Globalization and Inequality: the importance of nominal income series for understanding long term global development
Efforts to chart global economic development and increase our understanding of why some countries are rich and others poor requires detailed account of historical income estimates. Over the recent years, efforts to produce historical national account have spread to encompass increasingly more countries and time periods. Many of those estimates are gathered within the Maddison Project to represent the measurement of GDP and population in the world economy between Roman times and the present. To make these income estimates comparable over time and space, they are expressed in constant international prices.
Yet, to be able to answer questions such as ‘is the recent globalization movement that the world has witnessed a singular experience or is it related to earlier waves of globalization?’, or ‘what are drivers of income inequality?’ requires long term income series in current, nominal prices. Therefore the aim of the session at the XVIIIth World Economic History Congress is to bring together scholars working on issues that call for historical nominal GDP as mean to study comparative performance in the fields of globalization and openness to trade, inequality, fiscal history, and economic development.
Globalization and Trade: to answer questions like ‘is the world today more globalized than at the end of the 19th century?, requires a careful quantification of how open the world economy was prior to WWI. Up until now for example, work has often attempted to explain pre-1950 trade shares by combining import and export data expressed in current prices with GDP measures expressed in constant prices. Not accounting for the adjustment for purchasing power made to constant price GDP leads to systematic biases in measures of openness. Hence, historical nominal GDP series are needed as a denominator for studying globalization and trade topics.
Inequality: understanding the historical roots of inequality first of all a thorough measurement of inequality. Prominent inequality studies have relied on partial measures of inequality such as top income shares due to the superiority of information available on incomes at the top of income ranks (Kuznets, 1955; Piketty, 2014). For such partial inequality measures, nominal GDP provides the crucial total-income denominator. Increasingly, scholars have also attempted to extend inequality estimates into the past building on social tables (Lindert and Williamson 2016). Combining comprehensive information on incomes, these studies not only produce inequality estimates but also current GDP estimates from the income side.
Fiscal history and state capacity: the growth of states and its capacity to extract revenues from its citizens are important topics in economic history. For the 20th century relative accurate fiscal data for Europe and the US is widely available, Maddison (2001) documents a substantial increase in tax revenues as a share of GDP. But alike other local-currency magnitudes, dividing by GDP expressed in constant prices leads to systematic biases in measures of tax capacity. Hence for a accurate understanding of government’s fiscal capacity, and to extend such capacity measures into the deeper past will require the availability of historical nominal income estimates.
We explicitly invite scholars who work on the above and related fields and who built in their work on nominal income estimates. The discussion on these subjects has the aim to advance work on collecting and improving historical nominal GDP estimates, and stimulate a new generation of scholars working in this field.
- Jutta J Bolt, University of Groningen, email@example.com,
- Jan Luiten J.L. Van Zanden, University of Utrecht, firstname.lastname@example.org,
- Joost J. Veenstra, University of Groningen, email@example.com,
- Peter P.H. Lindert, University of California, Davis, firstname.lastname@example.org
- Leticia L Arroyo Abad, Middlebury college, email@example.com
- Giovanni G Federico, Università di Pisa, Giovanni.Federico@unipi.it
- Harry H.X. Wu, Hitotsubashi University, firstname.lastname@example.org
- Kyoji K. Fukao, Hitotsubashi University, email@example.com
- Morten M. Jerven, Norwegian University of Life Sciences, firstname.lastname@example.org
- Leandro L. Prados de la Escosura, Universidad Carlos III, email@example.com
- Jan Luiten J.L. Van Zanden, University of Utrecht, firstname.lastname@example.org
- Jutta J. Bolt, University of Groningen, email@example.com
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