Policy Bias Research Articles

Based on the Analysis of Existing Economic Policies, Compares the Response of Economic Policies to Population Ageing between the UK and Japan

In the context of the changing age structure of the global population, many countries are experiencing the economic consequences of this problem, such as severe dependency ratios and a reduction of qualified workers, which will harm the country's potential economic growth capacity. In order to mitigate the negative effects on the economy, both Japan and the United Kingdom have developed economic policies to deal with population ageing. These policies all revolve around healthcare, pension and employment. These economic policies are similar in some respects, such as in employment, where the two countries try to raise the retirement age of the elderly from various aspects, but show different policy biases in further policy-making for different reasons, such as different cultural backgrounds. This paper analyzes these similarities and differences by reviewing past literature and policy cases, making preliminary conjectures and summaries on the causes of these characteristics through past literature, and analyzing the effectiveness of some of these policies. Finally, we conclude from the study that these policy differences are due to different cultural, and historical backgrounds and current economic situations between the two countries.

Simmering Storm in the East China Sea: Shifting Dynamics in the Great Power Rivalries of East Asia

The East China Sea (ECS) is one of the region’s most significant and oft-times under-addressed potential flashpoints. In this article we take a holistic approach and reexamine the recent actions of East Asia’s two most significant powers, China and Japan, in relation to this important body of maritime space, as well as those of the United States (US), as the incumbent regional hegemon. Specifically, we examine the efficacy of each. This highlights a dynamism in Chinese actions, as well as elucidating how the US and Japan have come to adopt policies that are of dubious efficacy as a means of alleviating or nullifying the escalation of unwanted tensions across the ECS. In so doing, we draw upon two key concepts, immobilism and confirmation bias, that help us to understand why the US and Japan are failing to achieve their assumed objectives of regional stability and the maintenance of the status quo. Ultimately, we argue that a combination of immobilism and confirmation bias in American and Japanese foreign policy, in response to the greater dynamism and adaptability of regional great powers such as China, has led to latent changes in the status quo that risk undermining stability across the ECS.

Globalization after De-globalization

ABSTRACT: Globalization began in the late 1970s, shifted into "hyperglobalization" in the first decade of the twenty-first century, hit "peak globalization" around 2011, and has in the past 10 years given way to "de-globalization," characterized by nationalism and protectionism. The rules of globalization were built on a theory of competitive markets, while the world was in fact riven with growing asymmetries of power and a strong policy bias toward capital. This bias must be eliminated if the global economy is to become more democratic and sustainable as we emerge from this moment of de-globalization.

Assessing the Relationship between Political Financing and Policy Bias in B40 Welfare Policies during COVID-19

This conceptual paper delves into the intricate relationship between political financing and policy bias in the implementation of welfare policies for the B40 (Below 40 is the 40% of the total population that earns the lowest tier of household income compared to the first group of T20-Top 20% and second category M40-Middle 40%) cluster in Kuala Lumpur Malaysia amidst the COVID-19 pandemic. The B40 cluster comprises the most economically vulnerable segment of the population residing in public housing (the heavily subsidized government-owned residential areas for underprivileged citizens with certain household income categories). This study aims to unravel how political financing influences the formulation and execution of welfare policies, potentially leading to bias in resource allocation and support provision. Through an exhaustive review of existing scholarly literature and analysis of pertinent case studies, this paper embarks on examining the degree to which political financing impacts the decision-making process concerning welfare policies. The analysis focuses on identifying potential biases in policy formulation that may favor the interests of political entities providing financial backing to policymakers. Furthermore, the study also explores the implications of such biases on the equitable distribution of resources to the B40 cluster during the global COVID-19 crisis. The conceptual framework employed in this paper draws from theories of political economy, corruption, and public policy. By dissecting the extant body of literature, the paper aims to offer a nuanced comprehension of the dynamics between political financing and policy bias in the realm of welfare policies for the B40 cluster. The eventual findings are poised to enrich the theoretical understanding of the influence exerted by political financing on the formulation and execution of welfare policies, particularly during periods of global crisis. By identifying policy biases stemming from political financing, this study aspires to catalyze critical discussions on the imperative of transparent and accountable decision-making processes. Such deliberations are vital to ensuring the fair and effective implementation of welfare policies for the B40 cluster in Kuala Lumpur amidst the COVID-19 pandemic. This paper thus serves as a clarion call for enhanced governance mechanisms and institutional reforms to uphold the principles of fairness, equity, and social justice in welfare policy formulation and implementation. This study contributes to the existing literature by shedding light on the nexus between political financing and policy bias in the context of welfare policies for vulnerable populations. It underscores the importance of addressing systemic flaws in governance and decision-making processes to mitigate the adverse effects of political influence on welfare policy outcomes. Moreover, the findings of this study have practical implications for policymakers, advocacy groups, civil society organizations and other stakeholders involved in shaping welfare policy agendas and advocating for reform initiatives. In conclusion, this paper calls for a concerted effort to promote transparency, accountability, and integrity in the formulation and implementation of welfare policies. By fostering a culture of ethical governance and democratic participation, policymakers can ensure that welfare programs serve the interests of the most vulnerable members of society and contribute to equitable and sustainable development outcomes.

Understanding world models through multi-step pruning policy via reinforcement learning

In model-based reinforcement learning, the conventional approach to addressing world model bias is to use gradient optimization methods. However, using a singular policy from gradient optimization methods in response to a world model bias inevitably results in an inherently biased policy. This is because of constraints on the imperfect and dynamic data of state-action pairs. The gap between the world model and the real environment can never be completely eliminated. This article introduces a novel approach that explores a variety of policies instead of focusing on either world model bias or singular policy bias. Specifically, we introduce the Multi-Step Pruning Policy (MSPP), which aims to reduce redundant actions and compress the action and state spaces. This approach encourages a different perspective within the same world model. To achieve this, we use multiple pruning policies in parallel and integrate their outputs using the cross-entropy method. Additionally, we provide a convergence analysis of the pruning policy theory in tabular form and an updated parameter theoretical framework. In the experimental section, the newly proposed MSPP method demonstrates a comprehensive understanding of the world model and outperforms existing state-of-the-art model-based reinforcement learning baseline techniques.

An Inquiry Into the Use of Generative AI and Its Implications in Education

The emergence of generative AI technologies has provoked considerable debate among educators regarding their role in education. This study is an investigation of the benefits, disadvantages, and potential strategies for integrating generative AI in educational settings by analyzing societal impacts based on a literature review. We have surveyed the influence of generative AI in education through sources from peer-reviewed journals. The main findings show that generative AI can enhance accessibility and customization in learning for individual learners' needs and pacing. But there are problems with algorithmic biases, discrimination, and data privacy issues, too. This study advocates for mitigating bias, data transparency, and promotion and evaluation of AI policy and research. Generative AI in education hinges on how it is responsibly integrated and observes ethical guidelines, by way of the constant assessment that will guarantee its potential in revolutionizing learning.

Analysis of multi-factors affecting resource consumption efficiency in Vietnam

Theprimary objective of this study is todelve into the factors potentially influencing resource utilisationefficiency at the provincial level in Vietnam. We leverage 185 data points gathered from 63 Vietnamese cities or provinces, obtained from two key sources: the General Statistical Office of Vietnam and the Ministry of Information and Communications Vietnam. The collected data was subjected to analysis using an empirical econometric model that incorporates time-series characteristics to explore the connections between these potential determinants of resource consumption efficiency. The analysis revealed a strong correlation between resource utilisationefficiency and the quality of digital transformation, logistics performance, and various aspects of public governance,including accountability to higher authorities, control of corruption, administrative procedures, and public service delivery. Additionally, the provincial competitiveness index, encompassing factors like entry costs, policy bias, and labourpolicy, also demonstrated a significant association. These findings highlight a clear link between effective public governance and efficient resource utilisationin Vietnamese provinces. This suggests that robust public governance systems, combined with advancements in digitalisationand logistics, can play a crucial role in optimisingresource utilisationwithin Vietnamese provinces.

Systemic barriers preventing farmer engagement in the agricultural climate transition: a qualitative study

AbstractFarmers and the agricultural sector are facing an uncertain future, due to rapidly changing agricultural policy and a nexus of commercial, environmental and biodiversity challenges brought on by anthropogenic climate change. Balancing the interests of the farming community and natural environment is generating organisational and personal conflict, rather than effective agricultural adaption and a focus on the net zero transition. In this paper, we consider how farmers and the wider farming community can participate in engagement and decision-making for the transition, on their own terms, whilst ensuring social justice for fair and equitable decision-making. We discuss findings from our research project, that used questionnaires and follow up semi-structured interviews with farmers and farming groups to reveal systemic issues that prevent farmers engaging with the climate transition to net zero during their everyday practice. We highlight the structural relationships that form these barriers and frustrate farmers when designing and implementing farm interventions to agricultural systems during times of transition. Farmers have highlighted barriers to be associated with policy, (uncertain government strategy, landowner bias in government policy and tenancy restrictions, government bureaucracy—particularly environment schemes), commercial (market forces—downward pressure from supermarkets on price points, availability of labour due to seasonal migration restrictions, increasing cost of labour) and interpersonal (succession—uncertain future of the family farm due to limited income). This frustrates the ability of farmers to address climate change through farming practices and undermines the implementation of innovative engagement practices, such as co-produced interventions, in policy and decision-making settings.

Sustainable Innovation in the Biopharmaceutical Industry: An Analysis of the Impact of Policy Configuration

To achieve sustainable development, it is necessary to consider the business model adjustment of the industry in advance, from the development stage to the mature stage. In China, strategic emerging industries are industries that achieve technological breakthroughs, but such industries often have the characteristics of high investment, high technology, high risk, high returns, and long research and development times. This type of industry relies heavily on national resource support from the exploration period to the development period, but its high-profit characteristics also attract policy bias from the governments of other countries internationally. Therefore, understanding the resource requirements of such industries in different periods in advance will help the government to adjust resource allocation and strategic layout through policy means. This will facilitate the smooth transition of the entire industry from the development period to the mature period, and achieve its overall sustainable development. To assist the government in achieving reasonable predictions for policy adjustments, this study focuses on the biopharmaceutical industry, which is one of the representatives of the strategic emerging industries in China’s Yangtze River Delta. Considering that policies are not used in a single manner, and that the observation period needs to span the development and platform periods of the industry, the traditional Qualitative Comparative Analysis method (QCA) does not consider the analysis of data from multiple periods. Therefore, this study innovatively uses the Multi-Time Qualitative Comparative Analysis method (mtQCA), adding the dimension of time change and exploring the policy configuration logic behind the differences in local industrial innovation performance. Extracting general rules from specific policy configuration patterns is meaningful for a better analysis and resolution of complex, dynamic management issues, which will promote the sustainable development of strategic emerging industries.

Bias, Lotteries, and Affirmative Action in Science Funding Policy

Bias, Lotteries, and Affirmative Action in Science Funding Policy

Volunteer programs, empowerment, and life satisfaction in Jordan: mapping local knowledge and systems change to inform public policy and science diplomacy.

Volunteering in the community is thought to provide unique benefits to people who experience limited engagement in society. In the global South, volunteer programs are often framed as empowering women and benefiting the poor, without empirical evidence or systematic investigation of what this means from a local perspective. For this reason, it is critical to represent stakeholder knowledge, understand how change happens systemically, and reduce cultural bias in scientific inquiry and public policy. As such, efforts to respect diverse narratives and problem-solving approaches are key to science diplomacy - they help us understand cultural relevance, program efficacy, and for whom a program is considered transformative. This study shows how Syrian refugee and Jordanian women, living in resource-poor families, articulated (i) concepts of empowerment and life satisfaction and (ii) the benefits of engaging in community-based volunteering programs. Through engaging in a participatory methodology known as Fuzzy Cognitive Mapping, women generated visual representations of these constructs and cause-and-effect reasoning. They identified several dimensions of empowerment (e.g., cultural, financial, and psychological empowerment) and several meanings of life satisfaction (e.g. adaptation, acceptance, and contentment). They also mapped connections between variables, identifying those that might catalyze change. We were specifically interested in evaluating understandings of We Love Reading, a program that trains volunteers to become changemakers in their local community. In simulations, we modelled how employment, education, money, and volunteering would drive system change, with notable results on cultural empowerment. Through visual maps and scenarios of change, the study demonstrates a participatory approach to localizing knowledge and evaluating programs. This is key to improving scientific enquiry and public policy.

Ortho-Heterodox Biases and the Economist Algorithms of ChatGPT

Recommendations for economic policies can be based on different theoretical perspectives and may present hidden biases. Identifying these biases is challenging when they are embedded in recommendations from sources with high technological and social disruptive potential, where a good level of impartiality is expected, such as contemporary large language models. Thus, a questionnaire was administered to economists affiliated with the Brazilian academic community to assess their perception of orthodox/heterodox biases in economic policy recommendations derived from interactions with ChatGPT. The results showed that: i) there is still no consensus on the concepts of orthodoxy and heterodoxy in Brazil; ii) there are indications of a positive relationship between how self-proclaimed heterodox (orthodox) an economist is and how heterodox (orthodox) the perceived bias in an economic policy is; iii) it was not possible to identify a consistently orthodox or heterodox bias in ChatGPT's recommendations, which exhibited a good degree of impartiality.

Effect of the measurement period and spatial dependence on the accuracy of urban freight trip generation models

Despite recent advancements in the Freight Trip Generation (FTG) modelling literature, there is a lack of understanding on the effect of the choice of a regression model, measurement period (daily/weekly FTG), and spatial dependence on model fit and freight-related policies. This study addresses these research gaps by developing non-spatial and spatial autoregressive multiple linear regression and count models for daily and weekly Freight Trip Production (FTP) and Freight Trip Attraction (FTA). We model Freight Shipments (FS) as FTP and Freight Deliveries (FD) as FTA. The results show that the best model for daily and weekly FTP is the spatial Zero-Inflated Negative Binomial (ZINB) model. The best daily and weekly FTA model is the non-spatial Negative Binomial (NB) model. The findings indicate the presence of spatial dependence in the best FTP model, while it is absent in the best FTA model. The elasticity analysis shows that daily models may lead to bias and inaccurate prediction of policy impacts. The study recommends using count models that capture more FTG characteristics with a week as the measurement period and consider spatial dependence, if present.

Study on the driving factors and decoupling effect of carbon emission from pig farming in China—based on LMDI and Tapio model

AbstractThe livestock sector accounts for 18% of total anthropogenic carbon emissions and is an important source of global greenhouse gas (GHG) emissions. China occupies a large proportion of total livestock carbon emissions worldwide, especially in the pig industry, which is significant to China's agricultural economy and also a key area for China to achieve the "Carbon peaking and Carbon neutrality goals." This study uses the life cycle approach to calculate the GHG emission status of China's pig farming industry from 2001 to 2020, and then, we establish a logarithmic mean Divisia index (LMDI) model to identify the main driving factors and a Tapio decoupling model to analyze its decoupling status. We decompose the emission sources as well as decoupling index into five drivers: technological progress, livestock structure, policy bias, affluence, and population. The results reveal that the carbon emission of China's pig industry is in a weak growth trend and overall in a weak decoupling state but has volatility, which is closely related to the "Pig Cycle" in China. Decomposition analysis shows that increasing affluence and population growth are the main drivers of GHG emissions; simultaneously, technological progress, livestock structure, and policy bias are the main drivers of emission reduction. Meanwhile, technological and policy factors positively contribute to the decoupling status, while affluence level, population, and livestock structure changes negatively inhibit the decoupling status. The study concludes that technological advances, optimized economic structures, the guidance of green consumption patterns, and the solution to the "Pig Cycle" problem are crucial to further reduce GHG emissions from China's pig industry; meanwhile, technological changes have a dominant role in promoting carbon decoupling in pig farming.

Šeimos politika ir vienų motinų šeimų stigmatizacija

This article investigates the stigmatisation of single-mother families in Lithuania’s family policy through qualitative legal document content analysis and semi-structured interviews with single mothers. It reveals historical policy biases favouring nuclear families, resulting in marginalised single-mother households despite recognising their economic challenges. Analysis of single mothers’ experiences shows how stigmatization negatively impacts these families’ well-being and political representation, hindering rights advocacy.

Environmental Justice and Education: Bridging the Gap between Ecology, Equity, and Access

This study delves into the complex relationship between ecology, equity, and access, emphasizing the need for an integrated approach to address pressing environmental and societal challenges. Environmental disparities disproportionately affect marginalized communities, shaping their educational access and outcomes. These disparities are exacerbated by factors such as economic status, location, and policy biases, perpetuating a cycle of environmental and socio-economic inequality (Bullard, 1993). To mitigate these issues, various initiatives have been introduced to integrate environmental education into the academic curriculum, emphasizing interdisciplinary research, technology utilization, and community engagement. The National Curriculum Framework in India advocates for the inclusion of environmental concepts across subjects, aligning with global efforts to enhance environmental literacy (UNESCO, 2017). Additionally, community-based programs and advocacy campaigns have emerged to sensitize individuals, particularly the youth, about their role in achieving a sustainable and equitable future. Moving forward, holistic reforms encompassing policy advocacy, inclusive infrastructure development, and community empowerment are imperative to bridge the gap effectively. By fostering a culture of environmental consciousness, equitable resource distribution, and inclusive education, we can envision a society where every individual has equal access to a clean environment and the opportunity to prosper sustainably.

UNESCO world heritage branding: learning from some cases in Asia

UNESCO World Heritage listing is often misunderstood as branding to promote mass tourism or as a vehicle for economic development by a city or the state party. The reflection is aimed to shed light on the fundamental principles that underlie the listing and the integration of the World Heritage scheme with the Sustainable Development Policy. The reflection is done through the case study method of a specific real-life phenomenon in Asia to gain insights into complex and intricate situations and highlight the common misunderstanding through several cases. The study found the policy bias towards investment and tourism agenda and not towards the fulfilment of public welfare, often without ethics applying the top-down policy. The study also found evidence of irreversible damages to tangible and intangible aspects of cultural and natural heritage due to gentrification, commodification, decreasing carrying capacity, and loss of authenticity. The study aims to understand better the four pillars of integrating the World Heritage and Sustainable Development policy in achieving environmental sustainability, inclusive social development, inclusive economic development, and fostering peace and security.

Stability and change in the opinion–policy relationship: Evidence from minimum wage laws

Recent studies have documented large discrepancies between mass preferences and policies in U.S. states consistent with theories that highlight the oversized influence of affluent Americans on policymaking. In this note, we replicate and extend a recent such study (Simonovits, Guess, and Nagler, 2019) to assess how policy bias evolves in time. Specifically, relying on novel data and methods, we construct measures of minimum wage preferences and compare them to observed policies in each state for the years of 2014, 2016, 2019, and 2021. We demonstrate that, averaged across states, policy change closely tracked a pronounced increase in preferences for higher minimum wages, but the size of policy bias remained relatively stable. However, this national pattern hides an increasingly polarized policy landscape: in many states, insufficient responsiveness led to an increasing deviation between preferences and policies, while in other states policy changes—larger than preference changes—closed initial policy bias.

Power and politics in framing bias in Artificial Intelligence policy

AbstractBias is a key issue in expert and public discussions about Artificial Intelligence (AI). While some hope that AI will help to eliminate human bias, others are concerned that AI will exacerbate it. To highlight political and power aspects of bias in AI, this contribution examines so far largely overlooked topic of framing of bias in AI policy. Among diverse approaches of diagnosing problems and suggesting prescriptions, we can distinguish two stylized framings of bias in AI policy—one more technical, another more social. Powerful technical framing suggests that AI can be a solution to human bias and can help to detect and eliminate it. It is challenged by an alternative social framing, which emphasizes the importance of social contexts, balance of power and structural inequalities. Technological frame sees simple technological fix as a way to deal with bias in AI. For the social frame, we suggest to approach bias in AI as a complex wicked problem, for which a broader strategy is needed involving diverse stakeholders and actions. The social framing of bias in AI considerably expands the legitimate understanding of bias and the scope of potential actions beyond technological fix. We argue that, in the context of AI policy, intersectional bias should not be perceived as a niche issue but rather be seen as a key to radically reimagine AI governance, power and politics in more participatory and inclusive ways.

Does trade policy help or hinder global warming? A case study of Latin America and the Caribbean

AbstractThe dire prospects of global warming have been increasing the pressure on policymakers to use trade policy as a mitigation tool, challenging trade economists' canonical ‘targeting principle’. Even though the justifications for this stance remain as valid as ever, it no longer seems feasible in a world that is already engaging actively in using trade policy for climate purposes. However, the search for second‐best solutions remains warranted. In this paper, we focus on the climate benefits of tariff reform for a broad sample of Latin American and Caribbean countries, drawing on Shapiro's (2021, The Quarterly Journal of Economics, 136, 831) insights about the environmental bias of trade policy. Using a partial equilibrium approach and GTAP‐MRIO data for 2014, we show that even though there is evidence of a negative bias towards ‘dirty goods’ in half of the countries studied, translating this into actionable tariff reforms is plagued by interpretation and implementation difficulties, as well as by jurisdictional and efficiency trade‐offs. There are also questions about their efficacy in curbing greenhouse gas emissions.