فصلنامه اقتصاد مقداری
سال بیستم شماره 3 (پیاپی 78، پاییز 1402)

Although herding behavior is mainly defined on the basis of imitation and repetition in existing theories, it is difficult to provide a mathematical model which is able to identify this phenomenon.

Therefore, in this paper, using the Monte Carlo method and stock price data of Tehran Stock Exchange and OTC companies, during the years 2011 to 2019, the herding behavior among the sample companies is investigated. Given that the Iranian capital market is facing the phenomenon of closure, and this can affect the values ​​of price herding, the results are examined with the New York Stock Exchange as a market developed.

The first finding indicates the presence of herding behavior in 29.6% of possible cases in the sample. The second finding indicates the presence of herding at an average of 4.07%. The third finding reflects the increase in the amount of herding along with the increase in absolute return, which shows that as the stock prices change, the values of herding also increases.Also, the results show that the herding behaves almost symmetrically with increasing the absolute amount of stock returns, the amount of herding behavior is first decreasing and then increasing. Accordingly, when price change are slight, the amount of herding is small; But with drastic increases, the average herding behavior also becomes positive and upwards, reaching 16%. This means that as the price of one stock rises, the prices of other stocks also tend to rise, and the sharper the price increase, the greater the amount of imitation of price behavior. A similar trend is observed when prices fall. As prices fall and negative returns intensify, the average rate of herding behavior also increases, and the higher the decline in prices, the higher the rate of herding behavior. As a result, the higher the absolute amount of price return, the greater the amount of herding behavior.The fourth finding of the study indicates the possibility of a relationship between the herding and trading volume. To examine this relationship in the face of a sharp increase in the volume and number of trades, price data are classified into 20 groups based on the number and volume of trades. Then the amount of herding behavior of each group was calculated. The results show that the relationship between both indices of trading and herding is positive and significant and with a sharp increase in the number or volume of trades, herding measure approaches its maximum value. Similar results on the New York Stock Exchange are described below.

Evidence of herding behavior in the New York Stock Exchange also shows that this phenomenon occurs almost twice as much as in the Iranian capital market. Faster and more coordinated dissemination of news and faster reactions to them in NYSE can be the main reasons for this. These results are consistent with a study by Hwang and Salmon (2004) in which the amount of herding behavior in the US market was higher than the South Korean stock exchange. It seems that due to the less trading halts in NYSE, the significant values ​​of price herding in that market mean that the values ​​obtained for the Tehran market are probably not affected by the trading halts and the results can be reliable.

One of the most important goals of the countries, especially developing countries, is economic growth and development. Foreign direct investment along with human capital and financial development are important factors that can have a significant impact on economic growth. Human capital can be used as a facilitator for the transfer of technology related to foreign direct investment. In addition, evidences show that human capital plays a complementary role to the impact of foreign direct investment on economic growth. Technology overflow into domestic companies is also more efficient when financial markets in the host economy are more developed. Therefore, the purpose of this study is to evaluate the short-run and long-run effects of foreign direct investment, human capital and financial development on the economic growth of various income groups in developing countries.

The model used for unsustainable economies (developing countries) is designed to be appropriate for the current study. In this model, the effect of population growth rate and technology, physical capital, human capital, financial development, foreign direct investment, foreign direct investment interaction and financial development and the interaction of foreign direct investment and human capital on economic growth is examined. The statistical population is developing countries in the period 2000-2019. For the study, developing countries were divided into four categories: low-income, lower-middle-income, upper-middle-income, and high-income, which included a total of 27 countries (9 Asian countries, 9 African countries, and 9 South American countries). In this study, first, the significance of variables for developing countries by income groups was investigated. Then, based on the Kao test, the existence of long-run equilibrium relationships among the variables related to the income groups of developing countries was confirmed. FMOLS and DOLS methods were used to investigate the integration.

The results of long-run estimates show that foreign direct investment alone is 0.27 effective on the economic growth of high-income countries, and when combined with human capital and financial development, its impact on economic growth is 0.42 and It will be 0.104. Thus, the effect of the interaction of foreign direct investment and human capital is greater than the effect of foreign direct investment alone on the economic growth of high-income countries. But in other income groups of developing countries, the interaction of foreign direct investment with human capital and financial development has not had a positive effect on economic growth. Also, the results of short-run estimates showed that foreign direct investment alone does not affect the economic growth of high-income countries, but the interaction of foreign direct investment and financial development and the interaction of foreign direct investment and human capital by 0.48 and respectively. 2.13 affects economic growth. This result shows that in the short run, foreign direct investment alone has no effect on economic growth, but along with human capital and financial development have a positive effect on economic growth in these countries. In other income groups of developing countries, the interaction of foreign direct investment with human capital and financial development has not had a positive effect on economic growth. Also, the correct component of the error of foreign direct investment interaction with financial development and human capital shows that in each period, 78% and 95% of the imbalances in the system are eliminated to achieve long-run equilibrium, respectively. But in other income groups, the speed of adjustment is very low or non-existent.

These results show that high-income countries have realized that it is possible to increase productivity due to the high level of human capital and skilled labor through the transfer of new technologies and technology overflows, and this is a positive factor in Economic growth works. Also in high-income developing countries, due to the high institutional capacity in the economic structure that has led to comprehensive efficiency in the mechanism of credit allocation of these countries, the interactive effect of foreign direct investment and financial development on economic growth in the short-run and long –run is positive.

In national accounts, costs related to intangible capital include computer databases, research and development, design, brand equity, company-specific training, and organizational efficiency as investments has expanded (Corrado, Hulten, and Sichel, 2005). When this broad view of investment is included in the analysis of the sources of growth, intangible investments account for one-fifth to one-third of labor productivity growth in the industrial sector of the United States and the European Union and East and South Asia (India, China and Japan ((Liang,2021 ؛Bhattacharya and Rath,2020 ؛ Rico and Cebrer-Bares,2020 ؛Hintzmann, Masllorens& Ramos Lobo,2021 ؛ Corrado, Haskel, Jona- Iommi ,2013؛Corrado, Hulten,and Sichel ,2009 ؛Marrano, Haskel, and Wallis ,2009 ؛Fukao, Miyagawa, Mukai, Shinoda, and Tonogi, 2009 ؛ van Ark, Hao, Corrado, and Hulten, 2009). In order to manage intangible resources as a source of growth at the macroeconomic level and a driver of value creation for individual firms, their measurement is very important(Corrado,Jonathan HaskelCecilia, Jona-LasinioMassimiliano Iommi,2012). It is obvious that many studies have focused on intangible investment, which shows the importance of this topic.Therefore, in this research, focusing on intangible investment, intangible investment has been measured. In Iran, due to the lack of statistics on how to calculate and estimate this type of investment, few studies have been conducted in the field of intangible investment measurement.   For example, in the study of Ostadzad and Hadian (2014) on the effect of research and development on economic growth and the study of Rahimi Rad, Heydari and Najarzadeh (2016) investigated the factors affectingthe intensity of research and development costs in industrial workshops of Iran. But so far, this study has not been done to measure all the components of intangible investment in Iran and investigate its effect on economic variables.In Iran, studies have been conducted to identify effective factors in determining the value of intangible assets in companies listed on the Tehran Stock Exchange. However, the measurement of intangible investment with comprehensive international definitions and separated by the four-digit ISIC code has not been done so far. As a result, in this research, it has been tried to use the approach of Corrado et al. (2005), which is a method that can be cited in most Valid studies of the countries of the world have been defined to measure intangible investment, and also according to the comprehensive definitions accepted at the international level of intangible investment, this importance has been implemented for Iran and this important economic variable has been estimated (Mashaikhi, Birami and Birami, 2013; Corrado et al, 2005; Fukao, Miyagawa, Mukai, Shinoda, and Tonogi, 2009).Therefore, in this study, the CHS approach (a comprehensive and accepted approach that has been used in most important studies) has been used to measure intangible investment(Corrado, Hulten, and Sichel,2005). One of the important activities in the functions of Iran's economy is the activities of factory industries approved by the Iranian Statistics Center. This article first aims to estimate the share and trend of intangible capital in these activities with the CHS approach, which due to the lack of studies in this field is very important for Iran's economy.Also, how it affects the productivity of the entire production factors is also one of the next topics of this study, which can be the way of economic approaches and important decisions in this field. In order to find answers to the research questions andapproach its hypotheses, the studied time period of 1996-2018 has been used for industrial workshops with ten workers and above, separated by the four-digit ISIC code. The model used is panel data with GMM method. The results indicate that intangible investment has a positive and significant effect on the productivity of all production factors. In the continuation of the research, the workforce has been divided into professional and non-professional workforce. The estimation of the economic model of this research shows that the effect of skilled labor on the total productivity of production factors in Iranian industries is about 6 times compared to unskilled labor.

This section presents a model that describes the relationship between intangible investment and factor productivity. Assume that the added value of the industry according to the four-digit ISIC code and ten workers and above in industry i and time t can be written as follows:   On the right side, L and K are labor and capital. Similarly, R is the flow of intangible capital services and A is a change term that allows for changes in productivity as L, K, and R become returns. We get the differential from equation (1):   So that εX represents the production elasticity of factor X, which is basically different according to input, industry and time. To empirically examine the role of intangibles as growth drivers, existing literature is used and it is done in two stages. First, consider the condition of ε. For a company with the lowest cost, we have: where s is the share of payments of this invoice in relation to added value. So this simply expresses the first-order condition of a firm in terms of production elasticities. If equation (1) is Cobb-Douglas, ε is constant over time, and equation (2) may be transformed into a regression model with constant coefficients. . If (1), for example, the elasticity of substitution is constant, then ε will vary over time at all levels, so (2) may be written as a regression model with interactions among all inputs. Now suppose that a firm can K, L or R variables can be used in other companies, industries or countries. Therefore, as Griliches pointed out, the industry elasticity ΔlnR in lnQ ∆ is a combination of input and output elasticity. As a result, we can follow Stiroh and we have (Griliches, 1992; Stiroh, 2002): which shows that the productive elasticities of the factors are equal to the weight of the factors. In addition, here there is the flexibility of deviation from the weight of the factors due to spillover. All this can be shown in equation Second, consider Δln Qi,t. As Grilichs and Shankerman pointed out, if we include the R&D inputs in conventional L and K and use a regression model to determine the R&D production elasticity, the results will be biased. The main point of this argument is that intangibles (such as research and development) are long-term assets and not intermediate inputs, and should be included as value-added investments (Griliches, 1980; Schankerma, 1981). Adding the normal as V (where the intangibles are treated as intermediates), we can write:  So that N is a real intangible investment and we have approximated the share of intangible investment costs in nominal Q as sR. Substituting (6) into (5), we have:  If we have the above expression in terms of ΔlnVi,t:  For simplicity we have assumed that Δ lnR = lnN∆ (as in the steady-state "max consumption" case). To model the efficiency of factors, the studies of Caves, Christensen and Diewert have been used, and the ∆lnTFP index is constructed with the translog production function as follows (Caves, Christensen, & Diewert, 1982):  where ∆lnTFPi,t= ∆lnTFPQi,t and ∆lnTFPQi,t is calculated as follows: Obviously, these approaches have advantages and disadvantages, but the estimation of this regression potentially reveals the factors that played a significant role in TFP and were suppressed.

To measure the intangible capital from Iranian factory data with four-digit ISIC code during 1996 to 2018 is 2190. The data shows that the ratio of intangible capital to production for all industries is equal to 68.41%, which shows the high impact of intangible capital on Iranian industries[1].The results show that, contrary to the traditional approach, intangible investment is not included in intermediate goods, but is included as an important factor in the production function. The high number of observations has helped to estimate the model more accurately. The employment impact factor, including professional and non-professional labor force, is approximately equal to 22/, which has a positive and significant effect on TFP.Also, if the physical capital grows by ten percent, the total factor productivity will grow significantly by 2.3 percent on average in the same direction. The remarkable thing about the impact of intangible investment is that if this type of investment grows by 10 percent amongvarious manufacturing industries, it can help to increase the TFP by 3.5 percent on average, which actually shows the role of intangible investments in factory industries in today's world.These results indicate that the production share of Iran's industries is not only focused on physical investment and labor, and there is another type of investment called intangible investment, which is not only effective but also in the extent of its effect on industrial production is superior to the rest of the components.By studying the world's major industries such as Microsoft, it can be found that its market value in 2006 is much larger compared to the value of its physical investment and labor force, and this difference in value is included in the area of intangible investment.Therefore, for most industries in different countries, it is not far from reality that the amount of intangible investment has a greater impact than the rest of the effective components. As a result, the hypothesis that there is a positive and significant relationship between intangible investment on the productivity of all production factors can be accepted at the level of 99% with an impact factor of /356.Also, for a ten percent increase in skilled labor, the TFP changes on average by approximately 0.6 percent, while this figure is approximately 0.1 percent for unskilled labor. These results show that in addition to intangible investment having a positive and significant effect on productivity, skilled labor also helps to grow the productivity of factory industries.

The importance of intangible investment is growing relative to the level of investment in the tangible assets we intend to invest with, including transport infrastructure, machinery and power plants, etc. As a result, the role of intangible investment becomes increasingly important for understanding and predicting productivity trends, economic growth and innovation. The importance of intangible investment, such as research and development or software, to describe productivity, competitiveness and economic growth, has long been recognized by economic literature and statisticians.Considering investment in intangibles leads to a better understanding of differences in productivity trends across countries. In this research, firstly, the measurement of intangible investment with the CHS approach for the four-digit ISIC codes of factory industries with ten employees and above was discussed. The high share of intangible investment compared to the total production of each industry shows the importance of this issue. Then the intangible investment variable was brought as the main component next to physical investment and labor in the production function. Observations showed that intangible investment had a positive and significant effect on the productivity of all production factors. Its high coefficient indicates the impact of intangible investment on TFP, and its omission in the production function caused TFP estimation to have a bias error. In order to show the impact of the professional workforce on the productivity of production factors, we separated the workforce into skilled and unskilled, and the results show that the impact of professional and expert workforce is almost six times greater on TFP.[1] The Statistics Center of Iran has been used to obtain the data. The years used are from 1996 to 2018. The estimated components are intangible investment, physical investment, labor force and production volume, to calculate the productivity index of production factors. The obtained data have been used separately according to the economic activity ranking classification code with version 4 from the Statistics Center of Iran. In the codes, we merged them with similar codes, and as a result, the number of codes is 132, which is the total number of data for the years 1996 to 2018 is equal to 2190, and the volume of data is worth considering for the correct estimation.

Unequal distribution of income in society has caused many problems in the areas of economic, social and political will of each country, that's why one of the most important tasks of governments is proper income distribution in society.  Governments recommend short-term solutions to address income inequality, while implementing these short-term policies without recognizing the influencing factors can have adverse consequences on revenue distribution. In order to deal with the problem of income inequality and establishing a fair distribution of income should identify its risk factors and appropriate and effective policies be adopted to improve income distribution. Due to the growing trend of income inequality, high unemployment and economic inefficiency in various societies, especially in developing countries, and the special role of government in achieving a fair and equitable distribution of income, the issue of good governance needs to be addressed. Income inequality and good governance are among the most important economic issues in any country. The World Bank has good governance based on six indicators; It has introduced corruption control, government efficiency and effectiveness, political stability, quality of law and regulation, rule of law and the right to comment and be held accountable, one of the most important components of which is the corruption control variable. Indicators of governance play a very important role in the economic environment in terms of economic growth and development. Governance is considered as one of the basic inputs for the welfare of the people. Therefore, paying attention to the role of indicators of good governance and its absence, especially in developing countries and its impact on income distribution, which is one of the most important goals of governments, is very important and necessary. This study is one of the preliminary studies that can be compared to other studies from three aspects: technical, regional and temporal.The purpose of this study is to investigate the effect of good governance on income inequality in selected developing and developed countries, with emphasis on control of corruption over the period 2017-2000.

In this research, three indicators of political stability, rule of law and corruption control have been used as good governance. Using dynamic quantile panel regression, on one hand, the effect of good governance indicators on income inequality has been investigated, and on the other hand, the effect of tax revenues on Along with the inflation rate and GDP per capita, it has been investigated as instrumental variables on income inequality.

The results of the estimated the dynamic quantile panel regression model show that control of corruption and rule of law in both countries the quarters 25, 50 and 75 respectively positive and negative and significant effect on income inequality. Political stability in both groups of countries has negative effects in quarters 25 and 50 and in quarter 75 positive and significant. Also the tax revenues, inflation rate and GDP per capita these quarters in the selected countries have different effects on income inequality.

Rule of law and corruption control indicators have significant negative and positive effects on income inequality in both groups of selected countries and in all quartiles, respectively. Also, the coefficient of the political stability index shows that in the first two quarters in both groups of countries, it has a negative and significant effect on income inequality (Gini coefficient) and this can mean that with political instability, weak investment motivation and consumerism in these societies. be strengthened, which leads to a decrease in production and income inequality. Meanwhile, in the third quarter in both groups of countries under study, the political stability index has a positive and significant effect on income inequality, in the sense that with the increase in political stability, income distribution becomes more equal.

one of the important indicators of measuring the development of any country is the situation of females in each country. Overview of the status and income of females compared with males, it is clear that females are the most vulnerable parts of the population. In the last four decades, Iranian females have become half of the country's university graduates, but according to labor force statistics in 2017, the participation rate of females was 16%, relative to the participation rate of 64.5% male. According to the data of urban household income and expenditure survey in 2017, there is an increase in female's hourly wages relative to men, despite the small difference between the level of education of male and female, and the phenomenon of the gender pay gap in the Iranian labor market. This study aims to address the most fundamental and obvious inequality, namely wage inequality, and determine how much of this wage inequality is due to discrimination and how much is due to the characteristics of human capital.  In many cases, in the discussion of pay gap, we face censored data, which requires the estimation of a Tobit model; Because in the presence of censored data, ordinary least squares method will lead to inconsistent parameters and misleading results. Investigating the pay gap with a developed model of the Oaxaca decomposition method using Tobit models,is the pivotal issue in this research which has not been discussed in previous studies.

As stated, the purpose of this article is to investigate the fact that by controlling factors such as the level of education, occupational group, and age, there is still a clear discrimination between the wages of men and women in the  labor market.Therefore,This study aims to address the most fundamental and obvious inequality, namely wage inequality, and determine how much of this wage inequality is due to discrimination and how much is due to the characteristics of human capital.
According to theoretical studies, the technique of estimating the wage inequality has been estimated in two forms, the Ols and the definition of dummy variable, and the of Oaxaca and Blinder decomposition. This study attempts addition to OLS model, Provide accurate estimates of the gender pay gap with the help of the censored Tobit model in Iran labor market.

The results of the study of the gender wage gap of the censored Tobit model show that the wage inequality is positive  between men and women. A positive wage inequality means an increase in the gender wage gap to the loss of women. According to the results of the estimation of the models, with the increase in the level of education, significantly the wages of men and women increase. Because women have a higher educational attainment rate than men, wage increases for women are greater than for men. The results show, if is considered data censoring, the educational attainment of men and women significantly decreases. The impact of considering data censoring is much higher on  educational attainment  for women than men. Also, the private sector has a larger share of employment, but the wage gap in this sector is greater than in the public sector. The results show that potential experience follows an inverted u-shaped pattern. That is, people intermediate levels of potential experience have the highest wage compared to the two ends of the spectrum. In this study, due to the possibility of endogeneity of experience variable, an instrumental variable has been used as a substitute for work experience.

According to the available image of the censored data, the method of estimating the Conventional gender pay gap is biased and does not provide reliable results. This study attempts addition to OLS model, Provide accurate estimates of the gender pay gap with the help of the censored Tobit model in Iran labor market. Due to the indigenous of the experience, has been used the Tobit model with an instrumental variable. According to the results, with ignored the bias of censored data, is overestimated the part of the gender pay gap (GPG) attributable to gender differences in observed endowments and part of the GPG is due to that the same endowment generates different returns for male and female workers.

Housing, as an asset and shelter after food and clothing, is the most basic human need, which is very important for the life and survival of the individual as well as for the society. Changes in housing prices, on the one hand, as the main asset in households' portfolios, will lead to changes in individual wealth over time, and on the other hand, fluctuations in housing prices, especially their rapid increases, are among the threats that governments aim to provide access to. People face challenges in housing. This issue has become more important especially in recent decades with population growth, urbanization and the formation of new families. Air pollution as one of the most important environmental concerns of this century and the result of increasing population and urbanization and housing construction threatens the lives of millions of people in developing countries as well as in developed and industrialized countries. Therefore, considering the importance of air pollution and its impact on housing and housing prices, this research tries to use the panel data technique of the effect of air pollution on housing prices in developed countries (including: Norway, Switzerland, Australia, Iceland, Denmark, America , Japan and the Czech Republic) and developing countries (including: Turkey, Mexico, Brazil, China, Colombia, South Africa, Indonesia, India) during the period of time (2010-2017) and then this review for Iran using self-explanatory vector technique with extended non-linear intervals (NARDL) and during the years (1375-1996).

As mentioned in the introduction, the purpose of this research is to analyze the impact of air pollution and some economic variables on housing prices in developing countries, developed countries and Iran. For this purpose, the following model has been used:Which in this model: Logarithm of housing price in year t.Logarithm of air pollution index in year t.Logarithm of per capita income in year t.Logarithm of liquidity in year t.Logarithm of the price index of consumer goods and services in year t.Logarithm of exchange rate in year t.Interest rate in year t.It is important to mention two points here: First, the index considered for air pollution is the amount of suspended particles PM2.5. Second; The base year is 2010 for developed and developing countries and 2010 for Iran. The desired statistical information, which is in the form of a time series, was collected through the information portal of the World Bank, the International Monetary Fund, the Organization for Economic Cooperation and Development, the Central Bank of Iran, the Statistical Center of Iran, and the energy balance sheet.

The impact of air pollution on housing prices in developed and developing countries:Due to the short period of time (due to the limited access to information) and the invalidity of the results of the unit root test in these conditions, in the first step, the problem of heterogeneity of units was investigated using the F-test statistic of Limer. Considering that the probability value of the F test for both groups of developed and developing countries is less than 0.01, therefore, Panel Data technique is used to estimate the model in both groups of countries. As the next step to examine the correlation between disturbance components and explanatory variables (i.e. choosing between the fixed effects method and the random effects method), the Hausman test was performed, and the results of this test indicated a probability value smaller than 0.01 for both groups of countries. Investigating and applying the fixed effects method.The results of model estimation are as follows:In developed countries, air pollution leads to a decrease in housing prices by 30%. But in developing countries, there is no significant relationship between air pollution and housing prices. The effect of per capita income on housing prices is positive and significant in both groups of countries under investigation. The effect of liquidity on housing prices is positive and significant in developed countries, but the effect of liquidity on housing prices is not significant in developing countries. Considering the relative stability of the consumer goods and services price index in developed countries, this index has no effect on housing prices in the aforementioned countries. In developing countries, the effect of the price index of consumer goods and services on the price of housing is positive and significant at the rate of 48%.The stability of the exchange rate in developed countries has caused this variable to have no significant effect on housing prices. But in developing countries, an increase in the exchange rate causes a 4% decrease in housing prices. The increase in interest rate causes the housing price to increase by 0.5% in developed countries and decrease by 0.2% in developing countries.The impact of air pollution on housing prices in Iran:As the first step, the variables used in the model were tested for reliability. The results of the ADF unit root test showed that all the variables investigated in this research were I(1) and this means that the vector self-explanatory model with wide ARDL intervals is a suitable method for analyzing the short-term and long-term behavior of the variables in this research. Then, in order to check the classical assumptions, relevant diagnostic tests were used, and the obtained results indicated that the model does not have any problems in terms of the classical assumptions, that is, there is no autocorrelation and heterogeneity of variance, and the distribution of disturbance components is normal.The results of model estimation are as follows:The effect of negative and positive air pollution shocks on housing prices is significant, which means that the decrease in air pollution leads to an increase in housing rent and an increase in the level of air pollution leads to a decrease in the housing rent index in Iran. An increase in per capita income leads to an increase in housing rent index. The variable coefficient of liquidity logarithm is negative and meaningless. An increase in the price index of consumer goods and services and the exchange rate will lead to an increase in housing rent. The results of the model estimation indicate the insignificant effect of the interest rate of this variable on the housing rental index in the long term, but in the short term, with the increase in the interest rate, the demand for housing rent and consequently the rent will increase.

Based on the results obtained in this study, the effect of air pollution on housing prices was negative and significant in developed countries, while this effect was insignificant in developing countries. The results of the model estimation in Iran show that air pollution has a significant effect on housing rent.According to the obtained results, the following suggestions are presented in order to control housing prices:Stability of prices and prevention of excessive growth of the price index of consumer goods and services through control of liquidity and exchange rate.Exchange rate management: Since the fluctuations and instability of the exchange rate can leave destructive effects in the economy of the countries, therefore, the forecasting and use of tools that enable the economy of the countries to face the fluctuations of the exchange rate should be considered.