Estimating the critical and sensitive periods of investment in early childhood: a methodological note.

The identification of critical periods in early human development requires statistical analyses beyond simple cross tabulation of correlations of observed variables. This paper provides an overview of different quantitative methods available for the statistical analysis of longitudinal data regarding child development, and in particular the identification of critical and sensitive periods for later abilities. It draws heavily on the work on human skill formation developed by the economist James Heckman, which treats ability as a latent variable and explains its formation through the simultaneous estimation of structural equations of investments and achieved abilities across time. We distinguish between two specifications of the ability formation function. One of them (the 'recursive') format explains current ability as a function of the ability and investment at the immediately preceding period. The other (the 'non-recursive') format explains current ability as a function of a series of past investments. In order to fully examine critical and sensitive periods of investments, the non-recursive formulation needs to be used. Furthermore, true abilities of an individual cannot be directly observed: what we observe are the test scores, for example, on reading and writing. We outline an approach, structural equation modelling, that treats actual test scores as measurements of the latent ability variable, and show how it can be used in the recursive and non-recursive formulation. In order to fully examine critical and sensitive periods of investments, we argue that the non-recursive formulation of this structural model is necessary. However, the non-recursive formulation requires more data than the recursive formulation, and to the best of our knowledge, has never been used in the identification of critical and sensitive periods in early childhood development.

[1]  Petra E. Todd,et al.  On the Specification and Estimation of the Production Function for Cognitive Achievement , 2003 .

[2]  G. Leung,et al.  Socioeconomic influences at different life stages on health in Guangzhou, China. , 2011, Social science & medicine.

[3]  E. Hanushek,et al.  The Failure of Input-Based Schooling Policies , 2002 .

[4]  J. Heckman SUPPLEMENT TO “ESTIMATING THE TECHNOLOGY OF COGNITIVE AND NONCOGNITIVE , 2010 .

[5]  Esben Budtz-Jørgensen,et al.  Structural Equation Models , 2005 .

[6]  S. Hamilton Institute of Education , 2020, The Grants Register 2022.

[7]  Tanya N. Beran,et al.  Structural equation modeling in medical research: a primer , 2010, BMC Research Notes.

[8]  Petra E. Todd,et al.  The Production of Cognitive Achievement in Children: Home, School and Racial Test Score Gaps , 2004 .

[9]  J. Heckman,et al.  Understanding the Early Origins of the Education–Health Gradient , 2010, Perspectives on psychological science : a journal of the Association for Psychological Science.

[10]  J. Heckman,et al.  Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation , 2012 .

[11]  S. Morgan Handbook of Causal Analysis for Social Research , 2013 .

[12]  J. Heckman Role of Income and Family Influence on Child Outcomes , 2008, Annals of the New York Academy of Sciences.

[13]  J. Pearl,et al.  EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS , 2013 .

[14]  E. Knudsen Sensitive Periods in the Development of the Brain and Behavior , 2004, Journal of Cognitive Neuroscience.

[15]  J. Heckman,et al.  THE EDUCATION-HEALTH GRADIENT. , 2010, The American economic review.

[16]  D. Deeg,et al.  Early life undernutrition and chronic diseases at older ages: the effects of the Dutch famine on cardiovascular diseases and diabetes. , 2011, Social science & medicine.

[17]  J. Heckman,et al.  Investing in Our Young People , 2010 .

[18]  Petra E. Todd,et al.  The Production of Cognitive Achievement in Children: Home, School, and Racial Test Score Gaps , 2007, Journal of Human Capital.

[19]  Duncan C. Thomas,et al.  Does Head Start Make a Difference? , 1993 .

[20]  J. Heckman,et al.  Human Capital Policy , 2003, SSRN Electronic Journal.

[21]  J. Heckman,et al.  Investing in Our Young People. NBER Working Paper No. 16201. , 2010 .

[22]  J. Heckman,et al.  Economic, Neurobiological and Behavioral Perspectives on Building America’s Future Workforce , 2006 .

[23]  E. Hanushek,et al.  Conclusions and Controversies About the Effectiveness of School Resources , 1998 .

[24]  L. Fahrmeir,et al.  Analysis of childhood morbidity with geoadditive probit and latent variable model: a case study for Egypt. , 2009, The American journal of tropical medicine and hygiene.