

Tertiary

0.037

0.052

0.015

0.000

0.037

0.038

0.000

0.883

0.017

0.089

0.000

0.075

0.077

0.002

0.739

Locality (Rural Coastal = 1) Accra

0.078

Other Urban

0.279

0.278

0.000

0.947

0.289

0.288

0.001

0.912

Rural Coastal

0.120

0.124

0.004

0.474

0.125

0.125

0.000

0.909

Rural Forest

0.314

0.234

0.080

0.000

0.301

0.300

0.000

0.940

Rural Savannah

0.209

0.196

0.013

0.053

0.210

0.209

0.000

0.918

Instruments Electricity

0.241

0.329

0.088

0.000

0.246

0.244

0.002

0.787

Indoor plumbing

0.110

0.189

0.080

0.000

0.109

0.110

0.000

0.956

Employee

0.358

0.469

0.111

0.000

0.367

0.363

0.003

0.981

Flat

0.592

0.615

0.023

0.006

0.590

0.590

0.000

1.000

Notes: The P-values are that of the t-test. The difference is between the treated group and the control group.

Table A3　The Selected Instruments Proposed Instrument

Health Practitioner V


Source of HH energy is electricity

◦

◦

HH dwelling is a bungalow or flat

◦

◦

◦

◦

◦

Head is in-migrant Head is return migrant

◦

◦

Predicted value of health insurance

◦

◦

Hospital Admission Nights

◦

Note: This table reports the instruments used in the GMM estimation. Those in the first column are for health practitioner visits, followed by doctor consultations in the second column and hospital admission nights in the third column.


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