Backus, Peter

Clifford, David

Mohan, John

National Centre for Research Methods: NCRM EPrints Repository

1.7 million and counting: constructing and analysing alarge-scale panel of charitable organisations from the Register ofCharities, 1995 onwardsJohn Mohan, David Clifford & Peter BackusJuly 8, 20101/33Outline of presentation• A: Constructing the panel: data challenges• B: Analysing the panel: research agenda• C: An example of analysis: trends in income concentration2/33A: Constructing the panel: data challenges• We have a data set containing the population of registeredcharities in England and Wales between 1995 and 2008• Data and challenges in constructing a usable panel1. Removing compositional influences (cross-sectional)2. Tracking organisations through time (longitudinal)3. Identifying appropriate sub-populations3/33Data Challenges1.Removing compositional influencesChanges in the kind of organisations on the register over time.Therefore, use ’general charities’ definition (NCVO).1. formally structure their people and activities;2. are independent of government and business;3. are non profit-distributing;4. are truly self governing;5. have a meaningful degree of voluntary activity or funding;6. support the growth of a ’civil society’ by providing a widerpublic benefit that goes beyond any membership.4/33Data Challenges1. Removing compositional influencesTable: General charities exclusion rulebook5/33Data Challenges1. Removing compositional influencesChanges in threshold reporting requirements / accountingrequirements over time, e.g:• Threshold for registration: change from £1,000 income to£5,000 income in 2007.• Threshold for annual report and accounts: change fromincome>£10k to income>£25k in 2009.• Changes in procedures for reporting group, rather thanindividual charity, accounts (2006?)6/33Data Challenges1. Removing compositional influencesTherefore:• For specific research questions about changes over time, maywant to consider only population above £5,000 to ensureconsistency• Less easy to control for changes in reporting (group vsindividual)7/33Data Challenges2. Tracking organisations through timeIncorporation of a charitable trust/ unincorporated association to acharitable company: re-emerges on the register with a new charitynumber. Can lead to:• Overestimate of number of registrations and dissolutions• Affects building of panel with information on charities overtime• risk excluding potentially unrepresentative part of charitablesector• risk comparing income at beginning of period with income ofshell charity at end of period8/33Data Challenges2. Tracking organisations through timeLinking Strategy: linking current/former/ working names of anorganisation to the current/former/working names of old charity.Criteria:• Involving two records - one an unincorporated charity, theother a charitable company• Same local authority9/33Data Challenges2. Tracking organisations through timeResult - more charities, and more income, followed through time.• Example• Added an extra 1,566 general charities to a panel of 41,733 ofgeneral charities with income in both 1998 and 2007 (4% oftotal)• These extra charities accounted for £1.8bn of income in 2007(or 9% if the total £19.6bn)• A number of organisations remain unlinked, but may be bestwe can do.10/33Data Challenges3. Identifying appropriate subpopulations• Many of the interesting substantive questions relate to specificsubpopulations of charities rather than the whole register.• Classification very important part of our work.• General Charities definition• SIC (Standard Industrial Classification)• ICNPO (International Classification of NonprofitOrganisations)• Funding profiles11/33Data Challenges’Cross-sectional’ and ’Longitudinal’ questions1. For cross-sectional questions, helpful to remove compositionalinfluences which would hinder consistent comparison across thetime period - want to examine a specific (sub)sector over time1.1 changes in the kind of organisations on the register over time1.2 changes in threshold reporting requirements over time2. For longitudinal questions, there are the challenges involved inensuring we can track individual organisations over time.3. For both cross-sectional and longitudinal questions, identifyingappropriate and meaningful subpopulations is a real challenge.12/33B: Analysing the panel: research agendaThree kinds of questions1. The Historical• Are there long term trends and changes in the sector?2. The Aggregate and the Cross-Sectional• How has the sector considered as a whole changed over time?3. The Longitudinal• How have individual charities performed over time?13/33Research Agenda1. The Historical• Have the priorities of the sector changed over time?• What has been the impact of economic cycles on theestablishment of charities?• Such questions are essentially unexamined.• Identification of years of ’foundation’• Scope for contributions from qualitative researchers14/33Research Agenda1. The HistoricalFigure: Charitable causes over the last 100 years15/33Research Agenda2. The Aggregate and the Cross-Sectional• How has total sector income/expenditure grown?• How has the composition of that income/expenditurechanged?• How has the distribution of income across charities changed?• What is the extent of turnover in the sector?• How has the composition of new charities changed?• Often addressed by NCVO etc - we know a lot about these.• Much to add (e.g. geographical differences)16/33Research Agenda3. The Longitudinal• What is the relationship between initial size and growth?• To what extent has turnover affected the sector ?• What are the factors contributing to a charity’ssuccess/failure?• To what extent have established charities changed their focus?• What is the impact of changes in income streams onfundraising/charitable expenditure?• What is the relationship between publicly and privatelyprovided public goods (e.g. nursing homes)• Such questions will constitute much of our future work17/33C: An example of analysis: trends in charitable incomeconcentration‘There now seems to be an established trend towardsthe concentration of [total charitable] income in the veryrichest charities’ (Duncan Smith 2005)• There are two ways to consider this question of changingdistributions:1. The Cross-Sectional• Are larger charities getting an bigger piece of the ’pie’?2. The Longitudinal• Do larger charities grow faster than small ones?18/33C: An example of analysis: trends in charitable incomeconcentration‘There now seems to be an established trend towardsthe concentration of [total charitable] income in the veryrichest charities’ (Duncan Smith 2005)• There are two ways to consider this question of changingdistributions:1. The Cross-Sectional• Are larger charities getting an bigger piece of the ’pie’?2. The Longitudinal• Do larger charities grow faster than small ones?18/33C: An example of analysis: trends in charitable incomeconcentration‘There now seems to be an established trend towardsthe concentration of [total charitable] income in the veryrichest charities’ (Duncan Smith 2005)• There are two ways to consider this question of changingdistributions:1. The Cross-Sectional• Are larger charities getting an bigger piece of the ’pie’?2. The Longitudinal• Do larger charities grow faster than small ones?18/33Trends in income concentrationSector Growth• More well established (see NCVO Almanac)• Growth from 1995-2007 in terms of• total income (7.9% pa)• number of charities (~42,000 new registrations)• Questions remain as to how income is distributed and howthat distribution has changed.19/33Trends in income concentrationCross-sectional: n-charity concentration ratios“the richest 1.6% of charities account for 67.5% of all charityincome” (Duncan Smith 2005)Figure: Share of income captured by the largest 5, 10, 100 and 1000 ofcharities, 1995-200720/33Trends in income concentrationCross-sectional: the Growth Incidence Curve• Changes in the distribution outside the arbitrary largest ncharities or top p% are not captured• Single value global measures exist• Graphical summation of changes can be more revealing.• Growth Incidence Curve• plots the growth of each quantile from year t to t + τ .21/33Trends in income concentrationCross-sectional: the Growth Incidence CurveFigure: Growth incidence curve for 1995 to 200722/33Trends in income concentrationCross-sectional: concentration by ICNPO subsectorTable: Concentration ratios by ICNPO classification1995 2007 Change inICNPO Classification 5-charity 10-charity 5-charity 10-charity 5-charity 10-charityEducation 13.9 20.7 8.0 11.9 -5.9 -8.8Health 18.5 26.7 27.9 38.2 9.5 11.5International 49.1 66.0 33.2 47.4 -15.9 -18.7Social Services 18.9 30.3 14.3 23.7 -4.6 -6.623/33Trends in income concentrationCross-sectional: the Growth Incidence Curve for different subsectorsFigure: GIC for InternationalFigure: GIC for Education24/33Trends in income concentrationLongitudinal• Considering only the cross-sectional perspective provides anincomplete picture• composition of the top quantiles or largest n charities willchange• does not reveal the relationship between initial size and growth• We consider the longitudinal perspective• 41,732 charities from 1997-2008• We graphically summarise the relationship between a measureof initial size, charitable income in 1998 (on the x-axis), andsubsequent charitable growth between 1998 and 2007 (on they-axis).• Income Mobility Profile (median)• How did the distribution of relative growth vary with initialsize?25/33Trends in income concentrationLongitudinal: Income Mobility Profile (IMP)• IMP used in literature on individual income inequality (vanKerm 2009, Jenkins and van Kerm 2008).• Relative income growth y = z/x , where x is income at time t1and z is income at time t2• Uses local regression to summarise relationship betweenrelative growth y and initial size x• local linear regression for smoothed estimates of theconditional mean of the relative growth distribution• local quantile regression (Yu and Jones 1998, Koenker 2005)for smoothed estimates of conditional quantiles• Note that results are conditional on organisational survival26/33Trends in income concentrationFigure: Income Mobility Profile, 1997-200811.21.41.6Growth rate7,65215,67714,84611,6968,602 6,496 5,737 4,373 3,053 1,956 1,205 735 437 242 138 79 48 27 14Number of charities per bandwidth5,000 50,000 500,000 5,000,000 50,000,000Income in 199827/33Trends in income concentrationFigure: Income Mobility Profile for Social Services, 1997-2008.811.21.41.61.8Growth rate1,668 3,344 2,981 2,285 1,837 1,603 1,348 1,041 709 433 252 141 78 60 45 20 15 13Number of charities per bandwidth5,000 50,000 500,000 5,000,000 50,000,000Income in 199828/33Trends in income concentrationLongitudinal: Income Mobility Profile• Profile is consistent with the “Tesco-isation” hypothesis• Larger charities have highest median growth rate• Smaller charities have a capacity for high relative growth(reflected in income mobility profile for 90th percentile)• Profile is consistent with the “professionalisation” hypothesis• growth has favoured charities with sufficient income to supportpaid staff• Profiles differ across sub-sectors• Results are inconsistent with “Tesco-isation” for Social Services29/33Trends in income concentrationConclusions• The evidence from the cross-sectional perspective suggeststhat the charitable sector has experienced a fall inconcentration.• Little evidence of ’Tesco-isation’ for the sector as a whole• Results are consistent with ’professionalisation’• However, the larger charities at the start of the period havehad a higher median growth rate• Results are different in different sub-sectors.• Further work needed on ’turnover’ and how the survival ofcharities relates to initial size30/33Working Papers• Clifford, D & Backus, P.G., (2010): “Are big charitiesbecoming increasingly dominant? Tracking charitable incomegrowth 1997-2008 by initial size,” TSRC Working Paper• Backus, P.G. & Clifford, D., (2010): “Trends in theconcentration of income among charities,” TSRC WorkingPaper31/33Outline of presentation• A: Constructing the panel: data challenges• B: Analysing the panel: research agenda• C: An example of analysis: trends in income concentration32/33References• Roger Koenker. Quantile regression. Cambridge UniversityPress, 2005.• Keming Yu and M. C. Jones. Local linear quantile regression.Journal of the American Statistical Association,93(441):228237, 1998.33/33

1.7 million and counting: constructing and analysing a large-scale panel of charitable organisations from the Register of Charities, 1995 onwards

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1.7 million and counting: constructing and analysing a large-scale panel of charitable organisations from the Register of Charities, 1995 onwards

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