Journal of Economic Modeling Research

---

The Poverty measurement and its determinants are the most important factors in poverty alleviation programs. In this paper, we estimate poverty line and its dimensions by using Linear Expenditure System. To that end, we use household level data from the Statistics Center of Iran during the period of 1994-2008. We examine the main determinants of poverty for urban households using a probit model. In this model, we assess the impact of key household characteristics on poverty. The results show that the poverty likelihood decreases if the educational level of the head of the household increases. In addition, the chance of being poor declines in a household headed by a male. The age of the head of the household, the ratio of income earners in the household, and the household size are other factors that have a significant role in the probability of being poor.

---

The effect of equivalence scale on poverty line has been considered as a predetermined parameter in all previous studies about the estimation of poverty lines in Iran. As household’s members can benefit from economies of scale in consumption, the cost of reaching to a given level of welfare does not increase one to one in household scale. So the effect of household scale on poverty line as a predetermined parameter creates bias. The main purpose of this study is to estimate the poverty line regarding the household scale. The utility function and the indirect household expenditures under consumption behavior approach are estimated based on income-expenditure data for 204464 Iranian urban households during2001-2007. Then poverty lines (in nominal and real units) per household scale (1to 10 members) are calculated based on indirect household expenditures.

---

Targeted subsidies plan affects income distribution and poverty through several channels. On most of the analyzies, changes on labor supply are not considered. Increasing nominal income alone after paying cash subsidy rule can reduce labor supply in targeted subsidies. This issue may decrease effect of targeted subsidies. In this research by CGE we calculate the result of impact of energy price increase and direct cash subsidy transfer with considering labor supply decrease in the first phase of this plan. Then we use this CGE data to calculate the poverty index and income distribution. The model is calibrated based on 2001 Micro Consistent Matrix (MCM) designed by Research Institute of Planning and Management Deputy Strategic Planning and Control. The results of the model show that while the plan reduce supply of labor, it improve income distribution and poverty in Iran. The results also reveal that the percentage of improvement in purchasing power of rural deciles is more than the percentage of improvement in purchasing power of urban deciles.

---

The lack of social security supports and labor market laws in informal employment has strengthened the expectation that poverty in a family in which the head of the household chooses informal employment is greater than in a family in which the head of the household works in the formal sector. Hence, this study attempts to investigate the effect of informal employment with other factors affecting household’s poverty. To this aim, by using the microdata plan of costs and incomes of urban and rural households in 2018, first, the poverty line was calculated based on 66% of the average annual household expenditures by provincial division for urban and rural areas; and poor households were identified as well. Then, according to the presented index in this study, heads of households' employment types were formally and informally determined. In the primary data processing, a comparison between households with employed heads showed that the highest poverty rates were for households whose heads work in informal employment. Next, the estimation of the research model with the dependent variable limited to the basis of pseudo-panel data and random effects in logistic regression was performed in a separate format for 13248 urban households and 13115 rural households in 31 provinces. The results showed that the informal employment of the head of the households has a direct effect on the possibility of household poverty and the rate of influence in urban areas is higher than in rural areas. Furthermore, the head of the household's education, age, and gender have an indirect effect; and the square number of age and size of the household variables have a direct effect on the probability of household poverty. In such a way that the desired effect of education and age, and the undesired effect of the household dimension on the probability of household poverty in urban areas is greater than in rural areas.

---

The aim of this study was to analyze the effect of commodity group expenditures on the probability of urban and rural household poverty in Iran. First, using household expenditure-income data in 1399, the poverty line was calculated based on the method of 66% of the average per capita expenditure, which is higher than urban households than rural households. In the following, the research model is limited by a dependent variable and is estimated based on pseudo-panel data in logistic regression by random effects method. The results showed that the expenditures of the communication group have the greatest impact on the probability of household poverty in urban and rural areas and the impact of this group of goods in urban areas is greater than rural areas. In contrast, hotel and restaurant costs in urban areas have a greater positive effect on reducing the likelihood of household poverty than in rural areas. But the cost of durable goods in urban areas, transportation in rural areas, and the cost of recreation and cultural affairs do not have a significant effect on the likelihood of household poverty. There is also no significant difference between the impact of furniture and household appliances, clothing and footwear and health care in urban and rural areas. Between social characteristics of the household, variables of gender and education have a negative effect on the probability of poverty and variables of household dimension and marital status have a direct effect on the probability of poverty of urban households, but these variables have no significant effect on the probability of poverty of rural households.

---

Agbarakwe, U. H., & Okpe, E. A. (2024). An Analytical Inquiry into the Impact of International Trade on Poverty Reduction in Nigeria. South Asian Research Journal of Humanities and Social Sciences, 6(1), 40-55.
Asongu, S. A., & Eita, J. H. (2023). The conditional influence of poverty, inequality, and severity of poverty on economic growth in sub-Saharan Africa. Journal of Applied Social Science, 17(3), 372-384.
Atrkar Roshan, S., & Hashemi, Z. (2016). The Impact of Trade Openness on Poverty in Iran: Simultaneous Equations System Method. Quarterly Journal of Applied Theories of Economics, 3(1), 183-204. (in Persian)
Balasubramanian, P., Burchi, F., & Malerba, D. (2023). Does economic growth reduce multidimensional poverty? Evidence from low-and middle-income countries. World Development, 161, 106119.
Cao, Y., Tabasam, A. H., Ahtsham Ali, S., Ashiq, A., Ramos-Meza, C. S., Jain, V., & Shahzad Shabbir, M. (2023). The dynamic role of sustainable development goals to eradicate the multidimensional poverty: evidence from emerging economy. Economic research-Ekonomska istraživanja, 36(3).
Chatziantoniou, I, Duffy. D, Filis, G. (2013). Stock Market Response to Monetary and Fiscal Policy Shocks: Multi-country evidence. Economic Modelling, 30, 454–769.
D’Attoma, I., & Matteucci, M. (2024). Multidimensional poverty: an analysis of definitions, measurement tools, applications and their evolution over time through a systematic review of the literature up to 2019. Quality & Quantity, 58(4), 3171-3213.
Deaton, A. (2005). Measuring poverty in a growing world (or measuring growth in a poor world). The Review of Economics and Statistics, MIT Press, 87(1), 1–19.
Hosseinidoust, S. E., Sepehrdoost, H., & Moradi, F. (2024). Investigating the Affecting Factors on Capability Poverty in the Selected Islamic Countries Emphasizing on Globalization and Economic Growth. Economic Growth and Development Research, 14(56), -. (in Persian)
Ilyas, A., Banaras, A., Javaid, Z., & Rahman, S. U. (2023). Effect of Foreign Direct Investment and Trade Openness on the Poverty Alleviation in Burundi–Sub African Country: ARDL (Co-integration) Approach. Pakistan Journal of Humanities and Social Sciences, 11(1), 555-565.
Imam, M. F., Islam, M. A., & Hossain, M. J. (2018). Factors affecting poverty in rural Bangladesh: an analysis using multilevel modelling. Journal of the Bangladesh Agricultural University, 16(1), 123-130.
Iranmanesh, S. (2023). Analyzing the Role of Poverty in Financial Development in Selected Countries. Bi-quarterly Journal of development economics and planning, 10(1), 177-196. (in Persian)
Liu, M., Feng, X., Zhao, Y., & Qiu, H. (2023). Impact of poverty alleviation through relocation: From the perspectives of income and multidimensional poverty. Journal of Rural Studies, 99, 35-44.
Lundberg, L., & Squire, L. (2000). Inequality and growth; Lessons for policy. Mimeo, World Bank, Washington, D. C.
Mekonnen, A. G. (2024). Estimating multidimensional poverty: A new methodological approach. Journal of Poverty, 1-19
Motaghi, S., Gholami, R., Talei, S., & Ebrahimi, S. (2023). An Analysis of Regional Poverty and Factors Affecting it in Developed and Developing Countries. Journal of economics and regional development, 30(25), 39-66. (in Persian)
Nguyen, T. T., Hoang, V. N., Wilson, C., & Managi, S. (2019). Energy transition, poverty and inequality in Vietnam. Energy Policy, 132, 536-548.
Nurlina, N., Ridha, A., Syahputra, R., & Muda, I. (2024). Impact of selected macroeconomic on poverty alleviation in Indonesia: Evidence from NARDL approach. Journal of Infrastructure, Policy and Development, 8(8), 5166.
Saddique, R., Zeng, W., Zhao, P., & Awan, A. (2023). Understanding multidimensional poverty in pakistan: implications for regional and demographic-specific policies. Environmental Science and Pollution Research, 1-16.
Shabanzadeh-Khoshrody, M., Javdan, E., & Shemshadi, K. (2024). Spatial Distribution of Poverty, Food Insecurity and Factors Affecting it in Urban Areas of Iran. Journal of Agricultural Economics and Development, (), -.  (in Persian)
Tasan, M., Piraee, K., Nonejad, M., & Abdoshahi, A. (2021). The Effect of Economic Openness on Improving the Life of the Poor in Iran. Agricultural Economics Research, 13(1), 123-146. (in Persian)
Yan, J., Işık, C., & Gu, X. (2024). The nexus between natural resource development, trade policy uncertainty, financial technology and poverty in China: Contributing to the realization of SDG 1. Resources Policy, 95, 105154.
 

---

Introduction
 Poverty has become one of the major global challenges faced by most Asian countries. Although they have been able to achieve technology and increase productivity in the fields of production in recent decades, a high percentage of their society still lives in poverty. The current concern about the increase in chronic poverty in many countries of the developing world requires a deeper understanding not only of the number of poor people, but also of the nature of poverty. This issue has a widespread and devastating impact on the lives of millions of people around the world and is important because its effects go beyond the economic sphere and extend to the social, political and cultural spheres. Poverty reduction is one of the fundamental economic and social challenges in global societies. Therefore, it is very important to examine the factors affecting poverty reduction One of the ways to reduce poverty is the existence of institutional foundations and institutions. Since the second half of the twentieth century, numerous studies have been conducted on the role of institutional and political approaches in poverty reduction. According to these studies, the existence of strong institutions and institutions attracts investment, improves technology and employment, and consequently increases production and economic growth. Therefore, the existence of institutions is the main factor in the growth and development of countries. The existence of institutions and institutions can explain the differences in welfare, growth and development and economic well-being between countries. By creating a stable structure in the economy and society, institutions reduce risk and uncertainty, and thus reduce transaction costs. In short, understanding the interaction between institutional factors, spatial dynamics and poverty reduction is essential for designing effective policies and interventions. The aim of this study is to answer the question of how institutional factors and economic growth can reduce poverty in selected Asian countries?

 Method
 In this research, the research method is of the spatial analytical and econometric type. The data of this study were collected from the World Bank and the Macro Trends website. In estimating the spatial panel data model, it is necessary to mention a few points. First, the spatial effects in the calculations are factors that are related to the location of the variables. The first factor is the spatial dependence or autocorrelation between the observations of the sample data at different points and the second factor is the spatial structure or heterogeneity created by the model relations for moving on the plane. The coordinates change with the sample data. To detect the spatiality of the data, it is necessary to perform spatial detection tests. In this research, a weight matrix was formed for countries that have geographical connections. The weight matrix is ​​of the adjacency type. The adjacency or neighborhood matrix was formed for the 15 countries studied. In this way, the value of one is considered for neighboring or neighboring countries and the value of zero for non-adjacent countries. Therefore, the adjacency matrix is ​​a symmetric 15x15 matrix with a main diameter of zero and elements outside the main diameter of zero and one. Stata software is used to estimate the model. In panel data with spatial characteristics, fixed and random effects can be considered for the model and the best model was selected from SAR, SDM, SAC, SEM and GSPRE models using the spatial Hausman test, of which the spatial autocorrelation (SAC) model was selected.
Conclusion
 Based on the spatial effect of the disturbance components or dependent variables, the results of the spatial autocorrelation model (SAC) show that economic growth and the quality of institutional factors have a positive effect on poverty reduction. Also, increasing domestic investment also helps to reduce poverty. The spatial effects of poverty show that increasing poverty in a country can also cause poverty in neighboring countries. In general, economic growth can increase welfare and create new opportunities. Policies that support economic growth, such as financial development and economic stability, provide a favorable environment for poor households to increase their production and income. The research results show that institutional development and better quality of institutions (such as corruption control, government stability and democracy) have a positive effect on poverty reduction. Better institutional quality improves resource distribution and poverty reduction in the long run. Strong and reliable institutions can increase investment attraction and facilitate international trade. It also confirms the positive effect of domestic investment on poverty reduction. Increased investment increases production, income and welfare and reduces unemployment. Spillover effects of domestic investment can facilitate the transfer of knowledge and technology.