BY 4.0 license Open Access Published by De Gruyter August 7, 2020

Examining the Relationship between Different Types of Information Disclosure of Foundations and Chinese Donations

Zhijun Lin and Ying Li
From the journal Nonprofit Policy Forum

Abstract

The philanthropy industry in China has rapidly advanced in the past few decades and become more prominent in importance. In this study, we examine and compare how the disclosure of different types of information of Chinese foundations is associated with donations by exploiting a unique dataset from the China Foundation Center (CFC). Specifically, we explore a unique index of transparency on information disclosure or the Foundation Transparency Index (FTI) provided by the CFC and investigate how the donations from Chinese donors are associated with the disclosure of foundation’s basic information, its financial information, project information and its governance information. We find that Chinese donations are more sensitive to foundation’s governance information disclosure. These findings have important policy implications for both Chinese policy makers and practitioners in the industry. Specifically, the results lend strong support to advocating for the imposition of more mandatory regulations on the disclosure of foundation information, especially the governance information.

1 Introduction

Prior to 1978 or the economic reform of China, non-governmental organizations (NGOs) in public welfare were non-existent. After four decades of rapid yet steady economic development, almost 5000 foundations were in place at the end of 2015. The total amount of donations received was about 34.9 billion RMB (5.7 billion USD) with 104 billion RMB (16.9 billion USD) in total net assets. However, compared to the well-established philanthropy industry in the western countries, the philanthropy industry in China continues to lag behind its western counterparts, and is suffering from strong setbacks due to the lack of transparency. Although the Chinese government has passed several regulations that require foundations to disclose information, they are not mandatory and only provide guidance without a monitoring presence and disciplining process, which reduce inclination to disclose information (Dang 2015). In 2011, the notorious “Guo Meimei” scandal greatly impacted the reputation of the Chinese philanthropy industry as the incident created doubt on the information transparency of donation money use.[1]

Anecdotal stories report that the Chinese are less inclined to donate if they suspect that a charitable organization is hiding important information.[2] Some research has provided empirical evidence showing that Chinese donations are positively associate with the information disclosure of foundations. However, as foundations are disclosing all kinds of information, what type of information disclosure is more associated with Chinese donations? This is an interesting topic, but the evidence is still largely absent. In this research, we aim to fill in this research gap.

Many studies in the literature have focused on a list of related variables obtained from released financial reports that may affect charitable donation behaviors. They include but are not limited to the: price of the donation (Hyndman 1991), program ratio (Baber, Roberts, and Visvanathan 2001), administrative ratio (Greenlee and Brown 1999), adequacy of equity (Trussel and Parsons 2004, 2007), revenue concentration (Greenlee and Trussel 2000), operating margin (Greenlee and Trussel 2000), level of administrative costs (Greenlee and Brown 1999), other revenues (Posnett and Sandler 1989), fundraising expense (Weisbrod and Dominguez 1986), organization age (DiLorenzo and Bennett 1994), organizational size (Tinkelman 1999), and government grant support (Roberts, Smith, and Taranto 2003). The variables in these studies, most of which are based on philanthropy practices in the U.S., enhance current understanding on how different financial variables affect charitable donations. However, these financial variables cannot represent the transparency of foundations.

One of the related strands in the literature shows that ratings of charitable organizations provided by a third party have a significant impact on donations (Chhaochharia and Ghosh 2008; Gordon, Knock, and Neely 2009; Grant 2010; Harris and Neely 2016; Sloan 2009). However, this strand of studies focuses on the third party evaluation instead of examining how donors respond to the disclosure behavior of foundations.

Another strand of literature has directly studied the relationship between the donations and foundation information disclosure. Many papers have confirmed the positive relationship between the donations and the general information disclosure of foundations (Haski-Leventhal and Foot 2016), or the web information disclosure (Blouin et al. 2018; Saxton, Neely, and Guo 2014), or the accounting information disclosure (Buchheit and Parsons 2006), or the financial information disclosure (Blouin, Lee, and Erickson 2018; Parsons 2007). The consensus in the literature is that more disclosed information does bring more donation. However, as in reality foundations are revealing all kinds of information such as basic information, financial information, project information, and their own governance information, etc., one interesting follow-up question would be what type of disclosed information might be more effective in affecting people’s behavior. Nevertheless this question is not fully addressed as most of literature more focus on general or specific type of information disclosure, and lacks of examining and comparing how different type of information disclosure is associated with charitable donation.

In the literature, there are few studies on how the transparency of foundations affects donations in China. Deng, Lu, and Huang (2015) use survey data on 800 NGOs in human services and find that their donation income is positively related to their level of transparency. However, the scope of the survey data that they use is limited as they only focus on a subsample of the organizations in the charitable fund industry in China, and neglect many influential charitable fund organizations in China such as the Chinese Red Cross Foundation, China Soong Ching Ling Foundation, etc. Nie, Liu and Cheng (2016) examine how various factors might affect the information disclosure behaviors of foundations. They find that less government support and more donations will compel Chinese foundations to disclose more information. Ni and Zhan (2017) show that embedded government control might help foundations to receive more donations. Wei (2017) also explores factors that influence donation revenue to Chinese foundations from the perspective of government control. However, none of them distinguishes the types of information, and compares and examines how different type of information disclosure is associated with Chinese charitable donation.

In this research, we aim to fill in this research gap by exploiting a unique dataset constructed by the China Foundation Center (CFC).[3] This dataset offers a unique measurement: the Foundation Transparency Index (FTI) which reflects the transparency of Chinese philanthropic foundations. There are 41 items for consideration, which reflects the transparency of four types of information: the foundation’s basic information (e. g., address, mission statement, etc.), the financial information (e. g., revenue, assets, etc.), the project information (e. g., project location, expense, etc) and the governance information (e. g., corporate governance rules, asset management policies, etc.).[4]

A large sample of Chinese foundations is used in this study and the results first confirm the finding in the literature that there is a significant and positively robust relationship between the general transparency score of foundations and donations in China. More importantly, we examine and compare how different types of information disclosure affects charitable donation and find that Chinese are more sensitive to the disclosure of foundation’s governance information.

This study makes two contributions to the literature as follows. First, the work contributes to the growing body of literature on the Chinese philanthropy industry. Although this industry in China is growing at a steady pace, most of current research is still based on practices in the western countries. The current research will help to provide some evidence on the behaviors of Chinese foundations. Specifically, this research shows that among all types of information disclosure, Chinese donations seem to be more sensitive to one specific type of information disclosure: foundation’s governance information disclosure.

Second, the current research work has important policy implications for both policy makers as well as practitioners in the philanthropy industry. We argue that Chinese authorities should consider more mandatory policies that demand more transparency and information disclosure by Chinese foundations, especially the information related to the fund governance, hopefully to increase the participation of the Chinese in charitable activities and encourage their donations to promote the continual advancement of the philanthropy industry in China.

The remainder of this paper is organized as follows. Section 2 provides a brief introduction on the development of the Chinese philanthropy industry and the CFC which provides the FTI. Section 3 describes the research methodology. Section 4 provides the dataset. Section 5 presents the study results and Section 6 concludes.

2 Industry Background

2.1 Rapid Development of Philanthropy Industry in Post-reform China

After the establishment of People’s Republic of China in 1949, the Chinese government provided universal employment, basic welfare and disaster relief to all members of society, and completely eliminated non-governmental public welfare organizations. The economic reform and opening-up policy in 1978 however changed this situation, so that foundations and charitable activities emerged in response to help those who are in need as the government no longer provides a universal welfare system.

Many non-governmental foundations have emerged since 1980. Foundations are now playing an increasingly more important role in social development in China. Chinese social philanthropy efforts highlight social charity infused with love and vitality, and as a result, the sector has made steady progress in a period of less than 40 years. Figure 1 shows the rapid growth in the number of Chinese foundations since 1981 when charitable services received legal recognition. As of December 31, 2015, the total number of charitable foundations in China is 4954, with an average annual growth rate of 18% since 2004.

Figure 1: Growth trend of number of foundations in China. Source: China Foundation Center. Note: Number of foundations increasing at a rate of 18% since 1981. At end of 2015, the number of foundations in China was 4954.

Figure 1:

Growth trend of number of foundations in China. Source: China Foundation Center. Note: Number of foundations increasing at a rate of 18% since 1981. At end of 2015, the number of foundations in China was 4954.

2.2 Issues Around Regulations of Transparency of Foundations

Prior to the “Guo Meimei” scandal in 2011, some of the government regulations such as the Welfare Donations Law of the People’s Republic of China in 1999, Regulations on Administration of Foundations in 2004, and Measures for the Information Disclosure of Foundations in 2006 required charitable foundations to submit annual reports and donation information to the government and the public. However at that time, the laws did not clearly state the requirements for information disclosure.

After the notorious “Guo Meimei” scandal ensued in 2011, the government issued various new regulations that presided over the operations of charitable foundations in order to increase their transparency. For example, the Guidelines for Information Disclosure for Public Welfare Charitable Donations in 2011 regulated specific details that charitable foundations had to disclose, including the foundation name, address, contact details, specific information about donations, etc. The Guiding Opinions of the State Council on Promoting the Healthy Development of Charity Undertaking in 2014 reinforced the responsibility and supervision of the government over information disclosure and foundations in China.

These regulations have established the basic requirements for public foundations and/or private foundations to disclose information that may affect the interest of the stakeholders. However, as Dang (2015) points out, these regulations only provide guidance on the disclosure of foundation information, without a monitoring presence and disciplining process. All of these have eventually led to the current situation on information disclosure of foundations in China, which does not render disclosure mandatory nor has there been a comprehensive system instilled in place.

2.3 CFC and FTI Score

Potential donors often seek financial information about charities in order to make sound decisions about their donations. However, the information asymmetry between donors and charities means that it is difficult for the former to obtain reliable and useful information for their decision making. In response to such needs, different rating agencies have been established in the U.S. to rate charities. For example, the Better Business Bureau (BBB) started to rate charities in 1945 through its philanthropy advisory services (PAS). In 2001, the PAS merged with the National Charities Information Bureau (NCIB) to establish www.give.org which rates charitable organizations by using 20 standardized items that include fundraising governance and quality. Other agencies include: the Charity Navigator which was founded in 2001 for rating public charities that receive public donations and CharityWatch which was established in 1992, and previously known as the American Institute of Philanthropy.

The CFC is the first non-governmental agency to provide potential donors with an overview of Chinese foundations. The CFC was launched by 35 well-known Chinese charitable foundations, and established on July 8, 2010 to create an information disclosure platform for the charitable industry, provide capacity building services required for the development of the industry, promote the facilitation of industry self-regulation and industry credibility, and cultivate a culture of transparency and accountability.

To further enhance the professionalism and standardization of information disclosure, the CFC has developed the FTI to reflect the transparency of the philanthropy industry. The FTI is constructed by using 41 indicators on basic, financial, project and donor information. A higher calculated score indicates greater transparency.[5]

The FTI is calculated based on only whether the information is disclosed instead of the contents of the disclosed information. Foundations earn points by choosing whether they wish to disclose the required information, and if they do disclose, the channels that they use to disclose information – that is, on their official website or other channels. The contents of the disclosed information, i. e., their size, total revenue, assets, etc., are not considered in calculating their FTI score.

The CFC provided the first method in 2016 for calculating the FTI score as follows:

FTIn=(Ti×Wi×Si×Ci)

where:

  • FTIn denotes the transparency index score of foundation n;

  • i represents the sequence of indicators which ranges from 1 to 41;

  • Ti denotes the dummy number which equals to one if a foundation discloses the underlying indicator and zero otherwise;

  • Wi denotes the weight of each of the 41 underlying indicators, which ranges from 1 to 6, assigned by the 35 charitable foundations that launched the CFC;

  • Si represents the source, which is equal to 1.2 if the charitable foundation uses its own website to disclose information and 0.8 if the charitable foundation uses other channels to disclose information. Foundations can therefore earn a higher FTI score by disclosing the required information through their own website; and

  • Ci denotes coverage, that is, the percentage of program expenses that a charitable foundation discloses explicitly. The value of Ci is one when calculating the transparency index scores of all indicators for basic, financial, project and governance information. However, Ci is determined by the percentage of the summed expenses of all disclosed projects over annual giving in the profit and loss statement for the eight indicators of project information. The value of Ci ranges from 0 to 100%. The CFC expects that foundations can publicly show the detailed allocations of annual giving in terms of project costs.

A full FTI score is 100 points. The following four categories are considered in calculating the FTI score, which include the 41 indicators:

  1. Provision of basic information (10 indicators for 13.2 points in total), including mission statement, number of employees, names of council members, fund contact information, etc.

  2. Provision of financial information (15 indicators for 24 points in total), including donation revenue, expenses, assets, liabilities, etc.

  3. Provision of project information (8 indicators for 39.2 points in total), including location and concentrated field of projects, project revenue and expenses, etc.

  4. Provision of governance information (8 indicators with 23.6 points in total), mainly including the information about the foundation’s corporate governance rules, asset management policies, etc.

In summary, this calculation method shows that the FTI is essentially an average score that shows whether certain information based on a total of 41 indicators of the foundation is made publicly available.

3 Regression Model

A market where one party (seller) has more or better information about products over another party (buyer) creates information asymmetry (Akerlof 1978). More information disclosed or available in the market may mitigate asymmetric information problems and reduce transaction costs. Signaling is a widely used means to mitigate uncertainty and information asymmetry (Spence 1978).

In the context of charitable donations, charities disclose information and send signals to the public to attract donations. Harris and Neely (2016) argue that donors use disclosed information as creditable signals of charity success and conclude that charities that openly disclose information receive more donations than those that do not openly disclose details.

The literature has provided much empirical evidence which confirms this relationship. This study aims to push the research beyond the current frontier by examining and comparing the relationship between the behaviors of Chinese donors and different types of information disclosure of Chinese foundations. We decompose the FTI scores into four sub-dimensions: which are the transparency scores for foundation’s basic information, financial information, project information and governance information. These four variables will be selected as the main independent variables in the regression model.

Control variables selected for the regression are based on findings in the literature. Financial report related variables may affect charitable donations as per Trussel and Parsons (2004, 2007). Therefore, we follow Trussel and Parsons (2004, 2007) and apply their control variables which include: fundraising expenses, fundraising ratio, adequacy of equity, operating margin, revenue concentration, other revenue, organizational size, organization age, program ratio, administrative ratio, grant revenue and program revenue. The definition of each control variable is provided in Table 1.

Table 1:

Control variable definitions.

Fundraising expensesFUNDEXPPercentage of total expenses spent on fundraising expenses
Fundraising ratioFUNDCONTPercentage of income from charitable contributions spent on fundraising expenses
Adequacy of equityEQUITYNet assets of a foundation calculated as a percentage of its total revenue
Operating marginMARGINRevenue less expenditure divided by revenue
Revenue concentrationCONCENSum of squared percentage of each revenue source over total revenue
Other revenueOTHREVRevenue excluding program revenue, donations and government grants
Organizational sizeSIZEA logarithm of total assets
Organization ageAGEThe number of years since the establishment of the nonprofit organization
Program ratioPROGPercentage of total expenses spent on programs
Administrative ratioADMINPercentage of total expenses used for administration
Grant revenueGRANTSPercentage of total revenue from government grants
Program revenuePROGREVPercentage of total revenue from program revenue

To examine the relationship of donation with different types of information disclosure, the following OLS regression is adopted:

lnDoni,t=β0+β1BASICi,t1+β2FINANCIALi,t1+β3PROJECTi,t1+β4GOVERNANCEi,t1+β5Doni,t1+β6FUNDEXPi,t1+β7FUNDCONTi,t1+β8EQUITYi,t1+β9MARGINi,t1+β10CONCENi,t1+β11OTHREVi,t1+β12SIZEi,t1+β13AGEi,t1+β14PROGi,t1+β15ADMINi,t1+β16GRANTSi,t1+β17PROGREVi,t1+εi

where Don is revenue from donations in the regression model, i represents the different foundations, t represents information in 2015, and t−1 represents information in 2014. The reason for the years 2014 and 2015 is to reflect the two sets of data used here: the financial reports of all the charitable foundations registered with the CFC in 2014 and 2015. BASICt−1 is the FTI score for disclosing foundation’s basic information in the previous year; FINANCIALt−1 is the score for disclosing foundation’s financial information; PROJECTt−1 is the score for disclosing foundation’s project information and lastly, GOVERNANCEt−1 is the score for disclosing foundation’s governance information in the previous year.

The key regression result of interest is β1β4. If the coefficient is significantly positive, it means that the level of transparency for the corresponding type of information shown by the FTI score is positively associated with Chinese donations. We are interested in which type of information disclosure is associated with Chinese donations.

4 Data

As stated earlier, two sets of data are used in this study. One set includes the financial reports of all of the charitable foundations registered with the CFC in 2014 and 2015, which are used to calculate the control variables for the regression analysis and were downloaded from the CFC official website. The financial reports include a statement of financial position, profit and loss statement, and statement of cash flow. The original data contain an abundance of information, including annual revenue; revenue from donations, investments, programs, government grants and other; annual, program, general and administrative, fundraising, and other expenses; total and net assets; and so on and so forth. Descriptive information on the key variables is presented in Table 2.

Table 2:

Descriptive Statistics: 2014–2015.

VariableObsMeanStd. Dev.MinMax
Annual revenue620511,500,00070,100,00003,040,000,000
Revenue from donations62059,046,51556,800,00003,030,000,000
Revenue from investments6205802,0428,225,2070357,000,000
Revenue from programs620578,6931,312,924057,500,000
Revenue from government grants62051,002,25223,000,00001,270,000,000
Other revenue6205527,6869,774,7680736,000,000
Annual expenses61958,588,30860,500,00002,660,000,000
Program expenses61948,145,17158,700,00002,650,000,000
General & administrative expenses6191106,691610,716022,800,000
Fundraising expenses620535,423687,643034,400,000
Total assets620530,300,000154,000,00005,190,000,000
Net assets620528,500,000144,000,00005,170,000,000

The other set is information that demonstrates the transparency of 3626 foundations in 2014, which includes their FTI, e.g. total score and the score for each of the four components of the FTI: basic, financial, project and governance information. The details on the information for the 41 indicators are also included.

The descriptive information on the key variables for 2015 is presented in Table 3. The two sets of data were merged by using an object identifier (OID) to test the hypothesis.

Table 3:

Descriptive statistics.

VariableMinMaxMeanMedianSD
Dont03,030,000,00010,500,0001,000,00073,200,000
BASIC2.413.28.2197.22.641
FINANCIAL02415.41165.675
PROJECT039.223.1124.811.38
GOVERNANCE023.610.059.65.75
FTIt−111.210056.5652.821.84
Dont−101,610,000,0009,697,6881,201,50056,500,000

  1. Dont: Amount of charitable donations in current period.

  2. FTIt−1: FTI in the previous period.

  3. Dont−1: Amount of charitable donations in previous period.

Table 3 reports the summarized statistics of the key variables: the donation variables, the FTI and the transparency score of four components: basic, financial, project and governance information. The mean of FTIt−1 is 56.56 and the median of the FTI is 52.8. This could be taken to mean that the current information disclosure of foundations in China is still lacking, and according to the scores, about half of the information has not been disclosed by the foundations. For each category of information disclosure, there is also significant variation. For instance, the score of governance information disclosure has a standard deviation of 5.7 with the maximum value of 23.6 and minimum value of 0.

Table 3 also reports the summary statistics for the donation value. The mean value is 11,300,000 which is much greater than the median value of 1,300,000. This suggests that the distribution donations is highly left-skewed due to the relatively few large donations. In the regression, we take the log-transformed value of the donations. Figure 2 plots the histogram of the log-transformed donations. The distribution is much more symmetric and resembles the normal distribution.

Figure 2: The histogram of the log-transformed donation.

Figure 2:

The histogram of the log-transformed donation.

5 Results

Table 4 shows the correlation of all of the independent variables which are used in the regression analysis. The FTI score of the previous period and the donations received in the current period are relatively highly correlated. This suggests that there might be a trend in the impact on the donations. Therefore, in the regression, the donations in the previous period are also controlled.

Table 4:

Correlation table.

DontBASICFINANCIALPROJECTGOVERNANCEFTIDont − 1FUNDEXPFUNDCONTEQUITYMARGINCONCENOTHREVSIZEAGEPROGADMINGRANTSPROGREV
Dont1
BASIC0.371
FINANCIAL0.220.571
PROJECT0.160.470.651
GOVERNANCE0.390.850.660.541
FTI0.290.750.840.880.831
Dont−10.750.340.200.130.360.261
FUNDEXP0.030.01−0.09−0.080.010.010.031
FUNDCONT−0.060.000.000.000.00−0.01−0.010.211
EQUITY−0.14−0.07−0.07−0.04−0.06−0.07−0.26−0.10−0.061
MARGIN0.120.060.030.010.050.030.250.080.07−0.611
CONCEN−0.02−0.010.020.00−0.01−0.02−0.02−0.08−0.01−0.050.061
OTHREV−0.24−0.09−0.06−0.03−0.10−0.07−0.38−0.020.030.09−0.020.431
SIZE0.500.280.160.120.290.220.57−0.030.02−0.030.01−0.020.111
AGE0.150.150.090.050.140.110.120.030.01−0.020.03−0.010.090.341
PROG0.090.020.020.040.020.030.08−0.06−0.02−0.020.010.00−0.040.05−0.031
ADMIN−0.18−0.09−0.10−0.18−0.09−0.11−0.16−0.04−0.040.06−0.020.010.04−0.090.01−0.261
GRANTS−0.09−0.04−0.04−0.02−0.07−0.04−0.140.02−0.02−0.020.03−0.04−0.020.060.11−0.010.021
PROGREV−0.030.040.010.020.010.02−0.050.01−0.01−0.010.01−0.01−0.020.040.08−0.040.03−0.021

Table 5 presents the baseline regression results. First, the regression results show that the FTI score of the governance information in the previous year is significantly positively related to donation revenue in the current year, and the coefficient value is 0.0376 at the 0.001 significance level. Meanwhile, the regression coefficients of the basic information, financial information and the project information are all insignificant. All these suggest that among all four types of information disclosure, Chinese donations are more sensitive to the disclosure of the foundation’s governance information.

Table 5:

Baseline regression result.

Coefficientt-Statisticp-value
BASIC0.01520.660.511
FINANCIAL−0.000121−0.010.99
PROJECT−0.00547−1.270.203
GOVERNANCE0.03763.230.001
Dont − 10.71625.060
FUNDEXP0.5990.810.415
FUNDCONT0.01420.880.378
EQUITY−0.00205−2.150.032
MARGIN−0.0364−4.380
CONCEN0.2131.70.089
OTHREV0.3521.610.107
SIZE0.1313.730
AGE0.00771.720.086
PROG0.0290.350.723
ADMIN−2.817−4.810
GRANTS−0.0597−0.230.816
PROGREV0.340.610.541
Cons1.2462.930.003

  1. Number of obs = 1858.

  2. Prob > F = 0.

  3. Adj R-squared = 0.617.

  4. Root MSE = 1.335.

The regression results in Table 5 also show some other interesting information. First, the donations in the previous period are significantly correlated with the donations in the current period, which suggests that there may be an impact of the trend. However, even if donations in the previous year are controlled, the coefficient of the FTI score on current donations is still significantly positive. Table 5 also shows that the 12 control variables have a complex relationship with current year donations. Some are insignificant, while others may have an ambiguous relationship with donations. Actually, these complex and mixed results are expected as pointed out by Trussel and Parsons (2004, 2007).[6]

The main focus of this study is to examine and compare the relationship between different types of information disclosure of the foundations and Chinese donations. Baseline regression results show that only the coefficient of GOVERNANCE is positive. To examine the stability of the result, especially to address the concern whether the coefficients are affected by the inclusion of the 12 control variables, a robustness check was carried out by running a series of regressions and the control variables were added consecutively.

Table 6 shows the results for this series of regressions. The main dependent variable is still the donation amount received in the current year. The BASIC, FINANCIAL, PROJECT and GOVERNANCE are the key variables of interest. The first column shows the result of the first regression in which only these four variables are included as the regressors. Then, the other control variables are added consecutively to examine the robustness of the estimation results of β1β4 against the inclusion of the control variables. Altogether, 14 regressions were run and the results are shown in Table 6.

Table 6:

Robustness check.

VariableModel 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8Model 9Model 10Model 11Model 12Model 13Model 14
BASIC0.111***0.0340.0330.0330.0330.0390.0380.0340.0180.0160.0160.0160.0160.015
(0.033)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)(0.023)
FINANCIAL−0.0070.0080.0070.0070.0080.0070.0070.0080.0020.0010.0010.0000.0000.000
(0.013)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)(0.009)
PROJECT−0.009−0.004−0.004−0.004−0.004−0.004−0.004−0.005−0.005−0.005−0.005−0.005−0.005−0.005
(0.006)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)(0.004)
GOVERNANCE0.118***0.034***0.035***0.035***0.035***0.033***0.033***0.032***0.036***0.037***0.037***0.037***0.037***0.038***
(0.016)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)(0.012)
Dont − 10.755***0.754***0.754***0.758***0.766***0.768***0.808***0.731***0.732***0.731***0.716***0.713***0.716***
(0.017)(0.017)(0.017)(0.018)(0.018)(0.018)(0.020)(0.027)(0.027)(0.027)(0.027)(0.028)(0.029)
FUNDEXP0.7460.8650.8720.4190.3950.3620.4970.4910.5650.5890.6010.599
(0.525)(0.743)(0.743)(0.750)(0.751)(0.746)(0.735)(0.735)(0.739)(0.735)(0.736)(0.736)
FUNDCONT−0.0030.0000.0210.0210.0200.0140.0140.0130.0140.0140.014
(0.015)(0.016)(0.017)(0.017)(0.016)(0.016)(0.016)(0.016)(0.016)(0.016)(0.016)
EQUITY0.001−0.001−0.001−0.001−0.002**−0.002**−0.002**−0.002**−0.002**−0.002**
(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)
MARGIN−0.040***−0.040***−0.039***−0.036***−0.037***−0.036***−0.037***−0.036***−0.036***
(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)(0.008)
CONCEN−0.0830.216*0.2230.234**0.233**0.216*0.2030.213*
(0.103)(0.119)(0.118)(0.118)(0.118)(0.117)(0.124)(0.126)
OTHREV0.886***0.422**0.421**0.422**0.353*0.3310.352
(0.178)(0.203)(0.203)(0.203)(0.202)(0.216)(0.219)
SIZE0.143***0.129***0.128***0.131***0.133***0.131***
(0.033)(0.034)(0.034)(0.034)(0.035)(0.035)
AGE0.0070.008*0.008*0.008*0.008*
(0.005)(0.005)(0.005)(0.005)(0.005)
PROG0.0780.0270.0270.029
(0.082)(0.082)(0.082)(0.082)
ADMIN−2.826***−2.830***−2.817***
(0.585)(0.586)(0.586)
GRANTS−0.075−0.060
(0.255)(0.256)
PROGREV0.340
(0.557)
Constant12.380***2.721***2.741***2.742***2.673***2.561***2.603***1.693***0.812**0.959**0.914**1.246***1.257***1.246***
(0.197)(0.262)(0.262)(0.262)(0.275)(0.276)(0.281)(0.334)(0.408)(0.418)(0.420)(0.423)(0.425)(0.425)
n20781939193319331933193119311931185818581858185818581858
Adj. R20.1620.5900.5910.5910.5910.5940.5940.6000.6110.6120.6120.6170.6170.617

  1. Standard errors in parentheses.

  2. * p < 0.05, ** p < 0.01, *** p < 0.001.

First, the regression results in Table 6 show that the estimation results of β1β4 is robust. The inclusion of more control variables does not change the sign of the estimation results of β1β4. The estimation results of β1β3 are always insignificant. Meanwhile, the estimation result of β4 always remains significantly positive. While the values of β4 change slightly, most remain near 0.0375. Therefore, this robustness check of the regression results lends strong support to the conclusion of the baseline regression.

We further examine the heterogeneity of the relationship by dividing the sample into private foundations and public foundations. In China, there are two types of foundations: public foundations and private. We are interested in whether the baseline results still hold when we examine specific type of foundations. Table 7 reports the regression results. The first column shows the regression results for the private foundations and the second column shows the regression results for the public foundations with similar control variables as in the baseline regression. The results are also similar compared with the baseline regression results. The estimation results of β1 to β3 are both insignificant. Meanwhile, the estimation result of β4 always remains significantly positive. This suggests that the foundation type does not play an important role in Chinese donation behavior.

Table 7:

Robustness check: fundtype.

VariablePrivatePublic
BASIC−0.0160.044
(0.033)(0.033)
FINANCIAL0.011−0.012
(0.013)(0.014)
PROJECT−0.007−0.002
(0.006)(0.006)
GOVERNANCE0.043***0.028*
(0.016)(0.017)
Control variables includedYesYes
Constant1.678**0.015
(0.539)(0.798)
N1083775
Adj. R20.5730.653

The robustness of the results was further examined by adopting the Tobit Model. Since there are substantial proportion of the foundations that did not receive donations in 2015, we adopt the standard Tobit model to address the issue that some foundations received no donation in 2015. The function formula of the Tobit model follows the baseline regression function. In the Tobit model, the censored dependent variable is observable if the donation of the foundation is greater than zero in 2015; otherwise, the value of the censored dependent variable is not observed if the donation of the foundation is zero. Table 8 reports the regression results. The results show that β1β3 are insignificant; and the estimation result of β4 is significantly positive, which is consistent with the baseline regression results.

Table 8:

Robustness check – tobit model.

Coefficientt-Statisticp-value
BASIC0.0281.010.313
FINANCIAL−0.004−0.330.741
PROJECT−0.008−1.670.095
GOVERNANCE0.0755.340
Dont−1−0.006−5.130
FUNDEXP4.1885.30
FUNDCONT−0.039−2.830.005
EQUITY0.000−0.560.573
MARGIN0.000−1.620.106
CONCEN−0.333−2.260.024
OTHREV−2.959−14.840
SIZE0.69222.930
AGE0.0081.440.15
PROG0.0990.980.326
ADMIN−3.571−5.870
GRANTS−2.569−9.120
PROGREV−1.519−2.390.017
Constant3.3126.610

  1. Number of obs = 2552.

In sum, the results of above regressions suggest that Chinese donations are not significantly correlated with the disclosure of the basic, financial and project information of the foundations, but highly correlated with the disclosure of foundation’s governance information.

6 Conclusions

The philanthropy industry in China is becoming increasingly more prominent in public benefit activities as a supplementary means to the efforts of the state or local governments, in areas such as healthcare, education, natural disaster relief, rights of women and children, etc. Both the policy makers and the practitioners have also realized the importance of foundation information disclosure on the donation behaviors.

The literature has fruitful findings in showing that foundation information disclosure has strong impact on people’s donation. The consensus in the literature is that more disclosed information does bring more donation. Then, a more interesting and important question, which should be answered, is what type of disclosed information might be more effective in affecting people’s behavior, given the fact that foundations are revealing all kinds of information such as basic information, financial information, project information, and their own governance information, etc. In another word, current literature more focus on general information disclosure, and lacks of examining and comparing how different type of information disclosure is associated with charitable donation.

In this study, the aim is to fill in this research gap and to empirically investigate and compare how different types of information disclosure of Chinese foundations are correlated with Chinese donation behavior by examining a unique dataset of the CFC. By utilizing a large sample of Chinese foundations, the analysis results show a significant and robust result that Chinese donors are more sensitive to the disclosure of the governance information of foundations, and donations are not significantly correlated with the disclosure of the basic, financial and project information of foundations.

The evidence provided in this study also has meaningful policy implications for practitioners as well as the policy makers in the charitable industry. For instance, from industrial practitioner’s perspective, this finding gives them suggestive direction on what type of foundation information they should focus on to disclose. As for policy makers, it is recommended that they amend related laws or regulations so that more governance information about foundations can be released.

One of the limitations of this study is the relatively short time period of the data. Data that span a longer timeframe will be more robust in explaining for the donation behaviors towards Chinese foundations. Another possible research question might be to examine the impact of foundation transparency on the donation source diversification. However, due to the limitation of the data availability, current research cannot address this question. This interesting topic will be left for future study.


Corresponding author: Ying Li, School of Business, Macau University of Science and Technology, Taipa, Macao, E-mail:

Funding source: This study was supported by the Faculty Research Grant in Macau University of Science and Technology (FRG-20-006-MSB).

Acknowledgments

We are indebted to the three anonymous referees for helping us to substantially improve the paper. All errors are our own.

  1. Research funding: This study was supported by the Faculty Research Grant in Macau University of Science and Technology (FRG-20-006-MSB).

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Received: 2019-05-23
Accepted: 2020-05-24
Published Online: 2020-08-07

© 2020 Zhijun Lin, published by De Gruyter, Berlin/Boston.

This work is licensed under the Creative Commons Attribution 4.0 International License.