I6 29 Human Capital Development HCD Working Papers Human Capital Underdevelopment: The Worst Aspects HCDVP November 1996 HCDWP 76 Papers in this series are not formal publications of the World Bank. They present preliminary and unpolished results of analysis that are circulated to encourage discussion and comment; citation and the use of such a paper should take account of its provisional character. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. Human Capital Underdevelopment: The Worst Aspects HCDVP This report is the result of a team effort led by George Psacharopoulos and anchored by Robert Mattson, with contributions from Keiichi Ogawa, Anthanassios Katsis, and Hideo Akabayashi. Preface Today it is widely agreed that human capital development is the key factor underpinning a country's effort for economic and social development. Although much progress has been made recently in the developing world regarding health and education, much more remains to be done, especially at the sub-regional level. The purpose of this report is to bring to the surface some of the worst aspects of human capital underdevelopment, by dissaggregating to the extent possible the relevant indicators. (For a summary, see Annex Table 2 starting on page 44.) After all, when the illiteracy rate in a particular country is of the order of 70 percent, more than 80 percent of the children suffer from malnutrition, of less than 15 percent of the primary school age children are in school, it becomes obvious what are the investment priorities in that country. The report is produced in the hope that dissemination of such statistics would sensitize and instigate further action from governments and international assistance agencies to close the appalling gaps. George Psacharopoulos Senior Advisor Human Capital Development P. S. This is the last paper in the Human Capital Development series under HCDVP management. A new series will start under the HID Network. Contents I. I ntroduction ............................................. 1 II. Human Capital Development Indicators .............................................1 III. Poverty: The Catch-all Factor .............................................3 IV. Education .............................................6 V. Health...... .. 18 VI. Public Spending ............................................ 30 References and Sources ............................................ 35 Annex Table 1: The Poorest Countries ............................................ 43 Annex Table 2: Selected Worst Aspects Indicators by Country ............................................ 44 I. Introduction Development of human capital is a leading priority in the international community encompassing governments, intemational organizations, and academia. Increasing human capital levels is one part of a three-part strategy for alleviating poverty. The strategy aims to reduce poverty by encouraging broad-based growth while increasing the levels of human capital and providing safety nets (World Bank, 1990). Research abounds identifying issues affecting the development of human capital. As a result, governments have become more aware of, and focused on, problem areas for the development of their people's human capital, and significant progress has been made (Psacharopoulos, 1995). It is well known that poverty and human capital are closely related. Typically, where there is poverty one can find a poor stock of human capital. Increasing human capital levels has been associated with higher wages, better health conditions, and a myriad of other benefits-therefore increasing human capital levels is an integral part of the poverty alleviation strategy. In addition, human capital has been estimated to be over 50 percent of a country's total capital stock, 80 percent in the most developed countries (Becker, 1988 and 1995). Thus, human capital is a major asset of a country, and should be treated as such. Human capital development indicators reflect a country's investment in its people. These investments include schools, hospitals, books, teachers' services, medical equipment and other physical capital, along with recurrent expenditures associated with human capital investments- the mere construction of a school or hospital does little to increase literacy or treat illnesses without the staff to provide instruction or medical care. The identification of problem areas is another way of identifying where there is under-investment in human capital. After all, it is highly unlikely that an area has an 80 percent illiteracy rate because large sums of money were invested in its educational system. On the contrary, it is more probable that it was the result of under- investment in staff, materials, and other basic necessities for providing education. Once problem areas have been identified, they can be addressed by acting upon the constraints to the development of human capital. The purpose of this report is to identify and document some worst aspects of human capital development. This is done on the hope that dissemination of such statistics may instigate further action by governments and international donors to close the gaps. In the following section we discuss a taxonomy of human capital indicators and then present some broad statistics of poverty. The remaining sections deal with education, health, nutrition, and government expenditures on the social services. II. Human Capital Development Indicators Human capital indicators can be separated into two groups: monetary and physical. Monetary indicators fall into two categories: government expenditure and private expenditure. Government expenditures include those for the construction of facilities, infrastructure, and 2 recurrent outlays. Private expenditures are those incurred by individuals, which include tuition fees, books, drugs, and medical fees. Using expenditures as an indicator to human capital levels provides an insight into the investment priorities of governments and their public. However, a drawback to using expenditure indicators is that they tell little about the actual output, or effectiveness of those investments. A better proxy to assess human capital development is the use of physical indicators, for example, literacy or vaccination rates. The advantage of physical indicators is that most of them are the end product of human capital investments. For example, suppose we want an idea of the investment in expanding primary education. One investment indicator would be the number of schools built. While this tells us the potential capacity of the primary education system, is does not tell us the actual usage. In other words, is the additional capacity being utilized? A better indicator for this would be the primary school enrollment ratio. If a school system has 100,000 places, but only 50,0000 students are enrolled, it might not be a good decision to build more schools, at least not in that area. Though there is an investment in the form of extra capacity, the benefits are not necessarily being realized. Therefore a better proxy to the investment would be enrollment ratios. Even with the limitations of monetary indicators one cannot ignore them, for they are a complement to the physical indicators. For example, by looking at human capital expenditure as a percentage of GNP, one can get a feel for the investment priorities of society at large. In addition, the percentage of the government budget allocated to human capital investments provides a sense of the government's investment priorities. But the effectiveness of the investments is gauged by associating a monetary value with a quantified non-monetary measurement, in other words using both monetary and physical indicators (Pearce, 1986). Each of the physical indicators can be broken down into two sub-categories: those that look at the current human capital stock, and those that show the future potential of human capital stock, in terms of the current investment flows that build it, Figure 1. Examples of current stock indicators are literacy rates, educational attainment levels, life expectancy, or access to safe water. Examples of flow indicators include current enrollment levels, vaccination rates, and prenatal care. Looking at human capital indicators, monetary and physical, at the aggregate level of a country, though insightful, can be misleading. In some situations the indicators at the aggregate level are similar to dissaggregated indicators. Many countries have relatively encouraging aggregate level indicators, but after dissaggregating the data and looking at the regional and demographic breakdown, it is revealed that there are serious problems with the current and future human capital stock. 3 Figure 1: Human Capital Indicators Taxonomy | Human Capital Development | Stock FlwFIndicatorsl | Monetary | Physical l (Gov't Expenditure)Il (Private Expenditure)II |Educafion || Heath l Stock Flow Stock Flow (Literacy Rate) (Enrollment Ratios) (Life Expectancy) (Vaccination Rates) (Attainment Levels) (Student-Teacher Ratio) (Access to Safe Water) (Pre Natal Care) To exemplify the point, take the case of Brazil, and let us use the indicator of illiteracy. As a whole, Brazil's illiteracy rate is 19 percent.' However, the geographic distribution of illiteracy in Brazil is not uniform. Areas such as Brasilia and Sao Paulo have relatively low rates of illiteracy, while the state of Piaui has an illiteracy rate over 60 percent. Thus, Piaui would be identified as a human capital development hot spot. III. Poverty: The Catch-all Factor As a general rule, where there is poverty there is low human capital development (Psacharopoulos, et.al, 1994; De Geyndt, 1996). Since the correlation of low human capital levels to poverty is strong, the identification of poverty is a good place to start. However, it should again be pointed out that human capital is just one part of the welfare equation. The other part is economic growth (macroeconomic conditions). Therefore, the use of poverty as an indicator is not always indicative of low human capital levels. For instance, take the case of Sri Lanka. The incidence of poverty in Sri Lanka is 22 percent, but the overall human capital indicators are better than in other countries with the same incidence of poverty. This is a result of broad-based growth being poorly implemented. Cuba, Russia, and other former Soviet countries are also good examples of high levels of human capital and low economic growth. 1 All indicators listed in this paper are from World Bank sources that appear in the references section, unless otherwise stated. 4 Another limitation to using poverty as an indicator of human capital development, is that poverty levels are determined by establishing a minimum income and/or consumption level that has been deemed appropriate for survival. Though much thought and debate goes into the establishment of the poverty line, it is still an arbitrarily established line, or value, below which one is declared impoverished. This is further compounded by poor measurements of income in household surveys. Even with the limitation of using poverty incidence as an indication of human capital levels, it is a good place to start, particularly since we are interested in the alleviation of poverty. Identifying the poverty in a country is seldom as simple as looking at the overall national level data-though this would give the relative rating of a country to other countries, it provides little other useful information in targeting investments.2 The most useful information for targeting investments is derived from dissaggregated analysis. In the dissaggregation of the data, there are many sub-categories that have common patterns. One of these sub-categories is comparing the urban and rural populations-other sub-categories include various demographic, geographic, gender, and ethnic comparisons. Take for example Mozambique, arguably the poorest country in the world with a 1994 per capita GNP of $90, where one-third of the urban population is impoverished compared to two-thirds of the rural population. In Nicaragua, 75 percent of the rural population is poor compared to 32 percent of the urban population. Twenty percent of the urban population in Malawi is impoverished compared to 42 percent in the rural areas. Though the rural and urban poverty incidence may differ, it fails to adequately show the distribution of poverty between the rural and urban areas. For this reason we are also interested in the distribution of the poor. For example, the absolute poverty levels in Tanzania, are 48 percent in the rural areas and 11 percent in the urban areas is poor. However, 91 percent of the poor live in rural areas compared to 9 percent in the urban areas. An even more detailed and revealing dissaggregation is obtained by breaking the statistics into regions, states, or municipalities within a country. A good example of this is Indonesia, where the national poverty rate is 17 percent. Breaking this down by region, East Nusa Tenggara has the highest incidence of poverty at 45.6 percent. Or Malawi which has a 20 percent national incidence of poverty, but 51 percent of the population in the Southern regions are poor. Pakistan has a national poverty incidence of 33 percent, with the highest incidence of poverty being in south rural Punjab, 48 percent. High national levels of poverty do not inhibit an uneven distribution of poverty. Whereas the national incidence of poverty in Eritrea is 69 percent, the urban areas have a 62 percent incidence of poverty compared to 71 percent in the rural areas.3 Within the rural areas of Eritrea there is a wide variation in the incidence of poverty. The rural highlands have an 83 percent incidence compared to 52 percent in the lowlands. A good example that highlights this point is again Brazil. Though Brazil is considered an upper-middle income country, it has some pockets of poverty that rival areas in low-income 2Annex 1 shows the poorest countries in the world by per capita GNP ranked in ascending per capita GNP. 3Based on Table 1.2 World Bank, 1996i. 5 countries. For example, in Sao Paulo, 6.9 percent of the population lives below the poverty line, while in the North East, 32.4 percent live below the poverty line, Figure 2. Put another way, Sao Paulo accounts for 21.9 percent of the population and 8.6 percent of the poor, while the northeast accounts for 29.6 percent of the population and 55 percent of the population living in poverty. One state in the northeast of Brazil is particularly bad off, Piaui, with 50 percent of the state's population living in poverty. In the rural areas of Piaui, 67.8 percent of the rural population is impoverished. Regional variation of poverty can also be attributed or associated with racial or ethnic groups residing in that area. Take Guyana for example. Though poverty is widespread in Guyana, there are still regional pockets of extreme poverty. For instance 30.9 percent of the population in the Upper-Demerara-Berbice region live in poverty compared to 94.8 percent of those in the Potaro-Siparuni region. The Potaro-Siparuni region is mostly traditional Amerindian villages. The rate of poverty among the indigenous population is particularly severe in Guyana, where 87.5 percent of the Amerindians are categorized as poor. As a side note to this, there are other things to consider which affect poverty levels that might not be readily revealed in statistical tables. They both come from war. One is the influx of refugees and the other is the destruction of property. Ethiopia and Mozambique, Box 1, are suffering from the side effects of war. In Ethiopia, half of the population lives below the poverty line. In some regions 85 percent of the population lives below the poverty line. The poverty problem is compounded by the influx of refugees from Somalia and Sudan, demobilized soldiers, orphans, and redundant public servants. Box 1: War, Education, and Nutrition in Mozambique Arguably o-ne othe poorest countries in tie world, Mozambique is- a country with many problems inhbiting the :development human capital. In the course. of tre recent civil wax that raged-m Mozaibique; over half of the level I primary schools were t Te deion of these sools ot ufom arsreon. Te, 90 prent of the siool e deyi, cpred to 6pc in Ca Delgado. fta is nbad enough, approxintly 70 percent of All primary schools hive been losed. Aitis 67 p t of the oultIon15ys or o isillwiet. With tdestrucion closure of so any s, few will have ty to att4ini a n sol eduion, let uv sc ln. Theost re pri'' sho enrollmenratiowas6 percenit. Besides-the need for educationacilities, Mozai ique:suf ers nutritional problems. average persnis e t to csmonly 77 f thereuir daiy cc take. It ha bee estimated that 30 to 40 percent of thechidren ini- Mozambique suer from chroiuc malnutrition, This is evidW :vnwthh- uiermortaltraJutas tling is that 50 percentof the deaths of childre-repoted by th hpias,e asid by le diseases.- Source: Wol J;n, -a----cau 6 Figure 2: Poverty Incidence in Brazil, by Region and State 32.4 30 25 i20 15.5 14.6 14.1 ~15 1. 10 8.7 6.9 5 0 Z 0~~~~~~~~~~~~~~~ After the above overview of poverty conditions, we focus on the three traditional human capital indicators: education, health and nutrition. IV. Education Educational Attainment. One of the most common indicators used to evaluate the current human capital stock as it relates to education is literacy. Figure 3 shows the national illiteracy rates for those countries with some of the highest rates in the world. All of these countries have a per capita GNP of less than $600. 7 Figure 3: Adult Illiteracy Rates 90 86 81 80 73 '70 - - ~~69 69 6 65 65 64 63 62 62 62 61 60 60 ~50 140- .... .... .... .... . .. --- .... ... ... . .. ... .... .... --- Low-vircome 30 - lountry Avecge (34%1/) 20 10 0 ~~~~~~ 0.~~~~~~~~~~~ Table 1 shows the percent of the population over 18 years old in Pakistan by region that cannot read or write. Similar to the poverty indicators, the prevalence of illiteracy varies by region. Also, again, there is wide variation between urban and rural areas. This kind of dissaggregation can also be instructive by gender. Table 2 shows the adult illiteracy rate in Tanzania by region and gender, where particularly hard hit are females in Zanzibar. Table 1: Population over 18 Years Old That Cannot Read/Write, Pakistan (%) Urban Rural South Punjab 29.8 77.7 North-west Frontier 57.2 77.4 Province Balochistan 64.3 80.8 Overall 47.3 73.4 Source: Pakistan Integrated Household Survey, 1991 8 Table 2: Adult Illiteracy Rates, by Region in Tanzania (%) Mainland Zanzibar Female 31.8 58.1 Male 15.0 35.7 Total 23.8 46.7 Source: Tanzania Human Resource Development Survey, 1993 One of the primary goals of basic education is to bring about literacy. Literacy is generally believed to be attained after 4 to 5 years of schooling. So to have a high percentage of students not reach the fourth or fifth grade is detrimental to increasing literacy. Table 3 shows the effect of completing primary school on illiteracy in Tanzania. Those who complete primary school are literate, except for roughly 1 percent, while those who do not complete primary school have a higher probability of being illiterate. Thus the funds used to provide the education to those who dropped out were relatively ineffective. Table 3: Effect of Education on the Adult Illiteracy Rate (percentage) Primary School Unfinished Finished Total Whole Country Female 63.6 0.9 32.2 Male 35.5 0.7 15.4 Worst Four Regions* Female 79.3 0.7 45.1 Male 50.6 1.5 27.2 Source: Tanzania Human Resource Development Survey, 1993 * The rural regions of Dodoma, Arusha, Singida, Tabora. Years of schooling attained is another gauge of the educational level of the population. In Haiti, the average years of schooling is 1.7 years. Another area of particularly low attainment levels is in the northwest and northeast of Nigeria where the median years of schooling is .7 for males and .6 for females. In India, the mean years of schooling for females in the poorest income quintile and living in rural areas is .5. Or, in urban Bolivia, the mean years of schooling for non- 9 indigenous people is 8.4 years, while the urban mono-lingual indigenous population's means years of schooling is .3 years, and .2 for females (Psacharopoulos and Patrinos, 1994). Though a powerful indicator, information on educational attainment levels is not always available, but it can be proxied in other ways. One proxy for attainment levels is to look at the proportion of the adult population that has no schooling. Table 4 presents this proxy by gender and region. Across all countries the rural areas have a higher portion of adults with no schooling. With the exception of Brazil, females fare worse than the males. The definitive hot spots in Table 4 would be the rural areas of Egypt and Pakistan. Figure 4 shows the proportion of adult females with no schooling. Again, Pakistan and Egypt top the list, but the lack of schooling for females is still a major problem in the other four countries. Table 4: Population with No Schooling, 25 + Years Old (%) Country Both Sexes Female Bolivia Urban 13.1 18.4 Rural 37.5 50.9 Brazil' Urban 13.3 14.1 Rural 35.4 34.1 Egypt Urban 47.8 61.8 Rural 78.4 92.6 Pakistan Urban 59.2 na Rural 80.4 94.1 Venezuela Urban 17.2 19.2 Rural 45.2 48.1 Source: UNESCO, 199S. ' 10 + years of age 10 Figure 4: Females Over 25 Years Old with No Schooling (%) 87.9 90.0 78.6 80.0 68.3771.5 870.0 65.9 68.3 t 60.0 56.0 z q.-50.0 40.0 X 30.0 20.0 10.0 0.0 Source: UNESCO, 1995. Another proxy for attainment levels is the number of people reaching or attending particular levels of schooling. The national primary school graduation rate in Indonesia is 60 percent, but in the East Timor region it is only 20 percent. In Vietnam, 42 percent of the children do not reach grade 5.4 In India, only 51.3 percent of the 6 to 10 year old cohort in the state of Bihar are still in school. Figure 5 shows the percent of cohort to reach grade 4 by gender in selected countries.5 4UNICEF (1996). 5 Cohort is defined as those who had enrolled in primary school. 11 Figure 5 Percent of Cohort Reaching Grade 4 80- 74 73 70- 67 67 68 Xo~~~~~~~~~~ 63 60 ~~~~~~60 60 60 50 -45 46 4 ~O' 10 8 20 10 - 0 _ - _ E | | E _~~U o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~c U Female * Male School Enrollment. While illiteracy rates and attainment levels provide an indication of the current stock of human capital, enrollment ratios help provide an indication of the future stock of educated people. For example, in a recent household survey in Nicaragua, it was found that 22 percent of the children 6 to 14 years of age have never attended primary school. Here there is not only a difference within regions, but a great difference between the rural and urban areas within a region, particularly Matagalpa (Figure 6). In Nigeria the percentage of the population that never attended school is staggering, particularly in the rural areas where 65 percent of the females and 50 percent of the males never attended school. The two worst regions in Nigeria are the northeast and northwest with female non-attendance reaching well above 80 percent. In Guatemala, 61 percent of the indigenous people had no education. (Psacharopoulos and Patrinos, 1994). Table 5 gives an indication of a country's future human capital stock by looking at the primary school age children who have never attended school Pakistan. 12 Figure 6: Children 6 -14 Years Old That Have Never Attended Primary School, Nicaragua 50 - 46 45- 40 39 40 - 35 - 30- 25- 20- 15 12 1 10 - 7 _. 5- 0 Matagalpa Atlantica Norte Boaco E Urban * Rural Source: Nicaragua Living Standard Measurement Survey, 1993. Table 5: Children 6 - 13 Years Old Who Have Never Attended School, Pakistan, 1991 (%) Region Urban Rural North Punjab 16.7 29.3 South Punjab 22.7 48.4 Sind 30.0 56.6 Overall 25.7 45.9 Source: Pakistan Integrated Household Survey, 1991. 13 Table 6 presents the percent of children in a given age that are not attending school in Brazil. Across regions and age-groups the children of poor families are more likely not to be enrolled. In the south rural areas, a higher percent of the 7 to 9 year olds are in school than the 10 to 14 year olds. In the rural northeast the opposite is true. This suggest that while the children in the south are enrolling earlier and then dropout, the children in the northeast are enrolling later. Table 6: Children out of School by Area, Age Group and Poverty Level, Brazil 1990 (%) Age Group Poor Non-Poor (Years) All Brazil 7 - 9 42.0 19.0 10- 14 23.6 13.4 7 - 14 29.5 16.0 South (Rural) 7 - 9 20.0 11.0 10 - 14 31.9 24.9 7 - 14 27.2 19.5 Northeast (Rural) 7-9 63.0 52.0 10 - 14 31.1 30.2 7 - 14 44.6 38.3 Countries with both high illiteracy rates and low enrollment ratios face a serious hurdle because their current stock of human capital is low and their future stock is not promising. For example, Guinea has an 86 percent illiteracy rate and only a 44 percent net primary enrollment ratio (Figure 7). In Malawi, with a 59 percent illiteracy rate, the net primary enrollment ratio for the poorest male and female students is 34 and 31 percent, respectively. In Lao PDR, with a 36 percent incidence of illiteracy, the net primary enrollment ratio is 60 percent-54 percent rural and 78 percent urban. The net primary enrollment ratio for the lowest income quintile in Lao PDR is 44 percent, compared to 78 percent for the richest. Figure 8 shows a comparison of the rural and urban literacy rate and net primary enrollment ratios for Niger by household expenditure quintile. Clearly, across quintiles, the rural areas have a poor current human capital stock and a bleak future. Compared to the urban areas, the net primary enrollment ratio and literacy rate are relatively flat across expenditure quintiles. 14 Figure 7: Illiteracy Rate and Net Primary Enrollment Ratio 90 86 80 70-2 70 62 61 60 60 50 44 44 40 30 20 10 0 Guinea Pakistan Moznmbique MEllitrracy Rafte (%) U Net PrmBry Etmlront~ Ratki Figure 8: Net Primary Enrollment Ratios and Literacy Rates, Urban and Rural, Niger 80 - 70 --Urban net Primary Enrollment 60- 50 o- I | i i _ i | l | _~~~~~~~~~~- 40- - * -lhc RabUrban Literacy 30 20 Rural Net Primary Enrollment 10 - …… --… Rural Literacy 30 - __ 1 2 3 4 5 Expenditure Quindle 15 In Guyana, where the primary school enrollment ratio is 90 percent, 27 percent of the rural interior people do not receive primary education compared to 4 percent in Georgetown. In Sierra Leone the primary enrollment ratio in the Northern Province is 20 percent. Net primary enrollment is 26 percent in Eritrea, where the highest ratios are in the Asmara and Hamasien Provinces: 44 and 40 percent, respectively. The lowest ratios are found in the Sahel, Barka, Dankalia and Gash-setit Provinces; 5, 9, 13, and 15 percent respectively. In Uganda the primary gross enrollment ratio is 91 percent. However it is particularly low in the Kotido and Morodo districts-21 and 26 percent, respectively. The female enrollment ratios are especially low in these same districts-12 and 22 percent respectively. In Chad the gross primary enrollment ratios in Biltine, Batha and Quaddi are 10, 13 and 15 percent, respectively, while in the Lonone Occ. it is 115 percent. Box 2: Education in Northeast Brazil The -northeast-of EBrazil: isan area caght Grade Repetition. Even in the face of high in the trap-: ' of low ' - stock. and: flow- enrollment ratios, there can still be many obstacles to indicators of human capital.' In the rural overcome. One such obstacle is grade repetition. In areas of Brazil 60pecent of the -poor Lesotho, the primary school repetition rate is 22 household heads are illiterate, compared percent. In rural areas of Lesotho, the primary to 25-percent-in 'the urban 'areas. In the school completion rates are as low as 42 percent in northeast this climbs- to near -70 percent.- Qacha's Nek. Overall, 54 percent of the children in Eighty percent of the household heads in Lesotho do not reach grade 5 (UNICEF, 1996). In the:- norheast atteded between1 to- 4 Eritrea, repetition rates are almost 29 percent for years of schooling.- This- cycle does not females compared to 18 percent for males. appear to be abating.. Sixtytree percent of the children 7 to 9'years ofae are not' Peru is reported to have a 118 percent gross attending school. Fo thse aftending primary enrollment ratio. However, the repetition school, the repettiton rates fior, grade,s: 1t-o rate is 32 percent in the first grade alone. This is not 4 range from'' 74 to' 49 percent. It has surprising since 50 percent of the teachers are been estimated"'-that foar.Brazil as a whole, uncertified. In Guinea-Bissau the repetition rate for pmary schol repeition costs 30 prcent all grades, male and female, is 38 percent, but for of the current spencding for primary first and second grade it is 43 percent. This is not e:ducation. surprising considering that less than 13 percent of the Source: World ak jfia - primary schools teachers have only 4 to 5 years of lsource-. 11 .. . .. schooling, and 33 percent have 6 to 8 years. It is no wonder that with such poor schooling, 80 percent of the children do not reach grade 5 (UNICEF, 1996). The overall indicators in Mexico are good, but the indigenous population is a hot spot. While the national primary enrollment ratio is 100 percent, for the indigenous population it is only 72 percent. But the most telling figures are the primary repetition and completion rates. The primary repetition rate for the general population is 9 percent, but 42 percent for the indigenous. Fifty-eight percent of the general population complete primary school, whereas only 2 percent of the indigenous do. 16 The availability of textbooks is another crucial flow indicator. Text availability, or lack thereof, is an indication of the quality of schooling. In Lao PDR, 62 percent of the rural primary students have textbooks available to them. In Bolivia, it is estimated that only 20 percent of the students in grades 1 to 5 have textbooks available to them (Wolff, et al. 1994). Gender Gap& Figure 9 shows some of the worst female illiteracy rates, at the national level. Just like the other previous indicators, rural areas typically have worse indicators. In Lao PDR, for example, the illiteracy rate for females 36 to 55 years of age is 76 percent in the rural areas, with the highest incidence of illiteracy in the rural south-81 percent. Figure 9: Female Illiteracy Rate 100 - 91 90 86 82 BD X7 7 ~ 7 75 75 7 70 69 70 .8 60 50 -. Low4mcon CDuntry . 40 Avenge (45%) 30 20 10 X Enrollment ratios for females in developing countries are typically lower than that of males, and can reach extreme conditions. For example, the gross primary enrollment ratio for females in Ethiopia is 21 percent, and in Niger it is 23 percent. In Guinea, only 15 percent of females aged 7 to 12 are in school. In Mauritania the female illiteracy rate is 74 percent and the gross enrollment rate for females is 62 percent. As anticipated, regional disparities exist here as well. In Eritrea the national female gross primary enrollment ratio is 44 percent. However, in the Sahel Province it is 27 percent and in the Barka Province 29 percent. The lowest net enrollment ratio in Mauritania can be found in the rural, lowest expenditure quintile-4 percent. In Cambodia, adult illiteracy is 17 35 percent and the net enrollment ratio is 55 percent, but for females they are 50 and 43 percent, respectively. The overall female literacy rate in Niger is 10 percent, compared to 20 percent for males. However, there are significant differences between the rural and urban areas. Table 7 shows the male and female literacy rate by an urban-rural break down. Table 7: Literacy Rate in Niger, 1993 (%) Urban Rural Female 25 6 Male 43 15 In the rural areas of Niger there is not much variation in the literacy rate by expenditure quintile. However, in the urban areas females in the lowest expenditure quintile have a literacy rate of 16 percent, compared to 40 percent for females in the highest in expenditure quintile. This same pattern exists for net primary enrollment ratios. Table 8 shows the net primary enrollment ratio in Niger. Again, the rural females are worse off relative to males or urban females. Table 8: Net Primary Enrollment Ratios in Niger, 1993 (%) Urban Rural Female 61 12 Male 74 24 In Guinea, female students compose 33 percent of the primary school student population, and less than 15 percent of the girls 7 to 12 years of age are in school. Even though the rural areas represent over half of the potential students, fewer than 20 percent of all primary students are from the rural areas, and only 13 percent of the female primary school student are from rural areas. Increasing the enrollment rates and in turn the education of females is no easy task. Various cultural attitudes or school designs can prevent or cause students not to be enrolled. In Pakistan, 27 percent of the females age 5 to 10 who never attended school did so because their parents did not want them to. 18 V. Health Human capital is a vast web of complements and mutually reinforcing elements. For example, to do well in school, one needs to be able to attend school, and maintain concentration. To do this, one needs to be healthy and well fed. In turn, a well-educated person tends to live a healthier lifestyle and is more likely to use medical facilities, prenatal care, and put their children into private school, all of which tend to increase children's educational attainment levels, cognitive ability and academic achievement levels, because of their parents likelihood of having healthy children. The trap for the poor and uneducated is their tendency to live less healthy lives, not use medical facilities, prenatal care, and their children are less likely to enroll in school-thus perpetuating the cycle of poverty. Once a person has ceased their formal education and entered the work force, their human capital development becomes a function of growing experience, good health, and their educational attainment-which is complemented by additional training. Should one's health fail, their human capital can rapidly diminish. Therefore, while education is a primary component of the person's human capital, good health is also necessary to be educated and also to realize the benefits of being educated. One of the primary national level indicators of the overall health conditions of a country is life expectancy. Since life expectancy is the result of many inputs it acts as a gauge to summarize the overall health of a population. Life expectancy should only be used only as a rough indicator. It lends little insight into causes, problems, shortages, or geographic and demographic problem areas. Figure 10 shows some of the lowest life expectancies. 19 Figure 10: Life Expectancy 70 ....................................................................... .................................... - L ow-nm e Coutry 60 A-rape (63 years) 20 ~ ~ ~ 4 40 30 10~~~~~~~~~~~~~* 0 55 X 0 ] ] t } Figure 11: Access to Safe Water 80 75 70 70 59 2 60 52 50 44 s 40- 9 30- 27 18 17 20 < 20 14 1 i i _ 10 0 - * Urban * RuWal 20 Box 4: Human Capital Indicators in Tanzania * . . . . ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . ;.. . .. . . .P InTazaia 2 eren o eoleint..ailad.n.5.prcn inZ~Anzba arebelow they or-0T0-- t ttti ii0 pnva wwr0-i ..... .. .. ...... ....... W- 00. t. 0 0 ....... ....... ...... t: :.ti povrtyline. lPovetysemsto be arural gl phnmeo: 92 0 - g peren o- the poo liv in*ua:rasadte rey eaiy n gicltr. Therai incoe s five:0 tie les thtoewo reeiewae in te pbi or prvt setor. - areworse in Zanbr 9prcet5 : 9pmna noradrdwite. Thxe piay holgross erlmn ratiorapidlydeclined-betwn-1980-and1993:fro86to69percentforfemales,andfr 9 ...........9..t1........ percent for males. The grosssecondaryenrolments..... extr.me.y low:.. pecn for''-. ...emae ad C-6- p The poor receive m .uh. .l v The wo - poorest iis r"ceive only 0280 percent of the.total=education subsidies, while the two richesquintiles receive..7.percent. At the tsecodaan tertiayles,thetwo higest inom grup reetiv.ei 61 pece and 10 pecet fh Stotal benefits,respectively. In th0et0 pvate sector, t O9 pors q spend o r ' ' e 9 $6. per' hoehold foreducation,copaedtoUSi $23 pr hosoldi| spe t-0..-204400 by0t th rihes qwti Thefetiit ratpt. Tazani is 5.8 an oa ifeepcac s ers h vrg ubro ,~~~~~~~~~~~~~. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. childrenbelow the age 0 of 1840 pe houisehold 0is 4.14 Inthe botom- -unie comare t o- 2. in te} rih qnuintile.: Thgle infant imortality rate jis 84 per 10010: live births, Dmatena mortality is--748 per8 100:-000 ilive. birh, and th ner $ -mortlt rat is 15 pe 1,00 liv births.000 --.00t. 0.? . Publ00000000ic 00000Pl*ealth expniues ar-e pr-ich. Fo0r istanc, the 00poorest 40 percent recev onl00.y- 25 pecn of lhospital subsidies while thetp. 20 pecet rceve32 pret- of th hoptal. bidiesIn theT fprvtesector, fthe:poorest lquintile spens ol *0US $5 per.hiou:sehold; foirheath,whereas .the: richestuintilej can afford to spend US $39.~~~~~~~~~~f :bnw id -. -Fortyseven percen ofchildeunr theaeo re stute an 2 pecn of th reann iER. i..E .ER i j iiS~~. .. . i.iREE .... . i----E..E. E.--iE. ....... ... E ... . fchldrnr manorshed. I addtin t140 pecn ofault men an 34 pret 0of adut woenar Other indicators for gauging the health condition of a country include access to safe water, health facilities and other utilities, along with the incidence of illnesses. Safe Water. The ability for one to have access to safe water dramatically reduces their susceptibility to water-born diseases, thus increasing the likelihood of their being able to show up for work or school. Unfortunately, there are many areas in the world where access to safe water is rare. For instance, in Ethiopia, Guinea-Bissau, and Sierra Leone 82, 75, and 67 percent of the population does not have access to safe water, respectively. As with the other indicators, rural areas are particularly hard hit. In Mozambique, 75 percent of the population does not have access to safe water, but there is a heavy rural bias-83 percent of the rural population lacks access to safe water compared to 56 percent urban. In Cambodia, 88 percent of rural and 80 percent of the urban population do not have access to safe water. 21 Health Facilities. Figure 12 shows some of lowest indicators for access to health facilities. It is not uncommon that those with no access to safe water are double hit with no access to health care. Table 9 shows a few countries where both of these indicators were available. The problem of simultaneously lacking safe drinking water and health facilities can manifest itself in the rate of deaths for treatable diseases. For instance, in Mozambique only 25 and 40 percent of the population have access to water or health facilities, respectively. At the same time 50 percent of the deaths of children in Mozambique reported by hospitals were caused by treatable diseases. In northeast Brazil, infectious and parasitic diseases are the third leading cause of death. In Chad 74 percent of the population has no access to health care. Figure 12: Access to Health Facilities 18 18 16 14 ~14 12 *:- 12 10 10 lo 9 6 rA 6 4 3 4 2 0 0 4 z 22 Table 9: Access to Safe Water and Health Facilities (%) Country Safe Water Health Facilities Ethiopia 18 45 Guinea 40 45 Mozambique 25 40 Niger 37 30 The use or lack of health facilities is brought to light in another related set of indicators centered around pregnant women. For instance, Table 10 shows the proportion of pregnant women not receiving prenatal care in Nicaragua. Clearly, the highest incidence is in the rural areas. It should come as little surprise that the proportion of births performed outside a hospital has a simnilar distribution (Table 11). Table 10: Pregnant Women without Prenatal Care, Nicaragua (%) Region Overall Urban areas Rural areas Boaco 40.1 21.6 48.2 Matagalpa 42.4 17.4 52.9 Regi6n Autonoma 40.5 26.7 51.7 Atlantica Norte National 31.2 20.2 43.6 Source: Nicaragua Living Standards Measurement Survey, 1993 Table 11: Births Performed Outside of the Hospital, Nicaragua (%) Region Overall Urban areas Rural areas Boaco 64.6 39.6 78.8 Matagalpa 65.8 43.6 75.2 Regi6n Autonoma 64.1 41.1 83.0 Atlantica Norte National 50.7 33.0 73.0 Source: Nicaragua Living Standards Measurement Survey, 1993 23 Whereas in Nicaragua the use of prenatal care is low, particularly in the rural areas, in Tanzania the prenatal care indicators are even worse. As a whole, only 3.1 percent of the women receive prenatal care. The difference between the urban and rural areas is less than 1 percent. Table 12 shows the relationship of prenatal care and education in Tanzania. Table 12: Prenatal Care and Primary Education, Tanzania (%) Primary School Country Average Worst Six Regions6 Incomplete 1.4 1.0 Completed 9.9 7.3 Source: Tanzania Human Resource Development Survey, 1993. ilnesses. In a recent household survey in Pakistan, it was found that 80 percent of the households in the lowest expenditure quintile had at least one member sick in the 30 days preceding the survey.' Also, 25 percent of the children under 5 reported having diarrhea. In Nicaragua, almost 40 percent of the children under 1 year old in the rural Boaco region reported having diarrhea within 30 days of the survey. This is the same region where almost 40 percent of the children aged 6 to 14 never attended primary school. Vaccination. Even so, many countries have extremely low vaccination rates (Table 13). It should be noted, that, according to UNICEF (1996), in Haiti, Ethiopia and Mozambique none of the costs of vaccination are covered by the government. 6rSix ural areas of Ruvuma, Singida, Sinyanga, Kagera, Mara, and Coast. 7The highest quintile had a 74 percent incidence. 24 Table 13: Children Not Vaccinated (%) Country Measles DPT Dominican Republic 47 69 Ethiopia 63 57 Haiti 41 69 Mozambique 77 81 Paraguay 56 67 Table 14 presents the vaccination rates for Pakistan by region. There is 63 percent more chance for children from the lower income families not to be immunized relative to the children from upper income level families. Children from the urban areas have 72 percent more chance of being immunized relative to the children from the rural areas. In South Punjab this percentage increases to 200 percent. Table 14: Children under 5 Years Old That Have Never Been Immunized by Province/Area, Pakistan 1993 (%) Urban Rural South Punjab 11.1 33.2 Sind 22.2 41.4 North-west Frontier 22.8 40.7 Province Balochistan 38.8 51.5 Overall population 21.1 36.4 Source: Pakistan Integrated Household Survey, 1991 The reasons for parents not vaccinating their children can be as simple as not having someone visit them or the facilities being too far away or too expensive. From a recent Pakistan survey, Table 15 shows the percent of people not vaccinated because a team did not visit them. As with so many other indicators, there is a rural bias here. In the rural areas, the majority of the people's reason for not immunizing their children was the lack of a team visiting. 25 Table 15: People That Have Indicated "No Team Visited" As the Main Reason for Not Immunizing Their Children,8 Pakistan 1993 (%) Urban Rural Sind 26.4 60.1 Balochistan 17.9 57.9 Overall 27.9 51 Source: Pakistan Integrated Household Survey, 1991. Nutition. Nutrition plays a critical role in the development of human capital, in particular the educational and intellectual abilities of children. This is particularly true in the early developmental stages of the child. Unfortunately, many children suffer from malnutrition. The effects of malnutrition at the early stages of development can be devastating to the child's future development. In Bangladesh, 84 percent of the children under 5 are malnourished. In India and Ecuador, 63 percent of the children under 5 are malnourished. In Cambodia 40 percent of the children under 5 are malnourished. In Ethiopia the rate is 38 percent and in Indonesia it is 39 percent. In Zambia, 35 percent of the children under 5 were malnourished in the northem region. One of the worst case is in Mozambique where 30 to 40 percent of the children are estimated to be chronically malnourished. In Eritrea, almost 37 percent of the children under 5 are malnourished. However, the malnutrition rate climbs to almost 60 percent in the Hamasien Province and 54 percent in the Gash-setit province. The prevalence of stunting for children under 5 in Lesotho is 33 percent, versus 39.8 percent in Qacha Nek. In Malawi, 49 percent of those under 5 were stunted. In Nigeria, 43 percent of the children under 5 were malnourished. In Brazil, as a percentage of the age group, five time as many children were underweight in the northeast than in the south. Figure 13 presents the proportion of children under 5 with moderate to severe stunting in Afiica. Considering that the first four years of life are the most critical to the future human capital development of the child, the fact that a country has over 40 percent of their future students already suffering from malnutrition does not bode well. This suggests that this cohort will likely have high repetition and dropout rates, lower educational attainment and achievement, and most likely be left in poverty. N The three possible answers were "too far away", "team did not visit", "cost too much". 26 Figure 13: Children under Five with Moderate to Severe Stunting (%) 70 64 60 57 5_48 485 .0 505 inr 40 E 30 20 10 0 ln - Infant Mortality. Mortality rates, like life expectancy, are a good indicator of the overall health conditions within a county. Mortality rates are generally broken down into infant, child, and maternal. As with the previous indictors, high mortality rates are associated with other unfavorable human capital indicators. Figure 14 shows a geographic and demographic decomposition of infant mortality in Tanzania. The groups with the highest mortality rate are the illiterate, living in one of the four worst regions, and those who did not finish primary school. 27 Figure 14: Infant Mortality and Education, Tanzania, Worst Four Regions (deaths per thousand live births) The Whole Country 177 The Rest The Worst Four Regions of the Country in the Mainland 164 250 Primary School Primary School Finished Unfmished 211 275 252 284 Source: Tanzania Human Resource Development Survey, 1993 Under 5 Child Mortality. The second mortality indicator is that of children under 5. In Malawi, the overall under 5 mortality was 233 per 1000 live births-244 in the rural areas and 262 in the central regions. In Eritrea, the under 5 mortality rate is 203 per 1000 live births, with the highest rate in Dankalia, Sahel, and Sember: 318, 287, and 254 per 1000 live births, respectively. Maternal Mortality. The third mortality indicator is matemal mortality, which also has great variability within countries. For instance, in Tanzania, with a per capita GNP of $140, the matemal mortality rate is 748 per 100,000 live births, while in Benin, with a per capita GNP of $370, the matemal mortality rate is 2,500 per 100,000 live births. Mozambique, Ethiopia, Chad and Yemen, all have matemal mortality rates near 1,500 per 100,000 live births. Fertility. The issue of fertility as a human capital development factor might not be obvious on the surface. The development of human capital is partly a function of family resources: time 28 and money.9 As children are being added to the family, the resources used to educate and feed them are being spread out. Inherently, this means less resources per child, which in turn means lower human capital. Higher female education levels are associated with lower fertility rates, both of which are associated with higher educational attainment levels of children. Figure 15 presents some the highest fertility rates in the world. Box 5: Human Capital in Eritrea Erntrea: ws econonucal Vw:ell developed prt h bSa African couEtrie inte. 1950Xs. Hwever du toth&¢ irty ~0yearst ofwr ro to 0 lier tinn 191 titW i t beaeoeo thepors contie, it;ape capita icoe fabotess than$200. 0 9;:2rgu xa0 Theicdneof- wpverty $in Erie i5 ne; of th e ost; 0a tin thef wrd:6 percet. In0 rura areas, loland. Eveninurbman ra,62 percent liv -ide the30j poet line.)- f : 5 -perceiitin mSahelPrvice;: 10percent min arka Provine 11:3 -percenltin Dankalia Provice andll5 percn S.f SS.iE,iiS.";ff i.fTd SiS .E.S . .f.7 ..S.f .5-.f.-... S.0 ..-ff .... .. .f.av2fDD D .i.f.^D.fff.. . . .... in Gash-Setit Province. Accorin to- UNICtEf (1996),onl 25 v percento ch5ildrenl reacheda grade fIveC in-l The health s.taX-tuXls o Eiotre'ts populaionisoon ofisthe ooes-:1tr among low-incm icutries. ThlNe ytotal fertl^ity rateis5.7 births.perkwomenandlife epectancyat birthisoly!48 yea. The mor tlty ratenor thseunder thCe ageof fiv is 0203 pr100w 10 live birth. Conditionsithe rural ara ae mor sevre suc as in Dankalia l (318per".1,000 live e-birth) andSahel' ,1,:(28,7 pe :r l:1t00 live births). OethousandSl and four hundredper 0Qt0,0 l birtsinEartren 1990cituted one of th o m r tl rate 0 Sub-SC Afra F, 1 -996)L.-: -;f fft Thir00:000 0ty 0t-seven perent per:fof children:.under th.e a.ge .of fiv.et are. .. alnourishd. Rgionally,the. incidence of child mQlalnutri intha eia sienafnd Gash-it-Provinces is mostsevr-6percent ad 54 pent Source: UMC~~~~~~~~F The Pro s~~~~~~~~ Q N.... ion9 9See Becker and Tomes, 1986; Behrman, Pollak, and Taubman, 1989; Blake, 1989; Psacharopoulos and Matson, 1996. 29 Figure 15: Fertility Rate (births per woman) 8 7.5 7.4 7.1 6.9 67 6.7 6.7 6.6 6.6 6.5 6 5 3.---- - --- Low-income 3* Country Average 2 - (3.3) 1- 0 - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ One influence on fertility rates, besides the education of females, is the use of contraceptives. Figure 16 presents some of the countries with the lowest rates of contraceptive use. Figure 16: Contraceptive Use (%) 14- 13 12 12 12 11 10 0)8~~~~~~~~~~~~ 6 6- - 2 - 0 10 0)1 . 0).- 4 44 u L2.~ 30 Table 16: Population That Did Not Use Birth Control by Area/Region, Pakistan (percent) Urban Rural North Punjab 66.1 87.8 South Punjab 30.5 81.7 Overall 64.2 80 Source: Pakistan Integrated Household Survey, 1991 Table 16 presents contraceptive use by region in Pakistan. The proportion of the female population that does not use birth control is higher in the rural than in the urban areas, 80 and 64 percent respectively. In the rural areas of South and North Punjab the proportion of females not using contraception is 82 and 88 percent, respectively. In Guyana, the national level of contraceptive use in 29 percent. However, in the lowest per capita consumption quintile it is estimated to be only 12 percent. VI. Public Spending Our next task is to take a look at how governments are addressing, in terms of social spending, the issues listed above. For instance, if it is found that 95 percent of the urban population and only 10 percent of the rural population are literate, one would like to believe efforts are being made to educate those in the rural areas. So we look at the government's spending on education. Often the distribution of the benefits is pro-rich and poorly targeted, or not targeted at all, to the needy (Grosh, 1990: McGreevey 1980; Van de Walle and Nead, 1995). Table 17 shows the distribution of income and social spending in Brazil. The income distribution follows the typical pattern, the upper quintile has the majority of the income while the lower quintile has the least. Table 17 shows that the social spending in Brazil is pro-rich, i.e. the richest receives more benefits than the poorest. 31 Table 17: Income Distribution and Public Spending, Brazil 1990 (%) Income Quintile Income Share Social Spending* I Poorest 2.1 13 2 5.2 18 3 9.6 21 4 17.8 24 5 Richest 65.2 24 * Education, health, nutrition and social security. Education. Figure 16 shows the public expenditure on education as percent of GDP in a number of countries with the lowest such spending. This amounts to 1-2 percent of the GDP against 4% for developing countries. Figure 16: Public Education Expenditure (% of GDP) 4.5 - 4.0 -.- .------ - -- --------------------------- - --- DeveeoExng Country Average 3.5 -3.0 I 025 250 1.8 1.9 2.0 2.0 2.0 2.0 2.0~~~~~~~. 1.05 0.8 0.8 1l 0.5 0.0 *%of GNP 32 Table 18 shows the distribution of public education expenditure in Brazil. On the surface, when looking at the distribution for primary schooling, it would be commendable that the poor appear to be receiving most of the benefit. However, this could be misleading in the face of high private school enrollment, especially considering the wealthy typically send their children to private schools. At both the secondary and higher education levels the distribution of benefits is definitively pro-rich, and worsens from secondary to higher education. Table 18: Distribution of Public Education Spending, Brazil (%) Level of Schooling Quintile Total Primary Secondary Higher I Poorest 16 23 9 7 2 18 22 15 12 3 20 20 20 18 4 22 19 25 26 5 Richest 24 16 31 37 A similarly regressive education benefit distribution can be found in Ghana. In Ghana the poorest quintile receives only 14.9 and 6 percent of the secondary and higher education benefits, respectively. However, the richest quintile receives 18.6 and 45.2 percent of the secondary and higher education benefits, respectively. In Malawi, the poorest quintile receive 16 percent of the education subsidies, while the richest quintile receive 25 percent of the subsidies. In Lao PDR university students receive 24 times the subsidy of a primary school student. In Mongolia, investment in education dropped from 5.9 percent on GNP to 1.8 percent from 1990 to 1994 respectively. In 1994, 28 percent of the education budget in Mongolia was on primary education and 62 percent on secondary education. The distribution of education benefits was also pro-rich for both the primary and secondary levels. Tight budgets, strong teachers' unions, or simply poor management can often lead to an allocation of the education budget that deprives the students and teachers of the materials necessary for providing quality education. In 1991, 90 percent of the financial resources for textbooks in Bolivia came from the parents (Wolff, et.al. 1994). In 1989, a disproportionate allocation of resources caused 98 percent of the educational budget in Bolivia to be used for salaries. In India, it was estimated that 97 percent of the education budget for lower primary and 96 percent of the budget for upper primary was for salaries. Figure 17 shows the distribution of education expenditure in Guyana, by regional per capita consumption deciles. The four regions with the lowest average per capita consumption received less per capita education expenditure than any other region. In Guyana, university 33 students receive 33 times the education subsidy of the primary student. Also, the distribution of education expenditure is favored toward the rich areas. Healtk Figure 18 presents the public sector expenditure on health as a percent of GDP. The distribution of recurrent health expenditure in Guyana is similar to the distribution of its education expenditure. Figure 19 shows the distribution of recurrent health expenditure in Guyana. In Togo, the maritime regions have 35 percent of the country's population, and yet receive a disproportionate amount of the health related resources-90 percent of the country's drug supply, and 70 percent of the health personnel. Figure 17: Per Capita Recurrent Education Expenditure in Guyana 8000 7005 70000 6635 ;Ja 6000 0 ~~~~~~~~~~~4835 * 5000 4479 S~4000 3403 3148 3000 2000- - 1477 1500 1226 t 1000 -- 575 0 1 2 3 4 5 6 7 8 9 10 Poorest Richest Mean Per Capita Regional Consumption Ranking 34 Figure 18: Public Health Expenditure (% of GDP) 2.5 0 2 - ...................................................................................Develp Co t ry Aveage 1.5- Is .5 0. . . 0.9 0.9 1.0 1.0 1.0 1.0 i o5 OA| | | | X ~ iE i .] E E j 0 -~~~~~~~rfa E~~ Source: UNDP, 1996 Figure 19: Per Capita Recurrent Health Expenditure in Guyana 8000 7756 13 7000 .~6000 g'. 5000 X 4000 M 3000- 2825 2897 :4 2000 1741 1647 a 1000 611 ,,, 771 528 128 1 2 3 4 5 6 7 8 9 10 Poorest Richest Mean Per Capita Regional Consumption Ranking References and Sources Becker, Gary S. 1988. "Family Economics and Macro Behavior." American Economic Review. 78 (1): 1-11. Becker, Gary S. 1995. "Human Capital and Poverty Alleviation." 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New York: Oxford University Press. -1994a, Bangladesh: Poverty Alleviation Microfinance Project, Staff Appraisal Report, Report No. 15431-BD, South Asia Region, Country Department I, Washington DC, USA. -1994b, Bolivia: Education Reform Project, Report No. P-631 1-BO, Washington DC, USA. 38 -1994c, Bolivia: Education Reform Project, Staff Appraisal Report, Report No. 12863-BO, Latin America and the Caribbean Region, Country Department III, Washington DC, USA. -1994d, Brazil., Parana Basic Education Quality Project, Staff Appraisal Report, Report No. 12699-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1994e, Brazil: State Of Minas Gerais Basic Education Quality Improvement Project (Proqualidade), Staff Appraisal Report, Report No. 12477-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1994f, Eritrea: Options and Strategies for Growth, Report No. 12930-ER, Africa Region, Easter Africa Division, Washington DC, USA. -1994g, Guinea-Bissau: Poverty Assessment and Social Sectors Strategy Review, Report No. 13155-GUB, Africa Region, Sahel Department, Washington DC, USA. -1 994h, Guyana: Strategiesfor Reducing Poverty, Report No. 12861 -GUA, Latin America and the Caribbean Region, Country Department III, Washington DC, USA. -1994i, Mauritania: Poverty Assessment, Report No. 12182-MAU, Africa Region, Sahelian Department, Washington DC, USA. -1994j, Paraguay: Country Economic Memorandum, Report No. 11723-PA, Latin America and the Caribbean Region, Country Department IV, Washington DC, USA. -1994k, Paraguay: Poverty and the Social Sectors In Paraguay: A Poverty Assessment, Report No. 12293-PA, Latin America and the Caribbean Region, Country Department IV, Washington DC, USA. -19941, Rwanda: Poverty Reduction and Sustainable Growth, Report No. 12465-RW, Africa Region, South Central and Indian Ocean Department, Washington DC, USA. -1994m, Sierra Leone: Public Expenditure Policies for Sustained Economic Growth and Poverty Alleviation, Report No. 12618-SL, Africa Region, Western Africa, Washington DC, USA. -1994n, Tanzania: Role Of Government Public Expenditure Review, Report No. 12601-TA, Africa Region, Eastern Africa, Washington DC, USA. -1994o, Uganda: The Role Of Nongovernmental Organizations and Community-Based Groups In Poverty Alleviation, Report No. 12262-UG, Africa Region, Eastern Africa, Washington DC, USA. -1 995a, Brazil: A Poverty Assessment, Report No. 14323-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. 39 -1995b, Brazil: Country Assistance Strategy, Report No. 14569-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1995c, Brazil: Northeast Rural Poverty Alleviation Program, Report No. P-6604-BR, Washington DC, USA. -1995d, Brazil: Northeast Rural Poverty Alleviation Program; Rural Poverty Alleviation Project-Bahia, Staff Appraisal Report, Report No. 14390-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1 995e, Ethiopia: Country Assistance Strategy, Report No. 1498-ET, Africa Region, Country Department II, Washington DC, USA. -1995f, Ghana: Growth, Private Sector, and Poverty Reduction A Country Economic Memorandum, Report No. 1411 1-GH, Africa Region, West Central Africa, Washington DC, USA. -1995g, Guinea: Education Sector Adjustment Credit, Implementation Completion Report, Report No. 14617, Africa Region, Western Africa, Washington DC, USA. -1995h, Lao PDR: Social Development Assessment and Strategy, Report No. 13992-LA, East Asia and Pacific Region, Country Department I, Washington DC, USA. -1995i, Lesotho: Poverty Assessment, Report No. 13171-LSO, Africa Region, Southern Africa Department, Washington DC, USA. -1995j, Mozambique: Country Assistance Strategy, Report No. 15067-MOZ, Africa Region, Southern Africa, Washington DC, USA. -1995k, Pakistan: Country Assistance Strategy, Report No. 15115-PAK, South Asia Region, Country Department I, Washington DC, USA. -19951, Pakistan: Poverty Assessment, Report No. 14397-PAK, South Asia Region, Country Department I, Washington DC, USA. -1995m, Republic Of Nicaragua: Poverty Assessment, Report No. 14038-NI, Latin America and the Caribbean Region, Country Department II, Washington DC, USA. -1995n, Sri Lanka: Poverty Assessment, Report No. 13431-CE, South Asia Region, Country Department I, Washington DC, USA. -1995o, Viet Nam: Poverty Assessment and Strategy, Report No. 13442-VN, East Asia and Pacific Region, Country Department I, Washington DC, USA. - 1996. African Development Indicators 1996. Washington DC: World Bank. -1996b, Armenia: Confronting Poverty Issues, Report No. 15693-AM, Europe and Central Asia Region, Country Department IV, Washington DC, USA. 40 -1 996c, Bangladesh: Labor Market Policies for Higher Employment, Report No. 13 799-BD, South Asia Region, Country Department I, Washington DC, USA. -1996d, Bangladesh: Public Expenditure Review, Report No. 15905-BD, South Asia Region, Country Department I, Washington DC, USA. -1 996e, Brazil: Health Sector Reform Project - Reforsus, Staff Appraisal Report, Report No. 15522-BR, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1 996f, Brazil: Rural Poverty Alleviation and Natural Resources Management Project, Report No. P-6899-BR, , Washington DC, USA. -1996g, Cambodia: From Recovery To Sustained Development, Report No. 15593-KH, East Asia and Pacific Region, Country Department I, Washington DC, USA. -1996h, Caribbean Countries: Poverty Reduction and Human Resource Development In the Caribbean, Report No. 15342-LAC, Latin America and the Caribbean Region, Country Department III, Washington DC, USA. -1996i, Eritrea: Poverty Assessment, Report No. 15595-ER, Africa Region, Eastern Africa Region, Washington DC, USA. -1996j, Ethiopia: Ethiopian Social Rehabilitation and Development Fund Project, Staff Appraisal Report, Report No. 14907-ET, Africa Region, Eastern Africa Department, Washington DC, USA. -1996k, Ghana: Basics Education Sector Improvement Program, Staff Appraisal Report, Report No. 15570-GH, Africa Region, West Central Africa, Washington DC, USA. -19961, Guinea, Beyond Poverty: How Supply Factors Influence Girls' Education In Guinea, Report No. 14488-GUI, Africa Region, Western Africa, Washington DC, USA. -1996m, Guinea: Public Expenditure Review, Report No. 15147-GUI, Africa Region, Western Africa, Washington DC, USA. -1996n, Haiti: Country Assistance Strategy (Operations Committee Review), Latin America and the Caribbean Region, Washington DC, USA. -1996o, India: Primary Education Achievement and Challenges, Report No. 15756-IN, South Asia Region, Country Department II, Washington DC, USA. -1 996p, India: Second District Primary Education Project, Staff Appraisal Report, Report No. 15496-IN, South Asia Region, Country Department II, Washington DC, USA. -1996q, India: State Health Systems Development Project II, Staff Appraisal Report, Report No. 15106-IN, South Asia Region, Department II, Washington DC, USA. 41 -1996r, Indonesia: Dimensions Of Growth, Report No. 15383-IND, East Asia and Pacific Region, Country Department III, Washington DC, USA. -1996s, Indonesia: East Java and East Nus Tenggara Junior Secondary Education Project, Staff Appraisal Report, Report No. 15501-IND, East Asia and Pacific Regional Office, Country Department IL Washington DC, USA. -1996t, Indonesia: Social Sectors Strategy and Capacity Building, Technical Assistance Project, Report No. T-6868-IND, Washington DC, USA. -1996u, Lesotho: Country Assistance Strategy, Report No. 15510-LSO, Africa Region, Southern Africa, Washington DC, USA. -1 996v, Malawi: Human Resources and Poverty Profile and Priorities for Action, Report No. 15437-MAI, Africa Region, Southern Africa, Washington DC, USA. -1996w, Mongolia: Poverty Assessment In A Transition Economy, Report No. 15723-MOG, East Asia and Pacific Region, China and Mongolia Department, Washington DC, USA. -1996x, Niger: Health Sector Development Program, Staff Appraisal Report, Report No. 15443-NIR, Africa Region, Human Development III, Washington DC, USA. -1996y, Niger: Poverty Assessment, Report No. 15344-NIR, Africa Region, West Central Africa, Washington DC, USA. -1996z, Niger: Primary Education Development Project, Implementation Completion Report, Report No. 15748, Africa Region, West Central Africa, Washington DC, USA. -1996aa, Nigeria: Federal Public Expenditure Review, Report No. 14447-UNI, Africa Region, Western Central Africa Department, Washington DC, USA. -1 996ab, Nigeria: Poverty In the Midst Of Plenty the Challenge Of Growth With Inclusion: A World Bank Poverty Assessment, Report No. 14733-UNI, Africa Region, Western Africa, Washington DC, USA. -1996ac, Pakistan: Beaconhouse School System, Report No. IFV, Washington DC, USA. -1996ad, Pakistan, Improving Basic Education: Community Participation, System Accountability, and Efficiency, Report No. 14960-PAK, South Asia Region, Country Department I, Washington DC, USA. -1996ae, Pakistan: Northern Health Program Project, Staff Appraisal Report, Report No. 15133-PAK, South Asia Region, Country Department I, Washington DC, USA. -1996af, Pakistan: Public Sector Adjustment Loan/Credit, Implementation Completion Report, Report No. 15758, South Asia Region, Country Department I, Washington DC, USA. 42 -1996ag, Pakistan: Punjab Private Sector Groundwater Development Project, Report No. P- 6779-PAK, Washington DC, USA. -1996ah, Paraguay: Maternal Health and Child Development Project, Staff Appraisal Report, Report No. 15610-PA, Latin America and the Caribbean Region, Country Department I, Washington DC, USA. -1996ai, Peru: Irrigation Subsector Project, Report No. P-6958-PE, Washington DC, USA. -1996aj, Peru: Second Social Development and Compensation Fund Project, Staff Appraisal Report, Report No. 15497-PE, Latin American and the Caribbean Region, Country Department III, Washington DC, USA. -1996ak, Rwanda: Health and Population Project, Report No. P-6943-RW, Washington DC, USA. -1996al, Sri Lanka: Teacher Education and Teacher Deployment Project, Staff Appraisal Report, Report No. 15282-CE, South Asia Region, Country Department I, Washington DC, USA. -1996am, Tanzania: The Challenge Of Reforms: Growth, Incomes and Welfare, Report No. 14982-TA, Africa Region, Eastern Afica, Washington DC, USA. - 1996an, Uganda: The Challenge of Growth and Poverty Reduction. 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New York: Oxford University Press. 43 Annex Table 1: The Poorest Countries Country GNP/capita (dollars) Population mid- 1994 1994 (millions) Rwanda 80 7.8 Mozambique 90 15.5 Ethiopia 100 54.9 Tanzania 140 28.8 Burundi 160 6.2 Sierra Leone 160 4.4 Malawi 170 9.5 Chad 180 6.3 Uganda 190 18.6 Madagascar 200 13.1 Nepal 200 20.9 Vietnam 200 72.0 Bangladesh 220 117.9 Haiti 230 7.0 Niger 230 8.7 Guinea-Bissau 240 1.0 Kenya 250 26.0 Mali 250 9.5 Nigeria 280 108.0 Yemen, Rep. 280 14.8 Burkina Faso 300 10.1 Mongolia 300 2.4 India 320 913.6 Lao PDR 320 4.7 Togo 320 4.0 Gambia, The 330 1.1 Nicaragua 340 4.2 Zambia 350 9.2 Tajikistan 360 5.8 Benin 370 5.3 Central African Republic 370 3.2 Albania 380 3.2 Ghana 410 16.6 Pakistan 430 126.3 Mauritania 480 2.2 Azerbaijan 500 7.5 Zimbabwe 500 10.8 44 Annex Table 2: Selected Worst Aspect Indicators by Countr Country Education Health and Nutrition Angola Access to Safe Water, 19% Rural Bangladesh Child Malnutrition 84% Benin Illiteracy 65% Maternal Mortality, Per 2500 100,000 Live Births Bolivia Educational Attainment, .3 yrs Indigenous No Schooling, Rural 51% Females Brazil Illiteracy, Rural Poor Heads -Brazil 60% -Northeast 70%/o Repetition, Grades 1-4, 74 to Northeast 49% Public Education Expenditure Incidence -Poorest Quintile 16% -Richest Quintile 24% Cambodia Access to Safe Water, 12 % Rural Child Malnutrition 40% Central Africa Access to Safe Water Republic -Rural 11% -Urban 14% Chad Primary Enrollment Ratio Maternal Mortality, Per 1594 -Bilitine 10% 100,000 Live Births -Batha 13% -Quaddi 15% Congo Access to Health Facilities 9% Ecuador Child Malnutrition 63% Egypt No Schooling, Rural 78% Public Health 1% Population Expenditure/GDP - continued 45 Annex Table 2: continued Country Education Health and Nutrition Eritrea Primary Enrollment Ratio Under 5 Mortality, Per -Sahel 5% 1,000 Live Births -Barka 9% -Dankalia 318 -Dankalia 13% -Sahel 287 -Gash-Setit 15% -Sernber 254 Grade Repetition, Female 29% Child Malnutrition -Hamasien 60% -Gash-Setit 54% Ethiopia Illiteracy 73% Access to Safe Water, 11% Rural Primary Enrollment Ratio, 21% Females Access to Health Facilities 10% Maternal Mortality, Per 1528 100,000 Live Births Child Stunting 64% Ghana Public Education Expenditure Incidence -Secondary -Poorest Quintile 14.9% -Richest Quintile 18.6% -Higher -Poorest Quintile 6% -Richest Quintile 45.2% Guatemala No Schooling, Indigenous 61% Guinea Illiteracy 86% Access to Health Facilities 14% Net Primary Enrollment 44% Ratio - continued 46 Annex Table 2: continued Country Education Health and Nutrition Guinea- Grade Repetition Life Expectancy 38 yrs Bissau -First and Second Level 43% -Overall 38% Access to Safe Water 25% Public Education .8% Expenditure/GDP India Educational Attainment, .5 yrs Child Malnutrition 63% Rural Females Education Budget For Salaries -Lower Primary 97% -Upper Primary 96% Indonesia Public Health .7% Expenditure/GDP Lao PDR Net Primary Enrollment 54% Access to Health Facilities 4% Ratio, Rural Public Health Expenditure 1% Madagascar Access to Health Facilities 3% Child Stunting 51% Malawi Illiteracy 59% Child Stunting 49% Net Primary Enrollment Ratio (Poorest) -Females 31% -Males 34% Public Education Expenditure Incidence -Poorest Quintile 16% -Richest Quintile 25% Mauritania Illiteracy 81% Child Stunting 57% Net Primary Enrollment, Rural, Female Poorest 4% Quintile - continued 47 Annex Table 2: continued Country Education Health and Nutrition Mongolia Public Education Expenditure/GDP -1990 5.9% -1994 1.8% 1994 Budget -Primary 28% -Secondary 62% Mozambique Illiteracy 67% Access to Safe Water, 17% Rural Primary Schools Closed 70% Access to Health Facilities 10% Schools Destroyed, Tete 90% Maternal Mortality, Per 1512 100,000 Live Births Child Malnutrition 30-40% Nepal Access to Health Facilities 6% Child Malnutrition 50% Nicaragua No Schooling, Children 6- 46% 14, Rural Matagalpa Niger Illiteracy, Rural Females 94% Net Primary Enrollment 12% Ratio, Rural Females Nigeria No Schooling, Rural Access to Safe Water, 20% -Female 65% Rural -Male 50% No Schooling -Northeast 82% -Northwest 83% - continued 48 Annex Table 2: continued Country Education Health and Nutrition Pakistan Illiteracy, Rural 81% Balochistan No Schooling, Rural 80% Population No Schooling, Children 57% 6-13, Rural Sind Peru Repetition, First Grade 32% Sierra Leone Primary Enrollment Ratio, 20% Access to Safe Water 33% Northern Province Somalia Public Health .9% Expenditure/GDP Sudan Public Health .5% Expenditure/GDP Suriname Public Education .8% Expenditure/GDP Syria Arab Public Health .4% Republic Expenditure/GDP Tanzania Female Illiteracy, 59% Public Hospital Subsidies Zanzibar -Poorest Two Quintiles 25% -Richest Quintile 32% Public Education Expenditure Incidence -Poorest Two Quintiles 28% -Richest Two Quintiles 57% Uganda Primary Enrollment Ratio -Kotido 21% -Morodo 26% Yemen Maternal Mortality, Per 1471 100,000 Live Births Zaire Public Health .8% Expenditure/GDP I Human Capital Development Dissemination Notes Title Date 1 Tobacco Death Toll February 11, 1993 2 The Benefits of Education for Women March 8, 1993 3 Poverty and Income Dist. in Latin America March 29, 1993 4 BIAS is Here April 12, 1993 5 Acute Respiratory Infections April 26, 1993 6 From Manpower Planning to Labor Market May 10, 1993 Analysis 7 Enhancing Invest in Education Through May 24, 1993 Better Nutrition and Health 8 Indigenous People in Latin America June 7, 1993 9 Developing Effective Employment Services June 28, 1993 10 Social Security: Promise & Pitfalls in July 12, 1993 Privat. Experience from Lat Am 11 Making Motherhood Safe August 2, 1993 12 Indigenous People & Socioecon Devel in August 30, 1993 LA: The Case of Bolivia 13 Participatory Poverty Assessment September 13, 1993 14 World Population Surpasses 5.5 Billion in September 27, 1993 1993 15 Alcohol-Related Problems October 12, 1993 16 Hidden Hunger - I October 25, 1993 17 Hidden Hunger II - Micro Mal November 8, 1993 18 Barriers/Solutions to Gender Gap November 29, 1993 19 Higher Education in Singapore December 13, 1993 20 Five Criteria for Poverty Programs January 3, 1994 21 Operations Evaluation at the Bank January 18, 1994 22 Vocational Education for Chilean Farming January 31, 1994 23 Poverty Reduction Strategy - The Grameen February 28, 1994 Bank Experience 24 Social Security I - The Need for Reform March 14, 1994 25 Hidden Hunger III - Anemia March 28, 1994 26 Cross-Subsidies April 11, 1994 27 Social Security II - The Elements of Reform April 25, 1994 28 Intemational Migration and Trade Part 1 of June 20, 1994 2 Human Capital Development Dissemination Notes Title Date 29 International Migration and Trade July 11, 1994 Part 2 of 2 30 Women in Higher Education August 1, 1994 31 The Unit Cost of Family Planning August 15, 1994 32 Zimbabwe: Determinants of Contraceptive August 29, 1994 Use at the Leading Edge of Fertility Transition in Sub-Saharan Africa 33 Determining Contraceptive Use in Three September 12, 1994 Leading Countries 34 Self Employment for the Unemployed September 26, 1994 35 Supporting Entrepreneurship by Low- October 17, 1994 Income Women 36 The ORT Miracle October 31, 1994 37 Women's Health and Nutrition November 16, 1994 38 Poverty Reduction and Deregulation of November 28, 1994 Argentina's Microfirms 39 Poverty, Deregulation and Microfirms Part December 12, 1994 II: Mexico 40 Community-managed Schools Program in El December 19, 1994 Salvador 41 Retraining of the Unemployed: What January 9, 1995 Impact? The Case of PROBECAT in Mexico 42 Hidden Hunger IV January 23, 1995 43 The Caribbean Public Information Center February 6, 1995 44 Bringing Market Forces to Workers' February 27, 1995 Training 45 Dominican Education Reform Makes the March 13, 1995 Grade 46 Cervical Cancer: Promising Approaches March 27, 1995 47 Nutrition and Early Child Development: Into April 10, 1995 the Year 2020 48 Labor Market Outcomes of Technical April 27, 1995 Training: The Case of Mexico 49 Community-Managed Health Care May 8, 1995 Programs In Mali. 50 Do Women Workers Gain or Lose During May 22, 1995 Economic Growth or Adjustment 52 Priorities and Strategies for Education June 19, 1995 Human Capital Development Dissemination Notes Title Date 53 Does Good Economic Analysis Lead to July 10, 1998 Better Projects? 54 Payroll Taxes: Why Should Workers August 14, 1995 Worry About Them 55 The Ecoregional Factor: New Perspectives August 28, 1995 on Malnutrition and Poverty 56 Savings and Education: Some Empirical September 18, 1995 Evidence 57 Key Indicators for Family Planning September 25, 1995 58 Involving Schools and Communities in October 2, 1995 Education: An Analysis of World Bank Experience (FY1970 to FY 1995) 59 The Economic Value of Contraception October 10, 1995 60 Costs and Benefits of Bilingual Education in October 23, 1995 Guatemala 61 Are Donor-Supported Structural Adjustment November, 6, 1995 Programs Responsible for Reducaitons in Public Spending on Health 62 Economic Justification of Training November 13, 1995 Interventions: Should They Satisfy Needs or Correct Market Failures? 63 Cervical Cancer: The Case of Mexico November 20, 1995 64 Bank Lending for Labor Markets: 1991 to December 4, 1995 1995 65 Labor Market Outcomes, Output Growth, December 11, 1995 and Population Growth 66 Appraising a Health Project: Economic January 8, 1996 Benefits of the Onchoceriasis Control Programme in West Africa 67 Techniques for Evaluating the Impact of February 5, 1996 Interventions. 68 Costs and Effectiveness of Retraining in March 4, 1996 Hungary 69 What are the Effects of School Choice March 18, 1996 Programs? Evidence from the United States 70 Evaluating Retraining Programs in OECD April 1, 1996 Countries 71 Education Achievements and School August 5, 1996 Efficiency in Rural Bangladesh 72 Reflect: Evaluating a New Approach to October 31, 1996 Adult Literacy Human Capital Development Working Papers Contact for Title Author Date paper HROWP1 Social Development is Nancy Birdsall March 1993 L. Malca Economic Development 37720 HROWP2 Factors Affecting Achievement Eduardo Velez April 1993 B. in Primary Education: A Ernesto Schiefelbein Washington- Review of the Literature for Jorge Valenzuela Diallo Latin America and the 30997 Caribbean HROWP3 Social Policy and Fertility Thomas W. Merrick May 1993 0. Nadora Transitions 35558 HROWP4 Poverty, Social Sector Norman L. Hicks May 1993 J. Abner Development and the Role of 38875 the World Bank HROWP5 Incorporating Nutrition into F. James Levinson June 1993 0. Nadora Bank-Assisted Social Funds 35558 HROWP6 Global Indicators of Nutritional Rae Galloway June 1993 0. Nadora Risk (II) 35558 HROWP7 Making Nutrition Improvements Donald A.P. Bundy July 1993 0. Nadora at Low Cost Through Parasite Joy Miller Del Rosso 35558 Control HROWP8 Municipal and Private Sector Donald R. Winkler August 1993 E. De Castro Response to Decentralization Taryn Rounds 89121 and School Choice: The Case of Chile, 1981-1 990 HROWP9 Poverty and Structural Ishrat Husain September 1993 M. Youssef Adjustment: The African Case 34614 HROWP1O Protecting Poor Jamaicans Margaret E. Grosh September 1993 M.E. Quintero from Currency Devaluation Judy L. Baker 37792 M. Rodriguez 30407 HROWP1 1 Operational Education George Psacharopoulos September 1993 L. Malca Indicators 37720 HROWP12 The Relationship Between the John Clark October 1993 P. Phillip State and the Voluntary Sector 31779 HROWP13 Obstacles to Women's Access: Joseph Kutzin October 1993 0. Shoffner Issues and Options for More 37023 Effective Interventions to Improve Women's Health HROWP14 Labor Markets and Market- Arvil V. Adams October 1993 S. Khan Oriented Reforms in Socialist 33651 Economies HROWP15 Reproductive Tract Infections, May T.H. Post October 1993 0. Shoffner HIV/AIDS and Women's Health 37023 HROWP16 Job Security and Labor Market Ricardo D. Paredes November 1993 S. Khan Adjustment in Developing 33651 Countries Human Capital Development Working Papers Contact for Title Author Date paper HROWP17 The Effects of Wage Indexation Luis A. Riveros November 1993 S. Khan on Adjustment, Inflation and 33651 Equity HROWP18 Popular Participation in Philip R. Gerson December 1993 L. Malca Economic Theory and Practice 37720 HROWPl9 Economic Returns from Edwin Mansfield January 1994 I. Dione Investments in Research and 31447 Training HROWP20 Participation, Markets and Deepak Lal January 1994 L. Malca Democracy 37720 HROWP21 Safe Motherhood in Patricia Daly January 1994 0. Shoffner Francophone Africa Michael Azefor 37023 Boniface Nasah HROWP22 Indigenous People and Poverty George Psacharopoulos February 1994 I. Conachy in Latin America Harry Anthony Patrinos 33669 HROWP23 Is Grameen Bank Sustainable? Shahid Khandker February 1994 S. David Baqui Khalily 33752 Zahed Khan HROWP24 Concepts of Educational Marlaine E. Lockheed March 1994 M. Verbeeck Efficiency and Effectiveness Eric Hanushek 34821 HROWP25 Scientific Research for Erik W. Thulstrup March 1994 L. Malca Development 37720 HROWP26 Issues in Education Finance Stephen P. Heyneman April 1994 B. Cassorla and Management in ECA and 37172 OECD Countries HROWP27 Vocational Education and Julian Schweitzer April 1994 A. Gonzalez Training: The Role of the 37799 Public Sector in a Market Economy HROWP28 Social Security Issues and Nguyen X. Nguyen May 1994 M. Espinosa Elements of Reform 37599 HROWP29 Health Problems and Policies Mary Eming Young May 1994 0. Shoffner for Older Women: An 37023 Emerging Issue in Developing Countries HROWP30 Language and Education in S.M. Cummings May 1994 M. Espinosa Latin America: An Overview Stella Tamayo 37599 HROWP31 Does Participation Cost the Jesko Hentschel June 1994 D. Jenkins World Bank More? Emerging 37890 Evidence HROWP32 Research as an Input into Harold Alderman June 1994 P. Cook Nutrition Policy Formation 33902 HROWP33 The Role of the Public and Deepak Lal June 1994 M. Espinosa Private Sectors in Health 37599 Financing Human Capital Development Working Papers Contact for Title Author Date paper HROWP34 Social Funds: Guidelines for Soniya Carvalho July 1994 K. Labrie Design and Implementation 31001 HROWP35 Pharmaceutical Policies: Graham Dukes July 1994 0. Shoffner Rationale and Design Denis Broun 37023 HROWP36 Poverty, Human Development Harsha Aturupane August 1994 P. Cook and Growth: An Emerging Paul Glewwe 30864 Consensus? Paul Isenman HRO HRO Working Paper Series August 1994 M. Espinosa Abstracts: Numbers 1-35 37599 HROWP37 Getting the Most out of Helen Saxenian September 1994 0. Shoffner Pharmaceutical Expenditures 37023 HROWP38 Procurement of Denis Broun September 1994 0. Shoffner Pharmaceuticals in World 37023 Bank Projects HROWP39 Notes on Education and Harry Anthony Patrinos September 1994 I. Conachy Economic Growth: Theory and 33669 Evidence HROWP40 Integrated Early Child Mary Eming Young October 1994 0. Shoffner Development: Challenges and 37023 Opportunities HROWP41 Labor Market Insurance and Deepak Lal October 1994 M. Espinosa Social Safety Nets 37599 HROWP42 Institutional Development in Alberto de Capitani October 1994 S. Howard Third World Countries: The Douglass C. North 30877 Role of the World Bank HROWP43 Public and Private Secondary Marlaine E. Lockheed November 1994 M. Verbeeck Schools in Developing Emmanuel Jimenez 34821 Countries HROWP44 Integrated Approaches to T. Paul Schultz November 1994 M. Espinosa Human Resource Development 37599 HROWP45 The Costs of Discrimination in Harry Anthony Patrinos November 1994 I. Conachy Latin America 33669 HROWP46 Physician Behavioral Nguyen X. Nguyen December 1994 M. Espinosa Response to Price Control 37599 HROWP47 Evaluation of Integrated T. Paul Schultz January 1995 M. Espinosa Human Resource Programs 37599 HROWP48 Cost-Effectiveness and Health Philip Musgrove January 1995 0. Shoffner Sector Reform 37023 HROWP49 Egypt: Recent Changes in Susan H. Cochrane February 1995 0. Shoffner Population Growth Ernest E. Massiah 37023 HROWP50 Literacy and Primary Kowsar P. Chowdhury February 1995 M. Espinosa Education 37599 Human Capital Development Working Papers Contact for Title Author Date paper HROWP51 Incentives and Provider Howard Barnum March 1995 0. Shoffner Payment Methods Joseph Kutzin 37023 Helen Saxenian HROWP52 Human Capital and Poverty Gary S. Becker March 1995 M. Espinosa Alleviation 37599 HROWP53 Technology, Development, and Carl Dahlman April 1995 M. Espinosa the Role of the World Bank 37599 HROWP54 International Migration: Sharon Stanton Russell May 1995 0. Shoffner Implications for the World 37023 Bank HROWP55 Swimming Against the Tide: Nancy Birdsall May 1995 A. Colbert Strategies for Improving Equity Robert Hecht 34479 in Health HROWP56 Child Labor: Issues, Causes Faraaz Siddiqi June 1995 I Conachy and Interventions Harry Anthony Patrinos 33669 HCOWP57 A Successful Approach to Roberto Gonzales July 1995 K. Schrader Partcipation: The World Bank's Cofino 82736 Relationship with South Africa HCOWP58 Protecting the Poor During K. Subbarao July 1995 K. Labrie Adjustment and Transitions Jeanine Braithwaite 31001 Jyotsna Jalan HCOWP59 Mismatch of Need, Demand Philip Musgrove August 1995 Y. Attkins and Supply of Services: 35558 Picturing Different Ways Health Systems can go Wrong HCOWP60 An Incomplete Educational Armando Montenegro August 1995 M. Bennett Reform: The Case of 80086 Colombia HCOWP61 Education with and with out the Edwin G. West September, 1995 M. Espinosa State. 37599 HCOWP62 Interactive Technology and Michael Crawford October 1995 P. Warrick Electronic Networks in Higher Thomas Eisemon 34181 Education and Research: Lauritz Holm-Nielsen Issues & Innovations HCOWP63 The Profitability of Investment George Psacharopoulos December 1995 M. Espinosa in Education: Concepts and 37599 Methods HCDWP64 Education Vouchers in Practice Edwin G. West February 1996 M. Espinosa and Principle: A World Survey 37599 HCDWP65 Is There a Case for Antonio Zabalza March 1996 M. Espinosa Government Intervention in 37599 Training? HCDWP66 Voucher Program for Alberto Calder6n Z. May 1996 M. Espinosa Secondary Schools: The 37599 Colombian Experience Human Capital Development Working Papers Contact for Title Author Date paper HCDWP67 NGO-World Bank Toshiko Hino June 1996 A. Thomas Partnerships: A Tale of Two 31151 Projects HCDWP68 The Disability-Adjusted Life Nuria Homedes July 1996 L Arias Year (DALY): Definition, 35743 Measurement and Potential Use HCDWP69 Equitable Allocation of Ceilings Philip Musgrove August 1996 Y. Attkins on Public Investment: A 35558 General Formula and a Brazilian example in the Health Sector HCDWP70 The Economics of Language: Barry Chiswick September 1996 I Conachy The Roles of Education and 33669 Labor Market Outcomes HCDWP71 Agricultural Growth and Rashid Faruqee September 1996 C. Anbiah Poverty in Pakistan Kevin Carey 81275 HCDWP72 Measuring the Opportunity Andrew D. Mason September 1996 D. Ballantyne Cost of Children's Time in a Shahidur R. Khandker 87198 Developing Country: Implications for Education Sector Analysis and Interventions HCDWP73 The Full Social Returns to Alain Mingat September 1996 J_ Yang Education: Estimates Based Jee-Peng Tan 81418 on Countries' Economic Growth Performance HCDWP74 Costs and Benefits of Bilirngual Hary Anthony Patrinos October 1996 I Conachy Education in Guatemala: A Eduardo Velez 33669 Partial Analysis HCDWP75 What is Education Worth? Robert Picciotto November 1996 R. Wiemann From Production Function to 84572 Institutional Capital HCDWP76 Human Capital HCDVP November 1996 R. Mattson Underdevelopment: The Worst 31144 Aspects