## Flint indicators

This page is dedicated to tracking the City of Flint on subjective wellbeing, city financial condition and income inequality.

## Subjective Wellbeing

A distinction needs to be made between happiness or wellbeing, a more comprehensive and broader expression of happiness, and utility. Utility is measured by individual preferences. In regards to happiness, utility is defined by average happiness and the length of life of people. Much like general happiness, utility is about making people happier with a focus on the choices that improve net happiness. The main difference between utility and general happiness is the inclusion of both pecuniary aspects such as income and non-pecuniary aspects such as job satisfaction. Veenhoven (2000) labels the differences as objective and subjective wellbeing. Objective wellbeing is related to income and subjective wellbeing is related to one’s state of mind.

Objective wellbeing is a standard used in traditional economic measures of success such as per capita income or gross domestic product. The underlying assumption is that as one’s income increases or the general gross domestic product increases the wellbeing of the individual citizen increases along with it. The refocus on subjective wellbeing in contrast to these measures of wellbeing has challenged this traditional assumption. There has been a push to abandon these traditional measures of progress and utilize the subjective wellbeing scales as a measure of progress. Some countries have even adopted it as a measure of success for governmental policies such as Bhutan and its Gross National Happiness scale.

There are some challenges to measuring subjective wellbeing, however there have been some very comprehensive measures of it such as the Gallup-Healthways Wellbeing Index. This index ranks 189 communities across the United States based on a meta-score derived from five categories (purpose, social, financial, community and physical). It is based on more than 300,000 telephone interviews in which only MSAs that have more than 300 responses are included in the report. The scale ranges from 0 to 100 with 0 being the lowest wellbeing score. These data are used to rank Flint, MI. One can see from the graph below that between 2014/15 and 2015/16 the City of Flint has experienced a decline in wellbeing while the sample as a whole has experienced an increase in wellbeing. Flint increased slightly in 2016/17 while the sample as a whole continued to increase in wellbeing.

## Flint Wellbeing Score

## City Financial Condition

There are a number of ways of defining and/or measuring a city’s fiscal health. Hendrick (2004) defined fiscal health as the ability of a government to meet its financial and service obligations. Honadle, Costa and Cigler (2004) provided three tools on how to measure the fiscal health of a city. The first tool is a ten point test that uses ratios of revenues, expenditures, operating position and debt structure. The second tool is a fiscal capacity analysis which uses five year trends to forecast revenues and expenditures. The third tool uses the thirty six indicators of the International City/County Management Association to evaluate the financial condition of the city.

Kloha, Weissert and Kleine (2005) developed a fiscal health measure for the State of Michigan. Their measure has ten factors scored with zeros and ones which includes population growth, real taxable value, general fund expenditures / deficits (balance), major fund deficits and long term debt. The authors use the scores to develop tiers of fiscal health. Crosby and Robbins (2013) critiqued the Kloha, Weissert and Kleine (2005) measure and adapted it to include governmental and business activities, accurate population measures, inflation-adjusted real dollars, total debt and different thresholds and weightings. They did not change the scoring method.

Wang, Dennis and Tu (2007) developed a ten point system as well, but like the fiscal health measures cited previously they developed ratios which allow more variability. They defined fiscal health as determined by its level of financial solvency. They developed four major measures of financial solvency: Cash, Budget, Long-Run and Service. Each of these measures has a number of indicators which come from data found in the Comprehensive Annual Financial Reports in the Statements of Net Assets and Activities. These data are cash and cash equivalents, investments, current liabilities, receivables, current assets, total revenues, total expenditures, surplus/deficit, restricted/unrestricted assets, total assets, long-term liabilities, total taxes and total population. These include both government and business-like activities as that used by Crosby and Robbins (2013) while including standardized scores for each measure of financial solvency and different weightings. Arnett (2014) added a slightly different method in weighting and calculating the comprehensive fiscal condition index which combines the four different measures of solvency.

In the Wang, Dennis and Tu (2007) fiscal health measure along with the Arnett (2014) update there are eleven ratios and four indexes. The first ratio includes cash, cash equivalents and investments over current liabilities. The second ratio includes cash, cash equivalents and investments over receivables. The third ratio includes current assets over current liabilities. The fourth ratio includes total revenues over total expenditures. The fifth ratio includes total surplus or deficit over the total population. The sixth ratio includes restricted assets and unrestricted assets over total assets. The seventh ratio includes long-term liabilities over total assets. The eighth ratio includes long-term liabilities over total population. The ninth ratio includes total taxes over total population. The tenth ratio includes total revenues over total population. The eleventh ratio includes total expenses over total population.

The first three ratios are z-scored and the average score across the ratios is weighted according to the Arnett (2014) standard which creates the weighted cash solvency index score for each city for each year. The fourth and fifth ratios are also calculated together in the same method for the weighted budget solvency index score. The sixth to eighth ratios are calculated together in the same method except that the seventh and eighth ratios, long-term liabilities over total assets and over total population, are reversed scored. These ratios together produce the weighted long-run solvency index score. The ninth to eleventh ratios are also z-scored, averaged and weighted to produce the service solvency index score for each city. The index scores are then summed for each city to produce each city’s financial condition index score (Wang, Dennis & Tu 2007; Arnett 2014).

## Financial Condition Score

## Long-Term Liabilities Score

As can been seen in the graphs above, the City of Flint has an overall financial condition score that places it in the middle with other similarly large cities in the State of Michigan in 2017. Grand Rapids and Lansing are comparatively worse than Flint. The second graph above shows Flint near the bottom compared to its peers regarding long-term holdings and responsibilities. In addition, Flint is also at the bottom of this list when it comes to taxes collected per person, revenue collected per person and spending per person. These graphs are not shown here.

## Income Inequality

Increasing income inequality has a number of consequences. Boustan et al. (2013) noted that, “income inequality is correlated with several negative outcomes, including high crime rates, low levels of education achievement, and poor health” (1291). This describes a feedback loop in which income inequality is caused by a lack of training and education while causing a lack of training and education. This could be described as a self-reinforcing income inequality feedback loop which would require policy intervention to stop it.

Income inequality has also been associated with slowing economic growth through its propensity to stymie human capital development such as through education and training. This ultimately creates market instability further delaying economic growth which is needed to increase wages. Increasing inequality also creates group / class conflicts which hampers social consensus needed to create effective policy interventions.

Conversely, there is some literature that explore the positive effects from income inequality. One explanation for this positive effect is through creating entrepreneurial incentives. Those with lower incomes might seek means to increase those incomes through entrepreneurial activity. Investors might recognize the low cost of labor and invest in businesses in these areas. Chang, Gupta and Miller (2016) found that in some periods in U.S. history GDP led inequality (1949 to 1977) and in other periods inequality led GDP (1977 to 2012). Based on these findings the possible relationships between inequality and economic growth seem more nuanced and dependent on many factors.

Conard (2016) wrote a book making a case for the ‘upside of inequality’. Although the author explored many possible positive effects from income inequality he noted that, "increasingly deploying scarce resources to increase productivity of scarce resources – may be the gravest repercussion of growing income inequality” (211). In this statement the author recognized that there are negative consequences from income inequality which is exacerbated by limits on investment, increased savings, improper human capital development, trade deficits and low-skilled immigration.

The method of measuring income inequality here is based on the Gini coefficient. Although there are other prominent measures of income inequality including income shares of the top 1% or various forms of income ratios, the Gini coefficient is one of the most widely used measures in the literature. This measure ranges from 0 (complete equality) to 1 (complete inequality) and it is based on the Lorenz curve.

The first graph below shows several Gini coefficients in a bar chart for 2017. The Gini coefficients for the USA and the State of Michigan are calculated by the U.S. Census. There are two calculations of the Gini coefficient for Flint. The first is the calculation by the U.S. Census and the second is my own calculation based on available data from PUMS. My calculation shows higher income inequality in Flint than that calculated by the Census. If my calculation is accurate Flint has higher income inequality than the USA as a whole and the State of Michigan as a whole. If the ACS version is more accurate than the income inequality in Flint is higher than the State of Michigan, but only slightly lower than the USA as a whole.

The second graph below shows the distribution of wages in Flint in 2017 from the richest individual surveyed by the ACS to the poorest. The distribution shows a concentration of individuals at the bottom portion of the graph which is a typical distribution for wages in the United States. As can be seen in the graph, a majority of the individuals surveyed earn below 40K.

The third graph below shows the Real GDP per capita in 2017 in the metropolitan areas in which the five largest cities in Michigan are located. These are the same cities displayed above for the fiscal condition scores. As can be seen below, Flint has the lowest real GDP per capita than any of the other metropolitan areas. To test the assumption of GDP being higher when income inequality is higher with these cities the Gini coefficients as calculated by the ACS for these cities are compared in the fourth and final graph below. As can be seen, Flint has the second highest Gini coefficient after Detroit with Lansing having the lowest score. Meanwhile, Flint has the lowest real GDP per capita total which does not accord with the theory that having higher income inequality increases economic productivity at least with Flint, Michigan.