Introduction

In its essence, the Ottoman state was a dynastic one, administered by and for the Ottoman family, in cooperation and competition with other groups and institutions. In common with polities elsewhere in the world, the central dynastic Ottoman state employed a variety of strategies to assure its own perpetuation. It combined brutal coercion, the maintenance of justice, the co-option of potential dissidents, and constant negotiation with other sources of power. This chapter examines some of the obvious as well as the more subtle techniques of rule that it employed to domestically project its power over the centuries. Significantly, it explores the actual power of the central government in the provinces. It suggests that the older narratives stressing an extensive amount of administrative centralization are overstated.

The Ottoman dynasty: principles of succession

At the heart of Ottoman success lay the ability of the royal family to hold onto the summit of power for over six centuries, through numerous permutations and fundamental transformations of the state structure. Therefore, we first turn to modes of dynastic succession and how the Ottoman dynasty created, maintained, and enhanced its own legitimacy.

Globally, royal families have used principles of both female and male or exclusively male succession. In common with early modern and modern monarchical France (where the Salic law prevailed), but unlike the modern Russian and British states, the Ottoman family used the principle of male succession, considering only males as potential heirs to the throne.

Introduction

The present chapter focuses on international relations and addresses two complementary aspects of the place of the Ottoman Empire in the wider international community. Thus, it explores the empire's relations with other states, empires, and nations, as well as its diplomatic strategies. The chapter offers a distinctive commentary on the global order through the Ottoman perspective. It first focuses on the changing place of the Ottoman empire in the international order, 1700–1922, as it declined from first- to second-rank status. It then examines the changing diplomatic tools employed in dealing with other states, particularly the shift from occasional to continuous methods of diplomacy. Another diplomatic tool, the caliphate, gave the Ottoman state a special religious instrument that it increasingly used for secular state purposes from the eighteenth century onwards. And finally, the chapter provides an overview of Ottoman relations with Europe, central Asia, India, and North Africa.

The Ottoman Empire in the international order, 1700–1922

The place of the Ottoman state and any political system in the international order is a function of many factors, sometimes demographic and economic power. A large and densely settled population is not always a certain barometer of political importance: consider the vast power of eighteenth-century Prussia with its tiny population and the political weakness of nineteenth-century China, the world's most populous country at the time. In the Ottoman case, a relative decline in the global importance of its population paralleled its fading international political importance.

Introduction

During the long nineteenth century, 1798–1922, the earlier Ottoman patterns of political and economic life remained generally recognizable. In many respects, this period continued processes of change and transformation that had begun in the eighteenth century, and sometimes before. Territorial losses continued and frontiers shrank; statesmen at the center and in the provinces continued their contestations for power and access to taxable resources; and the international economy loomed ever more important. And yet, much was new. The forces triggering the territorial losses became increasingly complex, now involving domestic rebellions as well as the familiar imperial wars. Domestically, the central state became more powerful and influential in everyday lives than ever before in Ottoman history, extending its control ever more deeply into society. Its primary tools of control changed from consumption competitions and tax farms to a much larger and professional military and bureaucracy. As a part of the effort to more fully control its population, the state redefined the status of Muslims and non-Muslims and, after some delay sought, towards the end of the period, to re-order the legal status of women as well. And finally, a new and deadly element evolved in the Ottoman body politic – inter-communal violence among Ottoman subjects – that attested to the power of these accelerating political and economic changes.

The wars of contraction and internal rebellions

By the twentieth century, the Ottoman Empire in Europe had receded to a small coastal plain between Edirne and Istanbul.

Introduction

As the following chapter makes clear, history is not merely about leaders and politics but also the masses of people and their everyday lives. In the following pages, I tell about the ways Ottoman subjects earned livelihoods in the various sectors of the economy. This overview of the Ottoman economy is not a lesson in elementary economics, overflowing with statistics at micro- and macro-levels. Rather, it is designed to demonstrate how people in the Ottoman Empire made their livings and how these patterns changed over time. To achieve this goal, the chapter emphasizes a complex matrix that relates demographic information on population size, mobility, and location with changes in the significant sectors of the economy. After reviewing population changes, the chapter turns to the first sector, agriculture that, in 1700, was the dominant economic activity, as it was virtually everywhere else in the world. The chapter then turns to each of the other economic sectors in which people worked – manufacturing, trade, transport, and mining – in the rank order of importance just listed. As will become evident, although the economy remained basically agrarian, agriculture itself changed dramatically, becoming more diverse and more commercially oriented. In addition, Ottoman manufacturing struggled first with Asian, then with European competitors, yet obtained surprising levels of production. If these transformations did not lead to anything approaching an industrial revolution, they nonetheless did sustain improving levels of living until the end of the empire.

Introduction

The era from 1300 until the later seventeenth century saw the remarkable expansion of the Ottoman state from a tiny, scarcely visible, chiefdom to an empire with vast territories. These dominions stretched from the Arabian peninsula and the cataracts of the Nile in the south, to Basra near the Persian Gulf and the Iranian plateau in the east, along the North African coast nearly to Gibraltar in the west, and to the Ukranian steppe and the walls of Vienna in the north. The period begins with an Ottoman dot on the map and ends with a world empire and its dominions along the Black, Aegean, Mediterranean, Caspian, and Red Seas.

Origins of the Ottoman state

Great events demand explanations: how are we to understand the rise of great empires such as those of Rome, the Inca, the Ming, Alexander, the British, or the Ottomans? How can these world shaking events be explained?

In brief, the Ottomans arose in the context of: Turkish nomadic invasions that shattered central Byzantine state domination in Asia Minor; a Mongol invasion of the Middle East that brought chaos and increased population pressure on the frontiers; Ottoman policies of pragmatism and flexibility that attracted a host of supporters regardless of religion and social rank; and luck, that placed the Ottomans in the geographic spot that controlled nomadic access to the Balkans, thus rallying additional supporters.

Introduction

This chapter continues the emphasis presented in chapter 7, a depiction of the everyday lives of Ottoman subjects. To do so, it draws on an unusual body of literature to look at social organization, popular culture, and forms of sociability and it offers a cultural investigation into various forms of meaning. Societies as complex as the Ottoman are to be understood not only in terms of administrative decrees, bureaucratic rationalization, military campaigns, and economic productivity. They structure spaces within which people think about the common issues of life, death, celebration, and mourning. Often those spaces are highly gendered and at other times they bring men and women of certain classes together.

An overview of social relations among groups

All societies, including the Ottoman, consist of complex sets of relationships among individuals and collections of individuals that sometimes overlap and interlock but at other times remain distinct and apart. Persons assemble voluntarily or gather into a number of often distinct groups. On one occasion, they might identify themselves or be identified by others as belonging to a particular group, yet at other times another identity might come to the fore. At a very general level, the Ottoman world may be described as holding the ruling and subject classes and also divisions by religious affiliations such as Sunni Muslim or Armenian Catholic. There were also occupational groups, sometimes but not always organized as corporate groups (esnaf, taife) that we call guilds, as well as huge groups such as women, peasants, or tribes.

The writing of the history of the Ottoman Empire, 1300–1922, has changed dramatically during the past several decades. In the early 1970s, when I began my graduate studies, a handful of scholars, at a very few elite schools, studied and wrote on this extraordinary empire, with roots in the Byzantine, Turkish, Islamic, and Renaissance political and cultural traditions. Nowadays, by contrast, Ottoman history appropriately is becoming an integral part of the curriculum at scores of colleges and universities, public and private.

And yet, semester after semester I have been faced with the same dilemma when making textbook assignments for my undergraduate courses in Middle East and Ottoman history. Either use textbooks that were too detailed for most students or adopt briefer studies that were deeply flawed, mainly by their a-historical approach that described a non-changing empire, hopelessly corrupt and backward, awaiting rescue or a merciful death.

This textbook is an effort to make Ottoman history intelligible, and exciting, to the university undergraduate student and the general reader. I make liberal use of my own previous research. Moreover, I rely quite heavily on the research of others and seek to bring to the general reader the wonderful specialized research that until now largely has remained inaccessible. At the end of each chapter are lists of suggested readings, not always those used in preparing the section. Given the intended audience, only English-language works are cited (with just a few exceptions).

Introduction

This book owes its origins to an event that occurred in Vienna in the summer of 1983, when lines of schoolchildren wound their way through the sidewalks of the Austrian capital. The attraction they were lining up for was not a Disney movie or a theme park, but instead a museum exhibition, one of many celebrations held that year to commemorate the 300th anniversary of the second Ottoman siege of Vienna. In the minds of these children, their teachers, and the Austrian (and, for that matter, the general European) public, 1683 was a year in which they all were saved – from conquest by the alien Ottoman state, the “unspeakable Turk.”

The Ottoman state had emerged, c. 1300, in western Asia Minor, not far from the modern city of Istanbul. In a steady process of territorial accretion, this state had expanded both west and east, defeating Byzantine, Serb, and Bulgarian kingdoms as well as Turkish nomadic principalities in Anatolia (Asia Minor) and the Mamluk sultanate based in Egypt. By the seventeenth century it held vast lands in west Asia, North Africa, and southeast Europe. In 1529 and again in 1683, Ottoman armies pressed to conquer Habsburg Vienna.

The artifacts in the Vienna museum exhibit told much about the nature of the 1683 events. For example, the display of the captured tent and personal effects of the Ottoman grand vizier illustrated the panicky flight of the Ottoman forces from their camps that, just days before, had encircled Vienna.

Introduction

This chapter serves as an introduction to Bayesian econometrics. Bayesian regression analysis has grown in a spectacular fashion since the publication of books by Zellner (1971) and Leamer (1978). Application to routine data analysis has also expanded enormously, greatly aided by revolutionary advances in computer hardware and software technology. In the light of such major developments, a single chapter can never do adequate justice to the many facets of this subject. This chapter therefore has the very modest goal of providing a rough road map to the major ideas and developments in Bayesian econometrics. Despite this modest objective some parts are still quite technical.

The Bayesian approach, unlike the likelihood or frequentist or classical approach presented in previous chapters, requires the specification of a probabilistic model of prior beliefs about the unknown parameters, given an initial specification of a model. Many researchers are uncomfortable about this step, both philosophically and practically. This has traditionally been the basis of the concern that the Bayesian approach is subjective rather than objective. It will be shown that in large samples the role of the prior may be negligible, that relatively uninformative priors can be specified, and that there are methods available for studying the sensitivity of inferences to priors. Therefore, the charge of subjectivity may not always be as serious as many claim.

Bayesian approaches play a potentially large role in applied microeconometrics, especially when dealing with complex models that lack analytically tractable likelihood functions.

Introduction

Microeconometrics research is usually performed on data collected by survey of a sample of the population of interest. The simplest statistical assumption for survey data is simple random sampling (SRS), under which each member of the population has equal probability of being included in the sample. Then it is reasonable to base statistical inference on the assumption that the data (y i, x i) are independent over i and identically distributed. This assumption underlies the small-sample and asymptotic properties of estimators presented in this book, with the notable exception of sample selection models in Chapter 16.

In practice, however, SRS is almost never the right assumption for survey data. Alternative sampling schemes are instead used to reduce survey costs and to increase precision of estimation for subgroups of the population that are of particular interest.

For example, a household survey may first partition the population geographically into subgroups, such as villages or suburbs, with differing sampling rates for different subgroups. Interviews may be conducted on households that are clustered in small geographic areas, such as city blocks. The data (y i, x i) are clearly no longer iid. First, the distribution of (y i, x i) may vary across subgroups, so the identical distribution assumption may be inappropriate. Second, since data may be correlated for households in the same cluster, the assumption that (y i, x i) are independent within the cluster breaks down.

Introduction

The previous chapter focused on m-estimation, including ML and NLS estimation. Now we consider a much broader class of extremum estimators, those based on method of moments (MM) and generalized method of moments (GMM).

The basis of MM and GMM is specification of a set of population moment conditions involving data and unknown parameters. The MM estimator solves the sample moment conditions that correspond to the population moment conditions. For example, the sample mean is the MM estimator of the population mean. In some cases there may be no explicit analytical solution for the MM estimator, but numerical solution may still be possible. Then the estimator is an example of the estimating equations estimator introduced briefly in Section 5.4.

In some situations, however, MM estimation may be infeasible because there are more moment conditions and hence equations to solve than there are parameters. A leading example is IV estimation in an overidentified model. The GMM estimator, due to Hansen (1982), extends the MM approach to accommodate this case.

The GMM estimator defines a class of estimators, with different GMM estimators obtained by using different population moment conditions, just as different specified densities lead to different ML estimators. We emphasize this moment-based approach to estimation, even in cases where alternative presentations are possible, as it provides a unified approach to estimation and can provide an obvious way to extend methods from linear to nonlinear models.

Part 2 presents the core estimation methods – least squares, maximum likelihood and method of moments – and associated methods of inference for nonlinear regression models that are central in microeconometrics. The material also includes modern topics such as quantile regression, sequential estimation, empirical likelihood, semiparametric and nonparametric regression, and statistical inference based on the bootstrap. In general the discussion is at a level intended to provide enough background and detail to enable the practitioner to read and comprehend articles in the leading econometrics journals and, where needed, subsequent chapters of this book. We presume prior familiarity with linear regression analysis.

The essential estimation theory is presented in three chapters. Chapter 4 begins with the linear regression model. It then covers at an introductory level quantile regression, which models distributional features other than the conditional mean. It provides a lengthy expository treatment of instrumental variables estimation, a major method of causal inference. Chapter 5 presents the most commonly-used estimation methods for nonlinear models, beginning with the topic of m-estimation, before specialization to maximum likelihood and nonlinear least squares regression. Chapter 6 provides a comprehensive treatment of generalized method of moments, which is a quite general estimation framework that is applicable for linear and nonlinear models in single-equation and multi-equation settings. The chapter emphasizes the special case of instrumental variables estimation.

Introduction

The problem of missing data in survey data is one of long standing, arising from nonresponse or partial response to survey questions. Reasons for nonresponse include unwillingness to provide the information asked for, difficulty of recall of events that occurred in the past, and not knowing the correct response. Imputation is the process of estimating or predicting the missing observations.

In this chapter we deal with the regression setup with data vector (y i, x i), i = 1, …, N. For some of the observations some elements of x i or of both (y i, x i) are missing. A number of questions are considered. When can we proceed with an analysis of only the complete observations, and when should we attempt to fill the gaps left by the missing observations? What methods of imputation are available? When imputed values for missing observations are obtained, how should estimation and inference then proceed?

If a data set has missing observations, and if these gaps can be filled by a statistically sound procedure, then benefit comes from a larger and possibly more representative sample and, under ideal circumstances, more precise inference. The cost of estimating missing data comes from having to make (possibly wrong) assumptions to support a procedure for generating proxies for the missing observations, and from the approximation error inherent in any such procedure. Further, statistical inference that follows data augmentation after imputed values replace missing data is more complicated because such inference must take into account the approximation errors introduced by imputation.

In empirical work data frequently present not one but multiple complications that need to be dealt with simultaneously. Examples of such complications include departures from simple random sampling, clustering of observations, measurement errors, and missing data. When they occur, individually or jointly, and in the context of any of the models developed in Parts 4 and 5, identification of parameters of interest will be compromised. Three chapters in Part 6 – Chapters 24, 26, and 27 – analyze the consequences of such complications and then present methods that control for these complications. The methods are illustrated using examples taken from the earlier parts of the book. This feature gives points of connection between Part 6 and the rest of the book.

Chapter 24, which deals with several features of data from complex surveys, notably stratified sampling and clustering, complements various topics covered in Chapters 3, 5, and 16. Chapter 26 which deals with measurement errors in models studied in Chapters 4, 14, and 20. Chapter 27 is a stand-alone chapter on missing data and multiple imputation, but its use of the EM algorithm and Gibbs sampler also gives it points of contact with Chapters 10 and 13, respectively.

Chapter 25 presents treatment evaluation. Treatment is a broad term that refers to the impact of one variable, e.g. schooling, on some outcome variable, e.g. earnings. Treatment variables may be exogenously assigned, or may be endogenously chosen.