We investigate long-run effects of World War II on socio-economic status and health of older individuals in Europe. We analyze data from SHARELIFE, a retrospective survey conducted as part of SHARE in Europe in 2009. SHARELIFE provides detailed data on events in childhood during and after the war for over 20,000 individuals in 13 European countries. We construct several measures of war exposure—experience of dispossession, persecution, combat in local areas, and hunger periods. Exposure to war and more importantly to individual-level shocks caused by the war significantly predicts economic and health outcomes at older ages.
I. Introduction
The Second World War [WWII] was one of the major transformative events of the 20th century, with 39 million deaths in Europe alone. Large amounts of physical capital were destroyed through six years of ground battles and bombing. Many individuals were forced to abandon or give up their property without compensation and to move on to new lands. Periods of hunger became more common even in relatively prosperous Western Europe. Families were separated for long periods of time, and many children lost their fathers. Many, including young children, would personally witness the horrors of war as battles and bombing took place in the very areas where they lived. Horrendous crimes against humanity were committed. Due to WWII, political and economic systems in many countries would be permanently altered.
In this paper, we investigate long-run effects of World War II on late-life economic and health outcomes in Western continental Europe [health, education, labor market outcomes and marriage]. We explore several channels through which this war might have influenced individual lives, and document which groups of the population were most affected. Our research relies on retrospective life data from the European Survey of Health, Aging, and Retirement in Europe [SHARE] that have recently become available. SHARE covers representative samples of the population aged 50 and over in 13 European countries, with about 20,000 observations. We also collected external data on casualties, timing and location of combat action, yearly GDP by country, population movements, and male-female population ratios. To our individual-level analysis of the multidimensional effects of a major shock that affected life circumstances, we add new dimensions to a rapidly increasing literature that aims at explaining the causes of health and wealth gradients in labor and health economics [see ; ; ].
SHARE not only measures major contemporaneous economic and health outcomes of adults over age 50 in these European countries, but includes retrospective modules meant to capture salient parts of early life experiences, including those related to the war. There simply are no micro economic panel data in either the United States or in Europe that have prospectively tracked people for that long a time period. The co-existence of current prospective data combined with retrospective data on key events that preceded the survey baseline opens up important new research opportunities not before possible, and not simply those associated with the WWII. Since the end of WWII, western continental Europe has had a rich and sometime tumultuous economic and political history, the effects of which on its residents are not well documented.
There is legitimate concern about the quality of recall data, particularly for time periods decades in the past. But that concern has been lessened by a realization that recall of events during childhood is better than for other periods of life, particularly if events are salient as they certainly are in this application. investigated several quality markers and showed that his childhood health instrument was successful in matching known secular trends in childhood illnesses decades in the past. Moreover, we will provide evidence in this paper that these recalled events in the SHARE retrospective about the war matched the historical record.
One aim of the paper is to illustrate how such retrospective life data can further our understanding of effects of early-life conditions as affected by large external shocks, such as a war. The existing literature measuring impacts of macro-events mostly used “natural experiments” such as wars or famines to study effects of early-life conditions at the aggregate level. Largely due to data reasons beyond their control, the studies of which we are aware could not use individual-level measures of whether a particular person was affected by the war and through which channel. Retrospective life data, such as those from SHARE, contain detailed information and provide the opportunity of studying that issue.
Analyzing different outcomes is a first step in understanding the channels and mechanisms by which wars affect people’s lives. Another possibility is using different measures of war exposure such as the closeness of combat. We construct such measures from external data sources. In addition, SHARE data contain retrospective questions on several possible channels of war exposure: hunger, the absence of the father, dispossession, and persecution.
Given the scale of the war and number of ways it fundamentally changed the world, the existing economic literature using WWII as a natural experiment is surprisingly thin. Moreover, the literature that does exist using WWII is relatively recent and more American in context than European. This may reflect the fact that the popularity of ‘natural experiments’ framework in economics itself post-dated WWII by many decades. Still, it does suggest that excellent research opportunities remain, especially given the wide diversity of European experiences in WWII.
This paper is divided into six sections. The next highlights the main attributes of SHARE data and the additional data we collected for this research. Section III sets the stage for our analysis by presenting evidence of possible changes on which long-term effects of WWII may operate. The fourth section summarizes statistical models that capture impacts of the experience of WWII on individual adult labor market, demographic, and health outcomes. We also present our models of the influence of the war on some of the primary pathways through which it had long lasting impacts—hunger, dispossession, the absence of a father, and marriage. The final section highlights our main conclusions.
II. SHARELIFE Data
A. SHARE and Retrospective Early-life Data from SHARELIFE
SHARE is a multidisciplinary cross-national panel interview survey on health, socioeconomic status, and social and family networks of individuals aged 50 or over in continental Europe. The original 2004/2005 SHARE baseline included nationally representative samples in 11 European countries [Denmark, Sweden, Austria, France, Germany, Switzerland, Belgium, Netherlands, Spain, Italy, and Greece] drawn from population registries, or from multi-stage sampling [//www.share-project.org/]. For these countries, a second wave of data collection took place in 2006, and the third wave of data collection on this panel [SHARELIFE] was completed in 2008.
In addition to a standard set of demographic attributes [age, marital status, education], SHARE data include health variables [self-reported health, health conditions, health behaviors], psychological variables [e.g., depression and well-being] economic variables [current work activity, sources and composition of current income, and net wealth [including housing, cars, and all financial assets [stocks, bonds, and cash] minus all debts]].
SHARE’s third wave of data collection, SHARELIFE, collected detailed retrospective life-histories in 13 countries [Poland and the Czech Republic were added in wave 2] in 2008–09. SHARELIFE was based on life history calendar [LHC] methods. The interview starts with the names and birth dates of the respondent’s children [and other information about them including any deaths], which is followed by a full partner and residential history. This information is used to aid in dating of all other events.
The information in the life history includes family composition and type of home [number of rooms, running water, toilet, etc], number of books, and occupation of father. These measures were used to create an index of childhood SES at age 10. A childhood health history is also included based on the Smith module included in the PSID and HRS that queries about individual specific childhood diseases and an overall subjective evaluation of childhood health status []. In addition, respondents are asked about childhood immunizations and hunger during childhood. Adult health histories and job and income histories were also collected. Moreover, SHARELIFE provides detailed data on within-country region of residence and housing during the full life of respondents [childhood and adulthood].
B. Other Data Sources
In addition to SHARE data, we also use external data sources to identify aggregate channels of war-affectedness. Since WWII affected not only countries differentially, but also regions within countries, we constructed data on combat operations using sources from military history []. Using maps of within-country regions for each month during the war, we documented whether armies engaged in battle in that place at that time. We combined these data with information about the region in which respondents lived during each year of WWII and use it as one measure of individual war exposure.
Since we analyze data over a time period of 50 years, we also have to account for country-specific economic performance that may have affected childhood circumstances differently. We therefore use GDP data, which are available for each European country []. We also used external data on country-specific civilian and military causalities associated with WWII, population movements, and the sex ratio. Table 1 contains definitions of variables derived from SHARE and SHARELIFE that will be used in our analysis in this paper. provides a parallel list of variables constructed from external data sources with a documentation of the source that was used.
Table 1
Variable Definitions
Variable nameDefinitionBackground informationYear of birthYear of birthMaleDummy = 1 if respondent is maleChildhood SESUnifies four measures for SES at age 10: Logged number of books in household; logged number of rooms and persons in household; features in household; occupation of main breadwinnerOutcome measuresChildhood ImmunizationsDummy = 1 if respondent got any vaccinations during childhoodDepressionDummy = 1 if respondent suffers from more than three depression symptoms in EURO-D scaleDiabetesDummy = 1 if respondents has diabetes or high blood sugarEver marriedDummy = 1 if respondents was ever marriedHeart diseaseDummy = 1 if respondent has heart problems [including heart attack]HeightHeight in cmLife satisfactionLife satisfaction on a scale from 0–10 with 0 very unsatisfied and 10 very satisfiedLog[net worth]Logged household net worth as the sum of values from bank accounts, bonds, stocks, mutual funds, retirement accounts, contractual savings and life insurances minus liabilitiesSelf-rated healthCategorical variable for self-rated health with excellent health = 5Years of educationYears of educationChannels of war exposureDad absentDummy = 1 if biological father was absent at the age of 10DispossessionDummy = 1 if respondent reports ever being dispossessedHungerDummy = 1 if respondent ever suffered hunger and whenPersecutionDummy = 1 if respondent reports ever being persecutedWar variablesWarDummy = 1 if respondent was living in a war country during the war periodWar combat 0–2 monthsRespondent was living in a war country during the war period in a region within the country that experienced 0–2 months of combatWar combat 3–10 monthsRespondent was living in a war country during the war period in a region within the country that experienced 3–10 months of combat
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III. The Channels of Long-term Effects of WWII
This section presents descriptive data and reviews the current literature on possible major channels through which WWII might have affected people’s lives well into their older years. The channels include future per capita income growth of countries affected, mortality, changing sex ratios, absence of a father, periods of hunger, migration, dispossession, and persecution. This section is used to motivate the rationale for analyses pursued in section IV.
A. Per Capita Income Growth
If wars alter long-term economic growth, they would permanently depress economic prospects of future generations. Warfare reduces capital stock through the destruction of infrastructure, productive capacity, and housing through bombing and fighting, and results in a relocation of food and other production into military production. It obviously destroys human capital—but the real question for our analysis is will there be catch-up growth, or will the destruction show up many decades later?
Based on , table 2 displays GPD per capita of some of the major countries involved in the war relative to that of the US at key illustrative dates. The immediate impact of WWII was apparently quite destructive for the countries involved, especially so for those on the losing side—Germany, Japan, and Italy—presumably reflecting their much larger losses in both physical and human capital during the war. However, by 1973 and certainly by 1987, the European ‘losers’ actually had higher per capita growth than European ‘winners’. What appears to be essential in the long-term was not whether a country was on the winning or losing side, but whether or not they transited to democracy and open-market economies. The poor performance of USSR countries illustrates that point.
Table 2
GDP per Head Relative to US GDP per Head
Country1938195019731987UK.98.72.72.73Germany.84.45.79.82France.72.55.78.78Italy.53.36.63.70Japan.38.20.66.77USSR.35.30.36.33
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Source: , Table 1–10.
B. Mortality
In 1939, there were about 2 billion people in the world. The best estimates indicate that between 62 and 78 million of them would die due to WWII—more than 3% of the world’s population. While earlier wars also resulted in deaths of civilians, civilians were particularly heavily affected by WWII with about half of the WWII European casualties being civilians. Among civilian deaths, between 9.8 and 10.4 million civilians were murdered for political or racial reasons by the Nazi regime []. Deaths due to the war were very unequally distributed across countries, whether they were military deaths due to combat, civilian deaths, or the holocaust. Figure 1.A displays the fraction of the 1939 population who died in a large array of affected countries. Among European countries covered by our data, Germany and Poland bore the brunt of these casualties. In contrast and for comparative purposes only, American causalities in the European and Asian theatres combined were a bit over 400,000, the overwhelming majority of whom were soldiers. Similarly, total deaths in the UK are estimated to be about 450,000, 15% of whom were civilians.
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Figure 1
Figure 1.A.—WWII Casualties as Percent of the Population
Figure 1.B.—Total Number of WWII Casualties in the Population
Figure 1.B displays total number of deaths by type in the same countries. Deaths were highly concentrated in Germany and Poland where deaths measured around 5 million in both countries. In Germany, there were almost as many civilian deaths as military ones, while in Poland civilian deaths including the holocaust are by far the dominant ones. In many of the remaining countries in our data, deaths due to WWII are measured instead in the hundreds of thousands, but still often amount to a large fraction of the pre-war populations in several other countries, particularly Austria and the Netherlands. The other European countries that stand out are those that would comprise most of the Soviet Union, where one in seven perished in the war with about 10 million military deaths and 13 million civilian deaths. Unfortunately, data on these countries are not part of the SHARE network of European countries.
C. Sex Ratios and Absence of Father
Mostly men died during the war, producing low male/female ratios in Europe after the war as well as absence of many fathers during respondents’ childhood years. Since the male bias in deaths was concentrated among soldiers as civilian and holocaust fatalities were largely gender neutral, it is countries in figure 1 who experienced many military deaths that were most affected. With 3 million military deaths, the most affected country in our data was Germany.
The top left-hand side of table 3 shows one immediate demographic consequence of the war by listing by country and period when one was age 10 the fraction of individuals who had a biological father absent when they were 10 years old. Once again, the largest effects took place in the war-ravaged countries of Austria, Germany, and Poland. In Austria and Germany, about one in four children lived without their biological fathers when they were age 10 during the war. The legacy persists into years after the war since many who were age 10 during 1950–1955 had fathers who died during the war. In Germany, almost a third of those age 10 in these years were not living with their biological father. Absent father rates fall sharply in the post-1955 years since these children were born after the war. We observe war spikes in other countries as well [Italy, France, Denmark, and Belgium], but the contrasts with the pre- and post-war years are not as dramatic.
Table 3
Percent of SHARELIFE Respondents with Father Absent at Age 10; by Time Period
Country19551955% Father absent1% HungryAustria0.1950.2560.2740.2210.1520.0540.1160.0780.0260.015Germany0.1130.1910.2230.2970.0990.0060.2110.1620.0290.016Sweden0.1130.1260.0920.1400.1230.0060.0080.0050.0050.018Netherlands0.0740.0830.1020.0730.0440.0050.1160.0080.0010.003Italy0.0440.1030.0680.0690.0460.0320.1230.0530.0270.020France0.1060.1490.1580.1280.0560.0080.1170.0260.0140.026Denmark0.0730.0920.1180.1030.0760.0000.0040.0010.0010.007Greece0.0190.0240.0420.0490.0130.0320.1230.0460.0270.014Switzerland0.0670.0540.0850.0300.0460.0140.0130.0080.0070.013Belgium0.0460.0830.0650.0860.0550.0040.0700.0120.0060.014Czechia0.0800.0630.1340.0950.0810.0120.0380.0210.0060.004Poland0.0780.1150.1730.1540.0820.0150.1230.0540.0180.033
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% Dispossession% Ever DispossessedSex Ratios194545–48Austria0.0040.0150.0030.0000.000.0290.950.86-Germany0.0040.0390.0110.0070.006.0640.960.810.72Sweden0.0000.0020.0000.0000.001.0030.991.021.02Netherlands0.0020.0190.0000.0000.000.0150.990.97-Italy0.0010.0040.0000.0000.000.0040.940.950.94France0.0030.0240.0010.0010.006.0320.961.02-Denmark0.0000.0030.0000.0000.001.0040.950.99-Greece0.0140.0110.0010.0000.001.0140.920.99-Switzerland0.0060.0020.0000.0010.003.0110.960.990.98Belgium0.0070.0350.0030.0000.002.0360.991.02-Czechia0.0180.0120.0200.0780.023.1360.950.970.97Poland0.0020.0580.0080.0010.003.0650.89--
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Note: The countries are defined as of 2009 when SHARELIFE data were collected. One-year refers to year age 10.
Source: SHARELIFE; Calculations by authors. For sex ratios, see .
Sex ratios before, during, and after the war are contained in the bottom-right half of table 3. In Germany, the sex ratio dropped from 0.96 in 1939 to 0.72 men per women in the 15–45 age group after the war in 1946. Thus, many women did not marry, and many children grew up without a father. Even after the war, about 4 of the 11 million German prisoners of war remained in captivity, and the last 35,000 German soldiers returned from the Soviet Union in 1955 which further compounded the problem of absent fathers [].
D. Hunger
One channel by which WWII might have affected long-run adult health and SES outcomes is hunger. World War II caused several severe hunger crises which led to many casualties, and may have had long-term effects on the health of survivors. For example, since the beginning of the German occupation in Poland, the nutritional situation of the non-German population was poor. The average caloric intake for the Polish population was about 930 calories in 1941. The situation was worst in the Warsaw Ghetto where average food rations were limited to about 186 calories per day in 1941.
Similarly, in the fall and winter 1941/1942, Greece was struck by a severe famine with about 100,000 to 200,000 deaths []. In WWII, Greece was under Bulgarian, German, and Italian occupation. The famine was mainly caused by three factors: [1] occupiers imposed a naval blockade; [2] prices to farmers were fixed at such low levels that they were not willing to market their products; [3] mobility between different regions of the country was reduced due to occupation. The nutritional situation returned to acceptable levels towards the end of 1942. use Cohort Data to show that undernourishment of children who were 1 or 2 years old at the time of the famine had a significantly lower probability of being literate or to complete upper secondary education.
A combination of a food blockade and a harsh winter led to a severe hunger crisis in winter 1944/1945 in the Netherlands. About 20,000 deaths, mainly among elderly men, are attributed to this famine. The famine ended with the end of the German occupation in May 1945. The Dutch famine has been extensively studied because it affected an otherwise well-nourished population at a very specific time and region. Individuals exposed to this famine in utero are shown to suffer from cognitive and mental problems and addiction [; ], diabetes and coronary heart disease, and they also perform worse regarding anthropometric and socio-economic indicators [].
Germany suffered from hunger between 1945 and 1948 when the food supply from occupied countries ceased. In the US occupation zone, the Office of Military Government for Germany established a goal of 1550 calories per day in 1945, but in the first months of occupation, this goal often could not be met. There were regions where average calories per day were around 700 []. Death rates raised by the factor 4 for adults and 10 for infants during this period. With a good harvest and currency reform in June 1948, nutritional shortages were overcome [].
Figure 2 demonstrates that hunger episodes during the war were much more severe in war countries than in those countries that did not participate in the war. We also see that there was a great amount of diversity in periods of hunger within war countries. Hunger is more common in regions where combat took place within war countries. Finally and not surprisingly, the experience of hunger was far more common among those of low socio-economic background as a child. With respect to hunger, our analysis shows that the individual-level reports in SHARELIFE match well historical information on the timing and location of hunger episodes we collected from historic sources. To illustrate, in figure 2 the Greek hunger spike occurred in 1941–1942, the Dutch in 1944–45, and the German in 1946–1947.
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Figure 2
Percentage of SHARE Respondents Suffering from Hunger: War versus Non-war
E. Dispossession, Persecution, and Migration
SHARELIFE documents the extent of the experience of dispossession of property linked to WWII and its aftermaths. Dispossession was often associated with persecution and resulted in geographic displacement of populations during and immediately after the war. A further advantage of SHARELIFE is that we can observe where and when individuals moved during their lifetimes, including the wartime period.
There were three main periods when people were forced to flee their homelands. During WWII, millions of Jews, but also opponents of the Nazi regime, were expropriated, and often sent to concentration camps and were murdered there. Second, the end of WWII was associated with dramatic border changes in Eastern Europe. These border changes induced millions of individuals to leave their places of residence and flee to other parts of Europe. The Soviet Union annexed territory from some of its neighboring countries, inter alia from Czechoslovakia, Germany, and Poland. Poland in turn received one part of pre-war Germany in compensation. Those Poles having lost their homes in the part occupied by the Soviet Union were moved to the new part, so Poland and with it millions of people were moved westwards.
Figure 3 shows inflows and outflows of populations during and after the end of WWII into the new states in their new borders. Germany lost about one quarter of its territory. About 2 million people have been estimated to have died on the flight. After the war, the remaining territory of Germany was divided into four occupational zones. About 4 million people fled from the approaching Soviet armies to the British and US zone where the occupation was less severe. In Germany, destroyed cities had to accumulate millions of ethnic Germans from other parts of Europe. A further wave of dispossessions happened in Eastern countries after WWII when private property was nationalized in the socialist and communist economies. Even in France, there was a wave of nationalizations at the end of WWII. Mainly banks, energy, and transport firms were nationalized, but there were also some expropriations which happened as penalty for cooperation with the Nazi regime.
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Figure 3
Total Inflow and Outflow of Population 1939–1947
The bottom left-hand side of table 3 displays dispossession rates in our SHARE countries by time period with the final column indicating the percent ever dispossessed. Figure 4 complements the data in table 4 by showing the percentage of dispossessed individuals in SHARELIFE for the foreign and native-born populations. In the Czech Republic, Germany, and Poland more than 5% of respondents experienced dispossession during their lifetime. For respondents living in Germany and Poland, dispossession happened more frequently during the war period, while they happened after the war in Czechoslovakia. Dispossessed individuals in our sample are over proportionally born outside of the current borders of their country. Analyzing countries of origin, many of them came from Eastern Europe, thus they most probably lost their property with the big wave of nationalizations after WWII. Not surprisingly, it is the foreign-born living in our SHARE countries who were most likely to be dispossessed.
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Figure 4
Dispossession of Population in WWII
Table 4
Number of Observations Available in SHARELIFE; by Country
Observations CountryWar = 0War = 1TotalAustria146565711Germany4501,0011,451Italy8631,4702,333Czechia7239251,648Greece1,1491,4822,631Poland8197581,577Belgium1,0261,4802,506France7931,1051,898Netherlands8831,0691,952Denmark1,92701,927Sweden1,63901,639Switzerland9930993Total11,4119,85521,266
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Note: The countries are defined as of 2009 when the SHARELIFE data were collected. Native born only. War = 1 means that the respondent was living in a war country sometime during World War II.
Source: SHARELIFE; calculations by authors.
IV. WWII and Individual Outcomes: Analysis of SHARELIFE Data
Based on the descriptive data and review in the prior section, we find enormous variation even among war countries in the immediate impact of WWII. Long-term economic or population growth rates seem unlikely to be a primary pathway through which the war’s influence took place. Instead, changing gender ratios induced by differential male mortality in the war appear to be a more plausible pathway operating both through absence of fathers and difficulties faced by women in marrying. Hunger and immediate and long-term stress created by battles, dispossession, and persecution would also appear to be plausible pathways that could impact adult health, both physical and mental, and our later life measures of adult SES.
A. Measures of War Exposure
To analyze long-term impacts of WWII on health and economic outcomes, we use the fact that different countries in Europe and different people in those counties were differentially affected by WWII at different points in time. To study effects on adult outcomes, we use two indicators of being affected by World War II: [a] that one lived in a war country during the war period, and [b] that one was exposed to combat in the area within a country in which one lived during the war. Our first measure essentially creates a war dummy equal to zero for everybody in a non-war country [Denmark, Switzerland, and Sweden], and for everybody born after the war period no matter what country they lived in. Alternatively, it is equal to one for everybody alive in a war country [Austria, Belgium, Czech Republic, France, Germany, Greece, Netherlands, and Poland] during the war period. The war period ends in 1945 for all war countries, while it includes 1946 to 1948 in Germany and Austria, when they were under allied occupation. For these countries, the war period ended with the currency reform in Germany in 1948. Individuals could certainly have been affected by the war even if they were born after the war, but the channels we emphasize in this paper—combat, hunger, dispossession, persecution, and the absence of a father—were more likely to have affected those who lived during the war.
Our second war measure involves constructing a variable indicating whether there were combats and how many combats occurred in the region within the country in which the individual lived during WWII. Thus, in the war countries, we create two dummy variables based on the number of months of exposure the respondent had to combat in the place they lived during the war—0 to 2 months of exposure to combat and 3 or more months of exposure to combat. The purpose of this variable was to test whether actual exposure to combat was an important mechanism for the war effects that we estimate below.
Table 4 provides the list of SHARE countries that are part of our analysis with the sample sizes of those SHARELIFE respondents who experienced the war and those who had no direct experience of war. We did not include Spain in our analysis since Spain experienced a civil war in the late 1930s, so a distinction between whether Spain is a war country or not is very ambiguous. The results were not significantly different if Spain was included.
B. Micro-level Regressions of Adult Health and SES Outcomes
We next turn to our statistical modeling of whether individuals’ experiences during WWII predict their health and socio-economic status in their later adult life. For all of our later-life health and SES outcomes and channel outcomes, our estimating equation takes the form
Yitc = β1 ∗ waritc + β2 ∗ malei + λt + ηc + εitc
[1]
where Yitc is the late-life outcome of respondent i born in year t and living in country c. Male indicates a respondent was male. War is one of our two measures of war exposure outlined above, which vary by country [or region within a country] and year of birth. Because there may be unmeasured country and year effects associated with these outcomes, λt is a full set of year of birth dummies and ηc is a full set of country dummies. εitc is a random error term. Since error terms within country and within year may be correlated, we used the cluster option in STATA.
Our principal interest is to obtain estimates of β1—the ‘war’ effect in addition to birth-year and country effects. We estimate reduced form models using our two War variables on later adult life health and SES outcomes and the principal channels of war. We consider several adult dependent variables all measured in 2009, the year of SHARELIFE. Health outcomes include prevalence of diagnosed diabetes and heart disease, body height in centimeters [a summary measure of early-life health conditions], whether an individual is depressed using a dummy variable for presence of at least four symptoms on the EURO-D scale, and self-reported health status. Self-reported health status is recorded on a scale excellent, very good, good, fair, and poor which we have translated to a scale from one to five with five the best health status. Our adult SES and economic outcomes include log of household net worth, whether the individual was ever married, and life-satisfaction in 2009. SHARE respondents are asked “On a scale from 0 to 10 where 0 means completely dissatisfied and 10 means completely satisfied, how satisfied are you with your life?” We model this outcome as a score from 0–10.
We have two education measures in SHARE. The first is obtained from baseline SHARE in 2004 and, in an attempt to make the education variable comparable between individuals in the same country, assigns a standardized year for each education value. For example, university graduates in a country would be assigned a 16. The second education variable is available in the second SHARE wave and is equivalent to the actual number of years spent in education. We use the second measure because Poland and the Czech Republic were not part of baseline SHARE and for those two countries the first measure is not available. However, we hypothesize that WWII may have disrupted education for many respondents and resulted in a longer time to complete a given level of education. To test that hypothesis for the sub-sample of respondents who have both measures of education from the second and first SHARE waves, we estimated a model that amounts to the difference between the two education measures [the second-wave education minus the first-wave education variable].
Figure 5 displays the association of three of our key outcomes—education, self-reported health, and depression—with time period of birth using three sub-sets of countries—Germany and Austria combined, other war countries, and the non-war countries. These outcomes are each expressed as the difference between each of the first two kinds of war countries minus the outcome in the non-War countries. For all three outcomes, the outcomes deteriorate relative to the non-war countries for those born at a time they would experience war.
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Figure 5
Later Life Outcome Differences between War and Non-War Countries by Period of Birth
Table 5 summarizes results obtained for adult health outcomes and table 6 for adult SES outcomes. We present regressions in the A panels that use only the aggregate war exposure measures and in panel B the measure that distinguishes between very limited exposure to combat [two months or less, including zero] or an more extensive combat exposure [three or more months] with the left-out category being not exposed to war at all. In terms of right-hand side variables, there are no missing values for gender. If the outcome in any particular model is missing, this observation was not included in that specific model. Missing values in our outcomes are relatively rare. In terms of main channels [dad absence, dispossession, hunger, and persecution], missing values are in the order of one in a thousand observations.
Table 5
Adult Health Outcomes Associated with World War II
Variables[1][2][3][4][5]DiabetesHeartHeightDepressionSelf-reported HealthA. War variableWar0.026*** [0.009]0.014 [0.010]0.196 [0.179]0.058*** [0.014]−0.094*** [0.034]Male0.010** [0.004]0.045*** [0.004]11.579*** [0.106]−0.170*** [0.006]0.115*** [0.015]Observations21,22821,22821,11521,26621,254R-squared0.0220.0610.5100.0730.148B. Combat variableWar combat 0–2 months0.030*** [0.009]0.011 [0.010]0.138 [0.205]0.047*** [0.015]−0.091*** [0.036]War combat 3 or more months0.018* [0.010]0.018* [0.012]0.308 [0.221]0.078*** [0.016]−0.098*** [0.037]Male0.010** [0.004]0.045*** [0.005]11.576*** [0.086]−0.169*** [0.006]0.114*** [0.015]Observations21,21221,21221,09921,25021,238R-squared0.0220.0610.5100.0730.148
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OLS regressions include both country dummies and birth-year dummies. Robust standard errors in brackets allow for correlation at year/birth level.
***p