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Rural vs. Urban Inequality. Standards of living are significantly higher in most urban areas of developing countries than most rural areas. Not surprisingly, large numbers of people are moving to urban areas as a result. Rural-urban differences in standards of living are readily apparent if one looks at differences in per capita incomes, incomes per worker, wages, or poverty. Rural areas in Africa and Asia have 70-75% of total population but 80-90% of the poor population. Rural areas in Latin America have 25-30% of total population but over 50% of the poor population. It's not uncommon to find wages in developing country cities that are five or 10 times wages in rural areas of those countries. We know the rural-urban gap is big, but is it really that big? No, at least not when you consider rural-urban cost-of-living differences. Rural areas are usually less expensive, and often significantly less expensive. We should also take into account rural-urban differences in education. Urban wages are typically being paid to workers with at least a primary education, whereas rural wages are often paid to workers with little or no education. This doesn't lessen rural-urban differences, but it does suggest a different cause than simply being "rural" or "urban." Entrepreneurs vs. Others. Economic growth expands markets and opens up new opportunities. Finding and capitalizing on these opportunities is no easy task. Nevertheless, some entrepreneurs are able to do this and become extremely wealthy. Others, who aren't willing or able to become entrepreneurs, may become better off but don't become rich. Those with Connections vs. Those Without. As the saying goes, it's not what you know, it's who you know. Compared to the U.S., social and political connections tend to matter much more in most developing countries. The social and political status of young people's parents are often critical in determining which schools they attend, whether they're able to go to a university, whether they're able to study abroad, and their first job (and maybe jobs after that). Later in life, social and political connections can be critical in getting a good job, a lucrative government contract, or government favors of one sort or another. Female-Headed vs. Other Households. All over the world, including the U.S., female-headed households are much more likely to be in poverty than male- or dual-headed households. In developing countries, female-headed households are less likely to have access to potable water, sanitation facilities, medical facilities, and other public and private services. Religious, Ethnic and Racial Factors. As the events in Rwanda and Bosnia in recent years illustrate, being on the "wrong" side in a religious, ethnic or racial conflict can be very deadly. Even when there's no threat to life, liberty or property, and even when the law is officially neutral, religious, ethnic and racial factors can be very important. For example, the indigenous peoples of Peru have been isolated socially and economically for centuries. As a result, about 80% of the indigenous population in Peru are in poverty compared with about 50% of the non-indigenous population. Social and political connections often depend on religious, ethnic and racial factors. As we know from the U.S. experience, these factors can often come into play even when the law says they should not. One common method is to divide the population into successive quintiles (fifths) or deciles (tenths) according to ascending income or expenditure levels. You then determine what percentage of national income is received (or what percentage of national expenditures are paid) by each quintile or decile. A popular way to analyze the resulting statistics is to construct a Lorenz curve. A Lorenz curve is a diagram showing the cumulative percentage of national income received (or percentage of national expenditures paid) by a certain percentage of individuals or households. The purpose of the diagram is to depict the difference of actual income or expenditures from perfect equality among individuals or households. Under perfect equality, everyone would have the same income or expenditures. In other words, each quintile would have one-fifth of the income or expenditures, and each decile would have one-tenth of the income or expenditures. Thus, the first 20% would have 20% of the income, the first 40% would have 40% of the income, the first 60% would have 60% of the income, etc. In practice, of course, in most countries, the bottom 20% has significantly less than 20% of the income, the bottom 40% has somewhat less than 40%, etc. In countries with extreme inequality, the bottom 90% may only have 50% or 60% of the income, meaning that the top 10% has 40% or 50% of total income. Here's an example of a Lorenz curve in a country with a relatively unequal distribution of income. In this country, a small percentage of the population earns most of the income. The lower quintiles or deciles earn only a small fraction of national income. Notice how the Lorenz curve lies well below the perfect equality line. The perfect equality line shows what the income distribution would be in the case of perfect equality. It's a 45-degree line that starts of at 0 and ends at 100. Because of the inequality, the Lorenz curve starts off very slowly and doesn't rise rapidly until we include the wealthiest people or households.
Having drawn a Lorenz curve, one other popular method of measuring inequality is to use the Lorenz curve to calculate the Gini coefficient. The Gini coefficient is a measure of income inequality ranging from 0 (perfect equality) to 1 (perfect inequality). As the Gini coefficient increases, inequality increases. The Gini coefficient is defined as the area between the Lorenz curve and the perfect equality line, divided by the area underneath the perfect equality line:
The Gini coefficient is a measure of relative inequality, in that it shows how people in society are faring relative to each other, not relative to some absolute measure of poverty. A country with a reasonably good-looking Gini coefficient could still have millions of extremely poor people. Even as a measure of relative inequality, the Gini coefficient has its limitations. The bottom 10% or bottom 20% of the population or households could be in bad shape even if the Gini coefficient were small. This could happen if the bottom decile or quintile had little income while income distribution was relatively equal among the remaining deciles or quintiles. How do the ten largest developing countries and three largest developed countries fare in terms of Lorenz curves and Gini coefficients? Here are income and expenditure distribution statistics as collated by the World Bank from surveys done during 1993-2002 in these countries:
Notice the very large inequality in income distribution in Brazil. Brazil has one of the most unequal distributions of income of any country in the world. The tendency toward income inequality in Brazil and Mexico is typical of Latin America. Gini coefficients for Latin American countries are, with a few exceptions, all above 0.5. Also notice that the distribution of income isn't radically different between the poorest of the eight developing countries (Bangladesh and Pakistan) and the three developed countries (U.S., Japan and Germany).
In the diagram, Alternative A is consistent with the Kuznets curve hypothesis. By contrast, under Alternative B, there is no relationship between inequality and economic growth. Inequality in this diagram is measured using the Gini coefficient, although in principle one could use some other measure of inequality as well. Note that these are just two alternatives - many other relationships between inequality and economic growth are possible. Why might the Kuznets curve hypothesis be correct? One argument in favor of it is that economic growth, at least initially, tends to be concentrated in certain industrial sectors. The workers, managers, and owners in those sectors do quite well, but it takes time for growth to spread to other sectors. For example, a new, modern automobile plant may do much for the standard of living of the people who own and work at the plant, but not necessarily much for others. However, once growth does begin to spread to the economy at large, inequality declines. Is the Kuznets curve reality or just a piece of theoretical fiction? Studies of long-term tendencies in western countries (North America, Western Europe, Japan) don't support the Kuznets curve. However, studies of developing countries have produced conflicting results. In any event, the more important point is that the Kuznets curve doesn't need to be reality. Appropriate economic and social policies can produce economic growth while reducing inequality. Countries such as Taiwan, South Korea, China, Costa Rica, and Hong Kong have all done it. Corruption also includes using public office to take advantage of those who have dealings with the government through bribery, kickbacks, and extortion. Bribery and kickbacks on government contracts are commonplace in many developing countries. In countries that have limits on imports or controls on foreign exchange, bribery is a common tool for obtaining import licenses or foreign exchange needed to purchase imports. Extortion is also common in many countries, as for example when a corrupt police officer or judge demands payment to avoid a fine or jail term. Firm statistics on the magnitude of corruption are, because of the nature of the problem, impossible to collect. However, surveys have been done of those who have dealings with government officials in various countries, and the results of these surveys shed light on the degree of corruption. The table below presents ratings for various governance indicators from the World Bank, including corruption. The table also shows a corruption perceptions index from Transparency International, an international organization devoted to combating corruption.
Corruption is economically harmful in many ways. Corruption often means that low-quality products and services are bought at a high price, that unqualified applicants are given government contracts, and that contracts are awarded based on nepotism and favoritism. Corruption also reduces incentives for companies to invest, which can seriously damage long-run economic growth. If a company has to pay a bribe to get an investment license, the cost of the bribe can deter the company from even bothering to apply. In addition, when public funds meant for infrastructure investments instead wind up in a government official's bank account, a country loses the gains in productivity that the infrastructure would have made possible. A distinction is sometimes made between corruption of the type seen in East Asia and corruption of the type seen in many African countries. In East Asia, corruption tends to be "skimming off the top" – taking for example 10% or 15% of the value of a government contract. This is substantial but at least there are incentives for government officials to the keep the system moving by awarding contracts. In many African countries, corruption is more predatory, where contracts are never awarded to begin with because all the money was stolen. Some have argued that corruption is not always bad. There may be cases where corruption provides the grease for the squeaky wheels of a slow and cumbersome government agency. For example, starting or expanding a business can take months or years in some countries without bribes to help speed the paperwork along. The counterargument is that corruption reinforces unresponsive behavior on the part of government agencies, because bureaucrats come to expect bribes for what they should be doing anyway.
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