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Indicators Of Economic Development


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Indicators Of Economic Development

  1. 1. Development Economics Web Guide, Unit 5B 4 Indicators of development Compare and contrast GDP per Understand the limitations of in developing countries in capita and other measures of national income statistics as sub-Saharan Africa, Asia economic and social development, indicators of development. and Latin America e.g. life expectancy, literacy rates, Explain the inter-relationships the proportion of population between these indicators. Absolute and relative employed in agriculture. Understand poverty. the distinction between these terms. Differences between Compare how the record of Understand how there are developing countries. economic development differs in differences in countries both sub-Saharan Africa, Asia and Latin between and within the three America and explain reasons for continents and a consideration these differences. of these differences. Indicators of Economic Development Introduction The specification refers to two categories of country, ‘developed’ and ‘developing’. A variety of economic and social indicators can be used to classify countries in this way. However, some of these are more reliable than others. Further classifications are possible between countries within the ‘developing’ category – for example, strong differences by region emerge: Africa, South Asia, East Asia and Latin America have rather different sets of characteristics. However, it is also true that countries within each of these regions differ widely. Some Important Indicators A very wide variety of indicators can be used to characterise the difference between developed and developing countries. Only a small selection is considered here. The data reported below comes from the United Nations Human Development Report for 2001. 1 GDP per capita GDP per capita is the total value of (final i.e. not intermediate) goods and services produced within a country divided by the total population. The bar chart below shows the extraordinary difference between countries in terms of GDP per head. It also illustrates the relative difference between countries categorised as ‘developing’: Ghana had $1881 per capita in 1999, Zambia only $756. It is worth pausing to consider the figures for Zambia: on average, people there live on no more than about $2 a day. As a measure of development this seems to be the most important indicator: if people want to be in a position to buy commodities and enjoy high standards of health and education then they will need the income to match. Issue 1 – May 2003 Authorised by Peter Goff
  2. 2. Development Economics Web Guide, Unit 5B 5 GDP per capita, PPP US $, 1999 25000 20000 15000 10000 5000 0 UK Ghana Zambia There are some issues concerning the reliability of this indicator. One problem is measuring GDP in countries where much economic activity is unofficial. The data itself may be collected by governments who use different and more or less efficient methods of measurement. The measurement of inflation is also problematic: if inflation is under-estimated then real output will be over-estimated. Government officials may have an incentive to over-value output (particularly the unsold output of nationalised industries). Another major problem is the high level of subsistence farming in developing countries: non-marketed output may never get measured. To enable cross-country comparisons the data needs to be standardised to a particular currency. Using current exchange rates is unlikely to be appropriate for this – they are only based on traded goods and are greatly affected by speculative capital flows. The alternative, finding a purchasing power parity (PPP) rate with which to do the conversion, is non trivial in a world where goods and services differ so widely between countries. There are some other problems. First, it may be more informative to see patterns of GDP per capita growth over time, rather than just a snapshot of a particular year. Second, there is no sense in which this indicator can tell the whole story of a country’s economic or social situation – for example, there can be widely varying standards of health and education for countries with similar levels of GDP per head. The distribution of GDP may also vary, in some countries being much more uneven than in others. Third, increasing GDP per capita may bring with it costs as well as benefits, particularly if it is brought about in a non-sustainable way: the level of negative externalities needs to be considered. The rate of growth of GDP is also crucial. Over the last ten years real GDP per head in the UK has grown by 2.1% per year. Over the same period, the figure for Ghana was 1.6% and for Zambia minus 2.4%. Issue 1 – May 2003 Authorised by Peter Goff
  3. 3. Development Economics Web Guide, Unit 5B 6 2. Life Expectancy In 1999, Ghana had a life expectancy at birth of 56.6 years, contrasting with Zambia’s 41.0 years and the UK’s 77.7 years. A variety of factors may contribute to these differences – the stability of food supplies, the extent to which an area is contested by war, and the incidence of disease are all important. It is therefore possible for countries with similar levels of GDP per head to have very different life expectancies: for example, Vietnam currently has an almost identical income per head to Ghana, but a considerably higher life expectancy of 67.8 years. According to World Bank figures, over the past 40 years, life expectancy at birth in developing countries as a whole increased by 20 years. The figures above suggest that this was not evenly distributed. In many countries in sub-Saharan Africa life expectancy is now falling due to the AIDS epidemic. 3. Literacy Rates The UN Development Report defines adult literacy rates as the percentage of those aged 15 and above who are able to read and write a short, simple, statement on their everyday life. This is a very narrow definition of literacy. Interestingly, on this measure Zambia has a literacy rate of 77.2%, compared to Ghana’s 70.3%, and better than that of Saudi Arabia which has fourteen times higher GDP per head. However, and once again, a single indicator cannot tell the whole story – an important part of development economics consists in trying to understand the origins of such differences. More extensive definitions of literacy are available, for example ‘functional literacy’ based on the International Adult Literacy Survey. This survey tests people’s ability to understand printed text, to interpret documents adequately and perform basic arithmetic. One problem with such indicators is the care needed to ensure that the survey is appropriate to the local culture – you cannot ask people to interpret texts that refer to areas outside of their experience. ‘Literacy’ is likely to be considerably determined within a culture rather than across cultures. The wider the definition of literacy the greater this problem will be. Another problem is the distribution of literacy: a number of countries have a considerable gender divide, denying women access to the same levels of education as men. Issue 1 – May 2003 Authorised by Peter Goff
  4. 4. Development Economics Web Guide, Unit 5B 7 4 Measures of Poverty It is important to understand the difference between absolute and relative poverty. Absolute poverty refers to the inability to acquire goods necessary to satisfy basic needs e.g. the means to obtain the minimum level of nutrition necessary to sustain an active life. Basic needs also tend to include clothing and shelter. Put simply, absolute poverty is having ‘just enough to survive’ but no more. However, it is well worth considering whether what counts as ‘absolute’ poverty is, to some extent, relative to the culture concerned: the concept is by no means uncontroversial. Relative poverty refers to the differential of income and wealth between people or countries. That is, it involves some comparison across economies. One indicator of absolute poverty is the percentage of the population receiving less than the equivalent of $1 a day income. This stood at 38.8% for Ghana and 63.7% for Zambia in 1999. For most developed countries there is no absolute poverty according to this measure because of social security benefits. The World Bank estimates that 1.2bn people live off less than $1 a day, with a further 1.6bn existing on less than $2 a day. The figures for absolute poverty have to be treated with some caution for reasons similar to those discussed for GDP per capita. The concept is itself rather loose, and a $x a day measure is somewhat arbitrary: especially as local costs of living vary enormously and there are wide variations across countries of, for example, climate. There is also something of a preconceived idea involved in defining poverty in terms of income levels – it may be that for some people there are other more pressing objectives e.g. having shoes to wear or establishing a separation of living quarters for people and animals. These other objectives may be improving even when income is falling. Many commentators therefore prefer to see ‘poverty’ as a multidimensional concept. This is important because the way poverty is conceptualised will influence the policy measures adopted to deal with it. For example, a definition based exclusively on income will tend to see growth in GDP per head as the only solution to poverty. Other dimensions of absolute poverty might include access to ‘essential’ drugs (Ghana 44%, Zambia 66%, UK 100%) and the proportion of the population using regulated water supplies (only 64% in both Ghana and Zambia). To shed light on relative poverty it is possible to compare GDP per capita between countries or to look at income distributions within a particular country. The inequalities of income in developing countries can be very pronounced. In 1999 the richest 10% of the population in the UK had a 27.3% share of income. For Ghana the figure was 29.5%, for Zambia it was as high as 41%. Note that relative poverty is an issue even at a local scale of description. For example, within households there can be widely varying distributions of resources e.g. on the basis of age or gender. Issue 1 – May 2003 Authorised by Peter Goff
  5. 5. Development Economics Web Guide, Unit 5B 8 5 Demographic Indicators The table below contrasts Ghana and Zambia through a variety of further possible demographic (to do with population) indicators of development: Indicator UK Ghana Zambia Annual Population 0.1% 2.1% 2.3% Growth Rate Urban Population – percentage of 89.4% 37.9% 39.5% total Percentage of the Population Under 19.1% 41.4% 46.5% the age of 15 Infant Mortality Rate per 1,000 live births, 1999 6 (18) 63 (111) 112 (109) figures, (1970 figures in brackets) 6 Disease Indicators Disease is endemic in many developing countries due to low levels of health care, expensive drugs, contaminated water supplies, and poor health education. The figures in the table below speak for themselves. Indicator UK Ghana Zambia % of adult population with 0.11% 3.6% 19.95% HIV/AIDS Malaria cases (per 100,000 0 11,941 37,458 people) Tuberculosis cases (per 100,000 10 53 482 people) Issue 1 – May 2003 Authorised by Peter Goff
  6. 6. Development Economics Web Guide, Unit 5B 9 Aggregate Indices of Development To minimise the problems with individual indicators discussed above it is possible to combine a selection of indicators to form an index of development. Several of these are published by various organisations. The UN Development Report, for example, ranks countries by their “Human development index” which includes the major indices of life expectancy, adult literacy, and GDP per capita. This then creates a league table of development with the UK, at 14th, ranked as a country with “high human development”, Ghana at 119th classified as having “medium human development” and Zambia, at 143rd in the category “low human development”. As with any index, weights have to be used to construct the overall figure. These are to some extent arbitrary. However, it is interesting to see that some countries e.g. Pakistan, have relatively high GDP per capita but are much lower than this might suggest in the overall development index. This may suggest failures of government policy. Africa, Asia, Latin America Development indicators suggest pronounced regional differences. The countries of Latin America tend to be high up in the category “medium human development”. The countries of Asia also tend to be in the medium development classification, but lower down the ranking than countries in Latin America. The category of “low human development” is almost entirely made up of countries from sub-Saharan Africa. GDP per capita, 1999 US $ 8000 7000 6000 5000 $ 4000 3000 2000 1000 0 Latin East Asia South Asia Sub-Saharan America Africa Issue 1 – May 2003 Authorised by Peter Goff
  7. 7. Development Economics Web Guide, Unit 5B 10 However, growth within these regions has been by no means uniform: · Chile and Uruguay have grown so fast in the past few decades that they are now in the UN’s “high human development” group. Meanwhile, GDP in many Latin American countries was falling in the 1980s – as it is again during the current debt crisis. · Countries in East Asia, including most recently China, have grown far more rapidly than those in South Asia e.g. India. Thus, according to the World Bank, the number of people living in absolute poverty (less than $1 a day) fell by 139.2 million in East Asia and the Pacific between 1987 and 1998 whilst in South Asia the number increased by 47.6 million. However, the economic performance of countries in sub-Saharan Africa was not only poor but much less diverse – it appears to be very difficult for the very poorest countries to escape their poverty. According to the World Bank “sub-Saharan Africa as a region saw no increase in its per-capita incomes between 1965 and 1999, even with some improvement in the 1990s.” Inter-Relationships Between Indicators An important question is the extent to which the indicators outlined above are inter- related. This is a complex issue and only a few points are made here. There is a strong positive correlation between GDP per capita and life expectancy. However the graph below shows that this is non-linear – for the obvious reason that a small increase in wealth can enable basic standards of health and education to be established and thus dramatically improved increases in life span, whereas expenditure on advanced medical care in developed countries only brings marginal increases in longevity. Issue 1 – May 2003 Authorised by Peter Goff
  8. 8. Development Economics Web Guide, Unit 5B 11 Low and Medium Development Countries 80 75 70 Life expectancy, years 65 60 55 50 45 South Africa 40 Zimbabwe 35 30 0 5000 10000 15000 GDP per capita, $ 1999 PPP In the aggregate the correlation between these variables is striking. However, a number of countries seem to be separate from the overall pattern, from Zimbabwe, through Angola and Namibia to South Africa marked on the graph. The dramatic difference that levels of GDP per capita seem to be able to make to life expectancy in most countries is shown on the following scatter diagram for the poorest group: Countries of Low Human Development 65 60 Life expectancy, years 55 50 45 40 35 30 0 1000 2000 3000 4000 GDP per capita, 1999 PPP $ Issue 1 – May 2003 Authorised by Peter Goff
  9. 9. Development Economics Web Guide, Unit 5B 12 The relationship between GDP per head and adult literacy, whilst positively correlated when all countries are included, is much less clear for the poorest countries. The graph below shows the relation between Adult literacy and GDP per capita for countries of low human development: Countries of Low Human Development 90 80 70 Adult Literacy 60 50 40 30 20 10 0 0 1000 2000 3000 4000 GDP per capital, $ 1999 PPP This data is, in fact, slightly negatively correlated suggesting that in no sense are education programmes a sufficient condition for development. Resources for Pupils A very useful set of key economic indicators, some presented in graphical form, for each country. Includes data and further links on poverty. Suggested Activity Scroll down the page on the first link above to “Countries at a glance”. Print out the ‘at a glance’ pages for Brazil, Argentina, Ghana, Zambia, India and China. You should use these – or others of your choice - as case study countries. If you you’re your own choice make sure that you include countries from each of the three main regions mentioned in the specification: Latin America, Sub-Saharan Africa, and Asia. Also be sure to include two countries from each region so as to be able to draw out the Issue 1 – May 2003 Authorised by Peter Goff
  10. 10. Development Economics Web Guide, Unit 5B 13 differences within the region. You will need information on specific countries to help answer the ‘Questions for Discussion’ at the end of each section. Using the World Bank resource listed above, prepare for a class presentation a comparison of either Brazil and Argentina (Latin America) or Ghana and Zambia (Sub-Saharan Africa) or India and China (Asia). You should also try to get hold a recent issue of the United Nations Development Report to retrieve the Human Development Indices (HDI) for your chosen countries. Begin to collect newspaper reports and articles from The Economist about the selected countries. Questions for Discussion 1 After the class presentations, draw up a list of differences between Latin America, Sub-Saharan Africa and Asia. 2 Is ‘Asia’ too large an area to be treated as a single region? 3 Is the concept of ‘literacy’ of any interest in a discussion of economic development? 4 Examine the factors which might explain differences in infant mortality rates between developing countries. 5 How clear cut is the concept of ‘poverty’? Does it matter? 6 What factors might explain why some countries are rising and some countries falling in rank orders of human development? 7 “‘GDP per head’ is a very poor indicator of development.” Discuss. 8 Why is there so much discussion about what to call developing countries? 9 What is the significance of a negative figure for a GDP minus HDI ranking? 10 Examine the implications of the statement (page 10) that “a small increase in wealth can enable basic standards of health and education to be established.” Issue 1 – May 2003 Authorised by Peter Goff