Population

In order to facilitate development planning, programme and policy implementation of a district, it is important that population size, composition and distribution are analyzed. The population size and growth of a country is influenced by fertility, mortality and migration of the people over a period of time. The 2010 Census produced a de facto count in that all persons were recorded in the household where they spent census night, whether they were normally resident in that household or not. Currently, Upper Manya Krobo District has a projected population of 83,508 with males constituting 50.6 percent (42,280) and females 41,228 (49.4%).

Integration of Population and Development Planning.

Integration involves considering, and taking into account in the planning process, population factors as they significantly influence or are influenced by other variables relevant to development plans. The basis for integration is the recognition that population and development are inter-related, population variables affect, and are affected by socioeconomic development variables.

Experience has shown that increase in national income does not necessarily lead to the solution of social, economic and political problems. Not only does economic growth fail to resolve social, economic and political difficulties but inappropriate growth initiates and promotes the mentioned difficulties. Therefore, the prime and fundamental questions to ask about the Districts development performance are:

i. What has been happening to proportion of the population in poverty (deprivation trap)?

ii. What has been happening to unemployment, underemployment and disguised unemployment?

iii. What has been happening to inequality among different population categories?

Therefore, the DMTDP targets are focused on reducing poverty, inequality and unemployment and therefore it is on this basis that the 5-year Development Plan was prepared.

Population Size and Distribution

Table 1.18, shows the population distribution of the Upper Manya Krobo District as projected in 2017. The total projected population of the district is 83,508 with males constituting 50.6 percent (42,280) and females 41,228 (49.4%). Again, the table shows a very young population where 50.7 percent of the population falls between ages 0-19 years. The aged (65+), constitute only 5.9 percent. The age group 0-4 has the highest proportion of 14.1 percent with those 85+ recording the lowest proportion of 0.7 percent. Males aged 0-4 years constituted 14.1 percent compared to females (14.0%) in the same age group. Also males aged 0 - 5 are 13.8 percent compared to 13.5 percent of females. This shows that at birth, there are more males than females and as they grow older the females are more than the males as seen from ages 20-49 years.

Age-Sex Structure

The age structure of the country’s population is basically shaped by the effects of high fertility and decreasing mortality rate and the district data does not show any deviation from the national data. The population less than 15 years recorded the highest percentage (40.2%) of the total population. A similar pattern is observed in both sexes with slight variations as shown in Table 1.19.

Population pyramid by age and sex

Figure 1.1 shows the population pyramid of Upper Manya Krobo District. The figure depicts a youthful population consisting of a large proportion of children under 15 years, and a small number of elderly persons (65 years and older). The age structure of the district follows the regional and national patterns showing a pyramid that is broad based, consisting of large numbers of children at younger ages. The number reduces gradually in the higher age groups.

Sex Ratio

Table 1.20 shows the distribution of the population by locality of residence and sex ratio. The Upper Manya Krobo District is a predominantly rural district with 87.3 percent (62,903) of its population in rural areas. Again, the sex ratio of the district (the number of males per 100 females) of the district is 102.6. This means there are 102.6 males per every 100 females in the district. The sex ratio of the district could be due to the high agriculture potentials of the district. Male migrants are attracted to the area to engage in agricultural activities such as crop farming, livestock rearing and fishing including cage culture in the Volta Lake. Urban sex ratio (93.4) is less than rural sex ratio (104.1). This implies more females than males live in urban areas than in rural areas.

Age dependency ratio

Table 1.21 presents data on age dependency ratio. Dependency ratio is a measure of the dependent population made up of those below 15 years and 65 years and older to those in the working or productive age group of 15-64 years. The ratio could be used to measure the economic burden borne by those in the working ages. The age dependency ratio of the district is 85.5. This means that there are almost 86 persons in the dependent ages for every 100 persons in the working ages in the district. Child dependency ratio (74.6%) is higher than old age dependency ratio (10.9%). Child dependency ratio is also higher for males than females; however, older age dependency ratio for females exceeds that of males.

The District Population and Implications for Development

Education

The rate of population growth will affect the Districts efforts to achieve and sustain universal free and compulsory primary education for all. With high fertility continued, the number of primary school pupils will increase. With declining fertility, the pupil population would increase gradually. The minimal required number of primary school teachers would increase with high fertility continued. In contrast, few teachers would be needed with declining fertility. In addition to the need to train, recruit, and retain more teachers, and despite the current resource constraints and overdependence on conditional central Government Grants, the District will need more schools, classrooms and Teachers Houses.

Migration (Emigration and Immigration)

Migration is a socio-economic phenomenon which is a result of complex mechanisms involving social, psychological, economic, political and institutional determinants. The movement of population in space is incidental to carrying out daily activities in life, such as commuting to and from places of work and travelling for business or for pleasure. These movements are often monitored and analyzed for specific purposes. The duration of stay distinguishes the temporary stay from a short stay. However, when such mobility involves a permanent sojourn in the place of destination, it is considered as migration. Migration is therefore defined as a geographical movement involving a change from a usual place of residence over a defined territory beyond a defined period (United Nations, 2012). Migration can be measured in many ways, however, in this section; it is measured by birthplace and duration of stay as presented in Table 1.22.

The 2010 census collected data on birthplace and duration of residence of individuals in the place of enumeration. Table 1.22 provides information on the projected recent migration of the district. The total number of migrants in the district is 35,589 out of which 20,782 are born elsewhere in the region and 14807 are born elsewhere in another region. Majority of the migrant population of the district are born in the Volta region (6,758) while the lowest migrant population are born in the Upper West region (82). The presence of the Volta Lake in the district might have attracted most of the migrants to the district to engage in fishing and farming along the lake.

Migrants living in the district for less than five (5) years constitute the highest (28.4%) proportion of the migrant population while 12.9 percent have resided in the district for less than one (1) year. A relatively high proportion, (21.8%) have stayed for 20 and more years in the district.

The formal sector Gender Equality

Promoting gender equality, women rights and empowerment of women are the core values of the development of every society and Upper Manya is no exception. Table 1.33 shows the gender analysis matrix for the district.

From the issues discussed in the table 1.23, it is imperative to recommend the following measures to empower women;

 Capacity building of mothers economically as priority

Promote and empower women’s participation in political and decision making processes

Adopt and treat gender issues as cross-cutting issues

Reduce the household work burden on women so that they work efficiently in

Settlement Systems

Central Places

By their nature, high-order goods/services cannot be located ‘everywhere’ In any given region, high-order goods/services necessarily have to be located at just a few Central Places A Central Place is a settlement that provides a range of high-order goods/ services—it serves consumers both within and beyond its boundaries. To contribute to the threshold requirements of the high-order goods/ services, central places usually have higher population sizes than lower-level settlements A typical central place provides a range of both low- and high-order goods/services.

Hierarchy of Settlements

Through the interplay of various social, economic and political forces within the regional space economy, some settlements emerge as Central Places over time. These central places vary in size and importance. A system of settlements in a region that are ranked according to their ‘centrality’ is called Hierarchy of Settlements. Thus a typical region becomes a system made up of a network of goods/ services of varying ‘orders’, market areas of varying sizes and settlements of varying centrality.

Scalogram (also called Functional Matrix) is a tool used to analyse the Hierarchy of Settlements in a region based on the functions (goods and services) they provide It shows:

ï A list of settlements in the region (e.g. district)

ï The range and orders of services/facilities in each settlement

ï Level of centrality of each settlement, computed as an index called Centrality Index

ï The tool is based on the Central Place Theory

Fig. Hierarchy of settlements

The Scalogram Analysis was adopted to identify the presence or absence of essential services and facilities within the District. This is a non-statistical tool that arrays facilities and service by their ubiquity and ranks settlements by functional complexity on a matrix. By this, the settlements were ranked based on the different types of facilities available. The distribution of these facilities is presented on a settlement functional matrix as shown below. The construction of the settlement functionality matrix started with the arrangement of the settlement in descending order according to their population. The next step was to determine a cut-off point as all settlements in the District could not be considered in the settlement functional matrix.

This cut off point was set at settlements with a population of 534 people and above as it was observed that all settlement with a population less than 534 had either one or none of the essential facilities identified in the District. The various services and facilities were assigned weights in accordance with the level of function or importance within its defined sector, a centrality index was then taken to be 100 and a total centrality index which represent the degree to which each of the settlements provide functions to people in other areas was then calculated. Settlements with total centrality scores of 1000 and above formed the first hierarchy.

Settlements with total centrality scores of the range 300-499 formed the second hierarchy while the third level comprises settlements with centrality indices of 200-299. The 4th level settlements had total centrality scores of between of 100-199 while the 5th level settlements comprised of settlements with total centrality scores of below 100. The table below shows the Functional Matrix for the District.