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The Effects of the 1915 Gallipoli Campaign on Turkish Child Survivors in Anatolia

Cahit Guven and Mehmet Ulubasoglu

The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School or IBISWorld Pty Ltd.

The Effects of the 1915 Gallipoli Campaign on Turkish Child Survivors in

Anatolia

Cahit Güven, and Mehmet Ali Ulubaşoğlu*

Deakin University, Australia

December 2015

Abstract

Despite being one of the most significant wars in world history, the Gallipoli Campaign has been subject to little systematic investigation for its consequences. We investigate the long-term socioeconomic effects of this war on children who lived in Anatolia and were aged under five in 1915. Combining Turkish census data with military records on the province-level Turkish soldier mortality rate in the campaign, we find that, at the sample average of soldier mortality rate (3.18 soldiers per 1,000 people in a province), children under five in 1915 (i.e., treatment group) were 2.8% more likely to remain illiterate, or lost 0.2 years of schooling, compared to children born during 1916 to 1920 (i.e., control group). These are significant effects given that the literacy rate for the whole cohort is 35% and average years of schooling is 1.55. Our results are robust to controlling for birth-province fixed effects, other major shocks faced by the treatment and control groups during childhood, placebo tests, and alternative definitions of treatment.

Keywords: Gallipoli War; Natural Experiment; Socioeconomic Outcomes in Adulthood;

Children; Turkey.

* Corresponding Author: Department of Economics, Deakin University. Email: [email protected]

1 1. Introduction

The Gallipoli Campaign is one of the hardest fought wars in modern human history. It was hard fought because it engaged several different global powers and approximately 900,000 soldiers from around the world over a narrow strip of geographical space on the Gallipoli peninsula, involving heavy weaponry and chest-to-chest fighting, resulting in the death in action of about 140,000 soldiers from all sides within eight months during February to September 1915.

Bullets that hit each other in the air during fighting, epitomizing the intensity of the clashes, are still exhibited at the Gallipoli War Museum in Çanakkale, Turkey. This heavy fighting has produced such a strong legacy that the belligerent powers still commemorate the war 100 years later in different settings. The Turks celebrate their naval victory annually on 18 March, while in the antipodes, Australia and New Zealand observe 25 April as a public holiday (i.e., ANZAC day) to observe the anniversary of their amphibious landing on the peninsula. Legendary events that occurred during the campaign are still told with enthusiasm: the Man with the Donkey (John

Simpson Kirkpatrick), who carried wounded soldiers on his donkey for three and a half weeks under the bullets before being killed, is still told on the ANZAC side; while Seyid Onbaşı, a corporal who reportedly lifted and carried alone an artillery shell weighing 254 kg, which then hit the HMS Ocean of the British Navy, is a hero on the Turkish side. The campaign also had drastic political consequences; the inability of the Allied Powers to push further into the

Dardanelles and to capture Constantinople led to the Bolshevik Revolution and the collapse of the Tsarist regime in Russia in 1917.1

1 The war was also a strong contributor of the formation of national identity in Australia (Nelson, 1997); thousands of Australians visit the battlefield annually today (Hyde and Harman, 2011). The Gallipoli War also influenced some developments in modern medicine (Harrison, 1996) as it involved the use of chemicals, and played a crucial role in the introduction of chemical warfare to the Middle East (Sheffy, 2005). See Evans (2000) and Travers (2001).

2 The human cost of the Gallipoli Campaign was traumatic. The war theatre witnessed the death of approximately 140,000 soldiers from all sides. While the Allied Powers together lost

57,000 soldiers, Turkey lost 86,000. The total number of casualties on both sides was a whopping 500,000 (Erickson 2001). The impact of the war on Turkey was extreme. Defending the motherland had consumed years of the country’s economic, environmental, and demographic resources. Moreover, a large proportion of the soldiers killed were from Anatolia (the region that roughly makes up Turkey’s contemporary boundaries). The disappearance of thousands of labor force-age male individuals in a short period of time from a geographical space that is similar in size to Texas in the US, or New South Wales in Australia, created a massive vacuum in the country and left the predominantly agrarian population of Anatolia in dire circumstances. Less noticed, however, is the variation across Anatolian provinces in terms of the numbers of soldiers lost. Data available from the military records of Turkey document the fact that 75% of the total death toll came from provinces to the west of Ankara, a mid-Anatolian province. While this variation can be explained by the geographic proximity of the western provinces to the Gallipoli peninsula, it also points to the reality that the western part of the country was ravaged.

This paper seeks to examine the effects of the Gallipoli campaign on children who lived in Anatolia and were aged under five in 1915. We utilize the rich information in the Turkish census waves of 1985, 1990 and 2000 on the adulthood outcomes of the survivor children as observed in the census years, including literacy, schooling, welfare, and disability.

While the severe effects of the Gallipoli War on Turkey’s economy and manpower during the period of conflict are well known, little is known about its long-term consequences.

Theoretical predictions in the literature on the consequences of wars and conflicts are too ambiguous to allow a priori conclusions about the effects of wars. On one hand, individuals who

3 face war and conflict during childhood could have a lower human capital due to war-time stress, depression, and malnutrition (Akbulut-Yuksel, 2014; Akresh et al., 2012; Kesternich et al.,

2014). Individuals may even be hit by wars in utero. Barker’s hypothesis posits that adverse conditions in the foetal environment, such as malnutrition, have a lasting impact on individuals’ subsequent health and well-being (Stein et al., 1975; Glewwe and Jacoby, 1995; Maluccio et al.,

2009). On the other hand, wars and conflicts may be followed by “creative destruction,” due to post-war recovery and rehabilitation, such that they could even improve the economic conditions

(Blattman & Miguel, 2010; Miguel & Roland, 2011; Brakman et al., 2004; Kecmanovic, 2013).

The empirical evidence on the consequences of wars and conflicts is not straightforward, either. For instance, it has been found that prenatal exposure to the Korean War (1950–1953) had a negative effect on socioeconomic and health outcomes at older ages (Lee, 2014). Several studies also document wars as having negative effects on the schooling of school-aged children; see Swee (2015) for Bosnia and Herzegovina; Verwimp and Van Bavel (2013) for Burundi;

Chamarbagwala and Moran (2011) for Guatemala; Shemyakina (2011) for Tajikistan; and

Alderman et al. (2006) for Zimbabwe. Going further back in history, it is found that school-aged children in Germany and Austria during World War II received less education, and went on to earn less and have poorer health in adulthood (Ichino and Winter-Ebmer, 2004; Akbulut-Yuksel,

2014; Kesternich et al., 2014). On the other hand, de Groot and Goskel (2011) find the Basque

Region conflict to have had a positive impact on education. A recent study by Bozzoli et al.

(2013) finds that self-employment in Colombia is lower in regions that have been exposed to conflict, but higher in places with higher proportions of displaced persons. Perhaps the most surprising result is that found by Miguel and Roland (2011) in the context of the devastating

American bombing of Vietnam during the Vietnam War (1967–75). They find that local living

4 standards and human capital levels converged rapidly across bombed and non-bombed districts in Vietnam following the war, such that the differences today are statistically insignificant. This finding implies that a rapid post-war recovery can leave few visible economic legacies in 25 years, and is in contrast to the poverty-trap models of the implications of large shocks.

Our study is part of the literature that analyzes the early childhood outcomes of wars.

Empirically, a focus on young children could produce more reliable estimates, given that the selection bias concerning the impacts of war is likely to be smaller for children than for adults or combatants.2 Our focus on World War I is also notable, because there have been hardly any studies in the literature exploring the long-run impacts of WWI at the micro-level.3 One of the important exceptions is Abramitzky et al. (2011), who study the impact of male scarcity in

France as a consequence of WW I on assortative matching using French census data for early

1900s and French soldier mortality at WW I.

Our empirical framework takes a treatment versus control approach, in which we compare the adulthood socioeconomic outcomes of those who were born during the periods 1911 to 1915 (i.e., the treatment group) and 1916 to 1920 (i.e., the control group). We use the Turkish census waves of 1985, 1990 and 2000 to identify the individuals who were born during the period 1911 to 1920, and the socioeconomic outcomes of those individuals in adulthood. Our regression sample includes a total of 122,649 individuals, with 56,341 being in the treatment group and 66,308 in the control group. We utilize the treatment group as a whole group as well as with respect to the year in which the individuals were born (1911, 1912, 1913, 1914 and

1915). Importantly, our identification comes from the interaction of the treatment cohort with

2 The literature examining the impact of wars on combatants include Kecmanovic, 2013; Siminski, 2013; Siminski and Ville, 2011; Angrist, 1990; Angrist and Krueger, 1994; Angrist et al., 2010; and Bedard and Deschenes, 2010. Kondylis, 2010 analyize displaced civilians, while Blatmann and Annan, 2010 analyze child combatants in Uganda. 3 Most of other studies exploring WWI focus on the macroeconomic circumstances surrounding the war (see Barro, 1986, 1987; Barsky and De Long, 1991; and Broadberry and Harrison, 2005).

5 province-level soldier mortality rate in a child’s birth-province. This interaction allows us to capture the severity of the war exposure of children in different provinces. The available military records in Turkey identify 48,148 soldiers who were killed in the Gallipoli campaign with person-level detail, such as first name, family name, birth year, province of residence, death year, the battle in which they were killed, and the district-level military branch that conscripted the soldier. Although 48,148 is lower than the actual death toll of the Gallipoli Campaign for the

Turkish side, the level of detail in the data enables us to track the varying impact of the war on children in different provinces. The north-western province of Çanakkale, near Gallipoli, recorded the highest soldier mortality rate, 8.5 soldiers per 1,000 population, while Hakkari in the southeastern tip of Turkey is recorded as having a zero loss. The mean soldier mortality rate across Turkey’s 66 provinces is 3.18 soldiers, and the median is 2.78.4

We find strong evidence that the perishing of thousands of labor force-aged males from the agrarian economy of Anatolia adversely affected the subsequent lives of young children. We document that, for each additional soldier killed per 1,000 people, those born between 1911 and

1915 were 0.87% more likely to be illiterate than those born between 1916 and 1920. At the sample average of soldier mortality rate, this effect corresponds to 2.8% higher probability of being illiterate. Given that the literacy rate for the whole sample is 35%, this suggests the war explains an important proportion of this illiteracy for the treatment cohort. Similarly, at the average soldier mortality rate, children in the treatment group on average attained 0.2 lower years of schooling compared to the control cohort, which is again sizeable because the average years of schooling in the sample is 1.55. Moreover, we find that, for a given level of war exposure, the adverse impact of the war is relatively higher for elder children within the

4 In 1985, Turkey had 67 provinces. The Kars province was under Russian occupation in 1914, so we could not compute its soldier mortality rate and is excluded from our analysis. Province splits in Turkey increased the number of provinces from 67 to 81 by 1999. We conduct our analysis with respect to the provincial boundaries in 1985.

6 treatment group. Given the correspondence of the elder cohorts to child labor-age, this finding suggests that compulsion to work as child labor following the war was probably the dominant reason for the adverse effects of the war. Further, we find that at the sample average of soldier mortality rate, the treatment cohort is 0.6% more likely to be disabled in the census year.

Critically, this disability effect is the strongest for the 1915-born cohort, demonstrating that the disability impact of the war falls on those who were newborns or in utero during the war year.

Given that we focus on children in the whole geographic space of Anatolia and not specifically on the victims in the combat zone, our results cannot be explained by the destruction of schools, hospitals and other public properties or chemical warfare in the war theater. Our results are robust to controlling for other major shocks faced by the treatment and control groups during their childhood (such as Armenian deportation in 1915) or in temporal proximity to the sample period (the Turkish Independence War 1921-22, and the Greek-Turkish Population Exchange

1923), or comparing children only within a given birth province (i.e., birth-province fixed effects), as well as placebo tests, and alternative definitions of the treatment.

It is important to note that, despite several measures having been taken to address empirical biases, our estimates do still contain unavoidable biases, such as control group bias, infant mortality bias, and adult mortality bias in the census waves. For example, there are good reasons to think that our control group (i.e., those born after the war) might also have been affected by the war for other reasons. Although we use the province-level soldier mortality rate to obtain the identification for the treatment group, the control group bias, and the other biases mentioned above, could push our estimates downwards. Downward-biased effects mean that our estimates are likely to represent the bottom line of the true effect of the Gallipoli Campaign on

Turkish children; hence, the true effect is actually likely to be greater than our estimated effects.

7 Section 2 provides background on the Gallipoli Campaign and how the war affected the children. Section 3 describes the data, Section 4 explains the empirical approach, and Section 5 discusses the empirical results and some robustness tests. Finally, Section 6 concludes.

2. The Gallipoli Campaign and the Turkish Child Survivors

The Gallipoli War was fought between the Allied Forces, which included Britain, France,

Australia, New Zealand, British India and Newfoundland, and the Ottoman Empire, between 19

February 1915 and 16 January 1916, as part of World War I. Australia, New Zealand, British

India and Newfoundland contributed to Britain’s Mediterranean Expeditionary Force, while

France took part in the war via the Oriental Expeditionary Force. The Ottoman Fifth Army sourced the majority of its soldiers from Anatolia, though its territories outside modern Turkey also provided additional reserves. Germany helped the Ottomans with strategy and tactics.

The Gallipoli War had two phases: the naval battle and the land campaign. The official start of the war was on 19 February 1915, when the Allied Powers started bombarding the

Dardanelles in a naval push. The battle lasted for a month, and ended when the Allies withdrew on 18 March after losing 17 ships and submarines. The second phase of the war involved land offensives, starting with the amphibious landings of the ANZACs at Anzac Cove and of Britain and France at Cape Helles on 25 April 1915. This phase lasted for more than eight months, which saw repeated engagements between the Turks and the Allied Powers on fronts such as

Krithia, Chunuk Bair, Sari Bair, and the Lone Pine. The battle ended in January 1916, when the

Allied Powers withdrew their invasion force from the Gallipoli peninsula to Egypt. As indicated, the battles during those eight months involved numerous fatal engagements, including chest-to-

8 chest fighting. One example indicating the severity of the fighting is that the whole 57th Infantry

Regiment of the Turkish Army was killed in action, including its commander and officers.5

Although the systematic long-term consequences of the Gallipoli Campaign are unknown, there are several reasons why children might have been affected by this conflict. The first reason is malnutrition. Because most of the labor force-age men were in the battlefield in that year, the predominantly agrarian Anatolian economy harvested much smaller crop.

Moreover, the heavy combat conditions in Gallipoli meant that the available food and resources had to be sent to the war zone (Beşikçi 2012). The war cut off all economic activities in the area, including labor and production, and destroyed the existing crop, making it difficult for people to survive. Indeed, Özmucur and Pamuk (2002) argue that there was a significant increase in inflation in Anatolia in proportion to real wages during this period. Child malnourishment is a natural consequence in these circumstances.6

The second reason why children were affected by this conflict is that the death of thousands of labor force-age males in a short time generated a large vacuum in the agrarian

Anatolian labor market. As a consequence, a great majority of the children had to work as child labor following the war to contribute to the income of the household, and therefore did not attend school. Thirdly, it is argued that the war impacted both children and adults psychologically.

Living as orphan and widow had many psychological repercussions that often lead to poor productivity and efficiency on many economic outcomes (Doyle & Bennett, 1999).7

5 This information is attributed to Mustafa Kemal, the commander of the 19th Division at Gallipoli and founder of modern Turkey. In commemoration, the current 57th Regiment of the Turkish Army does not comprise any soldiers. 6 Erickson (2001) mentions that on some days during the war, about 3000 to 4000 wounded people were rushed to hospitals on a daily basis. 7 There is also a growing literature that analyzes the long-run effects of natural disasters on child survivors, citing similar mechanisms of impact as in wars. See, for example, Deuchert and Felfe (2015).

9 After the war, Turkey suffered from economic hindrance due to the lack of labor and income. It is reported that the excessive casualties, the movement of people and the extensive destruction of equipment and animals within the region led to the population being unable to maintain labor-intensive work in major sectors like agriculture, industry and mining (Pamuk,

2010; Scates, 2006). Given the interconnected need for material resources within most of the neighboring regions, the destruction of the means of production within Çanakkale, its surrounds

(the Marmara region), and greater Turkey caused significant economic deterioration. This meant that the economic stabilization took a substantial amount of time, given the reforms and the establishment of the independent Turkish state post-Ottoman rule. Moreover, the standards of living within the communities deteriorated exponentially as a result of scarce resources, rising costs, and instability due to being in a state of emergency with over two million army personnel mobilized (Pamuk, 2005, p. 117). The pre-existing budgetary decline of the Ottoman Empire added further to the many economic challenges faced by both the Empire and the locals

(Özmucur and Pamuk, 2002).

All in all, the literature on economic history puts forward a case for the Gallipoli War having extensive consequences for both adults and children, with the effects extending to range of human spheres (Scates, 2002). However, the long-term consequences of the physical, economic, social, or psychological impacts on the victims are largely unknown. Hence, there is significant room for the quantification of such implications using Turkey’s rich census data.

3. Data and Descriptive Statistics

We have obtained the Turkish national census data for 1985, 1995 and 2000 from the Integrated

Public Use Microdata Series website (Minnesota Population Center, 2013). The data provide person-level data from the 1985, 1995 and 2000 census waves, with 2,554,364, 2,864,207 and

10 3,444,456 observations, respectively (each representing 5% of the total population). The data include basic demographic information for each household member, such as age, education, employment status, disability status, birth province, and current residence (province or district).

The available census data include 56,341 individuals who were born within Turkey during the five-year period 1911–1915 (i.e., the treatment group) and 66,308 individuals who were born during the period 1916–1920 (i.e., the control group). Together, our regression sample includes 122,649 individuals from 66 provinces. Table 1 provides the descriptive statistics for key variables. Of all individuals in the regression sample, 35% were literate, 24% completed at least primary school, 4% completed at least secondary school, 45% were male, 6% were disabled, and the average number of rooms per household member is 1.1 in the census years. The average years of schooling for the sample is 1.55, while 50%, 36% and 14% of the individuals come from the 1985, 1990, and 2000 censuses, respectively. See also Figure 1.

In 1915, the Ottoman Empire was a multinational entity. The Ottoman census of 1914, presented in detail by Karpat (1985), reports the Empire’s population to be 18,520,016, of which

15,976,091 lived within the borders of modern Turkey.8 Average provincial population within these borders was 391,690, of which, on average, 83% was made up by Muslims (notably, Turks and Kurds) and 17% by minorities. The minorities were defined mostly based on their religious affiliations, and included Greek Orthodox, Armenian Orthodox, Jews, and to a smaller extent,

8 The 1914 Ottoman census presented in Karpat (1985) includes population information for muslims and ethno- religious minorities in 36 vilayets (province) of the Ottoman Empire. These 36 vilayets are further divided into 75 sancaks (i.e., sub-province), which are further divided into 427 kazas (i.e., districts). The borders of modern Turkey are made up by 31 of these 36 vilayets (i.e., the Beirut, Syria, Aleppo, Jerusalem, and Zor vilayets are beyond the current borders of Turkey, except a few sancaks and kazas of Aleppo). After identifying the populations of 366 kazas (i.e., districts) that are currently within Turkey, we have aggregated the kaza populations to the level of 66 contemporary provinces of Turkey as of 1985. In this process we used the method of name-matching between former kazas and contemporary districts, which, according to our historical information, provides a generally reliable picture of the population of contemporary provinces in 1914.

11 Greek Catholics, Armenian Catholics, Protestants, Syriacs, Maronites, and Gypsies. Ottomans drafted soldiers mostly among muslims; non-muslim ethnicies were typically not conscripted.

We have obtained detailed person-level data on 48,148 Turkish soldiers killed in the

Gallipoli Campaign from the National Defence Ministry of Turkey (1998).9 The data were sourced by the military through an extensive effort from district-level military branches, which were aided in turn by local officials (mukhtars) at the village or council level. Importantly, the available data identify the following detailed information for the soldiers killed at Gallipoli: name; last name (family’s nickname or father’s name); birth year; province of residence; military branch (at the district level); battle in which the soldier was killed; and year of death. This information suggests that average provincial soldier mortality rate at Gallipoli was 3.18 (per

1000 people in a province), see Figures 2 and 3. Figure 4 indicates that an important proportion of the soldiers killed were aged 25–29: 28.4%. The proportions of other age cohorts are as follows: 30–34 (23.1%); 20–24 (17.7%); 35–39 (13.1%); age unknown (12.8%); greater than 40

(3.8%); younger than 20 (0.7%). This information suggests that at least 64.4% of the soldiers killed were aged 25–39, and thus, were likely to have had children before going to the battlefield.

4. Empirical Framework

To investigate the impact of the Gallipoli Campaign on the socioeconomic outcomes of the child survivors later in life, we take a treatment–control approach where we compare the children who were born in the years 1911 to 1915 with those who were born during the period 1916 to 1920.

Our first empirical model is as follows:

9 The National Defence Ministry of Turkey (1998) provides detailed person-level data on most of the soldiers who were killed in action (commonly referred to as ‘martyrs’ in Turkey) in the wars that Turkey fought between 1857 and 1974. For the four wars that were fought between 1911 and 1922, the records indicate the numbers of soldiers killed from the 67 provinces of Turkey as follows: 375 soldiers in the Italio-Turkish War in Libya in 1911–12; 1,005 soldiers in the Balkan Wars in 1912–13; 86,054 soldiers in World War I, 1914–1918 (48,148 of whom were killed at Gallipoli, and the rest in battles on the Canal, Yemen, and Galicia fronts); and 14,658 soldiers during the Independence War in 1921–1922.

12 Yit = λ0 + λ1 Treatmenti + λ2 Soldier Mortality Ratep + λ3 Treatmenti × Soldier Mortality Ratep +

2 λ4 Malei + λ5 yobi + λ6 yobi +ηcensus + eit , (1)

where Y includes binary indicators of literacy, primary or secondary school completion, and disability status as well as continuous indicators of year of schooling and the number of rooms per household member, each utilized in alternative regressions, for child i in year t =

1985, 1990 or 2000. Treatment takes 1 if the child is born during the period 1911 to 1915 and 0 if s/he is born during 1916 to 1920; Soldier Mortality Rate is the number of soldiers killed per

1,000 people10 from province p in the Gallipoli campaign. The Male dummy takes the value 1 if the child is male and 0 otherwise; yob is the year of birth of child i. We also include the squared yob to account for possible nonlinearities in outcome trends. census is a vector that comprises three dummy indicators for each of three census waves 1985, 1990 or 2000.

The error term takes the form e = u + birth-year fixed effects, where u is random error and birth-year fixed effects captures permanent differences across birth cohorts. In an alternate specification, we employ e = u + birth-year fixed effects + birth-province fixed effects where the last term captures a broad set of time-invariant effects at the province level.11 However, this specification removes Soldier Mortality Rate from the regression, which makes λ2 not estimable, because both Soldier Mortality Rate and birth-province fixed effects are time-invariant for a province. In two additional error specifications, we use e = u + birth-year fixed effects + current-province fixed effects and e = u + birth-year fixed effects + birth-district fixed effects.

Controlling for birth province vis-à-vis current province tests indirectly whether migration from their birth province to another province could affect the results. Controlling for birth-district fixed effects is an even more restrictive specification, in that districts, being sub-units of

10 We use total population (i.e., both muslim and non-muslim groups) in computing this rate. In a robustness check below, we will compute soldier mortality rate as the ratio of number of soldiers killed to only muslim population. 11 Controlling for birth-province fixed effects enables us to compare individuals born within a given province.

13 provinces, might exhibit idiosyncratic time-invariant characteristics, and enable comparison of children within a narrower geographic area. Following the relevant literature (e.g., Akbulut-

Yuksel 2014), our preferred error specification is e = u + birth-year fixed effects where we control for Soldier Mortality Rate.

The control group includes the children who were born in 1916–1920, which is the first five years after the Gallipoli War. This period is the period closest to 1911–1915 that would ensure a relatively meaningful comparison of the effects of war across children of similar ages.

Importantly, the treatment cohort is interacted with the soldier mortality rate such that this interaction term identifies the effects of the war. Therefore, our coefficient of interest is λ3. This interaction is retained in all error term specifications above.

An important question is whether there are confounding factors in our analysis. A noteworthy shock that might threaten our empirical design is the Armenian deportation of 1915.

This was a significant demographic change within the Ottoman Empire in that it involved deportation of the Armenian population outside the Empire’s boundaries. The Ottoman census of

1914 reports a total of 1,190,296 Armenians living within the borders of modern Turkey (7.5% of the total population), residing mostly in Eastern, Central, and South-central Turkey as well as in Istanbul (Karpat 1985). Armenians formed an integral part of the Ottoman Empire’s population, contributing to daily life as artisans, white-collar workers, bankers, and even administrators. In a dramatic decision during political turmoil in 1915 the Young Turk regime of the Empire deported almost all Armenians in Turkey en masse.12 The concern here is that the vacuum left behind by the Armenians constitutes another shock to our regression sample and if this shock affected our treatment and control groups differentially. Our priors suggest that the

12 The tragic deaths of thousands of Armenians during this turmoil is subject to a huge political tension between the Armenian diaspora and modern Turkey. While the Armenian diaspora pushes for the recognition of deaths as killings, and thus, as genocide, the Turkish governments argue that the deaths occurred in a political turmoil.

14 latter is unlikely, because Armenians worked in different occupations than Muslims (who were mostly farmers), and it would have been difficult for Muslims to adjust to the Armenians’ professions within the sample period. In addition, our focus is on children, not on adults, but this may not rule out absence of any effect if children’s parents were also affected by the vacuum. In our benchmark analysis we bypass this issue given our soldier mortality-based identification, but controlling for this shock in the robustness tests makes little difference to our results.

Another important concern is that the birth cohorts in our regression sample were faced with other shocks later in life. These shocks included, among others, the Independence War

(1921-22),13 the Greek-Turkish Population Exchange (1923), as well as national literacy programs held in Turkey in second half of the century. Crucially, the inclusion of birth-year fixed effects in the model ensures that any shock that each birth cohort in the sample faces later in life is controlled for their effects on the outcomes of interest Y. Nonetheless, the Independence

War happened in western Turkey and in temporal proximity to our regression sample, and can thus constitute a problem for our empirical design. The National Defence Ministry of Turkey

(1998) reports 14,658 soldiers killed during the Independence War in 1921–1922. As noted, the actual figures are likely to be higher. Table 2 shows that the correlation between the province- level death toll in the Gallipoli War and that of other WWI fronts is 0.16, while the same correlation for the Gallipoli War and the Independence War is 0.46. In the robustness analysis below, we will control for the effects of the Independence War on our treatment and control groups, in order to isolate its possible confounding effect from the impact of the Gallipoli

Campaign. In that section we will also control for the potential effects of the Greek-Turkish population exchange in 1923 on our benchmark results. However, as will be shown, our

13 Even though the Turkish Independence War is considered to have started on 19 May 1919 with Mustafa Kemal’s arrival in Samsun to start the resistance to the Allied occupation of Asia Minor, the actual clashes took place during 1921-22.

15 benchmark results change only a little after these controls, suggesting that our identification based on provincial soldier mortality rate and controlling for birth-year fixed effects is doing a good job in capturing the Gallipoli War effects on children, and thus, our findings are unlikely to be driven by these shocks.

In an alternative specification, we adopt the following model:

Yit = β0 + β1 1911_Borni × Soldier Mortality Ratep + β2 1912_Borni × Soldier Mortality

Ratep +β3 1913_Borni × Soldier Mortality Ratep +β4 1914_Borni × Soldier Mortality Ratep +β5

1915_Borni × Soldier Mortality Ratep + β6 1911_Borni + ……. + β10 1915_Borni + β11 Soldier

2 Mortality Ratep + γ1Malei + γ2 yobi + γ3yobi + census’tη + eit , (2)

where 1911_Born, 1912_Born, 1913_Born, 1914_Born and 1915_Born are five dummy variables indicating whether the child was born in 1911, 1912, 1913, 1914 or 1915, respectively.

This specification divides the treatment group into five categories with respect to year born and enables us to see how each specific birth cohort was affected by the war.14 We use the four error term specifications above in estimating this model.

We estimate both equations (1) and (2) with ordinary least squares (OLS).15 Standard errors are clustered at the birth-province, current-province and current-district levels, depending on the error specifications.

Despite several measures that have been taken, there may be unavoidable empirical biases involved in our estimations. First, the control group itself might have been affected by the

Gallipoli Campaign. This effect is likely to occur through the vacuum created in labor markets, since the deaths of so many individuals might have pushed even the children born after the war

14In a yet different specification, we construct the treatment variable in such a way that it measures the age of child i in 1915. This model utilized a continuous age variable, which is set to 0 for the control cohorts and to 1, 2, 3, 4 and 5 for those born in 1915, 1914, 1913, 1912 and 1911, respectively. This specification informs on how Y changes with additional years of age. The findings from this specification were in line with equations (1) and (2). 15 We also perform probit estimations where applicable, and these yield estimates similar to those from OLS.

16 into work as child labor later on. The food shortage mechanism is less likely to be observed here, given that the war was already over in Anatolia, though malnutrition due to a compulsion to work as child labor could still exist. However, any war effect that has a negative influence on the control group would bias our coefficient of interest downwards.

The second source of bias is adult mortality. Obviously, the Turkish census waves of

1985, 1990 and 2000 include only those individuals who lived long enough to become part of the censuses. If these people are genetically healthier, such that they were affected less by the war, lived longer, and achieved better socioeconomic statuses later in life, this bias could push the coefficient of interest downwards likewise.

One may be concerned about the infant mortality that could have occurred during the

Gallipoli War. Reliable infant mortality data for the time period concerned are not available.

However, it is plausible to expect that the normal trend for infant mortality would have been present during the war; the question is whether the Gallipoli War accelerated this trend. If such was the case, it is not clear which direction the resultant bias would work. Malnutrition could have hit children with any ability. Moreover, soldiers’ conscription by the army was exogenous, meaning that soldiers were drafted from any income group or ability; hence, their descendants are equally likely to be hit by malnourishment. However, if genetically stronger children survived the war and are more likely to be literate or socioeconomically better off later in life, then this bias would push the coefficient of interest downwards.

Another concern is about the measurement of the number of soldiers. We rely on defence records that classify the soldiers killed with respect to their personal details. We do not have person-level data on volunteers, nor do we have such detailed data on other casualties, such as those who are wounded or lost, or become permanently ill. Therefore, the actual difficulties that

17 faced the remaining population after the war (e.g., the vacuum in the labor markets) are likely to be greater than what we can actually measure. This situation is likely to bias our estimates downwards. Since the four sources of bias mentioned so far all imply that any estimated coefficient is downwardly biased, the true effect is likely to be higher than our estimated effects.

Our final concern is the so-called “age heaping” problem in the censuses. Age heaping is a common problem in developing countries, and is generally associated with lower cognitive abilities (Baten et al., 2014). The census respondents in Turkey may have misreported their ages by rounding them to age years ending with 0 or 5. In other words, older people may have misreported their ages, such that the censuses exhibit “heaping” on 1915 and 1920. In fact, this problem is evident in Table 1, which shows that 15% of the sample reported having been born in

1915, while the proportions for those born in the years 1911 to 1914 are 5% to 7%. Crucially, our difference-in-difference approach is likely to cancel out the age heaping problem associated with 1915 and 1920, given that these years are included in the treatment and control groups, respectively. Note also that the treatment and control groups are free of the rounding problem in the majority of years. See also the discussion of a placebo test below and robustness checks.

5. Empirical Results

All of our tables report regression results for the following six socioeconomic outcomes: literacy, primary schooling completion, secondary schooling completion, years of schooling, rooms per household, and disability status. All of the tables comprise four columns. Column (1) controls only for Soldier Mortality Rate as the time-invariant variable, while column (2) instead controls for birth-province fixed effects. Columns (3) and (4) report the results when current-province fixed effects and current district fixed effects are controlled, respectively. We find that the coefficients in columns (2) and (3) are largely similar, confirming that domestic migration is

18 unlikely to be affecting our results. The results in column (4) are also similar to the previous two columns, suggesting that province characteristics largely mimic district characteristics. As noted, our preferred set of results is in column (1). Finally, our tables report only the coefficient estimates for the interaction of treatment indicators and soldier mortality rate (λ3 and β1 to β5).

Table 3 reports the benchmark estimation results of equation (1). As expected, the coefficient estimates of λ3 in column (2) are uniformly lower than their counterparts in column

(1). The estimates in columns (3) and (4) are analogous to columns (2). Focusing on column (1) in discussing the results, the coefficient estimates indicate that for each additional one soldier killed per 1,000 people, those born between 1911 and 1915 were 0.87% more likely to be illiterate than those born between 1916 and 1920. These estimates imply that for the sample average of soldier mortality, the effect corresponds to 2.8% (0.867×3.18). This is a sizeable impact, given that we are probably estimating the lower bound of the true effect. Similarly, at the average soldier mortality rate, children born between 1911 and 1915 were on average 2.4%

(0.763×3.18) less likely to have finished primary school or a higher degree, and attain 0.2

(0.051×3.18) lower years of schooling, compared to the control cohort. Note that the average years of schooling in the whole sample is 1.55. We find only marginally significant difference between the treatment and control groups in secondary or higher schooling completion, which is probably because only 3.73% of the sample attained that level of schooling.

Our results also indicate that the treatment group reports to be living in smaller houses in the census years, but this effect does not survive other error specifications in columns 2 to 4.

Importantly, the treatment cohort reports a significantly higher likelihood of having disability in the census years. Our estimate suggests that at the sample average of soldier mortality, the

19 treatment cohort is 0.6% (0.190×3.18) more likely to be disabled. Recall that the sample average of disability is about 6%.

Table 4 presents the estimation results of equation (2). Focusing on the estimates of β1 to

β5 in column (1), those born between 1911 and 1914 were significantly more illiterate than those born between 1916 and 1920, with the effect at average soldier mortality rate ranging between

3.5% and 4.9%. Moreover, the most adverse impact is estimated for the 1911-borns. The literacy effect is not statistically significant for 1915-borns using birth-province fixed effects. Similarly, at the average soldier mortality rate, children born between 1911 and 1914 were 3.2% to 4.3% less likely to have finished primary school or a higher degree, depending on the birth year.

Considering secondary or higher schooling completion as outcome, there are no significant differences between the treatment group and control group, except for 1911-borns and to some extent 1914-borns. We also estimate that, at the average soldier mortality rate, the average years of schooling is 0.3 lower for those born in 1911, the most affected group. Given that we are probably estimating the lower bound of the true effect due to several biases, these coefficients again point to a sizeable impact for the cohort in question.

Our results also indicate that each birth-year cohort in the treatment group reports to be living in smaller houses in the census years, which we interpret to be a uniform adverse welfare consequence of the war for these cohorts in the long-run. Finally, and critically, we find that the disability effect found above is the strongest for the 1915-born cohort, meaning that this cohort is more likely to report disability in the census years. This finding is worthwhile in that it demonstrates that the adverse health impact of the war is the most significant those who were newborns or in utero during the war year.

20 To summarize, our results show that the schooling effects are statistically and quantitatively much stronger for the 1911 birth cohort, with the effect somewhat reduced as children in the treatment group become younger and becoming insignificant for the 1915 cohort.

This result is consistent with the notion that the large vacuum in the labor markets, leading to the employment of children as labor, could be a dominant factor in producing the adverse outcomes of the war on children.16 Meanwhile, the disability effect is the strongest for the 1915 cohort.

In Table 5, we conduct a placebo test that considers a placebo treatment group of those born between 1921 and 1925 and a control group of those born between 1916 and 1920. We consider equation (1), with Treatment dummy as the treatment variable, and the same four error specifications as were mentioned above. The results of this test do not indicate any meaningful differences between the treatment and control groups when a placebo cohort is used as treatment.

We interpret this result as evidence that our empirical analysis is doing a good job of isolating the effects of the Gallipoli War, or of eliminating potential measurement problems such age heaping (which would also be the case for 1925 and 1920).

Having confidence in our empirical design, we next focus on the gender effects of war on children in Table 6. Considering equation (1) and the same four error specifications as above, the coefficient estimates demonstrate that the schooling effects of war are born by both male and female children, with males having been hit somewhat more strongly. In other words, both male and female children in the treatment group had a lower probability of literacy, a reduced likelihood of completing a primary school or higher degree, and fewer years of schooling. This result suggests that the war conditions and subsequent child employment (including inability to

16 A less likely reason for the insignificant difference between the 1915-borns and the control group is the age heaping problem. If those who were born in the control group period (e.g., 1916 or 1917) rounded their ages upwards such that they reported being born in 1915, then this might bias the coefficient of 1915-borns downwards. While this type of rounding may indeed have happened, we would expect the same to have happened to 1920 as well. Our difference-in-difference approach should have addressed an important part of the age-heaping problem.

21 attend school) affected male and female children in a relatively comparable manner. On the other hand, the disability effect of the war is predominantly driven by male children; the effect is insignificant for female children. Finally, neither male nor female children exhibit statistically significant difference between treatment and control groups in terms of secondary and higher schooling and the number of rooms per household member.

6. Robustness Tests

So far, our estimates show a robust significant and negative effect of an exposure to the

Gallipoli War on several adulthood outcomes of child survivors. There are, however, important empirical challenges that may affect our results.

Measurement error in soldier mortality. While historical estimates report 87,000 fatalities and roughly 250,000 casualties in the Gallipoli War for Turkey, military documents provide detailed information on only 48,148 soldiers killed. Independent of other biases, this under- measurement alone could push our estimated effects downwards. We provide some evidence for the extent of this problem by exploiting the high correlation between the provincial distribution of soldier mortality and the provincial distance to Gallipoli (DG). In this respect, first, we replace

DG with soldier mortality (in a reduced form setting) in equation (1). Panel A of Table 7 shows that the interaction between Treatment and DG significantly explains all of our outcomes, except disability (recall that only the 1915 cohort is affected by disability). Second, we use DG as instrument for soldier mortality in equation (1). It must be stressed that the exclusion restriction assumptions regarding DG as instrument could be violated due to other provincial characteristics that might have played role in children’s adulthood outcomes. So, we do not treat these results causal but rather suggestive. Panel B of Table 7 reports the second-stage results of the instrumental variable estimation. Crucially, the coefficient estimates across the four error term

22 specifications are uniformly higher than the OLS estimates for all outcomes and almost all estimates are significant. The first-stage F-statistic on the excluded instrument is estimated to be

44. Focusing on column (1), the effect on literacy is estimated with a coefficient of -1.446. The same coefficient using only OLS was -0.867 (see Table 3), pointing to a 1.7-fold higher IV coefficient. All other outcomes are similarly associated with higher IV estimates. These discrepancies are anticipated because the true losses in the war are much higher than 48,148 soldiers. Overall, these estimations, though focusing on only one source of bias, support the fact that our estimates are likely to represent the lower bound of the true effect.

Table 8 reports a number of other robustness checks, focusing mainly on literacy, years of schooling and the disability status as outcomes. Table A1 in Appendix presents the same checks for primary or secondary schooling completion and the number of rooms per household.

Armenian Deportation in 1915. To control for this shock, we include an interaction between Treatment and the Armenian share in provincial population in 1914 in equation (1), and observe how our coefficient of interest λ3 changes. Table 8 reports that controlling for this interaction results in only small reductions in the coefficients of the Gallipoli War effect for literacy and years of schooling, while making no change to the coefficient for disability. 17, 18 In another test, we compute the provincial soldier mortality as the ratio of the number of soldiers

17 The Muslim native population of Anatolia remained mostly bystander during this population movement. There are reports that some Armenians left their children to local Muslim families and the latter adopted those children. Turkey also argues that thousands of Muslims were killed in Eastern Anatolia by Dashnak guerillas before the deportations started. The extent of the adoption of Armenian children during deportation and that of Muslim deaths beforehand, is not clear. Thus, even though we cannot check the implications of these claims on the size of our estimates, these developments are unlikely to explain our results given that the soldier deaths were concentrated mainly in Western Anatolia. 18 One may ask how comparable is 48,148 soldiers killed to more than one million Armenians deported in terms of the size of the shock. Note that total human casualties for Turkey in the Gallipoli War are reported to be 250,000, and we consider the 48,148 soldiers killed as a proxy for the provincial distribution of total labor force-age male casualties. Assuming an average household size of seven and that we are considering an agrarian economy, the number of Turkish citizens affected by war casualties would well exceed one million.

23 killed at Gallipoli in Muslim population in 1914 rather than in total population, and our coefficient estimates remain unaffected, see Table 8.

The Turkish Independence War (1921-22). We have noted that our cohorts of interest were faced with other shocks in their life-times beyond the sample. Including birth-year fixed effects in the models aims to ensure that our estimates are not driven by these shocks. However, there is a relatively high correlation between provincial soldier mortality in the Gallipoli War and that in the Turkish Independence War in 1921–1922. To check the implications of this correlation, we include in equation (1) an interaction term between Treatment and the soldier mortality from each province during the Independence War. The resulting estimates of the

Gallipoli War exposure, reported in Table 8, are not meaningfully different than those in Table 3.

The Greek-Turkish Population Exchange (1923). Another demographic shock faced by our regression sample is the population exchange between Greece and Turkey in 1923. The

Ottoman census of 1914 reports 1,518,901 Greeks living within the borders of modern Turkey

(9.5% of the total population), residing mostly in Western, Central and North-Central Turkey

(Karpat 1985). A convention signed between Greece and Turkey in 1923 involved the mutual expulsion of a vast majority of Anatolian Greeks to Greece, and approximately 500,000 Muslims in Greece to Turkey. Even though our identification rests on province-level soldier mortality in the Gallipoli War and that we control for birth-year fixed effects in our models, this population movement might have affected the whole control group differently than the treatment group, given its temporal proximity to the control group.19 Controlling for the interaction between

Treatment and the provincial Greek share in 1914 in equation (1), our coefficients of interest are

19 There is also some historical debate as to when the Greek expulsion from Asia Minor started. It is argued that Greeks in North-Central Turkey were started to be deported in 1914. There are also claims that this expulsion amounted to genocide (called Pontic Genocide), but the political dispute between Greece and Turkey on this issue is much less severe than that between the Armenian diaspora and Turkey. Yet, there is no consensus on the number of Greeks that left Anatolia before 1923.

24 somewhat reduced in the preferred error specification (for example, the literacy effect is estimated to be 0.557). This reduction is anticipated because the majority of Greeks in Asia

Minor lived in the west. Next, this exchange also involved the injection of about 500,000 Muslim refugees into the Turkish population, which might affect our results. We do not have accurate information on the provincial distribution of these refugees, but we can compute from the census data the share of individuals in a province born abroad before 1923 as a proxy for the injections

(the average ratio of these individuals to our regression sample is 6.1%). Controlling for the interaction between Treatment and this share only reduces our estimates only marginally.20

In case other ethnicities were somehow affected by this or other events during 1910 to

1920, we control for an interaction between Treatment and the share of all non-Muslims in provincial population in 1914 in equation (1). Our main results remain unaffected, see Table 8.

On the whole, the preceding checks provide evidence that our identification strategy based on provincial soldier mortality rate is doing a good job in capturing the Gallipoli War effect on children, albeit its bottom line, and thus, our findings are unlikely to be driven by other shocks. We next carry out some other robustness checks regarding our empirical design.

Nonlinearity in soldier mortality. There are good reasons to believe that the higher the soldier mortality in a province, the more intense is the shock conceived by children. We check for non-linearity in soldier mortality by categorizing this variable into low, medium and high intensity based on its 33rd and 66th percentiles. The results, presented in Table 8, are striking.

Each additional soldier killed (in every 1000 people) in provinces that are classified medium and high intensity reduces literacy by more than 4% and years of schooling by 0.24 to 0.28, compared to provinces of low intensity. More importantly, the disability effect is significant only

20 We also revisit the placebo exercise in Table 5 to check for the implications of the Greek-Turkish population exchange in 1923 by including the same two interaction terms on the right-hand side, and the estimates concerning the Gallipoli War effect still remained insignificant across all outcomes and error specifications.

25 in provinces with high-intensity soldier mortality, whereby the estimated coefficient suggests that each additional soldier (in every 1000 people) increases the disability chance by 1%.

Age heaping revisited. We have already adopted different approaches to addressing the age heaping problem. In another check here, we simply exclude the cohorts whose ages end with

0 and 5 from our sample (i.e., 1915 and 1920-borns). The resulting coefficient estimates in Table

8 are only slightly higher than our benchmark estimate in Table 3.

Including the birth cohort 1906-1910. We also include an earlier birth cohort in our regression sample to better control for possible trend differences in outcomes. This makes no change our benchmark results in Table 3, see Table 8.21

7. Conclusions

The Gallipoli War was a major confrontation between the Allied Forces and the Ottoman Empire during World War I, and took place 100 years ago on a narrow geographical strip on the northwestern tip of contemporary Turkey. The embattled powers lost a total of approximately

140,000 soldiers in the course of the war. The Ottoman Empire alone lost 87,000 soldiers, with its total casualties standing at around 250,000. An important point that we have observed in connection with the Ottoman losses was that the majority of the death toll was borne by the western provinces of Anatolia.

This paper investigates the impact of the Gallipoli Campaign on children who lived in

Anatolia and were aged under five in 1915. Combining the Turkish census waves of 1985, 1990 and 2000 with detailed military records that provide person-level detail of 48,148 Anatolian soldiers who were killed at Gallipoli, we exploit the geographic variations in the death toll, as an indication of the severity of the war exposure, to explain the differences between the

21 In unreported regressions, we also checked the sensitivity of our results to alternative provincial boundaries in the census data, such as using five smaller provinces that had been split from larger provinces and that have the soldier mortality data available (i.e., Aksaray, Bartın, Bayburt, Karaman, Kırıkkale), and our results remained quite similar.

26 socioeconomic outcomes of children who were exposed to the war and those of subsequent birth cohorts who were affected less severely by the conflict. In spite of the major significance of the

Gallipoli War for modern human history, there has been little systematic inquiry into its consequences, or its impact on civilian victims.

There is reasonably reliable evidence that the disappearance of thousands of labor force- aged males in the agrarian Anatolian economy in a short space of time adversely affected the subsequent lives of young children. Our empirical estimates document that, for each additional soldier killed per 1,000 people in the population, those born between 1911 and 1915 were 0.87% more likely to be illiterate than those born between 1916 and 1920. At the sample average of soldier mortality, this effect corresponds to 2.8% higher probability of being illiterate.

Considering the literacy rate for the whole sample of 35%, these estimates suggest that the war explains an important proportion of this illiteracy for the cohort in question. Similarly, at the average soldier mortality rate, children born between 1911 and 1915 on average attained 0.2 lower years of schooling compared to the control cohort, which is again a sizeable effect given that the average number of years of schooling in the sample is 1.55. Moreover, we find that, for a given level of war exposure, the adverse impact of the war is relatively higher for elder children in the sample. This finding suggests that compulsion to work as child labor following the war was probably the dominant reason for the adverse effects of the disaster. Further, we find that at the sample average of soldier mortality rate, the treatment cohort is 0.6% more likely to be disabled in the census year. Critically, this disability effect is the strongest for the 1915-born cohort, demonstrating that the adverse health impact of the war is observed on those who were newborns or in utero during the war year.

27 There is also evidence to suggest that some consequences of the war, such as lower probability of literacy and schooling were born relatively comparably by both male and female children. However, the disability effect is driven primarily by the male cohort. Finally, we find that there is significant nonlinearity in the effects of soldier mortality rate in that the higher the soldier mortality in a province, the more intense is the shock conceived by children. In provinces where soldier mortality rate is beyond the 66th percentile of the sample, the war effect corresponds to more than 4.6% decline in literacy rate for every additional soldier killed (in every 1,000 people).

It is noteworthy to emphasize that our analysis focuses on children in the whole geographic space of Anatolia and not on the victims in the combat zone, and therefore, our results cannot be explained by the destruction of schools, hospitals and other public properties or chemical warfare in the war theater, but rather by the extent of resource mobilization from each province. It is also important to mention that the effects we found are likely to be the bottom line of the true effects of the Gallipoli Campaign, given the various potential empirical biases that could push our estimates downwards. Nonetheless, the results are robust to controlling for other major shocks that occurred within the sample (such as Armenian deportation in 1915) or in close temporal proximity to the sample period (Independence War 1921-22, and Greek-Turkish population exchange 1923), comparing children only within a given birth province (i.e., birth- province fixed effects), as well as placebo tests, and alternative definitions of the treatment.

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Bank Policy Research Working Paper, No. 6418, April.

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Table 1: Summary Statistics Mean Standard Minimum Maximum #Non-missing Deviation Observations Provincial Soldier Mortality (per 1000 people) 3.18 2.55 0.00 8.58 122649 Age <24 Soldier Mortality (per 1000 people) 0.58 0.45 0.00 1.42 122649 Age 24-29 Soldier Mortality (per 1000 people) 0.91 0.74 0.00 2.51 122649 Age 30-34 Soldier Mortality (per 1000 people) 0.74 0.63 0.00 2.60 122649 Age 35+ Soldier Mortality (per 1000 people) 0.55 0.48 0.00 1.95 122649 Total Population in Province in 1914 391690 361509 42352 1617840 122649 Muslim Population in Province in 1914 307786 232445 36122 1057352 122649 Greek Share in Province in 1914 0.07 0.07 0.00 0.38 122649 Armenian Share in Province in 1914 0.08 0.09 0.00 0.36 122649 Non-Muslim Share in Province in 1914 0.17 0.12 0.01 0.44 122649 Provincial Distance to Gallipoli (in km) 857.64 443.94 51.00 1877.00 122649 Treatment Dummy 0.46 0.50 0.00 1.00 122649 1911 Born Dummy 0.06 0.23 0.00 1.00 122649 1912 Born Dummy 0.07 0.26 0.00 1.00 122649 1913 Born Dummy 0.07 0.26 0.00 1.00 122649 1914 Born Dummy 0.08 0.27 0.00 1.00 122649 1915 Born Dummy 0.18 0.38 0.00 1.00 122649 Birth Year 1916.33 2.94 1911.00 1920.00 122649 Literate Dummy 0.35 0.48 0.00 1.00 122613 Primary and Higher Completed Dummy 0.24 0.43 0.00 1.00 122599 Secondary and Higher Completed Dummy 0.04 0.19 0.00 1.00 122599 Years of Schooling 1.55 3.12 0.00 15.00 122599 Rooms per Household 1.09 0.81 0.00 10.00 70699 Disabled Dummy 0.06 0.24 0.00 1.00 78456 Male Dummy 0.45 0.50 0.00 1.00 122649 Dummy of Census Round 1985 0.50 0.50 0.00 1.00 122649 Dummy of Census Round 1990 0.36 0.48 0.00 1.00 122649 Dummy of Census Round 2000 0.14 0.35 0.00 1.00 122649 Provincial distance to Gallipoli measures the driving distance from each provincial center to the district of Gelibolu (i.e., Gallipoli) as of 2015. Disabled dummy is the answer to the question: “do you currently have any physical or mental disability? Yes or No?”

Table 2: Pairwise Correlations among Soldier Mortality Rates by Provincial Residence

Soldier Mortality Rate in Soldier Mortality Rate in Soldier Mortality Rate the Gallipoli Campaign World War I (excluding during Independence 1915 the Gallipoli Campaign) War (1921-22) Soldier Mortality Rate in the Gallipoli Campaign 1.000 1915 Soldier Mortality Rate in World War I 0.158 1.000 (excluding the Gallipoli Campaign) Soldier Mortality Rate during Independence War 0.462 0.609 1.000 1921-22

35

Figure 1: Literacy and Years of Schooling By Birth Year Figure 2: Distribution of the Number of Soldiers Killed by Province of Residence 52.86

2 50

40 1.5

30 1 22.86 Percent 20

.5 11.43 10

4.286 4.286 2.857 0 1.429 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 0 Literacy Years of Schooling 0 500 1000 1500 2000 2500 3000 3500 4000 Number of Turkish Soldiers Killed Figure 3: Distribution of Soldier Mortality Rate by Figure 4: Total Number of Soldiers Killed by Age-Groups Province of Residence

0.4 15,000

0.3

10,000

0.2 Density

5,000 0.1

0 0 0 2 4 6 8 25-29 30-34 20-24 35-39 Unknown 40< < 20

The ANZAC Landing at Gallipoli Gallipoli War Sites

Source: Inside History Magazine, November 2013 Source: Revision World Networks Ltd.

36

Table 3: Gallipoli Campaign and Children Under Five: Coefficient on Treatment Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE

Literacy -0.867*** -0.492*** -0.508*** -0.503*** (4.37) (3.54) (3.77) (4.56)

Primary and Higher Completed -0.763*** -0.459*** -0.456*** -0.445*** (3.97) (3.48) (3.45) (4.17)

Secondary and Higher Completed -0.136* -0.0768 -0.0637 -0.050 (1.69) (1.35) (0.76) (0.90)

Years of Schooling -0.051*** -0.030*** -0.029** -0.027*** (3.19) (2.90) (2.31) (2.96)

Rooms per Household -0.007** -0.002 -0.003 -0.003 (2.37) (0.93) (1.16) (1.25)

Disabled 0.190** 0.153** 0.162** 0.163** (2.61) (2.12) (2.06) (2.36) Each cell reports the coefficient on Treatment Dummy*Soldier Mortality Rate in equation (1). Treatment group consists of people born between 1911 and 1915 while control group consists of those born between 1916 and 1920. Literacy, Primary and Higher Completed, Secondary and Higher Completed and Disabled are all multiplied by 100 in the regression to interpret a percentage-based interpretation of the coefficient estimates. Soldier mortality rate per 1000 people at the provincial level. All columns include birth-year fixed effects (dummies for each birth year), male dummy, birth year and birth year-squared (continuous variables) and census round dummies. Robust standard errors are clustered at birth-province levels in columns (1) and (2), at current province level in column (3), and at current district level in column (4). Absolute t-statistics in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

37

Table 4: Gallipoli Campaign and Children Under Five: Coefficient on Birth-Year Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE Literacy 1911 Born*Mortality Rate -1.537***(4.87) -0.951***(4.39) -0.918***(3.03) -0.879***(4.11) 1912 Born* Mortality Rate -1.254***(3.48) -0.626**(2.49) -0.589**(2.57) -0.590***(3.02) 1913 Born* Mortality Rate -1.087***(3.40) -0.426*(1.91) -0.465**(2.26) -0.482**(2.42) 1914 Born* Mortality Rate -1.262***(3.91) -0.756***(3.04) -0.702***(2.75) -0.676***(3.14) 1915 Born* Mortality Rate -0.213(1.35) -0.199(1.53) -0.274**(2.06) -0.277**(2.00) Primary and Higher Completed 1911 Born*Mortality Rate -1.342***(4.24) -0.830***(3.88) -0.772***(3.03) -0.723***(3.82) 1912 Born* Mortality Rate -1.158***(3.64) -0.632***(2.96) -0.571***(2.91) -0.567***(3.21) 1913 Born* Mortality Rate -1.023***(3.52) -0.468**(2.54) -0.485***(3.48) -0.505***(2.94) 1914 Born* Mortality Rate -1.015***(3.71) -0.604***(2.93) -0.536**(2.42) -0.507***(3.24) 1915 Born* Mortality Rate -0.185(1.28) -0.201(1.51) -0.260*(1.97) -0.252*(1.88) Secondary and Higher Completed 1911 Born*Mortality Rate -0.292**(2.43) -0.180**(2.25) -0.146(1.44) -0.118(1.56) 1912 Born* Mortality Rate -0.234*(1.90) -0.120(1.43) -0.080(0.90) -0.067(1.07) 1913 Born* Mortality Rate -0.170(1.42) -0.064(0.76) -0.046(0.56) -0.041(0.47) 1914 Born* Mortality Rate -0.212**(2.05) -0.132*(1.72) -0.086(0.69) -0.066(0.84) 1915 Born* Mortality Rate 0.003(0.05) -0.007(0.11) -0.028(0.35) -0.018(0.32) Years of Schooling 1911 Born*Mortality Rate -0.093***(3.72) -0.057***(3.66) -0.051**(2.56) -0.046***(3.31) 1912 Born* Mortality Rate -0.081***(3.16) -0.044***(2.82) -0.037**(2.31) -0.035***(2.90) 1913 Born* Mortality Rate -0.067***(2.80) -0.029*(1.99) -0.029**(2.25) -0.029**(2.00) 1914 Born* Mortality Rate -0.072***(3.36) -0.044***(2.89) -0.036*(1.86) -0.032**(2.74) 1915 Born* Mortality Rate -0.009(0.76) -0.011(0.97) -0.015(1.28) -0.014(1.35) Rooms per Household 1911 Born*Mortality Rate -0.018***(2.76) -0.009*(1.74) -0.01*(1.73) -0.010*(1.92) 1912 Born* Mortality Rate -0.016***(3.23) -0.007**(2.05) -0.008**(2.04) -0.007(1.56) 1913 Born* Mortality Rate -0.018***(2.96) -0.010*(1.96) -0.01**(2.12) -0.011**(2.15) 1914 Born* Mortality Rate -0.013**(2.41) -0.006(1.17) -0.006(1.28) -0.006(1.28) 1915 Born* Mortality Rate 0.006**(2.05) 0.006**(2.19) 0.005(1.29) 0.005(1.57) Disabled 1911 Born*Mortality Rate 0.180(1.63) 0.118(1.06) 0.136(1.14) 0.130(0.96) 1912 Born* Mortality Rate 0.087(0.74) 0.029(0.24) 0.041(0.33) 0.050(0.38) 1913 Born* Mortality Rate 0.215(1.65) 0.148(1.15) 0.166(1.14) 0.174(1.28) 1914 Born* Mortality Rate 0.203(1.48) 0.158(1.18) 0.168(1.13) 0.157(1.15) 1915 Born* Mortality Rate 0.214**(2.04) 0.206*(1.97) 0.207*(1.99) 0.211**(2.11) Each cell reports the coefficient on Year Born*Soldier Mortality Rate in equation (2). Treatment group consists of people born between 1911 and 1915 while control group consists of those born between 1916 and 1920. Literacy, Primary and Higher Completed, Secondary and Higher Completed and Disabled are all multiplied by 100 in the regression to interpret a percentage-based interpretation of the coefficient estimates. Soldier mortality rate per 1000 people at the provincial level. All columns include birth-year fixed effects (dummies for each birth year), male dummy, birth year and birth year-squared (continuous variables) and census round dummies. Robust standard errors are clustered at birth-province levels in columns (1) and (2), at current province level in column (3), and at current district level in column (4). Absolute t-statistics in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

38

Table 5: Placebo Test: Coefficient on False Treatment Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE

Literacy -0.003 -0.010 0.058 0.049 (0.02) (0.08) (0.46) (0.53)

Primary and Higher Completed 0.086 0.092 0.162 0.153* (0.64) (0.70) (1.37) (1.68)

Secondary and Higher Completed 0.019 0.023 0.055 0.052 (0.43) (0.54) (1.16) (1.19)

Years of Schooling 0.006 0.007 0.014 0.013** (0.69) (0.81) (1.66) (1.97)

Rooms per Household 0.002 0.002 0.002 0.002 (0.70) (0.55) (0.54) (0.65)

Disabled 0.048 0.055 0.055 0.052 (0.80) (0.91) (1.02) (1.02) Each cell reports the coefficient on Treatment Dummy*Soldier Mortality Rate in equation (1). Treatment group consists of people born between 1921 and 1925 while control group consists of those born between 1916 and 1920. Literacy, Primary and Higher Completed, Secondary and Higher Completed and Disabled are all multiplied by 100 in the regression to interpret a percentage-based interpretation of the coefficient estimates. Soldier mortality rate per 1000 people at the provincial level. All columns include birth-year fixed effects (dummies for each birth year), male dummy, birth year and birth year-squared (continuous variables) and census round dummies. Robust standard errors are clustered at birth-province levels in columns (1) and (2), at current province level in column (3), and at current district level in column (4). Absolute t-statistics in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

39

Table 6: Gender Subsample Regressions: Coefficient on Treatment Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE Panel A: Male Sample

Literacy -1.145*** -0.624*** -0.615*** -0.503*** (4.06) (3.11) (2.79) (4.56)

Primary and Higher Completed -1.032*** -0.605*** -0.551*** -0.445*** (3.59) (2.95) (2.92) (4.17)

Secondary and Higher Completed -0.212 -0.105 -0.064 -0.050 (1.50) (1.06) (0.57) (0.90)

Years of Schooling -0.071*** -0.040** -0.033** -0.027*** (2.87) (2.43) (2.12) (2.96)

Rooms per Household -0.006 -0.000 -0.002 -0.003 (1.26) (0.09) (0.41) (1.25)

Disabled 0.247** 0.175* 0.186* 0.163** (2.22) (1.65) (1.92) (2.36) Panel B: Female Sample

Literacy -0.644*** -0.415*** -0.444*** -0.429*** (3.44) (2.75) (3.08) (3.11)

Primary and Higher Completed -0.549*** -0.367*** -0.385** -0.369*** (3.42) (2.82) (2.60) (2.89)

Secondary and Higher Completed -0.081* -0.052 -0.052 -0.045 (1.96) (1.65) (0.81) (0.99)

Years of Schooling -0.035*** -0.024*** -0.024** -0.023** (3.24) (2.88) (2.06) (2.47)

Rooms per Household -0.008** -0.004 -0.005 -0.004 (2.16) (1.00) (1.00) (1.19)

Disabled 0.137 0.119 0.119 0.117 (1.36) (1.19) (1.15) (1.29) Each cell reports the coefficient on Treatment Dummy*Soldier Mortality Rate in equation (1). Treatment group consists of people born between 1911 and 1915 while control group consists of those born between 1916 and 1920. Literacy, Primary and Higher Completed, Secondary and Higher Completed and Disabled are all multiplied by 100 in the regression to interpret a percentage-based interpretation of the coefficient estimates. Soldier mortality rate per 1000 people at the provincial level. All columns include birth-year fixed effects (dummies for each birth year), birth year and birth year-squared (continuous variables) and census round dummies. Robust standard errors are clustered at birth-province levels in columns (1) and (2), at current province level in column (3), and at current district level in column (4). Absolute t-statistics in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

40

Table 7: Addressing Measurement Error in Soldier Mortality Rate with Provincial Distance to Gallipoli as Instrument (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE Panel A: Reduced Form: Coefficient on Treatment dummy*Distance to Gallipoli

Literacy 0.00550*** 0.00463*** 0.00439*** 0.00375*** (5.08) (5.08) (5.50) (5.30)

Primary and Higher Completed 0.00510*** 0.00460*** 0.00428*** 0.00353*** (6.88) (5.69) (5.50) (5.98)

Secondary and Higher Completed 0.00182*** 0.00183*** 0.00164*** 0.00125*** (3.37) (2.79) (2.73) (3.31)

Years of Schooling 0.000413*** 0.000389*** 0.000355*** 0.000281*** (6.22) (4.51) (4.34) (5.73)

Rooms per Household 0.0000377*** 0.0000279** 0.0000281 0.0000205 (2.77) (2.44) (1.56) (1.55)

Disabled -0.000114 -0.0000121 -0.0000490 -0.0000796 (0.27) (0.03) (0.10) (0.20) Panel B: Second-Stage: Coefficient on Treatment dummy*Soldier Mortality Rate

Literacy -1.446*** -1.230*** -1.167*** -0.995*** (4.73) (4.14) (4.21) (4.69)

Primary and Higher Completed -1.340*** -1.221*** -1.139*** -0.936*** (4.82) (3.83) (3.85) (5.31)

Secondary and Higher Completed -0.477** -0.486** -0.435** -0.331*** (2.47) (2.14) (2.20) (3.14)

Years of Schooling -0.108*** -0.103*** -0.0944*** -0.0746*** (3.82) (3.04) (3.15) (5.05)

Rooms per Household -0.00996*** -0.00743** -0.00752* -0.00548 (2.70) (2.35) (1.66) (1.56)

Disabled N.A. N.A. N.A. N.A.

Notes: Treatment group consists of people born between 1911 and 1915 while control group consists of those born between 1916 and 1920. Literacy, Primary and Higher Completed, Secondary and Higher Completed and Disabled are all multiplied by 100 in the regression to interpret a percentage-based interpretation of the coefficient estimates. Soldier mortality rate per 1000 people at the provincial level. All columns include birth- year fixed effects (dummies for each birth year), male dummy, birth year and birth year-squared (continuous variables) and census round dummies. N.A.: The IV coefficient could not be estimated, possibly due to small percentage of disabled people in the sample. Robust standard errors are clustered at birth-province levels in columns (1) and (2), at current province level in column (3), and at current district level in column (4). Absolute t-statistics in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

41

Table 8: Robustness Tests for Equation (1): Coefficient on Treatment Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current ↓ Rate Province FE Province FE District FE Literacy 1. Controlling for Treatment Dummy*Armenian -0.644***(4.17) -0.485***(3.60) -0.470***(3.71) -0.460***(4.20) Share in Province in 1914 2. Using Soldier Mortality Rate (per 1000 Muslims) -0.855***(4.68) -0.518***(3.82) -0.531***(4.03) -0.500***(4.65) 3. Controlling for Treatment Dummy*Soldier -0.846***(3.43) -0.449***(2.78) -0.477***(2.85) -0.441***(3.56) Mortality Rate in Independence War (1921-22) 4. Controlling for Treatment Dummy*Greek Share in -0.557**(2.27) -0.266(1.53) -0.298**(2.02) -0.311**(2.35) Province in 1914 5. Controlling for Treatment Dummy*Share of -0.634***(4.03) -0.400***(3.11) -0.393***(3.27) -0.412***(3.98) Citizens in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Non-Muslim -1.017***(5.09) -0.635***(3.73) -0.646***(3.98) -0.592***(4.60) Share in Province in 1914 7. Non-linearity in Soldier Mortality at Gallipoli: Treatment*Medium Intensity -4.092***(3.25) -3.816***(3.76) -3.751***(3.93) -3.129***(3.93) Treatment*High Intensity -4.589***(4.96) -3.150***(3.92) -3.067***(4.08) -2.966***(4.77) 8. Excluding Cohorts with Ages Ending with 0 and 5 -1.004***(3.60) -0.569***(2.69) -0.567***(3.08) -0.550***(3.57) 9. Including the 1906-1910 Birth Cohort into the -0.867***(4.37) -0.492***(3.54) -0.508***(3.77) -0.503***(4.56) Control Group

Years of Schooling 1.Controlling for Treatment Dummy*Armenian Share -0.036***(3.63) -0.029***(3.30) -0.026**(2.34) -0.023***(2.70) in Province in 1914 2. Using Soldier Mortality Rate (per 1000 Muslims) -0.056***(3.42) -0.038***(3.28) -0.037***(2.78) -0.032***(3.85) 3. Controlling for Treatment Dummy* Soldier -0.056**(2.51) -0.034**(2.56) -0.034**(2.30) -0.029***(3.07) Mortality Rate in Independence War (1921-22) 4. Controlling for Treatment Dummy*Greek Share in -0.035*(1.70) -0.019(1.31) -0.020(1.46) -0.019*(1.89) Province in 1914 5. Controlling for Treatment Dummy*Share of -0.0326***(2.93) -0.0201**(2.29) -0.0172*(1.78) -0.0179**(2.22) Citizens in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Non-Muslim -0.069***(4.11) -0.049***(3.69) -0.047***(3.30) -0.040***(4.14) Share in Province in 1914 7. Non-linearity in Soldier Mortality at Gallipoli: Treatment*Medium Intensity -0.282**(2.51) -0.301**(2.61) -0.289***(3.13) -0.212***(3.76) Treatment*High Intensity -0.243***(3.75) -0.180***(3.17) -0.159**(2.46) -0.142***(2.88) 8. Excluding Cohorts with Ages Ending with 0 and 5 -0.056**(2.62) -0.031**(2.33) -0.029*(1.82) -0.026**(2.25) 9. Including the 1906-1910 Birth Cohorts into the -0.051***(3.19) -0.030***(2.90) -0.029**(2.31) -0.027***(2.96) Control Group

Disabled 1.Controlling for Treatment Dummy*Armenian Share 0.180**(2.51) 0.154**(2.17) 0.161**(2.10) 0.163**(2.38) in Province in 1914 2. Using Soldier Mortality Rate (per 1000 Muslims) 0.157**(2.31) 0.123*(1.85) 0.131*(1.78) 0.133**(2.11) 3.Controlling for Treatment Dummy* Soldier 0.159**(2.01) 0.123(1.55) 0.131(1.41) 0.133*(1.75) Mortality Rate in Independence War (1921-22) 4. Controlling for Treatment Dummy*Greek Share in 0.208**(2.46) 0.175**(2.07) 0.177*(1.98) 0.180**(2.21) Province in 1914 5. Controlling for Treatment Dummy*Share of 0.198***(2.87) 0.168**(2.43) 0.176**(2.32) 0.178**(2.59) Citizens in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Non-Muslim 0.175**(2.30) 0.136*(1.82) 0.143*(1.70) 0.145**(1.97) Share in Province in 1914 7. Non-linearity in Soldier Mortality at Gallipoli: Treatment*Medium Intensity 0.537(1.37) 0.405(1.02) 0.430(1.09) 0.432(1.05) Treatment*High Intensity 1.044**(2.23) 0.843*(1.79) 0.886*(1.78) 0.890**(2.09) 8. Excluding Cohorts with Ages Ending with 0 and 5 0.235***(2.74) 0.188**(2.15) 0.205**(2.11) 0.205**(2.28) 9. Including the 1906-1910 Birth Cohorts into the 0.190**(2.61) 0.153**(2.12) 0.162**(2.06) 0.163**(2.36) Control Group

Each cell reports the coefficient on Treatment Dummy*Soldier Mortality Rate in equation (1), except row 7. In rows 1, 3, 4, 5 and 6, controlling for a variable means that, that variable is included in equation (1) as an additional control. In row 2, soldier mortality rate is computed as proportion in Muslim population in province, rather than in total population. In row 7, the specification is revised to estimate the coefficients of Treatment Dummy*Medium Intensity Soldier Mortality Rate and Treatment Dummy*High Intensity Soldier Mortality Rate, where medium and high intensities are defined with respect to 33rd and 66th percentiles of the provincial soldier mortality rate. In row 8, the sample excludes the 1915 and 1920-borns, while row 9 includes the 1906-10 birth cohort into the control group. All other table notes underneath Table 3 apply here as well. 42

ONLINE APPENDIX Table A1: Robustness Tests for Other Outcome Variables in Equation (1) Coefficient on Treatment Dummy*Soldier Mortality Rate (1) (2) (3) (4) Outcome Control for Control for Control for Control for Mortality Birth Current Current Rate Province Province District ↓ FE FE FE Primary and Higher Completed

1. Controlling for Treatment Dummy*Armenian Share -0.573***(4.38) -0.450***(3.89) -0.419***(3.63) -0.401***(3.90) in Province in 1914 2. Using Soldier Mortality Rate (per 1000 Muslims) -0.778***(4.35) -0.508***(3.93) -0.505***(3.78) -0.464***(4.75) 3. Controlling for Treatment Dummy* Soldier Mortality -0.770***(3.16) -0.449***(3.01) -0.459***(2.92) -0.415***(3.79) Rate in Independence War (1921-22) 4. Controlling for Treatment Dummy*Greek Share in -0.519**(2.19) -0.284*(1.71) -0.299**(2.08) -0.302**(2.45) Province in 1914 5. Controlling for Treatment Dummy*Share of Citizens -0.550***(3.76) -0.359***(2.99) -0.335***(3.14) -0.351***(3.56) in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Non-Muslim Share -0.947***(5.08) -0.644***(4.23) -0.637***(4.13) -0.570***(4.96) in Province in 1914 7. Non-linearity in Soldier Mortality at Gallipoli Treatment*Medium Intensity -3.574***(2.89) -3.624***(3.22) -3.528***(3.69) -2.788***(4.07) Treatment*High Intensity -3.777***(4.26) -2.714***(3.49) -2.541***(3.56) -2.405***(3.93) 8. Excluding Cohorts with Ages Ending with 0 and 5 -0.781***(3.22) -0.434**(2.63) -0.419**(2.54) -0.401***(3.13) 9. Including the 1906-1910 Birth Cohorts into the -0.763***(3.97) -0.459***(3.48) -0.456***(3.45) -0.445***(4.17) Control Group

Secondary and Higher Completed

1. Controlling for Treatment Dummy*Armenian Share -0.075(1.46) -0.073(1.44) -0.049(0.65) -0.034(0.65) in Province in 1914 2. Using Soldier Mortality Rate (per 1000 Muslims) -0.192**(2.01) -0.141*(1.95) -0.129(1.44) -0.102*(1.93) 3. Controlling for Treatment Dummy* Soldier Mortality -0.203(1.65) -0.132*(1.70) -0.125(1.32) -0.098(1.64) Rate in Independence War (1921-22) 4. Controlling for Treatment Dummy*Greek Share in -0.101(0.92) -0.063(0.71) -0.055(0.58) -0.041(0.69) Province in 1914 5. Controlling for Treatment Dummy*Share of Citizens -0.0483(0.90) -0.0183(0.40) 0.00120(0.02) 0.00238(0.05) in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Non-Muslim Share -0.236**(2.42) -0.186**(2.32) -0.169*(1.88) -0.130**(2.12) in Province in 1914 7. Non-linearity in Soldier Mortality at Gallipoli Treatment*Medium Intensity -1.182*(1.68) -1.369*(1.75) -1.288**(2.12) -0.851**(2.14) Treatment*High Intensity -0.596*(1.92) -0.505*(1.72) -0.363(0.80) -0.253(0.83) 8. Excluding Cohorts with Ages Ending with 0 and 5 -0.175(1.48) -0.097(1.23) -0.081(0.81) -0.058(0.82) 9. Including the 1906-1910 Birth Cohorts into the -0.136*(1.69) -0.077(1.35) -0.064(0.76) -0.050(0.90) Control Group

Rooms per Household

1.Controlling for Treatment Dummy*Armenian Share -0.006**(2.00) -0.002(0.93) -0.003(1.02) -0.003(1.08) 2. Soldier Mortality Rate (per 1000 Muslims) -0.007**(2.65) -0.003(1.36) -0.004(1.44) -0.003(1.48) 3.Controlling for Treatment Dummy*Mortality Rate in -0.007**(2.28) -0.003(1.07) -0.004(1.38) -0.003(1.33) Independence War 4. Controlling for Treatment Dummy*Greek Share -0.005(1.40) -0.002(0.77) -0.003(1.18) -0.003(1.20) 5. Controlling for Treatment Dummy*Share of Citizens -0.00600**(2.03) -0.00161(0.63) -0.00261(0.91) -0.00254(1.04) in Province Born Abroad before 1923 6. Controlling for Treatment Dummy*Minority Share -0.009***(3.09) -0.004*(1.74) -0.005*(1.81) -0.004*(1.81) 7. Non-linearity in Soldier Mortality Treatment*Medium Intensity -0.025(1.51) -0.013(1.05) -0.011(0.74) -0.003(0.19) Treatment*High Intensity -0.036*(1.75) -0.011(0.65) -0.015(0.73) -0.013(0.81) 8. Excluding Cohorts with Ages Ending with 0 and 5 -0.010**(2.53) -0.005(1.42) -0.006*(1.68) -0.005*(1.68) 9. Including the 1906-1910 Birth Cohorts into the -0.007**(2.37) -0.002(0.93) -0.003(1.16) -0.003(1.25) Control Group

See the notes to Table 8. 43