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Voluntary Contributions and Watchdog Ratings: Introduction and Signaling Effects

Voluntary Contributions and Watchdog Ratings: Introduction and Signaling Effects. Laura Ellyn Grant University of California, Santa Barbara. Question & Scope.

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Voluntary Contributions and Watchdog Ratings: Introduction and Signaling Effects

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  1. Voluntary Contributions and Watchdog Ratings: Introduction and Signaling Effects Laura Ellyn Grant University of California, Santa Barbara Laura Ellyn Grant

  2. Question & Scope • What are the effects of ratings, ranging from 0- to 4-stars in this study, in changing donations to charities? What do the responses indicate about donor behavior? • Motivation: • Identifying the extent of and response to missing information • Providing the ratings (financial metrics) publicly likely changes donations • Allowing donors to contribute strategically to orgs with better outcomes • Nearly $400 BILLION in donations in 2008; $3 TRILLION in revenue • Recession implies a tight spot for philanthropy • Need to know charities are reputable, putting $$ to the best use. • Outcomes will be relative to expectations and elasticities • Differentiate INTRODUCTION of ratings from SIGNALING by ratings Laura Ellyn Grant

  3. Broader Literature: Information Disclosure • Voluntary • Health Marketing: low-fat, natural • Eco-labeling: organic, marine stewardship • Social Responsibility: fair-trade • Government Policies • Education/School Performance • Nutrition and Content Labels • Hospital Performance • Restaurant Hygiene Report Cards • Third party • Media Campaigns: Fox News and Republican membership • Ratings Organizations: Morningstar, Moody’s, Standard and Poor’s Laura Ellyn Grant

  4. Approach • Conceptual Framework: • Expected demand for charity • Effects of information in the form of ratings • Data: • Charity Navigator (CN), complete data from the largest third-party evaluator; Ratings from 0- to 4-stars • 8-years of longitudinal ratings data on more than 5400 large charities • 8 additional years of previous tax data from IRS source • Total observations: 60,000+ • Econometrics: • Introduction Effect: Before-after, with-in charity effects • Signaling Effect: With-in charity effects of published levels/change in ratings • Heterogeneity by sector & size Laura Ellyn Grant

  5. Demand for Public Goods Standard charitable giving model max Ui(xi, G, gi) subject to xi + p*gi = wi potential donor with utility over a private good, a public good, and private benefits of giving to the public good Also called impure public goods [Andreoni (1990), Cornes and Sandler (1996), Kotchen (2005, 2006)] Can likely omit public good aspect: U(x,g) • Anonymous gifts to large charities likely independent of others donations • Effect of information likely acts on private benefits with no immediate consequence to public supply • Solve for demand/marginal benefit of giving: g(p, w) xi , gi Laura Ellyn Grant

  6. $ MB MClowRating MC0 MClowRating MC0 MB MB MC0 MChighRating MC0 MChighRating Q Introduction Effect: Expected Demand, Missing Information Elastic Inelastic $ MB Higher Expectations Lower Expectations Q 0 Laura Ellyn Grant

  7. Response to Introduction • Cannot observe original ‘expected equilibrium’, g0* • Define a º - 1 • a is non-zero & defined given information has value & g0¹ 0. • How consumers react to information will depend on BOTH expectations and elasticities. A priori, sign of the a effect is ambiguous: gR* g0* Elastic Inelastic DECREASE ( – ) INCREASE ( + ) Higher Expectations Lower Expectations INCREASE ( + ) DECREASE ( – ) Laura Ellyn Grant

  8. Signaling Effect: Expected Demand, Changing Information Elastic Inelastic $ MB MClowRating MChighRating Suppose that the empirically found sign of a is negative, these two cases remain Q 0 The intuitive outcome is that higher star-rating yields more donations, but effect is also unknown, a priori Can now measure response to changes in rating, from low to high, to deduce which case is correct MB MClowRating MChighRating Q 0 Laura Ellyn Grant

  9. Tax Data • Public Charity designated by US law 501(c)(3) • Tax exempt but must file IRS form 990 if receipts exceed $25000 • 280,000 filed in 2008 • Hundreds of fields on the tax form • Publically available • Estimated 1 million charities rated • Tax forms are complex, confusing, and incomparable Laura Ellyn Grant

  10. Laura Ellyn Grant

  11. CN Website • Launched in 2002 • Online only, over 5500 charities with $10bil/yr contributions • Can search for charities by name, location, attributes • “Guide to intelligent giving,” evaluating the financial health of each of the charities. • Third-party: not paid by charities, charities cannot opt-in or out • 0- to 4-Stars rank from ‘exceptionally poor’ to ‘exceptional’ Laura Ellyn Grant

  12. CN Website Laura Ellyn Grant

  13. Seeking Information Laura Ellyn Grant

  14. Ratings Calculations Trend data Expense data capital ratio = net assets/total exps fundraising efficiency = fund costs/contributions program expenses = programmatic costs/total func exps revenue growth = (rev_t2/rev_t1 - 1) fundraising expenses = fund costs/total func exps program exp growth = (prog_t2/prog_t1 - 1) administrative expenses = admin costs/total func exps Laura Ellyn Grant

  15. Ratings Calculations Trend data Expense data capital ratio = net assets/total exps fundraising efficiency = fund costs/contributions Convert all raw scores to a scale of 0 to 10 program expenses = programmatic costs/total func exps revenue growth = (rev_t2/rev_t1 - 1) fundraising expenses= fund costs/total func exps program exp growth = (prog_t2/prog_t1 - 1) administrative expenses = admin costs/total func exps Continuous re-scaling or thresholds Laura Ellyn Grant

  16. Ratings Calculations Trend data Expense data capital ratio = net assets/total exps fundraising efficiency = fund costs/contributions program expenses = programmatic costs/total func exps revenue growth = (rev_t2/rev_t1 - 1) fundraising expenses = fund costs/total func exps program exp growth = (prog_t2/prog_t1 - 1) administrative expenses = admin costs/total func exps capacity rating = 0 - 30, scaled to 0- to 4-stars efficiency rating = 0 - 40, scaled to 0- to 4-stars overall rating = efficiency rating + capacity rating = 0 - 70, scaled to 0- to 4-stars Laura Ellyn Grant

  17. Introduction Effect: Preliminary Specification • Publication Signal, before and after, with-in Charity (i), flexible time trend (t) • Treatment: Observerd publication, charities added over time • Control: Same charities, unpublished scores • Append historical data and calculate ratings using aforementioned process • Provides a with-in charity counterfactual/falsification ln_contit = f0*Star0 + f1*Star1 + f2*Star2 + f3*Star3 + f4*Star4 + + aK*StarK*Observed+ r*scoreit + f(Fundit, Prog_Serveit, Assetit, Liabsit) + qt +ni + eit fK*StarK Laura Ellyn Grant

  18. Comparing calculated to true scores Unpublished/Calculated Published/True • Thresholds of ratings: Star0 = 0-24.9, Star1 = 25-39.9, Star2 = 40-49.9, Star3 = 50-59.9, Star4 = 60-70 Laura Ellyn Grant

  19. Introduction Effect ln_contit = aK*StarK*Obs + fK*StarK + r*f(Score) + ln(covars) + qt +ni + eit Signaling Effect Laura Ellyn Grant

  20. Laura Ellyn Grant

  21. Results by Sector Laura Ellyn Grant

  22. Economic Impact • Calculate the median annual contributions by sector • Weight by average proportions in each star rating • Multiply respectively by estimated percent changes in contributions in each sector and rating $1 Billion/year loss, 2007 dollars Laura Ellyn Grant

  23. Discussion • Introduction: Unambiguously reduces donations, on average • Findings vary by sector and size • Signaling: Higher stars, greater contributions.  Together the effects imply demand for charity is overly auspicious & price elastic, on average • Is the money disappearing? • Some is lost in transactions costs • Transfer to other unrated charities is likely • If aggregate donations do not decrease, as if donors do not want to know the information. • May be particularly a problem if ratings cause distortions and/or are uncorrelated with social impact Laura Ellyn Grant

  24. Further Work • Can we predict the sensitivity to changes in the rating distribution or metrics used? • Macro-economic trend in contributions affected by ratings? • Trade-off between rating and reference charities? • Effect of other published charities gives cross-price of ratings • Effect of unpublished charities gives transfer • Does event analysis demonstrate trends of effects? • Learning versus salience • Growing popularity of ratings • Cohort effects and number of times rated Laura Ellyn Grant

  25. Thanks • Camp Resources Organizers & Funders • Charity Navigator • NCCS of The Urban Institute • Matt Kotchen, Paulina Oliva • Funding from NSF IGERT, UC Regents, & Bren School Toyota Fellowships, and UCSB Economics Dept Data Grants. Laura Ellyn Grant

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  27. Analysis of residuals Residit = ln_Contit – (ln_Covarsit + ni+ dt ) Laura Ellyn Grant

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