How Did the Great Recession Affect Inequalities in Entry to Tertiary Education? Evidence from 31 Countries

Abstract

This study examines how macroeconomic conditions affect inequality of educational opportunity. Using longitudinal data from 30 European countries and the United States, we study the effect of changing unemployment rates on the extent of social inequality in postsecondary transitions in the period (2004-2016), which includes the Great Recession. The findings from multilevel models with country fixed effects show that rising unemployment rates tend to increase social inequalities in postsecondary enrolment, as youth from low-educated families have increased risk for exclusion from both employment and education, while the likelihood of enrolment slightly rises for youth from higher-educated families. Yet, economic downturns also incentivize youth whose parents have middle level of education to opt for postsecondary education, especially in contexts where tertiary education is more affordable. Our findings suggest that changing opportunity costs and economic insecurity are the relevant factors to explain changing inequalities in access to higher education during a recession.

Handbook of Sociological Science: Contributions to Rigorous Sociology

“…Like with longitudinal designs in microlevel research, the principal logic of ITS designs is quintessentially suited to cases of some discrete historical event or policy change, but is also easily extended to a broader spectrum of quantitative (dosage) changes in some contextual condition of action. Classical examples would include studies that examine the effect of changing macroeconomic conditions on young adults' educational attainment or enrolment (e.g., Mayer 2001;Lindemann & Gangl 2020), on fertility decisions (e.g., Schneider 2015;Comolli & Vignoli 2021;Matysiak et al 2021), or on citizens' political preferences (e.g., Polavieja 2013;Gangl & Giustozzi 2018), as well as any other strand of research where contextual conditions such as neighborhood or school composition are observed over time and where the impact of any such change on outcomes is sought to be quantified. Evidently, there is a direct connection and a partially fluid distinction between ITS designs and data-analytic traditions of time-series (cross-sectional) regression as well as multilevel modeling, yet it again seems preferable to reserve the language of longitudinal designs to situations of plausibly (or near) exogenous changes in circumstances and a correspondingly limited role for individual agency, e.g.…”

Impact Analysis of Education Program, Unemployment Rate, and Income Gap on Social Mobility in Jakarta

“…High unemployment rates can limit job opportunities, hindering upward mobility [30]. Conversely, a dynamic job market with ample opportunities can contribute to increased social mobility [31]. Research in this field explores the intricate connections between employment dynamics and individuals' ability to move up the socioeconomic ladder, providing insights into the challenges and opportunities within urban settings [32].…”