My discussion of looking at the impact of resource sharing through long tail distribution of materials via holds between two similar consortia, looking at similarities and differences between the two regionally close primarily public library entities using the open source Evergreen ILS. As with many things in statistical modeling there are good indicators that we could find a predictive model but at this stage there are far more questions to answer.

1. A Tale of Two Consortia
Rogan Hamby

2. Feel free to interrupt.

4. Selected Reading
"The Consortial Effect in Detail:
The SCLENDS Experience"
by Robert Molyneux & Rogan Hamby
Library and Book Trade Annual,
57th Edition, 2012

6. Data Caveats
The data from both consortiums is from their
starts until the end of July 2015.
Data is aggregated by owner, library and
transactions but there is no copy, bibliographic
or user level data that would help answer some
of the long tail questions.

7. The Consortia
NC Cardinal
SCLENDS

8. Two Consortia - Similarities
well established 2011 / 2009
public w/ a state library
similar mix or urban and rural
similar economic diversity
similar cultural and regional influences
both are resource sharing

9. Two Consortia - Similarities
longer period
of constant
membership
more
municipal
more
regionals

10. Copyright by Puck, used under CC Attribution License 2.5

11. I did remove the standard deviants.

12. Circulations in Data Set
45,629,855 Circulations
0
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160000
180000
Jun-09
Aug-09
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Aug-11
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Apr-12
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Aug-12
Oct-12
Dec-12
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Jun-13
Aug-13
Oct-13
Dec-13
Feb-14
Apr-14
Jun-14
Aug-14
Oct-14
Dec-14
Feb-15
Apr-15
Jun-15

13. Holds in Data Set
3,526,910 holds
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Aug-09
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Jun-13
Aug-13
Oct-13
Dec-13
Feb-14
Apr-14
Jun-14
Aug-14
Oct-14
Dec-14
Feb-15
Apr-15
Jun-15

14. Terminology of Holds
Local Holds – within system
ICLs – Intra Consortia Loans
ILLs – Inter Library Loans (extra consortia)

15. ICLs 2009 – 2015 All Libraries
0
10000
20000
30000
40000
50000
60000
2009 2010 2011 2012 2013 2014 20

16. ICLs 2009 – 2015 Smaller Libraries
0
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4500
1 2 3 4 5 6

18. Question 1
Is there a long term pattern to ICLs?

19. Percentage of Holds Over Time
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6000.00

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
June 2009 - July 2015 Average ICLs as Percentages

21. Question 2
Do libraries have a demand threshold or does it
scale with available materials? Can we say there
is a point that ICLs will max out? Whey even
ask this?

22. Stable Sample
• SCLENDS Libraries that have been live since
December 2009
• 9 in sample
• State Library excluded because I can’t
meaningfully apply criteria like poverty and
population scores to it with it’s special user
base

23. 0.00
100.00
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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
SCLENDS ICLs as Percentages with Go Lives

24. 0
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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
SCLENDS Libraries as of Dec 2009 Only Onward
282%
247%
226%
235%
212%
166 %
vs 414%
For all
vs 349%
For all

25. NC Cardinal libraries did not have a similar go
live pattern. It was 16 months for their first nine
systems to go live. Alternatively they had 4
libraries go live in six month. I could have
chosen the first single go live but that would
have a high variance. I split the difference by
choosing the first 7 libraries, which went live
over 10 months.

26. 0.00
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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
Original Go Live Library’s Rise of ICLs as percentages month to month
max sclends 350%

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
NC Cardinal Libraries as of March 2012 w/ Percentage ICLs vs Later Go Lives ICLs as Percentages

28. 0.00
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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
Percentages of Holds Month to Month
SCLENDS NC Cardinal

29. 0
100
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1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374
Growth of ICLs of Early Libraries
SCLENDS NC Cardinal

30. Result is that consortia magnify the depth of
collections and have a significant impact but it is
inconclusive if there is a predictable saturation
point. SC data suggests a saturation point met
while NC data does not.

31. Is there a population difference that would
explain these discrepancies?

32. Population
58,192
101,374
mean
range
112,618
230468

33. Two Smaller Libraries From Early
Adopters
Smallest in early
adopter sets. Pops of
41,434 and 46,125.

34. 0
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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69
NC Cardinal vs SCLENDS Single Early Adopter

35. Two Large Libraries
Pops of 245,346 and
250,539.

36. 0
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100
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200
250
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
SC large library percent of holds vs NC large library percent of holds

37. This tells us we should probably dig deeper
controlling for population.
Small libraries have greater potential for
variation but here we saw it with the large
libraries.

38. In other variables …

39. Index Mundi’s 2009-2013 index of
population below poverty index

40. It would be critical that we additionally control
for patron, item and bibliographic level data to
better answer these questions.

41. Adding on … if I can
From Circ and Holds reports: Patron ID, Item ID
From Asset/Bib Tables: Item ID, Bib ID,
020/022/024, normalized title, author, TCN
SCLENDS, NC Cardinal, GA PINES