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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]

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]

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]

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]

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.)

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 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]

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]

This paper examines a linkage between consumer and producer prices within European countries. The research sample in this paper includes 23 European countries and monthly data between February 2000 and March 2023 for the harmonized index of consumer prices (HICP) and producer prices index (PPI). Using a wavelet coherence approach country country-specific linkage was provided for each of the considered countries. Empirical findings suggested diverse relationships. However, the most frequently observed linkage is the one with producer prices as leading and consumer prices as lagging variables and a positive correlation between the two. Furthermore, the relation is more prominent at higher scales or lower frequencies. Conclusively, monetary policy needs to monitor producer prices while targeting inflation rates. [ABSTRACT FROM AUTHOR]

Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE = ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper delves into the relationship between inflation and economic growth, two pivotal elements in contemporary economic discussions. It aims to demystify and elucidate these concepts, particularly examining how they are interlinked through the development of a comprehensive econometric model. The study pays special attention to key economic indicators such as the Consumer Price Index (CPI), Producer Price Index (PPI) and the growth rate of Gross Domestic Product (GDP). In addition, the paper considers the role of monetary and fiscal policies in managing inflation and fostering economic growth. This exploration not only contributes to a deeper understanding of economic dynamics but also offers valuable insights for policymakers and economists in formulating effective economic strategies. [ABSTRACT FROM AUTHOR]

The article aims to demonstrate their internal statistical consistency of the price series used by the U.S. Bureau of Labor Statistics (BLS) to track changes in gas prices throughout the supply chain. Cited are the impact of gas station markups on gasoline inflation, how BLS price measures are accurately simulated, and the inflationary impact of gas station markups over the course of 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 (BETPlus, 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. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has suppressed the economy and people's welfare, including in Indonesia and Central Java Province, as indicated by the weakening of the national economy by −2.06 percent and locally by −2.65 percent in 2020. Although the economy grew by 3.32 percent in 2021, societal welfare remains lower than in 2019, marked by an increase in unemployment and poverty throughout 2019–2021. Furthermore, the threat of COVID-19 including new variants of the virus continues to weigh on the economy, in 2022 and beyond. This study considered an industrial approach to production, based on inter-industrial linkages and policy simulations with input–output analysis. The objectives of this research are to analyze the impact of the COVID-19 pandemic on the economy of Central Java and to formulate an effective economic recovery policy for industry. The results show that the industries affected by the COVID-19 pandemic in Central Java can promote recovery of overall income in economic industry better than the leading industry and the industries with the highest output multipliers, expressed as a proportional increase in final demand for each industry. Meanwhile, the economic recovery strategy of increasing final demand in industries with high output multipliers results in a faster increase in economic output compared with increasing final demand in the affected industries or leading industries. [ABSTRACT FROM AUTHOR]

Introduction: Despite the positive effects of the liberalization of economic activity around the world, the Iranian government still has a major share in the country's economy. However, almost all economists agree on the low efficiency of government economic activities. Restricting government involvement in these activities is a move that has been proposed and pursued by the World Bank and other global economic organizations, especially in recent decades. One of the policies recently proposed to reduce government interference in the Iranian economy is the elimination of 42,000 Rials in the country. But reducing government interference in agricultural activities can have many positive and negative effects. One of these effects could be rising food prices. In recent years, the frequency of large food price increases has accelerated around the world. Due to the dramatic changes in food prices, there have been numerous studies postulated that the exchange rate and crude oil price instability are the main determinant of the food price crises in different countries. The exchange rate is at the center of the policy debate in both developed and developing economies. Therefore, the purpose of this study is to investigate the asymmetric effects of exchange rate changes on food price fluctuations in Iran. For this reason, the NARDL approach has been used. Materials and Methods: Due to the possibilities of asymmetric impact exits between the underlying variables (exchange rate and food price), the asymmetric Non-linear Unrestricted ARDL proposed by Shin et al. (2014) is widely used by the researchers, such as Ibrahim (2015) and Abdlaziz et al. (2016). The main purpose of this test was to test for the presence asymmetric effects in both long- and short-run relationships between economic time-series. Shin et al. (2014) applied the positive and negative partial sum decompositions to test the asymmetric effects. This asymmetric Unrestricted ARDL cointegration approach which allows the joint analysis of non-stationarity and non-linearity issues in the context of an unrestricted error correction model (ECM). In this study, in order to investigate the asymmetric effects of currency shock on food prices, the model introduced in the study of Wong and Shamsdin (2017) was used: ... . Results and Discussion: The findings of the study indicate several important relationships between key variables (oil price, per capita GDP, exchange rate, and trade liberalization) and food prices in Iran. Firstly, the study reveals that these variables exhibit long-run cointegration with food prices, suggesting a significant relationship between them. Secondly, the Unrestricted NARDL model demonstrates that exchange rates have a significant long-run asymmetric impact on food price changes in Iran. This implies that changes in the exchange rate can have a varying effect on food prices, depending on whether the exchange rate is appreciating or depreciating. Thirdly, the study finds that while longrun and short-run changes in oil prices do not have a significant impact on food prices in Iran, the long-run growth of per capita GDP and trade liberalization do have a significant impact on food price fluctuations. Specifically, the coefficients of the exchange rate variables indicate that a 1 percent increase in the exchange rate results in a 0.32 percent increase in the consumer food price index and a 1.05 percent increase in the producer food price index in the long run. The asymmetric impact of the exchange rate on Iranian food price fluctuations suggests that policymakers should prioritize stabilizing the national currency to manage the movement of food prices. This implies that economic policymakers, who aim to reduce inflation, protect vulnerable populations, and ensure food security, need to consider currency stability in their decision-making process. In addition to price liberalization, supportive policies should be implemented to prevent economic barriers to accessing food. Overall, these findings highlight the importance of understanding the relationships between key economic variables and food prices in Iran, and the need for policymakers to take these factors into account when making decisions regarding inflation, food security, and supporting vulnerable populations. Conclusion: In this study, the focus was on investigating the asymmetric impact of oil price, real GDP, and exchange rates on food price fluctuations in Malaysia. The findings revealed that the exchange rate had a significant asymmetric effect on the movement of food prices, indicating its importance in understanding the current food market situation in Iran. This suggests that policymakers should prioritize addressing exchange rate issues rather than solely focusing on crude oil prices when formulating food price policies. The study emphasizes that stabilizing the national currency is of greater importance than controlling oil prices, as demonstrated by the research findings. Rising food prices have a detrimental impact on the economic access of vulnerable groups to sufficient food and pose a significant threat to food security. Therefore, it is recommended to pay attention to the positive relationship between increasing exchange rates and agricultural product prices. In the short term, it is essential to implement supportive policies to prevent a reduction in economic access to food. These policies should aim to address the challenges faced by vulnerable groups in accessing affordable food. By taking these measures, policymakers can mitigate the negative consequences of rising food prices and ensure food security for all. [ABSTRACT FROM AUTHOR]

This study focuses on analysing the economic impact (in value-added terms) generated by Spanish wine exports during the period 2018–2020, characterized by significant geopolitical changes and market shocks produced by Brexit, the entry into force of the Federal Law on Viticulture and Winemaking in the Russian Federation, the imposition of tariffs by the USA and the COVID-19 pandemic. For this purpose, the multiregional input–output model was used to identify the overall effects (direct and indirect) in different countries deriving from the changes in final demand. The paper shows that both the world wine trade and Spanish wine exports have been negatively affected by the recent changes in the analysed period. It is estimated that the accumulated losses since 2018 as a result of the decrease in the value-added generated by Spanish wine exports exceed 300 million euros. In addition, the results suggest that the negative effects of COVID-19 are greater than the effects caused by the rest of the geopolitical changes. [ABSTRACT FROM AUTHOR]

Brands, and the trademarks that define them, now indicate much more than simply the source of a good. They represent a line of communication between producer and consumer, allowing the consumer to notify the producer of what they value, support, and desire. To do so successfully, however, trademarks require some level of protection. Setting this level is tricky due to the countervailing concern of quelling the public's freedom of expression. This Note examines the level of protection set by the Ninth Circuit in VIP Products v. Jack Daniel's and concludes that it was incorrectly set. It goes on to discuss the underlying policy goals that are involved when drawing this line and proposes a framework that appropriately balances the protection of both the trademark holder and the public. [ABSTRACT FROM AUTHOR]

The article has three aims. The first aim is to develop an improved version of the Keynes-Metzler-Goodwin (the KMG) monetary growth model originally presented and analysed in a series of publications by Carl Chiarella, Peter Flaschel and Willi Semler. The improvement of the model is obtained by modifying some of its equations in a way which ensures that they reflect real macroeconomic dependencies more properly. The equations that have been modified describe final demand expectations, determinants of production decisions, fixed capital accumulation, tax revenues, government budget deficit and money demand. The second aim is to transform the model into an intensive form described by seven non-linear differential equations and determine its unique steady state which shows proportions between variables on the balanced growth path. The third ultimate aim is to present a mathematical proof that the new improved version of the KMG model is locally asymptotically stable. [ABSTRACT FROM AUTHOR]

The main goal of this research is to study the impact of uncertainty on export performance, from a resource-based perspective. Despite the ample research on how economies behave during periods of high uncertainty, there is still a poor understanding of how this affects smaller sectors, particularly the most exposed to global competition. In this paper, we perform an evaluation of export performance for the Portuguese pharmaceuticals sector (PPI), before and after a period of considerable uncertainty. This study's results show strong incremental performance gains, during this period. It also confirmed the importance of marketing capabilities, innovation and networking resources in developing international businesses. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia Contemporânea is the property of Revista de Economia Contemporanea 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.)