metadata: authors: 0: author: Lofgren,Hans
1: author: Cicowiez,Martín
2: author: Mele,Gianluca
count: Yemen,Republic of,Yemen, Republic of
docna: 0: docna: Alternative Paths for Yemen up to 2030 : A
            CGE-Based Simulation Analysis
docty: Policy Research Working Paper
theme: Inclusive Growth,Economic Policy,Economic Growth and Planning,Fiscal Policy
abstracts: cdata!: Over nine years of violence and conflict
            have profoundly altered the Republic of Yemen&#x2019;s
            economy. The war has shattered the country&#x2019;s
            already fragile socioeconomic equilibria, affecting nearly
            every facet of life. Since the onset of the conflict,
            economic diagnostics have focused on descriptions of the
            deteriorating macro-fiscal and poverty conditions, lack of
            food security, and loss of capital accumulation. However,
            relatively little attention has gone toward the development
            of a forward-looking vision for the country, rooted in
            Yemen&#x2019;s current economic structure. This paper
            helps to fill this gap by presenting and analyzing a set of
            scenarios for Yemen&#x2019;s economy up to 2030. The
            analysis is based on a new version of the Sustainable
            Development Goal Simulation model, a dynamic computable
            general equilibrium (CGE) model, which is applied to a new
            social accounting matrix (SAM) for Yemen. The new social
            accounting matrix has the virtue of consolidating sparce and
            often inconsistent Yemeni data from multiple sources (the
            World Bank, the International Monetary Fund, and the United
            Nations system) into a coherent framework that reflects the
            basic structure of the economy, both at the macro and
            sectoral levels. The simulation analysis is built around
            three broad scenarios spanning 2022 through 2030. The
            results suggest that if the conflict subsides, governance is
            strengthened, and the donor community provides crucial aid,
            considerable progress, including reduced poverty rates and
            improved living conditions, can be achieved by 2030. Given
            Yemen&#x2019;s low levels of infrastructure and human
            development, the potential payoffs from investments in these
            areas are great.
display_title: Alternative Paths for Yemen up to
            2030 : A CGE-Based Simulation Analysis
listing_relative_url: /research/2023/11/34193411/alternative-paths-yemen-up-2030-cge-based-simulation-analysis
topicv3: Fragility, Conflict and Violence m1326586 2432,Fiscal Policy m1327144 445,Economic Growth Policy m1327634 1070,Computable General Equilibrium Modeling
            m1504533 3110
publishtoextweb_dt: 2023-11-08T00:00:00Z
dois: 10.1596/1813-9450-10599
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 747317
1: author_key: NA
2: author_key: 1141463
model_extractions: Policy Research Working Paper 10599 Alternative Paths for Yemen up to 2030 A CGE-Based Simulation Analysis _Hans Lofgren Martín Cicowiez Gianluca Mele_ Equitable Growth, Finance and Institutions Practice Group November 2023
Policy Research Working Paper 10599

# **Abstract**

Over nine years of violence and conflict have profoundly altered the Republic of Yemen’s economy. The war has shattered the country’s already fragile socioeconomic equilibria, affecting nearly every facet of life. Since the onset of the conflict, economic diagnostics have focused on descriptions of the deteriorating macro-fiscal and poverty conditions, lack of food security, and loss of capital accumulation. However, relatively little attention has gone toward the development of a forward-looking vision for the country, rooted in Yemen’s current economic structure. This paper helps to fill this gap by presenting and analyzing a set of scenarios for Yemen’s economy up to 2030. The analysis is based on a new version of the Sustainable Development Goal Simulation model, a dynamic computable general equilibrium (CGE) model, which is applied to a new social accounting matrix (SAM) for Yemen. The new social accounting matrix has the virtue of consolidating sparce and often inconsistent Yemeni data from multiple sources (the World Bank, the International Monetary Fund, and the United Nations system) into a coherent framework that reflects the basic structure of the economy, both at the macro and sectoral levels. The simulation analysis is built around three broad scenarios spanning 2022 through 2030. The results suggest that if the conflict subsides, governance is strengthened, and the donor community provides crucial aid, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gmele@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
# Alternative Paths for Yemen up to 2030: A CGE-Based Simulation Analysis

Hans Lofgren Martín Cicowiez Gianluca Mele

**_Key words: computable general equilibrium modeling, fragility conflict and violence, economic growth policy, fiscal policy_**

**_JEL Codes: C68 , D58, E16, E6, E17, O53_**
# 1. Introduction

Even before the start of its civil war in 2014, the Republic of Yemen had one of the lowest living standards in the world. Since 2014, its population has suffered from one of the world’s severest humanitarian crises with a serious further deterioration of living standards, manifested in increased poverty rates, severe malnutrition, food insecurity, and physical insecurity. Basic public health and education services have suffered, infrastructure (including road networks) has been destroyed, and government employees have been left without pay for extended periods. In the background, like many other low-income countries, Yemen is vulnerable to accelerating climate change. Given these circumstances, the country was ill-prepared to deal with the Covid-19 pandemic and international price shocks for food and energy.

Given this, it is more urgent than ever to put an end to the war and identify policies that will help to overcome the humanitarian crisis and put the country on a path toward a better future. With this aim, this paper explores potential scenarios for the economy of the country up to 2030, focusing on alternatives under which the conflict subsides, and the country manages to start addressing the urgent needs of the population and the constraints that stand in the way of resumed sustainable growth and improved living standards.

The analysis is based on simulations with the Sustainable Development Goal Simulation model (SDGSIM), a dynamic computable general equilibrium (CGE) model designed for country-level policy analysis.<sup>1</sup> For this analysis, the model was adapted to the Yemeni context and calibrated to a new dataset, including a social accounting matrix (SAM) for 2020, the most recent year with sufficient information. The analysis, which covers the period 2022-2030, presents a base scenario without significant improvements in the economy or the state of security and six alternative scenarios which, compared to the base, reflect varying degrees of improvement. The scenarios are underpinned by different trajectories along seven dimensions: health and education; infrastructure; social transfers; remittances from abroad; foreign direct investment (FDI); extraction growth for oil and gas production; and foreign grant aid.

The paper is organized as follows: After a brief overview of the model and its database (Section 2a), it presents a short review of literature and earlier applications of CGE modeling to Yemen (Section 2b), and detailed analyses of the base scenario with stagnant performance (Section 3) and the set of alternative scenarios (Section 4). The main findings are summarized in the conclusions (Section 5). The Appendix provides additional information about the model database.

> 1 SDGSIM is documented in detail in Lofgren and Cicowiez (2019). The starting point for SDGSIM was MAMS, a model designed for analysis of strategies related to the MDG agenda. See Lofgren et al. (2013) for documentation that also is relevant to SDGSIM.

1
# 2a. Model and database

SDGSIM, the model that is used for this analysis, is a CGE model for country-level analysis of medium- and long-run development policies with a focus on the SDG agenda and structural change. (For a review of literature and earlier CGE modeling applications to Yemen, see Section 2b.) The Yemen version of the model was adapted to better capture Yemen’s economic structure and the policy issues that are addressed in this paper. It is a suitable tool for analyzing issues related to a potential social and economic recovery for Yemen given the fact that it, in an integrated manner, captures fiscal issues, household spending and incomes, government services, private sector production, and links with the rest of the world, via trade, transfers, borrowing, and other foreign exchange flows. Moreover, the model relies on a social accounting matrix (SAM) for the bulk of its data – thanks to the ability of SAMs to impose coherence on data from different sources, they make it feasible to capture the structure of the modeled economy also in data-scarce contexts like Yemen’s.

Technically, the model consists of a set of simultaneous linear and non-linear equations. It is economy-wide, providing a comprehensive and consistent view of the economy, including linkages between disaggregated production sectors and the incomes they generate, households, the government (its budget and fiscal policies), and the balance of payments.

The linkages are summarized in Figure 2.1: the major building blocks in the model are activities (the entities that carry out production), commodities (activity outputs or, exceptionally, imports without domestic production; linked to markets), factors (also linked to markets), and institutions (households, the government, and the rest of the world). In the figure, the private capital account also has its own box given the multiple links that exist between private investment demands and their financing. In any SDGSIM application (and dataset), most blocks are disaggregated – the disaggregation in the Yemen application is shown below in Table 2.1.

In each period, the different agents (producers, households, government, and the nation in its dealings with the outside world) act subject to budget constraints: receipts and spending are fully accounted for and by construction equal (as they are in the real world). The decisions of each agent – for producers and households, the objective is to maximize profits and utility, respectively – are made subject to these budget constraints: for example, households set aside parts of their incomes to direct taxes and savings, allocating what is left to consumption with a utility-maximizing composition. For the nation, the real exchange rate adjusts to ensure that the external accounts are in balance, in a setting with exogenous foreign net financing and foreign reserve changes. Wages, rents, and prices play a crucial role by clearing markets for factors and commodities (goods and services). For commodities that are traded internationally (exported and/or imported), domestic prices are influenced by international price developments. It is assumed that Yemen is a small country in the sense that it is able to demand from and supply to international markets at prices that are exogenous in foreign currency.

2
<!-- Start of picture text --> Figure 2.1. Aggregate payment flows in SDGSIM<br>Factor domestic wages and rents private savings<br>Households<br>Markets<br>indir taxes<br>gov cons and inv Government Private<br>interm input demand<br>Investment<br>Financing<br>Activities<br>Domestic<br>Commodity Rest of<br>Markets imports World<br>domestic demand<br>exports<br>foreign wages and rents<br>private investment<br>dir taxes lending<br>trnsfr+interest<br>factor demand private consumption<br>trnsfr-interest<br>lending<br>trnsfr-interest FDI<br>lending<br><!-- End of picture text --> Source: Authors’ elaboration.

Over time, production growth is determined by growth in total factor productivity (TFP) and factor employment (made possible by better utilization of existing stocks and/or growth in stock supplies). TFP growth is made up of two components, one that responds positively to growth in government infrastructure capital stocks and one that, unless otherwise noted, is exogenous. Growth in private capital stocks is endogenous, depending on investment and depreciation. Public investment and capital stock changes are determined by policy. For other factors, the growth in employable stocks is exogenous. For labor and natural resources (with sector-specific factors for natural-resource-based sectors), the projected supplies in each time period are exogenous. For natural resources, they are closely linked to production projections. For labor, the projections reflect the evolution of the population in labor-force age, labor force participation rates, and educational attainment. In the labor market, unemployment is endogenous – the model includes a wage curve (a supply curve) that is upward sloping until full employment is reached, at which point it becomes vertical -- see Figure 2.2.

3
<!-- Start of picture text --> Figure 2.2. The labor market in SDGSIM<br>5<br>4<br>3 Wage<br>curve<br>Demand<br>2<br>1<br>0<br>85 90 95<br>100 - unemployment rate (%)<br>Wage<br><!-- End of picture text --> Source: Authors’ elaboration.

The basic accounting structure and much of the data needed to implement SDGSIM is derived from a social accounting matrix (SAM). The model also relies on complementary stock, elasticity, and base-year employment data.<sup>2</sup> For this study, a new Yemen SAM was built for 2020, primarily drawing on data from the World Bank, the IMF, and the UN. While the quality of data for Yemen is low, the construction of the SAM had the advantage of imposing consistency between data from different sources, as a result producing a coherent database that captures well-known structural features of the Yemeni economy. The disaggregation of the SAM and the model database is presented in Table 2.1.<sup>3</sup> It distinguishes 17 production activities and commodities (or products); seven primary production factors including two labor categories (male and female), private capital, and four natural resources (agricultural land, fish stock, and mineral stocks for oil and gas and other mining); and one household. The government is defined broadly to include donor expenses and receipts.

In 2020, the country’s GDP was 13,999 billion Yemeni riyals; with an exchange rate of 743 riyals per US$ and a population of 29.8 million, this corresponds to a GDP per capita of US$623. Tables 2.2a and 2.2b show a macro version of the 2020 SAM and its accounts, respectively. The SAM, which is expressed as percent of GDP, is highly aggregate – among other things, it has only one production sector (i.e., one activity producing one commodity) – making it easy to learn about the macro situation in 2020 from the SAM. The government current account deficit was 4.5 percent (of GDP) (cell [cap-gov,gov]) while government consumption and investment amounted

> 2 For additional details on the SAM, see the appendix.

> 3 The disaggregation is primarily based on the most recent UN data. Although it would be possible to further disaggregate the sector assuming that the patterns of the 2014 IFPRI SAM still are valid, this does not seem meaningful: it would not add anything significant to the analysis and, furthermore, the extent to which these patterns also were valid in 2020 is unknown.

4
to 19.4 and 0.7 percent, respectively (cells [c-nagr,gov] and [inv-gov,cap-gov]). The resulting government deficit (5.2 percent) was covered by domestic financing, shown in cell [cap-gov,capngov].<sup>4</sup> Yemen’s exports and imports amounted to 6.3 and 44.6 percent, respectively (cells [cagr,row] and [row,c-agr]), yielding a trade deficit of 38.3 percent. Thanks to a large transfer surplus – the sum of net transfers to households and government amounted to 32.6 percent (cells [hhd,row]+[gov,row])– the current account deficit (foreign savings) was much smaller, at 5.9 percent. Supplemented by domestic savings at 2.9 percent (sum of the cells [cap-ngov,hhd] and cap-gov,gov]), it financed total investment at 8.7 percent – the sum of government investment and private investment (8.0 percent; cell [total,inv-prv]). By international standards this is a very low investment level.

Table 2.1. Disaggregation of Yemen SAM and SDGSIM application

|**Group**|**Description**|**Group**|**Description**|
|---|---|---|---|
|Sectors|Qat**|Factors|Labor - Male|
|(activities &<br>commodities)*|Other agriculture<br>Fishing<br>Oil and gas**<br>Other mining<br>Manufacturing<br>Utilities (electricity, gas, and water)||Labor - Female<br>Capital - Private<br>Land<br>Natural resource - Fishing<br>Natural resource - Other mining<br>Natural resource - Oil andgas|
||Construction<br>Trade|Institutions|Households<br>Government|
||Hotels and restaurants||Rest of the world|
||Transport|Taxes and|Taxes - Income|
||Telecommunications<br>Financial serv|subsidies|Taxes - Imports<br>Taxes - Commodities|
||Real estate and business serv<br>Public administration|Institutions<br>(capital|Non-government<br>Government|
||Government education and health<br>Other services|accounts)|Rest of the world<br>Foreign reserves***|
|Trade &<br>transport<br>margins|Domestic products<br>Imports<br>Exports|Investment|Private investment (GFCF)<br>Government investment (GFCF)|

*Each sector has an activity and a commodity (output).

**Qat and Oil and gas are singled out from agriculture and mining, respectively, using CSO (Central Statistical Organization) estimates for 2017.

***Capital account for payments to foreign reserves; not associated with a current account.

> 4 The government deficit is defined as the difference between government investment and government savings. (Government savings may also be referred to as the government current account surplus.)

5
Table 2.2a. Macro SAM for Yemen 2020 (% of GDP)*

||act<br>com|f-lab<br>f-capprv|f-oilgas|tax-imp<br>tax-com<br>tax-dir|hhd|gov|row|cap-ngov|cap-gov|cap-row<br>drf|inv-prv<br>inv-gov|total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|act<br>com|147.6<br>49.3||||110.2|19.4|6.3||||8.0 0.7|147.6<br> 194.0|
|f-lab|59.7|||||||||||59.7|
|f-capprv|36.2|||||||||||36.2|
|f-oilgas|2.4|||||||||||2.4|
|tax-imp|0.8|||||||||||0.8|
|tax-com<br>tax-dir|0.9||||1.2|||||||0.9<br>1.2|
|hhd||59.7 36.1||||4.5|19.7|||||120.0|
|gov|||2.4|0.8 0.9 1.2|1.1||12.9|||||19.3|
|row|44.6|0.1||||||||||44.7|
|cap-ngov|||||7.4|||||8.4||15.8|
|cap-gov||||||-4.5||5.3||-0.1||0.7|
|cap-row|||||||5.9|||2.5||8.3|
|drf||||||||2.5||||2.5|
|inv-prv||||||||8.0||||8.0|
|inv-gov|||||||||0.7|||0.7|
|total|147.6 194.0|59.7 36.2|2.4|0.8 0.9 1.2|120.0|19.3|44.7|15.8|0.7|8.3 2.5|8.0 0.7||

*For notation, see Table 2.2b.

Source: Authors' calculations based on World Bank, IMF, UN, and WIDER.

Table 2.2b. Accounts in macro SAM for Yemen 2020

|**Account **|**Description**|**Account **|**Description**|
|---|---|---|---|
|act|activity|gov|government|
|com|commodity|row|rest of the world|
|f-lab|factor - labor|cap-ngov|capital acc - domestic non-gov|
|f-capprv|factor - private capital|cap-gov|capital acc - government|
|f-oilgas|factor - oil and gas|cap-row|capital acc - rest of the world|
|tax-imp|tax - imports|drf|change in international reserves|
|tax-com|tax - commodities|inv-prv|investment - private|
|tax-dir|tax - income|inv-gov|investment - government|
|hhd|households|||

Drawing on the disaggregated SAM used with the model, Figures 2.3-2.6 provide a summary of the structure of Yemen’s economy in 2020 from an aggregate sector perspective, showing (a) sectoral shares in value added, employment, exports, and imports (Figure 2.3); (b) export and import intensities (i.e., the shares of sector output that is exported and the share of domestic demand met by imports; Figure 2.4); (c) sectoral factor intensities (i.e., factor shares in the value added of each sector; Figure 2.5); and (d) the demand composition for each sector (i.e., the shares 6
of private consumption, government consumption, investment, intermediate consumption, and exports; Figure 2.6).

Figure 2.3 shows that agriculture is relatively important as a source of employment, while mineral extraction – particularly oil and gas -- mainly matters as a source of foreign exchange from exports. In turn, manufacturing is important both in terms of value added and foreign trade but less labor-intensive than agriculture and services.

Figure 2.4, which displays the shares of exports in output and imports in domestic demand, points to that (a) agriculture and mining production have a substantial export share while imports play a more moderate role; (b) for manufacturing, both exports and imports are important; and (c) as much as some 59.2 percent of domestic manufacturing demand – including food industry and fuel – is met by imports.

Figure 2.5 shows factor shares in the value added of each sector. Among other things, it indicates that agriculture, construction, and services are relatively intensive in their use of (male) labor. On the contrary, mining, manufacturing, and utilities are relatively intensive in their use of physical and natural capital. Currently, women are mainly employed in agriculture, education, manufacturing, and trade; their shares in total employment (male and female) are relatively larger in education, health, manufacturing, and agriculture. In CGE applications, the labor/capital ratios of the production sectors have a major impact on the results obtained from policy simulations. Hence, this information is useful when we analyze the results from the simulations.

The main features of the composition of domestic demand, covered in Figure 2.6, are that (a) agriculture and manufacturing are primarily sold domestically, either for private or intermediate consumption; and (b) compared to other products, mining products – particularly oil and gas -- are distinguished by significant exports.

7
**Figure 2.3. Yemen: sectoral structure in 2020 (%)**

<!-- Start of picture text --> a. Value added b. Employment<br>Other<br>services<br>Manufacturi<br>Other ng<br>services Agriculture<br>Agriculture<br>Government<br>Mining<br>Government<br>Mining<br>Transport Manufacturi<br>ng<br>Transport Utilities Utilities<br>Constructio Construction<br>Trade n Trade<br>Other Government<br>Transport c. Exports services d. Imports<br>Constructio Government Transport Other<br>n Trade Trade services<br>Utilities Construction<br>Mining<br>Agriculture Agriculture<br>Utilities<br>Manufacturi<br>ng<br>Manufacturi<br>ng<br>Mining<br><!-- End of picture text --> Source: Authors’ calculations based on the 2020 Yemen SAM.

8
**Figure 2.4. Yemen: export and import intensities in 2020 (%)**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EXP-OUTshr* IMP-DEMshr**<br>percent<br><!-- End of picture text -->

*Ratio between exports and production; **Ratio between imports and domestic demand. Source: Authors’ calculations based on the 2020 Yemen SAM.

**Figure 2.5. Yemen: sectoral factor cost composition in 2020 (%)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Male labor Female labor Capital Natural resources<br>porcentaje<br><!-- End of picture text --> Source: Authors’ calculations based on the 2020 Yemen SAM.

9
**Figure 2.6. Yemen: sectoral demand composition in 2020 (%)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>intermediate use* private cons government cons investment exports<br>percent<br><!-- End of picture text -->

*Includes trade and transport margins.

Source: Authors’ calculations based on the 2020 Yemen SAM.

# 2b. Literature overview

During the last decades, Yemen’s economy has been analyzed in a large number of applications of CGE models, an indication that it is relatively straightforward to apply such models even in a data-scarce environment like Yemen’s. This section provides a brief survey of the 15 Yemen studies that have been identified, with a focus on method and the issues addressed. The studies were published between 2007 and 2022 in academic journals, as book chapters, or as working papers. Most were undertaken by IFPRI as part of a research effort led by Clemens Breisinger; some work was also done at the World Bank. Readers interested in additional details are referred to the individual references for the studies.

The methodological framework of these models is rooted in the pioneering work at the World Bank in the late 1970s and early 1980s that is documented in Dervis, de Melo, and Robinson (1982). The approach that was developed may be described as neoclassical structuralist (Robinson 1989): its starting point is Walrasian general equilibrium theory, but it deviates from this theory whenever this is deemed necessary in order to incorporate essential features of the modeled economy.

In terms of the specifics of the Yemen applications, the IFPRI standard model (Lofgren et al. 2002), extended to recursive dynamics by Thurlow (2004), provides the mathematical structure and 10
computer code in GAMS that underpins most if not all Yemen CGE applications.<sup>5</sup> This model is user-friendly thanks to the inclusion of pre-programmed alternatives for factor and macro closures, separation between computer code and database (facilitating applications of new databases with different disaggregations), and permits regional disaggregation of sufficient data are available. As a complement to dynamic simulations with these CGE models, many of the Yemen applications have included different types of micro or household simulation modules that, drawing on the CGE simulation results, analyze the consequences for more disaggregated indicators, often related to households and nutrition.

The issues addressed in these studies were typically selected in the context of discussions between these international institutions and Yemeni authorities and on the basis of the issues that IFPRI and the World Bank identified as being of high priority for Yemen’s development progress. The analyses were most often forward-looking (at the time when the work was done), addressing the future impacts of packages of complementary policies. However, some studies were also designed to decompose the roles of past external and internal shocks on economic developments.

Among these studies, forward-looking analyses have addressed prioritization of public spending between health, education, and agriculture (Chemingui 2007); the long-run impact of climate change (Wiebelt et al. 2011); fuel subsidy reform combined with direct income transfers and infrastructure investments (Breisinger et al. 2012), strategies for achieving the millennium development goals (MDGs; Al-Batuly et al. 2013), the potential roles of growth and targeted measures for improving nutrition among poor groups (Breisinger and Ecker 2014), and conflict recovery (Mukashov et al. 2022). The backward-looking analysis of the determinants of recent economic performance have focused on the impacts of natural disasters and conflict on food security (Ecker et al. 2010), impacts of international price shocks and the financial crisis (Breisinger et al. 2011), and the effects of the 2008 tropical storm and flash flood (Breisinger et al. 2016).

In terms of topic, the analysis presented in this paper has most in common with Mukashov et al. (2022) who also address conflict recovery. Methodologically, it is most similar to Al-Batuly et al. (2013) who use the Maquette for MDG Simulations (MAMS), a model developed at the World Bank, which is the predecessor of SDGSIM, the model used in this paper.

> 5 The computer code is written in GAMS, a software for optimization and simulation modeling developed at the World Bank, also in the late 1970s and early 1980s. For more information, visit www.gams.com.

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# 3. Base scenario

The base scenario is designed to generate a plausible business-as-usual path for Yemen’s economy during the period 2020-2030, i.e., a path that assumes that fighting continues along the lines of the past eight years. As a result, economic growth is slow and living conditions do not improve. The subsequent analysis compares the base and non-base scenarios during the period 2022-2030. The solution for 2020 replicates the information in the SAM while, in 2021 and 2022, GDP grows at rates that are estimated or projected by the IMF (2022).<sup>6</sup> The assumptions are kept as simple and transparent as possible. Most importantly, it is assumed that (a) all international (export and import prices) are constant in real terms; and (b) for most institutional payments, including receipt and spending items in the government budget, the GDP shares are constant, kept at shares computed from the 2020 SAM. At the macro level, it is assumed that the government budget is cleared via tax adjustments, that the real exchange rate clears the balance of payments, and that household savings adjust to help maintain domestically financed private investment with an unchanged GDP share.

Figures 3.1-3.7 summarize results from the base scenario (covering indicators related to the macroeconomy, sectors, household welfare, and poverty); Tables 3.1-3.6 provide additional information about both base and non-base scenarios, including macro data (growth and GDP shares), the government budget, the balance of payments, and data on economic structure in 2022 and for the base scenario in 2030.

As intended, to facilitate comparisons to non-base scenarios, the picture is one of stable growth starting from 2023. GDP growth is simulated at an annual rate at 2.0 percent between 2022 and 2030 (Figure 3.1).<sup>7</sup> Absorption (the sum of domestic final demands) is boosted by Yemen’s trade deficit, permitting it to exceed, by a wide margin, GDP at purchasers’ prices and, even more so, GDP at factor cost (Figure 3.2). Among domestic demands, household consumption dominates (on the left-hand axis in Figure 3.3), followed by government consumption (which includes consumption financed by donors and managed by NGOs and others), private investment, and government investment (right-hand axis in Figure 3.3). In terms of GDP shares (Table 3.2), Yemen’s private consumption represents 108.6 percent while the sum of the other domestic final demands is as low as 28.1 percent, values that are extreme by international standards.

The macroeconomic aggregates covered in Figures 3.1-3.3 grow at annual rates of 1-2 percent (Figure 3.4). The growth rate for household consumption coincides almost exactly with the population growth rate, leaving per capita consumption unchanged with a headcount poverty rate of 74.4 percent both in 2022 and 2030. Among the sectors, the private service GDP is more

> 6 While GDP growth is exogenous for the base scenario, it is endogenous for all non-base scenarios, deviating from the base due to the shocks, policy and other, that are imposed.

> 7 Unless otherwise noted, the growth rates are geometric averages for the period 2022 to 2030.

12
than twice the level of the second sector, agriculture (Figure 3.5). The aggregate sectors grow at annual rates close to 2 percent with mining as the only exception (Figure 3.6), reflecting a balanced growth path. For mining (oil, gas and other), growth is constrained by the assumption that extraction rates are relatively stagnant. The resulting growth in employment does not keep up with the growth in the labor force, leading to increased unemployment rates both for men and women (Table 3.1).<sup>8</sup> In the base scenario, aggregate export growth is at 1 percent, kept in check by a negative growth rate for mining, which in 2022 represented around 62 percent of total exports (Figure 3.7; Table 3.6). For the non-mining sectors, the growth rates are around 3 percent.

As shares of GDP, the changes in size of the government and its different receipt and spending items are minor (Table 3.3). Foreign aid, which amounts to around 14 percent of GDP, is assumed to be almost entirely in grant form. Government foreign financing (borrowing net of interest) payments are close to zero, generating a virtually unchanged ratio between foreign government debt and GDP (Table 3.2).

> 8 The unemployment rate is the combined rate of time-related underemployment and unemployment (i.e., according to ILO terminology, the LU2 rate).

13
**Figure 3.1. Base: GDP at factor cost annual growth 2021-2030 (%)**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Source: Authors' calculations based on IMF (2022).

<!-- Start of picture text --> Figure 3.2. Base: Absorption, GDP, and trade 2022-2030 (bn 2020 YER)<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>Absorption Exports Imports GDP at factor cost<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

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**Figure 3.3. Base: Domestic final demands 2022-2030 (bn 2020 YER)**

<!-- Start of picture text --> 1,800 400<br>1,600 360<br>1,400 320<br>280<br>1,200<br>240<br>1,000<br>200<br>800<br>160<br>600<br>120<br>400 80<br>200 40<br>0 0<br>Hhd cons. Gov't cons. Private inv. Gov't inv.<br>Household consumption<br>Other indicators<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

**Figure 3.4. Base: Real annual macro growth 2023-2030 (%)**

<!-- Start of picture text --> 0.0 0.5 1.0 1.5 2.0 2.5<br>Absorption 2.0<br>Household consumption 2.0<br>Government consumption 2.0<br>Private investment 2.2<br>Government investment 2.0<br>Exports 1.0<br>Imports 1.8<br>GDP at factor cost 2.0<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

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**Figure 3.5. Base: Sector GDP 2022-2030 (bn 2020 YER)**

<!-- Start of picture text --> 800 400<br>700 350<br>600 300<br>500 250<br>400 200<br>300 150<br>200 100<br>100 50<br>0 0<br>Private services Agriculture Mining<br>Manufacturing Other industry Gov't services<br>Private services<br>Other sectors<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

|Figure 3.6. Base: Real annual sector GDPgrowth 2023-2030(%)<br>0.0<br>0.5<br>1.0<br>1.5<br>2.0<br>2.5<br>3.0<br>**~~Figure 3.6. Base: Real annual sector GDP growth 2023-2030 (%)~~**|
|---|
|~~1.9~~<br>0.1<br>~~2.4~~<br>2.1<br>2.1<br>~~Agriculture~~<br>Mining<br>~~Manufacturing~~<br>Other industry<br>Private services|
|Source: Authors' calculations based on simulation results.<br>~~2.0~~<br>2.0<br>Gov't services<br>Total|

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# Figure 3.7. Base: Real annual sector export growth 2023-2030 (%)

|-2.0<br>-1.0<br>**Figure 3.7. Base: Real ann**|0.0<br>**ual sector e**|1.0<br>**xport gro**|2.0<br>**wth 2023**|3.0<br>**-2030 (%**|4.0<br>**)**||||
|---|---|---|---|---|---|---|---|---|
|Source: Authors' calculations bas<br>-0.9<br>Agriculture<br>Mining<br>Manufacturing<br>Private services<br>Total<br>**Table 3.1. Macro indicators in 20**|ed on simu<br><br>**22 and by **|lation res<br>1.0<br><br>**scenario**|ults.<br><br> **(% annu**|3.0<br>**algrow**|3.2<br>3.4<br>**th 2023-2**|**030)**|||
||**2022**<br>|**base**<br>|**limbo**<br>|**rec-t**<br>|**rec-ta**<br>|**rec+t**<br>|**rec+ta r**<br>|**ec+ta+**<br>|
|**Absorption**|1,848.5|2.0|2.7|3.6|3.6|5.2|5.2|6.0|
|**Consumption - private**|1,460.5|2.0|2.6|3.2|3.2|4.2|4.2|5.1|
|**Consumption - government**|268.7|2.0|3.1|2.1|2.1|3.8|3.8|3.8|
|**Fixed investment - private**|109.4|2.2|3.1|8.6|8.6|13.6|13.6|14.7|
|**Fixed investment - government**|9.9|2.0|2.8|22.0|22.0|30.9|30.9|30.9|
|**Exports**|95.1|1.0|2.3|4.2|4.2|6.9|6.9|9.0|
|**Imports**|561.4|1.8|2.0|2.9|2.9|4.0|4.0|4.4|
|**GDP at factor cost**|1,361.5|2.0|3.1|4.0|4.0|6.0|6.0|7.1|
|**Total factor employment (index**|Eps|1.2|1.5|2.1|2.1|3.0|3.0|3.3|
|**Total factor productivity (index)**|Eps|0.8|1.6|1.9|1.9|3.0|3.0|3.8|
|**GNI**|1,382.8|2.0|3.0|3.7|3.8|5.5|5.5|6.6|
|**GNDI**|1,849.8|2.0|2.8|3.4|3.4|4.8|4.8|5.7|
|**GNI per capita**|0.4|0.0|1.1|1.7|1.8|3.4|3.5|4.5|
|**GNDI per capita**|0.6|0.0|0.8|1.4|1.4|2.8|2.8|3.6|
|**Real exchange rate (index)**|Eps|0.61|1.66|1.90|1.92|2.81|2.83|3.76|
|**Unemployment rate (%)**|25.4|31.9|29.8|29.1|29.0|25.5|25.3|23.3|
|**Male (%)**|24.4|31.0|29.0|28.2|28.1|24.6|24.5|22.4|
|**Female (%)**|36.8|42.3|40.0|39.5|39.4|35.8|35.8|33.7|
|**Headcountpoverty rate(%)**|74.4|74.4|72.0|70.0|69.8|66.0|65.8|62.4|

**Note:**

**1. Unless otherwise noted, column for initial year shows data in bn 2020 YER.**

**2. For the unemployment and poverty rates, the base-year and simulation columns show base-year rates and simulation-specific final-year rates, respectively.**

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**Table 3.2. Macro indicators in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**|**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta r**|**ec+ta+**|
|---|---|---|---|---|---|---|---|---|
|**Absorption**|136.7|138.5|138.9|138.7|138.7|137.7|137.8|137.7|
|**Consumption - private**|108.6|111.0|111.4|109.1|109.1|104.9|104.9|106.3|
|**Consumption - government**|19.3|18.8|18.8|15.9|15.9|15.4|15.3|14.0|
|**Investment - private**|8.0|8.0|8.0|11.2|11.2|13.7|13.7|13.8|
|**Investment - government**|0.7|0.7|0.7|2.5|2.5|3.8|3.8|3.6|
|**Exports**|7.5|7.2|8.1|8.9|8.9|10.1|10.1|11.9|
|**Imports**|44.2|45.7|47.0|47.6|47.6|47.9|47.9|49.6|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|3.1|2.6|2.5|2.3|2.3|3.0|2.9|1.9|
|**GDP at factor cost**|96.9|97.4|97.5|97.7|97.7|97.0|97.1|98.1|
|**GNI**|99.9|99.9|99.9|97.6|97.6|95.7|95.8|95.6|
|**GNDI**|136.6|138.5|138.9|135.4|135.4|131.6|131.7|131.6|
|**Foreign savings**|0.0|0.0|0.0|3.3|3.3|6.1|6.1|6.1|
|**Gross national savings**|8.7|8.7|8.7|10.4|10.4|11.4|11.4|11.3|
|**Foreign government debt**|43.1|43.7|44.1|41.7|41.7|38.7|38.6|38.7|
|**Domestic government debt**|6.5|10.9|10.4|9.7|9.7|8.7|8.7|8.2|

**Table 3.3. Government receipts and spending in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta re**|**c+ta+**|
|---|---|---|---|---|---|---|---|---|---|
|**Receipts**|**Direct taxes**|3.0|2.3|2.0|1.7|1.7|2.5|2.4|1.1|
||**Import tariffs**|0.8|0.8|0.9|0.9|0.9|0.9|0.9|0.9|
||**Other indirect taxes**|2.3|1.8|1.6|1.4|1.4|2.1|2.1|0.9|
||**Private transfers**|1.1|1.1|1.1|1.1|1.1|1.1|1.1|1.1|
||**Foreign transfers**|14.0|14.8|14.9|14.1|14.1|13.1|13.0|13.1|
||**Domestic financing**|0.3|0.3|0.3|0.3|0.3|0.2|0.2|0.2|
||**Foreign financing**|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
||**Total**|24.5|23.9|23.9|23.0|23.0|23.7|23.7|21.9|
|**Spending **|**Consumption**|19.3|18.8|18.8|15.9|15.9|15.4|15.3|14.0|
||**Fixed investment**|0.7|0.7|0.7|2.5|2.5|3.8|3.8|3.6|
||**Private transfers**|4.4|4.4|4.4|4.5|4.5|4.5|4.5|4.3|
||**Total**|24.5|23.9|23.9|23.0|23.0|23.7|23.7|21.9|

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**Table 3.4. Balance of payments in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta r**|**ec+ta+**|
|---|---|---|---|---|---|---|---|---|---|
|**Outflows **|**Imports**|44.2|45.7|47.0|47.6|47.6|47.9|47.9|49.6|
||**Factor payments**|0.1|0.1|0.1|2.4|2.4|4.3|4.2|4.4|
||**Change in foreign reserves**|0.2|0.2|0.2|0.2|0.2|0.2|0.2|0.2|
||**Total**|44.4|46.0|47.3|50.2|50.2|52.3|52.3|54.2|
|**Inflows**|**Exports**|7.5|7.2|8.1|8.9|8.9|10.1|10.1|11.9|
||**Transfers to households**|22.7|23.8|24.1|23.7|23.7|22.9|22.8|22.9|
||**Transfers to government**|14.0|14.8|14.9|14.1|14.1|13.1|13.0|13.1|
||**Private borrowing**|0.2|0.2|0.2|0.2|0.2|0.2|0.2|0.2|
||**Government borrowing**|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
||**FDI**|0.0|0.0|0.0|3.3|3.3|6.1|6.1|6.1|
||**Total**|44.4|46.0|47.3|50.2|50.2|52.3|52.3|54.2|

**Table 3.5. GDP at factor cost by sector -- level in 2022 (bn 2020 YER) and annual growth by scenario**

||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta**|**rec+ta+**|
|---|---|---|---|---|---|---|---|---|
|**Agriculture**|245.4|1.9|2.9|3.1|3.1|4.0|4.0|5.0|
|**Industry**|255.8|1.8|2.9|4.7|4.8|7.2|7.3|8.8|
|**Mining**|72.1|0.1|0.3|2.1|2.1|3.9|3.9|5.0|
|**Manufacturing**|128.5|2.4|4.1|5.3|5.4|7.8|7.8|9.7|
|**Other**|55.2|2.1|3.0|6.0|6.1|9.4|9.4|10.4|
|**Services**|797.9|2.1|3.2|4.1|4.1|6.2|6.2|7.2|
|**Private**|593.9|2.1|3.2|4.9|4.9|7.3|7.3|8.5|
|**Government**|204.0|2.0|3.1|1.3|1.3|2.3|2.3|2.3|
|**Total**|1,299.1|2.0|3.1|4.0|4.0|6.0|6.0|7.1|

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**Table 3.6. Sector structure in 2022 and for base in 2030 (%)**

||**Value**<br>**added**|**Produc-**<br>**tion**|**Employ-**<br>**ment**|**Exports**|**Imports**|**Export/**<br>**output**|**Import/**<br>**demand**|
|---|---|---|---|---|---|---|---|
|**2022**||||||||
|**Agriculture**|18.9|18.8|28.8|11.9|11.1|2.7|14.3|
|**Industry**|19.7|31.6|10.2|81.8|79.5|11.2|45.8|
|**Mining**|5.6|3.9|0.2|59.0|1.4|68.9|26.8|
|**Manufacturing**|9.9|16.4|4.7|22.8|77.8|5.3|57.8|
|**Other**|4.2|11.3|5.3|0.0|0.3|0.0|0.8|
|**Services**|61.4|49.6|61.0|6.3|9.4|0.6|4.9|
|**Private**|45.7|37.7|39.2|5.5|8.9|0.7|6.1|
|**Government**|15.7|11.9|21.8|0.9|0.5|0.3|1.0|
|**Total**|100.0|100.0|100.0|100.0|100.0|4.3|24.5|
|**2030**||||||||
|**Agriculture**|21.1|20.1|28.9|14.2|11.6|3.0|14.4|
|**Industry**|19.9|31.8|10.6|78.4|79.4|10.2|45.7|
|**Mining**|5.3|3.7|0.1|50.9|1.5|60.8|24.8|
|**Manufacturing**|10.5|17.1|5.2|27.5|77.6|6.0|57.4|
|**Other**|4.1|11.1|5.2|0.0|0.3|0.0|0.8|
|**Services**|59.0|48.1|60.5|7.4|9.0|0.7|5.0|
|**Private**|44.0|36.6|38.8|6.4|8.5|0.8|6.2|
|**Government**|15.0|11.5|21.7|1.0|0.4|0.4|1.0|
|**Total**|100.0|100.0|100.0|100.0|100.0|4.2|24.7|
|**Δ(2030-2022)**||||||||
|**Agriculture**|2.2|1.3|0.2|2.3|0.5|0.2|0.1|
|**Industry**|0.2|0.2|0.4|-3.4|-0.1|-1.0|-0.1|
|**Mining**|-0.3|-0.2|0.0|-8.1|0.1|-8.2|-2.0|
|**Manufacturing**|0.6|0.7|0.5|4.7|-0.2|0.7|-0.4|
|**Other**|-0.1|-0.2|-0.1|0.0|0.0|0.0|0.0|
|**Services**|-2.4|-1.5|-0.5|1.1|-0.4|0.1|0.1|
|**Private**|-1.7|-1.1|-0.4|0.9|-0.4|0.1|0.1|
|**Government**|-0.7|-0.4|-0.1|0.1|0.0|0.1|0.0|
|**Total**|0.0|0.0|0.0|0.0|0.0|-0.2|0.2|

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# 4. Non-base scenarios

The non-base simulations explore alternative potential paths for the economy of the country up to 2030, all assuming that, compared to the base, the security situation improves and that, in response, economic conditions improve due to more rapid private sector growth. Beyond this general description, there are significant differences between the non-base scenarios. While the paths are implemented in the context of a rigorous model and informed by cross-country evidence, they should merely be seen as pointing to possible gains from a conflict resolution, not as predictions of what will happen.

The non-base simulations start to deviate from the base in 2023 – all simulations are identical up to 2022. Given this, the analysis is based on comparisons between the non-base scenarios and the base scenario during the period 2023-2030, taking 2022 as the identical starting point.

Table 4.1 provides broad non-quantitative definitions of all non-base scenarios, linking each scenario to a broader context of political and military developments. In sum, the scenario limbo tries to capture the impact of continued and lasting but still relatively marginal security improvement compared to the preceding years whereas the recovery scenarios (rec- and rec+) assume that the political and military conflict ends; the result is more rapid productivity growth as the economy enjoys improved security, leading to some improvements in most economic indicators. The weak recovery scenarios (rec-) are associated with the emergence of a recentralized state in the context of military victory for one side or a political settlement while the strong recovery scenarios (rec+) assume that the result is a more developmental decentralized state, drawing on the decentralization that has emerged on the ground but strengthened by coalition-building among former enemies and rivals. For all recovery scenarios, the government expands spending in critical areas (health, education, social transfers, and infrastructure) at the same time as the outside world, encouraged by more positive domestic events, boost development via increases in private transfer inflows (“remittances”), foreign direct investment (FDI), and investments facilitating more rapid oil and gas extraction. Foreign grant aid does not increase for a subset of the recovery scenarios (rec-t and rec+t) whereas for others (rec-ta, rec+ta, and rec+ta+), it is above base levels 2023-2029 in support of government efforts to foster the recovery. The weak and strong recovery scenarios differ in that these positive shocks are twice the size for those that are stronger ones. In addition, the final strong scenario, rec+ta+, assumes that the government is able to better manage its infrastructure investments, something that is reflected in a doubling of the marginal productivity (MP) of new infrastructure capital.

21
**Table 4.1. Broad and contextual definitions of all non-base scenarios**

|**Name**|**Description**|
|---|---|
|**limbo**|**Emerging status quo**<br>Context<br>A fragile peace is brokered without any political settlement with intermittent fighting.<br>Simulation assumptions<br>:Same as**base**except for ....<br>... moderate growth accelerations in private and government sectors thanks to more rapid<br>productivity growth due to the security improvement.|
|**rec-t**|**Weak recovery scenarios***<br>Context<br>Emergence of a recentralized state as the military conflict is concluded due to victory for one<br>side or a political settlement.<br>Simulation assumptions<br>:Same as**base**except for ....<br>..._moderate_expansions in (a) government spending on health, education, social transfers, and<br>infrastructure; (b) transfers to households from abroad; (c) foreign direct investment (FDI); and<br>(d) the rates of oil and gas extraction.|
|**rec-ta**|Simulation assumptions<br>:Same as**rec-t**except for ....<br>... a moderate increase in foreign grant aid 2023-2029|
||**Strong recovery scenarios***<br>Context<br>Comprehensive political settlement enshrines decentralized state with strong powers at the<br>level of the governorates with coalition-building among former rivals and enemies.|
|**rec+t**|Simulation assumptions<br>:Same as**rec-t**except for ....<br>... expansions in areas a-d that are twice the size.|
|**rec+ta**|Simulation assumptions<br>:Same as**rec+t**except for ....<br>... an increae in foreign grant aid 2023-2029 that is twice the increase for rec-ta.|
|**rec+ta+**|Simulation assumptions<br>:Same as**rec+ta**except for ....<br>... a doubled marginal productivity (MP) of new government infrastructure capital.|

*The detailed assumption for the recovery scenarios (weak and strong) are presented in Table 4.2.

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# **Table 4.2. Detailed definitions of recovery scenarios**

|||**Scenario**<br>|**rec+t**<br>|
|---|---|---|---|
|||**rec-**|**rec+a**|
|**Shock area**|**Changes compared to base***|**rec-ta****|**rec+ta+*****|
|Health &|Government health & education spending (% of base GDP)|||
|education|(evenly split between consumption and investment)|+2.7|+5.4|
||Labor productivity in 2030 (%) -- gradual increase 2027-2030|+7.9|+15.8|
|Infrastructure|Government infrastructure spending (% of base GDP)<br>(32.5% consumption, 67.5% investment)|+3.3|+6.7|
|Social transfers|Transfers from government to households (% of GDP)|+0.6|+1.2|
|Remittances|Transfers to households from abroad (US$) (% annual growth)<br>(2023-2030)|+0.5|+1.0|
|FDI|Foreign direct investment (% of base GDP; in US$)|+3.45|+6.9|
|Oil-gas output|Change in oil & gas resource extraction levels (% annual growth)|+1.3|+2.4|

*Unless otherwise noted, changes are introduced gradually 2023-2026 and after that kept in place up to 203 **The difference between rec-t and rec-ta is that the latter enjoys foreign grant increases that in 20232026 gradually increase from 0.5 to 2 percent of base scenario 2022 GDP and in 2027-2030 gradually decline, 2030 being back at the 2030 base level.

***The difference between rec+t compared to rec+ta and rec+ta+ is that the latter two enjoy foreign grant increases that are twice as large as for rec-ta, peaking at 4 percent of 2022 GDP in 2026.  rec+ta+ differs from all other recovery scenarios in the MP of government capital is 0.6 instead of 0.3.

Table 4.2 provides additional technical and quantitative information about the different positive economic shocks that are implemented for the recovery scenarios. For the rec+ scenarios, the basic assumption is that Yemen by 2030 will have managed to take actions and realize improvements that bring the country up to outcomes typical of the 90<sup>th</sup> percentile among LICs; for the rec- scenarios, the basic assumptions is that, more modestly, the country closes half of the gap between its current government performance and that of the 90<sup>th</sup> percentile. Both for the rec+ and rec- scenarios, it is assumed that real government consumption not associated with health-education or infrastructure stays at the 2022 level (instead of growing at an annual rate of 2 percent under the base scenario); this may be seen as an efficiency gain since it makes it easier for the government to channel resources to high-priority spending without increases in taxes or foreign aid.

23
More specifically, for the rec+ scenarios, the increases in government health and education spending (split into consumption [current] and investment [capital]) during the period 2023-2026 are defined to permit the country to deliver services typical of the 90<sup>th</sup> LIC percentile starting from 2026.<sup>9</sup> It is assumed to generate a more gradual increase in Yemen’s human-capital index (HCI) to the 90<sup>th</sup> LCI percentile during the period 2023-2030 as a growing part of the labor force benefits from improved health and education. In its turn, labor productivity grows relative to the 2022 level at a rate that, according to cross-country evidence, is expected given the HCI increase.<sup>10</sup> For infrastructure, the rec+ scenarios impose gradual increases in consumption (operations and maintenance) and investment spending that, on average may be sufficient for low- and middleincome countries to achieve the infrastructure-related sustainable development goals (SDGs; Rozenberg and Fay 2019; Foster et al. 2022). Drawing on cross-country estimates (Gupta et al. 2014, p. 171), it is assumed that the infrastructure capital stock has a marginal productivity (MP) of 0.30 for rec+t and rec+ta, and 0.60 for rec+ta+. The gains are realized in the form of increases in total factor productivity (TFP); more specifically, once installed and other things being equal, value-added increases by 0.30 (0.60) YER for every additional YER of infrastructure capital with the gains spread across all sectors in proportion to their initial shares in value added. Finally, the scenarios rec+ta and rec+ta+ deviate from rec+t in that additional foreign grant aid is received: in each year 2023-2026 foreign grants increase by 1 percent of 2022 GDP (i.e., reaching 4 percent in 2026); the increases are reversed during the period 2027-2030 so that, in 2030, foreign grants are back at the level of the base scenario.

Similarly, the assumed increase in social transfers from the government to households would permit Yemen to reach the 90<sup>th</sup> percentile among LICs in terms of spending on social safety nets, assuming that Yemen’s initial level was in the bottom half among LICs (more specifically in the 25<sup>th</sup> percentile).<sup>11</sup> For remittances (i.e., transfers to Yemeni households from abroad expressed in foreign currency), the base scenario assumes that they grow at the same rate as the population, 2 percent, i.e., that they are unchanged in per-capita terms. For the rec+ scenarios, it is assumed that, starting from 2023, the annual growth rate (in foreign currency) increases by 1 percentage point. Such a development may require that the Gulf Cooperation Council (GCC) countries give preferential

> 9 More specifically, in each year the health and education spending increases for the rec+ and rec- scenarios are computed relative to base GDP and translated into increase in real service provision and real additions to the capital stock. Thus, the real changes are the same for all rec+ (rec-) scenarios, not depending on changes in GDP or prices in the individual rec+ (rec-) scenarios.

> 10 More specifically, the spending levels by LIC percentile were computed using World Development Indicators data for 2019 (World Bank 2020). The link between the spending percentile, the HCI, and labor productivity is based on data in World Bank (2021).

> 11 LIC percentile data for spending on social safety nets were computed on the basis of data in the World Bank’s ASPIRE dataset (World Bank 2018). For FDI, discussed in the following paragraph, the calculation was based on World Bank (2021).

24
treatment to worker migrants from Yemen and facilitate their remittance back home. In the model, the households primarily use the increases in social transfers and remittances to boost their consumption.

Currently, foreign direct investment (FDI) appears to be close to zero. In the rec+ scenarios, it is assumed that, during the period 2023-2026, FDI gradually reaches and after that stays at the LIC 90<sup>th</sup> percentile level among LICs. These increases in FDI accelerate the buildup of the private capital stock in Yemen, contributing to more rapid growth in production and incomes.

Finally, oil and natural gas dominate Yemen’s export revenues and, at least during the period up to 2030, these commodities are likely to continue to remain major foreign exchange earners. Under the base scenarios, the rate of extraction is assumed to grow at a modest annual rate of 0.1 percent. Under the rec+ scenarios, it is increased by 2.4 percentage points. For sectors other than oil and natural gas, productivity increases due to investment in human capital and infrastructure may contribute to more rapid export growth, especially if the sectors already have managed to export a significant share of its output and if domestic demand growth does not keep up with output growth.

As noted in Table 4.2, the rec- scenarios differ from the rec+ scenarios in that Yemen only eliminates half of the gap between its current situation and that of the 90<sup>th</sup> LIC percentile in terms of provision of health and education services, infrastructure, and social transfers; accordingly, the productivity gains that are realized thanks to these changes are only half the size. In other areas – remittances, FDI, and oil-gas output – the increases are half as large as for the rec+ scenarios. The difference between the rec-t and rec-ta scenarios is that the latter enjoys a foreign grant increase; however, the increase corresponds to 0.5 percent of base 2022 GDP (as opposed to 1 percent for rec+ta and rec+ta+ compared to rec+t).

Aside from the scenario features discussed above, the non-base scenarios have various assumptions in common that are different from those of the base: GDP is endogenous and, instead of letting private investment drive savings (as was done for the base), private savings (with exogenous savings rates) drive domestic private investment. This means that, other things being equal, increases in private incomes net of direct taxes translate into increases in private consumption, savings, investment spending, real investment, and capital stocks, the latter contributing positively to GDP.<sup>12</sup> For the government budget and the balance of payments, the base assumptions are retained: unless otherwise noted, foreign grant aid is at the same level (expressed in foreign currency); foreign financing of the government (borrowing net of interest payments) remains close to zero; and taxes and the real exchange rate adjust to clear the government budget and the balance of payments, respectively. The level of tax adjustments is in

> 12 The only exception is that, for the limbo scenario, it was for simplicity assumed that, just as for the base, GDP growth was assumed to be exogenous, thus endogenizing the TFP improvements. It would have been more cumbersome to implement the alternative of exogenously shocking TFP with endogenous GDP growth.

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fact an important part of the analysis as it will depend on government spending changes and the level of foreign grant aid and influence the level of household consumption.

The results for the different scenarios are summarized in Figures 4.1-4.9; as noted, additional results for both base and non-base scenarios are provided in Tables 3.1-3.6.

Figure 4.1 shows the deviations from the base for growth in aggregate government consumption and investment. In the absence of policy changes, the increases for limbo are small, merely reflecting that government spending responds to economic growth. For the recovery scenarios, they reflect policy changes, including larger increases for the rec+ scenarios. They are much larger for investment due to the fact that, under the base, it was at very low levels while, at the same time, government consumption growth is kept in check by a freezing of non-priority spending at 2022 levels throughout the simulation period.

As shown in Figure 4.2, across the board, growth increases with a strong correlation between the different scenarios: the gains in productivity, induced by improved security and policy changes, translate into higher GDP and income levels that generate higher household consumption, savings, private investment, exports, and imports, with the investment increases feeding back to higher growth. For the recovery scenarios, private investment growth is boosted by increases in FDI. The import and export gains may be seen as spillovers from increases in domestic supplies and demands that are imperfectly matched. Growth rates are higher for exports than for imports since the initial export value is smaller and the trade deficit is not permitted to expand compared to the base given exogenous values in foreign currency for other balance of payments items.<sup>13</sup> Respect for the balance of payments constraint is imposed via real exchange rate depreciation, encouraging exports and discouraging imports (cf. Table 3.1). The growth gains are weak for the limbo scenarios and strongest for the rec+ scenarios, exceeding the base by around 2-3 percent points for household consumption and 4-5 points for GDP (at factor cost), and exceeding the corresponding rec- scenarios by around 1 point for household consumption and 2 for GDP. Among the recovery scenarios, the growth gains are virtually identical for the scenarios with and without the additional aid (rec-ta vs. rec-t; and rec+ta vs. rec+t); however, as will be noted below, higher foreign aid has other more significant positive effects. On the contrary, the assumption of a higher MP of government capital for the final scenario, rec+ta+, makes a substantial difference, raising growth in household consumption and GDP by 0.8 and 1.1 percent points, respectively.

Deviations from base growth rates at the sector level are shown in Figure 4.3. For the limbo scenario, the expansion in manufacturing is strongest due to the combined impact of higher income elasticities of demand from households (compared to agriculture) and the absence of sectoral natural resource constraints. Across all recovery scenarios, the increases are strongest for other industry, reflecting strong increases in construction (part of other industry) to meet growing government and private investment demand. For mining, the growth gains reflect the

> 13 For the full-period growth figures, this also applies to the scenarios with increased foreign aid since these increases end in 2029.

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assumption that extraction rates are increased, most strongly for the rec+ scenarios. The small growth changes for government services (defined as public administration and public health and education) reflect the net of a policy-driven increase for education and health (which is stronger for the rec+ scenarios) and a decrease to zero growth for public administration. The gains in GDP growth raise employment growth sufficiently to reduce male and female unemployment rates relative to the 2030 base rate; the extent of the rate cuts is linked to differences in GDP growth gains. For the rec+ scenarios, female unemployment also reaches slightly below the 2022 level; for males, this is only the case for the rec+ta+ scenario (Table 3.1). The sectoral changes in export growth, shown in Figure 4.4, are highly correlated with the changes in sectoral GDP growth; the only partial exception is agriculture, for which exports are boosted by lower growth rate for domestic demands. (Government services are omitted from this figure since their exports are negligible.) The changes in headcount poverty – see Figure 4.5 -- are determined by changes in household consumption per capita, assuming an unchanged consumption distribution. As noted, the base rate in 2030 is identical to the 2022 rate, 74.4 percent, reflecting unchanged per-capita consumption. The rate declines slightly for the limbo scenario, to 72 percent. For the different recovery scenarios, the 2030 rates are in the range of 63-70 percent, corresponding to annual declines of 0.5-1.0 percent points. In addition, for the recovery scenarios, household welfare would improve thanks to better services in health and education, better infrastructure, and improved physical security.

As noted above, the differences between the scenarios with and without aid increases are negligible for indicators that only consider data for 2022 and/or 2030 as opposed to the evolution of the economy between these two years. By tracing the annual level of household consumption per capita between 2022 and 2030, indexed to 100 for 2022, Figure 4.6 highlights these differences. Compared to the scenarios rec-t and rec+t, the scenarios rec-ta and rec+ta, which only differ in that they enjoy the aid addition, are able to maintain a more even pace of consumption growth with noticeably stronger performance in the initial years when the gains from the policy shifts still are weak and, in the absence of the aid increases, the increases in domestic taxes are larger.

Figures 4.7 and 4.8 show the evolution over time for foreign grant aid and its implication for taxation for the rec- and rec+ scenario pairs; the tax increase in 2026 (the peak tax year) compared to 2022 amounts to 3.5 and 2.0 percent of GDP for rec-t and rec-ta, respectively; and to 8.3 and 5.5 percent for rec+t and rec+ta, respectively. If the total addition to fiscal space from aid, taxes or other resources is not sufficient, it would be necessary to opt for less ambitious government programs. However, given the humanitarian crisis in Yemen, it is evident that temporary additions to the aid flows may make an essential difference for living standards, which also may strengthen the prospects for a sustainable resolution of the conflict.

Finally, Figure 4.9 provides an alternative perspective on the macroeconomic gains under the different non-base scenarios and how these are distributed across the different types of final 27
demands. Relative to GDP in 2030 for the base scenario, the total gains in final demands range from less than 10 percent for limbo to close to 50 percent for rec+ta+. For the limbo scenario, which does not assume any significant structural change, the bulk of the modest increases accrue to private (or household) and government consumption whereas, for the recovery scenarios, roughly 80-85 percent of the increases are in the form of higher private final demands. In the background, the recovery scenarios assume a specific division of labor between the private sector and the government: the government represents a small but strategically important growthinstigating part of total final demands, helping the private sector to grow, while the private sector accounts for the bulk of final demands and an even larger part of production, given that the bulk of the supplies that meet government investment demands are provided by private sector producers.

<!-- Start of picture text --> Figure 4.1. Government demand growth by<br>scenario (% pt. deviation from base)<br>28.9 28.9 28.9<br>30<br>25<br>20.0 20.0<br>20<br>15<br>10<br>5<br>1.8 1.8 1.8<br>1.1 0.8<br>0.1 0.1<br>0<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br>Gov't cons'on Gov't inv'ment<br><!-- End of picture text --> 28
<!-- Start of picture text --> Figure 4.2. Growth in other macro indicators<br>by scenario (% pt. deviation from base)<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br>Hhd cons'on Private inv'ment Exports Imports GDP at f.c.<br>Figure 4.3. Sector GDP growth by scenario (%<br>pt. deviation from base)<br>9<br>8<br>7<br>Agriculture<br>6<br>Mining<br>5<br>4 Manufacturing<br>3 Other industry<br>2 Private services<br>1<br>Gov't services<br>0<br>-1<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br>12.5<br>11.4 11.4<br>8.0<br>6.5 6.5<br>5.9 6.0<br>5.1<br>4.0 4.0<br>3.2 3.3 3.1<br>2.0 2.0 2.2 2.1 2.2 2.2 2.6<br>0.7 0.9 1.3 1.1 1.2 1.1 1.2 1.1<br>0.2<br><!-- End of picture text --> 29
<!-- Start of picture text --> Figure 4.4. Sector export growth by scenario<br>(% pt. deviation from base)<br>13<br>12<br>11<br>10<br>9<br>8 Agriculture<br>7<br>6 Mining<br>5<br>Manufacturing<br>4<br>3 Private services<br>2<br>1<br>0<br>-1<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text -->

**Figure 4.5. Headcount poverty rate in 2022 and by scenario in 2030 (%)**

<!-- Start of picture text --> 80<br>74.4 74.4<br>75<br>72.0<br>70.0 69.8<br>70<br>66.0 65.8<br>65 62.4<br>60<br>55<br>50<br>2022 base limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text --> 30
<!-- Start of picture text --> Figure 4.6. Household consumption per capita<br>by year (index 2022=100)<br>130<br>125<br>base<br>120<br>limbo<br>115<br>rec-t<br>110 rec-ta<br>105 rec+t<br>rec+ta<br>100<br>rec+ta+<br>95<br>Figure 4.7. Grant aid for selected simulations (% of<br>GDP)<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2022 2023 2024 2025 2026 2027 2028 2029 2030<br>rec-t rec-ta rec+t rec+ta<br>Figure 4.8. Taxes for selected simulations (% of<br>GDP)<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2022 2023 2024 2025 2026 2027 2028 2029 2030<br>rec-t rec-ta rec+t rec+ta<br><!-- End of picture text --> 31
<!-- Start of picture text --> Figure 4.9. Increases in final demands in 2030 by<br>scenario (% of base GDP in 2030).<br>50<br>45<br>40<br>35<br>Gov't inv.<br>30<br>25 Gov't cons.<br>20 Private inv.<br>15 Private cons.<br>10<br>5<br>0<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text -->

# 5. Conclusions

This paper explores potential scenarios for Yemen’s economy up to 2030, drawing on a new database, including a SAM, put together from scattered data, and SDGSIM, a CGE model that imposes rigor based on first economic principles. The results suggest that, if the conflict subsides, the donor community is supportive, and the economy responds to increased spending on human development and infrastructure in ways that are typical of low-income countries, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great. However, high payoffs will require strong governance in general, including efficient government spending and the ability to raise additional taxes without a negative impact on income distribution or private sector incentives. Nevertheless, foreign aid may play a crucial role by reducing the need to raise domestic taxes, making it easier to realize badly needed improvements in living standards during years of expanded public investments in Yemen’s development.

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