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Published in: Journal of Business Ethics 4/2021

10-04-2020 | Original Paper

Looking Backward and Forward: Political Links and Environmental Corporate Social Responsibility in China

Authors: Peng Zhou, Felix Arndt, Kun Jiang, Weiqi Dai

Published in: Journal of Business Ethics | Issue 4/2021

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Abstract

This study aims to enrich our understanding of the relationship between political connections and the adoption of environmental corporate socially responsible (ECSR) investments. In addition to the individual-level political connections, i.e., entrepreneurs’ personal ties to government officials, we propose in China the creation of Communist Party of China (CPC) branches in privately owned firms serve as organizational and institutionalized dimensions of political connection building. Drawing on the social exchange theory, this paper details how CPC branches function in privately owned firms and how entrepreneurs are motivated by the reciprocal logic to engage in ECSR. We also supplement this main effect by examining the boundary conditions of it at the firm and regional level. Using a dataset with 17,690 firm observations in China, we find that the existence of CPC branches gives rise to the ECSR investments and the firm-level contingencies such as centralized governance and financial constraints, and the regional-level contingencies such as the development of the market system, are important moderators that shape the association of political links and ECSR investments. These findings have ample implications to understand the ECSR activities and extend social exchange theory beyond the individual level on which it is typically applied.

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Appendix
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Footnotes
1
Party branch (jiceng dangzuzhi) is the smallest unit of Chinese Communist Party (CCP) at the grassroots level. One of the unique characteristics of CCP is its “mass line” (qunzhong luxian) (Han 2015). In other words, CCP relays on numerous party branches to exercise its control over the state, economy and society in China.
 
2
China’s 5-year plans are a series of countrywide social and economic development initiatives containing detailed guidelines for economic and social development. The 10th 5-Year Plan (2001–2005) was the first to include an environmental policy by setting a 10% SO2 reduction target. In the 11th 5-Year Plan (2006–2010), the central government establishes a pollution reduction target 10% for the entire country with different provinces sharing different burdens. In 2006, the China State Council issued a document named “Reply to Pollution Control Plan During the Eleventh Five-Year Plan”, which handed down national goal to pollution reduction targets in provincial level. Formal contracts for the provincial pollution reduction targets were signed by the provincial vice presidents.
 
3
In this project, ECSR refers to a firm’s voluntary environmental investment, including investment in sewage treatment, air pollution treatment, soil pollution treatment, solid waste treatment, flue gas treatment, waste recycling and so on. These investments are not obligated. In other words, there is no explicit government policy which forces firms to invest in these items, but our data shows that some firms scarify their private benefits to do so.
 
4
Member organizations include All-China Federation of Industry and Commerce, State Administration for Market Regulation, Chinese Academy of Social Sciences, the China Society of Private Economy, and the United Front Work.Department of CCP.
 
5
According to the project team, to make the survey be representative to the population of nationwide registered private firms, a stratified random sampling procedure is applied. The stratifications include locations, industries, stages of economic development, and distribution of the private firms in urban and rural areas within each location (a city or a county). The original sample frame for this survey is a modified version of the private enterprise section sample frame used for its annual data collection by the China State Bureau of Statistics, which is considered to be representative of all registered private enterprises in China. In particular, the sampling involved two stages. In the first stage, a pre-specified number of counties were selected in each province based on economic development levels, so that both developed and less developed counties were represented. In the second stage, a pre-specified number of private firms were selected in each county based on location and primary industrial sector. Both urban and rural firms were chosen; and within each urban/rural area, firms from all industrial sectors were sampled. The number of sampled firms in each province, county, urban/rural area, and sector was proportionate to the population size of the private enterprises in each of these categories.
 
6
As a robustness check, we construct two variables to proxy the firm’s financial environment based on the firm’s basic characteristics: (1) Following Campello et al. (2010), we treated the financial crisis in 2008 as an external financial constraint shock for firms. FC1 equals to one if the time of the survey is 2010, and it equals to zero, otherwise; (2) FC2 equals to one if firms with the HP index higher than the average, otherwise zero. Prior studies show that small and young firms are more likely to suffer financial constraint. We constructed the HP Index, following Hadlock and Pierce (2010), as − 0.737 * Size + 0.043 * Size2 − 0.040 * Age, where Size equals the log of sales, and Age is the number of years the firm is established. The results of our robustness check are available on request.
 
7
We thank the anonymous reviewer for pointing out that hierarchical linear model (HML) is more appropriate for our nested data (firms within regions) than the OLS model. Although both our independent variable (PB) and dependent variable (ECSR) are at the firm level, one of our moderators is at the provincial level. According to Hofmann (1997), hierarchical linear models provide a conceptual and statistical mechanism for investigating and drawing conclusions regarding the influence of phenomena at different levels of analysis.
Our HML models consisted of two levels. At level 1, the unit of analysis was the firm, and each firm’s ECSR investment was a function of firm-level factors. The level 1′s model is: ECSR i,j = β0j + β1jXij + rij. i indexes each firm, j indexes each province, Xij is the vector of firm-level factors, including PB and other firm-level controls. β0j and β1j are, respectively, the intercept and slopes estimated for each province, and rij is the residual. At level 2, the unit of analysis was the province, where the dependent variable was hypothesized to depend on specific province factors adjusted for the regression coefficients in the level 1 model. Thus, we used an intercept-as-outcome models instead of a slopes-as-outcome model. The level 2′s model is: β0j = γ00 + γ01Gj + U0j. where β0j is the intercept from the level 1 equation above, Gj is the provincial-level variable, γ00 is the second level intercept terms and γ01 is the slope relating the province-level-independent variable G to the intercept, and U0j is the level 2 residual. Thus, to estimate the moderator effect of MarOrient using the HML model, we added the PB and its interaction term with MarOrient into level 1′s model and added the MarOrient into level 2′s model. This is because the interaction term between PB and MarOrient is actually a firm-level factor.
Specifically, we estimated the HML models by using the Stata commands “mixed” and “meprobit” (StataCorp 2013). A random-effects model, as opposed to a fixed-effect model, was used to deal with variance in our firm-level variables. This means, for the regressions that do not include the provincial-level independent variable, our HML models just include a random effect at the provincial level.
 
8
We calculate the centered variance inflation factors (VIFs) after each of our regression model. The centered VIFs for all variables are no higher than 1.5, indicating that our specification does not suffer multicollinearity. To save the space, the VIFs tables are available on request.
 
9
We conducted a Durbin–Wu–Hausman (DWH) Endogeneity tests. The p-value of the DWH test is 0.000, rejecting the null hypothesis that “the specified endogenous regressors can actually be treated as exogenous”. This means that PB is an endogenous variable and the results in Table 5 maybe bias.
 
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Metadata
Title
Looking Backward and Forward: Political Links and Environmental Corporate Social Responsibility in China
Authors
Peng Zhou
Felix Arndt
Kun Jiang
Weiqi Dai
Publication date
10-04-2020
Publisher
Springer Netherlands
Published in
Journal of Business Ethics / Issue 4/2021
Print ISSN: 0167-4544
Electronic ISSN: 1573-0697
DOI
https://doi.org/10.1007/s10551-020-04495-4

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