Economic Research and Perspectives

Economic Research and Perspectives

Targeting under Conditions of Imperfect Information and Budget Constraint: A Case Study of Rural Households in Iran

Author
Bagher Darvishi
Associate Professor of Economics, Faculty of Literature and Humanities, Ilam University, Ilam, Iran
10.22034/23.4.213
Abstract
Introduction:

In order to target the limited budgets of poverty alleviation programs and increase their efficiency, a wide range of targeting methods including means testing, proxy means testing, categorical targeting, geographical targeting, self-targeting, and community-based targeting has been used in developing countries (Coady, Grosh and Hoddinott, 2004). However, targeting in Iran, in the best case, has been based on the proxy means testing. Kidd and Wylde (2011) have criticized this method due to lack of transparency and poor predictions in the field of identifying the poor. The main purpose of this article is to compare the different economic and social characteristics of Iran’s rural households with the aim of finding the best characteristics in order to target subsidies in Iran. In this context, the following questions are raised: In a situation where accurate information about household income or expenses is not available, how should households be prioritized to receive subsidies based on their characteristics? How much cash subsidy should each household receive in order to reduce the aggregate poverty? To answer these questions, by following, Kanbur (1987), Ravallion and Chao (1989), Elbers, Fujii, Lanjouw, Özler and Yin (2007), Glewwe (1992) and Araar and Luca (2019), I use a new numerical algorithm, which acts as optimal poverty group targeting. This method is conceived to find the optimal group transfers that allow the largest possible reduction in any additive poverty indexes, like the Foster, Greer, Thorbecke (FGT) class of poverty indexes.

Methodology:

This article uses a new numerical algorithm, which acts as optimal poverty group targeting. This method was first presented by Kanbur (1987), which focused on the theoretical rules of optimization. Then, based on the theoretical findings of Kanbur (1987), Ravallion and Chao (1989) have proposed numerical method that maximizes the reduction in the FGT (α=2) index by group transfers, subject to a fixed budget. After that, Glewwe (1992) improved it theoretically, and finally, Araar and Luca (2019) proposed the method of optimal group targeting by modifying Glewwe (1992) method. In fact, the methodology of Glewwe (1992) is a generalization of Kanbor (1987) and Ravallion and Chua (1989) Method, but the mentioned methodologies focused on a subset of poverty indicators (e.g., squared poverty gap index) for which an analytical solution was possible. Contrarily, Araar and Luca (2019) proposed a new method, which is applicable to all additive poverty indices (such as the headcount or poverty gap rates and squared poverty gap indexes).This article uses income-expenditure data of rural households of Iran in 2020, and follows Araar and Luca (2019) methodology. In addition, to check the efficiency of this method, three indicators including the quality of targeting, inclusion and exclusion errors will be used.

Results and Discussion:

In this article, economic and social characteristics of rural households in Iran were compared to targeted poverty alleviation programs. Based on the results, for the head count ratio, the targeting efficiency based on different household characteristics changes between 23.67 and 31.03 %, the population coverage rate changes between 38.93 and 100 %, and the sum of the inclusion and exclusion errors changes between 41.62 and 52.53%. Now, if the targeting is done based on the poverty gap index, the targeting efficiency will be between 42.18 and 48.02 %, the population coverage rate will be between 86.21 and 100 %, and the sum of the exclusion and inclusion errors will be between 46.96 and 52.53%. Finally, if the poverty severity index is used as the basis for targeting, the targeting efficiency will change between 53.99 and 59.51 %, the population coverage rate will change between 99.33 and 100 %, and the sum of the inclusion and exclusion errors will change between 48.92 and 52.53%. It is interesting to note that in targeting based on all three mentioned poverty indicators, the family size, number of members under 7 years old and the education of the household head are always the best characteristics for targeting poverty.

Conclusion:

The main purpose of this article is to compare the different economic and social characteristics of rural households with the aim of identifying the best characteristics in order to targeting subsidies in Iran. According to the results of this article, the characteristic that should be taken into account in targeting is the family size, which the efficiency of targeting based on this characteristic is equal to 51.59% of targeting with complete information. The rate of exclusion and inclusion errors are zero and 52.53%, respectively. Finally, in the targeting based on the family size and squared poverty gap index, the population coverage rate is 100, which is very acceptable from the social point of view. Paying attention to the changes in the poverty indices based on the household demographic characteristics is very important, because if the family size increases, the poverty indices grow strongly. As a result, the headcount, poverty gap and squared poverty gap indexes for families with six and more people become 2.5, 3.5 and 2.4 times the same index for households with 1-2 people, respectively.
Keywords
Targeting Subsidies
Rural Households
Imperfect Information
Budget Constraint
Numerical Optimization Method

Subjects

1. پژویان، جمشید(۱۳۷۳). سیاست‌های حمایتی از قشرهای آسیب‌پذیر. تهران، معاونت اقتصادی وزارت امور اقتصاد و دارایی.
2. پژویان، جمشید(۱۳۷۴). شناسایی و حمایت از اقشار آسیب‌پذیر. تهران، معاونت اقتصادی وزارت امور اقتصاد و دارایی.
3. پژویان، جمشید(1375). فقر، خط فقر و کاهش فقر. مجله برنامه و بودجه، 1(2)،23-5.
4. خسروی‌نژاد، علی‌اکبر، مالکی، امین(۱۳۸۷). هدفمندسازی، شناسایی و طبقه‌بندی خانوارها. فصلنامه پژوهشنامه بازرگانی، 47، ۴۶-۱.
5. خسروی‌نژاد، علی‌اکبر، خدادادکاشی، فرهاد(۱۳۹۱). شناسایی اقتصادی خانوارها بر اساس روش دو مرحله‌ای: کاربرد آزمون تقریب وسع. فصلنامه علمی پژوهشی رفاه اجتماعی، 12(45)، ۱۲۶-۹۳.
6. درویشی، باقر، محمدیان، فرشته، احمدی‌خواه، مهین(۱۳۹۸). بررسی تأثیر هدفمندی یارانه‌ها بر مشاغل آزاد و مزد و حقوق‌بگیر: با تأکید بر فقر و نابرابری. فصلنامه علمی پژوهشی مطالعات اقتصادی کاربردی ایران، 8(29)، ۲۶۷-۲۳۹.
7. وزارت بهداشت، درمان و آموزش پزشکی ایران(۱۳۹۲)، سبد غذایی مطلوب برای جامعه ایرانی. دفتر بهبود تغذیه جامعه وزارت بهداشت، درمان و آموزش پزشکی دانشکده بهداشت دانشگاه علوم پزشکی تهران، انستیتو تحقیقات تغذیه‌ای و صنایع غذایی کشور.
8. Araar, A. & Luca, T.(2019). Optimal Targeting and Poverty Reduction with fixed budget and imperfect information ,( http://www.ecineq.org).
9. Bakhshoodeh M. (2013). Proxy Means Tests for Targeting Subsidies Scheme in Iran. Iranian Journal of Economic Studies, 2(2), 25-46. (https://doi.org/10.22099/ijes.2013.2718).
10. Coady, D., Grosh, M., & Hoddinott, J. (2004). Targeting of transfers in developing countries: Review of lessons and experience. The World Bank.
11. Coady, D. P., Grosh, M. & Hoddinott, J. (2002). Targeting Outcomes Redux, in World Bank Research Observer 19 (1): 61-85.
12. Devereux, S., Masset, E., Sabates‐Wheeler, R., Samson, M., Rivas, A. M., & Te Lintelo, D.(2017). The targeting effectiveness of social transfers. Journal of Development Effectiveness, 9(2), 162‐211.
13. Elbers, C., Fujii, T., Lanjouw, P., Özler, B., & Yin, W. (2007). Poverty alleviation through geographic targeting: How much does disaggregation help? Journal of Development Economics, 83(1), 198–213.
14. Foster, J., Greer, J., Thorbecke, E (1984). A class of decomposable poverty measures. Econometrica 52, 761–776.
15. Garfinkel, I. (ed.) (1982). Income-Tested Transfer Programs: The Case For and Against, Published by Elsevier Science & Technology Books (1982), ISBN 13: 9780122758805.
16. Glewwe, P. (1992). Targeting Assistance to the Poor: Efficient Allocation of Transfers when Household Income Is Not Observed. Journal of Development Economics, 38(2), 297–321.
17. Hanna, R., & Olken, B. A. (2018). Universal basic incomes versus targeted transfers: Antipoverty programs in developing countries. Journal of Economic Perspectives, 32(4), 201‐26.
18. Houssou, Nazaire S.I. (2010). Operational Poverty Targeting by Proxy Means Tests. Models and Policy Simulations for Malawi. Hohenheim.
19. Kanbur, R. (1987). Budgetary Rules for Poverty Alleviation. IMF Staff Papers.
20. Kidd, S. & Wylde, E (2011). Targeting the Poorest: An Assessment of the Proxy Means Test Methodology. AusAID Working Paper, Government of Australia
21. Klasen, S., Lange, S. (2015). Targeting Performance and Poverty Effects of Proxy Means-Tested Transfers: Trade-offs and Challenges. Göttingen, March.
22. Margitic, J., & Ravallion, M. (2019). Lifting the floor? Economic development, social protection and the developing World's poorest. Forthcoming in Journal of Development Economics.
23. Ravallion, M., & Chao, K. (1989). Targeted policies for poverty alleviation under imperfect information: Algorithms and applications. Journal of Policy Modeling, 11(2), 213–224.
Economic Research and Perspectives
Volume 23, Issue 4
Winter 2024
Pages 213-244