Diagnosing Sample-Selection Bias in Historical Heights: A Reply to Komlos and A’Hearn

2019 ◽  
Vol 79 (4) ◽  
pp. 1154-1175 ◽  
Author(s):  
Howard Bodenhorn ◽  
Timothy W. Guinnane ◽  
Thomas A. Mroz

Our 2017 article in this Journal stresses the pitfalls of using choice-based samples in economic history. A prominent example is the literature addressing the so-called antebellum puzzle. Heights researchers claim that Americans grew shorter in the first half of the nineteenth century, a period of robust economic growth. We argue that this result relies on choice-based samples. Without knowing the process that led to inclusion in the sample, researchers cannot properly estimate conditional mean heights. We proposed a diagnostic that can detect, but not correct for, selection bias. Komlos and A’Hearn’s interpretation of our analysis confuses diagnosis with cure. We dispute their view that selection bias has been appreciated in the heights literature.

2020 ◽  
Vol 16 (3) ◽  
pp. 241-268
Author(s):  
Dmitry Yu. Karasev

Introduction. The scope of regional economic inequality, its causes and consequences are relevant issues in the economic history. High regional inequality impedes representative estimation of national economic development and international comparison. The end of 19th and beginning of 20th centuries was the time when industrialization, states’ economic and political integration led to their regional divergence/convergence. Methods. The main challenge of measuring and accounting for 19th century regional economic growth is a scarcity of regional historical and economic statistics. Thus, the paper concerns with historiographical analysis of successful attempts to face this challenge in economic history. Results. It can be distinguished three approaches to historical regional economies accounting depending of relevant statistics availability: 1) for countries with high regional-data integrity, GRP can be estimated as a sum of its residents’ incomes (R. Easterling’s method); 2) for countries with moderate regional statistics being saved, it is possible to estimate GRP through distributing known GDP totals across regions on the basis of indicators of regional sectors’ shares (Geary-Stark method); 3) for countries with poor regional historical statistics it fits only short-cut approach on the basis of indirect regional economic indicators (Crafts’ approach and Good–Ma method). Furthermore, the paper deals with following methods and models used in quantitative explorations of unequal regional economic development: shift-share analysis, β and σ-convergence. Discussion. It appears that historical statistics from the Governors reports makes possible to distribute known national values added in the first and secondary sectors across provinces of the late-nineteenth century Russian Empire in the line with Geary–Stark methodology. The contribution of tertiary sector to the provinces’ economic growth could be estimated on the basis of indirect indicators from the same historical source and the other sources, following Good–Ma methodology. Finally, the cross-checking of the GRP to be calculated is possible through comparison with A. Markevich estimates for 1897.


2015 ◽  
Vol 2 ◽  
pp. 351-369 ◽  
Author(s):  
Richard Breen ◽  
Seungsoo Choi ◽  
Anders Holm

2018 ◽  
pp. 55-89
Author(s):  
Şevket Pamuk

This chapter looks at the role of institutions in economic development and the evolution of Ottoman institutions before the nineteenth century. It argues that while institutions are not the only things that matter, it is essential to examine their role in order to understand Turkey's experience with economic growth and human development during the last two centuries. The economics and economic history literature has been making a related and important distinction between the proximate and deeper sources of economic growth. The proximate causes refer to the contributions made by the increases in inputs, land, labor, and capital and the productivity increases. The deeper causes refer to the social, political, and economic environment as well as the historical causes that influence the rate at which inputs and productivity grow.


Author(s):  
Tao Lu ◽  
Ruimin Hu ◽  
Zhen Han ◽  
Junjun Jiang ◽  
Jun Chang

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