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This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

How do we make sense of urban life in the past? What do we do when we study urban history, and to what extent do our methods fully capture the complexities of historical city living? These are crucial questions for any scholar interested in the historical dimensions of urban experience. Notwithstanding the interest of most urban historians in the relationship between the physical form of urban space and its experience by inhabitants and visitors, very few scholars have written histories that systematically integrate these two areas of inquiry. In this chapter, we argue that such research requires a method and an accompanying tool that can analyze historical urban life in a more integrated, holistic way. We propose a way forward by introducing the Time Machine platform as a scalable data visualization and analysis tool for researching everyday urban experience across space and time. To illustrate the potential we focus on a case study: the area of the Bloemstraat in early modern Amsterdam. Unpacking a section of the Bloemstraat, house by house and room by room, we show how the Time Machine forms an instrument to connect spatial layouts to the arrangement of objects and to the practical and social use of the space by the inhabitants and visitors. We also sketch how this tool illuminates more dynamic spatial and temporal practices such as how people, goods, and activities are connected to locations in the wider city and beyond.

This paper uses a method devised by Geary and Stark to estimate regional GDPs for 24 Swedish provinces 1855–2000. In empirical tests, we find that the Swedish estimations yield results of good precision, comparable to those reported in the international literature. From the literature, we generate six expectations concerning the development of regional GDPs in Sweden. Using the GDP estimations, we test these expectations empirically. We find that the historical regional GDPs show a high correlation over time, but that the early industrialization process coevolved with a dramatic redistribution of productive capacity. We show that the regional inequalities in GDP per capita were at their lowest point in modern history in the early 1980s. However, while efficiency in the regional system has never been as equal, absolute regional differences in scale of production has increased dramatically over our investigated period. This process has especially benefited the metropolitan provinces. We present detailed sources of our estimations and also sketch a research agenda from our results.

This chapter assesses the extent to which historical levels of inequality affect the creation and survival of businesses over time. To this end, we use the Global Entrepreneurship Monitor survey across 66 countries over 2005–2011. We complement this survey with data on income inequality dating back to early 1800s and current institutional environment, such as the number of procedures to start a new business, countries’ degree of financial inclusion, corruption and political stability. We find that, although inequality increases the number of firms created out of need, inequality reduces entrepreneurial activity as in net terms businesses are less likely to be created and survive over time. These findings are robust in using different measures of inequality across different points in time and regions, even if excluding Latin America, the most unequal region in the world. Our evidence then supports theories that argue early conditions, crucially inequality, influence development path.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

This study investigates Rokkan's research programme in the light of the differences between case- and variables-based methodologies. Three phases of the research process are distinguished. Studying the way Rokkan actually proceeded in the research within his Europe project, we find that he follows the protocols of case-methodologies such as grounded theory. In the second phase of the research process, however, he constructs variables-based models as tools for his macro-historical comparisons. To get to variables from the sensitizing concepts coded in the first phase, Rokkan defines his variables as close to cases as possible: variables as nominal level typologies, types as variable values. He thus faces two interrelated dilemmas. First, a philosophy of science dissonance: he legitimates his research only with reference to a variable-methodology, while his research is thoroughly case based. Second, a paradox of double coding: using variable-based models in the second phase, the status of the knowledge available in the first phase memos is degraded. Rokkan cannot decide between the two main solutions to these dilemmas: The first solution is to discard his heterogeneous data, instead working only with homogeneous data that opens up to more consistently variables-oriented research. The second solution is to replace the notion of variables/variable values with typology/types, thereby returning to cases, pursuing comparative case reconstructions in the third phase of research. The study concludes in favour of the second solution.

Based on empirical patterns of annual earnings and saving from new micro-data covering a large sample of American workers around a hundred years ago, we develop a model for simulating the cross-section distribution of wealth at the turn of the twentieth century. Our methodology allows for a direct comparison with the wealth distribution from a sample of families in a comparable part of the contemporary income distribution. Our primary finding is that patterns of wealth accumulation among American workers at the turn of the century bear a striking resemblance to contemporary profiles.