Human Development: Simulations in a CGE Model for Haiti (IDB Technical Note 1570)

Human Development Country Department Simulations in a CGE model for Haiti Central America, Haiti, Mexico, Panama and Dominican Republic Martin Cicowiez Agustin Filippo TECHNICAL NOTE Nº IDB-TN-01570 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. http://www.iadb.org Copyright © 2019 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. Human Development Simulations in a CGE model for Haiti. Martín Cicowiez1 and Agustín Filippo2 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 offer equal opportunities to its citizens. 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, the 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 terms of promoting economic growth and reducing poverty. As before, the baseline scenario is the same as in the first set of simulations. On the other hand, the counterfactual model closure rule assumes that adjustments in public spending on human development clear the government budget. Specifically, 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 scenarios, the increase in labor productivity reflects the expected increase in the health status of the Haitian population that would be derived from increased/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 2016 (i.e., the year when all scenarios start deviating from the base) and 2030, 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 from the “Government and Institutional Capacity” simulations (Cicowiez and Filippo, 2018a) . Figures 2, 3 and 4 summarize the main transmission channels for human development scenarios through government spending in education or health and government financing. 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 labor. 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 structural 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, the 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 import 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 Figure 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 spending 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 increases 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: change in real private consumption 2013-2030 (percent deviation from base) 5 4 3 2 gconedu-tdir 1 gconedu-ftr 0 gconhlt-tdir gconhlt-ftr -1 -2 -3 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Figure 1b: change in real GDP at factor cost 2013-2030 (percent deviation from base) 7.0 6.0 5.0 4.0 gconedu-tdir gconedu-ftr 3.0 gconhlt-tdir 2.0 gconhlt-ftr 1.0 0.0 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Source: Author’s elaboration.  Table 1: change in real macro indicators (percent deviation from base) 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 Source: Author’s elaboration. Figure 2: main transmission channels education spending scenarios; through government spending ↑sk LS and ↓unsk ↑sk unemp and ↑hhd cons spnd ↑gov spnd edu LS ↓unsk unemp and sav Figure 3a: main transmission channels gconedu-tdir; through government financing ↓household ↑gov spnd edu ↑direct tax rate disposable income Figure 3b: main transmission channels gconedu-ftr; through government financing ↓real exchange ↓exports and ↑gov spnd edu ↑foreign aid rate ↑imports Figure 4: main transmission channels health spending scenarios; through government spending ↑labor ↑wages and ↑gov spnd health ↑output productivity ↓unemployment ↑hhd cons and ↑hhd income sav Figure 5: change in unemployment 2030 (percentage points from base) gconhlt-ftr gconhlt-tdir gconedu-ftr gconedu-tdir -10 -5 0 5 10 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 real value added in 2030 scenario gconhlt-dir (percent deviation from base) gconhlt-tdir 6.0 5.0 4.0 3.0 2.0 1.0 0.0 -1.0 Source: Author’s elaboration.  Table 2: change 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.): change 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 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 Source: Author’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 taxes and savings) income. In all four human development scenarios, the 2030 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-incidence 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 to 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 multidimensional measure of poverty were considered instead of only monetary poverty. Figure 7: change in poverty (percentage points from base) 2016 2030 gconhlt-ftr gconhlt-ftr gconhlt-tdir gconhlt-tdir gconedu-ftr gconedu-ftr gconedu-tdir gconedu-tdir -4 -3 -2 -1 0 -4 -3 -2 -1 0 Extreme poverty Poverty Extreme poverty Poverty Source: Author’s elaboration.  Figure 8a: growth-incidence curves scenario gconedu-ftr; 2030 household per capita income proportional changes by percentile 150 100 50 0 -50 -100 0 10 20 30 40 50 60 70 80 90 100 percentile gconedu-ftr fitted values  Figure 8b: growth-incidence curves scenario gconhlt-ftr; 2030 household per capita income proportional changes by percentile 80 60 40 20 0 0 10 20 30 40 50 60 70 80 90 100 percentile gconhlt-ftr fitted values 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 production and consumption elasticities for the simulations presented in previous sections. Table 4 shows the percentage change in private consumption estimated (i) under the central elasticities, and (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 weight. As can be seen, the results 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 gconhlt-ftr scenario has a positive effect on private consumption. Table 4: sensitivity analysis; real private consumption in 2030 percent deviation from base 95% confidence interval under normality assumption Central Standard Lower Upper Scenario elast Mean dev bound 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 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. 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 Figure A.1: real private consumption average annual growth rate 2014-2030; percent gconhlt-ftr gconhlt-tdir gconedu-ftr gconedu-tdir base 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80  Table A.1: real macroeconomic aggregates average annual growth rate 2014-2030; percent base gconedu- gconedu- gconhlt- gconhlt- Item 2013 base tdir ftr tdir ftr 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