Parenting and Child Development in Low- and Middle-Income Countries 2022016431, 2022016432, 9780367491765, 9780367491789, 9781003044925

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PARENTING AND CHILD DEVELOPMENT IN LOW- AND MIDDLE-INCOME COUNTRIES

This compelling volume advances the understanding of what parenting and related sociodemographic, demographic, and environmental variables look like and how they are associated with child development in low- and middle-income countries around the world. Specifically, expert authors document how child growth, caregiving practices, discipline and violence, and children’s physical home environments, along with child and primary caregiver sociodemographic characteristics and household and national development demographic characteristics, are associated with central domains of early childhood development across a substantial fraction of the majority world using contemporary 21st-century data from the UNICEF Multiple Indicator Cluster Surveys and the UNICEF Early Childhood Development Index.The lives of nearly 160,000 girls and boys aged 3 to 5 years in nationally representative samples from 51 low- and middle-income countries are sampled to address 7 principal questions about children, caregiving, and contexts. Parenting and Child Development in Low- and Middle-Income Countries takes an authentically international approach to parenting, the environment, and child development in cultural contexts that more fully characterize the world’s diversity. Parenting and Child Development in Low- and Middle-Income Countries is essential reading for researchers and students of parenting, psychology, human development, family studies, sociology, and cultural studies, as well as governmental and non­ governmental professionals working with families in low- and middle-income countries. Marc H. Bornstein holds positions at the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Institute for Fiscal Studies, and UNICEF. He is President Emeritus of the Society for Research

in Child Development, Founding Editor of Parenting: Science and Practice, Editor Emeritus of Child Development, and Editor of the Handbook of Parenting. W. Andrew Rothenberg is a research scientist at Duke University and a postdoctoral associate at the University of Miami Miller School of Medicine’s Mailman Center for Child Development. His specialties are preventing and treating the intergenerational transmission of deleterious parenting and child mental health. Andrea Bizzego is a postdoctoral researcher at the Department of Psychology and Cognitive Science of the University of Trento. His research focuses on improving methodological procedures and reproducibility in neuroscience and psychology. Robert H. Bradley is the director of the Center for Child and Family Success at Arizona State University. He developed the Home Observation for Measurement of the Environment Inventory and the Family Map. Kirby Deater-Deckard is a professor and program director of Developmental Science at the Department of Psychological and Brain Sciences of the University of Massachusetts Amherst. He is Associate Editor of the Handbook of Contemporary Family Psychology and Co-Editor of Frontiers in Developmental Science. Gianluca Esposito is a professor of Child Development and Chair of the PhD program in Cognitive Science at the University of Trento. He is Senior Associate Editor of Research in Developmental Disabilities. Jennifer E. Lansford is a research professor at the Sanford School of Public Policy and a faculty fellow at the Center for Child and Family Policy of Duke University. She leads the Parenting Across Cultures project, a longitudinal study of children, mothers, and fathers from nine countries. Diane L. Putnick is a staff scientist at the Eunice Kennedy Shriver National Institute of Child Health and Human Development. She serves on the editorial boards of Developmental Psychology, Parenting: Science and Practice, and Family Process. Susannah Zietz is a postdoctoral scholar at the Center for Child and Family Policy of Duke University Sanford School of Public Policy. Her research focuses on relations between exposure to adversity in childhood and aggression and health risk behaviors in adolescence and adulthood.

STUDIES IN PARENTING SERIES Marc H. Bornstein, Series Editor

The chief aim of this series of volumes is to provide a forum for extended and integrated treatments of fundamental and challenging contemporary topics in parenting. Each volume treats a different perspective on parenting and is selfcontained, yet the series as a whole endeavors to enhance and interrelate studies in parenting by bringing shared perspectives to bear on a variety of concerns promi­ nent in parenting theory, research, practice, and application. Reflecting the nature and intent of this series, contributing authors are drawn from a broad spectrum of the humanities and sciences—anthropology to zoology—with representational emphasis placed on active contributing authorities to the contemporary literature in parenting. Parenting Across Cultures Across Development Parenting from Childhood to Adolescence in Nine Countries Jennifer E. Lansford,W.Andrew Rothenberg, and Marc H. Bornstein Parenting, Infancy, Culture Specificity and Commonality in Argentina, Belgium, Israel, Italy, and the United States Marc H. Bornstein Parenting and Child Development in Low- and Middle-Income Countries Marc H. Bornstein,W.Andrew Rothenberg,Andrea Bizzego, Robert H. Bradley, Kirby Deater-Deckard, Gianluca Esposito, Jennifer E. Lansford, Diane L. Putnick, and Susannah Zietz

PARENTING AND CHILD DEVELOPMENT IN LOW- AND MIDDLE­ INCOME COUNTRIES

MARC H. BORNSTEIN, W. ANDREW

ROTHENBERG, ANDREA BIZZEGO,

ROBERT H. BRADLEY, KIRBY DEATER-DECKARD,

GIANLUCA ESPOSITO, JENNIFER E. LANSFORD,

DIANE L. PUTNICK, AND SUSANNAH ZIETZ

Cover image: Nastco/iStock via Getty Images First published 2023 by Routledge 605 Third Avenue, New York, NY 10158 and by Routledge 4 Park Square, Milton Park,Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 Marc H. Bornstein,W.Andrew Rothenberg,Andrea Bizzego, Robert H. Bradley, Kirby Deater-Deckard, Gianluca Esposito, Jennifer E. Lansford, Diane L. Putnick, and Susannah Zietz The right of Marc H. Bornstein,W.Andrew Rothenberg,Andrea Bizzego, Robert H. Bradley, Kirby Deater-Deckard, Gianluca Esposito, Jennifer E. Lansford, Diane L. Putnick, and Susannah Zietz to be identified as authors of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Names: Bornstein, Marc H., author. | Rothenberg,W.Andrew, author. | Lansford, Jennifer E., author. | Bradley, Robert H., 1946- author. | Deater-Deckard, Kirby D., author. | Zietz, Susannah, author. | Putnick, Diane L., author. | Bizzego,Andrea, author. | Esposito, Gianluca (Professor) author. Title: Parenting and child development in low- and middle-income countries / Marc H. Bornstein,W.Andrew Rothenberg, Jennifer E. Lansford, Robert H. Bradley, Kirby Deater-Deckard, Susannah Zietz, Diane L. Putnick,Andrea Bizzego, and Gianluca Esposito. Description: New York, NY : Routledge, 2023. | Series: Studies in parenting | Includes bibliographical references and index. Identifiers: LCCN 2022016431 (print) | LCCN 2022016432 (ebook) | ISBN 9780367491765 (hardback) | ISBN 9780367491789 (paperback) | ISBN 9781003044925 (ebook) Subjects: LCSH: Parenting—Developing countries. | Child development— Developing countries. | Child rearing—Developing countries. Classification: LCC HQ792.2 .B67 2023 (print) | LCC HQ792.2 (ebook) | DDC 649/.1091724—dc23/eng/20220411 LC record available at https://lccn.loc.gov/2022016431 LC ebook record available at https://lccn.loc.gov/2022016432 ISBN: 9780367491765 (hbk) ISBN: 9780367491789 (pbk) ISBN: 9781003044925 (ebk) DOI: 10.4324/9781003044925 Typeset in Bembo by Apex CoVantage, LLC

CONTENTS

Series Foreword List of Authors 1

2

3

4

Introduction and General Methods: Parenting, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Marc H. Bornstein,W.Andrew Rothenberg,Andrea Bizzego, Robert H. Bradley, Kirby Deater-Deckard, Gianluca Esposito, Jennifer E. Lansford, Diane L. Putnick, and Susannah Zietz Child Growth, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Susannah Zietz and W.Andrew Rothenberg Cognitive and Socioemotional Caregiving, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Marc H. Bornstein,W.Andrew Rothenberg, and Diane L. Putnick Parent Discipline and Violence, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Jennifer E. Lansford,W.Andrew Rothenberg, and Kirby Deater-Deckard

ix xi

1

52

79

128

viii Contents

5

6

7

Children’s Physical Home Environment, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Robert H. Bradley and W.Andrew Rothenberg Predictors of Early Childhood Development:A Machine Learning Approach Andrea Bizzego, Giulio Gabrieli, Mengyu Lim,W.Andrew Rothenberg, Marc H. Bornstein, and Gianluca Esposito The UNICEF Multiple Indicator Cluster Surveys and Early Childhood Development Index: Parenting, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Marc H. Bornstein and W.Andrew Rothenberg

Index

160

210

240

276

SERIES FOREWORD

Parenting is fundamental to the survival and success of the human species. Eve­ ryone who has ever lived has had parents, and the vast majority of adults in the world become parents. Opinions about parenting abound, but still surprisingly too little solid scientific information or considered reflection exist about parenting. Studies in Parenting redresses this imbalance.The chief aim of this series of books is to provide a forum for extended and integrated treatments of fundamental and challenging contemporary topics in parenting. Each book provides a different perspective on parenting and is self-contained, yet the series as a whole endeavors to enhance and interrelate studies in parenting by bringing shared perspectives to bear on a variety of concerns prominent in parenting theory, research, practice, and application. As a consequence of its structure and scope, Studies in Parenting will appeal individually or as a series to professionals and parents alike. Reflecting the nature and intent of this series, contributing authors are drawn from a broad spectrum of the humanities and sciences—anthropology to zoology—with rep­ resentational emphasis placed on active contributing authorities to the contem­ porary literature in parenting. Parenting is a job whose primary object of attention and action is the child— human children do not and cannot grow up as solitary individuals—but parenting is also a vital status in the life course with consequences for parents themselves. In this forum, parenting is defined by all of children’s principal caregivers and their many modes of caregiving. Studies in Parenting encompasses five central themes in parenting.

Who Parents Biological and adoptive mothers, fathers, single parents, and divorced and remar­ ried parents can be children’s principal caregivers, but when siblings, grandparents,

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Series Foreword

and nonfamilial caregivers as well as mentors mind children, their parenting is also pertinent.

Whom Parents Parent Parents parent infants, toddlers, children in middle childhood, and adolescents as well as special populations of children including multiple births, preterm, ill, developmentally delayed or talented, and aggressive or withdrawn children.

The Scope of Parenting Parenting’s direct effects include parents’ genetic endowment as well as the expe­ riences they provide children that are instantiated in parents’ cognitions and prac­ tices; parenting’s indirect influences take place through parents’ relationships with each other and their connections to community and culture. Parenting’s direct and indirect effects on children can be both positive and negative.

The Determinants and Consequences of Parenting Evolution and history; biology and ethology; family configuration; formal and informal support systems, community ties, and work; circumstances, tasks, and demands; social, educational, legal, medical, and governmental institutions; socio­ economic class, designed and natural ecology, and culture—as well as children themselves—each shapes parenting.

The Nature, Structure, and Meaning of Parenting Parenting’s pleasures, privileges, and profits as well as frustrations, fears, and failures are all explored in Studies in Parenting. Contemporary parenting studies are diver­ sified, pluralistic, and specialized.This fragmented state needs counterforce in an arena that allows the extended in-depth exploration of cardinal topics in parent­ ing. Studies in Parenting vigorously pursues that goal. Marc H. Bornstein Series Editor

AUTHORS

Andrea Bizzego holds an MS in Bioengineering from University of Padova and a PhD in Information and Communication Technology from University of Trento. Bizzego is a postdoctoral researcher at the Department of Psychology and Cognitive Science of the University of Trento. His research activity focuses on improving the methodological procedures and reproducibility in neuroscience and psychology, mainly processing physiological signals and the application of data science methods. Marc H. Bornstein obtained a BA from Columbia College, MS and PhD degrees

from Yale University, honorary doctorates from the University of Padua and Uni­ versity of Trento, and an Honorary Professorship at the University of Heidelberg. Bornstein holds positions at the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Institute for Fiscal Studies, and UNICEF. He is President Emeritus of the Society for Research in Child Development and has held faculty positions at Princeton University and New York University as well as visiting academic appointments in Bamenda (Cameroon), Bristol, London, Munich, New York, Oxford, Paris, Santiago (Chile), Seoul,Tokyo,Toronto,Trento, Tufts, and the Institute for Fiscal Studies (Lon don). Bornstein is Editor Emeritus of Child Development and Founding Editor of Parenting: Science and Practice. Robert H. Bradley holds a BA from the University of Notre Dame and MA and

PhD from the University of North Carolina. Bradley is Director of the Center for Child and Family Success at Arizona State University. Bradley formerly acted as Director of the Center for Applied Studies in Education at the University of Arkansas at Little Rock and was Adjunct Professor of Pediatrics at the Univer­ sity of Arkansas for Medical Sciences. He served as Associate Editor for Child

xii

Authors

Development and the Early Childhood Research Quarterly. He was also an investigator for several multi-site longitudinal studies, including the NICHD Study of Child Care and Youth Development and the Early Head Start Research and Evaluation study. Bradley is one of the developers of Home Observation for Measurement of the Environment (HOME) Inventory and the Family Map. Kirby Deater-Deckard received his BA from the Pennsylvania State University

and MA and PhD from the University of Virginia. He is Professor and Program Director in Developmental Science in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst and Fellow of the Associa­ tion for Psychological Science. He has been a visiting scholar or honorary faculty in Utrecht University (The Netherlands), University West (Vänersborg, Sweden), Kings College London (United Kingdom), and Shandong Normal University (Jinan, China) and currently consultants with the FinnBrain Study (University of Turku, Finland). He was a joint editor of The Journal of Child Psychology and Psy­ chiatry, an associate editor of the Handbook of Contemporary Family Psychology, and a co-editor of the book series Frontiers in Developmental Science. Gianluca Esposito holds an MS from the Second University of Naples and a PhD from the University of Trento. Esposito is Full Professor in Child Develop­ ment and Chair of the PhD program in Cognitive Science at the University of Trento. He has held positions at the RIKEN Brain Science Institute (Japan) and at Nanyang Technological University (Singapore) as well as visiting appointments at Chiba University and Nagasaki University (Japan), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (USA), and the Zayed Higher Organization (United Arab Emirates). He was Chair of the Inter­ national Affairs Committee of the Society for Research in Child Development. Esposito is Senior Associate Editor at Research in Developmental Disabilities and has held editorial roles at Child Development, Behavioral Brain Research, Parenting: Science and Practice, and Acta Psychologica. Jennifer E. Lansford holds a BA from Duke University and MA and PhD from

the University of Michigan. She is Research Professor at the Sanford School of Public Policy and Faculty Fellow of the Center for Child and Family Policy at Duke University. Lansford leads the Parenting Across Cultures Project, a longi­ tudinal study of children, mothers, and fathers from nine countries. She has con­ sulted for UNICEF on the evaluation of parenting programs in several countries and on the development of a set of international standards for parenting programs. She is Editor of the International Journal of Behavioral Development and chairs the U.S. National Committee for Psychological Science of the National Academies of Sciences, Engineering, and Medicine. She is Fellow of the American Psychologi­ cal Association and the Association for Psychological Science.

Authors

xiii

Diane L. Putnick holds a BA from the University of Delaware and a PhD in

Developmental Psychology from The George Washington University. Putnick is a Staff Scientist with the Eunice Kennedy Shriver National Institute of Child Health and Human Development and a participating member with the Center for Inter­ personal Acceptance & Rejection. Putnick was the first honorary member of the Bangladesh Psychometric Society and serves on the external advisory board of a grant to integrate global data on child health and development. She also serves on the editorial boards of Developmental Psychology, Parenting: Science and Practice, and Family Process. W. Andrew Rothenberg earned a BA from North Carolina State University and

MS and PhD in child clinical psychology from the University of North Carolina at Chapel Hill. Rothenberg holds positions as Research Scientist at Duke Univer­ sity and Postdoctoral Associate at the University of Miami Miller School of Medi­ cine’s Mailman Center for Child Development. He is interested in preventing and treating the intergenerational transmission of deleterious parenting and child’s mental health. He has collaborated with UNICEF, is a member of the UNICEF Inclusive Policy Lab, and has taught and trained scientists and clinicians in Kenya, Italy, Sweden, and Thailand. He is a winner of the United States’ National Insti­ tutes of Health’s Matilda White Riley Early Stage Investigator Award and is a practicing child clinical psychologist. Susannah Zietz holds a BA from Mount Holyoke College and MPH and PhD from the Gillings School of Global Public Health at the University of North Car­ olina at Chapel Hill. She is Postdoctoral Scholar with the Center for Child and Family Policy at Duke University Sanford School of Public Policy. Her research focuses on relations between exposure to adversity in childhood and aggression and health risk behaviors in adolescence and adulthood. She has consulted with international agencies and research organizations including the International Center for Research on Women, Pacific Institute for Research and Evaluation, UNICEF, and USAID on global qualitative and quantitative research on HIV among adolescents, adolescent mental health, cash transfers, child labor, and sexual harassment of girls in public places.

1 INTRODUCTION AND

GENERAL METHODS

MARC H. BORNSTEIN, ET. AL.INTRODUCTION AND GENERAL METHODS

Parenting, National Development, and Early Childhood Development in 51 Low- and Middle-Income Countries Marc H. Bornstein, W. Andrew Rothenberg, Andrea Bizzego, Robert H. Bradley, Kirby Deater-Deckard, Gianluca Esposito, Jennifer E. Lansford, Diane L. Putnick, and Susannah Zietz Introduction This volume in Studies in Parenting documents child growth, caregiving prac­ tices, discipline and violence, and children’s physical home environments, along with child and primary caregiver sociodemographic characteristics and house­ hold and national development demographic characteristics, in associations with multiple domains of early childhood development across a substantial fraction of the majority world of low- and middle-income countries (LMIC) using contem­ porary 21st-century data. Specifically, analyses presented in this volume examine three measures of child growth (height-for-age, weight-for-age, and weight-for­ height), two caregiving practices (cognitive and socioemotional), three discipline and violence forms of parenting (nonviolent, psychologically aggressive, and physically violent), and eight indexes of children’s physical home environments (quality of housing, construction materials, water and sanitation, cooking fuel and facilities, crowding, household electronic assets, household amenities, and learn­ ing materials), as instantiated in Rounds 4 (2009–2012) and 5 (2012–2017) of the UNICEF Multiple Indicator Cluster Surveys (2015), as well as two child (age and gender) and two primary caregiver (age and education) sociodemographic characteristics, and four household (crowding) and three national development (health, education, and wealth) demographic characteristics, in relation to five domains of children’s early development (literacy and numeracy, socio-emotional, physical health, approaches to learning, and overall development) as instantiated in the UNICEF Early Childhood Development Index (Loizillon et al., 2017) in

DOI: 10.4324/9781003044925-1

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Marc H. Bornstein et al.

nearly 160,000 girls and boys of 3 to 5 years of age using nationally representative data collected in 51 LMIC. This Study in Parenting marshals that information to address seven principal questions about children, caregiving, and contexts. Chapter 1 addresses the first two questions: (1) How do measures of five central domains of early childhood development compare across LMIC? (2) How do child and primary caregiver sociodemographic characteristics and household and national development demographic characteristics relate to the five central domains of early childhood development? Chapters 2–5 concern, respectively, child growth, caregiving practices, discipline and violence, and physical home environments and address the next four com­ mon questions: (3) How do measures of child growth, caregiving practices, discipline and vio­ lence, and physical home environments compare across child gender? (4) How does a measure of national development relate to measures of child growth, caregiving practices, discipline and violence, and physical home environments? (5) How do measures of child growth, caregiving practices, discipline and vio­ lence, and physical home environments relate to the five central domains of early childhood development? (6) How do measures of child growth, caregiving practices, discipline and vio­ lence, and physical home environments relate to the five central domains of early childhood development, after taking into consideration child and pri­ mary caregiver sociodemographic characteristics and household and national development demographic characteristics? Chapter 6 aggregates measures of child growth, caregiving practices, discipline and violence, and physical home environments with child and primary caregiver sociodemographic characteristics and household and national development demographic characteristics to address a seventh question using a machine learn­ ing approach: (7) How do measures of child growth, caregiving practices, discipline and vio­ lence, and physical home environments, child and primary caregiver soci­ odemographic characteristics, and household and national development demographic characteristics taken together rank in predicting the five central domains of early childhood development? This introductory chapter describes the database used to address these seven questions, and the UNICEF Multiple Indicator Cluster Surveys (MICS; UNICEF,

Introduction and General Methods

3

2015), with specific attention paid to how child growth, caregiving practices, dis­ cipline and violence, and physical home environments, child/primary caregiver sociodemographic and household/national development demographic character­ istics, and early childhood development were measured.The introduction to the MICS includes descriptions of MICS data collection processes and the 159,959 participant families, the 51 LMIC, and sociodemographics of the MICS samples. Next, the chapter describes the specific MICS measures used to capture child growth, caregiving practices, discipline and violence, and physical home environ­ ments, as well as the child’s development, and child and primary caregiver sociode­ mographic characteristics and household and national development demographic characteristics. The measure used to quantify national development, the Human Development Index (HDI; United Nations Development Programme [UNDP], 2014), is also described. Chapter 1 additionally includes a description of the gen­ eral data analysis plan used to address Questions 1–6. The same analytic plan is implemented in the four main substantive chapters as a means of consistently examining the questions with which this volume is centrally concerned.

International Social and Behavioral Science The social and behavioral sciences are consistently reminded of how little is still known about the activities, experiences, and life circumstances of children, caregiving, and contexts in majority world, non-North American, non-Western European settings (Arnett, 2016; Bornstein, 1980, 1991, 2001; Kennedy et al., 1984; Moghaddam, 1987; Nielsen et al., 2017; Russell, 1984; Saxena et al., 2006; Segall et al., 1998;Tomlinson et al., 2014). Most such research, and consequently an academic understanding of children, caregiving, and contexts, still derives from studies conducted in the minority world of Western, educated, industri­ alized, rich, and democratic—so-called “WEIRD” (Henrich et al., 2010)— nations. Regular estimates indicate that less than 10% of the literature in some topics in the social and behavioral sciences emanates from regions of the world that account for more than 90% of the world’s population. As a corollary, the societies typically included in comparative research are usually highly similar: they represent high-income countries, and in them, family members normally adhere to the same basic organization, play the same fundamental roles, and share many of the same parenting goals. In consequence, critics wisely reject broad generalizations about children, caregiving, and contexts derived from con­ textually restricted investigations. Exceptions to such criticisms focus on single society studies or comparisons among limited numbers of societies, but often these exceptions in comparative examinations are hampered by challenges to standardization. In a nutshell, context-related limitations defined by a narrow participant research database continue to constrain a global understanding of children, caregiving, and contexts. In consequence, much less is currently known scientifically than is commonly acknowledged about children, caregiving, and contexts generally.

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A more encompassing approach is advocated by empiricists and theoreticians alike as yielding a more comprehensive perspective on social and behavioral sci­ ences and as critical for testing the limits of generalization of social and behavioral phenomena. Science can only benefit from an enlarged representation of the world’s children, caregiving, and contexts. Studies that employ such a wider lens promise more penetrating and valid insights into development, parenting, and culture. Such lessons also illuminate how broad or circumscribed are presumed human universals as well as how experiential and environmental contexts shape children and caregiving. It is imperative to learn more about children, caregiving, and contexts so that parents, psychologists, practitioners, and policy makers can better understand the responsibilities and dynamics of caregiving and promote wholesome child development and well-being worldwide. This volume focuses on documenting child, caregiver, and context characteris­ tics associated with international variation in 51 low- and middle-income coun­ tries and on charting relations among child growth, caregiving practices, discipline and violence, and physical home environments, as well as child and primary car­ egiver sociodemographic characteristics and household and national demographic characteristics, with central domains of early childhood development. Compara­ tive multinational studies contribute to identifying, distinguishing, and explain­ ing general as well as specific patterns of childhood and caregiving. The LMIC reported about in this volume vary widely in terms of history and ideology, social and economic situations, beliefs and values, as well as other factors thought to influence child development and caregiving.This volume therefore moves toward the promise of a more global and inclusive social and behavioral science.

General Theoretical Framework A complex theoretical web of associations among individual, interpersonal, institu­ tional, and contextual factors that predict early childhood development organizes the studies presented in this volume.That theoretical underpinning is informally informed by Bronfenbrenner’s Bioecological Systems Theory of human develop­ ment (Bronfenbrenner & Morris, 2006; see also Bornstein & Leventhal, 2015). Bioecological theory defines development as a joint function of process, per­ son, context, and time (PPCT). For purposes here, processes refer to dynamic interactions that the child experiences. Person characteristics are indigenous to children and primary caregivers and include, for example, age, gender, personality, and intellect. Child development then proceeds within a hierarchically organized, interlinked set of nested contexts or systems, each of which harbors the potential to influence other systems. The proximal microsystem encompasses patterns of activities, roles, and interpersonal relationships (as with primary caregivers) that the child experiences in face-to-face settings defined by specific social and mate­ rial parameters. The most distal developmental influences from the child arise from overarching macrosystem patterns of society-level beliefs, values, customs,

Introduction and General Methods

5

traditions, and living conditions (e.g., culture, religion, and the socioeconomic organization).The macrosystem is not separate from the child’s microsystem but permeates and colors it. Understanding the meaning and impact of proximal influences on the child often requires placing each within broader distal contexts in which each is found (Bornstein, 1995). (The microsystem and macrosystem are connected through intervening mesosystem processes and links between two or more microsystems. They are further connected through the exosystem links between aspects of the environment the child does not directly encounter but which influence the child’s development through lower level micro- and mesosys­ tems. Moreover, crosscutting all these systems is time, the chronosystem. Mesosys­ tems, exosystem, and chronosystem are not explicitly examined or discussed here; see Chapter 7.) In overview, the child’s environment is complex, multidimen­ sional, and structurally organized into interlinked systems; as children are active producers of their own development, children (person) and their social and mate­ rial environments (contexts) are inextricably linked, and contributions of both person and context are essential to explain and understand human development. In anticipation of addressing the several substantive questions that motivate this Study in Parenting, this chapter presents associations between sociodemographic and demographic characteristics and the five early childhood development out­ comes as quantified in the Early Childhood Development Index (ECDI; Loizillon et al., 2017). Specifically, this chapter progresses toward the full PPCT frame­ work describing associations of the child sociodemographic characteristics with the ECDI, primary caregiver sociodemographic characteristics with the ECDI, household demographic characteristics with the ECDI, and national development demographic characteristics with the ECDI, as well as simultaneous associations between all sociodemographic and demographic characteristics and the ECDI in a linear regression framework. That analysis begins to meet the multisystem requirements of the PPCT model. In essence and faithful to the PPCT framework, this chapter presents the methodological and analytic roadmap followed in subsequent chapters in this volume. The aim is to increase an understanding of the varied and several asso­ ciations between child growth, caregiving practices, discipline and violence, and physical home environments with early childhood development in a substantial sample from a large number of contemporary LMIC.The chapter concludes with a brief recap of the UNICEF MICS sample, measures, and analytic plan and a discussion of sociodemographic and demographic covariates that matter to child development in LMIC.

The UNICEF Multiple Indicator Cluster Surveys This section of the chapter describes the MICS dataset used in analyses in this Study in Parenting, including the LMIC in the dataset, MICS Round 4 and Round 5 data collection, and selected MICS data used in this volume.

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A Brief History of the MICS The Convention on the Rights of the Child was adopted by the United Nations, signed on 20 November 1989, and came into effect on 2 September 1990. At the 29–30 September 1990 World Summit for Children, the UN subsequently adopted the World Declaration on the Survival, Protection, and Development of Children and its Plan of Action, and governments pledged to monitor progress toward achieving the 27 goals of the Declaration and Plan (Ki-Moon, 2007). To aid in that effort, UNICEF constructed the MICS with a goal of produc­ ing nationally representative and internationally comparable household surveys pertaining to child development and family life. Using the MICS, LMIC in dif­ ferent regions of the world are equipped to monitor and evaluate the progress of children, women, and men (UNICEF, 2015).The MICS supports evidence-based policy formulation, assesses trends, and measures disparities, and it has become a principal tool to assess achievements of the World Fit for Children (WFFC) Dec­ laration and Plan of Action (UNICEF, 2007), the UN Millennium Declaration and the Millennium Development Goals (MDG; United Nations, 2015), and the UN Sustainable Development Goals (SDG; United Nations, 2017).

The MICS 4 and MICS 5 All chapters in this volume use data from Rounds 4 (MICS 4) and 5 (MICS 5) of the MICS, carried out between 2009 and 2017.The MICS 4 and MICS 5 each comprised several questionnaires (UNICEF, 2009, 2013).Two questionnaires used in this volume are the Household Questionnaire and the Questionnaire for Chil­ dren under Five. The Household Questionnaire is administered to every house­ hold drawn for MICS samples (Bornstein et al., 2012). The Questionnaire for Children under Five provides information on all children under 5 years of age in a participant household (Bornstein et al., 2012). Each questionnaire is composed of sets of standardized questions grouped by topics (Bornstein et al., 2012). The specific sets of standardized questions from the Household Questionnaire and Questionnaire for Children under Five surveys used in this volume are discussed in greater detail later and in each substantive chapter in this volume. Nearly iden­ tical Household Questionnaires and Questionnaires for Children under Five were used in the MICS 4 and MICS 5. The basic criteria for inclusion of questions in the MICS 4 and MICS 5 Household Questionnaire and Questionnaire for Children under Five were (1) their relevance to WFFC, MDG, and UNICEF goals; (2) international agreement on the questions; (3) previous testing and feasibility of collecting data; and (4) proven quality (UNICEF, 2009, 2013). When countries administered the MICS 4 and MICS 5 Household Questionnaire and Questionnaire for Children under Five surveys, UNICEF recommended that they ask a specified set of core ques­ tions. UNICEF also provided countries with sets of ancillary questions and gave

Introduction and General Methods

7

countries the option of adding these ancillary questions to country-specific sur­ veys. For instance, measures of child growth status, such as height and weight, were core questions that UNICEF recommended countries ask in the Questionnaire for Children under Five. However, UNICEF also provided questions that, for example, asked how caregivers provided cognitive stimulation to their children. Countries were freer to choose whether to ask ancillary questions on the Ques­ tionnaire for Children under Five. The measures examined in this volume are derived from both core (e.g., child height and weight) and ancillary (e.g., car­ egiver cognitive stimulation) question sets. The MICS covers a wide array of issues, and flexibility of MICS in having both core and ancillary questions allows countries to adapt the surveys to their particular situations and needs. However, the MICS keeps comparability across countries through standardizations of both questions and administration. The questions in the MICS were designed to be understandable and easily answered by people with different education levels, from different cultures, and having dif­ ferent social backgrounds (UNICEF, 2009, 2013). In taking such an approach for measurement development and implementation, the MICS aspires to be an “etic” assessment instrument. Etic constructs consist of accounts, descriptions, and analy­ ses of constructs and activities expressed in terms of conceptual schemes and cat­ egories that are regarded as meaningful and appropriate by the broad community of scientific observers; contrasting “emic” constructs consist of accounts, descrip­ tions, and analyses expressed in terms of conceptual schemes and categories that are regarded as meaningful and appropriate only by members of a particular com­ munity (Harris, 1976; Jahoda, 1977; van de Vijver, 2010). Etic knowledge is essen­ tial for cross-cultural comparison because such comparison necessarily demands standard units and categories, and the etic approach validly treats diverse samples together at one time.

MICS 4 and MICS 5 Data Collection Each country designed and selected a probability sample that was national in cov­ erage, and then field implemented the MICS 4/MICS 5 with minimum deviation from an overall standard design. For data collection, a three-stage sample frame was used (UNICEF, 2009, 2013). In the first stage, primary sampling units (PSUs) were defined and selected with systematic probability proportionate to size. If possible, PSUs corresponded to cen­ sus Enumeration Areas (EA): the smallest geographical statistical unit created for a housing and population census. Referring to EA was a recommended practice because the PSU should be an area around which fieldwork could be conveniently organized. It should also be an area small enough for mapping, segmentation, or listing of households, but large enough to be easily identifiable in the field. The second stage involved selecting clusters within each selected PSU. Clusters are the smallest geographical area composed of a number of adjacent households.

8

Marc H. Bornstein et al.

In some cases, PSUs were themselves the clusters selected. When PSUs were selected, the second stage was eliminated. In the third stage, within each cluster, households that were to be interviewed were selected. Selection of the households was performed by sub-sampling the list of households within each cluster.To foster sample implementation, implicit strati­ fication was followed. Existing samples could be used only if they were valid prob­ ability samples (e.g., a Demographic and Health Survey or a labor force survey). The MICS 4 (UNICEF, 2009) and MICS 5 (UNICEF, 2013) manuals report the tables used to calculate the sample sizes and the segmentation that was used in each country. Depending on the country, the design might have varied with respect to the number of PSUs, the number of clusters per PSU, the number of households per cluster, and, hence, the overall sample size. In the MICS 5, prior to interviewing, all households located in the sampled clusters were listed.The main aim of the household listing operation was to create a complete and updated list of households for all selected clusters to better rep­ resent the total population.This list then served as a sampling frame for the final selection of households to be included in the MICS 5 sample. The average number of PSUs for each country was 649.5 (Mdn = 459, range = 1–3,658).The average overall country sample size of households included in this analysis was 3,136.45 (Mdn = 2,578, range = 119–13,960 (Table 1.1). Each country followed the same stages of implementing the MICS 4/MICS 5: making logistical arrangements, preparing the questionnaires and training materi­ als, training enumerators, collecting and preparing the equipment, carrying out pilot studies, setting up data processing (computers and staff ), and considering and solving ethical issues. Field teams (enumerators and supervisors) were recruited and trained in interview techniques, contents of the questionnaires, field proce­ dures, and use of equipment. All data were entered twice into standard databases, followed by internal consistency checks. After cleaning data files and checking data quality, countries prepared technical reports, and data were centrally archived (https://mics.unicef.org/). A MICS global team oversaw preparation of the survey tools and instruments, training country teams, follow-up of country performance, quality of data, and approved final reports. To minimize survey biases and ensure data reliability, the same MICS team standardized implementation procedures and prepared techni­ cal documents and programs to be used across participating MICS 4 and MICS 5 LMIC. Prior to MICS 4/MICS 5 implementations, UNICEF organized work­ shops in each region to review critical steps, such as survey design and prepara­ tion, data processing, data analysis and report writing, and data archiving and dissemination. At any time, individual governments could seek consultation from UNICEF. Global MICS 4 and MICS 5 evaluations confirmed that the tools, tech­ nical assistance, and data were of high quality (UNICEF, 2009, 2013). The MICS 4 and MICS 5 were conducted following the same standardized procedures.The surveys contained the same sets of questions with one small set of

Introduction and General Methods

9

exceptions in the Questionnaire for Children Under Five (where some questions concerning child disability were removed).These slight differences do not affect any of the measures examined in this volume.

Participant Countries and Households This section of the chapter describes the 51 LMIC and the 159,959 households whose data are used in this Study in Parenting.

Fifty-One Low- and Middle-Income Countries The 51 LMIC whose data are examined in this volume vary widely in terms of history and ideology, beliefs and values, and social and economic conditions.The 51 LMIC also vary with respect to other sociodemographic and demographic factors thought to influence child growth, caregiving practices, discipline and violence, and physical home environments as well as early childhood development. What these LMIC have in common is that they fall into the lower range of resources used by the World Bank (2015) to classify the strength of the over­ all national economy. This system considers gross national income per capita, quality of life (life expectancy and literacy rates), and economic diversification (labor force and consumption). LMIC generally have low standards of living, but conditions within and across LMIC still vary. For example, on a 0–5 index of household material resources (electricity, radio, telephone, television, and trans­ portation), LMIC average fewer than 1 to more than 4 (Bradley & Putnick, 2012). Multinational developmental inquiry, conducted within a sensitive contextual relational system framework (Lerner, 2018; Lerner et al., 2018), provides the basis on which to explore and distinguish uniformity and diversity in child growth, caregiving practices, discipline and violence, and physical home environments. As stated, most established relations of country-level factors with child growth, caregiving practices, discipline and violence, and physical home environments as well as early childhood development have been based on Western European and North American samples; in essence, samples are from technologically advanced, wealthier countries where only a minority of Earth’s inhabitants resides. The paucity of empirical investigation conducted with samples from LMIC leaves unclear whether child growth, caregiving practices, discipline and violence, and physical home environment effects on child development obtained in the minority world of high-income countries (HIC), and widely reported in the contemporary behavioral and social scientific literatures, apply to the majority world of LMIC.This volume seeks to expand an understanding of child growth, caregiving practices, discipline and violence, and physical home environments, early childhood development, as well as relations between them by examining a number of LMIC that represent a proportion of the 21st-century majority world

10 Marc H. Bornstein et al.

FIGURE 1.1

The 51 LMIC analyzed in this volume (designated in black).

(UNICEF, 2009, 2013).This volume explores child growth, caregiving practices, discipline and violence, and physical home environments, and early childhood development as well as relations between them in LMIC through the rich MICS data set. Although 70 LMIC conducted the MICS 4 and MICS 5, only data from 51 countries were used for analyses reported in this Study in Parenting. Nine­ teen countries were excluded because they did not include questions that contained data pertinent to the seven questions addressed in this volume. Additionally, 12 LMIC (Belize, Cuba, Eswatini [also known as Swaziland], Guinea-Bissau, Kazakhstan, Mauritania, Mongolia, Nigeria, Serbia, Palestine, Thailand, and Vietnam) participated in both MICS 4 and MICS 5 data col­ lection. Because a focus of this volume is to explore predictors of individual child development (as opposed to comparing specific nations), all families in these 12 countries are included in the sample used for analysis, regard­ less of whether these families participated in MICS 4 or MICS 5. The 51 LMIC represent 17 countries from Africa, 12 countries from Asia, 9 countries from North America, 8 countries from Europe, and 5 countries from South America (Figure 1.1).

Sociodemographics and Demographics of the MICS Samples The 159,959 households examined in this volume with MICS 4 and MICS 5 data can be characterized according to several child and primary caregiver soci­ odemographic and household and national demographic characteristics related to

Introduction and General Methods

11

their country of residence as well as the Human Development Index associated with that country. Children Data from 159,959 children/households in the 51 LMIC were analyzed. Chapters 2–5 in this volume use essentially the whole sample of 159,959 based on inclusion criteria for the questions of interest; Chapter 6 uses a subsample.The developmental science focus of reports in this volume concerns children aged 36 to 59 months. Children in the sample were, on average, 47.31 months old (SD = 6.89), and the sample was evenly divided with 49.30% girls and 50.70% boys. To obviate within-family or within-household variance, one child aged between 36 and 59 months was randomly selected from families with more than one child in the target age range. This selection was done one time, as the data set used in all analyses was compiled, thus ensuring that the samples used in each chapter are the same in this way. Although children of every age merit investigation in developmental science, the studies reported in this volume focus on children 3 to 5 years of age. First, only children in this age range were administered the Early Childhood Development Index (ECDI; Loizillon et al., 2017); and the primary questions addressed in this volume require direct measures of children’s development. Second, as children move beyond infancy, parents become less preoccupied with survival concerns and settle into routines more absorbed with child thriving.Third, the period just beyond infancy and toddlerhood (ages approximately 1–3 years;Verhoeven et al., 2019) but before middle childhood (ages approximately 5–12 years; Collins & Madsen, 2019) tends to be neglected in the contemporary developmental sci­ ence literature. Fourth, the lack of information on children 3 to 5 years old from LMIC is unfortunate given that children in this age range are growing cognitively, verbally, emotionally, and socially and are increasingly capable of formulating and expressing verbally their thoughts, beliefs, and desires; they are fast developing as persons in themselves and as social partners. Between 3 and 5 years of age, chil­ dren display declining tendencies to behave impulsively, and increasing tendencies toward planfulness and other executive processes; they possess greater capacities for understanding the impact of their actions on others and command and exhibit rudimentary knowledge of what is required to be helpful (Simpson et al., 2019; Wiebe et al., 2012; Willoughby et al., 2011). Fifth, at this point in development, children’s mental skills are acquiring greater power as predictors of their future cognitive abilities, and their personality or temperament characteristics and social skills are also coming to anticipate later patterns of friendship and peer status (Bayley, 1949; Bornstein et al., 2014, 2019; Bornstein, Hahn et al., 2016; Dodge & Crick, 1990; Ladd et al., 1988; Olson et al., 1992; Putnick et al., 2017;Walker et al., 1994).Thus, it is likely that the experiences children aged 3 to 5 years in LMIC have at home and in their immediate surroundings (such as those documented in the MICS and being examined in this volume) help to determine their devel­ opmental course. Sixth, as it happens, most children 3 to 5 years of age living in

12 Marc H. Bornstein et al.

LMIC do not attend school and so have not been exposed to the challenges and opportunities, and especially standardization, schools can bring to cognitive and socioemotional development. Rather, children in this age range are still mostly at home and under the purview of parents and other caregivers (Bornstein & Putnick, 2019). Last, despite the restricted age range of children included in these analyses, the findings likely hold substantial value for those interested in how envi­ ronmental circumstances influence child development in places around the world where multidimensional poverty is highly prevalent (Alkire & Santos, 2014). Primary Caregivers Respondents to the MICS 4 and MICS 5 identified the person who served in the social role of the child’s primary female caregiver in the household. In the dataset used in this volume, females who serve in the social role of primary caregiver, regardless of their biological relation to the child, are the main foci of investigation.Therefore, primary caregivers might include some adoptive mothers, stepmothers, aunts, grandmothers, and foster mothers (Leon, 2002). In a minority of cases (n = 1487), the main caregiver of the child was a male; children whose primary caregiver was a male were excluded from the dataset reported here (Bornstein et al., 2013; Jager et al., 2017). Regarding sociodemographic characteristics, primary caregivers in the cur­ rent sample were, on average, 32.03 years old (SD = 8.92). Primary caregivers’ educational achievements varied widely: 27.88% of primary caregivers reported receiving no formal education, 28.44% reported receiving primary education, 32.44% reported receiving secondary education, and 11.25% reported receiving higher education. All participants were treated in accordance with the Declaration of Helsinki and ethical standards for treatment of research participants consonant with the American Psychological Association. All caregivers provided verbal informed consent for their participation in the study before data collection commenced. Households On average, 6.62 people lived in each household in this sample (SD = 3.80). On average, 3.26 children under the age of 18 years (SD = 2.32) and 1.78 children under the age of 5 years (SD = 1.01) lived in these households. Households were somewhat crowded, as on average 3.19 people shared each bedroom (SD = 1.60).

MICS Measures The following sections of the chapter give general orienting descriptions of the MICS measures of child growth, caregiving practices, discipline and violence, and physical home environments; child and primary caregiver sociodemographic characteristics, household and national development demographic characteristics; and, finally, the measures of early childhood development used in this Study in Parenting.Additional details can be found in individual substantive chapters.

Introduction and General Methods

13

Several measures of child growth, caregiving practices, discipline and violence, and physical home environments (described later) are understood as indexes, rather than scales, as they are composed of formative or causal indicators (Bradley, 2015). The set of indicators used to compose a given index is placed together, not because they are thought to derive from a common cause or latent factor, but because they are assumed to produce (or cause) a common circumstance or outcome. An example of this type of measure is a quality-of-life questionnaire, where items such as happiness, high income, many friends, and the like each separately are assumed to promote or cause one’s quality of life to increase.There is no underlying latent phenomenon called “quality of life” that produces each of these indicators; rather, each indicator contributes to a higher quality of life (Streiner, 2003). Consequently, because there is no assumption that the items/ indicators used to represent a phenomenon such as quality of life are derived from the same underlying condition (i.e., having high income does not necessarily mean that someone has many friends), questionnaires constructed from causal or formative indicators are not concerned with internal consistency (Bradley, 2004, 2015; Streiner, 2003). To be more concrete as regards, for example, the caregiv­ ing practices examined in this Study in Parenting, a variety of parental actions may potentiate socioemotional well-being in children (e.g., singing songs for a child, playing with a child, and taking a child outdoors), but the issue is not that they are derived from a common source (e.g., a parent’s personality, the quality of inter­ personal relationships within the family, and living in a context of relative afflu­ ence). Rather, the reason for bundling them into a single index is that each action is thought to have a similar effect on the child (i.e., they help support the child’s socioemotional well-being), and exposure to more of such actions is assumed to be better than exposure to each one separately.

Child Growth First, this Study in Parenting investigates associations of measures of child growth including children’s height-for-age, weight-for-age, and weight-for-height as appropriate for their age, with early childhood development. Negative indicators of child growth, often associated with poor child nutrition in LMIC, therefore, include child stunting, child underweight, and child wasting.Thresholds for each of these measures are based on children being two or more standard deviations below the median of World Health Organization’s (WHO, 2006, 2014) global child growth standards (WHO Multicentre Growth Reference Study Group [WHO-MGRSG], 2006). Details appear in Chapter 2.

Caregiving Practices and Discipline and Violence Next, this Study in Parenting investigates associations of caregiving practices and discipline and violence with early childhood development.Two sets of caregiving

14 Marc H. Bornstein et al.

practices focus on parenting that facilitates the cognitive and socioemotional development of the child (UNICEF, 2007, 2009, 2013). Cognitive caregiv­ ing practices included reading, telling stories, and naming, counting, and draw­ ing with the child (UNICEF, 2009, 2013). Socioemotional caregiving practices included singing, playing, and taking the child outside (UNICEF, 2009, 2013). Discipline and violence focused on responding to children’s misbehaviors and setting rules and limits for children (UNICEF, 2009, 2013). The three types of strategies include nonviolent discipline, psychologically aggressive discipline, and physically violent discipline (UNICEF, 2009, 2013).To measure caregiving prac­ tices and discipline and violence, items in the MICS 4 and MICS 5 that reflected cognitive caregiving, socioemotional caregiving, and discipline were identified and placed into clusters. Summary scores for each cluster were then generated and used in the analyses. Detailed descriptions of selecting, processing, and combining MICS indicators for each parenting domain are provided in Chapters 3 and 4.

Children’s Physical Home Environment Finally, this Study in Parenting investigates associations of measures of the child’s physical home environment with early childhood development. Measures of the child’s physical home environment included material resources and infrastructure present in the child’s living area. Specifically, these were measures of the quality of housing, construction materials, water and sanitation, cooking fuel and facilities, crowding, household electronic assets, household amenities, and learning materi­ als (UNICEF, 2009, 2013). Details appear in Chapter 4.

Sociodemographic and Demographic Measures Child Sociodemographic Characteristics Child age in months and child gender (0 = female, 1 = male) were the two child sociodemographic characteristics examined in all analyses of associations with early childhood development.

Primary Caregiver Sociodemographic Characteristics Primary caregiver age in years and primary caregiver education (0 = no formal education, 1 = primary education, 2 = secondary education, and 3 = higher education) were the two primary caregiver sociodemographic characteristics examined in all analyses of associations with early childhood development.

Household Demographic Characteristics The number of people who lived in the household, the number of children under the age of 18 years who lived in the household, the number of children under

Introduction and General Methods

15

the age of 5 years who lived in the household, and household crowding were the four household demographic characteristics examined in all analyses of associa­ tions with early childhood development.The number of children under the age of 5 years who lived in the household was examined separately from the total number of children under 18 who lived in the household to explore if the unique effects of having multiple children under the age of 5 years were associated with slower gains in early childhood development because parents have to spread their cognitive, emotional, and financial resources among multiple children in the same developmental range (Bornstein, Putnick, & Suwalsky, 2016; Juhn et al., 2015; Zajonc, 2001; Zajonc & Markus, 1975). Additionally, a crowding variable was calculated by dividing the number of people who lived in the household by the number of bedrooms in the household. Consequently, crowding is a measure of the number of people-per-bedroom in the household.

National Development Demographic Characteristics This volume examines associations between child growth, caregiving practices, discipline and violence, and physical home environments with early childhood development in 51 LMIC. Given the focus on LMIC, it was essential to control the effects of conditions of national development when examining these associa­ tions. Around the globe, LMIC vary in their level of national support for human development. Perhaps, the most widely researched and empirically validated measure of a country’s support for human development is the United Nations’ Human Development Index (HDI; UNDP, 2014).The HDI is a composite indi­ cator of a country’s status with respect to health, education, and wealth and, thus, of nations’ conditions that foster human development. The HDI moves beyond assessing nations simply on the basis of economic resources by including three conditions in the country that promote the development of people and their capabilities (UNDP, 2014).The HDI has been associated with parenting practices, child health conditions, and early childhood development in numerous existing studies (e.g., Bornstein et al., 2012; Tran et al., 2017), further demonstrating its validity. The United Nations adopted the HDI as a way of representing the general standard of living present in a country. Most comparable national-level composite indexes (e.g., GDP) reflect solely a country’s level of wealth rather than convey an array of conditions available to support health and adaptive functioning in the population.Although the HDI has shortcomings (Bornstein et al., 2012), it stands as a reasonable proxy for national levels of support generally available for promot­ ing human development. Specifically, the HDI is derived from three dimensions of human development: (1) the ability for people in the nation to live a long and healthy life (measured by life expectancy at birth), (2) the ability for people in the nation to accrue knowledge (measured by expected years of schooling and mean years of schooling), and (3) the ability for people in the nation to achieve a decent standard of living (measured by Gross National Income per capita; UNDP, 2014).

16 Marc H. Bornstein et al.

Each of these measures is derived from UN data (UNDP, 2020).Through a series of mathematical transformations, these three dimensions of human development (and the measures that operationalize them) are transformed into indexes follow­ ing the same 0 to 1 scale as the HDI composite index (UNDP, 2020).The three indexes are called the Life Expectancy Index (LEI), the Education Index (EDI), and the Income Index (INI; UNDP, 2020).These three subindexes combined to create a composite HDI. The HDI varies from 0 (low) to 1 (high). Scores ≤ .550 on the HDI indicate low national development, .550–.699 indicate medium national development, .700–.799 indicate high national development, and ≥ .800 indicate very high national development.The 51 LMIC in this volume represent a range of human development conditions, as measured by the HDI. Of the 51 nations examined here, 14 fell in the low human development category, 14 fell in the medium category, 20 fell in the high category, and 3 fell in the very high category on the 2013 Human Development Index (UNDP, 2014). Country HDI scores are listed in Table 1.1. TABLE 1.1 The 51 LMIC, the Number of Children/Households (N), Geographical

Location (Continent), Country 2013 Human Development Index (HDI 2013), MICS Version Administered, and Years of Data Collection Country Algeria Argentina Bangladesh Barbados Belarus Belize Bhutan Bosnia and Herzegovina Cameroon Central African Republic Chad Democratic Republic of Congo Costa Rica Cuba Dominican Republic El Salvador

N

Continent

HDI 2013

MICS Administered

Years of Data Collection

5531 3047 8767 192 1406 783 2403 1026

Africa South America Asia North America Europe North America Asia Europe

0.745 0.820 0.575 0.796 0.804 0.705 0.589 0.747

4 4 5 4 4 4&5 5 4

2012–2013 2011–2012 2012–2013 2012 2012 2011–2016 2014 2010

2776 3705

Africa Africa

0.535 0.344

5 4

2014 2010

7011 4021

Africa Africa

0.397 0.426

4 4

2010 2010

912 2230 7703

North America North America North America

0.776 0.765 0.713

4 4&5 5

2011 2010–2014 2014

2964

North America

0.671

5

2014

Introduction and General Methods

17

Country

N

Continent

HDI 2013

MICS Administered

Years of Data Collection

Ghana Guinea Bissau Guyana Iraq Jamaica Kazakhstan Kosovo Kyrgyzstan Laos North Macedonia Malawi Mali Mauritania Mexico Moldova Mongolia Montenegro Nepal Nigeria Paraguay St. Lucia Sao Tome and Principe Serbia Sierra Leone State of Palestine Suriname Swaziland (also known as Eswatini) Thailand Togo Tunisia Turkmenistan Ukraine Uruguay Vietnam Zimbabwe

3024 2919 1309 13960 659 4200 660 1778 4472 553

Africa Africa South America Asia North America Asia Europe Asia Asia Europe

0.577 0.440 0.645 0.666 0.726 0.788 0.786 0.658 0.579 0.743

4 4&5 5 4 4 4&5 5 5 4 4

2011 2010–2014 2014 2011 2011 2010–2015 2013–2014 2014 2011–2012 2011

7664 6433 3690 3340 720 3672 639 2241 10151 1821 119 833

Africa Africa Africa North America Europe Asia Europe Asia Africa South America North America Africa

0.461 0.408 0.508 0.756 0.693 0.729 0.803 0.554 0.519 0.695 0.733 0.560

5 4 4&5 5 4 4&5 5 5 4&5 5 4 5

2013–2014 2009–2010 2011–2015 2015 2012 2010–2014 2013 2014 2011–2017 2016 2012 2014

2578 3599 3217

Europe Africa Asia

0.771 0.419 0.679

4&5 4 4&5

2010–2014 2010 2010–2014

1271 2129

South America Africa

0.715 0.572

4 4&5

2010 2010–2014

4214 1799 1163 1492 1897 747 2628 3891

Asia Africa Africa Asia Europe South America Asia Africa

0.728 0.472 0.723 0.692 0.745 0.797 0.675 0.516

4&5 4 4 5 4 4 4&5 5

2015–2016 2010 2011–2012 2015–2016 2012 2012–2013 2010–2014 2014

Note: HDI 2013 values reported in Table 1.1 and used in this volume are based on 2013 HDI values downloaded from this United Nations Development Programme Website: http://hdr.undp.org/en/ content/latest-human-development-index-ranking. Reported HDI values change slightly over time as the UN adjusts the HDI formula and revises figures. In consequence, HDI 2013 values in Table 1.1 may differ slightly from those reported in the original report of HDI 2013 values (UNDP, 2014).

18 Marc H. Bornstein et al.

The household-level average HDI score across the entire 51 LMIC is .60 (SD = .13), indicating that, on average, households in this sample are living in nations at the lower end of the “medium” human development level. The three HDI subindexes varied in their household-level averages in this sample.The average LEI score in the sample was .70 (SD = .13), indicating that, on average, households in this sample lived in nations in the high human develop­ ment range on the life expectancy index. The average EDI score in the sample was .54 (SD = .14), indicating that, on average, households in this sample lived in nations in the low human development range on the education index. The average INI score in the sample was .59 (SD = .15), indicating that, on average, households in this sample lived in nations at the low end of the medium human development range on the income index. Therefore, when it comes to creating conditions to promote human development, LMIC in this sample were overall most effective in creating conditions for long life, less effective in creating con­ ditions for knowledge creation and accrual, and somewhat effective in creating conditions for a decent standard of living. Administration of the MICS 4 and MICS 5 in the nations in this sample spanned the years 2009 to 2017.The UN calculates an HDI yearly. Because 2013 served as the mid-point in data collection, and because choosing this midpoint maximized the comparability of HDI effects across countries, the 2013 HDI was used in analyses in this volume.

Early Childhood Development Index In this volume, associations between child growth, caregiving practices, discipline and violence, and physical home environments are examined in relation to early childhood development.The analyses also consider measures of child and primary caregiver sociodemographic and household and national development demographic characteristics with early childhood development in the 51 LMIC.The same meas­ ures of early childhood development are used in each chapter; they are described in detail here and only explained briefly in the substantive chapters that follow. The MICS 4 and MICS 5 Questionnaire for Children under Five includes ten items that address target milestones of early childhood development. These ten items are collectively called the Early Childhood Development Index (ECDI; Loizillon et al., 2017). They were developed by UNICEF specifically to assess early childhood development in children aged 36 to 59 months in four develop­ mental domains: (1) literacy and numeracy, (2) socio-emotional functioning, (3) physical health, and (4) approaches to learning. The ten-item ECDI was devel­ oped from a list of 158 items provided by experts on early childhood develop­ ment using a multi-stage, multi-country pilot and validation process (Loizillon et al., 2017; McCoy et al., 2016). The final set of items was selected for their high test–retest and inter-rater reliability and validity when compared to existing measures of early childhood development (Janus et al., 2008; Loizillon et al., 2017; McCoy et al., 2016; UNICEF, 2019).

Introduction and General Methods

19

To complete the ECDI, the primary caregiver of the child was asked whether the child displayed a particular behavior related to early childhood develop­ ment, and the primary caregiver responded with a simple “Yes” or “No.” Based on these ten questions, UNICEF ECDI scoring guidelines were developed, and on that basis, the Early Childhood Development Index-Literacy and Numeracy (ECDI-LN; three items), Early Childhood Development Index-Socio-emotional (ECDI-SE; three items), Early Childhood Development Index-Physical Health (ECDI-PH; two items), and Early Childhood Development Index-Approaches to Learning (ECDI-AL; two items) subindexes were calculated. A total Early Childhood Development Index (ECDI; the combination of all ten items) was also calculated. Specifically, answers that indicated positive early childhood devel­ opment (e.g., a “Yes” that a child read at least four simple, popular words or a “No” that a child was easily distracted) were assigned a score of +1.These scores were then summed to compute overall scores on each of the ECDI-LN, ECDI­ SE, ECDI-PH, ECDI-AL, and ECDI indexes (see Table 1.2 for indexes and items). ECDI have demonstrated strong psychometric properties of criterion valid­ ity repeatedly in independent analyses (Loizillon et al., 2017; McCoy et al., 2016; Miller et al., 2016), and multiple independent factor analyses have demonstrated the validity and cross-national stability of these subindexes in the MICS 4 and MICS 5 samples (McCoy et al., 2016). The MICS ECDI was the first tool for capturing population-level development and is currently the most widely used population-level measure of early childhood development (Sincovich et al., 2020). Indeed, external evaluators of the ECDI have identified it as a critically

TABLE 1.2 Early Childhood Development Index Questions in the MICS, Grouped by

Developmental Domains Question

+1 Answer

Domain

Child identifies at least ten letters of the alphabet Child reads at least four simple, popular words Child knows name and recognizes symbol of all numbers from 1 to 10 Child gets distracted easily Child gets along well with other children Child kicks, bites, or hits other children or adults Child able to pick up small object with two fingers Child sometimes too sick to play Child follows simple directions Child able to do something independently

Yes

Literacy and Numeracy

Yes Yes

Literacy and Numeracy Literacy and Numeracy

No Yes No

Socio-emotional Socio-emotional Socio-emotional

Yes

Physical Health

No Yes Yes

Physical Health Approaches to Learning Approaches to Learning

Source: https://mics.unicef.org/tools

20 Marc H. Bornstein et al.

important MICS measure that leads to substantial worldwide benefit (Loizillon et al., 2017). The ECDI was adopted for reporting on MDG 4.2 in the UN Secretary-General’s Annual SDG Progress Report (United Nations Economic and Social Council, 2020). Therefore, ECDI indexes are used throughout this Study in Parenting. In the chapters that follow, relations between child growth (Chapter 2), cogni­ tive and socioemotional caregiving (Chapter 3), discipline and violence (Chap­ ter 4), and children’s physical home environments (Chapter 5) with scores on the four early childhood development domain indexes and the total ECDI score are examined. Each developmental domain was examined because each has been identified as critical to evaluating children’s overall well-being and ensuring that the world achieves the United Nations’ World Fit for Children Declaration and Plan of Action Goals, Millennium Development Goals, and Sustainable Develop­ ment Goals (UNICEF, 2007, 2013, 2017).

How Do Measures of Five Central Domains of Early Childhood Development Compare Across Low- and Middle-Income Countries? This section of this chapter addresses Question 1 introduced earlier. Table 1.3 provides the means and standard deviations for all ECDI scores in each coun­ try. Differences in levels of developmental achievement across these indexes are TABLE 1.3 Early Childhood Development Index and Subindex Scores in Each Country

Country

N

ECDI M (SD) (0–10 Scale)

ECDI-LN ECDI-SE ECDI-PH ECDI-AL M (SD) M (SD) M (SD) M (SD) (0–3 Scale) (0–3 Scale) (0–2 Scale) (0–2 Scale)

Whole sample Algeria Argentina Bangladesh Barbados Belarus Belize Bhutan Bosnia and Herzegovina Cameroon Central African Republic Chad

159959 5531 3047 8767 192 1406 783 2403 1026

5.86 (1.86) 5.70 (1.78) 7.14 (1.49) 5.62 (1.70) 8.42 (1.23) 7.40 (1.28) 7.21 (1.64) 5.94 (1.66) 7.33 (1.35)

0.71 (1.00) 0.84 (1.05) 1.23 (0.99) 0.70 (1.02) 2.47 (0.74) 1.35 (0.96) 1.34 (1.20) 0.77 (1.04) 0.82 (1.04)

2.05 (0.82) 1.88 (0.82) 2.19 (0.78) 1.85 (0.76) 2.16 (0.70) 2.17 (0.65) 2.13 (0.77) 1.96 (0.84) 2.72 (0.55)

1.50 (0.57) 1.35 (0.55) 1.81 (0.40) 1.44 (0.60) 1.85 (0.36) 1.92 (0.30) 1.83 (0.39) 1.56 (0.52) 1.84 (0.37)

1.61 (0.69) 1.64 (0.68) 1.93 (0.30) 1.63 (0.67) 1.94 (0.26) 1.96 (0.19) 1.91 (0.33) 1.65 (0.61) 1.95 (0.25)

2776 5.55 (1.68) 0.56 (0.96) 1.90 (0.78) 1.58 (0.54) 1.51 (0.73) 3705 4.70 (1.57) 0.24 (0.62) 1.76 (0.86) 1.41 (0.55) 1.31 (0.83) 7011 4.34 (1.60) 0.24 (0.64) 1.85 (0.87) 1.45 (0.57) 0.83 (0.90)

Introduction and General Methods

Country

Democratic Republic of Congo Costa Rica Cuba Dominican Republic El Salvador Ghana Guinea Bissau Guyana Iraq Jamaica Kazakhstan Kosovo Kyrgyzstan Laos North Macedonia Malawi Mali Mauritania Mexico Moldova Mongolia Montenegro Nepal Nigeria Paraguay St. Lucia Sao Tome and Principe Serbia Sierra Leone State of Palestine Suriname Swaziland Thailand Togo Tunisia Turkmenistan Ukraine

N

ECDI M (SD) (0–10 Scale)

21

ECDI-LN ECDI-SE ECDI-PH ECDI-AL M (SD) M (SD) M (SD) M (SD) (0–3 Scale) (0–3 Scale) (0–2 Scale) (0–2 Scale)

4021 4.71 (1.67) 0.44 (0.78) 1.90 (0.80) 1.19 (0.61) 1.18 (0.84)

912 6.50 (1.44) 0.76 (0.91) 2.08 (0.78) 1.74 (0.46) 1.91 (0.30) 2230 7.08 (1.36) 0.83 (0.97) 2.53 (0.67) 1.79 (0.43) 1.94 (0.28) 7703 6.75 (1.52) 0.73 (0.98) 2.41 (0.75) 1.72 (0.48) 1.90 (0.37) 2964 3024 2919 1309 13960 659 4200 660 1778 4472 553 7664 6433 3690 3340 720 3672 639 2241 10151 1821 119 833

6.38 (1.47) 5.88 (1.65) 5.08 (1.53) 7.34 (1.80) 5.59 (1.78) 7.62 (1.69) 6.81 (1.47) 6.65 (1.48) 5.74 (1.33) 6.28 (1.59) 7.53 (1.50) 5.28 (1.78) 5.18 (1.53) 5.21 (1.65) 6.52 (1.45) 6.84 (1.39) 6.49 (1.32) 7.36 (1.38) 5.55 (1.89) 5.22 (2.04) 6.41 (1.50) 7.66 (1.75) 5.32 (1.84)

0.67 (0.91) 0.61 (0.97) 0.20 (0.63) 1.79 (1.15) 0.61 (0.88) 1.90 (1.11) 1.03 (0.96) 0.66 (0.86) 0.65 (0.79) 0.64 (0.97) 1.32 (1.07) 0.58 (0.93) 0.30 (0.73) 0.62 (0.96) 0.84 (0.94) 1.13 (0.94) 0.59 (0.73) 0.88 (1.02) 0.83 (1.19) 0.81 (1.15) 0.73 (0.91) 1.97 (1.12) 0.60 (0.90)

2.15 (0.76) 2.06 (0.77) 2.06 (0.83) 2.07 (0.83) 2.11 (0.89) 2.08 (0.79) 2.20 (0.69) 2.40 (0.81) 2.00 (0.62) 2.37 (0.77) 2.47 (0.69) 2.10 (0.88) 1.87 (0.72) 1.73 (0.76) 2.06 (0.76) 2.02 (0.70) 2.16 (0.79) 2.75 (0.56) 1.77 (0.63) 1.87 (0.81) 2.14 (0.78) 2.15 (0.67) 1.82 (0.77)

1.68 (0.51) 1.66 (0.52) 1.25 (0.60) 1.69 (0.50) 1.34 (0.57) 1.76 (0.45) 1.76 (0.44) 1.71 (0.47) 1.33 (0.53) 1.51 (0.53) 1.84 (0.37) 1.26 (0.61) 1.50 (0.59) 1.16 (0.48) 1.73 (0.46) 1.75 (0.44) 1.85 (0.37) 1.79 (0.42) 1.41 (0.56) 1.28 (0.58) 1.67 (0.50) 1.74 (0.46) 1.45 (0.58)

1.88 (0.38) 1.56 (0.68) 1.57 (0.72) 1.80 (0.48) 1.55 (0.70) 1.88 (0.40) 1.84 (0.44) 1.88 (0.38) 1.77 (0.54) 1.77 (0.54) 1.89 (0.35) 1.35 (0.78) 1.52 (0.76) 1.72 (0.62) 1.89 (0.36) 1.94 (0.25) 1.89 (0.35) 1.95 (0.27) 1.54 (0.74) 1.26 (0.80) 1.87 (0.40) 1.81 (0.44) 1.46 (0.79)

2578 3599 3217 1271 2129 4214 1799 1163 1492 1897

7.50 (1.41) 4.57 (1.73) 5.84 (1.70) 5.88 (1.55) 5.58 (1.54) 7.74 (1.56) 4.80 (1.53) 5.92 (1.68) 6.84 (1.10) 6.99 (1.52)

1.12 (1.04) 0.31 (0.78) 0.71 (0.98) 0.60 (0.85) 0.55 (0.89) 1.77 (1.10) 0.35 (0.74) 0.97 (1.04) 0.96 (0.78) 1.32 (1.09)

2.64 (0.61) 1.79 (0.83) 2.02 (0.92) 1.85 (0.85) 1.86 (0.78) 2.45 (0.70) 1.87 (0.75) 2.03 (0.84) 2.15 (0.46) 2.06 (0.67)

1.78 (0.42) 1.21 (0.60) 1.39 (0.53) 1.58 (0.53) 1.43 (0.57) 1.61 (0.50) 1.21 (0.53) 1.19 (0.47) 1.89 (0.34) 1.74 (0.45)

1.97 (0.19) 1.26 (0.82) 1.72 (0.57) 1.87 (0.40) 1.74 (0.55) 1.92 (0.32) 1.37 (0.81) 1.74 (0.59) 1.84 (0.44) 1.88 (0.40) (Continued)

22 Marc H. Bornstein et al. TABLE 1.3 (Continued)

Country

Uruguay Vietnam Zimbabwe

N

ECDI M (SD) (0–10 Scale)

ECDI-LN ECDI-SE ECDI-PH ECDI-AL M (SD) M (SD) M (SD) M (SD) (0–3 Scale) (0–3 Scale) (0–2 Scale) (0–2 Scale)

747 7.48 (1.56) 1.41 (1.07) 2.31 (0.75) 1.82 (0.41) 1.95 (0.24) 2628 6.65 (1.70) 0.85 (1.03) 2.44 (0.68) 1.69 (0.51) 1.68 (0.61) 3891 5.37 (1.55) 0.41 (0.76) 1.88 (0.80) 1.55 (0.59) 1.54 (0.67)

Note: ECDI = Total Early Childhood Development Index, ECDI-LN = Early Childhood Devel­ opment Index-Literacy and Numeracy, ECDI-SE = Early Childhood Development Index-Socio­ emotional, ECDI-PH = Early Childhood Development Index-Physical Health, ECDI-AL = Early Childhood Development Index-Approaches to Learning.

noteworthy. Means for the Early Childhood Development Indexes aggregated across the 51 LMIC are presented next. The ECDI-LN mean was 0.71 (on a 0–3 scale; SD = 1.00). The findings show that, on average, of the three behaviors demonstrating literacy and numeracy development, the nearly 160,000 children in this sample displayed less than 1. Altogether, 99.9% of children had data on the ECDI-LN (nmissing = 134).Accord­ ing to UN criteria, 21.45% of children in this sample were on-track (see later) in their literacy and numeracy development (i.e., demonstrated at least two of the three behaviors; Loizillon et al., 2017). The ECDI-SE mean was 2.05 (on a 0–3 scale; SD = 0.82). These findings show that, on average, of the three behaviors demonstrating socio-emotional development, children in this sample displayed about 2.Altogether, 99.9% of chil­ dren had data on the ECDI-SE (nmissing = 226).According to UN criteria, 75.34% of children in this sample were on-track in their socio-emotional development (i.e., demonstrated at least two of the three behaviors). The ECDI-PH mean was 1.50 (on a 0–2 scale; SD = 0.57). These findings show that, on average, of the two behaviors demonstrating physical health devel­ opment, children in this sample displayed about 1.5. Altogether, 99.9% of children had data on the ECDI-PH (nmissing = 164). According to UN criteria, 96.20% of children in this sample were on-track in their physical health development (i.e., demonstrated at least one of the two behaviors). The ECDI-AL mean was 1.61 (on a 0–2 scale; SD = 0.69). These findings show that, on average, of the two behaviors demonstrating approaches to learn­ ing development, children in this sample displayed about 1.6. Altogether, 99.8% of children had data on the ECDI-AL (nmissing = 336). According to UN criteria, 87.98% of children in this sample were on-track in their approaches to learning development (i.e., demonstrated at least one of the two behaviors). The ECDI mean was 5.86 (on a 0–10 scale; SD = 1.86). These findings show that, on average, of the ten behaviors demonstrating overall adaptive early

Introduction and General Methods

23

childhood development, children in this sample displayed about 6. Altogether, 100% of children had data on at least some questions on the ECDI (nmissing = 0). Each subindex demonstrated distributions that approached normality. Specifi­ cally, none of these subindex score distributions demonstrated problematic nonnormality across the entire sample of families surveyed (i.e., where skewness of the distribution exceeded 2.0 and kurtosis exceeded 7.0; Curran et al., 1996). Therefore, traditional parametric statistical methods could be applied in examin­ ing these early childhood development variables. In 6.65% of cases (n = 10,640), children were missing data on at least one ECDI question. In these instances, sum scores were computed with available data (effectively meaning that if an item were missing, that developmental milestone was assumed not to be met by the child). This assumption was made instead of prorating the total scale score because, as is demonstrated by the vast differences in on-track development in aforementioned ECDI subindexes, substantial dif­ ferences appear in base levels of children’s attainment of different items. Amid such substantial differences, prorating scale scores could produce problematic and biased ECDI estimates. Assuming that developmental milestones were not met when data were missing ensures that estimates of children’s developmental capaci­ ties are conservative.According to UN criteria, 70.01% of children in this sample were on-track in their overall development (i.e., demonstrated on-track develop­ ment in at least three of the four ECDI-LN, ECDI-SE, ECDI-PH, and ECDI-AL developmental domains; Loizillon et al., 2017). To ease interpretability of Early Childhood Development Index, UNICEF (Loizillon et al., 2017) recommended dichotomizing each of the five ECDI to indicate whether a child is on-track or off-track in a typical developmen­ tal domain (as described earlier). The decision to dichotomize results to ease communicability for the widest possible audience is understandable. However, dichotomizing continuous quantitative measures has adverse effects when exam­ ining statistical relations between variables (which is the goal of this volume, viz. to examine associations between diverse domains and early childhood development).Therefore, when examining associations between continuous quantitative measures, dichotomization is generally to be avoided (MacCullum et al., 2002). Consequently, sum scores of early childhood development subindexes are uti­ lized (as opposed to dichotomous measures of early childhood development) in Chapters 2–5 in this volume; Chapter 6 uses on-track and off-track measures (for reasons specific to the analyses there). Nonetheless, continuous sum score measures of early childhood development correlated highly with the dichoto­ mous measures. For instance, the correlation between the continuous ECDI-LN score used here and the equivalent dichotomous measure is r = .89, p < .01, and similarly high correlations arise between continuous and dichotomous measures of ECDI-PH (r = .52, p < .01), ECDI-SE (r = .83, p < .01), ECDI-AL (r = .86, p < .01), and ECDI (r = .70, p < .01).Therefore, much of the information in the dichotomous and the continuous sum score measures is equivalent.

24 Marc H. Bornstein et al.

General Data Analysis Plan To investigate associations of child growth, caregiving practices, discipline and violence, and physical home environments as well as child and primary caregiver sociodemographic and household and national development demographic char­ acteristics with early childhood development indexes, the same general analytic plan was followed in each substantive chapter in the ensuing steps:

Step 1: Covariate Correlations Bivariate correlations between each child and primary caregiver sociodemo­ graphic and household and national development demographic characteristics (what are referred to as covariates for this study) and the five early childhood development indexes were computed.These sociodemographic and demographic covariate tests were conducted because doing so ensures that any subsequent anal­ yses of associations of child growth, caregiving practices, discipline and violence, and physical home environments with early childhood development indexes would control sociodemographic and demographic effects. For decisions about covariate use, see the discussion later on statistical significance versus effect size.

Step 2: Child Growth, Caregiving Practices, Discipline and Violence, and Children’s Physical Home Environment Psychometrics Indexes of child growth, caregiving practices, discipline and violence, and physical home environments that were of interest in a particular chapter were computed, and their statistical properties were explored. Details on this second step can be found in each substantive chapter.

Step 3: Child Growth, Caregiving Practices, Discipline and Violence, and Children’s Physical Home Environment Associations With Early Childhood Development Associations of child growth, caregiving practices, discipline and violence, and physical home environments with early childhood development indexes are inves­ tigated controlling for child and primary caregiver sociodemographic and house­ hold and national development demographic covariates as described earlier in Step 1. Unless otherwise noted, linear regression analytic frameworks were used to examine these child growth–child development, caregiving practices–child devel­ opment, discipline and violence–child development, and physical home environ­ ment–child development associations. Following best practices, full information maximum likelihood (FIML) estimation procedures were utilized to account for

Introduction and General Methods

25

partially missing data (e.g.,Arbuckle, 1996; Muthén & Muthén, 1998–2017).This decision ensured that all families, even those with partially missing data on some variables, were included in analyses. Additionally, following best practices, maxi­ mum likelihood estimation procedures that produced robust standard errors were utilized (MLR; Muthén & Muthén, 1998–2017).This MLR framework ensured that parameter estimates generated in the linear regression analyses were robust to non-normality. Finally, in this volume, child-/family-/household-level effects were estimated, but such child-/family-/household-level effects are clustered (also known as nested) in the 51 LMIC. Therefore, when correlations between a country-level variable (e.g., HDI score) and individual-level outcomes (e.g., ECDI score) were examined, clustering of households within country was con­ trolled and consequently adjusted standard error estimates of all correlations were calculated by utilizing the type = complex and cluster = country commands in Mplus 8.0 in accordance with expert recommendations (Bryan & Jenkins, 2016; Muthén & Muthén, 1998–2017). Attempts to do the same with regression models failed. Such models have nonpositive definite first-order derivative product matrices and therefore are poten­ tially non-identified. Such models still produce interpretable parameter estimates, but it is questionable whether such models should be interpreted (Asparouhov & Muthén, 2021). Given this uncertainty, regression models that are reported in this volume do not control for country-level clustering. However, they are compared in sensitivity analyses to the imperfect but interpretable regression models where country-level clustering is controlled. Fortunately, these two sets of model results rarely substantively differed.We note wherever such sensitivity analyses differ from the main analyses presented in this volume.

Statistical Significance and Effect Size Because of the large MICS sample sizes involved (i.e., nearly 160,000 children/ households), statistically significant results can emerge even when the actual effect sizes are small. A statistically significant result does not necessarily sig­ nify a result that is practically meaningful. For this reason, effect size metrics are reported, and results are distinguished by statistical and practical significance (Wilkinson, 1999). (1) Sociodemographic and demographic covariates were not meaningfully interpreted if they did not have zero-order correlation values of at least .10 or predict 2% or more of variance in the early childhood development indexes in regression analyses with the early childhood development indexes. In other words, a significant correlation between sociodemographic or demo­ graphic covariates and early childhood development indexes was not viewed as a practically significant finding unless zero-order correlations or percentages of variance explained exceeded these cut-offs.The .10 correlation and 2% of vari­ ance cut-offs were established on the basis of seminal recommendations in the

26 Marc H. Bornstein et al.

quantitative literature that label correlations less than .10 or explanations of less than 2% of variance as small effects (Cohen, 1988). (2) For the principal analyses, rules of thumb proposed by Cohen (1988) were also adopted.Where zero-order correlations were evaluated, r ≈ .10 is interpreted as a small effect, r ≈ .30 as a medium effect, and r ≈ .50 as a large effect.Where regressions were evaluated, if a variable explained at least 2% of the variance (R2 ≈ .02) in an early childhood development index, it was described as a small effect; if it explained at least 13% of the variance (R2 ≈ .13), it was described as a medium effect; and if it explained at least 26% of the variance (R2 ≈ .26), it was described as a large effect. In inter­ preting statistically significant results, effect sizes smaller than the reference for a small effect size are weighted less heavily. That said, as discussed in the Limita­ tions in Chapter 7, small effect sizes early in life can eventuate in large effect sizes later in life (Bornstein, 2014), and small effect sizes at an individual level could harbor greater explanatory potential at a population level (Funder & Ozer, 2019). Statistical analyses were performed in Mplus 8.0 (Muthén & Muthén, 1998–2017).

How Do Child and Primary Caregiver Sociodemographic Characteristics and Household and National Development Demographic Characteristics Relate to the Five Central Domains of Early Childhood Development? This section of the chapter addresses Question 2 introduced earlier out of the PPCT Bioecological Model framework. It includes findings pertaining to associa­ tions between child sociodemographic characteristics and early childhood devel­ opment indexes, primary caregiver sociodemographic characteristics and early childhood development indexes, household demographic characteristics and early childhood development indexes, and national development demographic char­ acteristics and early childhood development indexes, as well as the simultane­ ous examination of associations between all sociodemographic and demographic characteristics and early childhood development indexes in a linear regression framework. Subsequent chapters in this Study in Parenting focus on associations between child growth (Chapter 2), caregiving practices (Chapter 3), discipline and vio­ lence (Chapter 4), and physical home environments (Chapter 5) and indexes of early childhood development accounting for these sociodemographic and demo­ graphic characteristics. To do so, associations of sociodemographic and demo­ graphic characteristics with early childhood development indexes are examined first. These sociodemographic/demographic–child development associations are examined here to avoid repetition of these analyses in subsequent chapters. Later chapters collect all these variables together in two fuller comprehensions of the process-person-context-time Bioecological Model analyzing and sorting their

Introduction and General Methods

27

ordered predictive relations with the early childhood development indexes in complementary machine learning (Chapter 6) and regression (Chapter 7) formats.

Associations Between Child Sociodemographic Characteristics and Early Childhood Development Indexes Zero-order correlations between the two child sociodemographic characteristics (gender and age) and the early childhood development indexes can be found in Table 1.4. With the exception of child physical health development (ECDI-PH), correla­ tions between child gender and all early childhood development indexes were sta­ tistically significant, negative (indicating that girls scored slightly higher on child development indexes, as 0 = female and 1 = male in coding), and small in effect size. The correlation between child gender and physical health was non-significant. Results indicated that, with the exception of child physical health (M ECDI­ PHGirls = 1.50, SD = 0.57, M ECDI-PHBoys = 1.50, SD = 0.57, t(159792) = −0.16, p = .88, d = .00), boys score slightly lower than girls on indexes of overall early childhood development (M ECDIGirls = 5.95, SD = 1.86, M ECDIBoys = 5.78, SD = 1.85, t(159956) = 18.51, p < .01, d = .09), development of literacy and numeracy (M ECDI-LNGirls = 0.74,SD = 1.01,M ECDI-LNBoys = 0.69,SD = 0.98, t(159822) = 9.70, p < .01, d = .05), socio-emotional development (M ECDI­ SEGirls = 2.11, SD = 0.80, M ECDI-SEBoys = 2.00, SD = 0.83, t(159730) = 26.26, p < .01, d = .13), and approaches to learning (M ECDI-ALGirls = 1.61, SD = 0.69, M ECDI-ALBoys = 1.60, SD = 0.70, t(159620) = 4.89, p < .01, d = .02). However, given that all these correlations fell well below the r = .10 cut-off indicating even a small effect, these gender differences are practicably negligible.Therefore, 3- to 5-year-old girls and boys in this sample from 51 LMIC do not appear to differ meaningfully in any domain of early childhood development. TABLE 1.4 Zero-Order Correlations Between Child Sociodemographic Characteristics

and Early Childhood Development Indexes

Child gender (0 = female, 1 = male) Child age (months)

ECDI

ECDI-LN

ECDI-SE

ECDI-PH

ECDI-AL

−.05**

−.02**

−.07**

.00

−.01**

.18**

.21**

.03**

.04**

.10**

Note: ECDI = Total Early Childhood Development Index, ECDI-LN = Early Childhood Devel­ opment Index-Literacy and Numeracy, ECDI-SE = Early Childhood Development Index-Socio­ emotional, ECDI-PH = Early Childhood Development Index-Physical Health, ECDI-AL = Early Childhood Development Index-Approaches to Learning. Child gender correlations are point-biserial correlations. p < .01.

**

28 Marc H. Bornstein et al.

In contrast, and as might be expected, child age had small positive associations with overall early childhood development and the development of child literacy and numeracy skills. In both the cases, older children manifested higher levels of development. Statistically significant positive associations between child age and child socio-emotional, physical health, and approaches to learning development scores were also found. However, all three of those correlations fell at or below the r = .10 small effect cut-off, which means that these latter three associations are of questionable practical significance.

Associations Between Primary Caregiver Sociodemographic Characteristics and Early Childhood Development Indexes Zero-order correlations between the two primary caregiver sociodemographic characteristics (age and education) and early childhood development indexes can be found in Table 1.5. Primary caregiver age (M = 32.03 years, SD = 8.92, range = 10–95) demon­ strated small, but statistically significant, positive associations with all early child­ hood development indexes, except the child physical health index. However, all these associations fell below the r = .10 cut-off, indicating trivial effects. Past research indicated that associations between primary caregiver age and child intelligence might have an ∩-shape, such that 4-year-old children of younger primary caregivers (those below 30 years of age) and older primary caregivers (those above 40 years of age) had lower IQ scores than children of primary caregivers who were in between (i.e., aged 30–40; Bornstein & Putnick, 2019). To examine whether such a ∩-shaped effect emerged in associa­ tions between primary caregiver age and indexes of early childhood development studied here, sensitivity analyses were conducted to examine the correlation between the quadratic effect of primary caregiver age (age2, which measures

TABLE 1.5 Zero-Order Correlations Between Primary Caregiver Sociodemographic

Characteristics and Early Childhood Development Indexes

Primary caregiver age (years) Primary caregiver education (0–3 range)

ECDI

ECDI-LN

ECDI-SE

ECDI-PH

ECDI-AL

.02** .38**

.01** .32**

.02** .13**

.00 .20**

.01** .25**

Note: ECDI = Total Early Childhood Development Index, ECDI-LN = Early Childhood Devel­ opment Index-Literacy and Numeracy, ECDI-SE = Early Childhood Development Index-Socio­ emotional, ECDI-PH = Early Childhood Development Index-Physical Health, ECDI-AL = Early Childhood Development Index-Approaches to Learning. p < .01.

**

Introduction and General Methods

29

whether a U-shape exists in the data). None of these correlations was practi­ cally significant, as all fell between r = .00 and r = .01, which is well below the r = .10 cut-off used. Consequently, no ∩-shaped associations between primary caregiver age and the early childhood development indexes appear in this sample. In these 51 LMIC, primary caregiver age is not associated with early childhood development scores in a way that makes a practicable difference in early child­ hood development. In contrast, primary caregiver education is positively and meaningfully associated with all aspects of early childhood development. Specifically, more educated primary caregivers have children who score higher in overall early childhood development, literacy and numeracy development, socio-emotional development, physical health development, and approaches to learning devel­ opment. These effects are small (child socio-emotional development) to medium (overall early childhood development and literacy and numeracy) in size.

Associations Between Household Demographic Characteristics and Early Childhood Development Indexes Zero-order correlations (Table 1.6) indicated that all household demographic characteristics are negatively associated with overall early childhood development scores and literacy and numeracy development scores at significant levels. Specifi­ cally, households with more people, more children under 18 years, more children under 5 years, and more crowding have children with lower early childhood development scores. Still, these effects were small. TABLE 1.6 Zero-Order Correlations Between Household Demographic Characteristics

and Early Childhood Development Indexes ECDI ECDI-LN ECDI-SE ECDI-PH ECDI-AL Number of people in household Number of children in household < 18 years old Number of children in household < 5 years old Household crowding (number of people per bedroom)

−.17** −.13** −.19** −.15**

−.07** −.08**

−.09** −.10**

−.10** −.12**

−.16** −.12**

−.07**

−.08**

−.11**

−.14** −.12**

−.05**

−.09**

−.08**

Note: ECDI = Total Early Childhood Development Index, ECDI-LN = Early Childhood Devel­ opment Index-Literacy and Numeracy, ECDI-SE = Early Childhood Development Index-Socio­ emotional, ECDI-PH = Early Childhood Development Index-Physical Health, ECDI-AL = Early Childhood Development Index-Approaches to Learning. p < .01.

**

30 Marc H. Bornstein et al.

Moreover, having a larger household with more people, children under 18, and children under 5 had negative associations with the approaches to learning development scores.Thus, having more people of all ages in households is associ­ ated with lower early childhood development approaches to learning. However, these effects were also small. Correlations between each household demographic characteristic and meas­ ures of child socio-emotional or physical health development all fell at or below the r = .10 cut-off for a small effect. Even though these demographic variables are associated with slightly lower scores on these indexes, the effects are insufficiently large to be practicably meaningful.

Associations Between National Development Demographic Characteristics and Early Childhood Development Indexes National development demographic characteristics (i.e., the Human Devel­ opment Index and its constituent LEI, EDI, and INI indexes) demonstrated especially strong, positive associations with indexes of early childhood devel­ opment (see Table 1.7). It is important to note that these zero-order correla­ tions control for the effects of the clustering of households within countries, as described earlier in the General Data Analysis Plan.All associations between all Human Development Indexes exceeded the r = .10 cut-off indicating effects of practical significance, and many associations fell in the medium effect size (r = .30) range. Notably, a country’s overall HDI had an especially high (r = .40, 16% of variance in ECDI scores accounted for) positive association with overall early childhood development. The HDI encapsulates the story told by this collection of zero-order correlations: Countries that create more optimal conditions for human development, such as increasing life expectancy, TABLE 1.7 Zero-Order Correlations Between HDI Scales and Early Childhood

Development Indexes

HDI LEI EDI INI

ECDI

ECDI-LN

ECDI-SE

ECDI-PH

ECDI-AL

.40** .37** .39** .33**

.24** .21** .23** .21**

.18** .17** .16** .14**

.23** .20** .24** .18**

.33** .33** .31** .26**

Note: HDI = Human Development Index, LEI = Life Expectancy Index, EDI = Education Index, INI = Income Index. ECDI = Total Early Childhood Development Index, ECDI-LN = Early Child­ hood Development Index-Literacy and Numeracy, ECDI-SE = Early Childhood Development Index-Socio-emotional, ECDI-PH = Early Childhood Development Index-Physical Health, ECDI­ AL = Early Childhood Development Index-Approaches to Learning. p < .01.

**

31

Introduction and General Methods

Early Child Development Index Score

Association Between Human Development Index and Early Childhood Development Index 10 9 8 7 6 5 4 3 2 1 0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Human Development Index Score

FIGURE 1.2

Low Human Development (HDI < .550)

Medium Human Development (HDI .550-.700)

High Human Development (HDI = .700-.799)

Very High Human Development (HDI > .800)

The Association Between Country Human Development Index Score and Early Childhood Development Index Score.

access to education, and higher income, have children who are more optimally developed. As shown in Figure 1.2, when a country’s Human Development Index increases, its early childhood development scores increase too. Figure 1.2 shows this trend at the different UN-designated groupings of countries on the Human Development Index. Countries that the UN catego­ rizes in the low human development group are clustered in the lower left of the graph (i.e., the circles), where both human development scores and early child­ hood development scores are low. Countries that the UN categorizes in the high human development group are in the upper right of the graph (i.e., the squares), where both human development scores and early childhood development scores are high. Moreover, only 54.62% of children in countries from the low human development group met UN criteria for being on-track in their development (Loizillon et al., 2017), whereas 72.96% of children in countries from the medium human development group did, 84.46% of children in countries from the high human development group did, and 89.75% of children in countries from the very high human development group did. Importantly, these differences are statis­ tically significant, χ2(3) = 11994.61, p < .01, Cramer’s V = .27. It is clear from Table 1.7 and Figure 1.2 that a country’s measures of human development are strongly positively correlated with the early development scores of children in that nation.As human development scores increase, early childhood development scores increase as well.

32 Marc H. Bornstein et al.

Simultaneous Associations Between All Sociodemographic and Demographic Characteristics and Early Childhood Development Indexes in a Linear Regression Framework In the previous subsections, correlations between each specific child and primary caregiver sociodemographic and household and national development demo­ graphic characteristic and early childhood development indexes were examined separately.These analyses show how highly correlated each of these sociodemo­ graphic and demographic domains alone is with early childhood development. However, they do not allow a direct comparison among all characteristics to one another nor do they come close to fulfilling the explanatory promise of the PPCT Bioecological Model. Doing so would demonstrate which characteristic(s) emerge as the most powerful, unique associates of early childhood development even after controlling for all other characteristics.This goal of comparing all char­ acteristics directly and simultaneously can be accomplished using linear regression analysis.As shown in Table 1.8, regression models were created where all sociode­ mographic and demographic characteristics are simultaneously used to predict early childhood development scores. All parameter estimates remained the same when examined in sensitivity analyses that controlled for clustering of house­ holds within countries. As mentioned earlier, all analyses were conducted using robust maximum likelihood estimation procedures (MLR; Muthén & Muthén, 1998–2017) to protect against heteroskedasticity and non-normality. The total variance explained (R2) in ECDI scores was calculated by the com­ bination of all sociodemographic and demographic characteristics and is reported in the top row of Table 1.8. So, for instance, the model that used all demographic characteristics to predict total ECDI scores accounted for 23.7% of the variance in ECDI scores.The change in R2 (∆R2) in each model when a specific predic­ tor was excluded was also calculated. So, for instance, the lower left-hand corner of Table 1.8 shows that, by removing the Human Development Index from the model predicting overall Early Childhood Development Index scores, the model explains 4.9% less variance in ECDI scores. Therefore, HDI scores uniquely explain 4.9% of variance in ECDI scores, even after controlling for the effects of all other sociodemographic and demographic variables. (The ∆R2s do not com­ pletely add up to the total R2 reported in the top row because the predictors are correlated with one another; for example, the number of people in household is correlated with the number of children in household, and both are correlated with Early Childhood Development Indexes.) The ∆R2 controls for such correla­ tions, and therefore all ∆R2s, when added, can differ from the total R2. Results for each Early Childhood Development Index are described later. Sociodemographic and demographic characteristics together accounted for 23.7% of the variance in overall Early Childhood Development Index scores (ECDI).This impressive amount of variance, explained by sociodemographic and demographic characteristics alone, falls between medium (R2 = .13) and large

TABLE 1.8 Multiple Linear Regression Analyses Comparing the Simultaneous Associations of Child and Primary Caregiver Sociodemographic and

Household and National Demographic Characteristics with Early Childhood Development Indexes ECDI (R2 = .237) β (SE)

p

∆R2

β (SE)

ECDI-SE (R2 = .040) p

∆R2

β (SE)

ECDI-PH (R2 = .066) p

∆R2

−.05 (.00)