Explorations in Economic History最新文献

Nation-building is often proposed as a device for integration in ethnically divided societies. The determinants of national sentiment, however, remain imperfectly understood. This paper analyses the role of interethnic contact in the process of nation formation within multiethnic Yugoslavia, just before its disintegration in 1991. Using a variety of data sources and empirical strategies, I find that interethnic contact stimulated the formation of the Yugoslav nation. I argue that ethnic intermarriage is the key mechanism through which ethnic diversity influenced the adoption of a shared Yugoslav identity. These results illustrate the powerful effect that interethnic contact can have in reducing ethnic division even in a tense ethnic environment on the verge of conflict, like that of Yugoslavia.

War necessitates both allocating real resources to defense and certain interest groups being in favor of the government raising resources to wage war. Price controls can be a tool for governments to mobilize additional resources while buying the support of certain key interest groups, hence making war politically viable. France during the revolutionary Terror, the first instance of widespread price controls used in times of war, is used to illustrate this hypothesis. Urban capitalists benefited from price controls on agricultural output combined with forced sales. At the same time, I estimate that in the six months preceding the abolition of price controls, the government saved, by using them (and in real terms), the equivalent of roughly 40% of the annual 1790 central government budget. Consistent with my theory, once the exigencies of the war attenuated and as collective action became more costly for the urban population, price controls were abandoned.

This paper uses a structural time series model to explore U.S. commodity market convergence, efficiency, and intertemporal smoothing from 1750–1949. I find near-continuous convergence that is largely concentrated in the frontier, broad antebellum efficiency gains, and intertemporal smoothing from the 1880s onward among the most perishable goods. The results reveal new periods of integration across all three metrics and underscore the rapid rate of integration on the frontier.

This article exploits a unique district-level dataset to investigate the relationship between sugar cultivation, property rights systems and land distribution in colonial Java around the turn of the twentieth century. We demonstrate a negative and statistically significant relationship between sugar cultivation and the landholder Gini. An IV strategy, employing a newly computed index of sugar suitability as instrument, suggests that this effect is causal. It is argued that sugar production in the nineteenth and early twentieth centuries stimulated the expansion and persistence of communal landholding. This communal landholding consequently led to more equally distributed plots among landholders in the early twentieth century. We emphasize the importance of local property rights institutions in mitigating the effects of export production on socioeconomic outcomes.

Gender discrimination has been pointed out as a determining factor behind the long-run divergence in incomes of Southern vis-à-vis Northwestern Europe. In this paper, we show that women in Portugal were not historically more discriminated against than those in other parts of Western Europe, including England and the Netherlands. We rely on a new dataset of thousands of observations from archival sources covering six centuries, and we complement it with a qualitative discussion of comparative social norms. Compared with Northwestern Europe, women in Portugal faced similar gender wage gaps, married at similar ages, and did not face more restrictions on labor market participation. Consequently, other factors must have been responsible for the Little Divergence of Western European incomes.

I study the link between monetary policy and electoral outcomes by linking new data on the 1923 German hyperinflation and the vote share of the main parties of Weimar Republic from 1924 to 1933. Exploiting cross-sectional variation in prices in over 280 cities, I find that inflation predicts the vote share of the Volksrechtspartei, an association-turned-party of inflation victims, and positively correlates with the Communists in the 1932 elections. Hyperinflation also leads to a decline in turnout, with a loss of confidence in the German institutions. However, contrary to received wisdom, areas more affected by inflation did not see a higher vote share for the Nazi party. Results are robust to a range of specifications, including models in differences, panel data with fixed effects, Coarsened Exact Matching estimation, Conley standard errors, and an instrumental variable strategy.

We detail a methodology for estimating the textual similarity between two documents while accounting for the possibility that two different words can have a similar meaning. We illustrate the method’s usefulness in facilitating comparisons between documents with very different formats and vocabularies by textually linking occupation task and industry output descriptions with related technologies as described in patent texts; we also examine economic applications of the resultant document similarity measures. In a final application we demonstrate that the method also works well relative to alternatives for comparing documents within the same domain by showing that pairwise textual similarity between occupations’ task descriptions strongly predicts the probability that a given worker will transition from one occupation to another. Finally, we offer some suggestions on other potential uses and guidance in implementing the method.

Historical city directories are rich sources of micro-geographic data. They provide information on the location of households and firms and their occupations and industries, respectively. We develop a generic algorithmic work flow that converts scans of them into geo- and status-referenced household-level data sets. Applying the work flow to our case study, the Berlin 1880 directory, adds idiosyncratic challenges that should make automation less attractive. Yet, employing an administrative benchmark data set on household counts, incomes, and income distributions across more than 200 census tracts, we show that semi-automatic referencing yields results very similar to those from labour-intensive manual referencing. Finally, we discuss how to scale the work flow to other years and cities as well as potential applications in economic history and beyond.

Researchers use microdata to study the economic development of the United States and the causal effects of historical policies. Much of this research focuses on county- and state-level patterns and policies because comprehensive sub-county data is not consistently available. We describe a new method that geocodes and standardizes the towns and cities of residence for individuals and households in decennial census microdata from 1790–1940. We release public crosswalks linking individuals and households to consistently-defined place names, longitude-latitude pairs, counties, and states. Our method dramatically increases the number of individuals and households assigned to a sub-county location relative to standard publicly available data: we geocode an average of 83% of the individuals and households in 1790–1940 census microdata, compared to 23% in widely-used crosswalks. In years with individual-level microdata (1850–1940), our average match rate is 94% relative to 33% in widely-used crosswalks. To illustrate the value of our crosswalks, we measure place-level population growth across the United States between 1870 and 1940 at a sub-county level, confirming predictions of Zipf’s Law and Gibrat’s Law for large cities but rejecting similar predictions for small towns. We describe how our approach can be used to accurately geocode other historical datasets.

Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Perhaps the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors and although modern linking methods are designed to handle such challenges, these sources of errors are critical and should be minimized. For this purpose, improved transcription methods and large-scale databases are crucial components. This paper describes and provides documentation for HANA, a newly constructed large-scale database which consists of more than 3.3 million names. The database contains more than 105 thousand unique names with a total of more than 1.1 million images of personal names, which proves useful for transfer learning to other settings. We provide three examples hereof, obtaining significantly improved transcription accuracy on both Danish and US census data. In addition, we present benchmark results for deep learning models automatically transcribing the personal names from the scanned documents. Through making more challenging large-scale databases publicly available we hope to foster more sophisticated, accurate, and robust models for handwritten text recognition.