Volume 19 Issue 2

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Adjusting for reporting bias in the measurement of education-related health inequalities The health impact of China’s rapid urbanization Comparing health measures through a model for health care use Analyzing the effect of supplemental health insurance on inpatient care use in Belgium

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Contents 4

Adjusting for reporting bias in the measurement of education-related health inequalities Teresa Bago d’Uva

10

The health impact of China’s rapid urbanization

18

Comparing health measures through a model for health care use

Ellen van de Poel

A. Exterkate

26

Analyzing the effect of supplemental health insurance on inpatient care use in Belgium Tom Van Ourti

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MET | Volume 19 | Issue 2 | 2012

1

Special Edition about Health Economics

In this Special Edition of the MET we will be focusing on the subject of health economics. The global population is growing exponentially and the demand for efficient health care systems has attributed for the growing importance of health economics. This research area is very broad with many different disciplines. In this MET we will take a look at various researches from Erasmus University that are related to health economics, from a micro-economic level as the treatment of patients within hospitals to a macroeconomic level as the population health of an entire country.

This MET hosts a wide variety of articles right from the Erasmus University on the subject of health economics.

By reading these articles, we hope that you are more informed on the fascinating researches that are being done right now in this broad and interesting research area.

Enjoy!

Marijn Waltman

It is important to have an efficient method for gathering information about health. In her essay, Anneke Exterkate compares two different methods for obtaining health measures: one that uses a latent variable for general health and one that uses self-assessment questions on health, by making use of vignettes. She finds that the first method is helpful in finding relations between health indicators and true health, while the second explains health-related behavior better.

Does supplemental health insurance have an effect on inpatient care use? Tom van Ourti analyzes the adverse selection and the consumption of voluntary health insurance in Belgium, a country with a broad coverage in compulsory social health insurance.

In her essay, Teresa Bago d’Uva investigates the impact of reporting heterogeneity on the measurement of education-related health inequalities. She finds that there is a general tendency for the higher educated older Europeans to rate a given health state more negatively than their lower educated counterparts, except in Spain and Sweden.

In the last essay, Ellen van de Poel assesses the impact that the rapid urbanization of China has on people’s health.

She constructed an index of urbanicity and defined urbanization in terms of movements across the distribution of this index. She found that urbanization raises the probability of reporting poor health and that effect increases with the degree of urbanization.

2

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Adjusting for reporting bias in the measuremen Teresa Bago d’Uva Erasmus School of Economics Tinbergen Institute Erasmus University Rotterdam

Heterogeneity in the reporting of health by education may bias the measurement of health disparities. We use anchoring vignettes to identify educational differences in the reporting of health in six domains by older individuals, in eight European countries. Without correction for reporting differences, there is no evidence of health inequality by education in 32 of 48 (country-domain) cases. There is however a general tendency for the higher educated older Europeans to rate a given health state more negatively than their lower educated counterparts (except in Spain and Sweden).

Correcting for this leads to a general increase in measured health inequalities (except for Spain and Sweden) and, consequently, to the emergence of inequalities in 18 cases. Measured health inequalities by education are often underestimated, and even go undetected, if no account is taken of reporting differences.

4

1. Introduction The measurement of socioeconomic inequalities in health has often relied on self-rated health, SRH (eg, Van Doorslaer and Koolman, 2004; Kunst, Bos et al., 2005). This is partly due to its low cost and feasibility in large-scale surveys, but is also justified by extensive evidence demonstrating its predictive ability for mortality (see, eg, the review of Idler and Benyamini, 1997) and for health care use (eg, Van Doorslaer, Koolman and Jones, 2004). However, there have been concerns that, besides containing valuable information on health status, SRH may vary with conceptions of what constitutes good health and expectations for own health (Thomas and Frankenberg, 2000). If these vary systematically with socioeconomic status, then measures of socioeconomic inequality based on differences in SRH will be biased.

Figure 1 illustrates this problem. This shows the hypothetical mapping from latent true health into categorical responses of a SRH instrument for a representative high and low educated individual. For illustrative purposes, all response thresholds are assumed higher for the more educated person (which could arise if individuals report their health relative to that of peers, or if more highly educated individuals are better informed of treatment options and so are less tolerant of a given health condition).2 In this example, for any given true level of health, the more educated person reports worse health on the categorical scale. For example, if and represent the true latent health levels, then both report their health as “moderate� despite the fact that the more highly educated person has better true health. If we were to rely solely on SRH, we would falsely conclude that there is no socioeconomic inequality in health. With data on SRH only, this is inevitable, as differences in reporting behaviour cannot be disentangled from differences in true health.

A possible solution to this problem is the vignettes methodology, which identifies reporting behaviour through the rating of case vignettes describing fixed levels of functioning within a given health domain (King, Murray et al, 2004).3 Survey respondents are asked to rate both the vignettes and their own health on the same response scale and so different evaluations of the same hypothetical case represent reporting

MET | Volume 19 | Issue 2 | 2012

nt of education-related health inequalities

Very good Very good

Good Moderate

Good

•

H*H

Poor Moderate

H*L

•

Very poor

Poor

Very poor

L

H

Figure 1: Self-reported health for high (H) and low (L) educated individuals. True latent level H*L is perceived by person L as “moderate” and by person H as “very poor”.

Level H*H is perceived by person L as “good” and by person H as “moderate”.

differences. This makes it possible to identify systematic differences in response thresholds in relation to individual characteristics such as education. Assuming that individuals rate the vignettes in the same way as their own health (response consistency), the thresholds obtained from the vignette responses can be imposed on a model for reported own health. This enables identification of differences in true health by SES and not merely of a mixture of health and reporting differences. One can then measure health on a comparable scale, by estimating the level that each group would report if they all used the thresholds of a reference group. For example, in terms of Figure 1, the health of the high education individual, , could be re-labeled “good”, while that of the low education individual would remain “moderate”.

This study tests for reporting differences by education and aims at determining the impact of correcting for such differences on the measurement of health inequalities by education, using data on self-reported own health and vignette ratings for older individuals, in eight European countries.

2. Data The Survey of Health, Ageing and Retirement in Europe (SHARE) randomly sampled from the population aged

MET | Volume 19 | Issue 2 | 2012

50 years and over (plus spouses) in 12 countries (BörschSupan and Jürges, 2005). The first wave of SHARE data were collected in 2004-05 and released in June 2007 (Release version 2.0).4 Vignettes data are available for eight countries, which we analyse separately: Belgium, France, Germany, Greece, Italy, The Netherlands, Spain, and Sweden.

Respondents classify their own health in six domains, in response to the questions: “Overall in the last 30 days, how much...”: “of a problem did you have with moving around? (mobility); “difficulty did you have with concentrating or remembering things?” (cognition); “bodily aches or pains did you have?” (pain); “difficulty did you have with sleeping such as falling asleep, waking up frequently during the night or waking up too early in the morning?” (sleep), “of a problem did you have because of shortness of breath?” (breathing); “of a problem did you have with feeling sad, low, or depressed?” (emotional health). The response categories are: “None”, “Mild”, “Moderate”, “Severe” and “Extreme”. In addition, for each domain, respondents evaluate three vignettes, each describing a fixed level of difficulty in that domain, on the same response scale.5 We measure educational attainment in the categories: (i) finished at most primary education or first stage of basic education; (ii) lower secondary or second stage of basic education (reference category); (iii) upper secondary education; and (iv) recognized third level education, which includes higher vocational education and university degree. We control for age and gender by means of a continuous variable and a dummy variable, respectively. Sample sizes by country, and education are shown in Table 1.

To illustrate reporting differences by education we present in Table 2, for The Netherlands and Sweden, the proportions classifying a specific vignette for each domain as no or mild difficulty (specifically, the vignette corresponding to the middle level of difficulty, among the three vignettes for each domain). Except for cognition, the higher educated Dutch are much less likely than lower educated to designate the health problem described in the vignettes as one of no or mild difficulty. In general, the Swedes are less likely than the Dutch to report a given health state as representing no or mild difficulties and, more relevant for current purposes, they display less variability by education in the reporting 5

Table 1: Sample sizes, by country and education Belgium

France

Italy

Germany

Greece

Netherlands

Spain

Sweden

N

%

N

%

N

%

N

%

N

%

N

%

N

%

N

%

136

24.1

386

44.3

254

57.1

-

-

307

42.8

76

14.3

292

63.1

149

36.0

65

7.5

85

19.1

89

17.6

69

9.6

223

41.9

93

20.1

75

18.1

Primary Lower secondary Upper secondary

146

25.9

140

24.8

251

28.8

84

18.9

305

60.3

219

30.5

118

22.2

31

6.7

96

23.2

Tertiary

142

25.2

170

19.5

22

4.9

112

22.1

123

17.1

115

21.6

47

10.2

94

22.7

718

100.0

532

100.0

463

100.0

414

100.0

Total 564 100.0 872 100.0 445 100.0 506 100.0 Note: For Germany, the SHARE data do not distinguish between levels (i) and (ii).

Table 2: Proportions of respondents in The Netherlands and in Sweden who classify vignette corresponding to the middle level of difficulty as "no difficulties" or "mild difficulties" by educational level Netherlands

Sweden

Education level Primary Lower secondary Upper secondary Tertiary Primary Lower secondary Upper secondary Tertiary

Pain 0.26 0.15 0.17 0.08 0.05 0.03 0.08 0.03

Sleep 0.09 0.08 0.05 0.04 0.04 0.04 0.02 0.00

of health. The tendency for the better educated to hold higher health standards is evident for only half of the domains.

3. Econometric methods The standard analysis assuming reporting homogeneity consists of estimating an ordered probit model for selfreported health in each domain. The category reported, , , in domain , is assumed to be generated by the position of a latent health index , specified as:

(1)

relative to a set of fixed thresholds , such that, if

,

(2)

where includes education, age and sex. The assumption of reporting homogeneity is reflected in the fact that the thresholds are fixed.

From the estimates of the ordered probit model for each health domain, we compute the highest to lowest

6

Mobility 0.18 0.14 0.14 0.03 0.19 0.18 0.17 0.15

Emotional 0.17 0.10 0.09 0.04 0.09 0.03 0.07 0.09

Cognition 0.20 0.17 0.16 0.23 0.05 0.03 0.08 0.10

Breathing 0.17 0.06 0.07 0.03 0.09 0.07 0.08 0.06

education group rate ratio for reporting no problem or difficulty in that domain, for a reference individual (male aged 64, the sample mean age). This represents our measure of health inequality by domain, with no adjustment for reporting heterogeneity.

We allow for reporting heterogeneity by using an extended ordered probit model – hierarchical ordered probit model, HOPIT – in which the reporting thresholds are made functions of individual characteristics and so the parameters of the latent index represent true health effects, and not a mixture of health and reporting effects.

The first component of the HOPIT models respondents’ ratings of the vignettes. The perceived latent health level of vignette in domain , , is specified to depend solely on a dummy indicator identifying the vignette being rated and a random, normally distributed error: ,

.

The observed categorical vignette rating, through the reporting thresholds: if

,

(3) , relates to

(4)

, which are now

MET | Volume 19 | Issue 2 | 2012

defined as functions of the same covariates that enter the latent index of own-health in (1),

(5)

Note that observable individual characteristics are absent from (3), following from the assumption of vignette equivalence that respondents understand the vignette description as corresponding to the same level of functioning on a uni-dimensional scale. Consequently, effects of in the thresholds (5) are identified. In other words, all the systematic variation in the vignette ratings is attributed to reporting behaviour.6 The second component of the HOPIT models individual own health. This is assumed to be determined by the position of a latent health index in relation to thresholds as in (1)-(2) with the important difference that the thresholds are no longer assumed constant but are constrained to be equal to those in (5), identified from the vignettes component of the model. This follows from the response consistency assumption that respondents rate the vignettes in the same way as they do their own health. If this did not hold then it would not be valid to impose the thresholds identified from the vignettes ratings on the reporting of own health, and so the true health effects would not be identified. The HOPIT therefore consists of generalised ordered probit models for the reporting of own health and health of the vignettes with the cross-equation restriction that the threshold parameters are equal. It is assumed that the error terms in the vignette and own latent health equations, and , respectively, are independent for all and .

To obtain vignette-adjusted health inequalities, we first estimate the parameters of the HOPIT model and then predict latent health in each domain for males aged 64 with high/low education. We then predict the vignetteadjusted probabilities that each of these individuals has no problem or difficulty in that domain using their own predicted latent health and the estimated thresholds of males aged 64 with low education. Since thresholds are fixed, these probabilities vary with the impact of education on true latent health only. Finally, high to low education rate ratios are simply obtained by taking the ratio of the two probabilities.

MET | Volume 19 | Issue 2 | 2012

4. Results We test for differences in response scales by educational level using a log-likelihood ratio test for joint significance of the three education dummy variables in the four response thresholds in the HOPIT model. There are significant differences (at 5%) in 29 of the 48 domain/ country combinations (Table 3). Differential reporting by education appears especially in the domain of mobility (in seven of the eight countries), and in France and The Netherlands (in all domains). Italy and Germany display differential reporting scales in only one and two domains respectively. Although, before adjustment for reporting differences, high to low education rate ratios for reporting no problem or difficulty with own health are generally greater than one, they are not significantly different from one in 32 of the 48 cases (including all domains in Sweden, The Netherlands and Belgium, and all but pain in Germany, Table 4). Vignette-adjustment raises 39 of the 48 rate ratios, 18 of which become significantly higher than one. The countries where we observe the largest impact are Belgium, France, Germany and The Netherlands. Spain and Sweden display a different pattern: the more highly educated rate a given health state more positively in three and four domains respectively and, consequently, adjustment reduces the magnitude of the rate ratio (but does not change its significance). The domains that are mostly affected by the adjustment for differential reporting scales are those of sleep and breathing.

5. Conclusion This study uses ratings of hypothetical case vignettes to investigate: i) whether reporting of health in six domains differs by educational level; and ii) the degree to which any such heterogeneity biases the measurement of health inequalities among the older population in eight European countries. First, clear evidence of reporting differences is found for most of the health domains and countries analysed. In six of the eight countries (so, excluding Spain and Sweden), more highly educated individuals are more critical of a given health state.

When uncorrected, this would lead to underestimation of health inequalities by education. In particular for Belgium and The Netherlands, before correction, there is no evidence of inequalities by education in the 7

Table 3: P-values of tests of different response scales by educational level Country

Pain

Sleep

Mobility

Emotional

Cognition

Breathing

Belgium

0.006

0.002

0.012

0.011

0.943

0.042

France

0.000

0.000

0.000

0.000

0.000

0.001

Germany

0.189

0.743

0.010

0.879

0.941

0.013

Greece

0.000

0.000

0.008

0.185

0.002

0.000

Italy

0.786

0.040

0.276

0.487

0.352

0.102

Netherlands

0.032

0.000

0.000

0.000

0.003

0.000

Spain

0.118

0.108

0.049

0.031

0.452

0.600

Sweden

0.213

0.198

0.028

0.005

0.185

0.062

Notes: Likelihood ratio test of joint significance of all education dummy variables in the four response thresholds of the HOPIT model.

Table 4: High to low education rate ratios for no health problem or difficulty, with and without adjustment for reporting differences Rate ratio (95% confidence interval)

Country Belgium

France

Italy

Germany

Greece

Netherlands

Sweden

Spain

Pain

Sleep

Mobility

Emotional health

Cognition

Breathing

unadjusted

1.13 ( 0.78 - 1.48 )

1.11 ( 0.83 - 1.39 )

1.06 ( 0.86 - 1.25 )

1.10 ( 0.90 - 1.31 )

1.05 ( 0.75 - 1.35 )

1.09 ( 0.90 - 1.27 )

Adjusted

1.27 ( 0.89 - 1.66 )

1.33 ( 1.04 - 1.63 )

1.22 ( 1.02 - 1.42 )

1.24 ( 1.03 - 1.45 )

1.12 ( 0.76 - 1.48 )

1.22 ( 1.00 - 1.45 )

unadjusted

1.39 ( 1.08 - 1.70 )

1.05 ( 0.84 - 1.25 )

1.21 ( 1.06 - 1.35 )

1.05 ( 0.91 - 1.19 )

1.40 ( 1.13 - 1.66 )

1.10 ( 0.95 - 1.24 )

Adjusted

1.54 ( 1.22 - 1.86 )

1.32 ( 1.08 - 1.56 )

1.33 ( 1.19 - 1.48 )

1.23 ( 1.09 - 1.37 )

1.61 ( 1.32 - 1.90 )

1.27 ( 1.10 - 1.44 )

unadjusted

1.77 ( 0.98 - 2.57 )

1.09 ( 0.66 - 1.52 )

1.53 ( 1.24 - 1.82 )

1.43 ( 1.06 - 1.81 )

1.57 ( 1.03 - 2.11 )

1.24 ( 1.02 - 1.47 )

Adjusted

1.91 ( 1.06 - 2.75 )

1.34 ( 0.88 - 1.80 )

1.59 ( 1.32 - 1.85 )

1.53 ( 1.19 - 1.88 )

1.55 ( 0.99 - 2.11 )

1.34 ( 1.18 - 1.51 )

unadjusted

1.99 ( 1.14 - 2.84 )

1.31 ( 0.94 - 1.68 )

1.33 ( 0.95 - 1.71 )

1.06 ( 0.81 - 1.32 )

1.37 ( 0.92 - 1.81 )

1.26 ( 0.97 - 1.56 )

Adjusted

2.08 ( 1.25 - 2.91 )

1.42 ( 1.03 - 1.80 )

1.44 ( 1.04 - 1.84 )

1.14 ( 0.85 - 1.42 )

1.52 ( 1.01 - 2.04 )

1.40 ( 1.11 - 1.70 )

unadjusted

1.23 ( 0.94 - 1.51 )

1.29 ( 1.09 - 1.49 )

1.03 ( 0.92 - 1.14 )

1.34 ( 1.08 - 1.60 )

1.37 ( 1.11 - 1.63 )

1.15 ( 0.98 - 1.32 )

Adjusted

1.45 ( 1.14 - 1.76 )

1.34 ( 1.12 - 1.57 )

1.04 ( 0.92 - 1.16 )

1.33 ( 1.05 - 1.61 )

1.54 ( 1.26 - 1.83 )

1.04 ( 0.81 - 1.27 )

unadjusted

1.11 ( 0.77 - 1.44 )

0.99 ( 0.74 - 1.24 )

0.99 ( 0.79 - 1.19 )

1.10 ( 0.88 - 1.33 )

1.32 ( 0.88 - 1.76 )

1.17 ( 0.93 - 1.40 )

Adjusted

1.35 ( 0.97 - 1.73 )

1.31 ( 1.06 - 1.56 )

1.18 ( 1.00 - 1.36 )

1.38 ( 1.16 - 1.60 )

1.39 ( 0.94 - 1.83 )

1.40 ( 1.17 - 1.62 )

unadjusted

0.95 ( 0.77 - 1.14 )

1.09 ( 0.89 - 1.29 )

1.32 ( 0.91 - 1.73 )

1.38 ( 0.98 - 1.79 )

1.19 ( 0.94 - 1.44 )

1.10 ( 0.81 - 1.39 )

Adjusted

1.02 ( 0.82 - 1.21 )

1.07 ( 0.82 - 1.32 )

1.08 ( 0.64 - 1.52 )

1.27 ( 0.86 - 1.68 )

1.03 ( 0.75 - 1.32 )

1.38 ( 1.05 - 1.71 )

unadjusted

1.44 ( 1.07 - 1.80 )

1.40 ( 1.11 - 1.69 )

1.37 ( 1.12 - 1.62 )

1.26 ( 1.07 - 1.45 )

1.60 ( 1.29 - 1.90 )

1.12 ( 0.94 - 1.29 )

Adjusted 1.43 ( 1.02 - 1.84 ) 1.40 ( 1.06 - 1.75 ) 1.52 ( 1.25 - 1.79 ) 1.25 ( 1.02 - 1.47 ) 1.55 ( 1.21 - 1.89 ) 1.17 ( 0.95 - 1.38 ) Notes: Probability of reporting no health problem or difficulty in each domain for the highest education group relative to the lowest. Unadjusted ratios estimated from the ordered probit model that imposes homogeneous reporting thresholds. Adjusted ratios estimated from the HOPIT model allowing reporting thresholds to vary by education, age and gender. All ratios computed for males at sample mean age (64).

probability of reporting no health problem or difficulty.

Vignette-correction however increases the ratios for all domains and reveals inequalities favouring the higher educated in four domains in these two countries, and in 10 of the other domain-country cases analysed.

in self-reported health and inequalities in health and health care utilisation.

About the author Teresa Bago d’Uva is associate professor of health economics at the Erasmus School of Economics in Rotterdam. Her areas of interest are health economics, applied microeconometrics, and measurement of biases

Notes [1] This article is a summary of the published paper “Bago d’Uva T, O O’Donnell, E van Doorslaer.

Differential health reporting by education level and its impact on the measurement of health inequalities among older Europeans. International Journal of Epidemiology 2008, 37:1375–1383”.

[2] However, different scenarios are possible (for

8

MET | Volume 19 | Issue 2 | 2012

example, if health problems are reported as a justification for not working, Bound, 1991; or if higher income individuals, perhaps driven by a belief that they should be in good health, use more lenient standards in reporting their own health status, Melzer, Lan et al, 2004).

[3] Some authors have previously adopted alternative approaches that do not use vignettes but rely on objective health information. One approach is to test whether the ability of SRH to predict mortality varies across SES groups (eg, Burstrom and Fredlund, 2001). Another approach has examined whether, after controlling for a health indicator assumed to be more objective, there is any systematic variation in SRH, which is then attributed to reporting heterogeneity (Lindeboom and Van Doorslaer, 2004). Both approaches have produced mixed evidence on the existence and nature of reporting heterogeneity by SES. The vignette methodology has been applied recently, for example, by Bago d’Uva, Van Doorslaer et al (2008). These authors found that reporting heterogeneity leads to overestimation of health inequalities by education but underestimation of those by income, in China, Indonesia and India.

[4] The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life). Additional funding came from the US National Institute on Ageing (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064).

The Belgian Science Policy Office funded data collection in Belgium. Further support by the European Commission through the 6th framework program (projects SHARE-I3, RII-CT-2006-062193, and COMPARE, CIT5-CT-2005-028857) is gratefully acknowledged. For methodological details see BörschSupan and Jürges (2005).

[5] Descriptions of vignettes for all domains can be found here: http://ije.oxfordjournals.org/ content/37/6/1375/suppl/DC1 [6] In principle, it would be possible to include an error term in (6) representing unobservable heterogeneity in reporting styles. We do not do so since, with only three vignette ratings within each domain and relatively small samples, identification is likely to be weak.

MET | Volume 19 | Issue 2 | 2012

References

[1] Van Doorslaer EKA, Koolman X. Explaining the differences in income-related health inequalities across European countries. Health Econ 2004;13:609–28.

[2] Kunst AE, Bos V, Lahelma E et al. Trends in socioeconomic inequalities in self-assessed health in 10 European countries.

Int J Epidemiol 2005;34:295–305.

[3] Idler E, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997; 38: 21-37 [4] Van Doorslaer E, Koolman X, Jones AM. Explaining income-related inequalities in doctor utilization in Europe.

Health Econ 2004; 13: 629-647 [5] Thomas D and Frankenberg E. 2002. The Measurement and Interpretation of Health in Social Surveys. in Summary Measures of Population Health: Concepts, Ethics, Measurement and Applications, edited by C. Murray, J. Salomon, C. Mathers, and A. Lopez. Geneva, Switzerland: World Health Organization. 2002.

[6] Bound J. Self reported versus objective measures of health in retirement models. J Hum Resour 1991; 26: 107-37.

[7] Melzer D, Lan TY, Tom BD, Deeg D, Guralnik JM. Variation in thresholds for reporting mobility disability between national population subgroups and studies. J Gerontol A Biol Sci Med Sci 2004; 59: 1295-1303.

[8] Quesnel-Vallée A. Self-rated health: caught in the crossfire of the quest for ‘true’ health? Int J Epidemiol 2007; 36:11611164.

[9] Bago d’Uva T, van Doorslaer E, Lindeboom M, O’Donnell O. Does reporting heterogeneity bias the measurement of health disparities? Health Econ 2008; 17:351-375.

[10] Burstrom B, Fredlund P. Self rated health: is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes? J Epidemiol Community Health 2001; 55:836–40.

[11] Lindeboom M, van Doorslaer E. Cut-point shift and index shift in self-reported health. J Health Econ 2004; 23: 1083-1099.

[12] King G, Murray CJL, Salomon J, Tandon A. Enhancing the validity and cross-cultural comparability of measurement in survey research. Am Polit Sci Rev 2004; 98: 184-91 [13] Börsch-Supan A , Jürges H (eds). The Survey of Health, Ageing and Retirement in Europe - Methodology. MEA: Mannheim. 2005.

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The health impact of China’s rapid urbanization Ellen van de Poel Institute of Health Policy & Management Erasmus University Rotterdam

While highly pertinent to the human welfare consequences of development, the impact of rapid urbanization on population health is not obvious. This paper uses community and individual level longitudinal data from the China Health and Nutrition Survey to estimate the net health impact of China’s unprecedented urbanization. We construct an index of urbanicity from a broad set of community characteristics and define urbanization in terms of movements across the distribution of this index. We use difference-in-differences estimators to identify the treatment effect of urbanization on the self-assessed health of individuals. We find that urbanization raises the probability of reporting poor health and that effect increases with the degree of urbanization. The effect may, in part, be attributable to changed health expectations, but it also appears to operate through health behaviour.

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Introduction In recent decades, China has experienced urbanization on a scale unprecedented in human history. The proportion of the Chinese population living in urban areas increased from only 20% in 1980, to 36% in 2000, and is estimated to have reached 45% in 2010 (United Nations, 2007). In the space of a few decades, China will complete the urbanization process that lasted hundreds of years in the West. The consequences of such rapid urbanization for population health are not obvious.

On the one hand, urban living offers improved access to modern medicine and gains in income that can be invested in health. On the other, the health of city dwellers is threatened by air pollution, more sedentary and possibly more stressful work, social detachment, and Western, high-fat diets.

In this paper, we estimate the net effect of urbanization on health using longitudinal data from the China Health and Nutrition Survey (CHNS).

Besides being a household panel, this survey also collects data on the characteristics of communities, making it possible to identify what happens to individuals’ health when the environment in which they live becomes more urbanized. This identification strategy avoids the selection biases that may arise from point-in-time comparisons between the health of urban and rural populations.

We construct an index of urbanicity, define urbanization in terms of movement of a community up the distribution of this urbanicity index and compare changes in the health of individuals exposed to such urbanization with changes in the health of those living in communities that remain rural.

Data Sample We use the China Health and Nutrition Survey (CHNS) panel data from 1991, 1993, 1997, 2000 and 2004. The CHNS is a large scale longitudinal survey conducted in 9 provinces in China. Urbanization rates vary considerably within each province. The CHNS collects information on a wide range of individual, household and community

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characteristics. A community, which is the primary sampling unit (PSU), is a government-designated administrative district.

After dropping observations with missing information on any of the individual or household level variables used in the regression analysis, or missing community characteristics used in construction of the urbanicity index, we are left with 31333 personwave observations.

Measurement of urbanization In order to track the increasing urbanization that is taking place in communities across the survey waves, we construct an urbanicity index using factor analysis on a broad set of characteristics from the CHNS community level data pooled across all survey waves (Van de Poel, O’Donnell and Van Doorslaer, 2009). The urbanicity index captures information on population size, land use in the community, transportation facilities, economic activity and public services.

Since the index is constructed from factor analysis, it has no meaningful unit of measurement. We therefore identify the urbanization of a community through changes in its rank position in the (whole period) distribution of the index, conditioning on those that start off in the bottom part of the distribution. That is, we compare communities that move from the bottom to the top half of the distribution with those that remain in the bottom half. In 1991, 60% of the sample of communities were below the (all wave) median of the urbanicity index, while by 2004 61% of the sample was above the median. To investigate an exposure-response effect, we also compare those that remain in the bottom half of the distribution with those that move from there to the third and the upper quartile. The percentage of communities in the top quartile of the whole-period urbanicity distribution increased from 18% in the 1991 to 41% in 2004.

that of people their own age on a four-point scale consisting of excellent, good, fair and poor. In the analysis, we mainly use a binary indicator of reporting fair or poor health (poorhealth), but with one estimator we exploit the information contained in the full ordinal scale.

SAH is a popular instrument for health status that is very widely used in research based on large scale household surveys because of its availability, and because it provides a measure of general health status that contains information on health over and above that which can be measured objectively by physiology-based instruments (Idler and Benyamini, 1997). We also make use of the following more objective, but narrower, measures of health: mortality; obesity (Body Mass Index (BMI)>30); underweight (BMI<18.5); measured hypertension; and reported symptoms experienced in the four weeks preceding the survey (fever, headache, rash, diarrhea, joint pain, heart problems or others). Since most of the ill health symptoms have very low prevalence rates (Table 1), we create a binary variable that equals one if the respondent reported any of the symptoms in the four weeks preceding the survey.

Control covariates To identify the health effect of urbanization, we control for a set of individual and household level characteristics including demographics, socioeconomic status and household living conditions. Urbanicity, being a geographic characteristic, is measured by community level factors alone.

Measurement of health We use self-assessed health (SAH) as the principal measure of health. Respondents aged 18 years or over were asked to rate their health compared to

Identification strategy and estimation As explained in the previous section, urbanization is defined in terms of movement of a community up the distribution of the urbanicity index - from the bottom to the top half or from the bottom half to higher quartiles. We identify the health impact of such urbanization by using difference-in-differences (DID) methods to compare the changes in health of those living in communities that experience urbanization with the changes in health of those

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Table 1: Description and means (proportions) of ill health indicators: total and across areas classified as urban/rural by the CHNS urban/rural dichotomy.

Description of variables (1/0) reporting poor health BMI>30 BMI<18.5 diagnosed hypertension: average of three systolic blood pressure measurements (at time of survey) wasâ&#x2030;Ľ140mm Hg and/or average diastolic blood pressure was â&#x2030;Ľ90mm Hg and/or respondent was taking medication to lower blood pressure symptoms experienced in 4 weeks preceding the survey: fever, sore throat, cough headache, dizziness rash, dermatitis diarrhea, stomachache joint pain, muscle pain heart disease/chest pain other symptoms suffering from any of the above symptoms whether respondent dies by subsequent wave Observations

that do not experience urbanization.

total 0.31 0.025 0.079

urban 0.34 0.029 0.072

rural 0.3 0.022 0.083

0.185

0.223

0.169

0.044 0.037 0.003 0.020 0.026 0.009 0.020 0.097 0.021 31333

0.056 0.043 0.004 0.024 0.034 0.013 0.043 0.135 0.017 10970

0.038 0.034 0.002 0.017 0.023 0.007 0.007 0.093 0.023 22874

where 1(.) is the indicator function. The model includes a full set of time effects , which capture trends in health that are common across all individuals, community effects , which absorb time invariant geographic differences, including those between treatment and control communities, and time varying community specific dummies, , each of which is equal to one if the community experiences a defined degree of urbanization at time . When we restrict the sample to those in the bottom part of the distribution of the urbanicity

index at the beginning of the panel, these dummies are zero for all individuals at their first observation.

Controlling for time-varying, individual specific covariates takes account of the differences between treatment and control communities in trends of observable determinants of health. The error term, is assumed to be drawn from the logistic distribution, such that (1) is a logit model.

From the above model, the treatment effect of a given degree of urbanization on the treated at the time of treatment is given by the partial effect of the degree of urbanization dummy ( ) evaluated for the group of communities that urbanize in the posturbanization period. We compute these effects by averaging over individuals in the treatment communities within the treatment period (Puhani, 2008).

To fully exploit the panel nature of the data, and take account of the individual level unobserved heterogeneity that is correlated with any of the right-hand-side variables in (1), we also apply the conditional logit estimator to a model like (1), but

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Model and estimation Let equal one if an individual i belonging to community c reports to have fair or poor health at time . This, and each of the other dichotomous health outcomes, is assumed to be generated as follows:

Table 2: Marginal effects of urbanization and covariates on self-assessed health.

Probability of reporting fair/poor health logit marginal effect urbanization log income married edprim edmid edhigh age age squared male waterplant flush excreta fuel size 1993 1997 2000 2004 Observations

0.042* -0.019*** 0.014 -0.034*** -0.043*** -0.052*** 0.008*** 0.000 -0.053*** -0.072*** -0.059*** 0.047*** 0.049*** -0.007** -0.026 -0.009 0.109*** 0.120*** 17864

fixed effects logit standard error 0.024 0.005 0.010 0.011 0.014 0.015 0.002 0.000 0.007 0.023 0.025 0.013 0.014 0.003 0.023 0.024 0.024 0.024

marginal effect

standard error

Probability of reporting health deteriorating1 fixed effects ordinal logit marginal effect

standard error

0.055* -0.015* 0.058** 0.009 -0.046 -0.075

0.031 0.009 0.024 0.041 0.050 0.082

0.049* -0.017** -0.009 0.049 -0.006 0.023

0.028 0.007 0.024 0.037 0.049 0.074

-0.080** -0.064* 0.049*** 0.037* 0.000 -0.005 0.061* 0.259*** 0.306*** 8284

0.040 0.036 0.018 0.021 0.007 0.032 0.033 0.031 0.033

-0.067* -0.084** 0.044** 0.044* -0.001 0.020 0.088*** 0.229*** 0.263*** 10994

0.036 0.033 0.017 0.022 0.007 0.026 0.031 0.029 0.028

Notes: Marginal effects are mean effects computed over individuals in the treated (urbanized) communities in the post-treatment period (except for marginal effects of wave dummies). Urbanization equals one if community is in the upper half of the urbanicity index at time t. Logit model includes community dummies. Standard errors are adjusted for clustering on communities. 1 Dependent variable is 1 if SAH score (1,2,3,4 for excellent, good, fair and poor respectively) in period t is greater than individual specific average over all periods. * significant at 10%; ** significant at 5%; *** significant at 1%. including a fixed unobservable individual level effect and, consequently, no time invariant regressors; in particular, the community effects.

This comes at the cost of smaller sample size as the fixed effects logit model only uses those observations for which there is variation in the dependent variable.

With a third estimator, we exploit more of the information in the ordinal SAH variable by taking the approach of Ferrer-i-Carbonell and Frijters (2004). They have shown that an ordered logit model with fixed effects can be estimated as a fixed

effects logit model, where the ordered data are collapsed to binary data and the model allows individual-specific thresholds. This involves creating a binary health indicator ( ) that equals one if the individual reports worse health at time than the average he/she reports across all waves and then using this as the dependent variable in an individual fixed effects variant of (1) estimated by conditional logit (Bรถckerman and Ilmakunnas, 2009). In the remainder of the paper, we will refer to this as the fixed effects ordinal logit. Standard errors are adjusted for clustering at the community

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Table 3: Marginal effects of degrees of urbanization on self-assessed health Probability of reporting fair/poor health fixed effects logit urbanization bottom half to third quartile of urbanicity index bottom half to top quartile of urbanicity index Observations

Probability of reporting health deteriorating1 fixed effects ordinal logit marginal standard effect error

marginal effect

standard error

0.051

0.031

0.041

0.029

0.079

0.048

0.099**

0.045

8284

10994

Notes: All Standard errors are adjusted for clustering on communities. Specification of covariates as in Table 3. 1 Dependent variable is 1 if SAH score (1,2,3,4 for excellent, good, fair and poor respectively) in period t is greater than individual specific average over all periods.* significant at 10%; ** significant at 5%; *** significant at 1%. level.

Definition of urbanization In the first instance, we define the treatment of urbanization as a community moving from below the median (across all waves) of the urbanicity index to above it. We only use those individuals living in communities that lie below the median of the urbanicity index when they are first interviewed.

In this setting, model (1) will consist of only one treatment dummy , which is unity only in the periods when the individualâ&#x20AC;&#x2122;s community is above the median.

We can also use model (1), and its variants that take account of individual level fixed effects, to investigate whether the health effects vary with the intensity of urbanization by defining treatment indicators that distinguish between smaller and larger movements up the distribution of the urbanicity index. We consider the sample of individuals whose communities start off in the lowest half of the urbanicity index and define two treatments: a move to the third quartile of the urbanicity index by any subsequent wave and a move to the upper quartile of the index. This model has two time varying group dummies in , and their marginal effects evaluated for the respective

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treatment groups in the post-treatment period are the estimated treatment effects of the two intensities of urbanization.

Results Effects on self-assessed health We first look at the health effect of a jump from below to above the median of the urbanicity index.

Table 2 shows treatment effects, and the marginal effects of covariates, obtained from the logit, fixed effects logit and fixed effects ordinal logit estimators.

All three models indicate a positive and significant (at 10%) treatment effect, indicating that urbanization increases the probability of reporting poorer health. The magnitude of the effect is about 4 to 6 percentage points, an increase of almost onefifth in the baseline probability of reporting fair or poor health, where the baseline is defined as the probability that the treatment group would have reported fair/poor health in the post-treatment period had they not been treated. The estimates from the fixed effects logit (second column) indicate that urbanization raises the probability of reporting fair or poor health (5.5pp) by slightly more than

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Table 4: Marginal effects of urbanization on probability of experiencing different health outcomes and on health-related behaviors. Fixed effects logit models, except for mortality (1 logit model).

Probability of reporting fair/poor health fixed effects logit marginal standard effect of error urbanization Health outcomes hypertension observations BMI>30 observations BMI<18.5 observations any ill-health symptoms observations dying by next wave1 observations Health related behaviors consume alcohol daily observations too high fat intake observations insufficient physical activity observations currently smoking cigarettes observations

0.013 3858 0.055 427 -0.038 1617 0.052* 3817

0.027 0.050 0.105 0.029

0.003 17864

0.003

-0.008 2615 0.061* 9203

0.033

-0.034 3069

0.031

0.054* 2633

0.030

0.034

Notes: Urbanization is defined as crossing the median of the urbanicity index (as in Table 2). Standard errors are adjusted for clustering on communities. * significant at 10%; ** significant at 5%; *** significant at 1%. having excreta around the household dwelling (4.9pp) or using solid fuels indoors (3.7pp), and a little less than not obtaining water coming from a waterplant (8pp) or not having a flush toilet (6.4pp).

The marginal effect of 0.049 in the fifth column should be interpreted as the increase due to urbanization in the probability of an individual reporting worse health than he/she did on average across the panel.

Effects of varying intensities of urbanization We now examine whether the health effect varies with the intensity of urbanization. Of the 17864 individuals who live in communities that start off in the lower half of the distribution of the urbanicity index 30% move to the third quartile of the urbanicity index sometime in the period 19912004, and 13% move to the upper quartile. From the estimates obtained from both individual fixed effects estimators presented in the top panel of Table 3 it is clear that the treatment effect of moving from the lowest half to the third quartile of the

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urbanicity index is consistently smaller than that of moving from the bottom to the upper quartile of the index. Three of the four estimated effects are just below significance at 10%, while moving from the bottom half to the top quartile of the index increases the probability of reporting a deterioration in health by ten percentage points, an increase of about one third relative to baseline, and this is significant at 5%.

the risk of cardiovascular disease (Table 4, bottom panel).

Effects on other health outcomes and healthrelated behaviour To investigate whether the negative effect of urbanization on reported health is not simply attributable to changing health expectations that may accompany urbanization and to obtain insight into which aspects of health are most affected by urbanization, we now turn to estimates of the impact of urbanization on a set of more objective and specific health outcomes. Treatment is defined as moving from the lower to the upper half of the distribution of the urbanicity index (as in Table 2).

The estimated effects of urbanization on hypertension and obesity are positive, while that on undernutrition is negative, but none of these effects is close to being significant (Table 4, top panel).

Urbanization does, however, significantly, raise the probability of reporting symptoms of ill-health by more than five percentage points, equivalent to a 50% increase. Urbanization is also associated with an increased probability of dying, although the effect is not significant, which is perhaps not surprising given the low incidence of death.

The positive but insignificant associations between urbanization and both hypertension and obesity could reflect gestation effects in the relationship between health-related lifestyles and health outcomes. To investigate this, we estimate the immediate effect of urbanization on health-related behaviour represented by consumption of alcohol, smoking, fat intake and physical activity. Each outcome is defined dichotomously. Urbanization has positive and significant effects, again only at the 10% level, on high fat intake and smoking, both of which are very much related to hypertension and

Conclusion This paper uses individual and community level panel data from China to identify a health impact of the tremendous urbanization that has taken place in that country over the last two decades. The results reveal substantial and significant negative effects of urbanization on health, with the probability of reporting poor or fair health increasing by 4-5.5 percentage points, an increase of almost one fifth in the baseline probability, when communities rise from the bottom to the top half of the distribution of urbanicity. Larger degrees of urbanization have stronger and more significant effects on reporting bad health.

A partial answer to the question posed in the title is that urbanization imposes a penalty on perceived health in China. While our panel estimators are robust to any time-invariant heterogeneity across individuals in the way they report their health, we cannot rule out the possibility that our results reflect across time variation in the reporting of health in response to the experience of urbanization. For example, people who experience urbanization, and are awakened to the potential of medical treatment for example, might raise their health expectations and therefore become more likely to report fair or poor health, given the same objective health. If this phenomenon is present, our estimates reflect not only changes in objective medical conditions that respond to urbanization, but also the health consequences of the dissatisfaction individuals may derive from a changing environment. This is still a meaningful and relevant finding with respect to evaluation of the development process. But our results do not appear to derive only from an impact of urbanization on health expectations.

The limitation of using an urbanicity index is that it is difficult to pinpoint the specific aspects of urban life that are most harmful to health. Pollution is an obvious candidate. Estimates of mortality due to airborne pollution in China rely on relationships observed in data from other countries with often

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substantially different levels of pollution (Cohen et al, 2004) and, as yet, there are no studies, such as Chay and Greenstone (2003), which use exogenous variation in pollution levels to identify effects on health outcomes. We have explored another potentially important mechanism through which urbanization may damage health – lifestyle. We find urbanization significantly raises fat intake and smoking, two of the most important risk factors for cardiovascular disease and, in the case of smoking, cancer. While we find no significant impact on either obesity or hypertension, this may be due to the lags involved for changed health behaviour to impact on outcomes. We do find that both outcomes are positively correlated with urbanization.

We do not overlook the positive effects that urbanization can have on population health but our analysis suggests that currently in China these positive consequences of urbanization for health are outweighed by the negative ones – pollution, unhealthy lifestyles, overcrowding, social isolation and tension, and the unhygienic living conditions of urban slums – and by rising health expectations.

Given the inevitability of increasing urbanization in China, and elsewhere in the developing world, it is of utmost importance to shift the balance in the direction of the positive effects and to deal with the demands arising from changed expectations.

pollution. In: Ezzati M, Rodgers A, Lopez A, Murry C (Eds), Comparative Quantification of Health Risks, vol 2. Geneva: World Health Organization; p. 1353–1433.

Ferrer-i-Carbonell A, Frijters P. 2004. How important is methodology for the estimates of the determinants of happiness? Economic Journal 114: 641-659.

Idler E, Benyamini Y. 1997. Self-rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior 38(1): 21-37.

Puhani PA. 2008. The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. IZA Working Paper n°3478.

United Nations (UN). 2007. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2007 Revision.

About the author Ellen Van de Poel is assistant professor at the institute of Health Policy and Management of the Erasmus University Rotterdam and coordinator of the Global Health track in the master Health Economics Policy and Law. Her research interests are in the field of health economics applied to developing countries, more specially urbanization and health and equity in health and health care financing.

References Böckerman P, Ilmakunnas P. 2009. Unemployment and self-assessed health: Evidence from panel data.

Health Economics 18(2): 161-179.

Cohen AJ, Anderson HR, Ostro B, Pandey KD, Krzyzanowski M, Künzil N, et al. 2004. Urban air MET | Volume 19 | Issue 2 | 2012

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Comparing health measures through a model fo Anneke Exterkate

We consider two different methods of obtaining health measures. The first method consists of a health measurement model with a latent variable for general health, where a health index is constructed that is corrected for differences in reporting styles across countries. The second method uses subjective self-assessment questions on health in six different domains, where responses are corrected for reporting style by making use of vignettes. To assess the usefulness of both methods, the alternative health measures are included in a model for health care use.

We find that, although the method using the latent health model is found to be more helpful in finding relations between health indicators and “true” health, the method using vignettes seems to explain health-related behavior better.

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1 Introduction One of the major challenges in international research on health is the difficulty to measure an individual’s health.

Health is known to be a multidimensional concept, and thus cannot be measured accurately by only considering an individual’s physical symptoms and diagnoses; how an individual feels also seems to be of great importance.

Another issue is that individual health across countries cannot be directly compared; if individuals’ self-assessment of health is used, large social and cultural differences exist (Zimmer, Natividad, Lin and Chayovan [2000], J¨urges [2006]).

Accurate, corrected measures of health are, however, very useful in models for health-related behavior. Therefore, we want to construct health indices that re ect only an individual’s health and not also reporting style differences. To analyze these topics, we use individuals’ selfassessed information on health. This (subjective) information is known to be very useful, once it is corrected for di erences in reporting styles across countries and individuals. We implement two different existing methods for obtaining health measures: a structural equation model with a latent variable for health (Meijer, Kapteyn and Andreyeva [2011]), and a model that uses vignettes to correct self-assessed health for heterogeneity in reporting styles (Bago d’Uva, Lindeboom, O’Donnell and Van Doorslaer [2011a]). We then implement a model for health care use and include the obtained health measures to find out whether the measures are actually re ecting an individual’s health and thus capable of explaining and predicting health-related behavior.

2 Methods 2.1 The latent health method The first method is a health measurement model, following Meijer, Kapteyn and Andreyeva [2011]. This structural equation model states that many health indicators, the dependent variables in the model, can be explained by an individual’s (unobserved, latent) true health, which is represented as one explanatory factor.

This latent health factor, called the “health index”, in turn is in uenced by multiple other explanatory variables, known as “predictors”. The goal of the model is to obtain one health index, as a proxy for the latent health factor,

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or health care use

that is comparable across individuals and across countries.

Let denote a particular indicator We assume that the latent, continuous version of a health indicator, , depends structurally on an individual’s unobserved (latent) true health, :

(1) where is a country- and health indicator-specic intercept and is a country- and health indicator-specic factor loading. These parameters are all country-specic, to allow for dierent thresholds in reporting health diculties across countries. Further, is an error term which is often referred to as the “measurement error”, which is assumed to be independent and normally distributed, with variances captured in the diagonal covariance matrix .

The one health indicator for which the least countryspecic deviations are expected, is the one that measures grip strength, because grip strength is measured objectively with an electronic device. Therefore, we use grip strength as the reference indicator, to ensure crosscountry comparability: the parameters that indicate the relation of latent health with the other health indicators are relative to grip strength. Because it is expected that grip strength directly depends on an individual’s height and weight, we add height and weight to equation (1) for the grip strength indicator:

(3)

To estimate the model we use a two-step method, where in the rst step we correct grip strength for height and weight, for all countries jointly. In the second step, all other parameters are obtained for all countries of interest separately.

where GS denotes grip strength and is a vector that includes height, weight, their squares and the product of height and weight. Here, the parameters , and are restricted to be the same across countries. Because grip strength is the reference indicator in our model, for location and scaling purposes, the factor loading will be normalized to 1 and the intercept is set to 0, to identify the mean of the latent health index, . The parameters of the other health indicators are relative to =1 and =0.

Again following Meijer et al. [2011], to model latent health, we assume the following linear relationship between an individual’s unobserved health, nd demographic and socioeconomic characteristics, , also referred to as the “predictive health equation”:

2.2 The vignette method The second method, the vignette method, was originally developed by King, Murray, Salomon and Tandon [2004]. However, we build on a paper by Bago d’Uva, Lindeboom, O’Donnell and Van Doorslaer [2011a], who implemented the model. This method aims to construct six health measures, one in each of the following six health domains: mobility problems, cognition problems, pain, sleeping problems, breathing problems and emotional health problems. Respondents’ self-reported scores on a five-point scale in each of the six domains are used to construct these indices, by controlling for individual characteristics and more objective health indicators. More important, the method also corrects for heterogeneous reporting behavior using vignettes, where respondents were asked to rate the degree of severity of health-related problems in the six health domains that persons in vignette stories encounter.

By correcting individuals’ assessments of their own health for their reporting style in the vignettes, true health eects are purged of reporting eects and more useful measures for health problems can be obtained.

The above can be done by using a hierarchical ordered probit (HOPIT) model (King, Murray, Salomon and Tandon [2004]) that makes use of these described vignette ratings. This model builds on two main assumptions: vignette equivalence and response consistency. The former assumes that “all respondents understand the vignette description as corresponding to the same level of functioning on a uni-dimensional scale” (Bago d’Uva et al. [2011a]). Response consistency says that “respondents rate the vignettes in the same way as they do their own health”.

Following an ordered probit specication, the perceived latent health level of the personthat is described in the vignette in domain for respondent , relates to the observed ratings of the vignettes, , in the following

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(2)

way:

=

(4)

where the thresholds are domain- and individual-specic, and also country-specic, because the analyses are done separately for all countries of interest.

The thresholds are dened as functions of demographic and socioeconomic ( ), as well as health ( ) covariates. The demographic and socioeconomic variables are the same for all health domains, while the health covariates dier depending on health domain. The covariates relate to the thresholds in the following way:

(5)

with . In this way, we can correct individuals’ own health problems for dierences in their response styles, as thresholds will depend on their characteristics.

Under the assumption of response consistency, individuals rate health problems in the vignettes in the same way as they rate their own health problems.

Therefore, the same thresholds can be used to relate individuals’ responses to the categorical questions on own health problems in the six domains, , to their unobserved latent individual ill-health, , using an ordered probit specication as in (4):

(6)

The two parts (the vignette part and the part concerning own health problems) are estimated simultaneously using maximum likelihood, where the log-likelihoods for the two parts are derived similar to a standard ordered probit model. For both parts, the same thresholds are used and the log-likelihoods are simply added up.

Latent individual indices for health problems per domain (corrected for reporting style eects) can then be approximated by computing the linear combination of the covariates in (7).

2.3 Health care use model To nd out how well the above two methods explain health behavior, we include their measures in a model for health care use. The health care use model that is used here is similar to the one that Bago d’Uva et al.

[2011a] used. The aim is to explain the number of doctor visits an individual made in the past year. This process contains two dierent parts, namely the decision of whether one goes to the doctor or not, and the decision of how often one visits the doctor once she decided to go to the doctor (Pohlmeier and Ulrich [1995]). Therefore, the model is called a two-part model or a hurdle model and consists of two dierent steps.’ We use a logit specication for the probability of visiting a doctor, and a truncated negative binomial specication to determine the number of doctor visits. In this two-step model, we allow for the possibility that the frequent zeros and the positive numbers of doctor visits come from dierent underlying distributions. This particular hurdle model handles excess zeros well (Cameron and Trivedi [2005]).

Using the two decision processes, and letting denote the number of doctor visits, following Bago d’Uva et al.

[2011a], the probability of observing a given number of doctor visits is as follows:

This unobserved latent individual “level of health problems”, , also depends on the same covariates and .

20

(7)

(8)

where s an indicator that is 1 if an individual visited a medical doctor in the past twelve months (that is, ) and zero otherwise. Further, , is the probability of going to the doctor and is the distribution of how many times one visits the doctor, given that . To be more precise,

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(9)

where is a vector that includes demographic, socioeconomic and health variables. In (8), is the part following a truncated negative binomial specication:

(10)

where is a negative binomial specication that is truncated at zero in (10) (see alsoCameron and Trivedi [2005]).

We assume independence of the two decision processes, and therefore the two parts can be estimated separately by means of maximum likelihood and the log-likelihoods of the two models can simply be added.

3 Data The data are taken from the Survey of Health, Ageing and Retirement in Europe (SHARE). This is a crossnational biennial survey of individuals living in several countries in many parts of Europe who were aged 50 or over when rst interviewed. Individuals were interviewed on several topics, including social and demographic background, physical and mental health, health care, employment and nancial situation.

Next to comparing the latent health method with the vignette method, we also want to compare our results to earlier results by Meijer et al. [2011] and Bago d’Uva et al. [2011a], who all used the rst wave of SHARE. Therefore, for this research, the second wave of SHARE is used, for which data was collected in 2005-2006 from over 34,000 Europeans living in thirteen dierent countries.

3.1 Used variables Most of the variables used for the latent health method are similar to the ones that Meijer et al. [2011] used: 24 health indicators that are collected in the vector (except for grip strength all binary; 1 if a respondent encounters any diculty with an activity, 0 otherwise), and demographic and socioeconomic variables like age, gender, education and income, that are included in in equation (3).

For the vignette method, we use respondents’ self-rated MET | Volume 19 | Issue 2 | 2012

health problems in six dierent domains. Their responses are on a ve-point scale, ranging from “no diculties” to “extreme diculties”. Respondents also get six stories about non-existing persons (vignettes) and they have to rate the diculties that these persons encounter in the same way. We include demographic and socioeconomic variables as explanatory variables in both the thresholds and in the health equation, as well as health-related variables that dier according to the particular health domain.

For the model for health care use, as dependent variable we use the number of visits to a medical doctor (general practitioner and/or specialist), including conversations on the telephone, in the past twelve months. As explanatory variables, we use demographic and socioeconomic variables that do closely match the ones in a research by Majo [2010], who argues that these covariates are in line with the factors usually considered to explain the demand for health care. Additionally, we include several dierent health variables each time the model is estimated.

3.2 Sample selection Because both methods have been implemented before and the primary focus here is not to compare countries but to compare the two methods, we only include the Netherlands and Spain in our analysis. We chose these particular countries because of known dierences in various socioeconomic characteristics, health and the use of health care.

We deleted individuals with missing information. The remaining sample consists of 2661 individuals for the Netherlands and 2228 in Spain. The vignette method makes use of vignettes, which are only asked to a random third of the total sample, so that the vignette sample sizes are smaller: 523 for the Netherlands and 517 for Spain.

For the health care use model, we want to be able to compare dierent models (including dierent health measures), and therefore sample sizes become even smaller: 2390 for the Netherlands and 1757 for Spain in the ”full” sample, and 471 for the Netherlands and 424 for Spain in the vignette sample.

3.3 Descriptive statistics Table 1 shows descriptive statistics for the main 21

variables.

4 Results 4.1 The latent health method Our results for the latent health method using the second wave of SHARE are similar to the results of Meijer et al.

[2011]. Estimation results and explanations are available on request.

After estimating the model for two countries separately, the obtained corrected health indices are assumed to be cross-country comparable. Therefore, the estimated means and standard deviations of latent health in Table 2 can be directly compared. The numbers are consistent with what is widely known: average health is better among the elderly in the Netherlands compared to Spain.

Spain has a larger standard deviation and therefore more variation in health.

To give some idea of how well the health index reflects true health, Meijer et al. [2011] computed the reliability of the health index: the squared correlation between the health index ( ) and true health ( ), or as they call it,

22

the of the hypothetical regression of on . The relatively high reliabilities for our estimates show us that the information that is in the health indicators ( ) has much explanatory power additional to the other covariates ( ) for latent health.

4.2 The vignette method The HOPIT model is estimated twelve times: for the six dierent health domains, for two countries. Again, results and discussion are available on request. Due to a smaller number of observations for this method, it is dicult to nd signicant eects, although age, education and domainrelated health diagnoses seem to be important determinants for both explaining individual health and correcting for reporting heterogeneity Table 2: Estimated distribution of latent (true) health and reliability of the health index

4.3 Health care use model The model for health care use is applied several times with dierent variables on health. In all of nine sets of variables we included demographic and socioeconomic variables as controls. Results for three of the nine sets of variables of the logit model and the truncated negative binomial model are presented in Table 3 - other results are available on request. Next to this, we also provide likelihoodratio tests for the joint signicance of the health variables, in the case of more than one health variable.

The rst two columns of Table 3 show the results of the logit part of the model. For all of the estimates holds: the poorer the health is that individuals report, the larger is their probability to MET | Volume 19 | Issue 2 | 2012

visit a medical doctor. The coecients of both the latent health method and the vignette method are signicant, and larger in magnitude compared to models that include comparable health variables that are not corrected for reporting heterogeneity.

Correlations imply that health indices from the two methods contain overlapping information. The strongest correlation is visible between the latent health index and the index in the domain mobility problems from the vignette method (-0.866 (p<0.001)); all correlations between the latent health index and the vignette-indices are signicant.

The last model, at the bottom of Table 3, includes the health measures from both methods. Generally the same vignette measures are signicant as in the model without the latent health index, but now the coecients for mobility problems are insignicant for both countries. This conrms the implied relation between the two measures by the high correlation.

The likelihood-ratio test for joint signicance of all seven (ill-)health measures shows that jointly they are signicant. However, if we perform a likelihood-ratio test for only adding the health index from the latent health method to the six vignette measures, the latent health index does not have signicant additional explanatory power. This means that, for predicting doctor visits, the measures from the vignette method are sucient: inclusion of the latent health index does not improve prediction signicantly.

The last two columns of Table 3 show coecients for the truncated negative binomial model. Generally, the coecients are signicant and in the expected direction, where a poorer health leads to a higher number of doctor visits. Again, by including measures from both methods, the vignette measure mobility problems becomes insignicant, and adding the latent health index to the six vignette measures does not improve explanatory power significantly.

How well do the health measures from both methods perform? Table 4 gives an overview of all obtained loglikelihoods for the health care use model, as well as two information criteria: the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).

All are divided by the sample size of the estimation, thus the average contributions to the loglikelihood and information criteria per individual are reported. To make

the models with the vignette samples comparable to the larger models, we also re-estimated the rst ve models with only the vignette subsample.

Out of the ve models in the upper part of the table, which shows the results for the models that use the full sample, the model with self-assessed health performs best. This is not surprising, as it is widely known that an individualâ&#x20AC;&#x2122;s self-assessment of health is very useful to explain health-related behavior. The latent health index performs better than the average of the 23 health indicators or all 23 raw indicators and grip strength together, which is in favor of the use of the latent health method.

The lower part of Table 4 shows the log-likelihoods and AIC/BIC for all nine models using the smaller vignette sample. The log-likelihoods for the models with the six uncorrected categorical self-assessment variables are higher than the log-likelihoods of the models where we included the 23 health variables plus grip strength, which means that, without applying the methods, the variables that are used in the vignette method are more useful for predicting doctor visits, than the health indicators that are used in the latent health method are.

Once the six health measures from the vignette method are included, inclusion of the latent health index does not lead to an improvement in either log-likelihood or AIC/BIC. This conrms what the likelihood-ratio tests in Table 3 already found.

We can conclude that for prediction purposes, the latent health method is less useful than the vignette method.

However, instead of including the 23 health indicators and grip strength that are used to obtain the latent health index, using the latent health index improves the fit of the model.

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5 Conclusions The latent health method gives insight in the relations between health indicators and latent health, while the vignette method shows that individualsâ&#x20AC;&#x2122; response styles are generally in uenced by their own health problems in corresponding domains, when they rate vignettes. For explaining health-related behavior (visiting a medical doctor in this case), the latent health index can better be used than the 23 health indicators and grip strength. The health variables that are used in the vignette method contain more useful information in themselves then the variables that are used in the latent health method, even

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without controlling for reporting heterogeneity. Once the six vignette measures are included in the health care use model, adding the latent health index does not signicantly improve the model. We can say that for prediction purposes, the measures from the vignette method are more useful than the latent health index is.

In the end, we cannot say that one of the two methods is systematically better than the other one, as we noted before that both methods use dierent information on health. Both methods seem to perform better in explaining doctor visits than just using the raw information, although the vignette method can better be used for this purpose.

6 About the author Anneke Exterkate earned a Master’s degree in Econometrics and Management Science from the Erasmus University in Rotterdam. She now works as a Junior Consultant at Gibbs Quantitative Research & Consulting.

7 References [1] Bago d’Uva, T., Lindeboom, M., O’Donnell, O. and Van Doorslaer, E. [2011a], “Education- Related Inequity in Health Care with Heterogeneous Reporting of Health,” Journal of the Royal Statistical Society Series A 174(3), 639-664.

[2] Bago d’Uva, T., Lindeboom, M., O’Donnell, O. and Van Doorslaer, E. [2011b], “Slipping Anchor? Testing the Vignettes Approach to Identication and Correction of Reporting Heterogeneity,” Journal of Human Resources 46(4), 872-903.

[3] Bollen, K.A. [1989], Structural Equations with Latent Variables, John Wiley & Sons, Inc., New York.

[4] Cameron, A.C. and Trivedi, P.K. [2005], Microeconometrics: Methods and Applications, Cambridge University Press, New York.

[5] Jurges, H. [2006], “True Health vs. Response Styles: Exploring Cross-country Dierences in Self-Assessed Health,” German Institute for Economic Research Discussion Paper 588.

[6] King, G., Murray, C.J.L, Salomon, J. and Tandon, A. [2004], “Enhancing the Validity and Cross-cultural Comparability of Measurement in Survey Research,” American Political Science Review 98(1), 184-191.

[7] Majo, M.C. [2010], “A Microeconometric Analysis MET | Volume 19 | Issue 2 | 2012

of Health Care Utilization in Europe,” Dissertation, Tilburg University.

[8] Meer, J., Miller, D.L. and Rosen, H.S. [2003], “Exploring the health-wealth nexus,” Journal of Health Economics 22(5), 713-730.

[9] Meijer, E., Kapteyn, A. and Andreyeva, T. [2011], “Internationally Comparable Health Indices,” Health Economics 20(5), 600-619.

[10] Pohlmeier, W. and Ulrich, V. [1995], “An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care,” The Journal of Human Resources 30(2), 339-361.

[11] United Nations Educational, Scientic and Cultural Organization [2006], ISCED 1997. Downloaded from http://www.uis.unesco.org/Library/Documents/ isced97-en.pdf, accessed July 29, 2011.

[12] Van Soest, A., Delaney, L., Harmon, C.P., Kapteyn, A. and Smith, J. [2007], “Validating the Use of Vignettes for Subjective Threshold Scales,” IZA Discussion Paper 2860.

[13] Zimmer, Z., Natividad, J., Lin, H. and Chayovan, N. [2000], “A Cross-National Examination of the Determinants of Self-Assessed Health,” Journal of Health and Social Behavior 41(4), 465-481.

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Analyzing the effect of supplemental health insurance on Tom Van Ourti Erasmus School of Economics Tinbergen Institute, Erasmus University Rotterdam

This paper shows that the effect of supplemental health insurance on inpatient care use crucially depends on specific institutional features of the Belgian health care system. In Belgium, a country with a very broad coverage in compulsory social health insurance,

supplemental

insurance

mainly refers to extra-billing incurred when staying in one bed hospital rooms.

Within this institutional background, we find only weak evidence of adverse selection in the coverage of supplemental health insurance, and much stronger effects of socio-economic background. A flexible count data model for hospital care shows that supplemental insurance has no significant effect on the number of hospitalization spells, but a negative effect on the number of nights an individual spends in hospital each time/spell.

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Introduction In recent decades, many European countries have experienced a growing pressure on the financial resources of their public health care systems and a parallel increase in the importance of different forms of voluntary (“private”) health insurance (Mossialos and Thomson, 2002; OECD, 2004). There are worries that this development may threaten the ideal of equality of access to health care in these countries, as better-off groups in society may be more likely to purchase voluntary health insurance.

A typical problem in assessing the effects of insurance on health care use is the difficulty in distinguishing the insurance (moral hazard) effect from adverse selection (those with higher health care risks being more likely to take out supplemental insurance). Previous empirical work gives much evidence for the existence of a moral hazard effect, but the results with respect to adverse selection are mixed since “the nature of demand for private health insurance itself depends on the institutional context in which that insurance operates” (Harmon and Nolan, 2001, p. 135). It is indeed obvious that both the degree of adverse selection and moral hazard in the voluntary insurance system will crucially depend on the degree of population, service and cost coverage in the public (compulsory) system and thus on the types of voluntary insurance available to complement public health insurance. The wide variety of possible arrangements has been described in the international comparison reports (Mossialos and Thomson, 2002; OECD, 2004), but until now there have not been many structured attempts to formulate and test specific hypotheses which are linked to these institutional differences. In fact, a careful analysis of the institutional setting may in some cases lead to empirical predictions of an insurance effect that does not in the first place induce increased consumption.

In this paper, we analyse the take-up (adverse selection) and the consumption (moral hazard) effects of voluntary health insurance in Belgium. Our data for inpatient care distinguish explicitly between the number of hospitalization spells and the number of nights per spell.

This allows us to easily relax the single spell hypothesis, which has been common in previous research (Santos Silva and Winmeijer, 2001).

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n inpatient care use in Belgium

Supplemental health insurance in Belgium Belgium has a system of compulsory health insurance, covering the entire population, which is organized through private, non-profit sickness funds. The service and cost coverage within the compulsory system (basic package) and the social contribution rates levied are identical for all funds. Compulsory health insurance is combined with independent medical practice. Payment is mainly fee-for-service and patients have a large degree of freedom in their choice of provider. The system of hospital financing distinguishes between medical and non-medical services. The former are fully integrated into the system of health insurance and are covered by the sickness funds. Here also, remuneration is mainly fee-for-service. Perhaps due to the dominance of feefor-service (in addition to the relatively large number of providers per capita), there are hardly any waiting lists.

At the same time, the Belgian system is characterized by large out-of-pocket payments, covering overall about 20% of total health expenditures. These out-of-pocket payments consist of official co-payments, payments for health care items not included in the basic package, and extra-billing. Supplemental insurance is mainly relevant with respect to extra-billing (“supplements” in the Belgian terminology). Extra-billing plays an important role in hospital financing. On top of co-payments, patients opting for a single person room can be charged additional charges (i.e. “supplements”) for their treatment, while patients no opting for a single room do not pay these “supplements” even though they receive the same treatment. Hence, in the Belgian setting, extrabilling boils down to price discrimination enforced by hospitals upon patients option for a single person room.

Supplemental (“hospital”) insurance covering these costs is by far the most important type of supplemental health insurance in Belgium, and the only one analysed in this article.

Both sickness funds and private for-profit insurers provide supplemental insurance. In the private sector, both group contracts and individual contracts are offered. The private market share in supplemental health insurance has remained rather limited and private insurers focus on the higher-income market segment.

According to Berghman and Meerbergen (2005), supplemental insurance by the sickness funds and by

private insurers covered in 2001 – the year of our data – about 2.35% and 0.65% of total health care expenditures, respectively. However, since 2001, the importance of supplemental insurance has certainly grown.

It should be clear that this institutional background will influence both the coverage of the supplemental health insurance and its impact on health care use. As mentioned before, there are hardly any waiting lists and patients with and without supplemental insurance are treated in the same hospitals. Supplements in hospitals are strictly regulated for patients in two-person and in common rooms and it can reasonably be expected that most patients in single rooms have supplemental insurance.

While a stay in a single room will undoubtedly be more comfortable, there is no empirical evidence that it will also imply a larger consumption of health care or a better quality of care – in any case, if there is an effect, it must be rather due to differences in provider behaviour than to reactions by patients on price differences. We will analyse whether these predictions are confirmed by the data.

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Data Our data come from the Belgian Health Interview Survey (HIS) in 2001. A short description of the variables and summary statistics are given in Table 1. The HIS collects information on hospital care consumption and supplemental hospital insurance for all adults in the survey. It unravels the number of hospital nights during the last year into the number of hospitalization spells and the number of nights during each hospitalization.

Table 1 also summarizes the available demographic and socio-economic information. We equivalized income using the modified OECD scale and then categorized into a set of six income ranges in order to allow for a flexible functional form. Some individuals qualify for lower co-payments – such preferential treatment is provided by the compulsory health insurance system to patients with a weaker socio-economic background.

One of the main strengths of the Belgian HIS is the large battery of questions on health status. First, we use selfassessed health and a dummy indicating whether the individual suffers from a chronic illness or is handicapped. In addition, the HIS contains detailed

Panel A: Dependent Table 1: Summary statistics of variables in HIS variables

Variable

ins_individual ins_family hospspell nightspell hospnight Variable

Description

Obs Mean Supplemental hospital insurance individual has supplemental hospital insurance 5349 0,624 at least 1 household member has hospital insurance 5349 0,690 Health care consumption – general and psychiatric hospitals, excluding deliveries number of spells at hospital (1 year) 5349 0,127 number of hospital nights per hospital spell 645 8,852 number of hospital nights 5349 1,122

Panel B: Independent variables (dummies)

Min

Max

0,484 0,463

0 0

1 1

0,409 17,011 7,026

0 1 0

3 200 200

Mean Variable Description Mean Demographic variables age 15-24 15 <= age <= 24 0,055 single single without children° 0,170 age 25-29 25 <= age <= 29 0,085 single_child single with children° 0,031 age 30-34 30 <= age <= 34 0,104 couple couple without children° 0,328 age 35-39 35 <= age <= 39 0,116 couple_child couple with children (reference category) 0,304 age 40-44 40 <= age <= 44 (reference category) 0,106 complex complex household°° 0,168 age 45-49 45 <= age <= 49 0,102 Belgian Belgian nationality (reference category) 0,941 age 50-54 50 <= age <= 54 0,095 EUmember non-Belgian EU nationality 0,043 age 55-59 55 <= age <= 59 0,069 nonEU non-Belgian non-EU nationality 0,017 age 60-64 60 <= age <= 64 0,067 male male 0,496 age 65-69 65 <= age <= 69 0,067 age 70-74 70 <= age <= 74 0,053 age 75-79 75 <= age <= 79 0,045 age: 80-84 80 <= age <= 84 0,018 age: 85+ 85 <= age 0,016 Socioeconomic variables eqinc: 0-20 0 BEF<=...<20,000 BEF† 0,041 employee employee (reference category) 0,503 eqinc: 20-40 20,000 BEF<=...<40,000 BEF 0,385 self-employed self-employed 0,069 eqinc: 40-60 40,000 BEF<=... <60,000 BEF 0,361 retired (early) pensioned 0,248 eqinc: 60-80 60,000 BEF<=... <80,000 BEF 0,160 sick disabled or invalid 0,026 eqinc: 80-100 80,000 BEF<=... <100,000 BEF 0,035 unemployed unemployed 0,063 eqinc: 100+ 100,000 BEF<=...

0,018 other not working housework, student, not working 0,092 no_primary no or primary school 0,185 preftreat reduction of co-payments 0,119 secondary secondary school 0,529 sport practising sport 0,674 higher higher education 0,202 smoke_dai daily smoker 0,252 university university education 0,071 smoke_occ occasional smoker 0,046 otherdipl other diploma 0,013 smokerno non-smoker (reference category) 0,702 alcohol drinking alcohol 0,821 Health variables sahverygood SAH very good 0,233 parkinson idem for Parkinson's disease 0,003 sahgood SAH good (reference category) 0,525 depression idem for depression 0,063 sahfair SAH fair 0,203 epilepsy idem for epilepsy 0,004 sahpoor SAH poor or very poor 0,039 dizziness idem for "dizziness with falling" 0,033 chronic chronic illness or handicap 0,289 migraine idem for migraine 0,109 asthma having asthma 0,045 arthritis idem for arthritis of hands or feet 0,077 bronchitis idem for bronchitis/CNSLD†† 0,056 otherarthritis idem for other chronic rheumatism 0,040 allergy idem for allergy 0,132 stroke idem for brain haemorrhage 0,005 sinusitis idem for sinusitis 0,085 ulcer idem for gastric/small intestine ulcer 0,036 heart idem for serious heart condition 0,041 bile idem for bilestones 0,007 hypertension idem for hypertension 0,144 osteoporosis idem for osteoporosis 0,039 abdomen idem for abdominal disorders 0,032 wrist fracture idem for wrist fracture 0,006 liver idem for liver disorder 0,009 hip fracture idem for hip fracture 0,003 kidneystones idem for kidney stones 0,010 spine fracture idem for fracture of spinal column 0,002 kidney idem for kidney disorder 0,005 prostate idem for complaints of prostate 0,021 bladder idem for bladder infection 0,018 uterus idem for prolapse of the uterus 0,007 diabetes idem for diabetes 0,033 other1 1 if another disease, 0 otherwise 0,067 nd thyroid gland idem for thyroid gland disorder 0,042 other2 1 if a 2 other disease, 0 otherwise 0,013 glaucoma idem for glaucoma 0,023 bmi_018 BMI<18 (underweight) 0,018 cataract idem for cataract 0,017 bmi_1825 18<=BMI<25 (reference category) 0,521 skin disease idem for skin disease 0,031 bmi_2530 25<=BMI<30 (overweight) 0,338 cancer idem for cancer 0,018 bmi_30+ 30<=BMI (obesity) 0,123 tired idem for long-lasting tiredness 0,050 SF33 0<=SF-36<33 0,059 back idem for back complaints 0,125 SF66 33<=SF-35<66 0,089 arthrosis idem for arthrosis 0,148 SF100 66<=SF-36<100 (reference category) 0,852 Note: sampling weights of the HIS were used; °: children are household members who are 18 years and younger; °°: a complex household is a household which cannot be attributed to one of the other groups (e.g. three adults or more); †: 1€ = 40.3399 BEF; ††: CNSLD = Chronic non specific lung disease.

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Description

Stdev

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information on the type of chronic disease suffered by the respondent which has been included as a separate dummy variable for each disease. Second, we included four categories derived from the body mass index (see e.g. Garrow, 1992): an index between 18 and 25 indicates regular weight, while (>=25) <18 indicates (over-) underweight, and >=30 indicates obesity. Finally, the survey includes the SF-36 physical functioning score – in which higher values correspond to better physical functioning. We defined three dummy variables based on the ranges 0-33, 33-66 and 66-100.

Who takes up supplemental health insurance? A bivariate probit model Let us first look at the take-up of supplemental health insurance. We present in this section the results of a bivariate probit model that jointly models the uptake of supplemental insurance and the probability of at least one night in the hospital allowing to get a better insight into the issue of the endogeneity of insurance status for the explanation of hospital care use (see e.g. Holly et al., 1998). Columns (1) and (2) of table 2 shows the univariate partial effects, i.e. the change in the absolute probability of having supplemental insurance/at least one hospital night if a dummy takes the value 1 compared to 0.

The first column gives the results for the take-up equation. First, we find that among the demographic variables, only age, being single without children and being a non-EU member are relevant determinants of supplemental insurance. Compared to the reference age category of 40–44, persons aged between 50 and 70 are more likely to have supplemental insurance. This finding seems to be demand-driven, whereas the decline in insurance coverage for the 70+ (compared to those between 50 and 70) might result from exclusion restrictions in insurance policies or from higher prices offered to the elderly.

Second, there are strong socio-economic differences.

Individuals with a university and higher education degree are more likely, and individuals with no or primary education are less likely to have supplemental insurance. For equivalent income, a similar pattern is found, and this socio-economic gradient is confirmed for the occupational groups. Finally, whether an individual is eligible for reduced co-payments has a MET | Volume 19 | Issue 2 | 2012

negative effect on take-up.

Third, the results with respect to health and lifestyle variables are mixed. Individuals in very good health are less likely to buy supplemental health insurance, which may point to some adverse selection. However, individuals in fair and (very) poor health are also less likely to take out insurance. Moreover, and more importantly, except for two specific indicators of chronic diseases, none of the other health indicators is significant at the 5% level.

Summarizing our results, we find only weak evidence of adverse selection and much stronger evidence for socioeconomic inequalities in take-up. This is well in line with what could be predicted based on our description of the Belgian institutional setting, characterized by the very broad coverage of the compulsory system and by the (relative) luxury character of the items covered by supplemental insurance. One does not need supplemental insurance to be treated well when ill or to avoid waiting lists. However, for patients who can afford it, taking supplemental insurance may lead to a more comfortable stay in the hospital at a lower cost.

We estimated the bivariate probit model to test for the endogeneity of supplemental insurance. The model is identified without exclusion restrictions (Wilde, 2000).

The estimate of the correlation coefficient is insignificant, showing no evidence of such endogeneity.

We also re-estimated the model imposing some alternative exclusion restrictions but could never reject the hypothesis of exogeneity, except in a more restricted specification including supplemental hospital insurance as the sole regressor hospitalization probability equation.

Supplemental insurance and inpatient care use In this section, we exploit the distinction made in the HIS between the number of spells and the number of nights per spell during the last year. This may be important, since it can be argued that the decision on the number of occasions to go to the hospital (i.e. to “start” a spell) is different from the decision on the number of nights per spell, in that the patient has much less decision power on the latter than on the former. The literature on the determinants of the number of hospital nights has until now only focused on modelling the total number of nights without distinguishing between what drives the number of hospitalization spells and what drives the 29

Table 2: Determinants of ... in Belgium in 2001 number of hospital nights in each supplemental inpatient hospital hospital spells nights per spell spell. A notable exception is Santos insurance admission (1) (2) (3) (4) Silva and Windmeijer (2001), who P[ins_individual=1] P[hospnight>0] E(hospspell) E[nightspell] 0,002 -0,007 0,963 0,975 propose modelling strategies to male age 15-24 -0,101+ 0,091* 2,087* 0,567 25-29 -0,097* 0,027 1,283 0,391* account for multiple spells if only the age age 30-34 -0,044 0,017 1,029 0,612 35-39 -0,027 -0,013 0,742 0,771 total number of contacts/nights is age age 45-49 0,001 0,020 1,168 0,425* 50-54 0,112** 0,023 1,065 0,957 known. Since we observe the number age age 55-59 0,096* 0,003 0,729 0,377* age 60-64 0,211** -0,031 0,492* 0,990 of spells and the number of nights per age 65-69 0,115* -0,029 0,593 0,926 70-74 0,009 -0,046** 0,400** 1,334 spell directly, we can model the age age 75-79 -0,007 -0,027 0,500+ 1,250 age: 80-84 -0,015 -0,046* 0,446+ 1,029 individual decision process more age: 85+ -0,170 -0,040 0,386+ 0,395 single -0,092** -0,020 0,715+ 1,242 explicitly. single_child 0,009 -0,035* 0,621 1,069 -0,029 -0,034** 0,660* 0,902 We stick to the popular independence couple complex -0,051 -0,035** 0,576** 0,982 EUmember -0,013 -0,024 0,715 0,719 assumption of two-part models (e.g. nonEU -0,219** -0,006 0,854 0,741 -0,185** -0,001 0,999 0,727 Pohlmeier and Ulrich, 1995; Deb and eqinc: 0-20 eqinc: 40-60 0,059* -0,002 1,074 0,658** 0,087** -0,003 0,953 1,134 Trivedi 2002; Gurmu, 1997; van eqinc: 60-80 eqinc: 80-100 0,159** -0,008 0,985 0,389+ 0,039 -0,061** 0,208* 0,229** Doorslaer et al., 2004; Van Ourti, eqinc: 100+ no_primary -0,107** 0,004 1,022 1,122 2004; Bago dâ&#x20AC;&#x2122;Uva, 2005 & 2006), but higher 0,104** -0,009 0,830 0,982 university 0,071* -0,023 0,716 1,369 do not restrict to just one spell, i.e. we otherdipl 0,105+ 0,077 1,891 0,218** self-employed -0,071+ -0,013 0,801 0,712 assume that the data generating retired -0,071 0,029 1,569* 1,327 -0,126+ -0,011 1,070 1,287 process of the number of spells is sick unemployed -0,219** -0,024 0,728 2,059+ other not working -0,161** -0,009 0,899 1,327 independent from the data preftreat -0,087* -0,016 0,866 0,927 0,062** 0,005 1,068 1,211 generating process of the number of sport smoke_dai -0,056* -0,023* 0,776+ 1,139 -0,095* -0,048** 0,401** 0,921 nights per spell. We further assume smoke_occ alcohol 0,055* 0,008 1,017 0,838 sahverygood -0,068** -0,017 0,744 0,974 that the data generating process of the sahfair -0,062* 0,034* 1,458** 1,038 -0,135* 0,056+ 1,693* 0,916 number of nights per spell is similar sahpoor bmi_018 0,071 0,070+ 1,543 1,090 0,035+ -0,001 1,039 1,127 for each spell and independent bmi_2530 bmi_30+ -0,033 0,021 1,175 1,270 chronic 0,004 0,035** 1,517** 1,069 between spells (see further for SF33 0,011 0,086** 1,769** 2,335** 0,040 0,045* 1,540* 1,443+ additional argumentation). Both SF66 independence assumptions enable us to estimate two

(3) models (one for the number of spells and one for the ,where is a vector of explanatory variables, its number of nights per spell) separately, rather than associated parameter vector, and is an overdispersion jointly, which is easily seen from the conditional density: parameter. Equation (3) shows that the conditional variance is allowed to be larger than the conditional (1) mean â&#x20AC;&#x201C; a commonly observed characteristic of health care data. If the conditional mean and variance are equal and the model reduces to the Poisson regression

number op spells number of nights per spell model. ,where we have for ease of exposition not explicitly In the negative binomial model, the parameter vector accounted for conditioning on explanatory variables. does not represent effect of the determinants on the 1 denotes the number of nights individual spends in the number of spells . We can however derive the hospital during spell , is the number of spells, 1(.) is proportional change in the number of spells when each an indicator function.To analyse the number of spells, dummy goes from zero to one, by simply taking the we use the negative binomial regression model. It is exponent of the respective coefficient. well known that the conditional mean and variance of The second variable, i.e. the number of hospital nights the number of spells are then given by: per spell, can only take strictly positive and integer

(2) values. We therefore analyse this variable with the

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Table 2 continued

supplemental insurance (1)

P[ins_individual=1] -0,017 -0,022 -0,022 0,002 -0,036 -0,045 -0,018 0,087 -0,017 0,177* 0,071 0,076+ 0,045 -0,050 0,012 -0,095 0,048 0,047 0,042 -0,020 -0,061 0,030 0,073+ 0,032 -0,050 0,024 0,017 -0,096 -0,001 0,052 0,109* -0,176 -0,062 -0,208 -0,089 0,067 0,003 0,122+

inpatient hospital admission (2) P[hospnight>0] 0,012 0,058* -0,011 -0,023 0,013 0,016 0,042 0,119+ 0,094 0,197* -0,016 -0,013 0,007 -0,004 0,023 -0,032 0,050* 0,069 -0,004 -0,020+ 0,055+ 0,113* -0,006 -0,003 0,023 -0,011 0,010 0,170 0,014 0,207* 0,028 0,183 0,107 0,050 0,059 0,001 -0,009 0,003 -0,017

hospital spells (3)

E(hospspell) 1,228 1,508* 0,751+ 0,817 1,339 1,267 1,199 1,810+ 1,924+ 3,032** 1,084 0,761 1,072 1,042 1,187 0,521 1,482* 1,374 0,887 0,747+ 1,459+ 2,174** 0,951 0,881 1,252 0,761 1,195 2,559* 1,292 2,454* 1,352 4,191** 1,951 2,439 1,399 1,268 0,879 0,865 0,960

nights per spell (4)

E[nightspell] 0,496** 1,288 0,798 0,863 0,842 0,655* 1,142 2,873** 0,882 1,786 1,109 1,041 0,715 0,826 1,024 1,571 2,706** 0,421+ 0,720 0,859 0,562+ 1,124 0,948 0,794 0,727+ 1,043 1,853* 4,835** 0,853 0,939 0,672+ 0,115** 1,191 0,740 0,857 0,248* 0,599+ 1,076 0,672* 1,160 0,925

asthma bronchitis allergy sinusitis heart hypertension abdomen liver kidneystones kidney bladder diabetes thyroid gland glaucoma cataract parkinson depression epilepsy dizziness migraine skin disease cancer tired back arthrosis arthritis otherarthritis stroke ulcer bile osteoporosis wrist fracture hip fracture spine fracture prostate uterus other1 other2 ins_family spell2 spell3 Ď 0,061 alpha 0,456** 0,787** Observations 5349 5349 645 Log likelihood -4535 -1832 -1689 Note: we report univariate partial effects in columns (1) and (2), i.e. the change in the absolute probability of having supplemental insurance/at least one hospital night when a dummy takes 1 compared to 0 while using the average value for all other independent variables. 38 regional (district) control dummies are not reported. Exponents of coefficients (measuring the proportional change in the number of spells/nights per spell if the dummy goes from zero to one) are reported in columns (3) and (4). 38 regional (district) control dummies are not reported. Sampling weights of the HIS were used. Statistical inference is based on robust covariance matrices that allow for clustering at the household level: +: significant at 10%; *: significant at 5%; **: significant at 1%; shaded area: jointly not significant at 10%.

the second column. First, the effect of the socio-economic and demographic variables is rather weak. Second, the health variables are very significant in explaining the number of hospital spells.

Third (and most importantly), the number of hospital spells is not related to whether the individual or one of his/ her family members has supplemental health insurance for hospitalization.

Let us now turn to the estimation results for the number of nights per spell in the fourth column of Table 2. We included in the model dummies for the second and third spell (the first spell is the reference category). These dummies are jointly insignificant, which gives some justification (i) for our assumption of independence between the data generation process of the number of spells and the number of nights per spell, and (ii) for assuming that the data generating process of the number of nights per spell is similar for each spell.

Health indicators are the most important determinants of the number of nights.

Our most striking result is the strongly negative effect of having a supplemental

truncated at zero negative binomial regression model.

Analogous to the analysis of the number of spells, we present the estimation results in the form of exponentiated coefficients, which can be interpreted as the proportional increase in the untruncated number of nights.

The estimation results are shown in columns (3) and (4) of table 2. The second column gives the results for the number of spells; the third column gives the results for the number of nights per spell. In both cases, we introduced a dummy indicating whether the individual was living in a household with at least one member having supplemental insurance (ins_family). As mentioned before, all common supplemental insurance policies in Belgium include coverage of household members, and the previous section suggested that we can assume exogeneity of this dummy.

Let us now look at the results for the number of spells in

insurance on the number of nights per spell. This negative effect might be due to unobserved heterogeneity in health, leading to types of admissions with a shorter expected length of stay for individuals that have a supplementary insurance. Yet, the effect of insurance status hardly changes (and does not increase in absolute value) if we omit all the health information from the estimated model. An alternative hypothesis is that patients with a supplementary insurance (generally richer and better informed about the health care system), 1 express a desire for a shorter length of stay. In that case, a shorter stay in single rooms may be good for the reputation of the hospital among the groups concerned.

Since we have no direct information about possible differences in the quality of treatment, it would be misleading to derive from this any conclusions about a higher intensity of care in one-person rooms. We return

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to the issue of quality in the conclusion. However, whatever the interpretation of the negative effect, our most important finding is that there is not even the slightest indication of moral hazard in the form of an increase in the number of days spent in the hospital.

Remember that this is not surprising in the Belgian context, in which the supplemental insurance mainly covers luxury services.

in de tweede en derde pijler. Report for the Federal Science Department (AG/01/084): Social Policy Unit KULeuven, 2005.

[4] Deb P, Trivedi PK. The structure of demand for health care: latent class versus two-part models. Journal of Health Economics 2002; 21: 601–625.

[5] Garrow J. Treatment of obesity. The Lancet 1992; 340: 409-413.

[6] Gurmu S. Semi-parametric estimation of hurdle regression models with an application to Medicaid utilizations. Journal of Applied Econometrics 1997; 12: 225–242.

Conclusion When analysing the effects of supplemental health insurance, it is essential to take into account the overall institutional background of the health care system. Both the take-up of supplemental insurance and the (supplemental) insurance effect on health care consumption crucially depend on the specific features of the public (compulsory) system. This general idea is confirmed by our results for Belgium, a country in which the compulsory system has a very broad coverage, where there are no waiting lists in the public system and where supplemental insurance (at least until now) does not buy better health care quality. Moreover, supplemental insurance mainly relates to extra-billing, applied to patients who opt for a single room in the hospital.

This institutional setting leads to specific predictions that are corroborated in our empirical analysis. There are only weak indications of adverse selection in the take-up of supplemental insurance, but there is a strong socio-economic gradient. Moreover, a count data model for hospital care that explicitly distinguishes between effects on the number of spells and effects on spell length shows that supplemental insurance has no effect on the number of hospital spells and a significantly negative effect on the number of nights per spell. The latter result is in line with the finding of socio-economic stratification in supplemental insurance and in the ensuing choice of rooms.

[7] Harmon C, Nolan B. Health insurance and health services utilization in Ireland. Health Economics 2001; 10: 135-145.

[8] Holly A, Gardiol L, Domenighetti G, Bisig B. An econometric model of health care utilization and health insurance in Switzerland.

European Economic Review 1998; 42: 513-522.

[9] Mossialos E, Thomson S. Voluntary health insurance in the European Union. Report prepared for the European Commission: London, 2002.

[10] OECD. Private health insurance in OECD countries. OECD: Paris, 2004.

[11] Peters S. On the use of the RESET test in micro-econometrics models. Applied Economic Letters 2000; 7: 361–365.

[12] Pohlmeier W, Ulrich V. An econometric model of the two-part decisionmaking process in the demand for health care. Journal of Human Resources 1995; 30: 339-361.

[13] Santos Silva JMC, Windmeijer F. Two-part multiple spell models for health care demand. Journal of Econometrics 2001; 104: 67–89.

[14] Schokkaert E, Van de Voorde C. Health care reform in Belgium.

Health Economics 2005; 14: (S25-S39).

[15] Shmueli A. The effect of health on acute care supplemental insurance ownership: an empirical analysis. Health Economics 2001; 10: 341-350.

[16] Van Doorslaer E, Koolman X, Jones AM. Explaining incomerelated inequalities in doctor utilisation. Health Economics 2004; 13: 629-647.

[17] Van Doorslaer E, Masseria C, OECD Health Equity Group.

Income-related inequality in the use of medical care in 21 OECD countries. In Towards high-performing health systems, OECD (eds).

OECD Policy Studies: Paris, 2004; 109-166.

[18] Van Ourti T. Measuring horizontal inequity in Belgian health

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