Suchergebnisse

Since independence, agriculture has been the backbone of Tanzania's economy. Factors like good climatical condition, arable land, and manpower give the advantage to do well in agriculture as a tool for economic growth and self-sustaining. Despite having a good agriculture policy (Kilimo Kwanza), the country is not doing well in utilizing its full potential in the agriculture sector. This is due to several challenges like the political economy of agriculture, budget bias (constraints) against agriculture, and market bias against agriculture. To solve those problems and make this policy and agriculture more sustainable in achieving economic growth, industrialization (Tanzania ya Viwanda), and self-sustaining, this study is recommending the government to make some adjustments in the policy to match with the annual budget trends to avoid deficits. Moreover, the government should provide education to increase perceptions to the decision-makers about the importance of agriculture in the economy to reduce the biases on agriculture in their decision-making processes.

Data curated by humans reflects the biases and imperfections of humans (O'Neil, 2017; 2016). For example, in autonomous weapons systems, the initial data entered produces algorithms from which weapons systems learn, and, as a result, the systems mirror and amplify existing biases in the data sets (O'Neil, 2017). In political science and international relations, biases are also both inherent and amplified through the research approaches and methods adopted. They, too, are frequently hidden. A stark example of this is in the debate between area and disciplinary studies. Although there is a growing recognition that area studies can make valuable contributions to the study of international relations and that there is a need to 'decolonise' the discipline (Suzuki, 2021), the debate so far has not recognized the gulf of differences in research methods between these two approaches. This article argues that in the study of international relations and particularly regarding institutions, area studies approaches should be more frequently adopted. The limited use of these approaches not only hampers new research but also hides a colonial hangover.

The dominant paradigm of the estimation of causal partial effects of preferential economic integration agreements (PEIAs) on trade costs and trade flows is to rely on a selection on observables, with propensity‐score matching being the leading example. Conditional on some score of joint determinants of PEIAs and trade flows, causal partial effects of PEIAs on trade are obtained from a simple conditional mean comparison of trade between members and non‐members. Key for this approach to obtain consistent estimates is that the score used to form comparison groups is balanced: similarity of country pairs in the PEIA propensity core means similarity in all the observables. Otherwise, the effects may be biased, misascribing effects of differences in individual observables to PEIA membership. We employ a remedy for this bias through entropy balancing, demonstrate that there is an upward bias of PEIA effects on trade flows from lack of covariate balancing, and quantify the bias of the effects.

AbstractRather than exhibiting bias or open‐minded reasoning at baseline, we argue that information processing is motivated by whatever goals a context makes salient. Thus, if politics feels like debate, people will be motivated to argue for their side. If politics feels like deliberation, they will be motivated to seek consensus through open‐minded processing. Results from three experiments demonstrate: (1) Politics evokes thoughts similar to conflictual contexts and dissimilar from deliberative contexts. (2) Consequently, information labeled "political" primes the motivation to counterargue, leading to opinion polarization. Absent such labeling, no such motivation is evident, explaining why bias is common but not inherent to politics. (3) Despite this capacity for bias, people can be motivated to actively process and accept counterattitudinal information by simply making the value of open‐mindedness salient. This suggests open‐minded discourse is possible even absent motivation to evaluate information accurately. We conclude by discussing the implications of our research for political discourse.

AbstractWhen working with panel data, many researchers wish to estimate the direct effects of time-varying factors on future outcomes. However, when a baseline treatment affects both the confounders of further stages of the treatment and the outcome, the estimation of controlled direct effects (CDEs) using traditional regression methods faces a bias trade-off between confounding bias and post-treatment control. Drawing on research from the field of epidemiology, in this article I present a marginal structural modeling (MSM) approach that allows scholars to generate unbiased estimates of CDEs. Further, I detail the characteristics and implementation of MSMs, compare the performance of this approach under different conditions, and discuss and assess practical challenges when conducting them. After presenting the method, I apply MSMs to estimate the effect of wealth in childhood on political participation, highlighting the improvement in terms of bias relative to traditional regression models. The analysis shows that MSMs improve our understanding of causal mechanisms especially when dealing with multi-categorical time-varying treatments and non-continuous outcomes.

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.

AbstractChildren demonstrate a pervasive in‐group bias, preferring their in‐group across a range of contexts that encompass measures of liking, imitation, and, in some cases, resource allocation. A growing number of studies have begun to explore whether antisocial in‐group behavior reduces the robustness of this bias. However, these studies have focused on transgression evaluations, with only two studies focusing on social learning and none explicitly on imitation. This, therefore, limits the extent to which children's responses to interaction between in‐group bias and antisocial behavior can be fully understood. The current research expands on the prevailing literature, utilizing imitation as a behavioral measure to explore the reactions of children aged 4–5 and 7–8 years in response to antisocial in‐group behavior. Consistent with previous literature, antisocial in‐group behavior reduced in‐group liking ratings. Surprisingly, however, children's behavioral imitation preferences were guided solely by group membership, disregarding prosocial or antisocial behavior. These results indicate that children's explicitly reported social preferences and imitative preferences may be motivated by two independent drives.

Abstract
Organizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisions can lead to biases in the absence of social category data and in some contexts, may even sustain biases that arise by random chance. We then show how proactively using social category data can help illuminate and combat discriminatory practices, using cases from education and employment that lead to strategies for detecting and preventing discrimination. We conclude that discrimination can occur in any sociotechnical system in which someone decides to use an algorithmic process to inform decision-making, and we offer a set of broader implications for researchers and policymakers.

Abstract
Organizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisions can lead to biases in the absence of social category data and in some contexts, may even sustain biases that arise by random chance. We then show how proactively using social category data can help illuminate and combat discriminatory practices, using cases from education and employment that lead to strategies for detecting and preventing discrimination. We conclude that discrimination can occur in any sociotechnical system in which someone decides to use an algorithmic process to inform decision-making, and we offer a set of broader implications for researchers and policymakers.

Behavioural Industrial Organisation (BIO) incorporates psychological insights into traditional models of market interactions between firms and consumers. These models often have high predictive power relative to models based on the classical paradigm of the "rational" consumer in markets where the good has multiple features and at least a subset of consumers display biases such as inattention, overconfidence and self-control problems (present bias). This paper uses the Roaming Regulation as a case study to demonstrate the value of BIO to competition policy makers in such markets. To motivate the appropriateness of BIO models to roaming, the phenomenon of "bill-shock" is first shown to be plausibly caused or exacerbated by these biases. The paper discusses how insights from BIO models can inform an understanding of "demand substitution", and by deduction how policymakers conceive of market power. Finally, a counterfactual examination of two key junctures in the Roaming Regulation policy cycle highlights how the application of BIO is conducive to the formation of evidence-based policy.

Indonesia re-entered the new round of politics in 2018–2019. The vibrant political atmosphere is also reaching the public spaces, both in mass media and in online media. Although they often encounter differences due to the different political choices they face, audiences who have face-to-face discussions have the opportunity to clarify different conversational biases. However, many are discussing in the digital space, where it has the potential to cause a lot of message biases. The problem is political flaming often accompanies opinion bias. This study aims to find the characteristics of political flaming against Cak Imin since he runs as Vice President Jokowi unilaterally. Another goal is to see the attributes of the platform used in political flaming through content analysis approach. The results of this study show that political flaming against Cak Imin has a subtle tendency and jokes. The use of cultural elements is not intended as an expression of intolerance. Keywords: political flaming, reader comments, online media

The causal illusion is a cognitive bias that results in the perception of causality where there is no supporting evidence. We show that people selectively exhibit the bias, especially in those situations where it favors their current worldview as revealed by their political orientation. In our two experiments (one conducted in Spain and one conducted in the United Kingdom), participants who self-positioned themselves on the ideological left formed the illusion that a left-wing ruling party was more successful in improving city indicators than a right-wing party, while participants on the ideological right tended to show the opposite pattern. In sum, despite the fact that the same information was presented to all participants, people developed the causal illusion bias selectively, providing very different interpretations that aligned with their previous attitudes. This result occurs in situations where participants inspect the relationship between the government's actions and positive outcomes (improving city indicators) but not when the outcomes are negative (worsening city indicators).

This paper investigates whether the individual misperception of income distributions helps explain why, opposite to Meltzer and Richard (1981), higher initial inequality levels do not correlate positively with redistribution. I conduct a representative survey experiment in Brazil, France, Germany, Russia, Spain, and the United States, providing a personalized information treatment on the income distribution to a randomly chosen subsample. Most respondents misperceive their own position in the income distribution. These biases di_er by country and the true income position. Misperceptions of the median income relate negatively to misperceived income positions, showing evidence for biased reference points. Correcting misperceptions slightly shifts the demand towards less redistribution in Germany and Russia which appears to be driven by respondents with a negative position bias. Apart from Spain and the US, treatment reactions lead to a convergence of the demand for redistribution across countries. The treatment also alters trust levels in government and beliefs about the importance of luck but not equally across bias types.

This paper tests the long run risk and valuation risk model using a robust estimation procedure. The persistent long run component of consumption growth process is proxied by a news based index that is created using a random forest algorithm. This news index is shown to predict aggregate long term consumption growth with an R-square of 57% and is robust to inclusion of other commonly used predictors. I theoretically derive an estimatable bias term in adjusted Euler equation of the model that arises due to measurement error in consumption data and show that this bias term is non-zero. Using a three pass estimation procedure that accounts for this bias, I show that the long run risk and valuation risk model fails to explain cross section of equity returns. This contrasts to the results from regular two pass Fama-MacBeth estimation procedure that implies that the same model explains the cross section of asset returns with statistically significant risk premia estimates.

This paper investigates whether the individual misperception of income distributions helps explain why, opposite to Meltzer and Richard (1981), higher initial inequality levels do not correlate positively with redistribution. I conduct a representative survey experiment in Brazil, France, Germany, Russia, Spain, and the United States, providing a personalized information treatment on the income distribution to a randomly chosen subsample. Most respondents misperceive their own position in the income distribution. These biases differ by country and the true income position. Misperceptions of the median income relate negatively to misperceived income positions, showing evidence for biased reference points. Correcting misperceptions slightly shifts the demand towards less redistribution in Germany and Russia which appears to be driven by respondents with a negative position bias. Apart from Spain and the US, treatment reactions lead to a convergence of the demand for redistribution across countries. The treatment also alters trust levels in government and beliefs about the importance of luck but not equally across bias types.