Academic literature on the topic 'Population Average Causal Effect'
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Journal articles on the topic "Population Average Causal Effect"
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Ottoboni, Kellie N., and Jason V. Poulos. "Estimating population average treatment effects from experiments with noncompliance." Journal of Causal Inference 8, no. 1 (October 23, 2020): 108–30. http://dx.doi.org/10.1515/jci-2018-0035.
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AbstractRandomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart when compliance is relatively low and can be predicted by observed covariates. We apply the method to evaluate the effect of Medicaid coverage on health care use for a target population of adults who may benefit from expansions to the Medicaid program. We draw RCT data from the Oregon Health Insurance Experiment, where less than one-third of those randomly selected to receive Medicaid benefits actually enrolled.
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Li, Fan, Zizhong Tian, Jennifer Bobb, Georgia Papadogeorgou, and Fan Li. "Clarifying selection bias in cluster randomized trials." Clinical Trials 19, no. 1 (December 11, 2021): 33–41. http://dx.doi.org/10.1177/17407745211056875.
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Background In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic differences in baseline characteristics of the recruited patients between intervention and control arms, inducing post-randomization selection bias. We aim to rigorously define causal estimands in the presence of selection bias. We elucidate the conditions under which standard covariate adjustment methods can validly estimate these estimands. We further discuss the additional data and assumptions necessary for estimating causal effects when such conditions are not met. Methods Adopting the principal stratification framework in causal inference, we clarify there are two average treatment effect (ATE) estimands in cluster randomized trials: one for the overall population and one for the recruited population. We derive analytical formula of the two estimands in terms of principal-stratum-specific causal effects. Furthermore, using simulation studies, we assess the empirical performance of the multivariable regression adjustment method under different data generating processes leading to selection bias. Results When treatment effects are heterogeneous across principal strata, the average treatment effect on the overall population generally differs from the average treatment effect on the recruited population. A naïve intention-to-treat analysis of the recruited sample leads to biased estimates of both average treatment effects. In the presence of post-randomization selection and without additional data on the non-recruited subjects, the average treatment effect on the recruited population is estimable only when the treatment effects are homogeneous between principal strata, and the average treatment effect on the overall population is generally not estimable. The extent to which covariate adjustment can remove selection bias depends on the degree of effect heterogeneity across principal strata. Conclusion There is a need and opportunity to improve the analysis of cluster randomized trials that are subject to post-randomization selection bias. For studies prone to selection bias, it is important to explicitly specify the target population that the causal estimands are defined on and adopt design and estimation strategies accordingly. To draw valid inferences about treatment effects, investigators should (1) assess the possibility of heterogeneous treatment effects, and (2) consider collecting data on covariates that are predictive of the recruitment process, and on the non-recruited population from external sources such as electronic health records.
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Aronow, Peter M., and Allison Carnegie. "Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable." Political Analysis 21, no. 4 (2013): 492–506. http://dx.doi.org/10.1093/pan/mpt013.
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Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.
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Hu, Liangyuan, Jiayi Ji, Hao Liu, and Ronald Ennis. "A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations." International Journal of Environmental Research and Public Health 19, no. 22 (November 12, 2022): 14903. http://dx.doi.org/10.3390/ijerph192214903.
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Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence—average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.
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Schochet, Peter Z. "Estimating complier average causal effects for clustered RCTs when the treatment affects the service population." Journal of Causal Inference 10, no. 1 (January 1, 2022): 300–334. http://dx.doi.org/10.1515/jci-2022-0033.
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Abstract Randomized controlled trials (RCTs) sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting (IPW) estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply (reduce) to nonclustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children’s cognitive development scores. An R program for estimation is available for download.
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Bechtel, Michael M., and Kenneth F. Scheve. "Who Cooperates? Reciprocity and the Causal Effect of Expected Cooperation in Representative Samples." Journal of Experimental Political Science 4, no. 3 (2017): 206–28. http://dx.doi.org/10.1017/xps.2017.16.
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AbstractWhen do societies succeed in providing public goods? Previous research suggests that public goods contributions correlate with expectations about cooperation by others among students and other demographic subgroups. However, we lack knowledge about whether the effect of expected cooperation is causal and a general feature of populations. We fielded representative surveys (N = 8,500) in France, Germany, the United Kingdom, and the United States that included a public goods game and a novel between-subjects experiment. The experiment varied expectations about cooperation by others. We find that higher expected cooperation by others causes a significant increase in individual contributions. When classifying contribution schedules, we find that almost 50% of the population employs a conditionally cooperative strategy. These individuals are on average richer, younger, and more educated. Our results help explain the varying success of societal groups in overcoming cooperation problems and assist policymakers in the design of institutions meant to solve social dilemmas.
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Nethery, Rachel C., Fabrizia Mealli, and Francesca Dominici. "Estimating population average causal effects in the presence of non-overlap: The effect of natural gas compressor station exposure on cancer mortality." Annals of Applied Statistics 13, no. 2 (June 2019): 1242–67. http://dx.doi.org/10.1214/18-aoas1231.
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Choi, Byeong Yeob. "Instrumental variable estimation of truncated local average treatment effects." PLOS ONE 16, no. 4 (April 5, 2021): e0249642. http://dx.doi.org/10.1371/journal.pone.0249642.
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Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two nonrandomized treatment groups. The local average treatment effect (LATE) is a causal estimand that can be identified by an IV. The LATE approach is appealing because its identification relies on weaker assumptions than those in other IV approaches requiring a homogeneous treatment effect assumption. If the instrument is confounded by some covariates, then one can use a weighting estimator, for which the outcome and treatment are weighted by instrumental propensity scores. The weighting estimator for the LATE has a large variance when the IV is weak and the target population, i.e., the compliers, is relatively small. We propose a truncated LATE that can be estimated more reliably than the regular LATE in the presence of a weak IV. In our approach, subjects who contribute substantially to the weak IV are identified by their probabilities of being compliers, and they are removed based on a pre-specified threshold. We discuss interpretation of the proposed estimand and related inference method. Simulation and real data experiments demonstrate that the proposed truncated LATE can be estimated more precisely than the standard LATE.
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Kennedy, Amber L., Beverley J. Vollenhoven, Richard J. Hiscock, Catharyn J. Stern, Susan P. Walker, Jeanie L. Y. Cheong, Jon L. Quach, et al. "School-age outcomes among IVF-conceived children: A population-wide cohort study." PLOS Medicine 20, no. 1 (January 24, 2023): e1004148. http://dx.doi.org/10.1371/journal.pmed.1004148.
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Background In vitro fertilisation (IVF) is a common mode of conception. Understanding the long-term implications for these children is important. The aim of this study was to determine the causal effect of IVF conception on primary school-age childhood developmental and educational outcomes, compared with outcomes following spontaneous conception. Methods and findings Causal inference methods were used to analyse observational data in a way that emulates a target randomised clinical trial. The study cohort comprised statewide linked maternal and childhood administrative data. Participants included singleton infants conceived spontaneously or via IVF, born in Victoria, Australia between 2005 and 2014 and who had school-age developmental and educational outcomes assessed. The exposure examined was conception via IVF, with spontaneous conception the control condition. Two outcome measures were assessed. The first, childhood developmental vulnerability at school entry (age 4 to 6), was assessed using the Australian Early Developmental Census (AEDC) (n = 173,200) and defined as scoring <10th percentile in ≥2/5 developmental domains (physical health and wellbeing, social competence, emotional maturity, language and cognitive skills, communication skills, and general knowledge). The second, educational outcome at age 7 to 9, was assessed using National Assessment Program–Literacy and Numeracy (NAPLAN) data (n = 342,311) and defined by overall z-score across 5 domains (grammar and punctuation, reading, writing, spelling, and numeracy). Inverse probability weighting with regression adjustment was used to estimate population average causal effects. The study included 412,713 children across the 2 outcome cohorts. Linked records were available for 4,697 IVF-conceived cases and 168,503 controls for AEDC, and 8,976 cases and 333,335 controls for NAPLAN. There was no causal effect of IVF-conception on the risk of developmental vulnerability at school-entry compared with spontaneously conceived children (AEDC metrics), with an adjusted risk difference of −0.3% (95% CI −3.7% to 3.1%) and an adjusted risk ratio of 0.97 (95% CI 0.77 to 1.25). At age 7 to 9 years, there was no causal effect of IVF-conception on the NAPLAN overall z-score, with an adjusted mean difference of 0.030 (95% CI −0.018 to 0.077) between IVF- and spontaneously conceived children. The models were adjusted for sex at birth, age at assessment, language background other than English, socioeconomic status, maternal age, parity, and education. Study limitations included the use of observational data, the potential for unmeasured confounding, the presence of missing data, and the necessary restriction of the cohort to children attending school. Conclusions In this analysis, under the given causal assumptions, the school-age developmental and educational outcomes for children conceived by IVF are equivalent to those of spontaneously conceived children. These findings provide important reassurance for current and prospective parents and for clinicians.
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Bann, David, Emla Fitzsimons, and William Johnson. "Determinants of the population health distribution: an illustration examining body mass index." International Journal of Epidemiology 49, no. 3 (January 13, 2020): 731–37. http://dx.doi.org/10.1093/ije/dyz245.
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Abstract Most epidemiological studies examine how risk factors relate to average difference in outcomes (linear regression) or odds of a binary outcome (logistic regression); they do not explicitly examine whether risk factors are associated differentially across the distribution of the health outcome investigated. This paper documents a phenomenon found repeatedly in the minority of epidemiological studies which do this (via quantile regression): associations between a range of established risk factors and body mass index (BMI) are progressively stronger in the upper ends of the BMI distribution. In this paper, we document this finding and provide illustrative evidence of it in the 1958 British birth cohort study. Associations of low childhood socio-economic position, high maternal weight, low childhood general cognition and adult physical inactivity with higher BMI are larger at the upper end of the BMI distribution, on both absolute and relative scales. For example, effect estimates for socio-economic position and childhood cognition were around three times larger at the 90th compared with 10th quantile, while effect estimates for physical inactivity were increasingly larger from the 50th to 90th quantiles, yet null at lower quantiles. We provide potential explanations for these findings and discuss implications. Risk factors may have larger causal effects among those in worse health, and these effects may not be discovered when health is only examined in average terms. In such scenarios, population-based approaches to intervention may have larger benefits than anticipated when assuming equivalent benefit across the population. Further research is needed to understand why effect estimates differ across the BMI outcome distribution and to investigate whether differential effects exist for other physical and mental health outcomes.
Dissertations / Theses on the topic "Population Average Causal Effect"
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Genbäck, Minna. "Uncertainty intervals and sensitivity analysis for missing data." Doctoral thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-127121.
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In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR. The thesis covers different types of linear regression. In Paper I and III we have a continuous outcome, in Paper II a binary outcome, and in Paper IV we allow for mixed effects with a continuous outcome. In Paper III we estimate the effect of a treatment, which can be seen as an example of missing outcome data.
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Ujamaa, Dawud A. "Assessing the Effect of Prior Distribution Assumption on the Variance Parameters in Evaluating Bioequivalence Trials." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/math_theses/13.
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Bioequivalence determines if two drugs are alike. The three kinds of bioequivalence are Average, Population, and Individual Bioequivalence. These Bioequivalence criteria can be evaluated using aggregate and disaggregate methods. Considerable work assessing bioequivalence in a frequentist method exists, but the advantages of Bayesian methods for Bioequivalence have been recently explored. Variance parameters are essential to any of theses existing Bayesian Bioequivalence metrics. Usually, the prior distributions for model parameters use either informative priors or vague priors. The Bioequivalence inference may be sensitive to the prior distribution on the variances. Recently, there have been questions about the routine use of inverse gamma priors for variance parameters. In this paper we examine the effect that changing the prior distribution of the variance parameters has on Bayesian models for assessing Bioequivalence and the carry-over effect. We explore our method with some real data sets from the FDA.
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Licari, Federica. "Causal inference in irregular designs with intermediate variables and multivariate outcomes and the external validity of RCT results." Doctoral thesis, 2020. https://hdl.handle.net/2158/1288739.
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Xu, Yuanfang. "An OLS-Based Method for Causal Inference in Observational Studies." Diss., 2019. http://hdl.handle.net/1805/20225.
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Indiana University-Purdue University Indianapolis (IUPUI)Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.
Books on the topic "Population Average Causal Effect"
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Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.
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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of incorporating survey weights into propensity score matching procedures, as well as the problems that arise when survey weights are ignored. Finally, it explores the differences between population-based inferences and sample-based inferences using real-world data from the 2012 panel of The American Panel Survey (TAPS). The article highlights the impact of social media usage on political participation, when such impact is not actually apparent in the target population.
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Kaufman, Jay S., Dinela Rushani, and Richard S. Cooper. Nature versus Nurture in the Explanations for Racial/Ethnic Health Disparities. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190465285.003.0007.
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This chapter points out that some researchers explain the higher mortality rates among blacks in the United States as “nature”, blaming such rates primarily on blacks' genetic makeup. Others explain the phenomenon as “nurture”, blaming social status differences stemming from systemic discrimination. For a genetic difference to be used to explain an observed health disparity, the identified causal variant would have to have a large effect on the disease phenotype risk and would have to have a substantially different prevalence in the two racial populations, and the disease would have to be a significant contributor to mortality in the racial population. Genetic studies were done on cardiovascular disease, type II diabetes, homicide, and more. In evaluating results from these studies and previous knowledge, 3% of the entire racial disparity in mortality can be accounted for, which leaves 97% of disparities to social origin.
Book chapters on the topic "Population Average Causal Effect"
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Sowa, Anna S., Lisa Dussling, Jörg Hagmann, and Sebastian J. Schultheiss. "The power of next-generation sequencing and machine learning for causal gene finding and prediction of phenotypes." In Mutation breeding, genetic diversity and crop adaptation to climate change, 401–10. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0041.
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Abstract The wide application of next-generation sequencing (NGS) has facilitated and accelerated causal gene finding and breeding in the field of plant sciences. A wide variety of techniques and computational strategies is available that needs to be appropriately tailored to the species, genetic architecture of the trait of interest, breeding system and available resources. Utilizing these NGS methods, the typical computational steps of marker discovery, genetic mapping and identification of causal mutations can be achieved in a single step in a cost- and time-efficient manner. Rather than focusing on a few high-impact genetic variants that explain phenotypes, increased computational power allows modelling of phenotypes based on genome-wide molecular markers, known as genomic selection (GS). Solely based on this genotype information, modern GS approaches can accurately predict breeding values for a given trait (the average effects of alleles over all loci that are anticipated to be transferred from the parent to the progeny) based on a large training population of genotyped and phenotyped individuals (Crossa et al., 2017). Once trained, the model offers great reductions in breeding speed and costs. We advocate for improving conventional GS methods by applying advanced techniques based on machine learning (ML) and outline how this approach can also be used for causal gene finding. Subsequent to genetic causes of agronomically important traits, epigenetic mechanisms such as DNA methylation play a crucial role in shaping phenotypes and can become interesting targets in breeding pipelines. We highlight an ML approach shown to detect functional methylation changes sensitively from NGS data. We give an overview about commonly applied strategies and provide practical considerations in choosing and performing NGS-based gene finding and NGS-assisted breeding.
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Dunn, Graham. "Complier-Average Causal Effect (CACE) Estimation." In International Encyclopedia of Statistical Science, 273–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_13.
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Liu, Shirley H., and Frank Heiland. "New Estimates on the Effect of Parental Separation on Child Health." In Causal Analysis in Population Studies, 167–99. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9967-0_8.
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Francavilla, Francesca, and Alessandra Mattei. "Assessing the Causal Effect of Childbearing on Household Income in Albania." In Causal Analysis in Population Studies, 201–31. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9967-0_9.
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Turpin, Baptiste, Eline Y. Bijman, Hans-Michael Kaltenbach, and Jörg Stelling. "Population Design for Synthetic Gene Circuits." In Computational Methods in Systems Biology, 181–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85633-5_11.
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AbstractSynthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.
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Eastin, J. "Climate change, livelihoods and domestic violence in Indonesia." In Gender, climate change and livelihoods: vulnerabilities and adaptations, 94–106. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789247053.0008.
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Abstract This book chapter dicsusses the data, methodological strategies, and findings, and the final section concludes with a discussion of key policy implications and directions for future research regarding climate change, livelihoods, and domestic violence in Indonesia. This study argues that climate shocks in Indonesia elevate the incidence of domestic violence via their impact on agriculture and agrarian livelihoods. Those relying on agriculture as a primary income source in Indonesia-approximately 41% of the population=suffer when climatic stress diminishes earnings through job loss and reduced crop yields. The impact can reduce food security, especially for subsistence farmers, but also for the broader population when scarcity elevates local food prices. Food already consumes 70% of household budgets for half the population, with rice comprising the largest share-over 25% of total household expenditures for the poorest quintile. Thus, even minor reductions in yields or inflation in local rice markets can have dire effects. These impacts are anticipated to exacerbate social and psychological pressures-stress, anxiety, depression, trauma, substance abuse-commonly associated with domestic and intimate partner violence, which in turn should increase its incidence within affected regions. This study uses data from the Global SPEI database and the NVMS to model the relationship between climate change and domestic violence in Indonesia. It finds that positive and negative deviations from long-term climate averages, when occurring in December-the core month of the Indonesian rice-planting season-increase the incidence of domestic violence in the following year. This relationship likely reflects the negative impact of climate shocks on agricultural sectors and livelihoods, an outcome which aggravates the emotional and psychological preconditions for domestic violence and abuse, disproportionately diminishes women's bargaining power in the household, and reduces women's ability to escape abusive situations. These effects are especially prominent in areas with higher levels of poverty, further illustrating the economic dimension of the causal process.
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Fauzi, Achmad, Nazaruddin, and Rulianda Purnomo Wibowo. "Measurement of Supply Chain Management Performance on Toll Road Projects with Design and Build Contract Using SCOR and AHP Methods." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 1152–61. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_142.
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AbstractInfrastructure performance was a highlight in the World Economic Forum, thus encouraging countries worldwide, including Indonesia, to be motivated to accelerate the provision of infrastructure facilities. One of the infrastructures that continues to be improved is toll roads, and a design and build contract scheme is chosen, which in theory has the characteristics of a faster time in infrastructure provision. This project experienced delays caused by supply chain problems for the main materials, namely rebar and concrete. This study aims to measure the performance of supply chain management for rebar and concrete materials that cause project delays of 3.251%. The population in this study was 25 contractors who understood and were involved in the supply chain management process for rebar and concrete materials. This type of research is a descriptive causal study that uses a quantitative approach and uses SCOR and AHP as analytical tools. The results show that the supply chain management performance assessment for rebar and concrete materials is 64.36 out of a scale of 100. This indicates that the supply chain management performance in toll road construction projects is at an average level. This performance is much influenced by the scarcity of materials from suppliers at certain times and the design process that coincides with the construction period, causing long waiting times for adjusting the order quantity. Based on the AHP, it was found that the most dominant variables were Plan and Source. Thus, it can be concluded that the performance of delayed toll road construction projects is influenced by the performance of the rebar and concrete supply chain. For toll road projects with design and build contracts, the corrective steps must focus on Plan and Source so that the performance level increases and can avoid the risk of delays that are detrimental to service providers.
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Gustriandha, Ryan Dwi, Nazaruddin, and Isfenti Sadalia. "Effect of Occupational Health Safety and Rewards on Employee Performance and Work Motivation as Intervening Variables at PT Pertamina Geothermal Energy Area Sibayak." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 580–89. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_74.
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AbstractThese days, human resources are a vital role needed as a company asset to survive in the current business competition. Human resources as employees cannot be separated from problems related to occupational health and safety in the company as by ensuring occupational health safety, companies can foster morale and comfort of their employees. This study aims to determine whether the elements of occupational health and safety and rewards have an influence on employee performance and whether motivation is able to mediate occupational health safety and rewards for employee performance to be better. The population of this research was all employees of supporting service workers who work in a geothermal management business company. The data obtained were primary data through questionnaires distributed to employees in the company using the Likert scale method. This type of research was causal associative research with data analysis techniques used descriptive analysis and path analysis. The sampling technique was saturated sampling, which was collected from all 32 workers as samples. The results of the effect of occupational health safety and appreciation on employee performance through work motivation show a direct influence value of 0.782 and an indirect effect of 0.672. Based on the results of the study, it can be said that the direct effect is greater than the indirect effect (0.782 > 0.672). So that there is no significant effect of the Occupational Health Safety and Rewards variables on employee performance through work motivation. Based on these results, increasing the company’s commitment to occupational health and safety to carry out K3 procedures in order to comply with company regulations on an ongoing basis is needed.
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García Portilla, Jason. "Component 2 (Meso): Qualitative Comparative Analysis (QCA)." In “Ye Shall Know Them by Their Fruits”, 233–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78498-0_16.
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AbstractThis chapter contains the meso component (Qualitative Comparative Analysis, QCA). It discusses the QCA research model, the QCA methodology, and the analysis of the QCA results.QCA is used to analyse both quantitative and qualitative data, thus enabling causal inferences. However, QCA is not a statistical technique that focuses on the likelihood of the relations among variables. Instead, it is a method based on Boolean logic, rooted in set theory, and founded on the notions of sufficiency, the necessity of conditions, and conjunctural causation.QCA results indicate, among others, that for high competitiveness, high EPI suffices if Concordats with the Vatican are low and if the Roman Catholic and Orthodox population is low. No State Religion positively affects competitiveness. Having Concordats with the Vatican negatively influences competitiveness. Additionally, factors like German, English, and Scandinavian legal origin help to increase competitiveness.Oppositely, QCA results for high corruption indicate that Concordats in combination with Roman Catholic religion adherence increase corruption. Orthodox religion has a similar negative effect. Most countries with high corruption are of French legal origin and have high Concordats. This trend is robust.Colombia and Switzerland (the two extreme cases) exhibited several consistent QCA results. The other two cases (Cuba and Uruguay) only revealed one or two consistent outcomes.
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Atzeni, Gianfranco, Luca G. Deidda, Marco Delogu, and Dimitri Paolini. "Drop-Out Decisions in a Cohort of Italian Universities." In Teaching, Research and Academic Careers, 71–103. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07438-7_4.
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AbstractIn this chapter, we study the determinants of student drop-out decisions using data on a cohort of over 230,000 students enrolled in the Italian university system. The empirical analysis reveals that the probability of dropping out of university negatively correlates with high school grades and student age, controlling for the course of study and university fixed effects. The benchmark estimation suggests a negative correlation between high school final grade and drop-out probability. We also find that enrolling late at the university increases the likelihood of dropping out. In line with the literature, our results suggest that women have a lower propensity to drop out. Our dataset allows differentiating between students who leave their homes to enroll at university (off-site students) and on-site students. We find that off-site students drop out significantly less than those who study in their hometowns. We provide significant evidence that off-site students are a self-selected sample of the total population. Accordingly, we use an instrumental variable (IV) approach to identify the causal relationship. The IV estimation shows that studying off-site negatively affects drop-out decisions and more so for students growing up in the south of Italy who typically study off-site in the Center-North of Italy. Taking advantage of a more detailed dataset concerning students enrolled at the Università di Sassari, we show that the choice of the degree is also important to predict the magnitude of drop-out. Specifically, we resort to a bivariate probit specification to account for self-selection into the course of study, finding that the estimates of the determinants of drop-out and the predicted probabilities are heavily affected. Accounting for self-selection, we show that an unconditional comparison among degrees is misleading, as some degrees attract more heterogeneous students than others, as far as skills and motivation are concerned. For instance, regarding the effect of gender, we show that while the estimation without selection suggests that women drop out less, once we account for selection, the contribution of women to drop-out becomes either positive or negative, depending on which course of study they choose. In line with these results, policymakers should tailor drop-out reducing policy interventions to the specificities of each course of study.
Conference papers on the topic "Population Average Causal Effect"
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Nişancı, Murat, Aslı Cansın Doker, Adem Türkmen, and Ömer Selçuk Emsen. "The Determinants of Labor Productivity: Analyses on Chosen Countries (1960-2010)." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01550.
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Discussions on economic productivity, in micro analysis aspects there is direct causal relationship between increases or decreases in the production and productivity, whereas it can be said that productivity is based on economic recession or growth in macro analysis aspects. In the literature, while Classical theoreticians is attributed that the source of growth is the marginal productivity of capital, neoclassic school claims that marginal productivity difference provide benefit the country from behind for realization of the convergence hypothesis. Furthermore, increasing efficiency and as the factors this increase efficiency human capital, learning by doing concepts and technology are focused in the endogenous growth theories. In this study, human capital, physical capital per worker, exports per worker, gender differences, fertility, life expectancy and dependent population ratio were determined as determinants of labor productivity. In respect to labor productivity, variables are divided to three main groups in order to economic demographic and social and psychological factors. The variables are placed with taking five years average due to the fact that those variables’ effects reveal themselves more clearly in the long term. In the paper, it was investigated by panel data analysis considering groups of developed and developing countries between 1960 and 2010 period. In this context the degree of efficiency may well be discussed with parameters of selected variables for productivity of labor. Additionally, within framework of descriptive statistics the differences and similarities between countries were interpreted for political recommendations to developing countries how to increase productivity for catching developed countries’ growth trend.
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Drápela, Emil. "Spatial differentiation of housing construction in Czechia: Towards Florida's new urban crisis?" In XXIII. mezinárodní kolokvium o regionálních vědách / 23rd International Colloquium on Regional Sciences. Brno: Masaryk University Press, 2020. http://dx.doi.org/10.5817/cz.muni.p210-9610-2020-33.
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Since the 1990s, there has been an increase in interregional differences in housing prices in Czechia. These differences largely depend on the size and economic strength of the municipality or regional centre, but there exist also differences between regions. The article uses the indicator of housing construction intensity (HCI) for the twenty-two-year period 1997-2018 per 1 km2, which is processed at the level of municipalities using spatial autocorrelation (Anselin Local Moran's I) and hotspot analysis (Getis-Ord Gi*), and presented in GIS. By comparing the results of both methods it was found that the interregional differences in the popularity of the Czech regions are significantly influenced by their distance to the main economic centres. On the contrary, the hypothesis that some regions with a worse environment, a higher share of the socially weak population and a low supply of above-average jobs will be the cause of the negative push effect has not been confirmed. In discussion, the current situation is compared with Richard Florida's concept of “New Urban Crisis”, to which arrival in Czechia it indirectly points to, although the initial conditions in Czechia are significantly different than in the US.
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KALINOWSKI, Sławomir, and Barbara KIEŁBASA. "RISK OF POVERTY AND SOCIAL EXCLUSION IN THE EUROPEAN UNION IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.044.
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This paper addresses the poverty risk issues in the context of sustainable development of rural areas. Empirical materials included in this paper are based on EU-SILC (European Union Statistics on Income and Living Conditions) partial studies, and provide a reference point for comparing the EU income distribution and social integration statistics. Poverty reduction and counteracting social exclusion are among the key Millennium Development Goals. According to studies, one in four inhabitants of EU rural areas is at risk of poverty or social exclusion. While the highest shares of at-risk population are recorded in Bulgaria (54.8%) and Romania (50.8%), the levels reported by Poland and Lithuania are also above the EU average (by 4.5 and 9.2 percentage points, respectively). At the other end of the spectrum, the risk rate in the Netherlands and Czech Republic is 12.8%. For the households, income is a factor underpinning their economic safety and, thus, their confidence. The amount of incomes affects the objective poverty levels measured with a parametric method. In the EU, persons earning no more than 60% of the national median income are assumed to be at risk of poverty. Therefore, the risk of poverty affects nearly every fifth inhabitant of EU rural areas. Poverty and social exclusion are multidimensional aspects which result in unmet needs in multiple areas: healthcare, education, housing, culture and leisure. While triggering some kind of feedback loop, insufficient incomes are both the cause and the effect of deprivation of needs. Also, they provide favorable conditions for an unsustainable development of rural areas.
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Hughes, AM, H. Ask, T. Tesli, RB Askeland, T. Reichborn-Kjennerud, O. Andreassen, Ø. Helgeland, P. Njølstad, NM Davies, and A. Havdahl. "OP96 The causal effect of BMI on neurodevelopment: a within family mendelian randomization study using MoBa." In Society for Social Medicine and Population Health Annual Scientific Meeting 2020, Hosted online by the Society for Social Medicine & Population Health and University of Cambridge Public Health, 9–11 September 2020. BMJ Publishing Group Ltd, 2020. http://dx.doi.org/10.1136/jech-2020-ssmabstracts.94.
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Evju, Øyvind, Kent-Andre Mardal, and Kristian Valen-Sendstad. "On Non-Newtonian Effects in Cerebral Aneurysms: A Computational Study on 12 Patient Specific Aneurysms." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80565.
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The rupture of a cerebral aneurysm may cause subarachnoid hemorrhage (SAH), a type of stroke associated with a high risk of morbidity, and a mortality rate as high as 50% within 30 days. Cerebral aneurysms are relatively common. Roughly 6% of the population develop such during a life-time ([1]) and the average age of SAH is 51 years.
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Ermolaeva, S. V. "Drinking water contaminants as risk factors for the health of the Ulyanovsk region population." In VIII Vserossijskaja konferencija s mezhdunarodnym uchastiem «Mediko-fiziologicheskie problemy jekologii cheloveka». Publishing center of Ulyanovsk State University, 2021. http://dx.doi.org/10.34014/mpphe.2021-92-94.
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The main goal of health risk analysis is to obtain and generalize information about the possible influence of environmental factors on human health. As a result of hydrochemical analysis of drinking water supply sources in the Ulyanovsk region, a list of main contaminants has been established. It includes ammonium, iron, copper, phosphates, sulfates, chlorides, nitrates, zinc, manganese and chromium. Among them three pollutants - iron, manganese and sulfates – had surpassed maximum permissible concentration. The concentration of iron at the level of threshold chronic effects was found in drinking water of Baryshsky (0.13), Melekessky (0.16), Sengileevsky (0.13) districts. Severe chronic effects can be caused by the concentration of iron and manganese in the drinking water of the Staromainsky (0.4 and 0.3) and Cherdaklinsky (0.9 and 0.27) districts. Assessment of health risks led us to the conclusion that drinking water can serve as an additional risk factor and provoke disease development.
Key words: risk factors, morbidity, maximum permissible concentration, pollutants, relative conditional risk, average daily dose.
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Simon, Winfried, and Giorgi Bit-Babik. "Effect of the variation in population on the whole body average SAR of persons exposed to vehicle mounted antennas." In 2012 International Conference on Electromagnetics in Advanced Applications (ICEAA). IEEE, 2012. http://dx.doi.org/10.1109/iceaa.2012.6328735.
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Berk, H. R. "THE EFFECT OF SHEAR ON OLIGOMER FORMATION; EFFECTIVE REMOVAL OF MONOMERS." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1644220.
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Fibrin polymerization has been found to be influenced by shear flow conditions (Puryear,1980). In order to determine which mechanisms are responsible for theeffect reported it is necessary to look at the various stages in fibrin polymerization. It is known that the enzymatic attack of thrombin on fibrinogen is not influenced by shear (Sellers,1981). The next step, which is investigated in this study, is the oligomer-early protofibril stage.Fibrinogen (human, Kabi) is reacted with thrombin (human, Sigma) under Couette flow conditions (volume-averaged shear 0-250sec-l) in a pH 6.8, 4mM CaC12, HEPES buffered solution (I. S.-.15). The reaction time is chosen so that 6% of fibrinopeptide A (FPA) is released. The reaction is stopped by a 1,6 hexandiol-hirudin solution. The effect of shear on oligomer population is measured using large angle 1ightscattering techniques.In order to predict theoretical shear effects on oligomer formation, it is important to be able to predict the population size. This is done using Jamney’s (1983) predictions, for early reaction time, assuming q=16. Given a size distribution it is possible to apply low Reynold’s number hydrodynamic and Smoluchowski1 s( coagulation theories to predict possible shear affects.Hydrodynamic theory predicts no effect of shear on oligomer formation; Peclet numbers are too small. Smoluchowski coagulation theory, on the other hand, predicts that for oligomer sized particles in the shear range studied, orthokinetic (shear induced) coagulation becomes more important than peri kinetic (Brornian) coagulation.Results obtained from Zimm analysis show a dramatic increase in molecular weight, compared to the stagnant case, in the shear region corresponding to where orthokinetic coagulation dominates. The higher the thrombin concentration, the more extreme and earlier (i.e. lower shear) these effects are felt. After a peak is reached in molecular weight there is a sudden drop. This is caused by monomer exhaustion which shifts the population to a more homogeneous type. The concept of orthokinetic coagulation is important physiologically since it is advantageous to incorporate monomers onto fibers as quickly as possible.
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CIELAVA, Lāsma, Solvita PETROVSKA, Daina JONKUS, and Līga PAURA. "THE EFFECT OF DIFFERENT BLOODINESS LEVEL ON LATVIAN BROWN COW PRODUCTIVITY." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.038.
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Latvian brown (LB) cow breed is economically most important livestock breed in Latvia. In last 5 years in Latvian farms rapidly increases Holstain black and white cow population, however LB breed cows is more resistant against environment and climate changes than relatively new Holstain breed cows. More resistant is genetic resource LB cows. In study was observed data from Latvian Agriculture data center about Latvian brown (LB) cows with different breed blood admixture. Cows, included in data base was born in time period from 2003–2007 year and closed at least 1 full lactation. The aim of the study was to determine how different amount of Latvian brown cow bloodiness affects cow productivity and longevity. Latvian brown cow milk yield significantly lower (p<0.05) was in first lactation (4826.1 kg), but it increased until fifth lactation (5891.8 kg). Opposite situation was occurred with milk dry matter content, milk fat content significantly higher was in first lactation, but until 5th lactation it decreased by 0.84%, similar situation was occurred with milk protein content. Significantly highest milk yield grouped by dominant breed bloodines was observed in group with Brown Swiss bloodiness (4887.3 ± 26.3 kg; p<0.05), but highest protein content in genetic resources group (4.31 ± 0.02 %). Average lifespan in LB cow population was 2463.0 days (in average 6.7 years or about 5 lactations.
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Camilleri, Lawrence, Arthur Watson, Yan Liu, and Mohammed El-Gindy. "Keep Your ESPs Running: Case Studies Exhibiting a Holistic Methodology for Run-Life Improvement." In SPE Gulf Coast Section Electric Submersible Pumps Symposium. SPE, 2021. http://dx.doi.org/10.2118/204483-ms.
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Abstract Build a more robust ESP or reduce the stress it endures? Run-life improvement requires finding the right balance to suit the local well conditions and economics. Utilizing key case studies, the paper examines how operational stress caused by low flow rates can be avoided with the correct utilization of instrumentation, surveillance, and automation thereby providing practical solutions for extending the run life of already installed ESPs. The method starts with an extensive review of ESP failure mechanisms and their causes, supported by case studies and pictures illustrating the symptoms that can be observed during dismantling. This holistic technique is supported by several case studies. The common "thread" found on most failure mechanisms is temperature rise inside the ESP, which deteriorates properties of materials, including polymer insulation, elastomer seals, and metallic parts. Heat rise is attributed to three main causes: motor thermal losses, pump hydraulic losses, and frictional heat. Case studies and data sets are provided to confirm that a paradigm shift in mitigation improvement can be achieved by automating the identification of low flow events utilizing a downhole real-time flowmeter. Three reasons are given. Firstly, it is a leading indicator, whereas surface flow meters and temperature sensors are lagging indicators due to pump-up time and heat exchange respectively. Secondly, automation enables more consistent and cost-effective identification in large ESP populations. Thirdly, it enables deeper diagnostics of the cause of low flow (i.e., gas lock versus slugging, and even the source of slugging such as horizontal lateral versus production tubing). The authors provide an exhaustive list of case studies identifying sand fallback and scale as well as low flow causes and how they can be diagnosed, including differentiation between ESP, wellbore hydraulics, and reservoir inflow causes (e.g. depletion and skin.) Over the last 30 years, improvements in design and materials have tripled ESP run lives. Therefore, many fields attain six-year average run lives and 90-day survivability of 98%. Nevertheless, economics have tightened, which has raised the bar, and therefore, many operators still suffer uneconomical run lives. Case studies indicate that the next step-change in run life improvement will require a reduction in environmental stresses by mitigating the effect of low-flow events, scale, and sand.
Reports on the topic "Population Average Causal Effect"
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González Rozada, Martín, and Hernán Ruffo. Do Trade Agreements Contribute to the Decline in Labor Share? Evidence from Latin American Countries. Inter-American Development Bank, November 2021. http://dx.doi.org/10.18235/0003790.
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In this paper, we explore the role of trade in the evolution of labor share in Latin American countries. We use trade agreements with large economies (the United States, the European Union, and China) to capture the effect of sharp changes in trade. In the last two decades, labor share has displayed a negative trend among those countries that signed trade agreements, while in other countries labor share increased, widening the gap by 7 percentage points. We apply synthetic control methods to estimate the average causal impact of trade agreements on labor share. While effects are heterogeneous in our eight case studies, the average impact is negative between 2 to 4 percentage points of GDP four years after the entry into force of the trade agreements. This result is robust to the specification used and to the set of countries in the donor pool. We also find that, after trade agreements, exports of manufactured goods and the share of industry in GDP increase on average, most notably in the case studies where negative effects on labor share are significant. A decomposition shows that all the reduction in labor share is explained by a negative impact on real wages.
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Busso, Matías, and Juan Pablo Chauvin. Long-term Effects of Weather-induced Migration on Urban Labor and Housing Markets. Inter-American Development Bank, January 2023. http://dx.doi.org/10.18235/0004714.
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This paper explores the effects of weather-induced rural-urban migration on urban labor and housing markets in Brazil. In order to identify causal effects, it uses weather shocks to the rural municipalities of origin of migrants. We show that larger migration shocks led to an increase in employment growth and a reduction in wage growth of 4 and 5 percent, respectively. The increased migration flows also affected the housing market in destination cities. On average, it led to 1 percent faster growth of the housing stock, accompanied by 5 percent faster growth in housing rents. These effects vary sharply by housing quality. We find a substantial positive effect on the growth rates of the most precarious housing units (with no effect on rents) and a negative effect on the growth of higher-quality housing units (with a positive effect on rents). This suggests that rural immigration growth slowed down housing-quality upgrading in destination cities.
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van Walbeek, Corné, and Senzo Mthembu. The Likely Fiscal and Public Health Effects of an Excise Tax on Sugar sweetened Beverages in Kenya. Institute of Development Studies, May 2022. http://dx.doi.org/10.19088/ictd.2022.007.
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Historically, non-communicable diseases (NCDs) have typically been associated with tobacco and alcohol use. However, in recent decades increased levels of overweightness and obesity, mostly caused by poor eating habits and a sedentary lifestyle, have increased diabetes, cancers, and cardiovascular diseases. There is a general agreement that sugar sweetened beverages (SSBs) are bad for one’s health. As such, measures to reduce their consumption would be expected to positively impact population health. In this working paper, we develop and report on an Excel-based model, in which we simulate the impact of an SSB tax on the prevalence of overweightness and obesity. The model starts with a baseline scenario, which takes cognisance that a 10 KES specific tax already exists on all soft drinks. A sugar-based SSB tax is then introduced. The tax is levied as an amount per gram of sugar, with or without a tax-free threshold. Other than reducing the demand for SSBs, a sugar-based SSB also creates strong incentives for manufacturers to reformulate their products to reduce the sugar content. The model predicts that the average BMI would decrease across all age groups decreasing the prevalence of overweightness and obesity. The magnitude of the decrease in the prevalence of overweightness and obesity depends on the size of the SSB tax. For realistic and politically feasible values of the SSB tax, the prevalence of overweightness and obesity is expected to decrease by between 5 per cent and 10 per cent. Should Kenya implement a sugar-based tax on SSBs, over and above the current excise tax on soft drinks, the government should clarify that such a tax aims to enhance public health; raising additional revenue should be a secondary consideration. Also, implementing a sugar based SSB tax should be part of a more comprehensive strategy to reduce overweightness and obesity, because by itself the impact of the tax is modest.
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Acosta, Karina. Locked up? The development and internal migration nexus in Colombia. Banco de la República, January 2022. http://dx.doi.org/10.32468/dtseru.304.
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Although a sizable number of studies have been exploring the migration development nexus in international settings, there is still a reduced number on internal contexts in recent years. This research aims to estimate the causal effect of origin economic conditions on internal population migration using a time series of the Colombian states between 2012 and 2019. This analysis provides a macro perspective of associations and causation between population dynamics and development in the current changes observed using spatial interaction models. Likewise, it analyses the current portray of internal migration in Colombia (defined by five-years and one-year flows). The evidence shows that the migration hump depends on the scale at which it is analyzed. At an aggregated scale, initial economic conditions are negatively associated with migration until a threshold where this relationship is reversed. The opposite is observed in the rural migrants subsample.
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Ricciulli-Marín, Diana. The Fiscal Cost of Conflict: Evidence from La Violencia in Colombia. Banco de la República de Colombia, December 2020. http://dx.doi.org/10.32468/chee.53.
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This paper studies the effect of internal conflict on local fiscal capacity using evidence from Colombia’s political conflict in the mid-20th century, better known as La Violencia. Following a difference-in-differences strategy, I find that internal conflict has negative long-term consequences in local fiscal capacity. More precisely, municipalities affected by La Violencia experienced an average reduction of 10.3% in their tax revenue and a fall of 2.8 percentage points on their ratio of taxes to total revenue. Effects lasted for more than a decade and are only partially explained by a population and economic activity downturn. These results are consistent with previous evidence indicating a negative effect of violence on tax collection efficiency at the local level.
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Castro, Carolina Robledo, Piedad Rocio Lerma-Castaño, and Luis Gerardo Pachón-Ospina. Rehabilitation programs based on computational systems: effects in the executive functions in young and middle adulthood: A scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0052.
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Review question / Objective: To identify empirical studies that measured the feasibility and effect of computer-based executive function stimulation and rehabilitation programs in the young and middle adult population. Background: Reviews that evaluate the effectiveness of computerized cognitive training programs on executive functions in different population groups have shown contradictory results, to a certain extent associated with the methodological characteristics of said studies (Gates et al., 2019; 2020); most of them These reviews have focused on older adults (Ten Brinke et al., 2020; Yoo et al., 2015) with stroke sequelae, and adults with cognitive impairment. These studies have found improvements in general cognitive function in older adults (Ten Brinke et al., 2020); however, the effect on executive functions have not been studied. Only one review was carried out on the average adult (Gates et al., 2019); the authors restricted the search to interventions with more than 12 weeks and only found one article with eligibility criteria. Their work concluded that computerized cognitive training in midlife demonstrated lasting effects on general cognitive function after 12 weeks of training and on memory after 24 weeks of training.
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Rao, Krishna D., Andrés I. Vecino Ortiz, Tim Roberton, Angélica Lopez Hernandez, and Caitlin Noonan. Open configuration options Future Health Spending in Latin America and the Caribbean: Health Expenditure Projections & Scenario Analysis. Inter-American Development Bank, April 2022. http://dx.doi.org/10.18235/0004185.
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Latin American and Caribbean countries will face significant increases in future health expenditures. A variety of factors are responsible - population growth and aging, the epidemiological transition to noncommunicable diseases (NCDs), and economic growth and technology, among others. Increasing health expenditures are particularly concerning to countries in Latin America and the Caribbean (LAC) given growing levels of debt, insufficient fiscal revenues, and high out-of-pocket payments. The projected average annual per capita CHE growth rate from 2018-2050 is slightly higher in Latin American countries (3.2%) than in the Caribbean (2.4%). The share of health expenditure in GDP is projected to increase to 2030 in all LAC countries except for Guyana. The effect of demographics and epidemiology on health spending growth are more modest. Among strategies to control NCD risk factors, a focus on hypertension control generally had the strongest effect on restraining CHE growth except in countries where smoking is particularly prevalent. The main driver of health expenditure growth is economic growth and technology, demonstrating the importance of adopting policies such as explicit prioritization systems and benefit plans that establish common rules for payers and providers that encourage cost-effective decisions. The underlying model for making projections and analyzing alternative scenarios is publicly available.
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Gallien, Max, Giovanni Occhiali, and Hana Ross. An Overlooked Market: Loose Cigarettes, Informal Vendors, and Their Implications for Tobacco Taxation. Institute of Development Studies, February 2023. http://dx.doi.org/10.19088/ictd.2023.004.
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Recent years have seen the development of a substantial literature on tobacco taxation that has both noted its effectiveness as a tobacco control tool, and provided modelling of its implications. However, studies of tobacco taxation and tobacco consumption have largely ignored a crucial aspect of the market for cigarettes in many low- and middle-income countries – the prevalence of loose (single) cigarettes being sold, rather than cigarette packs. We argue that ignoring this market leaves room for unexpected dynamics and unintended policy effects. We develop this argument by establishing four aspects of the market for loose cigarettes. First, we show that it is sizeable and widespread. Second, we note that it has a consumer base that is on average poorer and younger than the overall population of smokers. Third, we show that the price dynamics for loose cigarettes are different to those for packs, that the price for a loose cigarette is typically higher than the equivalent per-cigarette price of a cigarette bought in a pack, and that the price of loose cigarettes and cigarette packs do not always move in parallel. Fourth, based on these dynamics, we show how the features of the loose cigarette market can affect the effectiveness of tobacco control policy, and in particular tobacco taxation. For example, we highlight that insufficient attention to the market for loose cigarettes might lead to a lower than anticipated effect of tax increases on demand, or might result in tax increases not being passed on to the consumers of loose cigarettes at all. Consequently, in order to ensure that tobacco tax increases immediately feed through to all consumers, policymakers in countries with markets for loose cigarettes should prioritise large rather than incremental tax increases.
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Manulis, Shulamit, Christine D. Smart, Isaac Barash, Guido Sessa, and Harvey C. Hoch. Molecular Interactions of Clavibacter michiganensis subsp. michiganensis with Tomato. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7697113.bard.
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Clavibacter michiganensis subsp. michiganensis (Cmm), the causal agent of bacterial wilt and canker of tomato, is the most destructive bacterial disease of tomato causing substantial economic losses in Israel, the U.S.A. and worldwide. The molecular strategies that allow Cmm, a Gram-positive bacterium, to develop a successful infection in tomato plants are largely unknown. The goal of the project was to elucidate the molecular interactions between Cmmand tomato. The first objective was to analyze gene expression profiles of susceptible tomato plants infected with pathogenic and endophytic Cmmstrains. Microarray analysis identified 122 genes that were differentially expressed during early stages of infection. Cmm activated typical basal defense responses in the host including induction of defense-related genes, production of scavenging of free oxygen radicals, enhanced protein turnover and hormone synthesis. Proteomic investigation of the Cmm-tomato interaction was performed with Multi-Dimensional Protein Identification Technology (MudPIT) and mass spectroscopy. A wide range of enzymes secreted by Cmm382, including cell-wall degrading enzymes and a large group of serine proteases from different families were identified in the xylem sap of infected tomato. Based on proteomic results, the expression pattern of selected bacterial virulence genes and plant defense genes were examined by qRT-PCR. Expression of the plasmid-borne cellulase (celA), serine protease (pat-1) and serine proteases residing on the chp/tomA pathogenicity island (chpCandppaA), were significantly induced within 96 hr after inoculation. Transcription of chromosomal genes involved in cell wall degradation (i.e., pelA1, celB, xysA and xysB) was also induced in early infection stages. The second objective was to identify by VIGS technology host genes affecting Cmm multiplication and appearance of disease symptoms in plant. VIGS screening showed that out of 160 tomato genes, which could be involved in defense-related signaling, suppression of 14 genes led to increase host susceptibility. Noteworthy are the genes Snakin-2 (inhibitor of Cmm growth) and extensin-like protein (ELP) involved in cell wall fortification. To further test the significance of Snakin -2 and ELP in resistance towards Cmm, transgenic tomato plants over-expressing the two genes were generated. These plants showed partial resistance to Cmm resulting in a significant delay of the wilt symptoms and reduction in size of canker lesion compared to control. Furthermore, colonization of the transgenic plants was significantly lower. The third objective was to assess the involvement of ethylene (ET), jasmonate (JA) and salicylic acid (SA) in Cmm infection. Microarray and proteomic studies showed the induction of enzymes involved in ET and JA biosynthesis. Cmm promoted ET production 8 days after inoculation and SIACO, a key enzyme of ET biosynthesis, was upregulated. Inoculation of the tomato mutants Never ripe (Nr) impaired in ET perception and transgenic plants with reduced ET synthesis significantly delayed wilt symptoms as compared to the wild-type plants. The retarded wilting in Nr plants was shown to be a specific effect of ET insensitivity and was not due to altered expression of defense related genes, reduced bacterial population or decrease in ethylene biosynthesis . In contrast, infection of various tomato mutants impaired in JA biosynthesis (e.g., def1, acx1) and JA insensitive mutant (jai1) yielded unequivocal results. The fourth objective was to determine the role of cell wall degrading enzymes produced by Cmm in xylem colonization and symptoms development. A significance increase (2 to 7 fold) in expression of cellulases (CelA, CelB), pectate lyases (PelA1, PelA2), polygalacturonase and xylanases (XylA, XylB) was detected by qRT-PCR and by proteomic analysis of the xylem sap. However, with the exception of CelA, whose inactivation led to reduced wilt symptoms, inactivation of any of the other cell wall degrading enzymes did not lead to reduced virulence. Results achieved emphasized the complexity involved in Cmm-tomato interactions. Nevertheless they provide the basis for additional research which will unravel the mechanism of Cmm pathogenicity and formulating disease control measures.
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Payment Systems Report - June of 2021. Banco de la República, February 2022. http://dx.doi.org/10.32468/rept-sist-pag.eng.2021.
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Banco de la República provides a comprehensive overview of Colombia’s finan¬cial infrastructure in its Payment Systems Report, which is an important product of the work it does to oversee that infrastructure. The figures published in this edition of the report are for the year 2020, a pandemic period in which the con¬tainment measures designed and adopted to alleviate the strain on the health system led to a sharp reduction in economic activity and consumption in Colom¬bia, as was the case in most countries. At the start of the pandemic, the Board of Directors of Banco de la República adopted decisions that were necessary to supply the market with ample liquid¬ity in pesos and US dollars to guarantee market stability, protect the payment system and preserve the supply of credit. The pronounced growth in mone¬tary aggregates reflected an increased preference for liquidity, which Banco de la República addressed at the right time. These decisions were implemented through operations that were cleared and settled via the financial infrastructure. The second section of this report, following the introduction, offers an analysis of how the various financial infrastructures in Colombia have evolved and per¬formed. One of the highlights is the large-value payment system (CUD), which registered more momentum in 2020 than during the previous year, mainly be¬cause of an increase in average daily remunerated deposits made with Banco de la República by the General Directorate of Public Credit and the National Treasury (DGCPTN), as well as more activity in the sell/buy-back market with sovereign debt. Consequently, with more activity in the CUD, the Central Securi¬ties Depository (DCV) experienced an added impetus sparked by an increase in the money market for bonds and securities placed on the primary market by the national government. The value of operations cleared and settled through the Colombian Central Counterparty (CRCC) continues to grow, propelled largely by peso/dollar non-deliverable forward (NDF) contracts. With respect to the CRCC, it is important to note this clearing house has been in charge of managing risks and clearing and settling operations in the peso/dollar spot market since the end of last year, following its merger with the Foreign Exchange Clearing House of Colombia (CCDC). Since the final quarter of 2020, the CRCC has also been re¬sponsible for clearing and settlement in the equities market, which was former¬ly done by the Colombian Stock Exchange (BVC). The third section of this report provides an all-inclusive view of payments in the market for goods and services; namely, transactions carried out by members of the public and non-financial institutions. During the pandemic, inter- and intra-bank electronic funds transfers, which originate mostly with companies, increased in both the number and value of transactions with respect to 2019. However, debit and credit card payments, which are made largely by private citizens, declined compared to 2019. The incidence of payment by check contin¬ue to drop, exhibiting quite a pronounced downward trend during the past last year. To supplement to the information on electronic funds transfers, section three includes a segment (Box 4) characterizing the population with savings and checking accounts, based on data from a survey by Banco de la República con-cerning the perception of the use of payment instruments in 2019. There also is segment (Box 2) on the growth in transactions with a mobile wallet provided by a company specialized in electronic deposits and payments (Sedpe). It shows the number of users and the value of their transactions have increased since the wallet was introduced in late 2017, particularly during the pandemic. In addition, there is a diagnosis of the effects of the pandemic on the payment patterns of the population, based on data related to the use of cash in circu¬lation, payments with electronic instruments, and consumption and consumer confidence. The conclusion is that the collapse in the consumer confidence in¬dex and the drop in private consumption led to changes in the public’s pay¬ment patterns. Credit and debit card purchases were down, while payments for goods and services through electronic funds transfers increased. These findings, coupled with the considerable increase in cash in circulation, might indicate a possible precautionary cash hoarding by individuals and more use of cash as a payment instrument. There is also a segment (in Focus 3) on the major changes introduced in regulations on the retail-value payment system in Colombia, as provided for in Decree 1692 of December 2020. The fourth section of this report refers to the important innovations and tech¬nological changes that have occurred in the retail-value payment system. Four themes are highlighted in this respect. The first is a key point in building the financial infrastructure for instant payments. It involves of the design and im¬plementation of overlay schemes, a technological development that allows the various participants in the payment chain to communicate openly. The result is a high degree of interoperability among the different payment service providers. The second topic explores developments in the international debate on central bank digital currency (CBDC). The purpose is to understand how it could impact the retail-value payment system and the use of cash if it were to be issued. The third topic is related to new forms of payment initiation, such as QR codes, bio¬metrics or near field communication (NFC) technology. These seemingly small changes can have a major impact on the user’s experience with the retail-value payment system. The fourth theme is the growth in payments via mobile tele¬phone and the internet. The report ends in section five with a review of two papers on applied research done at Banco de la República in 2020. The first analyzes the extent of the CRCC’s capital, acknowledging the relevant role this infrastructure has acquired in pro¬viding clearing and settlement services for various financial markets in Colom¬bia. The capital requirements defined for central counterparties in some jurisdic¬tions are explored, and the risks to be hedged are identified from the standpoint of the service these type of institutions offer to the market and those associated with their corporate activity. The CRCC’s capital levels are analyzed in light of what has been observed in the European Union’s regulations, and the conclusion is that the CRCC has a scheme of security rings very similar to those applied internationally and the extent of its capital exceeds what is stipulated in Colombian regulations, being sufficient to hedge other risks. The second study presents an algorithm used to identify and quantify the liquidity sources that CUD’s participants use under normal conditions to meet their daily obligations in the local financial market. This algorithm can be used as a tool to monitor intraday liquidity. Leonardo Villar Gómez Governor