Incorporating machine learning into sociological model-building

Quantitative sociologists frequently use simple linear functional forms to estimate associations among variables. However, there is little guidance on whether such simple functional forms correctly reflect the underlying data-generating process. Incorrect model specification can lead to misspecification bias, and a lack of scrutiny of functional forms fosters interference of researcher degrees of freedom in sociological work. In this article, I propose a framework that uses flexible machine learning (ML) methods to provide an indication of the fit potential in a dataset containing the exact same covariates as a researcher’s hypothesized model. When this ML-based fit potential strongly outperforms the researcher’s self-hypothesized functional form, it implies a lack of complexity in the latter. Advances in the field of explainable AI, like the increasingly popular Shapley values, can be used to generate understanding into the ML model such that the researcher’s original functional form can be improved accordingly. The proposed framework aims to use ML beyond solely predictive questions, helping sociologists exploit the potential of ML to identify intricate patterns in data to specify better-fitting, interpretable models. I illustrate the proposed framework using a simulation and real-world examples

Learning loss due to school closures during the COVID-19 pandemic

Suspension of face-to-face instruction in schools during the COVID-19 pandemic has led to concerns about consequences for students’ learning. So far, data to study this question have been limited. Here we evaluate the effect of school closures on primary school performance using exceptionally rich data from The Netherlands (n ≈ 350,000). We use the fact that national examinations took place before and after lockdown and compare progress during this period to the same period in the 3 previous years. The Netherlands underwent only a relatively short lockdown (8 wk) and features an equitable system of school funding and the world’s highest rate of broadband access. Still, our results reveal a learning loss of about 3 percentile points or 0.08 standard deviations. The effect is equivalent to one-fifth of a school year, the same period that schools remained closed. Losses are up to 60% larger among students from less-educated homes, confirming worries about the uneven toll of the pandemic on children and families. Investigating mechanisms, we find that most of the effect reflects the cumulative impact of knowledge learned rather than transitory influences on the day of testing. Results remain robust when balancing on the estimated propensity of treatment and using maximum-entropy weights or with fixed-effects specifications that compare students within the same school and family. The findings imply that students made little or no progress while learning from home and suggest losses even larger in countries with weaker infrastructure or longer school closures

Inequalities in healthcare use during the COVID-19 pandemic

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 – Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare

The challenges of rehabilitating denuded patches of a semi-arid environment in Kenya

Land degradation is a major problem in the semi-arid environments of Sub-Saharan Africa. Fighting land degradation is essential to ensure the sustainable and long-term productivity of the habited semiarid lands. In Kenya, grass reseeding technology has been used to combat land degradation. However, despite the use of locally adapted perennial grass species namely Cenchrus ciliaris (African foxtail grass), Eragrostis superba (Maasai love grass) and Enteropogon macrostachyus (Bush rye) failure still abound. Therefore, more land is still being degraded. The aim of this study was to determine the main factors which contribute to failures in rehabilitating denuded patches in semi-arid lands of Kenya. A questionnaire was administered to capture farmer perceptions on failures on rangeland rehabilitation using grass reseeding technology. Rainfall data was collected during the study period. Moreover, rehabilitation trials using the three grasses were done under natural rainfall. Results from this study show that climatic factors mainly low amounts of rainfall to be the main contributor to rehabilitation failures. 92% of the respondents asserted that reseeding fails because of low rainfall amounts received in the area. The study area received a total of 324 mm of rainfall which was low compared to the average annual mean of 600mm. Reseeded trial plots also failed to establish due to the low amounts of rainfall received. This showed how low rainfall is unreliable for reseeding. Other factors namely destruction by the grazing animals, pests and rodents, flush floods, poor sowing time, poor seed quality, lack of enough seed and weeds also contribute to rehabilitation failures in semi-arid lands of Keny

Dry matter yields and hydrological properties of three perennial grasses of a semi-arid environment in East Africa

Enteropogon macrostachyus (Bush rye), Cenchrus ciliaris L. (African foxtail grass) and Eragrostis superba Peyr (Maasai love grass) are important perennial rangeland grasses in Kenya. They provide an important source of forage for domestic livestock and wild ungulates. These grasses have been used extensively to rehabilitate denuded patches in semi-arid environment of Kenya. This study investigated the dry matter yields and hydrological properties of the three grasses under simulated rainfall at three phenological stages; early growth, elongation and reproduction. Laboratory seed viability tests were also done. Hydrological properties of the three grasses were estimated using a Kamphorst rainfall simulator. Results showed that there was a significant difference (p > 0.05) in dry matter yields and soil hydrological properties at the different grass phenological stages. Generally, all the three grasses improved the soil hydrological properties with an increase in grass stubble height. C. ciliaris gave the best soil hydrological properties followed by E. macrostachyus and E. superba, respectively. E. macrostachyus recorded the highest seed viability percentage. C. ciliaris and E. superba were ranked second and third, respectively. C. ciliaris yielded the highest biomass production at the reproductive stage followed by E. superba and E. macrostachyus, respectively. Key words: Cenchrus ciliaris, Enteropogon macrostachyus, Eragrostis superba, rangeland

Effects of legume cover crop and sub-soiling on soil properties and Maize (Zea mays L) growth in semi arid area of Machakos district, Kenya = Efecto del cultivo de cobertua y el subsolado sobre las propiedades del suelo y crecimiento de maiz (Zea mays L.) en la region semi arida de Machakos, Kenia

Low crop yields in the semi arid areas of Kenya have been attributed to, among other factors, low soil fertility, low farm inputs, labour constraints and inappropriate tillage practices that lead to pulverized soils. The aim of this study was to determine the effects of legume cover crops (LCC) on soil properties and maize growth in the semi arid area of Machakos District, Kenya. The study was undertaken in farmers’ fields. The field experiments were carried out in a RCBD with four treatments each replicated four times during the 2008 long (LR) and short rain (SR) seasons. The treatments were T1 = maize + dolichos (Lablab purpureus) + subsoiling; T2 = maize + dolichos + no subsoiling; T3 = maize alone + no subsoiling; T4 = maize alone with subsoiling). Results from the field experiments showed that rainfall amount and its distribution affected the growth and yield of dolichos and maize. There were significant differences in ground cover between the treatments at P = 0.05 in all the different weeks after planting when measurements were taken. The penetration resistance in all the plots ranged from 3.83 - 4.18 kg cm-2 with treatment T4 having the highest and treatment T1 lowest penetration resistance. There were also siginificant changes in soil N in plots which were under dolichos compared to plots without dolichos. The results obtained in this study also indicated that subsoiling in combination with dolichos had the greatest potential of improving soil properties and crop yields in semi arid environments of Kenya