!

!!

EXAMINING THE EFFECT OF MULTI-YEAR CAPITAL BUDGETING:

DOES FORWARD THINKING ENHANCE FINANCIAL CONDITION?

By

EMILY ERIN PORTNER

A paper submitted to the faculty of The University of North Carolina at Chapel Hill

in partial fulfillment of the requirements for the degree Master of Public Administration

FEBRUARY 15, 2011

This paper represents work done by a UNC-Chapel Hill Master of Public Administration student. It is not a formal report of the Institute of Government, nor is it the work of School of Government faculty.

EXECUTIVE SUMMARY

There is a common assumption among public administrators that multi-year capital budgeting, most commonly operationalized through a Capital Improvement Program (CIP), positively impacts the financial condition of governmental entities. Despite the prevalence of this assumption, empirical studies testing its validity are rare. The purpose of this capstone is to determine whether capital budgeting is a significant variant of financial condition among a sample population of North Carolina municipalities. Analyses demonstrate that capital budgeting yields no significant impact on financial condition; but also show that organizations which engage in sophisticated capital budgeting models tend to use capital budgeting “best practices” with greater frequency than organizations with more basic models. These findings provide local government managers with insight into the nature of governmental financial condition and the value of capital budgeting within their organization.

! 1!

BACKGROUND & RESEARCH QUESTION There exists a common assumption among public administrators that multi-year capital budgeting, most commonly operationalized through a Capital Improvement Program (CIP), positively impacts the financial condition of governmental entities.1 The central premise of this assumption is that strategic capital budgeting practices engage governments in a forward-thinking planning process, increasing the potential for effective resource allocation decisions and ultimately indirectly enhancing fiscal strength. The normative literature exploring the relationship between capital budgeting and financial condition in local government typically expresses it, as shown in Figure 1, as merely a component of a broader planning framework.2 Rarely does the literature involve a focused discussion of the direct impact of capital budgeting on financial condition.

Figure 13

Although the literature advocates systematic capital budgeting based on the aforementioned assumption, empirical studies investigating the tangible benefits of this practice are few. Such studies also are limited for the purposes of this capstone as they rarely focus their analysis on local government and predominantly investigate the impact of capital budgeting on broad economic variables such as Gross State Product (GSP),4 governmental productivity,5 or private-sector economic growth.6 In 2009, UNC School of Government faculty members developed a financial condition analysis model that allows local governments in North Carolina (counties and municipalities only) to evaluate their fiscal strength signified through a number of financial indicators and ratios calculated from annual external financial statements.7 There was previously no mechanism available to counties and cities for highly developed financial condition analysis. This capstone seeks to address the gap in the existing literature on capital budgeting by using this financial condition data to answer the following research question: Does effective multi-year capital budgeting, either through the use of a CIP or comparable capital-related financial policies, enhance the financial condition of municipalities in North Carolina? METHODOLOGY This study uses a mixed-method research design to determine whether advanced models of capital budgeting have a positive impact among North Carolina municipalities on any of the financial condition analysis model’s 14 general fund and governmental activities indicators.8 The quantitative component of this research involved performing multivariate regression analyses of variance (ANOVA) and Pearson’s chi-square tests for independence. Statistical significance was tested at the 95 percent threshold, representing a p-value of 0.05 or lower.9 An initial population of 126 was generated based on all those municipal units in the state with populations exceeding 5,000.10 An online survey was sent to finance directors and budget managers consisting of 12 questions inquiring as to the municipality’s capital planning strategies, financial policies, and the presence of professional “best practices” in the capital budgeting process. Question 1 was of primary interest in this study because it directly addressed the research question by inquiring as to the nature of the municipal capital budgeting strategy. Survey answer choices were arranged on a scale of “sophistication” from the more basic to the more sophisticated response.11 Answer choices were assigned a coded value along this scale (See Appendix A for survey questions, results, and coded values).12 The survey resulted in a final sample of 92 municipalities, representing a 73 percent response rate (See Appendix B for a list of the final sample municipalities).

Strategic Planning Capital Budget

(incl. CIP) Economic

Development Financial Condition

! 2!

Multiple chi-square tests were used on the coded survey data. All tests included survey question 1, with consecutive tests for each remaining question. The results were expressed in contingency tables of counts and percentages, and examined whether the way in which the municipality answered the capital budgeting question was independent of how it answered consecutive survey questions. This analysis answered a secondary research question of whether sophisticated capital budgeting models use selected “best practices” with greater frequency than more basic models. All financial indicator data were accessed through the existing financial condition database. The value of each indicator was representative of FY2009 financial statements. Data also were gathered on 10 independent variables for control use in the regression model (See Appendix C for a list of independent variables).13 Prior to use in the final regressions, tests of correlation and multicollinearity were performed on all variables to avoid invalid results or variable redundancy. Regression analyses were then performed for each of the 14 financial indicators (See Appendix D for a complete list of financial indicators). Every regression model included the capital budgeting variable and the 10 independent variables.14 The value of the capital budgeting variable was equal to the coded number value for survey question 1. The regression models examined the influence of the capital budgeting and independent variables on the value of the relevant financial indicator. FINDINGS & ANALYSIS Most Municipalities Engage in Sophisticated Capital Budgeting

In general, the survey data show that North Carolina municipalities are advanced in their approach to capital budgeting. “Sophisticated” forms of capital budgeting are defined as: the use of a CIP accompanied by an annual capital budget, an annual capital budget only, or the use of a CIP for planning purposes accompanied by separate project ordinances.15 Using this definition, 67 percent of municipalities employ advanced capital budgeting models (Figure 2), a majority of which (56 percent) supplement their capital budgeting strategy with formal capital-related financial policies (Figure 3).

Figure 2 – Capital Budgeting Strategy Figure 3 – Capital-Related Financial Policies

These results suggest that municipalities in North Carolina undoubtedly feel as though there is significant value in the capital budgeting exercise. As a majority of the sample practices a comprehensive approach, supplementing the capital budgeting process or CIP with formal capital-related financial policies, the data also imply that sophisticated capital budgeting includes both a planning and a policy component.

7%

26%

35%

2%

30%

0

10

20

30

40

All projects part of annual

operating budget

One single capital project ordinance for all projects

CIP (planning),

separate capital project

ordinances

Annual capital budget only

CIP, annual capital budget

25%

18%

27% 29%

0

10

20

30

40

No formal financial policies

Financial policies do not address capital management

De-centralized capital financing

policies

Centralized capital financing

policies

Basic Capital Budgeting Models

Sophisticated Capital Budgeting Models

No capital-related financial policies

Capital-related financial policies

! 3!

Sophisticated Capital Budgeting Models Encourage the Use of “Best Practices”

Overall, the results of the series of chi-square tests imply that advanced capital budgeting organizations tend to engage in select “best practices” with greater frequency (See Appendix E for complete chi-square results). Figure 4 presents the consolidated results of the statistically significant response patterns found throughout the survey data by providing the dominant strategy used by sophisticated capital budgeting models in comparison to basic models. Figure 4 – Consolidated Chi-Square Results, statistically significant response patterns found Sophisticated Capital Budgeting Models

Financial Policies *

More likely to have centralized capital-related financial policies, instead of de-centralized or no formal financial policies

Financing Strategy *

More likely to use a balanced approach of pay-as-you-go (PAYGO) and debt financing, instead of either PAYGO or debt financing

Financing Sources **

More likely to use a greater number (most commonly 5 or more) of distinct financing sources, instead of 4 or less

Review of Capital Plan **

More likely to review the capital plan separately from the annual budget, instead of as a part of the annual budget

Method of Prioritization *

More likely to use experience-based judgment to prioritize capital projects, instead of separate departmental prioritization methods

Capital Project Oversight *

More likely to manage project implementation formally through an individual or the budget function, instead of as needed on a case-by-case basis

* p ! .05 ** p ! .01 These relationships collectively suggest that sophisticated models encourage sound management practices in other aspects of the strategic capital budgeting process. The data show that sophisticated capital budgeting organizations tend to develop separate procedures pertaining to the capital plan and budget, and are often guided in the capital financing process by formal capital-related financial policies. These organizations tend to also be more forward-thinking in relation to project prioritization, open to a broader financing base, and are stricter about adhering to specified project expenditures and timelines. Correlation data echo this notion that advanced capital budgeting encourages other sound management decisions. Centralized capital-related financial policies are positively correlated with public input in the capital planning process (.402). In addition, municipalities with centralized financial policies also often include within their capital planning document a forecast of the impact of capital expenditures on future debt ratios (.432) (See Appendix F for complete correlation tables). While the tests imply that sophisticated capital budgeting organizations are more likely to use selected “best practices,” not all practices are consistently adopted in all sophisticated organizations, or across-the-board. For example, the chi-square tests found that these municipalities are not more likely to have a separate capital reserve fund16, nor do their capital planning documents forecast the impact of capital expenditures on future operating budgets or property tax rates with significantly greater frequency. Capital Budgeting Yields No Significant Impact on Financial Condition

When not controlling for other variables that may affect financial condition, regression results suggest that capital budgeting may be associated with greater debt levels. In these modified regression models, capital budgeting was found to have a positive impact on the general fund “Debt as a Percentage of Assessed Valuation” indicator (B = .290, p = .005), and the governmental activities “Debt Service Ratio” indicator (B=.251, p=.016).17 Figure 5 shows the average values for each indicator across all capital budgeting categories, showing higher values among sophisticated models.

! 4!

Figure 5 – Debt-Related Financial Indicators, Capital Budgeting Variable

Although the capital budgeting variable was found to be significant in the modified regressions, this variable was not a significant variant of financial condition in any of the complete 14 financial indicator regression models when including control variables (See Appendix G for full regression results).18 While the survey data suggest that North Carolina municipalities are advanced in their approach to capital budgeting and that this practice encourages forward-thinking in other aspects of strategic planning, capital budgeting yields no impact on financial condition when controlling for other variant factors. These results collectively support the null hypothesis that capital budgeting does not enhance financial condition. The data also confirm the suspicion that much of what affects financial condition still remains unknown. Adjusted R2 values for the complete regression models are relatively low (.529 max, .006 min), and somewhat surprisingly, macroeconomic variables which may be expected to be significant were rarely found to positively affect financial condition. Data Provide New Insight on the Nature of Governmental Financial Condition

While capital budgeting was not found to be a significant variant of financial condition, the complete study data set does provide some valuable insight into the nature of governmental financial condition. First, many of the forces which are commonly thought to influence a government’s financial strength (such as low unemployment or high household income) are in fact not associated with better financial condition. Local governments often find a way to effectively adapt to the external economic environment, given that many of these forces are outside of their short-term control, allowing their financial condition to remain somewhat stable. This ability to adapt is not unlimited, however, as the regression data show that governments which have higher municipal property tax rates are often already in poor financial condition, most likely because they have exhausted all other potential options to increase revenues. Secondly, a high number of large positive correlations among the financial indicators encourage the viewpoint that select aspects of financial condition are intricately linked. The most significant of these relationships indicate that as a government lives within its financial means, its financial condition improves (.831), and it is well poised to meet its long-term obligations (.875). In addition, the more resources dedicated to debt service, the greater the possibility that the organization relies too heavily on debt to finance assets (.681). Finally, if a government is able to meet its short-term obligations, typically it is able to meet its long-term obligations (.987). Collectively, these relationships suggest that indicators are rarely static, but are instead often influenced by other financial forces occurring within the organization.

0.403

0.385

0.289

0.218

0.043

0.087

0.140

0.054

0.062

0.043

0.000 0.100 0.200 0.300 0.400 0.500

CIP, annual capital budget

Annual capital budget only

CIP (planning), separate capital project ordinances

One single capital project ordinance for all projects

All projects part of annual operating budget

Sophisticated Capital Budgeting Models

Debt Service Ratio

Debt as a Percentage of Assessed Valuation

! 5!

RECOMMENDATIONS While this research study determines that capital budgeting yields no impact on financial condition, the statistical analyses highlight a number of considerations regarding financial condition and capital budgeting and provide three overarching recommendations for local governments and public administrators. Change the Common Perspective on Financial Condition

The regression results imply that financial condition is a very different type of fiscal concept. Local governments cannot passively operate within the constraints of their external environment, but must instead find a way to adapt and avoid being managed by it. The data suggest that sustaining or improving financial condition requires constant and active engagement on the part of elected and appointed officials to ensure that all planning and resource allocation decisions are pursued with the overall fiscal health of the organization in mind. In this way, the concept of governmental financial condition should be perceived as being influenced by both financial and policy choices. Do Not Expect Immediate or Easy Payoffs

While there are a number of forces that can potentially negatively affect fiscal health, it takes consistent and active effort to enhance financial condition. Additionally, decreases in one area of financial condition may be correlated with increases or decreases in another area. Public officials should be aware that attempts at reform are unlikely to produce immediate or significant increases to financial condition. In this way, financial condition may remain frustratingly stable despite multiple focused attempts at improvement. Consider the Value of Capital Budgeting

The lack of significance of the capital budgeting variable brings to light a lingering question: what is the value of capital budgeting for government organizations if not to enhance financial condition? The results of this study suggest that this value may lie in broadening the focus of government action to include a wider approach to capital project financing and greater attention to project oversight. Nevertheless, local governments should give greater consideration to the potential value of capital budgeting, beyond improving to financial health, within their organization and work to better communicate those objectives. CONCLUSION This capstone addresses a significant gap in the literature on capital budgeting by examining the direct effect of strategic capital budgeting on financial condition. The statistical analyses indicate that capital budgeting yields no impact on any of the 14 financial indicators for the general fund or governmental activities accounting levels, bringing into question the validity of the common assumption that multi-year capital budgeting enhances the fiscal strength of governmental entities. The data also introduce a greater empirical question: what is the nature of financial condition, and what variables do affect governmental fiscal strength? Additional research should be conducted on the concept of financial condition to answer this question, as well as to inform local governments on the methods and processes suggested to increase the financial condition of their organizations. Given the current macroeconomic climate, many local governments in North Carolina have experienced financial challenges in recent years. This trend, if spread over the long term, may seriously damage the financial condition of these localities. Provided these current pressures, further empirical research on the concept of financial condition is needed not only in this state but across the country as a possible way to help struggling government organizations make effective and responsible resource allocation decisions to improve long-term fiscal health.

! 6!

END NOTES !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 “Financial condition” is defined in this study as: a local government’s ability to meet its ongoing financial, service, and capital obligations based on the status of resource flow and stock as interpreted from annual financial statements. 2 Jane Beckett-Camarata, “An Examination of the Relationship between the Municipal Strategic Plan and the Capital Budget and Its Effect on Financial Performance,” Journal of Public Budgeting, Accounting and Financial Management, 15(1). 2003. 23-41. 3 Financial Performance outcomes in this study were measured by (a) Bond Rating, (b) Long-term Debt Per Capita, (c) General Fund Balance Per Capita, or (d) Own Source Revenue Per Capita. The analysis and recommendations are primarily founded on phone interview data collected on 4 municipalities, only 2 of which practice strategic planning or capital budgeting. 4 Arwiphawee Srithongrung. The Impacts of State Economic Capital Management Programs on State Economic Performance. Public Budgeting and Finance, 28(3), 2008. 83-107. 5 Jose Lobo and Norma Rantisi, “Investment in Infrastructure as Determinant of Metropolitan Productivity,” Growth and Change, 30(1), 1999. 106-127. 6 Samuel Nunn, “Public Capital Investment and Economic Growth in Fort Worth: The Implications for Public Budgeting and Infrastructure Management," Public Budgeting and Finance, 11(2). 1991. 62-94. 7 William Rivenbark, Dale Roenigk, and Gregory Allison, “Conceptualizing Financial Condition in Local Government,” Journal of Public Budgeting, Accounting & Financial Management, 22(2). 2010. 149-177. !8 The “general fund” is the fund that accounts for all general government services. “Governmental activities” account for these services in the government-wide financial statements using the economic resources measurement focus. This capstone does not include analysis of the financial condition analysis model’s indicators for enterprise funds. 9 “Regression” analysis is a technique that can be used to describe the nature of the statistical relationship between a group of independent variables and a single dependent variable. The “chi-square” test is a procedure for evaluating the level of statistical significance between two variables and determines whether any apparent patterns between the variables are attributable to chance. A p-value of .05 means that one can be 95 percent confident that the relationship between an independent variable and the dependent variable is statistically significant. 10 The municipal populations numbers are based on 2008 figures and were gathered from the North Carolina Office of State Management and Budget. 11 All answer choice scales based on relevant scholarly literature and Government Finance Officers Association (GFOA) suggested best practices. In nearly every case, a higher coded number value represented a more sophisticated or highly developed capital budgeting strategy. 12 Answer choice scales were not employed for survey questions 4 and 8. These answer choices were not coded based on their level of sophistication, but were only used for descriptive statistical purposes. 13 All independent variables selected based on financial indicators used in other financial condition analysis models. The majority of these independent variables were derived from Brown’s 10-Point Test for Financial Condition. 14 Individual regression models were also used for the financial policies variable (survey question 2) in the primary data analysis stage, but returned nearly identical regression results as those models including the capital budgeting variable (survey question 1). It was therefore decided to use the capital budgeting variable in the final regression model, as it more closely addresses the research question. 15 These models are consistent with answer choices 1, 2, and 3 in survey question 1. 16 Capital reserve funds are budgetary tools allowed by NC General Statue §159-18. For Generally Accepted Accounting Principles (GAAP) reporting purposes, these funds are typically reported as a separate capital project fund.

! 7!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!17 The term “modified regression models” refers to simple regressions performed for each of the 14 financial indicators that included only the capital budgeting variable as the independent variable. Although these regressions were performed on all indicators, the capital budgeting variable was only statistically significant in two (2) models: the general fund “Debt as a Percentage of Assessed Valuation” indicator, and the governmental activities “Debt Service Ratio” indicator. 18 Although this study uses the .05 threshold for significance, the capital planning variable would still not be significant in any of the final 14 models even at the .10 level.

! 8!

BIBLIOGRAPHY Beckett-Camarata, J. (2003). An Examination of the Relationship between the Municipal Strategic Plan and the Capital Budget and Its Effect on Financial Performance. Journal of Public Budgeting, Accounting and Financial Management, 15(1), 23-41. GFOA Committee On Economic Development and Capital Planning “Best Practice” Documents: “The Role of Master Plans in Capital Improvement Planning (2008)”, “Preparing and

Adopting Multi-Year Capital Planning (2006)”, “Incorporating a Capital Budget in the Budget Process (2007)”, “Capital Project Monitoring and Reporting (2007)”

Groves, Godsey, Shulman, (1981). Financial Indicators for Local Government. Public Budgeting and Finance, 1(2), 1-18 Honadle, Lloyd-Jones, (1998). Analyzing Rural Local Governments’ Financial Condition: An Exploratory Application of Three Tools. Public Budgeting and Finance, 18(2), 69-86 Hughes, Laverdiere, (1986). Comparative Local Government Financial Analyses. Public Budgeting and Finance, 6(4), 23-33 Lobo, J and Rantisi, N. (1999). Investment in Infrastructure as Determinant of Metropolitan Productivity. Growth and Change, 30(1), 106-127. Marlowe, J., Rivenbark, W. C., & Vogt, A. J. (2009). Capital Budgeting and Finance: A Guide for Local Governments, 2nd Edition. Washington, DC: International City/County Management Association Press. Miller, A. (1988). Selecting Capital Investment Projects for Local Governments. Public Budgeting and Finance, 8(3), 63-77. Nunn, S. (1991). Public Capital Investment and Economic Growth in Forth Worth: The Implications for Public Budgeting and Infrastructure Management. Public Budgeting and Finance, 11(2), 62-94. Rivenbark, W., Roenigk, D., Allison, G. (2010). Conceptualizing Financial Condition in Local Government. Journal of Public Budgeting, Accounting & Financial Management, 22(2). 2010. 149-177. Rivenbark, W., Roenigk, D., Allison, G. (2009) Communicating Financial Condition to Elected Officials In Local Government. Popular Government, Fall 2009. 4-13. Srithongrung, A. (2008). The Impacts of State Economic Capital Management Programs on State Economic Performance. Public Budgeting and Finance, 28(3), 83-107. Wang, W., Hou, W. (2009). Pay-as-You-Go Financing and Capital Outlay Volatility: Evidence from the States over Two Recent Economic Cycles. Public Budgeting and Finance, 25(1), 104-119.

! 9!

ACKNOWLEDGEMENTS I would like to recognize my committee members: William C. Rivenbark (chair), Dale J. Roenigk, and Gregory S. Allison, and thank them for their continued guidance throughout this process. I especially want them to know how much I value their treating me like one of their peers and not as a student. I would like to extend special gratitude to Bill for his advice and assistance regarding many different aspects of these past two years and for becoming a true mentor for me throughout my MPA experience. Lastly, I want to thank my friends and family, especially James Portner and Virat Mehta, for their constant support and encouragement throughout my research.

! 10!

APPENDIX A: SUMMARIZED SURVEY RESULTS

Survey Questions and Possible Answer Choices Responses % Coded Value

1. Which of the following best describes how your organization plans for capital projects? 1. The adoption of a multi-year Capital Improvement Program (CIP),

along with an annual capital budget by ordinance. 28 30% 5

2. The adoption of an annual capital budget by ordinance only. 2 2% 4 3. The adoption of a multi-year CIP by resolution for planning

purposes only, and separate capital project ordinances on a case-by-case basis.

32 35% 3

4. All capital projects are funded through the adoption of a capital project ordinance on a case-by-case basis. 24 26% 2

5. All capital projects are part of the annual operating budget (i.e. no capital project ordinances, no CIP) 6 7% 1

2. Which of the following best describes how your organization’s financial policies address capital planning and finance? 1. Centralized capital financing policies that address capital planning

(including a CIP) in a formal (i.e. Board adopted) and separate policy document.

27 29% 4

2. De-centralized capital financing policies or policy statements that address capital planning within the body of other financial policies. 25 27% 3

3. My organization has formal financial policies, but they do not address capital planning or finance. 17 18% 2

4. My organization does not have formal financial policies. 23 25% 1 3. Which of the following options best describes your organization’s capital financing strategies?

1. Predominantly pay-as-you-go (PAYGO) 7 8% 1 2. Predominantly debt financing 24 26% 1 3. A balanced approach of PAYGO and debt financing 61 66% 2

4. Which of the following sources are used to fund capital projects? Check all that apply. 1. General Obligation bonds (GO bonds) 40 --

Sum of all

funding sources

used

2. Revenue bonds 29 -- 3. Installment financing (installment purchases and certificates of

participation) 85 --

4. Operating revenues 76 -- 5. Capital reserves 58 -- 6. Grant funds 69 -- 7. Intergovernmental revenues 30 -- 8. Other: 10 --

a. General Fund fund balance 5 -- b. Impact fees 1 -- c. State loans 2 -- d. Private contributions 1 -- e. Transfers from other funds 1 --

5. Does your organization have a separate capital reserve fund? 1. Yes 50 54% 1 2. No 42 46% 0

6. Is the preparation and review of your capital planning process (including a CIP) separate from that of the annual operating budget? 1. Yes 53 58% 1 2. No 39 42% 0

! 11!

Survey Questions and Possible Answer Choices, continued Responses % Coded Value

7. Is public input involved in the preparation and review of the capital planning process? 1. Yes 26 28% 1 2. No 66 72% 0

8. Which of the following best describes how your organization prioritizes capital projects? 1. A weighted point rating system (An organization-wide ranking

system in which capital projects are assigned points and are evaluated using some criteria to address situations of greater urgency.)

8 9% 1

2. Category of need / Immediacy of need 34 37% 2 3. Experience-based judgment (The ranking of capital project requests

is based exclusively or primarily on the judgment of experienced managers, professional staff or elected officials.)

30 33% 3

4. Departmental or functional prioritization (Each department uses its own approach to prioritizing capital projects.) 12 13% 4

5. Organizational goals 8 9% 5 9. Does your organization require that the capital planning process forecast the impact of capital expenditures on

future operating budgets? 1. Yes 66 72% 1 2. No 26 28% 0

10. Does the capital planning process or CIP forecast the potential impact of capital expenditures on debt ratios? 1. Yes 43 47% 1 2. No 49 53% 0

11. Does the capital planning process or CIP forecast the potential impact of capital expenditures on the current property tax rate? 1. Yes 58 63% 1 2. No 34 37% 0

12. Which of the following best describes the way in which your organization manages the implementation of capital projects? 1. A formal process in which a designated individual or body manages

project expenditures including change orders. 31 34% 3

2. A formal process in which the finance or budget function manages project expenditures periodically throughout the fiscal year. 15 16% 2

3. On a case-by-case basis including project scope and timing. 46 50% 1 Descriptive Statistics - Survey Questions, based on coded values

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Mean 3.24 2.61 1.66 4.33 0.54 0.58 0.28 2.25 0.72 0.47 0.63 1.84 Variance 1.72 1.34 0.23 2.31 0.25 0.25 0.20 1.64 0.20 0.25 0.24 0.82 Standard Deviation 1.31 1.16 0.48 1.52 0.50 0.50 0.45 1.28 0.45 0.50 0.49 0.91

! 12!

APPENDIX B: LIST OF FINAL SAMPLE MUNICIPALITIES

Apex Fuquay-Varina Lincolnton Selma

Archdale Garner Marion Shelby

Asheboro Gibsonville Matthews Smithfield

Asheville Goldsboro Mebane Spring Lake

Boone Greensboro Monroe Stallings

Brevard Greenville Mooresville Tarboro

Butner Havelock Morehead City Thomasville

Carolina Beach Henderson Morganton Trinity

Chapel Hill Hendersonville Morrisville Unionville

Charlotte High Point Mount Airy Washington

Clayton Hillsborough New Bern Waynesville

Clinton Huntersville Oak Island Wendell

Concord Indian Trail Oxford Wesley Chapel

Conover Jacksonville Pinehurst Williamston

Cornelius Kannapolis Pineville Wilmington

Davidson Kernersville Pleasant Garden Wilson

Dunn Kill Devil Hills Raleigh Winston-Salem

Durham King Reidsville Winterville

Elizabeth City Kinston Roanoke Rapids Woodfin

Elon Knightdale Rockingham Zebulon

Fairview Laurinburg Rocky Mount

Fayetteville Lenoir Roxboro

Fletcher Lewisville Salisbury

Forest City Lexington Sawmills

! 13!

APPENDIX C: INDEPENDENT VARIABLES USED IN REGRESSION ANALYSIS

Independent Variable Operational Definition Data Source

Population Natural Log of the population of the municipality

North Carolina Office of State Management and Budget, 2008

County Tier Tier designation for the county in which the municipality is located

North Carolina Department of Commerce, 2008

Revenue Per Capita Total revenue/population North Carolina Department of the State Treasurer, Local Government Financial Data, 2009

Median Household Income Median Household Income ($) American Fact Finder, US Census

Data, 2000

Unemployment Unemployment rate, by county Employment Security Commission of North Carolina, June 2009 value

Assessed Valuation Per Capita

Assessed value of taxable property/population

Financial condition data as derived from annual financial reports

Median Age Median age of the municipal population

American Fact Finder, US Census Data, 2000

FY10 Property Tax Rate

FY10 property tax rate, municipality only

North Carolina Department of Revenue, FY2010

Business Activity Natural Log of the number of total business establishments (private industry), by county

North Carolina Department of Commerce, EDIS County Data, 2009

Education Level % of municipal population 25+ with a bachelor’s degree or higher

American Fact Finder, US Census Data, 2000

! 14!

APPENDIX D: FINANCIAL CONDITION INDICATORS

GOVERNMENTAL ACTIVITIES Economic resources measurement focus and accrual basis of accounting

Financial Indicator Calculation Interpretation

Resource Flow (Statement of activities)

Total Margin Ratio Total resource inflow divided by total resource outflow

Assesses whether a government lived within its financial means

Percent Change in Net Assets

Change in net assets divided by net assets, beginning

Indicates a government’s financial position

Charge to Expense Ratio

Charges for services divided by total expenses

Indicates whether a service is self-supporting

Debt Service Ratio Debt service divided by total expenses plus principal

Service flexibility is affected by debt service payment amounts

Resource Stock (Statement of net assets)

Quick Ratio Cash & investments divided by current liabilities

Suggests whether a government is able to meet its short-term obligations

Net Assets Ratio Unrestricted net assets divided by total liabilities

Suggests whether a government is able to meet its long-term obligations

Debt to Assets Ratio

Long-term debt divided by total assets

Assesses whether a government is over-reliant on debt for financing assets

Capital Assets Condition Ratio

1 - (accumulated depreciation divided by capital assets being depreciated)

Suggests whether a government is investing in its capital assets

GENERAL FUND Financial resources measurement focus and modified accrual basis of accounting

Financial Indicator Calculation Interpretation

Resource Flow (Statement of revenues, expenditures, and change in fund balances)

Operations Ratio Total revenues divided by total expenditures

Indicates whether a government has lived within its annual revenues

Intergovernmental Ratio

Total intergovernmental revenue divided by total revenue

Assesses the level of reliance of a government on other governments

Debt Service Ratio Debt services divided by total expenditures

Service flexibility is affected by debt service payment amounts

Resource Stock (balance sheet and Debt Management reports)

Quick Ratio Cash & investments divided by current liabilities

Suggests whether a government is able to meet its short-term obligations

Fund Balance as a Percentage of Expenditures

Available fund balances divided by total expenditures

Suggests whether a government is able to meet its long-term obligations

Debt as a Percentage of Assessed Value

Tax-supported, long-term debt divided by assessed value

Assesses whether a government is over-reliant on debt for financing assets

Source: adapted from Financial Condition Analysis Model, “How to Interpret Results”

! 15!

APPENDIX E: SURVEY QUESTION CHI-SQUARE RESULTS1 Capital Budgeting (Q1), Financial Policies (Q2):

Capital Budgeting Financial Policies Total 4 3 2 1

5 Count 15 7 4 2 28 % within Q1 53.6% 25.0% 14.3% 7.1% 100.0%

4 Count 1 0 0 1 2 % within Q1 50.0% 0% 0% 50.0% 100.0%

3 Count 8 11 5 8 32 % within Q1 29.6% 44.0% 29.4% 34.8% 100.0%

2 Count 2 7 6 9 24 % within Q1 8.3% 29.2% 25.0% 37.5% 100.0%

1 Count 1 0 2 3 6 % within Q1 16.7% 0% 33.3% 50% 100.0%

Total Count 27 25 17 23 92 % within Q1 29.3% 27.2% 18.5% 25.0% 100.0%

Chi-Square Value Value df Sig.

Pearson Chi-Square 22.021 12 .037

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 Question number ranking values based on survey answer choice coded values. With the exception of questions 4 and 8, a higher value represents a more “sophisticated” or “advanced” response. For a complete list of survey questions, answer choices, and coded values, refer to Appendix B. Only those chi-square contingency tables which are significant at the .05 level are shown.

! 16!

Capital Budgeting (Q1), Financing Strategy (Q3):

Capital Budgeting Financial Strategy Total 2 1

5 Count 21 7 28 % within Q1 75.0% 25.0% 100.0%

4 Count 2 0 2 % within Q1 100.0% 0% 100.0%

3 Count 25 7 32 % within Q1 78.1% 21.9% 100.0%

2 Count 9 15 24 % within Q1 37.5% 62.5% 100.0%

1 Count 4 2 6 % within Q1 66.7% 33.3% 100.0%

Total Count 61 31 92 % within Q1 66.3% 33.7% 100.0%

Chi-Square Value Value df Sig.

Pearson Chi-Square 12.878 4 .012 !

! 17!

Capital Budgeting (Q1), Financing Sources (Q4):!!

Capital Budgeting Number of Financial Sources Total 8 7 6 5 4 3 2 1

5 Count 0 5 3 9 8 1 2 0 28 % within Q1 0% 17.9% 10.7% 32.1% 28.6% 3.6% 7.1% 0% 100.0%

4 Count 1 0 0 1 0 0 0 0 2 % within Q1 50.0% 0% 0 50.0% 0% 0% 0% 0% 100.0%

3 Count 0 3 3 8 13 1 3 1 32 % within Q1 0% 9.4% 9.4% 25.0% 40.6% 3.1% 9.4% 3.1% 100.0%

2 Count 0 1 1 3 8 7 3 1 24 % within Q1 0% 4.2% 4.2% 12.5% 33.3% 29.2% 12.5% 4.2% 100.0%

1 Count 0 0 0 1 2 1 1 1 6 % within Q1 0% 0 0 16.7% 33.3% 16.7% 16.7% 16.7% 100.0%

Total Count 1 9 7 22 31 10 9 3 92 % within Q1 1.1% 9.8% 7.6% 23.9% 33.7% 10.9% 9.8% 3.3% 100.0%

Chi-Square Value Value df Sig.

Pearson Chi-Square 70.215 28 .000

! 18!

Capital Budgeting (Q1), Review of Capital Planning Process (Q6):

Capital Budgeting Review of Capital Planning Process Total 1 0

5 Count 21 7 28 % within Q1 75.0% 25.0% 100.0%

4 Count 0 2 2 % within Q1 0% 100.0% 100.0%

3 Count 22 10 32 % within Q1 68.8% 68.7% 100.0%

2 Count 9 15 24 % within Q1 37.5% 62.5% 100.0%

1 Count 1 5 6 % within Q1 16.7% 83.3% 100.0%

Total Count 53 39 92 % within Q1 57.6% 42.4% 100.0%

Chi-Square Value Value df Sig.

Pearson Chi-Square 15.905 4 .003

! 19!

Capital Budgeting (Q1), Prioritization of Capital Projects (Q8):

Capital Budgeting Method of Prioritization of Capital Projects Total 5 4 3 2 1

5 Count 1 5 10 6 6 28 % within Q1 3.6% 17.8% 35.7% 21.4% 21.4% 100.0%

4 Count 0 1 0 1 0 2 % within Q1 0% 50.0% 0% 50.0% 0% 100.0%

3 Count 3 4 11 14 0 32 % within Q1 9.4% 12.5% 34.5% 43.8% 0% 100.0%

2 Count 4 2 7 9 2 24 % within Q1 16.7% 8.3% 29.2% 37.5% 8.3% 100.0%

1 Count 0 0 2 4 0 6 % within Q1 0% 0% 33.3% 66.7% 0% 100.0%

Total Count 8 12 30 34 8 92 % within Q1 8.7% 13.0% 18.5% 37.0% 8.7% 100.0%

Chi-Square Value

Value df Sig.

Pearson Chi-Square 28.593 16 .027

! 20!

Capital Budgeting (Q1), Capital Project Oversight (Q12):

Capital Budgeting Capital Project Oversight Total 3 2 1

5 Count 14 4 10 28 % within Q1 50.0% 14.3% 35.7% 100.0%

4 Count 0 1 1 2 % within Q1 0% 50.0% 50.0% 100.0%

3 Count 14 3 15 32 % within Q1 43.8 9.4% 46.9% 100.0%

2 Count 3 6 15 24 % within Q1 12.5% 25.0% 62.5% 100.0%

1 Count 0 1 5 6 % within Q1 0% 16.7% 83.3% 100.0%

Total Count 31 15 46 92 % within Q1 33.7% 16.3% 50.0% 100.0%

Chi-Square Value !

Value df Sig. Pearson Chi-Square 15.872 8 .044 !

! 21!

APPENDIX F: CORRELATION TABLES FOR FINANCIAL INDICATORS AND CAPITAL BUDGETING VARIABLE, SURVEY QUESTIONS, AND INDEPENDENT VARIABLES Governmental Activities Financial Indicators

Total

Margin Ratio

% Change in Net Assets

Charge to Expense

Ratio

Debt Service Ratio

Quick Ratio Net

Assets Ratio

Debt to Assets Ratio

Capital Assets Condition Ratio

Capital Budgeting

Total Margin Ratio 1.000

% Change in Net Assets 0.831 1.000

Charge to Expense Ratio 0.057 -0.030 1.000

Debt Service Ratio 0.048 0.022 0.138 1.000 Quick Ratio 0.149 -0.011 0.027 -0.208 1.000 Net Assets Ratio 0.149 -0.006 0.048 -0.189 0.987 1.000 Debt to Assets Ratio -0.076 -0.029 0.137 0.681 -0.187 -0.164 1.000

Capital Assets Condition Ratio 0.168 0.199 -0.202 0.208 0.288 0.243 0.078 1.000

Capital Budgeting 0.097 0.138 0.163 0.251 -0.127 -0.136 0.066 0.016 1.000 General Fund Financial Indicators

Operations Ratio

Intergovernmental Ratio

Debt Service Ratio

Quick Ratio

Fund Balance as a % of Expenditures

Debt as a % of Assessed Value

Capital Budgeting

Operations Ratio 1.000 Intergovernmental Ratio 0.264 1.000

Debt Service Ratio -0.181 -0.280 1.000 Quick Ratio 0.407 0.108 -0.192 1.000 Fund Balance as a % of Expenditures 0.875 0.324 -0.270 0.483 1.000

Debt as a % of Assessed Value -0.193 -0.295 0.162 -0.258 -0.288 1.000

Capital Budgeting -0.148 -0.140 0.186 -0.185 -0.192 0.290 1.000

! 22!

Survey Data Results2

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q1 1.000 Q2 0.417 1.000 Q3 0.219 0.057 1.000 Q4 0.363 0.254 0.215 1.000 Q5 0.034 0.238 -0.099 0.255 1.000 Q6 0.309 0.301 -0.007 0.040 0.009 1.000 Q7 0.144 0.402 -0.063 0.168 0.139 0.001 1.000 Q8 0.167 0.119 -0.167 0.183 -0.026 0.255 0.142 1.000 Q9 0.189 0.290 0.063 0.199 0.006 0.145 0.126 0.161 1.000 Q10 0.212 0.432 0.023 0.317 0.159 0.319 0.235 0.261 0.394 1.000 Q11 0.020 0.229 0.026 0.106 0.067 0.027 0.230 -0.062 0.270 0.311 1.000 Q12 0.301 0.295 -0.027 0.135 0.028 0.187 0.194 0.282 0.342 0.145 0.036 1.000

Independent Variables

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2 For a complete list of survey questions, refer to Appendix B.

Population Tier Revenue Income Unemploy. Valuation Age Tax Rate Business Education Population 1.000 Tier 0.181 1.000 Revenue 0.057 -0.192 1.000 Income 0.076 0.546 -0.305 1.000 Unemploy. -0.114 -0.567 0.122 -0.194 1.000 Valuation -0.032 0.387 0.346 0.249 -0.295 1.000 Age -0.197 -0.031 0.224 -0.051 0.191 0.354 1.000 Tax Rate 0.117 -0.374 0.263 -0.514 0.097 -0.400 -0.150 1.000 Business 0.319 0.507 -0.161 0.614 -0.287 0.163 -0.231 -0.157 1.000 Education 0.255 0.471 -0.093 0.607 -0.400 0.239 -0.215 -0.179 0.543 1.000

! 23!

APPENDIX G: REGRESSION ANALYSIS OF VARIABLES IMPACTING FINANCIAL CONDITION Governmental Activities Flow Indicators

Total Margin Ratio % Change in Net Assets Charge to Expense Ratio Debt Service Ratio Adjusted R2 .195 .066 .145 .237 Significance of Model .002 .120 .012 .000 F 3.088 1.583 2.403 3.563

B* t Sig. B t Sig. B t Sig. B t Sig. (Constant) 3.209 .002 1.524 .131 .174 .862 -2.399 .019 Capital Budgeting .139 1.327 .188 .182 1.614 .110 .177 1.644 .104 .088 .869 .388 Ln Population -.048 -.393 .695 -.106 -.798 .427 -.109 -.858 .394 .297 2.473 .016 County Tier .234 1.509 .135 .198 1.185 .239 .002 .014 .989 .187 1.233 .221 Revenue Per Capita .056 .442 .660 -.019 -.139 .890 .316 2.423 .018 -.029 -.232 .817 Median House. Income .210 1.265 .209 .269 1.500 .138 -.229 -1.339 .184 .067 .412 .682 Unemployment Rate -.102 -.778 .439 -.045 -.321 .749 -.071 -.522 .603 .090 .682 .483 Assessed Valuation -.193 -1.341 .184 .036 .230 .819 -.327 -2.205 .030 .312 2.230 .029 Median Age -.001 -.009 .993 -.136 -1.065 .290 .285 2.340 .022 -.003 -.023 .982 FY10 Property Tax Rate -.389 -2.929 .004 -.118 -.823 .413 .029 .212 .832 .392 3.036 .003 Ln Total Businesses -.121 -.761 .449 -.161 -.938 .351 .089 .545 .587 -.092 -.592 .556 Education Level -.270 -1.944 .055 -.252 -1.685 .096 .120 .836 .406 .079 .582 .562

* - Standardized coefficient used for all Beta values

! 24!

Governmental Activities Stock Indicators

Quick Ratio Net Assets Ratio Debt to Assets Ratio Capital Assets Condition Ratio

Adjusted R2 .157 .144 .187 .006 Significance of Model .008 .013 .003 .409 F 2.540 2.390 2.904 1.054

B t Sig. B t Sig. B t Sig. B t Sig. (Constant) .342 .734 .092 .927 -1.279 .210 1.014 .314 Capital Budgeting -.064 -.598 .552 -.091 -.844 .401 -.065 -.615 .532 -.057 -.493 .623 Ln Population -.092 -.729 .468 -.073 -.572 .569 .189 1.524 .138 .154 1.121 .266 County Tier -.003 -.021 .983 .018 .113 .910 .160 1.023 .314 -.030 -.175 .862 Revenue Per Capita .045 .345 .731 .038 .292 .771 -.023 -.181 .843 .038 .271 .787 Median House. Income -.229 -1.344 .183 -.261 -1.520 .132 .201 1.200 .238 .067 .363 .717 Unemployment Rate -.023 -.172 .864 -.027 -.200 .842 .060 .451 .643 -.079 -.543 .589 Assessed Valuation -.379 -2.574 .012 -.371 -2.503 .014 .412 2.852 .006 .211 1.321 .190 Median Age .171 1.416 .161 .184 1.509 .135 -.061 -.516 .600 -.055 -.417 .677 FY10 Property Tax Rate -.521 -3.838 .000 -.503 -3.678 .000 .528 3.959 .000 -.160 -1.083 .282 Ln Total Businesses .317 1.946 .055 .333 2.027 .046 -.186 -1.165 .250 .120 .681 .498 Education Level -.046 -.322 .748 -.022 -.150 .881 .021 .152 .869 -.186 -1.208 .231

! 25!

General Fund Flow Indicators

Operations Ratio Intergovernmental Ratio Debt Service Ratio Adjusted R2 .406 .124 .091 Significance of Model .000 .024 .062 F 6.657 2.170 1.831

B t Sig. B t Sig. B t Sig. (Constant) 5.597 .000 3.476 .001 -2.125 .037 Capital Budgeting -.131 -1.457 .149 -.045 -.417 .678 .162 1.459 .148 Ln Population .060 .563 .575 -.238 -1.851 .068 -.016 -.121 .904 County Tier .074 .554 .581 -.105 -.649 .518 -.043 -.259 .796 Revenue Per Capita -.040 -.369 .713 .199 1.512 .134 -.069 -.516 .609 Median House. Income .427 2.991 .004 .299 1.722 .089 .250 1.416 .161 Unemployment Rate -.196 -1.737 .086 -.241 -1.754 .083 .154 1.100 .275 Assessed Valuation -.202 -1.635 .106 -.253 -1.687 .096 .231 1.511 .135 Median Age -.021 -.203 .840 -.027 -.222 .825 .051 .405 .686 FY10 Property Tax Rate -.462 -4.052 .000 -.242 -1.746 .085 .372 2.638 .010 Ln Total Businesses -.116 -.852 .397 .047 .284 .777 .191 1.131 .261 Education Level -.296 -2.482 .015 -.316 -2.177 .032 .022 .151 .880

! 26!

General Fund Stock Indicators

Quick Ratio Fund Balance as a % of Expenditures

Debt as a % of Assessed Value

Adjusted R2 .280 .529 .457 Significance of Model .000 .000 .000 F 4.224 10.305 7.950

B t Sig. B t Sig. B t Sig. (Constant) 1.860 .067 3.559 .001 -3.949 .000 Capital Budgeting -052 -.526 .601 -.087 -1.086 .281 .074 .865 .390 Ln Population -.209 -1.792 .077 -.077 -.819 .415 .456 4.495 .000 County Tier -.018 -.124 .902 .092 .777 .440 .261 2.043 .044 Revenue Per Capita .012 .098 .922 .059 .612 .542 .199 1.916 .059 Median House. Income -.170 -1.083 .282 .285 2.244 .028 .100 .729 .468 Unemployment Rate .055 .443 .659 -.093 -.925 .358 .077 .712 .479 Assessed Valuation -.323 -2.373 .020 -.367 -3.337 .001 .010 .081 .936 Median Age .017 .150 .881 -.017 -.191 .849 -.043 -.444 .658 FY10 Property Tax Rate -.563 -4.485 .000 -.660 -6.507 .000 .255 2.336 .022 Ln Total Businesses .278 1.852 .068 -.116 -.955 .342 -.062 -.473 .637 Education Level -.102 -.777 .440 -.226 -2.215 .037 .094 .826 .411