Document Type : Research Paper
Authors
1 Associate Professor, Department of Statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran.
2 Ph.d. of Statistics, Department of Statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran.
Abstract
Abstract
Most countries define poverty simply as a lack of money, yet poor individuals themselves often view their experience of poverty more broadly. A person living in poverty can face multiple overlapping disadvantages simultaneously, so focusing on a single factor, such as income, does not fully capture the reality of poverty. In Iran, several studies have attempted to calculate the multidimensional poverty index, yet most rely on household income and expenditure survey data, which is limited in calculating the relevant indicators. The present study aimed to calculate and measure multidimensional poverty at the provincial level in Iran, assessing the contribution of each dimension to overall poverty and using the Alkire–Foster method to inform policymakers in their poverty alleviation efforts. The data was collected from the 2015 Multiple Indicator Demographic and Health Survey (MIDHS), encompassing 33,013 households and a wider range of indicators. The results indicated that, aside from Khuzestan and Qom Provinces, the multidimensional poverty index was particularly high in provinces along the eastern borders, while provinces along the northern, southern, and parts of the western borders experienced less poverty. Additionally, the contribution of each dimension to overall poverty revealed that the types of deprivation experienced by households varied across provinces in 2015.
Introduction
Income poverty is an important dimension of poverty, but it fails to capture the full reality of deprivation. The global Multidimensional Poverty Index (MPI) provides an internationally comparable measure of acute multidimensional poverty across more than 100 countries. The global MPI identifies acute deprivations in health, education, and living standards that affect individuals simultaneously, thus complementing the traditional monetary poverty measures—such as the World Bank’s extreme poverty line. The national MPI is a measure of multidimensional poverty within a specific country, aligned with that country’s definitions of poverty. It can identify poverty across different population groups, such as by age or gender. The national MPI reveals not only who falls below the poverty threshold but also highlights specific deprivations that may affect even those above it. This insight allows policymakers to understand how certain deprivations impact both poor and non-poor segments of society. Using the Alkire–Foster method, the present study aimed to assess Iran’s national MPI and examine the contribution of each dimension to the overall MPI across its provinces. The analysis relied on data from the 2015 Multiple Indicator Demographic and Health Survey (MIDHS).
Materials and Methods
The Alkire–Foster method assigns a deprivation score ( ) to each household, calculated as the weighted average of deprivation across all selected dimensions. Households with a deprivation score at or above the established poverty cut-off are considered multidimensionally poor. The incidence of poverty is the proportion of the population that is multidimensionally poor, calculated as ( ). MPI is the product of poverty incidence (H) and the intensity of poverty, which is measured as the average deprivation score among the poor ( ).
All topics related to the national MPI were organized into seven dimensions, represented by 21 indicators. A poverty cut-off of 33% was applied, with equal weights assigned to each dimension and to all indicators within each dimension (see Table 1).
Table 1. Deprivation Cut-offs, Dimensions, and Indicators of National MPI
Dimensions
Indicators
Cut-off: Household is deprived if …
Health
Child mortality
Any child under the age of 18 years has died in the family in the five-year period preceding the survey.
Disability
At least one household member suffers from one of the types of disabilities.
Mental health
At least one household member aged 15 or older suffers from severe mental illness according to Kessler 6 scale (the score greater than or equal 19).
Education
School attendance
Any school-aged child is not attending school up to the age at which he/she would complete class eight.
Level of education
No household member aged 15 or older has completed primary schooling.
Well-being
Cooking fuel
The household cooks with dung, agricultural crop, shrubs, wood, charcoal or coal.
Sanitation
The household’s sanitation facility is not improved (according to SDG guidelines) or it is improved but shared with other households.
Drinking water
The household does not have access to improved drinking water (according to SDG guidelines) or improved drinking water is at least a 30-minute walk from home, round trip.
Electricity
The household has no electricity.
Assets
The household does not own more than one of these assets: Radio, television, telephone, computer, motorbike or refrigerator, and does not own a car.
Housing
The household with inadequate housing; the housing is made of low-quality materials (clay and mud/wood)
Overall life satisfaction
At least one household member aged 15 or older is dissatisfied or very dissatisfied with himself/herself, her/his family life, friends, current job, income or place of residence.
Employment
Unemployment
No household member aged 15 or older is employed or has an income without work.
Insurance
There is at least one household member without health insurance.
Security
Violent discipline
At least one child aged 1-14 has experienced some violent discipline.
Domestic violence
At least one woman aged 15 or older has agreed that her husband has the right to beat up his wife.
Culture
Mass media and information technology
At least one household member aged 15 or older does not read the newspaper or magazine, does not listen to radio or does not use the internet at all.
Access to cultural activities for children
At least one child does not have access to sport, poetry, painting, or religious classes.
Environment
Disaster preparedness
The household has not done any action in the past year to deal with natural hazards and disasters.
Drought-stricken people (1/21)
More than 50% of the population in a particular area is affected by severe drought.
Proximity to industrial pollution
At least 50% of the average industrial waste of the country is generated in the proximity of the household’s place of residence.
Source: Torabi, et al. (2021)
Results and Discussion
As shown in Table 2, in addition to Qom and Khuzestan provinces, all provinces bordering Afghanistan and Pakistan experience higher levels of multidimensional poverty. It also shows the contribution of each dimension to the overall MPI across Iran’s 31 provinces, ranked from the most prosperous to the poorest. Qom ranks highest in well-being and security, yet it is the most deprived in employment and environment. Hormozgan ranks best in health but is the most deprived in education. Ilam is the most deprived in security, while it ranks highest among provinces in environment and employment (with only a slight difference after East Azerbaijan).
Table 2. The Contribution of Each Dimension in Percentage in MPI by Province and National Level and the p-Values of the Wald Test
Health
Education
Well-being
Employment
Security
Culture
Environment
Population Share
Confidence Interval (95%)
MPI
Provinces
15.6
16.5
2.9
12.8
17.7
13.6
20.9
4.6
[0.004,0.010]
0.007
Mazandaran
6.7
17
5.3
15.6
20.4
19.8
15.2
1.1
[0.006,0.014]
0.010
Chaharmahal and Bakhtiari
13.3
14.9
1.7
6.2
32.1
21.8
10
0.7
[0.006,0.014]
0.010
Ilam
7.3
15.7
1.9
14.9
25.4
24.2
10.6
2.4
[0.007,0.015]
0.011
Golestan
6.7
14.9
1.7
14.3
23.8
19.9
18.7
1.3
[0.008,0.016]
0.012
Boshehr
9.9
14
6.2
14.8
17
11.4
26.7
3.6
[0.010,0.019]
0.015
Gilan
7.6
20.6
4.1
9.6
25.3
18.7
14.1
4
[0.010,0.020]
0.015
Western Azerbaijan
3.3
21
2.6
11.8
24.3
20.8
16.2
1.7
[0.010,0.020]
0.015
Hormozgan
10
17.8
4.4
14.2
24.5
17.6
11.5
2.4
[0.011,0.022]
0.017
Kermanshah
8.9
14.6
1.8
13.6
23.8
12.3
25
17.3
[0.010,0.024]
0.017
Tehran
10.9
16.2
4
9.4
27
18.8
13.7
2.3
[0.014,0.025]
0.019
Hamedan
10.7
14.4
2.8
15.6
18.3
12.4
25.8
6.9
[0.015,0.025]
0.020
Esfahan
9.8
15.2
3.2
6.1
30.9
18.3
16.5
4.8
[0.015,0.026]
0.021
Eastern Azarbaijan
8.2
11
0.8
17
14.8
16.6
31.6
3.6
[0.014,0.027]
0.021
Alborz
5.5
17
2.7
17.1
16.2
15.6
25.9
1.9
[0.017,0.028]
0.022
Markazi
8
19.1
2.9
15.9
16.2
12.7
25.2
0.8
[0.016,0.028]
0.022
Semnan
8.5
11
7.9
12.8
25.2
17.7
16.9
2.2
[0.016,0.032]
0.024
Lorestan
11
15.7
3.4
8.1
27.1
17.3
17.4
1.6
[0.018,0.029]
0.024
Ardebil
12
11.5
2.9
11.3
20.8
14.7
26.8
6.3
[0.019,0.032]
0.025
Fars
9.3
15.9
1.9
7.3
31.3
16.9
17.4
1.9
[0.023,0.036]
0.029
Kordestan
5.9
12.7
2.1
10.6
21.9
14.8
32
1.5
[0.023,0.035]
0.029
Yazd
7.6
12.6
3.7
9.7
24.2
18.5
23.7
1.4
[0.022,0.037]
0.030
Zanjan
8.5
13.1
1.6
11.7
20
14.1
31
1.7
[0.025,0.038]
0.031
Ghazvin
7.8
10.9
2.8
11.9
30
21.7
14.9
0.8
[0.027,0.043]
0.035
Kohgilouye & Boyerahmad
7.7
12
4.1
10.8
26.7
21.6
17.1
3.6
[0.024,0.048]
0.036
Kerman
6.5
20.2
4
10.3
25.7
16.9
16.4
0.9
[0.028,0.044]
0.036
Southern Khorasan
7.9
12.2
0.8
18.2
14.2
12.7
34
1.4
[0.028,0.045]
0.037
Ghom
8
13.8
2
12.7
22.9
13.8
26.8
8
[0.031,0.045]
0.038
Razavi Khorasan
6.3
14.8
3
9.9
22.7
16.3
27
5.6
[0.032,0.047]
0.039
Khuzestan
6.5
15.7
4.9
6.7
21.9
16.2
28.1
1.2
[0.039,0.055]
0.047
Northern Khorasan
5.8
16.9
7.3
13.8
20.1
22.3
13.8
2.5
[0.075,0.101]
0.088
Sistan & Balouchestan
8.3
14.6
3.3
12.2
22.6
16.2
22.8
100
[0.023,0.026]
0.025
National level
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-
-
p-values
Source: Research results
Conclusion
Overall, the three dimensions of culture, security, and environment were found to be the most significant contributors to deprivation in Iran, accounting for 16.2%, 22.6%, and 22.8% of the MPI, respectively. Improved access to MIDHS micro-data and administrative data (e.g., air pollution and crime statistics), as well as the inclusion of relevant items into the MIDHS questionnaire (e.g., social protection, violence against women, and nutrition), would improve the MPI measurement in Iran.
Keywords
Main Subjects
)