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This study examines the relationship between CO2 emissions, labor force participation, foreign direct investment (FDI), and trade openness on the Consumer Price Index (CPI) in Saudi Arabia, within the context of Vision 2030’s economic reforms. Vision 2030 aims to diversify the economy, reduce oil dependency, and promote sustainable growth, making it crucial to understand the factors influencing inflation and economic stability. Using annual data from 2001 to 2022 and the nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, the study analyzes both short- and long-term effects. The findings reveal that higher CO2 emissions have a deflationary effect, reducing the CPI in both the short and long term, while FDI shows an inflationary impact with a delayed effect. Labor force expansion contributes to lowering the CPI, reflecting its deflationary pressure, especially over the long term. Trade openness is also examined for its dual effects on CPI, In the short run, both positive and negative trade openness reduce consumer prices, while in the long run, positive trade openness increases inflation, and negative trade openness lowers prices. This shows the differing inflationary impacts of trade openness over time. These findings contribute to the policy discourse on balancing economic growth, environmental sustainability, and inflation management, offering strategic insights for policymakers in alignment with Saudi Arabia’s Vision 2030 objectives.

Scanner data can be obtained from a wide variety of retailers (supermarkets, home electronics, Internet shops, etc.) and provide information at the level of the barcode, i.e. the Global Trade Item Number or its European version: European Article Number. One of advantages of using scanner data in the Consumer Price Index measurement is the fact that they contain complete transaction information, i.e. prices and quantities for every sold item. One of new challenges connected with scanner data is the choice of the index formula which should be able to reduce the chain drift bias and the substitution bias. Multilateral index methods seem to be the best choice in the case of dynamic scanner data sets. These indices work on a whole time window and are transitive, which is a key property in eliminating the chain drift effect. Following the so-called identity test, however, one may expect that even when only prices return to their original values, the index becomes one. Unfortunately, the commonly used multilateral indices (GEKS, CCDI, GK, TPD, TDH) do not meet the identity test. The paper discusses the proposal of two multilateral indices, the idea of which resembles the GEKS index, but which meet the identity test and most of other tests. In an empirical study, these indices are compared, inter alia, with the SPQ index, which is relatively new and also meets the identity test. Analytical considerations as well as empirical study confirm the high usefulness of the proposed indices.

Abstract Socioeconomic scales are used to determine the socioeconomic status of a study subject which in turn affects the health and nutritional status. BG Prasad's scale of socioeconomic status uses only one criterion i.e. income. Therefore, it is easy to calculate and the most used socioeconomic scale to classify a study subject. It is used for both urban and rural areas. The consumer price index for industrial workers measures inflation at household level. As the consumer price index for industrial workers is used to calculate the income ranges for a study subject and it is released for every month by Labour Bureau, Ministry of Labour and Employment, it is imperative to update the BG Prasad's scale every month.

Accurate forecasting of China’s Consumer Price Index (CPI) is crucial for effective macroeconomic policymaking, yet remains challenging due to structural breaks and nonlinear dynamics inherent in the inflation process. Traditional linear models, such as ARIMA, often fail to capture threshold effects and regime shifts. This study introduces a Threshold Autoregressive Moving Average (TARMA) model that embeds a nonlinear threshold mechanism within the conventional ARMA framework, enabling it to better capture the CPI’s complex behavior. Leveraging an evolutionary modeling approach, the TARMA model effectively identifies high- and low-inflation regimes, offering enhanced flexibility and interpretability. Empirical results demonstrate that TARMA significantly outperforms standard models. Specifically, regarding the CPI Index level, the out-of-sample Mean Absolute Percentage Error (MAPE) is reduced to approximately 0.35% (under the S-BIC integration scheme), significantly improving upon the baseline ARIMA model. The model adapts well to inflation regime shifts and delivers substantial improvements near turning points. Furthermore, integrating an information-criterion-based weighting scheme further refines forecasts and reduces errors. By addressing the limitations of linear models through threshold-driven nonlinearity, this study offers a more accurate and interpretable framework for forecasting China’s CPI inflation.

This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.

We are all just a little bit plastic. Traces of bisphenol A or BPA, a chemical used in plastics production, are widely detected in our bodies and environment. Is this chemical, and its presence in the human body, safe? What is meant by safety? Who defines it, and according to what information? Is It Safe? narrates how the meaning of the safety of industrial chemicals has been historically produced by breakthroughs in environmental health research, which in turn trigger contests among trade associations, lawyers, politicians, and citizen activists to set new regulatory standards. Drawing on archival research and extensive interviews, author Sarah Vogel explores the roots of the contemporary debate over the safety of BPA, and the concerns presented by its estrogen-like effects even at low doses. Ultimately, she contends that science alone cannot resolve the political and economic conflicts at play in the definition of safety. To strike a sustainable balance between the interests of commerce and public health requires recognition that powerful interests will always try to shape the criteria for defining safety, and that the agenda for environmental health research should be protected from capture by any single interest group.

The Consumer Expenditure (CE) surveys are the only source of information on the complete range of consumers' expenditures and incomes in the United States, as well as the characteristics of those consumers. The CE consists of two separate surveys: (1) a national sample of households interviewed five times at three-month intervals; and (2) a separate national sample of households that complete two consecutive one-week expenditure diaries. For more than 40 years, these surveys, the responsibility of the Bureau of Labor Statistics (BLS), have been the principal source of knowledge about changing patterns of consumer spending in the U.S. population. In February 2009, BLS initiated the Gemini Project, the aim of which is to redesign the CE surveys to improve data quality through a verifiable reduction in measurement error with a particular focus on underreporting. The Gemini Project initiated a series of information-gathering meetings, conference sessions, forums, and workshops to identify appropriate strategies for improving CE data quality. As part of this effort, BLS requested the National Research Council's Committee on National Statistics (CNSTAT) to convene an expert panel to build on the Gemini Project by conducting further investigations and proposing redesign options for the CE surveys. The charge to the Panel on Redesigning the BLS Consumer Expenditure Surveys includes reviewing the output of a Gemini-convened data user needs forum and methods workshop and convening its own household survey producers workshop to obtain further input. In addition, the panel was tasked to commission options from contractors for consideration in recommending possible redesigns. The panel was further asked by BLS to create potential redesigns that would put a greater emphasis on proactive data collection to improve the measurement of consumer expenditures. Measuring What We Spend summarizes the deliberations and activities of the panel, discusses the conclusions about the uses of the CE surveys and why a redesign is needed, as well as recommendations for the future.

This study examines whether portfolio optimization can be effectively based on annual changes in the harmonized index of consumer prices (HICP) data. Specifically, we assess whether asset allocation based on consumer expenditure can generate superior returns compared to static or equal-weighted asset allocation. To explore this, we use consumer expenditure data from HICP statistics categorized by COICOP. Our findings indicate that this strategy outperforms a buy-and-hold benchmark by 13.32% in terms of the Sharpe Ratio and exceeds an annual equal-weighted rebalancing strategy by 3.11%. Additionally, both the Calmar and Sterling Ratios demonstrate improved performance, further reinforcing the robustness of this approach. Furthermore, a hypothetical scenario where sector weights from the end of the given year—though not yet available during the year—are used suggests even greater improvements in performance. A high-sample bootstrap simulation confirms that the observed performance differences are not random but reflect the independent effectiveness of asset allocation based on consumer expenditure trends. This result strengthens the validity of our backtesting findings, indicating that the examined strategy could generate excess returns compared to passive portfolio managment and fixed-weight rebalancing approaches. The result of the study is therefore the development of an effective portfolio rebalancing strategy.

In this book, Nobel Prize-winning economist Edmund Phelps draws on a lifetime of thinking to make a sweeping new argument about what makes nations prosper--and why the sources of that prosperity are under threat today. Why did prosperity explode in some nations between the 1820s and 1960s, creating not just unprecedented material wealth but \"flourishing\"--meaningful work, self-expression, and personal growth for more people than ever before? Phelps makes the case that the wellspring of this flourishing was modern values such as the desire to create, explore, and meet challenges. These values fueled the grassroots dynamism that was necessary for widespread, indigenous innovation. Most innovation wasn't driven by a few isolated visionaries like Henry Ford; rather, it was driven by millions of people empowered to think of, develop, and market innumerable new products and processes, and improvements to existing ones. Mass flourishing--a combination of material well-being and the \"good life\" in a broader sense--was created by this mass innovation. Yet indigenous innovation and flourishing weakened decades ago. In America, evidence indicates that innovation and job satisfaction have decreased since the late 1960s, while postwar Europe has never recaptured its former dynamism. The reason, Phelps argues, is that the modern values underlying the modern economy are under threat by a resurgence of traditional, corporatist values that put the community and state over the individual. The ultimate fate of modern values is now the most pressing question for the West: will Western nations recommit themselves to modernity, grassroots dynamism, indigenous innovation, and widespread personal fulfillment, or will we go on with a narrowed innovation that limits flourishing to a few? A book of immense practical and intellectual importance,Mass Flourishingis essential reading for anyone who cares about the sources of prosperity and the future of the West.

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.