DC City Government

As a city government, profits are not a relevant way to measure performance. But we can examine revenues vs costs as a salient substitute. Costs and revenues provide feedback for each other. For example, in the late 1980’s the real estate market collapsed and DC soon began to run into fiscal problems. The cuts to city services drove many middle class black families out of the city and the subsequent loss of income taxes further exacerbated the fiscal problems. Abandoned and boarded up houses attracted crime and violence. The city soon had the highest per capita murder rate, highest per capita HIV/AIDS rate and one of the highest high school drop-out rates in the country. Ironically, the city’s decline benefited neighboring jurisdictions as young professionals moved to the suburbs for better schools and safer neighborhoods. The DC government eventually was forced to give up power to a financial control board. The control board, free from the types of political pressures an elected government turned around city finances and economy1.

Given that revenues stem from income taxes, corporate taxes, sales taxes, property taxes (based on home values), and fees the core competence of a government is its ability to bolster the local economy.

Therefore, this paper will examine how Moore’s Law, Metcalfe’s Law and Bandwidth law can reinforce a positive or negative feedback loop in a city economy. I believe that four of the items in the exhibit have the potential to revolutionize the way that city governments perform.
Cities collect massive amounts of data on their citizens and businesses. Such information include licensures, use of services, location, payment of taxes, qualifications for and receipt of city services. However, today much of this information remains in data silo’s, inaccessible to other government entities and even public researchers seeking to find solutions to problems facing cities. As both bandwidth and processing power increase, the ability to send much of this data over networks is also enhanced, provided that such information can be linked together.

For a time, social security numbers were a de-facto national id number. Governments are going to increasingly search for ways to make their databases interoperable. This will allow them to set up more one-stop shops and reduce frustrations and runarounds.

Once such linkages are created, governments can aggregate data, make it anonymous, and publish it for study. Society at large can help governments answer some of their most pressing questions and in some cases, using data that spans decades. How can we reduce the amount of government services a person will need over their lifetime? What predictive factors are there for certain undesirable activities (crime, bankruptcy…).

Cities will be able to facilitate peer to peer networks to decrease the burden on government coffers. Today, Chicago uses a peer to peer network to monitor elderly during inclement weather. Similar efforts are in use to help those living with certain diseases, mental health illnesses, survivors of violent crimes and so forth. Social networking is also being used by federal agencies to monitor the activities of criminals. Police and neighborhood activists can coordinate to increase the amount of information on neighborhood crimes while limiting access to appropriate levels. Further, social networks can be used to enforce compliance

Cities will be able to develop predictive economic models. How much can we expect to collect in taxes from an individual (NPV of a taxpayer)? What programs will increase that person’s earning-power? If economic growth decreases, how much money will the city need to cope with increases in support payments such as unemployment and welfare? Which individuals are more likely to lose their jobs? Which of them are more likely to lose their homes? What can be done to help them in advance? What neighborhoods are more likely to see an increase in crime?