Mapping the History of Poverty

“Poverty has fallen faster over the past 50 years than in the previous 500.  But 1.2 billion people still live in extreme poverty.”  From http://www.povertyover.org/

Mapping the History of Poverty (and some other cool socio-demographic mapping projects)

This is an informative and innovative interactive map that lets you see an animated timeline of countries, showing when (and if) they transitioned out of poverty, as well as the degree of their development (symbolized by color intensity).  You can also tilt the map and see the poverty/development data symbolized by height, higher representing an increased degree of development. http://www.povertyover.org/

Check out Cuba!  It is one of the few countries that rises and falls, most others just rise as time goes by, or stay flat.  See if you can pick out a few other anomalous ones that go back and forth in development status. 

I’m not sure that I like the way that “development” is seen as the opposite of “poverty.”  As if those are the only two choices!  And that increasing development (however THAT is defined!) is the solution to poverty! The "growth is good" mentality, regardless of how that can possibly be sustained in even the short-run,  is what is sending this world of ours into the downward spiral. Not to mention the often-overlooked fact that if everyone in the world had the standard of living that we in the US, Canada, and western Europe enjoy, the earth would run out of resources in about 3 months.   But I’m not going to get all hung up on semantics – the concept of the map and the data are pretty interesting. 

MigrationsMap.net

Where are migrants coming from? Where have migrants left?

Another very interesting demographic website is: http://migrationsmap.net/  This interactive map shows you, for any given country, in flow map format, where people who are migrating OUT of the country are going TO (“Departures”), and conversely, where people who are migrating TO the country are coming FROM (“Arrivals”).  They also quantify the populations on the move.  The basis of these maps is something called the Global Migrant Origin Database, and you can download the actual spreadsheets used, and read about the methods and limitations in the construction of the database at: http://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.html

Also, view more detail about the database in a World Bank Discussion Paper at: http://econ.worldbank.org/external/default/main?pagePK= 64165259&piPK=64165421&theSitePK=469372&menuPK= 64166093&entityID=000016406_20070306151900

Population Pyramids, 1950-2050

             This is extremely cool!  View pop pyramids for individual countries, as well as world regions and globally, looking back to 1950, and projected forward to 2050, in 5-year increments. 

http://populationpyramid.net/

             Population pyramids are one of the best ways we have to explore the basic demographic structure of a place – they provide us with information on age cohorts, male/female ratios, and in most cases, absolute numbers of various population sub-groups.  According to Wikipedia, “a population pyramid, also called an age structure diagram, is a graphical illustration that shows the distribution of various age groups in a population (typically that of a country or region of the world), which forms the shape of a pyramid when the population is growing.  It typically consists of two back-to-back bar graphs, with the population plotted on the X-axis and age on the Y-axis, one showing the number of males and one showing females in a particular population in five-year age groups (also called cohorts).  Males are conventionally shown on the left and females on the right, and they may be measured by raw number or as a percentage of the total population.”  From: http://en.wikipedia.org/wiki/Population_pyramid

Generic Population Pyramids, showing the four main stages in demographic transition:

From: http://en.wikipedia.org/wiki/Population_pyramid

Example of a population pyramid from the Stage 1 “expanding” population category.  Nearly half of Libya’s 2011 population consists of youths under age 20.

From: http://en.wikipedia.org/wiki/Population_pyramid

America’s Demographic Opportunity – the Demographic Dividend

And as an important aside to the population pyramids, many of the more affluent countries are currently in the position of having “contracting” populations (the last stage in the demographic transition model, the last of the generic population pyramids).  This means that there are more people at the upper reaches of the pyramid (older populations, living longer) than there are younger ones.  This has serious implications for those nations where this is occurring, in terms of economic growth, employment, taxation, innovation, support for the elderly, and future development.  An interesting blog posting from New Geography recently detailed what this might mean for some of those contracting nations (Japan, Italy, etc.) and why the U.S. is not quite as badly off in terms of population numbers skewed toward the elderly.  I had sent this link around to those on my Listserv, but I include it again here, even though there are parts of it that I don’t agree with (as I rarely do with most of the New Geography posts! Especially their pro-urban sprawl tendencies).  Nevertheless, it is worth a read. BTW, notice how in the graph above, the legend and the symbols are shown incorrectly (they are reversed).  This was pointed out to me by an observant Jon Jenkins.  Thanks Jon, for your gimlet editor's eye for detail!  I looked at this graph dozens of times (and carefully, I thought!) and never noticed the error.  Of course, the little "%" symbols next to the numbers make the meaning clear, but STILL!  Shame on New Geography or whomever put these graphs together originally!

“Among the world’s major advanced countries, the United States remains a demographic outlier, with a comparatively youthful and growing population.  This provides an unusual opportunity for America’s resurgence over the next several decades, as population growth elsewhere slows dramatically, and even declines dramatically, in a host of important countries.” From: http://www.newgeography.com/content/002492-americas-demographic-opportunity?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Newgeography+%28Newgeography.com+-+Economic%2C+demographic%2C+and+political+commentary+about+places%29

The Spread of Immigrant Groups in the U.S.

And another really nice one on U.S. immigrant groups and their spread across the United States.  http://www.nytimes.com/interactive/2009/03/10/us/20090310-immigration-explorer.html
You can select specific nationalities or regions of origin, and the map changes from a multivariate choropleth to a proportional symbol map. You can also adjust the "bubble" size to see the detail better.  Thanks, Kristen Grady, for sending the link

Life expectancy across the U.S. / Global BMI and Diabetes

American women live an average of 2.5 years longer than men, but as life expectancies vary across the country, both men and women in certain counties, particularly in the South and Southeast, can expect to die more than a decade sooner than others.  See interactive map, where you can get data by county, at http://www.washingtonpost.com/wp-srv/special/nation/life-expectancy-map/

        Thanks, Urban Demographics, for the link.  On the same page, there is another link to an animated scatterplot showing how global BMI (Body Mass Index) has changed for men and women, 1980-2008, as a metric for cardiac risk. You can look separately at each nation, also.  Weight of the World:  http://www.washingtonpost.com/wp-srv/special/health/weight-of-the-world-bmi/
Another tab on that page shows Diabetes worldwide and for each nation. 


Bottom of the Heap
And, lastly, a sobering look at social justice in various nations around the world.  The United States, one of the overall richest countries in the world, is very low on the charts.  What does this say about the so-called "American Exceptionalism"? 
Table from: The New York Times, http://www.nytimes.com/imagepages/2011/10/29/opinion/29blow-ch.html?ref=opinion
from the editorial "American's Exploding Pipedream by Charles M. Blow at http://www.nytimes.com/2011/10/29/opinion/blow-americas-exploding-pipe-dream.html?nl=todaysheadlines&emc=tha212
"The differences in the prevention of poverty and access to educational opportunities are immense in the OECD.  The northern European countries are best of all at providing for equal opportunities for achievement. At the same time, many continental European and Anglo-Saxon states have considerable catching up." from the report at http://translate.google.com/translate?hl=en&sl=de&u=http://www.bertelsmann-stiftung.de/&ei=dh2rTqy2KsLh0QGgtJWPDw&sa=X&oi=translate&ct=result&resnum=1&ved=0CCIQ7gEwAA&prev=/search%253Fq%253Dhttp://www.bertelsmann-stiftung.de%2526hl%253Den%2526client%253Dsafari%2526rls%253
These (the successful nations) are the very countries that some of our would-be leaders in the U.S. deride as dangerous socialist welfare states.  Meanwhile, we (the U.S.) are literally at the bottom of the heap, just slightly above Greece, Chile, Mexico, and Turkey.  We are in the lowest category, "the bottom five," for Pete's sake!

Mapping Urban Inequality: Using the Gini Coefficient to Measure the Urban Divide

National Income Inequality Using Gini Coefficients, from CIA World Factbook, 2009.

Many of you have most probably seen maps of the Gini coefficient used to measure income inequality in nations.  (See the map above. If you don’t know what the Gini coefficient is, I’ll explain it in a minute.) 

            The United Nations Human Settlements Programme (known as UN-HABITAT) has put together a rather different perspective on the Gini coefficient.  Rather than measuring income inequality as averaged for an entire nation, they have teased apart the income inequality that exists in urbanized areas - an important thing to know, now that more than 50% of the world lives in cities.  Some of the most alarming and extreme statistics for income inequality exist in cities, and these facts are masked when looking at national averages. 

Even more surprising, in light of the extreme income inequality usually found in the developing countries, are the high Gini coefficients (i.e., high income inequality) found in U.S. cities.  This would not be apparent from just looking at the national Gini.  “The most surprising variations between national and city-specific Gini coefficients of income or consumption disparities are found in the United States of America, where around 2005 the national coefficient stood at 0.38, but exceeded 0.50 in many major metropolitan areas including Washington, DC; New York City; Miami; and others.  These values are comparable to the average Gini coefficients of cities in selected Latin American countries, where income inequality is particularly steep,” (UN-HABITAT, 2009:xiv).  And, in developed countries like the U.S., overall the national Gini coefficients have increased fairly steeply from the mid-1980’s to 2005. 

African cities have the highest Gini coefficient in the world, averaging 0.58.   Sub-Saharan Africa has the highest degree of poverty in the world, and the highest prevalence of slum populations in urban areas.  From: UN-Habitat “State of the World's Cities 2010/2011 - Cities for All: Bridging the Urban Divide”

        How is income inequality measured using the Gini coefficient, and indeed, what is the Gini coefficient?  The Gini coefficient is a number between 0 and 1, where 0 corresponds with perfect equality (where everyone has the same income) and 1 corresponds with perfect inequality (where one person has all the income, and everyone else has zero income).  It’s a measure of statistical dispersion – the inequality of a distribution - invented by an Italian statistician, Corrado Gini, about 100 years ago.  A low Gini coefficient indicates a more equal distribution, and a high score indicates a very unequal distribution.  While the Gini coefficient can be used in many disciplines (for instance, in ecology to measure biodiversity) it is most commonly used in economics, and specifically, to measure income inequality.  

Diagram from Southern African Regional Poverty Network http://www.sarpn.org.za/documents/d0000990/

“Graphically, the Gini coefficient can be easily represented by the area between the Lorenz curve and the line of equality. On the figure to the left, the Lorenz curve maps the cumulative income share on the horizontal axis against the distribution of the population [households] on the vertical axis. In this example, 70 percent of the total income is obtained by around 20 percent of the population [or households, in this example].  If each individual had the same income, or total equality, the income distribution curve would be the straight line in the graph – the line of total equality.  The Gini coefficient is calculated as the area A [between the curve and the line of equality] divided by the sum of areas A and B [everything under the line of equality].  If income is distributed completely equally, then the Lorenz curve and the line of total equality are merged and the Gini coefficient is zero.  If one individual receives all the income, the Lorenz curve would pass through the points (0,0), (100,0) and (100,100), and the surfaces A and B would be similar, leading to a value of one for the Gini-coefficient.”  From the Poverty Reduction and Equity website of the World Bank http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20238991~menuPK:492138~pagePK:148956~piPK:216618~theSitePK:430367,00.html

In a nutshell, then, the Lorenz Curve is established by plotting the cumulative percentage of income earned by the cumulative percentage of the population along two axes.  The Gini coefficient is then derived by creating a ratio of the area between the Lorenz Curve and the line of equality, divided by the total area under the 45 degree angle line that represents complete equality.  The lower the Gini coefficient, the more equality of income.

Because of the disparate ways that different countries calculate poverty and collect information on income, the Gini Index takes into account either or both “income” as earnings, and also by estimating “consumption,” or actual expenditures, as a proxy for income.  You will often see both used within the same map, depending on how the individual countries collected the data. 

Asia – relatively low Gini coefficient (0.38) in urban areas. 

From: UN-Habitat “State of the World's Cities 2010/2011 - Cities for All: Bridging the Urban Divide”

So, what are some of the impacts of income inequality in urban areas?  In addition to indicating simply economic disparities, a high Gini index represents barriers to cultural, political, social, environmental, and health equality.  These forms of exclusion continue to marginalize huge swaths of the population.  This marginalization is social as well as physical and translates into spatial inequalities and deeply divided cities.  Since virtually all of the world’s population growth in the coming decades will take place in cities, it is crucial to make cities more inclusive.  In general, countries with the highest per capita income are the most urbanized, and conversely, those with the lowest incomes tend to be the least urbanized.  So, overall, the process of urbanization should be seen as (and has been) overall an effective means of poverty reduction, and urban living has the potential to (and usually does) offer much more opportunity to people. 

Some areas have made progress in reducing the number of people living in slums.  In Northern Africa, for instance, both the proportion and absolute numbers of slum dwellers fell significantly, as it has in China as well.  What constitutes a “slum”?  According to the UN, a slum is defined as a part of the city where the residents do not have durable (permanent) housing, little or no acceptable water or sanitary infrastructure, severe overcrowding (more than 3 people to a room), and no secure tenure and protection against forced eviction.  By this definition, many cities are “slum cities,” with the majority of their residents living in slum conditions, which in Bangladesh includes 70 percent of urban dwellers; in Yemen, 65 percent; in Haiti, 76 percent; and in Bolivia, 61 percent.  Women bear the brunt of slum living, being more often the water bearers and waste disposers, spending more time within the slum than men, and thus being more exposed to the slum’s environmental burdens as well as the higher rates of crime and violence.

            This spatial inequality, the segmentation and marginalization of people and geography, the uneven distribution of amenities and disamenities, and fragmentation of opportunities, generate high social and economic costs for the poor as well as society as a whole.  Unequal income distribution can lead to social unrest and political instability, more than overall high poverty levels alone would produce.  The spatial “poverty trap” of income inequality stems from the physical and social distance between rich and poor neighborhoods, with concomitant adverse impacts: restrictions for the poor on job opportunities, gender disparities, deteriorated living conditions, social exclusion, and high crime rates.  The poor can not participate to the fullest degree in the “urban advantage.”  Considering that by 2050, nearly three quarters of the world’s population will be urban, and in the extreme example of South America, it is estimated that over 90% of its population will be urban by 2050, the fact that many of the world’s cities have such extremes of wealth and poverty does not bode well for our collective welfare. 

            There are a number of drawbacks to using the Gini coefficient as a measure of inequality.  As with any global index, it is an imperfect and imprecise measure for a complicated concept, and many factors are overlooked in its calculation, such as non-monetary economies, bartering, subsistence farming, welfare benefits from the state, governmental subsidies and services, and it measures income rather than wealth.  Income disparities also do not necessarily capture standard of living differences.  For instance, In Brasilia, there is a high Gini coefficient of 0.60, but nearly 95% of the population has piped in water and sewage provisions, indicating a higher quality of life for most of the population than might be assumed from a high Gini coefficient.  Most importantly, the Gini coefficient can not be used to measure poverty, per se, but merely income inequality.  Countries can be overall wealthy or overall poor, and therefore each have very low Gini coefficients, because they are homogeneous in terms of income.  That doesn’t mean, of course, that the two countries have similar standards of living, or poverty rates.  It just means that everyone in that country or city is approximately equally poor or equally wealthy.  The Gini coefficient is perhaps most useful when used in conjunction with other indices, such as the Human Development Index, and the GDP (Gross Domestic Product) of a nation.  Unfortunately, these are calculated at the national level, and can not usually be disaggregated to the municipal level to be compared with the urban Gini coefficients. 

Urban Gini Coefficient in Mexico, the Caribbean, and Central and South Americas,

From: UN-Habitat “State of the World's Cities 2010/2011 - Cities for All: Bridging the Urban Divide”

See these weblinks for further information on poverty mapping and urban income inequalities:

 UN-Habitat “State of the World's Cities 2010/2011 - Cities for All: Bridging the Urban Divide”

(Free download of pdf book)

http://www.unhabitat.org/pmss/listItemDetails.aspx?publicationID=2917

“More Than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions,” 2007, World Bank.  (Free download of pdf book)

http://siteresources.worldbank.org/INTPGI/Resources/342674-1092157888460/493860-1192739384563/More_Than_a_Pretty_Picture_ebook.pdf

“Where are the Poor? Experiences with Development and Use of Poverty Maps,” 2002, World Resources Institute (Free download of pdf book.)

http://pdf.wri.org/wherepoor.pdf

“Rich Map, Poor Map,” by WC Holton, 2004, in Environmental Health Perspectives.  http://ehp03.niehs.nih.gov/article/fetchArticle.action;jsessionid=F9C2755391DF98FBC51CD42BE955F5DE?articleURI=info%3Adoi%2F10.1289%2Fehp.112-a176

 “CIA World Factbook” – Gini coefficient https://www.cia.gov/library/publications/the-world-factbook/fields/2172.html