Pub Date : 2000-01-01DOI: 10.1080/01615440009598954
A B Kasakoff, J W Adams
D emographic indices are typically constructed out of aggregated statistical data derived from different subpopulations, and that is true not only on the national level, but also on the local level. Such indices are therefore potentially misleading means, because they obscure differences between component subpopulations. Moreover, even when attempts are made to disaggregate national statistics, the units used have many of the same faults. First, they mask much internal variation, and, second, it is often assumed that the units themselves are self-contained. Frequently, the units are administrative subdivisions of the larger nation and are conceived of as places, but migration in and out of these so-called places is rarely considered a factor influencing mortality within them. The challenge of demography, then, depends on knowing the changing composition and different vital rates of subgroups within mobile populations and on uncovering the various strands of the population and weighing their differences and interdependencies. But given the general lack of detailed data for even small subpopulations, such a task is often very difficult. Some might argue that the smaller the group and the smaller the variation within it, the more meaningful is the mean. But even micro populations are not fixed and uniform; within single families, there may be considerable variation (Adams and Kasakoff 1999). Others might argue that on the level of the large nation-state, small differences become inconsequential, especially as modern communications and standards of public health may tend to create a uniform population. But such homogeneity does not exist at at any level of analysis that we have examined. The underlying social reality is so complex that the usual demographic rates have to be taken as no more than summaries of dubious convenience. In this article, we use individual-level data to present three examples of the ways in which migration affected adult life expectancy in the American North from 1740 to 1880. We found that to understand mortality changes, particularly among adults, we had to consider the accumulated lifetime experiences of the population, experiences that for many, if not the majority, took place in more than one locality and occupation. People moving spatially and socially brought their demographic histories with them. Depending on the particular disease environment and the source of the migrants, new arrivals might have either raised or lowered mean life expectancy in their new location. Such mixtures are not only difficult to sort out statistically, but they also raise questions about the demographic stability of places. There is, therefore, a fundamental tension between looking at the lives of individuals who are parts of migration streams linking spatial and occupational units and the units themselves. Are the rates in a particular spatial unit the result of the migrants who entered into it or the people born there? (We will address this
{"title":"The effects of migration, place, and occupation on adult mortality in the American north, 1740-1880.","authors":"A B Kasakoff, J W Adams","doi":"10.1080/01615440009598954","DOIUrl":"https://doi.org/10.1080/01615440009598954","url":null,"abstract":"D emographic indices are typically constructed out of aggregated statistical data derived from different subpopulations, and that is true not only on the national level, but also on the local level. Such indices are therefore potentially misleading means, because they obscure differences between component subpopulations. Moreover, even when attempts are made to disaggregate national statistics, the units used have many of the same faults. First, they mask much internal variation, and, second, it is often assumed that the units themselves are self-contained. Frequently, the units are administrative subdivisions of the larger nation and are conceived of as places, but migration in and out of these so-called places is rarely considered a factor influencing mortality within them. The challenge of demography, then, depends on knowing the changing composition and different vital rates of subgroups within mobile populations and on uncovering the various strands of the population and weighing their differences and interdependencies. But given the general lack of detailed data for even small subpopulations, such a task is often very difficult. Some might argue that the smaller the group and the smaller the variation within it, the more meaningful is the mean. But even micro populations are not fixed and uniform; within single families, there may be considerable variation (Adams and Kasakoff 1999). Others might argue that on the level of the large nation-state, small differences become inconsequential, especially as modern communications and standards of public health may tend to create a uniform population. But such homogeneity does not exist at at any level of analysis that we have examined. The underlying social reality is so complex that the usual demographic rates have to be taken as no more than summaries of dubious convenience. In this article, we use individual-level data to present three examples of the ways in which migration affected adult life expectancy in the American North from 1740 to 1880. We found that to understand mortality changes, particularly among adults, we had to consider the accumulated lifetime experiences of the population, experiences that for many, if not the majority, took place in more than one locality and occupation. People moving spatially and socially brought their demographic histories with them. Depending on the particular disease environment and the source of the migrants, new arrivals might have either raised or lowered mean life expectancy in their new location. Such mixtures are not only difficult to sort out statistically, but they also raise questions about the demographic stability of places. There is, therefore, a fundamental tension between looking at the lives of individuals who are parts of migration streams linking spatial and occupational units and the units themselves. Are the rates in a particular spatial unit the result of the migrants who entered into it or the people born there? (We will address this","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598954","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26810874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598950
S R Johansson
n country after country, human life expectancy has doubled in the twentieth century. Although an enorI mous amount of information has accumulated about the decline of mortality, controversy continues about how to interpret the data (Schofield 1991). Some social scientists regard decreasing mortality as a side effect of economic growth, rising incomes, and better nutrition (Komlos 1998); others stress the importance of public health (Szreter 1988; Johansson 1994). Both explanations are concerned with those changes that mattered most to most people’s lives. At the macro level, the focus is on the average person and mean values (Hill 1997,224). At the micro level, the focus is on ordinary people who lived in specific contexts. Ordinary people live their lives as males and females, passing through different age groups, living in different places, earning a living in different ways while belonging to different religions and ethnic groups. Such differences expose people to different diseases or other hazards that must be avoided, resisted, or recovered from if individuals are to continue living until old age (Johansson and Mosk 1987). To prolong their lives, people must have access to a wide range of material resources, including food, of course, but they also require knowledge about how to convert their resources into better health and the resultant longevity. The conversion of knowledge and resources into health and longevity can never be observed directly. Trained observers can see only how specific groups of people tackled their specific health/disease-related problems in diverse material and cultural circumstances (Johansson 1990). But mainstream social scientists are not trained to do highly contextualized research grounded in specific details. As Stephen Kunitz (1996, 187) observed, conventional social scientists value truth only “to the extent that it transcends time and place.” This training automatically gives them a macro-level orientation to explaining change, including mortality change, and that perspective marginalizes the value of research on small-scale populations living in specific and often distinctive contexts. Such research may even be regarded as unscientific (Kertzer and Fricke 1997), because it is bogged down in “irrelevant” details that interfere with the search for sweeping generalizations about human beings in any context. In macro history, the average person is the only person who counts. But this statistical construct is not a real person who had specific nutritional or public health problems that led to his or her premature death. The average person is not even a conscious being who frames specific problems and tries to solve them in cooperation with others. Macro-level research makes room for “relevant” details about the average person by isolating separate components, such as age, sex, family size, household type, place of residence, class, occupation, income, religion, race, and ethnicity. Subsequently, these components
{"title":"Macro and micro perspectives on mortality history.","authors":"S R Johansson","doi":"10.1080/01615440009598950","DOIUrl":"https://doi.org/10.1080/01615440009598950","url":null,"abstract":"n country after country, human life expectancy has doubled in the twentieth century. Although an enorI mous amount of information has accumulated about the decline of mortality, controversy continues about how to interpret the data (Schofield 1991). Some social scientists regard decreasing mortality as a side effect of economic growth, rising incomes, and better nutrition (Komlos 1998); others stress the importance of public health (Szreter 1988; Johansson 1994). Both explanations are concerned with those changes that mattered most to most people’s lives. At the macro level, the focus is on the average person and mean values (Hill 1997,224). At the micro level, the focus is on ordinary people who lived in specific contexts. Ordinary people live their lives as males and females, passing through different age groups, living in different places, earning a living in different ways while belonging to different religions and ethnic groups. Such differences expose people to different diseases or other hazards that must be avoided, resisted, or recovered from if individuals are to continue living until old age (Johansson and Mosk 1987). To prolong their lives, people must have access to a wide range of material resources, including food, of course, but they also require knowledge about how to convert their resources into better health and the resultant longevity. The conversion of knowledge and resources into health and longevity can never be observed directly. Trained observers can see only how specific groups of people tackled their specific health/disease-related problems in diverse material and cultural circumstances (Johansson 1990). But mainstream social scientists are not trained to do highly contextualized research grounded in specific details. As Stephen Kunitz (1996, 187) observed, conventional social scientists value truth only “to the extent that it transcends time and place.” This training automatically gives them a macro-level orientation to explaining change, including mortality change, and that perspective marginalizes the value of research on small-scale populations living in specific and often distinctive contexts. Such research may even be regarded as unscientific (Kertzer and Fricke 1997), because it is bogged down in “irrelevant” details that interfere with the search for sweeping generalizations about human beings in any context. In macro history, the average person is the only person who counts. But this statistical construct is not a real person who had specific nutritional or public health problems that led to his or her premature death. The average person is not even a conscious being who frames specific problems and tries to solve them in cooperation with others. Macro-level research makes room for “relevant” details about the average person by isolating separate components, such as age, sex, family size, household type, place of residence, class, occupation, income, religion, race, and ethnicity. Subsequently, these components","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598950","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27346170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598960
M Ornstein
he sample of the nominal census for 1901 prepared by the Canadian Families Project is a sample of T households or dwellings, and the sampling point is the count of dwellings entered by the enumerator in column 1 of Schedule 1. Five percent of all dwellings on each microfilm reel were selected randomly; thus, the sample is stratified by microfilm reel. All individuals in each sampled dwelling were entered into the data set. Household samples for which information is gathered for every household member actually involve two levels of sampling and analysis. Usually, a simple random sample or stratified sample of households is selected. The resulting sample of individuals, however, is a cluster sample; it is a stratified cluster sample if the household sample is stratified. The selection probabilities are the same for individuals and for households. Thus, if the household sample is selfweighting, or epsem (equal probability of selection method)-which means that no weights are required to obtain unbiased estimates of population characteristicsthen so is the individual sample. The analysis of household characteristics is straightforward. For example, regional comparisons of household size require only the household sample. However, a cluster sample generally provides less information than a simple random sample of the same size, in this case, because members of the same household are less different than a simple random sample of individuals, who for the most part were from different households. In other words, the characteristics of one household member of a cluster usually go some way toward predicting the characteristics of the other household members. With household samples, that is often true for religion, for example. Usually, the religion of one household member is a good (but not perfect) predictor of the religion of all the other household members. The degree of within-household similarity is different for each variable. Because parents and children, and women and men, live together, households are not particularly homogeneous in age or sex composition. The consequence of within-cluster similarity is that estimates of statistical parameters generally have less precision than the parameters that would be obtained from a simple random sample of the same size. When cluster samples are used with computer programs that cannot, or are not “instructed” to, take account of clustering and assume a simple random sample, such as SPSS and SAS, erroneous standard errors, confidence intervals, and significance tests are computed. Almost always, standard errors are underestimated, the confidence intervals are too narrow, and statistical significance is overestimated. One can compute the degree of misestimation exactly by measuring the withincluster homogeneity, but the degree of misestimation cannot be predicted beforehand and is different for every variable. For that reason, the commonsense “fix” of decreasing the weight for each observation by some multiplie
{"title":"Analysis of household samples: the 1901 census of Canada.","authors":"M Ornstein","doi":"10.1080/01615440009598960","DOIUrl":"https://doi.org/10.1080/01615440009598960","url":null,"abstract":"he sample of the nominal census for 1901 prepared by the Canadian Families Project is a sample of T households or dwellings, and the sampling point is the count of dwellings entered by the enumerator in column 1 of Schedule 1. Five percent of all dwellings on each microfilm reel were selected randomly; thus, the sample is stratified by microfilm reel. All individuals in each sampled dwelling were entered into the data set. Household samples for which information is gathered for every household member actually involve two levels of sampling and analysis. Usually, a simple random sample or stratified sample of households is selected. The resulting sample of individuals, however, is a cluster sample; it is a stratified cluster sample if the household sample is stratified. The selection probabilities are the same for individuals and for households. Thus, if the household sample is selfweighting, or epsem (equal probability of selection method)-which means that no weights are required to obtain unbiased estimates of population characteristicsthen so is the individual sample. The analysis of household characteristics is straightforward. For example, regional comparisons of household size require only the household sample. However, a cluster sample generally provides less information than a simple random sample of the same size, in this case, because members of the same household are less different than a simple random sample of individuals, who for the most part were from different households. In other words, the characteristics of one household member of a cluster usually go some way toward predicting the characteristics of the other household members. With household samples, that is often true for religion, for example. Usually, the religion of one household member is a good (but not perfect) predictor of the religion of all the other household members. The degree of within-household similarity is different for each variable. Because parents and children, and women and men, live together, households are not particularly homogeneous in age or sex composition. The consequence of within-cluster similarity is that estimates of statistical parameters generally have less precision than the parameters that would be obtained from a simple random sample of the same size. When cluster samples are used with computer programs that cannot, or are not “instructed” to, take account of clustering and assume a simple random sample, such as SPSS and SAS, erroneous standard errors, confidence intervals, and significance tests are computed. Almost always, standard errors are underestimated, the confidence intervals are too narrow, and statistical significance is overestimated. One can compute the degree of misestimation exactly by measuring the withincluster homogeneity, but the degree of misestimation cannot be predicted beforehand and is different for every variable. For that reason, the commonsense “fix” of decreasing the weight for each observation by some multiplie","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598960","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26483905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598961
I Buck, D Jordan, S Manella, L McCann
he 1901 census of Canada, both in its original published form and in the sample created by the CanaT dian Families Project (CFP), offers very rich potential for geographical analysis of Canadian families. The reason is quite straightforward. When one examines documents of the 1901 census, the centrality of geographical space seems to permeate all of the surviving manuscript records and each of its printed volumes. Whether enumerating individuals or publishing results, census officials paid particular attention to geographical units. Their goal was to gather and disseminate population and economic information for provinces, cities, and census districts, as well as for smaller geographical areas such as towns, villages, and census subdistricts. In fact, the precise locational reference of any dwelling (its street address or legal property description), and therefore of any family occupying the dwelling, was carefully recorded in 1901 for the first time in Canadian census taking. Although census officials did devise a geographical framework for the census, it is no easy task to reconstruct the spatial parameters of this historical document. More important, the published volumes of the census, unlike later and even some earlier censuses, offer no cartographic evidence of the boundaries of the statistical units used to administer and report census results. Other than the locational references written on manuscript census schedules, little tangible evidence remains with which to retrace the actual boundaries and areas referenced and canvassed by census enumerators. Without a geographic& framework, it would be impossible to analyze the data sampled by the CFP when the purpose is to interpret the spatial processes, patterns, and structures associated with family life in Canada’s cities and rural areas. To meet this goal, methods have been devised to reconstruct the boundaries of the various types of statistical units that census officials used when gathering and publishing census information. These units include census districts, subdistricts, polling subdivisions, and urban places. Using areal units of varying size will, of course, influence the geographical interpretation of family life. To illustrate this fundamental fact, we have mapped, at different geographical scales, the patterns of average family size that characterize the social space of Montreal and its rural hinterland. With a population of 267,730 in 1901, Montreal was Canada’s largest city. This total does not include the population of twelve contiguous suburban municipalities that were incorporated as villages, towns, or cities and housed nearly 85,000 people. We make no attempt to explain the effect of scale differences on the revealed map patterns, other than to reaffirm the assertion of Fernand Braudel (1984, 21) that “geographical space as a source of explanation affects all historical realities, all spatially-defined phenomena.”
{"title":"Reconstructing the geographical framework of the 1901 census of Canada.","authors":"I Buck, D Jordan, S Manella, L McCann","doi":"10.1080/01615440009598961","DOIUrl":"https://doi.org/10.1080/01615440009598961","url":null,"abstract":"he 1901 census of Canada, both in its original published form and in the sample created by the CanaT dian Families Project (CFP), offers very rich potential for geographical analysis of Canadian families. The reason is quite straightforward. When one examines documents of the 1901 census, the centrality of geographical space seems to permeate all of the surviving manuscript records and each of its printed volumes. Whether enumerating individuals or publishing results, census officials paid particular attention to geographical units. Their goal was to gather and disseminate population and economic information for provinces, cities, and census districts, as well as for smaller geographical areas such as towns, villages, and census subdistricts. In fact, the precise locational reference of any dwelling (its street address or legal property description), and therefore of any family occupying the dwelling, was carefully recorded in 1901 for the first time in Canadian census taking. Although census officials did devise a geographical framework for the census, it is no easy task to reconstruct the spatial parameters of this historical document. More important, the published volumes of the census, unlike later and even some earlier censuses, offer no cartographic evidence of the boundaries of the statistical units used to administer and report census results. Other than the locational references written on manuscript census schedules, little tangible evidence remains with which to retrace the actual boundaries and areas referenced and canvassed by census enumerators. Without a geographic& framework, it would be impossible to analyze the data sampled by the CFP when the purpose is to interpret the spatial processes, patterns, and structures associated with family life in Canada’s cities and rural areas. To meet this goal, methods have been devised to reconstruct the boundaries of the various types of statistical units that census officials used when gathering and publishing census information. These units include census districts, subdistricts, polling subdivisions, and urban places. Using areal units of varying size will, of course, influence the geographical interpretation of family life. To illustrate this fundamental fact, we have mapped, at different geographical scales, the patterns of average family size that characterize the social space of Montreal and its rural hinterland. With a population of 267,730 in 1901, Montreal was Canada’s largest city. This total does not include the population of twelve contiguous suburban municipalities that were incorporated as villages, towns, or cities and housed nearly 85,000 people. We make no attempt to explain the effect of scale differences on the revealed map patterns, other than to reaffirm the assertion of Fernand Braudel (1984, 21) that “geographical space as a source of explanation affects all historical realities, all spatially-defined phenomena.”","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26481229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598959
L Y Dillon
A t the beginning of the twenty-first century, the field of historical demography has acquired new energy .through the proliferation of historical census microdata projects. Wedding traditional skills and new technology, census microdata projects in Canada, the United States, the United Kingdom, Norway, Denmark, and elsewhere are increasingly making historical and contemporary microdata available to researchers. One promising development in this work is the harmonization of census microdata across both time and space to facilitate comparative historical and demographic analysis. A group of census researchers drawn primarily from North and South America and Europe have formed the International Microdata Access Group (IMAG) to encourage the harmonization of historical census microdata. MAG facilitates the international consultation and cooperation of scholars who wish to unite multiple data files with a common set of comparably coded variables.' In a parallel initiative, the Social Sciences and Humanities Research Council of Canada (SSHRCC) and the Canadian Families Project (CFP), which created a machine-readable sample of the 1901 Canadian census, have sponsored the integration of these microdata with similar microdata from Canada in 1871 and the United States in 1870 and 1900.* The resulting integrated series is known as the Integrated Canadian-U.S. Historical Census Public Use Microdata Series (ICAPUMS), 1870-1901. I undertook this project on the heels of a similar initiative conducted between 1995 and 1997 in which I integrated the 1871 Canadian census microdata with U.S. microdata from 1850 and 1880. Integrating census microdata files from different nations and years enables scholars to compare individual, family, and social behavior across both space and time. Fully harmonized census microdata allow researchers to consider time and nation as independent variables together with others, such as sex, marital status, occupation, and age. Scholars can then analyze the strength with which individual behavior is associated with time and nation versus personal and subnational contextual variables. Such research allows historians to explore the extent to which social, economic, and cultural behaviors transcended national boundaries (Dillon forthcoming). This article describes the challenges of integrating the 1871 and 1901 Canadian and the 1870 and 1900 U.S. census microdata, considering some of the theoretical issues inherent in combining data from two countries that expressed somewhat different intentions for their censuses. The article is also informed by my experiences working with both the Minnesota and Canadian census microdata projects, which have treated their data in both similar and different ways.
{"title":"Integrating Canadian and U.S. historical census microdata: Canada (1871 and 1901) and the United States (1870 and 1900).","authors":"L Y Dillon","doi":"10.1080/01615440009598959","DOIUrl":"https://doi.org/10.1080/01615440009598959","url":null,"abstract":"A t the beginning of the twenty-first century, the field of historical demography has acquired new energy .through the proliferation of historical census microdata projects. Wedding traditional skills and new technology, census microdata projects in Canada, the United States, the United Kingdom, Norway, Denmark, and elsewhere are increasingly making historical and contemporary microdata available to researchers. One promising development in this work is the harmonization of census microdata across both time and space to facilitate comparative historical and demographic analysis. A group of census researchers drawn primarily from North and South America and Europe have formed the International Microdata Access Group (IMAG) to encourage the harmonization of historical census microdata. MAG facilitates the international consultation and cooperation of scholars who wish to unite multiple data files with a common set of comparably coded variables.' In a parallel initiative, the Social Sciences and Humanities Research Council of Canada (SSHRCC) and the Canadian Families Project (CFP), which created a machine-readable sample of the 1901 Canadian census, have sponsored the integration of these microdata with similar microdata from Canada in 1871 and the United States in 1870 and 1900.* The resulting integrated series is known as the Integrated Canadian-U.S. Historical Census Public Use Microdata Series (ICAPUMS), 1870-1901. I undertook this project on the heels of a similar initiative conducted between 1995 and 1997 in which I integrated the 1871 Canadian census microdata with U.S. microdata from 1850 and 1880. Integrating census microdata files from different nations and years enables scholars to compare individual, family, and social behavior across both space and time. Fully harmonized census microdata allow researchers to consider time and nation as independent variables together with others, such as sex, marital status, occupation, and age. Scholars can then analyze the strength with which individual behavior is associated with time and nation versus personal and subnational contextual variables. Such research allows historians to explore the extent to which social, economic, and cultural behaviors transcended national boundaries (Dillon forthcoming). This article describes the challenges of integrating the 1871 and 1901 Canadian and the 1870 and 1900 U.S. census microdata, considering some of the theoretical issues inherent in combining data from two countries that expressed somewhat different intentions for their censuses. The article is also informed by my experiences working with both the Minnesota and Canadian census microdata projects, which have treated their data in both similar and different ways.","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26483903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598963
B Bradbury
Anna Cottle was a 56-year-old widow living in St. John’s Ward, Toronto, in 1901. She and her two teenage daughters, Edith and Ellen, lived in the household of carpenter and widower William Wilkinson and his two sons. Anna was listed as a lodger, Edith and Ellen as her daughters. Another two families were also listed as lodgers and, like Anna, were given no separate family number in the manuscript census.’ Susan Mitchell (36) was listed as a lodger in the dwelling of a barber, Hugh Mitchell (43 , his wife, and their two children. Susan was inscribed as married, though her husband was not listed. Her 15-year-old son, Willie, lived with here2 Annie Tamousegusick, a 40-year-old Chippewan widow, lived in Nipissing County in the Nairn District when the enumerator visited her family in 1901. Her 24-year-old trapper son, George, was listed as family head, her other children as his brothers and sister^.^
{"title":"Single parenthood in the past: Canadian census categories, 1891-1951, and the \"normal\" family.","authors":"B Bradbury","doi":"10.1080/01615440009598963","DOIUrl":"https://doi.org/10.1080/01615440009598963","url":null,"abstract":"Anna Cottle was a 56-year-old widow living in St. John’s Ward, Toronto, in 1901. She and her two teenage daughters, Edith and Ellen, lived in the household of carpenter and widower William Wilkinson and his two sons. Anna was listed as a lodger, Edith and Ellen as her daughters. Another two families were also listed as lodgers and, like Anna, were given no separate family number in the manuscript census.’ Susan Mitchell (36) was listed as a lodger in the dwelling of a barber, Hugh Mitchell (43 , his wife, and their two children. Susan was inscribed as married, though her husband was not listed. Her 15-year-old son, Willie, lived with here2 Annie Tamousegusick, a 40-year-old Chippewan widow, lived in Nipissing County in the Nairn District when the enumerator visited her family in 1901. Her 24-year-old trapper son, George, was listed as family head, her other children as his brothers and sister^.^","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26369168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598964
D Gavreau, P Gossage, L Gingras
n demography, the most sophisticated measures of fertility are usually based on the registration of births I combined with census data. But where the registration of vital statistics is nonexistent-as in Canada before 1921 'demographers have found other ways of measuring fertility. Censuses are one source that can be used for this purpose, at both the aggregate and household levels. Aggregate measures of fertility-also referred to as macrolevel measures-are the most commonly used to describe historical fertility trends. They are comparatively easy to establish, because they can usually be derived from published material available for specific geographic areas.2 But the analytical power of such measures is limited to associations between fertility indices and the other characteristics of a given territory. The high aggregate fertility in Quebec counties with large proportions of Francophones, for example, tends to support a link between linguistic features and fertility beha~ior.~ But this "ecological" association actually tells us nothing about how Francophone women in various counties actually behaved in comparison with their English-speaking contemporaries. Household-level measures differ from aggregate measures in that they are based on a direct count of each mother's own children. That observation can then be linked to the mother's personal characteristics and to those of the household in which she lived. For obvious reasons, these microlevel research tools provide much greater analytical power. However, they are not particularly common in the literature. Furthermore, when they are used, it is most often with relatively small samples because household-level measures are much more difficult to establish than aggregate fertility indices. Constructing household-level fertility measures involves looking beyond published census tables to the raw data pertaining to each individual and family that were collected by enumerators, in this case almost a century ago. We have recently done this kind of work using the Canadian manuscript census of 1901, both in the context of the Canadian Families Project (CFP) and our separate research project on the Quebec fertility de~ l ine .~ The careful transcription of nominative data from enumerators' lists has allowed us to develop more detailed household-level measures of fertility. It thereby opened new opportunities for testing specific hypotheses about individual reproductive behavior, at a time when fertility rates in Quebec were showing some signs of a decline but when differentials with other parts of Canada (where fertility change had begun earlier) were still substantial. Our focus in this article on the Province of Quebec is of interest from the perspective of ethnic, religious, and linguistic variation. The Province of Quebec-where the quality of parish registers, at least for the Catholic majority, was high-also offers interesting possibilities for the independent testing of the fertility data derived
{"title":"Measuring fertility with the 1901 Canadian census: a critical assessment.","authors":"D Gavreau, P Gossage, L Gingras","doi":"10.1080/01615440009598964","DOIUrl":"https://doi.org/10.1080/01615440009598964","url":null,"abstract":"n demography, the most sophisticated measures of fertility are usually based on the registration of births I combined with census data. But where the registration of vital statistics is nonexistent-as in Canada before 1921 'demographers have found other ways of measuring fertility. Censuses are one source that can be used for this purpose, at both the aggregate and household levels. Aggregate measures of fertility-also referred to as macrolevel measures-are the most commonly used to describe historical fertility trends. They are comparatively easy to establish, because they can usually be derived from published material available for specific geographic areas.2 But the analytical power of such measures is limited to associations between fertility indices and the other characteristics of a given territory. The high aggregate fertility in Quebec counties with large proportions of Francophones, for example, tends to support a link between linguistic features and fertility beha~ior.~ But this \"ecological\" association actually tells us nothing about how Francophone women in various counties actually behaved in comparison with their English-speaking contemporaries. Household-level measures differ from aggregate measures in that they are based on a direct count of each mother's own children. That observation can then be linked to the mother's personal characteristics and to those of the household in which she lived. For obvious reasons, these microlevel research tools provide much greater analytical power. However, they are not particularly common in the literature. Furthermore, when they are used, it is most often with relatively small samples because household-level measures are much more difficult to establish than aggregate fertility indices. Constructing household-level fertility measures involves looking beyond published census tables to the raw data pertaining to each individual and family that were collected by enumerators, in this case almost a century ago. We have recently done this kind of work using the Canadian manuscript census of 1901, both in the context of the Canadian Families Project (CFP) and our separate research project on the Quebec fertility de~ l ine .~ The careful transcription of nominative data from enumerators' lists has allowed us to develop more detailed household-level measures of fertility. It thereby opened new opportunities for testing specific hypotheses about individual reproductive behavior, at a time when fertility rates in Quebec were showing some signs of a decline but when differentials with other parts of Canada (where fertility change had begun earlier) were still substantial. Our focus in this article on the Province of Quebec is of interest from the perspective of ethnic, religious, and linguistic variation. The Province of Quebec-where the quality of parish registers, at least for the Catholic majority, was high-also offers interesting possibilities for the independent testing of the fertility data derived ","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26369645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-01-01DOI: 10.1080/01615440009598949
S R Johansson, A B Kasakoff
he global transition to higher life-expectancy levels doubled the length of the average human life in less T than a century (Easterlin 1996, 69). Initially, life expectancy at birth values for most countries, Western and non-Western, were in the 20-to-40-year range; at present, they are in the 60-to-80-year range. For decades, social scientists have investigated this biological revolution without reaching a consensus about its causes. As Roger Schofield (1991,2) wrote in his introduction to The Decline of European Mortality, “It would only be a small exaggeration to say that our understanding of historical mortality patterns, and of their causes and implications, is still in its infancy.” If he is correct, we are dealing with a case of very prolonged infancy, because research on the decline of mortality was conducted throughout the twentieth century, and an enormous amount of solid factual information has been accumulated. The absence of an interpretative consensus after so much research reflects the covertly political nature of health and mortality history. Specific interpretations of the past have specific policy implications for the present that lead politically involved researchers to put a “spin” on the data (Kunitz 1987; Johansson 1994, 1996). But continuing controversy also reflects professional rivalry among social scientists who work in specialized fields of knowledge. More often than not, academics from different departments tend to treat each other as competitors instead of partners in the production of knowledge about the past; their rivalry encourages the habit of ignoring or dismissing evidence and arguments produced by the other side. This mentality discourages genuine interdisciplinary research in every aspect of demography and legitimizes the existence of highly artificial explanatory dichotomies (Lesthaeghe 1998). In mortality history, the artificial dichotomy currently generating more heat than light concerns the relative importance of better nutrition versus more public health in the world’s earliest mortality transitions, that is, those that began in Western Europe (see the essays in this issue by Haines and Kintner, Woods and Shelton, and Johansson). But both sides agree on the appropriateness of using national-level data to test competing hypotheses to the exclusion of doing more localized research. Before 1950, nationallevel research included some recognition of the importance of local differences to general explanations (Kreager 1997); but, subsequently, as the demography of currently developing countries came to be based on the analysis of nationallevel data and little else (Hill 1997), so did historical demography. Anthropological demographers objected to the increasing reliance on macro-level data and continued to do smallscale, micro-level population research.’ By now, they have exposed the empirical pitfalls of relying on national-level data as the basis for understanding fertility behavior (Kertzer and Fricke 1997).
{"title":"Mortality history and the misleading mean.","authors":"S R Johansson, A B Kasakoff","doi":"10.1080/01615440009598949","DOIUrl":"https://doi.org/10.1080/01615440009598949","url":null,"abstract":"he global transition to higher life-expectancy levels doubled the length of the average human life in less T than a century (Easterlin 1996, 69). Initially, life expectancy at birth values for most countries, Western and non-Western, were in the 20-to-40-year range; at present, they are in the 60-to-80-year range. For decades, social scientists have investigated this biological revolution without reaching a consensus about its causes. As Roger Schofield (1991,2) wrote in his introduction to The Decline of European Mortality, “It would only be a small exaggeration to say that our understanding of historical mortality patterns, and of their causes and implications, is still in its infancy.” If he is correct, we are dealing with a case of very prolonged infancy, because research on the decline of mortality was conducted throughout the twentieth century, and an enormous amount of solid factual information has been accumulated. The absence of an interpretative consensus after so much research reflects the covertly political nature of health and mortality history. Specific interpretations of the past have specific policy implications for the present that lead politically involved researchers to put a “spin” on the data (Kunitz 1987; Johansson 1994, 1996). But continuing controversy also reflects professional rivalry among social scientists who work in specialized fields of knowledge. More often than not, academics from different departments tend to treat each other as competitors instead of partners in the production of knowledge about the past; their rivalry encourages the habit of ignoring or dismissing evidence and arguments produced by the other side. This mentality discourages genuine interdisciplinary research in every aspect of demography and legitimizes the existence of highly artificial explanatory dichotomies (Lesthaeghe 1998). In mortality history, the artificial dichotomy currently generating more heat than light concerns the relative importance of better nutrition versus more public health in the world’s earliest mortality transitions, that is, those that began in Western Europe (see the essays in this issue by Haines and Kintner, Woods and Shelton, and Johansson). But both sides agree on the appropriateness of using national-level data to test competing hypotheses to the exclusion of doing more localized research. Before 1950, nationallevel research included some recognition of the importance of local differences to general explanations (Kreager 1997); but, subsequently, as the demography of currently developing countries came to be based on the analysis of nationallevel data and little else (Hill 1997), so did historical demography. Anthropological demographers objected to the increasing reliance on macro-level data and continued to do smallscale, micro-level population research.’ By now, they have exposed the empirical pitfalls of relying on national-level data as the basis for understanding fertility behavior (Kertzer and Fricke 1997).","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27346162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1999-01-01DOI: 10.1080/01615449909598927
N Bonneuil, P Rosental
{"title":"Changing social mobility in nineteenth-century France.","authors":"N Bonneuil, P Rosental","doi":"10.1080/01615449909598927","DOIUrl":"https://doi.org/10.1080/01615449909598927","url":null,"abstract":"","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615449909598927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29615055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1999-01-01DOI: 10.1080/01615449909598934
Ronald A Goeken, Marjorie Bryer, C. Lucas
{"title":"Making sense of census responses: Coding complex variables in the 1920 pums","authors":"Ronald A Goeken, Marjorie Bryer, C. Lucas","doi":"10.1080/01615449909598934","DOIUrl":"https://doi.org/10.1080/01615449909598934","url":null,"abstract":"","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615449909598934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59231356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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