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J Happiness Stud (2015) 16:515–541

DOI 10.1007/s10902-014-9521-8

Are Women Happier than Men? Evidence from the Gallup World Poll

Jacqueline S. Zweig

Published online: 2 April 2014

Springer Science+Business Media Dordrecht 2014

Abstract Women in nearly all countries of the world have lower incomes, are less educated, are more likely to be widowed or divorced, and report worse health than men. Based on the happiness literature, these inequalities should cause women to be less happy than men. This study investigates this hypothesis using the Gallup World Poll to estimate differences in happiness between men and women in 73 countries through country-specific ordinary least squares regressions. It then examines whether the magnitude of the female– male happiness gap can be explained by country characteristics, such as economic development, religion, or women’s rights. This paper provides evidence that women are either happier than men or that there is no significant difference between women and men in nearly all of the 73 countries examined; when comparing men and women with the same life circumstances, women are happier than men in nearly a quarter of the countries. The magnitude of the female–male happiness gap is not associated with economic development or women’s rights and there are no systematic patterns by geography or primary religion.

Keywords Happiness Gender Subjective well-being Life satisfaction

1 Introduction

The economics of happiness literature provides evidence that women are generally slightly happier than men after controlling for individual circumstances, but this evidence is largely based on analyses from developed countries (Blanchflower and Oswald 2004; Dolan et al. 2008; Easterlin 2001; Frey and Stutzer 2002). In a recent summary of the happiness literature, Helliwell et al. (2012) find a positive association between being female and happiness in three commonly used data sets. Stevenson and Wolfers (2009) present

J. S. Zweig (&)

University of Southern California, Los Angeles, CA 90089, USA e-mail: [email protected]

evidence that women are happier than men in the United States and Western Europe, but that the gap has declined for women relative to men in recent years.

The psychology literature is generally in agreement with the economics of happiness literature that women report being happier than men, but they also conclude that women are more likely to have anxiety or be depressed (Nolen-Hoeksema and Rusting 1999). Stone et al. (2010) found that age profiles for women and men in both global well-being and hedonic well-being are similar, though they confirm that sadness, stress, and worry were more common among women.

There is less clear evidence about the female–male happiness gap in transition and developing countries. Knight et al. (2009) show that rural women in China are happier than rural men. This result is found when they include both objective and subjective explanatory variables in their specification. Graham and Pettinato (2001) investigate the determinants of happiness for 15 Latin American countries using the Latinobarometer. They report that there is no happiness difference between men and women with the same circumstances. There is also some evidence that men are happier than women in Russia (Senik 2004).

Graham and Chattopadhyay (2012) also use the Gallup World Poll to investigate gender issues in well-being. They find that women are generally happier than men, but that the relationship is strongest for high income countries and there is no significant difference in low income countries. Although this paper uses the same data, the methodology and patterns investigated are different (as described in more detail in the following section).

In terms of explaining differences in male–female happiness, there is some evidence that expectations for equal pay for women has the opposite effect on women’s happiness than one might expect. Lalive and Stutzer (2010) use voting behavior on an equal rights amendment in Switzerland as a proxy for measuring gender equality. The authors find that women in more liberal communities (i.e., voted for the amendment) with a smaller male– female wage gap are less satisfied with their lives than women in communities with a larger male–female wag gap.

This paper contributes to the literature by presenting the patterns of female–male differences in life satisfaction across countries at various stages of development, including developed, developing, and transition countries. This study uses the Gallup World Poll to estimate differences in happiness between men and women in 73 countries and presents evidence on whether the magnitude of the female–male happiness gap changes after life circumstances are taken into account. One might expect that the magnitude of the female– male happiness gap depends on country characteristics, such as stage of development, geography, religion, or women’s rights. Thus, this paper presents information on the association between the female–male happiness gap and these country characteristics. As in much of the happiness literature, the terms life satisfaction, happiness, and subjective well-being are used interchangeably.

2 Methodology

This paper analyzes data for 73 countries included in the Gallup World Poll Survey (Gallup World Poll 2009a). The first three waves of the surveys, conducted between 2005 and 2008, are pooled to form a cross-sectional dataset. Due to geographic coverage and data availability 73 countries are analyzed. The sample contains 20 developed countries, 12 transition countries in Eastern and Central Europe, 16 Asian countries, 17 Latin American countries, and 8 African countries (see Appendix 1 for a list of the countries). The surveys are nationally representative and typically conducted by telephone in developed countries and in-person in developing countries. For additional information on the sampling procedure and data, see the Gallup World Poll Methodology (Gallup World Poll 2009b).

One of the benefits of these data is that the same questions are asked in all countries, which allows for comparisons to be made both within countries and across countries. It has been shown that time series and cross-section data yield different results in the happiness literature (Easterlin and Angelescu 2011). The focus of this paper is, therefore, on establishing the facts about the female–male happiness gap in countries of different geographic locations and stages of development at a point in time.

The life satisfaction question that is used as the outcome variable is:

‘‘Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. Suppose we say that the top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time, assuming that the higher the step the better you feel about your life, and the lower the step the worse you feel about it? Which step comes closest to the way you feel?’’

The respondents answer on a scale from 0 to 10.

The main independent variable is whether an individual is male or female. The average difference for each country is determined by a country-specific ordinary least squares (OLS) regression of life satisfaction on the female dummy variable. Standard errors are robust to heteroskedasticity. The first part of the analysis focuses on the average differences in life satisfaction allowing the respondents’ individual circumstances to vary. The average differences are compared to several country-level factors. Satisfaction with life for individual i, Si, is regressed on a dummy variable, Fi, which is equal to one if the individual is female and zero if the individual is male. The baseline specification is:

Si ¼ a þ bFi þ ei: ð1Þ

This regression is run separately for each of the 73 countries in the analysis, resulting in 73 estimates of b, where b indicates the size of the female–male happiness gap in a country. If b is positive and significant at the 5 % level, then women are on average happier than men.

In the second step of the analysis, objective factors typically included in happiness regressions are included in the model (Dolan et al. 2008). If differences in these variables account for the female–male happiness gap, then the coefficients on female should approach zero and should not be significant. A set of objective circumstances represented by Xi are added to each country’s regression:

Si ¼ a þ bFi þ dXi þ ei: ð2Þ

The demographic variables included in Xi are age, age squared, marital status, and level of education. Life circumstances include whether or not the individual has health problems, employment status, whether she attended a religious ceremony in the previous week, and whether she lives in a large city (with rural or small town as the reference group). Occupation and income are the economic factors included in Xi. In the analysis, the latter two variables are available in a limited number of surveys and countries. The 12 occupation groups in the survey are reduced to six categories: white collar, business owner, service worker, non-farm manual worker, farmer, and other. Other includes all individuals who chose ‘‘other’’ in the list of possible responses or reported having a job, but did not answer the occupation question. This category is only constructed for waves and countries where some of the respondents answered the occupation question. The income variable is a continuous measure that only includes cash income. I first include demographic characteristics, and life circumstances followed by economic factors, because the number of observations reduces considerably when different sets of variables are included. These regressions provide insight into whether women with the same circumstances as men are as happy as men, and can be thought of as ‘‘pure’’ difference between women and men.

The regressions are weighted by the weights provided in the Gallup data. The weights adjust for gender, age, and, where reliable comparative population data are available, education or socioeconomic status (Gallup World Poll 2009b). I include dummy variables for waves to control for any factors that affect all observations in a specific wave. Because some of the variables are the result of individual choices or subjective perceptions and the fact that this study uses cross-section data, the results that include all of the control variables cannot be interpreted as causal. The aim of this paper is, therefore, to present evidence on the patterns of the female–male happiness gap.

In all specifications, OLS is used rather than an ordered probit, which requires the assumption that the responses to the life satisfaction question are cardinal even though they are actually ordinal. The happiness literature suggests the results of ordinal and cardinal methods tend to be very similar in terms of levels of significance, especially for responses on a scale of zero to ten (Dolan et al. 2008, Powdthavee 2010; van Pragg 2005). In order to verify that the results are consistent, I run the baseline regressions using ordered probit and OLS. All of the female–male differences in life satisfaction that are significant in OLS regressions are also significant in the order probit regressions. Greece and Lithuania are the only countries where the differences are significant in the ordered probit regression and not in the OLS regression.

Because the number of observations changes depending on the variables included in the analysis, the coefficients on female from the regressions with controls are always compared to the coefficients on female when there are no controls, after restricting the number of observations to that in the controls case. For example, when the results for Eq. (2) are plotted against Eq. (1), the coefficients from (2) are plotted against the coefficients from a regression of life satisfaction on female without controls for observations where age, marital status, and education level, health, employment status, religiosity, and location are also available.

The methodology used in this paper differs from Graham and Chattopadhyay (2012) in three ways. First, the authors pool data from all countries in each income category in a single regression, while this paper examines each country separately and uses a crosscountry regression to explain the female–male happiness gap. By calculating mean differences in happiness for each country separately, this paper gives equal weight to each country, rather than weighting by population. Second, Graham and Chattopadhay include different control variables in their specifications.1Third, this paper attempts to explain differences across countries in the magnitude of the female–male happiness gap rather than explaining happiness.

Table 1 Descriptive statistics: average female–male difference

Means are computed using weights provided in the Gallup World Poll Survey data (2009a). Standard errors are computed from a paired two-tailed t test

3 Results

Table 1 shows the descriptive statistics for the average differences between women and men in the outcome and explanatory variables, where the unit of observation is a country and the averages give equal weight to each country. Women are more likely to be previously married, unemployed, only have an elementary education, have health problems, have lower income, and feel less safe. In addition, they are less likely to be white collar workers, farmers or non-farm manual workers.

Are women happier than men? In Table 2, female–male differences in life satisfaction are grouped by statistical significance and geographic region. There are ten countries where women are statistically significantly more satisfied than men, two where men are more satisfied and there is no significant difference between men and women in the remaining 61 countries. The only notable geographic pattern to the significance of the female–male happiness gap is that there is no significant difference between women and men in all of the transition countries. Without controlling for any individual factors, on average, there is either no difference in happiness between women and men, or women are happier than men in nearly all countries and there is not a systematic pattern by region. The coefficients and number of observations for each specification are presented in Appendices 3 and 4, respectively.

Is the female–male happiness gap larger at more advanced stages of development or in countries where women have more relative power? In Fig. 1, each country’s female–male happiness gap is plotted against its log GDP per capita obtained from the World Bank (2010). A positive difference indicates that women are happier and a negative difference indicates that men are happier. The female–male happiness gap is not a reflection of a country’s stage of development. In an OLS regression of the happiness gap on log GDP per capita, I cannot reject the null hypothesis at a 95 % significance level (but can at a 90 %) that the coefficient on log GDP per capita is equal to zero. There is a slightly negative slope, which suggests, if anything, that women are happier relative to men at lower levels of development and that at advanced stages of development, the happiness gap disappears. Figure 2 plots the female–male happiness gap and the percent of seats held by women in the national parliament for each country from the World Bank (2010). The figures show that the size of the happiness gap is not associated with this measure of women’s rights.

What about religion? The average differences in female–male happiness by a country’s primary religion are presented in Table 3. A religion is designated as primary if 50 % or more of the population practiced a specific religion based on the Central Intelligence Agency’s (CIA’s) World Fact Book (2010). The ‘‘other’’ category includes countries without a primary religion or with a primary religion other than Catholicism, Christianity, Buddhism, or Islam. Women are the happiest relative to men in Islamic and Buddhist countries, which are primarily in the Middle East and Asia.

The next step of the analysis is to determine whether women with the same circumstances as men are equally happy, happier, or less happy than men. The happiness gap would decrease compared to the average if the factors that make people happy are the same factors that are greater for women. Figure 3 shows the average female–male happiness gap on the x-axis and the female–male happiness gap after controlling for demographic characteristics, life circumstances, and economic factors on the y-axis. The dashed line is the 45-degree line; if the average differences and the ‘‘pure’’ differences were same, the data points would be on the 45-degree line. If the differences can be explained by the control variables, then the data points should be clustered around zero on the y-axis. Neither of these situations occurs. The ‘‘pure’’ female–male happiness gap is larger than the average gap and the data points are nearly all above the 45-degree line. Again, this indicates that the size of the gap is larger when you compare women and men with the same circumstances than when you allow those circumstances to vary.

The coefficient on female is now positive and significant in 16 of the 66 countries compared to 10 in the no-controls case based on a 5 % significance level. Eight of the 16 countries had positive and significant result in both the controls and no controls case. There are no countries where women are less happy than men. Seven countries are dropped from the original 73 when the economic variables are included and 25 % of the observations from the remaining countries are missing.2Figure 4 contains the results of the full sample when only demographic and life circumstances are included as explanatory variables and the findings are very similar. Women in the same occupations, with the same income, demographic characteristics, and life circumstances as men are significantly happier than men in 24 % of the countries studied.

Can GDP per capita, women’s rights, religion, or geography explain the ‘‘pure’’ differences in life satisfaction? The results in Tables 3 and 4 suggest that the answer is no. Neither the countries’ stage of development nor the percent of women serving in parliament is associated with the size of the ‘‘pure’’ female–male happiness gap. There is also no pattern by geography or religion. The findings for religion are slightly different than in the no-controls case; the female–male happiness gap is similar across religions and it is no

Table 2 Distribution of countries by average female–male difference in life satisfaction and geographic region

Mean difference in life satisfaction is the average of country-specific coefficients obtained from OLS regressions of life satisfaction on the female dummy variable and wave effects. The categories no significant difference, women happier, and men happier are based on whether the coefficient on the dummy variable for female is significantly different from zero at a 5 % significance level. Standard errors are in brackets

longer the case that women are happier relative to men in primarily Islamic or Buddhist countries (Table 5).

4 Robustness Checks

It is possible that the observations that are dropped in each specification are not random and could bias the results. Therefore, I test the equality of the female–male differences in life satisfaction in the baseline regressions with all observations and with only the observations available with controls using a Wald Test. For each specification, one cannot reject the null hypothesis that the no-controls coefficients are equal to the no-controls coefficients limited to the observations available when the control variables are included in the specification.

Concern that the missing observations are non-random is particularly important when the economic variables are included in the model. It is possible that the income variable would be missing more for women than for men. If that is true, and the women who have missing income data are the least happy, then the results would be biased upward. The percent of women and men in each country who are missing the economic variables, as well as the differences in these percentages, are presented in Appendix 5. The percent of the observations that are dropped in each country is similar across men and women. There are only three countries for which the difference in the percent missing for men and women is greater than 9 %: Nepal, Norway, and the Netherlands. On average, there is less than a 1 % difference in the number of observations missing for men and women. Unlike in the other regions, more male observations are dropped than female observations in Asia and the transition counties.

Log GDP per Capita

Fig. 1 Female–male difference in life satisfaction and log GDP per capita. GDP per capita data are for 2006 in PPP constant 2005 international dollars. GDP data are from World Bank (2010), World Development Indicators Online (WDI) database. Data retrieved March 1, 2011. The OLS regression is

SatLifef SatLifem ¼ 0:310:03lnGDP:R2 ¼ 0:04 N ¼ 73:

½0:16 ½0:17

Percent of Seats Held by Women in National Parliament

Fig. 2 Female–male difference in life satisfaction and percent of seats held by women in national parliament. The percent of seats held by women in national parliament are for 2005 or the closest year available from 2006 to 2008. Data are from World Bank (2010) World Development Indicators Online (WDI) database. Data retrieved March 1, 2011. The OLS regression is

SatLifef SatLifem ¼ 0:090:29Parliamentf: R2 ¼ 0:04 N ¼ 73:

½0:04 ½0:22

The coefficients on female could be overestimated in the regressions if (1) women are less likely to respond to the income question than men and (2) it is the women with lower life satisfaction who are less likely to respond. This is tested more formally using

Table 3 Average female–male difference in life satisfaction by religion

Mean difference in life satisfaction is the average of country-specific coefficients obtained from OLS regressions of life satisfaction on the female dummy variable and wave effects. Primary religion is determined by whether the CIA World Fact Book (2010) stated that at least 50 % of the population was a specific religion. Other includes religions that did not fall into one of the four categories and countries without a primary religion. Standard errors are in brackets

Fig. 3 Female–male difference in life satisfaction: average difference versus ‘‘Pure’’ difference after controlling for demographic characteristics, life circumstances, and economic factors. ‘‘Pure’’ differences are from regressions with the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, income, occupation, and wave effects. South Korea is beyond the range of the graph. The OLS regression is ðSatLifef SatLifemÞcontrols ¼ 0:11þ0:85ðSatLifef SatLifemÞnocontrols: R2 ¼ 0:7 N ¼ 66:

½0:02 ½0:08

Fig. 4 Female–male difference in life satisfaction: average difference versus ‘‘Pure’’ difference after controlling for demographic characteristics and life circumstance. The ‘‘pure’’ differences are from regressions with the following explanatory variables: age, age squared, marital status, health problems, employment status, education, attendance at a religious ceremony, residential location, and wave effects. South Korea is beyond the range of the graph. The OLS regression is ðSatLifef SatLifemÞcontrols ¼ 0:11þ0:85ðSatLifef SatLifemÞnocontrols: R2 ¼ 0:81 N ¼ 73:

½0:01 ½0:05

Table 4 Association between the ‘‘Pure’’ female–male difference in life satisfaction and log GDP per capita and percent of seats held by women in parliament

The percent of seats held by women in national parliament are for 2005 or the closest year available from 2006 to 2008. Data are from World Bank (2010) World Development Indicators Online (WDI) database.

Data retrieved March 1, 2011. Data on the percent of women in Parliament was not available for Finland

Standard errors are in brackets. ** Significant at 1 %; * Significant at 5 %; ? Significant at 10 %

Table 5 Average ‘‘Pure’’ female–male difference in life satisfaction by geographic region and primary religion

Mean difference in life satisfaction is the average of

country-specific coefficients obtained from OLS regressions of life satisfaction on the female

dummy variable, demographic

characteristics, life circumstances, economic factors, and wave effects. Primary religion is determined by whether the CIA World Fact Book (2010) stated that at least 50 % of the population was a specific religion. Other includes religions that did not fall into one of the four categories and countries without a primary religion.

Standard errors are in brackets

probit regression where the dependent variable is equal to one if the respondent did not answer the income question and zero otherwise. The independent variables are female, life satisfaction, and the interaction of female with life satisfaction. The female–male differences may be overestimated if the coefficient on the interaction term is negative and significant and the coefficient on female is positive and significant. This only occurs in seven out of 66 countries, which are shown in Appendix 6. The seven countries are Norway, Sweden, Australia, Columbia, Dominican Republic, Indonesia, and Tajikistan. In general, this suggests that the magnitudes of the coefficients on female in the regressions that include income are not driven by this type of selection. However, the results for the seven countries listed above should be interpreted with caution, especially Australia and the Dominican Republic which are two of the 16 countries women are significantly happier than men.

It is also important to verify that the female populations studied here are in fact representative of the populations as a whole. For example, if the Gallup Poll only surveyed educated women and people with higher education are happier, then this difference could be due to data limitations. Appendix 7 compares the percent of the female population with only an elementary education in the Gallup data to the United Nations Educational, Scientific, and Cultural Organization’s (UNESCO) data (2005–2008). There are large differences between Gallup and UNESCO data in Burkina Faso, Kazakhstan, and Cambodia. The Gallup data contain a larger percentage of the uneducated in Burkina Faso and Kazakhstan, which should drive down happiness, rather than increase it. After excluding Burkina Faso and Kazakhstan, the average difference increases to 0.17 for the Islamic countries.

Since this is cross-sectional data rather than longitudinal data, it could be that omitted variables bias is present. In the happiness literature, it is particularly important to consider personality; it could be that women are more likely to have a positive affect and therefore, by not controlling for personality, the coefficient on female is overestimated. Gallup asks a couple questions that could be used as a proxy for personality and are employed in Graham and Chattaopadhay’s study: ‘‘Did you experience enjoyment during a lot of the day yesterday?’’ and ‘‘Did you experience sadness during a lot of the day yesterday?’’. Including these two variables in the regression with life circumstances increased the number of countries where women were, on average, happier than men to 21; the remaining 45 countries were not significant. The results are included in Appendix 8.

5 Discussion

This paper has shown that women are happier than men or there is no difference between men and women in nearly all countries studied, regardless of a country’s stage of development, overall well-being, or geographic location. This is in spite of the fact that women are on average less educated, have lower income, and are more likely to be widowed or divorced. The finding that women are either happier than men or that there is no difference is consistent with prior research on developed countries and with the limited evidence on developing countries.

The results after controlling for individual circumstances show that the ‘‘pure effect’’ of being female is larger than the average effect. Women are statistically significantly more satisfied with life than men in about a 24 % of the countries. Women of the same age, education level, occupational status, etc. are either happier than their male counterparts or there is no significant difference. Helliwell et al. (2012) estimate using OLS regressions on three data sets that the female–male happiness gap ranges from 0.19 to 0.14. Graham and Chattopadhyay (2012) estimate that the female–male happiness gap is approximately 0.12. In this paper, the happiness gap after controlling for life and economic circumstances across all countries is approximately 0.15, which is similar to the findings in both papers previous papers. This is more than three times as large as the gap in the no controls specification. In terms of the magnitude of the female–male happiness gap, Helliwell, Layard, and Sachs, report that being unemployed is associated with a reduction in happiness of 0.33–0.66 depending on the dataset. This suggests that the male female happiness gap is about 22–45 % of the employed-unemployed gap.

The fact that there is no consistent pattern in the size of the differences across geographic regions or stage of economic development differs from the findings of Graham and Chattopadhyay (2012), who find that there is no significant difference between male and female happiness in the low income countries, but that women are happier in developed countries. This paper finds that there are no consistent patterns by geography or level of development. The differences in these results are likely due to differences in methodology, in particular: (1) the explanatory variables are different in the two studies,

(2) Graham and Chattopadhay pool the data from countries in each region rather than examining them separately, and (3) this study’s dependent variable is the female–male happiness gap rather than happiness when examining associations by stage of development or geography.

There are several important limitations to this study. First, the results are from crosssectional analyses rather than longitudinal analyses; longitudinal analyses would allow the use of individual fixed effects to control for personality. Second, as discussed in some detail above, the same questions were not asked of respondents in every wave and not all participants responded to every question, which raises concerns about missing data. Third, the financial crisis occurred during the time that the surveys were conducted, which may have an unknown differential impact on women and men’s responses to the life satisfaction question. Lastly, the interviews were conducted face-to-face in some countries and by telephone in other countries. All of these facts together highlight the fact that the results presented here are associations and that causal inferences should not be made from this paper.

These findings have possibly led to more questions than answers. If women’s objective circumstances cannot explain why women are happier, then what can explain it? Why are women in countries with low levels of women’s rights happier than men? There are a several possible explanations that are beyond the scope of the paper, but worth mentioning. Two possibilities, biology and personality, have largely been rejected by the psychology literature (Nolen-Hoeksema and Rusting 1999). The third and most likely explanation is that aspirations formed from culture and social norms play an important role in well-being. This could explain why women, who are less educated and have fewer rights than men, may be happier relative to men. It is possible that their aspirations are lower than men’s aspirations, so when they evaluate their circumstances in terms of life satisfaction, they report higher well-being. Plagnol and Easterlin (2008) show that early in adult life women have higher happiness because they are more likely than men to fulfill their material goods and family life aspirations. Later in life, the reverse is true leading men to be more satisfaction with life. Thus, this paper is a starting point for much additional research on why women are at least as happy as men despite their objective circumstances.

6 Conclusion

This study provides evidence using the Gallup World Poll that women are happier or that there is no significant difference in happiness between men and women in nearly all of the 73 countries examined. This is contrary to expectations based on the fact that men tend to have higher incomes, be employed, and be married. Women with the same life circumstances as men are also happier than men in 16 of the 66 countries examined with available data. The female–male differences in life satisfaction are larger after controlling for various individual circumstances, including, income, education, marital status, and health. This pattern of women being happier or there being no significant difference in happiness is true in all regions of the world and in countries at all stages of economic development.

Acknowledgments I am grateful to the Gallup Organization for providing me access to the Gallup World Poll and to Richard A. Easterlin for his guidance on this paper.

Appendix 2

See Table 7

Table 7 Description of variables in the analysis

Life satisfaction

Question Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. Suppose we say that the top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time, assuming that the higher the step the better you feel about your life, and the lower the step the worse you feel about it? Which step comes closest to the way you feel?

Answer Worst possible*01*02*03*04*05*06*07*08*09*Best possible

Don’t know

Refused

Marital status

In a small town or village

In a large city

In the suburb of a large city

Don’t know

Refused

Employment status

Question Do you currently have a job or work (either paid or unpaid work)?

Answer Yes No

Don’t know

Refused

Education

Question EDUCATION_CAT

Answer Elementary—Completed elementary education or less (up to 8 years of basic education)

Secondary—Completed some education beyond elementary education (9–15 years of education)

Tertiary—Completed 4 years of education beyond high school and/or received a 4-year college degree

Don’t know

Refused

Health problems

Table 7

Question Do you have any health problems that prevent you from doing any of the things people your age normally can do?

Answer Yes No

Don’t know

Refused

Attend a religious ceremony

Occupation

Appendix 3

See Table 8.

Table 8 Mean difference in life satisfaction between women and men from each specification

Table 8

Table 8

** Significant at 1 %

* Significant at 5 %

?

Significant at 10 %

a

DC & LC are coefficients from regressions including the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, and wave effects

b DC, LC, & EC are coefficients from regressions including the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, income, occupation, and wave effects. Standard errors are robust to heteroskedasticity

Appendix 4

See Table 9.

Table 9 Number of Observations in Regressions

Table 9

Table 9

a

DC & LC are from regressions including the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, and wave effects

b

DC, LC, & EC are from regressions including the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, income, occupation, and wave effects

Appendix 5

See Table 10.

Table 10 Percent of observations missing when economic factors are included in the analysis

Table 10

Table 10

Appendix 6

See Table 11.

Table 11 Probit regressions of whether a respondent did not answer the income question (dependent variable = 1 if income is missing; 0 otherwise)

Standard errors are in brackets

** Significant at 1 %

* Significant at 5 %

? Significant at 10 %

Appendix 7

See Table 12.

Table 12 Comparison of the Percent with Elementary Education from Gallup Data and UNESCO data

The reported percent with an elementary education is the sum of the following educational categories: no schooling, incomplete primary, and primary (ISCED 1). for Iran, Tajikistan, and Japan, elementary education also includes lower secondary (ISCED 2). Data are from United Nations Educational, Scientific, and Cultural Organization (2011). Educational Attainment of the Population Aged 25 years and Older/Latest

Year Available. Data retrieved on March 1, 2011

Appendix 8

See Table 13.

Table 13 Coefficient on female when enjoyment and sadness are included as control variables

Table 13 continued

Table 13 continued

Regressions include the following explanatory variables: age, age squared, marital status, health problems, employment status, education, residential location, attendance at a religious ceremony, income, occupation, enjoyment yesterday, sadness yesterday, and wave effects. Standard errors are robust to heteroskedasticity ** Significant at 1 %; * Significant at 5 %; ? Significant at 10 %

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