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The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exchange rate fluctuations have far-reaching consequences for national economies, companies and, above all, consumers. It is therefore important to understand the mechanisms by which these fluctuations affect food prices in domestic markets. In this context, we examine the complex relationship between exchange rates and domestic food prices and attempt to calculate the pass-through effect of the exchange rate on domestic prices. To examine the pass-through effect of the exchange rate on food prices, we use monthly USD/TRY exchange rate data from January 2003 to July 2023 as well as the global food price index, the export-import rate for food and the producer price index for food in Türkiye. We employ the vector autoregression (VAR) method to determine the optimal lags of the variables included in the analysis and the artificial neural network (ANN) algorithm to calculate the overall pass-through effect of the USD/TRY exchange rate on domestic food prices. The results show that the pass-through effect of the variable on food prices persists for three months: the current month, the previous month and the two previous months. The total pass-through effect of the exchange rate on food prices reaches up to 27%. Half of this impact comes from the previous month's food prices. In addition, the ratio of food exports to imports accounts for about 12% of the increase in domestic food prices. The results also show that the variable with the highest pass-through effect is the producer price index and that 90% of the increase in food prices is due to domestic dynamics, while the effect of price increases in foreign markets is only about 10%. [ABSTRACT FROM AUTHOR]

This research discusses the impact of climate change on inflation, as well as the limitations of existing econometric models in incorporating multiple climate-related variables in determining inflation. To address this limitation, the study introduces the climate condition index (CCI) for 153 countries covering the 1901–2021 period to investigate the relationship between climate and agricultural inflation. This index represents a weighted average of various climate variables, including cloud coverage, temperature, precipitation, vapor pressure, and wet day frequency. The weights are the long-term cointegration coefficients of the climate variables concerning the Agricultural Producer Price Index (APPI), which were obtained from separate vector error correction models (VECMs) constructed for each country. The study shows that climate change has a significant impact on agricultural prices globally, as evidenced by a median Granger causality p-value of 0.036 across 153 countries (CCI Granger causing APPI in 127 countries with a 90% confidence interval). The results align with panel vector autoregression and country-specific VECMs impulse responses, which indicate that climate conditions negatively impact agriculture in 142 countries, while 11 countries gain from climate change. The study's overall results suggest that climate change substantially affects agriculture globally. In light of the significant economic reliance on agricultural production, it is advisable for governments to consider the issue from the perspective of sustainable growth, while central banks should tackle it with a focus on the inflationary implications through green quantitative easing. [ABSTRACT FROM AUTHOR]

The article discusses the history and future of the "Prices of Music Monographs and Scores" column in the journal Notes. The column, which began in 1979, aimed to gather data on music score sales and identify trends in pricing. However, a recent survey revealed that the column was ranked as the least useful by readers. The article traces the evolution of the column's methodology and content over the years, highlighting the challenges of gathering accurate and comprehensive data in the digital age. Moving forward, the column will focus on music monograph and score publishing trends, updates, and innovations. [Extracted from the article]

This paper aims to examine the changes in global value chains (GVCs) in the years 2020–2022 (times of the COVID-19 pandemic) compared to 2019 (last year before the pandemic). GVCs are illustrated by diagrams based on the statistics on international trade measured by value-added exports and imports. In this paper, the final demand approach is applied to measure international trade by value-added exports and imports and they were computed based on inter-country input-output tables delivered by the Asian Development Bank (ADB). The catchphrase "Made in the World" makes an impression, but in reality, value chains are rarely global. They are rather regional with three centers. "Factory Europe", "Factory Asia", and "Factory America" can easily be identified in the years 2019–2022 as there were three large regional supply hubs in GVCs: Germany, China, and the United States. When the years 2020–2022 were compared to 2019, "Factory Asia" with center in China elevated their position in GVCs at the expense of "Factory Europe" with center in Germany and "Factory America with center in the USA." [ABSTRACT FROM AUTHOR]

Cost estimating in the construction industry is challenging due to the high uncertainty associated with the supply chain, especially after the COVID-19 pandemic. Some research studies have addressed such problems by developing models that predict material cost. In fact, all material can be interconnected and interrelated with lead-lag relationships such that any inflation in one material's price can be associated with inflation in other materials' prices, referred to as transmission of inflation. Despite the latter, none of the existing studies have investigated inflation transmission among all construction materials. This paper fills this knowledge gap. The authors used a multistep research methodology. First, Producer Price Index (PPI) data for 16 construction materials were collected and sorted. Second, the vector autoregression technique was used to model the relationships between each pair of material and subsequently validate the associations using the Granger causality test. Third, network analysis was performed to identify the inflation transmission capacity (out-degree centrality), inflation susceptibility (in-degree centrality), and inflation intermediatory capacity (betweenness centrality) for each material. Finally, modularity-based clustering was conducted to categorize the materials based on their price indices' interconnections and examine inflation transmission path among different sectors of construction-related material. The results show that metals and plastic products are generally the highest transmitters of inflation to other construction material including "Fabricated structural metal products" and "Plastic construction products." Furthermore, the results show that "Concrete products," "Flat glass," "Brick and structural clay tile," and "Architectural coatings" are also high transmitters of inflation and thus can be key indicators of increase in the overall construction cost. This paper provides the industry stakeholders with leading indicators and early warning signs for the inflated material prices. Contractors and owners can utilize those warning signs to enhance their procurement plans and budgeting decisions. [ABSTRACT FROM AUTHOR]

This article explores the use of a geometric-mean formula in calculating the Producer Price Index (PPI) research series. It discusses the advantages of this formula and compares the results with the official PPI series. The article also examines the use of geometric means in consumer price indexes and the mathematical considerations behind the choice of formula. It provides data on the PPI geomean research series for final demand and compares it to the official PPI series. Additionally, it compares the use of geometric means in the Consumer Price Index (CPI) and provides data on the CPI based on both arithmetic means and geometric means. The article highlights the impact of the geometric mean formula on index values, with smaller differences between official and geomean growth rates for goods compared to services. It attributes the larger difference in growth rates for services to price dynamics for trade services, which have more volatile and dispersed margin prices. The article also emphasizes the noticeable effect of formula choice on index values and suggests a trend of substitution towards less expensive goods in the PPI. [Extracted from the article]

Copyright of Journal of Islamic Research / İslâmi Araştırmalar Dergisi is the property of Turkey Economical & Cultural Solidarity (TEK-DAV) Foundation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Akademik Hassasiyetler is the property of Huzeyfe Suleyman Arslan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of industrial robotics is increasingly integrated within different production processes in manufacturing. This raises concerns about the potential impacts of automation on labor-intensive production tasks in the country of installation, but also in other economies along global value chains (GVCs). We analyze the relationship between the use of robots in Germany and the employment content of foreign exports serving German final demand for 14 manufacturing industries over the period 2005 to 2015. Our results indicate a positive relationship between robotization in Germany and employment in its upstream GVC trade partners in the long run. When comparing OECD and non-OECD countries, the positive association is more pronounced in the non-OECD sample, especially in low-tech industries. The positive association is stronger for industry-country combinations that have a high share of intermediates exports to Germany. Our findings indicate a positive productivity effect resulting from automation. • We examine the use of robots in Germany and the employment content of its imports. • German robots are positively related to employment in German imports in the long run. • This positive relationship is larger in non-OECD sample and in low-tech industries. • Stronger link for industry-country pairs with a high share of exported intermediates. • Indication of a positive productivity effect resulting from automation. [ABSTRACT FROM AUTHOR]

Purpose: Despite the central role of profitability in economic analysis, previous research has yielded diverse and often unstructured conclusions regarding its determinants. To address this gap, this empirical investigation aimed to explore the major determinants of company profitability. Design/methodology/approach: It conducted a comprehensive analysis of factors, encompassing: changes in the gross domestic product, Consumer Price Index, Producer Price Index, NBP’s Reference rate, investment outlays, intramural expenditures on research and development, expenditures on innovation activities in enterprises, and patents granted, alongside company-level profitability indicators. The study's sample consisted of companies representing 19 sectors of the economy, spanning from 2004 to 2021. For data analysis, a neural network was employed, specifically a multi-layer perceptron (MLP) utilizing the sigmoid activation function. Findings: The findings suggest that alterations in macroeconomic variables can significantly impact the profitability of companies. The analysis carried out revealed that consumer price index, reference rate, gross domestic product and producer price index were the most important exogeneous factors. Originality/value: This study introduces several novelties, including the application of neural networks, which are infrequently utilized in this field, and the simultaneous analysis of a comprehensive set of independent variables. [ABSTRACT FROM AUTHOR]

Competition is the catalyst of corporate innovation, which is the foundation of a country's core competitiveness. From the view of vertical industrial chain, this paper examines how downstream competition affects upstream firms' innovation output. Given innovation helps reduce the production cost of each product, increasing demand of intermediate products caused by downstream competition enhances the marginal revenue of upstream firms' innovation, thus leading to an innovation incentive effect. To identify causality, we utilize China's WTO accession, a quasi-natural experiment, to calculate downstream competition combined with import tariff reduction and input-output table, and then run a difference-in-difference regression. Using the data of Annual Survey of Industrial Firms and State Intellectual Property Office, our results find that downstream competition has a significantly positive impact on upstream firms' innovation, and this effect is more pronounced in firms with less industry competition, greater initial innovation capacity and weaker financial constraints. Moreover, the number of firms, the degree of industry competition and the scale of intermediate products in the downstream sector increase, confirming the market size channel. In sum, our findings document that downstream competition can motivate upstream firms to innovate through vertical industrial chain, providing some useful insights for China to build a fair market competition environment and promote innovation-driven growth regime. [ABSTRACT FROM AUTHOR]

This paper provides the evidence of a fall of markups of price over marginal costs in Poland over the period 2002–2016. Markups were calculated using a census of firms and the methodology proposed by De Loecker and Warzynski (2012; American Economic Review, 102(6)). The fall of markups is robust to several empirical identification strategies. Moreover, the decline of markups is not related to changes in a sectoral composition and firms demography and is most severe in exporting firms. Our empirical results relate the fall of markups to the globalisation and the emergence of the Global Value Chains. We show that an increasing reliance on imported components in production, together with a rising concentration of domestic firms on export markets are the main factors behind the observed compression of markups. We also document a hump‐shaped (U‐shaped) relationship between foreign value added in exports (distance from final demand) and markups. [ABSTRACT FROM AUTHOR]

Due to outdated weighting information, a Laspeyres‐based consumer price index (CPI) is prone to accumulating upward bias. Therefore, the present study introduces and examines simple and transparent revision approaches that retrospectively address the source of the bias. They provide a consistent long‐run time series of the CPI and require no additional information. Furthermore, a coherent decomposition of the bias into the contributions of individual product groups is developed. In a case study, the approaches are applied to a Laspeyres‐based CPI. The empirical results confirm the theoretical predictions. The proposed revision approaches are adoptable not only to most national CPIs but also to other price‐level measures such as the producer price index or the import and export price indices. [ABSTRACT FROM AUTHOR]

We propose an empirically estimated inter-regional input-output model of the Italian economy designed for COVID-19 impact assessment, intended as a tool for public authorities facing comparable adverse events and requiring timely estimates of sectoral and regional economic impacts. We evaluate the contributions of demand- and supply-side factors to output losses in Italy during the pandemic, providing insights on the suitability of demand- and supply-side policies. Supply-side shocks, as a consequence of mandated closures, are the primary driver of output losses only during the nationwide lockdown of spring 2020. During the following stages, changes in final demand due to income losses and changes in mobility play a pivotal role at the aggregate, regional, and sectoral levels. While this result supports demand-side policies, the efficacy of such policies may be hampered when final consumption demand is low chiefly due to reduced mobility rather than income losses. [ABSTRACT FROM AUTHOR]

Agriculture is associated with the production of essential food crops for decades and is the one which is playing an important role in the economy of a country as well as in life of an individual. Due to various uncertain variations in the climatic conditions such as rain and other affecting factors, crop prices vary in an unusual pattern. This variation of prices without the knowledge of the farmer may lead to losses in the economy of the individual who is involved in agriculture. In this paper, we have discussed a well-designed system which accurately predicts the crop prices of future months. We have used a Supervised Machine Learning algorithm that is Decision Tree Regression technique for the design of the prediction model as the data is of continuous form. The parameters, which are considered in the dataset, include crop name, month, year, rainfall and wholesale price index (WPI). We have considered the data of 22 crops in total with 4 parameters. We have developed a user-friendly user interface consisting of 22 crop profiles with the predicted prices. Our results show that the regression model achieved an accuracy of 97.32% which will help the farmer on decision of future crop selection for the growth and also hyperinflation can be avoided. [ABSTRACT FROM AUTHOR]

One of the crucial jobs of central banks is to rein in inflation as it creates uncertainty in the economy and in private investment and ultimately negatively impacts the economy. If the source of inflation is positive demand shock, then raising the federal funds rate target is the right way to rein in inflation. If the source of inflation is negative supply shock, then raising the federal funds rate target will make things worse. In this study, the impact of FFR (federal funds rate) on CPI (consumer price index) and producer price index (PPI) is examined. Findings indicate that raising the federal funds rate will have a negative impact on both CPI and PPI with a 2-period lag. The possible explanation of this finding is that raising federal funds rate lowers aggregate demand, lowers the price level and thereby the CPI. And when CPI falls, it lowers perunit profit, prompting producers to cut supply, which in turn lowers the demand for producer goods and services, and thereby lowers PPI. [ABSTRACT FROM AUTHOR]

This study investigates how household inflation expectations relate to realized inflation, and uses biased expectations hypothesis (BEH) to understand the association between inflation expectations and realized inflation in the food sector. Household survey data, wholesale price index (WPI), and consumer price index (CPI)-based data on inflation are used in the study and the data was sourced from the Reserve Bank of India (RBI) and Ministry of Statistics and Program Implementation, Government of India. The obtained results were cross-validated using auto-SARIMAX algorithm in a machine learning (ML) framework. The study finds that expected and realized inflation have significant causal relationships; this further supports BEH and reveals how household inflation expectations impact actual market inflation, and vice versa. Hence, these are broadly complementary. However, changes may occur in these aspects, and households and markets must prepare for these changes and plan their future accordingly. [ABSTRACT FROM AUTHOR]

Purpose: The 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/approach: Using 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. Findings: The 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/implications: The 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 implications: Since 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/value: While 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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi is the property of Journal of Economic Policy Researches / Iktisat Politikasi Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exchange rate is a crucial macro indicator for any open economy, whereby any change in the rate would influence, to a certain extent, various socioeconomic facets. One of the significant impacts is revealed in the pass-through of exchange rate variations to price inflation, also known as the exchange rate pass-through (ERPT). This paper explores the effect of ERPT on domestic inflation in Vietnam by comparing with other Asian economies. Specifically, pass-through mechanisms are examined for every single price as well as a distribution chain from import price index (IMP), producer price index (PPI) to consumer price index (CPI). The empirical results show that exchange rate shocks positively influence the price changes in Vietnam as well as in other Asian countries. According to Cholesky decomposition, the exchange rate plays a limited role in regulating CPI movements. Besides, world commodity prices are found to have a significant impact on domestic inflation across the sample-countries. Our findings reveal profound implications for monetary policy management and price stability. [ABSTRACT FROM AUTHOR]

This paper aims to study the relationship between producer price index (PPI) and consumer price index (CPI), as well as between components of producer prices and components of consumer prices in Azerbaijan by using monthly data covering the period from March 2004 to June 2023. Applying wavelet analysis approach, I find that there is a mixed relationship between CPI and PPI, and co-movement is observed mainly at medium and low frequencies. The results reveal that although there is a weak co-movement between aggregate PPI and CPI, a significant and strong relationship exists between food PPI and aggregate CPI, as well as between food PPI and food CPI. Additionally, producer prices of agricultural products demonstrates comovement with consumer prices in certain periods. In terms of lead-lag relationship, producer prices lead consumer prices. Although wavelet methodology does not necessarily determine causality, this study provides insights for policymakers about the direction of possible causality between producer prices and consumer prices. [ABSTRACT FROM AUTHOR]

To assess the economic ripple effect, this study integrates agent‐based modeling (ABM) with a multiregional input–output (MRIO) table to develop an assessment model that considers capacity recovery process. The intermediate and final demands in the MRIO table are used to describe the agents' interdependence. Survival analysis is used to construct capacity rate curves. By defining the first‐ and second‐order ripple effects, ABM is used to capture the ripple process in days. To conduct a case study, the service and retail sectors in Enshi in Hubei, China, are selected as disaster‐affected sectors (they were severely affected by the July 17, 2020 flood disaster). The main findings are as follows: (1) With the first‐order ripple effect, the losses caused by service and retail are concentrated within Enshi. Enshi's final demand, construction, and raw materials manufacturing sectors as well as Wuhan's construction sector are seriously affected. (2) With the second‐order ripple effect, the losses caused by the service and retail sectors expand, forming a prominent industrial ripple chain: "service (retail)–raw materials manufacturing–construction." (3) The direct and indirect losses caused by the service sector are more significant than those caused by the retail sector. However, the loss ratio of the service sector is smaller than that of the retail sector because of its sound industrial structure and strong resilience. Hence, the indirect losses caused by different sectors are not entirely determined by their direct losses; instead, they are also related to the degree of perfection of the structures of different sectors. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments. [ABSTRACT FROM AUTHOR]

This article uses for the first time inter-country input–output data and a structural decomposition method to assess the impact of the 2016 Brexit referendum on the United Kingdom's value-added exports. We find that final demand explains less than 30% of the 6.9-point decline in the UK's value-added exports in 2016 (with a market share loss that offset the positive impact of global demand), whereas more than 70% was due to extraordinary changes in input sourcing decisions of the rest of the world. This means that the UK's value-added exports were structurally affected by the weakening of the linkage effects of providing inputs to the rest of the world, especially to the EU. This article paves the way for further analyses of the structural effects of Brexit and other trade and industrial policies that alter the international allocation of inputs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two perspectives on the analysis of economic growth have developed within the input–output framework. On the one hand, Leontief proposed a dynamic model, where investment supplies the necessary capital stock needed to produce future output. On the other hand, Structural Decomposition Analysis assumes that changes in the output level are dependent on the behaviour of the technological and final demand components. This paper examines the differences between input–output tables of different periods to explain growth, as well as the contribution of the final demand components in the phenomenon. Further, the article provides examples based on the coefficient tables of five OECD economies, between 1995 and 2011, to illustrate the relevance of the method proposed here. [ABSTRACT FROM AUTHOR]

This paper shows that trade in services is still at its infancy in Africa. Its growth started later than for other developed and developing economies and, so far, it involves mostly low‐skilled services. Disentangling the different sources of trade growth, we find that demand and supply determinants have been relatively stable during the period 2002–2016, while service diversification and trade policy are the main propellants. In particular, trade in goods liberalisation increased services trade as well due to the complementarities between the two. In terms of geographical and industrial involvement, services produced in Africa are able to reach farther destinations than goods, but they are concentrated on industries close to final demand, thus missing high‐skilled services that are more upstream, but represent higher value‐added inputs. Therefore, there is still plenty of scope to consider trade in services as a potential source of growth and development for African countries. [ABSTRACT FROM AUTHOR]

This paper examines Sub‐Saharan Africa's (SSA) role in global value chains (GVCs) through a new comprehensive country‐sector level database. It reaffirms known aspects: SSA's involvement in GVCs has grown but still trails other regions, especially in manufacturing. Typically, SSA is involved in upstream production stages, exporting commodities that are later processed and re‐exported by other countries. Only few SSA countries have integrated into manufacturing GVCs, importing inputs for export production. SSA also engages more in GVCs with external partners than with other SSA countries. Yet, new insights also emerge. SSA increasingly provides inputs for its own export products, though not for domestic sales. Additionally, African GVCs are shifting alliances; China and India are rising as sources for supply of inputs and final demand, overtaking Europe. However, the United States and Europe remain pivotal final destinations for African goods and re‐export hubs of African value added. [ABSTRACT FROM AUTHOR]

Large‐scale platforms (LSPs) with valuation and awareness advantages have enabled competing small‐scale platforms (SSPs) to be embedded in their platforms. This compatibility strategy creates a new channel, that is, the compatible channel, through which customers can purchase services from SSPs via the LSPs. Additionally, numerous platforms have been introducing add‐on services to enhance their profitability. In this study, we develop stylized game models to characterize the interaction between an LSP and an SSP and explore their strategic and operational decisions on platform compatibility under add‐on services. Our major research findings are as follows: First, compatibility has opposite impacts on service pricing. That is, at a low proportion of demand through the compatible channel, the two platforms engage in a price war; otherwise, they both raise prices. Second, we identify the conditions for platform compatibility: Compatibility becomes an equilibrium strategy if the proportion of demand through the compatible channel falls within an intermediate range. Third, we find that homogeneous add‐on services stimulate rather than inhibit compatibility due to the different profit foci of two platforms. Finally, we conduct extensions to further verify the robustness of the conclusions. Our results provide important implications to the burgeoning debate on when platforms should implement compatibility to achieve a win–win scenario under a variety of settings. [ABSTRACT FROM AUTHOR]

The inflation rate was an important indicator for monitoring macroeconomic operations. In this study, we analysed the reasons for the divergence of consumer-side inflation (CPI) and production-side inflation (PPI) trends through large-dimensional data, so as to consider the impact of various macroeconomic factors on the inflation. From the perspective of the diversity of inflation rate formation mechanisms and model fitted performance, we used the large BVAR/large TVP-VAR models for analysis, considered the analysis capability and time-varying adaptability to large dimensional data. In view of the impulse response, we found that the impulse responses of CPI were relatively flat, and the responses of inflation were not exactly the same in both perspectives. The effects of various variables on the inflation in China were highly time-varying. In summary, the macro factors that led to significantly different responses and should be focused on energy prices, chemical prices, food prices, 7-day Inter Bank Offered Rate, narrow measure of money supply and the interaction between PPI and CPI. Accordingly, policymakers could develop more thoughtful and detailed scenarios from our findings. Moreover, the impulse response mechanism of each indicator to the inflation has validated the applicability of large dimensional analysis in macroeconomic studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]