Human Capital Development in the Middle East: Evidence from Ottoman Turkey, Syria, Palestine and Iraq the 18 th and 19 th century

Human Capital Development in the Middle East: Evidence from Ottoman Turkey, Syria, Palestine and Iraq the 18 th and 19 th century Rima Ghanem University of Tuebingen rimaghanem@hotmail.com INTRODUCTION There is a wide range of studies regarding the history of the Middle East. However, there is an evident lack of applied studies of the economic history of this area, and thus, more studies are needed to build a better understanding of the human capital development of this region. Nevertheless, the researchers of the Economic History of the Middle East are soon faced with the difficulty of data scarcity. Unfortunately, it can be particularly challenging to find the required data concerning this region for different reasons. In this paper, I will present a large dataset which is taken from documents stored in the Ottoman Archives in Istanbul, Turkey. These data will be analyzed for the purpose of getting a better perspective about the economic history of the Middle East during the 18 th and the 19 th century. This paper will apply the age heaping methodology which relies on the ability of people to declare their exact ages without rounding them to numbers ending with zero or five. A measurement that calculates the share of people who are able to state their age correctly is the ABCC index which reflects the estimated share of people who have minimum numerical skills in the sample. This index will be calculated for a sample of individuals that were living in the Middle East during the 19 th century. 1

The data used in this research stem from censuses that were carried out during different years in the 19th century in the Ottoman Empire. The sample covers several provinces in today s Turkey, Iraq, Syria and Palestine. It contains age statements of males who were living in 20 different places in the Ottoman Empire and were aged between 23 and 72 years old during the first half of the 19 th century. Around eight birth decades are covered beginning from the mid-18th century, the individuals are divided according to their religions and place of residence. Thus, the data allow us to calculate the different ABCC index values for the people according to their birth decades, place of residence, province and religion. Did the Muslims have better numeracy than the Christians and Jews? Were there a significant difference between the today s Arab regions and the Turkish ones relating to human capital? How big was the effect of living in a large city on the human capital of Ottoman regions in the 19 th century? And were there a significant influence of being a city located on a trade road on the human capital of this city? By applying the age heaping methodology, and using the mentioned dataset as well as including other variables containing information on the distance from a caravan route and the city size in addition to the geographical, religion and time variables, the regressions in this paper will try to answer the former questions in order to form a better understanding regarding the human capital development of the Middle East in the 19 th century. THE AGE HEAPING METHODOLOGY In order to measure the development of the human capital of a country, one can use a variety of indicators that cover a period of time. Indicators such as, the educational level, the investment in the country s education system, the literacy rates and some other measurements that indicate the human capital of the country can all be utilized. However, 2

the problem that can face the researchers in this domain, is the apparent lack of data when it comes to a historical period of time in which needed data might not even exist in order to calculate such indicators especially for ancient times. For this reason, the age heaping methodology was introduced to solve this problem partially. By relying on only age records, one can form an indicator of the human capital of a country. The age heaping is the phenomenon of rounding the people s ages to beloved number such as multiples of five. When the examined individuals lack sufficient education and good level of numeracy skills, they usually find it hard to calculate their exact ages accurately, therefore, they tend to round their ages to numbers ending with zero or five. In some countries people even tend to choose other numbers than zero or five, motivated by particular value within their cultures. Since the Mid-twentieth century, researchers managed to prove the age heaping phenomenon around the world. For example, through the results of the 1901 census of Ireland (Budd and Guinnane, 1991), in the 1950 census of the United States (Myers, 1954), and in different countries of Latin America during the former three centuries (Manzel et al, 2012). The researchers showed as well a correlation between the age heaping and the literacy rates. And thus ever since, this method has been effectively used to indicate the numeracy level of the people in a specific period as a tool of measurement of the human capital. THE WHIPPLE INDEX AND THE ABCC INDEX One of the most accurate measurements of the age heaping is the Whipple index (A Hearn, Baten and Crayen, 2009). By calculating the ratio between the number of rounded ages in a sample to the one fifth of the total number of stated ages, this index results in a number between 100 and 500. The index is calculated by including the age statements of the people 3

aged 23 to 72 years. The 500 result of the Whipple index indicates the lowest numeracy level when all the population state their ages as multiples of five. However, when the Whipple index is equal to 100, which means that all of the individuals were able to calculate their ages accurately and they have the basic numeracy level that enables them to calculate their ages. (age 25 + age 30 + + age 70) (1) Wh = [ 1 5 (age 23 + age 24 + age 25 + + age 72) ] 100 In their 2009 research, A Hearn, Baten and Crayen introduced a linear transformation of the Whipple index to give a simple interpretation of the age heaping. The new index was named after the initials of their names in addition to Greg Clack s who suggested this name. The ABCC index is used in this paper to measure the numeracy levels of different regions in the Ottoman Empire. This index shows the ratio of the individuals who were able to state their ages accurately. (Wh 100) (2) ABCC = [1 ] 100 if Wh 100 ; else ABCC = 100 400 An ideal value of the Whipple index with 100 will lead to the ideal ABCC value of 100, all the sample individuals could calculate their ages without rounding them and they have the best numeracy level. The other way around, when the Whipple index value is 500, the ABCC value will be zero showing that all of the population stated ages end with zero or five. The ABCC index shows us not only the average numeracy level of a country, but it can also be calculated for the different age groups in the sample enabling us to differentiate between their numeracy levels. In this paper the sample is divided into five age groups with ten years for each, and two rounded ages. The five age groups are: 23-32, 33-42, 43-52, 53-62 and 63-4

72. The birth decades are also calculated using the age groups and subtracting them from the census year. DATA DESCRIPTION The Ottoman Archives in Istanbul keep a treasure of documents covering not only Turkey, but also the former Ottoman states in the Middle East and Eastern Europe. The data in this research was collected at these archives and they stem mainly from thirty five different records. These records were parts of around fourteen censuses carried out in the different regions of the Ottoman Empire between 1830 and 1851. Those censuses included only the male inhabitants of the surveyed areas. The names of the male people, their ages and some other physical characteristics such as tall, short or facial beards were surveyed and registered in Ottoman language, which used to be the official language of the Ottoman Empire, using the Arabic alphabet around a century before the reforms applied on the Turkish language. Using census data enables to get rid of the potential selectivity problem of the dataset. Thus, the collected sample is supposed to be representative to the male population in the region. The area covered in this dataset is now located in today s Tukey, Iraq, Syria and Palestine. During the nineteenth century, these countries were states of the Ottoman Empire and their borders were overlapped. Back then they were called Eyalets and they were divided into Sanjaks, the sanjaks themselves were, in turn, divided into Kazas and the kazas into villages. The Data were collected from nine eyalets: Aydin, Bolu, Haleb, Istanbul, Marash, Mosul, Sham and Sayda. Around twenty kazas are included in these eyalets. In this paper the collected data is restructured geographically according to today s borders and countries locations and they were included into nine regions: five in Turkey and the other four in the 5

Arabic countries. The Turkish regions are divided into: Istanbul and Kocaeli located in northwestern Turkey in Marmara region. Aydin is in the west Turkey located in the Aegean region. Marash which is called nowadays Kahramanmarash and is located in the Mediterranean region in the south of Turkey. In the south there is also Diyarbekir which is now a province in the Southeastern Anatolia region. The Arabic regions are: Aleppo, which used to be an eyalet during the Ottoman Reign covering mainly the northwestern part of today s Syria Aleppo city and Edleb in addition to Iskenderun which became a part of Tukey since 1939 and Ayintab which is called today Gaziantep in the south of Turkey. The second Syrian region is Damascus which used to be called Sham and it was an eyalet as well and it used to cover a large part of middle and southern region of today s Syria, some parts of Jordan, Lebanon and Palestine. The fourth Arabic region is North Palestine covering today s Acre, Algalil and Nablus. In the first half of the nineteenth century, this area was a part of Sayda eyalet which used to be located in some parts of today s Lebanon and Palestine. The last region is Mosul, which was an eyalet located in northern Iraq. DATA ANALYSIS The censuses that include the collected sample were taken throughout thirteen different years between 1830 and 1851. Covering these twenty one years, this dataset enables us to calculate according to the five mentioned age groups, seven birth decades after subtracting each age group from its census year. These birth decades are the ones between 1760 and 1820 and the people who were born during those decades were between 23 and 72 years old between 1830 and 1851. The census records in the Ottoman Archives in Istanbul are organized not only according to their census year and geographical location, but also according to the religion of the 6

registered people. The Muslims were included in their own records. While other religions 1 were included together in registers under the classification of Reaya Defteri which meant the registers that used to refer to the non-muslims who lived in a Muslim state Reaya and they had to pay the Cizye, which is the head tax on the non-muslim individuals to their Muslim government (Shaw, 1978). This classification enables us to calculate the ABCC index for the Muslims and the non-muslims individually and compare the results to test whether there was a significant difference between the numeracy levels of these different religions. Unfortunately, the non-muslims were not clearly divided according to their distinct religions, and thus, it is not possible to differentiate the Christians from the Jews. It might be relatively possible to identify some of the known names into their religion, but it is not possible to estimate all of the names religions. Therefore, this research will be depending on dividing the religions into Muslims and non-muslims. The nine regions mentioned above are all in the today s Middle East region, which used to play a very important role in the international trade. However, not all of these regions have the same commercial importance. Some of them were located directly on important earlynineteenth century trade routes. The ports of Iskandarun and Latakia on the Mediterranean Sea, used to link Aleppo with Europe, while the caravan route across Iraq connected Aleppo with India. Additionally, Istanbul, Diyarbekir and Damascus were also located on caravan routes linking the East with the West and the North with the South (Owen, 1993). Being located on a caravan route is included in the research as a variable expected to have a positive influence on the numeracy level of the inhabitants of the cities on these caravan routes. This dummy variable is explained by 1 if the city is located on the trade route and 0 if 1 The non-muslims were from different religious communities. In 1885 the main communities were: Greek Orthodox, Armenian Gregorian, Bulgarian, Greek Catholic, Armenian Catholic, Protestant, Latin, Jew, Maronite, Frank and some other minor communities (See: Shaw, 1978). 7

it is not. The data of the caravan routes are taken from map of the Middle Eastern caravan routes in the Middle East economy in 1800 (Owen 1993, p.48). Age Heaping: Applying the age heaping method, Figure (1) shows that the inhabitants of the Ottoman States during the first half of the nineteenth century used to heap their ages to multiple of five. However, the younger people seemed to state rounded ages less than the older ones. Going into more details, the states which are located now in Turkey seem to heap their ages around multiples of five less than the ones which are located now in the Arabic countries (See Figure 2). For the youngest and the old age groups, the Arabic and the Turkish regions seem to have a similar behavior concerning the age heaping. For the middle age group, however, the Arabic regions seem to heap more to numbers ending with zero than the Turkish regions, the Turks tend to heap their ages less than the Arabs within this group. Dividing the data into two groups according to the religions, the Muslims present more rounded ages than the non-muslims unless for the ages of 25 and 30 (See Figure 3). ABCC Index: By calculating the ABCC index, the average value for the sample is 27.73. This means, according to the studied sample, only around one third of the Ottoman Empire population were able to calculate their ages accurately. This problem may result from the low education level in the Empire during the first half of the nineteenth century. Bayat (2003) states that the education institutions in this period were mainly religious schools, and the most important problem back then, that even if there were some schools, yet there was no coherent curriculum to be taught to the children, each teacher used to teach whatever he knew with no educational plan settled by a governmental committee. The Turkish regions, 8

however, were doing better with an average of ABCC value equal to 33.88 while the Arabic regions average ABCC value is only 16.59. The reason behind this might be including Istanbul in the Turkish sample, which was the most important city during that time, where a large number of educated people might immigrated to work in the official offices of the empire or in trade and other activities. But excluding Istanbul from the dataset does not change the difference between the Turkish and the Arabic regions. Another possible reason is the discrimination between the regions and preferring the Turkish regions with better public goods and education facilities than the Arabic ones. Another difference is also measured between Muslims and non-muslims. With an average ABCC for the Muslims in the whole sample of 24.97, the non-muslims used to have a better average ABCC of 33.28. In general, the analysis shows better results for the Turks, and better results of the non- Muslims within the ethnic groups. With a value of average ABCC equal to 11.84, the Muslim Arabs have the smallest numeracy level, while the non-muslim Turks exceed the value of 38 (See Figure 4). In order to see the development of the numeracy of this region, the ABCC index is calculated for the different birth decades between 1760 and 1820. The values begin around 10 for the first birth decade and reach 58 for the youngest group born during the 1820s. For the first and last birth decade, the Turks and Arabs look to have similar ABCC values, but this is not the situation for the other five birth decades, where the Turks exceed the Arabs by around 20 points (See Figure 5). The results of the ABCC index are different according to the religion. The values for both Muslims and non-muslims are similar with a difference of around ten points for all of the birth decades unless for the 1810 birth decade where surprisingly the difference exceeds the 30 points, the non-muslims ABCC values increase gradually. Whereas the Muslim ABCC values stay almost stable between 10 and 30 and then jump suddenly for 9

more than forty extra points for the last birth decade (See Figure 6). This huge difference might be influenced by the small number of observations for the Muslims born in the 1820s (See Figure 7). The difference between the ABCC values according to the religion in the Arabic area during the seven birth decades stay between seven and thirty eight points in the favor of non-muslims. But in the Turkish region, there are fluctuations of the values between the Muslims and the non-muslims during this period (See Figure 8). The difference seems to be small for the first five birth decades, but it becomes 28.5 points for the group born in 1810 in the favor of non-muslims, and then, to be dramatically changed for the next birth decade for around forty points, but in this decade the Turkish Muslims are on the top. However, the number of observations for the Turkish sample in the last birth decade of 1820, is low in comparison with the other birth decades, with around 1.5% of the total number of observations of Muslims, and 9.5% of the total number of non-muslim observations. Even though the average ABCC value of the non-muslims was better than the Muslims value for both the Arabic and the Turkish regions. But this was not exactly the case for each of the studied regions respectively. For two of the regions, only data for the non- Muslims were available. These records were for Istanbul which had the best ABCC average of around 72 and Kocaeli in the West of Turkey with an average of 37. For the other seven regions, the data are available for both Muslims and Non-Muslims. The non-muslims were doing better than the Muslims in Damascus, Mosul and Diyarbekir. The other way round was in Aleppo, North Palestine, Aydin and Marash where the Muslims seemed to have better ABCC average results (See Figure 9). The development of ABCC index in these nine regions according to the birth decades of the research sample is shown in Figure (10). Istanbul is on the front like mentioned above, the ABCC value begins with 46.6 for the 1780 birth decade and it improves for the people born in the later birth decades to reach the value of 76.7 for 10

those who were born in 1810. Kocaeli, Marash, Aleppo and Damascus results develop as well with time. Diyarbekir shows only a little development for the youngest generations. Mosul s results stay stable for the whole period. Aydin s results fluctuate a lot around the average of 32. In the small sample of North Palestine, the values are omitted for less than 30 observations, but the rest of the results fluctuate between the birth decades because of the small number of observations, which is between 37 and 106. REGRESSION ANALYSIS As it is shown above in the data analysis, the numeracy level of the non-muslims was better than the Muslims in the Ottoman Empire during the nineteenth century. The today s Arabic regions have a lower ABCC average value than the Turkish ones. To test whether the differences are significant or not, and to test the influence of living in a large city or on a caravan route on the numeracy, an OLS regression is done (See table 1). Being Muslim or not is depending on the data sources from the Ottoman Archives in Istanbul, were the Muslims were classified in separate registers than the other religions. The Arab variable is a dummy variable equal to one if the region is located now in an Arabic country, and zero if it is in Turkey. The variable large is a dummy variable for being from a large city or not. This dummy is estimated relying on 1885 population statistics of the Ottoman Empire cited in Shaw (1978). Istanbul, Damascus, Aleppo, Marash and Aydin are considered to be large cities, given the value of one and zero otherwise. The population statistics were taken around 35 years after the last census used in this research. However, it is the best available source for the total number of the provinces population in the nineteenth century. The borders and the size of the provinces would not be the same as in the sample period, but we can assume that they were not very different. The caravan routes used to cross through the 11

Middle East passing by the main cities, the variable caravan is a dummy variable taking the value of one if the caravan route passes by the province near the city. If not, it will take the value of zero, these values were estimated relying on Owen (1993). The time dummies are for the birth decades which were calculated by dividing the sample to age groups and then subtracting the center of the age group from the census year. The variable silk, cotton and textile production refers whether the region raised silk worms, planted cotton or produced textile. It takes the value of one if the region specialized in any of the mentioned activities. The data for this variable stem from the Tuebingen Atlas of the Near East (Hartmann et al, 1979). The rest of the variables are dummies for the nine regions included in the research. The first regression resulted in a 10% significant negative result of being Muslim on the ABCC by 7 points lower than the average, almost the same influence of being from a later Arabic county with 5% significance level and a lower ABCC index with 10 points. Being form a large city would increase the ABCC value by around 13.6 points this result is significant by 1%. Being on the caravan route does not have a significant influence on the ABCC index. The ABCC index is supposed to increase the later the birth decade is, but this increase is only significant for the last three birth decades which begin with the nineteenth century. In the second model, the dummy of the economic activity is added to the regression, having no significant influence itself, but it resulted in losing significance of the negative effect of being from an Arabic country and reduced the significance level of being from a large city as well. The last regression shows the average ABCC of the different regions, taking Mosul as a reference region, with the average ABCC value of around 11.29, it seems to have the lowest ABCC value among the nine studied regions. Diyarbekir is not significantly different from Mosul, but all of the other regions have significantly better numeracy levels. Istanbul leading, North Palestine comes second, then Marash, Aleppo, Damascus Kocaeli and Aydin. 12

CONCLUSION The studied dataset with more than 41,800 age statements of people used to live in different regions in the Ottoman Empire during the first half of the nineteenth century, in addition to the age heaping and the ABCC index methodology enabled us to have a close insight to the human capital situation in this region. The average numeracy level of the sample was low. By differentiating the observations by religion, the results showed a better numeracy level of the non-muslims in comparison with the Muslims. In general, the current Arabic states used to have a lower numeracy level than the Turkish regions. The size of the city had a positive effect on the numeracy of its population. But caravan routes did not have a significant influence on the numeracy. Generally, the numeracy level was developing with time, but the economic activities dummy had no significant influence on the ABCC index and the studied regions showed a variety in their numeracy levels. Thanks to the age heaping methodology, it was possible to benefit from these available data to have a better understanding of the human capital situation of the Ottoman Empire in the nineteenth century. 13

INDEX 16 14 12 10 8 6 4 2 0 25 30 35 40 45 50 55 60 65 70 Figure 1 Age heaping in percentage, the whole sample 20 18 16 14 12 10 8 6 4 2 0 25 30 35 40 45 50 55 60 65 70 turk arab Figure 2 Age Heaping in percentage, ethnic groups 14

18 16 14 12 10 8 6 4 2 0 25 30 35 40 45 50 55 60 65 70 muslims non muslims Figure 3 Age Heaping in percentage, religion 45 40 35 30 25 20 15 10 5 0 muslim not muslim muslim not muslim arab turk Figure 4 Average ABCC/ ethnic group/ religion 15

70 60 50 40 30 arab turk total abcc 20 10 0 1760 1770 1780 1790 1800 1810 1820 Figure 5 ABCC/ birth decades/ ethnic group 70 60 50 40 30 muslim not muslim total abcc 20 10 0 1760 1770 1780 1790 1800 1810 1820 Figure 6 ABCC/ birth decades/ religion 16

9000 8000 7000 6000 5000 4000 obs muslims obs non-muslims 3000 2000 1000 0 1760 1770 1780 1790 1800 1810 1820 Figure 7 Number of observations/ birth decades/ religion 100 90 80 70 60 50 40 30 20 10 0 1760 1770 1780 1790 1800 1810 1820 arab muslim arab not muslim turk muslim turk not muslim Figure 8 ABCC development/ birth decades/ ethnic group/ religion 17

aleppo damascus mosul north palestine aydin diyarbekir maras istanbul kocaeli 80 70 60 50 40 30 20 10 0 muslim not muslim arab turk Figure 9 Average ABCC/ ethnic group/ religion/ regions 90 80 70 60 50 40 30 20 10 aleppo aydin damascus diyarbekir maras kocaeli istanbul mosul north palestine 0 1760 1770 1780 1790 1800 1810 1820 Figure 10 The ABCC development/ birth decades/ regions 18

ABCC OLS(1) OLS(2) OLS(3) Muslim -7.332* -7.403* Standard errors in parentheses, (4.337) (4.368) *** p<0.01, ** p<0.05, * p<0.1 Arab -10.24** -7.801 (1)&(2)reference category: non- (4.651) (6.409) Muslim, Turk, born in 1760. Large 13.61*** 11.90** (4.744) (5.677) (3) reference category: Mosul Caravan -0.484 3.599 (4.298) (8.518) Bdec 1770 7.146 7.380 (10.32) (10.31) Bdec 1780 8.901 9.024 (9.36) (9.256) Bdec 1790 11.11 11.05 (9.195) (9.256) Bdec 1800 19.03** 18.58** (9.056) (9.153) Bdec 1810 28.83*** 28.14*** (9.668) (9.809) Bdec 1820 42.89*** 41.44*** (10.37) (11.35) Silk, cotton, textile- -6.032 production (10.84) Aleppo 32.03*** (9.407) Damascus 22.28*** (7.542) north Palestine 38.88*** (10.47) Istanbul 53.05*** (9.407) Kocaeli 18.69** (8.709) Aydin 18.09** (7.306) Marash 37.32*** (7.111) Diyarbekir 3.173 (6.947) Constant 14.54* 16.80* 11.29** (8.367) (9.348) (5.028) Observations 64 64 64 R-squared 0.501 0.504 0.538 Table 1 OLS regression, ABCC index/geographical/ time/ ethnic/ religion/ location 19

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