The Ipums Collaboration: Integrating and Disseminating the World’s Population Microdata

Ruggles, Steven; McCaa, Robert; Sobek, Matthew; Cleveland, Lara

doi:10.1017/dem.2014.6

Cited by 51 publications

(44 citation statements)

References 23 publications

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“…Information on unwanted births is not available in census data. Hence, we use Demographic 6 These data are especially valuable for studying trends and dierentials in the core demographic processes and have become a major source for the reports of the U.N. Population Division (Ruggles et al 2015).…”

Section: Datamentioning

confidence: 99%

Endogenous Childlessness and Stages of Development

Baudin

Croix

Gobbi

2018

Journal of the European Economic Association

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Although developing countries are characterized by high average fertility rates, they are as concerned by childlessness as developed countries. Beyond natural sterility, there are two main types of childlessness: one driven by poverty and another by the high opportunity cost of child-rearing. We measure the importance of the components of childlessness with a structural model of fertility and marriage. Deep parameters are identified using census data from 36 developing countries. As average education increases, poverty-driven childlessness first decreases to a minimum, and then the opportunity-driven part of childlessness increases. We show that neglecting the endogenous response of marriage and childlessness may lead to a poor understanding of the impact that social progress, such as universal primary education, may have on completed fertility. The same holds for family planning, closing the gender pay gap, and the eradication of child mortality.

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Section: Datamentioning

confidence: 99%

Endogenous Childlessness and Stages of Development

Baudin

Croix

Gobbi

2018

Journal of the European Economic Association

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“…We extracted household census data from the Integrated Public Use Microdata Series-International (IPUMS) database, which harmonizes household-level demographic variables across countries and years [31,32]. All records were extracted from the most recent available census for all available countries in Latin America, sub-Saharan Africa, and South and East Asia.…”

Section: Ipums Data Extractionmentioning

confidence: 99%

Subnational distribution of average farm size and smallholder contributions to global food production

Samberg

Gerber

Ramankutty

et al. 2016

Environ. Res. Lett.

341

207

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Smallholder farming is the most prevalent form of agriculture in the world, supports many of the planet's most vulnerable populations, and coexists with some of its most diverse and threatened landscapes. However, there is little information about the location of small farms, making it difficult both to estimate their numbers and to implement effective agricultural, development, and land use policies. Here, we present a map of mean agricultural area, classified by the amount of land per farming household, at subnational resolutions across three key global regions using a novel integration of household microdata and agricultural landscape data. This approach provides a subnational estimate of the number, average size, and contribution of farms across much of the developing world. By our estimates, 918 subnational units in 83 countries in Latin America, sub-Saharan Africa, and South and East Asia average less than five hectares of agricultural land per farming household. These smallholder-dominated systems are home to more than 380 million farming households, make up roughly 30% of the agricultural land and produce more than 70% of the food calories produced in these regions, and are responsible for more than half of the food calories produced globally, as well as more than half of global production of several major food crops. Smallholder systems in these three regions direct a greater percentage of calories produced toward direct human consumption, with 70% of calories produced in these units consumed as food, compared to 55% globally. Our approach provides the ability to disaggregate farming populations from non-farming populations, providing a more accurate picture of farming households on the landscape than has previously been available. These data meet a critical need, as improved understanding of the prevalence and distribution of smallholder farming is essential for effective policy development for food security, poverty reduction, and conservation agendas.

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“…The foundation of our improvements in investigating industrial landscapes lies in the creation and use of a robust and flexible digital infrastructure that we refer to as a Historical Spatial Data Infrastructure (HSDI). We introduce the HSDI concept and describe technical details relating to its construction elsewhere [18]; in brief, it is a digital infrastructure that links the research benefits of Historical GIS with the flexibility, scalability, and robustness of Spatial Data Infrastructures (SDIs) that underpin critical big data projects such as the Minnesota Population Center's Integrated Public Use Microdata Series (IPUMS) [38][39][40]. The Imag(in)ing London Historical GIS project [22] represents an early implementation of the HSDI concept [18] and this project (hereafter referred to as the London HSDI) forms the basis of the present longitudinal investigation of industrial hazards.…”

Section: New Infrastructures For Studying Industrial Activity Across mentioning

confidence: 99%

Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

Trepal

Lafreniere

2019

Urban Science

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We combine the Historical Spatial Data Infrastructure (HSDI) concept developed within spatial history with elements of archaeological predictive modeling to demonstrate a novel GIS-based landscape model for identifying the persistence of historically-generated industrial hazards in postindustrial cities. This historical big data approach draws on over a century of both historical and modern spatial big data to project the presence of specific persistent historical hazards across a city. This research improves on previous attempts to understand the origins and persistence of historical pollution hazards, and our final model augments traditional archaeological approaches to site prospection and analysis. This study also demonstrates how models based on the historical record, such as the HSDI, complement existing approaches to identifying postindustrial sites that require remediation. Our approach links the work of archaeologists more closely to other researchers and to municipal decision makers, permitting closer cooperation between those involved in archaeology, heritage, urban redevelopment, and environmental sustainability activities in postindustrial cities.

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The Ipums Collaboration: Integrating and Disseminating the World’s Population Microdata

Cited by 51 publications

References 23 publications

Endogenous Childlessness and Stages of Development

Endogenous Childlessness and Stages of Development

Subnational distribution of average farm size and smallholder contributions to global food production

Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

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