metadata: authors: 0: author: Devadas,Sharmila
1: author: Elbadawi,Ibrahim Ahmed
2: author: Loayza,Norman V.
count: Syrian Arab Republic
docna: 0: docna: Growth after War in Syria
docty: Policy Research Working Paper
subtopic: Armed Conflict,Labor Markets,Labor & Employment Law,Demographics,Macroeconomic Management
teratopic: Conflict and Development,Social Protections and Labor,Law and Development,Health, Nutrition and Population,Macroeconomics and Economic Growth
abstracts: cdata!: This paper addresses three questions: 1)
            what would have been the growth and income trajectory of
            Syria in the absence of war; 2) given the war, what explains
            the reduction in economic growth in terms physical capital,
            labor force, human capital, and productivity; and 3) what
            potential growth scenarios for Syria there could be in the
            aftermath of war. Estimates of the impact of conflict point
            to negative gross domestic product (GDP) growth of -12
            percent on average over 2011-18, resulting in a GDP
            contraction to about one-third of the 2010 level. In
            post-conflict simulation scenarios, the growth drivers are
            affected by the assumed levels of reconstruction assistance,
            repatriation of refugees, and productivity improvements
            associated with three plausible political settlement
            outcomes: a baseline (Sochi-plus) moderate scenario, an
            optimistic (robust political settlement) scenario, and a
            pessimistic (de facto balance of power) scenario.
            Respectively for these scenarios, GDP per capita average
            growth in the next two decades is projected to be 6.1, 8.2,
            or 3.1 percent, assuming that a final and stable resolution
            of the conflict is achieved.
geo_reg_key: 517202,517191
repnme: repnme: Growth after War in Syria
display_title: Growth after War in Syria
listing_relative_url: /research/2019/08/31316218/growth-after-war-syria
topicv3: Demographic Change m1327040 1448,Political Violence and War m1327310 2462,Labor Force Participation m1504050 2746,Working Age Population m1504053 2749,MFMod m1504542 3120
publishtoextweb_dt: 2019-08-06T00:00:00Z
historic_topic: Conflict and Development,Social Protections and Labor,Law and Development,Health, Nutrition and Population,Macroeconomics and Economic Growth
no_of_pages: 47
author_keys: 0: author_key: 1463367
1: author_key: 94054
2: author_key: 97109
model_extractions: Policy Research Working Paper 8967

# Growth after War in Syria

_Sharmila Devadas Ibrahim Elbadawi Norman V. Loayza_ Development Economics Development Research Group August 2019
Policy Research Working Paper 8967

# **Abstract**

This paper addresses three questions: 1) what would have been the growth and income trajectory of Syria in the absence of war; 2) given the war, what explains the reduction in economic growth in terms physical capital, labor force, human capital, and productivity; and 3) what potential growth scenarios for Syria there could be in the aftermath of war. Estimates of the impact of conflict point to negative gross domestic product (GDP) growth of −12 percent on average over 2011–18, resulting in a GDP contraction to about one-third of the 2010 level. In post-conflict simulation scenarios, the growth drivers are affected by the assumed levels of reconstruction assistance, repatriation of refugees, and productivity improvements associated with three plausible political settlement outcomes: a baseline (Sochi-plus) moderate scenario, an optimistic (robust political settlement) scenario, and a pessimistic (de facto balance of power) scenario. Respectively for these scenarios, GDP - per capita average growth in the next two decades is pro jected to be 6.1, 8.2, or 3.1 percent, assuming that a final and stable resolution of the conflict is achieved.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdevadas1@worldbank.org and nloayza@worldbank.org.

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

**Sharmila Devadas** Development Research Group, World Bank

## **Ibrahim Elbadawi**

Economic Research Forum

**Norman V. Loayza** Development Research Group, World Bank

**Keywords:** War, conflict, reconstruction, growth, factors of production, Syria **JEL Codes:** D74, F51, 011, 040, 053

Note: The authors are grateful to staff of the United Nations Economic and Social Commission for Western Asia including Ahmad Shikh Ebid for sharing information and data, as well as to Shanta Devarajan, Simeon Djankov, Young Eun Kim, Aart Kraay, Aljaz Kuncic, Rabie Nasser, and Nurlina Shaharuddin for helpful comments. All remaining errors are the responsibility of the authors. The views expressed here are the authors’, and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **1. Introduction**

The scale and intensity of the violence and destruction associated with the civil war that engulfed the Syrian Arab Republic since 2011 have very few parallels in recent history. The Syrian Observatory for Human Rights (SOHR) estimates the total death toll (from 15 March 2011 to 15 March 2019) at a staggering number of 570,000. The United Nations Economic and Social Commission for Western Asia (ESCWA 2018) ‐ which conducted an elaborate sectoral analysis of the economic cost of the Syrian civil war ‐ puts the destruction of the physical capital stock by end of 2017 at almost USD 120 billion. And, in terms of the cost to the overall economy, the World Bank (2017b) estimates that, from 2011 until the end of 2016, the cumulative losses in gross domestic product (GDP) reached a whopping USD 226 billion, about four times the Syrian GDP in 2010. These assessments seem to broadly cohere with the calculations of the country’s night‐light intensity by Ceylan and Tumen (2018) and Li et al. (2017), with the latter suggesting that by 2017, Syria had lost about 80 percent of its city night light.

Moreover, in addition to the massive death and destruction, this war has also created an unprecedented number of refugees and internally displaced persons (IDPs). According to the United Nations High Commissioner for Refugees (UNHCR), there are about 5.6 million registered refugees from Syria in neighboring countries. However, accounting for unregistered refugees in just the three countries of Egypt, Jordan and Lebanon would raise the aggregate number to more than 7 million (UNHCR, 2019). Adding these numbers to the about 6.3 million IDPs in Syria, we have almost two‐ thirds of the 21 million Syrian citizens who have been forced out of their homes. To appreciate the global impact of the Syrian refugees and IDPs crisis, it suffices to note that the former accounts for more than 23 percent of the total number of refugees worldwide, while the percentage of the latter is estimated at 20 percent of the total number of IDPs in the world.

The losses incurred by Syria are great, but it is not false hope to look toward recovery and further strengthening of the country’s socio‐economic fundamentals beyond its pre‐war situation. Chen, Loayza and Reynal‐Querol (2008) conduct a comprehensive evaluation of the aftermath of civil war using event‐study analysis across 41 countries over 1960‐2003. They show that recovery to pre‐ conflict levels and further improvements are possible for a country afflicted by war when relatively lasting peace is achieved. Other studies focusing on World War II (WWII) indicate countries returned to their pre‐war trends 15 to 20 years post war (Organski and Kugler 1977, 1980); and that countries suffering large negative output shocks grew systemically faster during the subsequent decades due to reconstruction dynamics (Milionis and Vonyo 2015). Because of the massive destruction of the factors of production in Syria at a scale more common in inter‐state wars than civil conflicts, the lessons from the post‐WWII reconstruction of Europe and insight from modern growth theory could be useful in assessing the post‐conflict growth potential for Syria. Janossy (1969) postulates that fast growth during reconstruction is not only the result of higher returns to physical capital accumulation (which diminish as capital grows in relation to output) but also depends on structural factors like the reorganization of economic activity and the reallocation of production factors. One of the key lessons from the experience of post‐WWII growth in the European countries and Japan, for example, was that the rapid growth impact of the massive re‐building of physical capital was made possible, not only by the Marshall Plan resources, but also by the relatively limited war time depreciation of the human capital base and technological potential (Smolny 2000).

The implication of the above for the post‐conflict economic reconstruction agenda for Syria ‐ with almost two‐thirds of its population internally displaced or living as refugees in foreign countries ‐ is that the restoration of human capital should be accorded the highest priority. And this should be 2
alongside the rebuilding of physical capital which will unavoidably be a key component of the agenda. Further, attention also needs to be paid to other factors contributing to total factor productivity (TFP), including institutions and market efficiency.

However, the prospects for mobilizing meaningful multi‐year financing for reconstruction and development and for achieving a critical mass of voluntary repatriation of refugees would hinge on the nature of the ultimate political settlement of the conflict. A lopsided political settlement may deter refugees, with strong lingering uncertainty about security and economic prospects, to return. Some of the main impediments hindering repatriation include the dispossession of refugees’ homes and mandatory military conscription for men of age. Therefore, and despite the “invitation” for refugees to return home and the refugee camps being set up within Syria, it is not surprising that only a few thousands returned in 2017, mostly motivated by push factors in the recipient countries. Indeed, this very limited response did not mark the opening of the flood gates for massive repatriation in the following years (POMEPS 2018).

Moreover, the volume of the funding required for reconstruction has been estimated at USD 250 billion by the United Nations to as high as USD1 trillion (POMEPS 2018), by far more than could be provided by Syrian allies. Thus, a genuine reconstruction plan for Syria would best be served by robust support from the wider international community, who have indicated a preference for a more robust political settlement (ERF 2019). The international community can provide some reconstruction aid that would support and encourage the return of refugees, infrastructure investment, and policy reform. This includes aid for geographically dispersed economic reconstruction (rebuilding infrastructure, access to health and education, etc.) and institutional reform (including security, property rights, and access to justice) that benefit various segments of the population fairly (Yahya 2017). Djankov and Reynal‐Querol (2010) find that both per capita income and civil war are jointly determined by country‐specific phenomena, such as colonial history. Consequently, policies are needed to rectify structural problems that make countries more prone to conflict.

Subscribing to the context discussed above, this paper uses the World Bank Long‐Term Growth Model ‐ Public Capital Extension (LTGM‐PC) by Devadas and Pennings (2019) ‐ see Appendix 1 for details ‐ to simulate a counterfactual of no conflict scenario (in Section 2), to estimate the impact of conflict (in Section 3), and to assess the potential post‐conflict growth for Syria (in Section 4). The after‐war projections are carried out for three political settlement scenarios: a baseline (Sochi‐plus, mainly operated by Iran, Russia, and Turkey, with some involvement from the United Nations) moderate scenario; a high (robust political settlement, brokered by the United Nations) scenario; and a pessimistic (de facto balance of power) scenario. The LTGM‐PC builds on the Solow‐Swan growth model, and is evolved from another World Bank tool, the Long‐Term Growth Model (LTGM) (Loayza and Pennings 2018; Hevia and Loayza 2012). The LTGM‐PC splits the accumulation of aggregate capital into public and private portions, while retaining other features of the LTGM including other growth drivers (demographics and the labor force, human capital, and total factor productivity). We run all our simulations using the LTGM‐PC Excel‐based toolkit.

Our paper complements earlier modeling work by World Bank (2017b) in four ways. One, it provides a straightforward and transparent analysis of how GDP evolves based on projections for the growth drivers. World Bank (2017b) uses a dynamic general equilibrium model to simulate the effects of the conflict through three channels – physical capital destruction, casualties, and economic disorganization; with the last calculated as a residual based on estimated GDP losses. Two, data‐wise, we use estimates of physical damage across all types of capital whereas World Bank (2017b) determines destruction in their simulations based on physical damage assessments in the housing 3
sector. Three, with a greater certainty of the end of conflict, we focus on growth scenarios in the aftermath of war, rather than mostly assessing conflict impact based on different end‐time scenarios. Four, we also attempt to provide a more up‐to‐date assessment of the impact from the conflict, that is until the end of 2018.

Under the counterfactual simulation (that is, if war had not occurred), our baseline projection shows average real GDP growth of 5.3 percent per annum over 2011‐2018 which would have led to real GDP rising from USD 60 billion in 2010 to USD 91 billion and real GDP per capita rising from USD 2,857 to USD 3,774 by 2018. In contrast, our simulations of the impact of conflict point to negative annual GDP growth of ‐12 percent on average over 2011‐2018, resulting in a GDP level of USD 23.2 billion in 2018. Comparing the conflict versus no conflict simulations suggest a cumulative loss in GDP potential of about USD 300 billion over 2011‐2018. The depletion of factors of production alone may account for about 87 percent of the negative GDP growth on average, and further, about 64 percent of the average negative growth is due to physical capital destruction. Physical capital destruction reflects the compounded effects of large outright damages, low new investments, and a falling output base, that is adversely affected by all growth drivers. Demographics and labor account for about 15 percent, human capital 7 percent, and total factor productivity 13 percent of negative GDP growth on average over the conflict years.

In our post‐conflict simulations, we assume that the three political settlement scenarios are associated with different levels of reconstruction assistance and different degrees of voluntary mobility of refugees. These in turn affect key drivers of growth: public and private investment, and the labor force. We also make different assumptions for human capital growth and TFP growth across the three scenarios. Depending on the scenarios, our simulation results suggest that it would take between 10 and 20 years for Syria to reach its pre‐conflict GDP level and between 10 and 30 years to reach its GDP per capita level (both at 2010 constant prices). If there were to be an unsanctioned and misguided “forced” repatriation of refugees, this would result in significantly lower GDP per capita compared to the voluntary mobility case. Under voluntary return, labor would adjust gradually to capital reconstruction, thus keeping labor productivity from falling.

# **2. Syria Pre‐Conflict Developments and Projections**

This section focuses on building a calibration using the LTGM‐PC for a no‐conflict scenario based on developments and projections prior to the conflict. Recent data indicate that average real GDP growth in Syria was around 4.7% over 2001‐2010 (Graph 1(a)). World Bank’s World Development Indicators (WDI)<sup>1</sup> data suggest an average growth of 4.9% over 2001‐2010, while IMF World Economic Outlook (WEO), points to 4.5%, over the same period.<sup>2</sup> Just prior to the conflict, projections for near‐ term growth were still robust: an average of 5.6% over 2011‐2012 in the World Bank Global Economic Prospects (GEP), January 2011<sup>3</sup> ; and an average of 5.0% over 2011‐2016 in the IMF WEO, April 2011 edition<sup>4</sup> ‐ see Graph 1(b). In the following subsection we look at pre‐conflict trends and projections

> 1 WDI data for Syria are publicly available only up to 2007. We supplement these data for the last three years up to 2010 with data on GDP growth from the World Bank’s internal macroeconomic model (MFMod), November 2017 vintage, which is consistent with the IMF WEO October 2018 data.

> 2 WDI: GDP is at market prices, 2010 USD. IMF WEO: GDP is at market prices, 2000 local currency.

> 3 Source: http://documents.worldbank.org/curated/en/293851468162862428/Global‐economic‐prospects‐January‐2011‐ <u>navigating‐strong‐currents. GDP growth of 5.0% for 2010 is an estimate. No GEP projections for Syria are available beyond</u> the January 2011 publication. GDP is at market prices, 2005 USD.

> 4 Source: https://www.imf.org/external/pubs/ft/weo/2011/01/weodata/index.aspx.

4
(where available) for key growth drivers (physical investment, demographics and the labor force, human capital and total factor productivity).

**<u>Graph 1: Pre‐Conflict Real GDP Growth and Projection</u>**

<!-- Start of picture text --> (a) September/October 2018 Vintage (b) April 2011 Vintage<br>% %<br>8.0<br>8.0<br>6.0 4.9<br>6.0<br>4.0<br>4.5 4.0 5.0<br>2.0<br>2.0<br>0.0<br>0.0<br>‐2.0<br>‐2.0<br>‐4.0<br>‐4.0<br>IMF WEO, Oct 2018<br>WDI/MFMod 2018 IMF WEO April 2011, 6‐year average, 2011‐2016<br>WDI/MFMod 2018, 10‐year average, 2001‐2010 IMF WEO, April 2011 (dashed line indicates estimate/projection)<br>IMF WEO Oct 2018, 10‐year average, 2001‐2010<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

# **2.1 Growth drivers**

In terms of **physical investment,** the investment‐to‐output ratio, IY⁄ (gross _<u>fixed</u>_ capital formation (GFCF), as a percentage of GDP) averaged about 21.5% over the 7‐year period, 2001‐2007 (Graph 2(a), blue line with marker).<sup>5</sup> For gross capital formation (GCF), which includes inventories, as a percentage of GDP, the IMF WEO October 2018 showed an average of 23.1% over 2001‐2010 (Graph 2(a) red line). Pre‐conflict data (IMF WEO April 2011) meanwhile suggested a lower average of 21.8% for GCF (% of GDP), albeit with a projected rise of about 3 percentage points over 2011‐2016 (Graph 2(b)). Of total IY⁄ , how much exactly can be attributed to public investment ( I<sup>�</sup> ⁄Y� is uncertain ‐ 40% seems plausible based on reported figures.<sup>6</sup>

**<u>Graph 2: Pre‐Conflict Investment and Projection</u>**

<!-- Start of picture text --> (a) September/October 2018 Vintage (b) April 2011 Vintage<br>% of GDP % of GDP<br>35.0<br>30.0<br>30.0<br>25.0<br>25.0<br>25.2<br>20.0<br>20.0<br>23.1<br>21.5<br>15.0 15.0<br>10.0 10.0<br>Gross fixed capital formation (GFCF) (WDI, 2018)<br>GFCF 7‐year average, 2001‐2007 (WDI, 2018) GCF (IMF WEO, April 2011), dashed line indicates estimate/projection<br>Gross capital formation (GCF) (IMF WEO, Oct 2018) GCF 6‐year average, 2011‐2016 (IMF WEO, April 2011)<br>GCF 10‐year average, 2001‐2010 (IMF WEO, Oct 2018)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

> 5 MFMod numbers suggest a slightly lower average of 20.5% over the same period (and 20.1% over 2001‐2010).

> 6 World Bank (2017b) reports I<sup>�</sup> ⁄Y of 9% and private fixed capital formation‐to‐output, I<sup>�</sup> ⁄ Y of 12% in 2010 giving a public investment share of total investment of 43%. Data in IMF (2010) indicate an average share of 38% over 2005‐2008, based on gross capital formation‐to‐output averaging 21.8%. Calculations using the IMF FAD Investment and Capital Stock Database 2017 suggest an average share of 47% over 2001‐2010, but the average value for total investment‐to‐output at 6% is relatively low compared to the other reported values.

5
**Demographics and the labor force** . The growth rates of total population and the working‐age population share averaged 2.5% and 0.8% respectively over 2001‐2010 based on latest United Nations (UN) estimates (United Nations 2017) ‐ Graph 3(a). As a gauge of projections prior to the conflict, United Nations (2011) indicated average growth rates for these two variables of 1.7% and 1.0% respectively over 2011‐2020 and 1.5% and 0.3% over 2021‐2030 compared to 2.5% and 0.5% over 2001‐2010 (Graph 3(b), dashed lines with markers).<sup>7</sup>

The labor force participation rate (LFPR)<sup>8</sup> meanwhile, had been on a moderating trend, declining from 54.5% in 1995 (Male: 84.3%; Female: 24.4%) to 44.9% in 2010 (Male: 75.6%; Female: 14.0%), with an average growth over 2001‐2010 of ‐1.5% (Male: ‐0.9%; Female: ‐3.9%) (Graph 4(a)). This phenomenon occurred despite relatively strong economic growth, distinguishing Syria from other countries – no other Middle East and North Africa (MENA) economy had a similar rate of decline in the LFPR over the same period, except Yemen. Nasser and Mehchy (2012) note that a sizeable portion of the economically active population that went out of the labor force in the 2000s consisted of women in rural areas, and workers becoming students. Women in the agriculture sector became jobless with the drought and higher fuel prices in the second half of the 2000s, and were unable to find other work, thus returning to domestic services in their homes. In the case of the increase in students, this was partly affected by the easing of entry into universities, and the development of the financial sector (entry of private banks and insurance companies) that required high‐skilled workers. It is not clear though, if the developments cited in relation to student enrollment were expected to have a long‐term impact on the LFPR. Early in the conflict, International Labour Organization (ILO)‐ modelled estimates and projections suggested a stabilizing participation rate after 2010 (Graph 4(b)), though some caution needs to be exercised in taking this at face value given uncertainty surrounding the underlying data.<sup>9</sup>

**<u>Graph 3: Pre‐Conflict Population Growth Rate and Projection</u>**

<!-- Start of picture text --> (a) 2017 Vintage (b) 2011 and 2013 Vintage<br>Average annual Average annual<br>growth growth<br>4.0% 4.0%<br>3.0%<br>3.0%<br>2.0%<br>2.0%<br>1.0%<br>1.0%<br>0.0%<br>0.0% 1991‐95 1996‐00 2001‐05 2006‐10 2011‐15 2016‐20 2021‐30 2031‐40<br>1991‐95 1996‐00 2001‐05 2006‐10 Total population (UN 2011)<br>Working‐age population/total population (UN 2011)<br>Total population (UN 2017) Total population (UN 2013)<br>Working‐age population/total population (UN 2017) Working‐age population/total population (UN 2013)<br>Note: Dashed lines indicate projections for years beyond UN report dates.<br><!-- End of picture text -->

> 7 This is based on the medium fertility variant – “...considered the most likely future scenario and that is widely used by the literature” (Barro and Lee 2015). This variant has “normal” fertility, mortality and migration rates.

> 8 Percentage of total population aged 15‐64.

> 9 Labor force participation rates for 2013‐2014 are projections. ILO‐modeled estimates and projections are based on estimates and projections for GDP‐related variables and population structure. The 2013 estimates and projections draw on the IMF WEO April 2013 and United Nations (2013). However, the IMF stopped publishing projections for Syria effective 2012, and ILO uses the regional median growth to extrapolate GDP growth for Syria.

6
**<u>Graph 4: Pre‐Conflict Labor Force Participation Rate and Projection</u>**

<!-- Start of picture text --> (a) 2018 Vintage (b) 2013 Vintage<br>%, labor force %, laborforce<br>participation rate (LFPR) %, growth participation rate (LFPR) %, growth<br>60.0 60.0<br>54.5 4.0 54.8 4.0<br>50.0 44.9 2.0 50.0 45.8 2.0<br>40.0<br>40.0 0.0<br>0.0 0.2<br>30.0 ‐1.3 ‐2.0<br>30.0 ‐1.5 ‐2.0<br>20.0 ‐4.0<br>20.0 ‐4.0<br>LFPR (WDI Archive, ILO‐modelled estimates, 2013), LHS<br>LFPR (WDI, ILO‐modelled estimates, 2018), LHS Growth in participation rate<br>Growth in participation rate Average growth, 2001‐2010<br>Average growth, 2001‐2010 Average growth, 2011‐2014<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Human capital** growth, based on Penn World Tables (PWT) 9, which for Syria, uses Cohen, Soto and Leker (CSL) data for the average years of schooling of the population aged 25 and above (Cohen and Soto 2007; Cohen and Leker 2014), averaged 1.0% for the 10 years up to 2010.<sup>10, 11</sup> Graph 5(a) shows average years of schooling based on select age groups under both CSL and Barro and Lee (BL) measures. Barro and Lee (2015) projections indicate a continued rise in the average years of schooling absent conflict, for the population aged 15‐64:<sup>12</sup> 1.6 years over 2011‐2030. Graph 5(b) shows human capital growth, based on the schooling years under CSL and BL measures. While fluctuations and differences are obvious decade to decade, there is consistency in a long‐term average of approximately 1.5%.

**<u>Graph 5: Pre‐Conflict Human Capital and Projection</u>**

<!-- Start of picture text --> (a) Average years of schooling (b) Human capital growth<br>Average years Average human<br>of schooling capital growth,<br>%<br>10.0<br>4.0<br>9.0<br>3.5<br>8.0<br>3.0<br>7.0<br>2.5<br>6.0<br>5.0 2.0<br>4.0 1.5<br>3.0 1.0<br>2.0 0.5<br>0.0<br>1981‐1990 1991‐2000 2001‐2010 2011‐2020 2021‐2030 2031‐2040<br>Cohen, Soto and Leker (25 and above) Cohen, Soto and Leker (25 and above)<br>Cohen, Soto and Leker (25‐64) Cohen, Soto and Leker (25‐64)<br>Barro and Lee (25 and above) Barro and Lee (25 and above)<br>Barro and Lee (25‐64) Barro and Lee (25‐64)<br>Barro and Lee (15 ‐ 64); dashed line indicates projection Barro and Lee (15 ‐ 64); patterned bar indicates projection<br>1980 1990 2000 2010 2020 2030 2040<br><!-- End of picture text -->

> 10 See the documentation, “Human Capital in PWT 9.0” (https://www.rug.nl/ggdc/productivity/pwt/). Human capital, h� e<sup>����</sup> , where s is average schooling years. Φ�s� = 0.134 ∙ s if s ≤ 4, Φ�s� =0.134 ∙ 4+0.101 ∙ (s − 4) if 4 < s ≤ 8, Φ�s� = 0.134 ∙ 4+0.101 ∙ 4+0.068 ∙ (s − 8) if s>8 (based on Caselli (2005) and Hall and Jones (1999)). For the first four years of education, a rate of return of 13.4 percent is assumed, corresponding to the average, reported by Psacharopoulos (1994) for Sub‐ Saharan Africa. For the next four years 10.1 percent is assumed, the average for the world.  For education beyond the eighth year the OECD value, 6.8 percent is assumed.

11 Using PWT 8.1 data points to a human capital growth rate 0.5% over 2001‐2010, but this is based on a previous version of Barro and Lee data (version 1.3, which has since been updated).

> 12 Barro and Lee (2015) do not appear to capture the population loss in Syria from the conflict, as they use projections from United Nations (2013).

7
**Total factor productivity (TFP)** growth, averaged 1.4% over 2001‐2010 based on calculations by The Conference Board (2018). Our own estimations following the methodology in Kim and Loayza (2019) also suggest an average growth rate of 1.4% for the same period.<sup>13</sup> See Graph 6.

**<u>Graph 6: Pre‐Conflict Total Factor Productivity (TFP) Growth</u>**

<!-- Start of picture text --> %<br>8.0<br>6.0<br>4.0 1.4<br>2.0<br>0.0<br>‐2.0<br>‐4.0 1.4<br>‐6.0<br>‐8.0<br>Kim and Loayza methodology (KL) (2019) The Conference Board (TCB) (2018)<br>KL (2019), average 2001‐2010 TCB (2018), average 2001‐2010<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

# **2.2 Simulation of what would have happened in the absence of conflict**

Table 1 details the baseline calibration of the LTGM‐PC for simulation under the no‐conflict scenario. The baseline assumptions are consistent with a long‐term GDP growth average of close to 5.0%. Average real GDP growth of 5.3% over 2011‐2018 would have led to real GDP rising from USD 60 billion in 2010 to USD 91 billion, and real GDP per capita rising from USD 2,857 to USD 3,774 by 2018. We discuss the baseline assumptions in sub‐sections 2.2.1 ‐ 2.2.2 below. Sub‐section 2.2.3 considers upper and lower estimates for the calibration.

## _2.2.1 Key parameters and initial conditions_

Key parameters and initial conditions (panels A and B of Table 1) either take their 2010 values or are calibrated. In panel A of Table 1, Syria’s share of labor income of 0.520 is proxied by the 2010 average value for relatively conflict‐free middle‐income MENA economies from PWT 9. This value is close to the average for lower‐middle income (LMI) countries (0.510), as well as “oil‐based” middle‐income economies with fuel exports amounting to less than 60% of their merchandise exports (0.540). The Conference Board (2018) also uses a similar value of 0.500. We calibrate the depreciation rate for aggregate capital to 4.8%, which is the PWT 8.1 data point for 2010 (PWT 9 gives a value of 5.5% for the same year). Generally, for Syria, PWT data suggests a relatively low share of structures (compared to other forms of physical capital) and hence a high depreciation rate, one that is above the MENA and LMI averages. So, we choose the lower of the two PWT depreciation values given the possibility of measurement error. The public capital depreciation rate of 3.1% follows the PWT 9 depreciation rate for non‐residential structures. Calculated as a residual, the private capital depreciation rate of

> 13 TFP is measured using growth accounting based on the standard Solow‐Swan growth model with human capital. Syria data for output, physical capital, human capital, and employed persons are taken from Penn World Tables (PWT) 9. Labor share is proxied by the average for four relatively conflict‐free middle‐income Middle East and North Africa (MENA) economies (Djibouti, Jordan, Morocco and Tunisia).

8
6.2% appears reasonable, consistent with the average of private capital depreciation rates for low‐ and middle‐income countries used in the IMF FAD Investment and Capital Stock Database 2017.<sup>14</sup> In panel B of Table 1, we calibrate the initial capital‐to‐output ratio, K�/Y� at 2.560 assuming steady‐state properties<sup>15</sup> and based on 30‐year averages of the investment‐to‐GDP share (22%), GDP growth (4.1%),<sup>16</sup> and aggregate depreciation rate (4.5% in PWT 8.1). Taking this approach at least provides us with some basis of setting an initial K�/Y� that is in between the PWT values for 2010 (2.384 based on PWT 9 and 2.632 based on PWT 8.1) especially since the PWT 9 value puts Syria on the border of the 75<sup>th</sup> percentile of lowest K/Y ratios, and is below the respective averages of LMI countries, low‐income countries as well as MENA countries.<sup>17</sup> Using the IMF FAD Investment and Capital Stock Database 2017 to calculate the shares of public and private capital shows that the share of public capital �K<sup>G</sup> /K� is relatively high in Syria, accounting for 0.610 of total capital. This is higher than the average for LMI countries of 0.399, but closer to the average for oil‐based economies in the Middle‐East and North Africa (MENA) region, 0.506. Still, Syria appears to have the highest ratio among oil‐based economies that have fuel exports accounting for less than 60% of exports (for which, the average is 0.397).<sup>18</sup> Taking all this into account, we use a “mid‐ point” public capital share of 0.450 for our simulations.

# _2.2.2  Central projections_

The central projected paths of key growth drivers over 2011‐2030 (panel C, Table 1) are based on:

- (i) average I/Y over 2001‐2007 of 21.5% (from WDI), and public investment share of total investment of 40%;

- (ii) average growth rates over 2001‐2010 for TFP of1.4% (according to The Conference Board 2018 and our own calculations following Kim and Loayza 2018) and human capital of 1.0% (following PWT 9);

- (iii) United Nations (2011) projections for growth rates of total population of 1.6% and of the working‐age population share of 0.6% (as discussed in Section 1.1); and

- (iv) zero growth in the LFPR (i.e. the 2010 participation rate level is maintained) given some signs of stabilization (and possible increase) from the 2013 ILO‐modelled estimations and projections.

> 14 4.25% for low‐income countries and 8.30% for middle‐income countries as of 2015.

> 15 In steady‐state/equilibrium, output grows at the same rate as capital stock, which allows us to write,

> � � � � ~~�~~ � ~~�~~ /�g� � δ � where g� is average output growth and δ the depreciation rate.

> 16 Investment‐to‐GDP and GDP growth are averages of WDI and WEO data (1981‐2007 and 1981‐2010 respectively).

> 17 Our calibrated value is also closer to what the calculated K/Y would be based on MFMod data (2.498).

> 18 Based on data from the IMF database for K�/K in 2010, and World Bank income and region classifications for the same year. Oil‐based economies are defined as those with fuel exports at least 30% of total merchandise exports (using WDI data).

9
# **<u>Table 1: No‐Conflict Baseline Simulation ‐</u>**

**Values for Parameters, Initial Conditions and Projected Variables**

|||**I**<br>**2010**|**nput value:**<br>**A**|<br>**A**|
|---|---|---|---|---|
|**Parameter/Variable***||**/**<br>**Calibrated**|**verage,**<br>**Pre‐**|**verage,**<br>**2011‐30**|
||**Note**||**Conflict**||
|**A. Consta**<br>|**nt Parame**<br>|**ters**<br>|||
|Labor shareβ|_(1)_|0.520|||
|Aggregate capital depreciation rateδ|_(2)_|0.048|||
|Public capital depreciation rateδ<sup>�</sup>|_(2)_|0.031|||
|Private capital depreciation rateδ<sup>�</sup>|_(3)_|0.062|||
|<br>**B. Initia**<br>|<br>**l Conditio**<br>|**ns**<br>|||
|Initial capital‐to‐output ratioK�/Y�|_(4)_|2.560|||
|Public capital share of total capitalK<sup>�</sup>/K|_(5)_|0.450|||
|Initialpublic capital‐to‐output ratioK�<br>�/Y�|_(6)_|1.152|||
|Initialprivate capital‐to‐output ratioK�<br>�/Y�|_(6)_|1.408|||
|**C. Projected Variables, C**<br>|**entral Pa**<br>|**th (2010/11—**|**30)**<br>||
|Investment‐to‐output ratioI/Y|_(7)_||0.215||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(7)_||0.086||
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(7)_||0.129||
|Human capital growthg�|_(8)_||0.010||
|TFP growthg�|_(9)_||0.014||
|Population growth rateg�|_(10)_|||0.016|
|Working age‐to‐population share, growthg�|_(10)_|||0.006|
|Labor force participation rate, growthg�|_(11)_||0.000||

- *Multiply by 100 to obtain parameter/variable values in percent share or growth terms (%). Note (source and calculation):

- _(1)_ Penn World Tables (PWT) 9: share of labor compensation in GDP at current national prices. There is no data for Syria. As a proxy, we use the 2010 average for four relatively conflict‐free middle‐income MENA economies (Djibouti, Jordan, Morocco and Tunisia).

- _(2)_ δ is PWT 8.1 data for Syria. δ<sup>�</sup> is the PWT 9 depreciation rate for non‐residential structures.

- _(3)_ δ<sup>�</sup> is derived as the residual from a weighted average calculation of δ based on δ<sup>�</sup> and K<sup>�</sup> ⁄K _. (4)_ Calibrated value based on long‐term averages of investment‐to‐output, GDP growth and the aggregate capital depreciation rate. See the discussion in Section 2.2.1.

- _(5)_ Calibrated based on average shares for lower‐middle income countries and oil‐based economies. See the discussion in Section 2.2.1.

- _(6)_ K��⁄Y� and K��⁄Y� are derived by applying K<sup>�</sup> ⁄ K to K�/Y� . _(7)_ Gross fixed capital formation (% of GDP), average for 1998‐2007 from WDI. Public investment share assumed at 40% based on World Bank (2017b) and IMF (2010).

- _(8)_ PWT 9: index of human capital per person based on years of schooling (Cohen and Soto 2007; Cohen and Leker 2014) and returns to education (Psacharopoulos 1994).  Average growth rate, 2001‐2010.

- _(9)_ The Conference Board (2017)/our own estimates using Kim and Loayza (2019). Average growth rate, 2001‐2010.

- _(10)_ United Nations (2011). _(11)_ Zero growth is assumed based on the stabilizing/marginally increasing participation rate observed in the 2013 ILO‐modelled estimates and projections.

# _2.2.3  Lower and upper estimates of projections_

Lower and upper estimates are set based on adjustments to some of the projected variables in panel C of Table 1. Table 2 displays these calibrations – which are those projections that are different from the central projections in panel C of Table 1.

10
- For the lower estimate, we introduce a decline in the LPFR based on the latest ILO‐ modelled estimates and projections that in turn draw on the most recent UN population estimates and regional GDP growth.<sup>19</sup> Other possible alternatives for this, though probably less satisfactory, are to: (i) assume the same decline in the participation rate as in the previous decade, but this would have likely been an unsustainable path; or (ii) use an earlier average growth, namely for 1991‐2000 (‐0.6%), which would be quite arbitrary.

- For the upper estimate, we use a higher I/Y of 23.5% since the IMF data presented in Section 2.1 indicates roughly a 2 ppt increase in average gross capital formation (from 23.1% over 2001‐2010 to 25.2% over 2011‐2016) and we assume the same for gross _fixed_ capital formation. We also use a higher long‐term average growth rate of 1.5% for human capital based on both CSL and BL data.

**<u>Table 2: No‐Conflict Simulation –</u> Upper and Lower Estimates for Projected Variables**

|**Variable**<br>**Note**|**Input value**|
|---|---|
|**Lower estimate onprojected variables(average, 201**<br> <br>|**1‐2030)**|
|Labor forceparticipation rate, growthg�<br>_(1)_|‐0.003|
|**Upper estimate onprojected variables(average, 2010**<br> <br>|**/11‐2030)**<br>|
|Investment‐to‐output ratioI/Y<br>_(2)_|0.235|
|Public investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.094|
|Private investment‐to‐output ratioI<sup>�</sup>/Y<br>_(2)_|0.141|
|Human capitalgrowthg�<br>_(3)_|0.015|
|*Multiply by 100 to obtain parameter/variable values in percent sh<br>terms (%).<br>Note (see Section 2.1 for further discussion):<br>_(1)_<br>Calculated using female and male LFPRs from ILO‐modelled<br>projections, May 2018. We carry out linear interpolation fo<br>based on the ILO‐reported values of series end points (2010<br>turning points (2020‐21 for the male participation rate) so<br>smoothened. Female and male shares of working‐age po<br>United Nations (2017) are kept unchanged at 2010 levels.<br>_(2)_<br>I/Yis based on IMF estimations suggesting higher GCF (% of G<br>The assumption of public investment share of total invest<br>used in the central projection is maintained.<br>_(3)_<br>Long‐term average based on calculations using both Cohen,<br>and Barro and Lee average years of schooling data respectiv<br>4(a)‐(b).|are or growth<br>estimates and<br>r annual values<br>and 2030) and<br>that trends are<br>pulation, from<br>DP) after 2010.<br>ment of 40% as<br>Soto and Leker;<br>ely. See Graph|

Graph 7 shows the trajectory for the level and growth of GDP in Syria based on the central calibrations in Table 1 and the calibrations for the upper and lower estimates from Table 2. Note that the lower estimate shows a slight upward trajectory – this is because of the ILO‐modelled estimates and projections, which show the LFPR for men gradually rising from 2021 onwards, after declining in the previous decade.

> 19 The estimates and projections do not fully account for the conflict. UN projections of population decline are captured but not GDP growth developments. See footnote 10 for further details.

11
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further damages of USD 7 billion to total USD 126.7 billion in 2018.<sup>20</sup> We assume that the conflict intensity index in the first three months of 2018 is at the 2017 level as estimated by the ESCWA, but then improves to somewhere between the 2011‐2012 level. Given the calculated average conflict intensity index for the year, we then derive an estimate of physical damage that is proportionate to the 2017 estimate of physical damage of USD 18.5 billion (see Appendix 2).<sup>21</sup> Such an assumption of improvement in conflict intensity is not inconsistent with the dread factor<sup>22</sup> in World Bank (2019), which reached new heights in 2017, continuing into the first quarter of 2018 before appearing to taper off to around the 2012 level by May 2018, the last data point. We also consider that the SOHR notes that a record low 20,000 lives were lost in 2018, and that big data (conflict‐related events) collated and analyzed by the Global Data on Events, Location and Tone (GDELT) project suggests that conflict intensity in 2018 was lower than in 2011.

World Bank (2017b, in their Figure 2.9) estimates a decline in private investment as a share of GDP �I<sup>�</sup> ⁄Y� from 12% in 2010 to 4% in 2015, and for the public investment‐to‐GDP ratio �I<sup>�</sup> ⁄Y� – a decline from 9% in 2010 to 1% in 2015. On the other hand, ESCWA (2018) sees a smaller decline from 12% to 9% for private investment as a share of GDP (averages for 2006‐2010 and 2011‐2016 respectively) and from 10% to 7% for public investment as a share of GDP. In our simulations, we use the World Bank estimates for our central projections, assuming I<sup>�</sup> ⁄Y and I<sup>�</sup> ⁄Y remain stable at 4% and 1% respectively over 2016‐2018. We then consider the ESCWA estimates as an upper estimate to our projections.

A deterioration in the efficiency of public investment is a likely concern. No Syria‐specific value of the Infrastructure Efficiency Index (IEI) (Devadas and Pennings 2019) is available. As a proxy for Syria’s pre‐conflict efficiency of new public capital, we use the LMI average of 0.734, and build in a decline in the efficiency of public investment to the low‐income (LI) group average of 0.570 by 2017 (at which point the World Bank’s income classification of Syria switches from LMI to LI).

# _3.1.2 Demographics and the labor force_

In terms of Syria’s total population, UN population statistics (United Nations 2017)<sup>23</sup> indicate negative average annual growth of ‐1.7% over 2011‐2018, with the population declining from 21 million in 2010 to 18.3 million in 2018. UN statistics also indicate for the working age‐to‐population share, negative average annual growth of ‐0.09% over 2011‐2018. The projections capture developments during the conflict. From mid‐2010 to mid‐2020, estimated deaths are higher at 1,036,445 compared with 657,131 the previous decade<sup>24</sup> and reflect the rise in the proportion of male deaths (68% versus 32% for women). Further, net migration abroad is also higher at 5,397,896 over mid‐2010 to mid‐2020.

> 20 In terms of other sources of data on physical damage, SCPR (2016) estimates total capital stock at 2010 prices to be down by 57% at end 2015 compared with 2010, but with no details of the breakdown of damages. World Bank (2017a), in a detailed but narrower assessment, estimates the damage to housing and infrastructure for six cities in the range of USD9.6 – USD11.1 billion at pre‐conflict prices, over 2011 – February 2017. The housing damages are extrapolated to eight governorates and used in the model‐based simulations of physical destruction effects on GDP in World Bank (2017b).

> 21 We use the most recent year’s value of physical damage as opposed to some longer‐term average measure, as the former would best reflect possible further damages given that much of Syria’s physical capital would have been destroyed by 2017/2018.

> 22 Shown graphically, this is an aggregate measure of the number of conflict events based on tank, artillery and air strikes of light, medium and heavy incidence levels.

> 23 Medium fertility variant.

> 24 607,685 on average per decade over mid‐1980 to mid‐2010.

13
This considers refugee numbers based on the UN Refugee Agency (UNHCR) populations of concern data up to March 2017 and builds in refugee returns over 2020‐2035.<sup>25</sup> There is a possibility though that the above numbers might still understate fatalities and migrants. The cumulative number of fatalities from the war is hard to ascertain. A source currently relied upon, the Syrian Observatory for Human Rights (SOHR) puts the death toll at 560,000 (civilians and combatants) over March 2011 – 10 December 2018 (with 367,965 documented victims). Netting off foreign combatants of 65,108, we would get a total of 494,892 Syrian fatalities.<sup>26</sup> This is higher than what the UN population statistics suggest in terms of conflict‐related deaths – 303,451, based on our calculations when we take the difference of average deaths between 2011‐2020 and 2001‐2010 (multiplied by eight years).

The UNHCR populations of concern data show that compared to 2016 total registered Syrian refugees and asylum‐seekers increased in 2017 by 746,811 (Graph 8(a)). While the equivalent 2018 data are not available, from the UNHCR Regional Refugee and Resilience Plan database specifically on refugees in selected neighboring countries ‐ Egypt, Iraq, Jordan, Lebanon, Turkey and North Africa,<sup>27</sup> we note that when compared to 2017, refugees had increased by 184,398 to 5,663,675 in 2018 (patterned bars in Graph 8(a), with the age and gender profile of these refugees shown in Graph 8(b)). Based on this, the UN population statistics may be understating refugee movement by about 900,000. But if we discount the 1 million refugees born in displacement/exile supposedly included in the UNCHR data, then the under‐representation of population “loss” in the UN population statistics due to the use of outdated UNHCR data disappears.<sup>28</sup> Perhaps of greater concern then is that the UN population statistics likely do not include non‐registered Syrians in neighboring countries, for which estimates vary but tend to go up to more than a million (see for instance, Vignal (2018) and World Bank (2019)). UNDP and UNHCR (2019) put the difference between estimated total Syrians<sup>29</sup> and registered Syrian refugees at about 1.6 million in December 2018. The difference is wholly accounted for by Egypt, Jordan and Lebanon.

Consequently, as a possible lower estimate to population growth estimates, we calculate an added decline in the Syrian population of 1.8 million, building in additional conflict deaths of 200,000, and unregistered Syrian migrants of 1.6 million. This would reduce the 2018 population from 18.3 million to 16.5 million giving a negative average growth of ‐3.0% over 2011‐2018.<sup>30</sup>

> 25 The UNHCR data available up to 2016 indicate a change of 5,688,897 in the number of refugees, asylum‐seekers (pending cases) and others of concern based on the 2010 and 2016 stock positions. UN data on the international migrant stock for 2017, show net migration for Syria of 5,784,696 over 2011‐2017 (2017 stock compared against 2010 stock).

> 26 Source: http://www.syriahr.com/en/?p=108723. Civilian casualties totaled 111,330 (22.5% of the 494,892 total Syrian casualties), and were mostly adult males (70.1%), as 20,819 were children under 18 (18.7%) and 13,084 were women over 18 (11.8%).

> 27 Syria Regional Refugee Response. <u>https://data2.unhcr.org/en/situations/syria</u>

> 28 <u>https://www.unhcr.org/refugeebrief/the‐refugee‐brief‐11‐december‐2018/</u>

> 29 Total estimated number of Syrians are government estimates which include registered Syrian refugees, unregistered Syrian refugees and Syrians residing in host countries under alternative frameworks.

> 30 UN population statistics suggest that births remained relatively high at 4,339,946 over mid‐2010 to mid‐2020 (about 434,000 births/year) versus 5,275,028 the previous decade (about 528,000 births/year). Some news reports (for example: <u>http://english.alarabiya.net/en/variety/2015/11/17/Births‐in‐Syria‐down‐more‐than‐50‐since‐war‐media.html)</u> suggest that births have fallen substantially, but absent more sources of concrete analysis, we do not make any adjustments to birth rates. Further, World Bank (2019) notes that adolescent fertility rates have only dropped to 40 per 1,000 women aged 15‐19 in 2016 compared to 45 in 2010, amid child marriages as a conflict coping mechanism.

14
**<u>Graph 8: UNHCR Registered Syrian Refugees and Asylum‐Seekers</u>**

<!-- Start of picture text --> (a) Total Syrian Refugees and Asylum‐Seekers (b) Profile of Syrian Refugees in Selected Neigboring Countries<br>Million All countries (source: UNHCR<br>persons populations of concern*)<br>7.00 Turkey and MENA (source: UNHCR 6.46 Total<br>6.00 populations of concern*) 5.72 5.48 5.66 60+ yrs<br>Selected neigboring countries 5.13 4.86 5.48<br>5.00 (source: UNHCR Regional Refugee 4.59 18‐59 yrs<br>and Resilience Plan) 3.97<br>4.00 3.73 12‐17 yrs<br>3.00 2.52 2.39 5‐11 yrs<br>2.00<br>0‐4 yrs<br>1.00 0.76 0.70<br>... ... 0 10 20 30 40 50 60<br>0.00<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 % of total refugees in Egypt, Iraq, Jordan, Lebanon, Turkey,<br>Female Male and North Africa ( 5,663,675 as at 31 December 2018 )<br>*Comprises refugees, asylum‐seekers (pending cases) and others of concern. Source: UNHCR and Government of Turkey<br><!-- End of picture text --> <!-- Start of picture text --> 6.00<br>5.00<br>4.00<br><!-- End of picture text --> ILO (2018) projects the overall LFPR at 43.0% in 2018 (female at 12.5% and male at 73.0%) down from 44.9% in 2010 (female at 14% and male at 75.6%), giving an average negative growth of ‐0.5% over 2011‐2018 (female LFPR growth at ‐1.4% and male at ‐0.4%). Data in World Bank (2017b) suggest the LFPR in 2015 stood at 47.2%, somewhat higher than the 2010 level of 44.6%.<sup>31</sup> World Bank (2019) meanwhile shows latest/2017 LFPRs for men and women above 15 years of age at 79.1% and 11.9% respectively versus 73.0% and 13.0% in 2010. We use the ILO estimates in our central projection, and for a possible upper estimate, consider an increment in the LFPR by 2018 based on the changes in participation rates of men and women reported in World Bank (2019).

# _3.1.3 Human capital_

Human capital would have been affected by (i) the **interruption of schooling** of the younger population, who represent future entrants into the labor force; and (ii) **migration** and **fatalities** which alter the distribution of years of schooling among the remaining population/existing labor force.

Regarding the **interruption of schooling** , according to SCPR (2016), almost half (45.2%) of all basic education school‐age children residing within the country were not attending school by 2014‐2015. They estimate a decline of 1.5 in the average years of schooling for population aged 15 and above _,_ based on these non‐attendance rates, from 6.8 in 2010 to 5.3 years in 2015.<sup>32</sup> We provide alternative calculations to assess the impact from the interruption of schooling as well as migration and fatalities on the national average of years of schooling. To do this, we use forward extrapolations, with the following assumptions:<sup>33</sup>  for age groups, a�3: 25 �29 to a�10: 60 �64 , the educational attainment (average schooling years), s, of gender g (either men, m or women, f� in age group, a, at time, t, is the same as that of the age group five years younger at time, t�5, as we assume these groups have completed their education, i.e.,

> 31 Though this LFPR increase contrasts with the ILO estimate of a decline, data in World Bank (2017b) suggests a sharp increase in the unemployment rate from 8.6% in 2010 to 52.9% in 2015. ILO estimates, on the other hand, point to a more moderate increase in the unemployment rate to 14.9% in 2018 (14.7% in 2015).

> 32 The same contraction rate in enrollment for basic education is assumed for secondary and tertiary education.

> 33 The nomenclature draws on Barro and Lee (2013, 2015).

15
a — a-1 gt — Sgt—5 _ a-1 a-1 ~ gjt-5 jt-5 a — gi g=m a t a=1:15-19 a = 2:20— 24, t-5 Gita gta —= Lij( gjt-sa + Aenrollsa ;,) Durjr_sa _=D (giesa jft-s+a Aenrollgj,a js)a —=aaa Soest djAenrollgi, — jt-s a t a— <u>Pope Pop?</u> c =Sr, oa X ~~—yt~~ +S a x ~~—m,t~~ fit Pop? mt Pope _y1o a <u>Pope</u> t — Ya=1 t x <u>Pop</u> t(15-64) t-—5 <!-- Start of picture text --> (15-64)<br><!-- End of picture text --> t
rather surprisingly suggest an increment of about 10 percentage points during the conflict period compared with the 2010 rate of 26%.<sup>36</sup> We build in this increment over 2010‐2015, applying the same enrollment rate for men and women, and keep it unchanged thereafter. Primary school duration is assumed to be 6 years (6‐11 years), secondary school, 6 years (12‐17 years), and university 4 years (18‐21 years). In Appendix 3, we provide further details on how we arrive at the average years of schooling in 2018.

By using the above methodology, we constrain changes to the national average of schooling years to arise from shifts in the distribution of the total population by age and gender, and in enrollment ratios. Because we use past composite values of average years of schooling, we do not consider changes in completion rates (that is, we are assuming completion rates are unchanged). This approach also does not consider other types of heterogeneity in educational attainment, for instance, that may depend on the socioeconomic status or geographical origination of migrants and conflict victims. Verme et al. (2016), focusing on the profile of refugees in Jordan and Lebanon, find that Syrian refugees in fact tend to have slightly lower levels of educational attainment than pre‐conflict Syrians.<sup>37</sup> To obtain human capital growth, we continue to assume the same calculations as in PWT 9 for the returns to education. Our calculations for the central projection using UN population statistics suggest that average years of schooling would have declined by 1.467 years for the population aged 15‐64 with human capital contracting by an annual average growth of ‐2.59% over 2011‐2015 and ‐ 0.56% over 2016‐2018 respectively. When we consider the additional decline of 1.8 million in the Syrian population discussed in the previous section, average years of schooling declines only marginally more, that is by 1.499 years. If we split this decline over time following the proportions in the first case, we obtain annual average growth rates in human capital of ‐2.64% over 2011‐2015 and ‐0.57% over 2016‐2018 respectively.

# _3.1.4 TFP_

The Conference Board estimates an average TFP growth of ‐9.5% over 2011‐2017 (from a low of ‐ 22.9% in 2012 rising to 2.8% in 2017). However, while the loss of TFP would be an important driver of loss in GDP potential, it could be that this high number reflects an underestimation of the destruction of physical and human capital.

Thus, as an alternative to The Conference Board’s calculations of TFP growth, we utilize Kim and Loayza’s (2019) model of determining TFP growth. The key element that feeds into this model is an overall index of TFP determinants, the determinants being education, infrastructure, innovation, institutions, and market efficiency. The composite index stood at 30.33 for Syria in 2010 on a scale between 1 and 100. We estimate the trajectory of this index over 2011‐2018 by calibrating its subcomponents. For the education index, we calculate a decline that is proportionate to the fall in average years of schooling of the working‐age population. Given that enrollment rates fall, and the exodus of Syrians increases noticeably only from 2013, we keep the average schooling years unchanged from 2010 to 2012, instead interpolating a decline over 2013‐2018, based on our calculations of average years of schooling for 2015 and 2018 respectively. For the infrastructure index,

> 36 See Milton (2019) for a discussion of how Syria’s higher education system survived quantity wise, despite general expectations that higher education suffers relatively more during conflict, but that quality had been eroded and political control over campuses increased.

> 37 Educational attainment for pre‐conflict Syrians five years and older (2007): 83 percent with primary education and below, 9.5 percent with secondary education and 7.2 percent with tertiary education. Syrian refugees five years and older (2014): 83.6 percent with primary education and below, 11.4 percent with secondary education and 5.1 percent with tertiary education (Jordan); 86.7 percent with primary education and below, 8 percent with secondary education and 5.3 percent with tertiary education (Lebanon).

17
G P <!-- Start of picture text --> GP<br>Kooj_ Ke,2011j<br>Yo Y2011<br>J J<br>c,2011 2011<br>Ko2011 j _ K>o10 j x 1+g k},2011 j<br>Y2011 Y2010 1+9y,2011<br>. j<br>JK3010<br>2010 Y2010<br>2010<br>(1- si )(1-d4o11)j + 13519/¥2010 J<br>1 + g j = K3010/¥2010<br>ko2011 (14.99.2011) (1+9,2011)(1+9n,2011)<br>.J<br>J— P2011<br>2011 7 ,j<br>K3010<br>14+ 9y20n = [A+ Tyo11)¢ -Oei(1 + Gazou)(1 + 96,2011) g (a + IxS2011) ’ (4 + GxP 2011) 1-B-¢h (4 + Gn2011) B<br>14+0oo1 = (1 + 9o2011)(1 + Gu2011)(1 + 9w2011)<br><!-- End of picture text --> Jj t
in USD 7 billion for 2018 as discussed in Section 2.1.1. Damages are apportioned between public and private capital based on their relative cumulative shares of damages as at end 2015, made available by ESCWA. We assume the same shares for each time t (that is, 40 percent of damages are attributable to public capital, 60 percent to private capital).

In Table 3, panel C, we present information on the central paths of projected variables based on the previous discussions. Under the central projection, both K<sup>�</sup> ⁄Y and K<sup>�</sup> ⁄Y are lower in 2018 (1.029 and 0.708 respectively) compared with 2010 (1.152 and 1.408 respectively) but more so for the former, since the damage for public capital was valued at less than for private capital.

**<u>Table 3: Simulation of Syria’s Conflict Years (2011‐2018)</u>**

|<br>**Parameter/Variable**|<br>**Note**|<br>**2010**|<br>**Average**<br>**2011‐18**|**2018**|
|---|---|---|---|---|
|**A. Constant**|**Paramet**|**ers**|||
|Labor shareβ|_(1)_||0.520||
|Aggregate capital depreciation rateδ|_(1)_||0.048||
|Public capital depreciation rateδ<sup>�</sup>|_(1)_||0.031||
|Private capital depreciation rateδ<sup>�</sup>|_(1)_||0.062||
|<br>**B. Capital‐to‐Ou**<br> <br>�|<br>**tput (K/Y**<br>|**) Ratios**<br>|||
|Initial public capital‐to‐output ratioK�<br>/Y�|_(1)_|1.152|||
|SimulatedK�<sup>�</sup>/Y (with damage)|_(2)_||1.141|1.029|
|Initialprivate capital‐to‐output ratioK�<br>�/Y�|_(1)_|1.408|||
|SimulatedK�<sup>�</sup>/Y (with damage)|_(2)_||1.144|0.708|
|**C. Projected Variables, C**<br>|**entral P**<br>|**ath (2011‐**|**2018)**||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.090|0.025|0.010|
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.120|0.064|0.040|
|Efficiency of new public investmentθ<sup>�</sup>|_(4)_|0.734|0.632|0.570|
|Human capital growthg�|_(5)_||‐0.018|‐0.006|
|TFP growthg�|_(6)_||‐0.016|‐0.022|
|Population growth rateg�|_(7)_||‐0.017|0.001|
|Working age‐to‐population share, growthg�|_(7)_||‐0.001|0.011|
|Labor force participation rate, growth,gρ|_(8)_||‐0.005|‐0.008|

*Multiply by 100 to obtain parameter/variable values in percent share or growth terms (%). Note (source and calculation):

- _(1)_ Unchanged from Table 1. See Table 1 for further details on source and computation.

- _(2)_ Calculations follow the discussion in Sections 3.1.1 and 3.2.

- _(3)_ Based on World Bank (2017b). Their estimates of 1% and 4% for I<sup>�</sup> /Y and I<sup>�</sup> /Y respectively in 2015 are also assumed for 2016‐2018 in our simulations.

- _(4)_ Based on the average IEI for LMI countries for 2010 which is assumed to gradually decline to 0.570 (the average IEI for LI countries) by 2018.

- _(5)_ See Section 3.1.3. Given that enrollment rates fall, and the exodus of Syrians increases noticeably only from 2013, we keep the index of human capital unchanged from 2010 to 2012, such that the contraction mainly occurs over 2013‐2015 with average growth of ‐4.28% (average growth over 2016‐2018: 0.56%).

- _(6)_ TFP growth is calibrated using the empirical methodology in Kim and Loayza (2019). See Section 3.1.4.

- _(7)_ Based on United Nations (2017).

- _(8)_ Female and male LFPRs are based on ILO‐modelled estimates, May 2018 (ILOSTAT). Female and male shares of the working‐age population follow United Nations (2017).

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In Table 4, we show the lower and upper estimate outcomes for capital‐to‐output ratios when we consider (i) lower population growth and the associated human capital growth for this population trend; and (ii) higher on average investment‐to‐output ratios and a higher LFPR. Other variables remain unchanged from their central paths in the simulations. For both the lower and upper estimate scenarios, the K/Y ratios in 2018 are slightly higher than in the central projections but remain lower than the 2010 levels.

**<u>Table 4: Simulation of Syria’s Conflict Years (2011‐2018)</u>**

**– Lower and Upper Estimates and Impact on Capital‐to‐Output Ratios**

|**Variable**|**Note**|**Average**|**2018**|
|---|---|---|---|
|||**2011‐18**||
|**A. Lower Estima**|**te**<br>|||
|Population growthg�|_(1)_|‐0.030|0.000|
|Human capital growthg�|_(2)_|_‐_0.019|‐0.006|
|<br>_Capital‐to‐Output (K/Y) Ratios_<br>|_(with D_|_amage)_||
|SimulatedK�<sup>�</sup>/Y||1.180|1.091|
|SimulatedK<sup>�</sup>/Y||1.175|0.737|
|�<br>**B. Upper Estima**<br>|**te**<br>|||
|Public investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.070|0.070|
|Private investment‐to‐output ratioI<sup>�</sup>/Y|_(3)_|0.090|0.090|
|Labor force participation rate, growth,g|_(4)_|0.007|0.006|
|ρ<br>_Capital‐to‐Output (K/Y) Ratios_<br>|<br>_(with D_|_amage)_||
|SimulatedK�<sup>�</sup>/Y||1.133|1.127|
|SimulatedK<sup>�</sup>/Y||1.139|0.784|
|�<br>*Multiply by 100 to obtain parameter/variable v<br>growth terms (%).|alues in p|ercentage sh|are or|

- _(1)_ Based on a further downward adjustment to the UN population statistics to reflect higher migration and fatalities than the central projection. See Section 2.2. Population changes over 2011‐2018 are calibrated such that they follow the trend in the UN population statistics but with steeper slopes.

- _(2)_ Human capital growth is calculated based on the adjusted population data. See Section 3.1.3. As in the central projection, we keep the index of human capital unchanged from 2010 to 2012, such that the contraction mainly occurs over 2013‐2015 with average growth of ‐4.37% (average growth over 2016‐2018: 0.57%).

- _(3)_ Investment‐to‐output ratios are based on ESCWA (2018) estimates. See Section 3.1.1.

- _(4)_ Calculated based on the growth in LFPRs for men and women above 15 years of age over 2010‐2018 reported in World Bank (2019). Female and male shares of the working‐age population follow United Nations (2017).

Graphs 9(a) ‐ 9(b) show the outcomes of simulations for GDP level and growth over the conflict years given the calibrations in Table 3 and Table 4. Our simulations across the three scenarios (central, lower and upper estimate projections) indicate that the depletion of factors of production alone may account for about 87 percent of the negative GDP growth on average, and further, that about 64 percent of the average negative growth is due to physical capital destruction. Demographics and labor 20
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<!-- Start of picture text --> ts,<br>se — — i — en — oe— oe — are<br>= NARS SRR Sy ee<br>eoo@e<br>=] fc] 7<br>B Py ——_<br>(a) GDP Loss (b) GDP Per Capita Loss<br>USD bil, 2010 USD, 2010<br>0 0<br>-10 = 500 SSe ~ Ss.<br>-20 SN “SS.<br>-30 -1000 SS,~~s<br>..<br>40 -1500 *,er<br>Cumulative boss: — Cumulative loss:<br>-6050 Cumulative loss: USD 289\ A b : 000 CumulativeUSD 10,763 loss: S~SS—USD 9,623wa<br>70 USD 300 b —S -2500 —<br>80 -3000<br>2010 «2011S 2012's SC(iiTC(éODB 2010 «-2013.««2022':s2ssi—i‘iCiSC(atiGSC(itiétSCi«éiB<br>= Conflict (Central) - No Conflict (Central) —_——= Conflict (Central) - No Conflict (Central)<br>=== Conflict (Lower Estimate} - No Conflict (Lower Estimate} === Conflict (Lower Estimate) - No Conflict (Lower Estimate)<br>--®- Conflict (Upper Estimate) - No Conflict (Upper Estimate) =--* = Conflict (Upper Estimate)-No Conflict (UpperEstimate)<br><!-- End of picture text --> <!-- Start of picture text --> =]<br>B<br><!-- End of picture text -->
# **4. Growth Scenarios Post Conflict**

This section discusses potential growth scenarios for Syria in the aftermath of war, exploring how long it would take for Syria to reach its pre‐conflict level of development and the number of “lost decades” under various assumptions for the growth drivers.

**<u>Table 5: Key Factors in the Post‐Conflict Scenarios for Growth Drivers</u>**

|**Broad Case**|<br>**Factor**|<br>**Baseline(Moderate)**|<br>**High (Optimistic)**|<br>**Low(Pessimistic)**|
|---|---|---|---|---|
|Voluntary<br>mobility OR<br>Forced<br>repatriation|Security|Partial political<br>settlement with<br>strong guarantees for<br>micro‐security and<br>property rights.|Robust political<br>settlement.|Political settlement<br>largely reflects de‐<br>facto balance of<br>power, with limited<br>guarantees for micro‐<br>security and property<br>rights.|
||Reconstruction<br>program|A substantial<br>reconstruction<br>program of USD 140<br>billion (average of the<br>high and low<br>scenarios), spread<br>evenly over a 20‐year<br>period.|A large<br>reconstruction<br>program amounting<br>to USD 250 billion to<br>meet UN‐estimated<br>reconstruction bill,<br>spread evenly over a<br>20‐year period.|Limited<br>reconstruction<br>program of USD 30<br>billion, largely relying<br>on China, Iran and<br>Russia, and spread<br>evenly over a 10‐year<br>period.|
|Voluntary<br>mobility|Refugee<br>returns|Of total refugees in<br>neighboring countries,<br>43 percent return rate<br>based on the ratio of<br>reconstruction funds<br>in the first 10 years<br>(moderate versus high<br>scenario).|76 percent rate of<br>refugee returns from<br>neighboring<br>countries, based on<br>the UNHCR (2018)<br>survey of refugees<br>intending to return<br>to Syria one day.|Of total refugees in<br>neighboring<br>countries, 18 percent<br>return rate based on<br>the ratio of<br>reconstruction funds<br>in the first 10 years<br>(low versus high<br>scenario).|
|Forced<br>repatriation|Refugee<br>returns|100 percent return|rate, of refugees in neig|hboring countries.|

<u>Notes: Under the voluntary mobility case, refugee returns follow the same time pattern across the three scenarios,</u> rising to peak around 2023‐2024, and gradually moderating thereafter. World Bank (2019) finds that refugee mobilization tends to be lower, the lower are security and infrastructure services.

Experiences of other countries in the Middle‐East suggest that longer‐lasting conflicts would entail a longer recovery period. Sab (2014) notes that it took Lebanon 20 years to reach its pre‐war GDP level (after the Lebanon Civil War from April 13, 1975 – October 13, 1990), Kuwait, seven years (Gulf War from August 2, 1990 – February 28, 1991), and Iraq, one year only (2003 Invasion of Iraq from March 19‐May 1, 2003).<sup>39</sup> Lebanon lost 70 percent of its GDP level while Kuwait 55 percent and Iraq 35 percent during their respective wars. Gobat and Kostial (2016) note that under the hypothetical assumption of reconstruction starting in 2018 and the Syrian economy growing at about 4.5 percent, it would take the country about 20 years to reach its pre‐conflict real GDP level. ESCWA

> 39 Sab (2014) highlights that while the 2002 invasion of Iraq did not last long, Iraq had experienced low‐grade civil war for much of the 2000s until the summer/fall of 2009.

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(2018) puts Syria’s real GDP at about USD 27 billion in 2017 – some 55 percent below the 2010 level and close to the level in the early 1990s. Our central projection of Syria’s potential GDP level in Section 3, at USD 21.2 billion in 2018, is 65 percent below the GDP level in 2010, close to the loss experienced by Lebanon.

To analyze the outlook for growth in Syria, we first consider a voluntary mobility case within which our projections for the growth drivers are guided by three plausible political settlement/security outcomes (baseline/moderate, optimistic, and pessimistic). These settings are associated with varying levels of reconstruction assistance, which in turn influences the voluntary mobility of refugees residing in neighboring countries.<sup>40</sup> With regard to linkages to the drivers of growth, the amount of reconstruction funds directly affects public and private investment.

Refugee returns, meanwhile, affect the size of the labor force. We also build in variation in human capital growth based on different assumptions for enrollment rates and vary the projections for TFP growth across the three scenarios. We then look at a second broad case of forced repatriation of refugees. Forced repatriation would contravene UN principles that care for the safety and welfare of refugees,<sup>41</sup> but it may be instigated by local and international voices eager for a quick resolution of the refugee issue.<sup>42</sup> Under forced repatriation, refugee returns are disconnected from the size of the reconstruction program, and instead, a “full” rate of refugee returns is assumed across the three scenarios. We thus have six scenarios in total – three for each of the two different broad cases (voluntary mobility and forced repatriation respectively). These are summarized in Table 5. We discuss the projections for the growth drivers in detail in Section 4.1 below. Then, we present the resulting simulations for Syria’s growth over the next 30 years in Section 4.2.

# **4.1 Prospective developments in growth drivers**

## _4.1.1 Physical investment_

The reconstruction and expansion of Syria’s physical capital will largely depend on the extent of foreign funds made available since its self‐financing capacity is likely to be limited, especially in the near term. We use Equation (7), a slight variant of the saving/investment ‐ balance of payments accounting identity, to link �� to the inflow of foreign funds.<sup>43</sup> �� (Note: S� = saving _excluding_ net current transfers, CAB� = current account balance _excluding_ net current transfers, TB� = trade balance, IB� = income balance, NCT� = net current transfers, ∆NFL� = change in net foreign liabilities.)

> 40 World Bank (2019) finds that pull factors, of which two prominent ones are security and infrastructure, have unambiguous effects on return behavior, in contrast to push factors in countries of asylum which have mixed implications.

> 41 The UN principle of non‐refoulement, codified in Article 33 of the 1951 UN Refugee Convention, requires that “no contracting state shall expel or return a refugee in any manner whatsoever to the frontiers of territories where his life or freedom would be threatened.”

> 42 The limitations that the UN non‐refoulement principle places on repatriation is frequently resented by states. Host countries are often impatient to see uninvited refugees leave. Countries of origin are sometimes impatient to see them return and signal the end of conflict. Moreover, donor states are eager to bring an end to the long‐term refugee assistance programs that they fund.

> 43 The standard identity has net current transfers as a component of the current account balance.

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In Equation (7), external financing may take the form of (non‐debt creating) aid and grants (which enter as higher NCT� ) or direct investment and loans (which result in an increase in foreign liabilities, thus increasing ∆NFL� ).<sup>44</sup> If the foreign funds lead to an equivalent amount being spent on tradables (for example, the imports of capital goods), the current account will be in deficit, ceteris paribus.<sup>45</sup> If the foreign funds do not lead to the purchase of tradables, the current account will be in balance, ceteris paribus (see for instance Elbadawi, Kaltani and Schmidt‐Hebbel (2008) for related discussion on how the utilization of aid monies affects current account balances and exchange rates). In our simulations, ����� <u>�</u> �� ∆���� <u>�</u> where NCT� �∆NFL� �∆FF�, the inflow of foreign funds, which leads to a corresponding amount being spent on tradables. This gives us Equation (8). ∆FF� varies across the three post‐conflict scenarios as described in Table 5: beginning 2019, USD 12.5 billion per year over a 20‐year period under the optimistic scenario; USD 7 billion per year over a 20‐year period under the baseline scenario; and USD 3 billion per year over a 10‐year period under the pessimistic scenario.

�� �� ∆��� � � (8) �� �� �� Regarding �� ~~,~~ if we assume �� of 5 percent (as per the central projection of our conflict �� �� simulation), and ���� of around ‐30 percent at the end of the conflict, this would give us �� of �� �� approximately ‐25 percent.<sup>46</sup> This is about 50 percentage points below Syria’s pre‐conflict long‐term average: 23 percent (20‐year average over 1991‐2010 based on WEO data). For the post‐conflict pessimistic scenario, we calibrate the transition for Syria’s �� by 50 percentage points to 25 percent, �� in eight years, based on the experience of Lebanon. Lebanon was subject to persistent political instability during its recovery, and external assistance specific to its reconstruction program was limited, though it did receive large capital inflows attracted by high interest rates that enabled it to run current account deficits. For the optimistic scenario, we assume that Syria’s saving ratio increases by 60 percentage points to 35 percent in five years, following the timeline and change experienced by Kuwait as it recovered to its pre‐conflict saving‐to‐GDP level. Resource‐rich, high‐income Kuwait made a strong recovery after the sharp decline as its oil production capacity was quickly restored amid a comprehensive economic recovery and reconstruction program.<sup>47</sup> For the moderate scenario, we take an average of the projections for �� under the other two scenarios. See Appendix 5 for further details. �� Once we have the projection for �� for each scenario, we then split it between public and private �� investment by continuing to assume that 40 percent of total investment is public, following pre‐ conflict trends. This is consistent with the estimated relative shares of destruction between public and private capital during the conflict. In terms of the efficiency of new public investment, we keep efficiency unchanged at 0.570 under the pessimistic scenario and assume a rise from 0.570 to 0.734 by 2038 under the baseline. For the optimistic scenario, we assume a rise to the average IEI for the upper‐middle‐income group of 0.769 by 2038.

> 44 This would be the “first leg” before the money is spent and both will have counter entries reflected as an increase in foreign assets.

> 45 In either instance of financing spent on imports – transfers or loans ‐ the trade deficit is the same, though the current account deficit _including_ transfers, will differ.

> 46 This is based on the 2017 estimate of the trade balance share of GDP by ESCWA (2018).

> 47 See Sab (2014) for discussion on the conflict and recovery stories of Kuwait and Lebanon.

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# _4.1.2 Demographics and the labor force_

As shown in Graph 8, registered Syrian refugees in neighboring countries numbered 5,663,675 at the end of 2018. A survey by UNCHR (2018) of a sample of 4,283 Syrian refugees in neighboring countries indicated that 76 percent intend to return to Syria one day. In the optimistic scenario of the voluntary mobility case, if we assume a 76 percent return rate, this would amount to 4.3 million returnees. We use the UN population statistics (United Nations 2017), which projects an average population growth of 2.5% over 2019‐2038 and builds in net migration into Syria of 4.21 million over 2020‐2035. Of this total, 66 percent return over 2020‐2025 (about 556,000 on average per year), 29 percent over 2025‐ 2030 (about 240,000 per year) and 5 percent over 2030‐2035 (about 46,000 per year).<sup>48,49</sup> For the moderate and pessimistic scenarios in the voluntary mobility case, we assume refugee return rates of 43 percent and 18 percent respectively based on the ratio of reconstruction‐related average funding per year in the first 10 years post conflict. For both these moderate and pessimistic scenarios, we still assume a similar time path as the optimistic scenario – with refugee returns rising to peak around 2023‐2024, and gradually moderating thereafter. Consequently, we obtain average population growth rates of 2.2 percent and 1.9 percent respectively over 2019‐2038 for the moderate and pessimistic scenarios. For all three scenarios in the forced repatriation case, we adjust the return rate to 100 percent (but keep the pattern of returns unchanged over time), thus giving us an average population growth rate of 2.7 percent over 2019‐2038.

We continue to draw on United Nations (2017) for projections of the working‐age population share, and ILO (2018) for LFPR projections – the latter is available until 2030, and so we keep the values thereafter stable at the 2030 level. The same working‐age population share and LFPR time series are used across all scenarios.

# _4.1.3 Human capital_

We follow the same approach as in Section 3.1.3. Changes in the average years of schooling are estimated based on shifts in the population (including the return of refugees) and improvements in enrollment rates. Where we reduce (voluntary mobility case – moderate and pessimistic scenarios) or increase refugee returns (forced repatriation case) compared to the UN statistics (voluntary mobility case – optimistic scenario), we apportion the adjustment to different age groups based on the UNHCR profile of the age distribution of refugees.

School destruction and/or non‐functioning schools are the key reasons for low enrollment rates (World Bank 2019). Further, displaced families will likely be hindered in their attempts to access education services. Since the prospects for reconstruction are relatively weak under the pessimistic scenario, we assume primary and secondary enrollment rates only return to pre‐conflict levels (93 and 67 percent respectively) by 2038, while the tertiary enrollment rate rises to 50 percent (from 36 percent) by that time. This timeline from the given initial levels is roughly in line with the trajectory of estimations/projections of enrollment ratios for developing countries in Barro and Lee (2015) (see their Chapter 3, Figure 3.5) and is longer than what Syria historically took to reach those rates.<sup>50</sup> We

> 48 In the UN population statistics, the net migration numbers are reported for the five‐year period between the middle of the respective bracketing years (for example, from mid‐2020 to mid‐2025).

> 49 From our calculations based on average births and deaths in the UN population statistics, this is in addition to about 112,000 inward migrants from mid‐2019 to mid‐2020, which gives us a total of 4.3 million returnees, or 76 percent of registered refugees residing in neighboring countries over 2019‐2035.

> 50 UNESCO UIS data for Syria indicate that the net primary enrollment rate rose from 81.9 percent in 1973 to 94.8 percent in 1987 (approximately 15 years) while the net secondary enrollment rate increased from 39.3 percent in 2000 to 66.9 percent in 2010 (approximately 10 years).

26
further assume that by 2048 enrollment rates reach 100, 80 and 60 percent respectively at the primary, secondary and tertiary levels. For the optimistic scenario, we assume primary and secondary enrollment rates reach pre‐conflict levels in half the time, that is, by 2028, and by 2038, 100 percent and 80 percent respectively. For the tertiary enrollment rate, we assume it rises to 50 percent by 2028 (also in half the time compared to the pessimistic scenario) and 60 percent by 2038. By 2048, we assume enrollment rates reach 90 and 70 percent respectively at the secondary and tertiary levels. With these calculations we obtain years of schooling of 8.449 and 6.991 respectively by 2038 in the optimistic and pessimistic scenarios of the voluntary mobility case. The projected years of schooling in 2038 under the pessimistic scenario is roughly the same as the pre‐conflict value of 7.080. For the moderate scenario, we take an average of years of schooling under the other two scenarios, which gives a value of 7.718 by 2038.

Using the above, we obtain an average annual growth rate in human capital of 1.3 percent over 2019‐2048 under the optimistic scenario (1.3 percent over 2019‐2028, 1.4 percent over 2029‐2038, and 1.1 percent over 2039‐2048). For the pessimistic scenario, average annual growth amounts to 0.9 percent. That is, 0.4 percent over 2019‐2028, 1.0 percent over 2029‐2038, and 1.2 percent over 2039‐ 2048.<sup>51</sup> The human capital growth rate under the moderate scenario is a simple average of the growth rates under the other two scenarios. The average years of schooling and human capital growth rates remain similar in the scenarios of the forced repatriation case compared to the voluntary mobility case as there is little change in the population distribution by age groups.

## _4.1.4 TFP_

Using the Kim and Loayza (2019) empirical model, we assume a gradual rebuilding of the TFP determinants index which was estimated at 15.98 in 2018. Under the optimistic scenario, we increase this index to 35.42 by 2028 and 75.76 by 2048 based on the trajectory of the Republic of Korea’s index over the 30‐year period, 1985‐2014. Korea is the best performer in the sample of countries used in Kim and Loayza (2019). This gives average annual TFP growth of 1.4 percent over 2019‐2048 (0.1 percent over 2019‐2028, 2.1 percent over 2029‐2038, and 2.0 percent over 2039‐2048).

For the pessimistic scenario, we repeat the exercise but based on index of the United Arab Emirates (UAE), the best performer for the sub‐sample of MENA countries. This would imply an increase in Syria’s index to only 21.72 by 2028 (still below pre‐conflict level) and 32.74 by 2048. The corresponding average annual TFP growth over 2019‐2048 would be 0.3 percent (‐0.9 percent over 2019‐2028, 0.9 percent over 2029‐2048). For the moderate scenario, we assume TFP growth rates that are the average of the rates under the optimistic and pessimistic scenarios.

# **4.2 Simulation of the post‐conflict growth outlook**

For the simulations in this section, we keep the constant parameters (labor share and depreciation rates) unchanged from the values used in the pre‐conflict and conflict projections. Default initial conditions as of 2018 (GDP level, GDP per capita level, and K/Y ratios) are drawn from the outcomes of the central projection in Section 3. Table 6 details the calibrations and projections of growth drivers post conflict based on the discussion in Section 4.1. Under the baseline/moderate scenario of the voluntary mobility case, I/Y averages about 43 percent and 39 percent over 2019‐ 2028 and 2029‐2038; and, at 23 percent and 25 percent, respectively, under the pessimistic scenario. The investment shares are exceptionally high under the optimistic scenario, averaging 63 percent and 46 percent respectively in the first and second decades; but are closer to pre‐conflict levels. The

> 51 The slightly faster human capital growth rate in 2039‐2048 compared with the optimistic scenario is mainly because of there still being room for improvement in the primary enrollment rate in the pessimistic scenario.

27
investment‐to‐output ratios only differ slightly in the forced repatriation case, at most 1 percentage point in the first decade.

# _4.2.1.  Post‐conflict GDP projections across the different scenarios under voluntary mobility_

The results of our simulations for Syria’s post‐conflict GDP in the different scenarios under the voluntary mobility case are shown in Graph 11(a). Under the baseline/moderate scenario, average GDP growth is 8.4 percent over 2019‐2038. As can be observed from the top right of Graph 11(b), with the inflow or reconstruction funds, the main growth driver over the 20‐year period is capital accumulation. As I/Y reverts to something close to pre‐conflict trends especially after the 20‐year annual inflow of reconstruction funds, the contribution from human capital growth and TFP are just as relevant as physical capital growth. In this scenario, Syria reaches its pre‐conflict GDP level by 2031, and its pre‐conflict GDP per capita level by 2033, thus losing about two decades.

In the optimistic scenario, average GDP growth is 10.9 percent over 2019‐2038 reaching a peak of 20.9 percent in 2022 with exceptionally high investments, and stronger contributions from other growth drivers relative to the moderate scenario (see Graph 11(a) and Graph 11(b), bottom left). Even with this strong performance, it would take Syria 9 years, that is by 2027, to surpass its 2010 GDP level and 11 years to surpass its pre‐conflict GDP per capita. In the pessimistic scenario, GDP growth averages 5.1 percent across the next two decades, only slightly higher than pre‐conflict levels, amid limited reconstruction funds from external sources (see Graph 11(b), bottom right, for the difference in growth drivers vis‐à‐vis the moderate scenario). In this case, it would take Syria at least 22 years to pass its pre‐conflict GDP level and almost 29 years to meet its GDP per capita level. This finding echoes the simulation in World Bank (2017b) where under the assumption that the conflict ends in its sixth year (2017), with investment recovering but remaining below its pre‐conflict level, Syria’s GDP remains below its pre‐conflict level even 20 years after the conflict.

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**<u>Table 6: Simulation for Post‐Conflict Syria – Projected Variables</u>**

|**Parameter/Variable***|**Note**|**2018**|||||**Scenario**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**Optimisti**|**c**||**Moderate**|||**Pessimist**|**ic**|
||||||||**_Average_**<br>|<br>||||
||||**2019‐**<br>**2028**|**2029‐**<br>**2038**|**2039‐**<br>**2048**|**2019‐**<br>**2028**|**2029‐**<br>**2038**|**2039‐**<br>**2048**|**2019‐**<br>**2028**|**2029‐**<br>**2038**|**2039‐**<br>**2048**|
|Public investment‐to‐output ratioI<sup>�</sup>/Y<br>‐ voluntary mobility case|_(1)_|0.010|0.251|0.185|0.140|0.170|0.157|0.120|0.091|0.100|0.100|
|‐ forced repatriation case|||0.250|0.183|0.140|0.168|0.155|0.120|0.088|0.100|0.100|
|Private investment‐to‐output ratioI<sup>�</sup>/Y<br>‐ voluntary mobility case|_(1)_|0.040|0.376|0.277|0.210|0.255|0.236|0.180|0.136|0.150|0.150|
|‐ forced repatriation case|||0.374|0.275|0.210|0.252|0.232|0.180|0.133|0.150|0.150|
|Efficiencyof newpublic investmentθ<sup>�</sup>|_(2)_|0.570|0.625|0.724|0.769|0.615|0.697|0.734|0.570|0.570|0.570|
|Human capitalgrowthg�|_(3)_||0.013|0.014|0.011|0.009|0.013|0.011|0.004|0.010|0.012|
|TFPgrowthg�|_(4)_||0.001|0.021|0.020|‐0.003|0.015|0.014|‐0.009|0.009|0.009|
|Population growth rateg�<br>‐ voluntary mobility case|_(5)_||0.034|0.017|0.011|0.027|0.017|0.011|0.021|0.017|0.012|
|‐ forced repatriation case|||0.039|0.016|0.010|0.039|0.016|0.010|0.039|0.016|0.010|
|Workingage‐to‐population share, growthg�|_(6)_||0.009|0.002|0.000|0.009|0.002|0.000|0.009|0.002|0.000|
|Labor force participation rate, growthg�|_(7)_||‐0.001|0.000|0.000|‐0.001|0.000|0.000|‐0.001|0.000|0.000|

*Multiply by 100 to obtain parameter/variable values in percent share or growth terms (%).

Notes (source and calculation): _(1)_ The 2018 values are from the central projection under conflict. Values under the scenarios are derived using the saving‐investment‐external financing accounting identity, based on assumptions on reconstruction funds and the saving‐to‐output ratio. See Section 4.1.1.

- _(2)_ The 2018 value is from the central projection under conflict. A gradual rise from low‐income efficiency to lower‐middle‐income efficiency and upper‐middle‐middle efficiency are assumed under the moderate and optimistic scenarios respectively. Efficiency is unchanged at the low‐income level under the pessimistic scenario.

- _(3)_ Average years of schooling by age groups are projected based on assumptions on enrollment rates (with the moderate scenario as a simple average of the optimistic and pessimistic scenario). Overall average years of schooling is based on population projections across the scenarios. See Section 4.1.3.

- _(4)_ Based on the Kim and Loayza (2019) model, assuming the TFP overall determinants index follows a similar trajectory as that of Korea (over 1985‐2014) under the optimistic scenario, and the UAE under the pessimistic scenario. TFP growth under the moderate scenario is a simple average of the values under the other two scenarios. See Section 4.1.4.

- _(5)_ The optimistic scenario under the voluntary mobility case is based directly on projections from United Nations (2017). For other scenarios, the UN projections are adjusted to reflect higher or lower refugee returns. See Section 4.1.2.

- _(6)_ United Nations (2017).

- _(7)_ Based on ILO projections up to 2030 of the labor force participation rates, held constant thereafter.

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Notes: GNI per capita based on the World Bank Atlas Methodology for Syria is only avaible up to 2007. We impute future values based on actual GDP per capita growth up to 2010 (at 2010 prices), our projections for GDP per capita growth over the conflict and post conflict periods (both also at 2010 prices). We account for inflation up to 2019 based on US inflation rates. On using GDP growth rates to proxy GNI growth rates, our calculations based on pre-conflict data suggest the the growth rates of GDP per capita in current USD and GNI per capita based on the Atlas Methodology on average are quite close (e.g. 1998 - 2007: 7 percent versus & percent; 1993-2007: 5 percent versus 4 percent).

The post-conflict scenarios (pessimistic, moderate and optimistic) reflect the voluntary mability case.
The post‐conflict outlook for the growth drivers in turn, depend on the political settlement outcome which directly affects the availability of reconstruction funds and the voluntary mobility of refugees. Voluntary mobility would not only be preferable on humanitarian grounds but also on economic terms. The political settlement scenario will also affect significantly human capital growth and productivity growth.

Focusing on the voluntary mobility case, under our moderate scenario of partial political settlement with strong guarantees for micro‐security and property rights, the average GDP growth and GDP per capita growth are 8.4 percent and 6.1 percent respectively over 2019‐2038, assuming that a final and stable resolution of the conflict is achieved. With the inflow of reconstruction funds amounting to USD 140 billion spread over 20 years, the main growth driver over the 20‐year period is physical capital accumulation amid investment‐to‐output of about 41 percent on average. As investment‐to‐output reverts to a lower level especially after the assumed 20‐year annual inflow of reconstruction funds, the contributions from human capital and TFP growth are just as relevant as physical capital growth. In this scenario, Syria reaches its pre‐conflict/2010 GDP level by 2031, and its pre‐conflict GDP per capita level by 2033, implying two “lost” decades from conflict.

Under the optimistic scenario of robust political settlement, with exceptionally high investment‐to‐output of over 60 percent in the first decade, it would still take Syria about one decade to surpass its 2010 GDP and GDP per capita levels. Under the pessimistic scenario of limited guarantees for micro‐security and property rights, reconstruction funds amounting to only USD 30 billion, and investment‐to‐output close to the pre‐conflict average, it would take Syria at least two decades to meet its pre‐conflict GDP level and close to three to surpass its pre‐conflict GDP per capita. Respectively for the optimistic and pessimistic scenarios, the average GDP per capita growth rate over the next two decades is projected to be 8.2 or 3.1 percent.

While the reconstruction and expansion of physical infrastructure is essential, the importance of strengthening human capital and the factors underlying TFP growth cannot be overstated. In this paper, we have only accounted for population and enrollment effects on human capital growth. However, the quality of education and health (as illustrated in the World Bank’s human capital index) will also likely be impeding factors that would have to be addressed in Syria’s quest for growth.

34