Search results

Emotional competence supports preschoolers’ social relationships and school success. Parents’ emotions and reactions to preschoolers’ emotions can help them become emotionally competent, but scant research corroborates this role for preschool teachers. Expected outcomes included: teachers’ emotion socialization behaviors functioning most often like parents’ in contributing to emotional competence, with potential moderation by socioeconomic risk. This paper aims to discuss this issue.

Participants included 80 teachers and 312 preschoolers experiencing either little economic difficulty or socioeconomic risk. Children’s emotionally negative/dysregulated, emotionally regulated/productive and emotionally positive/prosocial behaviors were observed, and their emotion knowledge was assessed in Fall and Spring. Teachers’ emotions and supportive, nonsupportive and positively emotionally responsive reactions to children’s emotions were observed during Winter. Hierarchical linear models used teacher emotions or teacher reactions, risk and their interactions as predictors, controlling for child age, gender and premeasures.

Some results resembled those parents’: positive emotional environments supported children’s emotion knowledge; lack of nonsupportive reactions facilitated positivity/prosociality. Others were unique to preschool classroom environments (e.g. teachers’ anger contributed to children’s emotion regulation/productive involvement; nonsupportiveness predicted less emotional negativity/dysregulation). Finally, several were specific to children experiencing socioeconomic risk: supportive and nonsupportive reactions, as well as tender emotions, had unique, but culturally/contextually explainable, meanings in their classrooms.

Applications to teacher professional development, and both limitations and suggestions for future research are considered.

This study is among the first to examine how teachers contribute to the development of preschoolers’ emotional competence, a crucial set of skills for life success.

The authors explore the hypothesis that some movements in commodity prices are anticipated (news shocks) and can trigger aggregate fluctuations in small open emerging economies. This paper aims to discuss the aforementioned objective.

The authors build a multi-sector dynamic stochastic general equilibrium model with endogenous commodity production. There are five exogenous processes: a country-specific interest rate shock that responds to commodity price fluctuations, a productivity (TFP) shock for each sector and a commodity price shock. Both TFP and commodity price shocks are composed of unanticipated and anticipated components.

The authors show that news shocks to commodity prices lead to higher output, investment and consumption, and a countercyclical movement in the trade-balance-to-output ratio. The authors also show that commodity price news shocks explain about 24% of output aggregate fluctuations in the small open economy.

Given the importance of both anticipated and unanticipated commodity price shocks, policymakers should pay attention to developments in commodity markets when designing policies to attenuate the business cycles. Future research should investigate the design of optimal fiscal and monetary policies in SOE subject to news shocks in commodity prices.

This paper contributes to the knowledge of the sources of fluctuations in emerging economies highlighting the importance of a new source: news shocks in commodity prices.

The goal of this work is to create a computational finite element model to perform thermoelastic stress analysis (TSA) with the usage of a non-ideal load frequency, containing the effects of the material thermal properties.

Throughout this document, the methodology of the model is presented first, followed by the procedure and results. The last part is reserved to results, discussion and conclusions.

This work had the main goal to create a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties. Loading frequencies out of the ideal range were applied and the model showed capable of good results. The created model reproduced acceptably the TSA, with the desired conditions.

This work creates a model to perform TSA with the usage of non-ideal loading frequencies, considering the materials’ thermal properties.

The purpose of this paper is to contribute to the teaching of undergraduate macroeconomics.

To suggest a roadmap, based on a consumption function, to be used by instructors willing to teach the Lucas Critique subject.

Therefore, this paper proposes a lesson, which consists of three parts, to help undergraduates better understand the subject: (1) a grading exercise to bring the topic closer to students’ lives; (2) a Keynesian and an optimal consumption function, followed by an example based on an unemployment insurance policy; and (3) two optional topics consisting of extensions of the optimal consumption function and some empirical results related to the Lucas Critique.

The Lucas Critique influenced the evolution of research in macroeconomics, but it is not easily grasped in a classroom.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

This study aims to identify the marginal impact of introducing a signal attribute of pasture-raised beef on consumer willingness to pay (WTP) for other independent attributes.

The study is divided into two steps. The first, qualitative, consisted of investigating the values consumers have regarding beef production. To this end, 52 interviews with Brazilian and US consumers were conducted using laddering. In the second, quantitative, six experiments, (face to face and online) with 267 consumers of beef were performed.

As a result, the main value found for the Brazilians was security, while for the Americans was self-direction. For consumers, the WTP for animal welfare was the most important in the choice experiments where this information was present.

These findings offer an alternate beef differentiation, enabling it to be sold with higher added value by integrating these.

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.

The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.

Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.

This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.