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.

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