Similasyon Devlopman Moun nan yon modèl CGE pou Ayiti

Human Development Simulations in a CGE model for Haiti Martin Cicowiez Agustin Filippo IDB-TN-01570 Country Department Central America, Haiti, Mexico, Panama and Dominican Republic TECHNICAL NOTE Nº January 2019 Human Development 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. Human development: simulations in a CGE model for Haiti / Martín Cicowiez and Agustín Filippo. p. cm. — (IDB Technical Note ; 1570) Includes bibliographic references. 1. Economic development-Social aspects-Haiti-Econometric models. 2. Government spending policy-Haiti-Econometric models. 3. Haiti-Social 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-1570 JEL Codes: C68, D58, E23, O47, O54. Keywords: Haiti, structural change, structural transformation, computable general equilibrium, economic development, human development. 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 Human Development Simulations in a CGE model for Haiti. Martín Cicowiez 1 and Agustín Filippo 2 Simulations This document presents the group of simulations related to “Human Development”, 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 In Haiti , public spending in health and education, and social protection remains limited, constraining the government’s ability to provide services and off e r equal opportunities to its c itizens. In addition, Haiti’s tax system generates limited resources for the government and tends to be regressive ( Singh and Barton - Dock , 2015) . In this set of simulations, more fiscal space is created through exogenous increases for foreign aid (grants) or increases in direct taxation . Then, t he government makes use of the resulting addition to fiscal space to expand 1 Universidad Nacional de La Plata, Argentina . 2 Inter - American Development Bank. spending and service delivery in education ( scenarios gconedu - tdir and gconedu - frt) and health (scenarios gconhlt - tdir and gconhlt - frt) . Thus, the purpose of this set of simulations is to assess what those different options entail in term s of promoting economic growth and reducing poverty . As before , 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 public spending on human development clear the government budget. Spe cifically, the following four simulations were implemented : • gconedu - tdir = increase in (real) public spending in education equivalent to 2.5 percentage points of GDP combined with increase in skilled labor supply ; specifically, the share of skilled labor in total labor supply gradually increases from 32 percent in 2015 to 47 percent in 2030 • gconedu - ftr = same increase in public spending in education as previous scenario combined with increase in skilled labor supply ; specifically, the share of skilled labor in total labor supply gradually increases from 32 percent in 2015 to 47 percent in 2030 • gconhlt - tdir = increase in (real) public spending in health equivalent to 2.5 percentage points of GDP combined with one percent yearly increase in labor productivity • gconhlt - ftr = same increase in public spending in health as previous scenario combined with one percent yearly increase in labor productivity In both health scenario s , the increase in labor productivity reflects the expected increase in the health status of the Haitian population that would be derived from increas ed/improved government provision of health - related services. 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, the economy evolves according to recent trends, as described in the companion document that presents the results f rom the “Government and Institutional Capacity” simulations ( Cicowiez and Filippo, 2018a) . Figure s 2 , 3 and 4 summarize the main transmission channels for human development scenarios through government spending in education or health and government financ ing . As explained in Cicowiez and Filippo (2018b) , our CGE model assumes that there is no full employment of labor. As shown by our results (see Figure 5) , this specification allows us to capture mismatches between the supply of and demand for skilled labo r. In fact, scenarios gconedu - tdir and gconedu - ftr show that investing in human capital without sufficient creation of skilled jobs results in higher rates of (skilled) unemployment and skill mismatches in the labor market. These outcomes can be catalysts of underemployment, resulting in negative repercussions in terms of rising inequality of income and opportunities, and less poverty reduction. These undesirable trade - offs can be avoided only if other policies improve the environment for stimulating a stru ctural change towards technologies and activities that absorb larger amounts of skilled labor, improve the content of education and ensure that skills created by the education system are in high demand by the productive sector (Sánchez and Cicowiez, 2014). In other words, unemployment of skilled labor, for example, may signal that investments in human capital do not go hand in hand with economic changes that are necessary to adequately absorb the population of skilled workers. As summarized in Figure 3 , t he impact on the rest of the economy from spending in human development depends on the financing mechanism. In case the marginal financing for education spending comes from direct taxes, growth declines for private consumption and investment. On the other hand, when marginal financing comes from foreign grants, the negative impact 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 im port growth, both will be induced by an appreciation of the real exchange rate. In the case of government spending in health, qualitative results are similar. However, in the longer run, the positive impacts of increased labor productivity dominate (see Fi gure 1 ). Figure 4 summarizes the main transmission channels in the health spending scenarios gcon - tdir and gcon - ftr. Naturally, impacts through the government financing mechanisms are the same as in the education spending scenarios. The additional public s pending in education and health has a positive impact on the relative demand for skilled workers, initially pushing up their relative wage. Thus, expanding expenditure in human development requires careful preparation to align the speed of expenditure incr eases with the ability of education and training programs to deliver properly educated workers. There may also be a need to monitor wage pressures to avoid large increases in the wage bill that could crowd out other expenditures. Figure 1a : 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. -3 -2 -1 0 1 2 3 4 5 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr Table 1 : c hange in real macro indicators (percent deviation from base) Source: A uthor’s elaboration. Figure 2 : main transmission channels education spending scenarios ; through government spending Figure 3 a : main transmission channels gcon edu - tdir ; through government financing base gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr Item 2013 2016 2030 2016 2030 2016 2030 2016 2030 Absorption 493,643 1.13 0.42 3.63 3.28 1.28 2.14 4.04 5.96 Private consumption 352,731 -0.87 -1.84 2.25 1.46 -0.89 -0.16 2.56 4.11 Fixed investment 109,528 -0.56 -1.77 0.68 0.63 -0.55 -0.15 0.84 3.49 Private fixed investment 50,796 -1.20 -3.83 1.47 1.36 -1.19 -0.32 1.81 7.52 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 28.59 28.59 28.59 28.59 31.00 31.00 31.00 31.00 Exports 44,879 -0.82 -4.07 -11.83 -12.40 -0.65 3.35 -12.21 2.78 Imports 171,307 -0.14 -1.03 2.81 1.78 -0.09 0.98 3.18 5.10 GDP at market prices 367,215 1.48 0.50 2.08 1.82 1.68 2.87 2.41 5.93 Net indirect taxes 19,907 -0.19 -1.40 0.90 0.15 -0.13 1.15 1.15 4.74 GDP at factor cost 347,308 1.58 0.61 2.16 1.95 1.78 2.97 2.50 6.02 Real exchange rate 1.00 0.12 -0.66 -3.17 -2.24 0.18 0.19 -3.36 -0.43 Wage, average 1.00 1.90 2.93 1.66 2.90 2.04 2.34 1.80 2.55 Capital return, average 1.00 -0.47 0.22 2.41 0.09 -0.44 1.06 2.75 0.80 Unemployment rate 31.72 -2.70 2.58 -6.60 -1.44 -3.12 -5.72 -7.47 -11.97 2013 = million gourdes ↑gov spnd edu ↑sk LS and ↓unsk LS ↑sk unemp and ↓unsk unemp ↑hhd cons spnd and sav ↑gov spnd edu ↑direct tax rate ↓household disposable income Figure 3 b : main transmission channels gcon edu - ftr ; through government financing Figure 4 : main transmission channels health spending scenarios ; through government spending Figure 5: c hange in unemployment 2030 (percentage points from base) ↓exports and ↑imports ↑gov spnd edu ↑foreign aid ↓real exchange rate ↑gov spnd health ↑labor productivity ↑output ↑wages and ↓unemployment ↑hhd income ↑hhd cons and sav -10 -5 0 5 10 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr skilled unskilled 3. Sectoral Results At the sectoral level, the wining sectors are those promoted by the government increased spending. For all other sectors, the impact on output is a function of their export and import orientation, and their importance in the consumption baskets of households. Figure 6 : change in sectoral r eal value added in 2030 scenario gconhlt - dir (percent deviation from base) Source: A uthor’s elaboration. -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 gconhlt - tdir Table 2 : c hange in sectoral real value added, exports, and imports (percent deviation from base) base gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Value added Agr, hunting and forestry; Fishing 67,345 -0.34 -1.50 -0.05 -0.92 -0.29 0.31 0.08 1.88 Mining and quarrying 560 -1.07 -2.40 1.72 1.02 -1.07 -0.10 2.04 4.96 Food prod and beverages 6,639 -0.50 -1.58 -0.24 -0.24 -0.48 0.14 -0.13 2.82 Tobacco prod 118 -0.65 -1.60 -0.16 0.06 -0.66 -0.01 -0.05 2.99 Textiles, wearing apparel and leather 9,609 -1.24 -4.91 -17.23 -17.65 -1.02 5.13 -17.78 2.93 Wood and of prod of wood and cork 1,227 0.09 -1.64 0.88 0.65 0.22 1.50 1.19 6.11 Paper and paper prod; Publishing 1,856 -0.83 -2.34 1.36 0.70 -0.79 0.53 1.69 5.86 Chemicals; Rubber and plastics 839 0.21 -1.22 1.47 1.46 0.32 1.49 1.78 6.29 Other non-metallic mineral prod 1,426 -0.21 -1.52 1.20 1.21 -0.17 0.54 1.44 4.87 Basic metals 204 -1.05 -2.79 0.27 -0.04 -1.05 0.00 0.50 5.12 Fabricated metal prod; Mach and equip 208 0.00 -2.63 -3.05 -2.91 0.14 1.61 -3.00 5.27 Other manufactures 2,449 -0.90 -3.97 -5.59 -5.70 -0.84 0.37 -5.73 3.23 Electricity and water supply 6,366 -0.10 -1.04 1.32 1.43 -0.06 0.72 1.54 4.67 Construction 83,021 -0.51 -1.72 0.71 0.66 -0.50 -0.08 0.87 3.54 Wholesale and retail trade 90,090 -0.15 -1.43 1.70 0.95 -0.08 1.04 2.03 5.18 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 -0.31 -1.35 0.99 0.95 -0.27 0.72 1.22 4.79 Financial intermediation 6,990 3.44 2.94 4.05 4.56 3.78 5.18 4.54 8.84 Other market services 11,490 -0.36 -1.69 1.49 1.16 -0.32 0.59 1.77 5.16 Education, government 770 800.00 800.00 800.00 800.00 0.00 0.00 0.00 0.00 Health, government 2,227 0.00 0.00 0.00 0.00 300.00 300.00 300.00 300.00 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 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 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr Commodity 2013 2016 2030 2016 2030 2016 2030 2016 2030 Exports Agr, hunting and forestry; Fishing 3,263 0.17 -2.19 -3.27 -4.26 0.38 0.74 -3.29 -0.10 Food prod and beverages 892 -0.50 -3.22 -6.21 -4.88 -0.37 0.79 -6.39 2.78 Textiles, wearing apparel and leather 21,600 -1.31 -5.22 -19.12 -19.18 -1.07 5.52 -19.75 2.87 Wood and of prod of wood and cork 906 -0.26 -4.65 -10.11 -7.54 0.03 2.16 -10.39 5.30 Chemicals; Rubber and plastics 599 -0.10 -2.50 -3.62 -1.93 0.03 1.50 -3.65 5.85 Other non-metallic mineral prod 6 -0.32 -3.44 -6.39 -3.76 -0.20 0.74 -6.62 4.69 Fabricated metal prod; Mach and equip 501 -0.49 -4.45 -9.36 -7.96 -0.32 1.56 -9.70 4.11 Other manufactures 8,161 -0.87 -4.65 -8.65 -8.29 -0.78 0.59 -8.98 2.73 Transport, storage and comm 3,801 -0.40 -1.66 -1.20 -0.31 -0.36 0.77 -1.12 4.76 Financial intermediation 566 2.69 2.91 1.43 3.60 2.93 5.00 1.71 8.77 Imports Agr, hunting and forestry; Fishing 26,478 -0.80 -1.08 2.46 1.70 -0.87 -0.01 2.72 3.68 Mining and quarrying 136 -1.26 -2.78 2.98 1.41 -1.25 -0.04 3.43 5.78 Food prod and beverages 24,386 -0.54 -1.03 2.49 1.78 -0.57 -0.15 2.75 2.95 Tobacco prod 546 -0.61 -1.19 2.93 2.04 -0.64 -0.22 3.23 3.28 Textiles, wearing apparel and leather 29,163 -0.63 -1.98 -0.92 -2.30 -0.60 1.25 -0.76 3.72 Wood and of prod of wood and cork 2,595 0.21 -0.92 4.73 3.30 0.32 1.38 5.30 6.80 Paper and paper prod; Publishing 2,185 -0.86 -2.11 3.59 1.90 -0.83 0.45 4.09 5.94 Chemicals; Rubber and plastics 25,695 0.41 -0.70 4.24 3.09 0.53 1.61 4.77 6.73 Other non-metallic mineral prod 2,098 -0.22 -1.31 2.93 2.06 -0.17 0.61 3.31 5.07 Basic metals 3,799 -1.02 -2.60 1.79 0.74 -1.01 -0.01 2.13 5.17 Fabricated metal prod; Mach and equip 19,595 0.69 -0.55 4.48 3.45 0.84 1.92 5.03 7.34 Other manufactures 1,204 -1.07 -1.84 4.64 3.44 -1.10 -0.34 5.15 5.30 Hotels and restaurants 2,047 2.85 1.41 12.46 7.94 3.19 3.86 13.73 11.30 Transport, storage and comm 27,048 -0.21 -1.02 3.35 2.28 -0.18 0.66 3.75 4.81 Financial intermediation 2,853 4.22 2.98 6.77 5.55 4.66 5.38 7.48 8.92 Other market services 1,476 -0.47 -1.28 4.34 2.43 -0.43 0.82 4.88 5.57 2013 = million gourdes taxes and savings) income. In all four human development scenario s , the 20 30 poverty rate is lower than for the baseline, mainly as a result of a decrease in unskilled unemployment (see Figure 5) , a higher average wage, and a decrease in the wage gap between unskilled and skilled labor. Once more, we use growth - incidenc e curves to assess the distributional impact of the various scenarios. In Figure 8a we see that the gconedu - ftr scenario has a pro - poor impact, with even negative impact on the highest percentiles of the income distribution. As explained, this is related t o the increase in the unemployment rate of skilled workers. On the other hand, the health scenario with foreign financing generates growth - incidence curve that is positive and is nearly flat. Certainly , the poverty effect would be larger if a multidimensio nal measure of poverty were considered instead of only monetary poverty. Figure 7 : c hange in poverty (percentage points from base) Source: A uthor’s elaboration. -4 -3 -2 -1 0 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr 2030 Extreme poverty Poverty -4 -3 -2 -1 0 gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr 2016 Extreme poverty Poverty Figure 8 a : growth - incidence curves scenario gconedu - ftr ; 2030 household per capita income pr oportional changes by percentile -100 -50 0 50 100 150 0 10 20 30 40 50 60 70 80 90 100 percentile gconedu-ftr fitted values Figure 8 b : growth - incidence curves scenario gconhlt - ftr ; 2030 household per capita income proportional changes by percentile 5. Sensitivity Analysis In 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 sensitivity analysis with respect to the values assigned to prod uction and consumption 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 average of the 5 00 observations generated by th e sensitivity analysis. For the second case, the 0 20 40 60 80 0 10 20 30 40 50 60 70 80 90 100 percentile gconhlt-ftr fitted values 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 signific ant, while estimates presented in Table 1 are within the confidence intervals reported in Table 3 . For example, there is virtual certainty that the gconhlt - ftr scenario has a positive effect on private consumption. Table 4: sensitivity analysis; real priva te consumption in 2030 percent deviation from base 95% 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 Development Bank. Cicowiez, Martin and Agustin Filippo, 2018b, A Computable General Equilibrium Analysis for Haiti, IDB Technical Note IDB - TN - 1486. Scenario Central elast Mean Standard dev Lower bound Upper bound gconedu-tdir -1.841 -1.682 0.221 -2.116 -1.248 gconedu-ftr 1.461 1.607 0.218 1.180 2.034 gconhlt-tdir -0.159 -0.186 0.082 -0.346 -0.026 gconhlt-ftr 4.112 4.017 0.178 3.669 4.366 Singh Raju Jan and Mary Barton - Dock , 2015, Haiti: Toward a New Narrative , Systematic Country Diagnostic , Washington, DC: World Bank . Sánchez, Marco V. and Martín Cicowiez, 2014, Trade - offs and Payoffs of Investing in Human Development, World Development, 62: 14 - 29. Appendix: Additional Simulation Results Figur e A .1: real private consumption average annual growth rate 2014 - 2030; percent 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80 base gconedu-tdir gconedu-ftr gconhlt-tdir gconhlt-ftr Table A .1: real macroeconomic aggregates average annual growth rate 2014 - 2030; percent base Item 2013 base Absorption 493,643 3.58 3.61 3.80 3.73 3.98 Private consumption 352,731 3.48 3.36 3.58 3.47 3.76 Fixed investment 109,528 3.60 3.48 3.64 3.59 3.84 Private fixed investment 50,796 3.60 3.33 3.69 3.58 4.10 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 6.25 6.25 6.38 6.38 Exports 44,879 4.36 4.07 3.44 4.59 4.55 Imports 171,307 3.81 3.74 3.94 3.88 4.16 GDP at market prices 367,215 3.57 3.60 3.69 3.77 3.97 Net indirect taxes 19,907 3.80 3.70 3.81 3.88 4.12 GDP at factor cost 347,308 3.57 3.61 3.70 3.77 3.97 Real exchange rate 1.00 -0.32 -0.36 -0.47 -0.30 -0.35 Wage, average 1.00 0.23 0.42 0.42 0.38 0.40 Unemployment rate 31.72 25.49 26.14 25.12 24.03 22.44 2013 = million gourdes gconhlt- ftr gconhlt- tdir gconedu- ftr gconedu- tdir