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Rocio Titiunik, University of Michigan

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Internal vs. external validity in studies with incomplete populations

Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation, it is common to have data on all households who received the program, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout, information on the total number of citizens who voted is usually complete, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology, information on arrests by race is available, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths, we lack complete information about the full population of drug users.

In all these cases, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference.

Co-sponsored with the Political Science Department, Statistics Department and the Center for Social Statistics 

Alison Norris, The Ohio State University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Abortion utilization in Ohio’s changing legislative context

Changes in Ohio, most notably legislation and policy changes since 2011, likely have impacted women’s access to abortion. Many abortion clinics in Ohio have closed in the past seven years, and several others are currently engaged in litigation and are at risk of closure. Clinic closures influence the distance that women travel when seeking abortion. Coupled with the impact of an Ohio law that mandates a 24-hour waiting period after a woman’s initial abortion consultation, loss of a nearby clinic may put abortion out of reach for many women. Other legislation limits where abortions can and cannot be performed and to what gestational stage abortions are performed. This presentation will provide preliminary findings about population-level shifts in abortion utilization, with special attention to change over time, geographic variation, and groups of women who may be underserved.

Co-sponsored with The Bixby Center

Michael Clemens, Center for Global Development

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Immigration Restrictions as Active Labor Market Policy: Evidence from the Mexican Bracero Exclusion

An important class of active labor market policy has received little impact evaluation: immigration barriers intended to raise wages and employment by shrinking labor supply. Theories of endogenous technical advance raise the possibility of limited or even perverse impact. We study a natural policy experiment: the exclusion of almost half a million Mexican bracero farm workers from the United States to improve farm labor market conditions. With novel labor market data we measure state-level exposure to exclusion and model the absent changes in technology or crop mix. We fail to reject zero labor market impact, inconsistent with this model.

Co-sponsored with the Public Policy and Applied Social Sciences Seminar 

Latest Past Events

Rocio Titiunik, University of Michigan

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Internal vs. external validity in studies with incomplete populations

Researchers working with administrative data rarely have access to the entire universe of units they need to estimate effects and make statistical inferences. Examples are varied and come from different disciplines. In social program evaluation, it is common to have data on all households who received the program, but only partial information on the universe of households who applied or could have applied for the program. In studies of voter turnout, information on the total number of citizens who voted is usually complete, but data on the total number of voting-eligible citizens is unavailable at low levels of aggregation. In criminology, information on arrests by race is available, but the overall population that could have potentially been arrested is typically unavailable. And in studies of drug overdose deaths, we lack complete information about the full population of drug users.

In all these cases, a reasonable strategy is to study treatment effects and descriptive statistics using the information that is available. This strategy may lack the generality of a full-population study, but may nonetheless yield valuable information for the included units if it has sufficient internal validity. However, the distinction between internal and external validity is complex when the subpopulation of units for which information is available is not defined according to a reproducible criterion and/or when this subpopulation itself is defined by the treatment of interest. When this happens, a useful approach is to consider the full range of conclusions that would be obtained under different possible scenarios regarding the missing information. I discuss a general strategy based on partial identification ideas that may be helpful to assess sensitivity of the partial-population study under weak (non-parametric) assumptions, when information about the outcome variable is known with certainty for a subset of the units. I discuss extensions such as the inclusion of covariates in the estimation model and different strategies for statistical inference.

Co-sponsored with the Political Science Department, Statistics Department and the Center for Social Statistics 

Alison Norris, The Ohio State University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Abortion utilization in Ohio’s changing legislative context

Changes in Ohio, most notably legislation and policy changes since 2011, likely have impacted women’s access to abortion. Many abortion clinics in Ohio have closed in the past seven years, and several others are currently engaged in litigation and are at risk of closure. Clinic closures influence the distance that women travel when seeking abortion. Coupled with the impact of an Ohio law that mandates a 24-hour waiting period after a woman’s initial abortion consultation, loss of a nearby clinic may put abortion out of reach for many women. Other legislation limits where abortions can and cannot be performed and to what gestational stage abortions are performed. This presentation will provide preliminary findings about population-level shifts in abortion utilization, with special attention to change over time, geographic variation, and groups of women who may be underserved.

Co-sponsored with The Bixby Center

Michael Clemens, Center for Global Development

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Immigration Restrictions as Active Labor Market Policy: Evidence from the Mexican Bracero Exclusion

An important class of active labor market policy has received little impact evaluation: immigration barriers intended to raise wages and employment by shrinking labor supply. Theories of endogenous technical advance raise the possibility of limited or even perverse impact. We study a natural policy experiment: the exclusion of almost half a million Mexican bracero farm workers from the United States to improve farm labor market conditions. With novel labor market data we measure state-level exposure to exclusion and model the absent changes in technology or crop mix. We fail to reject zero labor market impact, inconsistent with this model.

Co-sponsored with the Public Policy and Applied Social Sciences Seminar 

UCLA CCPR