بررسی اثر روش‌های تامین مالی دولت بر رشد اقتصادی در ایران: رهیافت مارکوف - سوئیچینگ

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه اقتصاد، پژوهشکده اقتصاد و مدیریت پژوهشگاه علوم انسانی و مطالعات فرهنگی، تهران، ایران

چکیده

با توجه به این‌که آثار روش‌های مختلف تامین مالی دولت در یک اقتصاد مشابه نبوده و این روش‌ها می‌توانند به‌گونه‌ای متفاوت متغیرهای کلان اقتصادی نظیر رشد اقتصادی را تحت تأثیر قرار دهند، مطالعه حاضر با استفاده از داده‌های دوره زمانی 1352-1397 و الگوی مارکوف – سوئیچینگ به بررسی اثرگذاری روش‌های مختلف تامین مالی دولت بر رشد اقتصادی ایران پرداخته است. یافته‌های این مطالعه نشان می‌دهد که نسبت مالیات به تولید ناخالص داخلی در هر دو رژیم رکود و رونق شناسایی شده، اثر مثبتی بر رشد اقتصادی دارد، اگرچه این اثرگذاری در رژیم رکود به لحاظ آماری معنادار نبوده است. همچنین و بر اساس یافته‌های این مطالعه، نسبت درآمدهای نفتی دولت به تولید ناخالص داخلی در هر دو رژیم شناسایی شده اثر منفی و معناداری بر رشد اقتصادی داشته که این مسأله به نوعی تاییدی بر نظریه پارادوکس فراوانی و یا پدیده نفرین منابع در اقتصاد ایران است. نهایتاً، یافته‌های این مطالعه نشان داد که نسبت بدهی دولت به تولید ناخالص داخلی در هر دو رژیم شناسایی شده اثر منفی و معناداری بر رشد اقتصادی طی دوره تحت بررسی داشته که این اثرگذاری منفی می‌تواند ریشه در این واقعیت داشته باشد که استقراض دولت در ایران بجای آن‌که صرف انجام سرمایه‌گذاری‌های بهره‌ور و ایجاد زیرساخت‌های لازم شود صرف جبران کسری‌های بودجه ساختاری می‌شود. در ضمن، یافته‌های این مطالعه نشان داد که نرخ رشد سرمایه‌گذاری، نرخ رشد جمعیت و نرخ رشد صادرات اثر مثبت، معنادار و محسوسی بر رشد اقتصادی ایران داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the Effect of Government Financing Methods on Economic Growth in Iran: Markov-Switching (MS) Approach

نویسنده [English]

  • Jalal Montazeri Shoorekchali
Associate Professor, Department of Economics and Management, Institute for Humanities and Cultural Studies, Tehran, Iran
چکیده [English]

Introduction: It is important to note that the effects of different methods of government financing (such as taxes, borrowing, selling natural resources, etc.) are not the same in the economy, and these different methods can affect macroeconomic variables such as economic growth in different ways. Therefore, answering the question of what is the optimal government-financing portfolio in the economy is of particular importance, especially in developing countries where the sale of natural resources plays an important role in their financing portfolio. According to Sachs and Warner, the abundance of natural resource encourages rent seeking, corruption, and poor government management. It also encourages developing countries to engage in protectionist paths through state-led projects of development, in fear of “Dutch disease effects of the resource abundance”. Yet, as de Ferranti et al. (2002) put it, ‘it is impossible to argue that Australia, Canada, Finland, Sweden and the United States did not base their development on their natural resources.’ (p. 6)
Accordingly, considering the three main current issues of Iran's economy based on a) inefficiency of the tax system, b) dependence of the budget on oil, and c) growing government debts, this paper investigates the effect of government financing methods on the economic growth in Iran from 1973 to 2018 using a Markov-Switching (MS) model.
Methodology: Following the generalized growth accounting model based on neoclassical and endogenous growth models, to investigate the effect of different methods of government financing on the economic growth, we used the following model based on the concept of the total production function:

 
 
 

 
where:
GY: GDP growth rate (constant 2011 LCU),
GK: Gross capital formation growth rate (constant 2011 LCU) as a proxy for investment growth rate,
GL: Population growth rate as a proxy for labor growth rate,
GX: Export growth rate (constant 2011 LCU) as a proxy for export growth rate,
TR / Y: Government tax revenue as a percentage of GDP,
OR / Y: Government oil revenue as a percentage of GDP,
GD / Y: Government debt as a percentage of GDP.
In addition, this study uses annual time series for Iran during 1973-2018.
Results and Discussion: Based on the specification tests, we estimated MSI (2) model using the EM algorithm as reported in Table 1. This model was tested for linearity using the LR linearity statistics assuming the null and alternative hypotheses to be a linear model and an MS model, respectively. The probability value of the Chi^2 statistic in this test (0.005) supports the existence of non-linearity in the data. Based on the transition probabilities, the probability of moving from regime zero (one) to one (zero) regime is 0.6879 (0.6374). Therefore, it can be said that the probability of staying in both regimes is moderate.
 




Table 1. Results from MSI (2)




 


Coefficient 


t-value 


t-prob




Switching variables in Regime 0




Intercept


0.60


0.294


0.771




GY (-1)


-0.04


-0.81


0.423




TRY


0.11


0.38


0.704




ORY


-0.18


-3.25


0.003




GDY


-0.44


-5.28


0.000




GDY (-1)


0.26


3.19


0.004




Switching variables in Regime 1




Intercept


1.47


0.69


0.496




GY (-1)


-0.30


-5.02


0.000




TRY


0.86


 2.54


0.017




ORY


-0.10


-1.84


0.076




GDY


-0.70


-8.80


0.000




GDY (-1)


 0.51


 7.55


0.000




Non- switching variables




GK


0.13


7.31


0.000




GL


4.09


4.86


0.000




GL (-1)


-1.24


-1.30


0.204




GX


0.24


16.1


0.000




AIC = 5.33                   SC = 6.09
Linearity LR-test Chi^2(9) =   42.361 [0.000]
p_ {0|0} = 0.6879;                       p_ {1|1} = 0.6374
p_ {0|1} = 0.3121;                       p_ {1|0} = 0.3626     
Normality Test: Chi^2(2) = 0.08 [0.959] 
ARCH 1-1 Test: F (1,26) = 1.13 [0.298] 
Portmanteau (6): Chi^2(6) = 10.46 [0.106] 




Note: In this study, in order to determine the optimal lag of variables, the autoregressive distributed lag (ARDL) technique was used. Also, the unit root tests results showed that all the variables were stationary.
* Annual data for all the variables were obtained from the Central Bank of Iran.
Source: Research findings




 
 
Figure 1. Regime classification based on the filtered and smoothed probabilities
Source: Research findings
 
In addition, the cumulative effects of explanatory variables are presented in Table 2:
 




Table 2. Cumulative effects of explanatory variables on economic growth in zero and one regimes




coefficient in regime one


coefficient in regime zero


variable




0.125


0.1


GK




2.74


2.19


GL




0.23


0.18


GX




0.11


0.66


TR/Y




-0.17


-0.08


OR/Y




-0.17


-0.14


GD/Y




Source: Research findings




                                     
 
Conclusion: This paper investigates the effect of government financing methods on the economic growth in Iran from 1973 to 2018. To this end, a Markov-Switching (MS) model was used. The results showed that the tax-to-GDP ratio had a positive effect on the economic growth in both recession and boom regimes, although this effect was not significant in the recession regime. Given the low tax-to-GDP ratio in Iran, these results have not been unexpected. Also, based on the findings of this study, the ratio of government oil revenues to GDP had a negative and significant effect on economic growth in both identified regimes, which confirms the theory of resource curse phenomena or paradox of plenty in the Iranian economy. In addition, the findings of this study showed that the ratio of government debts to GDP had a negative and significant effect on the economic growth in both regimes. The reason for this negative impact could be the fact that government borrowing in Iran is used to compensate for structural budget deficits instead of spending on productive investments and building the necessary infrastructure. Finally, the findings showed that the investment growth rate, population growth rate and export growth rate had positive, significant and tangible effects on Iran's economic growth, respectively.

کلیدواژه‌ها [English]

  • Financing
  • Taxation
  • Oil
  • Government debt
  • Markov-Switching (MS)

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سیاست گذاری اقتصادی
دوره 14، شماره 27
شهریور 1401
صفحه 113-153

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