Monday, February 17, 2014

The Human Development Index at the Sub-national Level

Porter, J. R., & Purser, C. W. (2008). Measuring relative sub-national human development: An application of the United Nation's Human Development Index using geographic information systems. Journal of Economic and Social Measurement33(4), 253-269.

There are a variety of ways to measure the development of a country. One method involves economic indicators, such as GDP growth or per capita income. However, this does not tell the whole picture. A country may experience high economic growth or raising incomes but the population does not experience progress in educational attainment, health outcomes, or human rights. A country can have a growing economy yet still have high unemployment rates or low education levels.  In 1989, the United Nations created the Human Development Index (HDI) to solve this problem. The UN views development as increasing people’s choices and improving their lives.

For this reason, the HDI measures health, education, and command of resources. Life expectancy at birth is used to measure the health component of the HDI; literacy rates and the percent of the country enrolled in school is used for the education measures; command over resources is measured via GDP per capita. The HDI measures human capital development because economic growth does not equal social growth. Each component of the HDI is equally weighted as one-third of the final index. All three components are added together and then divided by three to give a HDI score. Scores rage from zero to one, where a score of one indicates a highly developed country.

The HDI does have its critics but it is the first index to incorporate non-economic components in measuring human development. A highly developed country may have underdeveloped regions, cities, or counties. Porter and Purser (2008) used GIS to identify areas in the US that have a low HDI score. Literacy data was obtained from the National Institute for Literacy; the Census Bureau provided educational attainment levels; data on average life-expectancy of each county was obtained from the National Center for Health and Statistics; and per capita income at the county level were obtained through the Bureau of Economic Analysis. Sub-national HDI scores range from zero to three, where three indicates a highly developed county.

GIS was used to map out sub-national HDI at the county level and rank each county according to different levels of development: under-developed, moderately developed, or highly developed. Results indicate that the northeast region has the highest average HDI score of 1.65 and the south has the lowest score at 1.24. Metropolitan areas fare better than rural areas when it comes to HDI scores. The GIS map below indicates significant spatial clusters. A Moran’s I statistic of 0.5175 significant at the 0.001 level reveals that the clustering is not random.

Highly developed areas have more hospitals, less poverty, less violent crime, and more education than underdeveloped counties. Six of the top ten most developed counties are in the West and seven of those counties are metropolitan areas. As for underdeveloped counties, nine of the bottom ten counties are in the South. The most developed county is Marin County California, which is in the San Francisco Bay Area, while the least developed county is Shannon County South Dakota, which lies within the Pine Ridge Indian Reservation.

This study reveals that human development is uneven at the sub-national level in the US, even though the country is rated as being highly developed. The results show that human development varies a lot within the US. GIS allows people to visually see the spatial clusters and where underdeveloped areas are in the country. Similar studies can be carried out in developing countries to reveal if the benefits of development are being equally distributed. Just because a country experiences development, it does not mean that ever one’s lives will be improved.   


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  2. It's interesting to see this data clustered in regional areas, with the South having the lowest scores on the HDI. It would be interesting to see the data on average median income for each county, to see if this correlates in any way with a lower score on the HDI. Lower income areas imply a smaller tax revenue base, potentially leading to different spending levels by county on important qualifying factors, such as health and education.

  3. I'm a bit surprised seeing a lot of the midwest states being categorized as highly developed. The final list of states categorized by HDI score is something that I guess I understand but rarely see displayed in this manner. The difference in Bachelor's degree %age with KY having just above 9% and MA having 26% is another shocking parameter for me personally. On a terrible side note, the display on the bottom portion of Figure 1 really bugs me since I can hardly tell the slant of lines at first glance. Perhaps I'm too picky. Good stuff.