Causal Effect of Introducing Outsourcing on the Usage ofJapanese Public Libraries
Keita Tsuji 1 [0000-0003-2718-632X]
1 University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki 305-0821, Japankeita@slis.tsukuba.ac.jp

Abstract. For a long time, Japanese public libraries were managed by local
governments. However, in 2003, other organizations, including private
enterprises, superseded the management of libraries and introduced an
outsourcing system called “designated administrator system.” The suitability ofthis outsourcing system for libraries is now being debated, with many people
arguing that it is unfit. To provide basic data for this discussion, we conducted acausal analysis on the introduction of outsourcing to public libraries in Japan. We
performed a matching analysis, difference-in-differences (DD) analysis, and DD
analysis with matching on library usage in terms of three factors, namely, the gate
count, number of loans, and number of reference transactions. In the matching
analysis, the average gate count per capita of all outsourcing libraries was higher
than that of all direct management libraries. In the DD analysis with and without
matching, the average gate count per capita and number of loans per capita of all
the outsourcing libraries were higher than those of all the direct management
libraries. These differences were statistically significant at a 0.01 significancelevel. The results indicate that the introduction of the designated administrator
system increases the gate count per capita. Considering the difference incharacteristics of the matching and DD analyses, the number of loans per capita
may also be increasing with the introduction of the system.

Keywords: Outsourcing, Designated Administrator System, Japanese Public
Libraries, Causal Effect, Matching, Difference-in-Differences Analysis

1

Introduction

Before 2003, Japanese public libraries were managed by the local governments;
however, in 2003, other organizations, including private enterprises, superseded librarymanagement with the introduction of an outsourcing system named the “designatedadministrator system.” Although the number of public libraries managed using thisoutsourcing system (henceforth, “outsourcing libraries”) is increasing, whether local
governments should employ this system for managing public libraries is underdiscussion. Many people argue the unsuitability of the system for public libraries. Few
studies have examined the causal effect of introducing outsourcing to librarymanagement.

In this study, we used almost all Japanese public libraries for data and conducted
three types of causal analyses: (1) matching analysis, (2) difference-in-differences (DD)analysis, and (3) DD analysis with matching. The matching and DD analyses have beenfrequently used in econometrics to detect causal effects of certain events. We analyzed
the causal effects of introducing outsourcing on the usage of libraries in terms of thefollowing factors: (i) gate count per capita, (ii) number of loans per capita, and (iii)number of reference transactions per capita. For the matching analysis, we analyzed thedifference in the aforementioned three factors between outsourcing libraries andconventional libraries managed directly by local governments (henceforth, “direct
management libraries”) that were similar in (a) the number of holdings, (b) number ofacquisition, (c) size of floors, (d) number of staffs, and (e) service population. The
number of outsourcing and direct management libraries in the sample wereapproximately 400 and 3000, respectively (the numbers slightly differ depending on
the investigation). These data were obtained from Statistics on Libraries in Japan(2015) [1], which is published by the Japan Library Association. For (2) the DD
analysis, we compared changes in the aforementioned three factors of direct
management and outsourcing libraries before and after the introduction of outsourcing.
The data were obtained from Statistics on Libraries in Japan (2003–2015) [1]. For (3)the DD analysis with matching, we first analyzed direct management and outsourcing
libraries that were similar in terms of the aforementioned (a) to (e). The libraries werethen compared in terms of (i) to (iii).

2

Related Studies

In Japan, some librarians working in outsourcing libraries reported these changes
observed after introducing the outsourcing system [2][3]. In addition, Maeda (2007) [4],the Japan Library Association (2007) [5], Koyama and Nagata (2008) [6] conductedquestionnaire surveys to investigate the quality of outsourcing libraries and changes
observed after introducing the outsourcing system. These reports and surveys
ascertained that library services, including opening hours and days and library usage,
were increased with the introduction of the outsourcing system. However, these studies
included relatively small samples. Mouri and Ohba (2015) [7] conducted a comparative
study, with a focus on the certified directors in outsourcing and direct management
libraries.
Mizunuma and Tsuji (2016, 2017a, 2017b, 2018) [8][9][10][11] comprehensively
examined the differences between outsourcing and direct management libraries.
Mizunuma and Tsuji (2016) [8] examined the reference services and reported that direct
management libraries generally answer users’ questions directly, whereas outsourcing
libraries developed environments where users can find answers for themselves.
Moreover, they reported that outsourcing libraries received more reference questions
than direct management libraries. Mizunuma and Tsuji (2017a) [9] examined thechanges in library usage in terms of the gate count, number of loans, and number ofreference transactions, before and after introducing outsourcing. They asserted that ingeneral, these factors exhibited an increase after the introduction of outsourcing.
Mizunuma and Tsuji (2017b) [10] reported that (1) direct management libraries havemore novels than outsourcing libraries, whereas outsourcing libraries have morereference books than direct management libraries, and (2) the rates of borrowing books
in outsourcing libraries were higher than those in direct management libraries.
Mizunuma and Tsuji (2018) [11] stated that (1) the number of opening days andpercentage of certified directors in outsourcing libraries were higher than those in direct
management libraries, whereas (2) library usage, number of opening days, and numberof certified directors increased after introducing an outsourcing system.
The aforementioned studies by Mizunuma and Tsuji, particularly [9], are similar tothe proposed study. However, these studies did not conduct a strict causal analysis.

3

MethodAs mentioned in Section 1, we conducted three types of causal analyses, namely thematching analysis, DD analysis, and DD analysis with matching. In the following
subsections, we will explain the theoretical background of the conducted analyses andtheir implementation.
3.1

Theoretical Background

The following explanations are based on Angrist and Pischke (2009) [12], Hoshino andTanaka (2016) [13], and Meyer (1995) [14], with some modification.
Angrist and Pischke (2009) first asked whether hospitals make people healthier. To
discuss this problem precisely, they introduced some notations. First, a binary variable
𝑇𝑖 = {0, 1} was used to describe whether person 𝑖 received any treatment (in this case,
hospitalized). The outcome of interest—a measure of health status—was denoted using
𝑌𝑖 . The effect of hospital care on 𝑌𝑖 was in question. To address this concern, we
considered the possible situations that might have occurred if they had not went to thehospital and vice versa. Therefore, for any individual, there were two potential healthvariables, namely 𝑌1𝑖 (𝑇𝑖 = 1) and 𝑌0𝑖 (𝑇𝑖 = 0), which indicate the health of person 𝑖when they were and were not hospitalized, respectively. 𝑌1𝑖 − 𝑌0𝑖 was the causal effectof hospitalization on an individual1. However, because observing both the potential
outcomes for the same person (𝑌1𝑖 and 𝑌0𝑖 ) is impossible, we must understand theeffects of hospitalization by comparing the average health of people who were and were
not hospitalized. The two unobservable events 𝑌1𝑖 for 𝑇𝑖 = 0 and 𝑌0𝑖 for 𝑇𝑖 = 1
were counterfactual.
The comparison of average health conditional on hospitalization status is formallyassociated with the average causal effect based on the following equation:
E[𝑌𝑖 |𝑇𝑖 = 1] − E[𝑌𝑖 |𝑇𝑖 = 0]
= E[𝑌1𝑖 |𝑇𝑖 = 1] − E[𝑌0𝑖 |𝑇𝑖 = 0]
= E[𝑌1𝑖 |𝑇𝑖 = 1] − E[𝑌0𝑖 |𝑇𝑖 = 1] + E[𝑌0𝑖 |𝑇𝑖 = 1] − E[𝑌0𝑖 |𝑇𝑖 = 0]
= E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1] + E[𝑌0𝑖 |𝑇𝑖 = 1] − E[𝑌0𝑖 |𝑇𝑖 = 0]
Here, E[𝑌𝑖 |𝑇𝑖 = 1] − E[𝑌𝑖 |𝑇𝑖 = 0] is the observed difference in the average health and1

In general, there is likely to be a distribution of both 𝑌1𝑖 and 𝑌0𝑖 in the population, therefore,
the treatment effect can be different for different people.

thus can be calculated. E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1] is the average treatment effect on thetreated (ATET or ATT) and is the primary measure of causal inference. This term
provided the average difference between the health of the hospitalized group (i.e.,
E[𝑌1𝑖 |𝑇𝑖 = 1] ) and the potential outcomes if they had not been hospitalized (i.e.,
E[𝑌0𝑖 |𝑇𝑖 = 1]). Because the potential outcome term was counterfactual, the ATET
could not be directly calculated. The final term E[𝑌0𝑖 |𝑇𝑖 = 1] − E[𝑌0𝑖 |𝑇𝑖 = 0] is theselection bias, which is the difference in the average 𝑌0𝑖 between the hospitalized andnon-hospitalized groups. Because the unwell people are more likely to seek treatment
than the healthy people, the hospitalized people had worse 𝑌0𝑖 , and thus, the selection
bias was negative in this example. A large selection bias (in the absolute value) could
completely prevail over a positive ATET. Most studies on causal inference have aimed
to yield a zero selection bias and thus obtain the ATET by using E[𝑌𝑖 |𝑇𝑖 = 1] −E[𝑌𝑖 |𝑇𝑖 = 0] (which can be calculated using the acquired data).
Random assignment of 𝑇𝑖 to the sample population (so-called RCT (Randomized
Controlled Trial)) solves the selection problem. Because random assignment makes 𝑇𝑖
independent of potential outcomes, it makes selection bias zero (i.e. E[𝑌0𝑖 |𝑇𝑖 = 1] −E[𝑌0𝑖 |𝑇𝑖 = 0] = E[𝑌0𝑖 |𝑇𝑖 = 0] − E[𝑌0𝑖 |𝑇𝑖 = 0] = 0). However, the RCT is difficult toperform. In the hospitalization example, some unwell people are forced to not be
hospitalized in order to implement RCT.
Matching. First, consider the causal effect of an on-the-job training (OJT) on employee
wages in a company. Do employees have higher wages after the OJT because ofelements, such as improved skills and knowledge? Because of the selection bias, wecannot compare the wages of employees who did and did not receive the OJT. For
example, employees with less years of service in the company may receive the OJT,
whereas those who already have high wages may be reluctant.
Here, 𝑋1𝑖 and 𝑋2𝑖 represent employee 𝑖’s (1) years of service in the company and(2) the wage before the OJT, respectively. Furthermore, we assumed the following:{𝑌1𝑖 , 𝑌0𝑖 } ⊥ 𝑇𝑖 |𝑋1𝑖 , 𝑋2𝑖
That is, treatment 𝑇 was randomly assigned in the set of employees with identical 𝑋1
and 𝑋2 . The RCT was performed for the set of employees with identical 𝑋1 and 𝑋2 .
This approach is termed conditional independence assumption (CIA). With the CIA,
E[𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] = E[𝑌0𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]which indicates that conditional on covariates 𝑋1 and 𝑋2 , the selection bias
disappears. Therefore,
E[𝑌𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] − E[𝑌𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]= E[𝑌1𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] − E[𝑌0𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]= E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] + E[𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] − E[𝑌0𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]= E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ]Here, using the law of iterated expectations,

E{E[𝑌𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ]|𝑇𝑖 = 1} = E[𝑌𝑖 |𝑇𝑖 = 1]andE{E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ]|𝑇𝑖 = 1} = E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1] (=ATET)Therefore,ATET= E{E[𝑌1𝑖 − 𝑌0𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ]|𝑇𝑖 = 1}
= E{E[𝑌𝑖 |𝑇𝑖 = 1, 𝑋1𝑖 , 𝑋2𝑖 ] − E[𝑌𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]|𝑇𝑖 = 1}
= E{𝑌𝑖 − E[𝑌𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ]|𝑇𝑖 = 1}
In strict matching, E[𝑌𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ] is calculated as the mean of 𝑌𝑗 of employee𝑗, who belongs to the control group and satisfies equations 𝑋1𝑗 = 𝑋1𝑖 and 𝑋2𝑗 = 𝑋2𝑖 ,with employee 𝑖 from the treatment group. However, with the increasing number (𝑁)of covariates 𝑋, finding such employee 𝑗 in the control group (i.e., employee 𝑗 with
𝑋1𝑗 = 𝑋1𝑖 , 𝑋2𝑗 = 𝑋2𝑖 , ..., 𝑋𝑁𝑗 = 𝑋𝑁𝑖 ) is increasingly difficult. We can use thestandardized Euclidean distance between (𝑋1𝑗 , 𝑋2𝑗 , ..., 𝑋𝑁𝑗 ) and (𝑋1𝑖 , 𝑋2𝑖 , ..., 𝑋𝑁𝑖 )and adopt employee 𝑗 with the shortest distance from employee 𝑖 . However, an
excessively large number of covariates 𝑁 results in the curse of dimensionality.
Therefore, propensity score matching is generally employed. In propensity scorematching, E[𝑌𝑖 |𝑇𝑖 = 0, 𝑋1𝑖 , 𝑋2𝑖 ] is calculated as E[𝑌𝑖 |𝑇𝑖 = 0, 𝑝(𝑋1𝑖 , 𝑋2𝑖 )] , where𝑝(𝑋𝑖 ) is 𝑃(𝑇𝑖 = 1|𝑋𝑖 ) , which indicates the probability that employee 𝑖 withcovariate 𝑋𝑖 is from the treatment group. Propensity score matching is supported by
the following propensity score theorem.
Suppose the CIA holds for 𝑌1𝑖 and 𝑌0𝑖 . 𝑇ℎ𝑒𝑛 {𝑌1𝑖 , 𝑌0𝑖 } ⊥ 𝑇𝑖 |𝑝(𝑋𝑖 ).
DD Analysis. Let us begin with the following equation:𝑗𝑗𝑗

𝑌𝑖𝑡 = 𝛼 + 𝛽 𝑗 𝑑𝑡 + 𝛾𝑑 𝑗 + 𝐴𝑑𝑡 + 𝜀𝑖𝑡𝑗

In the aforementioned equation, 𝑌𝑖𝑡 is the outcome of interest for unit 𝑖 (= 1, … , 𝑁𝑡 )of group 𝑗 (= 0, 1) in period 𝑡 (= 0, 1). 𝑑𝑡 = 1 for 𝑡 = 1 and 𝑑𝑡 = 0 otherwise,𝑗whereas 𝑑 𝑗 = 1 for 𝑗 = 1 and 𝑑 𝑗 = 0 otherwise. 𝑑𝑡 is a dummy variable
representing people in the treatment group after receiving the treatment. 𝛽1 and 𝛽 0summarize the ways that group 𝑗 = 0 and group 𝑗 = 1 are influenced by time,
respectively. A time-invariant difference may be observed in the overall means between𝑗groups 𝑗 = 0 and 𝑗 = 1, which is represented by 𝛾. 𝐴 and 𝜀𝑖𝑡 are the true causal
effect of the treatment on the treatment group outcome (i.e., ATET) and an error term,
respectively.
The key identifying assumption is that 𝐴 is 0 in the absence of the treatment (i.e.,𝑗𝑗E[𝜀𝑖𝑡 |𝑑𝑡 ] = 0). In this case,𝑌̅11 = 𝛼 + 𝛽1 + 𝛾 + 𝐴
𝑌̅01 = 𝛼 + 𝛾

𝑌̅10 = 𝛼 + 𝛽 0
𝑌̅00 = 𝛼where the bar, subscript, and superscript indicate the average over 𝑖, time period, andgroup, respectively. The DD of the aforementioned four factors (i.e., 𝑌̅11 , 𝑌̅01 , 𝑌̅10 , and𝑌̅00 ) can be represented as follows:(𝑌̅11 − 𝑌̅01 ) − (𝑌̅10 − 𝑌̅00 ) = 𝐴 + 𝛽1 − 𝛽 0This approach assumes that if 𝛽1 and 𝛽 0 are identical, the ATET 𝐴 can be obtained
as follows: (𝑌̅11 − 𝑌̅01 ) − (𝑌̅10 − 𝑌̅00 ). This is called parallel trend assumption.
3.2

Implementation

We conducted the matching analysis, DD analysis, and DD analysis with matching. In
this study, treatment 𝑇 mentioned in the previous subsection is the introduction of thedesignated administrator system (outsourcing). Outcomes ( 𝑌 in the previous
subsection) are library usage in terms of the following factors: (i) gate count per capita,(ii) number of loans per capita, and (iii) number of reference transactions per capita.
For the matching analysis, the sample included 3,253 public libraries listed in theStatistics on Libraries in Japan (2015). For the DD analysis, 3,811 public libraries
listed in the Statistics on Libraries in Japan (2005–2015) were included in the sample
because the 2005 edition includes the data on the first outsourcing library. We classified
the libraries as outsourcing and direct management libraries based on The Report on
Public Libraries Managed by the Designated Administrator System [15].
Matching. As discussed in Section 1, we compared (i) gate count per capita, (ii)number of loans per capita, and (iii) number of reference transactions per capita ofoutsourcing and direct management libraries. These were matched in terms of (a) the
number of holdings, (b) number of acquisition, (c) size of the floors, (d) number ofstaffs, and (e) service population. In matching, the statistical computing software R [16]was used. First, glm—a generalized linear model function—was used for calculating
propensity scores. Then, match function in the Matching package was used [17].
Table 1 shows the numbers of sample libraries. In Table 1, OutSrc and DirectM
represent the outsourcing and direct management libraries, respectively. The sample
libraries were classified based on their serving municipalities, that is, prefectures,
ordinance-designated cities, Tokyo special wards, other cities, and towns and villages.
Furthermore, the libraries were divided into two types, namely main libraries andannexes. The results of the analysis were obtained for each type and for all libraries
combined. These data were obtained from Statistics on Libraries in Japan (2015) [1]published by the Japan Library Association.

Table 1. The number of sample libraries for matchingN
All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

OutSrc
431
4
4
0
55
1
54
99
3
96
216
94
122
57
52
5

DirectM
2,822
56
43
13
226
19
207
124
20
104
1,865
667
1,198
551
462
89

DD Analysis. Let M be the year when a certain outsourcing library introduced thedesignated administrator system. The difference in the gate count per capita is definedas (A) the difference between the mean of the gate count per capita in M + 2 and in M
+ 1 and the mean of the gate count per capita in M − 1 and in M − 2. For example, fora certain outsourcing library with M = 2010, its gate count in 2008, 2009, 2011, and2012 were 0.85, 0.95, 1.02, and 1.07, respectively. The difference of the gate count percapita for the library was calculated as follows: (1.02 + 1.07)/2 − (0.85 + 0.95)/2 = 0.15.
The difference in the number of loans per capita and in reference transactions per capita
were defined similarly. As mentioned in Section 1, we obtained such data from theStatistics on Libraries in Japan (2003–2015). Therefore, M varied from 2005 to 2013.
Table 2 presents the number of outsourcing libraries for this analysis. The sample sizeof the libraries differs based on the type of library usage because of the missing data in
the aforementioned Statistics on Libraries in Japan, which resulted in the exclusion ofsome of the libraries from the sample.
For the direct management libraries, the difference in the gate count per capita isdefined similarly. For example, in the year M, the difference in the gate count per capita
is defined as (B) the difference between the mean of the gate count per capita in M + 2
and in M + 1 and the mean of the gate count per capita in M − 1 and in M − 2. Contrary
to outsourcing libraries, in direct management libraries, M has no particular meaning(e.g., a year when the designated administrator system was introduced). Table 2 shows
the number of direct management libraries used for this analysis. The number ofsamples was higher than 10,000 because of numerous duplicates. For example, a librarythat was mentioned in the Statistics on Libraries in Japan from 2005 to 2015 appeared
seven times in the data (M = 2007, 2008, ..., 2013).
In the DD analysis, we examined the statistical difference between (A) foroutsourcing libraries and (B) for direct management libraries by using the Welch’s t test.

Table 2. The number of sample libraries for the DD analysis with and without
matching

All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

Gate Count
OutSrcDirectM
236
12,609
3
482
3
358
0
124
25
1,007
1
107
24
900
63
496
1
112
62
384
114
7,616
59
3,581
55
4,035
31
3,008
28
2,648
3
360

N
Loans
Reference Transactions
OutSrcDirectM
OutSrcDirectM
256
17,853
178
12,262
3
468
3
497
3
365
3
375
0
103
0
122
23
1,371
27
1,541
1
106
1
127
22
1,265
26
1,414
69
881
42
698
1
147
1
116
68
734
41
582
125
11,152
91
7,491
73
4,770
43
3,606
52
6,382
48
3,885
36
3,981
15
2,035
33
3,533
14
1,834
3
448
1
201

DD Analysis with Matching. In the DD analysis, the similarity between outsourcing
and direct management libraries was not considered. In the DD analysis with matching,
we first found the direct management libraries that were similar to each outsourcing
library and then calculated the difference between (A) and (B) mentioned in theprevious subsection. As mentioned in matching subsection, glm was again used for
calculating propensity scores and then match function was used to find similar libraries.
The number of sample libraries are the same as those in Table 2.

4

Results and Discussion

Table 3 presents the results of the straightforward comparison of outsourcing libraries
and direct management libraries (i.e., without using causal analysis). In Table 3, theleft-most column represents the types of municipalities of the libraries and the top row
represents library usage. The “**” and “*” in column “S” indicate that differences
between the left-hand outsourcing and direct management libraries were statisticallysignificant at 0.01 and 0.05 levels, respectively. For example, the average gate countper capita for all outsourcing and direct management libraries were 1.3557 and 0.9172,
respectively, and their difference was statistically significant at a 0.05 level.
The straightforward comparison of the gate count per capita of all the outsourcing
and direct management libraries did not exhibit statistical difference at a 0.01 level. By
contrast, the gate count and number of loans of libraries in the “Other Cities” category
exhibited statistical differences at a 0.01 level.

Table 3. Results of straightforward comparison between outsourcing and direct
management libraries
All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

4.1

Gate Count
OutSrc DirectM S
1.35570.9172 *0.50920.1706
0.50920.2070－0.05010.20280.18170.4141
0.5482
0.19890.14810.70290.6512
4.5794
1.66830.58170.4556
1.22190.7227 **2.09191.5744 *0.5516
0.2485 **4.16812.0130 *4.49692.3212 *0.74850.4131

OutSrc2.20950.38500.3850－0.27280.40780.27020.8932
3.14280.82292.32404.08800.96496.05856.48981.5734Loans
DirectM S
2.10630.18800.23330.03790.3454
1.29990.25771.01552.56040.7184
1.7782 **3.73340.6897 *4.3793 *5.0220 *1.0432

Reference Transactions
OutSrc DirectM S
0.01610.01550.02820.00850.02820.0102－0.00300.00540.00540.07440.02280.00410.00380.00850.01280.06970.03530.00660.00840.01730.01440.02920.02870.00810.00650.03430.0246
0.03600.02780.01590.0081

Matching

Table 4 shows the matching results. In Table 4, the left-most column and top row
represent the types of libraries and library usage, respectively. The “**” and “*” incolumn “S” indicate that the left-hand ATETs were statistically significant at 0.01 and0.05 levels, respectively. Table 4 shows that the average gate count per capita of all theoutsourcing libraries was 1.3557, which was higher than that for all the direct
management libraries by 0.4647. The difference was statistically significant at a 0.01
level, which was inconsistent with the results obtained through the straightforward
comparison. “Other Cities” libraries exhibited a statistical significance at a 0.01 levelin terms of the gate count, which was inconsistent with the results obtained through thestraightforward comparison.
Table 4 shows that almost all the ATETs are positive, which indicates that
outsourcing libraries exhibited higher usage in terms of all factors than direct
management libraries; however, the differences were not statistically significant.

Table 4. Matching results
All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

4.2

Gate Count
OurSrc
ATET
S
1.35570.4647 **0.50920.24070.50920.2513－－0.20280.03990.4141－
0.19890.04470.70290.1742
4.57942.74310.58170.11951.22190.3288 **2.09190.42510.55160.1767 *4.16811.40834.49691.88440.74850.5620 **OurSrc2.20950.38500.3850－0.27280.40780.27020.8932
3.14280.82292.32404.08800.96496.05856.48981.5734

Loans
ATET
S
0.24400.12370.1664－-0.0660－
0.02190.0434
1.3758-0.00070.3499 *0.27620.00980.7562
0.9769-0.7419

Reference Transactions
OurSrc
ATET
S
0.01610.00100.02820.01730.02820.0181－－0.00540.00200.0744－0.00410.00100.00850.00370.06970.05200.00660.00120.01730.00240.02920.00140.0081 -0.0017
0.03430.00650.03600.00830.01590.0062DD Analysis

Before performing the DD analysis, we examined the validity of the parallel trend
assumption (mentioned in subsection 3.1) for the data. Figs. 1, 2, and 3 show the trendof usage of the three libraries.

Fig. 1. Trend of gate count per capita for outsourcing and direct management libraries

Fig. 2. Trend of the number of loans per capita of outsourcing and direct management
libraries

Fig. 3. Trend of the number of reference transactions per capita of outsourcing anddirect management libraries
These figures indicate parallel trends. Furthermore, we conducted a regression analysis
by setting (1) the year as an explanatory variable and (2) the difference in the library
usage of outsourcing and direct management libraries as an explained variable. For all
library usage in terms of the three factors, a hypothesis of zero regression coefficientswas not rejected. These results indicate that the parallel trend assumption was valid forthe data. Figs. 1–3 show that curves of “OutSrc Libraries when they were DirectM
Libraries” were consistently lower than those of “DirectM Libraries.” It means thatusage of libraries which introduced outsourcing were relatively lower before theintroduction. We will discuss this point later.
Table 5 shows the results of the DD analysis. In Table 5, for example, the averagegate count per capita of all libraries that introduced outsourcing increased 0.2261 after
the introduction. This is (A) mentioned in subsection 3.2. By contrast, the average gate
count per capita for all the direct management libraries increased −0.0343 (or decreased
0.0343). This is (B) mentioned in subsection 3.2. The “DD” is the difference between(A) and (B), and for all libraries, this difference reached 0.2603. This difference
between (A) and (B) was statistically significant at 0.01 level, which is shown in the“S” column.
Furthermore, significant differences were observed in the gate count per capita,with a 0.01 significance level for libraries in the ordinance-designated cities and other
cities. Moreover, significant differences were observed in the number of loans, at a 0.01
significance level for all libraries, including those in ordinance-designated cities, Tokyo
special wards, and other cities.
The following observations were made: (1) Tables 4 and 5 revealed significant
differences in different types of library usage in different libraries. More significant
differences were observed in the DD analysis than in matching. (2) Although the DD
analysis revealed the difference in the chronological changes of the two investigation
targets, the matching analysis indicated the difference in the current status of the targets.
(3) Furthermore, we found that the curves of “OutSrc Libraries when they were
DirectM Libraries” (Figs. 1–3) were consistently lower than those of “DirectM
Libraries.” The aforementioned observations indicated that (i) outsourcing is generally
introduced to the libraries with relatively low usage, and (ii) outsourcing increases theusage significantly than the direct management libraries; (iii) however, the amounts ofusage have not reached the level where significant differences (from direct management
libraries) were observed. However, let us emphasize again that matching showed thatthe usage of outsourcing libraries was higher than direct management libraries in terms
almost all factors, although this increase was not statistically significant.
Table 5. Results of DD analysis
All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

4.3

Gate Count
DifferencesDD
S
OutSrc DirectM (ATET)
0.2261 -0.0343
0.2603 **0.3753 -0.0024
0.37770.3753 -0.0038
0.3791－0.0016－0.0208 -0.0087
0.0295 **-0.0060 -0.0195
0.01350.0219 -0.0075
0.0293 **0.2772 -0.0268
0.304013.8551 -0.0343 13.8894
0.0582 -0.0246
0.0829 **0.1872 -0.0094
0.1966 **0.3060 -0.0221
0.3281 **0.05970.00180.0579 **0.4163 -0.1120
0.5283 *0.4531 -0.1196
0.57270.0727 -0.0566
0.1293 *Loans
DifferencesDD
S
OutSrc DirectM (ATET)
0.1679 -0.0044
0.1723 **0.23680.00120.2356
0.23680.00100.2358－0.0020－0.0421 -0.0055
0.0477 **0.04420.03350.01070.0421 -0.0088
0.0509 **0.13890.01410.1248 **2.51640.04452.47190.10400.0081
0.0959 **0.27970.02540.2542 **0.4206
0.04110.3795 **0.08190.01380.0681-0.0901 -0.0922
0.0021-0.0905 -0.0946
0.0041-0.0855 -0.0734 -0.0121

Reference Transactions
DifferencesDD
S
OutSrc DirectM (ATET)
0.00410.00080.00330.01700.00040.01670.01700.00050.0165－-0.0001－
0.00050.00040.00010.00590.00120.00470.00030.00030.00000.0048 -0.0029
0.00770.2240 -0.0002
0.2242-0.0005 -0.0034
0.0029 *0.00730.00100.0062 *0.01110.00150.00950.00380.00060.0033 **-0.01310.0017 -0.0148-0.01460.0017 -0.0163
0.00750.00150.0060DD Analysis with Matching

Table 6 shows the results of the DD analysis with matching. In Table 6, the averagegate count per capita for all libraries that introduced outsourcing increased 0.2261 after
the introduction (consistent with the results in Table 5). This is (A) as mentioned insubsection 3.2. By contrast, the average gate count per capita of all direct management
libraries (and judged as similar to outsourcing ones by matching) increased 0.0203.
This is (B) as mentioned in subsection 3.2. The “DD” is the difference between (A) and(B), which reached 0.2058 for all libraries. This difference between (A) and (B) was
statistically significant at a 0.01 level ( the “S” column). Moreover, “－” in Table 6
indicates that the difference or ATET could not be calculated because of the small
sample size.
Significant difference was observed in the gate count per capita, at a 0.01
significance level for libraries in the ordinance-designated cities. Significant
differences were observed in the number of loans, at a 0.01 significance level for all
libraries, including those in ordinance-designated cities, Tokyo special wards, and other
cities.
Similar results are presented in Tables 5 and 6. However, Table 6 presents morereliable results than Table 5 because the compared direct management libraries are
similar to outsourcing libraries in terms of the following factors: (a) the number ofholdings, (b) number of acquisition, (c) size of the floors, (d) number of staffs, and (e)service population, as mentioned in subsection 3.2. The parallel trend assumption (𝛽1 =𝛽 0 in subsection 3.2) was more reliable in the DD analysis with matching than in thesimple DD analysis2.

2Here, we ignore the difference of the number of samples used in these methods.

Table 6. Results of DD analysis with matching
All Libraries
Prefectures
Main Libraries
Annexes
Ordinance-designated Cities
Main Libraries
AnnexesTokyo Special Wards
Main Libraries
Annexes
Other Cities
Main Libraries
Annexes
Towns and Villages
Main Libraries
Annexes

4.4

Gate Count
DifferencesDD
S
OutSrc DirectM (ATET)
0.22610.02020.2058 **0.37530.04510.33020.3753－－－－－0.02080.00120.0196 **-0.0060－－
0.0219 -0.0071
0.0289 **0.2772 -0.0179
0.295113.8551－－
0.0582 -0.0354
0.0936 **0.18720.03890.1483 *0.30600.05500.25110.05970.02780.03190.4163 -0.8283
1.24460.4531 -1.0244
1.47750.07270.03910.0337

Loans
DifferencesDD
S
OutSrc DirectM (ATET)
0.16790.00490.1630 **0.2368－－0.2368－－－－－0.0421 -0.0121
0.0543 **0.0442－－0.0421 -0.0113
0.0534 **0.1389 -0.0176
0.1566 **2.5164－－0.1040 -0.0299
0.1339 **0.27970.00430.2754 **0.42060.0356
0.3850 **0.08190.01780.0640-0.0901 -0.4522
0.3621-0.0905 -0.0727 -0.0178-0.0855 -0.3200
0.2345

Reference Transactions
DifferencesDD
S
OutSrc DirectM (ATET)
0.00410.00150.00260.0170－－0.0170－－－－－0.0005 -0.0005
0.00100.0059－－0.0003 -0.0003
0.00050.0048－－0.2240－－-0.0005 -0.0004 -0.0001
0.00730.00130.0060 *0.0111 -0.0006
0.01160.00380.00040.0034 **-0.0131 -0.0008 -0.0123-0.0146 -0.0023 -0.0123
0.0075－－Results on Particular Designated Administrator

The aforementioned results could be attributed to a particular designated administrator(or a particular corporation and foundation). We identified the three highest ranking
corporations and foundations administrating most libraries, which are denoted as A, B,
and C. Tables 7 and 9 presents the results of matching and DD analyses of the libraries
administrated by them. Tables 8 and 10 present the number of samples of these analyses.
Tables 7 and 9 reveal that numerous ATETs of “Others” (i.e., outsourcing libraries
administrated by other than A, B, and C) were statistically significant. This observation
indicates that significant ATETs in the previous subsections were not obtained by only
A, B, and C. Because of the length restrictions, the results of the DD analysis withmatching, which indicated similar tendencies, were omitted.
Table 7. Matching results on libraries administrated by A, B, and CABCOthersGate Count
OurSrcATET
S
1.13250.09180.3747 -0.4311
0.2514 -0.4220
1.52970.6260 **OurSrc2.20120.54430.36742.3748Loans
Reference Transactions
ATET
S OurSrc
ATET
S-0.05200.0139 -0.0032-0.9167 (**) 0.0053 -0.0103-1.0609 (**) 0.0142
0.00500.4759 *0.01760.0024

Table 8. The number of sample libraries for matching regarding A, B, and C
NABCOthers

OutSrc
104
17
11
299

DirectM
2,822

Table 9. Results of DD analysis on libraries administrated by A, B, and CABCOthersGate Count
DifferencesDD
S
OutSrc DirectM (ATET)
0.18160.2158 **0.02190.0562 **-0.03430.0296
0.0639 **0.27700.3113 **Loans
DifferencesDD
S
OutSrc DirectM (ATET)
0.20550.2098 **0.00020.0046-0.00440.04930.0537 **0.17060.1750 *Reference Transactions
DifferencesDD
OutSrc DirectM (ATET)-0.0008-0.00160.00090.00010.00080.0005-0.0003
0.00820.0074S

Table 10. The number of sample libraries for DD analysis regarding A, B, and CABCOthers5Gate Count
OutSrcDirectM
68
11
12,609
11
146

N
Loans
Reference Transactions
OutSrcDirectM
OutSrcDirectM
82
60
13
14
17,853
12,262
11
11
150
93

Conclusions

In this study, we examined the causal effects of introducing outsourcing on the usageof Japanese public libraries. In matching, the average gate count per capita of all theoutsourcing libraries was higher than that of all the direct management libraries. Similar
results were observed for “Other Cities” libraries. In the DD analysis, the average gate
count per capita and number of loans per capita of all the outsourcing libraries werehigher than those of all the direct management libraries, and similar results wereobtained for libraries in “Ordinance-designated Cities” and “Other Cities”. Furthermore,among libraries in “Tokyo Special Wards,” the average number of loans of outsourcing
libraries was higher than that of direct management ones. All of these differences were
statistically significant at 0.01 level. In DD analysis with matching, almost the same
could be said. Most of these results were different from the straightforward comparisonof outsourcing libraries and direct management ones (see Table 3). Furthermore, theseresults could not be attributed to the influence of particular designated administrators(Tables 7 and 9).
These results validate that introducing the designated administrator systemincreases the gate count per capita. Considering the difference in the characteristics ofthe matching and DD analyses (as mentioned in the last paragraph of subsection 4.2),
it is highly probable that the same can be said for the number of loans per capita. If theordinance-designated cities and other cities intend to increase the gate count and thenumber of loans of their libraries, introducing outsourcing will be beneficial.
We must consider whether outsourcing libraries are attracting people with “popular”
books, such as novels. However, a study by Mizunuma and Tsuji (2017b) [10] provides
contrasting results. They reported that direct management libraries generally have morenovels than outsourcing libraries, whereas outsourcing libraries generally have morereference books than direct management libraries.
In future, the condition of employment of librarians in outsourcing libraries,
especially per-hour salaries, can be investigated. We must examine whether the
aforementioned increase is achieved at the cost of outsourcing librarians overworking
with low salaries.

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