The IPUMS Multigenerational Longitudinal Panel (MLP) is a longitudinal population panel that links American censuses, surveys, administrative sources, and vital records spanning the period from 1850 to the present. This article explains the rationale for IPUMS MLP, outlines the design of the infrastructure, and describes the linking methods used to construct the panel. We then detail our plans for expansion and improvement of MLP over the next five years, including the incorporation of additional data sources, the development of a "linkage hub" to connect MLP with other major record linkage efforts, and the refinement of our technology and dissemination efforts. We conclude by describing a few early examples of MLP-based research.

Over the past five years, the IPUMS Multigenerational Longitudinal Panel (MLP) has constructed a massive longitudinal population panel by linking together American censuses, surveys, administrative sources, and vital records spanning the period from 1850 to the present. MLP is designed as a general-purpose resource for studying longitudinal processes and long-run social and economic change. By linking individuals across generations from birth through death using data from multiple sources, MLP enables reproducible research on shifting patterns of life course patterns and intergenerational processes. The large scale of the MLP database allows researchers both to study particular communities and small dispersed populations and to conduct big studies spanning many places and periods. Data that allow investigators to examine simultaneously the broad sweep of time and fine spatial detail will yield new insights into ongoing transformations of demographic behavior. The presentation will describe the creation of the IPUMS MLP database and the current MLP linking strategy. We then detail our plans for expansion and improvement of MLP over the next five years, including the incorporation of additional data sources, the development of a “linkage hub” to connect MLP with other major record linkage efforts, and the refinement of our technology and dissemination efforts. We will conclude by describing a few early examples of MLP-based research.

Academic researchers in the U.S. and Canada have partnered with genealogical organizations and statistical agencies to create massive new scientific data collections from census enumerations. These data are creating new opportunities for research across the social and health sciences. By linking individuals and families across censuses, analysts can create national longitudinal panels that trace the characteristics of individuals over their lives and families over multiple generations. IPUMS disseminates full count census enumerations for ten U.S. census years from 1850 to 1950. These full count data cover almost 800 million individual records and the IPUMS Multigenerational Longitudinal Panel (MLP) project links individuals' records across censuses. IPUMS data can be combined with data from the U.S. Census Bureau’s data linkage infrastructure to link historical records to numerous recent censuses, surveys and administrative data that measure social, economic and health outcomes.  The Canadian Peoples (TCP) is a comprehensive public research database of 40 million coded and georeferenced records found in Canadian censuses from the middle of the nineteenth century until after the First World War. The data include personal, family and household characteristics of every individual enumerated in each census from 1852 to 1921. TCP investigators are exploring record linkage technology to solve challenges presented with these historical records. The workshop included one presentation from each organization, with these objectives: introduce the data collections, explain the challenges and opportunities of census linkage, describe the linking strategies, and provide an overview on how to access the data.

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population.

Though many methods from ecology translate well to agroecosystems and still further, urban agroecosystems, systems of food production in areas of dense human occupation often necessitate a different approach to ecological research. This chapter provides a primer on natural science research methods for new researchers in UA. The chapter overviews recent ecological research in or relevant to urban agriculture, paying particular attention to methodological advancements made from a natural science perspective. Specifically, this chapter outlines 1) the application of classic ecological theory to urban agriculture's unique ecological conditions; 2) open questions in the ecology of UA, and broader questions in ecology that can be addressed in UA systems; 3) what methods from ecology and agroecology translate well and what must be newly adapted; and 4) the most pressing methodological issues for future urban agroecologial research to address.

This paper presents a new probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to a range of different sources of historical records. The method was designed to exploit a more comprehensive set of individual and contextual characteristics present in historical census data, aiming to obtain a machine learning algorithm that better distinguishes between multiple potential matches. Our results demonstrate that the method achieves a match rate that is twice as high other currently popular methods in the literature while at the same time also achieving greater accuracy. In addition, the method only performs negligibly worse than other algorithms in resembling the target population.

In September 2018, the Census Bureau announced a new set of methods for disclosure control in public use data products, including aggregate-level tabular data and microdata derived from the decennial census and the American Community Survey (ACS) (US Census Bureau 2018a). The new approach, known as differential privacy, “marks a sea change for the way that official statistics are produced and published” (Garfinkel, Abowd, and Powazek 2018). In accordance with census law, for the past six decades the Census Bureau has ensured that no census publications allow specific census responses to be linked to specific people. Differential privacy requires protections that go well beyond this standard; under the new approach, responses of individuals cannot be divulged even if the identity of those individuals is unknown and cannot be determined. In its pure form, differential privacy techniques could make the release of scientifically useful . . .

IPUMS-MLP will consist of nine censuses covering the entire U.S. population enumerated between 1850 and 1940 linked with public historical administrative data from Social Security, the military, and vital registration. The linked database will be invaluable for analyzing the impact of early life conditions on health and well-being in later life, and the large scale of the resource will allow study of very small population subgroups. IPUMS-MLP is not designed to answer any particular scientific question. Rather, we plan general-purpose data infrastructure, a permanent resource that can be continuously expanded to incorporate the latest data sources as they become available, ensuring its usage for decades to come. Former Census Bureau Director Robert Groves drew an insightful distinction between “designed data” and “organic data” [1]. Designed data, such as censuses and surveys, are created entirely to obtain information. Organic data are byproducts of transactions, including administrative records generated by Social Security, Medicare, the Internal Revenue Service, and the Armed Forces. Research on population aging currently relies primarily on designed data, despite the enormous potential of organic data to enrich our analyses. Groves argued that “the biggest payoff will lie in new combinations of designed data and organic data, not in one type alone.” Used in isolation, organic data have profound limitations that reduce their usefulness. They tend to be voluminous but shallow; they often are unrepresentative of the general population; and they frequently omit basic information about demographic behavior, economic status, education, work, and living conditions. IPUMS-MLP will enrich large sources of organic data—including Social Security, Medicare, and military records—by linking them to a century of designed census and survey data, thereby overcoming limitations of the organic data sources. Linking individuals from childhood to old age and death through both designed and organic data allows study of aging as a process over the entire life course, not just over a few years. Indeed, IPUMS-MLP will enable investigators to extend longitudinal analysis beyond individual life histories to investigate and understand processes of change over multiple generations [2]. In his 2010 presidential address to the Population Association of America, Robert Mare [3] argued that “the study of intergenerational mobility and most population research are governed by a two generation (parent-to offspring) view of intergenerational influence, to the neglect of the effects of grandparents and other ancestors and nonresident contemporary kin.” Mare called for the development of sources and methods that will support analysis of change over multiple generations. IPUMS-MLP will meet this need, allowing investigators to trace records back across multiple generations and making it possible for the first time to study the transmission of characteristics and behavior across centuries.

vFor the past decade IPUMS and the Census Bureau have been collaborating to recover, preserve, document, harmonize, and disseminate all surviving machine-readable population census microdata from 1960 to the present. The fruits of this collaboration are now available in Federal Statistical Research Data Centers (FSRDCs). With 1.3 billion person records and 530 million household records, IPUMS-FSRDC is the largest structured individual-level data collection describing the demographic characteristics of a population. This paper will describe the challenges of creating IPUMS-FSRDC and highlight the important research opportunities made possible by this new data resource. We will also provide details about the data, including differences between the public use data and these restricted files; description of constructed IPUMS variables, such as family interrelationships; an overview and explanation of consistent geography variables. Finally, we will discuss our plans to create harmonized versions of American Community Survey (ACS) microdata in the FSRDC.

For the past 80 years, social scientists have been linking historical censuses across time to study economic and geographic mobility. In recent decades, the quantity of historical census record linkage has exploded, owing largely to the advent of new machine-readable data created by genealogical organizations. Investigators are examining economic and geographic mobility across multiple generations, but also engaging many new topics. Several analysts are exploring the effects of early-life socioeconomic conditions, environmental exposures, or natural disasters on family, health and economic outcomes in later life. Other studies exploit natural experiments to gauge the impact of policy interventions such as social welfare programs and educational reforms. The new data sources have led to a proliferation of record linkage methodology, and some widespread approaches inadvertently introduce errors that can lead to false inferences. A new generation of large-scale shared data infrastructure now in preparation will ameliorate weaknesses of current linkage methods.

The first version of the Integrated Public Use Microdata Series (IPUMS) was released to users in 1993, and since that time IPUMS has come to stand for interoperable and accessible census and survey data. Initially created to harmonize U.S. census microdata over time, IPUMS now includes microdata from the U.S. and international censuses and from surveys on health, employment, and other topics. IPUMS also provides geo-spatial data, aggregate population data, and environmental data. IPUMS supports ten data products, each disseminating an integrated data collection with a set of tools that make complex data easy to find, access, and use. Key features are record-level integration to create interoperable datasets, user-friendly interfaces, and comprehensive metadata and documentation. The IPUMS philosophy aligns closely with the FAIR principles of findability, accessibility, interoperability, and re-usability. IPUMS data have catalyzed knowledge generation across a wide range of social science and other disciplines, as evidenced by the large volume of publications and other products created by the vast IPUMS user community.

This paper describes a new initiative to create and disseminate longitudinal data infrastructure for the United States based on the entire population enumerated between 1850 and 2020. The National Longitudinal Research Infrastructure (NLRI) aims to produce a foundational reference collection for demographic and health research. The availability of a massive collection of life histories of the U.S. population over 170 years will open new avenues for social and behavioral research, education, and policy-making. By disseminating the infrastructure to the broadest possible audience, the project will enhance scientific and public understanding of critical policy-related issues. We are developing the infrastructure through three closely interconnected research projects: (1) the Census Longitudinal Infrastructure Project (CLIP); (2) the American Opportunity Study (AOS); and (3) the Multi-Generational Longitudinal Panel (IPUMS-MLP). The paragraphs that follow briefly describe the origins of the project and our preliminary studies. We then explain how NLRI will overcome critical barriers and transform research on the effects of public policies, social institutions, and health care on the health, well-being, and functioning of people over the life course and in their later years.

For the past 80 years, social scientists have been linking historical censuses across time to study economic and geographic mobility. In recent decades, the quantity of historical census record linkage has exploded, owing largely to the advent of new machine-readable data created by genealogical organizations. Investigators are examining economic and geographic mobility across multiple generations, but also engaging many new topics. Several analysts are exploring the effects of early-life socioeconomic conditions, environmental exposures, or natural disasters on family, health and economic outcomes in later life. Other studies exploit natural experiments to gauge the impact of policy interventions such as social welfare programs and educational reforms. The new data sources have led to a proliferation of record linkage methodology, and some widespread approaches inadvertently introduce errors that can lead to false inferences. A new generation of large-scale shared data infrastructure now in preparation will ameliorate weaknesses of current linkage methods.

Terra Populus, part of National Science Foundation's DataNet initiative, is developing organizational and technical infrastructure to integrate, preserve, and disseminate data describing changes in the human population and environment over time. A large number of high-quality environmental and population datasets are available, but they are widely dispersed, have incompatible or inadequate metadata, and have incompatible geographic identifiers. The new Terra Populus infrastructure enables researchers to identify and merge data from heterogeneous sources to study the relationships between human behavior and the natural world.

This paper uses multiple U.S. National Health Interview Surveys (N=1,513,097) to describe and explain temporal patterns in racial health disparities using models that simultaneously consider the unique effects of age, period, and cohort. First, we employ cross-classified random effects age-period-cohort (APC) models to understand the patterns of self-rated health disparities over time. Second, we use decomposition models to understand how socioeconomic shifts in cohort composition explain the age and period adjusted racial disparities in successive birth cohorts. Third, we examine the extent to which exogenous conditions at the time of birth can explain disparities. Our results show that race disparities increase through the 1935 cohort for women, falling thereafter; disparities for men exhibit a similar pattern but begin their overall decline with cohorts born earlier in the century. Differences in socioeconomic composition consistently contribute to disparities across cohorts; notably, disparities in marital status emerge as an increasingly important predictor across cohorts for women whereas disparities in employment emerge as increasingly salient across cohorts of men. Finally, our cohort characteristic models suggest that cohort economic conditions (percent large family, farm or Southern birth) reduce both male and female disparities in health. Poor macro-economic conditions around the time of the great depression inflated disparities for those cohorts while more favorable conditions following World War II suppressed disparities. Relative cohort size had no impact on cohort disparities in health.

Climate variability is an inherent characteristic of the Earth's climate, including but not limited to climate change. It affects and impacts human society in different ways, depending on the underlying socioeconomic vulnerability of specific places, social groups, households and individuals. This differential vulnerability presents spatial and temporal variations, and is rooted in historical patterns of development and relations between human and ecological systems. This study aims to assess the impact of climate variability on livelihoods and well-being, as well as their changes over time and across space, and for rural and urban populations. The geographic focus is Southern Brazil-the states of Parana, Santa Catarina and Rio Grande do Sul-- and the objectives include (a) to identify and map critical areas or hotspots of exposure to climate variability (temperature and precipitation), and (b) to identify internal variation or differential vulnerability within these areas and its evolution over time (1980-2010), using newly available integrated data from the Terra Populus project. These data include geo-referenced climate and agricultural data, and data describing demographic and socioeconomic characteristics of individuals, households and places.

Climate variability affects and impacts human society in different ways, depending on the underlying socioeconomic and demographic vulnerability of specific places, social groups, households and individuals. This differential vulnerability presents spatial and temporal variations, and is rooted in historical patterns of development and relations between human and ecological systems. This paper aims to (a) identify and map critical areas or hotspots of vulnerability to climate variability and its evolution over time (1980-2010), and (b) identify internal variation or differential vulnerability within these areas, using newly available integrated data from the Terra Populus project. These data include geo-referenced climate data, and data describing demography and socioeconomic characteristics of individuals, households and places. This study focus on Southern Brazil Parana, Santa Catarina and Rio Grande do Sul and assess the impact of climate variability on livelihoods and well-being, and their changes over time and across space, for rural and urban populations.

The goal of Terra Populus (TerraPop) is to lower barriers to conducting interdisciplinary human-environment interactions research by making data with different formats from different scientific domains easily interoperable. TerraPop is developing organizational and technical infrastructure to integrate, preserve, and disseminate data describing population and environment over time. TerraPop will incorporate large international microdata and aggregate census datasets, along with land use, land cover, climate, and other environmental datasets. Currently, these data exist in a variety of data structures, have generally inadequate metadata, and have incompatible geographic identifiers. TerraPop is developing methods of integrating data from different domains and data structures based on spatio-temporal linkages among data contents. The new infrastructure will enable researchers to merge data from heterogeneous sources to study relationships between human behavior and the natural world. TerraPop will partner with data archives, data producers, and data users to create a sustainable international organization that will guarantee preservation and access over multiple decades. A beta version of the TerraPop data access system will be available in spring 2013, and initial public release is planned by the end of 2013.

Our paper examines the prevalence and determinants of children’s transitions into and out of food insecurity since 2001. We use longitudinally linked data from the Food Security Supplements to the Current Population Surveys to estimate one-year transition probabilities of entry and exit from food insecurity. Our results indicate that child hunger is typically short-lived, but children experiencing very low food security frequently experience multiple consecutive years of food insecurity. We demonstrate large demographic and socioeconomic differences in rates of entry into very low food security and persistence in children's food insecurity. Income and employment shocks are important predictors of child hunger transitions. Finally, we find that the Great Recession increased the likelihood that children entered into and persisted in food insecurity among children.

In 2007, the Current Population Survey (CPS) introduced a measure that identifies all cohabiting partners in a household, regardless of whether they describe themselves as unmarried partners in the relationship to householder question. The CPS now also links children to their biological, step-, and adoptive parents. Using these new variables, we analyze the prevalence of cohabitation as well as the demographic and socioeconomic characteristics of different-sex cohabiting couples during the years 20072009. Estimates of cohabitation produced using only unmarried partnerships miss 18 % of all cohabiting unions and 12 % of children residing with cohabiting parents. Although differences between unmarried partners and most newly identified cohabitors are small, newly identified cohabitors are older, on average, and are less likely to be raising shared biological or adopted children. These new measures also allow us to identify a small number of young, disadvantaged couples who primarily reside in households of other family members, most commonly with parents. We conclude with an examination of the complex living arrangements and poverty status of American children, demonstrating the broader value of these new measures for research on American family and household structure.