Problem

1. Measuring the Fiscal Health of Local Coastal Government Economies: Implications for Economic and Disaster ResiliencyJohn D. BarrecaJ. Matthew FanninCNREPMay, 28, 2010

2. Problem • Louisiana has been hit by several severe hurricanes • Destroyed large portions of the Louisiana coastline • Brought about tremendous costs • Historically, local governments have been reimbursed for all or nearly all of the disaster relief costs • For 2005 hurricanes, 100% of costs, but more recently 75% (eventually 90%) (Harper and Dyer, 2008) • Reimbursement does not come immediately, and local governments have to carry these costs • Little research has been done to determine the effects of these short-term financial burdens on fiscal health

3. Objectives General Research Objective • Using GDP and other economic indicators, evaluate the factors that drive the variation in the financial health of local governments in Louisiana. Specific Research Objectives • (1) Develop and test methods of estimating local area GDP, to determine which method is the most appropriate form of estimation. • (2) Estimate the effect of selected economic indicators on the fiscal health of parish governments.

4. Chapter 1 Methods GDP is the preferred metric because of its comprehensive nature Three methods were developed When earnings was disclosed, we used first method When earnings not disclosed, we used second method Table 1. Comparison across All Parishes, Industries, and Years

5. Chapter 1 Results Table 2. Identification of Highest Contributing Sectors

6. Chapter 1 Limitations Most of the time, variation in the GDP estimates was driven by the state-wide average industrial productivity If parish industrial productivity varied greatly from statewide average, forecast accuracy was reduced. Otherwise, the variation in GDP estimates was driven by the variation in the industrial earnings mix of the parish. If corporate earnings varied greatly from statewide average, this would also reduce forecast accuracy. This study requires very detailed data Should these data sources become unavailable (or less detailed) in the future, estimating county-level GDP using the methods contained here will be limited.

7. Chapter 2 Primary literature basis for research (Wang, Dennis, and Tu, 2007) (Cohen, 2008) Literature shortcomings Neither of the studies analyzed one-time events, such as hurricanes Our research checks the relative sensitivity of the financial ratios to one-time events as well as to annual economic factors.

8. Chapter 2 Ratio Analysis Profitability ratios - measure ability to efficiently utilize resources and finance growth (ROE, ROA) Liquidity ratios - indicate an organization’s ability to meet its short-term financial obligations (CR) Capital structure (or leverage) ratios - point toward how much an organization uses debt to fund its activities (D/E) Performance ratios - relate revenues and expenses (AT, OR) Taken together, these provide a good measure

9. Chapter 2 Methods Table 1: Ratio Formulas

10. Chapter 2 Results Table 2. Descriptive Statistics for Financial Ratios and Macroeconomic Factors

11. Chapter 2 The Model Log Financial Ratio = β0 + β1 Log LAG + β2 Log LAGSQ + β3 Log GDP + β4 Log ASVN + β5 Log DMG + ε Log Financial Ratio - log transform of ratios Log LAG - log transform of one-year lag of the ratio Log LAGSQ - log transform of the square of the LAG variable Log GDP - log transform of annual per capita GDP Log ASVN - log transform of annual per capita assessed valuation Log DMG - log transform of annual per capita value of the parish damage (2004 and 2005) Model run as double-log random effects

12. Chapter 2 The Change Model Δ Financial Ratio = β0 + β1 INITIAL + β2 INITIALSQ + β3 ΔGDP + β4 ΔASVN + β5 ΔDMG + ε Δ Financial Ratio represented the difference between the 2004 value and the 2007 value of one of the ratios from Table 1; INITIAL represented the initial 2004 value of the ratio used as the dependent variable INITIALSQ represented the square of the INITIAL variable previously discussed ΔGDP represented the difference between the per capita GDP in 2004 and the per capita GDP in 2007 for each parish ΔASVN represented the difference between the per capita value of the assessed valuation in 2004 and per capita value of the assessed valuation in 2007 for each parish annual ΔDMG represented the per capita value of the parish damage expense resulting from the year 2005 tropical events (Katrina, Rita, and Cindy).

13. Chapter 2 Results Table 3. Expected Signs for Each Combination of Ratios and Independent Variables

14. Chapter 2 Results Table 4. GLS Estimation of Financial Ratios Models

15. Chapter 2 Results Table 5. Estimation of Financial Ratios Models Using Changes

16. Chapter 2 Concluding Statements Limitations Lagged variable may be causing endogeneity Effects of 2008 hurricanes cannot be determined at present due to certain data having not yet been published Further research Explore other regression methods (between estimator) Attempt to improve methods and reduce the endogeneity Acquire more years of data

17. Policy Implications Relative growth of GDP and Employment Parish leaders should examine the causes for this occurrence Determine if a policy change could provide increased growth Highest contributing and highest growing industry sectors. Often mining or chemical, petroleum, and coal products manufacturing were the biggest sectors. Hurricane damage variable was found to be significant Hurricanes affect a parish’s financial situation. Decision makers from coastal parish should prepare Using financial ratios Compare one’s parish to similar parishes

18. Thank You Questions/Comments