BDI

Beck Depression Inventory 1

The Beck Depression Inventory in 28 Countries: A Meta-Analysis

Dianne A. van Hemert

Fons J. R. van de Vijver

Ype H. Poortinga

Tilburg University, the Netherlands

Corresponding author:Dianne A. van HemertDepartment of PsychologyTilburg UniversityP.O. Box 901535000 LE TilburgThe NetherlandsPhone: +31 13 466 3005Fax: +31 13 466 2370Email: [email protected]

mailto:[email protected]


Running head: THE BECK DEPRESSION INVENTORY IN 28 COUNTRIES



Abstract

The Beck Depression Inventory (BDI) is one of the most widely used instruments to

assess depression. Studies using the BDI in normal populations from 28 countries

were collected. Depression showed the same correlates at individual and country

level, which suggests that depression has the same meaning at the two levels. BDI

scores were negatively correlated with economical and political variables and with

indicators of societal equality. Splitting the sample in two subsets of more and less

affluent countries resulted in significant differences on various correlations with the

BDI. For example, BDI correlated significantly different with suicide rate and

Hofstede’s Masculinity for more and less affluent countries. Finally, after controlling

for GNP, correlations with well-being, Confucian Work Dynamism and Human-

Heartedness from the Chinese Value Survey, and Schwartz’s Autonomy value were

significant.

Keywords:

Depression

Beck Depression Inventory

Subjective well-being

Happiness

Country indicators

Functional equivalence



The present study focuses on negative affect across individuals and countries.

It is studied whether depression in normal populations, as measured by the Beck

Depression Inventory (BDI; Beck et al., 1961), has a similar meaning at individual

level and country level of analysis. This similarity is a prerequisite for interpreting BDI

scores at country level. Further, hypotheses are tested about correlates of

depression at country level.

In 1961, Aaron Beck and his colleagues (Beck et al., 1961) published the BDI

to measure the ‘behavioral manifestations of depression’. The BDI is not based on a

theory but on clinical observation of symptoms displayed by depressive patients

(Steer et al., 1986). These symptoms include disturbed mood (sadness, loneliness,

apathy) and a negative self-concept and self-punitive wishes. Further, depressive

persons can suffer from physical and vegetative symptoms such as anorexia,

insomnia, and loss of libido. Depressed individuals can also experience changes in

activity level: They show either retardation or agitation (Beck, 1967). The BDI

consists of 21 items describing symptoms and categories reflecting overt behavioral

manifestations of depression. The categories reflect both psychological and somatic

symptoms. The BDI is one of the most widely used depression self-rating scales.

More than 2,000 empirical studies have employed the BDI (Richter et al., 1998).

Although originally developed as a measure for clinical populations, it is now also

widely used as a measure of depression in non-clinical samples. This takes the form

of epidemiological research and research studying the correlates of depression, like

demographical and psychological variables. It should be kept in mind that there is a

difference between population depression scores as a continuous variable and the

prevalence of depression in a population, treating depression as a categorical


disorder. In the first instance the extent of depression can be taken as a continuous

variable with a mean level that is a characteristic of a population as a whole. In the

second instance there is a proportion of individuals scoring above a threshold.

According to Rose (1992), ‘’the visible part of the iceberg (prevalence) is a function of

its total mass (the population average)’’ (p. 72).

BDI scores of normal individuals can be aggregated to obtain country level

data. However, in order to be able to interpret such data, one has to know whether

the meanings of individual-level and country-level BDI scores are equivalent. This is

a question of functional equivalence (Van de Vijver & Leung, 1997). One way to

examine similarity of meanings is to make use of external variables that are part of

the nomological networks of depression across levels. In this method, correlations of

context variables with BDI scores are compared at individual and country level. This

strategy focuses on functional equivalence, as it refers to the functional context of the

concept (see also Van Hemert et al., submitted). An example of different correlations

at individual and country level was described by Myers and Diener (1996). Within

most nations there is hardly a relationship between income and happiness; although

Americans earn on average twice as much today as they did in 1957, they have not

become happier. At the same time people in rich countries are generally happier than

people in less prosperous countries.

To conceptionalize country-level correlations, it is useful to turn to the notions

of happiness and subjective well-being. Generally, three aspects of subjective well-

being are distinguished: positive affect, lack of negative affect, and satisfaction with

life (Diener, 1994; Myers & Diener, 1995). It seems reasonable to assume that

depression in normal populations is inversely related to subjective well-being and

happiness. Therefore, theories about cultural differences in these concepts can be at


the basis of cross-cultural hypotheses about correlates of depression cross-culturally.

A first theory, labeled livability theory by Veenhoven (1995), states that happiness is

influenced by objective living conditions. These are broadly defined, ranging from

education and income to equality, stability and freedom. Maslow's (1970) need-

gratification theory underlies this perspective (Oishi et al., 1999). The comparison

theory claims that happiness is dependent on comparison with other times' or other

people's conditions (Veenhoven, 1995). This theory stresses relative living conditions

as contrasted to absolute living conditions. Two types of comparison can be

distinguished: Social comparison involves comparison with other people and lifetime

comparison involves comparison with other times in life. The folklore theory

(Veenhoven, 1995) states that happiness is a reflection of the national character of a

country and is unrelated to the actual quality of life in that country.

In general, subjective well-being and happiness have been found to depend

on absolute (objective) quality of life (Diener et al., 1993; Veenhoven, 1995),

supporting the livability theory. Arrindell et al. (1997) related country scores on

subjective well-being to a number of country-level characteristics. Subjective well-

being was found to be higher in wealthy countries with a high level of civil rights.

Diener et al. (1995) reported positive correlations between subjective well-being and

economical and political variables, as well as variables related to equality and

individualism, in 55 nations. Similar findings were reported by Veenhoven (1999),

who examined happiness in 43 countries. He also found happiness to be related to

educational variables, variables concerning media availability, and Schwartz's

Autonomy. Inglehart (1997, 2000) found stability of democracy and to a lesser

degree level of democracy to go together with well-being. Further, a strong

relationship (r = .74; p < .01) was found between subjective well-being and wealth.


Inglehart added that above a threshold of about US$6,000 (in 1991 dollars) there

was hardly a relationship. Diener and Diener (1995) reported similar findings.

With respect to the comparison theory, Hagerty (2000) found that a more

positively skewed national income is associated with lower happiness. As for lifetime

comparison, Easterlin (1995) discusses evidence for a positive link between

economical growth and the increase of happiness. However, studies by Diener et al.

(1993) and Veenhoven (1995) did not find support for the idea that well-being is

dependent on relative quality of life (for instance through social comparison).

Some evidence has been found to support the folklore theory. In the study by

Arrindell et al. (1997) the Neuroticism and Psychoticism scales from the Eysenck

Personality Questionnaire (EPQ-N respectively EPQ-P) were negatively correlated

with subjective well-being (r = -.41, p < .05 respectively r = -.40, p < .05).

Furthermore, of the well-known dimensions of Hofstede (1980) uncertainty avoidance

and power distance were negatively correlated with subjective well-being, while a

high level of individualism went together with high subjective well-being. Although no

significant correlation was found with masculinity for the total sample, in poorer

countries masculinity and subjective well-being were positively correlated whereas in

richer countries the opposite was found.

Additionally, studies on depression and well-being suggest that depression

might also be positively correlated with marital status (Diener et al., 2000;

Mastekaasa, 1994; Myers, 1992; Myers & Diener, 1995; Veenhoven, 1983),

religiosity (Myers, 1992; Myers & Diener, 1995; Van den Eeden & Braam, 1999), and

employment (Myers, 1992).

No studies are available that relate (continuous) depression scores to other

psychological or contextual measures at country level. Yet, there are several studies


of such relationships at the individual level. In the following discussion we review

individual-level studies using context variables which will be used later on in the

country-level analyses. Several authors reported a negative correlation of depression

with socioeconomic status (Gibbs, 1985) and with level of education (Oliver &

Burkham, 1979; Williamson, 1989), as would be predicted from livability theory. As

for personality, numerous authors report a significant, positive correlation between

BDI scores and EPQ-N (e.g., Heaven & Shochet, 1995; Meites et al., 1980;

Tashakkori et al., 1989; Saklofske et al., 1995; Salamero et al., 1994; Ulusahin &

Ulug, 1997; Williams et al., 1998). The Marlowe-Crowne Social Desirability Scale was

significantly negatively correlated (r = -.36; p < .01) with the BDI scores in a study by

Carr et al. (1996). This finding is in agreement with results of Tashakkori et al. (1989)

who found more depressive participants to have lower scores on the Lie scale from

the Eysenck Personality Questionnaire (EPQ-L), which is considered to measure

social desirability or social conformity. Tanaka-Matsumi and Kameoka (1986) also

found negative correlations between BDI scores and social desirability scales.

Sometimes BDI scores were found to be negatively related to extraversion, as

measured by EPQ-E (Carrillo et al., 1996; Ulusahin & Ulug, 1997) and positively

correlated with EPQ-P (Saklofske et al., 1995; Tashakkori et al., 1989). Costa and

McCrae (1980) presented a model of personality influences on subjective well-being,

according to which extraversion enhances subjective well-being through positive

affect, whereas neuroticism is related to negative affect, thereby negatively

influencing subjective well-being or happiness. Similar findings were reported by

DeNeve and Cooper (1998) in their meta-analysis of personality traits and subjective

well-being.


The present study examines two questions. The first involves the equivalence

of depression in normal populations as measured by the BDI at individual and

country level. Correlations with context variables at individual and country levels are

compared to assess equivalence. Positive evidence with respect to this first question

is conditional for the second part of the study. This second part examines the

correlates of depression at country level. Based on the theories and literature

discussed, the following country-level hypotheses are tested.

General hypotheses:

1. Subjective well-being and happiness are negatively related to depression1.

2. Prevalence rates of (clinical) depression are positively related to

depression.

3. Suicide rate is positively related to depression.

Livability theory:

4. More affluent countries show lower depression scores. (According to the

livability theory affluence pertains to economical well-being, as well as

education and political stability.)

Comparison theory:

5. Equality of income distribution is negatively related to depression (social

comparison).

6. Economical growth is negatively related to depression (lifetime

comparison).

Folklore theory:

7. National character traits explain a significant portion of variance in

depression.

Demographic hypotheses:


8. The percentage of married people is negatively related to depression.

9. Religiosity is negatively related to depression.

10.Unemployment is positively related to depression.

Method

Data Collection

PsycInfo (formerly known as PsycLit) was searched for reports, using the

keywords BDI and Beck and Depression (time period covered: 1960- July 1999). Two

strategies were used to find additional studies. First, several authors of obtained

references were asked by letter or e-mail to provide all reports they had available on

the Beck Depression Inventory. Second, by looking at references in literature already

identified another part of the data set was found. Studies were included in the data

set if mean scores on the BDI and sample sizes were reported. If available, the

standard deviation of BDI score distribution, the internal consistency of the BDI

measured by Cronbach’s alpha, and the mean and standard deviation of the age

distribution were recorded. There was an important restriction concerning the type of

sample used; clinical samples and all other groups were excluded that were

expected to produce scores deviating from a representative sample in a national

population. Studies reporting ethnic diverse samples were only included if no

significant differences in means were reported between the ethnicities. This

restriction was needed because ethnic differences within a country were not the

focus of our study. Each separate group of participants in a single report, for instance

men and women or different age groups, was regarded as an independent study.

Two problems arose in the data collection. The first problem concerned the

different versions of the BDI. In 1979, a revised version of the BDI (copyrighted in

1978) was published (Beck et al., 1979). Instead of asking participants to report on


their feelings at the moment of administration, the revised version asks to report on

the “past week including today”. Also, the wording of several items was changed.

Although the number and global meaning of the 21 items remained the same, and

high correlations between the versions were found, significant differences in means

have been reported (Beck & Steer, 1984; Lightfoot & Oliver, 1985). The 1961 version

generally shows lower means. Most articles did not explicitly state what version was

used. To obtain this information various strategies were followed. First, if the version

was specified in the list of references, either 1961 or from 1978, this was used as an

indicator of the version. Nevertheless, Beck et al. (1988) acknowledge that “there is

no feasible way of determining which version researchers have actually employed

from the descriptions presented in the majority of studies”. They add that most

researchers cite the 1961 reference, although some of them actually used the 1978

version. Second, when no reference was given it was assumed that other studies by

the same author(s) and from the same country used the same version. In some

countries, different versions have been used. In countries in which several samples

were studied the ambiguous studies were eliminated, in others the year was decisive:

All studies published before 1981 were regarded to concern the 1961 version, the

others were considered to have used the 1978 version. A short form (Beck & Beck,

1972) was also introduced, consisting of 13 items from the original BDI. Gould (1982)

and Reynolds and Gould (1981) found this short version for patients to be reliable

and valid, and highly correlated with the standard from. Studies using this version

were also included. Generally, it was clear when this version was used.

The second problem in forming the data sets concerned translations and

adaptations in non-English speaking countries. Translations were not always literal.

Beck et al. (1988) also mentioned this problem. Some translations involved deleting


or adding items or modifying the content of existing items. Thus, several studies had

to be ignored in setting up the data set. In one case, the deletion only concerned one

item (Tashakkori et al., 1989). Here the mean total score was adjusted by adding

one-twentieth of the total score.

Data Sets

<Table 1>

Three data sets were composed. The first data set included the studies using

the 1961 version of the BDI. It consisted of 72 studies, providing data for 117

separate samples. Twenty-five countries were represented in this data set. Table 1

shows all countries in this data set with their means, standard deviations, and

reliabilities. The second data set contained the studies using the 1978 version of the

BDI (Table 2). Thirty-seven studies, with 56 samples from 13 countries made up this

data set. Finally, 13 studies using the short version of the BDI formed the third data

set (Table 3). It consisted of 33 samples and included 8 countries.

<Table 2>

<Table 3>

Publication years ranged from 1974 to 1999 in the 1961-version data set, from

1988 to 1999 in the 1978-version data set, and from 1984 to 1997 in the short-

version data set. Medians were 1993, 1995, and 1985 for the 1961-version, 1978-

version and short-version data set, respectively.

Many studies did not specify the number of men and women in their samples

or did not report the BDI scores for both genders separately. Thus, in the 1961-

version data set 43% of the studies either had mixed samples or did not provide

information on distribution of the sexes. It could be established for 19% of all

participants that they were men and for 28% that they were women. For the 1978-


version data set the corresponding figures were 7% men and 13% women. In 71% of

the studies in this data set, either the samples were mixed or no separate information

on sex was mentioned. Finally, the short-version data set contained 15% men and

16% women; 36% of the samples were mixed or had an unknown gender

distribution.

Age ranged from 15.00 to 74.10 years in the 1961-version data set, from 15.90

to 74.04 years for the 1978-version data set and from 17.27 to 84.50 years in the

short-version data set. Participants were on average 22.99 (SD = 11.21) year in the

1961-version data set, 25.42 (SD = 8.66) year in the 1978-version data set and 39.57

(SD = 21.12) year in the short-version data set. Data on age were available in 83%

and 89% of the studies in the 1961-version and the 1978-version data sets

respectively, and in all studies of the short-version data set.

Correlations across versions were calculated by correlating the means

(weighted for sample size) of all data sets at country level: r = .64 (ns) between the

1961-version and the 1978-version data sets ; r = .79 (p < .05) between the 1961-

version and the short-version data sets; and r = .42 (ns) between the 1978-version

and the short-version data sets. Compared with the correlation of .94 between the

1961 and the 1978 version reported by Lightfoot and Oliver (1985), correlations are

low. However, it should be noted that correlations were calculated at country level

instead of individual level. Unfortunately the data were not suited for individual-level

analyses.

Analysis and Testing of Hypotheses

Functional equivalence across levels was evaluated by comparing correlations

between context variables and depression scores at individual and country level. As

no data at the individual level were available, information about the nature of


correlations with the BDI at individual level had to be drawn from existing literature on

the subject. Country-level correlations were derived from the total data set collected

for the present study.

To test the hypotheses as formulated in the introduction several indicators at

country level were used.

General Hypotheses

Subjective well-being. Diener’s subjective well-being was derived from Diener

et al (1995). This measure combines scores from several surveys and is available for

25 countries. Inglehart’s (1997) measure of subjective well-being was derived from

questions concerning happiness and satisfaction with life as a whole (23 countries).

Happiness. Veenhoven (1999) reported data on happiness from the World

Database of Happiness (update 1996). His measure of happiness was used for 27

countries.

Prevalence rates. The World Health Organization (1999) reported 1998 data

on burden of disease by cause and by WHO Region for several neuropsychiatric

disorders. The disorders included unipolar major depression and bipolar affective

disorder. Percentages of sufferers could be calculated for the following regions:

Africa, America (subdivided in 'high income' and 'low and middle income'), Eastern

Mediterranean, Europe (subdivided in 'high income' and 'low and middle income'),

South-East Asia (subdivided in 'India' and 'other low and middle income'), and

Western Pacific (subdivided in 'high income', 'China', and 'other low and middle

income'). By assigning region scores to countries, data were available for all

countries.


Suicide rates. Death rates per 100,000 inhabitants by suicide were collected

using the Demographic Yearbook 1987 (United Nations, 1990a). Data were available

for 25 countries.

Livability Theory

Affluence. An overall score on Affluence (similar to Georgas et al., submitted)

was derived from factor analyzing (for the countries involved in the present study) all

variables constituting five ecosocial factors as used by Georgas and Berry (1995):

Ecological factor (temperature and precipitation), Economical factor, Educational

factor, Mass communication factor (number of telephones, newspapers, etc.) and

Population factor (mortality and birth rates). The scores on the resulting single factor

were used as a measure of Affluence. Where data on the indicators were not

available for a country in the data set, they were added by using the Demographic

Yearbook 1987 (United Nations, 1990a), the UNESCO Statistical Yearbook 1990

(United Nations, 1990b) and the historical weather data web site (Washington Post,

n.d.). Data on Affluence were available for all 30 countries.

Gross National Product. Data on the GNP per capita were obtained from the

Georgas and Berry (1995) database.

Political Rights and Democracy. We combined four variables concerning

political rights and democracy into one dimension. Humana (1986) collected data

from several United Nations instruments and constructed the Human Rights Index for

rights and freedoms in 40 categories (28 countries). The indices for Political Rights

and for Civil Liberties in the year 1984 to 1985 were available for 29 countries

(Freedom House, n.d.). Vanhanen’s (1997) Index of Democratization over the year

1980 is a weighted combination of two indicators of dimensions of democracy,

namely Competition (smaller parties’ share of the votes) and Participation


(percentage of total population who voted in the election). This index was available

for 29 countries. We factor analyzed all four variables. A one-factor solution was

found with an eigenvalue 3.33, explaining 83.24% of variance. Factor scores on this

Political Rights and Democracy factor were used for further analyses (27 countries).

Civil rights. For 24 countries Gupta’s measure for observance of civil rights, as

cited by Diener et al (1995), was used. The original sign was reversed so as to obtain

a higher score for more observance of civil rights.

Additional measures of democracy. Inglehart’s (1997) measure of the level of

democracy in 1990, as well as the measure for stability of democracy (expressed in

number of years of continuous democracy) were available for 23 countries.

Deaths from political violence. As a measure of political instability the data by

Taylor and Jodice (1983) on deaths of political violence between 1948 and 1977

were used. This measure was corrected for population size, and available for all

countries but one.

Comparison Theory

Gini index. The Gini index expresses the degree of income inequality in a

society. Indices for 21 countries, ranging from 1986 to 1995, were collected from the

World Development Report (World Bank, 1999). A higher value denotes more

inequality.

Economic growth. Average annual growth of the Gross Domestic Product

(GDP) between 1980 and 1987 was retrieved from the World Development Report

1989 (World Bank, 1989) and available for 27 countries.

Folklore Theory

Eysenck Personality Questionnaire. Studies reporting means on the four

scales of the Eysenck Personality Questionnaire (EPQ; Eysenck & Eysenck, 1975)


were collected across 38 countries. The four scales are the EPQ-P (Psychoticism),

the EPQ-E (Extraversion), the EPQ-N (Neuroticism), and the EPQ-L (Social

Desirability). The data set includes 153 studies, providing data for 333 separate

samples, and is described in more detail in Van Hemert et al. (submitted). Data on

the EPQ scales were available for 24 countries in the BDI data set.

Hofstede’s measures. Data for 27 countries were available on Hofstede’s

(1980, 2001) work-related values Individualism, Masculinity, Power Distance, and

Uncertainty Avoidance.

Values from Chinese Value Survey. The Chinese Culture Connection (1987)

reported factor scores of 22 countries (overlapping with 13 countries in the present

data set) on four dimensions of the Chinese Value Survey. The four dimensions

included Integration ('broadly integrative, socially stabilizing emphasis', but not

familial bonding), Confucian Work Dynamism (hierarchical work ethic), Human-

Heartedness ('gentleness, compassion', but also a 'legalistic approach'), and Moral

Discipline (moral self-control and moderation ('following the middle way')).

Schwartz’s Values. Schwartz (1994) provided data on values in 19 countries

of our sample. Factor analysis by Georgas et al. (submitted) yielded two bipolar

factors, labeled Autonomy and Hierarchy. Scores on these two dimensions were

used as indicators.

Demographic Hypotheses

Marital Status. Percentages of married females and males between 25 and 44

years old were taken from the Georgas and Berry (1995) database. Data were

available for 27 countries.

Religiosity. An analysis was done on a part of the 1990-1991 World Values

survey (Inglehart, 1993, 1997). This study included 47,871 participants from 39


countries. It provided data on religion, including a range of topics related to religion,

like the meaning of life, religious services, the role of churches and praying. We

selected six items to fit in a scale measuring religiosity. Examples of items are “Do

you find that you get comfort and strength from religion?”, “How important is God in

your life?”, and “How often do you pray to God outside of religious services?”. The six

items yielded a one-factor solution in factor analysis at country level, with an

eigenvalue of 5.39 (89.8% of variance explained). Cronbach’s alpha was .85. Data

on religion were available for 21 countries.

Unemployment. The percentage of unemployment in 1987 was available for

23 countries (United Nations, 1993).

Results

Descriptives

Means, standard deviations, and values of Cronbach’s alpha for the different

versions of the BDI in every country appear in Table 1, Table 2, and Table 3.

Moderate levels of depression according to Beck and Steer (mean score between 17

and 29; cited by Apostolou et al., 1999) were found in Israel and Kuwait, whereas

mild levels of depression (mean score between 10 and 16) were found in India, Iran,

Hong Kong, Australia, Japan, and Turkey. Cronbach’s alpha was higher than .80 in

all countries but former West-Germany.

In order to be able to calculate correlations between BDI scores and country

characteristics, mean scores were aggregated at country level (weighted by sample

size) and then standardized within each of the three data sets. Subsequently the

three data sets were combined into one total data set. Again scores were aggregated

to country level to obtain one final score per country. A boxplot of standardized BDI

mean scores at country level revealed two outliers, Kuwait and Israel (Figure 1), both


showing a z-score higher than 2.0. These countries (also) scored above the

threshold of 17 for mild depression as defined by Beck and Steer (Apostolou et al.,

1999). The Kuwait data were based on three samples from two studies (N = 274),

which were both published after the Gulf War. Possibly the influence of post-war

stress was visible in these studies (Al-Issa & Ismail, 1994; Lester & Abdel-Khalek,

1998). The Israel data (N = 574) were based on only one study in a Palestine sample

(Abdallah, 1997). Particularly in this group, the political situation is likely to enhance

feelings of unsafety and depression. The outliers used the 1961 version of the BDI,

but different translations. Kuwait and Israel were deleted from the data set, and

standardizations were carried out again in all three data sets before combining them

into the total data set, that was used for further analysis.

<Figure 1>

Correlations at Individual and Country Level: Establishing Functional Equivalence

<Table 4>

To establish functional equivalence of depression at individual and country

level, the relationships of BDI scores with other variables were examined at both

levels. The nature of correlations at both levels with selected context variables is

shown in Table 4. The individual level data were derived from studies discussed

before, complemented with information from Beck et al. (1988). The country level

data were derived from the total data set of the present study. Correlations with

income and education showed the same pattern across levels, but suicide rate and

alcohol use did not. Although the correlations with the EPQ scales showed the same

pattern at individual level and country level except for EPQ-L, none of the country

level correlations with EPQ scales reached significance. The small number of

countries might explain the absence of significant correlations at this level. Notable


differences between individual level and country level occur for suicide rate and

alcohol use. The overall similarities seem to suggest that functional equivalence of

the BDI can be assumed.

Correlations with Country Level Variables: Hypothesis Testing

Pearson correlations and significance levels were calculated both in the

regular way and with a bootstrapping procedure. For each correlation coefficient

10,000 bootstrapped samples were drawn with replacement from the original data

set. The advantage of this procedure is not having to rely on the bivariate normality of

the data and applicability to small samples.

General Hypotheses

<Table 5>

Measures of both subjective well-being and happiness showed negative

correlations with depression scores (Table 5). Thus, the first hypothesis was

supported. Although both depression prevalence rates correlated positively with BDI

scores, only one reached significance. Thus, the second hypothesis remains open.

Suicide rate was negatively related to depression, a rather unexpected finding, that

contrasts with other reports (Lester, 1988, 1986). This disconfirms the third

hypothesis.

Livability Theory

In general a higher level of Affluence was related to a lower level of

depression. This finding is in agreement with prior findings on the relationship

between subjective well-being and GNP per capita (e.g., Diener et al., 1993). Political

variables also showed a homogeneous picture: Political Rights and Democracy, civil

rights, and the stability of democracy all correlated negatively with the BDI mean

score. These findings indicate that the livability theory (fourth hypothesis) was


supported. A factor analysis of all variables testing the livability theory resulted in a

one-factor solution with an eigenvalue of 7.20, explaining 79.98% of variance. The

significant negative correlation between BDI score and factor scores on this Livability

factor (Table 5) confirms this conclusion.

Comparison Theory

The correlation between the Gini index, indicating equality of income

distribution, and BDI scores was not significant, contrary to expectation. Hypothesis 5

could not be confirmed. However, the correlation between economical growth and

depression was significant in the bootstrap procedure: Hypothesis 6 was confirmed.

In sum, social comparison does not appear to be associated with depression, but

lifetime comparison does.

Folklore Theory

None of the EPQ scales correlated significantly with BDI score. Hofstede’s

measures were related both positively (Masculinity and Power Distance) and

negatively (Individualism) to BDI scores, largely replicating earlier mentioned findings

by Arrindell et al. (1997). The Chinese Integration value correlated negatively to BDI,

and the Confucian Work Dynamism dimension showed a significant positive

correlation, but only when bivariate normality was assumed. Finally, Schwartz’s

Autonomy factor was negatively related to BDI. This finding is in agreement with

results reported by Sagiv and Schwartz (2000), who found values concerning

achievement, self-direction and stimulation to be positively correlated with the

affective component of subjective well-being across three cultural groups. It is difficult

to conclude whether this evidence does or does not support the rather unspecific

predictions of the folklore theory.

Demographic Hypotheses


None of the demographic hypotheses were confirmed. Percentages of married

females and males were positively correlated with the level of depression,

contradicting expectations. Correlations with religiosity and the percentage of

unemployed persons were not significant.

In sum, it can be concluded that wealthy, egalitarian, individualistic, and

politically stable countries appear to be less depressed. These findings mainly

support the livability theory.

Further Exploration of Country-Level Correlations

In general, country-level variables tend to be correlated with GNP. To explore

which other factors are relevant, relationships between depression and country level

indicators were further explored by (1) splitting the sample into more and less affluent

countries and (2) calculating partial correlations controlling for GNP per capita. More

affluent countries (N = 14) were defined as having a GNP per capita (data from 1987)

higher than the median of the distribution (9,210 US$). Less affluent countries (N =

14), consequently, had a GNP lower than 9,210 US$. The difference between the

two subsample correlations was tested for statistical significance by evaluating the

difference between the z-values of the correlation coefficients of both subsamples

(Hays, 1988, p. 591). For five variables a significant (p = .05) difference was found

between correlations of the two subsamples. A significant difference between more

and less affluent countries appeared in the correlation between suicide rate and

depression. Whereas a negative significant correlation was found for affluent

countries (r = -.70), a non-significant (but positive) correlation of .22 was found for

less affluent countries. Also, the difference in correlations between the Gini index and

depression was significant. In affluent countries income inequality was positively

correlated with depression (the more unequal, the more depressive; r = .46), in less


affluent countries the opposite was true (r = -.48). Hofstede’s Masculinity score also

showed different correlations (p = .05) in the two subsamples; in affluent countries a

high level of masculinity went together with a high level of depression (r = .77),

whereas in less affluent countries the opposite was the case (r = -.42). This finding is

congruent with Arrindell et al.'s (1997) results on subjective well-being, reported

before. Human-Heartedness correlated positively with BDI scores for affluent

countries (r = .84), whereas in less affluent countries a negative (although non-

significant) correlation of -.27 was found. Correlations with Moral Discipline also

differed significantly for the two subsamples (p = .05). Both correlations were positive

(resp. r = .12 and r = .79). A relationship between wealth and depression could not

be established in either subsample, in contrast to findings on subjective well-being in

poor and rich countries (Inglehart, 1997).

Briefly summarized, in more affluent countries depression levels correlated

positively with the Gini index, Neuroticism, Masculinity, Human-Heartedness, and the

percentage of married males, while negative correlations were found with variables

related to happiness, prevalence of depression, political rights, livability and with

suicide. Less affluent countries only showed significant correlations of depression

with the percentage of married females (positive) and Autonomy (negative). Thus,

Inglehart’s (1997) finding, that only for less affluent countries GNP shows a

relationship with well-being, could not be replicated.

Partial correlations, controlling for GNP, left only four significant correlations.

Inglehart’s well-being (r = -.53, p < .01), Confucian Work Dynamism (r = .64, p < .05),

Human-Heartedness (r = .60, p < .05), and Schwartz’s Autonomy (r = -.56, p < .05)

correlated with mean BDI score.

Discussion


In the present study differences between countries in the level of depression,

as measured by the Beck Depression Inventory, were investigated. Two research

questions were addressed. First, the equivalence of BDI scores across different

levels of analysis was examined. Patterns of correlations with context variables were

compared at individual level and country level. In general, meanings of depression

appeared to be reasonably similar at individual and country level. Differences

occurred for suicide rate and alcohol use. Myers (1992) reported a similar

inequivalence: Countries with higher levels of well-being also have higher suicide

rates, while obviously individuals attempting suicide experience relatively low levels

of well-being. It is not clear what the factors are behind these findings. However, both

suicide and alcohol use (death by liver cirrhosis) correlated significantly with GNP per

capita (resp. r = .59, p < .01, and r = .46, p < .05). This points to several possibilities.

First, suicide may be more visible in richer countries. Also, suicide may be more

accepted as a way to solve problems. Alcohol use may be related to GNP because

alcohol is more easily available in wealthier countries. Alternatively, other ways to

deal with problems are more common in poorer countries. Differences in correlations

with the EPQ scales were quite small, except for the EPQ-L. Van Hemert et al.

(2001) studied equivalence of the four EPQ scales at individual level and at country

level, and found unsatisfactory equivalence for both the EPQ-P and the EPQ-L.

Therefore, it may well be that differences in meaning of the EPQ-L at individual and

country level contributed to the difference in correlations with BDI scores. Further,

small sample sizes at country level may have prevented correlations from reaching

significance. A negative answer to this first question would imply that the second

question would be difficult to analyze. We concluded that overall the evidence was


sufficiently positive on functional equivalence across levels to allow analysis of the

second question.

Second, an attempt was made to explain country differences in BDI score in

terms of contextual factors. As for the concepts of depression and subjective well-

being, BDI scores were negatively correlated with subjective well-being measures.

Furthermore, prevalence rates of clinical depression were positively correlated with

depression score levels. The relatively low level of correlations can be explained by

considering the nature of the populations involved. As self-reported depression

inventories rely on the voluntary participation of individuals, extremely depressed

participants are likely to be missed in questionnaire studies of non-clinical samples.

Mean depression scores were lower in affluent countries, that is countries with

a high GNP, a high level of education, and a high level of information available

through media. Also, countries with a low level of depression were more egalitarian,

more democratic, and had more civil, human, and political rights. All these findings

confirm the livability theory, explaining happiness through objective living conditions

like income and education. This theory gets most support from the present results.

These findings are in line with results reported by Veenhoven (1995, 1999), Diener et

al. (1995), and Arrindell et al. (1997).

For the comparison theory, claiming that happiness is dependent on

comparison with other times' or other people's conditions, little support was found, as

only economic growth was related to depression. This latter finding is not in

agreement with Diener and Oishi (2000), who concluded from an examination of

Veenhoven’s surveys that people in countries with high economic growth did not

report more happiness.


Also for the folklore theory, stating that happiness is a reflection of the national

character of a country, only mixed evidence was found. Some variance could be

explained by national character of a country, but mostly this was a function of GNP.

Some value indices, Confucian Work Dynamism, Human-Heartedness and

Schwartz's Autonomy, were related to depression when GNP was controlled for. An

obvious difficulty with folklore theory is the lack of precision of the predictions that

can be derived from it. Any significant correlation provides supporting evidence, even

independent of sign.

In general, several reasons can be named for the sometimes unclear results.

First, many of the samples in the data set consisted only of students. Possibly, these

samples are not representative. In poor countries students are relatively rich, in rich

countries they are relatively poor (Diener et al., 1993). Apart from their extreme

economic position (either relatively rich or relatively poor), student samples tend to

be uniform in many other aspects. This argument was supported by Veenhoven

(1995), who found the correlation between average happiness and GNP per capita to

be larger in a general population than in a student population. Second, besides

differences in means a shift in symptoms could take place, with more somatic and

psychological symptoms scoring higher in one country than another. No raw data

were available to do analyses on this point. A third reason is the presence of cultural

bias. Bias can limit the validity of findings considerably (cf. Van de Vijver & Leung,

1997). For example, item bias should be taken seriously in cross-cultural

administration of instruments. Byrne and Campbell (1999) illustrated this by showing

that BDI item scores can vary across countries even when the factorial structure is

equivalent cross-culturally. Unfortunately, the lack of raw scores at item level in the

present data set did not allow for an investigation of item bias. Another source of bias


could emerge from the composition of the sample. It is clear that western, affluent

countries are overrepresented in the present study. Splitting the sample in more and

less affluent countries partly controls for this, but at the same time implies a

restriction of range. A third kind of bias pertains to the indicators. Some indicators like

GNP and suicide rate are proxies for psychological variables with partly unknown

ramifications.

In summary, depression scores seemed sufficiently equivalent at the individual

level and country level to allow country-level analyses with individual-level data.

Cross-national differences in depression among normal samples were found to be

connected to mainly economical and political variables. These connections could be

best explained in terms of livability theory. At the same time, these variables did not

explain all variance in depression. Psychological variables at the level of nations

should be explored further to obtain a more differentiated picture of processes related

to levels of depression across countries.


Acknowledgement

The authors would like to thank Jim Georgas from the University of Athens for

providing the data on the Affluence indicator.


Footnote

1Depression refers to country-level mean scores on the BDI


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Table 1

Study Characteristics of the 1961-Version Data Set

Number of BDIa

Country Resp. Samples Mean SD Alpha

Australia 225 3 7.57 6.50 --

Bulgaria 691 1 9.14 -- --

Canada 2140 13 8.85 5.84 0.82

China 100 1 6.68 4.18 --

Denmark 217 1 5.00 0.40 --

Finland 285 4 2.59 1.17 --

Germany West 42 1 5.30 -- 0.60

Greece 37 1 4.89 5.40 --

Iceland 187 2 5.77 5.50 --

India 490 3 10.71 7.02 0.88

Iran 405 1 11.35 -- 0.82

Ireland 359 3 7.32 6.57 0.85

Israel 574 2 17.75 10.02 0.91

Italy 445 6 8.16 6.46 --

Kuwait 274 3 16.59 9.27 --

Netherlands 359 3 4.62 4.98 0.82

New Zealand 844 4 7.64 7.03 0.88

Nigeria 180 1 7.30 7.90 --

Norway 100 1 5.92 5.33 --

Spain 1524 3 7.34 7.09 --

Sweden 3913 5 6.18 6.37 0.89


United Kingdom 4481 14 7.25 5.85 --

USA 5456 34 6.75 6.37 0.85

Hong-Kong 2780 7 11.44 8.29 0.86

Total mean 26108 117 7.98 6.50 0.86

aAll numbers are weighted by sample size


Table 2

Study Characteristics of the 1978-Version Data Set

Number of BDIa


Argentina 608 2 8.46 7.26 0.86

Australia 1343 3 9.55 7.50 0.86

Brazil 3203 6 8.47 6.69 0.83

Canada 139 3 7.53 4.61 --

Greece 98 1 8.17 6.69 0.86

Japan 116 2 10.99 8.04 --

Mexico 1012 12 7.64 7.58 --

Netherlands 10 1 1.00 1.10 --

Spain 2407 5 6.44 5.81 0.83

Turkey 1514 4 13.06 7.88 --

United Kingdom 47 1 4.60 4.20 --

USA 1602 13 8.56 6.63 0.86

Hong-Kong 675 3 10.86 7.90 0.86

Total mean 12774 56 8.81 6.88 0.85



Table 3

Study Characteristics of the Short-Version Data Set

Number of BDIa


Australia 1659 5 3.45 4.25 0.88

Canada 499 2 7.28 6.07 --

Finland 1070 4 2.69 2.88 0.73

France 376 3 2.85 2.50 --

Germany West 60 1 3.72 3.24 --

Iran 305 1 8.03 3.29 0.83

Netherlands 156 1 2.80 3.50 --

New Zealand 1091 16 2.50 3.09 0.81

Total mean 5216 33 3.67 3.68 0.84



Table 4

Correlations Between BDI Score and Context Variables Within Countries and

Between Countries

Context variables Individual levela Country levelb

Income (SES) - -.46*

Educationc - -.54**

Suicide + -.46*

Alcohol used + -.25

Anxietye + -.02

EPQ-P + .29

EPQ-E - -.05

EPQ-N + .23

EPQ-L - .23

aResults are based on literature reviewed in the introduction section, and studies

reported by Beck et al. (1988)

bResults are based on analyses from the present study

cAt country level measured by the Education factor (Georgas & Berry, 1995),

consisting of the teacher-pupil ratio in the first level, the gross enrollment in the

second and the third level, and the percentage of adult illiterates

dAt country level measured by death rate through liver cirrhosis (United Nations,

1990a)

eAt country level measured by mean scores on the question ‘How often do you feel

nervous or tense at work?’ (Hofstede, 1976) as cited in Lynn (1981), with reversed

sign


*p < .05 **p < .01


Table 5

Correlation Coefficients and Significance Levels (Regular and After Bootstrapping) Between Standardized BDI Scores and Context

Variables for More Affluenta and Less Affluent Countries

BDI

Context variables N

countries

Total sample More affluent countries

(Nmax = 14) a

Less affluent countries

(Nmax = 14)

General hypotheses

Subjective Well-Being

Diener 24 -.46*† -.42 -.29

Inglehart 23 -.66**†† -.74**†† -.49

Happiness (Veenhoven, 1999) 26 -.24†† -.59*† -.03

Prevalence rates

Unipolar major depression 28 .19 -.52†† -.23

Bipolar affective disorder 28 .33† -.50 .24

Suicide rate 23 -.46*† -.70*†† .22


Livability theory

Affluence 28 -.50**†† -.18 -.31

Gross National Product 28 -.46*†† -.14 -.18

Political Rights and Democracy 25 -.46*†† -.73**†† -.33

Civil rightsb 23 -.46*† -.62*†† -.40

Level of democracy 23 -.29 .15 -.04

Stability of democracy 23 -.46*† -.28 -.12

Death by political violence (corrected) 28 -.02 .12 -.16

Factor score Livability 20 -.40† -.62*† -.11

Comparison theory

Gini Index 20 .17 .46† -.48

Economic growth 25 .34† .45 .34

Folklore theory

Eysenck Personality Questionnaire

Psychoticism (EPQ-P) 23 .29 .33 .13

Extraversion (EPQ-E) 23 -.05 .18 -.19


Neuroticism (EPQ-N) 23 .23 .49† -.11

Social Desirability (EPQ-L) 22 .23 -.10 -.10

Hofstede's Measures

Individualism 26 -.45†† -.07 -.26

Masculinity 26 .38† .77**†† -.42

Power Distance 26 .50**†† .40 .30

Uncertainty Avoidance 26 .07 .36 -.26

Chinese Values

Integration 13 -.67*† -.41 -.68

Confucian Work Dynamism 13 .64* .33 .76

Human-Heartedness 13 .48 .84**†† -.27

Moral Discipline 13 .40 .12 .79

Schwartz’s Values

Autonomy factor 18 -.62**†† -.36 -.72*††

Hierarchy factor 18 .35 .44 .05

Demographic hypotheses


Percentage of married females 25 .65**†† .41 .74**††

Percentage of married males 25 .52**† .40† .43

Religiosity (Inglehart, 1997) 21 .29 .36 .10

Percentage unemployed 22 -.12 .07 -.36

aMore affluent countries have a GNP (1987) higher than 9210 US$, less affluent countries have a GNP lower than 9210 US$

bOriginal sign changed

*p < .05 **p < .01 (conventional significance test)

† p < .05 †† p < .01 (significance in bootstrap prcedure)


Figure Caption

Figure 1. Boxplot of standardized mean BDI scores

BDI

Documents

world wide

confucian

kong total

historical

world health

oxford university

objective

total data