Antebellum Urbanization in the American Northeast

1965 ◽  
Vol 25 (4) ◽  
pp. 592-608 ◽  
Author(s):  
Jeffrey G. Williamson

No one seriously questions the familiar association either between levels of urban development and degrees of industrial maturity or between rates of change in these two indices. It has even become commonplace in macroeconomic growth theory to simplify the complexities of structural change into some variation of the urban-rural two-sector model although, in the real world, shifts from commercial to industrial urban employment, let alone more complex intersectoral shifts, are of prime importance. Indeed, many of these growth models place great emphasis not upon changes in sector productivity but upon resource shifts between low- and high-productivity employment, while in empirical studies urban and rural population data very often appear as explicit substitutes for sectoral employment. But given the paucity of macroeconomic data for the antebellum period, especially prior to 1839, one is left puzzled by our relative inattention to the wealth of population census data by residence. American economic history textbooks are stuffed with quantitative information on the number of cities, their spectacular growth, and the percentage of population urbanized, but these may not be the most effective uses of this great data pool. The plethora of urban histories and the abundant attention to urban rivalry may be useful complements, but they are not very effective substitutes for quantitative analysis of overall American urbanization and experience with city size distribution. The very attention which the topic is currently receiving by economists, geographers, and historians suggests that much remains undone in the study of early American urbanization.

Author(s):  
Nurkhalik Wahdanial Asbara

Technological developments and changes in government systems are developing rapidly. Both of these lead to efforts to carry out duties, protect functions and serve the community. This encourages the government to take various adjustment steps quickly in line with the dynamics of development that occur. One of them is through a population census. The population census is an important issue that must be handled properly. The population census in this study takes population data in an area based on the number of male population, female population, ratio, and population density. The data was taken and submitted to the Makassar City Statistics Agency. Population Census is a presentation of information that has the ability to present accurate information, and helps facilitate the search for a population census data. The population census is carried out every 5 years which is carried out by census officers to carry out data collection to each resident's house, the data collection process is carried out by conventional recording and submitting it to the central statistics agency for database entry. With this application, it is expected to provide convenience to Population census officers to perform the process of inputting population data and the data is directly stored in the database without having to return to the office to input again.


2014 ◽  
Vol 53 (1) ◽  
pp. 15-23
Author(s):  
Daumantas Stumbrys ◽  
Domantas Jasilionis ◽  
Dalia Ambrozaitienė ◽  
Vlada Stankūnienė

This paper presents the results of a study on sociodemographic mortality differentials in Lithuania based on censuslinked mortality data. Population data come from the individual records of the 2011 Population and Housing Census of the Republic of Lithuania. The results of the research demonstrate that education and marital status are very strong predictors of alcohol-related mortality. Among males aged 30 and older, the alcohol-related mortality risk in non-married groups is up to 3.4 times as high as in the group of married males. The alcohol-related mortality risk in lower-education groups is up to 3.7 times as high as in the group of those with higher education. The findings of the study suggest that the elimination of educational differences would allow avoiding 55.7 %, the elimination of marital status differences – 40.2 %, the elimination of ethnic group differences – 11.1 % of alcohol-related deaths.


2020 ◽  
Vol 49 (Supplement_1) ◽  
pp. i15-i25
Author(s):  
Daniela Fecht ◽  
Samantha Cockings ◽  
Susan Hodgson ◽  
Frédéric B Piel ◽  
David Martin ◽  
...  

Abstract Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.


2013 ◽  
Vol 52 (1) ◽  
pp. 34-38
Author(s):  
Audronė Jakaitienė ◽  
Vaidutis Kučinskas

The studies about the homogeneity of the Lithuanian population according to ethnolinguistic groups were carried out in various areas – psychological, anthropological, genetic, etc. From diverse research results, we know that the ethnic Lithuanian population is not homogeneous. For the analysis of the Lithuanian population according to ethnolinguistic groups, one should first know the population structure according to the mentioned groups so that a two-stage stratified random sample could be drawn. As to author’s knowledge, there are no publications about the structure of the Lithuanian population by ethnolinguistic groups. Statistics Lithuania does not produce such information. In the paper, the expert estimation of the Lithuanian population structure by ethnolinguistic groups is provided, based on annual population figures at the beginning of the year (2001–2013) from Statistics Lithuania. According to final 2001 and preliminary 2011 population census data, it was calculated that the ethnic Lithuanian population consists of 24.5 per cent of žemaičiai and 75.5 per cent of aukštaičiai. The latter is rather the estimation of the Lithuanian population structure living in the ethnolinguistic areas than the structure of the Lithuanian population by ethnolinguistic groups. Therefore, the results obtained should be used as an approximate estimate of the Lithuanian population structure by ethnolinguistic groups in empirical studies.


2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Frank Popham ◽  
Elise Whitley ◽  
Oarabile Molaodi ◽  
Linsay Gray

Abstract Background Health surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are becoming less representative as response levels fall. Routinely collected administrative data offer more extensive population coverage but typically comprise fewer health topics. We explore whether data combination and multiple imputation of health variables from survey data is a simple and robust way of generating these variables in the general population. Methods We use the UK Integrated Household Survey and the English 2011 population census both of which included self-rated general health. Setting aside the census self-rated health data we multiply imputed self-rated health responses for the census using the survey data and compared these with the actual census results in 576 unique groups defined by age, sex, housing tenure and geographic region. Results Compared with original census data across the groups, multiply imputed proportions of bad or very bad self-rated health were not a markedly better fit than those simply derived from the survey proportions. Conclusion While multiple imputation may have the potential to augment population data with information from surveys, further testing and refinement is required.


Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.


1994 ◽  
Vol 22 (3) ◽  
pp. 131-138 ◽  
Author(s):  
R. C. S. Taragi ◽  
K. S. Bisht ◽  
B. S. Sokhi

2018 ◽  
Vol 12 (3) ◽  
pp. 305-325 ◽  
Author(s):  
Aalok Ranjan Chaurasia

The present article uses data available through the 2011 population census to analyze the state of development in the villages of India on the basis of a village development index that has been constructed for the purpose following the capabilities expansion as development approach. The analysis reveals that the state of development in the villages of the country varies widely and there is only a small proportion of the villages where the state of development can be termed as satisfactory. The analysis also reveals that the state of development in the village is influenced by its selected defining characteristics. The article calls for a village-based planning and programming approach for meeting the development and welfare needs of the village people.


2002 ◽  
Vol 3 (3) ◽  
pp. 339-346 ◽  
Author(s):  
Lutz G. Arnold

Abstract Standard R&D growth models have two disturbing properties: the presence of scale effects (i.e., the prediction that larger economies grow faster) and the implication that there is a multitude of growth-enhancing policies. Recent models of growth without scale effects, such as Segerstrom's (1998), not only remove the counterfactual scale effect, but also imply that the growth rate does not react to any kind of economic policy. They share a different disturbing property, however: economic growth depends positively on population growth, and the economy cannot grow in the absence of population growth. The present paper integrates human capital accumulation into Segerstrom's (1998) model of growth without scale effects. Consistent with many empirical studies, growth is positively related not to population growth, but to investment in human capital. And there is one way to accelerate growth: subsidizing education.


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