This paper presents a theoretical framework for understanding the role of human capital in endogenous growth models. It addresses two key questions: how to differentiate between intangible inputs like education and experience, and knowledge or science, and how knowledge and science actually affect production. The paper argues that the initial level of a variable like literacy is important for understanding subsequent growth, contrasting with the usual focus on the rates of change of inputs in growth accounting. The main empirical finding is that literacy has no additional explanatory power in cross-country regressions of growth rates on investment and other variables, but the initial level of literacy helps predict subsequent investment and growth. The paper outlines a theoretical framework that allows for an explicit research and development activity to foster new goods. It suggests that simple growth accounting relationships do not hold when such activity is considered. The framework emphasizes the importance of human capital variables in explaining output growth and highlights the need for a more detailed analysis of data. The paper also discusses the effects of measurement error and the importance of considering the level of inputs rather than just their rates of change. The theoretical framework is detailed, with a focus on the production of goods and the role of different types of skills. It distinguishes between educational skills, scientific skills, and experience, and shows how they can enter the production technology. The paper also discusses the production of designs and the role of basic science in the creation of new goods. It highlights the nonrival and nonexcludable nature of some inputs and the challenges this poses for economic models. The paper concludes that the role of human capital in growth is complex and requires a more nuanced understanding of the interactions between different types of inputs. It emphasizes the importance of considering the level of inputs and the need for further research on the empirical implications of these interactions. The paper also discusses the implications of the model for growth accounting and the need for more detailed data on the factors that influence growth.This paper presents a theoretical framework for understanding the role of human capital in endogenous growth models. It addresses two key questions: how to differentiate between intangible inputs like education and experience, and knowledge or science, and how knowledge and science actually affect production. The paper argues that the initial level of a variable like literacy is important for understanding subsequent growth, contrasting with the usual focus on the rates of change of inputs in growth accounting. The main empirical finding is that literacy has no additional explanatory power in cross-country regressions of growth rates on investment and other variables, but the initial level of literacy helps predict subsequent investment and growth. The paper outlines a theoretical framework that allows for an explicit research and development activity to foster new goods. It suggests that simple growth accounting relationships do not hold when such activity is considered. The framework emphasizes the importance of human capital variables in explaining output growth and highlights the need for a more detailed analysis of data. The paper also discusses the effects of measurement error and the importance of considering the level of inputs rather than just their rates of change. The theoretical framework is detailed, with a focus on the production of goods and the role of different types of skills. It distinguishes between educational skills, scientific skills, and experience, and shows how they can enter the production technology. The paper also discusses the production of designs and the role of basic science in the creation of new goods. It highlights the nonrival and nonexcludable nature of some inputs and the challenges this poses for economic models. The paper concludes that the role of human capital in growth is complex and requires a more nuanced understanding of the interactions between different types of inputs. It emphasizes the importance of considering the level of inputs and the need for further research on the empirical implications of these interactions. The paper also discusses the implications of the model for growth accounting and the need for more detailed data on the factors that influence growth.