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This Chapter presents a set of quantitative modelling approaches, connected to various steps of the policy cycle, that aim at helping policy-makers and all social actors involved, by providing a scientific sound framework for a systematic, coherent and transparent analysis. Practical guidelines for structuring policy problems by using uncertainty a...
Contexts in source publication
Context 1
... contexts (see, e.g., Figueira et al., 2016). SMCE accomplishes the goals of being inter/multidisciplinary (with respect to the research team), participatory (with respect to the community) and transparent (since all criteria are presented in their original form without any transformations in money, energy or whatever common measurement rod) (see Fig. 3). In operational terms, the application of an SMCE framework involves the following main ...
Context 2
... contexts (see, e.g., Figueira et al., 2016). SMCE accomplishes the goals of being inter/multidisciplinary (with respect to the research team), participatory (with respect to the community) and transparent (since all criteria are presented in their original form without any transformations in money, energy or whatever common measurement rod) (see Fig. 3). In operational terms, the application of an SMCE framework involves the following main ...
Citations
... Research can and shall play a vital role in supporting policy makers, industry, farmers, and other stakeholders in establishing more sustainable production systems through the provision of evidence-based information from ex-ante impact assessments and ex-post policy assessments (e.g. Head, 2010, Munda et al., 2020. ...
... More specifically, combining such tools in a "policy cycle" can help to continuously adapt policies and regulations to quickly changing realities. This gives stakeholders the possibility to react flexibly to different regional and production contexts (Cagliero et al., 2021) while increasing effectiveness, consistency, and transparency of assessments (Munda et al., 2020). ...
... Yet, combinations of ex-ante assessments with ex-post analyses are still rare, despite their potential to improve information for stakeholders and effectiveness and efficiency of assessment tools (e.g. Head, 2010, Munda et al., 2020. ...
Sustainable intensification of agriculture requires the adoption of new production techniques, tools, and programs on a large scale. This implies substantial shifts in established ways of farming under uncertain information about potential economic and environmental outcomes. Research can support stakeholders such as farmers, industry, and policy in this transformation by providing evidence-based information. The provision of such information can be improved by combining ex-ante and ex-post assessment tools at different stages of policy cycles and projects. We here present a unique combination of ex-ante bio-economic modelling analysis and ex-post econometric analyses based on survey data using the example of a novel pesticide-free wheat production program in Switzerland. We exemplify how ex-ante and ex-post evaluation can be combined to increase robustness of results for stakeholders, e.g. on yield losses from pesticide-free production or farmer typologies important for adoption. Further, we show how their alignment can improve future assessments in project and policy cycles, e.g. through the choice of suitable variables explaining farmer decision-making and priors on the distribution of their characteristics. Despite the identified synergies between assessments, we also find that their integration is limited by timing and information requirements of stakeholders at different stages in the project. Finally, potential synergies in our case study strongly depended on the type of ex-ante models chosen for evaluation and their alignment with ex-post methods. Especially integrating farmer behaviour in ex-ante assessments seems to be crucial to arrive at holistic evaluations of large-scale programs for sustainable agricultural practices and providing useful information to stakeholders.
... On the one hand, there are authors like Marttunen et al. (2019) who advocate for the inclusion of not or negatively correlated indicators as they can be more informative for a decision since they bring unique perspectives on the aspects under evaluation. On the other hand, there are other authors like Munda et al. (2020) who warn about the risk of including indicators with low or negative correlations as their information might not be represented in the CI. Our research advocates for a balanced reasoning between these perspectives as follows. ...
Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information to the CI. A measure of information transfer from each indicator is proposed along with two weight-optimization methods, which allow the weights to be adjusted to achieve either a targeted or maximized information transfer. The tools presented in this paper are applied to a case study for resilience assessment of energy systems, demonstrating how they can support the tailored development of CIs. These findings enable analysts bridging the statistical properties of the index with the weighting preferences from the stakeholders. They can thus choose a weighting scheme and possibly modify the index while achieving a more consistent (by correlation) index.
... 11 Multicriteria decision analysis can also be used to help reach consensus on the impacts on multiple outcomes; however, consideration needs to be given to consistency across decisions and whether opportunity costs are appropriately considered. [42][43][44] An alternative approach is to define a single (universal) outcome measure; examples include approaches such at the QALY, capability approaches, or extended QALYs. [45][46][47][48] However, such an approach imposes a value judgment that all issues of consequence and tradeoffs between them are appropriately captured within the measure. ...
Background
Policy evaluations often focus on ex post estimation of causal effects on short-term surrogate outcomes. The value of such information is limited for decision making, as the failure to reflect policy-relevant outcomes and disregard for opportunity costs prohibits the assessment of value for money. Further, these evaluations do not always consider all relevant evidence, other courses of action, or decision uncertainty.
Methods
In this article, we explore how policy evaluation could better meet the needs of decision making. We begin by defining the evidence required to inform decision making. We then conduct a literature review of challenges in evaluating policies. Finally, we highlight potential methods available to help address these challenges.
Results
The evidence required to inform decision making includes the impacts on the policy-relevant outcomes, the costs and associated opportunity costs, and the consequences of uncertainty. Challenges in evaluating health policies are described using 8 categories: 1) valuation space; 2) comparators; 3) time of evaluation; 4) mechanisms of action; 5) effects; 6) resources, constraints, and opportunity costs; 7) fidelity, adaptation, and level of implementation; and 8) generalizability and external validity. Methods from a broad set of disciplines are available to improve policy evaluation, relating to causal inference, decision-analytic modeling, theory of change, realist evaluation, and structured expert elicitation.
Limitations
The targeted review may not identify all possible challenges, and the methods covered are not exhaustive.
Conclusions
Evaluations should provide appropriate evidence to inform decision making. There are challenges in evaluating policies, but methods from multiple disciplines are available to address these challenges.
Implications
Evaluators need to carefully consider the decision being informed, the necessary evidence to inform it, and the appropriate methods. [Box: see text]
This study provides an in-depth bibliometric assessment of the Library and Information Science (LIS) sector within the ASEAN region from 2018 to 2022, leveraging data from the Scopus core collection. The overarching goal was to uncover current research patterns, collaborations, and productivity, subsequently crafting a strategic blueprint to enhance ASEAN LIS research’s global prominence. Methodologically, the research employed Scopus All Science Journal Classification Codes (ASJC) for LIS to retrieve a comprehensive set of relevant publications. Out of an initial count of 65,822 documents, refined search parameters narrowed this to 2768 outputs, or 4.2% of total LIS documents, for the specified timeframe and region. Key observations from the data depict a significant shift in 2020, likely influenced by the COVID-19 pandemic, underscoring the importance of timely, relevant research. Countries such as Malaysia and Singapore emerged as leading contributors, emphasizing quality research, while Indonesia’s substantial output did not necessarily guarantee citation impact. The study accentuates the increasing importance of interdisciplinary collaborations, as evident from platforms like the International Journal of Information Management. For ASEAN’s sustained growth in the global LIS arena, the emphasis should be on leveraging individual nation strengths, reinforcing international ties, and prioritizing globally relevant research themes.
Introduction:
During recent disease outbreaks, quantitative research has been used to investigate intervention scenarios while accounting for local epidemiological, social, and clinical context. Despite the value of such work, few documented research efforts have been observed to originate from low-income countries. This study aimed to assess barriers that may be limiting the awareness and conduct of quantitative research among Liberian public health graduate students.
Methods:
A semi-structured questionnaire was administered September-November 2021 to Master's in Public Health (MPH) students in Liberia. Potential barriers around technology access, understanding of quantitative science, and availability of mentorship were interrogated. Associations between barriers and self-reported likelihood of conducting quantitative research within six months of the investigation period were evaluated using ordinal logistic regression.
Results:
Among 120 participating MPH students, 86% reported owning a personal computer, but 18.4% and 39.4% had machines with malfunctioning hardware and/or with battery power lasting ≤2 hours, respectively. On average, students reported having poor internet network 3.4 days weekly. 47% reported never using any computer software for analysis, and 46% reported no specific knowledge on statistical analysis. Students indicated spending a median 30 minutes per week reading scientific articles. Moreover, 50% had no access to quantitative research mentors. Despite barriers, 59% indicated they were very likely to undertake quantitative research in the next 6 months; only 7% indicated they were not at all likely. Computer ownership was found to be statistically significantly associated with higher likelihood of conducting quantitative research in the multivariable analysis (aOR: 4.90,95% CI: 1.54-16.3).
Conclusion:
The high likelihood of conducting quantitative research among MPH students contrasts with limitations around computing capacity, awareness of research tools/methods, and access to mentorship. To promote rigorous analytical research in Liberia, there is a need for systematic measures to enhance capacity for diverse quantitative methods through efforts sensitive to the local research environment.
