Transformation structurelle en Haïti

Structural Transformation in Haiti Martin Cicowiez Agustín Filippo IDB-TN-1487 Country Department Central America, Haiti, Mexico Panama and Dominican Republic TECHNICAL NOTE Nº September 2018 Structural Transformation in Haiti Martin Cicowiez Agustín Filippo September 2018 Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Filippo, Agustín. Structural transformation in Haiti / Agustín Filippo and Martín Cicowiez. p. cm. — (IDB Technical Note ; 1487) Includes bibliographic references. 1. Economic development-Haiti-Econometric models. 2. Haiti-Economic policy- Econometric models. 3. Haiti-Economic conditions-Econometric models. I. Cicowiez, Martín. II. Inter-American Development Bank. Country Department Central America, Haiti, Mexico, Panama and the Dominican Republic. III. Title. IV. Series. IDB-TN-1487 Copyright © Inter-American Development Bank. 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The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. http://www.iadb.org 2018 - 1 - Structural Transformation in Haiti Agustín Filippo and Martín Cicowiez this version: June 18 , 201 8 1. Introduction Undoubtedly, Haiti has endure d incredible hardship, and, d espite its resilience , the country’s social and economic structure s have not created an environment for prosperity. Consequently , statistics are usually grim , starting with Haiti’s GDP per capita of US $761 for 201 7. Of Haiti’s population of 10. 8 million, 58.6 percent are considered poor based on the national poverty line and the most recent estimate. Haiti is also extremely unequal; based on the 2012 household survey data, Haiti has a Gini coefficient of 0.61 0 , which has remained constant since 2001 ( ECVMAS , 201 2 ). In this paper , we assess the impact of various scenarios related to accelerating growth and structural change in Haiti . The analysis is based on simulations with a relatively standard recursive dynamic computable general equilibrium (CGE) model designed for development policy analysis. For the purpose of this p aper , the model was adapted to the Haitian context and calibrated to a database for 201 3 , the most recent year with sufficiently detailed information. The simulations cover the period 201 3 - 2030 and consider alternative policies and shocks starting from 201 9. In outline, we proceed as follows. In Section 2, we provide context and assess the long - term (1950 - 2013) structural changes of Haiti. Section 3 describes the model and its database. Section 4 presents and analyzes CGE simulations designed to assess the impact – in terms of macro and sectoral variables -- of alternative patterns of sectoral productivity growth . Finally, Section 5 summarizes our main findings. More details on the model structure, data, and additional simulation results are presented in two appendices. 2. Historical Trends in Structural Change In the period 1950 - 2013, Haiti’s economy experienced a change in its sectoral structure against tradable sectors such as agriculture and manufacturing (Figure 2.1). Currently, - 2 - agriculture and manufa cturing account for 24.5 and 8.5 percent of GDP, respectively. In the 1970s, the corresponding figures were 37.3 and 15.1, respectively. On the other hand, the service sector grew to represent 55.9 percent of GDP in 2013. Figure 2.1: sectoral structure (pe rcent) Source: Haitian Institute of Statistics; see data table in annex. Productivity Gains and Structural Constraints Here , we start by estimating the value added (VA) per worker across economic sectors. To that end, we used information from the nation al accounts. Table 2 .1 shows the sectoral structure of the Haitian economy in terms of employment (column 1 - 2) and VA at current (columns 3 - 4) and constant (columns 4 - 5) prices. For instance, in 2013, 39.6 and 2.2 percent of workers were employed in the pr imary sector and manufacturing, respectively. Besides, Table 2 .1 shows that VA per worker was higher in manufacturing than in primary sectors. In turn, this translates into wage differentials across sectors. For example, for unskilled labor, wages are 490 percent higher in manufacturing than in agriculture. Generally speaking, urban industries (i.e., manufacturing and services) show higher VA per worker than ( typically ) rural industries such as agriculture . Indeed, such disparity in VA per worker is related to differences in wages and poverty rates between rural and urban areas. In 2012, 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 Services Construction Elect and water Manufacturing Primary - 3 - the extreme poverty rates were 38 and 12 in rural and urban areas, respectively. Moreover, population statistics show that population migrates from rural to urban areas . Int erestingly, we find large differences between sectoral VA measured at current and constant prices. For example, construction VA represents 23.9 and 10.6 percent of GDP at current and constant prices, respectively. Certainly, p art of that difference may be explained by higher prices for construction services after the 2010 earthquake. In turn, construction accounts for 5.8 percent of total employment. Table 2 .1: employment and value added per worker 2013 Source: Haiti CGE model dataset except VA at constan t prices from IH SI . Figure 2 . 2 : total and rural population, 1960 - 2016 Employment share (%) number (#) share (%) VA per worker share (%) VA per worker (1) (2) (3) (4) (5) (6) Agriculture, forestry, fishing and mining 39.6 1,240,306 19.6 1,391 24.5 1,738 Manufacturing 2.2 69,747 7.1 8,958 8.5 10,724 Electricity and water 0.3 8,194 1.8 19,331 0.5 5,370 Construction 5.8 180,819 23.9 11,631 10.6 5,159 Services 52.1 1,633,227 47.6 2,565 55.9 3,012 Total 100 3,132,292 100 2,809 100 2,809 Value Added, current pr Value Added, constant pr 0 10 20 30 40 50 60 70 80 90 0 2 4 6 8 10 12 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 rural population, percent population, million Total population (left) Rural population, share of total (right) - 4 - Based on Table 2 .1, we can conduct a (partial equilibrium) thought experiment in which workers move from primary to manufacturing activities under the assumption that wages remain con stant. Thus, moving one worker from agriculture to manufacturing would cause the economy to gain $7,567 ($8,958 - $1,391). Then, under the assumption that the workforce remains constant, increasing the share of manufacturing employment to 15 percent would increase VA per worker from $2 , 809 to $3 , 776, an increase of 34.4% (see Table 2 . 2) . Certainly, this is a simple partial equilibrium computation that did not consider the economic interrelation of agents in the economy – e.g., we assume that output per work er remained constant. I n the next section , we use a computable general equilibrium model to conduct similar, but more realistic, experiments. In fact, the estimations reported here suggest that promoting the manufacturing industries may have strong growth potentia l. In this regard, the African Development Bank has recently released a report stat ing that “ Africa must industrialize to end poverty and to generate employment for the 10 - 12 million young people who join its labor force every year ” ( AfDB, 2017) . A lso, Rodrik (2016) makes a similar argument in favor of manufacturing. Table 2 .2: thought experiment, increasing the share of manufacturing employment to 15 percent 3. Haiti CGE Model and Dataset CGE Model The fact that many policies and external shocks induce complex interactions between numero us agents makes it diffi cult to predict what effects they will have, including who will win and who will lose . CGE modelling offers a systematic method for predicting both the Employment Value Added number (#) share (%) VA per worker share (%) Agriculture, forestry, fishing and mining 840,209 26.8 1,391 9.90 Manufacturing 469,844 15.0 8,958 35.60 Electricity and water 8,194 0.3 19,331 1.40 Construction 180,819 5.8 11,631 17.80 Services 1,633,227 52.1 2,565 35.40 Total 3,132,292 100.0 3,776 100.00 - 5 - directi on and approx imate sizes for th e impacts of policies and external shocks on different agents. This study employs a single - country recursive dynamic CGE model to evaluate the impact of alternative scenarios on the Haitian economy. 1 The mathematical statement of the model is provided in A ppendix A . The model integrates a relatively standard recursive dynamic computable general equilibrium model with additional equations and variables that single out: (a) the impact of public capital investment in infrastructure on sectoral productivity, ( b) the workers migration between rural /informal and urban /formal sectors , and (c) the foreign tourism demand . Thus, this CGE model offers a combination of policy - relevant features for the study of various policy counterfactual scenarios for Haiti. Figure 3 . 1 depicts , for each simulation period, the circular flow of income within the economy and between the economy and the rest of the world. Figure 3 .1: c ircular income flow in the Haiti computable general equilibrium; within - period module 1 In Banerjee et al. (201 5 ), a similar model was used to assess the impact of a tourism - related investment in the Sud department of Haiti . Factor Markets Activities Households Commodity Markets Rest of World Government Non-Gov Capital Account domestic wages and rents factor demand dom demand exports imports interm input dem private consumption gov cons and inv indirect taxes hhd net savings transfers transfers transfers financing financing non-gov investment direct taxes financing foreign wages and rents - 6 - Source: Author ’ s own elaboration. In any single year, the Haiti CGE has the structure summarized in the above figure. Activities produce, selling their output at home or abroad (i.e., the rest of the world), and using their revenues to cover their costs (of intermediate inputs, factor hiring and taxes). Their decisions to pursue particular activities with certain levels of factor use are driven by profit maximization. The shares exported and sold domestically depend on the relative prices of their output in world and dome stic markets. The model identifies four types of institutio ns: households, government, the rest of the world , and foreign tourists . Households earn incomes from factors and transfers. These are used for consumption, direct taxes, and savings. Their consumption decisions change in response to income and price changes. By construction (and as required by the household budget constraint s), the consumption value of the households equals their income net of direct taxes and savings. The government gets its receipts from taxes and transfers from abroad; it uses these for consumption, transfers to households, and investment, drawing on the l oanable funds market for supplementary funding. To remain within its budget constraint, it either adjusts some part(s) of its spending on the basis of available receipts or mobilizes additional receipts in order to finance its spending plans. The rest of t he world (which appears in the balance of payments) sends foreign currency to Haiti in the form of transfers to its government and households. In turn, Haiti uses these inflows to finance its imports. The balance of payments clears (inflows and outflows ar e equalized) via adjustments in the real exchange rate (the ratio between the international and domestic price levels), influencing export and import quantities and values in foreign currency. Investment financing is provided from savings by households, go vernment, and the rest of the world. Tourism demand from rest of the world ( exports ) is modeled as an exogenous volume . In turn, total tourism demand is disaggregated across locally produced commodities using fixed coefficients. In domestic commodity mark ets, flexible prices ensure balance between demands for domestic output from domestic demanders and supplies to the domestic market from domestic suppliers. The part of domestic demands that is for imports from the rest of the world faces exogenous prices – Haiti is viewed as small in world markets, without any - 7 - impact on the import and export prices that it faces. Domestic demanders decide on import and domestic shares in their demands on the basis of the relative prices of commodities from these t wo source s. Similarly, domestic suppliers (the activities) decide on the shares for exports to the rest of the world and domestic supplies on the basis of the relative prices received in these two markets. Factor markets reach balance between demands and supplies via wage (or rent) adjustments. Across all factors, the factor demand curves are downward - sloping reflecting the responses of production activities to changes in factor wages. In the case of labor, unemployment is endogenous . F or each labor type, the model includes a wage curve that imposes a negative relationship between the real wage and the unemployment rate (Blanchflower and Oswald, 2004). This type of wage equation can be derived from trade union wage models, as well as from efficiency wage models (see , for example, Devarajan et al. (1999) and Cicowiez and Sánchez (2010)). For non - labor factors, the supply curves are vertical in any single year. Model D ynamics In our CGE model , growth over time is largely endogenous. The economy grows due to accumulatio n of capital determined by investment and depreciation, labor (determined by exogenously imposed projections), as well as because of improvements in total factor productivity (TFP) which have both endogenous and exogenous components. Apart from an exogenou s component, TFP of any production activity potentially depends (usually, positively) on the levels of government capital stocks and economic openness. The accumulation of capital is through investment financed by domestic savings and foreign inflows. Incr eased capital is allocated across sectors according to their relative profitability. Once installed , capital becomes sector - specific and can only by adjusted through exogenously - determined depreciation and the attraction of new investments. Social Accounting Matrix and Other Data The basic accounting structure and much of the underlying data required to implement our Haiti CGE model is derived from a Social Accounting Matrix (SAM) for Haiti. A SAM is a comprehensive, economy - wide statistical represe ntation of the modeled economy at a specific point in time. It is a square matrix with identical row and column accounts where - 8 - each cell in the matrix shows a payment from its column account to its row account. It can be used for descriptive purposes and i s the key data input for a CGE model . Major accounts in Haiti SAM are: activities that carry out production; commodities (goods and services) which are produced and/or imported and sold domestically and/or exported; factors used in production which include labor, capital, land , and other natural resources; and institutions such as households, government, and the rest of the world. Generally speaking, most features of the SAM are familiar from social accounting matrices used in other models. 2 As is usually done, we use the SAM to define base - year values for the bulk of the model parameters, including production technologies, sources of commodity supplies (domestic output or imports), demand patterns (for household and government consumption, investment and e xports), transfers between different institutions, and tax rates. A stylized ( m acro - )SAM for Haiti is provided in Table 3 .1. Haiti GDP reached 367,215 million gourdes in 2013, based on data from the supply and use table. 3 In 2013, the government current ac count surplus was around 1.1% of GDP and government current consumption was 8.5 of GDP. Regarding international trade, Haiti exported 12.2 percent of GDP and imported 46.7 percent of GDP (Table 3 .2). Remittances (transfers) are the single largest source of earnings in the current account balance of Haiti, equiva lent to 21.1 percent of GDP in 2013. 2 See Pyatt and Round (1985) or King (1981) for a more detailed introduction to SAM construction and interpretation. 3 GDP in 2013 was 364,526 million gourdes according to the latest IHSI report on national accounts. - 9 - Table 3 .1: m acro - SAM for Haiti 2013 ( percent of GDP ) where a/c - agr : agriculture activi ties and commodities; a/c - nagr: non - agriculture act ivities and commodities; f - lab: labor; f - cap: capital; tax - ind: domestic indirect taxes; tax - i mp: import tariffs; tax - dir : direct taxes; marg : trade and transport margins; h - rur and h - urb : rural and urban a-agr a-nagr c-agr c-nagr f-lab f-cap tax-act tax-com tax-imp tax-dir marg-d marg-m marg-e h-rur h-urb gov row sav invng invg dstk total a-agr 31.0 0.0 31.0 a-nagr 0.6 118.6 119.2 c-agr 9.1 4.7 13.1 15.6 0.9 0.0 0.0 0.0 43.3 c-nagr 4.0 34.9 12.1 19.2 1.8 22.0 45.3 8.5 11.3 13.8 16.0 0.0 189.0 f-lab 7.5 43.8 51.4 f-cap 10.4 32.8 0.8 44.1 tax-act 0.0 3.0 3.1 tax-com 0.0 -0.9 -0.9 tax-imp 0.3 3.0 3.3 tax-dir 0.4 2.1 2.6 marg-d 3.4 8.8 12.1 marg-m 0.8 18.5 19.2 marg-e 0.1 1.7 1.8 h-rur 18.4 7.6 0.1 4.9 31.0 h-urb 32.9 36.3 0.2 16.2 85.6 gov 3.1 -0.9 3.3 2.6 0.1 0.7 8.9 17.7 row 7.2 39.4 0.2 0.5 2.4 0.0 49.8 sav -5.2 19.5 8.9 6.7 29.8 invng 13.8 13.8 invg 16.0 16.0 dstk 0.0 0.0 total 31.0 119.2 43.3 189.0 51.4 44.1 3.1 -0.9 3.3 2.6 12.1 19.2 1.8 31.0 85.6 17.7 49.8 29.8 13.8 16.0 0.0 762.9 - 10 - representative households, respectively; gov: government; row : rest of the world; sav: savings; inv and invg : private and government investment, respectively. Source: A uthor’s elaboration. - 11 - As explained, the Haiti CGE was calibrated to a 2013 SAM and other data for Haiti. The main sources of information for the construction of the Haiti 2013 SAM were the supply and use tables for the same year, complemented by data on th e balance of payments and government finance statistics as well as the ECVMAS 2012. Table 3 .2 shows the accounts in the SAM, which determine the size (i.e., disaggregation) of the model. Thus, the SAM/model identifies 2 2 activities and commodities. The fac tors of production include two types of labor, each of which is linked to a level of education ( unskilled is less than completed secondary , and skilled is completed secondary or above). The growth in the labor force and changes in its composition are exoge nous, allowing us to consider alternative counterfactual scenarios. The non - labor factors include public capital stock s (i.e., one for each government sector ) , a private capital stock, land, and a natural resource used/extracted in mining. The SAM also inc ludes current and capital accounts for instituti ons (household, government, rest of world , and foreign tourists ), investment accounts (one per capital stock), and auxiliary accounts for taxes and trade and transport margins. Table 3 . 2 : accounts in the Hait i 20 13 Social Accounting Matrix Category (#) Item Category (#) Item Agr, hunting and forestry; Fishing Labor, unskilled Mining and quarrying Labor, skilled Food prod and beverages Capital Tobacco prod Land Textiles, wearing apparel and leather Natural resource, extractive Wood and of prod of wood and cork Dist marg, domestic Paper and paper prod; Publishing Dist marg, imports Chemicals; Rubber and plastics Dist marg, exports Other non-metallic mineral prod Taxes on activities Basic metals Taxes on commodities Fabricated metal prod; Mach and equip Subsidies on commodities Other manufactures Tariffs Electricity and water supply Taxes on income Construction Household, rural, quintiles (5) Wholesale and retail trade Household, urban, quintiles (5) Hotels and restaurants Government Transport, storage and comm Rest of world Financial intermediation Foreign tourists Other services Savings Education, government Investment, private Helath, government Investment, government agriculture infra Public administration Investment, government transport infra Investment, government education Investment, government health Investment, government other Stock change Sectors (activities and comm) (22) Services (10) Manufact (10) Primary (2) Investment (8) Institutions (13) Taxes and subsidies (5) Trade and transport margins (3) Factors (5) - 12 - Source: A uthor’s elaboration. On the basis of SAM data, Table 3 . 3 summarizes the sectoral structure of Haiti’s economy in 2013: sectoral shares in value - added, production, employment, exports and imports, as well as the split of domestic sectoral supplies between exports and domestic sales, and domestic sectoral demands between imports and domestic output. For instance, while (primary) agriculture represents a significant share of employment (around 4 1 percent) , its share s of value added , production, and exports are much smaller (in the range of 7.5 - 20 percent). The share of its output that is exported is around 2.5 percent while 19.6 percent of domestic demands are met via imports. On the other hand, t extiles, wearing app arel and leather products represent a significant share of export revenue (around 48.1%), while their share in total value added is about 2.8% (column VAshr). The Haiti 2013 SAM also reports taxes paid by institutions, commodity sales, value added, activit ies, exports, and tariffs; total tax revenue reached 9% of GDP in 2013 , a relatively low figure when compared to other LDCs (see WDI (2018) ) . - 13 - Table 3 . 3: sectoral structure of Haiti’s economy in 201 3 (percent) where VAshr: value - added share (%); PRDshr : production share (%); EMPshr : share i n total employment (%); EXPshr: sector share in total exports (%); EXP - OUTshr : exports as shar e in sector output (%); IMPshr: sector share in total imports (%); IMP - DEMshr : imports as share of domestic demand (%). Source: Author’s calculations based on 2013 Haiti SAM and employment data. Table 3 .4 shows the factor shares in total sectoral value added. For example, the table shows that agriculture is relatively intensive in the use of unskilled labor and labor; this information will be useful to analyze the results from the Haiti CGE simulations. In turn, G overnment services (i.e., education, health and public administration ) and Financial intermediation are relatively intensive in the use of skilled labor. Sector VAshr PRDshr EMPshr EXPshr EXP- OUTshr IMPshr IMP- DEMshr Agr, hunting and forestry; Fishing 19.4 21.0 41.0 7.3 2.5 15.5 19.6 Mining and quarrying 0.2 0.2 0.0 0.0 0.0 0.1 12.0 Food prod and beverages 1.9 4.6 0.5 2.0 2.5 14.2 52.0 Tobacco prod 0.0 0.1 0.0 0.0 0.0 0.3 58.2 Textiles, wearing apparel and leather 2.8 4.4 1.2 48.1 81.1 17.0 87.3 Wood and of prod of wood and cork 0.4 0.5 0.1 2.0 12.4 1.5 51.0 Paper and paper prod; Publishing 0.5 0.9 0.1 0.0 0.0 1.3 31.0 Chemicals; Rubber and plastics 0.2 0.5 0.0 1.3 13.0 15.0 92.0 Other non-metallic mineral prod 0.4 0.6 0.1 0.0 0.1 1.2 39.7 Basic metals 0.1 0.2 0.0 0.0 0.0 2.2 76.0 Fabricated metal prod; Mach and equip 0.1 0.1 0.0 1.1 45.0 11.4 98.7 Other manufactures 0.7 1.5 0.2 18.2 66.7 0.7 33.0 Electricity and water supply 1.8 2.7 0.3 0.0 0.0 0.0 0.0 Construction 23.9 19.0 5.9 0.0 0.0 0.0 0.0 Wholesale and retail trade 26.0 22.0 28.6 0.0 0.0 0.0 0.0 Hotels and restaurants 0.3 0.8 0.3 10.2 100.0 1.2 100.0 Transport, storage and comm 14.2 13.9 3.7 8.5 5.0 15.8 27.1 Financial intermediation 2.0 2.0 2.1 1.3 5.0 1.7 21.1 Other services 3.3 3.5 11.0 0.0 0.0 0.9 7.1 Government services 1.6 1.4 4.8 0.0 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0 7.0 100.0 31.3 - 14 - Table 3 . 4 : sectoral factor intensity , Haiti 2013 (percent) Source: Author’s calculations based on 2013 Haiti SAM. In addition to the SAM, our Haiti CGE model requires (a) base year estimates for capital stocks and sectoral employment levels and unemployment esti mates for the different labor types , (b) a set of elasticities (for production, consumption , and trade) , (c) population projections by household group (i.e., rural and urban), and (d) a baseline projection for growth in GDP at factor cost (see below) . In order to estimate sectoral employment , we combined population data from UN with estimates for the unemployment rate computed from the ECVMAS (2012). In turn, elasticities were given a value based on the available evidence for comparable countries; given th e implied uncertainty, we performed a systematic sensitivity analysis of the results with respect to their value. For elasticities, the following values were used: (a) the elasticity of substitution among factors is in the 0.2 – 1.15 range, relatively low f or primary sectors and relatively high for Sector Labor, unskilled Labor, skilled Capital Nat Res Total Agr, hunting and forestry; Fishing 40.3 3.8 9.5 46.4 100.0 Mining and quarrying 14.0 31.6 31.5 23.0 100.0 Food prod and beverages 18.6 41.9 39.6 0.0 100.0 Tobacco prod 21.9 49.4 28.7 0.0 100.0 Textiles, wearing apparel and leather 27.4 61.7 10.9 0.0 100.0 Wood and of prod of wood and cork 17.2 38.8 44.1 0.0 100.0 Paper and paper prod; Publishing 18.3 41.3 40.3 0.0 100.0 Chemicals; Rubber and plastics 13.1 29.6 57.3 0.0 100.0 Other non-metallic mineral prod 11.8 26.7 61.5 0.0 100.0 Basic metals 21.7 48.9 29.4 0.0 100.0 Fabricated metal prod; Mach and equip 13.6 30.8 55.6 0.0 100.0 Other manufactures 15.4 34.8 49.8 0.0 100.0 Electricity and water supply 10.1 22.7 67.2 0.0 100.0 Construction 18.1 19.4 62.5 0.0 100.0 Wholesale and retail trade 44.0 25.3 30.7 0.0 100.0 Hotels and restaurants 40.4 23.2 36.4 0.0 100.0 Transport, storage and comm 21.5 36.4 42.1 0.0 100.0 Financial intermediation 13.2 66.1 20.8 0.0 100.0 Other services 18.6 20.3 61.1 0.0 100.0 Government services 5.3 85.1 9.6 0.0 100.0 Total 29.3 24.9 37.0 8.8 100.0 - 15 - manufacturing and services (see Narayanan et al. (2015)) ; (b) the wage curve has an unemployment - elasticity of - 0.1 (see Blanchflower and Oswald (2005) ); and (c) based on S adoulet and de J anvry ( 1995 ), trade elast icities are in the 0.5 - 2 range. Finally, note that for each set of simulations we conduct ed a systematic sensitivity analysis of our CGE model results with respect to their value. 4 4. Results and Analysis Scenarios The simulations consist of a base simulat ion and a set of non - base simulations that quantitatively explore alternative growth options for Haiti . The Haiti CGE simulations cover the period 201 3 - 2030. The initial year, 201 3 , was selected in light of data availability (see Section 3 ). Base Scenario The base run is designed to replicate trends since 201 3 at the macro and sectoral levels. From 201 8 on, this first simulation assumes that past trends will continue into the period from 201 8 to 2030. More specifically, the model’s base - run scenario simula tes a Haitian economy that grows at an average annual growth rate of 1.5 percent for the period 2018 - 2030, based on recently observed growth rates and consistent with the long - run growth rate estimated by Katz (2016). Thus, relative to IMF World Economic O utlook projections (IMF, 2017), our baseline scenario is pessimistic in terms of expected growth. In contrast, our non - base simulations are designed to assess alternative option s to bring about an increase in the Haiti’s growth rate, closer to the estimati ons by the IMF. In the base scenario, GDP growth is imposed by endogenously adjusting (overall) total factor productivity . 5 Besides, w e assume that government demand for government services, transfers from government to households, and domestic and foreign government net borrowing are all maintained at their base - year shares of GDP. In turn, t axes are fixed at 4 The results from the sensitivity analysis are available from the authors upon request. 5 In the non - base simulations, TFP is invariably exogenous. - 16 - their base - year rates, which means that they will grow roughly at the same pace as the overall economy. Figures 4 . 1 - 4 . 4 show key macroeconomic results for the base. (Tables B .1 - B .5 show additional results for base and non - base scenarios, covering macro and sector indicators as well as the government budget and the balance of payments.) Given that it is intended to be a bus iness - as - usual scenario, the base is set up to maintain the observed macroeconomic structure. Figures 4 . 1 and 4 . 2 show the evolution of the levels of GDP, foreign trade, and domestic final demand aggregates . I n Figure 4 . 3 , this information is translated in to average annual growth rates . As shown, most macro aggregates grow at 1.5 - 1.7 percent yearly, with exports growing at 2.2 percent yearly. Figure 4 . 4 simply reflects our baseline assumption that sectoral TFP grows at the same pace for all sectors. In fact , the baseline TFP growth of about 0.2 is consistent with the annual GDP growth rate of 1.5 percent for the period 2018 - 2030 . 6 Thus, based on Katz (2016), the computed TFP is below the one estimated for the 70s but above the one estimated for recent period s. The real wage grows slightly at a rate of 0.1 percent per year on average. For the base scenario, the growth rates in household consumption per - capita for rural and urban households are 1. 3 and - 0. 2 percent, respectively, yielding a national average of 0 . 4 percent ( Figure 4 . 5 ) . In turn , these changes in per - capita consumption are reflected in changes in headcount poverty. In fact, the extreme poverty rate falls from 26.6 in 2013 to 17.4 in 2030 (Figure 4 . 6 ) At the sector level , growth for agriculture – 0 .8 percent yearly -- is constrained by the land supply, which is assumed to grow only 0.1 percent annually. On the other hand, textiles – which is the m ost export - oriented sector -- grows at 2.5 per year during the period 2018 - 2030 (Figure 4 . 7 and Table B . 3 in Appendix B ). GDP grows at 1.5 percent yearly is not strong enough to reduce the unemployment rate, which stays relatively constant at 31.7 percent (see Table B .1). Figure 4 . 8 shows output per worker in four (aggregated) sectors: agriculture, manufacturing, other industries (i.e., mining, construction, and electricity and water supply ), and services. As discussed, output per worker is lower for agricultural activities. In the b ase, we see that sectoral output per worker and the sectoral structure of the economy remains relatively constant for the whole simulation period. For example, the share of non - 6 In a companion paper , we consider long - run scenarios that are also consistent with a 1.5 percent annual GDP growth rate. - 17 - agricultural VA increases by 1.6 percentage points – from 60 to 61.6 percent -- at the expense of agricultural VA (Figure 4 . 9 ) . Finally, the base scenario replicates the trend in population migration from rural to urban areas described above (Figure 4 . 1 0 ). Figure 4 . 1 : base scenario; selected macroeconomic indicators (2013 prices, bil lion gourdes) Source: Authors’ calculations based on simulation results. 0 100 200 300 400 500 600 700 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 billion gourdes Absorption Exports Imports GDP factor cost - 18 - Figure 4 . 2 : base scenario; domestic final demands ( 2013 prices, billion gourdes ) Source: Authors’ calculations based on simulation results. Figure 4 . 3 : base scenario; real annual macroeconomic growth 2018 - 2030 (%) Source: Authors’ calculations based on simulation results. 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 250 300 350 400 450 500 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 other final demands private consumption Consumption, private Investment, private Consumption, government Investment, government 0 0.5 1 1.5 2 2.5 Absorption Consumption, private Consumption, government Investment Exports Imports GDP factor cost - 19 - Figure 4 . 4 : base scenario; TFP annual growth 2018 - 2030 (%) Figure 4 . 5 : base scenario; r eal household consumption per capita by region Source: Authors’ calc ulations based on simulation results. 0.00 0.05 0.10 0.15 0.20 0.25 Agriculture Manufact Other indust Services 0 10,000 20,000 30,000 40,000 50,000 60,000 Nation Rural Urban Real household consumption per capita (2013 prices, gourdes) 2013 2030 - 20 - Figure 4 . 6 : base scenario; headcount extreme poverty by region Figure 4 . 7 : base scenario; real annual sector growth 2018 - 20 30 (%) Source: Authors’ calculations based on simulation results. 0 5 10 15 20 25 30 35 40 45 Nation Rural Urban Extreme poverty rate (%) 2013 2030 0 0.5 1 1.5 2 2.5 Agriculture Manufacturing Other indust Services - 21 - Figure 4 . 8 : output (VA) p er worker (2013 prices, gourdes per capita) Source: Authors’ calculations based on simulation results. Figure 4 . 9 : base scenario; employment structure (%) Source: Authors’ calculations based on simulation results. 0 100,000 200,000 300,000 400,000 500,000 600,000 Agriculture Manufact Other indust Services 2013 gourdes per worker 2013 2030 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Agriculture Manufacturing Other indust Services - 22 - Figure 4 . 10 : base scenario; rural and urban population (%) Source: Authors’ calculations based on simulation results. Non - Base Scenarios The non - base scenarios are defined in Table 4 . 1 . As shown, and b ased on the experiences of other successful developing countries 7 , we simulate the effects of rapid growth in agriculture, export - oriented manufacturing such as textiles and apparel , and logistic services such as trade and transport. Specifically, the non - base scenarios assume increases in labor productivity, increases in land productivity – use d in agriculture -- , and increases in foreign direct investment (FDI). 7 For example, countries such as Brazil, Malaysia, Thailand, China, India, and Vietnam have reached MIC status within a span of approximately 10 years. In none of these countries rapid growth was driven by booms in (extractive) natural resources . Instead, in all six countries t he share of agriculture in total GDP declined in fav or of manufacturing . 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Rural Urban - 23 - Table 4.1: d efinitions of non - base scenarios * *Note: Except for the changes indicated in the description, the scenarios are otherwise identical to the base scenario. By assumption, the overall (economy - wide) TFP growth is 0.5 percent in all non - base scenarios (see text). Hence, the increase in FDI does not increase TFP. Source: Authors’ elaboration. For agriculture, the non - base increase in agriculture productivity i s justified because yields per hectare cultivated in Haiti are low when compared to the average for least developed countries in 2014; i.e., there is room for significant improvement. For example, the average yield of the areas cultivated with corn was 9.2 and 16.4 tons per hectare in Haiti and in least developed countries, respectively (FAO , 2017 ) (see Table B .1 in Appendix B ). In turn, the selection of manufacturing industries in the mnfc and mnfc+fdi scenarios is based on (a) the current sectoral export pattern of Haiti, highly concentrated in textiles (Figure 4 .1 1 ) ; (b) the labor intensity of textiles, based on the Leamer product classification (Leamer, 1984 ); and (c) the current location of Haiti in the Product Space as developed by Hidalgo et al. (2007 , 2011) (Figure 4 .1 2 ) (The Product Space is a network representation of the relatedness or proximity between products traded in the global market. The product space is a network connecting products that are likely to be co - exported and can be used to predi ct the evolution of a country’s export structure.) In all cases , the increase in FDI is determined such that the FDI in Haiti increases to the same FDI - to - GDP ratio as the average for low income countries in 2016. (In 2016, FDI in Haiti and in low income countries was 1.3 and 4 percent of GDP, respectively.). Implicitly, we assume Name Description agr 25 percent increase in labor and land productivity in agriculture during 2018-2030 agr+fdi same as agr combined with increase in FDI equivalent to 2.7 percent of base GDP mnfc increased labor productivity in manufacturing during 2018-2030; overall TFP increase same as agr mnfc+fdi same as mnfc combined with increase in FDI equivalent to 2.7 percent of base GDP svc increased labor productivity in services during 2018- 2030; overall TFP increase same as agr svc+fdi same as svc combined with increase in FDI equivalent to 2.7 percent of base GDP - 24 - that the increase in FDI would bring about an increase TFP; for example, through a better organization of production and an improvement in the business climate. In all cases , shocks are introduced during the period 2018 - 2030; i.e., the non - base simulations deviate from the base for the period 2018 - 2030; thus, base and non - base scenarios are the same for the period 2013 - 2017. Figure 4 .1 1 a: exports Haiti 2015 (%) Figure 4 .1 1 b: imports Haiti 2015 (%) - 25 - Figure 4 .1 2 : Product Space Haiti 2015 To facilitate comparisons across the different scenarios, all shocks are imposed during the same period (2018 - 2030) and are of the same size in terms of their impact on overall Total Factor Productivity. In other words, in all non - base scenarios factor - specific productivity in the target sector is increased up to a point where aggregate TFP growth rate reaches 0.5 percen t annually . In turn, the non - promoted sectors maintain their baseline pr oductivity level. In fact, our simulation design takes into account the differences in size between the productive sectors. For example, agriculture and manufacturing represent 40 and 2.2 percent of total employment, respectively. Thus, in order to obtain the same increase in overall TFP, the required increase in factor - specific productivity is smaller for agriculture than for manufacturing. In other words, the different simulations are comparable even though the increase in factor - specific productivity is applied to different sectors (see Figure 4 .2 0 ) . Specifically, i n scenarios mnfc and svc, labor productivity in manufacturing and services increases by 3.1 and 1 percent annually , respectively. 8 8 Mathematically, the VA production function can be written as 𝑄𝑉𝐴 𝑎 , 𝑡 = 𝑡𝑓𝑝 𝑎 , 𝑡 ⋅ 𝜑 𝑎 𝑣𝑎 ( ∑ 𝛿 𝑓 , 𝑎 𝑣𝑎 𝑓 ∈ 𝐹 ⋅ ( 𝑓𝑝𝑟𝑑𝑎 𝑓 , 𝑎 , 𝑡 ⋅ 𝑄𝐹 𝑓 , 𝑎 , 𝑡 ) − 𝜌 𝑎 𝑣𝑎 ) − 1 𝜌 𝑎 𝑣𝑎 , where 𝑄𝑉𝐴 𝑎 , 𝑡 is value added, 𝑄𝐹 𝑓 , 𝑎 , 𝑡 is factor demand, 𝑡𝑓𝑝 𝑎 , 𝑡 is an an index of - 26 - Figure 4 .1 3 summarizes the main transmission channels in the agricultural productivity scenarios agr and agr+fdi. For the other labor productivity scenarios, the main transmission channels are similar, although the targeted sector differs. In all scenarios, increased factor productivity results in increased output of the promoted sector. Thus, real GDP and household income rise in all scenarios . Consequently, household consumption per capita also rises while poverty declines when compared to the base scenario . For ins tance, i n the agriculture scenarios, yearly GDP growth gains between 0.5 (agr) and 1 (agr+fdi) percentage points . As expected, the increase in GDP is accompanied by expansion in private consumption and private investment as additional output permit private incomes and savings to grow more rapidly with a positive feedback into the growth process. In turn, t he increase in supply of the sector’s goods (or services) results in a decline in its price since demand increases (brought about by increases in household incomes and investment demand) are in general less than the increase in supply. In other words, domestic markets clear at a lower price level . Howev er, the higher the exports - to - output ratio, the lower is the decrease in the domestic price . In all scenarios, overall TFP growth increases from about 0. 2 percent in the base to about 0. 5 in the non - base simulations. The size of the change in real GDP, the changes in output quantities and prices, and changes in incomes of various household groups all vary according to which sector is shocked (see Figure s 4 . 1 4 and 4.15 ). Needless to say, GDP growth also depends on capital accumulation and the employment leve l. The income sources of rural and urban households are distinct, with rural households depending more on unskilled labor while urban households rely more on skilled labor and (private) capital. The scenarios differ in terms of their impact on different s ectors and, thereby, on the distribution of factor incomes, household incomes, and household consumption. Figure 4 .1 6 shows the deviations of the per - capita consumption growth rates from the base rates reported in Figure 4 . 5 , both at the national level and for rural and urban households separately. The scenarios that promote the agricultural and manufacturing sectoral total factor productivity, 𝑓𝑝𝑟𝑑 𝑓 , 𝑎 , 𝑡 is an index of sectoral factor - specific productivity , and 𝛿 𝑓 , 𝑎 𝑣𝑎 , 𝜑 𝑎 𝑣𝑎 , and 𝜌 𝑎 𝑣𝑎 are parameters. In the simulation, we increase 𝑓𝑝𝑟𝑑𝑎 𝑓 , 𝑎 , 𝑡 in selected sector s so that the change in overall TFP is the same in all non - base scenarios. In turn, overall TFP in year 𝑡 is defined as the ratio between ∑ 𝑄𝑉𝐴 𝑎 , 𝑡 𝑎 ∈ 𝐴 and ∑ 𝑄𝑉𝐴 𝑎 , 𝑡 ∗ 𝑎 ∈ 𝐴 , where 𝑄𝑉𝐴 𝑎 , 𝑡 ∗ = 𝑡𝑓𝑝 𝑎 00 ⋅ 𝜑 𝑎 𝑣𝑎 ( ∑ 𝛿 𝑓 , 𝑎 𝑣𝑎 𝑓 ∈ 𝐹 ⋅ ( 𝑓𝑝𝑟𝑑𝑎 𝑓 , 𝑎 00 ⋅ 𝑄𝐹 𝑓 , 𝑎 , 𝑡 ) − 𝜌 𝑎 𝑣𝑎 ) − 1 𝜌 𝑎 𝑣𝑎 . - 27 - sectors are more advantageous for rural households, since agricultural and manufacturing sectors are relatively intensive in the use of unskilled labor. For the services scenarios, the consumption growth gains favor the urban households. In all cases, the gap between rural and urban household per capita incomes is reduced when the increase in factor productivity is combined with FDI . Interestingly, and due to its positive impact on the (unskilled - intensive) construction sector, FDI has a stronger positive impact on the wages of unskilled workers . These changes in per - capita consumption are reflected in changes in headcount poverty. Figure 4 .1 7 shows the deviations of the extreme poverty rates in 2030 from the base rates reported in Figure 4 . 6 . In a ll scenarios , we observe a reduction in poverty at the national level. For the scenarios that favor agriculture and manufacturing, the declines in the pover ty rate are stronger for rural households where poverty is most severe. By 2030, in the mnfc+fdi scenario, rural and urban extreme (moderate) poverty rates are 7.4 and 3.4 (11.8 and 14.1) percentage points lower than in the base, respectively. Generally sp eaking, the most favored sectors are those with a higher ratio between exports and output -- i.e., the most export - orientated sectors. Specifically, t extiles, wearing apparel and leather show the largest increases in sectoral value added (VA). For all othe r sectors, the ratio between exports and output is relatively small ( see Table 3 . 3 in Section 3 ). As explained, Haiti is assumed to be a price taker in world markets. Therefore, the said sectors are the ones that can expand production and increase sales wi th a relatively minor decrease in domestic prices. In other words, the increase in TFP favors the current pattern of sectoral specialization for exports . Overall, exports (and imports) increase the most under the manufacturing scenarios. As expected (Figur e 4 . 8 ) 9 , the higher VA per worker and the higher export orientation of manufacturing leads to higher GDP growth rate. Interestingly, growth in the manufacturing sector does not spill over the agriculture sector . For instance, i n the mnfc scenario, the increase in agriculture GDP growth is insignificant (Figure 4 .1 8 ) . However, this is due to poor integration of local agriculture into manufacturing supply chains and high import shares of consumer and industrial goods (again, see Table 3 .3 in Section 3 ). Besides , when FDI also increases we see a similar growth rate for manufacturing sectors combined with higher growth rates for services such as 9 Also, see the discussion at the end of Section 2. - 28 - electricity, construction, trade, and transport. Therefore, our results indicate that, once manufacturing incr eases its growth rate, bottlenecks in (non - tradable) infrastructure - and logistic - related services emerge. In turn, the increase in the relative profitability of these sectors attracts new investments with the consequent increase in their respective capita l stocks. In the agr and agr+fdi scenarios, the agricultural real VA share increases relative to the base (Figure 4 . 19 ) . At the same time, employment share in agriculture decreases, from 38.4 to 36.9 percent in 2030. In turn, manufacturing scen a rios mnfc a nd mnfc+fdi show a decrease in agriculture real VA share from 17.3 to 14.8 in 2030 combined with an increase in manufacturing real VA share from 7.7 to 14.1 percentage points. To explain changes in overall labor productivity (Figure 4 .2 0 ) , we implement a decomposition procedure based on McMillan and Rodrik (2011) . Specifically, we decompose the changes in labor productivity relative to the base scenario in two components: (a) the within - sector component that captures how much of overall la bor productivity growth can be attri buted to changes within sectors; and (b) the structural change component that captures how much of overall labor productivity growth can be attributed to movements of workers across sectors. Mathematically, ∆ 𝑙𝑝𝑟𝑑 𝑡 , 𝑠 𝑖𝑚 = ∑ 𝑤𝑔𝑡 ℎ 𝑎 , 𝑡 , 𝑏𝑎𝑠𝑒 𝑎 ∈ 𝐴 ( 𝑙𝑝𝑟𝑑𝑎 𝑎 , 𝑡 , 𝑠𝑖𝑚 − 𝑙𝑝𝑟𝑑𝑎 𝑎 , 𝑡 , 𝑏𝑎𝑠𝑒 ) + ∑ 𝑙𝑝𝑟𝑑𝑎 𝑎 , 𝑡 , 𝑠𝑖𝑚 ( 𝑤𝑔𝑡 ℎ 𝑎 , 𝑡 , 𝑠𝑖𝑚 − 𝑤𝑔𝑡 ℎ 𝑎 , 𝑡 , 𝑏𝑎𝑠𝑒 ) 𝑎 ∈ 𝐴 where 𝑎 ( ∈ 𝐴 ) are activities, 𝑡 ( ∈ 𝑇 ) are time periods, 𝑠𝑖𝑚 ( ∈ 𝑆𝐼𝑀 ) are simulations/scenarios, 𝑙𝑝𝑟𝑑 𝑡 , 𝑠𝑖𝑚 is overall labor productivity, 𝑙𝑝𝑟𝑑𝑎 𝑎 , 𝑡 , 𝑠𝑖𝑚 is activity - specific labor productivity, and 𝑤𝑔 ℎ 𝑡 𝑎 , 𝑡 , 𝑠𝑖𝑚 is the weight of activity a in base - year employment . Figure 4 .2 1 shows the results. In the agr scenario, structural change does not play a significant role to explain the overall increase in labor productivity. In fact, the structural change in favoring agricultural activities has a negative impact on overall labor productivity. On the other hand, once FDI is increase d (see scenar io agr+fdi), the structural change favoring industrial and service activities also has a positive impact on overall labor productivity. In turn, all manufacturing and services scenarios show that structural change has a positive impact on overall labor pro ductivity. - 29 - Hence, our results are consistent with the evidence that suggest that s tructural change and higher growth go hand in hand (Rodrik , 2016). At any rate, both the increase in sector - specific productivity and the sectoral composition effect have a positive impact on VA per worker, wages, and household income. Figure 4 .1 3 a: main transmission channels for an increase in agriculture TFP Source: A uthor’s elaboration. Figure 4 .1 3 b: main transmission channels for an increase in FDI Source: A uthor’s el aboration. Needless to say, our CGE model for Haiti has some limitations worth mentioning. I t does not fully capture all the issues involved in bringing about structural changes. For instance, our scenario design does not consider expenditures related to skill upgrading that would likely complement the increase in factor productivity and FDI. Also, we are assuming that government investment in infrastructure remains constant at its baseline values. However, we should expect that public investment in infras tructure