• BMI;
  • ethnicity;
  • food habits;
  • outsourcing;
  • socioeconomic status


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objective: To study the effects of lifestyle variables and socioeconomic status on overweight among native Dutch and immigrants in The Netherlands.

Research Methods and Procedures: Data were used from a survey sample (N = 2551) of native Dutch and immigrant respondents (Surinamese/Antilleans, Turks, and Moroccans). BMI was calculated using self-reported weight and height. Lifestyle variables such as modern food habits (take-out food and eating out) and participating in sports were included, as well as socioeconomic and demographic background variables. Bivariate and ordinary least squares analyses were performed to study BMI and the determinants of overweight among the different groups.

Results: All immigrant groups had a higher prevalence of overweight than the Dutch, except Moroccans. Men were overweight more frequently than women. Take-out food, eating out, and fresh vegetables were related to a decrease in BMI, whereas convenience foods were related to an increase in BMI. For ready-to-eat meals, the results were mixed. In all groups, age was associated with a higher BMI, and a higher level of education was associated with a lower BMI. Immigrants participated in sports less frequently than native Dutch people.

Discussion: One percent to 5% of the total public health costs can be attributed to costs for overweight-related diseases. Public health policies should aim at stimulating healthy lifestyles and discouraging bad food habits through higher taxes on high-calorie foods. In particular, Dutch immigrants should be encouraged to lose weight, because they have a higher risk for overweight-related diseases.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Genetic, socioeconomic, and cultural factors such as poor housing, low incomes, lifestyle, and individual (subjective) health can be the underlying factors that result in differences in overweight among different groups of people in society. Earlier research has shown that immigrants into The Netherlands have a higher risk of becoming overweight than the native Dutch people (1, 2, 3). Therefore, it is of interest to study differences in determinants of overweight between the native Dutch and the largest immigrant groups: Surinamese/Antilleans, Turks, and Moroccans.

More than 40% of the Dutch population is overweight. In 2002, the prevalence of overweight (BMI ≥ 25 kg/m2) was 39% for women and 48% for men (4). In comparison, data from 1980 indicated that 30% of the Dutch population was overweight. Thus, the prevalence of overweight has increased alarmingly. The Netherlands is one of the countries with the highest prevalence of overweight compared with other countries, along with the United States, the United Kingdom, and Germany, where a similar pattern of overweight has been observed (2).

There are no exact figures available to indicate how obesity varies across the population in The Netherlands. However, it is known that, for immigrant populations, the risk of being overweight is higher among both children and adult women compared with the Dutch. There is also a higher risk of being overweight for Turkish and Moroccan groups compared with members of the native Dutch population (1). In general, immigrant groups in The Netherlands have a lower level of education and fall into lower-income groups, which indicates a higher risk for being overweight, independent of ethnicity (1, 2, 3, 5).

In The Netherlands, outsourcing food preparation has become more popular than in previous decades. For example, in 1975, 40% of Dutch households visited restaurants between one and nine times a year compared with 61% in 1995 (6, 7). In addition, in 1995, people chose to eat take-out food quite frequently, with 63% of all Dutch households eating take-out food more than once a month (8). There is, however, a paucity of data available regarding differences in the outsourcing of food preparation between immigrants and the native Dutch. It is known that immigrants (mainly the Surinamese/Antilleans) visit fast food restaurants more often than native Dutch people. Moroccans visit cafeterias and snack bars less frequently than the Surinamese/Antilleans, Turks, and the native Dutch (9).

Overweight is associated with energy intake and physical activity. There is strong evidence to suggest that the lack of physical activity (especially in leisure time) is of great importance as a factor contributing to the increase in overweight and obesity (10, 11). Current public guidelines advocate 30 minutes of moderately intensive exercise at least 5, if not all, days per week (12). About one half of the Dutch population does not meet this minimum requirement (13).

This paper focuses on socioeconomic determinants of overweight, such as income and level of education, and some lifestyle variables (such as food habits and engaging in sporting activities) to study whether lifestyle and/or cultural factors affect overweight for different groups.

The purpose of this paper is 2-fold: 1) to study whether native Dutch and immigrants have a different prevalence of overweight and 2) to study socioeconomic and lifestyle determinants of overweight and differences between the native Dutch and immigrants regarding these determinants of overweight. Specifically, it is hypothesized that the determinants of overweight may differ across the ethnic groups as a consequence of cultural differences in modern food habits, lifestyle factors (such as physical activity), and socioeconomic status. It is expected that immigrants will have a higher BMI than the native Dutch, because immigrants have lower levels of education and income (1, 3). In addition, lifestyle factors such as food habits may contribute to overweight.

In the analyses, BMI, which is calculated according to the respondent's self-reported weight and height, is used as a dependent variable. The relation between being overweight and socioeconomic status will be studied using the respondent's income and level of education.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References


An immigrant is defined as a person who has at least one parent born abroad (14). Until this analysis, the number of immigrants included in cross-sectional analyses of the Dutch population has been low. This is because they represent only 10% of the Dutch population, which makes their inclusion in survey statistical analyses complicated. A stratified sample was needed to be able to compare in absolute numbers immigrants with the native Dutch respondents. To obtain enough information to compare sexes and different household types, ∼700 respondents per ethnic group were interviewed. The total response rate of 20,738 numbers drawn was 23.4%. Cooperation was refused (mainly because of lack of time) in 44% of telephone contacts, 13% of the phone lines were closed, 6% were not answered, 6% were business numbers, and 7% of the contacts were not successful because of other reasons (fax number, double in pool, inaccessible). Approximately 47% of the 4857 successful contacts fell outside of the target group (screeners).

It is always difficult to get a high response rate in such cross-sectional surveys, and we are aware that a low response could affect the results. Therefore, we compared our sample with other samples from the Netherlands divided by ethnicity (data from Dutch Central Bureau of Statistics and Social and Cultural Planning Bureau). The levels of education were comparable with the available data, but highly educated immigrants were somewhat over-represented. The levels of income of all groups were close to general statistics. There was an over-representation of non-working Dutch women in our sample, which caused a relatively low mean level of income for the native Dutch. However, all analyses corrected for level of education and income, which would minimize these over-representations.

All respondents were older than 18 years of age. The Moroccans, Surinamese, Turkish, and Antilleans were selected on the basis of belonging to the largest immigrant groups in The Netherlands. In 2003, the percentage of non-Western immigrants (including Moroccans, Turks, Surinamese, and Antilleans) within the Dutch population was 10%, which is ∼1,483,000 immigrants in a total population of ∼16 million. The share of Moroccans, Turks, Surinamese, and Antilleans of the non-Western immigrants was ∼70% (5). The Surinamese and Antillean respondents were considered as one group, because they are from comparable cultural origins. The final sample consisted of 701 native Dutch, 701 Surinamese/Antilleans, and 700 Turks. Four hundred forty-nine Moroccan respondents agreed to participate in the study. Essential (socioeconomic) variables such as age, income, and level of education were compared with existing data provided by the Dutch Central Bureau for Statistics. The levels of education in the sample were comparable with available data, but more highly educated immigrants were somewhat over-represented. This may be because younger immigrants were slightly over-represented in the sample; in general, younger immigrants are more highly educated. The levels of income of all groups in the sample were similar to the Dutch population in general.


The data were collected in The Netherlands between September and November 2001 by a Dutch organization for market research. Interviewers who could speak both Dutch and Moroccan or both Dutch and Turkish were hired to conduct interviews in appropriate languages. This was not needed for the Surinamese/Antillean group, who use Dutch as their mother language.

The Dutch subsample was drawn randomly from the total pool of phone numbers (∼6.8 million) administered by the Dutch Telephone Co. in 2001. The immigrant subsamples were drawn from a sample of ∼80,000 names provided by the market research company that performed the interviews. The immigrants were selected on the basis of their names, which were used as an indication of their ethnicity. In total, 2551 respondents were included in the sample.

Demographic and Socioeconomic Variables

In the multivariate analyses, age and age squared were both used to determine whether BMI increases up to a certain age or not. The respondent's sex was included in the analyses to study whether there were differences between men and women with respect to BMI. Previous research has indicated a lower BMI for women (15, 16), although there is evidence that, after pregnancy, women report a higher weight for a time (17).

To determine the socioeconomic status of the respondents, their level of education and their net monthly household income were included in the analyses. Marital status (e.g., whether respondents were married or lived with a partner) and whether the respondents have children have a potential effect on people's BMI and were, therefore, included in the analyses. People may be less careful of their weight once they are married or live together, which may be related to an increase in BMI. On the other hand, with the arrival of children, people may reconsider their food habits and lifestyle, which may induce a BMI decrease, apart from the postpartum BMI increase for women.

Food Habits and Lifestyle

Several modern food habits were included in the analyses. Respondents were asked to indicate the number of times per month they bought take-out food (e.g., Chinese meals), bought home delivery food (e.g., a pizza delivered to the home), or ate outside the home (e.g., in restaurants or fast food outlets). Take-out food and delivery food were expected to affect an increase of BMI, assuming that foods with a high-calorie value are associated with an increase of weight. The effect of eating out in restaurants was uncertain, because restaurants can cook low-calorie food just as easily as high-calorie food (10). Conclusions should be drawn carefully from the analysis of the data, because only the frequency of outsourcing meal preparation was measured and not the type of food. However, it does supply us with information that is valuable: 1) it gives an indication of cultural differences in lifestyle among the different ethnic groups and 2) in The Netherlands, one could almost predict what was eaten when delivery food was ordered (pizza in most cases) or when take-out food was ordered (in general, Asian meals).

People were also asked to indicate the number of times they ate ready-to-eat meals, convenience food (such as canned or pre-cut vegetables), and fresh vegetables per week or per month. The effect of ready-to-eat meals on BMI can be either positive or negative. Nowadays, in supermarkets, there is a great variety of ready-to-eat meals, and the quality of ready-to-eat meals may have improved. Fresh vegetables are considered to be good for health and low-fat; therefore, a negative effect on BMI is expected. Because of cultural differences in food patterns, the effects of food habits may differ across the groups.

We followed the convention in the literature by hypothesizing a negative sign effect for smoking on BMI (18, 19). The literature suggests that immigrants engage in sports less frequently than the native Dutch. In 1999, 66% of the native Dutch between 6 and 79 years of age engaged in some kind of sports activities. For immigrants, this figure was 51.5% (20). As a consequence of problems related to endogeneity, we performed the analyses including and excluding sports participation. The endogeneity problem refers to the fact that people who are overweight may not have the physical capacity to participate in sports. The effect of participation in sports on BMI is expected to be negative.

Data Analysis

Bivariate analyses were performed to obtain an indication of the distribution of BMI among the different groups. To study the determinants of overweight, multivariate analyses were carried out. We used ordinary least squares (OLSs)1 regressions. An OLS regression is a regression where the line through the middle of the data points minimizes the sum of squared distances between data points to study the relationship between a dependent variable and one or more independent variables (21).

BMI was normally distributed and could, therefore, be used as a dependent continuous variable on which linear regressions could be performed. First, an OLS regression was applied to the whole sample, including the demographic, socioeconomic, and lifestyle variables discussed earlier. Second, OLS regressions were performed for each ethnic group to study whether the determinants of BMI differed across the four groups. The purpose of these analyses was to gain insights into the determinants of overweight across four different groups. Therefore, a stratified sample as described above was used. Moreover, the aim of this study was to test whether hypotheses about socioeconomic, demographic, and lifestyle variables hold. Therefore, finding the model that best predicts BMI was not the aim; causal relationships were studied, meaning that corrections for multiple testing were not necessary, because it was not a process of data-mining (22, 23) or sequential testing.

Table 1 shows an overview of the distribution of the socioeconomic and demographic variables across the different groups in the sample.

Table 1.  Distribution of socioeconomic and demographic variables
SexN = 701N = 700N = 447N = 700
Married/cohabitingN = 701N = 700N = 449N = 699
Children at homeN = 701N = 701N = 449N = 700
AgeN = 701N = 699N = 444N-700
 Standard deviation16.8513.7212.4410.93
Level of educationN = 695N = 679N = 366N = 674
 N = 701N = 701N = 449N = 700
Net household income mean/month€1694/$2185€1667/$2150€1335/$1722€1413/$1823
 (SD = €1011/$1304)(SD = €1000/$1290)(SD = €721/$930)(SD = €704/$908)

In our sample, the Turkish respondents reported most frequently that they were married/cohabiting, followed by the Moroccans and the native Dutch. The Surinamese/Antillean group had the most single respondents. The mean age of the Turks and the Moroccans in our sample was lower compared with the mean age of the native Dutch and the Surinamese/Antilleans. The latter was a consequence of the age distribution of non-Western immigrants being younger than that of the native Dutch (5). The lower mean age may also partly explain the higher percentage of children living at home for immigrant respondents.

The native Dutch and the Surinamese/Antilleans had higher levels of education compared with the Moroccans and Turks. The relatively higher level of education of the Surinamese/Antilleans may indicate a higher level of integration into Dutch society and a higher socioeconomic status compared with the Turks and Moroccans. This is confirmed by the relatively high level of household income for the Surinamese/Antilleans compared with the other immigrant groups included in the study. Moreover, our data show (results not shown) that younger Moroccans and Turks are more highly educated than older Moroccans and Turks. This effect was not found in the Surinamese/Antillean group, where the younger generation had about the same level of education as the elder generation.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Overweight among Native Dutch People and Non-Western Immigrants

In the sample, the Turkish respondents had the highest prevalence of overweight, followed by the Surinamese/Antillean respondents. The Moroccans had the lowest prevalence of overweight. The BMI of the native Dutch respondents was in between the Moroccan and Surinamese/Antillean respondents. Within the entire sample, few people were underweight. Table 2 shows the distribution of the groups over the BMI classes divided by age and sex over the four groups. In the bivariate analyses, significance was tested with a one-way ANOVA. The results are mentioned within the text.

Table 2.  BMI distribution among native Dutch and immigrants (%)
    BMI ≥ 25 kg/m2
Dutch (N = 682) 18 to 34 years (n = 188) 29.3
  35 to 44 years (n = 162) 37.0
  45 to 64 years (n = 214) 49.1
  ≥ 65 years (n = 118) 59.4
Surinamese/Antillean (N = 679) 18 to 34 years (n = 221) 33.0
  35 to 44 years (n = 197) 41.1
  45 to 64 years (n = 220) 56.3
  ≥ 65 years (n = 40) 72.5
Moroccan (N = 421) 18 to 34 years (n = 258) 31.8
  35 to 44 years (n = 90) 48.9
  45 to 64 years (n = 57) 63.2
  ≥ 65 years (n = 12) 75.0
Turkish (N = 684) 18 to 34 years (n = 404) 36.6
  35 to 44 years (n = 191) 70.6
  45 to 64 years (n = 76) 71.1
  ≥ 65 years (n = 13) 76.9
WomenDutch (N = 426)Surinamese/Antillean (N = 384)Moroccan (N = 187) 
 BMI < 18.5 kg/m22.32.64.3 
 18.5 ≤ BMI <25 kg/m258.551.056.2 
 BMI ≥ 25 kg/m228.229.728.3 
 BMI ≥ 30 kg/m211.016.711.2 
MenDutch (N = 256)Surinamese/Antillean (N = 294)Moroccan (N = 232) 
 BMI < 18.5 kg/m201.02.2 
 18.5 ≤ BMI <25 kg/m252.054.855.6 
 BMI ≥ 25 kg/m240.638.831.9 
 BMI ≥ 30 kg/m27.45.410.3 

Table 2 shows that BMI increased with age. The immigrant groups showed a higher prevalence of obesity (BMI ≥ 30 kg/m2) for people >65 years of age compared with the native Dutch. However, conclusions should be drawn carefully, because the values of the higher age categories for Turks and Moroccans were based on a small number of observations. Turkish men had the highest prevalence of overweight and obesity. Moroccans had the lowest incidence of overweight among men in the sample, although their prevalence of obesity was higher compared with native Dutch and Surinamese/Antillean men. Surinamese/Antillean women had a higher prevalence of overweight than men with the same ethnicity. In all groups, women were more often obese (as opposed to overweight) compared with men. The four groups differed significantly in their BMI.

Table 3 shows the results of the OLS regression on BMI. The OLS regression showed that all immigrant groups had a higher BMI compared with the native Dutch respondents. This is in contrast with the findings in the bivariate analyses, which did not show a higher BMI for the Moroccans compared with the native Dutch respondents. This means that the BMI of the Moroccans was not different from the native Dutch, if corrections for income, level of education, etc., are not applied. However, when corrected for age, level of education, income, etc., the BMI of Moroccans was higher compared with the native Dutch.

Table 3.  Parameter estimates of BMI with dummies for Turks, Surinamese/Antilleans, and Moroccans, with the Dutch as reference group
  • *

    p< 0.01.

  • p< 0.05.

  • p< 0.10.

Native DutchReference group
Take out food (times per month)−0.009
Delivery food (times per month)0.051
Eating out (times per month)−0.054
Convenience food seldom/neverReference group
Convenience food 1 to 2 per week0.104
Convenience food 2 to 4 per week0.231
Convenience food >4 per week0.755
Raedy-to-eat meals seldom/neverReference group
Ready-to-eat meals 1 to 5 per month−0.083
Ready-to-eat meals 5 to 10 per month0.223
Ready-to-eat meals >10 per month0.579
Fresh vegetables seldom/neverReference group
Fresh vegetables 1 to 2 per week1.304
Fresh vegetables 2 to 4 per week0.404
Fresh vegetables >4 per week0.313
Smoking (yes/no)−0.750*
Married/cohabiting (yes/no)0.160
Children at home (yes/no)0.318
Low education levelReference group
Medium education level−1.123*
High education level−1.683*
Age squared−0.002*
Household income−0.001
No. observations2213
Adjusted R20.144
F statistic16.555

Demographic and Socioeconomic Characteristics

The OLS regression in Table 3 shows that BMI increased with age. The literature suggests that BMI increases up to the age of 50 for men and into the 70s for women (2, 3, 31). With the result of age squared from Table 3, the partial derivative of age squared on BMI can be calculated: ΔBMI/Δ age squared = 0. In our research, taking the partial derivative of BMI/age gives the maximum of 62 years, meaning that BMI increased up to the age of 62 and then decreased.

As expected, a higher level of education indicated a lower BMI for all groups. Household income gave no significant results, which can be attributed to the strong effect of education (which is also an indication of income) and age.

Whether people were married/lived together or had children at home did not affect their BMI. However, the effects of marital status and children on BMI may have been overruled by the strong effect of age (which is closely connected to being married/cohabiting and to having children). Therefore, the OLS regression was repeated excluding age and age squared (results not shown). This analysis showed that being married/living together or having children was related to a significant increase in BMI.

Food Habits and Lifestyle

The univariate analyses on food habits revealed some differences between native Dutch and immigrants. The Turkish respondents were found to eat take-out and delivery food and eat out more frequently than the other groups. In contrast, the native Dutch respondents were found to eat convenience food and ready-to-eat meals most frequently. Thus, the Turks were more likely to outsource the complete meal preparation, whereas the native Dutch outsourced only a part of the meal preparation. In general, immigrant groups ate take-out food more frequently than the native Dutch. Table 4 shows the distribution of the lifestyle variables over the four groups.

Table 4.  Distribution of lifestyle variables
Take-out food per monthN = 688N = 696N = 440N = 601
 Standard deviation2.763.875.664.8
Delivery food per monthN = 668N = 683N = 431N = 559
 Standard deviation2.901.951.852.15
Eating out per monthN = 689N = 690N = 440N = 548
 Standard deviation2.613.102.734.55
Ready-to-eat mealsN = 700N = 698N = 443N = 674
 1 to 5 times per month23.9%10.7%15.8%16.5%
 5 to 10 times per month4.0%2.1%3.2%2.8%
 >10 times per month2.3%1.0%3.4%2.1%
Convenience foodN = 695N = 696N = 437N = 673
 1 to 2 times per week38.9%25.6%24.5%29.1%
 2 to 4 times per week13.8%11.1%8.7%10.7%
 >4 times per week6.1%2.7%2.5%3.7%
Fresh vegetablesN = 701N = 701N = 447N = 697
 1 to 2 times per week8.6%7.1%6.5%12.2%
 2 to 4 times per week26.5%18.5%20.6%24.4%
 >4 times per week63.8%72.8%72.5%62.2%
SmokingN = 701N = 701N = 449N = 699
Do sportsN = 701N = 701N = 449N = 700
 ≤ 1 time per week21.1%18.0%19.8%17.6%
 2 to 3 times per week25.8%24.0%18.3%16.1%
 >3 times per week10.1%13.1%14.3%10.9%

Across the whole sample, the frequency of eating fresh vegetables was quite low. Eating fresh vegetables had a significantly negative effect on BMI, whereas eating convenience food resulted in a positive effect on BMI. In contrast to the hypothesis, eating out was not related to an increase, but to a decrease, in BMI (Table 3). In the bivariate analyses, the groups included in the study did not differ significantly in their food habits.

The highest frequency of smokers in the sample was found in the Turkish group. The Moroccan group had the lowest frequency of smokers (significant results). The OLS regression showed that smoking was associated with a decrease in BMI.

The different effects of food habits across the four groups are shown in Table 5.

Table 5.  Parameter estimates of BMI for Dutch, Surinamese/Antilleans, Moroccans, and Turks
  • *

    p < 0.01.

  • p< 0.05.

  • p< 0.10.

Take-out food (times per month)0.077−0.0140.016−0.063
Delivery food (times per month)0.1450.0750.030−0.099
Eating out (times per month)−0.125−0.043−0.078−0.024
Convenience food seldom/neverReference groupReference groupReference groupReference group
Convenience food 1 to 2 times per week0.235−0.2930.1220.576
Convenience food 2 to 4 times per week0.2580.438−0.0260.244
Convenience food >4 times per week0.6020.0100.1662.059
Ready-to-eat meals seldom/neverReference groupReference groupReference groupReference group
Ready-to-eat meals 1 to 5 times per month0.0560.440−1.2610.063
Ready-to-eat meals 5 to 10 times per month−0.2362.156*−2.1990.703
Ready-to-eat meals >10 times per month−0.3162.832*0.4180.013
Fresh vegetables seldom/neverReference groupReference groupReference groupReference group
Fresh vegetables 1 to 2 times per week1.4920.1254.6420.232
Fresh vegetables 2 to 4 times per week0.808−0.4112.257−0.217
Fresh vegetables >4 times per week0.782−0.8222.791−0.337
Smoking (yes/no)−1.029*−0.890−1.007−0.142
Married/cohabiting (yes/no)0.3580.0240.063−0.044
Children at home (yes/no)−0.0500.557−0.3750.423
Low education levelReference groupReference groupReference groupReference group
Medium education level−1.449*−0.933−1.116−0.738
High education level−2.034*−1.188−1.860*−1.844*
Age squared−0.0010.000−0.002−0.006*
Household income−0.001−0.0010.000−0.001
No. observations646657396511
Adjusted R20.1050.1190.2000.229
F statistic4.6075.2015.7268.224

The regressions for the different groups showed that, in the Dutch group, there was a significant effect of eating out on BMI. As seen in Table 5, this effect was negative. For Surinamese/Antilleans, consumption of ready-to-eat meals was associated with an increase in BMI. In contrast, the effect of ready-to-eat meals on the BMI of Moroccans was negative. Moroccans showed an unexpected positive effect of eating fresh vegetables on BMI, in the category of eating fresh vegetables one to two times per week. This indicates that a less “healthy” diet with only few fresh vegetables was related to a decrease in BMI for Moroccans. The Turkish respondents showed a BMI decrease related to consumption of take-out food but a BMI increase associated with consumption of convenience food. The differences between the groups could be caused by (cultural) diversity in food choices.

It might be possible that eating out covers both dining in a restaurant and eating at McDonalds. It may be that younger people eat out at McDonalds. To test this, the analysis was performed excluding age. The results on eating out did not change, but the results on take-out food did. In the analysis excluding age, take-out food had a significant negative effect on BMI.

As mentioned before, the relationship between engaging in sporting activities and BMI may be endogenous. Therefore, being an active participant in sports was not included in all regression analyses. Table 4 shows that, in our sample, the participation of immigrants in sports was lower than the participation of the native Dutch.

The figures of the Surinamese/Antillean group corresponded to the figures of the native Dutch, with the exception that the frequency of exercising of the Surinamese/Antilleans was higher. The largest share of respondents not participating in sports at all was found among the Turks.

To measure the effect of engaging in sporting activities on BMI, we repeated the OLS regressions from Table 5 (results not shown). If the OLS regressions were re-estimated including sports participation, the estimations did not change, which indicated that participating in sports had no significant effect on BMI in any of the groups except for the Dutch group (if the frequency of doing sports was more than three times per week). When age was excluded from the regression for the whole sample, participating in sports two to three times per week became significantly negative, which suggests an age effect (as could be expected). On the whole, the effect of participating in sporting activities on BMI was small. Apparently participating in sports per se was neither a very good nor a complete indication of physical activity. For example, physical activity during daily walking or activities related to housekeeping obviously may not be perceived by respondents as sports activities.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The purpose of this paper was to study whether native Dutch and immigrants into The Netherlands have a different prevalence of overweight and to study the socioeconomic and lifestyle determinants of overweight.

Our results showed that immigrants had a significantly higher BMI than the native Dutch. The statistics for overweight for the native Dutch in our sample corresponded to those from the Dutch Central Bureau of Statistics (4). The higher prevalence of overweight among immigrants was caused partly by their lower socioeconomic status (1, 2, 3, 5).

On the basis of the literature, socioeconomic status affects BMI negatively (2, 24, 25). This is shown for level of education in all groups in this study; higher education level was associated with lower BMI. Nevertheless, there were no significant results for income, which could have been over-ruled by the strong effects associated with age and education. In other words, education was a more important determinant for overweight than income. More highly educated people will have more knowledge regarding what constitutes a healthy lifestyle and, therefore, watch their diet more closely than less educated people.

A lower socioeconomic status relates to an unbalanced diet, which may cause overweight and damage health in the long term (24, 26). Time preferences among the studied groups were different. Time preference means, for example, that more highly educated people are better informed about the long-terms effects on their health (27), whereas less educated people may lack knowledge about healthy lifestyles or prefer consumption-related gratification without thinking about the consequences of their behavior over consumption oriented toward delivering long-term positive health effects in the future (28, 29). This confirmed the idea that, in Western countries, people with lower socioeconomic status are overweight, whereas in non-Western countries, people with higher socioeconomic status are overweight.

Food habits contributed to the differences in overweight among the groups. Although our results may give an indication of different food habits and lifestyle, conclusions should be carefully drawn from the data, because only information regarding the frequency of eating out and making use of convenience food, delivery, or take-out food was provided by our survey, and it is not known what type of food people chose and how much they ate. Eating out was significant only for the native Dutch, but the effect on BMI was negative instead of positive. An explanation could be 2-fold: mainly people with higher incomes (who are already more aware of a healthy diet) are more likely to eat out, and they might choose restaurants that serve lower-calorie menus rather than cheaper restaurants such as McDonalds, because, in general, healthy food is more expensive than unhealthy food. The analyses showed an age effect or generation effect for take-out food. Younger people ate more take-out food, which was related to a BMI decrease.

In contrast to Moroccans (whose BMI was negatively related to their increased consumption of ready-to-eat meals), the BMI of Surinamese/Antilleans was positively related to the frequency of eating ready-to-eat meals. If Surinamese/Antilleans—who are known for their “fatter” diet (30) —eat ready-to-eat meals, they may eat meals with a high-calorie value, and this may be related to a BMI increase. On the other hand, Moroccans may choose more healthy ready-to-eat meals. For the Turks, increased consumption of take-out food was associated with a decrease in BMI, indicating that they chose take-out food that did not have a high-calorie value. However, convenience food was associated with an increase in BMI in the Turkish respondents.

Apparently, people make different (less or more healthy) choices with respect to outsourcing food preparation. These choices may be culture-driven. Our results certainly indicated that not all modern food habits increase BMI (which is an indication of health).

Smoking related to a decrease in BMI. Of course, this does not mean that smoking is good for people's health. On the contrary, smokers die earlier than non-smokers (31). On the basis of the literature, one could expect a lower BMI for the group with the highest percentage of smokers (18, 19). This was, however, not seen in our sample, where Turks smoked most frequently compared with the other groups. It is suggested that other lifestyle factors (such as food habits and lack of physical activity) are related to a relatively high BMI for Turks.

In the total sample, BMI was found to increase up to 62 years of age and then decrease. This is a finding that is consistent with the literature and that can be attributed to reduced food intake (2, 3, 16, 32).

All immigrant groups, especially the Turkish respondents, participated in sporting activities less frequently than the native Dutch. This result might be ascribed partly to cultural differences in sports participation among ethnic groups (30) and between men and women, because women participate in sports less frequently than men (20).

In the literature, physical activity is negatively associated with overweight. However, in our sample, the effects of sports participation on BMI were very small, indicating that this was not a complete measure of physical activity. Nevertheless, it does give some indication about people's lifestyle and patterns of physical activity. About one half of our sample did not participate in sports, which corresponds to the literature (13).

As mentioned above, the relative low response rate led to a slight over-representation of higher educated immigrants. The results for the lifestyle variables might be stronger for lower-educated immigrants. However, this over-representation was corrected by including level of education in the analyses, which minimized the effect. The effect of the over-representation of nonworking Dutch women (causing a relatively lower household income) was abolished by correcting for household income in the analyses.

Overweight leads to higher expenditures on national health care, primarily because of the extra costs of chronic diseases caused by overweight (such as diabetes). Research shows that costs related to overweight constitute 1% to 5% of total public health costs, which in The Netherlands would be between 400 million and 1 billion Euro in terms of additional public health costs per year (29, 33). The latest figures from the United States show that nearly 12% of private health costs are a result of obesity-related diseases (20). These extra costs are not yet reflected by higher health insurance contributions to cover the overweight prevention costs.

Public policies should aim at encouraging people to adopt healthier diets by subsidizing healthy food (like fruit and vegetables) and by putting higher taxes on high-calorie foods. Immigrants should be encouraged to become more physically active as part of a healthy lifestyle. Campaigns against overweight should aim to address less-educated people, because they have higher risks for overweight, and may need to specifically target immigrant populations.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

This research was supported by the Wageningen University and the University of Amsterdam.

  • 1

    Nonstandard abbreviation: OLS, ordinary least square.

  • The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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