metadata: authors: 0: author: Redaelli,Silvia
1: author: Infanzon Guadarrama,Michelle
2: author: Moreno Herrera,Laura Liliana
count: Syrian Arab Republic
docna: 0: docna: Assessing the Extent of Monetary Poverty in
            the Syrian Arab Republic after a Decade of Conflict
docty: Policy Research Working Paper
theme: Inclusive Growth,Economic Policy,Social Development and Protection,Economic Growth and Planning,Fragility, Conflict and Violence,Forced Displacement
abstracts: cdata!: The data for estimating monetary poverty
            in the Syrian Arab Republic are outdated. In the context of
            data scarcity, this paper aims to propose a methodological
            approach to address the knowledge gap regarding welfare in
            Syria over the past decade. In particular, the analysis
            provides (i) updated pre-conflict poverty baseline estimates
            based on grouped data from the 2009 Household Income and
            Expenditure Survey; (ii) supporting evidence on the
            viability of using Humanitarian Needs Assessment Programme
            Demographic and Water Supply, Sanitation, and Hygiene 2022
            survey data for the estimation of monetary poverty in 2022;
            and (iii) supporting theoretical and empirical evidence to
            identify growth in per capita gross domestic product in
            current prices deflated by Consumer Price Index as the best
            metric to project poverty using a nowcasting approach. Based
            on this analysis, the paper proposes to use 2022
            Humanitarian Needs Assessment Programme&#x2013;based
            poverty estimates to anchor the most recent estimates to the
            best available evidence, and to interpolate the poverty
            evolution obtained from back-casting 2022 and nowcasting
            2009 poverty estimates over 2009&#x2013;22 using the
            growth rate of per capita gross domestic product in current
            prices, deflated by the Consumer Price Index with a
            passthrough of 0.7.
display_title: Assessing the Extent of Monetary
            Poverty in the Syrian Arab Republic after a Decade of Conflict
listing_relative_url: /research/2024/03/34288659/assessing-extent-monetary-poverty-syrian-arab-republic-after-decade-conflict
topicv3: Fragility, Conflict and Violence m1326586 2432,Poverty Measurement and Analysis m1326941 707
publishtoextweb_dt: 2024-03-26T00:00:00Z
dois: 10.1596/1813-9450-10736
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 44
author_keys: 0: author_key: 901788
1: author_key: 2204109
2: author_key: 1337572
model_extractions: Policy Research Working Paper 10736 Assessing the Extent of Monetary Poverty in the Syrian Arab Republic after a Decade of Conflict _Silvia Redaelli Michelle Infanzon Laura Liliana Moreno Herrera_ Poverty and Equity Global Practice March 2024
Policy Research Working Paper 10736

# **Abstract**

The data for estimating monetary poverty in the Syrian Arab Republic are outdated. In the context of data scarcity, this paper aims to propose a methodological approach to address the knowledge gap regarding welfare in Syria over the past decade. In particular, the analysis provides (i) updated pre-conflict poverty baseline estimates based on grouped data from the 2009 Household Income and Expenditure Survey; (ii) supporting evidence on the viability of using Humanitarian Needs Assessment Programme Demographic and Water Supply, Sanitation, and Hygiene 2022 survey data for the estimation of monetary poverty in 2022; and (iii) supporting theoretical and empirical evidence to identify growth in per capita gross domestic product in current prices deflated by Consumer Price Index as the best metric to project poverty using a nowcasting approach. Based on this analysis, the paper proposes to use 2022 Humanitarian Needs Assessment Programme–based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over 2009–22 using the growth rate of per capita gross domestic product in current prices, deflated by the Consumer Price Index with a passthrough of 0.7.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at sredaelli@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
# Assessing the Extent of Monetary Poverty in the Syrian Arab Republic after a Decade of Conflict

Silvia Redaelli <u>sredaelli@worldbank.org</u> Michelle Infanzon <u>minfanzonguadarr@worldbank.org</u> Laura Liliana Moreno Herrera <u>lmorenoherrera@worldbank.org</u>

**JEL codes:** I32 – Measurement and Analysis of Poverty

**Keywords:** poverty measurement, poverty nowcasting, fragility and conflict, data deprivation
# Contents

|**Introduction**.................................................................................................................................................. 4|
|---|
|**1**<br>**Updating pre-conflict poverty estimates**............................................................................................. 6|
|**2**<br>**Post-conflict poverty projections: Review of existing estimates**....................................................... 8|
|**2.1**<br>**Post-conflict estimates based on nowcasting methodologies**................................................... 8|
|**2.2**<br>**Post-conflict estimates based on HNAP 2021 Demographic and WASH survey**...................... 10|
|2.2.1<br>Measure of welfare used in HNAP analysis ........................................................................ 11|
|2.2.2<br>Poverty line used in HNAP analysis ..................................................................................... 13|
|**3**<br>**Poverty estimation using the HNAP 2022 Demographic and WASH Household Survey**................. 13|
|**3.1**<br>**Improvements to the survey’s expenditure module**................................................................ 14|
|**3.2**<br>**Monetary poverty estimates based on the HNAP 2022 Demographic and WASH survey**...... 15|
|**4**<br>**Nowcasting monetary poverty in Syria: Challenges and sensitivity analysis**................................... 19|
|**4.1**<br>**Data challenges**.......................................................................................................................... 19|
|4.1.1<br>Measuring “growth” ........................................................................................................... 19|
|4.1.2<br>Population estimates .......................................................................................................... 21|
|**4.2**<br>**Distribution neutral poverty projections: Sensitivity analysis**................................................. 22|
|**5**<br>**Triangulating poverty projections with available evidence**............................................................. 26|
|**6**<br>**Proposed way forward**....................................................................................................................... 28|
|**References**.................................................................................................................................................. 29|
|Annex 1: Post-Conflict Poverty Estimation Studies in Syria ........................................................................ 31|
|Annex 2: The Humanitarian Needs Assessment Programme (HNAP) ........................................................ 33|
|Annex 3: Geospatial population estimates for Syria................................................................................... 36|
|Annex 4: Distribution neutral poverty projections using private per capita consumption from NA ......... 38|
|Annex 5: Distribution neutral poverty projections using GDP per capita, and poverty lines at 2011 PPP . 41|
|Figure 1: FCV Pass-through rate for Syria 2009-2020 ................................................................................... 9|
|Figure 2: Share of population in extreme poverty, by subdistrict (HNAP extreme poverty definition) ..... 11|
|Figure 3: Contribution to total household monetary income, by source ................................................... 12|
|Figure 4: Average household income and share of households reporting income sufficiency, by|
|governorate ................................................................................................................................................ 12|
|Figure 5: Distribution of monthly per capita expenditure, nominal and spatially adjusted ....................... 17|
|Figure 6: Distribution by governorate and quintiles of spatially adjusted and nominal monthly per capita<br>expenditure ................................................................................................................................................. 17|
|Figure 7: Governorate level poverty estimates based on nominal and spatially adjusted welfare ........... 18|
|Figure 8: Poverty estimates based on HNAP 2022 data, nominal aggregate ............................................. 18|
|Figure 9: GDP deflator and CPI growth index (2003=100) .......................................................................... 21|
|Figure 10: Population estimates for Syria ................................................................................................... 22|
|Figure 11: Sensitivity Analysis of Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) ...... 23|
|Figure 12: Sensitivity Analysis of Poverty at LMIC international poverty line ($3.65, 2017 PPP)............... 23|
|Figure 13: Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) and growth based on GDP|
|---|
|current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates .................... 25|
|_Figure 14: Poverty Projections at LMIC international poverty line ($3.65, 2017 PPP) and growth based on_|
|_GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_............. 25|
|Figure 15: Benchmarking of poverty estimates for Syria, 2020 .................................................................. 26|
|Figure 16: Trends in night-time light emissions .......................................................................................... 27|
|Figure 17: Poverty trends 2009-2022 ......................................................................................................... 28|
|Table 1: Official poverty rate and national poverty lines ............................................................................. 6|
|Table 2: Poverty estimates using income class international poverty lines ................................................. 7|
|Table 3:Comparative Overview of Post-Conflict Poverty Estimation Studies in Syria ................................ 9|
|Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey ............... 14|
|Table 5: Analysis of relation between food share and total household expenditure ................................ 15|
|Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB) ................................................ 16|
|Table 7: National poverty estimates based on nominal and spatially adjusted welfare ............................ 17|
|Table 8:Ratio of survey household consumption to Private Consumption and GDP ................................. 19|
|Table 9: Average annual growth rate 2003-2009 ....................................................................................... 20|
|Table 10: Comparison between baseline poverty estimates and projections in 2009 ............................... 24|
# Introduction

**More than a decade of conflict has had devastating socio-economic consequences for the Syrian Arab Republic** . In 2011 Syria was a fast-growing middle-income country. Fast forward 10 years, Syria is a lowincome country still engulfed in violence and struggling with a deep economic crisis. Conflict has claimed the lives of at least 350,209 individuals<sup>1</sup> and more than half of Syria’s pre-conflict population remains displaced, including 6.6 million IDPs in Syria and 6.2 million Syrian refugees displaced abroad. Between 2011 and 2019, the Syrian economy is estimated to have contracted by more than 50 percent in real terms. Over the same period, extreme poverty is projected to have increased manyfold, reflecting deterioration of livelihood opportunities and progressive depletion of household coping capacity.<sup>2</sup>

**Over the last four years, the negative welfare impact of conflict has been further exacerbated by regional and global shocks** .  The conflict in Syria has exacerbated the country’s vulnerability to external shocks. The intensification of US sanctions on the Islamic Republic of Iran after the collapse of the Nuclear Treaty in November 2018 impacted Syrian imports of Iranian fuel, leading to petrol and gas shortages which sparked sharp price increases for both food and non-food items in early 2019. Moreover, the financial crisis in Lebanon by end 2019 had ripple effects on the Syrian economy, further contributing to soaring prices.<sup>3</sup> The COVID-19 crisis and drought conditions in the Northeastern region, Syria’s traditional breadbasket which is still heavily affected by conflict and new waves of population displacement, further contributed to the food security challenge of the Syrian population. As of February 2022, before the war in Ukraine, WFP estimates that 12 million people, about 55 percent of the Syrian population, were facing acute food insecurity, up 51 percent compared to 2019.

**Household welfare analysis in Syria is severely constrained by data availability** . Similar to other conflictaffected countries, estimates of the living standards of the Syrian population are outdated due to the lack of official and comparable household budget surveys after the onset of war. The latest official household budget survey data available on the World Bank’s Poverty and Inequality Platform (PIP)<sup>4</sup> dates back to 2003, despite the existence of two more recent budget surveys conducted by Syria’s Statistical Office in 2007 and 2009.<sup>5</sup> As such, the World Bank has not been publishing poverty estimates for Syria post 2003.

> 1 Estimates of the total death toll of the Syrian war vary depending on the methodology and reporting agency. In 2021, the UN’s human rights office (OHCHR) released a tally of <u>350,200 deaths</u> including both civilians and combatants. The count is based on a strict methodology requiring the deceased’s full name, date of death and location of the body and should therefore be interpreted as an under-estimation of the actual number of war-related deaths.  The Syrian Observatory for Human Rights (SOHR) estimates an overall death toll of 610,000 people over 11 years of conflict, of which 160,681 are represented by civilians (120,158 men, 15,237 women and 25,286 children).

> 2 <u>ESCWA (2020) “Syria at war: eight years on”, United Nations Economic and Social Commission for Western Asia, Beirut, 2020.</u>

> 3 Syrians, especially traders, have been depending on banks in Lebanon for accessing foreign currency (dollars). However, due to the financial crisis in Lebanon and with Lebanese banks limiting the release of funds, many Syrians have lost access to their savings. The heavily reduced release of cash from Lebanese banks has meant that dollars have become scarce in Syria, pushingup the informal market exchange rate. As a result, prices of food and non-food items have risen across Syria.

> 4 The Poverty and Inequality Platform <mark>(PIP) i</mark> s a publicly available dataset of World Bank’s official country-level, regional, and global estimates of poverty. Most of the data in PIP comes from the Global Monitoring Database, which is the World Bank’s repository of harmonized multitopic income and expenditure household surveys used to monitor global poverty. PIP contains more than 2,000 surveys from 168 countries covering 98% of the world’s population. The data available in PIP are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption.

5 These surveys were used by UNDP and ESCWA to estimate national poverty estimates (see Abu-Ismail, Abdel-Ghadir and ElLaithy (2011) “Poverty and Inequality in Syria 1997-2007”, Arab Development Challenges Report, Background paper 2011/05).
Still, lineup estimates of poverty in Syria based on a distribution neutral nowcasting approach using growth in Household Final Consumption Expenditure (HFCE – constant prices, unit-passthrough) have so far been used for the production of global and regional poverty estimates.

**In the context of data scarcity, this paper aims to propose a methodological approach to address the knowledge gap regarding welfare in Syria over the last decade** . In particular, the objective of this paper is to (i) fill the knowledge gap on the extent of monetary poverty pre-conflict using international poverty lines for 2007 and 2009; (ii) assess the strengths and weaknesses of poverty estimates based on household survey data collected in 2021 and 2022 under the Humanitarian Needs Assessment Programme (HNAP); (iii) conduct a sensitivity analysis of distribution neutral nowcasting approaches, identifying the most credible approach given NA data limitations; (iv) triangulate estimates obtained through various approaches with indirect evidence on economic activity based on georeferenced data; and lastly (v) propose a methodological approach to assess the evolution of monetary poverty in Syria during the conflict period.
# 1 Updating pre-conflict poverty estimates

Official poverty estimates for Syria are available for 1997, 2003 and 2007 and produced with UNDP technical assistance. Poverty measurement builds on nationally representative household budget survey data from the Household Income and Expenditure Surveys (HIES) conducted by the Central Bureau for Statistics (CBS – the official statistical agency in Syria).<sup>6</sup> CBS conducted another round of the HIES in 200910, but no poverty estimates were released. Since the conflict erupted in 2011, no new rounds of HIES were conducted.

The methodology adopted by UNDP to compute official poverty estimates builds on the construction of “household-specific poverty lines”, reflecting caloric requirements which vary by household composition and regional variation in the cost per calory and non-food allowance.<sup>7</sup> Two different poverty thresholds are considered, one reflecting basic needs (referred to as the “extreme” or “lower” poverty line – henceforth LPL) and a more generous one reflecting a “reasonable” level of basic needs (referred to as the “moderate” or “upper” poverty line – henceforth HPL). The LPL – once expressed in USD using the 2011 PPP conversion factor – is close to the international poverty line for lower-middle-income countries ($3.65 at 2011 PPP), while the UPL is close to the one for upper-middle-income countries. Table 1 below summarizes the available poverty estimates.

_Table 1: Official poverty rate and national poverty lines_

||**1997**|**2003**|**2007**|
|---|---|---|---|
|||**_Poverty rate (%)_**||
|**Lower Poverty line (LPL)**|14.26|11.39|12.3|
|**Upper Poverty line (UPL)**|33.22|30.13|33.6|
||**_National average_**|**_of household specific poverty_**|**_lines - SYP per month_**|
|**Lower Poverty line (LPL)**|NA|1458|2183|
|||_($3.6 daily, 2011PPP)_<br>_($3.9 daily, 2017PPP)_|_($4.2 daily, 2011PPP)_<br>_($4.6 daily, 2017PPP)_|
|**Upper Poverty line (UPL)**|NA|2052<br>_($5.1 daily, 2011PPP)_|3037<br>_($5.8 daily, 2011PPP)_|
|||_($5.5 daily, 2017PPP)_|_($6.4 daily, 2017PPP)_|

_Source:_ Abu-Ismail et al. (2011); El Laithy, Abu-Ismail (2005). World Bank staff estimates for 2011 PPP conversion. Notes: SYP value of the poverty lines is expressed in current prices. Daily 2011 PPP value of the PL provided in parenthesis for reference _._ At present, the World Bank Poverty and Inequality Platform (PIP) only includes poverty estimates at international poverty lines for 2003, the only year for which microdata are available in the Global Poverty Monitoring Database. However, building on tabulations available in Abu-Ismail et al. (2011) and on the CBS website, it is possible to gather all relevant information to compute grouped-data poverty estimates at international poverty lines for 2007 and for 2009, hence to update the available pre-conflict baseline poverty series (Box 1).

> 6 Information from the HIES 1997 was collected between October 1996 and September 1997; for the HIES 2003 information was collected between July 2003 and June 2004; for the HIES 2007, information was collected between November 2006 and October 2007.

> 7 El Laithy, Abu-Ismail (2005).
# _Box 1: Grouped data poverty estimates_

<mark>Grouped data are consumption expenditure or income organized in intervals or bins, such as deciles or</mark> percentiles. These bins are used to derive a continuous Lorenz curve, which plots the cumulative welfare share (on the y-axis) against the cumulative population share (on the x-axis). Together with information about mean welfare, the Lorenz curve can be used to construct a full distribution. Two approaches are used to derive a Lorenz function, the general quadratic (GQ) Lorenz function and the Beta Lorenz function (Datt 1998). Both functions are parameterized and estimated. The function that provides the best fit is selected for poverty and distributional statistics, conditional on passing normality and validity tests. The GQ Lorenz function is estimated using the following specification: _L_ (1− _L_ )= _a_ ( _p_<sup>_2_</sup> _−L_ )+ _bL_ ( _p_ −1)+ _c_ ( _p−L_ ), where _p_ is the cumulative proportion of the population, L is the cumulative proportion of consumption expenditure or income, and a, b, c are parameter estimates. Poverty and inequality measures are based on the parameter estimates.

The Beta Lorenz function is estimated using the following specification: L(p)=p−θp<sup>γ</sup> (1−p)<sup>δ</sup> , where θ, γ, and δ are parameter estimates.

The methodology to estimate poverty starting from grouped data is applied on grouped data information on per household consumption by decile of the welfare distribution in 2007 and in 2009 HIES available from the CBS website. Information on average household size by decile, which is needed to convert household expenditure in per capita terms, was obtained from Prof. Heba El-Laithy, one of the authors of the Abu-Ismail et al. (2011) study who was in possession of 2007 HIES microdata. Through this consultancy, grouped data poverty estimates for 2007 were compared and validated with estimates based on the full distribution. Lacking information on average household size by decile for 2009, per capita expenditure by decile in 2009 is obtained using 2007 estimates. _Source_ : <u>Poverty and Inequality Platform Methodology Handbook</u> Table 2 below reports grouped data estimates of poverty incidence at different values of international poverty lines for 2007 and for 2009. Based on grouped-data estimation approach, in 2009, the latest year for which data are available before conflict started, Syria’s poverty at LMIC was 14.50 percent using 2011 PPP and 16.02 percent using 2017 PPP, whereas corresponding estimates based on the UMIC poverty line were 40.29 percent (2011 PPP) and 47.52 percent (2017 PPP). When comparing to 2007 group-based estimates, evidence suggests that poverty had stagnated, possibly reflecting the impact of the drought that affected the country over the period.

_Table 2: Poverty estimates using income class international poverty lines_

||||**Pover**|**ty Rate (%)**<br>||
|---|---|---|---|---|---|
||||**Estimates based on microdata**|**Estimates based**|**on grouped-data**|
||**PPP**|**PL value**|**2003**|**2007**|**2009**|
|**LIC**|2011|1.9|0.79|2.43|2.08|
||2017|2.15|0.93|2.83|2.44|
|**LMIC**|2011|3.2|9.48|14.90|14.50|
||2017|3.65|10.89|16.36|16.02|
|**UMIC**|2011|5.5|38.37|39.47|40.29|
||2017|6.85|47.26|46.43|47.52|

_Source:_ World Bank staff estimates
# 2 Post-conflict poverty projections: Review of existing estimates

## 2.1 Post-conflict estimates based on nowcasting methodologies

Nowcasting methodologies are typically used to fill data gaps in countries that lack up-to-date surveybased poverty estimates. Nowcasting approaches vary in terms of both methodological assumptions as well as in terms of data sources used in the analysis.

The simplest nowcasting approach – which is used by the World Bank in the production of global poverty estimates – relies on information from national accounts (per capita GDP or household final consumption expenditure) to predict poverty. More specifically, this nowcasting methodology assumes that growth (or shrinkage) in GDP per capita registered since the country’s last poverty estimate is fully passed through to the consumption vector that is measured in household surveys (Prydz et al. 2019). Poverty is then estimated using this predicted consumption vector under a “distribution neutral” assumption i.e., assuming that inequality does not change at that each individual is equally affected by the growth/contraction in welfare. While the impact of growth on poverty reduction is well-known (Kraay, 2006; Ferreira and Ravallion, 2009), concerns have emerged related to possible un-reliability of GDP measures in developing countries (Angrist, 2022), as well as the extent to which growth is passed through to welfare in fragile and conflict affected countries (Box 2).

_Box 2: Growth rate pass-through in FCS Economies_ The assumption of full pass-through works fairly well in economies that are not in Fragile and Conflict-Affected Situations (FCS). However, available evidence suggests that a full pass through assumption is likely inappropriate in case of conflict.

In order to estimate any difference in pass-through in FCS settings, Corral et al. (2020) fitted a regression model on a sample including all countries for which survey consumption estimates are available in PIP over the period 2000 to 2019. In particular, the annualized growth rate in the survey per capita consumption is regressed on annualized growth in GDP per capita, together with an interaction of annualized growth in GDP per capita and the share of time (if any) that each country spent as FCS between 2000 and 2019. G G s s s = 0.964 ∗G G G G s −0.530 ∗G G G G s mmssm GGmmG GGmm GGmmG GGmm According to this model, for economies that have never been FCS, 96 percent of GDP growth is passed through GGh GGh GGh ∗SShaaGGa aaSS to welfare. However, for economies that have been in FCS throughout the entire period, only 43 percent (0. 64 - 0. 30) is passed through.

The reduced pass-through rate indicates that in economies experiencing conflict, violence, or fragility, changes in national accounts are larger than changes in welfare. In particular, for economies in FCS where GDP per capita declined, this implies that the poverty rates estimated using this adjusted pass-through would be lower compared to those estimated under a full pass-through assumption.
<!-- Start of picture text --> Figure 1: FCV Pass-through rate for Syria 2009-2020<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Share of time since 2009 as FCS Pass-through Rate<br>Source: Corral et al. (2020)<br><!-- End of picture text --> An alternative nowcasting approach, which bypasses the distribution neutral assumption, relies on growth elasticities of poverty (GEP). Growth elasticities of poverty reveal the percentage change in poverty associated to a 1 percent growth in GDP per capita. Implicitly, the elasticity takes into account both the impact of growth on average welfare as well as changes in the distribution of welfare (Shorrocks, 2013; Datt and Ravallion, 1992). Still, this approach relies on the strong assumption that the relation between growth and poverty does not change over time.

In some cases, poverty nowcasting builds on more articulated microsimulation models. More complex examples of microsimulation models are based on computable general equilibrium (CGE) or general equilibrium macroeconomic models that rely on social accounting matrices or time series macroeconomic data to create “linkage aggregate variables” (LAVs) that are fed into microsimulation models that build on household survey data. However, the high information demands of these models makes them hard to apply in data deprived contexts. A less data demanding approach to microsimulation relies on building behavioral models on household surveys (typically linking labor market outcomes and remittances to individual and households’ incomes and consumption) which are then linked to aggregate and sector level macro-economic projections.<sup>8</sup> Table 3 below provides a summary of the results and methodologies employed in the limited number of studies that have attempted to estimate poverty in Syria during the post-conflict period. Despite their notable limitations, all these studies consistently indicate substantial increases in poverty.<sup>9</sup> _Table 3: Comparative Overview of Post-Conflict Poverty Estimation Studies in Syria_

|**Author**|**Poverty line**|**Estimates**|**Methodological approach**|
|---|---|---|---|
|World<br>Bank<br>(2017)|Lower national PL (LPL)|2007: 12.3%<br>2016: 54.5 – 66.5%|Extrapolation using a growth<br>elasticity of poverty<br>estimated over the period<br>2003-2007.|
|ESCWA (2016)|Lower national PL (LPL)|2010: 14%; 2015: 50%|NA|
||Upper national PL(UPL)|2010: 28 %;2015: 83.4%||
|ESCWA<br>(2020b)|$1.90 (2011 PPP) intl PL<br>$3.50(2011 PPP)intl PL|2010: <1%; 2019: 40%<br>2010: 19%;2019: 77%|Distribution neutral<br>projections based on the<br>growth ofprivate household|

8 Olivieri et al. (2014).

> 9 For a detailed description of each study see Annex 1.
||||consumption per capita<br>from NA;unitpass-through|
|---|---|---|---|
|Hamati (2018)|Lower national PL (LPL)<br>Upper national PL (UPL)|2009: 14.9%; 2015: 85.8-75.9%<br>2009: 29.4%; 2015: 93.1% - 87.5%|Dynamic microsimulation<br>model projections that<br>introduce price,<br>demographic, labor market,<br>and income shocks.|

# 2.2 Post-conflict estimates based on HNAP 2021 Demographic and WASH survey

The scale of Syria’s humanitarian crisis and the necessity to inform operations on the ground has prompted UN agencies to develop an articulated system of welfare monitoring: the _Humanitarian Needs Assessment Programme_ (HNAP). The HNAP is a joint UN program established in April 2018 with the objective of tracking displacement and return movements, conducting multi-sectoral assessments, and monitoring humanitarian needs inside the country.<sup>10</sup> In addition to collecting data at the community level through key informant interviews, the HNAP also implemented sample-based household surveys whose frame relies on community level population data.<sup>11</sup> The HNAP Demographic and WASH survey is a large-scale survey aimed at providing national and subnational-level estimates of demographic and basic socio-economic conditions of the population living inside Syria (resident, IDPs and returnees). The survey is primarily used to inform the programming of assistance of various UN agencies and humanitarian actors. The questionnaires of this survey have evolved over time.<sup>12</sup> In 2021, in order to assess the monetary deprivation of Syrian households, a module on household income and household expenditures was included in the survey. Based on this information, _the HNAP Socio-Economic situation report of the summer 2021 estimated that 98 percent of the Syrian population was living in extreme poverty_ (Figure 2). The poverty measure is obtained by taking the sum of all individual and household incomes obtained from employment only, converting it in per capita terms using household size, and then assessing whether this welfare indicator is below $1.90, a value converted in local currency using the exchange rate at the time of the survey.

> 10 The HNAP network presently consists of more than 30,000 community focal points and 475 full time staff on the ground in Syria. HNAP is implemented through local Syrian NGOs, with technical support from UN agencies. Information is collected across all regions inside Syria through face-to-face consultations and direct field observations. See Annex 2 for details.

> 11 Stratified random sampling is used to draw a 95% confidence / 10% margin of error sample at the subdistrict level and the number of household interviews per community (p-coded location) within a subdistrict was obtained by randomly sampling locations proportionate to population size, with replacement.

> 12 The main survey modules include questions on demographic characteristics of current household members and of members who have left the household, individual level basic information on labor market engagement (adult population) and school attendance (children below age 18); household level information on shelter, WASH, access to assistance, sources of income, copying strategies and priority needs.
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controlling for spatial price variation<sup>15</sup> – governorates in which income levels are lower would have a relatively lower incidence of households reporting that their income is sufficient to satisfy needs. On the other hand, as shown in Figure 4, while average household income is by far the lowest in Idleb governorate, the share of households in Idleb that report income being sufficient to satisfy household needs is the third highest after Damascus, Aleppo and Ar-Raqqa. This finding, coupled with background information of high levels of both cash and in-kind humanitarian assistance being provided to households in Idleb underscore the limitations of a partial welfare metric not comprehensive of in-kind income receipts.

_Figure 3: Contribution to total household monetary income, by source_ <!-- Start of picture text --> IDLEB<br>QUNEITRA<br>TARTOUS<br>AS-SWEIDA<br>RURAL DAMASCUS<br>ALEPPO<br>HAMA<br>DAR'A<br>LATTAKIA<br>DAMASCUS<br>HOMS<br>DEIR-EZ-ZOR<br>AR-RAQQA<br>AL-HASAKEH<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Labor Remittances (domestic) Remittances (Intl.) Pension Cash Assistance<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data _Figure 4: Average household income and share of households reporting income sufficiency, by governorate_ <!-- Start of picture text --> 500,000 45%<br> 450,000 40%<br> 400,000 35%<br> 350,000 30%<br> 300,000 25%<br> 250,000<br>20%<br> 200,000<br> 150,000 15%<br> 100,000 10%<br> 50,000 5%<br> - 0%<br>Income % income sufficient<br>(SYP)<br>Average household income<br>sufficient to satisfy needs<br>% household reporting income<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data Overall, the fact that the income metric used in the HNAP report is solely based on labor income and that it does not include in-kind income receipts leads to the conclusion that the 98 percent extreme poverty estimate is significantly over-estimated.

> 15 Due to data limitation, a basic food spatial deflator was constructed using the cost of the WFP basic food basket at the governorate level.
## 2.2.2 Poverty line used in HNAP analysis

Another source of poverty over-estimation in the HNAP report’s methodology is related to the definition of the poverty line. The poverty line used in HNAP analysis is $1.90, converted in local currency using the 1USD: 3200 SYP exchange rate prevailing at the time of the survey. This approach implies a poverty line of 6,080 SYP per person per day.

Despite the fact that the report uses the “extreme poverty” terminology – the approach deviates substantially from the one that underlines extreme poverty estimation. The $1.90 extreme poverty line in fact is defined in 2011 PPPs terms.<sup>16</sup> Used as a benchmark to compare poverty across low income countries and to obtain global poverty estimates, the $1.90 value is obtained as an average of LIC national poverty lines, all expressed in PPP values. The correct approach for using the $1.90 international poverty line corresponding to extreme poverty should have been to first use 2011 PPP value<sup>17</sup> to convert 1.90 US dollars in local currency (SYP) and then express it in 2021 prices using CPI inflation over the 2011-2021 period. Following the correct approach leads to a 2021 value of the $1.90 2011 PPP extreme poverty line of 1,537,153 SYP per person per day, which would be approximately one-fourth the value of the poverty line used in the HNAP report. The incorrect use of the international poverty line implies that the 98 percent extreme poverty estimation provided in the HNAP report is grossly over-estimated.

# 3 Poverty estimation using the HNAP 2022 Demographic and WASH Household Survey

The analysis of data from the HNAP 2021 Demographic and WASH survey revealed severe limitations in the design of both the household income and expenditure module which limit the possibility to use these data for poverty measurement.<sup>18</sup> In particular, both income and expenditure modules fail to capture the value of in-kind assistance and the value of household own production in agriculture. In-kind assistance and own-production of food are likely to be sizeable in Syria, particularly in areas most affected by conflict where markets are disrupted and incidence of humanitarian assistance more consistent. As a result, welfare aggregates based on either income or expenditure provide a very poor proxy of welfare that does not allow to consistently rank households from poorest to richest.

Based on the analysis of the HNAP 2021 Demographic and WASH survey, the team collaborated with the HNAP team to improve the design of the instruments for the 2022 summer round of the survey. Results of this engagement are described in the sections below.

> 16 Purchasing power parities (PPPs) are used in global poverty estimation to adjust for price differences across countries. PPPs are price indices published by the International Comparison Program (ICP) that measure how much it costs to purchase a basket of goods and services in one country compared to how much it costs to purchase the same basket of goods and services in a reference country, typically the United States. PPP conversion factors are preferred to market exchange rates for the measurement of global poverty because the latter overestimate poverty in developing countries, where non-tradable services are relatively cheap (a phenomenon known as the Balassa-Samuelson-Penn effect). The ICP updates PPP data periodically. For example, in the last three ICP rounds, PPPs were released for the 2005, 2011 and 2017 reference years. The 2017 PPPs are currently used to convert household welfare aggregates, expressed in local currency units in 2017 prices, into a common internationally comparable currency unit.

> 17 The revised 2011 PPP value for Syria is 22.26 (see Jolliffe et al. 2022, Table B.3). 18 See Section 2.2.1.
# 3.1 Improvements to the survey’s expenditure module

In lower income countries, a welfare metric based on consumption is generally preferred to income. As discussed in Mancini, Vecchi (2020), the choice between consumption and income would be inconsequential whenever all income is consumed, and all consumption is financed by income. However, in low-income economies and in contexts characterized by shocks, a metric of welfare based on consumption – which is smoother than income through households’ reliance on savings and borrowing – provides a better measure of living standards.

In order to make the Demographic and WASH survey better suited for poverty measurement, efforts were made to improve the design of the expenditure module of the survey questionnaire within the limits in terms of overall questionnaire length and complexity deemed feasible by the HNAP team.

In particular, efforts were made to improve the measurement of in-kind assistance and own-production (particularly for food); to use an annual recall period for items consumed infrequently; to expand (to the extent possible) the list of non-food items (Table 4).

_Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey_

|**SUMMER2021 EXPENDITURE MODULE**|**REVISEDSUMMER2022 EXPENDITURE MODULE**|
|---|---|
|_How much did your household spend on the following items_<br>_on a monthly basis?_**_(average of last 3 months)_**|_Please estimate the value of_**_monthly_**_household_<br>_consumptionfor thefollowing items._<br>|
|Food|Food and beverages (_bought or bartered_)<br>Food and beverages(_ownproduced or received_)|
|Electricity|Electricity|
|Cooking|Fuel for cooking|
|Heating/cooling|Heating/cooling|
|Safe water|Water|
|Hygiene items|Hygiene items|
|Transportation(public/private all costs)|Transportation(public/private all costs)|
|Communication|Communication|
|Covid related items|--|
||Personal care|
||Infant care(diapers,babyformula)|
||Childcare|
||Other|
||_Please estimate the value of_**_annual_**_household consumption_<br>_for thefollowing items._|
|Shelter maintenance|Regular shelter maintenance|
||Furnishings and household consumables(e.g. lightbulbs...)|
||Clothingand footwear(_bought or bartered or received_)|
|Education|Education|
|Healthcare (including medicines, visit to healthcare facilities,<br>medicalprocedures)|Healthcare (including medicines, visit to healthcare facilities,<br>medicalprocedures)|
|NFIs|NFIs (blankets, mats, kitchenware) (_bought or bartered or_<br>_received_)|
|Repair and maintenance of hh appliances and machines|Repair and maintenance of hh appliances and machines<br>Desludging|
||Other|
A validation exercise which is typically conducted to check the overall quality of a welfare measure based on consumption expenditure is to rank households based on such metric and then check whether Engel’s Law is satisfied, i.e. check if poorer household devote a higher share of total consumption to food.<sup>19</sup> As shown in Table 5 below, the expected negative relation between food shares and total household expenditure is only confirmed in 2022 data, possibly suggesting that the changes apported to the questionnaires improved the correspondence between the measure of welfare based on expenditure and households’ living conditions.

_Table 5: Analysis of relation between food share and total household expenditure_

|**Dependent variable: share of total con**|**sumption devoted to food**<br> <br>|
|---|---|
||**2021 data**<br>**2022 data**|
|(Log) Total monthly expenditure|0.0253<br>-0.0205|
||(13.51)<br>(-13.15)|
|Household size|-0.002<br>0.0014|
||(-2.75)<br>(2.38)|
|Nr. Observations|23166<br>26074|
|R<sup>2</sup>|0.0079<br>0.0068|

_Note_ : Model include a constant term _Source_ : World Bank staff calculations, based on HNAP 2021 and 2022 summer round of the Demographic and WASH survey Despite such improvements, the measure of consumption based on the 2022 Demographic and WASH survey is still far from being an accurate welfare metric for poverty estimation in Syria, particularly for the purpose of assessing the evolution of poverty over time using pre-conflict estimates from the HIES as a baseline.

In fact, the consumption module in each of the three rounds of HIES conducted prior to the conflict in 2003, 2007 and 2009 was based on an extensive diary account of household expenditures covering more than 500 items (goods and services). According to Beegle and others (2012), use of diary versus recall, and differences in the number of consumption items collected in surveys drastically affect poverty and inequality measures.

- 3.2 Monetary poverty estimates based on the HNAP 2022 Demographic and WASH survey

While differences in survey instruments between the HNAP 2022 and the HIES constrain the possibility of obtaining accurate pre-post conflict poverty comparisons, it is important to assess whether poverty estimates from the HNAP 2022 can be used to assess the profile of monetary poverty in Syria in 2022.

The welfare metric used for poverty analysis is constructed aggregating all available information on household expenditure, with the sole exclusions of expenditures recorded under the “Other” categories for both monthly and annual consumption items (see Table 4). Compared to a “standard” consumption aggregate for welfare measurement, the consumption aggregate based on information collected in the HNAP 2022 has three major drawbacks:

> 19 Engel’s law – the fact that the expenditure share dedicated to food consumption decreases as income rises – has been proved to be extraordinarily consistent across time and space. As such, the predictive character of this relationship has been widely used in poverty measurement, from the estimation of poverty lines to PPP assessment.
- (i) The list of consumption items included in the questionnaires is mostly consistent with major headings in COICOP classification (2 digit), with the sole omission of expenditure on “alcoholic beverages and tobacco” and “recreation and culture”.<sup>20</sup> However, in some cases the list of consumption items in the HNAP 2022 questionnaire includes items at lower levels of aggregation, including some coarse expenditure headings open to interpretation by respondents (for example: personal care, childcare, NFIs...).

- (ii) No information is available to properly account for housing costs. Information on expenditure on rent paid is available for market tenants, which only constitute 10 percent of households, a sample to small and geographically segregated to attempt estimating any model to impute for the value of housing services consumed by owners or other nonmarket tenants.

- (iii) No information is available on consumer durables possessed by the household.

Another important challenge that needs to be addressed to estimate monetary poverty, is to control for spatial price differences. Unfortunately, CBS does not produce any subnational CPI index and lacking detailed information on food and non-food consumption items, it is not possible to estimate one using HNAP survey data. A possible approach is to use a proxy food price index constructed using “standard food basket” price estimates at the governorate level produced by WFP (Table 6). In Syria, WFP standard food basket is set at a group of dry goods providing 2,060 kcal a day for a family of five during a month. The basket includes 37 kg bread, 19 kg rice, 19 kg lentils, 5 kg of sugar, and 7 liters of vegetable oil. WFP provides governorate level estimates on such basket in its monthly “market price watch bulletin”, building on market level price information it has been collecting in the country since 2011.

_Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB)_

||Standard food basketprice(SYP)|Spatialprice deflator|
|---|---|---|
|Damascus|342,867|106.2|
|Aleppo|310,336|96.1|
|Rural Damascus|340,465|105.4|
|Homs|318,922|98.8|
|Hama|334,474|103.6|
|Lattakia|355,953|110.2|
|Idleb|322,260|99.8|
|Al-Hasakeh|253,223|78.4|
|Deir-ez-Zor|273,609|84.7|
|Tartous|346,070|107.2|
|Ar-Raqqa|290,455|90.0|
|Dar’a|352,944|109.3|
|As-Sweida|347,134|107.5|
|Quneitra|341,690|105.8|
|National*|322961.6|100.0|

_Notes_ : National average weighted by population _Source_ : World Bank calculation using April 2022 market price watch bulletin and HNAP population estimates 20 The Classification of individual consumption by purpose, abbreviated as COICOP, is a classification developed by the United Nations Statistics Division to classify and analyze individual consumption expenditures incurred by households, non-profit institutions serving households and general government according to their purpose. The classification is generally used by countries in NA estimations and budget surveys, particularly in relation to CPI weights estimation.
_Figure 5: Distribution of monthly per capita expenditure, nominal and spatially adjusted_ <!-- Start of picture text --> Spatially Adjusted<br>Nominal<br>0 200000 400000 600000<br>Per capita Montly HH Expenditure<br>Density<br><!-- End of picture text --> _Notes:_ The vertical line indicates the monthly value of the $2.15 poverty line (2017 PPP) _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 5, the spatially adjusted and unadjusted (nominal) welfare metrics display a very similar distribution, although the spatially adjusted one is relatively more concentrated. Table 7 below reports national level poverty estimates at LIC $2.15 (2017 PPPP) as well as LMIC $3.65 (2017 PPP) international poverty lines based on the two welfare metrics.

_Table 7: National poverty estimates based on nominal and spatially adjusted welfare_

||Nominal|SpatiallyAdjusted|
|---|---|---|
|$2.15 2017 PPP|26.92|25.62|
||(0.008)|(0.008)|
|$3.65 2017 PPP|69.34|70.41|
||(0.011)|(0.001)|

_Note:_ Standard errors in parenthesis _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 6, the distribution by governorate and the welfare quintiles constructed on each of the two welfare metrics are substantially different, with clear implications in terms of poverty profile (Figure 7).

_Figure 6: Distribution by_ _<u>governorate and quintiles of spatially adjusted and nominal monthly per capita expenditure</u>_ <!-- Start of picture text --> spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Aleppo Rural Damascus Idleb Damascus Homs Hama Lattakia<br>Al-Hasakeh Dar'a Tartous Deir-ez-Zor Ar-Raqqa As-Sweida Quneitra<br>Q5<br>Q4<br>Q3<br>Q2<br>Q1<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP May 2022 Demographic and WASH survey
_Figure 7: Governorate level poverty estimates based on nominal and spatially adjusted welfare_ <!-- Start of picture text --> $2.15 - 2017 PPP $3.65 - 2017 PPP<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>nominal spatially adj nominal spatially adj<br>Source : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey<br>Overall, while acknowledging the importance of controlling for spatial price differences, we do not think<br>that using an imperfect spatial deflator would be the best way forward, particularly when considering how<br>much a fixed basket approach would deviate from the theoretical underpinnings of welfare measurement.<br>Moreover, using a nominal aggregate could also be aligned with the impossibility to account for spatial<br>price differences in pre-conflict poverty estimates based on 2009 grouped data.<br>Figure 8 below maps poverty at the governorate level, using the nominal aggregate.<br>Figure 8: Poverty estimates based on HNAP 2022 data, nominal aggregate<br>$ 2.15 (2017 PPP) $3.65 (2017 PPP)<br>Al-Hasakeh Al-Hasakeh<br>Aleppo Aleppo<br>Ar-Raqqa Ar-Raqqa<br>Idleb Idleb<br>Lattakia Lattakia<br>Hama Hama<br>Deir-ez-Zor Deir-ez-Zor<br>Tartous Tartous<br>Homs Homs<br>Damascus Rural Damascus Poverty Rate(%)88 - 100 Damascus Rural Damascus Poverty Rate(%)88 - 100<br>Quneitra 75 - 8862 - 75 Qu neitra 75 - 8862 - 75<br>Dar'a As-Sweida 50 - 6238 - 5025 - 38 Dar'a As-SweidaAs-Sweida 50 - 6238 - 5025 - 38<br>12 - 25 12 - 25<br>0 - 12 0 - 12<br>Percent Percent<br><!-- End of picture text --> _Overall, while acknowledging the importance of controlling for spatial price differences, we do not think that using an imperfect spatial deflator would be the best way forward, particularly when considering how much a fixed basket approach would deviate from the theoretical underpinnings of welfare measurement. Moreover, using a nominal aggregate could also be aligned with the impossibility to account for spatial price differences in pre-conflict poverty estimates based on 2009 grouped data._ _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey
- 4 Nowcasting monetary poverty in Syria: Challenges and sensitivity analysis

# 4.1 Data challenges

As discussed in Section 2.1, national accounts – the comprehensive economic statistics that measure economic activity in a country – become an input for poverty projections for years in which no survey has been conducted. The typical (distribution neutral) approach applies per capita growth rates of GDP or private consumption (household final consumption expenditure in WDI) to the baseline mean of per capita expenditure from a household survey to extrapolate poverty beyond the survey period. Besides concerns pertaining the validity of the assumption that each household’s consumption expands/contracts at the same rate of the overall economy, the reliability of NA statistics can pose further challenges. In the case of Syria, _the base year for NA estimation is 2000_ . Hence, NA statistics published by the national statistical agency available up to 2021 assume that the structure of the economy has not changed since 2000, an assumption clearly problematic given the substantial economic changes associated to economic liberalization between 2000 and 2010 and conflict thereafter.<sup>21</sup> In what follows, we discuss possible options for the use of NA statistics in poverty projections taking into account the challenges posed by data quality and data reliability issues in the context of Syria.

## 4.1.1 Measuring “growth”

Should poverty projections be based on GDP or private consumption data from national accounts? Private consumption is generally preferred as it captures a set of goods and services that more closely mirrors consumption from household surveys. In practice however, considerations such as the availability and quality of GDP and private consumption data, as well as the strength of correlations between data from national accounts and household survey data typically influence the choice.

Prior to conflict, for the years in which both NA and survey data are available, the average ratio of average per capita consumption from household survey to average private consumption per capita from NA was 0.93, whereas the corresponding ratio using GDP per capita was 0.61 (Table 8).<sup>22</sup> _Table 8:Ratio of survey household consumption to Private Consumption and GDP_

||**Ratio of average pc consumption from HH**<br>**survey to average pc private consumption**<br>**from NA**|**Ratio of average pc consumption from HH survey**<br>**to pc GDP**|
|---|---|---|
|**2003**|1.18|0.71|
|**2007**|0.84|0.56|
|**2009**|0.79|0.57|
|**Average**|0.93|0.61|

_Source_ : World Bank staff calculations, based on WDI and CBS data

> 21 The international recommendation is to update the base year at least every five years.

> 22 The lower ratio when using GDP is expected because GDP includes more than private household consumption.
While NA estimates of average PC private consumption are closer to those in household survey compared to per capita GDP, the result are reversed when looking at growth rates. From 2003 to 2009, the average annual growth of per capita GDP was 1.6 percent, which closely aligns with the 2 percent estimated from household surveys (Table 9).

_Table 9: Average annual growth rate 2003-2009_

||**A**<br>|**verage Growth**<br>|<br>|
|---|---|---|---|
||**2003-2009**|**2003-2007**|**2007-2009**|
|**Per capita Consumption from household surveys**|-0.2%|-0.7%|0.4%|
|**Per capita GDP**|1.6%|1.8%|1.3%|
|**Per capita Private consumption from NA**|3.4%|2.0%|5.6%|

_Source_ : World Bank staff calculations, based on WDI and CBS data Pre-conflict data already revealed a substantial discrepancy between the growth-rate of per-capita welfare estimates based on NA private consumption and those coming from household surveys.<sup>23</sup> Over time, such discrepancy is expected to have further increased together with the deteriorating capacity of NA aggregates to reflect changes in the economy as a result of conflict. In particular, the likely increase of the informal economy as a result of conflict might have created an additional source of measurement error in available NA indicators. In principle, it could be assumed that an increase in informality would affect measurement error of GDP more compared to household final expenditure. However, as household final consumption expenditure in Syria’s NA is computed as a residual,<sup>24</sup> it could be assumed that the loss in reliability of growth estimates based on this aggregate is more severe than that based on GDP growth estimates.<sup>25</sup> Another important choice when identifying the growth rate in the per capita NA indicator to be used for poverty projections pertains the choice of the deflator. The general practice is to work with per capita NA aggregates expressed in constant terms using the GDP deflator, an implicit deflator that is meant to reflect price changes in goods and services purchased by consumers, businesses, government and foreigners (not importers). On the other hand, when assessing changes in consumption between household surveys collected at different points in time, the typical choice is that of using the Consumer Price Index (CPI), which measures the price changes in goods and services purchased by consumers (households). CPI is also used when expressing PPP conversion factors from 2011 or 2017 base years into the corresponding values in the years in which survey data are available for global poverty measurement. As shown in Figure 9, the GDP deflator and CPI trends, while generally aligned before conflict, diverge post 2011, possibly reflecting

> 23 El-Laithy, Aby-Ismail (2005). 24 Ibid.

25 Another element of concern regarding the reliability of Household and NPISHs Final Consumption Expenditure (HFCE) from NA pertains the lack of information on how its constant price estimates are computed by CBS. In fact, while GDP constant price estimates provided by CBS and included in WDI can be replicated using the GDP series in current prices and deflating it using the GDP deflator, the same does not hold in the case of HFCE. In fact, the constant price HFCE series provided by CBS and included in WDI cannot be replicated starting from the corresponding series in current prices neither using the GDP deflator (as it should) nor using the CPI. As CBS does not publish any documentation on the construction of the constant price HFCE series, the reliability of the metric is questionable.
the combined effect of the collapse in domestic production, sanctions, devaluation and increasing reliance on imports of basic commodities.

_Figure 9: GDP deflator and CPI growth index (2003=100)_ <!-- Start of picture text --> 3300<br>3000<br>2700<br>2400<br>2100<br>CPI GDP Deflator<br>1800<br>1500<br>1200<br>900<br>600<br>300<br>0<br><!-- End of picture text --> _Source:_ WB staff calculations based on WDI and CBS _Based on these considerations, the most appropriate approach to project welfare growth would be to use per capita GDP in local currency units deflated using the CPI._<sup>_26_</sup>

# 4.1.2 Population estimates

Data challenges also affect population estimates, which are needed to convert macro aggregates in per capita terms.

The last population Census in Syria was conducted in 2004. Since then, population estimates for Syria have been revised multiple times. Pre-conflict population estimates and projections to account for the tremendous impact of conflict which claimed the lives of at least 350,209 individuals<sup>27</sup> and forced millions of Syrians to leave the country.<sup>28</sup> As shown in Figure 10, sizeable differences exist between different revisions of Population Prospects data post conflict. In particular, the latest available estimates from the 2022 Prospects revision – which have been recently adopted in WDI – place the Syrian population for 2022 at about 22 million, in line with direct population counts from HNAP and with the 2015 Prospects revision, but as much as 15 percent higher compared to the 2019 Prospects revision. Moreover, while population estimates for most recent years are relatively similar between the 2015 and 2022 population prospects revisions, major differences emerge for the years between 2009 and 2015.

> 26 See Section 4.2 and Section 5 for further discussion.

> 27 Estimates of the total death toll of the Syrian war vary depending on the methodology and reporting agency. In 2021, the UN’s High Commissioner for Human Rights (OHCHR) office released a tally of <u>350,200 deaths including both civilians and combatants.</u> The count is based on a strict methodology requiring the deceased full name, date of death and location of the body and should therefore be interpreted as an under-estimation of the actual number of was related deaths.  The Syrian Observatory for Human Rights (SOHR) estimates an overall death toll of 610,000 people over 11 years of conflict, of which 160,681 are represented by civilians (120,158 men, 15,237 women and 25,286 children).

> 28 5.6 million Syrian refugees are currently hosted in neighboring countries and an additional 1 million reside in Europe, mostly in Germany and Sweden (UNHCR).
_Figure 10: Population estimates for Syria_ <!-- Start of picture text --> 30,000,000<br> 25,000,000<br>POP CENSUS<br> 20,000,000<br> 15,000,000<br>HNAP MNM<br> 10,000,000<br>CONFLICT STARTS<br> 5,000,000<br> -<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>CBS - HNAP Prospects 2010 Prospects 2015<br>Prospects 2019 Prospects 2022<br><!-- End of picture text --> _Source:_ WB staff calculations based on World Population Prospects (UNDESA), HNAP and CBS _Note:_ Population projections based on medium fertility scenario _Population data from the 2022 revision of the World Population Prospects (which is included in WDI) will be used for the poverty projections._<sup>_29_</sup>

- 4.2 Distribution neutral poverty projections: Sensitivity analysis

This section presents the results of distribution neutral poverty projections for Syria. As the country was re-classified as low-income country in 2018, estimates have been computed using both the lower-middleincome countries international poverty line ($3.65 in 2017 PPP) and low-income countries international poverty line ($2.15 in 2017 PPP).

For an analysis of the sensitivity of poverty projections to underlying assumptions, we present results obtained using baseline poverty estimates from all years available (2003, 2007 and 2009); growth estimates based on per capita GDP in constant terms and per capita GDP in current LCU adjusted using CPI; and using either a unit pass-through or a “conflict-adjusted” pass through (Box 2).<sup>30</sup> Figure 11 show poverty projections results at the Lower income countries international poverty line (in 2017 PPP), using different poverty baselines and different NA aggregate growth with a unitary pass through. Figure 12 displays a comparable analysis at the Lower Middle Income countries international poverty line.<sup>31</sup>

> 29 Additional analysis was further conducted to contrast national poverty estimates provided in the 2022 revision of the Population Prospects with different geospatial population estimates. Results of this analysis are discussed in Annex 3. 30 Corresponding estimates obtained using private per capita consumption from NA are provided in Annex 4.

> 31 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5.
_Figure 11: Sensitivity Analysis of Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Constant GDP Pov. Baseline 2003 Constant GDP Pov. Baseline 2007<br>Constant GDP Pov. Baseline 2009 GDP current prices - CPI Pov. Baseline 2003<br>GDP current prices - CPI Pov. Baseline 2007 GDP current prices - CPI Pov. Baseline 2009<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data _Figure 12: Sensitivity Analysis of Poverty at LMIC international poverty line ($3.65, 2017 PPP)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Constant GDP Pov. Baseline 2003 Constant GDP Pov. Baseline 2007<br>Constant GDP Pov. Baseline 2009 GDP current prices - CPI Pov. Baseline 2003<br>GDP current prices - CPI Pov. Baseline 2007 GDP current prices - CPI Pov. Baseline 2009<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data
Irrespective of the growth, poverty projections show sizeable differences depending on the baseline year used: 2003, 2007 and 2009. In particular, estimates based on the latest HIES 2009 deliver consistently higher levels of poverty projections. This finding is not surprising however, as none of the projection approaches is able to match actual pre-conflict poverty dynamics (Table 10). _Accordingly, to make best use of pre-conflict data, preferred poverty projections should be based on HIES 2009 baseline poverty estimates_ .

_Table 10: Comparison between baseline poverty estimates and projections in 2009_

||**Poverty**<br>**line**<sup>**32**</sup>|**Poverty estimates**<br>**based on grouped data**<br>**from HIES 2009**|**Distribution neutral**<br>**poverty projections**<br>**baseline 2003**|**Distribution neutral**<br>**poverty projections**<br>**baseline 2007**|
|---|---|---|---|---|
|**Constant pc GDP**|$2.15|2.4%|0.9%|2.7%|
|**Current pc GDP & CPI**|$2.15|2.4%|0.7%|2.8%|
|**Constant pc GDP**|$3.65|16.0%|10.2%|16.1%|
|**Current pc GDP & CPI**|$3.65|16.0%|9.5%|16.3%|

_Source_ : World Bank staff calculations, based on WDI and CBS data The analysis further reveals the substantial sensitivity of results to the choice of the NA aggregate growth to be used in the projections, with poverty projections based on the growth rate of constant per capita GDP being sizably different compared to those in which growth is estimated using per capita GDP in current prices (LCU) deflated using CPI. In particular, the while projections based on growth in constant pc GDP show a monotonic increase in poverty over the period 2011-2020, projections based on current pc GDP deflated using CPI show a marked increase in poverty between 2011-2016 – which corresponds to the most intense phase of the Syrian conflict – a decline in poverty between 2016 and 2019 – which correspond to a subdued phase of the conflict and pick up again post 2019, reflecting the compounding impact of the Lebanon financial crisis. _Based on considerations related to the reliability of constant GDP estimates_<sup>_33_</sup> _and a better match with observational accounts regarding the evolution of welfare in Syria based on non-monetary indicators, poverty projections based on current pc GDP growth deflated using CPI are preferred._ Lastly, Figure 13 and Figure 14 show poverty projection results using preferred poverty baseline (HIES 2009) and growth estimates (per capita GDP in current prices, deflated with CPI) at different values of the pass through: unitary, “conflict-adjusted” (which varies over the conflict years from 0.7 to 0.48 - see Box 2), as well as the pass through observed between 2007 and 2009 (0.29), a period characterized by drought and regressive welfare dynamics.<sup>34</sup> As expected, employing a lower pass-through rate leads to considerably lower poverty estimates possibly reflecting household capacity to smooth consumption in face of a shock. However, the possibility for households to continue smoothing their consumption over a prolonged conflict spell is dubious. Given the amount of uncertainty around the most appropriate passthorough rate to be used in the case of Syria, we can use the poverty projections based on different values of the pass-through as providing likely lower and upper bounds for the extent of poverty in the country.

> 32 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5. 33 See section 4.1.1.

> 34 Corresponding estimates obtained using poverty lines in 2011 PPP  ($3.2  and $2.15) are provided in Annex 5.
_Figure 13: Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) and growth based on GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>42%<br>40% 35%<br>28% 28% 28%<br>22% 23% 21%<br>19%<br>17%<br>20% 15% 15% 14%<br>12% 12% 12%<br>8%<br>2% 2% 6%<br>0% 5% 9% 10% 10% 11% 9% 8% 8% 11%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Unit Passthrough Clonflict Adjusted Passthrough Passthrough=0.7<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data _Figure 14: Poverty Projections at LMIC international poverty line ($3.65, 2017 PPP) and growth based on GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_ <!-- Start of picture text --> 100%<br>80%<br>71%<br>65%<br>58% 58% 57%<br>60%<br>51% 51%<br>49%<br>47%<br>43%<br>40% 40% 40%<br>40% 36% 36% 35%<br>29%<br>24% 31% 33% 32% 33% 34%<br>29% 29% 28%<br>20%<br>13% 23%<br>15%<br>14%<br>0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Unit Passthrough Clonflict Adjusted Passthrough Passthrough=0.7<br>Poverty Rate<br><!-- End of picture text --> _Source:_ World Bank staff calculations, based on WDI and CBS data
Figure 15 below benchmarks poverty estimates for Syria obtained using the distribution neutral approach based on GDP current priced adjusted by CPI and baseline 2009. Overall, estimates based on both unit value pass through and conflict adjusted pass through lead to estimates that could be consistent with countries at similar levels of development, as proxied by per capita GDP at current prices.

_Figure 15: Benchmarking of_ _<u>poverty estimates for Syria, 2020</u>_ <!-- Start of picture text --> LIC($2.15,2017 PPP) LMIC($3.65, 2017 PPP)<br>100% 100%<br>UZB<br>ZMB<br>SYR-Unit<br>80% MDG 80% Passthrough TLS<br>COD MWI TZA PNGSLB<br>BDI MOZ ZWE COG<br>60% CAF 60% SYR- 0.7 HTIBENLSOKEN NGA SWZ<br>NER RWA Passthrough BGDGHA AGO<br>40%20% SYR-Unit PassthroughSYR-Conflict-adjusted GNBTCDGMBLBRMLIETHSLE GINBFA SYR- 0.7 Passthrough UGATGO SDN 40%20% SYR-Conflict-Passthroughadjusted NPL CMRTJKCOMKGZSENKIRNICSTPPAKHNDCIVMRTMMRINDBOL BTNFSMDJILAOCPVPSEMARVUTPHL IDNSLVDZAMNG WSMEGYLKAIRN<br>Passthrough 0% UKR VNM TUN<br>0% $- $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500 $5,000 $5,500<br> $- $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800 $2,000<br>GDP per capita, 2020 (constant 2015 US$) GDP per capita, 2020 (constant 2015 US$)<br>Poverty Rate Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data

# 5 Triangulating poverty projections with available evidence

As discussed in Section 3, the HNAP 2022 survey could provide a reasonably good assessment of the current extent of monetary poverty in Syria. While the consumption aggregate based on the HNAP 2022 survey is not comparable with the comprehensive recording of consumption from pre-conflict official household budget surveys, it is most likely providing a more accurate characterization of monetary poverty in Syria compared to projections based on a distribution neutral nowcasting approach, including on the subnational profile of monetary poverty. Using the nominal consumption aggregate obtained from the HNAP 2022, monetary poverty in Syria is the highest in northeastern governorates (Figure 8). Can this evidence be corroborated?

Similarly, as discussed in Section 4, distribution neutral approaches could lead to very different dynamics of the temporal evolution of poverty, depending on the underlying NA aggregate used to project growth in average household consumption. Based on the assessment of the likely quality of each aggregate, estimates of growth based on per capita GDP in current prices deflated using the CPI should be preferred over the default nowcasting estimates obtained using constant prices per capita GDP. As shown in Figure 11 and Figure 12, poverty trends based on per capita GDP in constant prices would lead to a monotonic poverty increase over the conflict period, whereas estimates using per capita GDP in current prices deflated with the CPI would lead to an increase in poverty over the period 2011-2016, followed by a poverty decline between 2016 and 2019, and again an increase thereafter. Can these trend dynamics be corroborated?
In order to answer these two questions might be useful to use georeferenced data, in particular looking at night-time light data as well as data on spatial and temporal incidence of droughts.

As shown in Figure 16, night-time light emission – a metric often used to proxy for economic activity - collapsed after the start of the conflict in 2011 and kept declining till 2016. In the following years, the rebound that followed the phase of conflict de-escalation was short lived, with emission declining again starting in 2019, the year of the financial crisis in Lebanon.<sup>35</sup> Interestingly, a drop in night-time light emissions is also visible in 2008-2009, corresponding to the severe drought that affected the country prior to the conflict start. Other drought periods affected agriculture production in 2014, 2016 and 2018 possibly driving additional decline in economic activity, as captured by night-time light emissions. More recently, drought conditions affected northeastern regions of Syria in 2021 and 2022.

Overall, evidence emerging from satellite data seems to suggest that – should a distribution neutral approach be used to nowcast poverty in Syria – using projections based on GDP in current prices deflated by the CPI is likely to provide a more accurate picture of poverty dynamics compared to GDP in constant prices which would provide monotonic poverty trends over the entire conflict period.

<!-- Start of picture text --> Figure 16: Trends in night-time light emissions<br>80000 conflict<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>NTL NLT - Adj<br>Note: Night-lights data series shows in orange adjusted for intensity variations emanating from gas and oil flares.<br>Source: World Bank estimates based on Chen et al. (2021)<br>Night time light intensity<br><!-- End of picture text --> 35 In order to consistently analyze variation in night time light emissions over the period 2000-2020, we use the combined VIIRS, DMSP series. See Chen et al. 2021.
# 6 Proposed way forward

Estimating monetary poverty trends in Syria post 2011 is severely constrained by data quality and availability issues. Evidence presented in this paper suggests that – among possible distribution neutral nowcasting approaches – the one in which baseline poverty estimates based on 2009 grouped data and average consumption growth based on per capita GDP in current prices, deflated by the CPI, would likely lead to the most sensible trend profile of poverty over the conflict period. Moreover, given data constraints, the analysis of consumption data based on the HNAP 2022 Demographic and WASH survey does not evidence any concern regarding the possibility to use these data for the purpose of poverty measurement.

Based on these considerations, the proposed way forward is to use 2022 HNAP-based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over the 2009-2022 period using the growth rate of per capita GDP in current prices, deflated by the CPI with a passthrough of 0.7 (Figure 17).<sup>36</sup> <!-- Start of picture text --> Figure 17: Poverty trends 2009-2022<br>$2.15,2017 PPP $3.65,2017 PPP<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Backcasting, base 2022 Backcasting, base 2022<br>Source : World Bank staff calculations based on CBS and HNAP 2022<br><!-- End of picture text --> The proposed approach has three main advantages. First, it makes the best use of the best available evidence in a simple and transparent way, therefore substantially improving on current World Bank poverty estimates available in PIP. Second, it is grounded in the standard methodology used in PIP for the analysis of poverty trends in other countries, including in cases of non-comparability of poverty estimates between baseline and end-line surveys. Lastly, it will allow an updating of poverty estimates moving forward even in absence of new household survey rounds.

> 36 See <u>https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nationalaccounts</u>