Macro and micro perspectives on mortality history.

IF 1.6 2区 历史学 Q1 HISTORY Historical Methods Pub Date : 2000-01-01 DOI:10.1080/01615440009598950
S R Johansson
{"title":"Macro and micro perspectives on mortality history.","authors":"S R Johansson","doi":"10.1080/01615440009598950","DOIUrl":null,"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 become independent variables that, through linear relationships, cause variation in the dependent variable (in this case, some index of mortality). The goal of macro-level research is to identify which independent variable(s) accounts for most of the observed variance in the dependent variable, irrespective of local contextual considerations (Ruzika, Wunsch, and Kane, eds. 1989; Smith 1983). The regression methods used to explain mortality history at the macro level tell us a lot about how variables behave in large data sets, whereas they shed very little light on how real people address specific health-related problems in specific circumstances. Clarification of those problems remains the goal of most micro-level mortality research. To explain problem-solving behavior, micro historians rely heavily on conventional narrative methods. They read primary sources and select a set of details that when arranged in chronological order, tell a plausible story about who did what and why and how others reacted. Story formats are based on linking details, but not in any highly standardized way. The flexibility and diversity associated with narrative methods make it difficult to see what specific stories or cases have in common, if anything (Levine 1987, 129-32). Micro-level research tends to pull researchers toward the","PeriodicalId":45535,"journal":{"name":"Historical Methods","volume":"33 2","pages":"59-72"},"PeriodicalIF":1.6000,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/01615440009598950","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Historical Methods","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1080/01615440009598950","RegionNum":2,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HISTORY","Score":null,"Total":0}
引用次数: 12

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 become independent variables that, through linear relationships, cause variation in the dependent variable (in this case, some index of mortality). The goal of macro-level research is to identify which independent variable(s) accounts for most of the observed variance in the dependent variable, irrespective of local contextual considerations (Ruzika, Wunsch, and Kane, eds. 1989; Smith 1983). The regression methods used to explain mortality history at the macro level tell us a lot about how variables behave in large data sets, whereas they shed very little light on how real people address specific health-related problems in specific circumstances. Clarification of those problems remains the goal of most micro-level mortality research. To explain problem-solving behavior, micro historians rely heavily on conventional narrative methods. They read primary sources and select a set of details that when arranged in chronological order, tell a plausible story about who did what and why and how others reacted. Story formats are based on linking details, but not in any highly standardized way. The flexibility and diversity associated with narrative methods make it difficult to see what specific stories or cases have in common, if anything (Levine 1987, 129-32). Micro-level research tends to pull researchers toward the
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
死亡率历史的宏观和微观视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Historical Methods
Historical Methods Multiple-
CiteScore
3.20
自引率
7.10%
发文量
13
期刊介绍: Historical Methodsreaches an international audience of social scientists concerned with historical problems. It explores interdisciplinary approaches to new data sources, new approaches to older questions and material, and practical discussions of computer and statistical methodology, data collection, and sampling procedures. The journal includes the following features: “Evidence Matters” emphasizes how to find, decipher, and analyze evidence whether or not that evidence is meant to be quantified. “Database Developments” announces major new public databases or large alterations in older ones, discusses innovative ways to organize them, and explains new ways of categorizing information.
期刊最新文献
A New Strategy for Linking U.S. Historical Censuses: A Case Study for the IPUMS Multigenerational Longitudinal Panel. Simple Strategies for Improving Inference with Linked Data: A Case Study of the 1850-1930 IPUMS Linked Representative Historical Samples. Reconstruction of Birth Histories for the Study of Fertility in the United States, 1830-1910. Linking the 1940 U.S. Census with Modern Data. Union Army Veterans, All Grown Up.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1