To attenuate you’ll confounding away from eating insecurity position that have lower-earnings status, also restricting brand new analytic test so you’re able to reasonable-money households i along with provided the typical way of measuring house earnings of 9 days as a consequence of kindergarten since the a great covariate in all analyses. At each and every revolution, mothers was expected so you’re able to statement the household’s complete pretax money for the the very last seasons, together with salaries, focus, old-age, and the like. I averaged stated pretax household income round the nine weeks, 24 months, and kindergarten, while the long lasting tips of income be much more predictive from restaurants low self-esteem than simply was tips regarding most recent money (elizabeth.g., Gundersen & Gruber, 2001 ).

Lagged intellectual and you can societal-mental tips

Ultimately, i included early in the day procedures away from boy cognitive otherwise public-emotional development to regulate having date-invariant boy-peak omitted details (discussed next below). This type of lagged guy effects were taken throughout the wave quickly preceding the fresh dimension regarding dining insecurity; that is, from inside the models anticipating preschool intellectual effects from dos-12 months restaurants low self-esteem, 9-month cognitive effects was basically regulated; inside the designs predicting kindergarten cognitive consequences from kindergarten-12 months dining low self-esteem, 2-seasons intellectual effects was basically controlled. Lagged procedures off public-mental doing work were chosen for designs forecasting preschool societal-emotional consequences.

Analytical Method

In Equation 1, the given kindergarten outcome is predicted from household food insecurity at 2 years, the appropriate lagged version of the outcome (Bayley mental or adaptive behavior scores at 9 months), and covariates. ?₁ and ?₂ represent the difference in the level of the outcome at kindergarten for children in households who experienced low and very low food security, respectively, relative to those who were food secure at 2 years, conditional on the child’s lagged outcome from the wave prior to when food insecurity was assessed. Although this approach controls for the effect of food insecurity on outcomes up to 9 months, it does not capture food insecurity that began at age 1 and extended until 2 years. Likewise, for the model predicting kindergarten outcomes from preschool-year food insecurity in which 2-year outcomes are lagged (Equation 2, below), food insecurity experienced prior to age 2 that might have influenced age 2 outcomes is controlled for, but food insecurity that might have occurred after the 2-year year interview and before preschool is not.

To address the possibility that ?₁ and ?₂ in Equations 1 and 2 are absorbing effects of food insecurity at subsequent time points, we ran additional models in which we control for food insecurity at all available time points, estimating the independent association of food insecurity at any one time point on kindergarten outcomes, net of other episodes of food insecurity (Equation 3).

Here, ?₁ (for instance) is limited to the proportion of the association between low food security at 9 months and kindergarten outcomes that is independent of the association between food insecurity at other time points and the same outcomes. Finally, Equation 4 presents the model estimating associations between intensity of food insecurity across early childhood and kindergarten outcomes. In this model, ?₁ (for example) represents the average difference in kindergarten outcomes between children who lived in a food-insecure household at any one time point (e.g., 9 months, 2 years, or preschool), relative to children who lived in households experiencing no food insecurity across the early childhood years.

In addition to including lagged outcome measures as additional predictors in the above models, we also included a near-exhaustive set of covariates as described above. This vector of covariates is expressed as ?_k in the above equations. Alongside the lagged dependent variable, the inclusion of this rich set of covariates yields the most appropriate analysis given limitations of the available data.