The crowding-out effect of sugar-sweetened beverages (SSBs) on household expenditure patterns in Bangladesh.

BACKGROUND: Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities. Reducing expenditure for consumption of other household commodities is known as the crowding-out effect of SSB. We aimed to determine the crowding-out effect of SSB expenditure on other household commodities. In addition, we also identified the factors influencing the household's decision to purchase of SSBs. METHODS: We used the logistic regression (logit and multinomial logit models) and the Seemingly Unrelated Regression (SUR) models. In order to find the probability of a given change in the socio-demographic variables, we also estimated the average marginal effects from the logistic regression. In addition, we regressed the SUR model by gender differences. We used Household Income and Expenditure Survey (HIES) 2016 data to estimate our chosen econometric models. HIES is nationally representative data on the household level across the country and is conducted using a multistage random sampling method by covering 46,075 households. RESULTS: The findings from the logit model describe that the greater proportion of male members, larger household size, household heads with higher education, profession, having a refrigerator, members living outside of the house, and households with higher income positively affect the decision of purchasing SSB. However, the determinants vary with the various types of SSB. The unadjusted crowding out effect shows that expenditure on SSB or sugar-added drinks crowds out the household expenditure on food, clothing, housing, and energy items. On the other hand, the adjusted crowding out effect crowds out the spending on housing, education, transportation, and social and state responsibilities. CONCLUSION: Although the household expenditure on beverages and sugar-added drinks is still moderate (around 2% of monthly household expenditure), the increased spending on beverages and sugar-added drinks is a concern due to the displacement of household expenditure for basic commodities such as food, clothing, housing, education, and energy. Therefore, evidence-based policies to regulate the sale and consumption of SSB are required for a healthy nation.

Research on economic and social impact of carbon emissions in the construction industry at a regional scale: a case of Shandong, China.

To identify the impact of carbon emissions in the construction industry on the society and economy at a regional scale is of great significance for the sustainable development of the construction industry. Taking Shandong, China, as an example, this study uses a process-based carbon emission assessment method to calculate the carbon emissions of the construction industry in Shandong from 2010 to 2020. Based on specific situations at different phases, ten major factors affecting the society and economy have been analyzed and identified. Then, the Seemingly Uncorrelated Regression (SUR) model was used to analyze carbon emissions and economic and social factors at each stage. The results show the following: (1) The overall carbon emissions of the construction industry in Shandong are on an increasing trend, the carbon emission intensity is on a decreasing trend, and the material production and operation stages are the main source of carbon emissions; (2) There is a significant relationship between carbon emissions in the construction phase and all influencing factors, so it should be the main phase for macro-regulation; (3) Unlike in the Ordinary Least Square (OLS) model, the error terms of the gross output value of the construction industry and the total turnover of the main building materials equations are independent; however, there is an interaction between the error terms of the remaining equations. The results provide a theoretical reference for governmental departments to set up environmental targets and make policy regulations.

The impact of natural disasters on China's macroeconomy.

This study attempts to construct an econometric model using China's natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China's macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China's accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.