Appendix Table of Contents Appendix I - HCI in selected Brazilian municipalities 5 Appendix II - Are SAEB and PISA comparable? 37 Appendix III - How is the HCI-SES calculated? 39 Appendix IV - Human Capital Accumulation 41 Amidst the COVID19- Pandemic Appendix V - Drivers of Human Capital Formation 49 Appendix VI - Subnational Human Development Policies 63 Appendix HCI in selected Brazilian municipalities Aracaju, Sergipe Human Capital Index: 0.586 As part of the institutional agenda, health and education conditions are average, 58.6% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Aracaju had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 22nd human talent. the conditions are for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Aracaju had an HCI of 0.586 in 2019. (fig. 3). Aracaju’s per capita income the productivity of a child born today This means that the future productivity would be 70.8% higher if human capital when entering the job market if current of a child born in this year was, on was fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Aracaju’s rank Aracaju’s rank Brazil in Brazil in Sergipe 0.601 3,268th 5th 6 Belém, Pará Human Capital Index: 0.561 As part of the institutional agenda, health and education conditions are 56.1% of its potential. This value is below the World Bank launched the Human maintained. It is a prospective indicator the national average of 0.601 (fig. 2) Capital Project, which emphasizes based on three components: (i) health, and is largely explained by educational the importance of investing in the measured by the adult survival rate performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Belém success of a society lies in its ability child survival (up to the age of 5). had insufficient performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 26th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), despite HCI growth above the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Belém had an HCI of 0.561 in 2019. This Belém’s per capita income would be the productivity of a child born today means that the future productivity of a 78.2% higher if human capital were fully when entering the job market if current child born in this year was, on average, promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Belém’s rank Belém’s rank Brazil in Brazil in Pará 0.601 4,160th 17th 7 Belo Horizonte, Minas Gerais Capital Index: 0.619 Human As part of the institutional agenda, health and education conditions are on average, 61.9% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value was above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Belo success of a society lies in its ability child survival (up to the age of 5). Horizonte had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 11th human talent. the conditions for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth below This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Belo Horizonte had an HCI of 0.619 (fig. 3). Belo Horizonte’s per capita the productivity of a child born today in 2019. This means that the future income would be 61.6% higher if human when entering the job market if current productivity of a child born in this year is, capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Belo Horizonte’s rank Belo Horizonte’s rank in Brazil in Brazil Minas Gerais 0.601 1,720th 285th 8 Boa Vista, Roraima Human Capital Index: 0.586 As part of the institutional agenda, health and education conditions are value is below the national average of the World Bank launched the Human maintained. It is a prospective indicator 0.601 (fig. 2) and is largely explained by Capital Project, which emphasizes based on three components: (i) health, educational performance. the importance of investing in the measured by the adult survival rate and fundamental skills of individuals. The the child stunting rate; (ii) quantity and In relation to other state capitals, Boa message of this project is that the quality of education; and (iii) the child Vista had insufficient performance. The success of a society lies in its ability survival (up to age of 5). The closer the municipality occupies the 21st position to promote, allocate, and strengthen index is to 1, the better the conditions among the 27 state capitals in Brazil human talent. are for human capital accumulation. (fig. 4), and has had a growth below the national average in recent years (fig. 3). This effort is materialized in the Human Boa Vista had an HCI of 0.586 in 2019. Boa Vista’s per capita income would be Capital Index (HCI), which illustrates This means that the future productivity 70.7% higher if human capital were fully the productivity of a child born today of a child born in this year is was, on promoted. when entering the job market if current average, 58.6% of its potential. This Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Boa Vista’s rank Boa Vista’s rank in Brazil in Brazil Roraima 0.601 3,250th 1st 9 Brasília, Distrito Federal Capital Index: 0.632 Human As part of the institutional agenda, health and education conditions are average, 63.2% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0,601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational and health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Brasília had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 6th human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), and has had a growth This effort is materialized in the Human above the national average in recent Capital Index (HCI), which illustrates Brasília had an HCI of 0.632 in 2019. years (fig. 3). Brasília’s per capita the productivity of a child born today This means that the future productivity income would be 58.3% higher if human when entering the job market if current of a child born in this year is was, on capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Brasília’s rank Brazil in Brazil 0.601 1,166th 10 Campo Grande, Mato Grosso do Sul Capital Index: 0.646 Human As part of the institutional agenda, health and education conditions are is, on average, 64.6% of its potential. the World Bank launched the Human maintained. It is a prospective indicator This value was above the national Capital Project, which emphasizes based on three components: (i) health, average of 0.601 (fig. 2) and is largely the importance of investing in the measured by the adult survival rate explained by educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Campo success of a society lies in its ability child survival (up to the age of 5). Grande had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 3rd human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), and has had a growth This effort is materialized in the Human above the national average in recent Capital Index (HCI), which illustrates Campo Grande had an HCI of 0.646 years (fig. 3). Campo Grande’s per the productivity of a child born today in 2019. This means that the future capita income would be 54.7% higher if when entering the job market if current productivity of a child born in this year human capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Campo Grande’s rank Campo Grande’s rank in Mato Grosso Brazil in Brazil do Sul 0.601 625th 3rd 11 Cuiabá, Mato Grosso Capital Index: 0.592 Human As part of the institutional agenda, health and education conditions are average, 59.2% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Cuiabá success of a society lies in its ability child survival (up to the age of 5). had insufficient performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 17th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth below the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Cuiabá had an HCI of 0.592 in 2019. Cuiabá’s per capita income would be the productivity of a child born today This means that the future productivity 68.9% higher if human capital were fully when entering the job market if current of a child born in this year was, on promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Cuiabá’s rank Cuiabá’s rank in Brazil in Brazil Mato Grosso 0.601 2,984th 61st 12 Curitiba, Paraná Human Capital Index: 0.649 As part of the institutional agenda, health and education conditions are average, 64.9% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational and health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Curitiba had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 2nd human talent. the conditions for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth below This effort is materialized in the Human the national average in recent years Capital Index (HCI) which illustrates Curitiba had an HCI of 0.649 in 2019. (fig. 3). Curitiba’s per capita income the productivity of a child born today This means that the future productivity would be 54.0% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Curitiba’s rank Curitiba’s rank in Brazil in Brazil Paraná 0.601 543rd 49th 13 Florianópolis, Santa Catarina Capital Index: 0.637 Human As part of the institutional agenda, health and education conditions are This value is above the national average the World Bank launched the Human maintained. It is a prospective indicator of 0.601 (fig. 2) and is largely explained Capital Project, which emphasizes based on three components: (i) health, by educational and health performance. the importance of investing in the measured by the adult survival rate and fundamental skills of individuals. The the child stunting rate; (ii) quantity and In relation to other state capitals, message of this project is that the quality of education; and (iii) the child Florianópolis had satisfactory success of a society lies in its ability survival (up to age of 5). The closer the performance. The municipality occupies to promote, allocate, and strengthen index is to 1, the better the conditions the 5th among the 27 state capitals human talent. are for human capital accumulation. in Brazil (fig. 4), despite HCI growth below the national average in recent This effort is materialized in the Human Florianópolis had an HCI of 0.637 years (fig. 3). Florianópolis’ per capita Capital Index (HCI), which illustrates in 2019. This means that the future income would be 56.9% higher if the productivity of a child born today productivity of a child born in this year human capital were fully promoted. when entering the job market if current was, on average, 63.7% of its potential. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Florianópolis’ rank Florianópolis’ rank in Brazil in Brazil Santa Catarina 0.601 942nd 109th 14 Fortaleza, Ceará Human Capital Index: 0.614 As part of the institutional agenda, health and education conditions are average, 61.4% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Fortaleza had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 12th human talent. the conditions are for human capital position among the 27state capitals in accumulation. Brazil (fig. 4), and has had a growth This effort is materialized in the Human above the national average in recent Capital Index (HCI), which illustrates Fortaleza had an HCI of 0.614 in 2019. years (fig. 3). Fortaleza’s per capita the productivity of a child born today This means that the future productivity income would be 62.8% higher if human when entering the job market if current of a child born in this year was, on capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Fortaleza’s rank Fortaleza’s rank in Brazil in Brazil Ceará 0.601 1,915th 45th 15 Goiânia, Goiás Human Capital Index: 0.641 As part of the institutional agenda, health and education conditions are average, 64.1% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Goiânia had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 4th human talent. the conditions for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth above the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Goiânia had an HCI of 0.641 in 2019. Goiânia’s per capita income would be the productivity of a child born today This means that the future productivity 56.0% higher if human capital were fully when entering the job market if current of a child born in this year was, on promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Goiânia’s rank Goiânia’s rank in Brazil in Brazil Goiás 0.601 821st 60th 16 João Pessoa, Paraíba Human Capital Index: 0.592 As part of the institutional agenda, health and education conditions are was, on average, 59.2% of its potential. the World Bank launched the Human maintained. It is a prospective indicator This value is below the national average Capital Project, which emphasizes based on three components: (i) health, of 0.601 (fig. 2) and is largely explained the importance of investing in the measured by the adult survival rate by educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, João success of a society lies in its ability child survival (up to the age of 5). Pessoa had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 16th human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), despite HCI growth This effort is materialized in the Human above the national average in recent Capital Index (HCI), which illustrates João Pessoa had an HCI of 0.592 years (fig. 3). João Pessoa’s per capita the productivity of a child born today in 2019. This means that the future income would be 68.8% higher if human when entering the job market if current productivity of a child born in this year capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in João Pessoa’s rank João Pessoa’s rank in Brazil in Brazil Paraíba 0.601 2,979th 44th 17 Macapá, Amapá Human Capital Index: 0.550 As part of the institutional agenda, health and education conditions are average, 55.0% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Macapá had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 27th human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), and has had a growth This effort is materialized in the Human below the national average in recent Capital Index (HCI), which illustrates Macapá had an HCI of 0.550 in 2019. years (fig. 3). Macapá’s per capita the productivity of a child born today This means that the future productivity income would be 81.7% higher if human when entering the job market if current of a child born in this year was, on capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Macapá’s rank Macapá’s rank in Brazil in Brazil Amapá 0.601 4,549th 4th 18 Maceió, Alagoas Human Capital Index: 0.573 As part of the institutional agenda, health and education conditions are 57.3% of its potential. This value is below the World Bank launched the Human maintained. It is a prospective indicator the national average of 0.601 (fig. 2) Capital Project, which emphasizes based on three components: (i) health, and is largely explained by educational the importance of investing in the measured by the adult survival rate performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) of In relation to other state capitals, Maceió success of a society lies in its ability the child survival (up to the age of 5). had insufficient performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 24th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), despite HCI growth above the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Maceió had an HCI of 0.573 in 2019. This Maceió’s per capita income would be the productivity of a child born today means that the future productivity of a 74.6% higher if human capital were fully when entering the job market if current child born in this year was, on average, promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Maceió’s rank Maceió’s rank in Brazil in Brazil Alagoas 0.601 3,764th 31st 19 Manaus, Amazonas Human Capital Index: 0.584 As part of the institutional agenda, health and education conditions are average, 58.4% of its potential. This value the World Bank launched the Human maintained. It is a prospective indicator is below the national average of 0.601 Capital Project, which emphasizes based on three components: (i) health, (fig. 2) and is largely explained by health the importance of investing in the measured by the adult survival rate performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, success of a society lies in its ability child survival (up to the age of 5). Manaus had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 23rd human talent. the conditions are for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Manaus had an HCI of 0.584 in 2019. (fig. 3). Manaus’ per capita income the productivity of a child born today This means that the future productivity would be 71.1% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Manaus’ rank Manaus’ rank in Brazil in Brazil Amazonas 0.601 3,314th 9th 20 Natal, Rio Grande do Norte Human Capital Index: 0.587 As part of the institutional agenda, health and education conditions are 58.7% of its potential. This value is below the World Bank launched the Human maintained. It is a prospective indicator the national average of 0.601 (fig. 2) Capital Project, which emphasizes based on three components: (i) health, and is largely explained by educational the importance of investing in the measured by the adult survival rate performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, Natal success of a society lies in its ability the child survival (up to the age of 5). had insufficient performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 20th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth below the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Natal had an HCI of 0.587 in 2019. This Natal’s per capita income would be the productivity of a child born today means that the future productivity of a 70.3% higher if human capital were fully when entering the job market if current child born in this year was, on average, promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Natal’s rank Natal’s rank in Brazil in Brazil Rio Grande do Norte 0.601 3,199th 26th 21 Palmas, Tocantins Human Capital Index: 0.623 As part of the institutional agenda, health and education conditions are average, 62.3% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, success of a society lies in its ability the child survival (up to the age of 5). Palmas had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 8th human talent. the conditions are for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth below This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Palmas had an HCI of 0.623 in 2019. (fig. 3). Palmas’ per capita income the productivity of a child born today This means that the future productivity would be 60.5% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Palmas’ rank Palmas’ rank in Brazil in Brazil Tocantins 0.601 1,531st 10th 22 Porto Alegre, Rio Grande do Sul Capital Index: 0.621 Human As part of the institutional agenda, health and education conditions are was, on average, 62.1% of its potential. the World Bank launched the Human maintained. It is a prospective indicator This value is above the national average Capital Project, which emphasizes based on three components: (i) health, of 0.601 (fig. 2) and is largely explained the importance of investing in the measured by the adult survival rate by educational and heath performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, Porto success of a society lies in its ability the child survival (up to the age of 5). Alegre had satisfactory performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 9th human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), and has had a growth This effort is materialized in the Human above the national average in recent Capital Index (HCI), which illustrates Porto Alegre had an HCI of 0.621 years (fig. 3). Porto Alegre’s per capita the productivity of a child born today in 2019. This means that the future income would be 61.1% higher if human when entering the job market if current productivity of a child born in this year capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Porto Alegre’s rank Porto Alegre’s rank in Brazil in Brazil Rio Grande do Sul 0.601 1,620th 266th 23 Porto Velho, Rondônia Human Capital Index: 0.607 As part of the institutional agenda, health and education conditions are was, on average, 60.7% of its potential. the World Bank launched the Human maintained. It is a prospective indicator This value is above the national average Capital Project, which emphasizes based on three components: (i) health, of 0.601 (fig. 2) and is largely explained the importance of investing in the measured by the adult survival rate by educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Porto success of a society lies in its ability child survival (up to the age of 5). Velho had satisfactory performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 13th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth above the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates Porto Velho had an HCI of 0.607 Porto Velho’s per capita income would the productivity of a child born today in 2019. This means that the future be 64.7% higher if human capital were when entering the job market if current productivity of a child born in this year fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Porto Velho’s rank Porto Velho’s rank in Brazil in Brazil Rondônia 0.601 2,273rd 27th 24 Recife, Pernambuco Capital Index: 0.619 Human As part of the institutional agenda, health and education conditions are 61.9% of its potential. This value is above the World Bank launched the Human maintained. It is a prospective indicator the national average of 0.601 (fig. 2) Capital Project, which emphasizes based on three components: (i) health, and is largely explained by educational the importance of investing in the measured by the adult survival rate performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Recife success of a society lies in its ability child survival (up to the age of 5). had satisfactory performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 10th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Recife had an HCI of 0.619 in 2019. This Recife’s per capita income would be the productivity of a child born today means that the future productivity of a 61.6% higher if human capital were fully when entering the job market if current child born in this year was, on average, promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Recife’s rank Recife’s rank in Brazil in Brazil Pernambuco 0.601 1,715rd 21st 25 Rio Branco, Acre Human Capital Index: 0.598 As part of the institutional agenda, health and education conditions are average, 59.8% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Rio success of a society lies in its ability child survival (up to the age of 5). Branco had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 15th human talent. the conditions are for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Rio Branco had an HCI of 0.598 in 2019. (fig. 3). Rio Branco’s per capita income the productivity of a child born today This means that the future productivity would be 67.2% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Rio Branco’s rank Rio Branco’s rank in Brazil in Brazil Acre 0.601 2,700th 4th 26 Rio de Janeiro, Rio de Janeiro Capital Index: 0.589 Human As part of the institutional agenda, health and education conditions are was, on average, 58.9% of its potential. the World Bank launched the Human maintained. It is a prospective indicator This value is below the national average Capital Project, which emphasizes based on three components: (i) health, of 0.601 (fig. 2) and is largely explained the importance of investing in the measured by the adult survival rate by educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) the In relation to other state capitals, Rio de success of a society lies in its ability child survival (up to the age of 5). Janeiro had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 18th human talent. the conditions are for human capital position among the 27 state capitals accumulation. in Brazil (fig. 4), and has had a growth This effort is materialized in the Human below the national average in recent Capital Index (HCI), which illustrates Rio de Janeiro had an HCI of 0.589 years (fig. 3). Rio de Janeiro’s per the productivity of a child born today in 2019. This means that the future capita income would be 69.7% higher if when entering the job market if current productivity of a child born in this year human capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Rio de Janeiro’s rank Rio de Janeiro’s rank in Brazil in Brazil Rio de Janeiro 0.601 3,102nd 49th 27 Salvador, Bahia Human Capital Index: 0.566 As part of the institutional agenda, health and education conditions are average, 56.6% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) of In relation to other state capitals, success of a society lies in its ability the child survival (up to the age of 5). Salvador had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 25th human talent. the conditions are for human capital position among other state capitals in accumulation. Brazil (fig. 4), despite HCI growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Salvador had an HCI of 0.566 in 2019. (fig. 3). Salvador’s per capita income the productivity of a child born today This means that the future productivity would be 76.7% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Salvador’s rank Salvador’s rank in Brazil in Brazil Bahia 0.601 3,998th 87th 28 São Luís, Maranhão Human Capital Index: 0.588 As part of the institutional agenda, health and education conditions are average, 58.8% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational and health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, São success of a society lies in its ability the child survival (up to the age of 5). Luís had insufficient performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 19th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), and has had a growth below the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates São Luís had an HCI of 0.588 in 2019. São Luís’ per capita income would be the productivity of a child born today This means that the future productivity 70.1% higher if human capital were fully when entering the job market if current of a child born in this year was, on promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in São Luís’ rank São Luís’ rank in Brazil in Brazil Maranhão 0.601 3,174th 9th 29 São Paulo, São Paulo Human Capital Index: 0.626 As part of the institutional agenda, health and education conditions are average, 62.6% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is above the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate educational and health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, São success of a society lies in its ability the child survival (up to the age of 5). Paulo had satisfactory performance. The to promote, allocate, and strengthen The closer the index is to 1, the better municipality occupies the 7th position human talent. the conditions are for human capital among the 27 state capitals in Brazil accumulation. (fig. 4), despite HCI growth below the This effort is materialized in the Human national average in recent years (fig. 3). Capital Index (HCI), which illustrates São Paulo had an HCI of 0.626 in 2019. São Paulo‘s per capita income would be the productivity of a child born today This means that the future productivity 59.8% higher if human capital were fully when entering the job market if current of a child born in this year was, on promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in São Paulo’s rank São Paulo’s rank in Brazil in Brazil São Paulo 0.601 1,411th 390th 30 Teresina, Piauí Human Capital Index: 0.600 As part of the institutional agenda, health and education conditions are average, 60.0% of its potential. This the World Bank launched the Human maintained. It is a prospective indicator value is below the national average of Capital Project, which emphasizes based on three components: (i) health, 0.601 (fig. 2) and is largely explained by the importance of investing in the measured by the adult survival rate health performance. fundamental skills of individuals. The and the child stunting rate; (ii) quantity message of this project is that the and quality of education; and (iii) In relation to other state capitals, success of a society lies in its ability the child survival (up to the age of 5). Teresina had insufficient performance. to promote, allocate, and strengthen The closer the index is to 1, the better The municipality occupies the 14th human talent. the conditions are for human capital position among the 27 state capitals in accumulation. Brazil (fig. 4), despite HCI growth above This effort is materialized in the Human the national average in recent years Capital Index (HCI), which illustrates Teresina had an HCI of 0.600 in 2019. (fig. 3). Teresina’s per capita income the productivity of a child born today This means that the future productivity would be 66.8% higher if human capital when entering the job market if current of a child born in this year was, on were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Teresina’s rank Teresina’s rank in Brazil in Brazil Piauí 0.601 2,615th 27th 31 Cocal dos Alves, Piauí Human Capital Index: 0.740 As part of the institutional agenda, education; and (iii) the child survival (up of a child born this year is, on average, the World Bank launched the Human to the age of 5). The closer the index is to 74% of its potential. This value is above Capital Project, which emphasizes 1, the better the conditions are for human the national average of 0.601 (fig. 2) and the importance of investing in the capital accumulation. is largely explained by educational and fundamental skills of individuals. health performance. The municipality of Cocal dos Alves shows This effort is materialized in the Human a strong performance in the Human Compared to the other cities in the state, Capital Index (HCI), which illustrates the Capital Index, in comparison to the state Cocal dos Alves occupies the 1st position productivity of a child born today when average. As such, it was chosen to in Piauí’s ranking (fig. 4). Moreover, in entering the job market if current health participate in this research. recent years the municipality’s HCI has and education conditions are maintained. significantly increased (fig. 3): from 2007 It is a prospective indicator based on Cocal dos Alves stands out for having to 2019, there was an increase of 29.9% three components: (i) health, measured one of the highest HCIs in Brazil. The in its HCI. The municipality’s per capita by the adult survival rate and the child municipality had an HCI of 0.740 in 2019. income would be 35.1% higher if human stunting rate; (ii) quantity and quality of This means that the future productivity capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Cocal dos Alves’ rank Cocal dos Alves’ rank Brazil in Brazil in Piauí 0.601 3rd 1st 32 Vitória, Espírito Santo Capital Index: 0.652 Human As part of the institutional agenda, the and education conditions are maintained. 65.2% of its potential. This value is above World Bank launched the Human Capital It is a prospective indicator based on the national average of 0.601 (fig. 2) and Project, which emphasizes the importance three components: (i) health, measured is largely explained by educational and of investing in the fundamental skills of by the adult survival rate and the child health performance. individuals. The message of this project stunting rate; (ii) quantity and quality of is that the success of a society lies in its education; and (iii) the child survival (up In relation to other state capitals, Vitória ability to promote, allocate and strengthen to the age of 5). The closer the index is to had satisfactory performance. The human talent. 1, the better the conditions are for human municipality occupies the 1st position in capital accumulation. the rank of capitals in Brazil (fig. 4) and This effort is materialized in the Human has had a growth above the national Capital Index (HCI) which illustrates the Vitória had an HCI of 0.652 in 2019. This average in recent years (fig. 3). Vitória’s productivity of a child born today when means that the future productivity of a per capita income would be 53.3% higher entering the job market if current health child born in this year was, on average, if human capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Vitória’s rank Vitória’s rank in Brazil in Brazil Espírito Santo 0.601 469th 14th 33 Guanambi, Bahia Human Capital Index: 0.575 As part of the institutional agenda, stunting rate; (ii) quantity and quality of a child born this year is, on average, 57.5% the World Bank launched the Human education; and (iii) the child survival (up of its potential. This value is below the Capital Project, which emphasizes to the age of 5). The closer the index is to national average of 0.601 (fig. 2) and is the importance of investing in the 1, the better the conditions are for human largely explained by health performance. fundamental skills of individuals. capital accumulation. Compared to the other cities in the state, This effort is materialized in the Human The municipality of Guanambi shows Guanambi occupies the 56th position Capital Index (HCI), which illustrates the a strong performance in the Human in Bahia’s ranking (fig. 4). Moreover, in productivity of a child born today when Capital Index, in comparison to the state recent years the municipality’s HCI has entering the job market if current health average. As such, it was chosen to significantly increased (fig. 3): from 2007 and education conditions are maintained. participate in this research. to 2019, there was an increase of 15.3% It is a prospective indicator based on in its HCI. The municipality’s per capita three components: (i) health, measured Guanambi had an HCI of 0.575 in 2019. income would be 73.8% higher if human by the adult survival rate and the child This means that the future productivity of capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Guanambi’s rank Guanambi’s rank Brazil in Brazil in Bahia 0.601 3,673rd 56th 34 Ibimirim, Pernambuco Capital Index: 0.661 Human As part of the institutional agenda, stunting rate; (ii) quantity and quality of 66.1% of its potential. This value is above the World Bank launched the Human education; and (iii) the child survival (up the national average of 0.601 (fig. 2) Capital Project, which emphasizes to the age of 5). The closer the index is to and is largely explained by educational the importance of investing in the 1, the better the conditions are for human performance. fundamental skills of individuals. capital accumulation. Compared to the other cities in the state, This effort is materialized in the Human The municipality of Ibimirim shows an Ibimirim occupies the 4th position in Capital Index (HCI) which illustrates the accentuated growth in the HCI over the Pernambuco’s ranking (fig. 4). Ibimirim productivity of a child born today when years, and for this reason was chosen to stands out for the accentuated growth entering the job market if current health participate in this research. of its HCI (fig. 3): from 2007 to 2019, and education conditions are maintained. there was an increase of 45.0% in the It is a prospective indicator based on Ibimirim had an HCI of 0.661 in 2019. municipality’s HCI. The municipality’s per three components: (i) health, measured This means that the future productivity capita income would be 51.3% higher if by the adult survival rate and the child of a child born this year is, on average, human capital were fully promoted. Figure 1 Figure 2 Brazil Human Capital Review Figure 3 Figure 4 Average in Ibimirim’s rank Ibimirim’s rank in Brazil in Brazil Pernambuco 0.601 292nd 4th 35 Appendix II Are SAEB and PISA comparable? The Sistema Nacional de Avaliação da Educação Básica (SAEB) is a national learning assessment in Brazil. SAEB is applied every two years and administered to students at the end of each education cycle: grade five (end of primary school), grade nine (end of lower secondary school), and grade 12 (end of upper secondary school) for private and public schools. From 2015 onwards, SAEB has been applied in every public school with at least 20 students, for a sample size of four million students in 2015. To gain geographic granularity and the possibility of disaggregating HCI data, SAEB is a better option than the Programme for International Student Assessment (PISA). PISA is a similarly large-scale education assessment that compares students’ knowledge and ability across countries. However, different from SAEB, PISA is applied every three years worldwide and evaluates science in addition to languages and mathematics. The sample is composed of 15-year-old students in 7th grade. In the 2015 Brazilian edition, the sample covers 841 schools and 23,141 students (OECD, 2016). There are some critical differences between PISA and SAEB. First, the targeted population differs greatly. The typical profile of PISA participants is a high school student in a state-administered school, typically located in an urban area (OECD, 2016). For this reason, PISA fails to be a representative for northern regions in Brazil, which are characteristically more rural. SAEB successfully represents these regions. Despite the challenge of comparing these assessments, SAEB and PISA have some points in common. Comparing the pedagogical matrix from SAEB (2015) in 9th grade and PISA (2015), both assessments evaluate students’ capacity to understand a text beyond decoding. PISA goes further by considering the capacity of students to reflect and analyze a situation critically. In mathematics, SAEB and PISA assess a student’s ability to employ mathematical reasoning and create mathematical situations. For these reasons, when analyzing the results at the state-level in 2015, PISA correlates strongly with SAEB, both in mathematics and in reading (correlation of 0.83) (OECD, 2016). Brazil Human Capital Review Brazil Human Capital Review 38 33 Appendix III How is the HCI-SES calculated? The proxy for socioeconomic status is tertiary education completion. Maternal educational level is used for the HCI components referring to childhood education and child survival. For the adult survival rate, an individual’s educational attainment is used. Each component is calculated at the state level. Different strategies are used to construct the different parts of the index, as explained below: Child Survival: The primary data source for this component is the SINASC/Datasus database. The database includes maternal educational level for deaths between 0-1 years old. The share of deaths according to the SES is applied to all deaths between 0-4 years old. For the population data, the percentage of individuals older or equal to 25 years old who completed higher education (from PNAD 2019) is divided by the population according to this share. Child survival is calculated child using the same HCI methodology for each state and each socioeconomic group. Adult survival rate: The primary data source for adult survival rate is the SIM/Datasus database, which has individual information on educational attainment. To calculate this component, the deaths that occurred in each five-year age group (15-19 years old, 20-24 years old, 25-29 years old, and so on) are used. Because individuals younger than 25 years old may still be studying and there might not be information on maternal educational level, the share of individuals in the 25- to 29-year-old group who have completed higher education is added to these two age groups. For the population, the same strategy as the Child Survival component is used. Adult mortality is calculated using the same HCI methodology for each state and socioeconomic group. Quality of education: The primary data source for HLO is SAEB, a national assessment system that has information on maternal educational level. When an individual’s mother has completed tertiary school, they are considered high-SES; when an individual’s mother has not completed tertiary school, they are considered low-SES. The same HCI methodology for each state and socioeconomic group is used to calculate HLO. Quantity of education: The primary data source for EYS is the School Census. However, that database does not include the mother’s education. Therefore, EYS is estimated based on the SES-HLO. Since HLO is directly correlated with EYS, the correlation from 2007 to 2019 is estimated at the state level, after which the SES-EYS is predicted based on the SES-HLO. Brazil Human Capital Review Brazil Human Capital Review 40 35 Appendix IV Human Capital Accumulation Amidst the COVID-19 Pandemic In this Appendix, we describe in detail how we estimate the impact of COVID-19 on the components of HCI. We present the adult survival rate and child survival together, because we rely on the same methodology; we have estimated the number of deaths per age group. Next, we show the methods for calculating the impact on Not Stunting. Lastly, we explain how we calculate the effects on the Education component: Harmonized Learning Outcomes and Expected Years of School. Adult Survival Rate and Child Survival To estimate the impact of COVID-19 on Adult Survival Rate and Child Survival, we estimate the number of deaths per age group for 2021. The estimated number of deaths for each age group is given by three components: i) projected deaths; ii) COVID-19 excess deaths; iii) normal non-COVID-19 deaths (deaths that would have occurred anyway, without COVID-19). We represent this estimation using the following equation: Below we describe each component. Projected deaths The projected deaths are the expected number of deaths in a scenario without COVID-19, calculated using age-specific mortality projections. where the estimated mortality rate is given by: where mortality rate is defined as: COVID-19 excess deaths COVID-19 excess deaths are the number of direct and indirect deaths due to COVID-19, given by: Direct COVID-19 deaths Brazil Human Capital Review Brazil Human Capital Review Direct COVID-19 deaths are those from 1 January 2021 to 31 December 2021 recorded in the Severe Acute Respiratory Syndrome (SRAG) database1 provided by the Health Ministry. This data includes all hospitalizations due to respiratory diseases, including COVID-19. As such, if a death by COVID-19 occurred 1 Available here: https://opendatasus.saude.gov.br/dataset/bd-srag-2021 42 37 without hospitalization, it does not appear in the database, leading to a sub-estimation.2 For example, using this source, the total COVID-19 deaths in 2021 is 358,100, and if we use the data from the COVID-19, which come from state Health Secretaries,3 the number of COVID-19 deaths in 2021 is 413,400, 15 percent higher (reference date 7 November 2021). However, in this last source, we cannot access deaths by age group, which is essential to calculating the adult survival rate and the child survival rate. Below, we describe two other possible sources of information about COVID-19 direct deaths, and we explain why they are not well-suited for our analysis. An alternative data source for COVID-19 direct deaths is preliminary results in the Mortality System Information,4 which we use to calculate mortality for other years. This database registers all deaths, both in and out of hospitals. Also, this database includes better information about race, schooling, and occupation than the SRAG database. However, this data is only available until May 2021, thus, we did not use this data for the HCI simulations.5 We could have also used the Civil Registry database,6 which records all registered deaths independent of hospitalization. However, data is unavailable for the five years intervals, which we need to calculate this disaggregation to determine the adult survival and child survival rates. Non-direct deaths rate Some deaths are indirectly related to COVID-19. For example, deaths caused by the overloading of the healthcare system or the postponing of health treatments. Also, there are sub-notifications of COVID-19 deaths, which is not exactly indirect deaths, but deaths that are not counted in the direct deaths described in the last subsection. These non-direct deaths are not observable. As such, we need to estimate the number of non-direct deaths. For that, we use the excess deaths estimates of a different source. Put simply, the idea of excess deaths is to project the deaths in 2020 and 2021 through a time series where there was no special event. This is called the expected deaths. The excess deaths are the observed deaths during the pandemic (observable) less than the expected deaths. This remainder are the deaths directly and indirectly attributable to COVID-19. The better the model for predicting expected deaths, the better the approximation of excess deaths from the non-observed reality. We have three different sources for COVID-19 excess deaths for Brazil: the National Council of Health Secretaries (CONASS),7 the Institute for Health Metrics and Evaluation (IHME),8 and The Economist.9 The first two have excess deaths calculated by state, and the latter only at the national level. These data sources use different expected deaths models.10 For our estimations, we used IHME excess deaths, since it it is available by state and has lower bound, point, and upper bound estimates, producing three different scenarios (which we call optimistic, realistic, and pessimistic). CONASS has only the point estimate. Why did we choose not to use only excess mortality? We needed the deaths per age group, and excess 2 To extract only the deaths of the database, we filtered: i) case evolution (evolução caso), classified as deaths or deaths by other factors; and ii) final classification (classificação final do caso) as Severe Acute Respiratory Syndrome by COVID-19. 3 Available here: https://covid.saude.gov.br/ 4 Available here: https://opendatasus.saude.gov.br/dataset/sistema-de-informacao-sobre-mortalidade/resource/da17c5f6-aa89- 4e7d-b7e2-ec4b77a5dc31 5 We used Mortality System Information data for other purposes, e.g., when we count deaths by race and schooling level. Brazil Human Capital Review Brazil Human Capital Review 6 Available here: https://transparencia.registrocivil.org.br/especial-covid 7 Available here: https://www.conass.org.br/indicadores-de-obitos-por-causas-naturais/ 8 Available here: http://www.healthdata.org/covid/data-downloads 9 Available here: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-tracker 10 CONASS uses exponential regression; IHME uses spline regression; and The Economist uses an algorithm to train an ordinary least squares model. 38 43 mortality estimates are unavailable by age group, so we could not use it directly in our estimations. Since we have the projected deaths and the direct deaths per age group, we created a proxy11 for non-direct deaths rate using the excess deaths estimations, given by: We used this proxy for all age groups. The basic assumption is the rate of indirect deaths is the same across age groups. Normal Non-COVID-19 deaths This component accounts for the deaths that would have occurred anyway, without COVID-19, among those who died from COVID-19. We estimate: Note that all the components are calculated separately for each age group (except the percentage of indirect deaths). Stunting The subnational level estimates are obtained from the Institute of Health Metrics and Evaluation (IHME on an annual basis between 2007 and 2017. National country-level estimates are available from the UNICEF/ WHO/World Bank Group joint child malnutrition estimates (JME) on an annual basis between 2007 and 2020. For Brazil, the latest available survey datapoint for malnutrition is dated from 2006.12 National level estimates are produced by JME, and according to these national estimates, stunting is estimated to remain constant, at around 6.1 percent between 2014 and 2020.13 Because of that, and since the JME and IHME stunting estimates are very much correlated across countries, we have used the estimates from 2017 for 2019. The figure below shows the correlation between JME and IHME estimations, resulting in a correlation of 0.957. We used the national estimates from the Goalkeepers Report (Bill & Melinda Gates Foundation) to estimate the impact on stunting among children under age five in Brazil. According to their estimates, we have the following: Brazil Human Capital Review Brazil Human Capital Review 11 Since this rate is non-observable, we call it a proxy. 12 From Pesquisa nacional de demografia e saúde da criança e da mulher - PNDS 2006. Relatório da pesquisa. Sao Paulo: CEBRAP, 2008 13 United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: key findings of the 2021 edition of the joint child malnutrition estimates. New York: United Nations Children’s Fund; 2021. Licence: CC BY-NC-SA 3.0 IGO. 44 39 (a) Worst scenario: stunting would increase by 2.44 percent in comparison to the scenario without COVID-19. (b) Reference scenario: stunting would increase by 0.59 percent in comparison to the scenario without COVID-19. (c) Best scenario: stunting would decrease by 1.20 percent in comparison to the scenario without COVID-19. To simulate the Not Stunting component of HCI, we considered 2017 rates for the scenario without COVID-19. This is because national estimates indicate no variation in stunting in Brazil from 2014 to 2020 in a scenario withoutCOVID-19.14 Harmonized Learning Outcomes To calculate harmonized learning outcomes, we need SAEB scores for Mathematics and Portuguese. Since we do not have that information for 2021, we assume that the scores would be the same as 2019, but discounted for the as learning loss (ll) rate. In that sense, the Mathematics and Portuguese score for ninth grade in 2021 is estimated as: Where s stands for state and the SAEB score refers to the average of the ninth grade. The learning loss rate is estimated in three ways, giving us three scenarios. All three depend on another variable: the number of days of school closure. Number of days closed The number of days closed is retrieved from the INEP questionnaire about school responses during COVID- 19.15 INEP has assessed how many days face-to-face activities in each school were suspended during the pandemic. The questionnaire was administered between February and May 2021. Since most states opened schools either at the end of the first semester or at the beginning of the second semester of 2021, the average number collected by INEP is likely a good measure of the length of school closure in each state. Because we are interested in ninth grade scores, we use the average days in primary and lower secondary schools. Since INEP collected the data at school level, they published the statistics of the distribution of school closure length in each state. For the optimistic scenario, we use the number of days corresponding to the first quartile of the distribution. For the realistic and pessimistic scenarios, we use the average number of days. The number of days closed functions as a weight to measure the learning loss rate. Learning loss rate The learning loss rate is calculated in the following three scenarios. The learning loss rate in SP is described in Figure 6.4.c and Figure 6.4.d: -5.34 percent in Mathematics and -4.58 percent in Portuguese. • Optimistic: Learning loss rate in state s and subject p (Mathematics or Portuguese): • Realistic: Learning loss rate in state s and subject p (Mathematics or Portuguese): Brazil Human Capital Review Brazil Human Capital Review 14 According to estimates from joint child malnutrition (JME) - UNICEF/WHO/World Bank 15 https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/censo-escolar/pesquisas-suplementa- res/pesquisa-covid-19 40 45 • Pessimistic Learning loss rate in state s and subject p (Mathematics or Portuguese): The parameter 0.004 represents that for each day more (less) that the schools of state s were closed compared to those of the state of São Paulo, the learning loss rate increases (decreases) by 0.4 percent. The parameter was chosen by: i) calculating the learning loss rate in the optimistic and realistic scenarios; ii) choosing the minimum parameter that generates a learning loss rate more than or equal to the realistic scenario in all states. Alternative learning loss rate At the beginning of March 2020, the São Paulo education secretary released the results of SARESP, the state standard assessment. This assessment reached 83.1 percent of ninth graders in the state schools of São Paulo. The loss rate was lower than the sample study used in the previous estimation. For ninth grade, the loss was -5.05 percent in Mathematics and -3.33 percent in Portuguese. Because of that difference, and since this rate is an important parameter for the simulations, we have estimated the scenarios for the harmonized learning outcomes using this lower rate. HARMONIZED LEARNING OUTCOMES- RESULTS FOR BRAZIL Main estimation Alternative estimation Optimistic 0.630 0.633 Realistic 0.628 0.632 Pessimistic 0.628 0.631 Expected Years of School The expected years of school was estimated through a panel regression. It was estimated using a state and year fixed effects regression, as follows: Where s is the state and t is the dummy for time (2007-2019). The parameters in the regression were used to predict the expected years of school for 2021, given by: The regression results are presented in the following table: RESULTS: FIXED EFFECTS REGRESSION - EXPECTED YEARS OF SCHOOL Brazil Human Capital Review Brazil Human Capital Review EYS HLO 7.1285** (3.0235) year=2009 0.1296** (0.0523) 46 41 RESULTS: FIXED EFFECTS REGRESSION - EXPECTED YEARS OF SCHOOL (CONTINUED) year=2011 0.2479** (0.1000) year=2013 0.5396*** (0.0685) year=2015 0.4779*** (0.1095) year=2017 0.6328*** (0.1234) year=2019 0.7763*** (0.1512) Constant 5.4116*** (1.8233) R-Squared 0.816 Observations 189 F-statistic 36.26 Robust Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01 Brazil Human Capital Review Brazil Human Capital Review 42 47 Brazil Human Capital Review 48 Appendix V Drivers of Human Capital Formation Variable Definitions Fiscal variables For all expense variables, data from FINBRA (Finanças Municipais)16 on expenses by function is used. For 2013 and onwards, “paid expenses” are used, which are the expenses that the municipality definitely paid. Before 2013, there were no differences in expense type, so what accounts for “expenses” is used. Basic health spending per capita The variable Despesa Atenção Básica is used and divided it by the projected population. • Health spending per capita The variable Despesa Saúde is used, which corresponds to total health expenses, and divided it by the projected population. • Sanitation spending per capita The variable Despesa Saneamento is used, which corresponds to total sanitation expenses, and divided it by the projected population. • Education spending per student The variable Despesa Ensino Fundamental is used, which corresponds to expenses for grades one to nine, and divided it by the number of enrollments in first to ninth grade (from the School Census). Bolsa Família • Number of families receiving Bolsa Família This variable is constructed using Ministerio do Desenvolvimento Social database. It refers to the average number of families receiving PBF in a given municipality and in each year. The raw database was available for each month and each municipality, and calculated the average for each year and each municipality. Education-specific variables • School Infrastructure Index The following variables are used from School Census 17: sports court, library, computer lab, science lab, existing plumbing, existing energy, existing water. Only the standard basic schools (Ensino Regular Fundamental e Médio) are selected from first to twelfth grade. The following variables are changed: a) sports court (for 2011 and 2013, the variable was split in two: sports court covered and sports court uncovered, so the variable ‘sports court’ is constructed from these two); b) existing plumbing, existing energy, existing water: the original variables were in the negative form (non-existence plumbing, water, and energy), so they are recoded to the positive form. Brazil Human Capital Review The index using Multiple Component Analysis is generated. The predicted score was standardized to vary from 0 to 1. 16 Data available at: https://siconfi.tesouro.gov.br/ 17 Data available at: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-escolar 50 44 • Number of municipal schools This variable is constructed using the School Census. It is the sum of the number of municipal schools in each municipality each year. Only the standard basic schools (Ensino Regular Fundamental e Médio) were selected (from first to twelfth grade). • Dummy has private school This variable is constructed using the School Census. It is a dummy variable that assumes a value of 1 if there is any private school in the municipality in each year, and 0 otherwise. Only the standard primary schools (Ensino Regular Fundamental e Médio) were selected (from first to twelfth grade). • % of white students This variable is constructed using SAEB/Prova Brasil data. Only ninth grade students are considered. The sum of ninth grade students who self declared that they are white are calculated, divided by the number of students who answered the question about race, and then multiplied by 100. • % sudents living with mother & father This variable is constructed using SAEB/Prova Brasil data.18 Only ninth grade students are considered. The sum of ninth grade students who declared that they live with their mother and the sum who declared that they live with father are calculated, divided by the number of students who answered both questions, and then multiplied by 100. • % parents with tertiary education This variable is constructed using SAEB/Prova Brasil data. We considered only ninth grade students. The sum of ninth grade students who declared their mother or father completed tertiary school is calculated, divided by the number of students who answered either of the questions about the parents’ level of education, and then multiplied by 100. • % HS full-time school This variable is constructed using the School Census - Classes database. Only high school classes (variable TP ETAPA ENSINO from 25 to 38) and only classes from municipal and state schools are selected. Full- time classes are defined are those with more or equal to 420 minutes of duration during a school-day.19 • Number of teenage pregnancies We constructed this variable using SINASC-Datasus.20 We counted the number of births that the mother had when they were less than or equal to 17 years old. We multiplied this number by 100,000 and divided it by the projected population for each municipality. Hospitalization variables For all variables from hospitalizations, microdata from SIH-Datasus is used.21 The municipality of residence, the date of birth, the date of hospitalization, and the ICD code22 of the primary diagnostic is used. 18 Data available at: https://siconfi.tesouro.gov.br/. Brazil Human Capital Review 19 The standard school in Brazil is part-time, where the length of a school-day ranging from 4 to 5 hours. Full-time school is usually defined as equal to or more than 7 hours per day (https://inepdata.inep.gov.br/analyticsRes/res/pne/ficha_tecnica/FICHAS%20 TECNICAS_06.pdf). 20 Data available at: https://datasus.saude.gov.br/ 21 Data available at: https://datasus.saude.gov.br/. 22 International Classification of Diseases 51 45 • Malnutrition The number of hospitalizations due to malnutrition (ICD codes: from E40 to E46) is used, divided by the 23 projected population from 0 to 4 years old, multiplied by 100,000. • Sanitation-related The number of hospitalizations due to lack of sanitation divided by the projected population from 0 to 4 years old, multiplied by 100,000. These hospitalizations include fecal-oral transmissions diseases, vector- borne and water-borne diseases, diseases associated with hygiene, and geohelminths and taeniasi. ICH codes: diarrhoeas (A09), typhoid fever (A25), hepatitis A (B15), dengue (A90), yellow fever (A95), leishmaniasis (B55), cutaneous leishmaniasis (B55.9), visceral leishmaniasis (B55.0), lymphatic filariasis (B74), malaria (B50), Chagas’ disease (B57), leptospirosis (A27), schistosomiasis (B65), leptospirosis (A27) schistosomiasis (B65), eye disorders (Z13.5), trachoma (H54.3), conjunctivitis (H10), skin diseases (B08) and superficial mycosis (B36), helminthiasis (B82.0), and taeniasis (B83.9).24 • Asthma The number of hospitalizations due to asthma (ICD codes: J45 and J46) is used25 divided by the projected population from 0 to 4 years old, multiplied by 100,000. • Hypertension The number of hospitalizations for which the direct cause was hypertension (ICD code: I10) is used26 divided by the projected population from 15 to 59 years old, multiplied by 100,000. • Obesity The number of hospitalizations for which the direct cause was obesity (ICD code: E66) is used27 divided by the projected population from 15 to 59 years old, multiplied by 100,000. • Diabetes The number of hospitalizations for which the direct cause was Diabetes mellitus (ICD codes: E10-E14) are used28 divided by the projected population from 15 to 59 years old, and multiplied by 100,000. • Alcohol Hospitalizations The number of hospitalizations related to the alcohol consumption (ICD codes: K700, K704, K709, F100, F101, F102, F103, F104, F105, F106, F107, F108, F109, G312,G621, X450-X459, X650-X659, Y150-Y159, Y900-Y909,Y910-Y919, E244, G721, I426, K292, K852, K860, O354, P043, Q860, R780) is used29 divided by the projected population from 15 to 59 years old, multiplied by 100,000. 23 Definition used in (Otero et al., 2002). 24 Definition used in . (Siqueira, et al., 2017). Brazil Human Capital Review 25 Definition used in (Comaru et al 2016). 26 Definition used in (Siqueira, et al., 2017). 27 Definition used in (Siqueira, et al., 2017). 28 Definition used in (Quarti Machado Rosa et al., 2018) 29 Definition used in (Garcia et al., 2015). 52 46 Hospital Structure These variables are constructed using CNES-Datasus.30 Since the database is available for each month- year, the average of the year is calculated. • X-ray The average number of x-rays in a given year and municipality is used, divided by the projected population, multiplied by 100,000. • Hospital Beds The average number of hospital beds for hospitalization in a given year and municipality is used, divided by the projected population, multiplied by 100,000. • Family Health Strategy physicians The average number of physicians Médicos de estratégia Saúde da Família (Family Health Strategy) in a given year and municipality are used, divided by the projected population, multiplied by 100,000. In 2007, only information for August to December is available, so the average for that year is used. • Urgency stations The average number of SUS establishments that serve on an urgency basis are used31 in a given year and municipality, divided by the projected population, multiplied by 100,000. Birth related variables For all variables, SINASC-Datasus is used. 32 • % of mother with no school All births in a given year and municipality where the mother has less than or equal to three years of schooling are counted. This number is divided by the total number of births and multiplied by 100. • % of poor birth outcomes All births in a given year and municipality in which the APGAR score in 5 minutes is lower than 7 are counted (Thorngren-Jerneck, Herbst, 2001). This number is divided by the total number of births and multiplied by 100. • % of cesarean births All births in a given year and a municipality delivered by cesarean are counted. This number is divided by the total number of births and multiplied by 100. • % insufficient prenatal care All births in a given year and municipality in which the mother had fewer than four prenatal appointments Brazil Human Capital Review are counted. This number is divided by the total number of births and multiplied by 100. 30 We extracted the data directly from Tabnet (http://tabnet.datasus.gov.br). 31 Available here http://tabnet.datasus.gov.br/cgi/deftohtm.exe?cnes/cnv/aturgbr.def. 32 Data available at: https://datasus.saude.gov.br/. 53 47 Other variables • Adult Sex Ratio The sex ratio using population estimates is constructed.33 Only the population from 15 to 59 years old is taken. The men’s population is divided by the women’s population and multiplied the result by 100. • Homicides These variables are constructed using SIM-Datasus.34 It corresponds to the deaths from aggression (ICD code: X85 to Y09).35 Only the homicides that occurred when the victim was between the ages of 15 and 59 are considered. That number is divided by the projected population and multiplied it by 100,000. • % schools with plumbing Data from the School Census is used.36 All schools in a given year are counted and municipality that have a public sewage system. The total number of schools is divided and multiplied by 100,000. Descriptive statistics TABLE V.1 Descriptive statistics: Dependent variables 2007 2019 Variables Mean CV Mean CV Expected Years of School 9.83 0.63 11.01 0.27 Harmonized Learning Outcomes 0.60 0.20 0.65 0.20 HCI - Education Component 0.62 0.32 0.68 0.20 Adult Survival Rate 0.85 0.15 0.87 0.16 Not Stunting 0.89 0.35 0.91 0.25 HCI - Health component 0.88 0.13 0.89 0.15 HCI - Child Survival component 0.98 0.03 0.99 0.02 HCI 0.53 0.45 0.60 0.34 Mean and coefficient of variation (CV) are weighted using population estimates as weight. Brazil Human Capital Review 33 Available here http://tabnet.datasus.gov.br/cgi/deftohtm.exe?popsvs/cnv/popbr.def. 34 We extracted the data directly from Tabnet (http://tabnet.datasus.gov.br). 35 Definition used in https://www.conass.org.br/guiainformacao/notas_tecnicas/NT6-MORTALIDADE-HOMICIDIOS.pdf. 36 Data available at: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-escolar 54 48 TABLE V.2 Descriptive statistics: Child Survival 2007 2019 Variables Mean CV Mean CV Socioeconomic % of mothers with no school 9.88 4.72 1.95 5.52 Proxy poverty (Families in PBF) 5,810.51 5.04 6,558.79 3.68 Public Expenditure Basic health spending per cap.² 200.24 5.25 270.72 3.16 Sanitation spending per cap² 63.38 11.02 71.20 8.50 Hospital Structure No. X-ray¹ 25.85 4.84 40.19 6.22 Physicians ESF¹ 8.70 4.05 12.61 2.65 Hospitalizations % schools with plumbing 57.43 5.36 67.06 3.57 Hosp. malnutrition¹ 37.74 6.85 26.82 7.35 Hosp. sanitation-related¹ 587.67 6.12 339.40 6.49 Hosp. asthma¹ 729.35 4.70 221.87 6.57 Birth & Pre-birth conditions % births poor health 1.42 2.06 1.03 1.24 % insufficient prenatal 9.23 3.33 6.50 3.60 Mean and coefficient of variation (CV) are weighted using population estimates as weight. ¹Variables per 100,000 inhabitants ²Deflated using IPCA (date of reference: December 2019) TABLE V.3 Descriptive statistics: Not Stunting 2007 2019 Variables Mean CV Mean CV Socioeconomic Brazil Human Capital Review % of mothers with no school 9.88 4.72 1.95 5.52 Proxy poverty (Families in PBF) 5,810.51 5.04 6,558.79 3.68 Public Expenditure Basic health spending per cap.² 200.24 5.25 270.72 3.16 55 49 TABLE V.3 Descriptive statistics: Not Stunting (continued) Hospital Structure Physicians ESF¹ 8.70 4.05 12.61 2.65 Hospitalizations Hosp. malnutrition¹ 37.74 6.85 26.82 7.35 Birth & Pre-birth conditions % births poor health 1.42 2.06 1.03 1.24 % insufficient prenatal 9.23 3.33 6.50 3.60 Mean and coefficient of variation (CV) are weighted using population estimates as weight. ¹ Variables per 100,000 inhabitants. ² Deflated using IPCA (date of reference: December 2019) TABLE V.4 Descriptive statistics: Education 2007 2019 Variables Mean CV Mean CV Socioeconomic % white students 34.74 2.10 30.13 2.18 % students living with mother & father 61.73 0.67 68.64 0.35 % parents with tertiary education 10.38 1.60 17.02 1.82 Proxy poverty (Families in PBF) 5,810.51 5.04 6,558.79 3.68 Expenses Education spending per student1 2,416.84 2.60 4,015.63 2.56 School Structure School infrastructure index 0.33 2.37 0.30 1.63 No. municipal schools 116.10 32.19 115.95 33.11 Dummy has private school = 1 0.88 1.15 0.90 0.96 % full-time HS classes2 0.35 15.41 10.15 5.63 Others No. teenage pregnancy3 154.75 2.95 92.69 3.43 Mean and coefficient of variation (CV) are weighted using population estimates as weight. Brazil Human Capital Review 1 Deflated using IPCA (date of reference: December 2019) 2 HS: high school 3 Variables per 100,000 inhabitants 56 50 2007 2019 Variables Mean CV Mean CV Socioeconomic Proxy poverty (Families in PBF) 5,810.51 5.04 6,558.79 3.68 % parents with tertiary education 10.38 1.60 17.02 1.82 Expenses Health spending per cap. 492.23 2.37 737.90 2.21 Hospital Structure Hospital beds¹ 245.41 2.75 206.05 2.86 No. urgency stations¹ 3.69 5.72 4.92 4.14 Hospitalizations Hosp. diabetes¹ 45.70 3.77 42.61 3.14 Hosp. hypertension¹ 45.68 3.48 16.35 4.62 Hosp. obesity¹ 4.22 7.80 11.49 9.47 Hosp. alcohol¹ 76.98 5.44 34.89 4.71 Others Adult sex ratio: 100¹(men/women) 96.58 0.54 97.22 0.43 Homicides¹ 36.00 4.12 29.61 5.90 Mean and coefficient of variation (CV) are weighted using population estimates as weight. ¹ Variables per 100,000 inhabitants ² Deflated using IPCA (date of reference: December 2019) Regression tables TABLE V.6 Results: Fixed Effects Regression - Harmonized Learning Outcomes & Expected Years of School (1) (2) Log(HLO) Log(EYS) log(school infrastructure index) 0.0030 0.0165*** (0.0129) (0.0030) log(no. municipal schools) -0.0097*** -0.0005 (0.0032) (0.0023) Dummy has private school = 1 0.0026 0.0005 Brazil Human Capital Review (0.0017) (0.0024) log(education spending per student) 0.0008 0.0009*** (0.0007) (0.0002) log(no. families PBF¹) -0.0041 0.0028 57 51 TABLE V.6 Results: Fixed Effects Regression - Harmonized Learning Outcomes & Expected Years of School (continued) (0.0039) (0.0032) % white students 0.0004*** -0.0000 (0.0001) (0.0001) % parents with tertiary education 0.0016*** 0.0001 (0.0002) (0.0001) % students living w/ parents 0.0001 0.0002** (0.0001) (0.0001) % of full-time school 0.0002** 0.0002*** (0.0001) (0.0000) log(no. teenage pregnancy¹) 0.0011 -0.0038*** (0.0008) (0.0009) Constant -0.3611*** 2.4295*** (0.0316) (0.0240) R-Squared 0.431 0.694 Observations 37042 37042 F-statistic 373.89 382.14 All regressions are with municipality, year, and state-trend fixed effects Robust Standard errors in parentheses ¹ Variables per 100,000 inhabitants * p<0.10, ** p<0.05, *** p<0.01 TABLE V.7 Results: Fixed Effects Regression - Adult Survival Rate (1) Log(Adult Survival Rate) log(no. urgency stations¹) 0.0015*** (0.0006) log(hosp. beds¹) 0.0003 (0.0003) log(hosp. alcohol¹) -0.0001 (0.0002) log(hosp. hypertension¹) -0.0001 Brazil Human Capital Review (0.0002) log(hosp. diabetes¹) -0.0009*** (0.0002) log(health spending per cap.) -0.0002 58 52 TABLE V.7 Results: Fixed Effects Regression - Adult Survival Rate (continued) (0.0006) log(no. families PBF¹) -0.0035*** (0.0011) % parents with tertiary education 0.0001 (0.0001) adult sex ratio: 100*(men/women) 0.0001** (0.0000) log(homicides¹) -0.0081*** (0.0003) Constant -0.0946*** (0.0125) R-Squared 0.249 Observations 37123 F-statistic 125.83 All regressions are with municipality, year, and state-trend fixed effects Robust Standard errors in parentheses ¹ Variables per 100,000 inhabitants * p<0.10, ** p<0.05, *** p<0.01 TABLE V.8 Results: Fixed Effects Regression – Not Stunting & Child Survival (1) (2) Log(Not Stunting) Log(Child Survival) log(Family Health Strategy physicians¹) 0.0004*** -0.0001 (0.0001) (0.0001) log(hosp. malnutrition¹) -0.0001* -0.0001*** (0.0000) (0.0000) % poor birth outcomes 0.0000 -0.0008*** (0.0001) (0.0002) % insufficient prenatal -0.0000 -0.0001*** (0.0000) (0.0000) log(basic health spending per cap.) -0.0000 -0.0001 (0.0001) (0.0000) Brazil Human Capital Review % of mothers with no school -0.0004*** -0.0001*** (0.0001) (0.0000) log(no. families PBF¹) -0.0008* -0.0004* (0.0004) (0.0002) 59 53 TABLE V.8 Results: Fixed Effects Regression – Not Stunting & Child Survival (continued) log(no. raiox¹) 0.0002 (0.0001) log(hosp. asthma¹) -0.0000 (0.0000) log(hosp. sanitation-related¹) -0.0000* (0.0000) log(sanitation spending per cap.) -0.0000 (0.0000) % of schools with plumbing 0.0000** (0.0000) Constant -0.0888*** -0.0120*** (0.0035) (0.0019) R-Squared 0.756 0.080 Observations 37969 37969 F-statistic 431.02 38.47 All regressions are with municipality, year, and state-trend fixed effects Robust Standard errors in parentheses ¹ Variables per 100,000 inhabitants * p<0.10, ** p<0.05, *** p<0.01 TABLE V.9 Results: Fixed Effects and IV Regression - Expected Years of School FE IV First stage Log(EYS) Log(EYS) Log(No. families PBF) log(no. families PBF¹) 0.0028 0.0313*** (0.0032) (0.0111) log(school infrastructure index) 0.0165*** 0.0147*** 0.0456 (0.0030) (0.0035) (0.0326) log(no. municipal schools) -0.0005 0.0004 0.0104 (0.0023) (0.0021) (0.0342) Dummy has private school = 1 0.0005 -0.0004 0.0243*** Brazil Human Capital Review (0.0024) (0.0021) (0.0069) log(education spending per student) 0.0009*** 0.0015*** -0.0190** (0.0002) (0.0005) (0.0094) % white students -0.0000 -0.0001 0.0008 60 54 TABLE V.9 Results: Fixed Effects and IV Regression - Expected Years of School (continued) (0.0001) (0.0001) (0.0007) % parents with tertiary education 0.0001 0.0000 0.0018* (0.0001) (0.0001) (0.0010) % students living w/ parents 0.0002** 0.0002** 0.0005 (0.0001) (0.0001) (0.0008) % of full-time school 0.0002*** 0.0002*** -0.0011*** (0.0000) (0.0000) (0.0003) log(no. teenage pregnancy¹) -0.0038*** -0.0042*** 0.0099* (0.0009) (0.0008) (0.0056) log(no. elderly BPC¹) 0.0298*** (0.0053) lag: log(no. families PBF¹) 0.1966*** (0.0248) Constant 2.4295*** (0.0240) R-Squared 0.694 0.685 Observations 37042 37027 37027 F-statistic 382.14 386.05 62.93 Hansen-J (p-value) 0.88 All regressions are with municipality, year, and state-trend fixed effects Robust Standard errors in parentheses ¹ Variables per 100,000 inhabitants * p<0.10, ** p<0.05, *** p<0.01 TABLE V.10 Results: Fixed Effects and IV Regression - Not Stunting FE IV First stage Log(Not Stunting) Log(Not Stunting) Log(No. families PBF) log(no. families PBF¹) -0.0008* 0.0085** (0.0004) (0.0042) log(Family Health Strategy physicians¹) 0.0004*** 0.0005*** -0.0111 Brazil Human Capital Review (0.0001) (0.0001) (0.0073) log(hosp. malnutrition¹) -0.0001* -0.0001* -0.0004 (0.0000) (0.0000) (0.0013) 61 55 TABLE V.10 Results: Fixed Effects and IV Regression - Not Stunting (continued) % poor birth outcomes 0.0000 0.0001 -0.0018 (0.0001) (0.0000) (0.0011) % insufficient prenatal -0.0000 -0.0000 -0.0031*** (0.0000) (0.0000) (0.0008) log(basic health spending per cap.) -0.0000 -0.0001 0.0086** (0.0001) (0.0001) (0.0035) % of mothers with no school -0.0004*** -0.0004*** -0.0025*** (0.0001) (0.0000) (0.0007) log(no. elderly BPC¹) 0.0382*** (0.0064) Constant -0.0888*** (0.0035) R-Squared 0.756 0.732 Observations 37969 37968 37968 F-statistic 431.02 488.59 35.39 Hansen-J (p-value) All regressions are with municipality, year, and state-trend fixed effects Robust Standard errors in parentheses ¹Variables per 100,000 inhabitants * p<0.10, ** p<0.05, *** p<0.01 Brazil Human Capital Review 62 56 Appendix VI Subnational Human Development Policies ANNEX 1: Survey Questions EDUCATION NUMBER PROPOSED QUESTIONS Municipal schools have the autonomy to: [ ] Develop pedagogical curriculum [ ] Develop performance goals [ ] Implement extracurricular actions and thematic projects [ ] 1 Financially manage the school [ ] School maintenance[ ] No, the secretariat centralizes the school administration [ ] ] Other____ Who prepares the pedagogical project of schools? [ ] The secretariat provides a ready- made model [ ] The schools adapt a model offered by the Secretariat [ ] The schools 2 prepare their own project (with autonomy) [ ] The schools prepare their own project with the support of the Secretariat Does the Department of Education use data from students, teachers, and schools as information for decision making? In what way? [ ] Performance Goals [ ] Resource 3 Allocation [ ] Teacher Performance Assessment [ ] Principal Performance Assessment [ ] Development of specific programs adapted to the school context [ ] Does not use data Does the Department of Education support schools to reduce school dropout? [ ] Yes 4 [ ] No Does the Secretariat identify students who drop out of school? [ ] Yes, through a data system [ ] Yes, the secretariat supports the schools to 5 carry out this monitoring [ ] No, the school does not carry out this monitoring (If so, what was the biggest challenge to identifying student dropouts before the pandemic?) Does the Department of Education provide structured material for elementary school 6 teachers for the initial and final years? [ ] Yes, prepared by the secretariat [ ] Yes, prepared by another authority [ ] No Does the Department of Education have a program that promotes child literacy? (e.g., 7 the federal government’s More Literacy Program) [ ] yes [ ] no (If so, what were the challenges in implementing this program before the pandemic?) Are there any municipal programs/actions that personalize/customize teaching to the 8 pedagogical needs of students? [ ] Yes, for all students [ ] Yes, for low-achieving students only [ ] Yes, for Special Education [ ] Yes, for the Indigenous curriculum [ ] No Does the municipality have a program to adapt the school structure for students and 9 teachers with special needs? [ ] Yes [ ] No (If so, what were the challenges in implementing this program before the pandemic? Brazil Human Capital Review Do schools in the municipality have an internet provision program? (e.g., the Connected 10 Education Program (Programa Educação Conectada). [ ] Yes [ ] No (If so, what were the challenges in implementing this program before the pandemic?) 11 Does the Department of Education monitor the quality of school meals? 64 58 NUMBER PROPOSED QUESTIONS Does the Department of Education monitor the performance of students in the municipal 12 network? [ ] Yes [ ] No ( If yes, what is the frequency of monitoring? What is monitored?) 13 Does the Department of Education monitor teacher absenteeism? [ ] Yes [ ] No There is a policy of continuing education for professionals able to work in: [ ] early childhood education (nursery and primary schools) [ ] Indigenous education [ ] 14 education for students with special needs [ ] board members [ ] teachers [ ] schools in vulnerable areas [ ] There are no ongoing training activities promoted by the Secretariat How are principals selected for the municipality’s schools? 1 - Exclusively by management appointment 2 - Qualified selection process and management appointment 15 3- Specific public competition for the position of school manager 4 – Electoral process with the participation of the school community 5 - Qualified selection process and election with the participation of the school community 6. Other Do teachers or principals earn salary bonuses if the school they work for performs above 16 the expected/target? What were the three main challenges faced by the department of Education before the 17 pandemic? Regarding the COVID-19 pandemic, when were face-to-face school activities suspended 18 and what was the return date? In which media format did the Secretariat offer distance learning during the pandemic? 19 [ ] TV [ ] Radio [ ] Printed Material [ ] Online Classes [ ] Digital Applications [ ] No Offer [ ] Others How did the Secretariat deal with the lack of connectivity? [ ] provided internet (e.g., prepaid chips) [ ] provided internet and devices (e.g., prepaid chips and tablets/notebooks) 20 [ ] provided devices (tablets/notebooks) [ ] did not provide connection options[ ] other (box to describe other) 21 Did the school provide alternatives to school meals during the pandemic? [ ] Yes [ ] No 22 What were the main challenges for the department during the COVID-19 pandemic? Brazil Human Capital Review 23 Cite the three main challenges for reopening schools post-pandemic. 65 59 HEALTH NUMBER PROPOSED QUESTIONS Does the municipality have a program to promote a pharmaceutical assistance network? 1 e.g., Popular Pharmacy Program (Programa Farmácia Popular) [ ] Yes [ ] No Does the municipality have an effectively operationalized vitamin A supplementation program? e.g., National Vitamin A Supplementation Program (Programa Nacional de 2 Suplementação de Vitamina A). [ ] Yes [ ] No (If so, describe the challenges before the pandemic in implementing this program.) Does the municipality have a human milk bank program? [ ] Yes [ ] No (If so, what were 3 the challenges in maintaining this program before the pandemic? ) Does the municipality have a program that provides public space for exercise and leisure 4 activities? e.g., Federal Health Academy Program (Programa Federal Academia da Saúde). [ ] Yes [ ] No Does the municipality reach the target for HPV vaccination? (80 percent of the population 5 of girls aged 9 to 14 years, and of the population of boys aged 11 to 14 years). [ ] Yes No [ ] Is there an HPV immunization plan in schools? [ ] Yes [ ] No (If so, what were the 6 challenges to maintaining this program before the pandemic? Does the municipality promote immunization actions that integrate the health, social 7 assistance, and education departments? [ ] Yes [ ] No (If so, what is the name of this action/program?) Does the municipality offer a permanent training program for primary health care 8 professionals to promote the practice of breastfeeding? [ ] Yes [ ] No Is there a municipal breastfeeding campaign? e.g., National Breastfeeding Program of 9 the federal government (Programa Nacional de Aleitamento Materno). [ ] Yes [ ] No (If so, what challenges did you face in implementing this program before the pandemic? ) Does the municipality have actions/plans to reduce teenage pregnancy? [ ] Yes [ ] No 10 (If so, what actions are taken?) Are there integrated actions between the department of Health and the department 11 of Education regarding family planning policies in schools? [ ]Yes [ ] No (If so, what actions are performed?) 12 Does the municipality have a family planning promotion plan? [ ] Yes [ ] No Are there coordinated actions between the Department of Social Protection and the 13 Department of Health in the execution of family planning actions? [ ] Yes [ ] No (If so, what actions are performed?) Brazil Human Capital Review Does the municipality promote follow-up actions for chronic diseases? [ ] Yes [ ] No 14 (If so, what actions are performed?) Does the municipality have an agenda for child malnutrition? [ ]Yes [ ] No (If so, what 15 were the challenges faced in dealing with child malnutrition before the pandemic? ) 66 60 NUMBER PROPOSED QUESTIONS Does the municipality have a municipal health plan? [ ] Yes [ ] No (If so, how often is 16 the municipal health plan reviewed?) Does the municipality have guidelines and an implementation plan for labor and birth 17 care? (If so, what challenges did you face in implementing these guidelines before the pandemic?) Is there a training plan for primary care workers? [ ] Yes [ ] No (If so, is there a specific 18 space for the training of primary health care employees in the units of the municipality?) Does the municipality have a plan for ongoing training of health professionals? [ ] Yes 19 [ ] No (If so, is the municipality part of a municipal network focused on the continuing education of health professionals?) Does the municipality have an education, training, and improvement plan for professionals 20 from the Municipal Health Network Specialized in STI/AIDS? [ ] Yes [ ] No Does the Health Department have a Control, Assessment, Regulation, and Audit Service 21 plan on the quality and resolution of health actions and services for SUS users? [ ] Yes [ ] No Does the municipality have a mobile pre-and inter-hospital care system? [ ] Yes [ ] No 22 (If so, how many functioning ambulances does the municipality have? ) 23 What actions does the municipality take to respond to COVID-19 in terms of planning? What actions does the municipality take to respond to COVID-19 in relation to monitoring 24 practices and actions? 25 What actions does the municipality take to respond to COVID-19 in relation to acquisitions? 26 What actions does the municipality take to respond to COVID-19 in terms of hiring staff? 27 What other actions does the municipality carry out to respond to COVID-19? SOCIAL PROTECTION NUMBER PROPOSED QUESTIONS What challenges does the municipality face in meeting the criteria adopted for the territorial 1 distribution of CRAS and CREAS defined by the Federal Government (pre-COVID-19)? Brazil Human Capital Review What is the estimated ratio of CRAS population coverage to the vulnerable population 2 of the municipality? Is CRAS running the Comprehensive Family Care Service (Serviço de Atendimento Integral 3 à Família - PAIF)? [ ] Yes [ ] No (If so, what is the estimated coverage of monitoring of families in situations of social vulnerability in the municipality?) 67 61 NUMBER PROPOSED QUESTIONS Does the municipality have a permanent training plan for social assistance professionals? 4 [ ] Yes [ ]No Does the municipality have a Social Assistance Surveillance area in place? [ ] Yes [ ] 5 No (If yes, do you carry out a socio-territorial diagnosis? [ ] Yes [ ] No Have the goals of the last municipal social assistance plan been achieved? [ ] 0-25 6 percent [ ] 26-50 percent [ ] 51-75 percent [ ] 76-100 percent 7 If below 75 percent, list the top 3 challenges to achieving the goals. Does the municipality have its own systems for georeferencing, monitoring, and evaluation 8 of its social assistance policy? Is there a municipal plan to actively seek out specific vulnerable groups? [ ] Yes [ ] No 9 (If so, which groups?) Does the municipality use the information from the Single Registry to formulate public 10 policies on social assistance? [ ] Yes [ ] No 11 Is there a referral and counter-referral protocol between CRAS and CREAS? [ ]Yes [ ] No 12 Is there a protocol for the Integration of SUAS offers? [ ] Yes [ ] No Is there a specific municipal program for entering the job market (check all options): [ ] 13 women [ ] people with disabilities [ ]socio-learning [ ] homeless people [ ] other [ ] There is no action /program for the insertion of the population in the labor market Does the municipality have an early childhood care program? [ ] children’s visitation 14 program [ ] coexistence and strengthening of ties (parents) [ ] Workshops with families [ ] community actions [ ] others ____specify [ ] there is no early childhood care program 15 What are the challenges associated with implementing an early childhood care program? Is there a protocol to identify exposure to domestic violence and care for victims? 16 [ ] Yes [ ] No Does CRAS/CREAS have a protocol for identifying and dealing with child labor? 17 [ ] Yes [ ] No 18 Does the municipality have a municipal food and nutrition security policy? [ ] Yes [ ] No Does the municipality have a food and nutrition education program integrated with SUAS? 19 [ ] Yes [ ] No (If so, what program?) Brazil Human Capital Review Does the municipality promote integrated actions for insertion into the labor market 20 through family farming? [ ] Yes [ ] No 68 62 NUMBER PROPOSED QUESTIONS 21 What was the biggest supply demand of SUAS in the COVID-19 pandemic? Services: [ ] Protection and Comprehensive Care for the Family (Proteção e Atenção Integral à Família - PAIF) [ ] Coexistence and Strengthening of bonds  [ ] Flying/Sporadic Teams [ ] Basic Social Protection at Home for the Elderly and for People with Disabilities [ ] Protection and Specialized Care for Families and Individuals - PAEFI [ ] Social Approach [ ] Social Protection for Homeless People [ ]Special Social Protection for People with Disabilities, the Elderly and their Families [ ] Institutional Shelter Services [ ]Social Protection for Adolescents in Compliance with the Socio-educational Freedom Measure [ ] Provision of Services to the Community (Prestação de Serviços à Comunidade - PSC)  [ ] Social Protection in a Situation of Public Calamity 21 Programs:  [ ] Long-term Benefit at School (BPC na Escola) [ ] BPC at School [ ] Long-term benefit at work (BPC Trabalho) [ ] Acessuas Trabalho [ ] Strategic Actions of the Child Labor Eradication Program (Programa de Erradicação do Trabalho Infantil) [ ] Criança Feliz [ ] Bolsa Família [ ] Programs (state and municipal) Benefits:  [ ] Occasional benefit (birth aid; death grant; assistance in situations of temporary vulnerability; assistance in disaster and public calamity situations) [ ] Continued benefit [ ] Income transfer benefits (state and municipal) What were the three main challenges faced by the social protection network to ensuring 22 the management of Bolsa Família and Cadastro Único during the COVID-19 pandemic? 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