Evaluation Review最新文献

This study examines the relationship between foreign direct investment (FDI), institutional quality and human development) in host countries from 2002 to 2019, using the Human Development Index [HDI] as the measure of human development. This study utilized a panel dataset of 143 countries, including both developed and developing economies, over a 17-year period. Additionally, the study employed a GMM (generalized method of moments) estimator to address unobservable heterogeneity and simultaneity. This study reveals a significant positive relationship between FDI and human development, with a stronger effect observed in developing countries compared to in developed countries. Notably, the impact of FDI-HDI nexus is larger in countries with moderately high-quality institutions, irrespective of their income level. Furthermore, good governance plays a crucial role in enhancing human development, as developing economies with high governance quality experience a greater impact of FDI on HDI compared to other countries. The findings of this study suggest that attracting FDI can be beneficial for enhancing the HDI, especially in developing countries. Additionally, the study highlights governance as a moderating factor in the relationship between FDI and HDI. Improving governance quality can enhance the positive impact of FDI on human development in host countries, especially in developing countries.

To improve one of the lowest rates of literacy and numeracy in the world, the government of Brazil has targeted public education reform, given the strong link between an educated population and economic growth. This study examines the academic performance of the Brazilian public primary school system. It addresses the empirical shortcomings of prior research to examine the dynamics of the relationship between academic performance scores and several demographic and institutional variables, such as socioeconomic characteristics, variations in school infrastructure and school complexity, and teachers' human capital. We employed quantile regression to explore the determinants of academic performance across 35,490 schools in rural and urban environments in Brazil. The dependent variable in our analysis captures the academic performance score, as measured by Brazil's education authorities, of each school in our dataset. The model includes several education-related indices used in prior research and, as explanatory factors, measures of teachers' human capital and the students' socioeconomic level, which synthesizes information on parents' education and household income. The results suggest that several institutional variables, including access to school libraries, computer facilities, projectors, and televisions, are positively and significantly related to the academic performance of primary students in Brazil's system of public education. Furthermore, students' socioeconomic level is positively associated with their academic performance.

Successfully designing and implementing a program is complex; it requires a reflexive balance between the available resources and the priorities of various stakeholders, both of which change over time. Logic models are theory-based evaluation approaches used to identify and address key challenges of a program. This article describes the process of building a logic model on advanced theories in complexity studies. The models aim to support a province-wide multispecies monitoring system of antimicrobial use (AMU), designed in collaboration with the animal health sector in Quebec (Canada). Based on a rigorous theoretical foundation, the logic model is built in three steps: (1) mapping, a narrative review of literature on similar programs in other jurisdictions; (2) framing, iterative consultations with project members to elaborate the logic model; (3) shaping, hypotheses based on the logic model. The model emerges from the reflexive balancing of current scientific knowledge and empirical insights to gather relevant information about stakeholders from interdisciplinary experts that led a 3-year consensus-building process within the community. Recognizing the challenge of unpacking theories for practical use, we illustrate how the process of an "open" logic model building could enable governance coordination in complex processes. Logic models are useful for evaluating public, private, and academic partnerships in One Health programs that characterize an adaptive governance process.

Sustainable agriculture is crucial for achieving SDG2 and building a resilient climate-food system. This study provides a nuanced understanding of factors that influence the adoption of precision agriculture technology by Vietnamese smallholder rice farmers as a sustainable agricultural solution. The study's objectives are: (1) to provide a nuanced understanding of factors that influence adoption of precision agriculture technology by Vietnamese smallholder rice farmers; and (2) to identify factors associated with agricultural practice in Vietnam that may impact the adoption of precision agriculture technology. The study conducted 35 semi-structured interviews with smallholder rice farmers and agriculture experts. Data were analyzed using iterative thematic analysis. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used for empirical analysis. The UTAUT constructs of Performance expectancy, Effort expectancy, Government support, and Social influence were found to be determinants of adoption. Trialability and Observability impact Effort expectancy and Performance expectancy. We also discovered that the performance of agricultural cooperatives and support of lead firms play a crucial role in facilitating agricultural technology adoption by Vietnamese smallholder rice farmers. The results confirm the applicability of UTAUT in defining smallholders' behavioural intention and predicting agricultural technology adoption. The study also provides constructive feedback on the UTAUT model by highlighting the effect of agricultural cooperatives' performance as innovation intermediaries and of the support of lead firms.

The COVID-19 pandemic has exacerbated the local debt in Spanish municipalities with negative consequences on the macroeconomic financial stability at national and eurozone level. The main objective is to identify the causes of public debt per capita in four groups of Spanish municipalities according to size. It is based on a quantitative analysis based on correlational and causal-comparative approaches. It consists in the construction of panel quantile regressions (MMQ) and mean group (ME) estimators to explain public debt per capita in Spanish municipalities. Moreover, the Juodis test for causality analysis in panel data is applied. The research is constructed around various types of potential determinants related to economic factors (GDP per capita and unemployment rate), demographic factors (population under 15 and population over 65), and political factors (political party, ideology, and political strength). The results based on MMQR for the period 2011-2020 indicate common factors that reduce local debt (short-run economic growth), but also differences between clusters in what concerns factors that increase or decrease the debt. The Juodis et al. (2021) test shows that growth and unemployment are factors that influence the level of public debt in all groups of municipalities except one (5001-20,000 inhabitants) where political party affects debt. These empirical findings support policy proposals to reduce the local debt in Spanish municipalities. The main initiatives to reduce debt should be based on the promotion of economic growth and creation of new jobs, especially for young people.

This paper describes a process evaluation of a 'wise' intervention that took place in six acute care units in two medical-surgical teaching hospitals in the United States during 2016-2017. 'Wise' interventions are short, inexpensive interventions that depend on triggering specific psychological mechanisms to achieve behaviour change. This study sought to increase the hand hygiene compliance (HHC) rates before entering a patient's room among nurses. The intervention centred on the use of threat to professional identity to prompt improved HHC. Through questionnaires administered to intervention participants and the implementation facilitator, together with independent observation of intervention delivery, we examined whether the steps in the Theory of Change occurred as expected. We found that aspects of the implementation-including mode of delivery, use of incentives, and how nurses were recruited and complied with the intervention-affected reach and likely effectiveness. While components of the intervention's mechanisms of impact-such as the element of surprise-were successful, they ultimately did not translate into performance of the target behaviour. Performance was also not affected by use of an implementation intention as repeated performance of HHC over years of being a nurse has likely already established well-ingrained practices. Context did have an effect; the safety culture of the units, the involvement of the Nurse Managers, the level of accountability for HHC in each unit, and the hospitals themselves all influenced levels of engagement. These conclusions should have implications for those interested in the applicability of 'wise' interventions and those seeking to improve HHC in hospitals.

Background: While randomized controlled trials (RCTs) are typically considered the gold standard of program evaluation, they are infrequently chosen by public sector leaders, defined as government and nonprofit decision-makers, when an impact evaluation is required. Objectives: This study provides descriptive evidence on RCT aversion among public sector leaders and attempts to understand what factors affect their likelihood of choosing RCTs for impact evaluations. Research Design: The authors ask if public sector leaders follow similar preference patterns found among non-public sector leaders when choosing either an RCT or a quasi-experimental design and use a survey experiment to determine which factors affect the RCT choice. Subjects: The study sample includes 2050 public sector leaders and a comparison group of 2060 respondents who do not lead public sector organizations. Measures: The primary outcome measure is selecting an RCT as the preferred evaluation option. Results: When asked to make a decision about an impact evaluation, the majority of people do not choose an RCT. While also averse to RCTs, public sector leaders are about 13% more likely to prefer a RCT to a quasi-experimental evaluation compared to the general population. Public sector leaders are less likely to use RCTs for evaluations of more intense interventions, potentially because they are perceived to be superior to the options available for the control group. Conclusion: Funders should be aware that when given a choice, public sector leaders prefer other options to RCTs. Greater awareness of the benefits of RCTs could increase their use in the public sector.

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India's booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors' early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.