2.5.2 Linkages at the Macro Level

2.1. How Population Growth is Linked to Development and Poverty Reduction at the Macro Level

Facts/messages: The association between poverty and population dynamics through its effect on economic growth is probably the most traditional, at least in the economic literature. In the past, inverse correlations between demographic and economic growth have often been difficult to demonstrate, but this is in part because many of the early models were not correctly specified, e.g. because they did not distinguish between the effects of fertility and mortality. In a more recent study of 86 countries, fertility and mortality effects, duly separated, have been credited with causing 21 % of the average economic growth of 1.5 % between 1960 and 1995. In terms of poverty reduction effects, it was estimated that the average poverty incidence in 45 countries would have fallen by one-third if the crude birth rate had fallen by an additional 5 per 1,000 in the 1980s. Historically, much emphasis has been placed on the presumably higher savings in households with fewer children. This may be a relevant factor in Asia, where household savings rates have historically been high, but Latin American economists have typically been skeptical about this link in the context of the region of Latin America and the Caribbean, where household savings are low.

The economic growth argument is not limited to mere population growth. Since a skewed income distribution also tends to be associated with high differentials of fertility rates between income groups, the effect of fertility reduction on poverty can even be stronger in high inequality contexts. It has also been suggested that faster reduction of gender inequality, inter alia, would enhance economic growth, e.g. in South Asia, where this reduction has been slow. If South Asia had advanced as rapidly as East Asia, some argue that its annual economic growth between 1960 and 1992 might have been higher by 0.7 percentage points. In turn, population growth not only affects the level of per capita growth, it also affects the distribution of economic resources. In countries with rapid population growth, returns to labour tend to fall faster than returns to capital, thereby effectively increasing income inequality.

Although fertility decline can boost economic growth and help to reduce poverty, the poverty effects also depend on where fertility decline takes place. In situations where the fertility amongst the poor households is declining at a slower rate than fertility among non-poor households, the poor population will have an intrinsic tendency to grow in relation to the non-poor population, unless this tendency is counteracted by economic mobility.

Methodology: The empirical demonstration of the savings effect, in countries where this is relevant, is not always feasible, due to the difficulty of obtaining information from traditional economic sources, such as Central Bank and others, and the difficulty of interpreting data from household surveys that refer to different cohorts, at different times in their life cycle. It is recommended not to analyze this effect in detail unless previous research has been carried out in the country. The line of argument that associates population growth with different categories of social spending (demographic investments versus productive investments) was very common in the 1960s and 1970s, when resources such as the Long-Range Planning Model (LRPM) and RAPID were developed to assist in the interpretation of data. In order to use LRPM and other resources, one normally needs age and sex-specific population projections first. Both the population projections and their application in the analysis of the social and economic consequences of high fertility and rapid population growth for such sectors as labor, education, health, urbanization, and agriculture are part of the SPECTRUM package that is distributed by USAID: the former in the DemProj module and the latter in the RAPID module. The latter is intended to raise policy-makers’ awareness of the importance of fertility and population growth as factors in social and economic development. However, neither the population projections nor the results of LRPM or RAPID are customarily differentiated by poverty strata.

Another potentially useful resource is the Threshold 21 modeling software developed by the Millennium Institute. This is a dynamic simulation tool designed to support comprehensive, integrated long-term national development planning. It supports comparative analysis of different policy options, and helps users to identify the set of policies that tend to lead towards a desired goal. This insight into how different indicators of development interact with one another to produce an outcome deepens users understanding of development challenges. Customized country models are based on the T21 Starting Framework, a set of interconnected sectors combined into a macroeconomic framework. The sectors and their interrelations represent the fundamental mechanisms that are responsible for socio-economic development. Once a country identifies its vision, and key goals are determined, it generates scenarios describing the future consequences of the proposed strategies. T21 is an especially useful tool for preparing Poverty Reduction Strategies based on the MDG framework, and for monitoring progress towards the MDGs or other national goals. More specifically, T21 supports stakeholder consultations, preparing strategy documents that address sectoral or industrial interests, preparing data and analyses for loan negotiations, and monitoring and evaluating national plans. To date there are more than 15 unique, customized T21 models with applications in less-industrialized countries such as Jamaica, Malawi, Mozambique, and Bangladesh, and industrialized countries such as the United States and Italy.

Primary Sources:

  • National Statistical Offices: Participation rates can be obtained directly from the CSO or calculated by means of household surveys of the Living Standards Measurement Study (LSMS) type;
  • National accounts (Central Banks) or household budget surveys (NSOs) for data on household savings.

Secondary Sources:

  • UN Population Division: Population projections;
  • ECLAC. Long-Range Planning Model (LRPM) programme;
  • IADB: Country Cases (for example Mexico) regarding the rates of saving by phase of the household life cycle;
  • More information and resources with respect to Threshold 21 can be found at http://www.threshold21.com.


2.2. How Changes in Age Structures and Ageing are Linked to Poverty Reduction and Development

Facts/messages: The increase of the number of people in active ages (15-64 years) compared to inactive ages (notably the 0-14 year age group) has been widely publicized as the “demographic bonus”. It has been suggested that this bonus – or dividend or window of opportunity as it is sometimes referred to – explains up to one third of Asia’s economic miracle. In principle, Sub-Saharan Africa could also benefit from a demographic bonus as it enters the next stage of its demographic transition, but for countries to reap this bonus it is equally important the youthful population finds productive and remunerative employment. Furthermore, numerous countries in Africa are confronted by an erosion of their labour force because of the rapid spread of communicable diseases, particularly HIV/AIDS. Critics of the concept of the “demographic bonus” argue that this advantage is nonexistent if one does not have the capacity necessary to absorb the entire economically active population productively. This concern, however, may be exaggerated. The only situation under which the demographic bonus would actually have negative consequences is if the effective demand created by the population under 15 years that ended up not being born would have been sufficient to employ the population whose absorption into the labour force was complicated by the “bonus”.

Methodology: The two methodologies discussed here are both relatively complex, although the NTA methodology more so than the computations involved in DMPAP. In the countries that are part of the National Transfer Accounts (NTA) project, it may be possible to take advantage of the results of the local country study. The Demographic Model for Poverty Analysis and Projection (DMPAP) is based on household surveys and makes a breakdown of the income of each household in one component that is strictly economic and another that is associated with the composition of the household. It then carries out projections in which this household structure varies and evaluates the impact of these changes on the incidence of poverty. The latter are based on demographic projections by age and sex and economic projections that have been previously prepared (generally the country’s official projections) which serve as a basis for defining the parameters of 1) aggregated economic growth; 2) change in the Gini index; and 3) specific assumptions regarding the way in which aggregate demographic change affects the composition of families. The other factor that determines results is the profile of the income-generating capacity of household members by age and sex can be determined in a number of ways, depending on available information in the country. This latter procedure currently constitutes one of the most complicated issues in the use of the methodology. In addition to the projection procedure (DMPAP), a simple methodological resource has been developed based on the direct standardization of the compositions of households, to quantify the contribution of changes in these factors to changes in the incidence of poverty in the past. To the extent that there is an increase in the number of heads of households as a percentage of the population and there is a reduction in dependency ratios, both in poor and non-poor families, the trend points towards a reduction in poverty. In Brazil, for example, the National Report on the Millennium Development Goals (2007) acknowledges for the first time that the change in demographic structure was one of the four factors responsible for the reduction in poverty over the last decade. The quantification of these effects is also important for defining targets, so that countries do not set poverty reduction targets whose fulfillment is already built into current population trends.

For the analysis of the demographic bonus, the main indicators are the demographic dependency ratio and the economic dependency ratio. Here one can also use LRPM, but in general it is easier simply to use a spreadsheet containing the projected populations by sex and age and a set of economic participation rates.

Primary Source:

  • Household surveys of a LSMS type or income;
  • Expenditure surveys;
  • DHS surveys. Available at www.measuredhs.com;
  • Surveys of sexual and reproductive health.

Secondary Sources:

  • Kannan Navaneetham (2002). Age Structural Transition and Economic Growth: Evidences from South and SouthEast Asia. Asian Metacentre, Singapore, Research Paper 7. This study analyzes Age Structural Transformations in Bangladesh, India and Sri Lanka, (Indonesia, Malaysia, Philippines, Singapore and Thailand and their influence on economic growth in the 1950-92 period. It controls for macroeconomic variables such as investment share of GDP, net foreign balance, share of public consumption expenditure, inflation rate and openness. It concludes that the demographic bonus had a positive impact on economic growth in all Southeast Asian countries except the Philippines. The South Asian countries did not perform well in terms of economic growth at the onset of the window of opportunity. The results also indicated that countries that have had open economies and had excellent human capital benefited more. The DMPAP model is described in Research Paper 3 of Project RLA5P201;
  • ECLAC: Social Panorama. Useful information about families;
  • For more information on the NTA project, consult the NTA website at http://www.ntaccounts.org;
  • Hakkert (2007). The demographic bonus and population in active ages. Brasília, UNFPA/IPEA Project RLA5P201, Research Document 7.


  • UNFPA: The Demographic Model for Poverty Analysis and Projection (DMPAP) model requires household surveys of a LSMS type or income and expenditure surveys of another type.

2.3. On the Needs for Social Protection Linked to the Change of the Age Structure, especially Ageing

Facts/messages: Old age brings with it a reduced capacity for work, and increasing demand for health services, but also difficulties in accessing health care and other basic services. As was noted in Section 2 of Chapter IV, this increases the likelihood of older people becoming and remaining poor. However, the extent to which this actually happens depends greatly on the quality and coverage of the old-age support system. Formal social security systems, if they exist at all, do not always provide adequate coverage for all older persons. In some developing countries, social security is limited to a small minority of older persons, mostly professional and urban-based. In the absence of formal systems of social protection, informal support systems constitute the main source of economic security for older people. Therefore, changing family structures have affected the security of the elderly. While in many cases families provide the necessary support, not all of them are able to do so. The level of support that families can provide is influenced to a great extent by the level of support which families receive from Governments. If there are no programmes and safety nets in place, families that are themselves struggling to make ends meet may find it extremely difficult, if not impossible, to provide for the needs of elderly family members. If older family members become frail or disabled, families alone may not be able to provide the necessary medical care. Older men, while better placed with respect to formal social security than women, tend to be more socially isolated and receive less family support than women.

In most of Latin America and the Caribbean, Eastern Europe and elsewhere, where formal social security systems are relatively well developed, the average incidence of poverty among the elderly is actually lower than among the population in general, but where formal old-age security systems have low coverage, as in Southern and Eastern Asia, old-age poverty is widespread, despite massive transfers from the younger generation to its elders. In such countries, there is cause for concern, particularly now that populations are rapidly ageing. In theory, most formal old-age social security systems have a built-in mechanism for transferring resources from men to women because women in most parts of the world retire earlier and live longer than men. In practice, however, older women have less financial autonomy because formal social security systems based on past economic participation are biased against them. Consequently, they are among the main potential beneficiaries of non-contributory systems that recognize the intrinsic value of unpaid domestic work.

Social protection systems need to adapt themselves to demographic scenarios in which older people will have increasing representation. In many parts of the developing world, ageing occurs in conditions of poverty and the absence of such a system of social protection, so that a central challenge is to build these systems, thus preparing for the fast-approaching scenario of an ageing population. The growing number of older persons calls for appropriate policies and programmes to ensure their equal access to health care, basic social services and social safety nets to protect them from falling into poverty. Of particular concern is the need to provide affordable, accessible and appropriate health-care information and services, but also pensions, social safety nets, and social protection schemes to help alleviate poverty among older persons and promote financial independence and empowerment. Regular cash transfers to older persons increase their access to services, especially health care, and increase their standing within the family, their dignity and empowerment. They also play an important role in breaking the inter-generational cycle of poverty because the elderly often share resources with younger family members, thereby contributing to the household income and improving nutrition and school attendance among children.

Many countries are facing increasing structural deficits of their pension systems. Demographic change is often not the primary cause of these disequilibria, but it is an aggravating factor. An important determinant is the financial mechanism for funding these pension systems. The two most common modalities are the pay-as-you-go system, in which the current contributions of the economically active population are used to pay the pensions of those who are currently retired, and the individual capitalization modality, which is a form of forced individual saving. The former system is particularly vulnerable to population ageing. This is why some countries are converting to individual capitalization mechanisms. According to some economists, like Mason and Lee, this holds the promise of a ”second demographic bonus” as ageing populations start to accumulate savings for their retirement, which can be applied for investment. On the other hand, it also has a number of disadvantages. The short and medium-term cost of converting from one system to the other can be prohibitively high. More importantly, individual capitalization is not a viable solution for those who do not earn enough to accumulate a realistic individual pension fund. At the other extreme, some developed countries have entirely abandoned the principle of pension contributions based on labour income and now frame the entitlement to a government pension as a basic human right, based on the number of years that the person has resided in the country since age 15.

Methodology: It is recommended to use indicators for the coverage of security and health, both with regard to contributions and pensions, by sex, broad age groups (differentiating at least between 60-74 and 75 and over), and socio-economic status. Other relevant factors are expenditure on health by age bracket and building scenarios of the potential costs involved in different arrangements for the delivery of care. Indicators on the patterns of co-residence, exchange, and family support, and the role these play in the living conditions of older people. Indicators quantifying the monetary and non-monetary contribution of older people in the households in which they live. Analyze indicators on the needs for coverage by the social safety net for other vulnerable groups and public and private transfers received and given by them. In countries that are part of the NTA Project (see previous section), it may be possible to obtain a quantitative estimate of the ”second demographic bonus”, if it exists. In countries that do not participate in this project, constructing the necessary indicators may be too time-consuming. If there is a pension system in place with sufficient coverage, it may be possible to analyze the role of the ageing process in the financial health of the system. It may also be possible to estimate what the incidence of poverty among the elderly would be without the existence of the pension system. In countries with a pension system based on individual capitalization, one may try to evaluate to what extent the system attends to the need of the lowest income groups.

Primary Sources:

  • National censuses and statistics with regard to expenditure on social protection, administrative registers of the public and private social protection system;
  • Household surveys and specialized surveys on health, living standards or poverty, budget, free time, etc.;
  • Regional surveys, including the SABE survey on Health, Well-Being, and Ageing in Latin America and the Caribbean from the Pan American Health Organization (PAHO), the Survey of Health, Ageing and Retirement in Europe (SHARE).

Secondary Sources:

  • WHO. Study on global AGEing and adult health (SAGE). Available at: http://www.who.int/healthinfo/systems/sage/en/index.html;
  • The World Bank has a web page on Providing Security in Old Age Through Sustainable Pension Systems that Support Development, which refers to a number of relevant documents with respect to pension systems and pension reform, including Averting the Old Age Crisis: Policies to Protect the Old and Promote Growth (1994);
  • Mason and Lee (2006). ”Reform and support systems for the elderly in developing countries: capturing the second demographic dividend.” Genus 62 (2): 1-25.

2.4. The Links between Migration and Spatial Distribution with Poverty

Facts/messages: This issue involves at least four sub-themes, namely

  • How rural-urban migration affects poverty in urban areas, in rural areas, and at the national level;
  • The positive aggregate effects of international migration on the balance of payments and development financing;
  • The positive effects of remittances at the household level; and
  • The negative effects of international migration through mechanisms such as the brain drain.

In addition, internal migration may have other undesired effects, such as the propagation of HIV/AIDS. In the past, the negative aspects of migration (such as the losses for sending communities and the spread of urban slums) were often highlighted. Poorer countries are marked by higher levels of rural to urban migration and this process tends to make overall poverty more visible, given the higher poverty levels characteristic of rural areas. Much of the urbanization literature has also been concerned with the rapid growth of poor populations and of environmental degradation in cities due to rural-urban migration. However, the overall impacts of such movements are increasingly viewed in a positive light. Rural-urban migration, analyzed from the economic standpoint, is a stabilizer of human resources, the effects of which tend to be positive in the long term. The increase of urban poverty is offset by the reduction of the number of rural poor, and since upward economic mobility is higher in the cities than in the countryside, the overall effect is often one of poverty decline at the national level. Most urban growth in developing countries is now due more to natural increase in the cities than to migration, a fact which alters the locus of public policy. Moreover, the deterioration of urban environment that is often attributed to urban growth is not an inevitable process, but rather the consequence of misguided policies that try to stop growth by not planning for it.

Moreover, cities provide many more opportunities for social participation and empowerment of different social groups. In particular, urbanization can be a powerful factor in creating conditions for women’s empowerment. Participation in an organization allows them to reduce the vulnerability, insecurity and dependence which is even more typical in rural habitats. Finally, urban concentration can be helpful for environmental well-being, provided cities make a sustainable use of space and foment sustainable economic practices. In order to argue that the predominant effects are negative, one must show that these perceptions are mistaken or inspired by non-economic factors such as the extreme precariousness of access to basic services in the regions of origin or that the presence of migrants exercises a negative effect on relevant urban markets.
International migration also presents both opportunities and challenges for growth and poverty reduction. Benefits include new investments, learning opportunities, professional competencies, brain gain and remittances that can contribute to poverty reduction (at the household level) and development (at the community level).

Migrant remittances have emerged as a major source of external financing (helping to achieve an equilibrium of the balance of payments) in developing countries. Remittances play a central role in the provision of foreign exchange and poverty reduction. In analyzing the impact of remittances, both the positive and negative effects should be considered at household and national level. Migration can also lead to other forms of beneficial transfers back to the countries of origin in the form of “social remittances”. The financial assets, education and skills migrants have acquired abroad may stimulate innovation, create employment and boost economic growth in emigration countries. Whether migrants will invest largely depends on the economic conditions and the governance in the home country. At the family level remittances constitute an important source of additional income that can mean the difference between overcoming or continuing in a state of poverty. In some cases it is possible to measure the latter effect at the household level. At the global level, remittances sent by migrants to their families (250-400 billion dollars in 2006) exceed official development assistance (ODA) and in some countries even direct foreign investment. According to a World Bank study, a 10 % increase in the share of remittances in a country’s GDP leads to a 1.2 % decline in poverty.

However, the social costs of migration should not be overlooked. Challenges associated with migration include brain drain, heavy social cost for the people left behind, the spread of HIV and other diseases, the possibility of exploitation and abuse, particularly against women, and various pressures resulting from any influx of refugees and internally displaced persons (IDPs). The brain drain and economic dependency of the main regions of emigration are the main negative effects of international migration. According to the ILO, developing countries are currently experiencing a 10-30 % loss of skilled manpower, with 75 % of persons emigrating from Africa having a tertiary level education. The depletion of human resources in sectors such as health and education may present a challenge to development efforts and potentially contribute to increases in poverty. Depending on the specifics of the country situation, the brain drain may be counteracted by two other phenomena: on the one hand, the return migration of natives who acquired education and practical experience abroad, and the potential stimulus to local education of those who are preparing themselves to migrate, but end up staying. Migrants are also important vehicles for transmitting “social remittances” including new ideas, products, information, and technology.

There are also other significant social costs of migration which are not always obvious: children without mothers, husbands without spouses and families left behind. The migration of mothers often results in children dropping out of school or finding themselves in situations of neglect and abuse. Perhaps the most painful social cost of migration is the knowledge of migrant mothers that they are taking care of other people’s children (or elderly parents) while they leave their own children (or elderly parents) to be cared for by others. This has negative effects on social cohesion of the family and society. In the Philippines, for example, an increase in incest has been noted as mothers migrating to work as maids in the Gulf States have to leave their children alone with their fathers. In some cases, older persons are left on their own with no one to take care of them. In some families, grandparents assume care-giving roles, taking care of grandchildren in the absence of adult children who go abroad. This has important implications for inter-generational relations.

The participation of women in migration has raised both prospects and challenges. Female migration has a tremendous potential. It can advance gender equality and women’s empowerment through opportunities that it opens for greater independence and self-confidence. It can be a vehicle for enhancing the status of women. It can give rise to structural and institutional changes as well as changes in mind set, understanding and lifestyle. It can redress social and economic imbalances. Migration provides women with income and the status, autonomy, freedom and self-esteem that come with employment. Women become more assertive as they see more opportunities opening up before them. However, female migration can also involve a significant amount of tension, especially since it often breaks through established values and practices. In some environments, female migration is accompanied by exploitation and abuse. Women migrants are found predominantly in the service and welfare sectors in traditionally female occupations. Those in unregulated and the informal sectors of the economy are at greater risk of exploitation, including harsh working and living conditions, low wages, illegal withholding of wages, and illegal and premature termination of employment. Many lack access to much-needed health and legal services.
Methodology: Not all effects can be demonstrated easily. In addition, there are methodological difficulties. For example, in the case of remittances found in household surveys, one has to be mindful of the fact that remittances are normally underreported and that the analysis of volumes by poverty levels can be affected by the phenomenon that the households that most benefit from remittances tend to emerge from poverty. This picture can erroneously suggest that the poorest households do not receive remittances.

Use economic indicators to measure the economic implications of migration. Assess the effects on real income in terms of households as opposed to the national level. Define the proportion of homes by type (poverty, household size, educational level) that receive remittances and the characteristics of recipients (sex, age, etc.). Break down the household’s welfare between the change in private consumption and the change in the consumption of public services. To account for changes in prices faced by migrants in the countries of destination, consumption patterns in the country of destination country should be adjusted to reflect the differences in the cost of living, use Purchasing Power Parity (PPP) exchange rates from the World Bank’s database. With respect to the brain drain, analyze emigration data by occupational group (e.g. doctors, nurses and teachers). Compare these data with the situation in the country of origin, including unemployment levels. Consider the total size of the labour force in the occupations subject to emigration and the unemployment in these sectors before drawing any conclusions on the loss that it represents for the country.

National banks report their remittance data as part of a country’s national accounts. The International Monetary Fund (IMF) compiles and publishes the data. The World Bank monitors and estimates remittance flows for all countries at regular intervals. In order to maximize the remittance levels received by families in countries of origin, global policies have been adopted to reduce transaction costs. Regional development banks monitor these transfer costs. Official remittance data tend to under-report actual flows as they exclude informal remittances. In some countries, data from money transfer agencies and small transactions are not included. Model-based estimates and household surveys suggest that informal flows could add at least 50 % to the official estimate, with significant regional and country variation. Household surveys are a growing source of information, particularly on the impact of remittances at the household level. Consider using indicators on the flow of officially recorded remittances in absolute terms, per capita, as a share of GDP and as a proportion of total financial flows. Use labour force surveys and household surveys to analyze the detailed nature and impact of remittance flows on household members.

With regard to rural-urban migration, it is possible to make a breakdown of the reduction (or possible increase) of urban and rural poverty at two points in time and the residue of poverty reduction at a national level, which is precisely the component attributable to rural-urban migration. It is also possible to analyze internal migration by sex, age, levels of education, and income. Gender differences should be highlighted although scarce information is available about various aspects of this question, such as trafficking in women for certain kinds of clandestine employment in the countries of destination.

Primary Sources:

  • Central Banks and Central Statistical Offices (in some countries): National accounts provide information about part of international remittances (aggregate figures) even when these are underestimated because they do not capture money and goods that migrants carry with them;
  • Population censuses and population registers, national administrative sources;
  • Labour Force Surveys and specialized surveys;
  • Household surveys of the LSMS type (poverty and social indicator monitoring): These household surveys contain information about remittances at the household level , by characteristics of the recipients (sex, age, etc.), which can be analyzed by poverty strata (subject to the restrictions mentioned above). For rural-urban migration, normally one has to make do with indirect inter-census estimates given that censuses often do not collect information about the area of residence of migrants at their origin;
  • National accounts for remittances (aggregate figures).

Secondary Sources:

2.5. The Links between Population Dynamics and the Labour Market (MDG 1.B)

Facts/messages: The essential prerequisite for harnessing the full potential of the “demographic bonus” is the need to improve employability (level and relevance of the education received) and to create more employment for those entering the labour market. It has been noted, for instance, that while in some countries of the Middle East educational levels have improved markedly during the past few decades, young people still have difficulty finding jobs because their skills are not adapted to the needs of the market. For women, in particular, university education in these countries often does not lead to future employment. One of the sub-issues that can be analyzed in this context is access of young people to (training in) information technology. One can also analyze inflows and outflows as the result of mortality or retirement, in the course of a more or less extensive historical period, to assess the demand for jobs from those entering the labour market in the near future, and compare it with historical trends. On the other hand, one may analyze the rates of youth unemployment of the last 20 years and relate these results with the previous ones. Another topic for research is the relationship between unemployment and adolescent fertility, given that there is the possibility that in some countries the increase in adolescent fertility is related with trouble to find work.

Methodology: The main indicators are rates of youth unemployment (15-19 and especially 20-24 years old) and the economic participation of women by age group and educational level. For a more detailed analysis, one can use household surveys to relate rates of participation to family structure. This is similar to what the DMPAP model does, with the difference that DMPAP does not analyze the rates of participation as such, but the average income generated as a result of certain rates of participation and remuneration. Another possible type of analysis relates the fluctuations of populations by age, sex and educational level to the level of (un)employment or wage levels in the corresponding group or age-sex groups that may be in direct competition with it, the rationale being that a relative “glut” of population in some age and sex categories will negatively affect the market position of those population groups and the ones that compete in the same markets.

To elaborate on the issue of youth employment, analyses can be made of those entering and leaving the labour market, as suggested above. For the relationship between youth unemployment and adolescent fertility, it may be appropriate to analyze the activity of adolescent mothers in the 6 months before their pregnancy, in those cases in which it is possible to obtain this information. Another important indicator is the proportion of the adolescent population that neither works nor studies. Where possible, one should analyze how this population occupies its time.

Primary Sources:

  • National censuses;
  • National surveys on employment/unemployment and qualification of the workforce;
  • Household surveys of the LSMS type;
  • DHS surveys. DHS surveys can only be used to analyze the current economic activity of women in relation to their reproductive history, but they do not have any information about their history of activity or the conditions of current employment.

Secondary Sources:

  • ECLAC. Social Panorama. Rates of employment and unemployment;
  • Hakkert (2007). The demographic bonus and population in active ages. Brasília, UNFPA/IPEA Project RLA5P201, Research Document 7.


  • UNFPA (2007). Demographic Model for Poverty Analysis and Projections (DMPAP).

2.6. The Links between Population, Climate Change and Environment (MDG 7)

Facts/messages: From the environmental point of view, population trends are one of the most important determinants of pressure on ecosystems, although their impact is mediated by other factors such as consumption patterns, the level of economic development, technological progress, and environmental policies. This generates an enormous policy conundrum. Patterns of growth in developed countries are responsible for the major environmental threats to the planet, but developing countries are anxiously trying to emulate these patterns and, if successful, will magnify current threats of global change many times over. In particular, the reduction of poverty, which under current development models is associated with increased consumption, would inevitably contribute to increase GHG emissions. But of course all countries must have the right to develop. Meeting desired fertility levels with better access to RH would mitigate this problem somewhat over the long term, but the main tradeoff between development as we know it and sustainability remains a fundamental challenge.

The impacts of climate change are already apparent, from droughts and floods to changing weather patterns and destabilized livelihoods, and they are being felt disproportionately by those who are already impoverished. Adaptation means managing the unavoidable consequences of climate change. Analyzing population dynamics can clarify who is most vulnerable, why, and how interventions can most effectively reach them. Some groups of people are particularly vulnerable to impacts of climate change, including women, children, single, female-headed households, and the elderly. These groups tend to be most at risk for poverty and have the most tenuous livelihoods. In addition, living in urban slums can exacerbate vulnerability — housing stock and infrastructure is often substandard or non-existent in urban slums, and many are also located in flood plains or low coastal elevation zones where the risks from the impacts of climate change will be the greatest. The factors influencing population vulnerability therefore include location, poverty, demographic characteristics, and extent of protection provided to people by their housing, infrastructure, and social and economic support structures.

Population-poverty-environment linkages tend to be vicious cycles: poverty is linked to high fertility through higher rural labour demand, high infant mortality and gender inequalities. Population growth due to high fertility results in increased demand for limited food and environmental resources. The decreased per capita resource base in turn results in small, inefficient farm plots, soil fertility loss, and increased incentives for short term resource exploitation over longer term sustainability. Rapid population growth and increased population density in forest areas can cause deforestation through agricultural expansion. Deforestation results in increased vulnerability, particularly of the poor, to storms, floods and other disasters, for instance in Costa Rica, Bangladesh or Nepal, where deforestation has left low lying lands at greater risk.

Fast-paced urban slum growth has contributed to deforestation due to increased demand for charcoal for cooking fuel, for instance in the DRC, Tanzania and Kenya. The two main population interventions that can break this vicious circle are the promotion of family planning in rural areas, particularly rural areas that experience environmental stress; and the acceptance of some degree of rural-urban migration as inevitable and consequently planning ahead for it.

An important aspect of the environment-population interaction is how a specific area, with its regional variations of natural aptitudes, can be managed in the most rational way by a population with characteristics that also vary in space. This is the perspective known as “sustainable use of space”, which has its greatest potential impact on spatial planning. One should analyze the national spatial planning strategies and indicate how the characteristics of the population have or should have repercussions in this context. Specific issues in this regard are the urban slums and possibilities for providing basic infrastructure, including basic sanitation. Although rural-urban migration can generate pressures on local governments to attend to this demand, one should also consider that at an aggregate level the concentration of population facilitates the provision of these services to the greatest number of users possible. Finally, one should analyze to what extent existing settlement patterns in the country contribute to environmental vulnerability, a subject that has taken on considerable importance after the experience of a number of meteorological catastrophes in high-risk human settlements.

Age structure, household size and spatial distribution all affect per capita emissions, and should be integrated into climate change modeling. Older people who are past their peak working years consume less and produce fewer greenhouse gas emissions than working-age people. Worldwide, the proportion of older persons is rising, with UNDP projecting an increase in the proportion of people over 60 years of age from 10 % in 2005 to 22% in 2050. All things being equal, this will result in a reduction in emissions over time. Household sizes are declining in many places around the world, linked to processes like urbanization and fertility decline. Many argue that the household, and not the individual, is the best base unit for measuring emissions, as households generally consume together and often produce together. If household sizes are shrinking and the total population remains stationary, the total number of households will increase. Due to economies of scale, larger households, while emitting more in total, emit less per capita. Decreases in household size therefore mean more emissions, even without more people.

The urban-rural distribution of the population is a major determinant of emissions levels, though not always in predictable ways. Due primarily to income differences, urban areas tend to produce more emissions than rural areas. Yet, greater density in urban areas also allows urban residents in some cities to have lower per capita emissions relative to those living outside of the cities. Better urban planning, so essential to poverty reduction, women’s empowerment and slum improvements, could help mitigate greenhouse gas emissions, while also providing resilient and adaptive environments to reduce vulnerability, particularly for impoverished urban dwellers.

Methodology: At this time, links between local population dynamics and emissions levels have yet to be formalized in a way that allows for a simple methodology for assessing this relationship. On the adaptation side, it is important to identify the size and composition of at-risk populations. First, identify projected impacts of climate change under different scenarios for the country, and where possible examine sub-national variation, with particular attention to geographic extent of potential impacts. Identify the projected urbanization rate, and the extent to which urban areas are located in places where climate change impacts are likely. Use the most recent census or sub-nationally sampled survey data to generate a demographic breakdown of the population that resides in the projected locations of climate change impacts or is food/water insecure. For broad geographic impacts, like changing temperatures and rain patterns, this can be done at the national or provincial level. For more localized impacts, like flooding or sea level rise, spatial analysis of the links between populations and impacts may be necessary.

The Urban Risk Assessment by the World Bank is being developed to standardize a methodology for a cost-effective tool to assess vulnerability in cities which will harmonize the information on disaster and climate risk at both the city level and within cities, identifying areas which are most vulnerable. Such information will also provide a mapping of slums which can be used for improving basic services for the urban poor.

The main indicators are demographic density, sanitation infrastructure (percentage of households served by running water and sewer systems), and the local availability of water, although these, in isolation, mean relatively little. If there are national spatial planning directives, these should be analyzed in the light of the demographic situation. Areas of environmental risk should be compared with the geographical distribution of the population, also taking into account the spatial distribution of economic activities, especially when these are based on comparative advantages of location. The UNFPA project with the Ministry of Environment of Colombia has produced a set of manuals (in Spanish) about population analysis in the context of spatial planning which can serve as a useful reference.

Primary Sources:

  • Censuses: Environmental modules of national censuses are the best source for mapping demographic densities and basic sanitation infrastructure;
  • DHS;
  • Specialized surveys;
  • The Environmental Ministries of many countries in the region have maps of ecological risk. Add specific sources for other regions.

Secondary Sources:


  • World Bank (2010). Understanding Urban Risk. An Approach for Assessing Disaster and Climate Risk in Cities;
  • Population Action International (PAI). Mapping Population and Climate Change, Available at: http://www.populationaction.org/Publications/Interactive_Databases/ climate_map.shtml;
  • African Development Bank and UNFPA (2005). Training Module on Integration of Population Issues into African Development Bank programmes and projects. Module 3, Session 4 on Integration of Population Issues in the Agricultural Sector;
  • UNFPA Colombia. Enfoque poblacional para revisión y ajuste de planes de ordenamiento territorial. Guía de aplicación.

99   International development partners include: USAID through the Health Systems 20/20 project and Management Sciences for Health, the Norwegian Agency for Development Cooperation, PMNCH, UNAIDS, UNDP, UNFPA, UNICEF, WHO, and the World Bank.
100   Both studies can be found in Birdsall et al. (eds.). Population matters: demographic change, economic growth, and poverty in the developing world. New York, Oxford University Press.
101   Bloom, David; David Canning and J. Sevilla (2003). The demographic dividend: a new perspective on the economic consequences of population change. Santa Monica CA, Rand Corp.
102   There is concern, for instance, with respect to ageing men in China who, due to prevailing sex imbalances resulting from the one-child policies of the Chinese government since the late 70s, tend to age without families to support them.
103   European University Institute, Florence Schuman Center for Advanced Studies (2008). Return Migration and Small Enterprise Development in the Maghreb.
104   Adams, R. and J. Page (2003). International migration, remittances and poverty in developing countries. Washington DC, World Bank Policy Research Paper 3179.
105   World Bank (2006). Global Economic Prospects 2006. Economic Implications of Remittances and Migration.
106   See Guzmán, J. M. et al. (2009). Population dynamics and climate change. New York, UNFPA/IIED.