Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Educational Leadership and Policy Studies

First Advisor

Bernice Garnett


A joint report from the United Nations Development Program and the Oxford Poverty and Human Development Initiative indicates that while the number of people living with less than $1.90 a day declined globally, dropping from 2 billion in 1990 to 736 million in 2015, the number of people who experienced non-income poverty reached 1.3 billion in 2020. Non-income poverty, referred to as multidimensional poverty, assesses the extent to which people are deprived from accessing basic services such as health, education, or attaining decent living standards, despite having income levels well above $1.90.

Research on development and welfare economics points to assets as the missing piece in the poverty puzzle because they can build capacity. In general, assets can be used to generate income or to enhance quality of life. Income-generating assets such as bonds, credit, or home ownership help people gain economic stability, acquire other assets, and prepare for economic shocks. Quality-of-life-enhancing assets help people improve their living standards, develop agency, and participate in political as well as in social life. Examples of quality-of-life-enhancing assets include education, social capital, and durable goods such as TVs or computers.

Most research on assets examines the relationship either between financial assets and poverty or between financial assets and education. An exploration of durable goods and education was the focus of this dissertation. Although not a nascent field, most studies in this area have focused on analyzing how durable goods relate to academic achievement and school attendance mainly in African and Asian countries. From a methodological standpoint, these studies have modeled durable goods utilizing a binary approach, where ownership of durable goods is measured as possession of any durable good, or as an index, using principal component analysis (PCA), which research suggests is not the most robust method for index creation. Such methodological decisions have provided only a partial understanding of the relationship between durable goods and education. For example, findings indicate that possession of durable goods improves achievement in reading, but not in math. However, further research is needed to assess whether different types of durable goods have differential effects on educational outcomes.

Hence, this study explored the relationship among durable goods, academic achievement, and school attendance in Colombia through three methodological approaches to operationalize durable goods: inventory, attributional, and index approaches. Data come from the 2017 SABER test, a nation-wide examination that assesses reading and math skills, for fifth and ninth grade students, (N = 621,218). Students with complete durable goods information (N = 364,436) were included. This research added to the existing literature on this field by using different methodological approaches to model durable goods, including the construction of a durable goods index employing exploratory factor analysis (EFA), and by expanding the geographic scope to Latin America. By using hierarchical linear and nonlinear modeling, this study found that, overall, durable goods were positively associated with reading and math outcomes, particularly for fifth graders. Similarly, results indicated that students whose families owned washing machines, computers, or who had Internet access were more likely to go to school.



Number of Pages

348 p.