IDS held an online zoom seminar on 25th January 2024, 2.00pm - 3:30 PM EAT. Moderated by Prof. Winnie Mitullah, the following were the presenters.
Speaker: Dr. Moses Muriithi. Department of Economics and Development Studies.
Discussant 1: Prof. Akanni Olayinka Lawanson. Department of Economics, University of Ibadan.
Discussant 2: Prof. Corti Paul Lakuma. Economic Policy Research Center
Chairing: Prof. Winnie Mitullah. Departments of Economics and Development Studies, University of Nairobi.
Welcome remarks were made by Prof. Mitullah who chaired the session, the first IDS seminar of the year 2024. The seminars will be running every thursday to present the research works by the Department of Economics and Development Studies as well as other researchers. She welcomed Prof. Kamau on behalf of the director Prof. Kanyinga. Prof. Kamau welcomed everyone and echoed the chairs’ sentiments on the seminars being done weekly to disseminate findings from different research projects as a platform for knowledge exchange.
Prof. Mitullah welcomed the presenter and the discussants and opened the floor for the presenter. Dr. Muriithi.
The first discussant Professor Akanni Olayinka Lawanson begins by highlighting the critical importance of the topic, particularly in light of recent global events spanning the past two to three years. He emphasizes the significance of policy measures implemented by nations during this period to address challenges in school attendance.
He points out certain discrepancies in the descriptive statistics, noting that variables such as education level and type (private vs. public) should ideally sum up to 100%. For instance, while one might expect each respondents education level (e.g., no education, primary, secondary) to collectively account for 100%, this doesn’t always hold. Similarly, the sum of private and public education should also equal 100%.
He finds particular interest in the finding that being a child belonging to a household head decreases the probability of school attendance by 0.2%. While one might intuitively expect parental involvement to positively impact a child's education, he suggests a compelling explanation rooted in the context of the COVID-19 pandemic. He posits that household heads may prioritize their children safety by keeping them away from environments where they could easily contract the virus, such as school settings.
Moreover, the variable indicating whether schools serve meals shows a reduction in the probability of attendance. However, he argues that when considering the context of COVID-19, the perceived benefits of attending school to access meals must be weighed against the safety concerns posed by the pandemic. The trade-off between nutrition and health becomes particularly salient in this discussion.
Lastly, he addresses the finding that maternal employment increases school attendance. He reasons that parents engaged in economic activities may have less time to provide home care, thereby incentivizing regular school attendance for their children. This observation underscores the complex interplay between socioeconomic factors and educational outcomes.
Mr. Corti Paul Lakuma, as the second discussant, commends the paper for its significant contributions to the education discourse, particularly in the realm of Human Capital Development (HCD). He suggests that the abstract should be summarized to primarily focus on the problem statement, methodology, results, and policy recommendations, ensuring a concise yet comprehensive overview of the paper's key aspects.
Lakuma raises a pertinent question regarding the distinction between the incidence of COVID-19and county incidence of COVID-19, noting the potential for a high correlation between the two. He suggests that understanding this difference is crucial, as it could clarify whether COVID-19 incidence is exogenous or endogenous to the study’s context.
Furthermore, Lakuma highlights the importance of explicitly stating insignificant results in the paper, such as those pertaining to the gender of the child, household head status, household income, age of household head, maternal and paternal employment, school meals, and pre-primary education level.
He also critiques the treatment of the variable “age’ noting a negative relationship between age and school attendance. Lakuma recommends introducing a non-linear transformation, such as age squared, to better capture the relationship and its implications. Additionally, he suggests specifying the base variable in the explanation for clarity.
Regarding policy recommendations, Lakuma raises concerns about the feasibility of implementing income transfer programs in developing countries due to their high cost. He proposes exploring the possibility of interacting income transfer data with key policy variables in the model to derive more concrete and context-specific recommendations.
The presenters were engaged by more than 70 attendants who gave their input and asked several questions. Key among them;
Mr Njoka- Responded to the query on augmenting quantitative with qualitative data, especially in the matter of statistical significance. The World Bank data set was highly quantitative. He also responded that the study was not a pre and post-study but a study done during the pandemic.
Dr. Mugo suggested that the issue of inequality can be addressed as further research.
Dr. Gachuki mentioned that technology comes up in policy recommendations and was not there as one of the variables. He asked that an explanation be provided as to why technology was not used as a variable in the analysis, in the form of online learning for some students and not for others. Dr. Muriithi responded that technology was not measured directly, but was rather used as a dependent variable to proxy for learning or schooling.
Paul Kariuki – He asked that more information be provided on how the education sector is recovering from the COVID-19 shocks. Dr. Muriithi responded that that was not done, and could be considered as an area for further study.
Prof Paul Kamau provided the context of the paper. The paper was a study on school attendance during COVID-19 by looking at access to learning without physically going to school. The data used was collected by the World Bank concerning household factors. Learning was measured by the access that the student had to technological gadgets such as TV, radio, computers, mobile phones, etc. The findings may not be applicable in the normal learning situation. The age of the parents was an important factor as younger parents were more open to learning using technology, compared to older parents. He said that the project is still ongoing. He mentioned that it was also important to consider variations from county to county.