Simulations de la production et des secteurs productifs dans un modèle d'équilibre général calculable pour Haïti

Production and Productive Sectors Simulations in a CGE model for Haiti Martin Cicowiez Agustin Filippo IDB-TN-01569 Country Department Central America, Haiti, Mexico, Panama and Dominican Republic TECHNICAL NOTE Nº January 2019 Production and Productive Sectors Simulations in a CGE model for Haiti Martin Cicowiez Agustin Filippo January 2019 Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Cicowiez, Martín. Production and productive sectors: simulations in a CGE model for Haiti / Martín Cicowiez and Agustín Filippo. p. cm. — (IDB Technical Note ; 1569) Includes bibliographic references. 1. Economic development-Haiti-Econometric models. 2. Industrial productivity-Haiti- Econometric models. 3. Haiti-Economic policy-Econometric models. 4. Haiti-Economic conditions-Econometric models. I. Filippo, Agustín. II. Inter-American Development Bank. Country Department Central America, Haiti, Mexico, Panama and the Dominican Republic. III. Title. IV. Series. IDB-TN-1569 JEL Codes: C68, D58, E23, O47, O54. Keywords: Haiti, structural change, structural transformation, computable general equilibrium, economic development, production, productive sectors. Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 Attribution- NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license. Note that link provided above includes additional terms and conditions of the license. 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 2019 - 1 - Production and Productive Sectors Simulations in a CGE model for Haiti. Martín Cicowiez 1 and Agustín Filippo 2 Simulations This document presents the group of simulations related to “ Production and Productive Sectors ”, and analyzes the results for both the CGE model and the microsimulation model. In a companion document ( Cicowiez and Filippo 2018 a ) , we provide a detailed description of the reference scenario results . In addition, a document that provides an introduction and describe s the method and data use d in this study is also available (Cicowiez and Filippo 2018b) . 1. Scenarios In t he first set of simulations presented here we show the effects of increased exogenous total factor productivity growth in various sectors of the Haitian economy. It should be noted that w e do not model the source of this productivity growth . I n the agricultural sector , productivity growth could be the result of investments in agricultural research and extension and/or increased us e of improved seeds. In the non - agricultural sec tors , productivity growth could be the result of technical change and/or improved management. Instead, our focus is on the 1 Universidad Nacional de La Plata, Argentina . 2 Inter - American Development Bank. - 2 - effects of these productivity changes on other sectors of the economy (spillover effects) and on household incomes. As documented in , among others, Singh and Barton - Dock (2015), and Katz (2018) Haiti’s suffers from insufficient and poor infrastructure . Naturally , i sland economies such as Haiti are extremely dependent on the quality, frequency and cost of the means of transport that link them to export and import markets. Accordingly , t he efficiency and effectiveness of transport contribute to the competitiveness of these countries. I n th is second group of simulations, we therefore consider increase s in government investment in agriculture and transport infrastructure. For agriculture, even though 40 percent of jobs in Haiti are in agriculture, the country is far from developing a commercial agribusiness sector. In fact, the agriculture sector in Haiti ha s been declining for many years, the result of neglected rural infrastructure, weak research and extension, poorly defined land tenure, limited access to credit, and under - investment in human capital (Singh and Barton - Dock, 2015). In both cases, the infra structure simulations were run under alternative assumptions about the source of financing for the required additional government capital spending: foreign direct taxes (tdir), domestic borrowing (dbor), and foreign borrowing (fb or ). Technically, this mean s that the rules for balancing the government accounts varied across scenarios, with sufficient increases in the indicated financing source playing the role of clearing the government balance. Compared to the base, another change in these scenarios is a mo dification in the rule for achie ving savings - investment balance; specifically, private investment adjusts endogenously to maintain balance between total savings (from different sources) and total investment (i.e., inves tment becomes savings - driven). Conseq uently, these scenarios capture the crowding - out - 3 - of private investment when domestic sources are used to finance the increase in government investment in infrastructure . In our Haiti CGE model, infrastructure stocks, determined by publicly financed investm ent, affects growth in sectoral total factor productivities. 3 Largely , tourism is viewed as a sector t hat can be a driver of economic growth and development , with significant potential for poverty alleviation. Thus, within the productive sector scenarios, we also assess the effects of an exogenous increase in foreign tourism arrivals. To that end, we extended our Haiti CGE model following Banerjee et al . (2015) . Briefly, such extensions imply that foreign tourism is a source of (a) demand for (mostly) domes tic commodities (goods and services), and (b) foreign exchange. Specifically, the following non - base simulations were simulated: • tfpagr = 25 percent increase in agriculture TFP • tfpagr - ex = 25 percent increase in agriculture TFP combined with increase in agriculture export intensity (i.e., the ratio between exports and output is exogenously increased) 4 • tfpmnf = 25 percent increase in manufactures TFP • tfpsvc = 25 percent increase in (non - government) ser vices TFP • in fagr - tdir = increase in agriculture infrastructure equivalent to 2.5 percent of GDP, with direct tax financing • infagr - dbor = increase in agriculture infrastructure equivalent to 2.5 percent of GDP, with domestic borrowing financing • infagr - fbor = increase in agriculture infrastructure equivalent to 2.5 percent of GDP, with foreign borrowing financing 3 For a more detailed description of the links between infrastructure and TFP, see Appendix A. 4 Technically, we re - calibrate the behavioral parameters of the Constant Elasticity of Transformation function so that its so that, at given prices (PE0 and PD0) and given output level (QX0=QX1), the optimal QE/QD ratio changes as imposed with unchanged revenue at the new optimal quantities ; i.e., PX0*QX0 = PX0*QX1 = PE0*QE0 + PD0*QD0 = PE0*QE1 + PD0*QD1 . - 4 - • inftrns - tdir = increase in transport infrastructure equivalent to 2.5 percent of GDP, with direct tax financing • inftrns - dbor = increase in transport infrastructure equivalent to 2.5 percent of GDP, with domestic borrowing financing • intrns - fbor = increase in transport infrastructure equivalent to 2.5 percent of GDP, with foreign borrowing financing • tourism = 25% increase in tourist arrivals 2. Aggregate Results Figure 1 and Table 1 show key macroeconomic results for the base and the non - base scenarios for the year 201 6 (i.e., the year when all scenarios start deviating from the base ) and 20 3 0 , the last simulation year . In the base scenario, t he economy evolves according to recent trends, as described in the companion document that presents the results from the “Government and Institutional Capacity” simulations (Cicowiez and Filippo 2018a) . Figure 2 summarizes the main transmission channels in the total factor productivity scenario tfpagr. For the other TFP scenarios, the main transmission channels are similar , although the targeted sector differs . In the four TFP scenarios, increased total factor pr oductivity results in increased output of a sector, but a reduction in the amount of labor, land (only for agriculture), and capital used in that sector. (In the tfpagr - ex scenario, the increase in export orientation for agriculture lessens the decrease in sectoral factor use.) The increase in supply of the sector’s goods (or services) results in a decline in the real price since demand increases (brought about by increases in household incomes and investment demand) are in general less than the increase in supply. At the same time, the reduction in the use of factor s of production from the - 5 - sector experiencing the productivity shock frees up these factors for use in other sectors of the economy. Thus, real GDP and household income rise in all scenarios. T he 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 Table 1) . Figure 1a : c hange in real private consumption 201 3 - 20 3 0 (percent de viation from base) 0 5 10 15 20 25 30 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 tfpagr tfpagr-ex tfpmnf tfpsvc - 6 - -5 0 5 10 15 20 25 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 infagr-tdir infagr-dbor infagr-fbor -1 0 1 2 3 4 5 6 7 8 9 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 inftrns-tdir inftrns-dbor inftrns-fbor - 7 - Figure 1b : c hange in real GDP at factor cost 201 3 - 20 3 0 (percent deviation from base) 0 5 10 15 20 25 30 35 40 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 tfpagr tfpagr-ex tfpmnf tfpsvc 0 5 10 15 20 25 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 infagr-tdir infagr-dbor infagr-fbor - 8 - Source: A uthor’s elaboration. Table 1 (cont.) : c hange in real macro indicators (percent deviation from base) (*) -4 -2 0 2 4 6 8 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 inftrns-tdir inftrns-dbor inftrns-fbor tourism base tfpagr tfpagr tfpagr-ex tfpagr-ex tfpmnf tfpmnf tfpsvc tfpsvc Item 2013 2016 2030 2016 2030 2016 2030 2016 2030 Absorption 493,643 7.20 11.72 7.17 11.58 4.05 5.11 10.82 26.01 Private consumption 352,731 8.74 13.32 8.71 13.17 5.19 6.38 9.67 24.69 Fixed investment 109,528 4.36 10.54 4.30 10.39 1.57 2.75 17.75 38.84 Private fixed investment 50,796 9.41 22.73 9.27 22.41 3.38 5.94 38.27 83.74 Government fixed investment 58,732 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Government fixed inv, infra 56,624 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Change in stocks 57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Government consumption 31,327 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Exports 44,879 27.77 49.29 27.79 48.44 37.24 42.42 45.48 109.91 Imports 171,307 7.00 13.36 6.99 13.12 9.32 11.47 11.67 30.06 GDP at market prices 367,215 9.88 16.15 9.84 15.97 5.73 7.21 14.77 35.72 Net indirect taxes 19,907 8.71 16.50 8.47 16.08 9.49 11.85 16.41 41.95 GDP at factor cost 347,308 10.13 16.07 10.07 15.84 5.65 7.25 14.84 36.11 Real exchange rate 1.00 3.04 4.58 2.70 4.45 -7.58 -7.78 10.59 11.99 Wage, average 1.00 6.06 8.09 5.68 7.81 1.39 2.21 -1.09 5.03 Capital return, average 1.00 13.83 7.37 13.78 7.33 7.36 4.55 4.32 -16.95 Unemployment rate 31.72 -13.33 -28.87 -13.96 -28.90 -16.74 -21.55 -18.24 -44.45 2013 = million gourdes - 9 - (*) N ote: exports in Table 1 include tourism exports . Table 1 (cont.) : c hange in real macro indicators (percent deviation from base) base infagr-tdir infagr-dbor infagr-fbor Item 2013 2016 2030 2016 2030 2016 2030 Absorption 493,643 0.42 15.55 0.00 11.64 2.54 17.34 Private consumption 352,731 -0.34 17.30 0.00 14.83 2.31 19.30 Fixed investment 109,528 2.99 15.19 0.00 5.40 4.02 16.88 Private fixed investment 50,796 -4.07 20.82 -10.52 -0.29 -1.84 24.46 Government fixed investment 58,732 9.10 10.32 9.10 10.32 9.10 10.32 Government fixed inv, infra 56,624 9.43 10.70 9.43 10.70 9.43 10.70 Change in stocks 57 0.00 0.00 0.00 0.00 0.00 0.00 Government consumption 31,327 0.00 0.00 0.00 0.00 0.00 0.00 Exports 44,879 -0.61 60.33 0.00 47.84 -10.06 52.91 Imports 171,307 -0.14 16.35 0.00 12.93 2.34 17.60 GDP at market prices 367,215 0.56 21.40 0.00 16.05 1.05 22.17 Net indirect taxes 19,907 -0.01 19.88 0.00 15.05 0.89 20.42 GDP at factor cost 347,308 0.59 21.52 0.00 16.15 1.08 22.27 Real exchange rate 1.00 -0.88 4.34 0.00 3.24 -3.63 4.14 Wage, average 1.00 0.89 12.95 0.00 10.85 0.69 12.75 Capital return, average 1.00 3.10 21.98 0.00 26.55 5.58 21.03 Unemployment rate 31.72 -0.69 -38.76 0.00 -32.35 -4.00 -40.41 2013 = million gourdes - 10 - Source: A uthor’s elaboration. Figure 2 : main transmission channels agriculture TFP scenario Source: A uthor’s elaboration. Figure 3 and Figure 4 summarize the main transmission channels for the second group of counterfactual simulations, through government investment in infrastructure and government base inftrns-tdir inftrns-dbor inftrns-fbor tourism Item 2013 2016 2030 2016 2030 2016 2030 2016 2030 Absorption 493,643 0.42 3.74 0.00 -2.37 2.54 7.60 0.12 0.05 Private consumption 352,731 -0.34 3.19 0.00 -0.24 2.31 7.68 0.16 0.08 Fixed investment 109,528 2.99 6.71 0.00 -9.92 4.02 9.87 0.03 -0.02 Private fixed investment 50,796 -4.07 -3.07 -10.52 -38.91 -1.84 3.76 0.06 -0.03 Government fixed investment 58,732 9.10 15.16 9.10 15.16 9.10 15.16 0.00 0.00 Government fixed inv, infra 56,624 9.43 15.72 9.43 15.72 9.43 15.72 0.00 0.00 Change in stocks 57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Government consumption 31,327 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Exports 44,879 -0.61 7.16 0.00 -9.55 -10.06 -3.84 0.94 0.58 Imports 171,307 -0.14 2.01 0.00 -2.56 2.34 5.72 0.34 0.20 GDP at market prices 367,215 0.56 5.05 0.00 -3.27 1.05 6.92 0.12 0.05 Net indirect taxes 19,907 -0.01 4.49 0.00 -2.59 0.89 6.60 0.31 0.19 GDP at factor cost 347,308 0.59 5.35 0.00 -3.13 1.08 7.22 0.11 0.04 Real exchange rate 1.00 -0.88 0.76 0.00 -1.52 -3.63 -1.09 -0.40 -0.26 Wage, average 1.00 0.89 0.85 0.00 -2.13 0.69 0.74 -0.02 -0.04 Capital return, average 1.00 3.10 11.36 0.00 18.58 5.58 10.71 0.34 0.20 Unemployment rate 31.72 -0.69 -13.22 0.00 -2.83 -4.00 -18.62 -0.66 -0.48 2013 = million gourdes ↑agriculture TFP ↑agr output ↓agr factor use ↑non-agr factor use ↑wages and ↓unemployment ↑hhd income ↑hhd cons and sav - 11 - financing , respectively. In the agriculture infra structure scenario s , yearly GDP growth gains between 1 (domestic borrowing) and 1.4 (foreign borrowing) percentage points and is accompanied by expansion, not only in government demands, but also in private consumption and private investment as additional infrastructure permit privat e incomes and savings to grow more rapidly with a positive feedback into the growth process (see Table 1 ). Moreover, an increase in the agriculture - specific infrastructure capital stock raises total factor productivity in agriculture . For the transport in frastructure scenarios , given its smaller direct contribution to GDP, the acceleration of growth in GDP is weaker . 5 Besides, note that t he impact on the rest of the economy from increased investment in transport infrastructure depends on the financing mech anism. In case the marginal financing comes from domestic borrowing, growth declines for private consumption, investment, and GDP. On the other hand, when marginal financing comes from foreign sources (in the form of grants or borrowing), the negative impa ct from increased domestic resource mobilization on private investment will be absent. However, the inflow of foreign resources will give rise to a slower export growth and faster import growth, both will be induced by an appreciation of the real exchange rate. As showed, f inancing the infrastructure investments through increased foreign financing allows each of the domestic household groups to increase their consumption level at a higher growth rate (see Table C.1). This may seem to suggest that increasing foreign financing is a better alternative but, in reality, it simply reflects that the analysis that we are conducting ignores the 5 For transport, we assume that infrastructure investment ha s a positive effect on transport sector TFP. However, we may also assume that it also has a positive impact on other sectors TFP; see Perrault et al. (2012). - 12 - accumulation of assets by the actors in the model: if foreign financing consist of foreign direct investment or foreign borr owing, they will tend to reduce the share of output that is available to Haitian residents. Figure 3 : main transmission channels infrastructure scenarios; through government investment Source: A uthor’s elaboration. Figure 4 a: main transmission channels infra - tdir; through government financing Figure 4b : main transmission channels infra - dbor; through government financing ↑inv gov infra ↑infra capital stock ↑sector-specific TFP ↑GDP ↑inv gov infra ↑direct tax rate ↓household disposable income ↓hhd cons spnd and sav ↓private investment ↓GDP ↓GDP ↑inv gov infra ↑gov domestic borrowing ↓private investment ↑domestic debt stock - 13 - Figure 4c : main transmission channels infra - fbor; through government financing Source: A uthor’s elaboration. Figure 5 summarizes the main transmission channels in the tourism scenario. Overall, higher household income growth is achieved with increased foreign tourism demand , because these inflows of foreign exchange increase total resources in the economy. However, a s shown in Table 1, the expansion of tourism demand tends to expand domestic absorption more rapidly than it expands GDP , also causing deterioration in the trade balance. In other words, the increase in “tourism exports” also generates an appreciation of the real exchange rate that hurts the tradable sectors. Figure 5: main transmission channels tourism Source: A uthor’s elaboration. ↑inv gov infra ↑gov foreign borrowing ↓real exchange rate ↓exports and ↑imports ↑foreign debt stock ↑hhd cons and sav ↑foreign tourism ↓real exchange rate ↓exports and ↑imports ↑dem tourism- related svc ↑wages and ↓unemployment ↑hhd income - 14 - 3. Sectoral Results In all TFP and infrastructure scenarios we found that the most favored sector in terms of VA growth acceleration is Tex tiles, wearing apparel and leather . Again, this is explained by its relatively high export - to - output ratio. For the tourism scenario, service industries selling directly to tourists, including Hotels and restaurants, are strongly stimulated by the expansio n in to urism . On the other hand, the upward pressure on prices and the real exchange rate leads to reduced competitiveness of traditional export sectors (see Table 2) . Figure 6 : change in sectoral real value added in 2030 scenario tfpagr (percent deviation from base) Source: A uthor’s elaboration. 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 tfpagr - 15 - Table 2 : c hange in sectoral real value added, exports, and imports (percent deviation from base) base tfpagr tfpagr-ex tfpmnf tfpsvc Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Value added Agr, hunting and forestry; Fishing 67,345 19.98 23.78 20.56 24.06 1.85 2.49 4.84 11.27 Mining and quarrying 560 7.26 12.70 7.20 12.53 3.54 4.42 17.67 40.18 Food prod and beverages 6,639 6.86 11.11 6.70 10.96 6.25 6.05 8.81 21.92 Tobacco prod 118 6.87 11.53 6.69 11.36 4.50 4.92 10.28 23.19 Textiles, wearing apparel and leather 9,609 43.06 75.11 39.05 71.31 67.05 72.18 57.15 104.17 Wood and of prod of wood and cork 1,227 10.32 16.99 10.01 16.69 6.92 6.42 21.18 47.67 Paper and paper prod; Publishing 1,856 8.99 15.66 8.79 15.37 11.65 12.30 19.89 48.23 Chemicals; Rubber and plastics 839 7.80 14.61 7.68 14.41 10.09 9.54 17.17 50.51 Other non-metallic mineral prod 1,426 5.53 12.08 5.46 11.91 6.51 6.65 15.95 43.25 Basic metals 204 6.12 13.32 5.95 13.08 5.86 6.70 24.77 69.03 Fabricated metal prod; Mach and equip 208 2.35 10.46 1.90 10.06 7.23 5.18 32.00 134.61 Other manufactures 2,449 1.41 9.19 0.85 8.72 8.90 6.59 37.69 165.12 Electricity and water supply 6,366 5.46 12.44 5.37 12.22 4.06 6.11 17.27 42.99 Construction 83,021 4.63 10.77 4.58 10.62 1.67 2.86 17.54 38.73 Wholesale and retail trade 90,090 9.50 15.52 9.43 15.30 7.54 8.92 15.62 39.91 Hotels and restaurants, foreign tourism 1,134 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transport, storage and comm 34,190 7.19 13.02 7.10 12.84 3.16 4.53 20.87 46.47 Financial intermediation 6,990 6.84 12.28 6.66 12.04 3.74 4.56 22.67 51.31 Other market services 11,490 8.40 15.56 8.37 15.37 5.01 6.97 10.99 39.15 Education, government 770 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Health, government 2,227 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other government services 18,552 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2013 = million gourdes - 16 - Table 2 (cont.) : c hange in sectoral real value added, exports, and imports (percent deviation from base) base tfpagr tfpagr-ex tfpmnf tfpsvc Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Exports Agr, hunting and forestry; Fishing 3,263 55.63 53.38 92.45 90.63 -9.79 -10.89 2.18 -0.29 Food prod and beverages 892 9.82 16.58 8.98 16.16 4.49 1.32 27.71 50.07 Textiles, wearing apparel and leather 21,600 46.65 80.32 42.23 76.22 72.88 77.23 62.38 111.41 Wood and of prod of wood and cork 906 10.86 22.78 9.47 22.07 -5.67 -7.77 56.19 109.19 Chemicals; Rubber and plastics 599 4.15 14.20 3.67 13.87 9.17 6.86 28.22 82.46 Other non-metallic mineral prod 6 -1.11 9.88 -1.70 9.54 6.72 2.67 32.12 98.34 Fabricated metal prod; Mach and equip 501 -2.34 7.92 -3.19 7.34 3.79 0.25 48.01 201.64 Other manufactures 8,161 -0.84 7.83 -1.63 7.26 9.16 5.69 46.18 205.78 Transport, storage and comm 3,801 5.58 12.46 5.33 12.22 -2.26 -0.83 36.46 70.91 Financial intermediation 566 6.69 12.37 6.35 12.08 -1.26 -0.57 39.45 74.05 Imports Agr, hunting and forestry; Fishing 26,478 -4.34 2.84 -3.91 2.87 11.92 14.45 9.11 25.36 Mining and quarrying 136 9.16 14.50 9.15 14.33 6.30 7.06 23.06 43.24 Food prod and beverages 24,386 4.07 7.12 4.22 7.10 3.15 5.38 4.08 13.14 Tobacco prod 546 3.59 7.07 3.74 7.03 4.21 6.27 4.02 14.05 Textiles, wearing apparel and leather 29,163 12.79 24.97 12.08 24.01 17.47 22.58 15.72 37.61 Wood and of prod of wood and cork 2,595 8.42 12.87 8.48 12.75 6.04 7.38 15.29 31.65 Paper and paper prod; Publishing 2,185 10.95 16.41 10.89 16.16 7.93 9.36 17.40 36.66 Chemicals; Rubber and plastics 25,695 9.91 14.55 9.97 14.42 7.13 8.24 15.06 35.25 Other non-metallic mineral prod 2,098 7.50 12.56 7.53 12.43 1.84 4.06 17.71 33.31 Basic metals 3,799 7.40 13.64 7.33 13.43 6.71 7.51 20.31 57.76 Fabricated metal prod; Mach and equip 19,595 8.30 13.94 8.29 13.77 5.62 6.91 18.17 38.20 Other manufactures 1,204 9.16 14.14 9.32 14.07 3.78 6.29 12.75 17.33 Hotels and restaurants 2,047 16.37 18.84 16.62 18.70 17.82 15.68 14.69 30.89 Transport, storage and comm 27,048 8.90 13.61 8.99 13.47 9.15 10.43 6.00 23.81 Financial intermediation 2,853 7.00 12.21 6.98 12.02 9.04 10.04 7.53 30.86 Other market services 1,476 13.82 16.77 13.96 16.63 13.08 13.03 15.25 27.71 2013 = million gourdes - 17 - Table 2 (cont.) : c hange in sectoral real value added, exports, and imports (percent deviation from base) base infagr-tdir infagr-dbor infagr-fbor Commodity 2013 2016 2030 2016 2030 2016 2030 Value added Agr, hunting and forestry; Fishing 67,345 -0.06 39.08 0.00 36.77 0.19 39.60 Mining and quarrying 560 0.20 13.97 0.00 9.07 2.56 16.18 Food prod and beverages 6,639 -0.19 13.48 0.00 10.76 0.04 14.64 Tobacco prod 118 -0.29 13.85 0.00 11.20 0.14 15.23 Textiles, wearing apparel and leather 9,609 -0.97 94.75 0.00 78.12 -14.75 82.03 Wood and of prod of wood and cork 1,227 -0.44 18.77 0.00 14.05 0.24 20.78 Paper and paper prod; Publishing 1,856 -0.42 16.75 0.00 12.04 1.43 18.68 Chemicals; Rubber and plastics 839 -0.33 15.10 0.00 10.21 0.76 17.14 Other non-metallic mineral prod 1,426 1.16 14.16 0.00 6.93 2.35 16.10 Basic metals 204 0.42 13.40 0.00 6.78 1.53 15.54 Fabricated metal prod; Mach and equip 208 -0.24 4.28 0.00 -3.07 -2.83 5.43 Other manufactures 2,449 0.17 1.89 0.00 -7.02 -3.80 2.47 Electricity and water supply 6,366 -0.29 12.88 0.00 8.51 0.91 14.41 Construction 83,021 2.85 15.42 0.00 5.87 3.87 17.09 Wholesale and retail trade 90,090 -0.07 19.09 0.00 14.82 1.49 20.47 Hotels and restaurants, foreign tourism 1,134 0.00 0.00 0.00 0.00 0.00 0.00 Transport, storage and comm 34,190 -0.18 14.48 0.00 10.32 0.92 16.00 Financial intermediation 6,990 -0.01 13.43 0.00 8.99 0.53 14.59 Other market services 11,490 -0.19 17.57 0.00 12.71 1.38 19.38 Education, government 770 0.00 0.00 0.00 0.00 0.00 0.00 Health, government 2,227 0.00 0.00 0.00 0.00 0.00 0.00 Other government services 18,552 0.00 0.00 0.00 0.00 0.00 0.00 2013 = million gourdes - 18 - Table 2 (cont.) : c hange in sectoral real value added, exports, and imports (percent deviation from base) base infagr-tdir infagr-dbor infagr-fbor Commodity 2013 2016 2030 2016 2030 2016 2030 Exports Agr, hunting and forestry; Fishing 3,263 -0.16 102.41 0.00 101.23 -3.05 101.53 Food prod and beverages 892 -0.33 17.00 0.00 11.08 -5.14 17.62 Textiles, wearing apparel and leather 21,600 -1.04 101.30 0.00 83.45 -16.38 87.41 Wood and of prod of wood and cork 906 -0.56 18.56 0.00 8.66 -8.82 19.50 Chemicals; Rubber and plastics 599 -0.35 9.47 0.00 2.05 -3.27 11.17 Other non-metallic mineral prod 6 -0.12 2.31 0.00 -7.66 -5.24 3.82 Fabricated metal prod; Mach and equip 501 -0.43 -5.07 0.00 -14.85 -7.92 -4.77 Other manufactures 8,161 -0.18 -3.34 0.00 -13.28 -6.77 -3.34 Transport, storage and comm 3,801 -0.20 11.27 0.00 5.87 -0.87 12.64 Financial intermediation 566 -0.10 11.98 0.00 6.88 -1.09 12.94 Imports Agr, hunting and forestry; Fishing 26,478 0.02 0.27 0.00 -2.46 2.78 1.34 Mining and quarrying 136 0.29 17.59 0.00 12.61 3.89 20.12 Food prod and beverages 24,386 -0.13 9.31 0.00 8.08 2.43 10.71 Tobacco prod 546 -0.19 8.89 0.00 7.66 2.81 10.49 Textiles, wearing apparel and leather 29,163 -0.38 31.59 0.00 26.27 -0.67 29.95 Wood and of prod of wood and cork 2,595 -0.43 15.24 0.00 12.38 3.39 17.61 Paper and paper prod; Publishing 2,185 -0.40 20.20 0.00 16.65 3.36 22.31 Chemicals; Rubber and plastics 25,695 -0.35 17.88 0.00 14.59 2.90 20.09 Other non-metallic mineral prod 2,098 1.67 17.45 0.00 11.04 4.34 19.44 Basic metals 3,799 0.51 15.63 0.00 9.78 2.89 17.86 Fabricated metal prod; Mach and equip 19,595 -0.02 16.62 0.00 12.46 3.20 19.01 Other manufactures 1,204 1.42 20.99 0.00 15.75 6.28 23.81 Hotels and restaurants 2,047 -0.72 24.18 0.00 20.91 7.41 27.65 Transport, storage and comm 27,048 -0.17 17.93 0.00 15.18 2.85 19.63 Financial intermediation 2,853 0.09 14.96 0.00 11.20 2.19 16.33 Other market services 1,476 -0.20 23.97 0.00 21.52 3.86 25.71 2013 = million gourdes - 19 - Table 2 (cont.) : c hange in sectoral real value added, exports, and imports (percent deviation from base) base inftrns-tdir inftrns-dbor inftrns-fbor tourism Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Value added Agr, hunting and forestry; Fishing 67,345 -0.06 0.55 0.00 -2.29 0.19 1.33 0.03 0.01 Mining and quarrying 560 0.20 2.22 0.00 -5.76 2.56 6.75 0.11 0.03 Food prod and beverages 6,639 -0.19 0.33 0.00 -3.93 0.04 2.17 0.79 0.94 Tobacco prod 118 -0.29 1.17 0.00 -2.96 0.14 3.44 -0.07 -0.06 Textiles, wearing apparel and leather 9,609 -0.97 2.86 0.00 -15.24 -14.75 -14.14 -2.32 -2.42 Wood and of prod of wood and cork 1,227 -0.44 1.07 0.00 -6.19 0.24 4.23 0.13 0.14 Paper and paper prod; Publishing 1,856 -0.42 2.26 0.00 -5.10 1.43 6.35 0.09 0.01 Chemicals; Rubber and plastics 839 -0.33 2.93 0.00 -5.22 0.76 6.62 -0.01 -0.06 Other non-metallic mineral prod 1,426 1.16 4.12 0.00 -7.96 2.35 7.78 0.03 -0.02 Basic metals 204 0.42 4.92 0.00 -6.84 1.53 8.59 -0.07 -0.14 Fabricated metal prod; Mach and equip 208 -0.24 3.04 0.00 -11.81 -2.83 2.95 -0.52 -0.57 Other manufactures 2,449 0.17 7.09 0.00 -12.14 -3.80 4.64 -0.68 -0.76 Electricity and water supply 6,366 -0.29 2.65 0.00 -4.28 0.91 6.03 0.11 0.05 Construction 83,021 2.85 6.52 0.00 -9.66 3.87 9.66 0.03 -0.01 Wholesale and retail trade 90,090 -0.07 3.15 0.00 -3.34 1.49 6.34 0.23 0.14 Hotels and restaurants, foreign tourism 1,134 0.00 0.00 0.00 0.00 0.00 0.00 25.00 25.00 Transport, storage and comm 34,190 -0.18 23.48 0.00 15.42 0.92 27.37 0.04 -0.02 Financial intermediation 6,990 -0.01 8.18 0.00 0.37 0.53 10.52 0.03 -0.03 Other market services 11,490 -0.19 2.22 0.00 -5.33 1.38 6.05 0.14 0.07 Education, government 770 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Health, government 2,227 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other government services 18,552 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 20 - Table 2 (cont.) : c hange in sectoral real value added, exports, and imports (percent deviation from base) Source: A uthor’s elaboration. 4. Distributive Results The poverty impact captured in the microsimulation model depends essentially on two factors: the change in the labor market conditions and the increase in per capita disposable (i.e., net of base inftrns-tdir inftrns-dbor inftrns-fbor tourism Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Exports Agr, hunting and forestry; Fishing 3,263 -0.16 -1.06 0.00 -3.19 -3.05 -3.51 -0.43 -0.30 Food prod and beverages 892 -0.33 -0.82 0.00 -11.26 -5.14 -2.61 -0.22 0.29 Textiles, wearing apparel and leather 21,600 -1.04 2.99 0.00 -16.37 -16.38 -15.64 -2.57 -2.62 Wood and of prod of wood and cork 906 -0.56 -0.05 0.00 -17.04 -8.82 -3.18 -1.20 -0.70 Chemicals; Rubber and plastics 599 -0.35 1.57 0.00 -12.11 -3.27 2.70 -0.51 -0.39 Other non-metallic mineral prod 6 -0.12 0.34 0.00 -18.52 -5.24 0.40 -0.76 -0.52 Fabricated metal prod; Mach and equip 501 -0.43 1.62 0.00 -19.66 -7.92 -2.54 -1.13 -1.06 Other manufactures 8,161 -0.18 8.06 0.00 -14.52 -6.77 3.05 -1.01 -1.05 Transport, storage and comm 3,801 -0.20 53.66 0.00 39.95 -0.87 56.75 -0.16 -0.15 Financial intermediation 566 -0.10 9.46 0.00 -0.28 -1.09 10.58 -0.18 -0.17 Imports Agr, hunting and forestry; Fishing 26,478 0.02 1.98 0.00 -1.93 2.78 5.53 0.40 0.25 Mining and quarrying 136 0.29 3.38 0.00 -4.23 3.89 8.99 0.21 0.09 Food prod and beverages 24,386 -0.13 1.15 0.00 -0.09 2.43 4.81 1.31 1.28 Tobacco prod 546 -0.19 1.01 0.00 -0.15 2.81 5.20 0.27 0.17 Textiles, wearing apparel and leather 29,163 -0.38 1.62 0.00 -4.19 -0.67 1.11 -0.22 -0.41 Wood and of prod of wood and cork 2,595 -0.43 1.78 0.00 -2.13 3.39 7.36 0.61 0.45 Paper and paper prod; Publishing 2,185 -0.40 2.45 0.00 -2.28 3.36 7.74 0.32 0.16 Chemicals; Rubber and plastics 25,695 -0.35 4.02 0.00 -0.83 2.90 9.11 0.24 0.12 Other non-metallic mineral prod 2,098 1.67 5.85 0.00 -4.49 4.34 10.32 0.18 0.09 Basic metals 3,799 0.51 4.07 0.00 -5.78 2.89 8.46 0.08 -0.04 Fabricated metal prod; Mach and equip 19,595 -0.02 5.43 0.00 -1.02 3.20 10.83 0.18 0.07 Other manufactures 1,204 1.42 3.28 0.00 -4.01 6.28 10.20 0.40 0.29 Hotels and restaurants 2,047 -0.72 2.68 0.00 -1.12 7.41 11.22 0.65 0.29 Transport, storage and comm 27,048 -0.17 -3.03 0.00 -6.59 2.85 1.33 0.28 0.15 Financial intermediation 2,853 0.09 6.90 0.00 1.05 2.19 10.48 0.24 0.12 Other market services 1,476 -0.20 3.34 0.00 0.49 3.86 8.16 0.43 0.23 2013 = million gourdes - 21 - taxes and savings) income. In all TFP and agriculture infrastructure scenarios , the 20 30 poverty ra te is lower than for the baseline (see Figure 7) , mainly as a result of a decrease in unemployment, a higher average wage, a nd, given that agriculture is relatively intensive in the use of unskilled labor, a decrease in the wage gap between unskilled and s killed labor. For example, due to the decrease in unemployment (i.e., from 25.5 to 15.5 in 2030), the poverty rate decreases 5.4 percentage points in the infagr - fbor scenario. In turn, the increase in the average wage level decreases poverty by additional 4.4 percentage points. It is interesting to note that the sectoral change (i.e., increase in the employment share of manufactures and services ) also has a positive impact on poverty. Again, we use growth - incidence curves to assess the distributional impact across the whole income distribution. In the tfpagr and infagr - fbor scenario s , growth is pro - poor (i.e., decreasing) ; see panels (a) and (b) of Figure 8, respectively. As expected, t he poverty impact is stronger under infagr - fbor scenario, given that the increase in public investment has a positive (Keynesian; i.e., increase in final demand) effect that is absent in the tfpagr scenario. For the tourism scenario, we do not find signifi cant impacts of poverty and inequality. However, it is expected that an increase in foreign tourism will have positive and significant local (i.e., regional/sub - national) impacts (see Banerjee et al. (2015)). - 22 - Figure 7 : c hange in poverty (percentage points from base) Source: A uthor’s elaboration. -12 -10 -8 -6 -4 -2 0 2 tfpagr tfpagr-ex tfpmnf tfpsvc infagr-tdir infagr-dbor infagr-fbor inftrns-tdir inftrns-dbor inftrns-fbor tourism 2030 Extreme poverty Poverty -12 -10 -8 -6 -4 -2 0 2 tfpagr tfpagr-ex tfpmnf tfpsvc infagr-tdir infagr-dbor infagr-fbor inftrns-tdir inftrns-dbor inftrns-fbor tourism 2016 Extreme poverty Poverty - 23 - Figure 8a: growth - incidence curves scenario tfp - agr ; 2030 household per capita income proportional changes by percentile 0 50 100 150 0 10 20 30 40 50 60 70 80 90 100 percentile tfpagr fitted values - 24 - Figure 8 b : growth - incidence curves scenario inf agr - fbor ; 2030 household per capita income proportional changes by percentile 5. Sensitivity Analysis I n a companion document (i.e., “Government and Institutional Capacity”), we discuss the relevance of conducting sensitivity analysis when applying the CGE method. In this section, we focus on sen sitivity analysis with respect to the values assigned to production and consumption elasticities for the simulations presented in previous sections . Table 3 shows the percentage change in private consumption estimated (i) unde r the central elasticities, an d (ii) as the average of the 500 observations generated by the sensitivity analysis. For the second case, the upper and lower bounds under the normality assumption were also computed; notice that all runs from the Monte Carlo experiment receive the same we ight. As can be seen, the results 0 50 100 150 200 0 10 20 30 40 50 60 70 80 90 100 percentile infagr-fbor fitted values - 25 - reported above are significant, while estimates presented in Table 1 are within the confidence intervals reported in Table 3 . For example, there is virtual certainty that the t ourism scenario has a small but positive effec t on private consumption. Table 3 : sensitivity analysis; real private consumption in 2030 percent deviation from base 95% confidence interval under normality assumption Source: Author’s elaboration. References Banerjee, Onil, Martin Cicowiez and Sébastien Gachot, 2015, A Quantitative Framework for Assessing Public Investment in Tourism – An Application to Haiti, Tourism Management 51: 157 - 173. Scenario Central elast Mean Standard dev Lower bound Upper bound tfpagr 13.321 13.504 0.892 11.756 15.253 tfpagr-ex 13.171 13.356 0.880 11.632 15.080 tfpmnf 6.384 6.306 0.679 4.975 7.637 tfpsvc 24.694 24.314 1.506 21.363 27.265 infagr-tdir 17.297 17.606 1.700 14.273 20.939 infagr-dbor 14.829 15.183 1.828 11.601 18.765 infagr-fbor 19.305 19.702 1.379 16.999 22.405 inftrns-tdir 3.193 3.117 0.829 1.491 4.743 inftrns-dbor -0.237 -0.192 0.772 -1.706 1.322 inftrns-fbor 7.684 7.710 0.394 6.939 8.482 tourism 0.078 0.075 0.014 0.048 0.103 - 26 - Cicowiez, Martin and Agustin Filippo, 2018a, Government and Institutional Capacity. Simulations in a CGE Model for Haiti , Project Document, Inter - American Development Bank . Cicowiez, Martin and Agustin Filippo, 2018b, A Computable General Equilibrium Analysis for Haiti, IDB Technical Note IDB - TN - 1486. Estache , Antonio, Jean - François Perrault and Luc Savard , 2 012, The Impact of Infrastructure Spending in Sub - Saharan Africa: A CGE Modeling Approach , Economics Research International 2012: 1 - 18. Katz, Sebastian, 201 8 , ¿Podrá, Ayiti, volver a ser el Reino de este Mundo?, IDB Technical Note IDB - TN - 1484 . Singh Raju Jan and Mary Barton - Dock , 2015, Haiti: Toward a New Narrative , Systematic Country Diagnostic , Washington, DC: World Bank . - 27 - Appendix: Additional Simulation Results Figure A .1: real private consumption average annual growth rate 2014 - 2030; percent 0.00 1.00 2.00 3.00 4.00 5.00 6.00 base tfpagr tfpagr-ex tfpmnf tfpsvc infagr-tdir infagr-dbor infagr-fbor inftrns-tdir inftrns-dbor inftrns-fbor tourism - 28 - Table A .1: real macroeconomic aggregates average annual growth rate 2014 - 2030; percent base Item 2013 base tfpagr tfpagr-ex tfpmnf tfpsvc Absorption 493,643 3.58 4.35 4.34 3.92 5.19 Private consumption 352,731 3.48 4.35 4.34 3.91 5.02 Fixed investment 109,528 3.60 4.30 4.29 3.79 5.89 Private fixed investment 50,796 3.60 5.03 5.01 4.00 7.89 Government fixed investment 58,732 3.60 3.60 3.60 3.60 3.60 Government fixed inv, infra 56,624 3.60 3.60 3.60 3.60 3.60 Change in stocks 57 3.57 3.57 3.57 3.57 3.57 Government consumption 31,327 4.49 4.49 4.49 4.49 4.49 Exports 44,879 4.36 7.18 7.14 6.85 9.65 Imports 171,307 3.81 4.68 4.67 4.57 5.65 GDP at market prices 367,215 3.57 4.61 4.60 4.05 5.70 Net indirect taxes 19,907 3.80 4.86 4.83 4.58 6.25 GDP at factor cost 347,308 3.57 4.60 4.59 4.05 5.72 Real exchange rate 1.00 -0.32 -0.02 -0.03 -0.85 0.44 Wage, average 1.00 0.23 0.75 0.73 0.37 0.56 Unemployment rate 31.72 25.49 18.13 18.12 19.99 14.16 2013 = million gourdes - 29 - Table A .1 (cont) : real macroeconomic aggregates average annual growth rate 2014 - 2030; percent base Item 2013 base infagr-tdir infagr-dbor infagr-fbor Absorption 493,643 3.58 4.58 4.34 4.69 Private consumption 352,731 3.48 4.59 4.44 4.71 Fixed investment 109,528 3.60 4.58 3.96 4.68 Private fixed investment 50,796 3.60 4.92 3.58 5.12 Government fixed investment 58,732 3.60 4.28 4.28 4.28 Government fixed inv, infra 56,624 3.60 4.31 4.31 4.31 Change in stocks 57 3.57 3.57 3.57 3.57 Government consumption 31,327 4.49 4.49 4.49 4.49 Exports 44,879 4.36 7.69 7.11 7.35 Imports 171,307 3.81 4.87 4.66 4.94 GDP at market prices 367,215 3.57 4.92 4.60 4.96 Net indirect taxes 19,907 3.80 5.06 4.77 5.09 GDP at factor cost 347,308 3.57 4.92 4.61 4.96 Real exchange rate 1.00 -0.32 -0.03 -0.10 -0.05 Wage, average 1.00 0.23 1.04 0.92 1.03 Unemployment rate 31.72 25.49 15.61 17.24 15.19 2013 = million gourdes - 30 - Table A .1 (cont) : real macroeconomic aggregates average annual growth rate 2014 - 2030; percent base Item 2013 base inftrns-tdir inftrns-dbor inftrns-fbor tourism Absorption 493,643 3.58 3.83 3.41 4.09 3.58 Private consumption 352,731 3.48 3.70 3.47 4.00 3.49 Fixed investment 109,528 3.60 4.05 2.88 4.25 3.60 Private fixed investment 50,796 3.60 3.39 0.25 3.86 3.60 Government fixed investment 58,732 3.60 4.58 4.58 4.58 3.60 Government fixed inv, infra 56,624 3.60 4.61 4.61 4.61 3.60 Change in stocks 57 3.57 3.57 3.57 3.57 3.57 Government consumption 31,327 4.49 4.49 4.49 4.49 4.49 Exports 44,879 4.36 4.84 3.66 4.09 4.40 Imports 171,307 3.81 3.95 3.63 4.20 3.83 GDP at market prices 367,215 3.57 3.91 3.34 4.03 3.57 Net indirect taxes 19,907 3.80 4.10 3.62 4.24 3.81 GDP at factor cost 347,308 3.57 3.93 3.35 4.05 3.57 Real exchange rate 1.00 -0.32 -0.27 -0.42 -0.39 -0.33 Wage, average 1.00 0.23 0.28 0.08 0.28 0.22 Unemployment rate 31.72 25.49 22.12 24.77 20.74 25.37 2013 = million gourdes