2.5.1 Linkages at the Micro Level

1.1. How Women’s Empowerment is Linked to Poverty Reduction and to MDGs 2 and 4

Facts/messages: Gender issues are relevant to poverty reduction in several ways. At the macro level, faster reduction of gender inequality would increase economic growth, as has been argued for the case of South Asia, for example. At the micro level, the participation of women in the labour force of low-income countries is often undermined by the vital roles they play at home, including the collection of fire woods and fetching drinking water, particularly in rural areas. While family structure and household composition do not have a significant impact on male participation in the labour force, they certainly do in the case of women, inasmuch as women without children earn more than those with children. The domestic functions of women, especially women with children, are vital, yet they are undervalued and unpaid and they effectively confine women to the household and to informal economic activities, rather than participating in the formal labour market where they could earn a wage. When policies are in place to attend to these issues, women participate more fully in the labour market and operate under better conditions. A specific area of intervention at the micro level which could yield important dividends for poverty reduction is the promotion of “reconciliatory policies”, to allow women to reconcile their productive and reproductive roles, thereby increasing the total income of poor households. In developed and middle income countries there is considerable evidence that providing affordable and reliable child care is crucial to women’s labour force participation, particularly in the formal sector.

Population behaviours are also linked to social participation. Traditionally, the reproductive and domestic role assigned to women has militated against their involvement in public life. As a result, an increase of women’s capacity to achieve their full decision-making potential in this field increases their autonomy in individual and social terms. Yet this increase is not sufficient in and of itself, as there is also a need for meaningful opportunities and real instruments to make these decisions a reality, as well as measures that give incentives to men to play their part in reproductive and domestic activities.

With regard to MDGs 2 and 4, it has been shown that households where women have a larger say in the redistribution of resources tend to allocate a larger share of resources to health and education. Hence, efforts to empower women help to ensure that a larger share of the social transfers is used to support the most vulnerable household members and thus has a stronger poverty reducing effect. This is the reason why most poverty reduction programmes based on conditional transfers make the cash transfers available to the oldest woman in the household, rather than to the household head per se. More specifically, there are many studies showing that female education (which may be a proxy for women’s influence on the distribution of resources) is the best predictor for the survival probability of newborns and children under age 5. In countries where female enrollment rates are substantially lower than male enrollment rates, it may be worthwhile to invest some time in analyzing the implications of eliminating these differences for future labour force participation rates, productivity, and ultimately for poverty.

In countries where sons are strongly preferred over daughters, poverty may also increase as a consequence of the tendency to have more children than are actually desired, just to attain the minimum desired number of sons. In contexts where women are more empowered, this is less likely to happen. Finally, in several countries, studies exist to evaluate the economic costs of gender violence, although mostly at the aggregate level, without a focus on poverty implications, in the strict sense. If such studies exist in the country, they should be referred to and, if possible, an attempt should be made to gauge the poverty effects of the aggregate economic costs identified in these studies.

Methodology: Analyze labour force participation rates by age and sex. If there are micro-data on poverty and household structure, it may be possible to perform more detailed analyses. The argument as to whether lower fertility contributes to the higher labour force participation of women and thereby to poverty reduction is quite intricate because of the ambiguity of the causal relationship. Unless previous studies exist in the country that attempt to untangle this relationship through econometric techniques, it may be better not to invest in its analysis. Another area that needs to be treated with caution is the relationship between female household headship and poverty as international evaluations in recent years suggest that this relationship is not as straightforward as was once thought. Rather than looking only at female headship, it is better to consider the entire structure of the household and its relationship to poverty, i.e. to compare households with similar compositions where the only major difference is the sex of the head of household. Although there are various methodologies for evaluating the economic costs of violence against women, applying them for the costing of violence against women from the ground up may be time-consuming, so building on pre-existing studies, if at all available, may be the only viable strategy. The analysis of conciliatory policies or of various kinds of discrimination or non-enforcement of rights can normally be carried out only in a qualitative manner, but if research is available dealing with productivity losses or gains among women and men based on these factors, it should be taken advantage of. UNFPA is conducting research on this subject in several countries. Analyzing the impact of raising female enrollment rates on future productivity and poverty requires fairly detailed information on female labour force participation rates by educational level at the macro level and at the household level, where the number of children and other determinants of female labour force participation have to be considered. Secondary effects, such as the effect of higher female education on marriage rates or on fertility behaviour may also have to be considered, inasmuch as they may have an effect on incomes and poverty. In some countries, studies may exist (or they may be prepared), based on DHS or other survey data, to assess the fertility-enhancing effect of son preference, which may then be used to estimate the effect on poverty at the household level.

Primary Sources:

  • National censuses or household or labour surveys for labour force participation rates by age and sex;
  • LSMS and other specialized surveys, including time use surveys. Analyses of the composition of households (including the sex of the head of household) are best based on LSMS or other surveys that contain both poverty data and information on household composition.

Secondary Sources:

  • Caldwell (1979). “Education as a factor in mortality decline: an examination of Nigerian data”. Population Studies 33:395–419. This is the standard reference on the effects of maternal education on child survival. It was later followed up by several other studies in different countries. Basu, A.M. (1994). “Maternal education, fertility and child mortality: disentangling verbal relationships.” Health Transition Review 4:207-15, for example, enters more into the interpretation of the observed relationship;
  • ICRW/UNFPA (2009). Intimate partner violence: high cost to households and communities, for the methodology of costing of violence against women;
  • Review of the national literature, legislation and public policies;
  • Specialized surveys about issues of violence or female employment in the country;
  • Day, McKenna and Bowlus (2005). The economic costs of violence against women: an evaluation of the literature. New York, United Nations;
  • Specific country studies on the economic costs of intimate partner violence in countries like Chile and Nicaragua.

1.2. How Reproductive Health and Reducing Unwanted Births Contribute to Poverty Reduction

Facts/messages: The number or the proportion of unwanted births is higher among the poor. More important is that unwanted births actually increase poverty, in several ways. Fewer (unwanted) children in the household imply a lower dependency ratio, i.e. fewer mouths to feed with the income earned by the same number of adults. In addition, fewer dependent children to care for make it easier for women to generate income by finding employment outside the home, even though it should be borne in mind that the relationship between fertility and female labour force participation is complex and controversial (see the previous section). For example, the combination of both effects has been estimated for the cases of Honduras and Colombia, suggesting that the effect of eliminating all unwanted fertility on poverty would be equivalent to raising the incomes of the poor by 10-20 %.

There is also evidence that households with fewer children have higher intra-generational mobility rates. For example, urban Nicaraguan families with little education and 4 or more children living in extreme poverty in 1998 had a 57 % chance to continue living in this situation in 2001, but those who had fewer than 4 children had only a 36 % chance to continue in extreme poverty. More recently, higher socio-economic upward mobility of families with fewer children was also found in a study of Nairobi slum areas.

The benefits of family planning (FP) and sexual and reproductive health (SRH) programmes resulting from their effect on fertility have historically been the most publicized, but there is growing evidence that they also improve women’s health, productivity and economic prosperity in other ways. Some researchers have found statistically significant associations between FP interventions and improved economic security for families, without detailing the specific pathways through which the relationship operates. Poor health and nutrition due to poverty during their own childhood and adolescence, compounded by early childbearing, mean that pregnant women have higher risks of maternal and child morbidity and mortality. One should analyze whether policies promoting access to SRH in the country have had direct repercussions for the budgets of (poor) families, reducing expenditures on services associated with SRH or improving the quantity and quality of these services for the same expenditure. Access of the population to medication, especially those drugs that play an important role in SRH, such as contraceptives, antibiotics, antiretrovirals etc., and the differential impact of such access on the budgets of households may also be of interest.

The financial mechanisms in the previous paragraph have to do with routine expenses and how saving on them may increase real income. A different financial mechanism that can push a household into poverty is a major episode of illness, particularly of one of its active members, resulting in so-called “catastrophic health expenditures”. An important element of the analysis, therefore, is how households cover their expenditure on SRH: out-of-pocket disbursements, private insurance schemes, or through services supported with public funds. A high share of out-of-pocket health expenditure can be a major obstacle to achieving poverty reduction and the MDGs. Moving to the establishment of prepayment schemes (e.g. social health insurance) and pooling risk across individuals while putting in place incentives to promote the efficient and responsive provision of care should go a long way in curbing this kind of poverty determinant. To the extent possible, this analysis should not only address health services, but also actions to promote health, prevention and health care assistance, even when in these cases the relationships can be more difficult to quantify. Less evidence is available on the costs associated with insufficient SRH care than on other health issues, including HIV/AIDS (see next section), but one should try to make the best of whatever evidence can be obtained, bearing in mind that unintended pregnancies, for example, also imply short-term costs to households. Another issue deserving consideration is maternal mortality and the economic cost, which it can have for families.

In general, one of the most important pathways of interaction between health and poverty passes through the increase in the average productivity of individuals due to the decline of their morbidity. This connection may be more difficult to establish, especially from a quantitative perspective, in the case of reproductive health issues than that of major debilitating diseases. Nevertheless, one should analyze the morbidity trends in the country and especially morbidity associated with reproductive issues, establishing the way in which it impacts on productivity and thereby on poverty in households.

Methodology: The problem of estimating the effect of unwanted fertility on poverty is that these pieces of information are available from different sources: DHS or other reproductive health surveys in the former case and LSMS or other household surveys in the latter. The estimation methodology consists in using the former surveys to develop a regression model for the expected number of unwanted children under 15 as a function of the total number of children, the age of the woman and some socio-economic stratification variable(s). This model is then applied to the latter kind of survey to estimate how many unwanted children there are in each household and by how much per capita income would increase if they were not part of the household. In addition to assessing the effect on poverty, one may also want to quantify the effect on the inequality of per capita incomes. In Section 1.1 of Chapter IV, an assessment has probably been made of how unwanted fertility varies by income strata or at least by wealth quintiles, with the likely conclusion that unwanted fertility is much higher in the lower income strata than among the rich. Considering that the reduction of unwanted fertility raises income per capita, it should be possible to make an approximate assessment of how much income or consumption inequality will be reduced by specific changes in the percentage of unwanted births by income or consumption level.

The intra-generational mobility study is more difficult to carry out because, strictly speaking, it requires knowing the poverty status of the same households at two different points in time. Sometimes this information is available if a poverty survey has been carried out in panel format, as in Nicaragua and Peru. If only the number of households by poverty status and number of children is known at two different points in time, without information on the transition of households between poverty statuses, it is still possible to estimate approximate transition possibilities or to apply direct standardization methods, but the number of assumptions that one has to make increases.

Analyze the expenditures at the household level associated with different health interventions, such as antenatal checkups, childbirth, routine gynecological exams, emergency obstetric care, hospitalization due to cancers of the reproductive system, prevalence of breast cancer, number of pap smears carried out, vaccinations given to women and children, purchase of contraceptives etc. and also the costs of different kinds of essential drugs. In all these cases, the goal is to analyze not only the frequency of curative or preventive actions, but also mechanisms through which the population has access to such services (e.g. contraceptives that are freely distributed by the Ministry of Health or bought in pharmacies). Indicators for quality of care include the number of antenatal checkups, the frequency of routine gynecological examinations, the level of training of birth attendants, and available information concerning the quality of health-care centres. What should be attempted here is to establish links between these expenses and broader poverty issues, and not simply to evaluate the situation or overall cost of SRH in the country. Neither is this the place to review how access to SRH varies by social strata, since this issue should have been addressed in Chapter IV, although it is appropriate to evaluate the differential impact of SRH interventions on different social strata, in order to see how they would contribute to the reduction of social inequality. When assessing repercussions on poverty, it is important to consider the share of treatment costs that are paid out of pocket by the patient and his/her family. The World Health Organization (WHO) has developed methodologies for estimating the incidence of catastrophic health expenditures in general, but estimates for specific costs associated with SRH are lacking.

Rarely, it is possible to analyze the trajectory of individual households with respect to morbidity and poverty over time. The use of Disability Adjusted Life Years (DALYs) for quantifying the impact of morbidity on poverty is not recommended, given that this is a macro indicator that was developed for other purposes and does not have a clear interpretation when measuring impacts on poverty, especially at the household level.

Primary Sources:

  • Statistics of the Ministries of Health, health care authorities and social security institutes;
  • DHS surveys and other surveys of SRH (evaluation of emergency obstetric care needs)
  • Household surveys;
  • Living Standards Measurement Study (LSMS) type or income and expenditure surveys of another type;
  • Qualitative studies and interviews with key informants to complete the information.

Secondary Sources:

  • WHO. (2003): Studies on catastrophic health expenditures, such as Xu et al. “Household catastrophic health expenditure: a multicountry analysis”. The Lancet 362: 111-117;
  • UNFPA. (2010) Reproductive Health Costing Tool;
  • Project RLA5P201. The effect of avoiding unwanted fertility has been investigated in some detail in Project RLA5P201, particularly in Research Papers 8 and 11;
  • UNFPA. Impacts of population dynamics, reproductive health and gender on poverty (PDB/TD, 2010). This paper underlies much of the discussion in this section;
  • Hakkert (2007). Un análisis del efecto de la fecundidad no deseada sobre la pobreza a nivel de los Departamentos y zonas de residencia de Honduras. Brasília, UNFPA/IPEA Proyecto RLA5P201, Documento de Investigación 11. Alonso et al. (2008). Informe del estudio en profundidad de Colombia. Brasília, UNFPA/IPEA Proyecto RLA5P201, Documento de Investigación 11.

1.3. How HIV/AIDS is Linked to Other MDG Outcomes

Facts/messages: In the case of AIDS, the effects of the catastrophic health expenditures mentioned in the previous section are particularly crippling. AIDS can drive households into poverty for a number of reasons, including loss of income and property, while finding money for health care and funeral costs. AIDS affects adults in the prime active ages, tends to require prolonged expenditures, and ultimately brings about a large number of orphans. Productivity losses and loss of human capital in the present generation (e.g. deaths of substantial numbers of school teachers in many African countries) have direct implications for poverty in the short run, as do the direct costs associated with the treatment of AIDS patients. Poor female-, and increasingly grandmother-headed households that care for AIDS-orphans have very few coping capacities to re-establish self-sustaining livelihoods. Children may need to drop out of school and, especially if they become orphaned, are less likely to complete primary education. This in turn will have consequences for the incidence of poverty in the next generation. As in other sections of this chapter, the challenge consists not so much in identifying these relationships as in trying to quantify them.

Apart from direct effects at the micro-level, disease affects poverty at the level of the economy as a whole, where economic growth rates may suffer systematically as a consequence of major epidemics. World Bank estimates suggest that when the prevalence of HIV/AIDS reaches 8 % – which it is today in 13 African countries – annual GDP growth falls by about 1 %. The relevance of an increase in these rates because of HIV/AIDS has other economic implications. In addition to the reduction of labour supply, there is a decline in productivity as a consequence of increased morbidity.

Methodology: Several African countries have either Epidemiological Surveillance Sites or special surveys that allow the measurement of the impact of AIDS on the households affected, either in monetary terms or in terms of social costs. Data on AIDS-orphanhood can be obtained from DHS or MICS surveys. WHO has developed methodologies for the measurement of catastrophic health expenditures which are particularly relevant to the case of HIV/AIDS. For methodologies to measure the impact of HIV/AIDS at the macro-level, consult the HIV/AIDS website at the World Bank.

Primary Sources:

  • Both DHS and MICS surveys allow for the estimation of AIDS-orphanhood in many countries.
  • Epidemiological Surveillance Sites

Secondary Sources:

  • Children on the brink is a joint UNICEF/UNAIDS biannual publication that tracks the incidence of AIDS-orphanhood.
  • Ke Xu (2003). “Household catastrophic health expenditure: a multicountry analysis.” The Lancet vol. 362: 111-117.

1.4. How the Better Use of Household Resources and Better Birth Spacing are Linked to Poverty and Malnutrition

Facts/messages: If poverty is conceptualized in the way proposed in the capability framework, one of its essential components is how income and other resources are converted into actual capabilities, i.e. potential choices for wellbeing. One might think that larger households have greater economies of scale, but because over 70 % of consumption/income near the poverty line is food consumption, there is less room for such economies. To the extent they exist, they are countered to a much greater degree by the adverse effects of crowding and resulting risk of infection and by the waste of resources that occurs when closely spaced births lead to higher infant mortality. This makes it is more costly for these households to generate an additional household member, even if the economic context favours a relatively large number of children, e.g. because children represent an old age investment for the parents. This, in turn, makes it more difficult for households to rise out of poverty. It has been suggested that much can be done to reduce inefficiencies by providing information on nutrition and basic hygiene. Obviously, better SRH care is also an important ingredient, but again the argument is more forceful if it can be quantified.

In addition to its economic determinants, the malnutrition of mothers and children is also affected by reproductive patterns, especially birth spacing intervals and, to a lesser extent, by the age of the mother, by birth orders, and whether or not the birth was wanted or not. Action to influence these variables (mainly birth spacing intervals and ensuring that every child is wanted) can reduce malnutrition in these groups by several percentage points. It is clear that malnutrition also varies as the result of other factors, notably the socio-economic level of the family, the age of the child and the area of residence, and these should be taken into account.

Methodology: The DHS contain various indicators of chronic and acute malnutrition of children under the age of five years: weight for height, height for age, and weight for age. Normally the most interesting indicator is chronic malnutrition, expressed by insufficient height for age. One should investigate how this relationship is associated with the reproductive factors mentioned before among various population segments, and what is their potential for reduction by means of suitable policies in the field of SRH. Given that malnutrition is also associated with other factors, it is necessary to perform the analysis carefully, using multivariate statistical models, to control the interference of these factors. The object of the exercise is not so much to show the existence of the relationship, which has been amply documented, but to estimate the extent to which this has contributed to reducing malnutrition in children under five years (both the average and the distribution between strata) in recent periods or how this will be able to contribute in the future. As is the case with other issues in this chapter, the relationship can be looked at in average terms (i.e. how much child malnutrition can be reduced in the aggregate through better birth spacing) or in terms of inequality between social strata. Because both short birth intervals and child malnutrition are mostly concentrated in the lower income strata, it is to be expected that better birth spacing will reduce inequality between income strata in terms of child malnutrition and this relationship can likely be quantified.

The issue of maternal malnutrition is normally more difficult to investigate due to the lesser abundance of data on the nutritional status of mothers, but some information does exist (e.g. the Administration Committee on Coordination–Sub-Committee on Nutrition (ACC/SCN) study. However, in some countries there are special surveys about nutrition, even for adults.

Primary Sources:

Secondary Sources:

  • UNFPA. Impacts of population dynamics, reproductive health and gender on poverty (PDB/TD, 2010) for more information on the capability approach and the conversion concept;
  • Administration Committee on Coordination–Sub-Committee on Nutrition (ACC/SCN). 1990. Women and nutrition. Symposium report, Nutrition Policy Discussion Paper No. 6;
  • Conde Agudelo (2002). Optimal birth spacing: new research from Latin America on the association of birth intervals and perinatal, maternal and adolescent health. Washington DC, Catalyst Consortium.

1.5. How Population Factors at the Household Level are Linked to the Formation of Human Resources (MDG 2)

Facts/messages: At the micro level, there are two issues that merit attention here. The first has to do with how family composition affects the educational performance of children. It is frequently stated that one of the advantages of having a small family is the possibility to invest more in the education of each child. There is even some evidence to this effect, from studies undertaken by Cynthia Lloyd and other researchers during the 1990s. The more recent literature, which tends to be more stringent in terms of its econometric requirements on the evidence, has been more skeptical, although the results generally tend to be stronger if the focus is not just the number of children, but also their spacing and other issues with respect to relative crowding. The other issue refers to teenage pregnancy and its effects on the educational outcomes for the teenage mother.

The other two issues are actually macro-issues, but because of their implications for human capital formation, they are addressed here. One has to do with the profile (quantity and quality) of the demand for education, especially the needs and opportunities for human capital formation deriving from the demographic bonus. As birth rates fall, the number of children requiring schooling diminishes, thereby placing less demand on the school system. On the one hand, this reduces the need for investment in the educational infra-structure, just to keep up with the growing number of children. On the other hand, it creates opportunities for enhanced investments in the quality of schooling. Both of these effects (the former for several Latin American countries, the latter only for the case of Brazil) are analyzed in a study by Soares, written as a part of the RLA5P201 project, using the demographic bonus as a reference concept. The other, parallel issue concerns the profile of the demand for health services, including SRH. Many types of health costs are strongly dependent on age, with most of the “cheap” health interventions occurring at younger ages, whereas the “expensive” health interventions tend to be more typical of older ages. SRH interventions, of course, affect primarily though not exclusively women of reproductive age. As age structures change, so do overall health costs and their composition by category. Population projections can be used as an instrument to anticipate and quantify these trends.

Methodology: The analysis of factors in the family environment on the school performance of children should be based on multivariate analyses. However, given what was said above, unless relatively sophisticated econometric analyses of this issue exist in the country, it may be better not to pursue it. In the case of adolescent fertility, care should be taken not to draw naive conclusions based on the direct comparison of the educational indicators of adolescent women that did or did not get pregnant because these women are also different in many other ways, including their educational motivation. At the very least, it is necessary to include certain controls for rural-urban residence and socio-economic stratum. But even this may not be enough. In ideal cases, one may be in a position to compare twins with different histories of pregnancy during adolescence. Barring that, econometric techniques such as instrumental variables may be needed to control the simultaneity bias. Needed resources in education can be projected, based on the assumption of continuity of the historic trends in school enrolment by age. One can also investigate how the demographic transition is influencing school enrolment and educational lags.

A particularly interesting attempt to integrate educational issues with population dynamics, although at the macro rather than the micro-level, is the work developed in recent years by Wolfgang Lutz, on integrated population projections which consider not only the evolution of the basic demographic variables, but also of education and its impacts on fertility. These projections are being executed for all countries of the world and are available from the International Institute for Applied Systems Analysis (IIASA) or the Vienna Institute for Demography (VID). For the study of relative and absolute demographic bonuses in the education system, use the study by Soares cited below. Use population projections by age and sex, in conjunction with age-specific health expenditures by categories of diseases to project the total cost of health care implied by demographic trends and its composition by categories.

Primary Sources:

  • Civil register;
  • Surveys of the DHS type for adolescent fertility rates. Unfortunately, the DHS do not provide much information about the relationship between early pregnancy and school dropout rates, nor about the school performance of children. In countries where reproductive health surveys based on the Centers for Disease Control (CDC) methodology are available, the latter information does exist sometimes;
  • Household surveys of the LSMS type allow for the analysis of school performance by some variables of the family structure, but they do not have information about some relevant issues in this context, such as wanted fertility;
  • National population projections. The basic information about age and sex structure can be obtained there;
  • National censuses for enrollment rates by age and sex.

Secondary Sources:

  • UN Population Division. World Urbanization Prospects. The basic information about age and sex structure can be obtained from population projections. Available at: http://esa.un.org/unpp/ index.asp;
  • Project RLA5P201. Research Paper 11 includes an example of the use of controls, instrumental variables, for the determination of the educational effect of teenage pregnancy;
  • Soares (2007). Relative and absolute demographic bonus in schooling. UNFPA/IPEA Project RLA5P201, Research Document 1;
  • Wolfgang Lutz (2009). “Sola schola et sanitate: human capital as the root cause and priority for international development”. Phil. Trans. Royal Soc. B 27: 364:3031-3047. doi:10.1098/rstb.2009.0156.

1.6. How Reproductive Health is Linked to the Other Health MDGs

Facts/messages: The objective of the PSA is to advance in the quantification of the relationships between reproductive health and other health issues and, if possible, even to estimate budgetary impacts. A typical example of the latter would be the study by Moreland and Talbird, which estimated that, for every dollar spent in family planning, 2-6 dollars can be saved in interventions aimed at achieving the MDGs for health and other issues.

The level of fertility has a number of health impacts in other areas. It is well known that infant and child mortality vary by birth order, even if other factors are controlled for, and that children of high birth orders (4 or higher) are typically at an increased risk. Similarly, it has been established that fertility is correlated with maternal mortality. To some extent, this relationship is mechanical as each additional pregnancy brings with it a new episode of maternal mortality risk. But the cross-country evidence collected in the process of defining the 2008 maternal mortality estimates suggests that the relationship goes beyond that and that even the Maternal Mortality Ratio is affected by fertility levels.

Short birth intervals (less than 36 months) are known to have a number of negative health effects on the neonatal, infant, under-five mortality and nutritional status of children. Effects on the health status of the mother are less well-documented, except for those of very short birth intervals. Several studies have demonstrated the negative effects of very low maternal ages on child survival. With respect to maternal mortality, there is substantial evidence that very low maternal ages (16 or lower) are an important risk factor; ages of 18 years or higher do not appear to represent any special risk, except for the fact that women who give birth at these ages are generally poorer and therefore more vulnerable to complications than those who do so later.

A different set of benefits derives from the promotion of condom use which, apart from its contraceptive function, provides protection against STIs and particularly HIV. With respect to the risks and benefits of using oral contraception the evidence is mixed. While there is considerable evidence that oral contraception reduces the risk of ovarian and endometrial cancer, there are also indications that it leads to a slight increase in the risk of breast and liver cancer, as well as the incidence of circulatory problems.

These are the direct benefits of family planning. There are, however, several more indirect benefits deriving from the fact that women who are planning their families are also being drawn into the wider primary health system, where they enjoy a wider range of reproductive health and other health services. Systematic overviews of these benefits can be found in a booklet published by the Wolrd Health Organization (WHO) in 1995, entitled Health Benefits of Family Planning, and in Family Planning Saves Lives (4th edition), published by the Population Reference Bureau in 2009. Reproductive health, of course, also includes several other elements that improve the health of mothers and children, including antenatal care, the promotion of breastfeeding, and counseling for STIs and HIV/AIDS.

Methodology: If specific studies are available in the countries, that quantify the importance of any of these links, they should be used. Otherwise, one may resort to international studies that quantify these impacts and, based on the national indicators on family planning and reproductive health, try to assess their impact on the wider quality of health. For methodologies to assess potential budgetary impacts see the previously cited study by Moreland and Talbird. As mentioned earlier, there are also a number of costing models that exist which reflect the dynamics between investments in family planning and the resulting impacts on fertility including the SPECTRUM tools (Futures Institute), Reproductive Health (RH) Costing Tool by UNFPA, Marginal Budgeting for Bottlenecks (MBB) by the UNICEF and the Unified Health Model by the Inter-Agency Working Group on Costing (IAWG Costing). See Section 2 of Chapter III for the references.

The Rapid Assessment Tool for Sexual and Reproductive Health and HIV Linkages: A Generic Guide was designed to assess HIV and sexual and reproductive health bi-directional linkages at the policy, systems (partnerships, coordination, capacity building, logistics, monitoring and evaluation, etc.) and service-delivery levels. The objective is to identify gaps and contribute to the development of country-specific action plans to strengthen linkages. It acknowledges the importance of and outlines the principles behind SRH and HIV linkages and the need for a comprehensive approach to strengthen such linkages.

Primary Source:

  • DHS and Reproductive health surveys administered by the CDC were specifically designed to address these issues.

Secondary Sources:

  • WHO (1995). Health Benefits of Family Planning. Geneva, WHO, Division of Family Health;
  • Population Reference Bureau (2009). Family Planning Saves Lives (4th edition). Washington DC, PRB.

Tools:

  • SPECTRUM tools (Futures Institute);
  • UNFPA (2010) Reproductive Health (RH) Costing Tool;
  • UNICEF. Marginal Budgeting for Bottlenecks (MBB);
  • Unified Health Model by the Inter-Agency Working Group on Costing (IAWG Costing). See Section 2 of Chapter III for the references;
  • The appropriate use of costing tools can help shape national health policies, strengthen advocacy for increased investments to achieve health targets, and inform planning and budgeting processes. But how can stakeholders select, and access, an appropriate costing tool? To help users such as policy makers, technical staff, technical assistance agencies, and non-governmental organizations, decide which costing tool to use, several international development partners have developed an interactive online costing tool guide. Available at: http://apps.who.int/pmnch/topics/costingtool/.

89   See, for example, Doss, C. (1996). “Testing among models of intrahousehold resource allocation.” World Development 24(10):1597–609; Thomas, D. (1990). “Intra-household allocation: an inferential approach.” Journal of Human Resources 25(4):635–64; A.R. Quisumbing, ed. (2003). Household decisions, gender, and development: a synthesis of recent research. Washington, D.C.: International Food Policy Research Institute.
90   Andersen (2004). Proyecciones de población y pobreza para Nicaragua 1995-2015. Mexico City, CST/UNFPA. Ousmane Faye (2009). Poverty dynamics in Nairobi’s slums, testing for state dependence and heterogeneity. Paper presented at the IUSSP Conference in Marrakesh.
91   For example, Joshi and Schultz (2007). Family planning as an investment in development. Evaluation of a program’s consequences in Matlab, Bangladesh. New Haven CT, Yale University Economic Growth Center Working Paper.
92   An alternative strategy is to do the opposite, i.e. try to estimate income or consumption (and not merely wealth quintiles) from the data available in the DHS or other reproductive health surveys.
93   This is one of the three links discussed in Eastwood, R. and M. Lipton (2001). “Demographic transition and poverty: effects via economic growth, distribution, and conversion”. In: Birdsall et al. (eds.). Population matters: demographic change, economic growth, and poverty in the developing world. New York, Oxford University Press.
94   Lloyd (1994). “Investing in the next generation: the implications of high fertility at the level of the family.” In: Cassen (ed.). Population and development: old debates, new conclusions. Washington DC, Overseas Development Council.
95   Moreland and Talbird (2006). Achieving theMillennium Development Goals: The contribution of fulfilling the unmet need for family planning. Washington DC, USAID.
96   WHO/UNICEF/UNFPA/World Bank. Trends in Maternal Mortality: 1990 to 2008. Geneva, WHO.
97   See, for example, Rutstein (2005). “Effects of preceding birth intervals on neonatal, infant, under five-years mortality and nutritional status in developing countries: evidence from the Demographic and Health Surveys.” International Journal of Gynaecology and Obstetrics 89: S7-S24.
98   See, for example, Hobcraft, McDonald and Rutstein (1985). ”Demographic determinants of infant and early child mortality.” Population Studies 39 (3): 363-385.