تبیین جغرافیای سرمایه انسانی خلاق و با مهارت بالا به عنوان موتور محرکه ای توسعه منطقه ای استان های ایران (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
تبیین جغرافیای سرمایه انسانی خلاق و با مهارت بالا به عنوان موتور محرکه ای توسعه منطقه ای استان های ایرانقرن بیست ویکم، قرن شهرها، ایده ها و خلاقیت هاست. خلاقیت انسان و استعداد او به عنوان سرمایه انسانی خلاق، محرک اصلی توسعه اقتصادی، اجتماعی و فرهنگی تلقی میشود که رقابت روزافزون شهرها، مناطق و کشورها به طور فزاینده به پرورش، حفظ و جذب آن وابسته خواهد بود. در حال حاضر رقابت فزاینده ای بین شهرها، مناطق و کشورها برای جذب استعدادها و افراد خلاق و با مهارت بالا وجود دارد. بدیهی است مکان هایی که بتوانند این طبقه خلاق را پرورش، حفظ و جذب کنند، در رقابت پذیری آینده دست برتر را خواهند داشت و رشد و شکوفایی اقتصادی و توسعه را به طورکلی برای شهر و منطقه خود به ارمغان خواهند آورد. هدف این پژوهش تبیین جغرافیای سرمایه انسانی خلاق و با مهارت بالا به عنوان موتور محرکه ای توسعه منطقه ای استان های ایران است. این پژوهش از لحاظ ماهیت یک تحقیق تبیینی است که با استفاده از مدل سازی معادلات ساختاری در PLS انجام گرفته است. یافته ها تمرکز روبه رشد و جغرافیای واگرای سرمایه انسانی خلاق و با مهارت بالا را در استان های ایران را نشان می دهد. یافته ها همچنین نقش عوامل مکان محور را در توزیع سرمایه انسانی خلاق و با مهارت بالا نشان می دهد. تسامح، مدارا و تنوع؛ استعداد و خدمات مصرف کنند رفاهی مهم ترین عواملی هستند که توزیع سرمایه خلاق را توضیح می دهند. علاوه بر این یافته ها ما را به این نتیجه رساند که سرمایه خلاق تأثیر مثبت و معناداری بر عملکرد اقتصادی و فناورانه شهری-منطقه ای دارد و موجب شکوفایی اقتصادی و توسعه شهرها و مناطق می شود.Explaining the geography of creative and highly skilled human capital as a driving engine for the regional development of Iran's provinces
Extended Abstract
Introduction
Investigating the important factors on the prosperity of cities and urban-regional development has a rich history and has attracted the attention of various scientific disciplines, including urban economics, economic geography, and regional sciences. In the meantime, it deals with the roles of places in natural resources and enterprises to human resources in the visible development process. According to common thinking and research in economics, geography and social sciences, the main drivers of economic development and regional development are people with high skills and education, factors that some of them call talent and economists and social science experts consider it as human capital. Many researches have been done in the field of the creative class, the spatial distribution of the creative class, the effects of the creative class, and the factors affecting the geography of the creative class. But the problem here is that these studies (domestic and foreign) had shortcomings. The purpose of this research is to investigate some significant weaknesses of previous studies in explaining the uneven geography of talented and highly skilled people and urban-regional development. First, some studies reduce Florida's explanatory model to a measurement model. In other words, they take Florida's findings as definitive for their context without testing them, and simply measure the creativity index. Secondly, methodologically, many of these studies use single-equation regression, and Florida suggests the use of structural equations and path analysis in his review of this topic. Structural equation modeling has received less attention in creative class studies. Using this method can solve the validity and reliability problems of previous studies, especially the problem of construct validity. The advantages of structural equation modeling over regression have been discussed in some studies. But the main problem is that Florida et al., despite emphasizing the importance of using the structural equation method instead of regression, have difficulty using this method and their model only uses path analysis. In such a way that the results obtained from the use of structural equation modeling in the context of the measurement model (investigating the relationship between hidden variables and their measurements) and the fit of the model, which measures the validity and reliability of the model, have not been examined. Thirdly, most of the studies on the creative class and explaining the uneven geography of talents and urban-regional development have been done in the context of developed countries, and the creative class, geography, economic effects and its determinants are not well documented in developing countries. Hence, what we know about creative class theory may be somewhat biased. To clarify the last issue, we chose Iran as a case study. In general, according to the gaps in the theoretical literature, three key issues are addressed in this research. The first goal is to create a standard index to measure the concept of the creative class in Iran, which is internationally acceptable and applicable in other cities and regions. The second goal is to investigate the geography of the creative class in Iran and its effects on economic performance and urban and regional technology. Finally, in order to explain the factors affecting the geography of the creative class in Iran by using structural equation modeling, we create an explanatory model to achieve these goals.
Methodology
In this research, structural equation modeling was used to test the relationship between hidden variables, which is called second generation modeling. There are two main approaches to structural modeling, covariance-based structural modeling (CB-SEM) and variance-based structural modeling (PLS-SEM). They differ not only in their assumptions and initial results, but also in their estimation methods. While, CB-SEM minimizes the difference between the observed and predicted covariance matrix by maximum likelihood (ML) estimation method, partial least squares (PLS-SEM) from regression-based ordinary least squares (OLS) to explain the variance of latent constructs with It uses covariance score maximization between dependent and predictor latent variables. In this research, data was analyzed using SPSS 24 and SmartPLS2 software. Model analysis in SmartPLS has two stages, measurement model evaluation in the first stage and structural model evaluation in the second stage.
Results and Discussion
The findings show that the relationship between tolerance and diversity with creative capital (β = 0.409; t-value = 3.129; p = .002) is positive and significant and confirms the hypothesis that
the diversity of a city or region and its openness It has a positive and significant effect on diversity in maintaining and attracting creative capital in that place. Similarly, there is a positive relationship between consumer services and creative capital (β = 0.315; t-value = 3.060; p = .002), and talent and creative capital (β = 0.383; t-value = 4.061; p = .000). There is a meaningful Similarly, there is a strong and significant relationship between creative capital and economic development (β = 0.757; t-value = 5.411; p = .000) and it shows that creative capital has a positive effect on the economic development and prosperity of cities and regions. One of the noteworthy points in the field of factors affecting capital is that the variable of tolerance and diversity has the highest effect in explaining creative capital. In addition, the proposed relationship between creative capital and technology (β = 0.722; t-value = 5.980; p = .000) is positive and significant, and it shows that creative capital acts as a driver of technology in a city or region and affects it. (Figure 3). In general, R2 values can be interpreted as significant (above 0.75), moderate (0.50) and weak (0.25). According to these rules, the table shows that the proposed model has an explanatory power of 77% in explaining the creative class variable with R2=0.7225, which is a significant value. This R2 value is high and significant. Blindfolding is an example reuse technique. This method provides the calculation of Q² Stone-Geisser index. The Q² index represents an evaluation criterion for the predictive relevance of cross-validation of the PLS path model. This criterion is known as the Stone and Geiser (1975) index. The Q² index was introduced by Stone and Geiser (1975) and determines the predictive power of the model in the dependent variables. This index seeks to measure the predictability of the PLS model and is presented in a tabular form with the Q2 symbol in the blindfolding test of the Smart PLS software. In addition to evaluating the magnitude of R2 values as a measure of predictive accuracy, researchers also examine Aston and Geisser's Q2 value. The Q2 value of the hidden variables in the PLS path model is obtained using the blindfolding method. In general, the explanation of the factors affecting the location decisions and the geography of creative capital at the regional level shows factors such as tolerance, tolerance and diversity; Amenities or consumer services and talent play an important role in explaining the geography of creative capital in Iran's provinces. The results of the structural model of the research also show that creative capital is also related to the technology component with the indicators of the number of creative and knowledge-based companies, the number of research and development centers; And the number of research and development projects measured has a positive and significant effect. Also, creative capital can well explain the development of the regional economy and generally has a positive effect on the economic and technological performance of Iran's provinces and results in regional development.
Conclusion
The research results show three main findings. First, we used skill level 4 occupations in ISCO-08 classification to measure creative and highly skilled human capital called creative class. In this classification, skill level 4 requires complex problem solving, decision-making, and creativity based on a broad set of theoretical and practical knowledge in a specialized field. Empirical evidence from Iran shows that the creative class is unevenly distributed and follows an unbalanced and concentrated pattern. The largest cluster of the creative class is formed in the capital and surrounding provinces. Second, the creative class leads to better economic and technological performance at the urban-regional level and regional development in general. It should be noted that the dominant occupational class in terms of income and wealth is the creative class, so the income of its members is on average twice that of other occupational classes, and in general, their wages, salaries, and savings are higher than other occupational classes. The high wages of the creative class lead to the flourishing of the urban-regional economy in various forms such as consumption of goods and services and investment. On the other hand, by increasing productivity and creating knowledge-based, creative and cultural goods and services, the creative class helps to increase the GDP per capita and create more
added value and ultimately the economic prosperity of cities and regions. Third, the findings in the field of factors affecting the geography of creative capital in the provinces of Iran show that tolerance and diversity, talent and services and amenities respectively have the greatest impact on the geography of creative capital. Together, these factors play a complementary role and shape the diverse distribution of the creative class. As a conclusion, it can be stated that there is currently an increasing competition between cities, regions and countries to attract talents and creative and highly skilled people. It is obvious that the places that can cultivate, maintain and attract this creative class will have the upper hand in future competitiveness and will bring economic growth and prosperity and development to their city and region in general. Cities, regions and countries will not succeed in this direction unless they build a place that is tolerant, open and diverse and can accept and recognize diverse and even dissident ideas of creative capital. In the next stage, this place should have an attractive cultural and artistic atmosphere and top universities and research institutes. Also, this place should be more attractive than other places in terms of providing consumer welfare services and other location features.
Funding
There is no funding support.
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
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