Use It for Good – Page 15 – News and information from IPUMS

Introducing CPS-ASEC Longitudinal Extracts

December 20, 2021December 10, 2021 by mpcblog

By Renae Rodgers

The panel component of the Current Population Survey and new Longitudinal Extracts

Did you know that the Current Population Survey (CPS) – an important source of information on unemployment, poverty, and many other topics – has a panel component? If you didn’t, you’re not alone. The CPS rotation pattern is complex and can be difficult to work with. In fact, IPUMS CPS has held multi-day workshops intended to introduce researchers to the CPS panel component, help them understand the rotation pattern, and show some convenient IPUMS CPS features that make working with CPS panel data a little easier. If you’re completely new to the CPS panel, check out the materials from our latest workshop!

Maybe you did know about the CPS panel component, but looked at the complex rotation pattern, the Census Bureau guidelines and linking keys, and decided that this was for the birds. If this sounds like you, then our newest IPUMS CPS feature may be right for you! IPUMS CPS users can now download CPS-ASEC panels that contain two observations per person across a one-year period as longitudinal extracts. The rest of this blog post will explain what you are getting when you make a CPS-ASEC longitudinal extract and will walk you through how to create one for yourself.

IHGIS Research Example: Fertilizer Use from Agricultural Census Data

October 6, 2021October 6, 2021 by mpcblog

By Chris M. Boyd

The IPUMS International Historical Geographic Information System (IHGIS) provides subnational data from agricultural and population and housing censuses from around the world. The agricultural census data cover a wide range of information on agricultural inputs, labor, production, and more, which can be used to explore a variety of research questions. IHGIS data can help understand, for instance, which factors contribute to better crop productivity, including the role of fertilizer use. Researchers have used agricultural census data at the subnational level to analyze the negative relationship between farm size and fertilizer overuse in China¹; the relationship between maize yield, farm size and fertilizer and irrigation use in Mexico²; the use of chemical fertilizers in direct market farms in the U.S.³; and the environmental sustainability of using fertilizers, insecticides and pesticides in Pakistan⁴.

To date, IHGIS has released Agricultural Census tables for ten countries (see https://ihgis.ipums.org/dataset-descriptions), including seven developing countries in Africa and the Pacific Islands. These seven datasets include information about fertilizer use, though each measures it in a different way (see Table 1). Despite the differences, these data can reveal broad patterns in the use of fertilizer by farmers among these countries.

Conceptualizing Structural Racism with IPUMS Time Use Data

September 29, 2021September 14, 2021 by mpcblog

By Solvejg Wastvedt and Yash Singh

A major challenge for researchers studying racism is measuring the consequences of this macro-level phenomenon in people’s everyday lives. For the purposes of this post we define structural racism as a system of racialized advantage and disadvantage underlying and structuring multiple domains of life, including housing, interactions with government, education, the criminal legal system, and more. ¹ ² ³ ⁴ ⁵ Structural racism does not refer to individual prejudices or discriminatory beliefs. Rather, it is a macro-level phenomenon: a principle upon which structures in our society are built. When researchers study racism as a structural force, its impacts in any one area affect and reinforce impacts in every other area.

The consequences of structural racism for individuals are well-documented; for example, research shows structural racism produces poorer health outcomes for racialized groups. ⁶ However, research often focuses on a single aspect of structural racism, such as police brutality, and struggles to capture the full impact of this multidimensional phenomenon. Measures used to assess impacts of structural racism typically differ by domain, and while robust research documents these impacts, it can also be difficult to capture the mechanisms through which they occur.

Time — specifically time use — is a type of data that can unite studies of structural racism across domains and offer a glimpse into how this macro-level force acts on individuals.

Time is a form of capital: we only have so much of it, and time spent in one place is not spent elsewhere. For example, an individual living in a segregated neighborhood because of residential discrimination may be required to drive long distances for work or health care. This reduces time available for all other activities, including exercise. The resulting lack of time may lead, in turn, to negative health outcomes. While simply an illustration of one possible pathway linking the macro and micro levels, this example shows how time use data can capture impacts across multiple domains and, ultimately, on daily life.

IPUMS Time Use provides data from the American Time Use Survey (ATUS), a nationally representative U.S. time diary survey. In this post, we highlight measures from the ATUS corresponding to two aspects of structural racism: residential segregation and discrimination in government services. Researchers interested in other aspects can create and select variables from IPUMS ATUS data to match their areas of interest.