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Nicoletta Batini, Mario di Serio, Matteo Fragetta, and Mr. Giovanni Melina
This paper estimates multipliers for spending in clean energy and biodiversity conservation to help inform stimulus measures for a post-COVID-19 sustainable recovery. Using a new international dataset, part of which was especially assembled for this analysis, we find that every dollar spent on key carbon-neutral or carbon-sink activities—from zero-emission power plants to the protection of wildlife and ecosystems—can generate more than a dollar’s worth of economic activity. The estimated multipliers associated with green spending are about 2 to 7 times larger than those associated with non-eco-friendly expenditure, depending on sectors, technologies and horizons. These findings survive several robustness checks and suggest that ‘building back better’ could be a win-win for economies and the planet.
Nicoletta Batini, Mario Di Serio, Matteo Fragetta, Mr. Giovanni Melina, and Anthony Waldron

Model B. Data Coverage and Specification V. Results VI. Robustness Analysis VII. Conclusions and Policy Implications References Appendix A. Data

International Monetary Fund. Statistics Dept.

rehabilitation and employment of invalid persons Fund; ;e3.4 Child Protection Fund of the Republika Srpska ;c State Governments ;dSubsector 4. State governments ;e4.1 Not applicable ;c Local Governments ;dSubsector 5. Local governments ;e5.1 139 municipalities ;e5.2 4 cities ;b Data coverage Data in central government tables cover operations of subsectors 1–3 Data in local government tables cover operations of subsector 5 ;b Accounting Practices ;f1

International Monetary Fund. Statistics Dept.

governments ;e4.1 Not applicable ;c Local Governments ;dSubsector 5. Local governments ;e5.1 139 municipalities ;e5.2 4 cities ;b Data Coverage Data in central government tables cover operations of subsectors 1–3 Data in local government tables cover operations of subsector 5 ;b Accounting Practices ;f1. Bases of recording: Revenues are reported on a cash basis, expenditure on an accrual basis;. ;f2. Liquidation or complementary period: - ;f3. Valuation of assets and liabilities

Ms. Era Dabla-Norris and John M. Matovu

.9 3.5 15.0 10.0 13.5 14.9 source: IMF and World Bank a Regional averages are weighted by current GDP for the year b Data coverage varies for the different variables Table 7. Countries With Primary Gross School Enrollment Ratios Below 90 Percent, 1996 Region and country Gross enrollment ratio Region and country Gross enrollment ratio 50-90 percent Sub-Saharan Africa Middle East and North Africa Benin 78 Morocco 86 Burundi 51 Oman 76

International Monetary Fund. Statistics Dept.

previously). ;b Data Coverage Data in central government tables cover operations of subsector 1. Uganda has no state governments; therefore, there are no state government tables (subsector 4). Data in local government tables cover operations of subsector 5. At this time, Uganda is not producing GFS for subsectors 6 and 7 (public corporations). Uganda’s GFS time series extends currently back to 1998. There are no critical series breaks for the data that are presented. However, “…”.is used to indicate data gaps. ;b Accounting Practices 1

Mr. John J Matovu and Ms. Era Dabla-Norris
This paper addresses the potential effects on human capital accumulation and economic growth of the alternative compositions of public expenditures in the context of a computable dynamic general equilibrium model of overlapping generations and heterogeneous agents in which altruistic parents make schooling decisions for their children. In the presence of fixed and variable costs for different levels of schooling, we show that reducing household costs of primary education has the largest positive impact on growth and poverty reduction in the short run. Moreover, an increase in higher education spending increases long-run growth. These effects can be substantial even when increasing education spending comes at the expense of public infrastructure investment.
Nicoletta Batini, Mario Di Serio, Matteo Fragetta, Mr. Giovanni Melina, and Anthony Waldron

to be strong determinants of spending decisions. For each of 10,000 draws from the posterior distribution, 19 we derive impulse responses for a time horizon of 5 years, saving the median response and the 16th and 84th percentile of their distribution as confidence bands. Given that the model requires the estimation of many parameters, for the sake of parsimony, we produce the baseline results with a uniform lag structure of one year and conduct robustness checks with a lag structure of two years in Section VI . B. Data Coverage and Specification Details

International Monetary Fund. Statistics Dept.

;b Data Coverage Data in central government tables cover operations of subsector 1. The changes in currency and deposits are included under loans in financial assets because the information available is for net credit to government (loans minus deposits). For this reason Statement II shows zeros as values under Net change in the stock of cash. ;b Accounting Practices ;f1. Bases of recording: The data compiled for budgetary central government are on a modified cash basis. ;f2. Liquidation or complementary period: Not reported