FISCAL POLICY SPACE CHANGING THE DISCOURSE FROM CITY FISCAL CONDITION TO CITY FISCAL BEHAVIOR

Transcription

1 FISCAL POLICY SPACE CHANGING THE DISCOURSE FROM CITY FISCAL CONDITION TO CITY FISCAL BEHAVIOR Center for Local, State, and Urban Policy (CLOSUP) University of Michigan 18 March 2015 Michael A. Pagano Dean, College of Urban Planning and Public Affairs University of Illinois at Chicago) The support of the John D. and Catherine T. MacArthur Foundation is gratefully acknowledged.

2 Fiscal Policy Space Set Up Reframing Background Need for Reframing from Annual Report Need for Reframing from Research FPS The Frame The Project Data and Measures Clusters Analysis Next?

3 1. A Different Perspective, a New Frame

4 2. Moving from Sameness to Uniqueness

5 3. Best Practices, Mimicking Behavior

6 Objectives Reframe the dialogue about cities from one that uses a city fiscal conditions lens to a city behavioral lens. Identify pathways of adjustment by filling data gaps and fostering an informed dialogue about city fiscal behavior. Identifying, comparing and analyzing the behavior of clusters of like cities.

7 Fiscal Policy Space Set Up Reframing Background Need for Reframing from Annual Report Need for Reframing from Research FPS The Frame The Project Data and Measures Clusters Analysis Next?

8

9 Summary of Findings Finance officers projected a small year-over-year decline in general fund revenues in 2014 (constant dollars) following a small increase in 2013: Property tax revenues were expected to increase in 2014 for the first year since 2009 Sales tax revenues and local income tax revenues experienced marked increases in 2013, with further growth in 2014; Ending balances increased in 2013 for the third year in a row, returning to 2004 levels. Factors pressuring city budgets include infrastructure costs, public safety costs, employee-related costs for health care, pensions, wages, and cuts in state and federal aid Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

10 Percentage of US Cities "Better Able/Less Able" to Meet Financial Needs 100% 80% 60% % of Cities 40% 20% 0% -20% -40% 33% -67% 21% 22% -79% -78% 34% -66% 75% 65% 68% 69% 73% 54% 58% -46% -42% -35% -32% -31% -25% -27% 56% 45% -44% -55% 19% -81% 37% -63% 63% 65% 70% -37% -35% -30% 36% -64% 12% 13% -88% -87% 43% -57% 57% -43% 72% 80% -28% -20% -60% -80% Less able Better able -100% Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

11 Percentage of Cities "Better Able/Less Able to Meet Financial Needs Next Year 80% 60% 40% 20% 29% 49% 50% 53% 55% 61% 64% 63% 46% 33% 17% 40% 59% 56% 55% 21% 11% 20% 39% 56% 65% 73% % of Cities 0% -20% -40% -60% -80% -100% -27% -39% -36% -37% -35% -41% -45% -44% -45% -44% -51% -50% -48% -54% -60% -61% -67% -72% Less able Better able -79% -80% -83% -89% Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

12 6.0% 4.0% 2.0% 0.0% -2.0% 4.2% 3.8% 0.3% -0.1% 3.6% 2.0% 0.6% Recession July 1990-March % Year-to-Year Change in Municipal General Fund Revenues and Expenditures (Constant Dollars) 0.8% 0.5% -0.2% -0.2% -0.5% -0.5% -0.7%-0.8% 1.5% 1.2% 0.9% 0.5% 3.9% 2.9% 2.1% 1.4% 1.4% 1.3% 1.1% 1.0% 0.8% 0.1% Recession March November % -0.6% 3.3% 0.2% -1.0% -1.0% -1.5%-1.6% 1.9% 1.9% 1.6% 0.0% 2.6% -0.2% Recession December 2007 June % 0.5% -1.2% -0.8% -1.5% -1.8% 2.8% 2.2% 1.0% -0.5% -2.7% -4.0% -3.5% -4.5% -6.0% Change in Constant Dollar Revenue (General Fund) -5.1% Change in Constant Dollar Expenditures (General Fund) Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

13 8% Year-to-Year Change in Municipal General Fund Tax Receipts (Constant Dollars) 6% 4% 2% 0% 3.6% 1.3% -0.1% 3.4% 2.0% 1.2% 6.0% 4.2% 2.4% 1.5% 1.4% 0.9% 2.8% 1.0% Recession March November % -0.1% -0.2% 4.4% 4.0% 3.3% 3.0% 2.2% 2.3% 1.0% 0.6% 0.5% Recession December 2007 June % 6.2% 2.3% 2.2% 4.2% 1.3% 1.6% 6.2% 4.6% 4.4% 4.3% 3.6% 1.6% 0.6% -2% -4% -3.4% -3.2% -2.3% -1.1% -0.3% -2.5% -1.0% -2.0% -2.5% -3.9% -0.4% -0.4% -6% -5.3% -5.1% -4.7% -6.6% -8% -8.4% -10% Sales Tax Collections Income Tax Collections Property Tax Collections Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

14 Ending Balances as a Percentage of Expenditures, Municipal Sector (General Fund) Recession July 1990-March Recession March November Recession December 2007 June Actual Ending Balance Budgeted Ending Balance 0 Source: Christiana McFarland and Michael A. Pagano, City Fiscal Conditions in 2014 (Washington, DC: National League of Cities, 2014)

15 Research on Fiscal Distress/Financial Condition During 1980s and 1990s there had been significant research into city s fiscal problems and underlying factors (Bradbury, 1983; Bradbury et al, 1984; Bunce and Neal, 1984;Bahl, Martinez-Vasquez, and Sjoquist, 1992; Bahl, 1994). Bradbury (1983) introduces the concept of structural distress, which is a long-term imbalance between a government s functional responsibilities, service demands, capital investment needs and revenue raising capacity. Ladd and Yinger (1989) define standardized fiscal health as the difference between standardized revenue-raising capacity and standardized spending need. Their concept of actual fiscal health is the balance between restricted revenue-raising capacity and actual spending need. RTS is calculated as the weighted sum of major taxes potentially available to jurisdictions being compared (Advisory Commission on Intergovernmental Relations, 1981; Tannenwald, 1999). Financial management scholars have sought improved measurement of financial condition of governments (Clark and Ferguson, 1983; Berne and Schramm, 1986; Groves and Valente, 2003; Hirschman- Herfindahl index to measure revenue diversification Recent work to conceptualize financial condition process and determinants (Hendrick, 2011; LaPlante, 2013).

16 Context City fiscal conditions declined dramatically during and after the Great Recession City fiscal declines exposed gaps Data Knowledge and research Policy solutions Diagnosis and treatment cycle Each city has as a unique set of challenges Policy makers search for similar/same solutions The solution sets don t work for the individual cities

17 Fiscal Policy Space Set Up Reframing Background Need for Reframing from Annual Report Need for Reframing from Research FPS The Frame The Project Data and Measures Clusters Analysis Next?

18 Fiscal Policy Space Framework Attributes The intergovernmental context within which municipal corporations exist, including state-imposed limitations on taxes and expenditures, access to taxes. The underlying economic base from which cities derive resources and its linkage to cities fiscal architecture. The local legal context that regulates fiscal choices and behavior. The demands and preferences of citizens for a quantity and quality of services. The local political culture that creates a set of expectations, norms of conduct and behavior, and informal rules. Source: Michael A. Pagano and Christopher W. Hoene, States and the Fiscal Policy Space of Cities in Michael Bell, David Brunori, and Joan Youngman, eds. The Property Tax and Local Autonomy (Cambridge, MA: Lincoln Institute of Land Policy, 2010), pp

19 Institutional Analysis & Development Framework Biophysical Conditions Attributes of Community Action Situation Interactions Rules-in-Use Outcomes The Fiscal Policy Space (FPS) framework is comparable to the Institutional Analysis and Development (IAD) framework developed by Ostrom and her colleagues (Ostrom et al, 1994). The key conceptual unit of the IAD framework is the action situation, which refers to the social spaces where individuals interact, exchange goods and services, solve problems, dominate one another, or fight.

20 IAD Framework vs. FPS Framework IAD Framework FPS Framework Action Situation City Fiscal Action Biophysical Conditions Underlying Economic Base Rules-in-Use Intergovernmental Context Attributes of Community Local Political Culture Demands and Preferences of Citizens Local Legal Context

21 Fiscal Policy Space Framework Economic base Intergovernmental context Tax base (residents, business) Revenue restraints and transfers Revenue decisions Local legal context Local legal context Spending decisions Service assignment and spending limit Demand/preference (residents, business) and political culture Intergovernmental context Economic base Source: Michael A. Pagano, Christopher Hoene, Yonghong Wu, The Fiscal Policy Space: A Comprehensive Framework for City Fiscal Decision-making, paper presented at the National Tax Association annual meetings, Tampa, Nov 21-23, 2013.

22 Sample Cities Goals Representative of the municipal sector A varied and diverse sample Feasibility of data collection Selection Method Largest central cities Largest Metropolitan Statistical Areas

23 Sample Cities by U.S. Census Region

24 Sample cities by tax authority

25 Data Collection Efforts State attribute Data on state-imposed TELs since 1980 for all 50 states were collected and coded according to the constraining effect and its actual property tax gap Each city is coded according to its authority to tax three general tax sources (property, sales, income). The economic base attribute. Data from the Census Bureau were collected for the 100 cities, linking the fiscal architecture of cities to their underlying economic base. The local legal context attribute. Locally-imposed TELs were identified for the 100 cities and changes in municipal ordinances were traced from 1980 to the present. Policy Actions. Data on major tax/spending actions were collected, coded, and fact checked for 1980 to present using newspapers, LexisNexis, etc. City Financial Data CAFR data from Electronic Municipal Market Access for 1992, 1997, 2002, 2007, Pension and infrastructure data were provided by Merritt Research for 2007 and Incomplete and inconsistent data prior to 2007 reduced time frame.

26 Clustering Cities: Data & Methods TEL data: legal research on state constitutions, statutes, and laws Replicable search strategies using Lexis Legal database Cross-referenced with other academic sources Tracking TEL amendments over time actual gap calculated for property tax Economic base data: US Census of Governments, city CAFRs Demand for services: proxy comprised of following measures: % union, Democratic vote, percent poverty

27 TEL Measurement TEL restrictiveness (Mullins & Wallin, 2004) Non-binding TEL: either rate limit or assessment growth Potentially binding TEL: Levy limit; or Rate limit + assessment limit; Binding TEL: Levy limit + rate limit + assessment limit

28 Source: Christiana McFarland and Christopher Hoene, Cities and State Fiscal Structure, 2015 (Washington, DC: National League of Cities, March 2015).

29 Actual Property Tax Gap % Property Tax Gap = Max. property tax levy allowed by state TEL Actual levy Maximum Property Tax Levy allowed

30 YEAR MEAN OF PROPERTY LIMITATION GAP Binding-TEL cities Non-binding TEL cities TEL gap is standardized as the ratio of TEL gap to the actual levy. Binding is defined as the presence of a levy limit, the presence of both a rate limit and an assessment limit, or the presence of all these three types of limit (ACIR, 1995; Mullins & Wallin, 2004).

31 City Maximum Permissible Levy 2012 Actual Tax Levy 2012 Actual TEL gap TELgap/ Actual Levy Raleigh $ 7,581,141,504 $ 190,578,000 $ 7,390,563, Durham $ 3,455,963,648 $ 129,656,385 $ 3,326,307, Tampa $ 2,609,307,392 $ 122,960,000 $ 2,486,347, Virginia Beach $ 6,807,038,464 $ 529,296,087 $ 6,277,742, Portland (OR) $ 5,652,188,160 $ 445,043,642 $ 5,207,144, Cincinnati $ 652,395,648 $ 53,807,385 $ 598,588, Dayton $ 186,444,672 $ 15,724,338 $ 170,720, Pittsburgh $ 1,665,642,624 $ 147,335,000 $ 1,518,307, St. Louis $ 532,083,008 $ 57,723,000 $ 474,360, Kansas City $ 832,032,832 $ 105,208,148 $ 726,824, Oklahoma City $ 583,655,680 $ 74,379,000 $ 509,276, Tulsa $ 380,382,272 $ 62,334,000 $ 318,048, Phoenix $ 1,324,701,824 $ 223,483,000 $ 1,101,218, Baltimore $ 3,848,798,464 $ 811,224,000 $ 3,037,574, Philadelphia $ 2,200,399,872 $ 508,600,000 $ 1,691,799, Detroit $ 1,031,383,424 $ 251,399,000 $ 779,984, Austin $ 633,758,272 $ 381,126,366 $ 252,631, Des Moines $ 138,991,872 $ 137,113,954 $ 1,877, Denver $ 269,993,504 $ 295,438,000 $ (25,444,496)

32 Timeline of Locally Imposed TELs

33 Tax authority & Economic base = Fiscal Base Tax authority Number of general taxes that a municipality is authorized to impose (property tax, sales tax, personal income tax) Economic base Percentage of total municipal revenue generated from sales, property and income tax. Growth in retail sales, assessed property, per capita income linked to the percentage that each contributes to the city s fiscal base

34 Source: Christiana McFarland and Christopher Hoene, Cities and State Fiscal Structure, 2015 (Washington, DC: National League of Cities, March 2015).

35 State City Growth of Total Private Income Growth of Market Value of Properties Growth of Values of Retail Sales Economic Base Index - Weighed Growth of Economic Bases NC Raleigh VA Virginia Beach AZ Phoenix NC Durham TX Austin OR Portland (OR) FL Tampa MD Baltimore IA Des Moines PA Pittsburgh OK Oklahoma City CO Denver OK Tulsa MI Detroit MO Kansas City MO St. Louis PA Philadelphia OH Cincinnati OH Dayton

36 Fiscal Base per capita (constant dollars) 2000 Fiscal Base per capita (constant dollars) 2010 Per Capita Fiscal Base change City Tulsa $5,822 $12, % Phoenix $20,053 $37,588 87% Virginia Beach $72,881 $122,862 69% Denver $32,638 $53,136 63% Austin $54,776 $77,487 41% Baltimore $36,097 $50,548 40% Tampa $76,648 $99,608 30% Portland (OR) $114,059 $147,793 30% Detroit $23,130 $29,911 29% Pittsburgh $41,417 $53,491 29% Oklahoma City $7,988 $9,861 23% Kansas City $25,029 $29,659 18% Des Moines $44,481 $52,676 18% St. Louis $19,150 $20,833 9% Durham $70,822 $76,373 8% Raleigh $86,570 $90,788 5% Cincinnati $31,831 $30,057-6% Philadelphia $24,957 $19,153-23% Dayton $25,496 $18,449-28%

37 CITY Demand index Virginia Beach 1.08 Oklahoma City 0.97 Tulsa 0.82 Raleigh 0.72 Phoenix 0.69 Durham 0.41 Des Moines 0.32 Kansas City (MO) 0.24 Cincinnati 0.18 Austin 0.15 Denver 0.09 Dayton 0.06 Tampa 0.03 Portland (OR) St. Louis Pittsburgh Baltimore Philadelphia Detroit -1.78

38 2010 Scatterplot (Z-Scores) More FPS Z_Demand Pressure Less FPS Z_Economic Base Level

39 Comparison of City FPS Virginia Beach Detroit Population Steady since 1990 Decreasing since 1950 Housing market Property values increasing Property values decreasing Local tax authority Property, Sales Property, Income Income per capita in 2010 (constant) State imposed TELs Tax revenue reliance (2011) $33,000. $13,000 No About 50% of general revenue is from taxes Debt Capacity (2012) $4.3 billion $76 million Governmental Fund Balance in 2012 Positive Yes (Levy, assessment, rate ) About 35 % of the general revenue is from taxes Negative

40 Approved Policy Actions Virginia Beach Detroit Property Tax Increase Other Taxes FY(1996), FY(1997), FY(2013) FY(2001), FY(2002), FY(2004), FY(2009), FY(2011) Charges and Fees FY(2012) FY(2008) Spending Cut Strategies FY(2005) Layoffs FY(2010) File for Bankruptcy FY(2013)

41 Fiscal Policy Space Set Up Reframing Background Need for Reframing from Annual Report Need for Reframing from Research FPS The Frame The Project Data and Measures Clusters Analysis Next?

42 What s next? Cluster Analysis. Modeling fiscal behavior of cities by city cluster. What pathways of adjustment do clusters of cities propose and adopt? Do fiscal policy actions create more FPS or less? Development of a data portal for access by others New additions to the initiative Infrastructure Pensions The deep dive comparative case study analyses of similarly situated cities

43 Michael A. Pagano, Spatialization of Revenue Structures, Government Finance Review, August Adapted from Ann O M. Bowman and Michael A. Pagano, Terra Incognita: Vacant Land and Urban Strategies (Washington, DC: Georgetown University Press, 2004).

44 Fiscal Policy Space is a frame for understanding constraints on cities adjustments to their changing environment and a new approach to understanding fiscal policy behavior. Papers, blogs, and other information on the FPS project can be found at our website at:

45 Thank you. FISCAL POLICY SPACE CHANGING THE DISCOURSE FROM CITY FISCAL CONDITION TO CITY FISCAL BEHAVIOR Project Team: Michael A. Pagano (UIC), Christopher Hoene (California Budget Project), Yonghong Wu (UIC), Christy McFarland (NLC), Richard Mattoon (Chicago Federal Reserve), Darrene Hackler (consultant), Shu Wang (UIC), Yu Shi (UIC), Ayman Bari (UIC), Nisa Yazici (UIC), Sam Bassett (UIC), Jill Mason Terzakis (UIC), Paul Lewis (Arizona State University), Katie Cowlin (UIC) The support of the John D. and Catherine T. MacArthur Foundation is gratefully acknowledged.