Mothers’ Employment and their Children’s Schooling: a Joint Multilevel Analysis for India

This paper studies the relation between mothers’ employment and their children’s schooling in India, where a high number of children are not attending school at compulsory school age. Using the second National Family Health Survey, the results of a joint multi-level random effects model show that, controlling for covariates, the correlation between mothers’ employment and children’s schooling is negative. A sensitivity analysis on wealth and education deciles shows that this relation disappears in urban areas and becomes weaker in rural areas only at the top wealth deciles, but persists for the more educated mothers. The last result may be driven by the low number of females with a high level of education in India, but it also seems to envisage that, for mothers with lower education, being literate does not increase pay conditions. These findings suggest that policies aiming at improving both women’s and children’s welfare should not only pursue higher levels of education, but also target improvements in women’s conditions in the labour market.women’s employment, children’s schooling, household allocation of time, random effects, India, NFHS-2

Multiple imputation and selection of ordinal level 2 predictors in multilevel models. An analysis of the relationship between student ratings and teacher beliefs and practices

The paper is motivated by the analysis of the relationship between ratings and teacher practices and beliefs, which are measured via a set of binary and ordinal items collected by a specific survey with nearly half missing respondents. The analysis, which is based on a two-level random effect model, must face two about the items measuring teacher practices and beliefs: (i) these items level 2 predictors severely affected by missingness; (ii) there is redundancy in the number of items and the number of categories of their measurement scale. tackle the first issue by considering a multiple imputation strategy based on information at both level 1 and level 2. For the second issue, we consider regularization techniques for ordinal predictors, also accounting for the multilevel data structure. The proposed solution combines existing methods in an original way to solve specific problem at hand, but it is generally applicable to settings requiring to select predictors affected by missing values. The results obtained with the final model out that some teacher practices and beliefs are significantly related to ratings about teacher ability to motivate students.Comment: Presented at the 12th International Multilevel Conference is held April 9-10, 2019 , Utrech

A comparison between the varying-thresholds model and quantile regression

The varying-thresholds model is a new modelling approach capable of estimating the whole conditional distribution of a response variable in a regression setting. The varying-thresholds model can be used for continuous, ordinal and count responses. Conditional quantiles estimated through the varying-thresholds method are compared to those of quantile regression. The comparison is based on models' simulations to assess the performance of the two methodologies regarding the coverage and width of prediction intervals. The simulation study encompasses eight different settings with several functional forms and types of errors. In addition, a discrete variation of the continuous ranked probability score is proposed as a way to choose the best link function for the binary models used to estimate the varying-thresholds model. The comparison shows that the varying thresholds model performs better whenever the functional form of the true data generating model is non-linear

School attendance of children and the work of mothers: a joint multilevel model for India

This paper investigates the determinants of school attendance of children and their mother´s working status when the mother decides how to allocate her time and that of her children. A multilevel random effects model is applied to study the mother´s participation and the schooling status of her children in a joint framework. Using the second National Family Health Survey (NFHS-2) for India, we find that, controlling for many covariates among which wealth is the most powerful predictor, children of working mothers have a lower probability of attending school. This, together with the result that only illiterate and poor mothers with unskilled or unemployed partners have a high probability of working, points to the need for decent labour market opportunities for females. An implication of our findings is that any policy aiming both at enhancing women´s empowerment through labour and increasing children´s welfare should also target improvements in women´s conditions in the labour market

Frugivory and ornitochorous fruits removal in Chaco forest fragments of Córdoba (Argentina)

La pérdida de bosques naturales y su fragmentación en el paisaje por actividad agrícola pueden afectar procesos ecológicos como la dispersión biótica y también el mantenimiento de la diversidad de especies nativas y la invasión de plantas exóticas. En el contexto de la fragmentación del bosque chaqueño de la provincia de Córdoba se evaluó remoción y frugivoría de frutos carnosos en especies de dos fragmentos con superficie menor a 5 ha y dos de bosque continuo superior a 300 ha. Una vez por semana durante dos meses los frutos de cada individuo se contabilizaron tomándose como indicador de la dispersión biótica a la probabilidad de supervivencia de los frutos (PSF). Los elementos florísticos ornitócoros incluyen a 15 especies, principalmente leñosas. En todas las especies ornitócoras presentes en cada sitio se encontraron diferencias en la PSF, siendo mayor en los fragmentos pequeños respecto a los sitios de bosque continuo. Asimismo, la PSF fue menor en la especie arbustiva de origen exótico en relación con la nativa. Contrariamente, en las especies exóticas leñosas la PSF fue mayor que en las nativas. Estos resultados sugieren que la dispersión de frutos es afectada por la reducción del bosque chaqueño y que los fragmentos pequeños son aún utilizados por las aves como áreas de alimentación. Este comportamiento de las aves sería importante para la persistencia y colonización de nuevos sitios por las plantas con frutos carnosos, así como para la conservación de los pequeños fragmentos de bosque inmersos en los agroecosistemas del centro de Argentina.Forests loss and habitat fragmentation due to agricultural activity could be related to different ecological processes such as biotic dispersion, as well as the maintenance of native plant diversity and exotic plant invasions. The objective of this work was to analyze fruit removal and frugivory by animals in the Chaco forest of Córdoba, in the context of landscape fragmentation. The study was carried out in two continuous (> 300 ha) and two small forest fragments (< 5 ha). We randomly selected three to five individuals with ornitochorous fruits at the sampling time (March-June, 2006). We counted the fruits in focal plants once a week during two month. Fruit survival probability (FSP) was used as an indicator of fruit removal. Ornitochorous plants included 15 species, mainly woody. The FSP for the plant community showed statistically significant differences between small fragments and continuous sites. FSP was lower in continuous forests than in small fragments. FSP in the exotic bush was lower than in the native bush species. Contrary to bushes, FSP values in woody exotic species were higher than those for native species. Results suggest that the dispersion of the ornitochorous fruits would be related to the process of fragmentation, but small fragments are used by birds as feeding sites, which confers them a high conservation value. This behavior of bird species would be important for the persistence and colonization of new sites for plant species with ornitochorous fruits, as well as for the conservation of small forests fragments disseminated within agro-ecosystems of central Argentina.Fil: Ponce, Analía Melisa. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Grilli, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Galetto, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina. Universidad Nacional de Córdoba; Argentin

Chapter Random effects regression trees for the analysis of INVALSI data

Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability