What factors drive individual misperceptions of the returns to schooling in Tanzania? Some lessons for education policy

Abstract

Evidence on educational returns and the factors that determine the demand for schooling in developing countries is extremely scarce. Building on previous studies that show individuals underestimating the returns to schooling, we use two surveys from Tanzania to estimate both the actual and perceived schooling returns and subsequently examine what factors drive individual misperceptions regarding actual returns. Using ordinary least squares and instrumental variable methods, we find that each additional year of schooling in Tanzania increases earnings, on average, by 9 to 11 percent. We find that on average individuals underestimate returns to schooling by 74 to 79 percent and three factors are associated with these mis-perceptions: income, asset poverty and educational attainment. Shedding light on what factors relate to individual beliefs about educational returns can inform policy on how to structure effective interventions in order to correct individual misperceptions.

Vocational training programs and youth labor market outcomes: Evidence from Nepal

“…10 Most of the vocational training literature on similar programs in high-or middle-income countries finds low or insignificant effects (Card et al, 2010;Kluve, 2010;Dar and Tzannatos, 1999). 11 Other authors document similarly sized impacts based on related active labor market policies such as wage subsidies (Galasso et al, 2004;Groh et al, 2016;Levinsohn et al, 2014)) or search and matching assistance (Abebe et al, 2016a(Abebe et al, , 2016bFranklin, 2015;Jensen, 2012;Groh et al, 2015;Dammert et al, 2015;Beam, 2016;Abel et al, 2016;Bassi and Nansamba, 2017) or education-related interventions documented by Nikolov and Jimi (2018).…”

The Importance of Cognitive Domains and the Returns to Schooling in South Africa: Evidence from Two Labor Surveys

“…2 For surveys of empirical estimates in developed countries, see Card (1999) and Harmon et al (2003). For empirical estimates in developing countries, see Psacharopoulos (1994), Psacharopoulos and Patrinos (2004) and Nikolov and Jimi (2018). Chakravarty et al (2019) review estimates in the context of vocational schooling in developing countries.…”

The Effects of Access to Credit on Productivity: Separating Technological Changes from Changes in Technical Efficiency

“…The impact of other production inputs on productivity has also been examined in relation to inputs such as credit (de Mel, McKenzie and, capital (Karlan et al 2015), labor (Shearer 2004), information (Beaman and Magruder 2012), monitoring (Nagin et al 2002), and managerial practices (Bloom andVan Reenen 2010, Karlan andValdivia 2011;Drexler et al 2014). 8 Chakravarty et al (2019) examine the role of human capital constraints on productivity; Nikolov and Jimi (2018) review the role of informational constraints. 9 Credit has been shown to affect the risk-taking behavior of producers (Boucher et al 2008;Eswaran and Kotwal 1989), thereby affecting technology choices and adoption by farmers.…”