We draw on administrative data from the country of Colombia to assess differences in student learning in online and traditional on-campus college programs. The Colombian context is uniquely suited to study this topic, as students take a compulsory exit examination at the end of their studies. We can therefore directly compare the performance on the exit exam for students in online and on-campus programs both across and within institutions, degrees, and majors. Using inverse probability weighting methods based on a rich set of background characteristics coupled with institution–degree–major fixed effects, our results suggest that bachelor’s degree students in online programs perform worse on nearly all test score measures (including math, reading, writing, and English) relative to their counterparts in on-campus programs. Results for shorter technical certificates are more mixed. While online students perform significantly worse than on-campus students on exit exams in private institutions, they perform better in SENA—the main public vocational institution in the country.
We draw on administrative data from the country of Colombia to assess differences in student learning in online and traditional on-campus college programs. The Colombian context is uniquely suited to study this topic, as students take a compulsory exit examination at the end of their studies. We can therefore directly compare performance on the exit exam for students in online and on-campus programs both across and within institutions, degrees, and majors. Using inverse probability weighting methods based on a rich set of background characteristics coupled with institution-degree-major fixed effects, our results suggest that bachelor's degree students in online programs perform worse on nearly all test score measures (including math, reading, writing, and English) relative to their counterparts in on-campus programs. Results for shorter technical certificates are more mixed. While online students perform significantly worse than oncampus students on exit exams in private institutions, they perform better in SENA-the main public vocational institution in the country.
Despite improvements in the design of development interventions from the perspective of the Sustainable Development Goals (SDGs), there is still a lack of evaluation methods able to estimate the impact of these interventions on multiple and interrelated outcomes. This paper proposes a methodological framework for complex causal inference in international development that combines machine learning and econometric designs for causal inference. As a study case, the relationship between multidimensional poverty and violence in Colombia is evaluated following this framework. First, Bayesian networks (BN) are used to create a directed acyclic graph (DAG) able to predict how multidimensional poverty components are interrelated and affected by a violence indicator. Second, the DAG output is used to identify instrumental variables (IV) in order to test the effect of multidimensional poverty on a household’s likelihood to be a victim of violence. Minimum living standards—measured in terms of access to water, connection to the sewage system, and the quality of walls and floors—are strong predictors of the education and health dimensions of poverty. Using 2SLS, the results show that having an illiterate person within a household increases by 0.4% the household’s likelihood to be a victim of violence. BNs have the potential to predict complex causal patterns helping to understand the effect of development interventions on multidimensional outcomes such as poverty. Quasi-experimental econometric designs can then be used to test some of these predicted causal connections.
This paper combines machine learning and econometrics to explore the relationship between multidimensional poverty components and violence in Colombia. First, I create a directed acyclic graph (DAG) using Bayesian networks and census data to predict how multidimensional poverty components are interrelated. I find that minimum living standards— measured in terms of access to water, connection to the sewage system, and the quality of walls and floors—are strong predictors of the education and health dimensions of poverty. Second, I use the DAG output to identify potential instrumental variables (IV) that may be used to test the effect of multidimensional poverty on a household’s likelihood to be a victim of violence. Illiteracy is predicted by a complex set of poverty indicators in the DAG, yet it seems to be the only connecting point between multidimensional poverty and violence showing potential to meet the validity assumptions in an IV approach. Using 2SLS, I show that having an illiterate person within a household increases by 0.4% the household’s likelihood to be a victim of violence.
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