In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N =4857) and test (N =2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self- report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ~36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N =3214). Validating the PES-S in the UK biobank (N =40,614), we find that the UK biobank PES-S explains about ~12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one’s genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.
MULTIFILE
We show how to estimate a Cronbach's alpha reliability coefficient in Stata after running a principal component or factor analysis. Alpha evaluates to what extent items measure the same underlying content when the items are combined into a scale or used for latent variable. Stata allows for testing the reliability coefficient (alpha) of a scale only when all items receive homogenous weights. We present a user-written program that computes reliability coefficients when implementation of principal component or factor analysis shows heterogeneous item loadings. We use data on management practices from Bloom and Van Reenen (2010) to explain how to implement and interpret the adjusted internal consistency measure using afa.
Estimation of the factor model by unweighted least squares (ULS) is distribution free, yields consistent estimates, and is computationally fast if the Minimum Residuals (MinRes) algorithm is employed. MinRes algorithms produce a converging sequence of monotonically decreasing ULS function values. Various suggestions for algorithms of the MinRes type are made for confirmatory as well as for exploratory factor analysis. These suggestions include the implementation of inequality constraints and the prevention of Heywood cases. A simulation study, comparing the bootstrap standard deviations for the parameters with the standard errors from maximum likelihood, indicates that these are virtually equal when the score vectors are sampled from the normal distribution. Two empirical examples demonstrate the usefulness of constrained exploratory and confirmatory factor analysis by ULS used in conjunction with the bootstrap method.
Since the 1970s, Caribbean reefs have transitioned from coral-dominated to algal-dominated ecosystems. The prevalence of algae reduces coral recruitment, rendering the reefs unable to recover from additional disturbances and jeopardizing crucial ecosystem services, including coastal protection, fisheries, and tourism. One of the main factors to the proliferation of algae is the scarcity of grazers, which is a result of overfishing and disease outbreaks. While fishing supports livelihoods, enhances local food security, and is an integral part of the Caribbean communities' culture, it remains a significant threat to coral reefs. Consequently, the Nature and Environmental Policy Plan (NEPP) 2020-2030, outlining conservation and restoration priorities in the Caribbean Netherlands, underscores the necessity of an integrated approach to tackle the complex challenges of coral reef restoration and fisheries development. The Saba government, and nature management organizations of Bonaire, St. Eustatius, and Saba are implementing the NEPP. Together with University of Applied Sciences Van Hall Larenstein, Wageningen University and WWF, they aim to identify novel species of native invertebrate grazers with the dual purpose of reef restoration and fisheries diversification. The Caribbean king crab (Maguimithrax spinosissimus), the West Indian sea egg (Tripneustes ventricosus), and the West Indian top shell (Cittarium pica) have been identified as potential candidates. Despite their preference to graze on macroalgae, their current densities are inadequate. Population enhancement of these species holds promise for reducing algae, promoting biodiversity, and simultaneously supporting small-scale fisheries. However, there is limited knowledge regarding the ecological effects and socio-economic potential of these grazers. The ReefGrazers project aims to assess the current densities of these herbivores around the BES islands, analyze their impacts on the reef, and evaluate their retention post-restocking. Socio-economic research will quantify current small-scale fishing practices, while market analysis will help assess the potential for the development of these novel resources as sustainable fisheries.
