Arab Development Challenges Report

Background Paper 2011/15

Poverty and Inequality in Syria (1997-2007)

Khalid Abu-Ismail, Ali Abdel-Gadir and Heba El-Laithy

United Nations Development Programme Arab Development Challenges Report

Background Paper 2011/15

Poverty and Inequality in Syria (1997-2007)

Khalid Abu-Ismail, Ali Abdel-Gadir

and Heba El-Laithy

Khalid Abu-Ismail is Poverty and Macroeconomics Advisor at United Nations Development Programme- Regional Centre in Cairo (UNDP-RCC). E-mail: Khalid.Abu-Ismail@undp.org

Ali Abdel Gader is Acting Director of the Arab Planning Institute. E-mail: aali@api.org.kw Heba El-Laithy is Professor of Statistics at Cairo University. E-mail: hlaithy@gmail.com

Comments should be addressed by email to the author(s)

Acronyms and Abbreviations CBS Central Bureau of Statistics CSO Central Statistical Office FPL Food Poverty Line GDP Gross Domestic Product HDI Human Development Index HIES Household Income and Expenditure Survey HPI Human Poverty Index IMF International Monetary Fund ISI Import-Substitution Industrialization LAS League of the Arab States LPL Lower Poverty Line MDGs Millennium Development Goals SL Syrian Lira UN United Nations UNDP United Nations Development Programme UPL Upper Poverty Line USA United States of America USSR Union of Soviet Socialist Republic

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Introduction

Since the early 2000s, in the endeavour to understand the unprecedented levels of rising poverty in Syria in the last decade, the Syrian State Planning Commission, supported by the UNDP, in partnership with the Central Bureau of Statistics, has undertaken a number of studies to support the decision making of policy makers for the achievement of the MDGs and to directly contribute to the poverty reduction objectives stipulated in the National 10th five year plan of the Government of Syria. Some of these notable efforts have included:“Poverty in Syria” (2005), which identified the baseline for expenditure poverty and provided evidence of the geographic distribution and concentration of poverty;“Macroeconomic Policies for Poverty Reduction” (2005), which highlighted the results of the 2004 Household Income and Expenditure Survey (HIES) and provided a menu of policy options at the sectoral and macroeconomic levels to tackle the phenomenon of poverty;“Poverty and Income Distribution in Syria” (2009,) which sketched a profile of the primary features of poverty in Syria based on the expenditure data from the 2006-7 HIES survey and updated the findings of the first UNDP poverty assessment report by providing a comprehensive overview of the evolution of poverty and income distribution since 2004. Two other UNDP-led studies in 2009: "Understanding Poverty and Inequality Dynamics in Syria" and "Pro-Poor Growth in Syria: Diagnosis and Policy Considerations" were undertaken to spur further research and analysis, as well as to inform development practitioners of the structural issues of poverty in Syria. Drawing on the main findings of the above-mentioned reports, in this paper, we undertake an inquiry into the imperative question of the extent to which state actions and markets have affected social outcomes, such as the overall spread of poverty and inequality in expenditure and access to social services over the period from 1997 to 2007, for which we have data. This paper, which is a compilation of the findings of the papers “Pro-poor Growth in Syria: Diagnosis and Policy Considerations” (2009) by Abdel-Gadir and Abu-Ismail, “Poverty and Distribution in Syria” (2009) by Abu-Ismail and El-Laithy, and “Understanding Poverty and Inequality Dynamics in Syria” (2009) by Abu-Ismail and El-Laithy,is organized in the following manner: First, we explain the main definitions and methodologies applied to achieve a comparable assessment of the trends in poverty in Syria across the two discernible periods 1996-2004 and 2004-2007. We then present stylized facts taking stock of poverty, growth and inequality trends in Syria over these periods(1996-2007), exposing regional and rural-urban discrepancies. In our assessment of the depth of poverty and inequalities in Syria we paint the relationship between income and human poverty over time to shed some light on the possible links between these results and policy stances as well as the impact of extraneous factors such as drought. We then assess the pro-poorness of the growth processes that took place during these periods 1996-2004 and 2004-2007, again examining the nature of this growth process at the national, regional and rural-urban level. We interpret these results of human deprivation with an explanation of the underlying root cause of the shifts in these indicators by taking into account the major shift in macroeconomic policies that took place in Syria during the 1990s and 2000s. Finally, we strive to present Syrian policy makers with a menu of policy interventions to enhance the processes of pro-poor growth. To start with, we must note that our approach is anchored in the UNDP’s human development approach, namely in the conviction that understanding the dynamics of poverty and inequality in any developing country requires the adoption of a broad definition for the "development process". Such a definition, which has gained world-wide acceptance, is that “development can be seen as a process of expanding the real freedoms that people enjoy”1. Without getting involved in the philosophical foundations of this approach we need to note that it requires judging the welfare of individuals neither in terms of the utility of goods and services, nor in terms of primary goods, but in terms of substantive capabilities to choose a

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life one has reason to value. “A person’s capability to achieve functioning’s that he or she has reason to value provides a general approach to the evaluation of social arrangements, and this yields a particular way of viewing the assessment of equality and inequality”2. “Development as freedom” is a much broader approach to understanding what is meant by development compared to other approaches that identify development with increases in per capita income, or with industrialization, or with technological advance, or with social modernization. Being broad, the capability perspective is closely related to the concept of “equality of opportunities”; though it is not identical. “In a very real sense, a person’s capability to achieve does indeed stand for the opportunity to pursue his or her own objectives. But the concept of ‘equality of opportunities’ is more commonly used in the policy literature in more restrictive ways, defined in terms of the equal availability of some particular means, or with reference to equal applicability of some specific barriers or constraints. Thus characterized, ‘equality of opportunities’ does not amount to anything like equality of overall freedoms”3; (emphasis is in the original)4. As is well known, the UNDP pioneered the measurement of “development as freedom” in terms of the by now famous Human Development Index (HDI). Capabilities included in the HDI are: the ability to live longer (as reflected in life expectancy at birth, to proxy health); the ability to read and write and to have access to available information (as reflected in literacy rates, as well as educational enrolment rates); and, the ability to have a decent standard of living (as reflected by real per capita income). The HDI is now very widely used to guide development policy. In addition to the substantive influence of the HDI, being a measure of the broad definition of the development process, on policy, "development as a process of expanding the freedoms that people enjoy" has found international recognition in the Millennium Development Goals (MDGs) adopted by the United Nations in September 2000. Over the past nine years, since their formulation, the MDGs and the broader definition of development, on which they are based, influenced the development policy debate around the world (see, for example, the Commission for Africa (2005); Sachs (2005); UN Millennium Project (2005); the World Bank (2006); and, the Commission on Growth and Development (2008))5. The IMF, the World Bank, and the UNDP are all involved in helping least developed countries in estimating the feasibility of achieving these goals and the cost of doing so! Furthermore, as is well known under the MDGs, the overarching objective of development in developing countries is the reduction of poverty. The first MDG requires the eradication of extreme poverty and hunger, where poverty is expressed as the proportion of people living below one dollar a day (i.e. the head-count ratio). Four of the remaining MDGs look at poverty from a capability, and achievements, perspective (education, gender equality, health and the environment). Under the dominant money-metric approach to poverty reduction, over a 25-year horizon both economic growth (changes in real per capita consumption expenditure) and changes in the inequality in the distribution of consumption expenditure matter. Thus, changes in poverty over time have a growth component and a distribution component: if a country experiences both economic growth and a decline in inequality it can be assured of reduced poverty. Otherwise, it will all depend on the relative strength of each of the two components. Thus, understanding the dynamics of inequality in any developing country is an important guide to devising a development policy anchored on the overarching objective of development, namely poverty reduction over a long horizon. Indeed, one of the indicators identified to measure progress towards achieving poverty reduction is the share of the 20% of the population in total consumption expenditure. Given the above noted two components

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of changes in poverty over time (i.e. the growth and inequality components), concern about the share of the poorest fifth of the population gave rise to a huge literature on measuring the pro-poorness of growth processes. Such literature provides the relevant theoretical, and empirical, framework to understand the dynamics of inequality in a developing country such as Syria. This is the framework that we adopt in this chapter.

Methodological Preliminaries For the purpose of an objective assessment of the evolution of poverty, the methodology for measuring income poverty applied in this report is consistent with the other World Bank and UNDP-led poverty assessment reports for the Arab Region (see for example, UNDP and LAS (2008) for the Arab Region, UNDP (2006) for Yemen, UNDP (2007) for Lebanon and the earlier UNDP (2005) Syria poverty assessment (Box 1)). The results are presented using three main definitions, which are:

1. “Abject poverty”: defined as the share of population whose expenditure lies under the food poverty line (FPL);

2. “Extreme poverty”: defined as the share of population whose expenditure lies under the lower poverty line (LPL); and

3. “Overall poverty”: defined as the share of population whose expenditure lies under the upper poverty line (UPL).

The first Poverty Assessment Report (UNDP 2005) concluded that, in 2003-2004, almost 2 million individuals in Syria (11.4% of the population) were extremely poor. Using a higher expenditure poverty line, overall poverty in Syria was estimated at 30.1%, representing almost 5.3 million individuals. In addition, the 2005 report identified the following major trends and characteristics of poverty:

1. Poverty in 2004 was generally more prevalent in rural than in urban areas of Syria (62% in rural areas). The North-Eastern region (Idleb, Aleppo, Al Raqqa, Deir Ezzor and Hassakeh), had the greatest incidence, depth and severity of poverty;

2. Poverty decreased between 1996-1997 and 2003-2004 for Syria as a whole, but regional patterns were different. The incidence of poverty declined rapidly in the Central and Southern regions, especially in rural areas. The decline was moderate in urban areas of the North-Eastern and Coastal regions, and poverty actually rose in the rural parts of these regions;

3. At the national level, growth was not pro-poor. Non-poor individuals (above the third decile in the expenditure distribution) benefited proportionally more than the poor from economic growth. Between the years 1997-2004, inequality in Syria, as a whole, rose (the Gini index rose from 0.33 to 0.37). In 2003-2004, the bottom 20% of the population consumed only 7% of all expenditure in Syria while the richest 20% consumed 45%;

4. Poverty in Syria is shallow, with most people clustered just below the poverty line; and

5. Education was the single characteristic with the strongest correlation to poverty risk in Syria. More than 18% of the poor population was illiterate, and poverty was highest, deepest and most severe for these individuals.

As in the 2005 UNDP poverty report, this report adopts a well-established cost-of-basic-needs methodology that accounts for: (i) differences in consumption patterns and prices across regions, (ii) different ‘basic needs’ requirements of different household members – young versus old, male versus female, (iii) the cost of the actual diet, and (iv) ‘economies of scale’ within households – the fact that non-food items can be shared among household

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members. The cost-of-basic-needs methodology thus yieldsan unbiased absolute poverty line, poverty lines that are household-specific objective, and regionally consistent. As these poverty lines account for regional differences in relative prices, expenditure patterns, activity levels, as well as the size and age composition of poor households, they are expected to vary from one region to another and from one household to another within the same region. However, it is intuitively clear that households with similar age and gender compositions who live in the same region will face similar poverty lines. The first step in constructing any income poverty assessment is to construct the food poverty line. This is completed by identifying a food bundle that yields the predetermined caloric requirements, but with a composition that is consistent with the consumption behaviour of the poor. This bundle was defined for individuals in different age brackets, gender, and activity levels (using tables from the World Health Organization). FPLs were then set using the cost of the required calories based on consumption pattern of the second quintile (using the prices for the food in each region and at each time period). Thus the relative quantities observed in the diet of the poor (proxied by the second quintile) and the prices they face were taken into account while constructing the FPL. As noted above, households whose expenditure is below the FPL are referred to as "abjectly poor".

BO

X

1 Comparability between the 2004 and 2007 surveys

The second step is estimating the LPL, which is basically the FPL augmented by an allowance for expenditure on essential non-food goods. The latter is determined based on expenditures by households who have to forego food consumption to allow for non-food expenditures that are deemed indispensable. The non-food allowance can be estimated by identifying the share of non-food expenditure for households whose total expenditure was equivalent to the food poverty line. Any household that spends less than the LPL is

The HIES surveys in 2004 and 2007 are the main source of data for the poverty analyses contained in this report.

Based on data in those surveys, we are able to record household income and consumption expenditures on more

than 550 goods and services, which is a valuable source of information on the level and distribution of welfare for

the Syrian society. The sampling, questionnaire design, and the administration of the two surveys are very similar

to allow for comparability. Thus, in both surveys selected households were asked to report their daily expenditures

during a ten-day period only and other regular expenditures were reported using the same recall period; data on

the ownership of assets and imputed rent for owned houses which are very important in the course of poverty

assessment were also similarly recorded in both surveys. However, there are two main differences between the

surveys, which do not affect their comparability. First, the 2006-07 survey includes more detailed questions on

employment characteristics of household members. Another major difference between 2006-07 and 2003-04

surveys is the sample size. Although the sample design is similar in both surveys and about one third of the

selected PSU's in 2003-04 were reselected in 2006-07, the selected households were different. Nonetheless, the

sample of the 2006-07 survey is statistically representative at the regional level. The governorates comprising

each region and sample size at national, regional and governorate levels for both surveys is listed in the table

below.

Region Governorates Sample size (2003-2004)

Sample size (2006-2007)

South Urban

Damascus, Rural Damascus, Deraa, El Suaida and El Quneitra 6336 2879

Rural 3284 958

North-East

Urban Idleb, Aleppo, Al Raqqa, Deir Ezzor and Hassakeh

6261 2631

Rural 6161 2339

Middle Urban

Homs and Hama 2275 954

Rural 2448 1025

Coastal Urban

Tartous and Latakkia 1314 522

Rural 1711 701

Total

Syria 29790 12009

Source: HIES 2004 and 2007

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considered extremely poor. Therefore, the abjectly (food) poor group are just a sub set of the extremely poor. Finally, for a more inclusive poverty measure, we construct the UPL by enlarging the non-food component to include a more reasonable minimum required level of non-food expenditures. This UPL is estimated when the FPL is augmented by the non-food expenditure of households whose food expenditure is equivalent to the food poverty line. The report refers to households whose expenditure lies under the UPL as the "poor" which include the extremely poor and abjectly poor sub-groups. Overall poverty in this report thus refers to the share of population whose mean expenditure lies below the UPL. One final observation with respect to the construction of the poverty lines is that all poverty lines for 2006-07 were estimated using the reference food baskets of 2003-04, but with a different set of regional unit prices derived from the 2007 survey.6 Household specific food poverty lines were thus derived from multiplying the caloric cost by the required calories of household members. Similar to the 2003-04 poverty lines, the non-food poverty lines are then estimated based on an Engle’s curve regression. Table 1: Average monthly per capita consumption expenditure and poverty lines, 2004 and 2007

2004 2007

Expenditure (SL) FPL LPL UPL Expenditure (SL) FPL LPL UPL

Southern Region

Urban 4647 1149 1663 2441 5058 1538 2351 3465

Rural 3174 1062 1500 1978 4590 1474 2238 3242

North-Eastern Region

Urban 3775 979 1454 2144 4887 1519 2287 3042

Rural 2472 904 1279 1694 2636 1162 1730 2250

Central Region

Urban 4016 1029 1482 2047 4806 1463 2255 3081

Rural 3074 924 1304 1748 4692 1428 2186 2950

Coastal Region

Urban 4274 1081 1591 2412 6592 1642 2513 3754

Rural 3844 1103 1584 2303 5828 1713 2627 3944

All Syria

Urban 4177 1058 1548 2264 5058 1527 2324 3267

Rural 2885 965 1366 1833 3770 1336 2017 2766

Total 3541 1012 1458 2052 4467 1439 2183 3037

Source: Complied based on UNDP 2005 and CBS Household income and Expenditure Survey, 2006-2007.

As shown in table 1, the estimated poverty lines (averaged to produce per capita figures) show that the thresholds for different types of poverty vary across the regions. Accordingly, in 2006-07, on average, individuals who spent less than SL 1439 per month (equivalent to 32% of average per capita expenditure) are considered to be in abject poverty while those who spent less than SL 2183 monthly (equivalent to 48% of average per capita expenditure) are extremely poor (Table 1). Using data in table 1, we observe that the ratios of the value of the poverty lines to total expenditure for 2004 and 2007 were, respectively, as follows: FPL (0.29 and 0.32); LPL (0.41 and 0.48); UPL (0.58 and 0.68). These results indicate that the minimum cost of living in Syria (relative to the average expenditure) has increased.

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Poverty, Growth, and Inequality Stylized Facts

Poverty trends, 1997-2007 Figure 1 and table 2 present an overall picture of poverty in Syria for 2007 based on the most recent HIES. The former shows that overall poverty (share of the population under the UPL) is relatively widespread in Syria, affecting 33.6% of the population. This implies that 6.7 million Syrian people are considered poor. Of this larger group, almost 2.4 million (12.3% of the population) are estimated to live in extreme poverty. Hence, the average expenditure of approximately 4.3 million Syrian individuals (21.3% of the population) lies between the thresholds of extreme poverty (i.e. under the lower poverty line) and overall poverty (i.e. under the upper poverty line) (Figure 1). The rural-urban discrepancies characterizing poverty in Syria become immediately discernible from the data presented in Figure 1 and table 2. Figure 1: Distribution of Syrian population (%) across poor and non-poor categories, 2007

Urban Rural National

Source: UNDP (2009)

These rural-urban discrepancies translate across all regions as well, as demonstrated by table 2, which show cases the strong regional dimension of poverty in Syria. As the table shows, in 2007, 46% of the sampled population in Syria lived in rural areas, but the poor in those areas accounted for about 56% of the extremely poor and 50% of the overall poor population. Rural areas thus recorded higher poverty measures than their urban counterparts in all regions throughout the region. The Coastal region was the least poor with an extreme poverty rate of 7.68%, while the North-Eastern region was the poorest in the country with an extreme poverty rate of 15.4%. The results also show that the poor in Syria were mostly concentrated in the North-Eastern region, particularly in the rural areas of the region, for which the share of the extremely poor and overall poor population (37% and 28.8%, respectively) exceeded by far its population share of 23.1%.When the overall poverty rate is considered, both the North-Eastern and Southern regions had nearly the same incidence of poverty. However, the expenditure levels of the poor in the Southern urban region were, on average, far below the poverty line. Consequently, the poverty gap for the Southern urban region is 25% higher than the poverty gap for urban Syria using the UPL (Table 2). Now turning to the evolution of poverty over the period from 1997 to 2007, table 3 gives a summary of the main changes observed by comparing the results from the three surveys. Accordingly, overall poverty in Syria decreased over the first period (from 33.2 % in 1997 to 30.1 % in 2004) before rising in the second period (to reach 33.6 % in 2007). Thus, the

1% 9%

22%

68%

Below FPLAbove FPL and Below LPLAbove LPL and Below UPLAbove UPL

2% 13%

23%

62%

Below FPLAbove FPL and Below LPLAbove LPL and Below UPLAbove UPL

1% 11%

22%

66%

Below FPLAbove FPL and Below LPLAbove LPL and Below UPLAbove UPL

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deterioration since 2004 has offset the poverty reduction achieved over the period from 1997 to 2004. However, not all poverty indicators moved in the same direction from 2004 to 2007: as reported in UNDP (2009), abject (food) poverty decreased (from 2.2 % in 2004 to 1.2 % in 2007), extreme poverty increased slightly (from 11.39 % to 12.3 %), and overall poverty increased rather more significantly (from 30.1 % to 33.6 %). Table 2: Poverty measurements by region, 2007

Using Lower Poverty Line Using Upper Poverty Line % of Individuals

Po P1 P2 % of Poor Po P1 P2 % Poor

Southern region

Urban 10.56 1.8 0.49 18.9 35.07 8.37 2.79 23.04 22.06

Rural 12.75 2.23 0.65 8.8 35.6 8.53 2.99 9.04 8.53

Total 11.17 1.92 0.54 27.7 35.22 8.41 2.84 32.08 30.59

North-Eastern region

Urban 10.75 1.6 0.38 18.5 29.58 5.78 1.63 18.63 21.15

Rural 19.73 3.08 0.76 37 41.97 9.28 2.87 28.82 23.06

Total 15.43 2.37 0.58 55.5 36.04 7.6 2.28 47.45 44.21

Central region

Urban 7.76 0.85 0.15 4.6 23.8 4.56 1.21 5.18 7.31

Rural 9.05 1.38 0.34 6.7 26.8 5.58 1.68 7.22 9.05

Total 8.47 1.14 0.26 11.3 25.46 5.12 1.47 12.4 16.36

Coastal region

Urban 5.6 1.16 0.37 1.6 25.33 5.97 2.02 2.69 3.57

Rural 9.09 1.94 0.66 3.9 34.16 8.06 2.98 5.36 5.27

Total 7.68 1.62 0.54 5.5 30.59 7.22 2.59 8.05 8.84

All Syria

Urban 9.93 1.55 0.39 43.6 30.76 6.68 2.07 49.56 54.1

Rural 15.1 2.46 0.65 56.4 36.9 8.27 2.67 50.44 45.9

Total 12.3 1.97 0.51 100 33.58 7.41 2.35 100 100

Source: Complied based on UNDP Poverty Assessment reports (2005) and (2009) and data from HIESs produced by the CBS in 1997, 2004 and 2007. Note: P0 represents the incidence of poverty, P1 represents the poverty gap index, and P2 represents the severity index of poverty.

Those changes indicate that although poverty most likely increased in Syria over the past few years, there was an improvement in the conditions of the poorest of the poor. Distributional changes over the period from 2004 to 2007 thus affected the poor and the non-poor differently, with the bottom 5% actually showing an improvement in their living standards. Table 3: Incidence of extreme and overall poverty for rural and urban areas (in per cent), 1997-2007

Incidence of Poverty

1997 2004 2007

Urban Rural Total Urban Rural Total Urban Rural Total

Extreme Poverty 12.6 15.9 14.3 8.7 14.2 11.4 9.9 15.1 12.3

Overall Poverty 31.5 35 33.2 28.5 31.8 30.1 30.8 36.9 33.6 Change in incidence of Poverty

% Change 1997-2004 % Change 2004-2007 % Change 1997-2007

Urban Rural Total Urban Rural Total Urban Rural Total

Extremely Poor -3.9 -1.7 -2.9 1.2 0.9 0.9 -2.7 -0.8 -2

Poor -3 -3.2 -3.1 2.3 5.1 3.5 -0.7 1.9 0.4

Source: ibid.

Changes in poverty varied greatly among Syrian regions. Two consistent patterns of poverty evolution in 1997-2004 emerge when the lower poverty line is considered. First, the incidence of poverty increased in the rural areas of the North-Eastern and Coastal regions, especially in the North-Eastern region (from 15.2% to 18%). The poverty gap and severity indices followed a similar pattern. Second, during this first period all other regions

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experienced declines in their poverty measurements (1997-2004), with substantial decreases in extreme poverty in the urban and rural areas of the Central region (from 14.8% to 9.02%, for P0 in urban areas and from 22.6 to 11.1%, in rural areas). In the rural Coastal region, although there was a slight increase in extreme poverty, overall poverty substantially declined for the same 1997-2004 period. In this region, the 35th-60th percentile of expenditure distribution experienced a higher change in their expenditure than in the poverty line. Figure 2: Changes in the incidence of the extreme poverty (A) and overall poverty (B) by region, 1997-2007

(A) (B)

Source: ibid

Regarding the changes in patterns of geographic distribution of poverty over the recent period 2004-2007, clearly, poverty is still more concentrated in the North-Eastern region, especially the rural areas, where 19.7% of the population is extremely poor (i.e. below the lower poverty line). Also, the urban Southern region experienced a considerably large increase in extreme poverty with a poverty incidence in 2007 that is almost twice that of 2004 (Figure 2.A). Accordingly, this region, which had the lowest levels of poverty in 2004, became the second poorest region in 2007. In fact, as shown in UNDP (2009), the overall increase in extreme poverty nation-wide between 2004 and 2007 was mainly driven by the sharp rise in poverty in the Southern region. Changes in overall poverty (i.e. percentage of population under the upper poverty line) also varied significantly between regions. Four out of the eight regions in Syria followed the overall nation-wide trend of increasing poverty (from 2004-2007). However, the pace of change was not homogeneous across those regions. As in the case of extreme poverty, overall poverty in the urban Southern region showed the largest increase (by 50%). However, contrary to the national trend, overall poverty rates declined in some regions, particularly in the urban areas of the North-Eastern region. They also declined rather insignificantly in the Central region and in urban areas of the Coastal region (Figure 2.B).

Growth in per capita consumption expenditure At the outset, it must be noted that striking inconsistencies exist between growth rates obtained from the Syrian national accounts and Household Surveys. The data on growth in per capita consumption expenditure used in this paper, as stated previously, are mainly derived from two UNDP-led poverty assessment studies conducted in 2005 and 2008 (El-Laithy and Abu-Ismail (2005) and El-Laithy and Abu-Ismail (2008)), which are based on the Household Income and Expenditure Surveys conducted by the Central Statistical Office in 1997, 2004 and 2007.It should be recalled that in developing countries real per capita consumption expenditure is the most widely used indicator of the standard of living relevant to the analysis of poverty.

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On the basis of the survey data, real per capita expenditure in 2003-2004 prices are reported in El-Laithy and Abu Ismail (2008). Such estimates are reported for the level of the country as well as for geographical regions, with their residential sectors (urban/rural). For the purposes of our analysis, we use the regional distribution of the population of the country in 2004 population as reported in El-Laithy and Abu Ismail (2005: 42, annex table 2) with a total population for the country of about 17.7 million. It is not surprising that the rural-bias of poverty incidence, previously demonstrated in table 3 and Figure 2, reflects onto the per capita consumption growth process over this period, through a clear urban-bias of per capita consumption growth at the country level (Table 4). Table 4 shows that over the period 1996-2004, real per capita consumption expenditure increased by an annual rate of growth of about 1.6%nationwide, with the urban per capita consumption increasing by an annual rate of about 2.1% compared to a growth rate of only 0.5% for the rural sector. Table 4: Real per capita consumption expenditure, 1996-2007

Region Sector

Real Per Capita

Expenditure 1996 (£S)*

Real Per Capita

Expenditure 2004 (£S)*

Real Per Capita

Expenditure 2007(£S)*

Annual Growth

Rate (%): 1996-2004

Annual Growth

Rate (%): 2004-2007

Annual Growth

Rate (%): 1996-2007

Southern

Urban 3796 4646 3777 2.6 -6.7 -0.5

Rural 3085 3174 3307 0.4 1.4 0.6

Total 3533 4140 3570 2 -4.9 0.1

North-Eastern

Urban 3027 3775 3150 2.8 -5.9 0.3

Rural 2613 2472 2051 -0.7 -6 -2.2

Total 2917 3126 2632 0.9 -5.6 -0.9

Central

Urban 3022 4016 3379 3.6 -5.6 1

Rural 2375 3074 3037 3.3 -0.4 2.3

Total 2858 3527 3202 2.7 -3.2 1

Coastal

Urban 3857 4274 4339 1.3 0.5 1.1

Rural 3876 3844 3751 -0.1 -0.8 -0.3

Total 3862 4031 4001 0.5 -0.2 0.3

Syria

Urban 3558 4192 3528 2.1 -5.6 0.4

Rural 2804 2922 2728 0.5 -2.2 -0.3

Total 3222 3612 3194 1.6 -4 -0.1

Source: ibid Note: * £S denotes Syrian Lira (called Syrian Pound by the IMF) per person per month in 2004 constant prices.

This feature of the growth process from 1996 to 2004 is reflected at the level of the regions, except for the Central Region, which posted the highest rate of real per capita consumption expenditure with an overall annual rate of growth of about 2.7% and with both sectors recording a fairly high rate: 3.6% for the urban sector and 3.3% for the rural sector. The Southern Region was the second fastest growing region posting an annual rate of growth of 2%, with the urban sector growing at a rate of 2.6% compared to only 0.4% for the rural sector. The North-Eastern Region was the third fastest growing region posting an annual rate of growth of 0.9% and with the urban sector growing at a rate of 2.8% compared to a negative growth rate of 0.7% for the rural sector. The lowest growing region was the Coastal Region, which posted an overall rate of growth of only 0.5%, with the urban sector reporting a growth rate of 1.3% compared to a negative growth rate of 0.1% for the rural sector. From table 4 it is clear that over the period 2004-2007 real per capita consumption expenditure declined rather substantially by an annual rate of decline of about 4% at the level of the country, with the urban per capita consumption declining at an annual rate of about 5.6% compared to a rate of decline of 2.2% for the rural sector. However, it is not clear whether such a per capita consumption growth process could be considered as having been characterized by a clear rural-bias! Though all regions reported negative growth rates for per capita consumption expenditure, they varied in terms of the rural-bias feature. The best

10

performing region was the Coastal region, which posted the lowest rate of decline of only 0.2% (with the urban sector posting a positive growth rate of 0.5%, and the rural posting a negative growth rate of 0.8%). The highest rate of decline, of 5.6%, was reported for the North-Eastern region (with both sectors posting a rate of decline of about 5.9%). In the Southern region, which is the second worst performing region with an annual rate of decline of 4.9%, the rural sector posted a positive rate of growth of 1.4% while the urban sector declined at an annual rate of 6.7%. Over the longer period 1996-2007real per capita consumption expenditure as recorded in household budget surveys was stagnant at the country level, declining as it did by an annual rate of only 0.1%. The urban sector reported a positive rate of growth of about 0.4% per annum while the rural sector recorded a negative annual rate of growth of about 0.3%. The Central region posted positive rates of growth for both the urban (about 1% per annum) and the rural (about 2.3%) sectors. As is clear from the table, both the Coastal and the Southern regions recorded positive, but very low, overall rate of growth, while the North-Eastern region recorded a negative overall rate of growth of 0.8%. The urban-bias pattern of the growth process over this longer period was reported for all regions except for that of the Southern region where there was a clear rural-bias.

The state of inequality in Syria To appropriately evaluate the state of inequality in the distribution of consumption expenditure in Syria we need to specify an international benchmark. To do so we use the most recent Gini coefficient results on the distribution of consumption expenditure in a sample of 84 countries reported by Ferreira and Ravallion (2008: table 1: 35-40) for the period 2000-20047. Without loss of generality we consider such results to represent the state of inequality in the world for the 2000 decade. For some of the countries the Gini coefficients were reported for the distribution of income. We adjusted these coefficients by subtracting 6.6% to get the Gini coefficients for expenditure distribution as per the results of Deininger and Squire (1996). . For the above sample of countries we calculated the mean (0.374 or 37.4%), the median (0.371 or 37.1%) and the standard deviation (0.092 or 9.2%). On the basis of these parameters we could classify countries with Gini coefficients falling in the range [0.328-0.420] as countries that have a medium degree of inequality8. Countries with low degrees of inequality would be those with Gini coefficients less than 0.328, while those with high degrees of inequality will be those with Gini coefficients of 0.42 and above. With the help of the above benchmarks, we are now in a position to look at the state of inequality in the distribution of consumption expenditure in Syria for the three years for which household budget survey data is available: 1997, 2004 and 2007. The information is reported in the following tables as readings of the respective Lorenz curves in terms of decile shares together with the calculated Gini coefficients. The results are reported for the four geographical regions of the country: Southern, North-Eastern, Central and Coastal, and for urban and rural sectors in each region, as well as for the country as a whole with its sectoral composition.

We hasten to note that the detailed results reported in this section are based on the original information provided by UNDP based on CSO unit record data.9 Table 5 provides the evidence on the degree of inequality for the four geographical regions, each split into urban and rural sectors, for 1997.

Overall, the degree of inequality in Syria in 1997,as per the international benchmark of a Gini of 32.8%, was low, with a Gini coefficient of about 32.6%. The share of the lowest 20% of

11

the population was about 8% of expenditure (with a marginal difference between the urban and rural sectors), compared to a share of about 41% for the richest 20% (again with a marginal difference between the two sectors). As is well known, a standard result of the development-inequality literature is that in developing countries in their early stages of development it is expected that rural sectors would display a more equal distribution compared to urban sectors. Such a result is confirmed for the distribution of consumption expenditure in Syria in 1997 for two geographical regions (North-Eastern and Coastal). For the other two regions the reverse result holds. However, the difference in the Gini coefficients between the two sectors is marginal for all geographical regions except for that of the Coastal region, where inequality in the urban sector is markedly higher than that in the rural sector. The degree of inequality in the distribution of consumption expenditure for the urban sector in the Coastal region (a Gini of about 36%) was also the highest nationwide. For this sector the lowest 20% of the population enjoyed about 7.5% of total expenditure compared to a share of 45% for the richest 20%. The lowest degree of inequality of about 31.76% was recorded for the rural sector of the North-Eastern region. For this sector the lowest 20% of the population received about 8% of total expenditure compared to a share of about 41% for the top 20%.

Table 5: Distribution of consumption expenditure by region in Syria (Decile shares in percentages), 1997

Region Sector Deciles Gini

Coefficient (%)

1 2 3 4 5 6 7 8 9 10

Southern

Urban 3.5 4.8 5.7 6.5 7.5 8.6 10 11.8 15 26.8 32.3

Rural 3.4 4.7 5.6 6.6 7.5 8.5 10 12 14.9 27 32.8

Total 3.4 4.8 5.7 6.5 7.5 8.6 10 11.9 14.9 26.8 32.5

North-Eastern

Urban 3.4 4.7 5.5 6.4 7.4 8.5 9.9 11.9 15 27.3 33.3

Rural 3.3 4.7 5.7 6.7 7.7 8.8 10.3 12.1 15.1 25.6 31.8

Total 3.4 4.7 5.6 6.5 7.4 8.5 9.9 11.8 15 27 32.8

Central

Urban 3.6 4.8 5.8 6.7 7.5 8.5 9.8 11.7 14.8 26.8 31.8

Rural 3.4 4.7 5.6 6.8 7.7 8.8 10.3 11.8 14.8 26.2 31.9

Total 3.4 4.7 5.6 6.6 7.5 8.6 10.1 11.9 15.1 26.6 32.6

Coastal

Urban 3.1 4.4 5.3 6.2 7.1 8.2 9.6 11.4 14.7 30.1 35.6

Rural 3.5 4.8 5.6 6.6 7.3 8.6 10 11.7 14.8 27.1 33.4

Total 3.6 4.8 5.8 6.7 7.5 8.6 9.9 11.7 14.8 26.7 31.8

Syria

Urban 3.5 4.8 5.7 6.6 7.5 8.6 10 11.9 14.9 26.7 32.2

Rural 3.4 4.7 5.6 6.5 7.5 8.5 10 11.9 15 27 32.9

Total 3.4 4.7 5.6 6.5 7.5 8.6 10 11.9 15 26.8 32.6

Source: UNDP calculations based on HIES unit record data.

In 2004, there was an increase in the overall degree of inequality in Syria, as summarized by table 6, compared to the levels of inequality in 1997.This degree of inequality is nevertheless medium by international standards, with a Gini coefficient of about 37.5%. The share of the lowest 20% of the population was about 7.8% of expenditure (7.4% for the urban, and 8.3% for the rural sectors), compared to a share of about 44% for the richest 20% of the population (45% for the urban, and 42% for the rural sectors).Once again, these results correspond with the inequality-development result in the sense that the degree of inequality was higher in the urban sector (with a Gini coefficient of about 38%) than that in the rural sector (with a Gini coefficient of about 34%).This rural-urban discrepancy held throughout all regions, which collectively recorded medium degrees of inequality as per the international benchmark of a Gini of 42% (upper bound).

12

The urban sectors in the North-Eastern and Central regions recorded the highest degrees of inequality, with respective Gini coefficients of 38.4%, and 39.5% In these higher inequality sectors the lowest 20% of the population enjoyed about 7% of total expenditure compared to a share of about 47% for the richest quintile of the population. The lowest degree of inequality was recorded for the rural sector of the Southern region, where the lowest quintile of the population enjoyed about 9% of total expenditure while the top quintile enjoyed about 40% of total expenditure. Table 6: Distribution of consumption expenditure by region in Syria (decile shares in percentages), 2004

Region Sector Deciles Gini

Coefficient (%)

1 2 3 4 5 6 7 8 9 10

Southern

Urban 3.2 4.5 5.2 6 6.9 8 9.6 11.5 14.4 30.7 36.9

Rural 3.7 5.1 6 6.9 7.7 8.7 10.1 11.8 14.5 25.6 31

Total 3.4 4.7 5.4 6.3 7.2 8.2 9.7 11.6 14.7 29.1 36.2

North-Eastern

Urban 3.1 4.1 4.9 5.7 6.7 7.9 9.6 12 15.7 30.4 38.4

Rural 3.6 4.8 5.7 6.6 7.5 8.6 10 11.8 14.7 26.7 32.6

Total 3.3 4.4 5.2 6.1 7.1 8.2 9.7 11.9 15.5 28.5 37.3

Central

Urban 3 4.1 4.9 5.6 6.6 7.7 9.3 11.5 15.2 32.2 39.5

Rural 3.4 4.6 5.3 6.2 7 8 9.5 11.7 14.9 29.4 35.8

Total 3.2 4.3 5.1 5.9 6.8 7.9 9.4 11.6 15.2 30.6 38.2

Coastal

Urban 2.9 4.4 5.4 6.4 7.5 8.7 10.4 12.3 15.2 26.9 34.7

Rural 3.4 4.7 5.6 6.5 7.5 8.7 10.1 12.1 14.8 26.8 33.3

Total 3.1 4.6 5.5 6.4 7.5 8.7 10.2 12.2 15.2 26.6 34

Syria

Urban 3.1 4.3 5 5.9 6.9 8 9.6 11.7 15.3 30.2 38

Rural 3.6 4.8 5.7 6.6 7.5 8.6 9.9 11.9 14.9 26.7 34

Total 3.3 4.5 5.3 6.2 7.1 8.2 9.7 11.8 15.1 28.8 37.5

Source: Complied based on UNDP Poverty Assessment reports (2005) and (2009) and data from HIESs produced by the CBS in 1997, 2004 and 2007.

In 2007, Syria recorded a medium degree of inequality in the distribution of consumption expenditure lower than the level in 2004, as summarized by table 7.It is also worthy to note that in 2007, although not on a national level, for all regions, with the exception of the Central region, inequality in the urban sectors was also higher than for the rural sectors, Moreover, it should be noted that for the regions, confirming the observation of the inequality-development literature, the difference in the degree of inequality between the sectors was very pronounced especially for the North-Eastern region, where the difference between the two sectors wasnearly10%! Further comparison of the geographical regions, reveals that the North-Eastern and Coastal regions recorded a medium degree of inequality compared to the Southern and Central regions, which recorded a low degree of inequality as per the international benchmark of a Gini of 32.8%.The results show that the Central region had the lowest inequality (with a Gini coefficient of about 30%), and the North-Eastern region had the highest inequality (with a Gini coefficient of about 34%).

13

Table 7: Distribution of consumption expenditure by region in Syria (Decile shares in percentages), 2007

Region Sector Deciles Gini Coefficient

(%) 1 2 3 4 5 6 7 8 9 10

Southern

Urban 3.7 5 5.8 6.6 7.5 8.5 9.9 11.9 15 26.2 32.6

Rural 3.6 5 5.9 6.7 7.6 8.9 10.2 12.1 15 25.2 31.6

Total 3.6 5 5.9 6.7 7.5 8.6 10 11.9 15 26 32.4

North-Eastern

Urban 3.6 4.8 5.6 6.4 7.2 8.2 9.7 11.9 15.4 27.2 34.1

Rural 4.7 6 7 7.8 8.6 9.3 10.2 11.7 13.9 20.9 24.2

Total 4 5.2 6.1 6.9 7.7 8.6 9.9 11.8 14.8 24.9 34

Central

Urban 4.1 5.5 6.3 7.3 8.1 9.3 10.6 12.1 14.8 22 27.7

Rural 3.9 5.1 6 6.7 7.4 8.4 9.5 11.3 13.7 28 32.5

Total 4 5.3 6.1 7 7.7 8.8 10 11.6 14.2 25.3 30.5

Coastal

Urban 3.5 4.7 5.7 6.4 7.2 8.4 9.6 11.3 14 29.3 34.8

Rural 3.7 5.2 6.2 7.1 7.7 8.6 9.8 11.3 13.4 27.2 31.8

Total 3.6 5 6 6.8 7.5 8.5 9.7 11.3 13.7 28.1 33.3

Syria

Urban 3.7 5 5.8 6.6 7.4 8.5 9.9 11.9 15 26.3 33

Rural 4.1 5.4 6.3 7.1 7.9 8.9 10 11.6 14 24.7 33.5

Total 3.8 5.1 6 6.8 7.6 8.6 9.9 11.8 14.6 25.7 34.2

Source: ibid

From the table it is also clear that the urban sector in the Coastal region was the highest inequality sector, with a Gini coefficient of about 35%, and where the lowest 20% of the population enjoyed about 8% of total expenditure compared to a share of 43% for the richest quintile of the population. The lowest degree of inequality was recorded for the rural sector of the North-Eastern region, with a Gini coefficient of about 24%, and where the lowest quintile of the population enjoyed about 11% of total expenditure while the top quintile enjoyed about 34% of total expenditure. To summarize, for the three years under study, using the benchmark of a Gini coefficient of about 32.8% or lower to identify countries as having low degrees of inequality, it can safely be concluded that Syria is a medium inequality country. One way of appreciating this is to recall the past history of Syria as an Arab socialist country, prior to its recent adventures into liberalizing its economic system. Low degrees of inequality on a world scale are a feature of fairly advanced Western countries (except for the USA) and ex-socialist countries. Moreover, it needs to be recalled that inequality, being a structural characteristic of societies and economic systems, does not change dramatically during short periods of time unless subjected to huge shocks.

Human poverty

Finally, to supplement the story of poverty revealed by the money-metric indicators presented above, an assessment of the depth of inequalities in Syria must also take into account the spread of human deprivation. Several human poverty indicators at the national and regional level are reported in the HIESs, which allowed a calculation of our estimation for the HPI, which of course, due to data limitations, differs from the global definition of HPI. The indicators reported in table 8 are: the percentage of households without access to safe water, the illiteracy rate and the percentage of children living in extreme poverty. The table shows that much of the progress in reducing human poverty is related to significant gains in access to safe water and declines in illiteracy rates, but there are discernable regional variations. Consistent with the results from previous indicators of poverty, the North-Eastern region, urban and rural, demonstrates relatively poor performance on each of the components of the HPI presented in table 8. Most notably, the region demonstrates the worst performance in literacy rates, in rural and urban regions, for all three years. Also, it is worthy to highlight that the rural sector of this region is the worst performing sector each year for all components of the HPI, with the exception of 1997 (for the incidence of children living in poor families).

14

Table 8: Incidence of human poverty by region, 1997-2007

Year Unsafe water

Illiteracy rate

Children living in poor families

HPI Unsafe water

Illiteracy rate

Children living in poor families

HPI

Southern Urban Southern Rural

1997 18.1 10.1 9.4 13.8 15.9 14.7 13.7 14.8

2004 21 10.1 4.9 15.1 18.6 15.5 8.2 15.3

2007 24 5.1 9.2 17 22.6 9.7 12.4 16.8

North-Eastern Urban North-Eastern Rural

1997 10.2 21.1 14.6 16.6 55.7 36.1 12.9 42

2004 0.6 16.8 9.3 12.3 44.3 29.2 14.3 33.7

2007 0.4 8.8 10.3 8.4 40.1 18.9 15.4 29.2

Central Urban Central Rural

1997 5.7 10.6 12.2 10.2 40.7 16.8 20.1 29.9

2004 1.1 11.3 7.6 8.6 6.4 16.6 6.8 12

2007 0.8 5.3 6.1 5 7.3 10.2 5.3 8.1

Coastal Urban Coastal Rural

1997 1.6 10.6 9.1 8.6 20.3 14.6 6.2 15.7

2004 1.2 12.1 10 9.7 25.8 14.1 4.8 18.8

2007 0.6 7 4.4 5.2 18 9.8 5.7 13.2

All Urban All Rural

1997 10.8 14.5 12.2 12.7 35.6 22.7 13.9 27.1

2004 8.7 13.1 7.5 10.4 28.9 22.2 11 23

2007 10.1 6.7 8.9 8.8 27.9 14.2 12.1 20.6

Source: ibid

To shed further light on regional disparities, Figure 3 paints a picture of the relationship between income and human poverty over time by plotting headcount poverty rates (for extreme poverty) against the HPI index for 1997 and 2007 (data points representing 1997 are depicted in blue). The figure is split into eight quadrants (four for each year) separated by the nation-wide average HPI and extreme headcount poverty. Thus, areas located in the upper right quadrants are in the worst position with a high headcount poverty rate and HPI score. Conversely, the lower left quadrant represents the best position. Figure 3: Changes in extreme poverty and the HPI by region, 1997-2007

Source: ibid Notes: NE is an acronym for the North-Eastern region, Cn for the Central region, Cs for the Coastal region, and S for the Southern region. T indicates the total and R or U indicate rural and urban, respectively so that, for example, TNER and SU stand for the total North-Eastern rural region and the Southern urban region, respectively.

CsR

CnR

TCn NER*

NER

TNER

TR

CsU*

CnU* TCs*TCn*

CnR*CsR*

SU*

CnUSR

TU

TS*

TCs

SU

SR*

NEU T

TNER*

NEU*TU*

CsU

TR*

TST*

4

6

8

10

12

14

16

18

20

22

24

4 9 14 19 24 29 34 39 44

HPI

P0

National Average HPI 1997National Average HPI 2007

National Average P0 1997

National Average P0 2007

High P0 and HPIHigh P0 and Low HPI

High HPI and Low P0Low P0 and HPI

15

Three main conclusions emerge from Figure 3, above. First, the HPI for Syria declined, rather significantly, from 19.7% in 1997 to 14 in 2007 with the rural areas of the Central and North-Eastern regions recording the highest rate of decline. Second, the level of deprivation in living standards is generally commensurate to the level of headcount poverty (as indicated by the upward sloping scattering of the data points). In other words, regions with high (low) income poverty also tend to have high (low) human poverty. Third, the gap between the North-Eastern rural region and other regions is clearly depicted by its situation in the upper right quadrant. Fourth, most regions witnessed a significant improvement in both HPI and extreme poverty rates over the last decade (as represented by the length of the downward sloping green arrows).This is particularly visible for the Central region due to the significant decline in poverty and in access to safe water, as earlier demonstrated in table 8. However, rural areas in the North-Eastern and Southern regions witnessed an improvement (or stagnation) in one poverty measure and deterioration in the other (as indicated by the blue arrows). Only one region (the Southern urban region) showed deterioration in both income and human poverty (albeit a very slight one). Finally, annex table 1 and figure 3 examine the inequality in the distribution of human poverty by deciles (of expenditure) and by region. This is important for investigating the evolution of the gap between the richest and poorest from the angle of human deprivation over the past decade. Figures 3.A and 3.B present the HPI data ranked by decile and the ratio of the HPI for the top to bottom deciles and quintiles, respectively. In annex table 1, deciles which experienced an increase in human poverty are marked in red. Accordingly, the Southern region (both urban and rural) is the only region that saw deterioration for most deciles (which is expected given the earlier reviewed data). Figure 3: Ratio of HPI for top to bottom deciles (A) and quintiles (B) by region, 1997-2007

(A) (B)

Source: ibid

However, the gap between the highest and the lowest deciles appears not to have been altered significantly for the Southern urban and the North-Eastern rural regions compared to other regions, as shown in the above figure. It is also worthy to note that the lowest quintile of the Coastal region (both urban and rural) witnessed a sharp deterioration in its HPI, a pattern which begs further investigation.

Pro-poor growth Given the complexity of the distributional changes presented above, it suffices to state that what matters for poverty reduction is not the average overall growth per se, but rather the growth in the income or consumption of the poor specifically. In other words, if the growth rates for the poor exceeds the growth rate in the mean for the entire population growth is judged to be pro-poor. Using the methodology outlined in the Annex Technical Notes, we

0

5

10

15

20

So

uth

ern

Urb

an

So

uth

ern

Rura

l

Nort

h E

ast

Urb

an

Nort

h E

ast

Rura

l

Centr

al

Urb

an

Centr

al R

ura

l

Coasta

lU

rba

n

Coasta

l R

ura

l

1997 2007

0

5

10

15

20

So

uth

ern

Urb

an

So

uth

ern

Rura

l

Nourt

h E

ast

Urb

an

Nort

h E

ast

Rura

l

Centr

al U

rban

Centr

al R

ura

l

Coasta

lU

rba

n

Coasta

l R

ura

l

1997 2007

16

now look at the nature of the growth process that has taken place in Syria over three periods of time: 1996-2004, 2004-2007 and 1996-2007. We hasten to note that only the results for the two relatively longer periods of 1996-2004 and 1996-2007 should be considered as credible in view of the fact that changes in inequality over short periods of time must be reflecting great shocks as we noted earlier. Recall that in order to judge the pro-poor nature of the growth process we need to compare the average of the growth rates of per capita consumption expenditure of the deciles [γ*], which will be called the effective growth rate, with the overall average growth rate of per capita consumption expenditure, [G(μ)] for the whole society, which will be called the actual growth rate. When the effective growth rate is greater than the actual growth rate there is said to be a gain in growth and the growth process will be designated as pro-poor, and vice versa.

At the national level: We start with the first period 1996-2004 at the level of the country. According to official information in 1996, average real per capita consumption expenditure ( 2003-2004 prices) amounted to SL 3224 per person per month. By 2004 this real average consumption per capita level increased to SL 3611 per person per month. Thus, over this 7-years period the actual annual growth rate was 1.63%. The growth rate of real per capita consumption expenditure for the deciles is reported in table 9. Table 9: Pro-poor growth in Syria, 1996-2004

Deciles Per Capita

Expenditure (SL per month) 2004

Per Capita Expenditure (SL per

month) 1996

Annual Effective Growth Rate (%) [Actual Growth Rate (%)]

1 1192 1102 1.13

2 1618 1521 0.89

3 1910 1814 0.74

4 2232 2101 0.88

5 2567 2407 0.92

6 2972 2758 1.07

7 3506 3216 1.24

8 4254 3818 1.56

9 5460 4820 1.8

10 10407 8645 2.69

Average 3611 3222 1.29 [1.63]

Source: Abdel Gadir and Abu-Ismail (2009)

According to the results in the table, the effective annual per capita growth rate (i.e. the average of the growth rates of deciles) over the period 1996-2004 was about 1.29% compared with the actual growth rate of 1.64%, representing a loss in per capita growth. Hence the growth process of 1996-2004 was anti-poor, meaning that the growth process during this period was accompanied by deterioration in the distribution of consumption expenditure. Indeed our earlier results on the state of inequality in Syria confirm this as evidenced by the increase in the Gini coefficient of the distribution of consumption expenditure between the two years. Thus the gains of the poor from the growth process that took place in the country over the period 1996-2004 were comparatively less than those of the rich. We now move to the second period 2004-2007 at the level of the country. The growth rate of real per capita consumption expenditure for the various deciles is reported in table 10. As noted above average real per capita consumption expenditure amounted to SL 3611 per month in 2004, declining to SL 3194 per person per month by 2007. Thus, over this 3-years

17

period the actual annual growth rate of real per capita consumption expenditure was negative 4%. Table 10: Pro-poor growth in Syria, 2004-2007

Deciles Per Capita

Expenditure (SL per month) 2007

Per Capita Expenditure (SL per

month) 2004

Annual Effective Growth Rate (%) [Actual Growth Rate (%)]

1 1227 1192 1

2 1635 1618 0.35

3 1916 1910 0.1

4 2175 2232 -0.86

5 2431 2567 -1.8

6 2753 2972 -2.52

7 3169 3506 -3.31

8 3753 4254 -4.09

9 4666 5460 -5.1

10 8209 10407 -7.6

Average 3194 3611 -2.38 [-4.01]

Source: ibid

Comparing the effective growth rate of real per capita consumption expenditure of negative 2.38% over the period 2004-2007, with the actual growth rate of negative 4% we see that there was a gain in per capita growth. Hence the growth process of 2004-2007 was pro-poor, meaning that the growth process during this period was accompanied by an improvement in the distribution of consumption expenditure. Indeed our earlier results on the state of inequality in Syria confirm this as evidenced by the decrease in the Gini coefficient of the distribution of consumption expenditure. Thus the gains of the poor from the growth process that took place in the country over the period 2004-2007 were comparatively more than those of the rich. Last, we consider the whole period 1996-2007 at the level of the country, as reported in table 11. In 1996 , average real per capita consumption expenditure amounted to SL 3222 per month and SL 3194 per month in 2007. Thus, over this 11-years period the actual annual growth rate of real per capita consumption expenditure was negative and amounted to about 0.08%. This, we hasten to note, is tantamount to saying that over the period under consideration real per capita consumption expenditure in the country stagnated. Table 11: Pro-poor growth in Syria, 1996-2007

Deciles Per Capita Expenditure

(SL per month) 2007 Per Capita Expenditure

(SL per month) 1996 Annual Effective Growth Rate (%)

[Actual Growth Rate (%)]

1 1227 1102 0.98

2 1635 1521 0.66

3 1916 1814 0.5

4 2175 2101 0.32

5 2431 2407 0.1

6 2753 2758 -0.02

7 3169 3216 -0.13

8 3753 3818 -0.16

9 4666 4820 -0.3

10 8209 8645 -0.47

Average 3194 3222 1.48 [-0.08]

Source: ibid

Comparing this actual negative growth rate of about 0.1% with the effective annual per capita growth rate (i.e. the average of the growth rates of deciles)of about 1.5 %, we can see that there was a gain in per capita growth. Hence the growth process over the period 1996-

18

2007 was pro-poor, meaning that the growth process during this period was accompanied by an improvement in the distribution of consumption expenditure. Thus the gains of the poor from the growth process that took place in the country over the period 1996-2007 were comparatively more than those of the rich.

At the level of the regions Using the information in tables 12, 13, 14 and 15 we can have a look at the pro-poorness of the Syrian growth processes at the level of the four regions. In this respect it will be recalled that real per capita consumption expenditure per person per month in 1997 amounted to SL 3533 for the Southern region; SL 2917 for the North-Eastern region; SL 2858 for the Central region; and SL 3862 for the Coastal region. By 2004 all regions witnessed an increase in their standard of living, recording an annual rate of actual growth rate (in squared brackets) of 2.29% for the Southern region, 0.99%for the North-Eastern region, 3.05%for the Central region, and 6.1%for the Coastal region. Table 12: Pro-poor growth in Syrian regions, 1996-2004

Reg

ion

Year

Real Per Capita Consumption Expenditure by Decile (L. S. per month) Real Per

Capita

Cons. Exp.

(LS. /

month)

1 2 3 4 5 6 7 8 9 10

So

uth

ern

1996 1215 1678 2000 2304 2636 3021 3515 4190 5275 9483 3533

2004 1399 1925 2232 2621 2960 3403 4020 4798 6069 12035 4140

Growth (%)

2.03 1.98 1.58 1.86 1.67 1.72 1.94 1.95 2.02 3.46

2.02

[2.29]

Nort

h-

Ea

ste

rn 1996 998 1374 1634 1887 2167 2485 2897 3451 4364 7888 2917

2004 1032 1360 1629 1907 2497 2576 3042 3723 4842 8912 3126

Growth (%)

0.48 -0.13 -0.04 0.15 2.05 0.52 0.70 1.09 1.50 1.76

0.81

[0.99]

Cen

tra

l

1996 960 1343 1598 1889 2146 2464 2875 3410 4301 7602 2858

2004 1132 1531 1799 2074 2402 2793 3298 4098 5375 10775 3527

Growth (%)

2.38 1.89 1.71 1.34 1.62 1.81 1.98 2.66 3.24 5.11

2.37

[3.05]

Coa

sta

l

1996 1370 1854 2221 2584 2912 3318 3835 4526 5766 10300 3862

2004 1266 1834 2205 2592 3035 3515 4116 4918 6111 10718 4031

Growth (%)

-1.12 -0.16 -0.10 0.04 0.59 0.83 1.02 1.19 0.83 0.57

0.37

[0.61]

Source: ibid

Comparing these actual growth rates with the effective growth rate (i.e. the average of the growth rates of deciles) over the period 1996-2004,it is clear that for all regions the effective growth rate was less than the actual growth rate: about 2% compared to about 2.3% for the Southern region; about 0.8% compared to about 1% for the North-Eastern region; about 2.4% compared to about 3.1% for the Central region; and, about 0.4% compared to about 0.6% for the Coastal region. These results mean that the growth process during this period was anti-poor in the sense that it was accompanied by deterioration in the distribution of consumption expenditure in each of the four regions. Thus, the earlier result pertaining to the level of the country is confirmed at the level of the regions, namely that the gains of the poor from the growth process that took place in the country over the period 1996-2004 were comparatively less than those of the rich at the level of each region. Similar to the reading of the results in table 12, the last column of table 13 gives the effective annual per capita growth rate (i.e. the average of the growth rates of deciles) over the period

19

2004-2007 for each of the four regions. It also gives the actual growth rate in squared brackets. Comparing these two growth rates it is clear that for all regions the effective growth rate was greater than the actual growth rate albeit that the effective growth rates were negative for all regions except for the Coastal region: about (-3.8)% compared to about (-4.8)% for the Southern region; about (-3.4)% compared to about (-5.6)% for the North-Eastern region; about (-0.4)% compared to about (-3.2)% for the Central region; and, about 0.3% compared to about (-0.3) % for the Coastal region. These results mean that the growth process during this period was pro-poor in the sense that it was accompanied by an improvement in the distribution of consumption expenditure in each of the four regions. Table 13: Pro-poor growth in Syrian regions, 2004-2007

Reg

io

n

Year Real Per Capita Consumption Expenditure by Decile (Ls. per month)

Real Per Capita Cons.

Exp. (LS. / month) 1 2 3 4 5 6 7 8 9 10

So

uth

ern

2004 1399 1925 2232 2621 2960 3403 4020 4798 6069 12035 4140

2007 1296 1781 2089 2374 2670 3060 3559 4263 5344 9264 3570

Growth

(%) -2.52 -2.56 -2.18 -3.25 -3.38 -3.48 -3.98 -3.86 -4.15 - 8.35

-3.77

[-4.82]

Nort

h-

Ea

ste

rn 2004 1032 1360 1629 1907 2497 2576 3042 3723 4842 8912 3126

2007 1234 1379 1608 1813 2029 2271 2611 3106 3898 6546 2632

Growth

(%) 6.14 0.46 -0.43 -1.67 -6.68 -4.11 -4.97 -5.86 -6.97 -9.77

-3.39

[-5.57]

Cen

tra

l

2004 1132 1531 1799 2074 2402 2793 3298 4098 5375 10775 3527

2007 1287 1691 1950 2232 2478 2818 3208 3724 4537 8099 3202

Growth

(%) 4.37 3.37 2.72 2.48 1.04 0.30 -0.92 -3.14 -5.49 -9.08

-0.44

[-3.17]

Coa

sta

l

2004 1266 1834 2205 2592 3035 3515 4116 4918 6111 10718 4031

2007 1428 1989 2393 2725 2981 3409 3873 4509 5461 11239 4001

Growth

(%) 4.10 2.74 2.76 1.68 -0.60 -1.02 -2.01 -2.85 -3.68 1.60

0.27

[-0.25]

Source: ibid

Similar to the reading of the results in tables 12 and 13, the last column of the table 14 gives the effective annual per capita growth rate (i.e. the average of the growth rates of deciles) over the period 1996-2007 for each of the four regions. It also gives the actual growth rate in squared brackets. Comparing these two growth rates it is clear that for all regions the effective growth rate was greater than the actual growth rate except for the Coastal region where the two growth rates were almost equal: about 0.2% compared to about 0.1% for the Southern region; about (-0.5)% compared to about (-0.9)% for the North-Eastern region; about 1.2% compared to about 0.8% for the Central region; and, about 0.3% compared to about 0.32% for the Coastal region. These results mean that the growth process over the period 1996-2007 was pro-poor in the sense that it was accompanied by an improvement in the distribution of consumption expenditure in each of the four regions. Thus, the earlier result pertaining to the level of the country is confirmed at the level of the regions namely that the gains of the poor from the growth process that took place in the country over this period were comparatively more than those of the rich at the level of each region, albeit the gains were relatively small over this longer time period.

20

Table 14: Pro-poor growth in Syrian regions, 1996-2007 R

eg

ion

Year

Real per capita consumption expenditure by decile (LS. per month) Real Per Capita Cons. Exp (LS. /

month) 1 2 3 4 5 6 7 8 9 10

So

uth

ern

1997 1215 1678 2000 2304 2636 3021 3515 4190 5275 9483 3533

2007 1296 1781 2089 2374 2670 3060 3559 4263 5344 9264 3570

Growth

(%) 0.59 0.54 0.40 0.27 0.12 0.12 0.11 0.16 0.12 -0.21

0.22

[0.10]

No

rth

-

Ea

ste

rn 1997 998 1374 1634 1887 2167 2485 2897 3451 4364 7888 2917

2007 1234 1379 1608 1813 2029 2271 2611 3106 3898 6546 2632

Growth

(%) 1.95 0.03 -0.15 -0.36 -0.60 -0.82 -0.94 -0.95 -1.02 -1.68

-0.45

[-0.93]

Ce

ntr

al 1997 998 1374 1634 1887 2167 2485 2897 3451 4364 7888 2917

2007 1287 1691 1950 2232 2478 2818 3208 3724 4537 8099 3202

Growth

(%) 2.34 1.91 1.62 1.54 1.23 1.15 0.93 0.69 0.35 0.24

1.20

[0.85]

Co

asta

l

1997 1370 1854 2221 2584 2912 3318 3835 4526 5766 1030

0 3862

2007 1428 1989 2393 2725 2981 3409 3873 4509 5461 1123

9 4001

Growth

(%) 0.38 0.64 0.68 0.48 0.21 0.25 0.09 -0.03 -0.49 0.80

0.30

[0.32]

Source: ibid

At the rural and urban levels

Using the information in tables 12 and 14 we can also have a look at the pro-poorness of the Syrian growth processes at the level of the two sectors of the economy. We start with looking at the urban sector reported in table 15. In this respect it will be recalled that real per capita consumption expenditure per person per month in 1996 in the urban sector amounted to SL 3558; by 2004 the sector witnessed an increase in its standard of living to SL 4192, recording an actual annual rate of growth of about 2.4%. By 2007, however, the sectors saw its welfare level decline to SL 3528, recording an annual rate of decline of about 5.6% over the period 2004-2007. Over the longer period 1996-2007 the urban sector also recorded an annual rate of decline of about 0.1%, implying a period of stagnation. Table 15: Pro-poor growth in the Syrian urban areas, 1996-2007

Decile Per Capita Expenditure (SL per month)

Annual Effective Growth Rate (%) [Effective Growth Rate (%)]

1996 2004 2007 1996-2004 2004-2007 1996-2007

1 1231 1308 1302 0.87 -0.15 0.51

2 1697 1786 1750 0.73 -0.68 0.28

3 2028 2104 2043 0.53 -0.98 0.07

4 2338 2486 2329 0.88 -2.15 -0.04

5 2669 2876 2621 1.07 -3.05 -0.17

6 3056 3358 2988 1.36 -3.82 -0.20

7 3558 4020 3493 1.76 -4.58 -0.17

8 4198 4917 4181 2.28 -5.26 -0.37

9 5312 6401 5292 2.70 -6.15 -0.03

10 9489 12672 9286 4.22 -9.84 -0.20

Average 3558 4192 3528 1.64

[2.37]

-3.67

[-5.59]

-0.03

[-0.08]

Source: ibid

The results in the last row of the above table enable us to compare the above noted actual annual rates of growth (in squared brackets) with the effective rates of growth (the average rate of growth of the deciles). For the period 1996-2004 the table shows that the urban

21

effective rate of growth of about 1.6% was less than the actual rate of growth (about 2.4%). This means that the growth process during this period was anti-poor in the urban sector: meaning that the poor in urban areas gained less than the urban rich during the period. For the period 2004-2007, which saw a decline in the standard of living of the urban sector, the table shows that the growth process was pro-poor in view of the fact that the effective rate of decline of per capita consumption expenditure (about 3.7%) was lower than the actual rate of decline (of about 5.6%). This means that the poor suffered less than the rich during the process of decline over this period. A similar result obtains for the longer period 1996-2007, which also recorded a decline in the welfare of the urban sector. The table shows that the growth process was pro-poor in view of the fact that the effective rate of decline of per capita consumption expenditure (about 0.03%) was lower than the actual rate of decline (of about 0.08%). While this means that the poor suffered less than the rich during the process of decline over this period, the extremely low value of two rates suggests that indeed the urban sector stagnated over the period without any significant movements in the welfare of urban residents. Having noted the above regarding the urban sector we now move to consider the pro-poor nature of the Syrian growth processes in the rural sector. In this respect it will be recalled that real per capita consumption expenditure per person per month in 1996 in the rural sector amounted to SL 2804; by 2004 the sector witnessed an increase in its standard of living to SL 2922, recording an actual annual rate of growth of about 0.6%. By 2007, however, the sectors saw its welfare level decline to SL 2728, recording an annual rate of decline of about 2.3% over the period 2004-2007. Over the longer period 1996-2007 the rural sector also recorded an annual rate of decline of about 0.3%, implying a period of stagnation. Table 16: Pro-poor growth in the Syrian rural areas, 1996-2007

Decile Per Capita Expenditure (SL per month) Annual Effective Growth Rate (%) [Effective Growth Rate (%)]

1996 2004 2007 1996-2004 2004-2007 1996-2007

1 948 1040 1110 1.33 2.20 1.45

2 1312 1403 1473 0.96 1.64 1.06

3 1559 1660 1730 0.90 1.39 0.95

4 1811 1914 1948 0.79 0.59 0.67

5 2089 2186 2155 0.65 -0.48 0.28

6 2395 2504 2420 0.64 -1.13 0.09

7 2993 2893 2717 -0.48 -2.07 -0.88

8 3226 3466 3167 1.03 -2.96 -0.17

9 4200 4357 3817 0.52 -4.32 -0.87

10 7565 7808 6744 0.45 -4.77 -1.04

Average 2804 2922 2728 0.68 [0.59] -0.99 [-2.26] -0.16 [-0.25]

Source: ibid

The results in the last row of table 16 enables us to compare the above noted actual annual rates of growth (in squared brackets) with the effective rates of growth (the average rate of growth of the deciles). For the period 1996-2004 the table shows that the rural effective rate of growth of about 0.68% was more than the actual rate of growth (about 0.59%). This means that the growth process during this period was pro-poor in the rural sector: meaning that the poor in urban areas gained more than the rural rich during the period. For the period 2004-2007, which saw a decline in the standard of living of the urban sector, the table shows that the growth process was pro-poor in view of the fact that the effective rate of decline of per capita consumption expenditure (about 1.0%) was lower than the actual rate of decline (of about 2.3%). This means that the poor suffered less than the rich during process of decline over this period. A similar result obtains for the longer period 1996-2007, which also recorded a decline in the welfare of the rural sector. The table shows that the growth process was pro-poor in view of the fact that the effective rate of decline of per capita consumption

22

expenditure (about 0.12%) was lower than the actual rate of decline (of about 0.25%). While this means that the rural poor suffered less than the rural rich during the process of decline over this period, the relatively low value of the two rates suggests that indeed the rural sector stagnated over the period without any significant movements in the welfare of rural residents.

Historical Views and Prospective Analyses Any explanation of the underlying root cause of the shifts in inequality over the period since the mid-1990s must take into account the major shift in macroeconomic policies which took place in Syria during that period. Historically, Syria has been long characterized as a highly centralized state-led economy, with an extensive import-substitution industrialization (ISI) program, with pervasive restrictions covering foreign trade, foreign investment and with a limited level of export orientation (unlike East Asian Tigers). By virtue of the special relations between Syria and the former soviet bloc, a segment of the Syrian industry managed to break out and successfully penetrate East European and former USSR markets; however, the dismantling of the former USSR broke this peculiar economic relation. The political and economic ramifications of the collapse of the Soviet Union, coupled by the global onslaught of economic neo-liberalism, provided a strong rationale for opening up the Syrian economy, but stronger pressures for economic liberalization grew out of a domestic balance of payments constraint during the mid-1980s. In Syria, as elsewhere, ISI tended to foster mass consumption over capital accumulation, establishing the building of a national market as its main raison d’être. This created dependency on imported capital goods and high tech goods, without fostering the export capacity needed to earn foreign exchange, thereby precipitating balance of payments difficulties. When aid from Arab countries and oil revenues declined, along with the fall of global oil prices in the 1980s, Syria’s trade imbalance deteriorated sharply. The resulting fiscal and foreign exchange crises forced austerity cutbacks in public spending, and the state began to turn to the private sector to finance investment and create new jobs. In return, private business was awarded concessions, leading to the further opening of the economy in the early 1990s with legislation that allowed and encouraged private investment. Subsequently, the fiscal situation eased up considerably as oil exports grew to replace these traditional sources of foreign exchange. Against this backdrop, the first objective of this section is to explain the rather substantial shifts in distribution witnessed over the period from 1997 to 2007, first by giving an account for the first period (1997-2004) during which the poor benefited proportionately less from growth, second by providing some possible explanations for the opposite trend witnessed in the second period (2004-2007) wherein growth was pro-poor, and last, by explaining the long term inequality dynamics.

The period from 1997 to 2004 As shown earlier, at the national level, from 1997 to 2004, growth was not pro-poor. Non-poor individuals (above the third decile in the expenditure distribution) benefited disproportionally more than the poor from economic growth), thus inequality rose. However, large increases in per capita expenditure outweighed the effect of this worsening distribution as the fall in income poverty over the period was driven by a growth of per capita real expenditure of 2.0% per annum (Table 17). Yet, data from national accounts reviewed in UNDP (2005) suggest that per capita GDP growth between 1997 and 2004 was less than 1% per annum. The increase in average salaries, after adjusting for inflation, was also a

23

rather moderate 0.8% per annum. Moreover, a breakdown of per capita GDP by expenditure reveals private (real) consumption grew at only 0.3% per annum.10 Turning to the sources of growth, annex tables 3 and 4 reveal that oil exports (as indicated by mining and manufacturing) and private consumption contributed the most to growth during the period. On the supply side, oil-led growth fuelled a major expansion in services. However, over the period from 2000 to 2004, the contribution of the mining and manufacturing sector was negative due to declining oil production (-6.37%). As argued in Abu-Ismail et al (2005), the expansion in trade and services was essentially due to economic liberalization policies, in particular trade and capital account liberalization, reduction ofagricultural and public sector subsidies, deregulation of investment and financial sector reform11. Table 17: Average and annual percentage change in per capita expenditure by region, 1997-2004 in 2004 prices

Region

Average expenditure (SL) per capita in 2003-04

Gini coefficient

1997 2004 Annual % growth 1997 2004 Actual change

Urban

Southern 3796 4646 2.926 0.334 0.368 0.034

North-Eastern 3036 3775 3.16 0.328 0.383 0.055

Central 3022 4016 4.147 0.324 0.394 0.07

Coastal 3857 4274 1.48 0.359 0.346 -0.012

Rural

Southern 3085 3174 0.406 0.332 0.309 -0.023

North-Eastern 2613 2472 -0.788 0.325 0.326 0

Central 2375 3074 3.755 0.327 0.357 0.029

Coastal 3876 3844 -0.118 0.333 0.333 0

Total 3085 3541 1.988 0.337 0.374 0.037

Source: UNDP (2005)

In this context of economic liberalization and urban-centred service-led growth, the major beneficiaries were urban areas, which were best situated to take advantage of the shift in economic policy regime. This is clearly confirmed in table 17 above which shows that urban areas experienced significantly larger growth in real per capita expenditure and rise in inequality compared to rural areas.

The period from 2004 to 2007 Growth-distribution dynamics over the second period indicate an opposite trend. Distribution improved significantly particularly for the poorest of the poor. At the same time, however, growth in expenditure faltered. Thus, monthly real per capita expenditure (2004 prices), at the national level, declined from SL3541 in 2004 to SL3141 in 2007. This represents an annual decrease in the average real per capita expenditure (expressed as a welfare ratio to the value of poverty line) of 3.92% (Table 18). As in the earlier period, this does not sit well with the story coming from national accounts data which shows GDP growth has picked up since 2004 despite the rapid depletion of oil reserves mainly due to demand stimulus, which in turn is partially explained by the large presence of Iraqi refugees. Iraqis presence in Syria is therefore considered as a favourable factor for economic growth since the majority live on savings and/or transfers from a third country. This de facto amounts to a kind of export of services (tourism) to the Syrian economy. Hence, it is conventionally assumed 12 that the Iraqi refugees had significant positive impact on growth via private consumption which mitigated the negative shock from declining oil revenues

24

Table 18: Average and annual percentage change in per capita expenditure by region, 2004-2007 in 2004 prices

Region

Average expenditure (SL) per capita in 2003-04

Gini coefficient

2004 2007 Annual % growth 2004 2007 Actual change

Urban

Southern 4646 3777 -6.7 0.37 0.32 -0.05

North-Eastern 3775 3150 -5.6 0.38 0.34 -0.04

Central 4016 3379 -6 0.39 0.27 -0.12

Coastal 4274 4339 0.5 0.36 0.35 -0.01

Rural

Southern 3174 3307 1.4 0.31 0.31 0

North-Eastern 2472 2051 -6 0.33 0.24 -0.9

Central 3074 3037 -0.4 0.36 0.32 -0.04

Coastal 3844 3751 -0.8 0.33 0.32 -0.01

Total 3541 3141 -3.9 0.36 0.32 -0.04

Source: UNDP (2009)

. Notwithstanding the discrepancy between national accounts and survey data, it is clear that any explanation for the distributional shifts over the period must take into account the fact that not all regions experienced the same rate of decline in expenditure and inequality. In this regard, three questions warrant further attention. First, what caused the significant decline in inequality in the North-Eastern rural region? Second, what caused the significant decline in growth in the North-Eastern region (both urban and rural) and Southern urban regions? Third, what has been the impact of the Iraqi refugees on poverty and inequality? Regarding the first question, there are two possible explanations. One is that the government interventions have been effective in reaching the poorest of the poor in that region and thus decreased the gap between the rich and poor. Although this explanation is backed by official policy, which has since 2005 identified the North–Eastern rural region as the primary target of state intervention in the 9th five year plan, it is less probable, as the mid-term review of the plan (2006 to 2010) has raised several questions regarding its effectiveness in reaching targeted beneficiaries in that region. In addition, as noted by the IMF (2007), over the period from 2004 to 2007, fiscal policy had to contend with a cumulative loss of oil revenues in excess of 10% of GDP, owing both to a sharp contraction in oil production and rapid growth in the consumption of subsidized petroleum products (reflecting, in part, increased smuggling and the impact of the Iraqi refugees). A cumulative improvement of 7% of GDP over 2004–06 narrowed the non-oil budget deficit to about 10% of GDP in 2006. However, the bulk of the adjustment was achieved through rationalization of investment spending and taxation reform. This implies that the fiscal adjustment has been mainly anti-poor. A more plausible explanation for the reduction in inequality should take into account internal migration dynamics which took place from the North East rural region as a result of the successive droughts. If, as one would expect, the migration pattern was characterized by an overwhelming majority of the poorest strata, then this would in effect play a major role in reducing inequality in that region, while simultaneously reducing per capita expenditure growth at the destination region. Given that the majority of these poor migrants are more likely to have targeted the Southern urban region (as discussions with Syrian officials suggest), this would go a long way in answering the second question.

25

Figure 4: Distribution of Iraqis by governorate as of July 2007

Source: Abu-Ismail et al (2008).

The third question is perhaps the most difficult to address. At the outset, as shown below in Figure 4, there is little dispute among Syrian officials that the influx of Iraqis has been concentrated in the Southern urban region and that it has increased demand for state subsidized goods and services13, which may explain the fact that this region is the only one which witnessed a deterioration in human poverty index over the period from 1997 to 2007. One possible hypothesis is that higher demand for consumption goods and the housing sector by Iraqis was the primary cause of the unprecedented inflation since 2003, which was the root cause behind the decline in real expenditure. Such a conviction is not unfounded, especially if we take into account that the Syrian economy was under a tight price control regime due to high levels of direct and indirect government subsidies, and it experienced relatively few episodes of double-digit inflation during the past few decades. It is important, therefore, to underline the actual factors that have been driving the rate of inflation up. However, this theory is challenged by the composition and distribution of inflation. Data suggests inflationary pressures started to build up in mid-2005, and the average inflation increased to 10% in 2006. According to the CBS, half of the increase in the latter is attributed to higher food pricing due to weather-related shortages in domestic production and higher demand in food prices in the international markets. In addition, although it is true that from 2001 to 2003 a big jump took place on the rent index due to a legislative reform which liberalized rents and eased rent control, housing inflation in the Southern urban region does not appear to be significantly higher than in other regions14. In short, there have been many internal and external factors that contributed to the high rates of inflation in Syria since 2003.One of the reasons can be the influx of Iraqis, but it is neither the only one nor necessarily the most important one at the aggregate or sub-national levels. A more plausible theory is that the large influx of Iraqi refugees and internal migrants from the North-Eastern rural region may have exerted significant pressure on wages in the informal sector of the Southern urban. This may have been an important determining factor for growth and inequality, in decreasing the growth rate in the urban sector of the Southern region and in decreasing the Gini coefficient of the North-Eastern region. In this regard, one of the important findings in the UNDP (2009) poverty assessment report is that the percentage of unpaid workers increased by almost 50% among poor individuals in urban areas from 2004 to 2007. On the other hand, the percentage of unpaid workers declined for both poor and non poor groups in rural areas. Another important feature that could explain the significant increase of poverty in the urban Southern region is the disproportionate rise in irregular work (seasonal, temporary or occasional work) amongst the poor (and non poor) in that region. As shown in Figure 5, the irregular workers among poor persons in urban Southern region increased from 13.6% in 2004 to 20.2% in 2007. The corresponding figures

62%

5%

4%

6%

23%

Rural Damascus Aleppo Homs Other Damascus

26

for the Southern rural areas are 27.3% and 36.6%, respectively. Furthermore, irregular workers among the poor in the urban sector of the Southern region showed a higher incidence, depth and severity of poverty for 2007 compared to 2004. Thus, in 2007, the risk of poverty for a person in urban Southern region engaged in temporary work is almost 54% compared to 32% among persons engaged in permanent work. Figure 5: Per cent of individuals engaged in irregular jobs in the Southern region by poverty status, 2004-2007

Source: UNDP (2009)

Future scenarios The degree to which poverty responds to growth is an important part of explaining the change in poverty incidence as well as a valuable input for the formulation of regionally-based public policy interventions. In general, the elasticity would be negative, as growth and poverty tend to move in opposite directions – positive (negative) growth typically means a decline (increase) in poverty. The elasticity of poverty measures to changes in the mean expenditure and inequality may thus explain the impact of growth and distribution on poverty trends (as previously illustrated by the simple arithmetic of poverty reduction decomposed into a ‘growth effect’ and a ‘distribution effect’). The growth elasticity therefore measures the percentage change in poverty for a one per cent change in mean income, holding the distribution constant. Growth elasticity of poverty in Syria as a whole is quite high (-3). This means that if the real per capita consumption of all Syrians increases by 3%, this will result in half a million people moving out of extreme poverty (from 12.2% to 11.1%, or by 10%). Likewise, a growth of 3% in per capita consumption per year, over the period 2007-2011, is likely to reduce extreme poverty in Syria to around 8.9% of the population. However, as demonstrated by previous results, regions differ in terms of the responsiveness of poverty reduction to any given growth rate. The elasticity of poverty measures to changes in the mean expenditure and inequality may explain the impact of growth and distribution on poverty trends. Calculations of the elasticity of poverty to growth (i.e., the percentage change in the poverty rate given a percentage change in mean regional consumption levels) show that poverty in urban Southern region is more responsive to growth compared to other regions. Poverty in this region is also sensitive to inequality changes. Consequently, a relatively higher impact of growth and inequality on poverty levels in this region were observed (Table 19). On the other hand, this elasticity was least for the rural North-Eastern region, where poverty rates are the highest (Annex tables 5 and 6 also support this by demonstrating the responsiveness of extreme poverty in different regions to variations in growth rates and income distribution. The rural sector in North-Eastern region registers the lowest reductions in extreme poverty to such variations, compared to the other three regions).

0

10

20

30

40

Urban poor Urban non-poor Rural poor Rural non-poor

2004 2007

27

Table 19: Elasticity of headcount poverty to mean consumption in 2004 and 2007

Region Growth Elasticity Distribution Elasticity Growth Elasticity2 Distribution Elasticity 3

2004 2007

Urban

Southern -5.923 10.625 -5.731 4.295

North-Eastern -3.498 5.583 -3.301 3.752

Central -4.033 6.896 -5.145 5.82

Coastal -3.089 5.209 -4.648 7.544

Rural

Southern -3.543 3.953 -3.093 3.25

North-Eastern -2.761 2.579 -2.709 1.947

Central -3.996 5.424 -4.873 5.586

Coastal -3.547 5.061 -4.071 4.96

All Syria -2.956 4.223 -2.911 3.046

Source: ibid.

The high growth-poverty elasticity implies the poverty rate in Syria is expected to be quite sensitive to economic fluctuations, including rapid changes in growth. To illustrate, we conducted a simple simulation analysis to examine the impact of the unfolding global economic crisis on growth of real private consumption and hence on poverty rates in Syria over the period from 2008to 2015. To this end, we constructed three scenarios for private consumption growth. The first scenario is rather gloomy. It assumes that private consumption will grow at an average of 3% annually which implies annual per capita consumption expenditure growth is not likely to exceed 1%. Scenario 2 is based on the assumption that there will be a slightly more rapid rebound with real per capita consumption averaging 2% from 2009 to 2015. Scenario three is the most optimistic one which incorporates the growth projections of the IMF. The methodology applied here15 also factors in changes in income distribution. Thus, there are three possible scenarios for income distribution. If growth is distribution neutral, there will be no change in the Gini over the period. Distribution scenario 2 assumes that the Gini will decline by 0.5% annually. Conversely, Distribution Scenario 3 assumes that the Gini will increase by 0.5% annually. Table 20: Private consumption growth projections, 2008-2015

2008 2009 2010 2011 2012 2013 2014 2015

Population 2.4% 2.4% 2.3% 2.3% 2.2% 2.2% 2.1% 2.1%

Growth Scenario 1 (low)

Private Consumption 0.5% 1.0% 2.0% 3.0% 3.0% 3.5% 4.0% 4.0%

Per Capita Consumption -1.9% -1.4% -0.3% 0.8% 0.8% 1.4% 1.9% 2.0%

Growth Scenario 2 (medium)

Private Consumption 0.5% 2.0% 3.0% 4.0% 4.0% 4.5% 5.0% 5.0%

Per Capita Consumption -1.9% -0.4% 0.7% 1.8% 1.8% 2.4% 2.9% 3.0%

Growth Scenario 3 (high)

Private Consumption 5.2% 3.9% 3.3% 5.3% 5.2% 5.7% 5.0% 5.0%

Per Capita Consumption 2.8% 1.6% 1.0% 3.1% 3.0% 3.6% 2.9% 3.0%

Source: UNDP (2009)

The results of the poverty projections are shown in annex tables 5 to 7. Assuming the first growth scenario and the second income distribution scenario hold (i.e. assuming the lower bound GDP growth and higher bound reduction in Gini), extreme poverty will decline by 66% from its level in 2008 (Annex table 5). The corresponding rate of decline for growth scenarios 2 and 3 are 78% and 87% respectively (Annex tables 6 and 7). Hence, with an improvement in income distribution, halving extreme poverty is an achievable goal regardless of our

28

assumptions regarding the order of magnitude of the impact of the financial crisis on consumption growth. In the case of lower and distribution-neutral growth (i.e. growth scenario 1 and distribution scenario 1), the reduction in extreme poverty is expected to be marginal (10%) (Annex table 5). Poverty reduction increases to 27% and 46% under the distribution-neutral and medium and high growth scenarios, respectively (Annex tables 6 and 7). Assuming deterioration in income distribution and low growth yields a significant increase of 46% in extreme poverty (Annex table 5). When the medium growth assumption is applied this yields a 23% rise in poverty (Annex table 6). Even in the high growth scenario, poverty remains virtually unchanged (declining by 8% merely). Last but by no means least, with the assumption of deterioration in income distribution, there were large variations in the projected regional outcomes due to differences in growth and distribution elasticities across regions, as demonstrated earlier in table 19. To sum, notwithstanding the importance of the growth impact of the crisis, projected poverty outcomes for Syria over the medium term are very sensitive to changes in income distribution. It follows that redistributive policies in Syria are of crucial importance to override the negative impact of the financial crisis.

Policy Considerations An interesting observation emerges regarding Syria’s long-term poverty and inequality profile: poverty rates in Syria are almost half the rate prevailing in other countries with similar levels of GDP per capita such as Egypt and almost at par with the rates prevalent in more affluent countries such as Lebanon. Explaining this impressive record is easy when considering the role of the state versus market. As in many other Arab countries, the fundamental features of the social contract in Syria is that it was interventionist and redistributive in nature and may be summarized as follows:

a preference for redistribution and equity in economic and social policy;

a preference for states over markets in managing national economies; the adoption of import-substitution industrialization and the protection of local markets from global competition;

reliance on state planning in determining economic priorities;

an encompassing vision of the role of the state in the provision of welfare and social services

The argument here is that Syria’s state-led development policies in general, and in particular, its agricultural policy and rural development policies were the main vehicles that delivered this relatively impressive social development record. In this regard, the agrarian structural transformation, instituted by the bold Public Act of 1958 and strengthened further by the Land Reform Laws of 1973 and 1980, was targeted at many objectives of the social contract outlined above: food security or malnutrition reduction as a necessary improvement of human capital among the poor peasantry; correcting inequality of the distribution of income and power in favour of the rural sector; raising land productivity and intensive labour absorption via secure access to both land and agricultural credit; attaining sustainable agricultural output growth; and making the participation of the land reform beneficiaries in the development process the social foundation of the Syrian political system. Overall, state activity stimulated enough development to permit peasants to diversify their resources and strategies; many, taking advantage of new opportunities in off-farm activities and of state credit and inputs, acquired the resources to significantly intensify production. This equitable rural development approach of the 1960s, 1970s and most of the 1980s, was closely associated with agricultural output growth, at an average annual rate of 4.4% (1960-

29

70) and much faster at8.6% in 1970-80, raising total GDP annual growth rates to 5.7% and to a record of 10.0%, respectively. During this “golden age of Syrian agriculture”, investment in rural infrastructure and human capital in agriculture was intensified; irrigated areas more than doubled; fertilizer consumption increased four times and food production per head was almost maintained above the average level in 1960-65, despite rainfall instability and rapid population growth at an annual rate of 3.5%. This brief account of Syrian development experience suggests the strong dynamic links between equitable distribution of land, agricultural growth and poverty reduction. Indeed, according to the most recent agricultural census, land distribution in 1994 was more equitable than in 1970. Despite state-led development programs with heavy social policy focus, the rural-urban economic and human development divide is still large in Syria 16 .It follows that rural development policies, which played a major role in reducing such inequalities in the past three decades, present the single most effective component of any national poverty reduction programme. Distributional elasticities are also quite high in Syria. This implies that small changes in expenditure inequality may have a strong impact on poverty. These stylized facts should be taken into account in any assessment of the present and future macroeconomic policies and development priorities. UNDP has, since 2003, produced several reports on the macroeconomic and broader social and economic development challenges facing Syria. Based on the findings of these studies and given the rapidly changing regional and global economic context, four other broad policy directions may be pertinent for Syrian policy makers when formulating the five year plan:

1. At the global level, Syria should closely examine the Malaysian experience where a clear development vision and effective development planning models played an essential role in leading the socioeconomic transformation. At the regional level, the Tunisian experience may be considered, particularly with regards to the impact of state policies on rural poverty and the rules of engagement with global (and particularly European) markets.

2. At a sub-national level, the focus of agricultural and rural development strategies should mitigate the impact of the successive droughts in the North-Eastern region. The Southern Urban region should also receive attention given the burden of the successive waves of internal (from the NER) and external (from Iraq) migration on its public services.

3. The importance of developing the tourism sector to mitigate the fiscal constraints imposed by declining oil exports.

4. Social policies are of crucial significance for Syria’s socio-economic development prospects. Within the social services sector, based on the most recent MDG achievement data, particular attention should be devoted to education.

Endnotes 1 Sen 1999

2 Sen 1992

3 ibid

4 In presenting empirical evidence in the context of this broader approach to development Sen (1999: 38-40)

identified five instrumental freedoms that have immediate policy relevance: political freedoms; economic facilities; social opportunities; transparency guarantees; and, protective security. Sen (1999: 38) notes that these

“instrumental freedoms tend to the general capability of a person to live more freely, but they also serve to complement one another”. A careful reading of the content of the above instrumental freedoms clearly shows the political economy nature of issues of the inequality in the distribution of income, wealth, and indeed opportunities.

30

5 Reflecting this world wide consensus the Commission on Growth and Development (2008: 1) specifically notes

that growth "is not an end in itself. But it makes it possible to achieve other important objectives of individuals and societies". The Commission goes on to refer to the MDGs. 6As noted by Kakwani, one appealing guiding principle in poverty comparisons is that, “within a given standard of

living, poverty should not depend on which sub-group in the poverty profile the person with that standard of living happens to belong.” A poverty profile would be "consistent" if the poverty line is fixed in real terms of the indicator of living standards used. “Consistent poverty comparisons imply that two persons at the same real consumption level are deemed to be either "poor" or "not poor" irrespective of the time or place under consideration, or the presence or absence of policy change within the relevant domain. To monitor poverty, we need to have poverty profiles that are comparable over time. The comparability of poverty profiles requires that the minimum standard of living implied by the poverty line should not change over time. It means that the real poverty line is fixed over time. Thus, the poverty line should change over time only because of changes in prices.” 7 In the original compilation by Ferreira and Ravallion (2008) there were 83 countries; we added Syria 2004.

8 Note that the above distribution of Gini coefficients across countries is almost normal (in view of the near

equality of the mean and the median), enabling us to use the mean and the standard deviation to specify the ranges for the various classifications. The medium degree of inequality is defined as the mean plus or minus half a standard deviation. 9 See, for example, El-Laithy and Abu-Ismail, (2005: appendix table A.2.6; p. 133) and the UNDP (2008)

regarding the 2007 household budget survey. 10

The inconsistency between the growth rates of the Household Budget Survey and the national accounts is quite striking, but not uncommon. It can be explained with reference to various informal income generation mechanisms such as informal sector activities and workers remittances. 11

UNDP 2005 12

As in IMF 2007 and 2008 6Despite data limitations, a recent paper by UNDP has courageously estimated the total subsidies consumed by

and services rendered to Iraqis over the 2004 to 2006 period. It is projected at 2.5% of GDP in 2006 (UNDP 2008). Evidently, this is a significant amount when we consider that it represents over half of the Syrian budget deficit in 2006.

14 Abu-Ismail et al 2007

15 Based on Kakwani et al 2005

16El Laithy and Abu-Ismail 2009

31

REFERENCES

Abu-Ismail K., A. Moustafa and M. Vernengo. 2008. “Trade and Industry”. Background Paper for the Macroeconomic Policies for Poverty Reduction in Syria Report.

Arodki, Y. 1972. “Al-Iqtisad al-Soury al-Hadith (The Modern Syrian Economy)”. Al-Tarabishy Publishing House, Damascus.

Asian Development Bank. 2000. “Transforming the Rural Asian Economy”. Oxford University Press.

Birdsall, N. and L. Londono. 1997. “Asset Inequality matters: An Assessment of World Bank’s Approach to Poverty Reduction”, The American Economic Review, Vol. 87: 2, pp. 32-37.

Central Bureau of Statistics. 2003. “Statistical Abstract”. Office of the Prime Minister, Damascus.

Datt, G., and M. Ravallion. 1993. “RegionalDisparities, Targeting, and Poverty in India”. In Michael Lipton and Jacques Van Der Gaag, eds., Including the Poor, Washington D.C., The World Bank.

Deininger, K. and L. Squire. 1998. “New Ways of Looking at Old Issues. Inequalities and Growth”. Journal of Development Economics, Vol. 57:2.

Demery, L. 2000. “Benefit incidence: a Practitioner’s Guide, Poverty and Social Development Group”. The World Bank (unpublished mimeo).

El Laithy, H. and K. Abu-Ismail. 2005. “Poverty in Syria”. UNDP Syria CO, UNDP.

El-Ghonemy, M. Riad. 2005. “Agriculture and Rural Poverty”, Background Paper for the Macroeconomic Policies for Poverty Reduction in Syria Report.

IMF (International Monetary Fund). 2007. “Syrian Arab Republic, IMF Article IV Consultations, Mission Preliminary Conclusions”. http://www.imf.org/external/np/ms/2007/051607.htm

IMF (International Monetary Fund). 2008. “Syrian Arab Republic-2008 Article IV Consultation, Concluding Statement”. http://www.imf.org/external/np/ms/2008/102908.htm

Jalan, J. and M. Ravallion. 1998. “Are There Dynamic Gains from a Poor- Area Development Program?”, Journal of Public Economics, 67(1): pp. 65–85.

Kakwani, N., H. Son, K. Abu-Ismail and J. Roberts. 2005. “Growth and Investment Requirements for Halving Poverty by 2015 in Yemen”. UNDP Yemen.

Keilani, Z. 1980. “Land Reform in Syria”, in Niblock T. and W. Rodney (ed.), The Political Economy of the Middle East, Vol. 1, Edward Elgar Publishing.

Lewis, A. 1955. “Theory of Economic Growth”, London: Unwin University Books.

Maletta, H. 2003. “Private investment in Syrian Agriculture and Agribusiness”. In FAO 2003: Chapter 3.

McKinley, T. 2001. “Macroeconomic Policy, Growth and Poverty Reduction”. Basingstone, UK: Palgrave.

NAPC (National Agriculture Policy Centre). 2000. “Food Security”. Hamdi Salem, Damascus.

NAPC (National Agriculture Policy Centre). 2003. “Syrian Agricultural Trade”. Ravallion, M and S. Chen. 2001. “Measuring Pro-Poor Growth”. Development Research Group, World Bank

Ravallion, M. 1995. “Growth and Poverty: Evidence for Developing Countries in the1980s”, Economic Letters, 48: pp. 411-417.

32

Sarris, A. and A. Corsi. 2003.“The Syrian Agricultural Producers: Structural and Distributional features”, in FAO 2003: Chapter 11.

Smith, A. 1776. “The Wealth of Nations”. Random House Inc. 1937 edition.

UNDP (United Nations Development Programme) and LAS (League of Arab States). 2009. “Development Challenges for the Arab Region”. A Human Development Approach, Forthcoming.

UNDP (United Nations Development Programme). 1990, 1993, 2007. “Human Development Report”. Oxford University Press.

UNDP (United Nations Development Programme). 2007. “Poverty, Growth, Employment and Income Distribution in Yemen -1998-2006”, UNDP Yemen.

UNDP (United Nations Development Programme). 2008. “Poverty, Growth and Income Distribution in Lebanon”, UNDP Lebanon.

UNDP (United Nations Development Programme). 2008. “The Socioeconomic Impact of Iraqi Refugees on Syria”. Abu-Ismail K., T. El Khalidi, H. El Laithy and S. Al-Jondi, UNDP Report.

UNDP (United Nations Development Programme). 2009. “Poverty and Distribution in Syria”.

UNIDO (United Nations Industrial Development Organization). 1997. “Industrial Development Global Report”. Oxford University Press.

Varela-Ortega, C. and J. Sagardoy. 2003. “Irrigation Water Policy in Syria” In FAO, Chapter 13.

Westlake, M. 2003. “The Economics of Strategic Crops”. Chapters 6 in FAO.

World Bank. 2004. “Unlocking the Employment Potential in the Middle East and North Africa: Towards a New Social Contract”. Washington D.C

33

Technical Notes

Methodology on Measuring Pro-Poor Growth At the outset of this note we need to recall that the most widely used measure of inequality in the distribution of consumption expenditure is the Gini coefficient. The Gini coefficient is based on the Lorenz curve. It will be recalled that the Lorenz curve describes the relationship between the cumulative percentage share of expenditure, L(ρ), enjoyed by the bottom ρ% of the population, where individuals are arrayed from the poorest to the richest. A valid Lorenz curve needs to satisfy the following restrictions: (1) L(0) = 0; L(1) = 1; L(ρ) ≤ ρ for all 0 ≤ ρ ≤ 1; L'(0) ≥ 0 ; L"(ρ) ≥ 0. The first two restrictions respectively mean that zero per cent of the population have no consumption expenditure, and that 100% of the population exhaust100% of consumption expenditure. The third condition notes that the Lorenz curve lies in a unit square below the diagonal (defined by the line of complete equality L(ρ) = ρ for all 0 ≤ ρ ≤ 1). The fourth condition implies that the Lorenz curve increases monotonically, while the fifth condition means that the curve is convex. The most widely used measure of inequality is the Gini coefficient, which is defined as the area between the Lorenz curve and the diagonal of the right angle triangle to the total area of the triangle17. In technical terms it is defined as follows where the integration is over the closed interval [0, 1]: (2) Gini Coefficient = 2 ∫ [ρ – L(ρ)] d ρ The Gini coefficient varies from zero (a value that represents the state of complete equality where the Lorenz curve coincides with the diagonal meaning that every one in society gets the average income), to unity (a value that represents the state of complete inequality when one individual gets all the expenditure in the society and all others have zero expenditure). Thus, a higher value for the Gini coefficient means a higher degree of inequality and vice versa. Without getting involved into a detailed survey of various contributions to the meaning, and measurement, of pro-poor growth it is perhaps sufficient to note that Kakwani and Pernia (2000), and Ravallion and Chen (2001) were among the pioneers in this research area. These two contributions defined pro-poor growth in terms of the total change in a poverty index as a result of a change in per capita income after allowing for the change in the distribution of income. Kakwani and Pernia (2000) used a generalized measure of poverty while Ravallion and Chen (2001) used the Watts measure of poverty18. According to both definitions economic growth is pro-poor if it leads to a decline in the poverty measure, after taking into account the changes in the distribution of income!! However, the distributional changes required for growth to be pro-poor are closely related to the stage of development of the country in question on the development-inequality relationship (i.e. the Kuznets' curve)!! According to the Kuznets' hypothesis inequality will be expected to increase at an early stage of development (meaning fairly low real per capita income), to peak and to decline at later stages of development (meaning medium levels of real per capita income)19. A more transparent interpretation of Kakwani and Pernia's definition of pro-poor growth is provided by Son (2004: 308). According to this interpretation "economic growth is pro-poor if the poor enjoy the benefits of growth proportionately more than the non-poor. In this scenario, inequality is concurrently declining during the course of growth. A change in the

34

Lorenz curve indicates whether inequality is increasing or decreasing with economic growth. Thus, growth in unambiguously pro-poor if the entire Lorenz curve shifts upward" (emphasis in not in the original). Thus, Son's interpretation confirms our observation noted above that the distributional changes required for growth to be pro-poor are closely related to the stage of development of the country in question on the development-inequality relationship (i.e. the Kuznets' curve)!! It is perhaps in recognition of the inadequacy of the original Kakwani-Pernia measure of pro-poor growth that Son and Kakwani (2006) proposed a "new measure" of pro-poor growth. To develop this new measure the authors started with defining pro-poor growth as "growth that benefits the poor proportionately more than the non-poor. When there is a negative growth rate, growth is defined as pro-poor if the loss from the growth is proportionately less for the poor than for the non-poor" (Son and Kakwani (2006: 2)). The new measure is based on the generalized Lorenz curve. As noted above the Lorenz curve, L(ρ), describes the percentage share of expenditure enjoyed by the bottom ρ% of the population. The generalized Lorenz curve is defined as the product of mean income, μ, and the Lorenz curve (i.e. μL(ρ)). The new measure for pro-poor growth is now expressed in terms of growth rates of income, a vast improvement over the original Kakwani-Pernia measure!!! From the definition of the Lorenz curve we have the share of the bottom ρ% of the population given by: (3) L(ρ) = [μρ ρ]/ μ Where μρ is the average expenditure of the bottom ρ% of the population. Note that from equation (3) we can immediately get an expression for the generalized Lorenz curve as: (4) μL(ρ) = μρ ρ Son and Kakwani (2006: 4) use equation (4) to derive the new measure of pro-poor growth relying on Atkinson's (1987) relationship between the shift in the generalized Lorenz curve (∆ (μL(ρ)) and the general class of additive poverty measures, such that as the generalized Lorenz curve shifts upward for all ρ, then poverty declines for all poverty lines. Taking the logarithm of the two sides of equation (4) we can express the relationship between the average consumption expenditure of the poor and the average consumption expenditure in society as follows: (5) Ln (μρ ) = Ln [μL(ρ)] - Ln (ρ) By taking the first difference in (4) we have: (6) ∆ Ln (μρ ) = ∆ [Ln (μL(ρ)] The left-hand side of (6) can be taken as the percentage change in mean consumption expenditure of the bottom ρ percentage of the population (when individuals are ranked from poorest to richest), which can be denoted as G(ρ). G(ρ) as a function of ρ in the range from 0 to one, is called the poverty growth curve by Son (2004: 309). The right hand-side is the percentage change in the generalized Lorenz curve. Thus, if G(ρ) is positive for all ρ, then poverty has decreased unambiguously between two periods, given Atkinson's result. Expanding the right-hand side of equation (6), we have:

35

(7) G(ρ) = G(μ) + ∆ [Ln L(ρ)] Where G(μ) = ∆ Ln (μ) is the growth rate of per capita consumption expenditure (or per capita income) of the whole society. Note that, given the definition of the Lorenz function, L(ρ), the growth rate for ρ=1 is equal to G(μ). From equation (7), Son (1984: 309) argues that if G(ρ) > G(μ) for all ρ < 1, then growth "is pro-poor because the entire Lorenz curve shifts upward for all ρ. For empirical applications it is noted that the poverty growth curve "can easily be calculated if we know the decile or quintile shares and the mean income for two periods" (Son (2004: 311)). Son and Kakwani (2006: 4) integrate equation (7) appropriately to get an index of pro-poor growth rate, and they call it γ*, as follows (where the limits of integration are [0, 1]: (8) γ* = ∫ G(ρ) d ρ = ∫ ∆ [Ln (μL(ρ)] d ρ = G(μ) - ∆ Ln Q* Where Q* is a measure of inequality given by: (9) Ln Q* = ∫ [Ln (ρ) – Ln (L(ρ))] d ρ The second term on the extreme RHS of equation (8) measures the rate of change in inequality. "If inequality measured by Q* decreases (increases) in a period, then the pro-poor growth will be greater (less) than the actual growth rate of mean income. Thus, there will be a gain or a loss in growth rate due to changes in inequality. Growth will be pro-poor if there is a gain in growth rate and anti-poor if there is a loss in growth rate" (Son and Kakwani (2006: 4)). Note that for applied purposes γ* is the weighted average of growth rates of real consumption expenditure of the deciles, while G(μ) is the actually observed overall growth rate of real consumption expenditure. The above analysis, we suggest, confirms our intuitive understanding of what is expected to affect pro-poor growth processes. If a growth process is accompanied by improvements in the distribution of consumption expenditure one would be tempted to expect that it will be pro-poor.

Endnotes 17

A number of measures of inequality exist in the literature; an excellent review of which is to be found in Duclos and Araar (2006: pp.49-80). 18

A general measure of poverty can be written as: P = P (z, μ, L(ρ)); where z is the poverty line, μ, is average per capita consumption expenditure, and L(ρ) is the share of the poorest ρ of the population in consumption expenditure (i.e. the Lorenz curve). The Watts measure of poverty is given by: P = 1/n Σ Ln z – Ln μi; where the summation is over the number of poor q. 19

Estimates of the turning point on the Kuznets' curve differ among authors.

36

ANNEX TABLES

Table 1. Human poverty by region and deciles, 1997 and 2007

Deciles Southern North-East Central Coastal

Urban Rural Urban Rural Urban Rural Urban Rural

1-1997 55.5 65.0 66.2 68.1 59.8 71.4 53.5 47.9

1-2007 69.6 66.0 58.7 40.5 65.0 54.1 69.4 71.4

2-1997 17.9 22.2 27.1 36.8 15.0 56.5 7.6 14.4

2-2007 27.4 22.8 8.6 31.6 13.8 9.1 69.3 58.8

3-1997 14.4 13.8 16.2 40.0 9.9 32.0 8.2 16.3

3-2007 22.4 13.1 6.2 26.8 2.1 7.4 4.4 13.0

4-1997 13.6 15.7 17.7 46.9 8.0 26.2 7.3 14.2

4-2007 21.7 14.7 6.4 26.0 3.1 5.7 4.8 10.5

5-1997 16.5 17.5 15.3 39.7 7.7 27.9 8.2 19.8

5-2007 20.7 13.1 6.5 29.8 2.5 7.7 5.6 9.9

6-1997 13.1 13.0 14.4 42.6 7.6 29.8 7.2 19.3

6-2007 14.6 14.7 7.4 31.5 4.4 5.6 5.9 12.5

7-1997 10.7 11.6 14.0 41.3 6.3 28.9 7.1 12.7

7-2007 18.1 10.5 6.8 30.2 3.1 9.7 5.1 9.8

8-1997 12.2 12.3 14.1 43.6 6.9 26.6 6.0 13.0

8-2007 15.9 12.6 8.1 26.2 4.6 7.3 7.2 9.2

9-1997 9.8 11.8 13.7 44.8 6.7 27.1 5.7 15.7

9-2007 11.2 8.4 5.6 29.1 3.7 9.8 3.9 8.2

10-1997 8.3 12.9 11.4 44.6 7.2 26.2 6.7 14.5

10-2007 10.8 8.1 4.1 32.1 3.9 10.0 3.7 10.6

All Deciles 1997 13.8 14.8 16.6 42.0 10.2 29.9 8.6 15.7

All Deciles 2007 17.0 16.8 8.4 29.2 5.0 8.1 5.2 13.2

1 to 10 ratio 1997 6.7 5.0 5.8 1.5 8.3 2.7 8.0 3.3

1 to 10 ratio 2007 6.5 8.1 14.4 1.3 16.5 5.4 18.7 6.8

Source: HIES produced by the CBS in 1997 and 2007.

Table 2. Projected population and GDP per capita in Arab countries, 2008

Country Group Total Population

(million) Population Share

(%) GDP (US$ billions)

GDP Share (%)

Per Capita GDP (US$)

Syria 19.7 6.50% 92.8 3.80% 4711

Egypt 81.7 26.90% 443.1 18.20% 5424

Jordan 6.2 2.00% 30.3 1.20% 4887

Lebanon 3.9 1.30% 45.8 1.90% 11744

Morocco 34.3 11.30% 138.2 5.70% 4029

Tunisia 10.3 3.40% 83.1 3.40% 8068

DE 156.1 51.50% 833.3 34.20% 5338

Algeria 33.7 11.10% 241.1 9.90% 7154

Libya 6.1 2.00% 90.6 3.70% 14852

MOE 39.9 13.20% 331.7 13.60% 8313

Bahrain 0.7 0.20% 26.5 1.10% 37324

Kuwait 2.6 0.90% 141 5.80% 54231

Oman 3.3 1.10% 67.7 2.80% 20515

Qatar 0.8 0.30% 95.1 3.90% 115976

Saudi Arabia 28.1 9.30% 600 24.60% 21352

United Arab Emirates 4.6 1.50% 186.2 7.60% 40478

OE 40.2 13.30% 1116.5 45.90% 27774

Djibouti 0.5 0.20% 1.9 0.10% 3800

Mauritania 3.3 1.10% 6.4 0.30% 1939

Sudan 40.2 13.30% 89.3 3.70% 2221

Yemen 23 7.60% 55.2 2.30% 2400

PEE 67.1 22.10% 152.8 6.30% 2277

Total 303.3 100.00% 2,434 100.00% 8026

Source: IMF (2008) and CIA World Factbook

37

Table 3. Contribution to GDP growth by expenditure (%), 1995-2004

1995

1996

1997

1998 1999

2000 2001 2002 2003

2004

Average 1996-

1999

Average 2000-

2004

Average

1996-2004

Total

GDP

Growth rates

7.0 9.8 5.0 6.8 -

3.6 0.6 5.1 5.9 1.1 2.0 4.5 3.0 3.7

Exp

ort

s

Growth rates

2.7 13.5

15.1 0.5 9.6 0.5 13.0 8.7 -

23.3

13.3 9.7 2.4 5.7

Share in GDP

31.1 29.9

30.9 33.8 31.8

36.2 36.1 38.8 39.9

30.2 33.2 35.7 34.6

Contribution to Growth

0.8 4.0 4.7 0.2 3.1 0.2 4.7 3.4 -9.3 4.0 3.0 0.6 1.7

Share in Growth

11.9 40.9

93.0 2.5 -

86.2

28.6 91.5 57.4 -

836.6

196.5

65.9 20.1 45.3

Imp

ort

s

Growth rates

-0.1 -

4.1 -1.6 -6.4

11.5

-2.4 10.4 6.8 -4.1 27.0 -0.1 7.5 4.1

Share in GDP

38.8 36.3

31.7 29.7 26.0

30.1 29.2 30.7 30.9

29.3 29.4 31.3 30.4

Contribution to Growth

0.0 1.5 0.5 1.9 -

3.0 0.7 -3.0 -2.1 1.3 -7.9 0.2 -2.2 -1.1

Share in Growth

0.3 15.0

10.3 27.8 84.4

120.5

-59.0 -35.3 113.8

-387.

0 4.8 -74.6 -30.9

NFD

Contribution to Growth

0.9 5.5 5.2 2.1 0.1 0.9 1.7 1.3 -8.0 -3.9 3.2 -1.6 0.5

Pri

vate

Co

ns

um

pti

on

Growth rates

8.1 6.1 1.0 5.9 -

4.5 -2.9 1.2 3.4 6.9 6.5 2.1 3.0 2.6

Share in GDP

71.3 72.0

69.5 66.9 66.3

65.6 63.4 61.0 59.6

63.0 67.1 62.5 64.6

Contribution to Growth

5.8 4.4 0.7 3.9 -

3.0 -1.9 0.8 2.1 4.1 4.1 1.5 1.8 1.7

Share in Growth

82.0 44.5

14.2 58.0 84.2

-314.

7 15.2 35.5

368.7

200.5

33.3 62.1 46.3

Pu

bli

c

Inv

estm

en

t

Growth rates

3.3 8.6 15.6 4.5 -

1.2 4.4 16.6 4.5

21.5

-13.0

6.8 6.8 6.8

Share in GDP

10.1 9.7 9.6 10.6 10.3

10.6 11.0 12.2 12.0

14.5 10.3 12.4 11.5

Contribution to Growth

0.3 0.8 1.5 0.5 -

0.1 0.5 1.8 0.6 2.6 -1.9 0.7 0.7 0.7

Share in Growth

4.7 8.5 29.7 7.0 3.6 78.0 35.7 9.3 232.4

-91.9

14.8 24.0 18.9

Pri

vate

In

vestm

en

t Growth rates

-2.4 -

7.2 -

23.8 1.5

-4.6

-12.2 9.7 20.9 16.5

27.6 -8.5 12.5 3.2

Share in GDP

13.7 12.5

10.5 7.7 7.3 7.2 6.3 6.6 7.5 8.6 8.2 7.9 8.0

Contribution to Growth

-0.3 -

0.9 -2.5 0.1

-0.3

-0.9 0.6 1.4 1.2 2.4 -0.9 0.9 0.1

Share in Growth

-4.7 -

9.1 -

49.9 1.7 9.3

-146.

6 11.9 23.2

111.1

116.4

-20.0 31.9 3.3

Pu

bli

c

co

ns

um

pti

on

Growth rates

3.3 0.3 1.2 2.0 -

1.6 19.1 2.0 4.9

10.3

10.3 0.5 9.3 5.4

Share in GDP

12.7 12.2

11.2 10.8 10.3

10.5 12.4 12.0 11.9

13.0 10.7 12.7 11.8

Contribution to Growth

0.4 0.0 0.1 0.2 -

0.2 2.0 0.2 0.6 1.2 1.3 0.1 1.1 0.6

Share in Growth

5.9 0.3 2.8 3.2 4.7 334.

2 4.7 9.9

110.6

65.6 1.2 36.5 17.1

Source: Authors calculations based on data from various Annual Statistical Abstracts (CBS)

38

Table 4. Contribution to GDP growth by sector (%), 1995-2004

1996

1997

1998

1999

2000 2001

2002

2003 2004

Average 1996

-1999

Average

2000-2004

Average 1996

-2004 Total GDP Growth rates 9.8

% 5.0%

6.8%

-3.6%

0.6% 5.1%

5.9%

1.1% 2% 4.52%

2.96%

3.65%

Agriculture

Growth rates 14.6%

-2.9%

22.3%

-15.1%

9.3% 8.1%

7.9%

-2.7%

1.2% 4.73%

4.77%

4.75% Share in GDP 23.4

% 24.4%

22.6%

25.9%

22.8%

24.8%

25.5%

25.9%

25% 23.93%

25.18%

24.62% Contribution to

Growth 3.4%

-0.7%

5.0%

-3.9%

2.1% 2% 2% -0.7%

0.3% 0.96%

1.15%

1.07% Share in GDP

Growth 34.8%

-14.1%

74.2%

109.9%

352%

39.2%

34%

-61.9%

15.1%

21.26%

38.88%

29.19%

Mining & Manufacturing

Growth rates 22.1%

15.8%

3.9%

-3.4%

-8.1%

1% -3.7%

-6.1%

-14.9%

9.58%

-6.37%

0.72% Share in GDP 27.7

% 30.7%

33.9%

33.0%

33% 30.1%

29%

26.3%

24.5%

32.65%

26.06%

28.99% Contribution to

Growth 6.1%

4.8%

1.3%

-1.1%

-2.7%

0.3%

-1.1%

-1.6%

-3.6%

2.79%

-1.74%

0.27% Share in GDP

Growth 62.1%

96.5%

19.5%

31.7%

-447.8%

5.7%

-18.1%

-143.7%

-178.5%

61.65%

-58.90%

7.41%

Building and Construction

Growth rates 9.8%

5.2%

0.5%

-5.1%

1.9% -0.4%

0.5%

35.6%

-17.9%

2.61%

3.95%

3.35% Share in GDP 3.4

% 3.4%

3.4%

3.2%

3.2% 3.2%

3% 2.9% 3.9% 3.29%

3.21%

3.25% Contribution to

Growth 0.3%

0.2%

0.0%

-0.2%

0.1% 0% 0% 1% -0.7%

0.09%

0.08%

0.08% Share in GDP

Growth 3.4%

3.5%

0.3%

4.6%

10.2%

-0.2%

0.2%

92% -33.7%

2.02%

2.69%

2.32%

Wholesale & Retail Trade

Growth rates -4.4%

-5.1%

0.9%

1.4%

-7.9%

10.8%

13.1%

-2.8%

18.9%

-1.78%

6.43%

2.78% Share in GDP 20.8

% 18.1%

16.3%

15.4%

16.2%

14.9%

15.7%

16.7%

16.1%

16.53%

16.43%

16.48% Contribution to

Growth -

0.9%

-0.9%

0.2%

0.2%

-1.3%

1.6%

2.1%

-0.5%

3% -0.37%

0.99%

0.39% Share in GDP

Growth -

9.2%

-18.5%

2.2%

-6.2%

-213.4%

31.4%

34.7%

-41.4%

148.9%

-8.09%

33.56%

10.65%

Transport & Communication

Growth rates 7.2%

10.6%

-0.5%

8.6%

5.4% 6.7%

9.1%

11.2%

5.5% 6.50%

7.59%

7.11% Share in GDP 11.1

% 10.9%

11.5%

10.7%

12% 12.6%

12.8%

13.2%

14.5%

11.25%

13.60%

12.56% Contribution to

Growth 0.8%

1.2%

-0.1%

0.9%

0.7% 0.8%

1.2%

1.5% 0.8% 0.71%

0.99%

0.86% Share in GDP

Growth 8.2%

23.0%

-0.9%

-26.0%

108.6%

16.6%

19.6%

133.1%

38.9%

15.63%

33.35%

23.60%

Finance & Insurance

Growth rates -5.1%

7.6%

2.0%

18.5%

-7.4%

-2.1%

5.3%

12.5%

21.3%

5.75%

5.93%

5.85% Share in GDP 3.8

% 3.2%

3.3%

3.2%

3.9% 3.6%

3.3%

3.3% 3.7% 3.41%

3.67%

3.55% Contribution to

Growth -

0.2%

0.2%

0.1%

0.6%

-0.3%

-0.1%

0.2%

0.4% 0.8% 0.18%

0.20%

0.19% Share in GDP

Growth -

1.9%

4.9%

1.0%

-16.5%

-48%

-1.4%

3% 37.4%

38.5%

3.92%

6.89%

5.26%

Social Services

Growth rates 0.0%

19.2%

8.2%

8.4%

25.6%

7.2%

19.8%

1.1% 1.8% 8.93%

11.11%

10.14% Share in GDP 1.6

% 1.5%

1.6%

1.7%

1.9% 2.3%

2.4%

2.7% 2.7% 1.66%

2.57%

2.17% Contribution to

Growth 0.0%

0.3%

0.1%

0.1%

0.5% 0.2%

0.5%

0% 0% 0.14%

0.24%

0.20% Share in GDP

Growth 0.0%

5.5%

2.0%

-4.0%

80.2%

3.3%

8% 2.6% 2.4% 3.06%

8.13%

5.34%

Other Services

Growth rates 3.2%

-0.5%

1.5%

-3.3%

22.1%

3.3%

13.1%

10.2%

14.4%

0.20%

12.63%

7.11% Share in GDP 8.3

% 7.8%

7.3%

7.0%

7% 8.5%

8.4%

8.9% 9.7% 7.27%

9.28%

8.39% Contribution to

Growth 0.3%

0.0%

0.1%

-0.2%

1.5% 0.3%

1.1%

0.9% 1.4% 0.02%

1.05%

0.59% Share in GDP

Growth 2.7%

-0.8%

1.6%

6.5%

258.3%

5.5%

18.5%

81.9%

68.3%

0.55%

35.39%

16.22% Source: ibid

39

Table 5. Extreme poverty projections for low growth scenario under three distribution scenarios (%), 2008-2015* Dist. SC1 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.7 11.42 8.55 6.09 13.47 20.74 9.88 9.79 12.98

2009 12.6 11.93 9.17 6.46 14.02 21.49 10.53 10.33 13.49

2010 12.81 12.05 9.38 6.64 14.13 21.59 10.72 10.48 13.6

2011 12.26 11.75 9 6.41 13.81 21.16 10.32 10.16 13.3

2012 11.7 11.44 8.61 6.17 13.48 20.7 9.92 9.83 12.99

2013 10.8 10.93 7.98 5.79 12.94 19.94 9.27 9.29 12.48

2014 9.63 10.25 7.16 5.28 12.2 18.92 8.41 8.57 11.79

2015 8.56 9.59 6.41 4.81 11.48 17.92 7.61 7.89 11.12

% Change -19% -11% -17% -14% -10% -9% -16% -13% -10%

Dist. SC2 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.47 11.22 8.32 5.88 13.26 20.54 9.63 9.56 12.79

2009 11.88 11.31 8.45 5.8 13.37 20.9 9.74 9.62 12.9

2010 11.31 10.77 7.8 5.16 12.82 20.45 9.03 8.98 12.41

2011 9.69 9.58 6.34 3.95 11.59 19.18 7.47 7.63 11.29

2012 7.86 8.2 4.66 2.52 10.15 17.71 5.66 6.05 9.98

2013 5.5 6.43 2.54 0.77 8.3 15.76 3.39 4.06 8.29

2014 2.77 4.35 2.23 0.52 6.12 13.41 0.78 1.76 6.29

2015 2.84 4.28 2.26 0.49 3.84 10.97 0.77 -0.59 4.21

% Change -73% -60% -71% -91% -70% -44% -91% -107% -66%

Dist. SC3 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.92 11.62 8.78 6.3 13.68 20.93 10.13 10.01 13.16

2009 13.09 12.35 9.67 6.92 14.46 21.89 11.08 10.81 13.88

2010 14.1 13.14 10.63 7.74 15.27 22.7 12.1 11.71 14.62

2011 14.64 13.74 11.35 8.52 15.88 23.1 12.9 12.44 15.16

2012 15.38 14.52 12.28 9.49 16.68 23.68 13.93 13.38 15.87

2013 15.97 15.3 13.17 10.52 17.46 24.15 14.94 14.32 16.56

2014 16.41 16.05 14.01 11.55 18.22 24.5 15.89 15.24 17.22

2015 16.99 16.94 14.95 12.69 19.12 25 16.97 16.29 18.01

% Change 61% 58% 93% 127% 50% 27% 88% 79% 46%

Source: Authors estimates based on IMF 2009 * NE is an acronym for the North-Eastern region, Cn for the Central region, Cs for the Coastal region, and S for

the Southern region. R or U indicates rural and urban so that, for example, SU and SR stand for the Southern

Urban region and the Southern Rural region, respectively. Table 6. Extreme poverty projections for medium growth scenario under three distribution scenarios (%), 2008-2015 Dist. SC1 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.7 11.42 8.55 6.09 13.47 20.74 9.88 9.79 12.98 2009 11.93 11.55 8.71 6.18 13.61 20.93 10.05 9.93 13.11 2010 11.45 11.28 8.26 5.8 13.36 20.71 9.64 9.59 12.86 2011 10.31 10.62 7.48 5.33 12.66 19.73 8.82 8.9 12.21 2012 9.25 9.99 6.75 4.89 11.98 18.77 8.05 8.25 11.57 2013 8.01 9.22 5.9 4.36 11.13 17.58 7.13 7.46 10.78 2014 6.69 8.34 4.97 3.78 10.16 16.2 6.12 6.58 9.87 2015 5.56 7.52 4.18 3.27 9.26 14.91 5.24 5.79 9.02 % Change -47% -30% -46% -42% -27% -24% -42% -36% -27% Dist. SC2 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.47 11.22 8.32 5.88 13.26 20.54 9.63 9.56 12.79 2009 11.22 10.93 7.99 5.52 12.97 20.34 9.26 9.22 12.53 2010 9.99 10.03 6.9 4.63 12.02 19.34 8.09 8.2 11.66 2011 7.9 8.55 5.17 3.3 10.46 17.56 6.22 6.57 10.23 2012 5.77 6.94 3.36 1.87 8.76 15.66 4.24 4.83 8.67 2013 3.38 5.07 1.34 0.25 6.76 13.39 2.01 2.83 6.83 2014 0.89 3.01 1.26 0.16 4.54 10.83 -0.35 0.67 4.79 2015 1.15 3.05 1.43 0.24 2.35 8.31 -0.19 -1.41 2.76 % Change -89% -72% -82% -96% -82% -58% -102% -115% -78% Dist. SC3 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 11.92 11.62 8.78 6.3 13.68 20.93 10.13 10.01 13.16 2009 12.42 11.97 9.21 6.64 14.05 21.33 10.59 10.41 13.5 2010 12.7 12.35 9.65 7.14 14.43 21.55 11.09 10.87 13.84 2011 12.53 12.54 9.83 7.55 14.61 21.38 11.33 11.15 13.97 2012 12.56 12.9 10.21 8.12 14.96 21.39 11.78 11.61 14.27 2013 12.5 13.25 10.54 8.71 15.3 21.3 12.21 12.07 14.55 2014 12.38 13.58 10.84 9.29 15.62 21.14 12.59 12.5 14.81 2015 12.39 14.03 11.23 9.93 16.06 21.13 13.09 13.04 15.18 % Change 17% 30% 45% 77% 26% 7% 45% 43% 23%

Source: ibid

40

Table 7. Extreme poverty projections for high (IMF) growth scenario under three distribution scenarios (%), 2008-2015 Dist. SC1 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 8.87 9.76 6.59 4.88 11.68 18.24 7.82 8.05 11.3 2009 8.09 9.26 6.04 4.53 11.14 17.48 7.23 7.55 10.79 2010 7.63 8.99 5.6 4.16 10.89 17.26 6.83 7.22 10.55 2011 6.3 8.09 4.68 3.58 9.9 15.84 5.82 6.32 9.62 2012 5.23 7.29 3.92 3.08 9.01 14.55 4.97 5.55 8.78 2013 4.17 6.43 3.17 2.58 8.05 13.16 4.11 4.75 7.87 2014 3.48 5.82 2.67 2.23 7.35 12.13 3.53 4.19 7.2 2015 2.89 5.25 2.25 1.93 6.7 11.16 3.02 3.68 6.59 % Change -73% -51% -71% -66% -47% -43% -67% -59% -46% Dist. SC2 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 8.65 9.56 6.36 4.67 11.47 18.05 7.57 7.83 11.11 2009 7.5 8.7 5.43 3.96 10.55 16.93 6.55 6.92 10.26 2010 6.53 7.88 4.58 3.24 9.68 15.95 5.6 6.06 9.45 2011 4.57 6.32 3.01 2.04 7.98 13.87 3.84 4.45 7.88 2012 2.83 4.78 1.58 0.88 6.29 11.82 2.2 2.9 6.31 2013 1.13 3.12 0.15 -0.31 4.46 9.59 0.53 1.28 4.61 2014 -0.17 1.69 0.45 -0.13 2.86 7.68 -0.83 -0.09 3.12 2015 -0.03 1.71 0.54 -0.08 1.27 5.79 -0.74 -1.41 1.64 % Change -100% -84% -93% -101% -90% -71% -108% -115% -87% Dist. SC3 SU NEU CnU CsU SR NER CnR CsR All Syria 2008 9.1 9.96 6.81 5.09 11.89 18.43 8.06 8.28 11.48 2009 8.46 9.62 6.42 4.9 11.52 17.83 7.66 7.95 11.13 2010 8.52 9.83 6.62 5.2 11.73 17.87 7.9 8.2 11.31 2011 7.83 9.6 6.36 5.25 11.44 17.12 7.65 8.03 11.02 2012 7.43 9.55 6.29 5.43 11.37 16.62 7.6 8.05 10.92 2013 7.03 9.52 6.23 5.63 11.3 16.08 7.58 8.08 10.83 2014 6.97 9.75 6.39 5.97 11.53 15.96 7.8 8.36 11.02 2015 6.98 10.06 6.6 6.35 11.85 15.95 8.08 8.7 11.29 % Change -34% -6% -15% 13% -7% -19% -11% -4% -8%

Source: ibid