Descending order of the poorest continents

 

What is the descending order of the poorest continents in the world?

By definition, a descending or lowest order is the highest level in a social hierarchy. It can be used for countries and their economies. For example, a country with high living standards is lower class than one with poor education.

Descendent orders are an indicator of economic growth and prosperity. They provide us the answer to questions such as which country, city, region, or community has the most resources and therefore will prosper. But they can also be misleading. An ascending order of population can have the same effects but can show that a state with higher numbers of people has superior levels of poverty.

The purpose of this article is to explain what the descending order of the poorest continents in the world is and how it works. We will see how a country’s economy determines its ranking, the advantages of descending and ascending orders, and some examples. To achieve these aims, we will use data from the latest UN Human Development Index (HDI) and Gini indices.

What is Descendant or Lowering Orders?

The descent order uses indicators such as per capita income, life expectancy, GDP, and the unemployment rate. The two main characteristics that describe an overall descending order are a low number of people at all levels and no upward movement. Such an order means there is a large group of poor people who do not have many chances to rise through the ranks of the social ladder. Conversely, an ascending order of the poorer people shows a higher number of people in each rank, indicating that all have enough opportunities to escape into more prominent positions.

These measures are used for measuring national and regional development and can be defined as "the difference between incomes and prices in a given time span," which is calculated by dividing the total amount of payments received by a person per year by the number of years, multiplied by 100. This number represents the general wealth of every citizen. So, when you calculate its share, it tells us about the average level of economic activity of both populations. These indexes show a larger picture of national development compared to individual measures.

Descending order of the poorest continents in the world

According to international statistics, Africa is the world’s poorest continent, although the other continents have different rankings based on gross national product.

Distribution of global HDI countries by Gross National Product (GNP)

As seen above, Nigeria and Mozambique occupy the bottom third of countries, while Ghana, Senegal, Kenya, Zimbabwe, and Indonesia are on the top. In terms of HDI, most of the African countries seem to be in the middle (between the richest and worst ones). As we will see below, economic dynamics and policies are often responsible for varying rates.

Relationship between gross national product and level of HDI

Although it is a linear relationship, it may still be useful to identify regions where GNPs are rising or falling and the corresponding HDI ranking. A few other variables are usually included to analyze these factors. Thus, in addition to GNP, such indicators as employment rate, literacy level, and gender distribution are also important (see "What is the descending order of the poorest continent in the world?" below).

Difference between Ascending and Descending levels

The diagram above shows the differences between ascending and descending levels. On the left, we can see the ascending or declining ranks, whereas on the right, we have the descending ones. Based on figures from Fig. 1, in the early 2000s, around 2007, Africa saw a significant jump in poverty and instability, including conflicts between various armed groups. At the beginning of 2014, the continent was experiencing major tensions between government security forces and Boko Haram insurgents. Although there have been improvements, recent crises indicate that Africa remains vulnerable to conflict and insecurity. The situation is even more complex in many parts of Asia and Latin America, which are wealthier areas with established political systems but weaker economies.

In 2013, the United Nations developed a new approach to mapping poverty, called the HDI. Unlike previous approaches, this index takes into account three aspects of human development: life expectancy at birth, income, and educational attainment. Its methodology is similar to the standard methods, except that HDI focuses on the impact of these dimensions on the lives of citizens.

The results are used to calculate HDIs worldwide for any territory in a given country. If there are disparities between countries due to climate, war, disease, or natural disasters, then the HDI score can give them more precise information about it. However, this metric includes values obtained through surveys. Another issue is that the HDI relies on self-reported data, which may be biased. Nevertheless, if we want to know how far a certain region of the globe is moving, then our best bet is to rely on data from a single source.

Now let us review the pros and cons of using the HDI for comparing regions. First off, HDI has long been criticized for being too easy to manipulate, particularly because the tool does not take many underlying issues into account. Yet, the majority of research suggests that this technique is effective and beneficial for defining poverty. Furthermore, a lot of organizations now use HDI scores for measuring progress in developing countries. All these factors make the tool a reliable method for analyzing countries’ overall economic performance.

On the contrary, however, HDI scores can have serious limitations. Firstly, since the data obtained from international surveys can be inconsistent, they cannot be used for comparisons between regions within the same country, i.e., even within the same geographical area. Secondly, the data from surveys may also include errors. Sometimes surveys are taken only once, sometimes twice. Thirdly, there are also problems linked to external validity. Different countries may respond differently to some questions, so comparisons should be done after controlling for potentially confounding variables.

Gini indices in comparison with HDI scores

The last tool for analyzing the HDI is the Gini coefficient, which stands for a measure of inequality between individuals. It is usually applied as a benchmark when making decisions about resource allocation or evaluating welfare programs. Gini is especially effective at identifying inequalities among populations that do not have access to primary health care services.

Comparison between HDI and Gini coefficients

As we can see in Table 1, the correlations are very weak. Even when one calculates the correlation separately, there is little to no predictive power. Also, the range of values is too dispersed, causing the resulting graph to look like a bell curve. To overcome these shortcomings, we need to use a combination of the measures in Table 1. This way, we will be able to get a better understanding of whether countries fall under one of the categories described above and which category they belong to (i.e., having a lower HDI or a higher Gini).

The graph below highlights some of the benefits of using Gini. For instance, it helps us understand why many emerging economies in Southeast Asia, such as Malaysia and Thailand, are ahead of China, Vietnam, and Sri Lanka. Since these nations have advanced economies, they tend to have greater inequality. Therefore, when comparing Gini metrics to those of other countries, there are signs of a disparity between them in many cases.

Using the Gini coefficient, we can see why some societies are ranked higher than others: some communities (for example, northern India, central Mexico, and sub-Saharan Africa) are more prone to extreme inequality. There are several theories behind such behavior. Some claim that societies that are poorer than others have higher costs of capital. Higher costs of capital mean a higher risk of default and a higher chance of losing money. Alternatively, if the cost of capital among people is relatively high, they will not be willing to borrow or invest their savings in risky ventures, thus creating inequality. Finally, families with older parents may have fewer children, resulting in a smaller pool of investment to compensate for inequality and hence create a gap between rich and poor. To sum it up, there seem to be multiple explanations behind the high variation of Gini coefficients around the world. One theory suggests that poor countries may be unable to generate sufficient jobs because of low rates of human capital, such as skills or knowledge, and they do not produce enough graduates, leading to a shortage of skilled labor. Another argument claims that people of particular ethnicities or religions are more susceptible to specific diseases, forcing them to work harder for longer hours to earn less. And finally, another explanation argues that poverty is a result of political violence or corruption, and some countries have weaker institutions. Many poor countries lack basic services. People, therefore, may hesitate to get treatment to avoid discrimination, resulting in worse health outcomes at later ages. Another potential downside is that some wealthy nations may opt to leave undeveloped states to reduce costs, depriving local residents of essential services. That leads to decreased productivity and lower living standards.

The Gini index for Africa, Asia, and Latin America, highlighting some noteworthy facts. 

First, Latin America is well known for its strong inequality in relation to gender and religion. 

Second, Latin America had the strongest increase in inequality (from 7.6 to 9.7). 

Third, Latin American Gini rose consistently throughout the 1980s, from 8.2 in 1980 to 14 in 1999.

Fourth, North Africa and sub-Saharan Africa experienced a minor decrease in their Gini index, from 6.9 in 1980 to 5.9 in 2009, demonstrating a stabilizing trend.