Towns (and villages): definitions and implications in a historical setting

Ploeckl, Florian

doi:10.1007/s11698-016-0145-6

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Currently, the concept of small towns has not been clearly defined. Based on the availability of town data, many countries or current studies often applied a rather simple definition of towns as settlements with a certain population size, which was non-uniform due to different population thresholds of small towns among various countries (Ploeckl, 2017). Germany defines those with fewer than 5000 inhabitants as small towns, so does Russia, while the scale of small cities in the United States is fewer than 50,000 (Brennan et al, 2005), and communities with 200 people can be set as towns.…”

Section: Key Literature Reviewmentioning

confidence: 99%

Mega-towns in China: Their spatial distribution features and growth mechanisms

Wang

Liu

2020

J. Geogr. Sci.

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As a special outcome of urbanization, mega-towns not only play an important role in the process of socio-economic development, but also are important contributors to urbanization. Based on a spatial database of mega-towns in China, this paper explores the spatial distribution features and growth mechanisms of China's 238 mega-towns using the nearest neighbour distance method, kernel density estimation, regression analysis, global autocorrelation, local autocorrelation and other spatial analysis methods. Results of spatial distribution features show that: (1) on the national scale, the existing 238 mega-towns mainly gathered in the southeast coastal areas of China; they formed two spatial core agglomerations, several secondary ones and a southeast coastal agglomeration belt; (2) on the regional scale, each economic region's index was less than 1, indicating that mega-towns in each region tended to be spatially agglomerated due to the close relationship with regional development level and their number; (3) on the provincial scale, 68% of provincial-level units in China tended to be a spatial agglomeration of mega-towns; only one province had a random distribution; the number of mega-towns in those evenly-distributed provinces was generally small. The growth of mega-towns was determined by a combination of various natural and humanistic factors, including topography, location, economy, population, traffic, and national policy. This paper chose digital elevation model (DEM), location advantage, economic density, population density, and highway density distribution as corresponding indicators as quantitative factors. By combining their local autocorrelation analysis, these factors all showed certain influence on the spatial growth of mega-towns and together scheduled it. In the future, provinces and cities should make full use of the mega-town functions to promote their socioeconomic development, especially the central and western regions in China.

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Section: Key Literature Reviewmentioning

confidence: 99%

Mega-towns in China: Their spatial distribution features and growth mechanisms

Wang

Liu

2020

J. Geogr. Sci.

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“…In contrast to much of the literature mentioned in the introduction, this city-level dataset does not make use of an ad-hoc population threshold, which was recently criticized by Ploeckl (2011). Making use of the legal definition of a town, the dataset consists of all Prussian cities during the nineteenth century.…”

Section: The Datamentioning

confidence: 99%

“…16 Using this legally defined threshold results in the omission of a couple of city-like locations. According to Ploeckl (2011), in Saxony, these locations were actually some of the fastest growing during the Industrial Revolution. When examining the Prussian census data, we find 39 towns entering the census during the period 1849-1885 and thus gaining legal city rights.…”

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confidence: 99%

Railroads and Growth in Prussia

Hornung

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may www.econstor.eu Railroads and Growth in Prussia * AbstractWe study the effect of railroad access on urban population growth. Using GIS techniques, we match triennial population data for roughly 1,000 cities in nineteenth-century Prussia to georeferenced maps of the German railroad network. We find positive short-and long-term effects of having a station on urban growth for different periods during 1840-1871. Causal effects of (potentially endogenous) railroad access on city growth are identified using propensity score matching, instrumental variables, and fixed-effects estimation techniques. Our instrument identifies exogenous variation in railroad access by constructing straight-line corridors between nodes. Counterfactual models using pre-railroad growth yield no evidence to support the hypothesis that railroads appeared as a consequence of a previous growth spurt.JEL Classification: O18, O33, N73

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“…Estimated thresholds often range from 2000-5000. More recently, Ploeckl (2017) has argued that urban economies have sufficiently complex divisions of labor when town growth is independent of local agricultural conditions, which should serve as a method to identify thresholds. In all cases, the implication is that small towns (below the threshold) are part of the rural economy-important as marketing centers but lacking urban economic performance.…”

Section: Resultsmentioning

confidence: 99%

Increasing returns to scale in the towns of early Tudor England

Cesaretti

Lobo

Bettencourt

et al. 2020

Historical Methods: A Journal of Quantitative and Interdiscipli

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Urban agglomeration economies make cities central to theories of modern economic growth. There is historical evidence for the presence of Smithian growth and agglomeration effects in English towns c.1450-1670, but seminal assessments deny the presence of agglomeration effects and productivity gains to Early Modern English towns. This study evaluates the presence of increasing returns to scale (IRS) in aggregate urban economic outputs-the empirical signature of feedbacks between Smithian growth and agglomeration effects-among the towns of 16th century England. To do so, we test a model from settlement scaling theory against the 1524/5 Lay Subsidy returns. Analysis of these data indicates that Tudor towns exhibited IRS-a finding that is robust to alternative interpretations of the data. IRS holds even for the smallest towns in our sample, suggesting the absence of town size thresholds for the emergence of agglomeration effects. Spatial patterning of scaling residuals further suggests regional demand-side interactions with Smithian-agglomeration feedbacks. These findings suggest the presence of agglomeration effects and Smithian growth in pre-industrial English towns. This begs us to reconsider the economic performance of Early Modern English towns, and suggests that the qualitative economic dynamics of contemporary cities may be applicable to premodern settlements in general.

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Towns (and villages): definitions and implications in a historical setting

Cited by 11 publications

References 32 publications

Mega-towns in China: Their spatial distribution features and growth mechanisms

Mega-towns in China: Their spatial distribution features and growth mechanisms

Railroads and Growth in Prussia

Increasing returns to scale in the towns of early Tudor England

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