Economic Development Blog

A study published in the latest issue of Economic Development Quarterly compares the relationship between growth and prosperity for the 100 largest U.S. metropolitan areas, which combined represent 66 percent of the U.S. population.

The report originally authored by Eben Fodor (Fodor & Associates) is a bit dated, but the data, methodology and findings are still relevant enough.

Growth vs Prosperity for US metros (Source – Fodor & Associates)

The study takes the average annual population growth rate of each metro area from 2000 to 2009 and compares it individually with per capita income, unemployment rate and the poverty rate.

The same thing is done all over again for the Great Recession period from 2007-09. The study also shows a head to head comparison of the economic well-being of the 25 fastest growing cities against the 25 slowest growing cities.

The results, to put it mildly, are surprising. Faster growth is directly related to lower incomes, greater income declines, and higher poverty rates. Unemployment rates tend to be higher in faster growing areas. The 25 slowest growing metro areas outperformed the 25 fastest growing in every category.

The 2000-09 change in the unemployment rate was 5.4 percent for all the 100 MSAs and for the 25 slowest growing MSAs. It was 5.7 percent for the 25 fastest growing MSAs. The 2009 poverty rate for all 100 MSAs was 13.7 percent. It was 13 percent for the 25 slowest growing MSAs, and 15.5 percent for the 25 fastest growing MSAs.

The 2009 per capita personal income for all 100 MSAs was $39,190. It was $42,908 for the slowest 25 MSAs and $34,454 for the fastest 25 MSAs. That points to a huge $8,455 difference in the per capita personal income between the slowest growing cities and the fastest growing ones.

The 2007-09 statistics show that the fastest growing cities were more severely affected by the recession. The 25 slowest growing MSAs are spread around in 13 states, including New York, Connecticut and Ohio. The fastest growing MSAs are in 12 states, with a majority in California, Texas and Florida.

Fodor concludes the report by saying that, “These findings clearly indicate that there is more to the economic development equation than merely growth and that growth may not even be a contributing factor.”

He adds that a possible explanation may lie in the fact that growth itself does not promote general welfare, and it may be other factors associated with growth that used to promote prosperity in the past. For example, growth before globalization would have given a boost to domestic manufacturing and production capacities.

Now, when a U.S. metro area expands and increases its demand for goods, foreign jobs are created to fill that demand and prosperity is generated elsewhere.

Read the full study on growth vs. prosperity in U.S. metros – Download (pdf)

A report was launched at UN Headquarters in New York which highlights the predictive power of “Big Data for Development.”

Big Data for Development (Photo – UN Global Pulse)

The report was produced by UN Global Pulse, which is supposed to be an innovation lab that was created in 2009, bringing together expertise from inside and outside the UN.

This white paper has been available since May 29, 2012, but was officially launched and highlighted at the UN round table discussion a couple of days ago.

It explores how data from social media, mobile phone usage and other sources can yield information about population well-being, including responses to job losses, changing food prices and other economic indicators.

The study demonstrates that social media chatter can be an early indicator of spikes in unemployment in countries such as the United States and Ireland and showed how tweets related to the price of rice in Indonesia mirrored food price inflation statistics.

There are stunning examples throughout the report showing how data mining unearths trends, but the most interesting stuff is on page 20, with concrete examples of the predictive power of data in the fields of social science and public policy.

A team of physicists from Northeastern University conducted a study in which they were able to predict, with over 93% accuracy, where a person is physically located at any given time based on analysis of cell-phone information generated from their past movements.

A country’s GDP could be estimated in real time by measuring light emissions at night through remote sensing.

The study also cites research regarding Google Flu Trends – “because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms,” it was possible to “accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day.” The conclusion was that it is “possible to use search queries to detect influenza epidemics in areas with a large population of web search users.”

The study discusses the possibilities of Twitter data at length. It says tweets can be mined to find out the prevalence and spread of health conditions and ailments in a community, including flu, obesity and cancer. Twitter users’ location that they provide publicly can be used to study the geographic spread of a disease or virus.

Tweets also provide detailed information about the use and misuse of medication and exercise routines among various socioeconomic groups. Facebook updates, photos and posts could help identify drinking problems among college students, a major public health concern.

Another huge application is disaster response. The study cites the example of the Haiti quake, where a centralized text messaging system was set up to allow cell-phone users to report on people trapped under damaged buildings. Analysis of the data found that the concentration of aggregated text messages was highly correlated with areas where the damaged buildings were concentrated.

Read the full “Big Data for Development” report – Download (pdf)