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This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

Studies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.

The authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.

The results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.

This study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

This study examines the impacts of the Ethiopian developmental state model on the competition, efficiency and profitability of banks.

The competition, efficiency and profitability of the Ethiopian bank are measured using Panzar Rose, data envelopment analysis and financial ratio. Fixed-effect panel regression methods are applied to test the direction and strength of association between the Ethiopian developmental state model and the competition, efficiency and profitability of the country's banks while controlling bank-specific market structure and macroeconomic factors.

The Ethiopian developmental state model embeds the state-directed financial system, which affects the banking industry using a range of credit allocation instruments. Of which, directed credit schemes, interest rate control and the lack of financial freedom reduce the competition and efficiency of banks. The National Bank of Ethiopia (NBE) advances to the government and the sale of Treasury bills to a captive market enhances banking competition while negatively affecting banking efficiency. Interest rate control and the lack of financial freedom lower banking profitability. Unexpectedly, directed credit schemes improve banking profitability.

As with any study, this one has limitations. The intra-period comparison of efficiency is based on balanced data. Future studies can use methods that can measure the efficiency of banks using unbalanced data. The computation of the yearly H-statistic is constrained by the small sample size. The use of high-frequency data for measuring competition can provide us with better insights into banking competition in Ethiopia. Furthermore, there are a number of methods for measuring banking competition, efficiency and profitability with different assumptions. Approaching the subject of this study by applying different methods will offer different insights.

The contributions of this study to practice are at two levels. First, at the policy level, it enhances our understanding of the impacts of developmental state model policies, as implemented in Ethiopia, on the banking industry and therefore provides suggestions to policymakers to reform the sector's policies. Second, it offers input to the management of banks regarding the factors that impact the industry.

The banking industry is often studied in the context of financial liberalisation. The originality of this study lies in investigating how the competition, efficiency and profitability of banks are affected when operating in the context of significant state interventions in the industry.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

The purpose of this paper is to examine the hotel growth model including hotel brand, culture and life cycle phases of the Myrtle Beach, South Carolina, the fastest growing tourism destination in the United States.

Culture reflecting consuming behaviour of low-context innovators and high-context imitators is measured by the price elasticity of demand (PED). Hotel brand reflecting guests’ hotel class is measured by the income elasticity of demand. Autoregressive distributed lag has been conducted on the Smith Travel Research data in 33 years (1989–2022) to determine the relationship among hotel brand, culture and life cycles.

Skilled labour is the key to make hotels grow. Therefore, increase room rates when hotels possess skilled professionals and decrease room rates when hotels have no skilled professionals. During the rejuvenation in Myrtle Beach (1999–2003), hoteliers increased room rates for innovators due to skilled professionals to increase revenue. Otherwise, a decrease in room rates due to lack of skilled professionals would lead to increase revenue.

(1) Although Myrtle Beach is one of the fastest growing tourism destinations in the US, it has a relatively small geographic area relative to the country. (2) Data cover over one tourist life cycle, so the time span is relatively short. Hoteliers can forecast the number of guests in different culture by changing room rates.

To optimize revenue, hoteliers can select skilled labour in professional design hotel brands which could make an increase in demand for leisure transient guests no matter what room rates increase after COVID-19 pandemic.

The study has considered the applied ethical processes regarding revenue management that would maximize both revenue and customer satisfaction when it set up an increase in room rates to compensate for professional hotel room design or it decreases room rates for low-income imitators in exploration and development.

This research highlights that (1) skilled design in the luxury hotel brand is the key for the hotel growth and (2) there is a steady state of the growth model in the destination life cycle.

The purpose of this study is to analyze the affordability and sustainability of residential water consumption in Ecuador, a developing country.

To determine affordability, the authors assess water consumption and its distribution across different income levels of the population in the three main cities: Cuenca, Guayaquil and Quito. The authors then simulate the effects of a pricing policy on sustainability by deriving demand elasticities through a quadratic almost ideal demand system (QUAIDS).

Results show that Ecuador has low water tariffs with respect to other cities in Latin America, which points to its affordability. However, sustainability might not be guaranteed since consumption is overall higher than the level suggested by the World Health Organization. From an economic point of view, this could be motivated by the low tariffs. Indeed, the simulation of a pricing policy shows that higher prices can significantly reduce demand for most of the sample. However, in low-income households this reduction may compromise vital consumption.

This research has important implications in terms of stimulating the discussion of how water affordability and sustainability can be achieved. For the case study, Ecuador, the results indicate that sustainability may not be guaranteed and that policies need to be designed to encourage it. Another implication is that population income levels should be considered to avoid negative effects for the most vulnerable groups. The main limitations of this study are methodological. First, the QUAIDS model is based on a conditional demand model (Zhen et al., 2013), which does not take into account spending on other goods. Second, data availability is limited and prevents a deeper analysis.

To the best of the authors' knowledge, this is the first study to analyze these issues in Ecuador, a country that recognizes the human right to water at the constitutional level.

This study investigated the asymmetric effects of changes in policy uncertainty on real sector variables in Brazil, China, India and South Africa.

The study used the nonlinear autoregressive distributed lag (NARDL) modeling framework.

The results showed that both in the long run and short run, rising uncertainty not only increases consumer prices significantly in these economies, but also impedes aggregate and sectoral output growths, and deters investment, employment and private consumption. Contrary to economic expectation, the results also showed that in the long run, declining uncertainty impedes aggregate and sectoral output growths in these economies, and significantly hinders employment in South Africa and Brazil. This suggests that in the long run, economic agents in these economies somewhat behave as if uncertainty is rising. The authors also found significant asymmetric effects in the response of real sector variables to uncertainty both in the long run and short run, which justifies the choice of NARDL framework for this study.

The sample is limited to Brazil, India, China and South Africa. While Brazil, India and China are three of the most prominent large emerging market economies, South Africa is the largest emerging market economy in Africa.

To lessen the adverse effects of policy uncertainty observed in the results, there is need for sound institutions and policy regimes that can promote predictable policy responses in these economies so that policy neither serves as a source of uncertainty nor as a channel through which the effects of other shocks are transmitted.

Apart from using the NARDL framework to capture the asymmetric effects of policy uncertainty, this study also accounted for the sectoral effects of uncertainty in emerging markets.