پژوهشنامه اقتصاد کلان
سال شانزدهم شماره 32 (پاییز و زمستان 1400)

The value added of manufacturing activities has positive effects and side effects on the national economy. Improving productivity, increasing the complexity of economy and strengthening capital accumulation are among these consequences. Therefore, in some studies, the role of manufacturing has been interpreted as a growth engine. In addition, improving the economic conditions and per capita incomes usually increase the share of household expenditures in high-tech industrial products. This will increase the demand for factory products and possibly increase the activity of manufactures. The main purpose of this study is to investigate the relationship between the value added induced by the activity of manufacturing industries and economic growth among different countries using PVAR model. The limitation of having a consistent set of data has reduced the number of sample countries to 21. The data belongs to the years 1990-2020. The average share of manufacturing in the total production of the selected countries is 15% and the average economic growth in these countries is about 2%. The result of the study, the stability of which has also been confirmed, does not rule out the existence of a simultaneous and reciprocal relationship between the two main variables of the research. In case of a positive 10% shock in the value added of manufacturing, the economic growth of the selected countries of this study will increase by 1% after 7 years and in case of a positive 10% shock in the economic growth, the activity of manufacturing will increase by about 0.5% after 7 years. In other words, the effect of manufacturing on the economic growth is greater than the effect of the economic growth on factory activity.

Health as a special commodity and a fundamental right of all people has always been considered by policymakers. Corruption as one of the indicators of institutional quality can lead the resources of society to specific groups and cause a lack of resources in areas such as health. Therefore, the phenomenon of corruption can be one of the obstacles to achieving the health goals in any society. The purpose of this article is to examine the impact of two different indicators of corruption on life expectancy in 10 selected Asian developing countries for the period 1996 to 2015. For this purpose, Pooled Mean Group Econometric (PMG) method was used. The results showed that in both models, reducing corruption had a positive and significant effect on life expectancy in the long run. However, in the short term, only the control of corruption index introduced by the World Bank had a significant and negative effect on life expectancy. In other words, in the short term, corruption has been able to positively affect health by bypassing the legal barriers in the health sector. In the long run, on the contrary, the negative effects of corruption are greater and therefore corruption has a negative impact on health in the long run.

An important part of an individual's human wealth, which directly affects his or her well-being and that of society, is formed through education. This requires an equal distribution of educational opportunities among the people. Today, with the change in educational processes due to the growth of communication technologies and the role of Covid 19, access to the Internet and government spending, along with other relevant factors in the literature have affected educational inequality. Based on the importance of education in economic development and progress, this study investigates the impact of Internet access on educational inequality in the selected West Asian countries during the period 2010-2019 using the panel Fully Modified Ordinary Least Squares method (FMOLS).The results of the model estimation show that public government expenditures, Internet access and investment have a negative and significant effect on educational inequality in the countries .This means that these variables lead to a more equal distribution of education among individuals. Another empirical finding is that income inequality and gender inequality both significantly increase educational inequality in West Asian countries.

Foreign direct investment has been the subject of many studies due to its importance in providing financial resources for companies, governments, and technology transfer. Its effects and relationship with various economic factors, including employment, have been the focus of researches. However, there is still no coherent review study that takes into account other aspects of the issue and draw a general conclusion from the studies along with investigating the effect of foreign direct investment. This study is designed to fill this information gap. Among the searched articles, 329 published English papers about the effect of foreign direct investment on employment were studied and the results were presented according to dimensions such as the effect of short-term and long-term employment generation, the effect on skilled and simple labor force, etc. Finally, this general result was extracted that the maximum positive effect of Foreign Direct Investment (FDI) on employment is achieved when the FDI enters the host country in the form of Greenfield, and the training of human resources should be done by the FDI, especially the simple workforce, to match the level of technology imported by it. Policy making and legislation should support employment to maximize the country's absorption capacity and oblige the FDI to supply labor, raw materials, and required services from the host country.

Modeling of oil price behavior depends on whether we distinguish the observed data as examples of deterministic or random trend processes. The distinction between these processes is crucial because of the long-term response of data series to shocks. Generally, in standard significance tests, the null hypothesis is tested for the difference between the insignificant and significant hypotheses. In the present study, to investigate the root of the non-difference stationary unit from Bayesian methods, we estimate the RCAR model considered for oil prices. The results of this study show that despite confirming the unit root in oil price data, it is not clear that whether a random step process is suitable for predicting Brent crude oil prices or not because confirming the existence of a random unit root does not necessarily mean that the crude oil spot prices follow a Brownian process. Therefore, it is not possible to conclude from such results that the efficiency of the oil market is insignificant. However, because the unit root in the crude oil data is random, it does not persist with the differentiation of the crude oil data; therefore, the use of the differentiated variable in conventional co-integration models will be incorrect. Also, because the crude oil is the input of many industries, this anonymity could be transferred to other macroeconomic variables. Therefore, theories of business cycles that portray temporary economic shocks will have weak support.

This study aimed to investigate the effect of social security expenditure on the convergence of social welfare in provinces of Iran. Results of estimating models, using Spatial Econometrics, revealed that social security expenditure has a positive and significant effect on the convergence of social welfare in provinces of Iran during the period 2006-2020. Moreover, findings demonstrated that direct and indirect effect of social security expenditure is also positive, in the sense that the increases in social security expenditure has not only improved the convergence of social welfare in all provinces, but its spillover effects have also improved the convergence of social welfare in neighboring provinces on average. Besides, the Absolute Convergence Rate was 0.053 and in the conditional models, its’ values were estimated to be 0.054 and 0.059, respectively. These findings show that the estimated Conditional Beta-Convergence Rate is higher while considering social security expenditures. Additionally, results revealed that the Misery Index and the Government Size have a negative and significant effect, while the Information and Communication Technology (ICT) and Human Capital have a positive and significant effect on the convergence of social welfare. Finally, an estimation of the Spatial Spillover Effects of social security expenditures in 2019 showed that each one of the provinces has the highest direct effect coefficient on itself, and then has the highest effect on neighboring provinces.

The relationship between inflation and inflationary uncertainty has attracted the attention of many theoretical and empirical macroeconomic studies. Despite extensive studies in this field, there is still no consensus regarding the flow of causality between these two variables. In this regard, the current research uses continuous wavelet transform and analysis in the time-frequency domain to provide new insights into the relationship between inflation and its uncertainty in Iran during the period 1991 - 2022. According to the obtained results, the flow of causality between the two variables is not established and the theories related to this issue are not relevant to Iran's economy in the sense that, firstly, economic agents are generally not able to correct their forecast in the face of inflation. And secondly, the monetary authorities do not take necessary measures to stabilize the prices when they rise. In the long run, the causal flow from inflation to inflation uncertainty is established with significant intensity. The interpretation of the results in the dimension of time and its compatibility with the theoretical foundations and conditions of Iran's economy indicates that in the long run, the policymaker, in response to the increase in the general level of prices, has relatively less sensitivity to reducing inflation compared to other goals.

.Controlling corruption for more than two thousand years old has always challenged economies. The present paper, with the goal of explaining the gender behavior of human beings in the form of government institutions, attempted to answer the question of whether reducing the inequality between genders decreases financial corruption or if financial corruption is an obstacle to achieving gender equality. This study also aimed to find whether the realization and spread of corruption are more dependent on gender or institutional (government and religion) factors. This paper used statistical data from 89 countries including Iran from 2008 to 2017. The dynamic panel data approach system generalized method of moments (GMM-SYSTEM) was utilized for answering the above questions. In this regard, the Gender Inequality Index (GII), the Corruption Perceptions Index (CPI), and the Money Laundering Index (AML) were used as alternatives to the Corruption Perceptions Index. Also, to evaluate the sensitivity of government institution performances, two groups of good governance tools and freezer foundation indicators were used. The results of estimation systems showed that corruption decreased following the increase of women's participation in the public sector and corruption reduced women's participation in the public sector. Also, the result of systematic estimates seeking the key to solving the corruption riddle indicated that regardless of any gender behavior, the institution of government as a missing link between gender and corruption has a significant impact on the effectiveness of these two indicators. Moreover, despite government institutions, religion doesn’t affect the relationship between two indicators of corruption and gender inequality.

Education, especially in academic fields of basic sciences, technology, engineering, and mathematics (STEM), is one of the significant indicators of human capital. STEM represents the economy's capacity for innovation and creativity, which forms the core of economic growth. The current research used the model of Knowles et al. (2002), who have added gender inequality in education to the economic growth model of Mankiw, Romer, and Weil (1992), to investigate the effect of gender inequality in STEM and deal with the economic growth of Iran in the period of 1988 to 2020. In this study, due to the random nature of unobservable factors affecting economic growth such as technological progress, the structural time series econometric model (STSM) approach was used. The results of the estimation showed that the positive effect of women's enrollment in STEM on economic growth is greater than the effect of men's enrollment, and the increase in gender inequality in STEM has a negative and significant effect on economic growth in Iran. Therefore, adopting appropriate policies in the labor market to eliminate gender discrimination will lead to gender equality in the education of these academic fields; because the sustainable growth of the economy depends on the intertwining of labor market policies and other institutions, including educational institutions.

The purpose of this research is to compare the relationship between corruption and human capital in 19 countries with very high and high human development (first group) and 35 countries with average and low human development (second group). To conduct this study and investigate the long-term and short-term relationships between the variables of the model from 2012 to 2018, a descriptive-analytical method and the panel econometric method were used. Results show that in the first group of countries (high human development), about 71.48% of corruption changes were caused by corruption-related shocks, 5.30% by inflation-related shocks, and 9.75% by education-related shocks at the end of the period. The findings also revealed that 3.17% of corruption changes were explained by shocks caused by GDP per capita, 7.65% by shocks caused by health, and 2.62% by shocks caused by good governance. However, in the second group (low human development), the results showed that at the end of the period, about 62.26% of corruption changes were caused by corruption-related shocks, 21.54% fluctuations by inflation-induced shocks, 0.2% by education shocks, 1.34% by shocks due to GDP per capita, 9.33% by shocks due to health, and 5.30% by shocks due to good governance. In conclusion, it can be said that the indicators of human capital in countries with high human development (the first group) explained 17.4% of corruption changes, while this factor is 9.53% in countries with low human development (the second group). In other words, in the first group of countries, the changes and structure of corruption are better explained by human capital indicators.

With the emergence and expansion of the globalization phenomenon and the deepening of interactions and economic and trade relations between the countries of the world, the role and place of attracting foreign capital in the patterns of growth and development have become very important for countries that are facing financial difficulties, in a way that since the 80s, new factors and variables have been proposed as the stimulus for attracting foreign direct investment and have attracted the attention of economists. Therefore, according to this necessity, the purpose of the current research is to investigate the relationship between innovation and the inflow of foreign direct investment in the top 47 science-producing countries in the world from 2011 to 2020. For this purpose, the econometric approach of dynamic panel data and dynamic generalized moments estimator have been used to estimate the model. The results of the research model show that all the studied variables with different estimated coefficients have a positive and significant effect on foreign direct investment in a way that the improvement of each of the five variables of innovation inputs and the variable of corruption control, exchange rate, and degree of development of the countries used as control variables increases the volume of foreign direct investment in this category of countries.

Today, the financial condition index is introduced as a tool for forecasting macroeconomic variables in many countries of the world. The aims of the present study are, first, to construct the financial condition index in different patterns of time-varying parameters and then, to analyze the performance of the financial conditions index in order to forecast the macroeconomic variables using the mean squared forecasting error approach (MSFE) and the cumulative sum of log-predictive likelihoods.The results show that the constructed financial conditions index can adapt to different states of Iran's economy and its fluctuations increase over time. Based on the obtained results, the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) shows less forecast error in some macroeconomic variables compared to the factor-augmented vector autoregressive model (FAVAR) and also the vector autoregressive time-varying parameter factor-augmented model (FA-TVP-VAR). The use of combined time-varying parameter factor-augmented vector autoregressive models when the coefficients and selected variables of the financial conditions index change depending on the economic condition, improve the performance of the financial conditions index in forecasting macroeconomic variables. The cumulative sum of log-predictive likelihoods shows that using variable parameter patterns does not have a great effect in reducing the forecast error of some macro variables.