Simulations de chocs macroéconomiques dans un modèle d'équilibre général calculable pour Haïti

Macroeconomic Shocks Simulations in a CGE model for Haiti Martin Cicowiez Agustin Filippo IDB-TN-01571 Country Department Central America, Haiti, Mexico, Panama and Dominican Republic TECHNICAL NOTE Nº January 2019 Macroeconomic Shocks 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. Macroeconomic shocks: simulations in a CGE model for Haiti / Martín Cicowiez and Agustín Filippo. p. cm. — (IDB Technical Note ; 1571) Includes bibliographic references. 1. Economic development-Haiti-Econometric models. 2. Haiti-Economic policy- Econometric models. 3. 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-1571 JEL Codes: C68, D58, E23, O47, O54. Keywords: Haiti, structural change, structural transformation, computable general equilibrium, economic development, macroeconomic shocks. 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 Macro economic Shocks Simulations in a CGE model for Haiti. Martín Cicowiez 1 and Agustín Filippo 2 Simulations This document presents the group of simulations related to “Macroeconomic Shocks”, and analyzes the results for both the CGE model and the microsimulation model. In a companion document, we provide a detailed description of the reference scenario results (Cicowiez and Filippo, 2018a). In addition, a document that provides an introduction and describes the method and data used in this study is also available (Cicowiez and Filippo, 2018b). 1. Scenarios The apparel industry has expanded rapidly since 2009 with exports especially to the US market helped by preferential access agreements. These exports have been growing at 18 percent per y ear. Thus, in the first scenario (pwetex) in this set, we simulate an increase in the world export price of Textiles, wearing apparel and leather , the main export product of Haiti (see Table 2.2). In other words, this scenario represents an improvement in the terms of trade for Haiti. Next, the second scenario (pwm) simulates an across the board decrease in the world price of imports; i.e., also an improvement in the terms of trade for Haiti. In the third scenario (remit), 1 Universidad Nacional de La Plata, Argentina . 2 Inter - American Development Bank. we simulate an increase in remitta nces, both to rural and urban households. Finally, we assess the impact of a negative shock such as the decrease in foreign capital inflows. In this set of simulations, the magnitude of the different shocks was decided rather arbitrarily, with the aim of emphasizing the main qualitative results. As explained, t he baseline scenario is the same as in the first set of simulations . O n the other hand, the counterfactual model closure rule assumes that adjustments in the direct tax r ate clear the government budget. Specifically, the following four simulations were implemented: • pwetex = 25 increase in world export price of Textiles, wearing apparel and leather • pwm = 25 percent decrease in world price of imports • remit = 25 perce nt incre ase in remittances • forcap = 25 percent decrease in foreign capital inflows; this is equivalent to an average decrease in capital inflows of 1 . 5 and 12 percent of baselin e GDP and exports, respectively 2. Aggregate Results Figure 2 and Table 3 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, the economy evolves according to recent tr ends, as described in the companion document that presents the results from the “Government and Institutional Capacity” simulations (Cicowiez and Filippo, 2018a) . Figure s 3 , 4, 5 and 6 summari ze the main transmission channels in the pwetex, pwm, remit and forcap scenarios, respectively . In scenarios pwetex and pwm, c ompared to the baseline, better terms of trade for Haiti lead to improvements in the macroeconomic situation (see Table 1). This includes GDP growth, private consumption and investment , and trad e indicators. In the pwetex scenario, the annual growth rate of the GDP at factor cost for the 2013 - 20 30 period rises by 0. 9 percentage points. As expected, t he in crease in the growth rate is higher for Textiles, wearing apparel and leather than for other activities (see Table 2). In addition , the unemployment rate de creases by 11 . 5 percent age point in 20 30 with respect to the baseline scenario. On the other hand , the outward orientation of the expanding industry appreciates the real exchange rate which ge nerates a form of “Dutch disease” for the rest of the tradables (again, see Table 2) . In the remittances scenario (i.e., remit) , the exchange rate appreciates at the same time as the trade deficit increases with a surge in imports and a decline in exports . Undoubtedly, Dutch Disease effects can be a serious concern (see Katz , 2018) . In our case, remittances - induced appreciation of the real exchange rate and the drop in exports are severe in view of the large (absolute) increase in remittances under conside ration . In fact, exports in 20 30 are 15 .8 percent lower than in the base scenario, while the real exchange rate appreciates by 3.1 percent. In the scenario with foreign capital outflows, the decrease in foreign savings has a strong negative impact on inve stment and consequently growth. Interestingly, in the short run, the real exchange rate depreciation promotes an increase in exports. In the long run, however, the impact of a smaller capital stock dominates and, with the slower growth in GDP, exports and imports decrease. Overall, GDP growth is , on average, 0.4 percentage point s lower during 2013 - 2030 than in the baseline scenario. Figure 1 a: c hange in real private consumption 201 3 - 20 3 0 (percent deviation from base) Figure 1b : c hange in real GDP at factor cost 201 3 - 20 3 0 (percent deviation from base) Source: A uthor’s elaboration. -10 -5 0 5 10 15 20 25 30 35 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 pwetex pwm remit forcap -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 pwetex pwm remit forcap Table 1 : c hange in real macro indicators (percent deviation from base) Source: A uthor’s elaboration. Figure 2 : main transmission channels pwetex scenario base pwetex pwm remit forcap Item 2013 2016 2030 2016 2030 2016 2030 2016 2030 Absorption 493,643 9.41 12.27 20.02 27.38 4.57 5.69 -1.90 -4.88 Private consumption 352,731 12.27 15.84 25.00 32.56 5.72 6.60 -1.34 -4.01 Fixed investment 109,528 2.97 5.05 9.85 20.07 2.20 4.71 -4.29 -9.26 Private fixed investment 50,796 6.41 10.89 21.24 43.28 4.75 10.16 -9.25 -19.97 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 64.37 77.07 19.26 44.42 -18.78 -15.80 7.83 -3.80 Imports 171,307 24.07 30.58 37.40 46.84 5.59 5.73 -1.34 -4.24 GDP at market prices 367,215 9.42 12.41 11.74 20.30 1.16 2.68 -0.95 -5.03 Net indirect taxes 19,907 19.95 25.81 25.00 36.01 2.20 3.21 -0.44 -4.69 GDP at factor cost 347,308 9.56 13.07 12.21 21.23 1.17 2.71 -0.97 -5.07 Real exchange rate 1.00 -19.90 -22.47 -9.58 -6.67 -5.98 -3.17 2.85 -0.47 Wage, average 1.00 7.29 9.86 8.24 13.42 -0.27 -0.12 -0.78 -1.03 Capital return, average 1.00 16.97 13.23 35.72 15.01 5.35 0.08 -5.04 4.30 Unemployment rate 31.72 -32.78 -45.07 -50.10 -64.74 -7.39 -8.81 2.77 8.32 2013 = million gourdes ↑world exp price tex ↑exports of textiles ↑output ↑wages and ↓unemployment ↑hhd income ↑hhd cons and sav ↓real exchange rate ↓exports of non- textiles Figure 3 : main transmission channels pwm scenario Figure 4 : main transmission channels remit scenario Figure 5 : main transmission channels forcap scenario 3. Sectoral Results At the sectoral level, our results show that promoted sectors (pwetex scenarios) an d import - oriented sector and non - tradables (remit scenario) are gain ing most in terms of VA. In turn, the forcap scenario shows a negative impact across the board, given the smaller capital stock in 2030. In the pwm scenario, the decrease in the price of i mported inputs promotes an increase in production in most sectors of the Haitian economy. ↑GDP ↓price interm inputs ↑output ↓world price imports ↑imports invest goods ↓price invest goods ↑private investment ↑remittances ↑hhd income ↑hhd cons and sav ↑private investment ↑GDP ↓real exchange rate ↓exports and ↑imports ↓foreign cap inflows ↓private investment ↓GDP ↑real exchange rate ↑exports and ↓imports Figure 6 : change in sectoral real value added in 2030 scenario abscap - g (percent deviation from base) Source: A uthor’s elaboration. -30.0 -25.0 -20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 remit Table 2 : c hange in sectoral real value added, exports, and imports (percent deviation from base) base pwetex pwm remit forcap Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Value added Agr, hunting and forestry; Fishing 67,345 3.14 4.28 3.45 5.84 0.53 1.17 -0.08 -1.95 Mining and quarrying 560 7.59 9.24 16.13 23.75 4.92 6.69 -1.86 -6.36 Food prod and beverages 6,639 -2.98 -2.19 -1.39 4.47 0.40 2.57 0.03 -3.24 Tobacco prod 118 -3.00 -1.69 -2.50 4.23 0.84 3.23 0.01 -3.37 Textiles, wearing apparel and leather 9,609 139.58 154.38 37.14 68.66 -26.81 -23.89 11.89 -1.95 Wood and of prod of wood and cork 1,227 -2.08 -1.64 7.95 15.55 1.43 4.46 0.04 -5.61 Paper and paper prod; Publishing 1,856 9.15 11.29 15.29 23.92 3.93 6.01 -0.73 -6.00 Chemicals; Rubber and plastics 839 1.58 3.04 12.13 23.30 2.29 5.25 -0.36 -6.10 Other non-metallic mineral prod 1,426 3.01 5.09 8.37 18.64 2.52 5.34 -2.24 -7.73 Basic metals 204 0.24 1.51 11.75 23.20 2.63 5.60 -1.29 -7.84 Fabricated metal prod; Mach and equip 208 -19.75 -23.96 -2.72 9.10 -5.28 -0.57 1.98 -8.33 Other manufactures 2,449 -28.28 -34.89 -0.11 15.07 -8.41 -3.73 2.42 -9.34 Electricity and water supply 6,366 7.66 11.85 11.92 23.70 2.53 4.81 -0.44 -5.18 Construction 83,021 3.08 5.19 9.82 20.00 2.18 4.66 -4.11 -9.06 Wholesale and retail trade 90,090 12.72 16.45 22.42 31.87 3.51 4.84 -0.89 -4.99 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 5.16 7.60 11.70 20.96 2.38 4.51 -0.52 -5.07 Financial intermediation 6,990 3.34 4.50 9.10 16.93 1.16 3.14 -0.34 -4.99 Other market services 11,490 9.19 13.30 16.11 28.11 3.38 5.66 -0.80 -5.81 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 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 As explained in Cicowiez and Filippo (2018 b ) , the microsimulation model can decompose the poverty impact of a given non - base scenario into the following effects related to labor market base pwetex pwm remit forcap Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Exports Agr, hunting and forestry; Fishing 3,263 -23.67 -28.34 -7.46 -9.43 -6.29 -4.02 2.00 0.06 Food prod and beverages 892 -38.78 -42.50 -8.04 1.28 -10.51 -3.51 3.40 -4.90 Textiles, wearing apparel and leather 21,600 156.20 169.11 42.23 75.33 -29.87 -26.19 13.18 -1.87 Wood and of prod of wood and cork 906 -61.67 -64.93 -23.71 -6.89 -17.92 -6.03 5.93 -8.00 Chemicals; Rubber and plastics 599 -28.34 -29.88 0.16 19.49 -6.57 0.93 2.17 -7.85 Other non-metallic mineral prod 6 -41.67 -42.58 -16.22 4.70 -11.22 -0.94 3.28 -10.31 Fabricated metal prod; Mach and equip 501 -54.27 -61.37 -18.87 -1.32 -16.10 -7.20 5.31 -10.17 Other manufactures 8,161 -45.81 -54.97 -6.10 12.32 -14.07 -7.63 4.50 -10.11 Transport, storage and comm 3,801 -8.60 -8.28 6.59 18.25 -1.54 2.54 0.61 -6.04 Financial intermediation 566 -9.21 -10.26 6.65 15.18 -2.19 1.24 0.71 -5.43 Imports Agr, hunting and forestry; Fishing 26,478 29.62 38.89 46.81 57.05 6.12 5.53 -1.67 -3.80 Mining and quarrying 136 14.71 16.93 30.47 37.11 7.67 8.40 -2.72 -6.64 Food prod and beverages 24,386 16.19 21.72 31.00 38.72 5.57 5.60 -1.46 -2.49 Tobacco prod 546 18.54 24.45 35.94 44.19 6.56 6.48 -1.67 -2.74 Textiles, wearing apparel and leather 29,163 42.57 56.31 36.52 52.47 0.06 -0.25 1.04 -3.03 Wood and of prod of wood and cork 2,595 18.36 22.17 36.24 43.23 8.21 8.42 -1.98 -4.91 Paper and paper prod; Publishing 2,185 25.05 28.96 44.19 51.34 8.21 8.07 -1.91 -5.20 Chemicals; Rubber and plastics 25,695 16.73 20.43 34.90 42.75 7.05 7.58 -1.69 -5.05 Other non-metallic mineral prod 2,098 12.78 16.03 27.27 35.64 5.73 6.70 -3.72 -7.01 Basic metals 3,799 9.63 11.80 30.41 40.73 5.43 6.90 -2.16 -7.25 Fabricated metal prod; Mach and equip 19,595 14.25 17.77 30.23 38.92 7.24 8.28 -2.10 -5.89 Other manufactures 1,204 28.24 36.87 50.19 57.91 10.56 10.61 -4.74 -6.66 Hotels and restaurants 2,047 49.45 47.48 50.29 44.35 17.92 13.09 -4.29 -6.31 Transport, storage and comm 27,048 21.52 26.77 41.40 49.32 6.65 6.61 -1.69 -4.03 Financial intermediation 2,853 17.60 21.72 34.61 43.20 4.66 5.12 -1.40 -4.55 Other market services 1,476 30.40 33.73 58.12 59.71 9.00 7.51 -2.30 -4.03 2013 = million gourdes parameters: unemployment, sectoral structure, relative wages, and average wage. In terms of pove rty, our results show that the poverty headcount ratio in the last year of the simulation period falls in the first three scenarios and increases in the last one (forcap) (Table 7) . In general, t he main drivers of th e decrease in poverty are, again, decrease s in unemployment and higher average wage s . In the remit scenario, increases in non - labor income also contribute to the decrease in poverty, but not so much to the decrease in extreme poverty. Figure 7 : c hange in poverty (percentage points from ba se) Source: A uthor’s elaboration. 5. Sensitivity Analysis In a companion document ( Cicowiez and Filippo , 2018a ), we discuss the relevance of conducting sensitivity analysis when applying the CGE method. In this section, we focus on sensitivity analysis with respect to the values assigned to production and consumption -20 -15 -10 -5 0 5 pwetex pwm remit forcap 2030 Extreme poverty Poverty -15 -10 -5 0 5 pwetex pwm remit forcap 2016 Extreme poverty Poverty elasticities for the simulations presented in previous sections. Table 4 shows the percentage change in private consumption estimated (i) unde r the central elasticities, and (ii) as the aver age 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 weight. As can be seen, the results reported above are significant, while estimates presented in Table 1 are wi thin the confidence intervals reported in Table 4. For example, there is virtual certainty that the forcap scenario has a negative effect on private consumption. Table 3 : sensitivity analysis; real private consumption in 2030 percent deviation from base 9 5% confidence interval under normality assumption Source: Author’s elaboration. References Cicowiez, Martin and Agustin Filippo, 2018a, Government and Institutional Capacity. Simulations in a CGE Model for Haiti, Project Document, Inter - American Developm ent Bank. Scenario Central elast Mean Standard dev Lower bound Upper bound pwetex 15.840 15.911 1.932 12.125 19.698 pwm 32.559 33.033 2.455 28.221 37.844 remit 6.599 6.590 0.248 6.103 7.076 forcap -4.007 -3.933 0.304 -4.528 -3.338 Cicowiez, Martin and Agustin Filippo, 2018b, A Computable General Equilibrium Analysis for Haiti, IDB Technical Note IDB - TN - 1486. Katz, Sebastian, 201 8 , ¿Podrá, Ayiti, volver a ser el Reino de este Mundo?, IDB Technical Note IDB - TN - 1484 . 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 pwetex pwm remit forcap Table C.1: real macroeconomic aggregates average annual growth rate 2014 - 2030; percent base Item 2013 base pwetex pwm remit forcap Absorption 493,643 3.58 4.38 5.26 3.96 3.23 Private consumption 352,731 3.48 4.50 5.45 3.93 3.20 Fixed investment 109,528 3.60 3.94 4.87 3.92 2.93 Private fixed investment 50,796 3.60 4.32 6.11 4.27 2.07 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 8.41 6.95 3.17 4.09 Imports 171,307 3.81 5.68 6.51 4.20 3.51 GDP at market prices 367,215 3.57 4.38 4.85 3.75 3.21 Net indirect taxes 19,907 3.80 5.40 5.95 4.02 3.47 GDP at factor cost 347,308 3.57 4.42 4.90 3.75 3.21 Real exchange rate 1.00 -0.32 -1.99 -0.78 -0.53 -0.35 Wage, average 1.00 0.23 0.86 1.07 0.22 0.16 Unemployment rate 31.72 25.49 14.00 8.99 23.24 27.61 2013 = million gourdes