Household Labour Migration and Farmers’ Access to Productive Agricultural Services: A Case Study from Chinese Provinces

Household labour migration experiences may have a staggering impact within developing countries, especially in dynamic societies like China, where labour migration is obvious. The present study’s objective is to investigate whether household labour migration contributes to the probability of farmers’ access to productive agricultural services. The study’s empirical setup is comprised of household survey data of 541 farmers in Shaanxi, Henan, and Sichuan provinces. The study proposes a counterfactual model to evaluate the average processing effect of an urban migrant with the help of the endogenous transformation of the Probit model. The results show that labour migration for work directly affects farmers’ access to productive agricultural services and indirectly affects farmers’ access to productive agricultural services through three channels: labour input, land transfers, and planting structure adjustments. The study further confirms that labour migration for work has a significant heterogeneity in the probability of obtaining productive agricultural services for farmers with or without non-agricultural income. Simultaneously, the labour migration area for work has significant heterogeneity in the probability of farmer households’ access to productive agricultural services. The government should extend support towards productive agriculture services. Agricultural demonstration services and on-hand training of migrant labour should be highlighted.

The effect of non-pharmaceutical interventions on the demand for health care and on mortality: evidence from COVID-19 in Scandinavia

We study the effectiveness of non-pharmaceutical interventions (NPIs) against COVID-19 on the allocation of scarce resources in the hospital sector in Scandinavia. Denmark and Norway imposed strict NPIs, but Sweden followed an extraordinarily lenient approach. We use an event study to compare COVID-19 hospitalizations, intensive-care (ICU) patients, and deaths in Sweden with Denmark and Norway. The outcome variables initially follow a common trend, but diverge 2–3 weeks after lockdown. Both the timing of the effect and the similarity in the trend between Denmark and Norway are highly consistent with a causal effect of the lockdown. We use our event study to build a counterfactual model that predicts the outcome variables for Denmark and Norway if they had followed Sweden’s approach. In the absence of strict NPIs, the peak number of hospitalizations would have been 2.5 (3.5) times as large in Denmark (Norway). Overall, Denmark (Norway) would have had 334 (671) percent more hospital-patient days, 277 (379) percent more ICU-patient days, and 402 (1015) percent more deaths. The benefit of lockdown in terms of healthcare and mortality costs amounts to between 1 and 4 (0.9 and 3.5) percent of GDP in Denmark (Norway).

High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities

Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Information on vaccine effectiveness in these settings is essential to improve mitigation strategies, but evidence remains limited. To evaluate the early effect of the administration of BNT162b2 mRNA vaccines in LTCFs, we monitored subsequent SARS-CoV-2 documented infections and deaths in Catalonia, a region of Spain, and compared them to counterfactual model predictions from February 6th to March 28th, 2021, the subsequent time period after which 70% of residents were fully vaccinated. We calculated the reduction in SARS-CoV-2 documented infections and deaths as well as the detected county-level transmission. We estimated that once more than 70% of the LTCFs population were fully vaccinated, 74% (58%-81%, 90% CI) of COVID-19 deaths and 75% (36%-86%) of all documented infections were prevented. Further, detectable transmission was reduced up to 90% (76-93%). Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic.

High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities

Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Information on vaccine effectiveness in these settings is essential to improve mitigation strategies, but evidence remains limited. To evaluate the early effect of the administration of BNT162b2 mRNA vaccines in LTCFs, we monitored subsequent SARS-CoV-2 documented infections and deaths in Catalonia, a region of Spain, and compared them to counterfactual model predictions from February 6th to March 28th, 2021, the subsequent time period after which 70% of residents were fully vaccinated. We calculated the reduction in SARS-CoV-2 documented infections and deaths as well as the detected county-level transmission. We estimated that once more than 70% of the LTCFs population were fully vaccinated, 74% (58%-81%, 90% CI) of COVID-19 deaths and 75% (36%-86%) of all documented infections were prevented. Further, detectable transmission was reduced up to 90% (76-93% 90%CI). Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic.

Counterfactual Thinking and Recency Effects in Causal Judgment

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of the extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one (Experiment 2). Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens (and sometimes eliminates) the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure (Experiments 3 & 5). We discuss these results in relation to work on counterfactual thinking and causal modeling.

The Design of Clustered Observational Studies in Education

Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practices for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in a large U.S. school district.

A definition of the causal effect of a political party’s nominee on the U.S. presidential general election using counterfactual response types

The electability of the candidates for the 2020 Democratic U.S. presidential nomination was frequently debated. In general, arguments regarding a given candidate’s electability often claim that they would affect the general election by changing the behavior of a certain subset of eligible voters. For example, is it more important electorally that a candidate drives turnout or swing voting? As lay consumers of political opinion, we were having difficulty weighing these claims from a strategic standpoint.Although candidate electability is a nebulous term that might be interpreted in various ways, one interpretation of the term is a population-based causal question: what would the effect of the Democratic nominee be on the presidential election result? Population-based causal questions are commonly studied in epidemiology. To aid interpretation of electability arguments, we frame the question through a counterfactual model used in epidemiology.Specifically, we define the causal effect by characterizing the population of eligible voters into nine counterfactual response types. The definition clarifies our ability to interpret arguments regarding the electability of the candidates. For example, the causal effect can be subdivided into three parts: the effect of the nominee on 1) Democratic turnout, 2) Republican turnout, and 3) swing voting. We show using notation that the third part has twice the weight as the other two.The definition follows intuition. However, we hope its formalization using counterfactual response types may foster inter-disciplinary communication.

Dynamics of COVID-19 transmission including indirect transmission mechanisms: a mathematical analysis

The outbreak of the novel coronavirus severe acute respiratory syndrome-coronavirus-2 has raised major health policy questions and dilemmas. Whilst respiratory droplets are believed to be the dominant transmission mechanisms, indirect transmission may also occur through shared contact of contaminated common objects that is not directly curtailed by a lockdown. The conditions under which contaminated common objects may lead to significant spread of coronavirus disease 2019 during lockdown and its easing is examined using the susceptible-exposed-infectious-removed model with a fomite term added. Modelling the weekly death rate in the UK, a maximum-likelihood analysis finds a statistically significant fomite contribution, with 0.009 ± 0.001 (95% CI) infection-inducing fomites introduced into the environment per day per infectious person. Post-lockdown, comparison with the prediction of a corresponding counterfactual model with no fomite transmission suggests fomites, through enhancing the overall transmission rate, may have contributed to as much as 25% of the deaths following lockdown. It is suggested that adding a fomite term to more complex simulations may assist in the understanding of the spread of the illness and in making policy decisions to control it.

Evidence-Based Practices in a Changing World: Reconsidering the Counterfactual in Educational Research

Experimental and quasi-experimental designs are used in educational research to establish causality and develop effective practices. These research designs rely on a counterfactual model that, in simple form, calls for a comparison between a treatment group and control group. Developers of educational practices often assume that the population from which control groups are drawn is unchanging in its behavior or performance. This is not always the case. Populations and study samples can change over time—sometimes dramatically so. We illustrate this important point by presenting data from 5 randomized control trials of the efficacy of Kindergarten Peer-Assisted Learning Strategies, a supplemental, peer-mediated reading program. The studies were conducted across 9 years and involved 2,591 students. Findings demonstrate a dramatic increase in the performance of control students over time, and suggest the need for a more nuanced understanding of the counterfactual model and its role in establishing evidence-based practices.