examining the gender gap in life expectancy: a cross-national analysis, 1980–2005

Examining the Gender Gap in LifeExpectancy: A Cross-National Analysis,1980–2005∗

Rob Clark, University of Oklahoma

B. Mitchell Peck, University of Oklahoma

Objectives. This study examines predictors of the gender gap in life expectancy acrossa large cross-national sample. Methods. We employ random effects and fixed effectsmodels of the gender difference (female–male) and gender ratio (female/male) inlife expectancy during the 1980–2005 period. Results. Women’s status, traditionalmale hazards, and development/modernization processes tend to widen the gendergap in life expectancy. In addition, income inequality expands the gender gap, whilefemale representation in parliament reduces it. We argue that these latter effects area function of (1) the steeper socioeconomic gradient for men in predicting mortalityand (2) the protection of economically vulnerable groups by female parliamentarians,which provides greater health returns to males. Conclusion. Advances in genderequity along economic, political, and cultural lines appear to exert countervailingeffects, both expanding and reducing the gender gap in mortality.

In almost every society across the world, women live longer than men. Bi-ological and genetic differences have been considered as possible explanationsfor this gender disparity. However, there is considerable variation in the gendergap across time and space. For example, the female advantage in life expectancytends to be smaller among poor nations (Nathanson, 1984). By contrast, thegender gap in Eastern European and Central Asian countries is famously large(Jasilionis et al., 2007) due to the relatively high rates of alcohol consumptionand cigarette smoking for men (Anderson and Silver, 1986), as well as theirunder-utilization of health-care services relative to women (Cashin, Borowitz,and Zuess, 2002).

In addition, the gender gap has not remained constant within societiesover time. For example, among wealthy nations, the female advantage in lifeexpectancy began to rise considerably during the late 1800s and early 1900s(Pampel, 2002). However, women’s relative gains in life expectancy eventuallystalled and have even begun to reverse course over the past several decadesdue primarily to changes in smoking behavior by men and women (Pampel,

∗Direct correspondence to Rob Clark, Department of Sociology, University of Oklahoma,Kaufman Hall 331, 780 Van Vleet Oval, Norman, OK 73019 〈[email protected]〉. The authorswill share all data and coding with those wishing to replicate the study.

SOCIAL SCIENCE QUARTERLYC© 2012 by the Southwestern Social Science AssociationDOI: 10.1111/j.1540-6237.2012.00881.x

2 Social Science Quarterly

2002). Clearly, net of whatever biological or genetic advantages that femalesmay enjoy relative to males, environmental factors are operative in influencingthe magnitude of the gender gap in life expectancy.

In this study, we update Ram’s (1993) investigation of the gender gap in lifeexpectancy, examining a larger set of countries over a more recent period. Wefirst report the gender difference (female–male) and gender ratio (female/male)in life expectancy across 195 countries between 1985 and 2005. In doing so,we highlight the major cross-sectional patterns and longitudinal trends. Wethen examine predictors of both the gender difference and gender ratio inlife expectancy, employing random effects (REM) and fixed effects models(FEM) for a large cross-national sample covering the 1980–2005 period. Wefind support for most of our hypotheses and offer explanations for severalnoteworthy findings.

Male Hazards

The primary sources of female advantage in life expectancy among afflu-ent nations stem from mortality differences in heart disease, cancer, accidents,and violence (Nathanson, 1984). Previous research has emphasized gender dif-ferences in smoking prevalence, alcohol consumption, homicide and suiciderates, as well as exposure to occupational hazards, traffic accidents, and war(Moller-Leimkuhler, 2003; Nathanson, 1984). In particular, about 75 percentof the gender gap in Italy is explained by cancer, cardiovascular disease, and vio-lence (Conti et al., 2003). Similarly, in the United States, about three-quartersof the gender gap in mortality can be attributed to gender differentials intraumatic deaths (especially traffic accidents, suicide, and homicide), cardio-vascular disease (especially heart disease), and cancer (especially lung cancer;Wong et al., 2006). By contrast, the gender gap in Israel has been narrowrelative to that of Western nations primarily due to Israeli males experiencingfewer deaths from lung cancer, cirrhosis of the liver, and hazardous behavior(Staetsky and Hinde, 2009).

Tobacco deaths, in particular, contribute substantially to the gender gapin both the United States (Rogers and Powell-Griner, 1991) and Europe(Bobak, 2003). About half of the gender gap in life expectancy in Finlandduring the 1990s can be attributed to tobacco- and alcohol-related deaths(Martelin, Makela, and Valkonen, 2004), while other estimates suggest thatsex differences in smoking alone accounted for 30–45 percent of the gendergap in life expectancy across Denmark, Finland, Norway, Sweden, and theNetherlands during the 1970s and 1980s (Valkonen and van Poppel, 1997).

Gender differences in smoking may also contribute to the gender gap inmortality among developing nations. While cigarette use is higher amongmales (35 percent) than females (22 percent) across wealthy countries, thisdisparity is even greater in the developing world, where approximately halfof all males smoke compared to only 9 percent of females (Pampel, 2007).

Gender Gap in Life Expectancy 3

Moreover, while cigarette use has declined among rich nations, it has increasedin the developing world (Pampel, 2007, 2008). Thus, we expect tobacco-related deaths to play a significant role in shaping the gender gap across bothdeveloped and developing countries. Specifically, we hypothesize that thegender ratio in smoking deaths will widen the gender difference and genderratio in life expectancy. In addition, we expect the prevalence of violence ina society, measured by each country’s homicide rate, to widen the gender gapin mortality.

Women’s Status

In our dataset, only eight countries feature a male-favored gender gap inlife expectancy in any one of the five waves during the 1985–2005 period,and these states are concentrated in South Asia. As Ram (1993:86) notes, “InSouth Asian societies, discriminatory treatment of females over males in termsof allocation of food and health care has been found to be associated withexcess female mortality.” In six countries (Afghanistan, Bangladesh, India,Nepal, Pakistan, and Zimbabwe), the male advantage appears in one to threewaves, and the gender difference is less than one year. In two other countries(Maldives and Niger), the male advantage appears in four to five waves, andthe gender difference is one to three years.

Ram (1993:84) argues that “the more highly regarded women are in a so-ciety, the higher their life expectancy is relative to men.” For example, sexdifferences in resource allocation toward children (e.g., food, medical care) inthe developing world disadvantage females with respect to infant and child-hood mortality (Nathanson, 1984), thereby reducing their life expectancyrelative to males. In Ram’s (1993) province-level study of India, gender ratiosin literacy (female/male) were positively associated (r = 0.60) with genderdifferences in mortality (female–male), while fertility rates were negativelyassociated (r = −0.73). In another region-level study of India, women’s laborforce participation rates were found to be associated with lower child mortalityrates for females, which may indicate a family’s tendency to invest more inyoung females when they are viewed as economic assets (Basu, 1993). Indeed,a district-level study in India finds that female literacy and labor force par-ticipation are negatively associated with female disadvantage in child survivalbecause, presumably, economic activity increases the returns of investing ingirls and reduces gender bias in child preference (Murthi, Guio, and Dreze,1995). Other cross-national studies of developing countries have found thatwomen’s formal education, industrial employment, and reproductive auton-omy all significantly contribute to female life expectancy (Williamson andBoehmer, 1997), and that education is associated with a reduction in mater-nal mortality rates (Buchmann, 1996).

Ram’s (1993) study of the gender gap in life expectancy covers 83 devel-oping nations over the 1960–1980 period. Ram includes several measures of


women’s status, including fertility rate, the secondary school enrollment ratio(female/male), and the labor force participation ratio (female/male). Ram findsthat all three measures are significant predictors of the gender gap, with theeducation and labor force ratios expanding the gap, and fertility rate reducingit. In our study, we similarly expect our measures of women’s status (fertilityrate, primary school ratio, and labor force ratio) to significantly impact thegender difference and gender ratio in life expectancy. In addition, we considerthe percent of seats in parliament held by women. Although one previousstudy finds that female representation in parliament does not significantlyimprove female life expectancy among less developed countries (Williamsonand Boehmer, 1997), we hypothesize that it will broaden the gender gap inlife expectancy.

Development/Modernization

By some accounts, the gender gap in mortality is also responsive to develop-ment and modernization processes. In general, wealthy nations tend to featurelarger gender differences in life expectancy than poor countries (Nathanson,1984). Several explanations are possible. First, economic development mayraise a country’s overall life expectancy, thereby magnifying absolute differencesin life expectancy between males and females. Ram’s (1993) study illustratesthe importance of development among poorer countries, showing that a na-tion’s energy consumption increases its gender difference in life expectancy.Second, economic development is associated with a number of societal changes(e.g., industrialization, the demographic transition) that elevate living stan-dards, thereby reducing maternal mortality rates, and lowering fertility, therebyincreasing women’s status. Drawing on data from 42 countries between 1861and 1964, Preston (1976) finds that economic modernization explains most ofthe historical variation in the gender gap via improvements in women’s statusin mortality. Other work suggests that political modernization may also im-prove women’s relative standing in society. One study finds that democraciesgrant suffrage to women more quickly than nondemocracies (Paxton, Hughes,and Green, 2006). And another finds that political and civil rights positivelyaffect female life expectancy among less developed countries (Williamson andBoehmer, 1997). Accordingly, we expect economic development and democ-ratization to both widen the gender gap in life expectancy. However, it isalso possible that women’s status mediates much of these relationships, whichcould dampen the direct effects of development and modernization.

Moreover, we anticipate that the gap-expanding effect of economic de-velopment may be weaker due to recent trends among advanced countries.Specifically, a number of studies have found that the gender gap has begunto narrow among wealthy nations as female and male life expectancy havestarted to converge. In the United States, the gender gap in life expectancypeaked in the 1970s and began to decline thereafter (Hummer, Rogers, and


Eberstein, 1998). This narrowing of the gender gap has also been observed inEngland (Raleigh and Kiri, 1997), France (Mesle, 2004), Italy (Conti et al.,2003), the Netherlands (Perenboom et al., 2005), and across the developedworld (Staetsky and Hinde, 2009). Research investigating the cause of thisnarrowing gender gap among affluent nations generally points to the diffusionof smoking from males to females (Pampel, 2002; Valkonen and van Poppel,1997), with cigarette use and tobacco-related deaths becoming more similarbetween males and females.

Economic Inequality

Low socioeconomic status is considered a risk factor for mortality (Hummer,Rogers, and Eberstein, 1998; Jasilionis et al., 2007; Raleigh and Kiri, 1997).In one study, Hahn et al. (1995) attribute 17.7 percent of mortality in theUnited States to poverty. In their sample, poor people were less educated, lesslikely to marry, less likely to exercise, but more likely to have higher bloodpressure and to be obese. Most importantly, they found that females weremore likely to experience poverty than males, but that males were more likelyto suffer from poverty. Among both blacks and whites, the poverty-attributablemortality rate for men was twice as large as that for women.

Similarly, numerous other studies have shown that the relationship betweenmortality and socioeconomic status (as measured by education and/or income)is stronger for men than for women (e.g., Hummer, Rogers, and Eberstein,1998; Jasilionis et al., 2007; Montez et al., 2009; Raleigh and Kiri, 1997). Thissuggests that poverty does not impact women to the same degree that it harmsmen. For example, in India, poverty is associated with male disadvantage inchild survival (Murthi, Guio, and Dreze, 1995), and adult males tend to sufferthe most during periods of famine or food shortages (Basu, 1993).

Part of this gender effect may involve the more dire consequences of haz-ardous behavior to those individuals who are economically disadvantaged.For example, similar to advanced nations, socioeconomic status and genderare important predictors of smoking in sub-Saharan Africa, such that (a) lesseducated and lower status workers are more likely to smoke than others, and(b) males are more likely to smoke than females (Pampel, 2008). Thus, tothe extent that low socioeconomic status exacerbates the mortality risks ofsmoking via less access to medical care, the impact of poverty on males maybe greater than its impact on females.

Alternatively, part of this effect may operate through differences in maritalstatus across socioeconomic positions (Montez et al., 2009). Marriage providesgreater health and mortality benefits for men than women, as men rely moreon their partners for social support than vice-versa (Hummer, Rogers, andEberstein, 1998; Jasilionis et al., 2007; Moller-Leimkuhler, 2003; Nathanson,1984). Thus, to the extent that the poor and less educated are less likely to


marry (Goldstein and Kenney, 2001; McGinnis, 2003), being impoverishedmay harm males more than females.

Finally, males are more emotionally affected by socioeconomic stressors thanfemales. Across most societies, males are far more likely to commit suicide thanfemales, and the sex difference has been growing in recent decades (Moller-Leimkuhler, 2003). Conversely, males are less likely to seek help for depression.Males are socialized to endure or ignore (rather than report) symptoms ofphysical or emotional pain, as seeking help implies dependency and a lossof status. Accordingly, males are more likely to conceal their condition or toself-medicate with alcohol than to seek medical help for emotional problemsor depressive disorders (Moller-Leimkuhler, 2003). Indeed, research showingthat females make greater use of health-care services suggests that there aresex differences in self-protective behavior (Nathanson, 1984). One study inKazakhstan and Uzbekistan found that women were more than twice as likelyto visit primary health-care facilities as men (Cashin, Borowitz, and Zuess,2002). The above tendencies thus suggest that financial insecurity poses agreater health risk to men than women.

In sum, we expect economic inequality to widen the gender gap via thesteeper socioeconomic gradient for men in shaping mortality outcomes. Thatis, when larger portions of the population are enduring poverty, this shoulddisproportionately affect males. Ram (1993) finds that inequality is not sig-nificantly associated with the gender gap in life expectancy. However, thismeasure is calculated at the sectoral (rather than household) level. By contrast,when employing a more precise measure, we expect income inequality to pushthe gender gap upward.

Methods

Dependent Variables

Gender Difference in Life Expectancy. The gender difference is calculated aseach country’s female life expectancy minus its male life expectancy. GenderRatio in Life Expectancy. The gender ratio refers to each country’s female lifeexpectancy divided by its male life expectancy. Life expectancy data for bothfemales and males come from the World Population Prospects: The 2006 Revision(United Nations, Department of Economic and Social Affairs, PopulationDivision, 2007). Life expectancy indicates the number of years a newborninfant would live if prevailing patterns of mortality at the time of its birthwere to stay the same throughout its life.

Independent Variables

Unless otherwise noted, the following predictors come from the WorldDevelopment Indicators (World Bank, 2010). We logged several of the following


measures to reduce skew (as noted below). Year. In order to control forlongitudinal trends in the gender gap, we include a year covariate in thesubsequent analyses to represent each wave of the sample period (1 = 1985;2 = 1990; 3 = 1995; 4 = 2000; 5 = 2005). World Region. We also controlfor the considerable cross-national variation in the gender gap by includingworld region as a predictor in the analyses. We classify states as belonging toone of the following six world regions: (1) Europe and the West (the excludedreference category), (2) Latin America and the Caribbean, (3) Central andSub-Saharan Africa, (4) North Africa and the Middle East, (5) East Asia andthe Pacific, and (6) Eastern Europe and Central Asia.

Development/Modernization

GDP PC (PPP) (log). We measure economic development with each coun-try’s GDP PC based on purchasing power parity. Data are in constant 2005 in-ternational dollars. An international dollar has the same purchasing power overGDP as the U.S. dollar has in the United States. Democratization. We measurepolitical modernization with each country’s level of democracy. Democracyscores come from Freedom in the World 2010 (Freedom House, 2010). FreedomHouse’s annual survey measures democracy according to two broad categories:political rights and civil liberties. Each country is rated on a seven-point scalein both categories, with 1 representing the most free, and 7 representing theleast free. We calculated each country’s average score across both categoriesand inverted this value so that higher numbers represent greater levels ofdemocracy.

Economic Inequality

Gini Coefficient (log). We use the Gini coefficient based on net incometo measure economic inequality within each country. Inequality estimatescome from the Standardized World Income Inequality Database (SWIID),Version 3.0 (Solt, 2009). The SWIID provides comparable Gini scores for171 countries from 1960 to the present. Scores range from 0 to 100, with 0indicating perfect equality and 100 indicating perfect inequality.

Women’s Status

We use four indicators to measure women’s status in a global context. Fer-tility Rate (log). Fertility rate represents the number of children that wouldbe born to a woman if she were to live to the end of her childbearing yearsand bear children in accordance with current age-specific fertility rates. Pri-mary School Ratio. This measure refers to the ratio of female students to male


students enrolled in primary school. Labor Force Ratio. This ratio refers tothe percentage of the total labor force that is female. Parliament Ratio. Theparliament ratio indicates the proportion of seats held by women in nationalparliaments. In separate analyses, we also considered the impact of each coun-try’s maternal mortality rate. This measure was nonsignificant in both thegender difference and gender ratio models, most likely due to its high degreeof correlation with fertility rate (r = 0.838).

Male Hazards

Finally, we use two indicators to capture traditional male hazards. Smok-ing Mortality Ratio (log). Our first indicator is the male-to-female smok-ing mortality ratio. We constructed the measure by dividing the propor-tion of adult deaths due to smoking for males by the proportion of adultdeaths due to smoking for females. Data come from the Tobacco Atlas(http://www.tobaccoatlas.org). There are 14 unique smoking mortality ra-tios across 180 countries in our sample, ranging from 1.27 to 19.00. Un-fortunately, data for this measure are only available for a single year (2000).Therefore, we use this measure as a time-invariant predictor that estimates theeffect of cross-national differences in gendered smoking behavior during thelatter portion of our sample period. Homicide Rate (log). We also estimate theimpact of a country’s intentional homicide rate, which refers to the numberof unlawful deaths (per 100,000 people) purposefully inflicted on a personby another person resulting from domestic disputes, interpersonal violence,violent conflicts over land resources, intergang violence over turf or control,and predatory violence by armed groups. Similar to the smoking mortalityratio, homicide rates are also only available for a single year (2004). Therefore,we use this measure as a time-invariant predictor that estimates the effect ofcross-national differences in societal violence during the latter portion of oursample period.

Analysis

The panel structure of our data allows us to use estimation techniquesthat deal with potential heterogeneity bias (the confounding effect of un-measured time-invariant variables), which can seriously affect ordinary leastsquares (OLS) coefficient estimates. The REM and the FEM represent twoestimation strategies designed to correct for the problem of heterogeneitybias. Both procedures “simulate” the unmeasured time-invariant factors ascountry-specific intercepts (Nielsen and Alderson, 1995: 685). We presentresults using both a generalized least squares estimator of the REM, as well asa within estimator of the FEM, both of which include a first-order autocor-relation correction. The REM represents the matrix weighted average of the


estimates produced by the FEM (focusing on variation within cases) and thebetween-effects model (BEM) (focusing on variation between cases).

While the FEM ignores all cross-sectional variation in its estimates bysubtracting all variables from their case-specific means, and the BEM ignoresall temporal variation by using the case-specific means as predictors, the REMincorporates both the fixed and between-effects components, thereby makingthe REM advantageous for capturing both cross-sectional and longitudinalvariation. In other words, REMs are useful because it is important to explainwhy some states feature a larger gender gap than others, while also explainingwhy the gender gap has changed within countries over time. In addition,REMs are able to estimate the effects of time-invariant measures due totheir between-effects component. Nevertheless, we also present results whenrestricting attention to longitudinal effects by estimating our time-varyingpredictors using FEMs.

Sample

Data for the dependent variables cover the years 1985, 1990, 1995, 2000,and 2005, while data for the predictors cover the prior five-year period foreach wave (1980–1984, 1985–1989, 1990–1994, 1995–1999, and 2000–2004), where each data point for each measure represents a state’s averagevalue across the entire wave. Our REMs include all predictors, which restrictsthe time period available for these models. Data for the parliament ratio areonly available during the final three waves of the sample period (1990–2005),while the smoking mortality ratio and the homicide rate are time-invariantpredictors whose data reflect cross-national differences toward the end ofour sample period. Consequently, we restrict our REM sample to the 1990–2005 period, which produces 337 observations across 139 countries coveringthree waves. Our FEMs exclude the parliament ratio and all time-invariantmeasures, which allows us to estimate the longitudinal effects of our remainingpredictors across a longer time span. Specifically, our FEM sample includes355 observations across 126 countries covering all five waves during the 1980–2005 period.

Results

Descriptives

Table 1 reports cross-regional patterns and longitudinal trends in female lifeexpectancy, male life expectancy, the gender difference, and the gender ratiofor 195 nations in the world between 1985 and 2005. The first row reportsthe mean value of all four measures for all 195 countries, while the subsequentrows report mean values for different regions of the world. Across all countries,


TABLE 1

Gender Differences and Ratios in Life Expectancy by Region, 1985–2005

Region 1985 1990 1995 2000 2005 Change

All States(N = 195)

Female 65.22 66.86 67.89 68.72 69.41 6.42%Male 60.47 62.06 62.89 63.69 64.61 6.85%Difference 4.75 4.80 5.00 5.03 4.80 1.05%Ratio 1.077 1.076 1.079 1.079 1.073 −0.37%

Europe & theWest (N = 23)

Female 77.90 78.92 79.71 80.62 81.60 4.75%Male 71.36 72.42 73.56 74.79 76.23 6.82%Difference 6.54 6.50 6.16 5.83 5.36 −18.04%Ratio 1.092 1.090 1.084 1.078 1.070 −2.01%

Latin America & theCaribbean (N =37)

Female 70.07 71.74 73.13 74.17 75.35 7.54%Male 64.55 66.16 67.39 68.35 69.53 7.71%Difference 5.52 5.58 5.75 5.82 5.82 5.43%Ratio 1.087 1.085 1.085 1.085 1.084 −0.28%

Central &Sub-SaharanAfrica (N = 47)

Female 52.61 54.27 54.43 54.04 53.12 0.97%Male 49.24 50.72 50.64 50.34 50.27 2.09%Difference 3.37 3.55 3.79 3.70 2.85 −15.43%Ratio 1.068 1.069 1.076 1.074 1.056 −1.12%

North Africa & theMiddle East(N = 25)

Female 63.44 66.16 68.20 69.88 71.25 12.31%Male 60.42 63.01 64.89 66.50 67.83 12.26%Difference 3.02 3.15 3.32 3.37 3.42 13.25%Ratio 1.049 1.049 1.050 1.050 1.050 0.10%

East Asia & thePacific (N = 34)


Eastern Europe &Central Asia(N = 29)


NOTES: Each cell reports the average female and male life expectancy for each region, followed bythe sex difference (female–male) and sex ratio (female/male). Europe and the West = Australia,Austria, Belgium, Canada, Channel Islands, Denmark, Finland, France, Germany, Iceland, Ire-land, Italy, Luxembourg, Malta, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden,Switzerland, United Kingdom, United States; Latin America and the Caribbean = Argentina, Aruba,Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Re-public, Ecuador, El Salvador, French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti,Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay,Peru, Puerto Rico, Saint Lucia, Saint Vincent-Grenadines, Suriname, Trinidad-Tobago, Uruguay,Venezuela, Virgin Islands (U.S.); Central and Sub-Saharan Africa = Angola, Benin, Botswana,Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo(DR), Congo (R), Cote d’Ivoire, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia,Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania,Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome-Principe, Senegal,Sierra Leone, Somalia, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe;North Africa and the Middle East = Afghanistan, Algeria, Bahrain, Cyprus, Egypt, Iran, Iraq, Israel,Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan,Syria, Tunisia, Turkey, United Arab Emirates, Western Sahara, Yemen; East Asia and the Pacific =Bangladesh, Bhutan, Brunei, Cambodia, China, East Timor, Fiji, French Polynesia, Guam, HongKong, India, Indonesia, Japan, Korea (North), Korea (South), Laos, Macao, Malaysia, Maldives,Micronesia, Mongolia, Myanmar, Nepal, New Caledonia, Papua New Guinea, Philippines, Samoa,Singapore, Solomon Islands, Sri Lanka, Thailand, Tonga, Vanuatu, Vietnam; Eastern Europe andCentral Asia = Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, Croatia,Czech Republic, Estonia, Georgia, Greece, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania,Macedonia, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan,Turkmenistan, Ukraine, Uzbekistan.


females have been living approximately five years longer than males. Acrossthe sample period, the average female and male life expectancy increased byabout four years. Thus, changes in the gender difference (1.05 percent) andthe gender ratio (−0.37 percent) during the 1985–2005 period were not verylarge.

However, there is considerable regional variation with respect to both thelevel of the gender gap, as well as the trends over time. Throughout the sampleperiod, the gender difference (7.20–8.04) and the gender ratio (1.109–1.124)were both largest among Eastern European countries, leading other regionsby fairly wide margins. Western and Latin American countries feature thenext largest gender gaps (where the gender difference has been about fiveto six years), followed by East Asia, the Middle East, and Africa (where thedifference has been about three to four years). As of 2005, the gender differencewas lowest in Africa (2.85) because overall life expectancy is lowest there, butthe gender ratio was actually lowest in the Middle East (1.050).

Regarding trends, the decline in the gender gap is most noticeable in theWest, where the difference and ratio declined by 18.04 and 2.01 percent,respectively, and Africa, where the declines were 15.43 and 1.12 percent,respectively. Among Western states, both female (4.75 percent) and male lifeexpectancy (6.82 percent) increased, but the latter grew considerably more. Infact, the gender gap in the West has narrowed so much that, by the year 2005,Latin America actually featured a larger gender difference (5.82) and ratio(1.084) than the West (5.36 and 1.070, respectively). Meanwhile, in Africa,both female and male life expectancy began to decline during the 1990s dueto the AIDS crisis. However, female life expectancy was hindered to a greaterdegree (0.97 percent) than male life expectancy (2.09 percent).

By contrast, the gender gap increased in Eastern Europe between 1985and 2005 (7.06 and 0.54 percent, respectively), thereby distinguishing thisregion from Latin America and the West to an even greater extent. However,even larger increases in the gender gap occurred in East Asia (20.44 and0.57 percent, respectively) and the Middle East (13.25 and 0.10 percent,respectively). In all three regions (Eastern Europe, East Asia, and the MiddleEast), gains in female and male life expectancy were fairly similar to oneanother (slightly favoring females). It is in the West and Africa, where femaleand male life expectancy growth rates were noticeably discrepant (heavilyfavoring males), that the gender gap narrowed.

Analyses

Why is the gender gap larger in Eastern Europe than elsewhere? Why didthe gender gap decline in the West and Africa, but increase in East Asia andthe Middle East? We turn now to explaining these cross-national patternsand longitudinal trends. Table 2 reports results from REMs and FEMs whereour dependent variables are the gender difference and gender ratio in life


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expectancy. Model 1 presents results from the REM of the gender difference,where we estimate our focal predictors net of temporal and regional controls.Each hypothesis receives at least some support. Economic development, asmeasured by GDP PC, widens the gender difference in life expectancy at amarginal level of significance (p < 0.10). We suspect that the recent narrowingof the gender gap in advanced countries may be weakening the positive effectof GDP PC. Our other indicator in this category, democratization, does notachieve statistical significance at all. Thus, the development/modernizationindicators, as a whole, do not perform well. However, it is possible that thewomen’s status measures may be mediating part of the effects of developmentand modernization.

By contrast, the economic inequality hypothesis receives greater supportin this model. The Gini coefficient is significant (p < 0.05), indicating thatinequality expands the gender difference in life expectancy. Thus, when in-come inequality is greater, the gender gap increases. We interpret this findingto mean that inequality harms male life expectancy more than female lifeexpectancy due to the steeper socioeconomic gradient for males in shapingmortality outcomes. In addition, the women’s status measures generally per-form well. Fertility rate reduces the gender gap and achieves the highest level ofsignificance (p < 0.001). That is, in societies where the fertility rate is higher,gender differences in life expectancy tend to be narrower. Meanwhile, genderratios in primary school enrollment (p < 0.01) and labor force participation (p< 0.05) both widen the gender gap. In sum, where women’s status is greater,the gender difference in life expectancy tends to be greater, as well.

However, our final women’s status measure, the parliament ratio, achievessignificance (p < 0.05) as a negative predictor of the gender gap. In otherwords, societies with a larger share of females in national parliament havemore narrow gender differences in life expectancy. As a measure of women’sstatus, this is contrary to what we hypothesized. We cannot readily attributethis effect to the narrowing gender gap among advanced, Western nations(where the parliament ratio tends to be higher) because we are controlling forsocioeconomic development via GDP PC, democratization, and the regionaldummies. We also cannot attribute this finding to collinearity. In separatediagnostics, we calculated variance inflation factor (VIF) scores for our REMsgenerated through OLS estimation. In both models, the maximum VIF scoreis below 10 (maximum VIF = 7.81), suggesting that collinearity is not prob-lematic (Chatterjee, Hadi, and Price, 2000:240). Rather, we suggest that theparliament ratio reduces the gender gap because female parliamentarians aremore likely to protect vulnerable populations, which yields greater health re-turns to males. Previous work has linked women’s political mobilization to theformation of welfare states across Scandinavia (Hobson and Lindholm, 1997;Sainsbury, 2001) and the United Kingdom (Lewis, 1994). Within the UnitedStates, female political representation at the state level is associated with thepresence of child support and welfare policies, as well as unemployment ben-efits (Caiazza, 2004). And one study of 12 Western nations shows that the


share of women in parliament positively affects welfare spending (Bolzendahland Brooks, 2007). In this way, gender egalitarian governments may buffermales against the greater health risks they face when experiencing poverty.

Finally, both time-invariant measures representing traditional male hazardsachieve significance. The smoking mortality ratio (p < 0.01) and the homiciderate (p < 0.001) both expand the gender difference in life expectancy. That is,in countries where the male/female smoking mortality ratio is greater, genderdifferences in life expectancy are significantly higher. Thus, smoking patternsnot only shape sex differences in mortality across the developed world, butthey contribute, more generally, to the gender gap at a global level. Similarly,in those countries where homicide rates are higher, the gender gap is likewisehigher, as these deaths disproportionately select for males. Collectively, theindependent variables explain over two-thirds of the overall variance in genderdifferences in life expectancy (R2 Overall = 0.680).

In model 2, we examine the longitudinal effects of our time-varying predic-tors on the gender difference in life expectancy by estimating a FEM. Two ofthe women’s status measures, primary school ratio and labor force ratio, dropout of significance. However, GDP PC (p < 0.05), the Gini coefficient (p <0.001), and fertility (p < 0.01) all remain significant. Thus, our hypotheseseach continue to receive at least some support when restricting attention tolongitudinal effects within countries. In short, the findings from models 1and 2 suggest that traditional male hazards, improvements in women’s status,and economic growth all tend to elevate gender differences in life expectancy,while increases in income inequality also widen the gender gap via the moreharmful consequences of inequality for males.

In models 3 and 4, we replicate our analyses with the gender ratio measure.The REM replication in model 3 produces fairly similar results. One apparentdifference between models 1 and 3 is that GDP PC is nonsignificant in thegender ratio model. Thus, the development/modernization hypothesis contin-ues to receive less support. Conversely, the remaining hypotheses concerningeconomic inequality, women’s status, and male hazards continue to hold up,with the noteworthy exception of the parliament ratio’s gap-reducing effect(p < 0.05). Similar to model 1, higher female representation in parliament isassociated with a more narrow gender gap, contrary to what we hypothesized.Collectively, the predictors explain over half of the overall variance in thegender ratio (R2 Overall = 0.577).

Finally, in model 4, we examine the longitudinal effects of our time-varyingpredictors on the gender ratio in life expectancy. In this model, the develop-ment/modernization indicators perform better, with GDP PC (p < 0.001)and democratization (p < 0.05) both achieving significance as predictors thatincrease the gender ratio. Thus, economic development and political modern-ization are fairly successful in explaining gender gap changes within countriesover time. The Gini coefficient remains significant in expanding the gap (p <0.001), as do several of the women’s status measures, primary school ratio (p< 0.05) and labor force ratio (p < 0.001). However, fertility rate drops out of


significance in this model. In sum, the models presented in Table 2 providebroad support for most of the hypotheses set forth in this study, revealing theimportance of development/modernization processes, economic inequality,women’s status, and traditional male hazards.

Conclusion

In this study, we update previous work, describing cross-national patternsand longitudinal trends in the life expectancy gender gap across 195 nationsduring the 1985–2005 period. Eastern European nations exhibit the largestgender gap, while the female advantage in life expectancy is smallest in Africaand the Middle East. Alternatively, the gap has narrowed considerably amongWestern and African nations, while increasing the most in East Asia and theMiddle East. In addition, we test a number of common explanations for thegap, focusing on both gender differences in life expectancy, as well as the genderratio. We find broad support for most of our hypotheses, including the gap-widening impact of traditional male hazards, women’s status, developmentand modernization, as well as income inequality. Specifically, we show thatthe male-to-female ratio in smoking mortality rates is a successful predictorof cross-national variation in the gender gap, as are homicide rates. Net ofthese male hazards, our women’s status indicators also perform well, capturingthe importance of gender role patterns across several dimensions (includingfertility rates and female participation in formal education and paid labor).We also find that economic growth and democratization both widen the gap,but these effects are primarily longitudinal.

Finally, our models show that income inequality widens the gender gapin life expectancy, while the parliament ratio reduces it. We suggest thatthese latter findings are a function of the greater impact of poverty on malemortality. That is, as income inequality increases, greater segments of thepopulation reside in more vulnerable economic positions, a condition thatdisproportionately harms males. Conversely, when females occupy a greatershare of the legislature, the welfare state expands, serving as a cushion forimpoverished groups, a condition that disproportionately helps males.

Interestingly, our findings show that political and economic inequality exertlongitudinal effects on the gender gap, shaping male and female life expectancydifferently. In separate analyses, we replicated our FEMs when decomposingour dependent variable into female and male life expectancy. We find thatdemocratization positively affects female life expectancy (p < 0.05), but hasno significant impact on male life expectancy. Conversely, the effects of incomeinequality are nonsignificant for females, but negative for males (p < 0.05).In this way, our results reveal the benefits of political equality for females, andthe harmful effects of economic inequality for males.

In conclusion, we note that the diffusion of traditionally male behaviors tofemales does not portend any simple, linear impact on the gender gap in life


expectancy across nations. As females begin to participate in traditional maleinstitutions, such as formal schooling and paid employment, and begin toshift away from traditional female roles, as indicated by a decline in fertility,this increase in women’s status uniformly serves to widen the gender gap.Furthermore, the extension of political rights (e.g., suffrage) to females andother marginalized populations empowers women to promote policies thathelp them. However, further increases in women’s status begin to reducethe gap. As additional behaviors diffuse from males to females (e.g., lifestyleand occupational hazards), and as women continue to acquire greater politicalpower (e.g., participation in the legislature), these mechanisms serve to narrowthe gender gap due to negative effects on female health and positive effectson male health. In sum, this male-to-female diffusion seems to produce aninverted U-shaped effect on the gender gap in life expectancy, a process whichour focal predictors holistically capture.1 We call on future studies to furtherexamine the long-term mortality effects of gender equity (or lack, thereof )among both developed and developing nations.

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examining the gender gap in life expectancy: a cross-national analysis, 1980–2005

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