Partial Identification Approach Research Articles

Does college selectivity reduce obesity? A partial identification approach.

We use data from the National Longitudinal Study of Adolescent to Adult Health to investigate whether the quality of tertiary education -measured by college selectivity-causally affects obesity prevalence in the medium run (by age 24-34) and in the longer run (about 10years later). We use partial identification methods, which allow us, while relying on weak assumptions, to overcome the potential endogeneity of college selectivity as well as the potential violation of the stable unit treatment value assumption due to students interacting with each other, and to obtain informative identification regions for the average treatment effect of college selectivity on obesity. We find that attending a more selective college causally reduces obesity, both in the medium and in the longer run. We provide evidence that the mechanisms through which the impact of college selectivity on obesity operates include an increase in income, a reduction in physical inactivity and in the consumption of fast food and sweetened drinks.

HIV estimation using population-based surveys with non-response: A partial identification approach.

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non-random missingness. Using worst case identification bounds that do not make assumptions about the non-response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non-response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility.

Measuring economic mobility in India using noisy data: a partial identification approach

AbstractWe examine economic mobility in India while accounting for misclassification to better understand the welfare effects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find overall mobility has been remarkably low: at least 65% of poor households remained poor or at-risk of being poor between 2005 and 2012. We also find Muslims, lower caste groups, and rural households are in a more disadvantageous position compared to Hindus, upper caste groups, and urban households. These findings cast doubt on the conventional wisdom that marginalized households in India are catching up.

What can we learn about the effect of mental health on labor market outcomes under weak assumptions? Evidence from the NLSY79

We employ a nonparametric partial identification approach to bound the causal effect of poor mental health on employment and earnings using the National Longitudinal Study of Youth 1979. Our approach allows us to provide bounds on the population average treatment effect based on relatively weak, credible assumptions. We find that being categorized as depressed decreases employment by 10% and earnings by 27% at most, but we cannot statistically rule out a zero effect. We also provide insights into the heterogeneity of the effects on labor market outcomes at different levels of adverse mental health experienced (no, little, mild, moderate, and severe depressive symptoms). We find that going from having no (little) to severe depressive symptoms reduces employment by 3–18% (3–16%) and earnings by 11–44% (12–36%). The estimated bounds statistically rule out null effects for earnings but not for employment.

The Effectiveness of Safety Education on Workforce Risks: Evidence from South Korea

This paper investigates how effective safety education reduces workforce risks by using an establishment-level dataset in South Korea. We focus on identification and estimation of causal effects. Since provision of safety education depends on establishment’s decision, the main variable of interest is endogenous. We adopt a partial identification approach to identifying and estimating the causal effects. The empirical results indicate that we cannot rule out the possibility that safety education may not be effective in most cases. We then discuss potential reasons that safety education would not help reduce workforce risks. Our findings are expected to be helpful to develop an effective policy to reduce workforce risks and improve work environments.

Input motion extraction and closely-spaced modes recognition through partial system identification: An orthogonality-aided blind source separation approach

This research proposes an output-only procedure to extract seismic input motion and identify systems with severe closely-spaced modes incorporating a partial system identification approach. The proposed framework employs new methods to detect free vibration portion of the response histories and clustering the candidate points to find the first mode shape in a blind modal identification (BMI) procedure. The first mode frequency and damping along with mass matrix distribution of the system are calculated then using an orthogonality-based optimization process. The mentioned identified features are utilized next for the input motion extraction without any need to know the higher mode characteristics of the system. Next, higher mode characteristics are detected in succeeding steps considering the removal of each identified mode share from the total response at the end of each step. The perfect performance of the framework was proved against two simulated synthetic structures with mild and severe closely-spaced modes.

The Dynamics of Global Sourcing

<ns3:p>This paper studies an import model that incorporates both static crosscountry interdependence and dynamic dependence in firm-level decisions. I find that the benefit of sourcing from one country increases as a firm imports from more countries. Furthermore, using a partial identification approach under the revealed preferences assumption, I provide evidence for the sunk costs of importing, which make establishing relationships with new sellers costlier than maintaining existing ones. The coexistence of cross-country interdependence and sunk costs implies that temporary trade policy changes can have long-lasting effects on both the targeted and non-targeted markets through firm-level decisions.</ns3:p>

A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers

A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers

A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers

A Partial Identification Approach to Identifying the Determinants of Human Capital Accumulation: An Application to Teachers

Bounding the Joint Distribution of Disability and Employment with Contaminated Data

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial identification approach to bound the joint distribution of disability and employment status in the presence of contaminated data. Allowing for a modest amount of contamination leads to bounds on the labor market status of the disabled that are not overly informative given the relative size of the disabled population. Thus, absent further assumptions, even a modest amount of contamination creates much uncertainty about the employment gap between the non-disabled and disabled. However, additional assumptions considered are shown to have some identifying power. For example, under our most stringent assumptions, we find that the employment gap is at least 15.2% before the Great Recession and 22.0% afterwards.

The Black Market for Beijing License Plates

Black markets can reduce the effects of distortionary regulations by reallocating scarce resources toward consumers who value them most. The illegal nature of black markets, however, creates transaction costs that reduce the gains from trade. We take a partial identification approach to infer gains from trade and transaction costs in the black market for Beijing car license plates, which emerged following their recent rationing. We find that at least 11% of emitted license plates are illegally traded. The estimated transaction costs suggest severe market frictions: between 61% and 82% of the realized gains from trade are lost to transaction costs.

Re-visiting the Conditional Cash Transfer in India through the Partial Identification Approach

This study re-estimates the causal impacts of a conditional cash transfer (CCT) programme in India, namely the Janani Suraksha Yojana (JSY) scheme, on maternal and child healthcare use. The main goal is to provide new evidence and to assess the validity of the identification assumptions employed in previous studies on JSY. We achieve this by implementing a conservative partial identification approach. We find that the average treatment effects estimated under the conditional independence assumption are below the lower bound of the treatment effect estimated under weaker but more credible assumptions for institutional delivery, skilled birth attendance and postnatal care use. For antenatal care use and intakes of iron and folic acid supplements, and uptakes of tetanus toxoid injections, the average treatment effects under the conditional independence assumption are above the upper bound. These findings suggest that selection bias could not have been fully controlled for by the observable characteristics.

Out-of-pocket costs for preventive care persist almost a decade after the Affordable Care Act

Higher cost-sharing reduces the amount of high-value health care that patients use, such as preventive care. Despite a sharp reduction in out-of-pocket (OOP) costs for preventive care after the implementation of the Affordable Care Act (ACA), patients often still get unexpected bills after receiving preventive services. We examined out-of-pocket costs for preventive care in 2018, almost ten years after the implementation of the ACA. We quantify the excess cost burden on a national scale using a partial identification approach and explore how this burden varies geographically and across preventive services. We found that in addition to premium costs meant to cover preventive care, Americans with employer-sponsored insurance were still charged between $75 million and $219 million in total for services that ought to be free to them ($0.50 to $1.40 per ESI-covered individual and $0.75 to $2.17 per ESI-covered individual using preventive care). However, some enrollees still faced OOP costs for eligible preventive services ranging into the hundreds of dollars. OOP costs are most likely to be incurred for women's services (e.g., contraception) and basic screenings (e.g., diabetes and cholesterol screenings), and by patients in the South or in rural areas.

Bounding the joint distribution of disability and employment with misclassification.

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial identification approach to bound the joint distribution of disability and employment status in the presence of misclassification. Allowing for a modest amount of misclassification leads to bounds on the labor market status of the disabled that are not overly informative given the relative size of the disabled population. Thus, absent further assumptions, even a modest amount of misclassification creates much uncertainty about the employment gap between the non-disabled and disabled. However, additional assumptions considered are shown to have some identifying power. For example, under our most stringent assumptions, we find that the employment gap is at least 15.2% before the Great Recession and 22.0% afterward.

Segregsmall: A command to estimate segregation in the presence of small units

Suppose that a population, composed of a minority and a majority group, is allocated into units, which can be neighborhoods, firms, classrooms, etc. Qualitatively, there is some segregation whenever allocation leads to the concentration of minority individuals in some units more than in others. Quantitative measures of segregation have struggled with the small-unit bias. When units contain few individuals, indices based on the minority shares in units are upward biased. For instance, they would point to a positive amount of segregation even when allocation is strictly random. The command segregsmall implements three recent methods correcting for such bias: the nonparametric, partial identification approach of D’Haultfœuille and Rathelot (2017, Quantitative Economics 8: 39–73); the parametric model of Rathelot (2012, Journal of Business & Economic Statistics 30: 546–553); and the linear correction of Carrington and Troske (1997, Journal of Business & Economic Statistics 15: 402–409). The package also allows for conditional analyses, namely, measures of segregation accounting for characteristics of the individuals or the units.

Preferences for Government Assistance to Forced Migrants: Do Perceptions of Disadvantage Matter?

Developing regions have received a significant share of refugees over the past decade yet knowledge on willingness to extend government support to these groups in host countries is limited. This paper explores how perceived disadvantage of refugees and internally displaced persons (IDPs) affects individual preferences for increased government support to these groups in developing countries. We use unique data from the Caucasus Barometer 2011 survey, conducted by the Caucasus Research Resource Centers in three post-Soviet countries in the South Caucasus – Armenia, Azerbaijan, and Georgia. The survey uses multi-stage stratified clustered sampling and consists of a combined cross-sectional sample of 2851 respondents. The analysis is restricted to the non-migrant population aged 21-65 in each country. We apply Probit analyses to test for the existence of an effect of perceived refugee disadvantage on individual support for increased government assistance for refugees/IDPs. In addition, we carry out robustness tests taking two different approaches to mitigate the effect of unobserved heterogeneity. Our findings indicate that the perception that refugees and IDPs are disadvantaged is positively associated with support for increased government assistance for them, and this positive effect is highly significant in all three countries. The marginal effects are 19.4 percentage points in Georgia, 27.4 percentage points in Azerbaijan and 33.2 percentage points in Armenia. The models control for a range of demographic and socioeconomic factors, including sex, age, family structure, education, employment, household income and rural/urban residence. Using a partial identification approach, we also show that these associations are likely to be causal. This study makes a significant contribution to the research gap on the social and economic issues surrounding refugees and IDPs in developing countries. The outcomes of this study may inform policy efforts to address the complex interplay between attitudes towards immigrants and support for government assistance to these vulnerable groups in the era of increasing inflow of forced migrants in developing countries. In particular, the findings suggest that raising public awareness and particularly provision of objective information on disadvantage faced by forced migrants might be essential in generating public support for policies to extend assistance to these groups.

Estimation of Covid-19 prevalence from serology tests: A partial identification approach

We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%–1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate (∼0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%–17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%–8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.

Re-evaluating the early child stimulation programme in Bangladesh: evidence from the partial identification approach

ABSTRACT This study complements the previous evaluation study of the Early Child Stimulation programme conducted in Bangladesh. Despite a rigorously designed randomised control trial, the presence of non-compliers made it impossible to point-identify the average treatment effect (ATE) on targeted outcomes without additional strong identification assumptions. This study provides new evidence through the partial identification approach, which estimates the ATE bound with weak but credible assumptions. The results show that the ATE bounds include the local average treatment effects and we do not find strong evidence to suggest the ATE among compliers is greatly different from that among the entire population.

Estimation of Covid-19 Prevalence from Serology Tests: A Partial Identification Approach

We propose a partial identification method to estimate COVID-19 prevalence in the US. Our data are results from antibody tests (serology tests) in a population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The key advantage of our method over more standard approaches is that it is valid in finite samples, requiring only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent COVID-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot yet support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-6%, and are therefore inconclusive. However, this range could be narrowed down to 0.3%-1.8% if the actual false positive rate of the antibody test was near its empirical estimate (∼0.5%). In a study from New York State, COVID-19 prevalence is confidently estimated in the range 11%-18%, which also suggests significant geographic variation in COVID-19 exposure across the US. Combining all datasets yields a 3%-9% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.

Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies

The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.