metadata: authors: 0: author: Atamanov,Aziz
1: author: Mostafavi,Mohammad-Hadi
2: author: Salehi Isfahani,Djavad
3: author: Vishwanath,Tara
count: Iran, Islamic Republic of
docna: 0: docna: Constructing robust poverty trends in the
            Islamic Republic of Iran : 2008-14
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Inflation,Macroeconomic Management,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping
abstracts: cdata!: This paper constructs and tests the
            robustness of consistently measured poverty trends in the
            Islamic Republic of Iran after 2008, using international
            poverty lines based on U.S. dollars at 2011 purchasing power
            parity. The constructed estimates reveal three distinct
            periods of welfare in the Islamic Republic of Iran: increase
            in poverty and inequality between 2008 and 2009, decline in
            poverty and inequality between 2009 and 2012, and gradual
            deterioration of both indicators again after 2012. The
            results are robust regardless of the choice of welfare
            aggregate, inclusion or exclusion of different components,
            and spatial adjustment accounting for regional variation in
            food and housing prices.
geo_reg_key: 517202,517191
repnme: repnme: Constructing robust poverty trends in the
            Islamic Republic of Iran : 2008-14
display_title: Constructing robust poverty trends
            in the Islamic Republic of Iran : 2008-14
listing_relative_url: /research/2016/09/26818983/constructing-robust-poverty-trends-islamic-republic-iran-2008-14
topicv3: Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Poverty Measurement and Analysis m1326941 707,Health m1504661 3480,Deflation m1505334 3102
publishtoextweb_dt: 2016-09-27T00:00:00Z
no_of_pages: 27
author_keys: 0: author_key: 1182029
1: author_key: 428618
2: author_key: 124046
model_extractions: WPS7836 Policy Research Working Paper 7836 Constructing Robust Poverty Trends in the Islamic Republic of Iran 2008–14 _Aziz Atamanov Mohammad-Hadi Mostafavi Djavad Salehi-Isfahani Tara Vishwanath_ Poverty and Equity Global Practice Group September 2016
Policy Research Working Paper 7836

# **Abstract**

This paper constructs and tests the robustness of consistently measured poverty trends in the Islamic Republic of Iran after 2008, using international poverty lines based on U.S. dollars at 2011 purchasing power parity. The constructed estimates reveal three distinct periods of welfare in the Islamic Republic of Iran: increase in poverty and inequality between 2008 and 2009, decline in poverty and inequality between 2009 and 2012, and gradual deterioration of both indicators again after 2012. The results are robust regardless of the choice of welfare aggregate, inclusion or exclusion of different components, and spatial adjustment accounting for regional variation in food and housing prices.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aatamanov@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Constructing Robust Poverty Trends in the Islamic Republic of Iran: 2008–14**<sup>1</sup>

Authors<sup>2</sup> : Aziz Atamanov, Mohammad-Hadi Mostafavi, Djavad Salehi-Isfahani, Tara Vishwanath JEL codes: D31, D63 Key words: Iran, international poverty, inequality

> 1 Authors are thankful to Dean Jolliffe for peer-reviewing the paper, Nobuo Yoshida and Sergio Olivieri for useful comments and suggestions.

> 2 Aziz Atamanov and Tara Vishwanath are with the World Bank. Mohammad-Hadi Mostafavi and Djavad SalehiIsfahani are with Virginia Polytechnic Institute and State University. Contact email: <u>aatamanov@worldbank.org.</u>
# **1. Introduction**

Accurate and timely welfare measurement is a necessary step for effective and informed policy making. Even though there are no official publicly available poverty estimates in the Islamic Republic of Iran, estimates of poverty trends exist in the academic literature. These are calculated either based on the authors’ own assessment of an appropriate national line or according to international poverty lines based on US dollars. Examples of such estimates cover different periods between 1984 and 2009 and are available in Assadzadeh and Paul (2004), Salehi-Isfahani (2009), Mahmoudi (2011), and Maasoumi and Mahmoudi (2013).

According to Salehi-Isfahani (2009), the 1979 Revolution and the subsequent war disrupted economic growth, which contributed to a deterioration of welfare for Iranian households. Using the poverty lines defined in Assadzadeh and Paul (2004),<sup>3</sup> Salehi-Isfahani shows that poverty increased dramatically following the revolution from 29 percent in 1984 to 42 percent in 1989. After the end of the war in 1988, the period of reconstruction and reforms started. The poverty rate fell by more than 30 percentage points, declining from 42 percent in 1989 to 12 percent in 2005. Poverty gap and severity indicators improved as well which may suggest that welfare improved for the population close to the poverty line, but also for those at the bottom of the distribution (Salehi-Isfahani 2009).

The World Bank (2008) analyzed regional dimensions of poverty in the Islamic Republic of Iran in 19982005 using both the international poverty line ($2.9 in 2005 PPPs) and poverty lines in local currency units. Regardless of the choice of poverty line, poverty fell both in urban and rural areas and in most of the provinces. According to Maasoumi and Mahmoudi (2013), who decomposed poverty changes during the period 2000 to 2009 into growth and redistribution components, poverty declined between 2000 and 2004 and increased afterwards both in rural and urban areas. This is in line with findings in Mahmoudi (2011) who also reported an increase in poverty between 2004 and 2007.

There is, however, little knowledge about poverty trends in the Islamic Republic of Iran in the recent past, in particular after 2008, when the country’s economy entered a turbulent period as a result of energy price reform, the tightening of international sanctions, and falling oil prices. The most recent international poverty estimates reported by the World Bank and based on the group data from the Household Expenditures and Income Survey (HEIS) contain only two data points, for 2009 and 2013.<sup>4</sup> According to this source, poverty fell between 2009 and 2013 regardless of the choice of poverty line. Inequality, measured by the Gini coefficient and based on expenditure per capita, also declined, from 42 to 37.4 percent. This limited empirical evidence should be treated with utmost caution because it is based on two points in time and could mask volatility in poverty rates especially given the shocks the Islamic Republic of Iran has experienced during this time. In particular, during this period the Islamic Republic of Iran went through sanctions, removal of energy subsidies and initiated the distribution of universal benefits through cash transfers. Therefore, constructing a consistent and reliable poverty trend by including all years is essential to better understand welfare changes in the Islamic Republic of Iran after 2008.

This working paper fills the existing knowledge gap by constructing welfare aggregates and measuring poverty rates in the Islamic Republic of Iran using publicly available data from the Household Expenditure

> 3 In particular, the rural daily poverty line in 2004 was set at 7,679 rials and the urban one at 10,886 rials.

> 4 Datt (1998) discusses how group data can be used to calculate poverty rates.

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and Income Survey (HEIS) that is conducted annually since 1963 in rural areas and 1968 in urban areas. Unit record data are available in an electronic format since 1984.This paper constructs the trend for the most recent 2008-2014 period. Besides the knowledge gap, this particular time period was chosen to minimize the risk of comparability between surveys due to changes in instruments and data collection process.<sup>5</sup> The paper explores different analytical approaches for constructing the components of the welfare aggregate as well as price adjustments, and conducts various robustness checks of poverty trends. This work may be helpful to inform discussions about establishing official poverty measurement methodology and necessary data requirements.

Poverty rates in this paper are measured using different international poverty lines expressed in USD at 2011 purchasing power parity (PPP). Using an international line helps to avoid arbitrariness and sensitivity of establishing a line in local currency units. In addition, selected international lines in this paper are used only for illustrative purposes.

This paper consists of seven sections. Section 2 briefly discusses main methodological issues in poverty measurement. Section 3 explains construction of different components of expenditure based welfare aggregate. Section 4 discusses the methodology for price adjustment. Section 5 presents robustness tests of expenditure based poverty and inequality measures. A brief robustness test of poverty trends by using income data is done in section 6. Section 7 concludes.

# **2. Methodological issues in poverty measurement**

Measuring poverty requires two broad steps. The first step is to define an indicator to measure welfare or living standards. The second step requires setting a poverty line - the minimum welfare level below which a person is considered to be poor.

There are two most common welfare indicators for poverty measurement: consumption and income per capita derived from survey data. The choice between them is often made based on the social-economic conditions of the country chosen for the analysis and microdata collected. Income is usually used to measure welfare in rich and developed countries. For example, poverty in most of European Union countries is measured by income, but in most of the developing world consumption is deemed a better measure for poverty as income suffers from measurement error and tends to be underreported (Azzarri et al. 2010). Consumption includes both purchased goods, services and home production and is believed to be a better candidate to measure lifetime welfare in less developed countries. Consumption can be better measured than income in countries where agriculture and self-employment are important employment options. Consumption is also less subject to seasonal volatility in rural areas compared to income and is more likely to be representative of general welfare (Coudouel, Hentschel and Wodon 2012; Haughton and Khandker 2014). Using income for poverty measurement may have its benefits as well. For example, contributions to change in income poverty can be established by income sources and this information will be very useful and easy to use for policy recommendations. Indeed, a recent paper by Meyers and Sullivan (2009) argues

> 5 One caveat is that information on changes in sample frame in the selected period was not available in open sources. In order to test the robustness of obtained trend the team re-estimated poverty without survey weights. Results are qualitatively the same and available upon request.

3
that even for a developed country like the United States, consumption is a better measure especially for those at the lower end of the distribution.

In this paper we use expenditure as a basis for poverty and inequality measurement but also examine the income data and compare trends using both income and consumption measures.

Whatever welfare indicator is chosen, it is necessary to aggregate information provided at the household or individual level. The main guiding principle for the welfare aggregate is to make it as comprehensive as possible, but minimizing measurement errors through inclusion of components which may be noisy. For example, food consumption, typically, includes own production, transfers and food eaten outside home which pose challenges during the data collection phase. Nonfood consumption usually excludes lumpy infrequent expenditures (e.g. funerals, weddings), investment and items which are hard to estimate accurately (public goods). Durables require a special treatment because they are bought at a particular point in time, but consumed over a period of several years. Therefore, consumption should include only the annual use value of a durable rather than purchase value. The same principle should be applied to measure the value of housing services (Deaton and Zaidi 2002, Haughton and Khandker 2014).

There is no consensus in the literature on whether health and education expenditures should be included. One argument to exclude health expenditures is because they may reflect a regrettable necessity and do not contribute to welfare (e.g. expenditure to treat cancer). In contrast, preventive care expenditure clearly contributes to higher utility and welfare, but practically it is often difficult to distinguish between them. Education expenditures are another controversial item because they are associated with investment in the future rather than current consumption. Nevertheless, this component is often included in the welfare aggregate following standard national income accounting practice (Deaton and Zaidi 2002).

Having constructed the welfare aggregate, there is a need to make additional adjustments to facilitate ranking of individuals or households. One of the most important adjustments is spatial and inter-temporal deflation to account for temporal and spatial difference in prices faced by the population. The second important change is related to adjustment of the welfare aggregate for household composition. This can be done by simply dividing household consumption by total household size or by taking into account economy of scale and equivalence scale.

After adjusting the welfare aggregate as described above, the second step is construction of a poverty line which is intrinsically linked to the measure of welfare. The welfare aggregate and the poverty line should be coherently measured. Households whose consumption expenditures or income fall below this line are considered poor. Defining a national official poverty line is a long empirical process led by national authorities of the country. Broadly speaking, it requires many steps including defining an average nutritional requirement or multiple requirements depending on the method chosen, costs of meeting energy requirement and the nonfood component (Ravallion 1998).

For the purpose of international comparison, international poverty lines are often used. The most widely used poverty lines are 1.25 USD at 2005 PPP and its equivalent in 2011 PPP - 1.90 USD (Ferreira et al. 2015). They were established by the World Bank as averages of the national poverty lines of the 15 poorest developing countries expressed in PPP terms to monitor global extreme poverty (Chen and Ravallion 2010). Higher international poverty lines can be used by countries to measure wellbeing if necessary. In this report international poverty lines are used based on USD in 2011 PPPs.

4
In order to estimate poverty trends it is important to make sure that numbers are consistently measured across time (Haughton and Khandker 2014). Firstly, the sampling frame and survey instruments should not change during the period selected for the analysis. As shown in Beegle et al. (2010), changes in recall period and how consumption data are collected may result in significantly different poverty rates. Secondly, the welfare aggregate should be defined consistently across years. For example, excluding health expenditures in the first year and including them in the second will make poverty estimates between these years not comparable. Thirdly, the timing of survey work should be the same across years. This is especially important for the surveys without inter-temporal stratification within a year and with high seasonality in economic activities.

# **3. Expenditure‐based welfare aggregate and its components**

The Household Expenditure and Income Survey (HEIS) has been conducted annually by the Statistical Center of Iran (SCI) since 1963 in rural areas and 1968 in urban areas. The unit record data from 1984 onward are publicly available.<sup>6</sup> The survey is nationally representative and two-stage stratified. Strata information, however, is not publicly available for all years. Households are distributed randomly and evenly throughout the year, making one-twelfth of the sample interviewed each month. However, the month of interview information is publicly available only from 2008 onwards. Sample sizes vary over the years ranging from 5,759 households in 1986 to 39,856 households in 2014.

Gregorian notations for years are used in this report, but the actual survey period is left as it is shown in the HEIS: March to March. For example, year 2010 means the survey period between March 2010 and March 2011.

HEIS includes both demographic and income information but its main focus is on expenditures. Surveys collect expenditure information on more than 1,000 items. The recall period of expenditures for most items is the last month. For durables, some education expenses, insurance expenditures, investment in housing and farms, and income components, the recall period is the last 12 months. Like in most surveys of such type, income and expenditure data are self-reported. HEIS does not distinguish between how much is spent and how much is consumed and reports only expenditures on a particular item. For items, which were not purchased, an estimated value is provided.

The food component of the welfare aggregate includes purchases or the estimated value of all food products bought or received for free. Food expenditures in restaurants and dining facilities are also included in food. The non-food component aggregates the following groups of expenditures: tobacco, miscellaneous, hotel, leisure, communication, transport, furniture, housing, clothing, actual and estimated rent, education, and health insurance.

As discussed above, there are no clear rules on whether education and health expenditures should be included or excluded from the welfare aggregate and the decision depends mainly on the country context. Following Deaton and Zaidi (2012), the elasticity of education and health expenditures with respect to total expenditure per capita has been estimated to determine whether education and health expenditures are closely associated with wellbeing. The point is to keep expenditures with high elasticities to total expenditures. As is often observed in other countries, elasticity of education expenditures is higher than

> 6 Data points were downloaded in January 2016.

5
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Month of interview is known for each household in the survey. Given that for all expenditures (except durables) the recall period is last month, shifting the time of interview by one month gives the actual period of expenditures reported. Monthly rebased CPI is then applied to deflate the components of welfare aggregate within each year.

Across year deflation is done to keep welfare aggregates in similar prices. Given that expenditure per capita is transformed into USD in 2011 PPPs, welfare aggregates in each year are inflated or deflated to be in 2011 prices using SCI CPI.

# 4.3 Spatial deflation

Spatial deflation is another important step to make sure the welfare aggregate is corrected for differences in prices across space. Two different deflators were constructed for this purpose: food and rent. The food spatial deflator is constructed from unit values of purchased food products (excluding tobacco) and is based on Paashe price index following Deaton and Zaidi (2002). Paashe index is calculated using the formula below where ��� is the share of household h's budget devoted to good k. P0 is reference price vector. Ph is vector of prices household face. For spatial deflator, food unit prices are used to calculate household level deflators, which are then averaged to the regional level separately for urban and rural areas (called subregions further in the text). The whole process is described below.

Unit food prices are calculated by dividing inter-temporally (within-year) adjusted expenditures by purchased quantities. To make sure prices are calculated for consistent units, all quantities are transformed to kilograms. Products with zero or missing quantities are dropped. Products observed less than six times at the national level are dropped as well. Finally, outliers in unit prices at sub-regional level - lower or higher than 2.5 standard deviation from the mean – were identified and replaced by missing values. Missing values were replaced by median values at the lowest possible level starting from sub-regional level, followed by rural/urban and finally the national level. Households with zero expenditure were assigned imputed values using unit prices and quantities.

Household level deflators are constructed for each product by dividing household level prices over median prices at the national level. For each household average deflator is constructed using expenditure shares of different products as the weight. Thereafter, average deflators for each rural and urban area of seven regions are calculated and will be applied to each household for spatial adjustment.<sup>10</sup> Simple illustration of this procedure is shown in table 2.

> 10 Using sub-regional averages as deflators is easier for communication than having household specific deflators and is more robust to extreme values.

9
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<!-- Start of picture text --> 2.00<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>0.40<br>0.200.00 |<br>urban rural urban rural urban rural urban rural urban rural urban rural urban rural urban<br>Tehran Caspian Northwest Northeast Central Southeast Persian Gulf Zagros<br>2008 @2009 @2010 ©2011 2012 M2013 m2014<br><!-- End of picture text -->
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# **7. Conclusion**

This working paper constructed poverty trends in the Islamic Republic of Iran during 2008-2014 using international poverty lines expressed in USD 2011 PPPs. The constructed estimates reveal three distinct periods regardless of the choice of poverty line (4-10 USD 2011 PPP daily), the way the welfare aggregate is constructed and adjusted, and whether income or expenditure is used. We distinguished three periods: The first period is between 2008 and 2009 when both poverty and inequality were increasing, the second period is between 2009 and 2012 when poverty and inequality were declining, and the third period spanning from 2012 to 2014 exhibited increasing poverty and inequality.

Proposed exclusion of durables and health components from welfare aggregate increases poverty rates measured at 5.5 USD 2011 PPP by 2-3 percentage points, but does not affect the trends. Correcting the welfare aggregate for spatial differences in food prices has a minimal impact on poverty rates. However, spatial adjustment for differences in nonfood prices substantially narrows the welfare gap between regions and rural and urban areas given the high share of nonfood expenditures and large variation in rents used to spatially deflate the rent component of the welfare aggregate.

Ownership of selected durables was constructed across the distribution of the preferred spatially adjusted welfare aggregate. The better-off households were naturally found to have better access to cars, computers and dishwashers, which can be viewed as supporting evidence that the constructed the welfare aggregate appears to be sorting households in the way one expects.

Finally, as an additional robustness check, income-based welfare measures were constructed. Income poverty is higher than expenditure poverty, probably because of underreporting. Nevertheless, trends in both series are qualitatively very similar.

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