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Author: Hjorth-Trolle, Anders
Resulting in 3 citations.
1. Hjorth-Trolle, Anders
Beliefs, Parental Investments, and Intergenerational Persistence: A Formal Model
Rationality and Society 30,1 (February 2018): 108-154.
Also: http://journals.sagepub.com/doi/abs/10.1177/1043463117754076
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Sage Publications
Keyword(s): Children, Home Environment; Home Observation for Measurement of Environment (HOME); Intergenerational Patterns/Transmission; Parental Investments; Parenting Skills/Styles; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Socioeconomic Status (SES)

Permission to reprint the abstract has not been received from the publisher.

Empirical research documents persistent socioeconomic and race gaps in parental investments in children. This article presents a formal model that describes the process through which parents' beliefs about the returns on investments in children evolve over time in light of new information that they receive regarding the outcomes of past investments. The model, which is based on Bayesian learning, accounts for how parents of low socioeconomic status may come to underinvest in their children because they have false low beliefs about the returns on investments. Moreover, the model describes how beliefs are transmitted across generations, thus creating dynasties of underinvesting parents who reproduce inequalities in children's socioeconomic outcomes. Finally, this article uses National Longitudinal Survey of Youth data to provide illustrative empirical evidence on key aspects of the proposed model. The main contribution of this article is to integrate parents' beliefs about returns on investments into existing models of intergenerational transmissions.
Bibliography Citation
Hjorth-Trolle, Anders. "Beliefs, Parental Investments, and Intergenerational Persistence: A Formal Model." Rationality and Society 30,1 (February 2018): 108-154.
2. Hjorth-Trolle, Anders
Molitoris, Joseph
Do Siblings Take the Weight Off Our Shoulders? The Causal Effect of Family Size on the Risk of Overweight and Obesity During Childhood
Presented: Chicago IL, Population Association of America Annual Meeting, April 2017
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Population Association of America
Keyword(s): Childhood; Family Size; Obesity; Siblings

Permission to reprint the abstract has not been received from the publisher.

Nearly 20% of children are currently considered to be obese in the United States, a figure that continues to rise (Ogden et al., 2016). In recent years, researchers have increasingly focused attention on family composition as one possible risk factor associated with childhood obesity and overweight, as there are consistent indications that singletons are at significantly higher risk than children with siblings. Unfortunately, few studies reporting such findings have attempted to account for the likely endogenous relationship between family size and children's obesity and overweight risks, making it difficult to ascertain if these are causally related. This study addresses this deficiency by using data from the NLSY79 to estimate difference-in-differences models to identify the causal effect of an increase in family size on children's risk of obesity. Preliminary results suggest that, once unobserved heterogeneity is accounted for, the relationship between family size and obesity and overweight risks disappears.
Bibliography Citation
Hjorth-Trolle, Anders and Joseph Molitoris. "Do Siblings Take the Weight Off Our Shoulders? The Causal Effect of Family Size on the Risk of Overweight and Obesity During Childhood." Presented: Chicago IL, Population Association of America Annual Meeting, April 2017.
3. Holm, Anders
Hjorth-Trolle, Anders
Andersen, Robert
Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate
Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0).
Also: https://doi.org/10.1177/00491241231176845
Cohort(s): Children of the NLSY79
Publisher: Sage Publications
Keyword(s): Methods/Methodology; Peabody Individual Achievement Test (PIAT- Reading); Statistics

Permission to reprint the abstract has not been received from the publisher.

Lagged dependent variables (LDVs) are often used as predictors in ordinary least squares (OLS) models in the social sciences. Although several estimators are commonly employed, little is known about their relative merits in the presence of classical measurement error and different longitudinal processes. We assess the performance of four commonly used estimators: (1) the standard OLS estimator, (2) an average of past measures (AVG), (3) an instrumental variable (IV) measured at one period previously (IV), and (4) an IV derived from information from more than one time before (IV2). We also propose a new estimator for fixed effects models—the first difference instrumental variable (FDIV) estimator. After exploring the consistency of these estimators, we demonstrate their performance using an empirical application predicting primary school test scores. Our results demonstrate that for a Markov process with classic measurement error (CME), IV and IV2 estimators are generally consistent; LDV and AVG estimators are not. For a semi-Markov process, only the IV2 estimator is consistent. On the other hand, if fixed effects are included in the model, only the FDIV estimator is consistent. We end with advice on how to select the appropriate estimator.
Bibliography Citation
Holm, Anders, Anders Hjorth-Trolle and Robert Andersen. "Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate." Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0).