Producer Price Index Research Articles

Relationship between organic agriculture and export of olive oil in Tunisia via an ARDL model

This paper aims to study the presence of the relationship between the organic olive-growing area and the export of olive oil in the Tunisian economy; such as the producer price index for the case of olives and the exchange rate. A cointegration approach through an Auto Regressive Distributed Lag model (ARDL model) was mobilized for the period (2001-2021). The test results show the existence of a significant short and long-terms cointegration relationship between the olive area, the exchange rate and the export of olive oil. Similarly, unidirectional causal relationships have also been determined between the producer price index and the olive area on the one hand, a unidirectional causal relationship is caused by the exchange rate on the olive-growing area and this exerts unidirectional causality on the export of olive oil. The promotion of organic farming in Tunisia depends not only on the development of organic areas but also on the economic situation of the country. Thus, it can be concluded that limiting the depreciation of the national currency and improving the producer price index can improve the exchange rate and contribute positively to the development of organic olive areas and the export of olive oil in general

The Impact of Retail and E-commerce Sales on Inflation Including the Covid-19 Period: Evidence from Turkiye

The aim of this study is to evaluate the effect of retail or online shopping on the producer price index before and during the Covid-19 pandemic period. Thus, the impact of crisis periods will be clearly revealed. For this reason, In the study, data between January 2010 and November 2022 are used in the case of Turkey. As a result of the empirical analyses, it was determined that the causality relationship between the Producer Price Index and the Internet and Mail Order Turnover Index, which was valid in every canton without exception until the Covid-19 pandemic, disappeared with the pandemic. No causal relationship has been identified from Internet and Mail Orders Turnover Index to Producer Price Index. While bi-directional causality was detected between Retail Turnover Index and Producer Price Index in some quantiles, unidirectional causality was detected from Producer Price Index to Retail Turnover Index in some quantiles.

Asymmetric effects of international oil prices on China's PPI in different industries——Research based on NARDL model

In theory, changes in the price of oil, which is a critical raw material for production, can affect the Producer Price Index (PPI). In this study, the PCA method is first employed to extract factors from the PPI of 39 industrial sectors in China, followed by the application of the NARDL model to analyze the nonlinear effects of international oil price fluctuations on the PPI of three industries. The results indicate that the long and short-term fluctuations in international oil prices have a significant impact on the PPI of resource-intensive industries and labor-intensive industries, but the impact on capital and technology-intensive industries is not significant. However, it is worth noting that the long-term increases in oil prices will have a negative impact on the PPI of resource-intensive industries. From the perspective of asymmetry, the positive and negative shocks of international oil prices will have an asymmetric impact on the PPI of labor-intensive industries, while the impact on resource-intensive industries is symmetric. Based on the existing conclusions, this article proposes relevant policy recommendations from the perspectives of both government and enterprises.

Futures Markets and Macroeconomics: Empirical Evidence from China

This paper uses the methods of Granger causality test, cointegration relationship test, error correction model and shock response function in cointegration correlation theory, and uses the monthly data from 2003.1 to 2023.5 to study the role and influence of futures market variables (price index, total turnover, total open interest) on sixteen macroeconomic variables. The results show that the three indicators of the futures market have a guiding effect on industrial added value, net export, fiscal revenue and fiscal expenditure at the same time. The price index and total turnover also have a guiding effect on the interbank 7-day interest rate and the RMB real effective exchange rate index. The total turnover and total open interest also have a strong guiding effect on the total import and export volume, the investment amount of fixed assets, and the total retail sales of social consumer goods. The price index also has a strong guiding effect on the producer price index, total loans, and the exchange rate of the US dollar against the RMB. The total open interest also has a strong guiding effect on the total loan. The cointegration test and error correction model show that in the short and long term, the futures market variables have a strong or weak quantitative impact on twelve macro variables, and the shock response function curve shows that the futures market variables have different impact effects on twelve macro variables. It shows that the futures market has a relatively strong impact on the macroeconomy, but there is still much room for improvement in the scope and depth of influence. It is suggested that the futures market should make efforts in terms of variety expansion, internationalization and supervision to further promote the comprehensive development of the domestic macroeconomy.

The dynamic effect of macroeconomic factors on housing prices: Evidence from South Africa.

This study examines the dynamic short- and long-run causal relationship between South African real house prices and key macroeconomic fundamentals (gross domestic product(GDP), mortgage rate, exchange rate-USDZAR, affordability, household debt to disposable income, unemployment rate, share prices (JSE ALL share index), foreign direct investment, and producer price index) over the period 2000Q1-2019Q4. The study uses a vector error correction model (VECM) to estimate the relationships while accounting for endogeneity and reverse causality. Although, there seems to be a significant association(both short and long-run) between house prices and all macroeconomic fundamental variables, GDP and producer price index appear to have the greatest impact. Further, our results suggest that any short-term disequilibrium in house prices always self-corrects in the long-run.

Re-examining asymmetric dynamics in the relationship between macroeconomic variables and stock market indices: empirical evidence from Malaysia

PurposeThe aim of this research is to re-examine the dynamic linkages between macroeconomic variables and the stock market indices in Malaysia following some transformational changes in the policies and the exchange rate regime.Design/methodology/approachUsing monthly data points for all the economic variables and the stock market index (KLCI Index), the authors applied vector autoregression (VAR) model to examine the relationship. The authors also used impulse response function (IRF) in order to explore the effect of one-unit shock in “X” on “Y” under the VAR environment.FindingsThe authors' study finds a significant relationship between all the macroeconomic variables and the stock market index of Malaysia. The cointegration results indicate a long-term relationship, whereas the vector autoregressive-based impulse response analysis suggests that the Malaysian stock index (KLCI) responds negatively to the money supply, inflation and producer price index (PPI). However, the authors' results indicate a positive response from the stock index to the exchange rate.Research limitations/implicationsThe authors' study's results are based on selected macroeconomic variables and the VAR model. Researchers may find other variables and methods more useful and may provide findings accordingly.Practical implicationsSince the results are quite asymmetric, it would be interesting for the market players, policymakers and regulators to consider the findings and explore appropriate opportunities.Originality/valueWhile the relationship between macroeconomic variables and stock market indices has been widely examined, a significant gap in the literature remains concerning the role of exchange rate variable on the stock market in an emerging economy context.

Studying the economic burden of premature mortality related to PM2.5 and O3 exposure in Greece between 2004 and 2019

The detrimental direct effects of air pollution on public health have been well-determined; however, of relevant importance are also the indirect effects on the economy. Based on the value of statistical life (VSLs) and the willingness to pay (WTP) method, we estimated the economic cost resulting from premature mortality associated with fine particulate matter (PM2.5) and ground-level ozone (O3) exposure across Greece during 2004–2019, under a constant and a changing income elasticity scenario. The spatiotemporal trends of the economic burden were also investigated. The total economic burden attributable to premature mortality due to exposure to PM2.5 and O3 for the 16-year period was estimated at € 66.34 billion under the constant scenario and at € 273.65 billion under the changing scenario. A dramatic decline of the mortality cost was observed from 2009 to 2019 in the changing scenario, reflecting various socio-economic factors and income losses, as opposed to a less profound downward trend under the constant scenario, where the annual Gross Domestic Product (GDP) and Consumer Price Index (CPI) variances were not taken into account. During the study period, PM2.5 contributed the most to the economic loss, while the contribution of O3 was much less. The spatiotemporal patterns observed here may prove an effective means of communicating these trends to policy-makers and may feed the design of strategies for protecting public health and safeguarding the economic growth.

Analysis on the influence of digital economy on manufacturing transfer

Based on the panel data of 30 provinces in China from 2010 to 2020, the entropy method is used to measure the digital economy, and the fixed-effect model is used to explore the impact of digital economy on manufacturing transfer. The results show that: (1) The digital economy will have an inverted U-shaped impact on the manufacturing industry, that is, the digital economy will promote the transfer of manufacturing within a certain range, but after reaching a certain extent, it will inhibit the transfer of manufacturing; (2) An increase in the producer price index and a decline in the per capita road area will significantly promote the transfer of manufacturing; (3) The level of digital economy in Guangdong, Jiangsu and Zhejiang provinces is among the best.

Carbon Substitution Effect Through the Utilization of Wood Products in Major Carbon-intensive Industries: Focusing on the Construction Industry in Korea

This study aims to examine the carbon substitution effect in Korea's major carbon-intensive industry, the construction industry. To accomplish this, we estimate the factor demand functions for the building construction and maintenance industry as well as the civil construction industry using the SUR model. Subsequently, we conduct a simulation analysis to assess the carbon substitution effect resulting from the replacement of wood with other factor inputs in each industry. The data utilized in this study are gathered from the Bank of Korea's Input-Output table and the Bank of Korea's producer price index. The estimated results of the factor demand functions reveal a substitutive relationship between wood and wood products with processed metal products within the construction industry. The simulation analysis indicates that an 8.75% increase in demand for wood and wood products within the construction industry can lead to the most significant reduction in total greenhouse gas (GHG) emissions for the industry, amounting to 277.52 thousand tons. This study is the first to quantify carbon substitution effects from an economic perspective, thereby contributing to providing insights for governments in formulating policies related to carbon neutrality.

Analysis of the Structural Change in the Price Transmission from the Agricultural Producer Price Index to the Consumer Price Index

Analysis of the Structural Change in the Price Transmission from the Agricultural Producer Price Index to the Consumer Price Index

Catalyzing the Impact of High Cost of Living on Demand and Supply of Textile Industry A Prospective of Emerging Economy

The high cost of living is unrestful for all economic agents. The shocks in the cost of living badly affect emerging economies like Pakistan. This study provides a novel analysis of the impact of “different types of living costs” on the demand and supply of the textile industry of Pakistan. It used the Structural Vector Autoregressive (SVAR) model to analyze the impact of the shocks in cost of living on textile industry demand and supply using monthly data from July 2008 to June 2020. The study's findings suggest that rising energy, housing, clothing, and footwear cost weaken demand for the textile sector, mostly through lower purchasing power. While education costs diminish supply, food costs raise producer price index, and transportation costs have no effect. Our findings contribute to the body of knowledge and give vital information on which types of living costs affect the textile sector and which might lead to improvement. The study's findings aid in the establishment of strategies for the textile industry's long-term viability and development. Furthermore, the study provides a novel insight that not all sorts of costs are detrimental to the textile industry.

Determining the variables affecting the prices of animal products by the network analysis in Türkiye

Recently, Türkiye has seen significant increases in animal products and input prices. These recent increases in prices have made it even more important to examine the prices of animal products and their associated factors. This study aimed to reveal the pattern network structure and characteristics of the prices of animal products and related variables for the 2010-2020 period in Türkiye. For this aim, a network analysis covered the prices of animal products, input prices used in producing these foods, and some economic variables. The study results revealed that Producer Price Index (PPI) and Agricultural PPI are key variables that are highly active in the network and act as a bridge between other unconnected variables. In addition, the results of the analysis suggest that the overall network consists of highly correlated variables and that the PPI and Agricultural PPI are the two most important variables. As a result, the pattern network structure shows that economic variables have a high impact on the prices of animal products. The network structure visualizes Türkiye's import-substitution model in animal production and foreign dependency on feed inputs, and the market structure affected by high exchange rates. In addition, as can be understood from the positive connections between feed inputs, the pattern network of the variables affecting the prices of animal products is largely shaped by feed prices and the internal dynamics of economic variables.

Investigating Factors Influencing Inflation in the USA

The determination of inflation has been extensively explored in economic literature and it remains an ongoing and developing field of study. Researchers have examined diverse econometric methodologies and analyzed data from varying time spans to identify the factors influencing inflation. In this research paper, we explore the factors influencing inflation in the USA by utilizing the monthly data from 2002 to 2020. To achieve our aim, we employ error correction model (ECM) and then granger causality method. The findings indicate that certain lagged changes in inflation, consumer price index (CPI) for fuel and utilities, CPI all city average and production price index play a significant role in accounting for the variations in inflation in the USA but the lagged changes in money supply are not a significant determinant in accounting for the fluctuations in inflation in the USA. Moreover, the results of causality analysis suggest that all factors have significant Granger causality on inflation, except money supply.

Examining the Effect of Crude Oil Shock on the U.S. PPI Through Time Series Analysis

Nowadays, people are becoming more curious about how changes in oil prices affect inflation, particularly how they impact macroeconomic indicators like the PPI (Producer Price Index) and CPI (Consumer Price Index), due to the ongoing pandemic and war's impact on oil prices. The present study examines the relationship between oil price changes and the PPI in selected industries. Using data from the trucking, chemical manufacturing, semiconductor, and electronic device industries PPI from 2020, with WTI (West Texas Intermediate) as the reference of U.S. oil price. The Pearson correlation coefficient and linear regression model were employed to analyze the correlations and evaluate the impact of oil price changes. The results demonstrate that the PPI for the trucking industry had the strongest correlation with oil prices over the same period, while the PPI for the chemical manufacturing industry demonstrated the strongest relationship with oil prices a month earlier. The PPI for the semiconductor industry displayed a strong correlation with oil prices six months ago. Overall, the transportation industry appears to be highly sensitive to changes in contemporaneous oil prices, and oil prices one month ago have a significant concussion on the current PPI of the chemical lines, and the effect of oil prices on the electronic device industry is weaker and more delayed.

The Relationship Between Money Supply and Price Movements from A Structural Perspective

This paper examines the impact of money creation and flow on prices, including house prices and financial asset prices, from a structural perspective. By analyzing central bank monetary statistics, the study highlights the relationship between money supply and price changes, emphasizing long-term effects on consumer prices and short-term effects on incremental household deposits. Furthermore, the study emphasizes the importance of monitoring changes in the money supply, both domestically and internationally, with specific attention to the PPI (Producer Price Index) changes. The real estate market is identified as a significant driver of money creation, particularly through deposits in non-bank financial institutions. Additionally, deposits in non-bank financial institutions are found to be an important indicator of stock market volatility. The paper recommends that asset prices be included as a key target of monetary policy and advocates closely monitoring trends in international commodity prices.

Predicting Financial Distress Using a MIDAS Hazard Model: Evidence from Listed Companies in China

ABSTRACT This study aims to predict financial distress in an emerging country using data on ST listed companies in China from 2001 to 2021. A new Aalen hazard model with mixed data sampling (MIDAS) is adopted to investigate the impact of monthly macroeconomic variables and quarterly financial variables on financial distress. The empirical results show that the current ratio, operating profit ratio, current capital ratio, retention ratio, profit ratio and income ratio of listed companies have a significant impact on the time-varying intensity of financial distress. The consumer price index has a negative relation with the intensity of financial distress, while the production price index and credit spreads have a positive influence. Finally, the results of the robustness tests are consistent with those with different lag orders.

The effects of the macroeconomic factors on the Bucharest Stock Exchange during the Covid-19 pandemic

"The purpose of the paper is to observe and analyze how the dynamics of macroeconomic factors influence the evolution of the Bucharest Stock Exchange (BVB) through the lens of the main stock indices (BET, BET-Plus, BET-FI), but also of the stock market capitalization. The research carried out consists of four stages. First, the monthly values of the macroeconomic factors considered were collected (inflation rate, reference interest rate, unemployment rate, RON-EURO and RON-USD exchange rates, producer price index, gold price, average salary and the Covid-19 number of cases) during the period 2020-2021, as well as the monthly values of the Bucharest Stock Exchange market indices (BET-Plus, BET and BET-FI) and of the stock market capitalization. These data were collected from the websites: www.bvb.ro (Bucharest Stock Exchange), www.bnr.ro (National Bank of Romania), www.innse.ro (National Institute of Statistics) and www.ms.ro (Ministry of Health). Then, the descriptive statistics was used to describe the collected data (mean value, standard deviation, minimum/ maximum value). This step is followed by the use of the Pearson correlation to capture the correlation between each stock market index and the macroeconomic factor included in the analysis. And finally, multiple regression was used to see exactly how the Romanian stock market indices are influenced by any changes of macroeconomic factors. Keywords: macroeconomic factors, Bucharest Stock Exchange, stock market indices, Covid-19 pandemic JEL Classification: B22, C32, G10"

FOREIGN ECONOMIC STRATEGIC PRIORITIES OF UKRAINE

The paper is devoted to the analysis of modern foreign economic strategic priorities of Ukraine, features of their implementation in the context of the key purposes of economic development of the country. Emphasis is placed on the need to use modern tools to support domestic producers and limit import expansion in the context of the formation of a new export strategy of Ukraine. Ukraine is a small open economy that makes extensive use of external factors of economic development both in the context of markets for its products, especially the agricultural sector, and attracting important resources of critical imports, including oil and natural gas, electronic equipment and more. From this point of view, the analysis has revealed the role and importance of two key players in the world market – the United States and China – in ensuring Ukraine’s external economic balance. The paper empirically examines the dependence of certain macroeconomic indicators of Ukraine on similar indicators of the United States and China (the so-called ‘large’ and systemically important economies for Ukraine). Vector autoregression models were used as a research tool to explore the dynamic interdependencies between macroeconomic indicators in the case of explaining their present values by the previous ones. For this purpose, percentage changes in gross domestic product and consumer price index compared to the corresponding period of the previous year for the USA, China and Ukraine were selected. As a result of the research, impulse-response functions from ‘large’ economies showed the dependence of indicators that characterise economic development in Ukraine from them and their long-term absorption, both in the context of economic growth in these countries and inflation imports from abroad. Moreover, it should be noted that the influence of China is more significant than that of the United States, especially if we consider the impact of fluctuations in the consumer price index in China on economic growth and inflation in Ukraine. This necessitates further research on this issue in line with the formation of scientifically sound foreign economic strategy and policy of Ukraine. The impact of economic growth in the United States and China on Ukraine’s GDP fluctuations is positive and almost the same (peaking in the second quarter and gradual levelling over two years). In turn, the variance decomposition of forecast errors for Ukraine’s GDP shows that in the long run about 52% of its variability result from modelled external factors, which in our opinion is due to significant openness of Ukraine’s economy and, consequently, ‘large’ economies. Finally, paper emphasizes the need to assess their international economic policy to minimize risks in the implementation of the country’s foreign economic strategy.

A Cyclical Phenomenon among Stock & Commodity Markets

Considerable studies have examined the relationship between commodity markets and stock markets. This paper studies the cyclical relationship between commodity markets and stock markets with implications for investing based on index relationships. Stock markets are represented by the U.S. S&P 500 index and aggregate commodity markets by the U.S. producer price index (PPI). Tradeable market indexes readily available to investors, namely the S&P GSCI Index and the Bloomberg Commodity Index (BCOM), are also studied. An optimal bandpass filter is used to estimate the cyclical component using a pricing-performance measure of the S&P 500 relative to the PPI, based on annual data from 1871 to 2022. The S&P GSCI and the BCOM indexes are also used to test the robustness of the findings. The impacts of the financial crisis of 2008 and the coronavirus pandemic are also assessed. The overriding conclusion of the study is that a cyclical relationship exists between stock markets and commodity markets for both aggregate and tradeable indexes which can last, from peak to peak, approximately 31 years. Measuring returns and risks in a manner consistent with these cycles can shed new light on the usefulness of commodity investing via tradeable indexes in seeking efficient portfolios.

Evaluating the cost impacts to meet China’s renewable electricity portfolio standard target in 2030

ABSTRACT The power sector plays a pivotal role in China’s carbon peak and carbon neutrality targets. To build a low-carbon power system, it is important to develop wind and solar. This study aims to evaluate the economic impacts of the newly launched renewable portfolio standard in 2030 in China using a cost minimization model and an input-output model. The results show that to accomplish the renewable electricity portfolio standard in 2030, the installed wind and solar capacity will have to reach 1451.9 gigawatts (GW) in 2030. The Northeast, Northwest, and North regions will deploy the most installed capacity, and Inner Mongolia will take on the most renewable energy generation tasks. The annual cost of wind and solar development is expected to be 506.6 billion CNY in 2030, 94.7% of which are new construction costs and storage costs. Renewable energy growth will result in a national average electricity price increase of 5.4 CNY¢/kWh compared to 2019, and Heilongjiang, Gansu, and Shanxi are the most affected. The rapid development of renewable electricity in the next decade will increase the Consumer Price Index (CPI) by 0.4%, Producer Price Index (PPI) by 0.9%, and Gross Domestic Product (GDP) deflator by 0.5% in 2030. Based on the results, we propose to improve the electricity market mechanisms, enhance the electricity transmission stability, and develop policies appropriate to local conditions.