Corporate Power for Poverty Alleviation: Evidence from the Poverty Alleviation Results of Chinese Listed Companies
-
Liya Liu
and
Xuehan Guo
Abstract
Government-led poverty alleviation faces multiple constraints and limitations fighting poverty, and it is urgent to mobilize social forces as many as possible, especially the involvement of corporate forces. To figure out whether corporate participation is effective to poverty alleviation and whether regional differences have an impact on it, this paper probes into results of corporate involvement in poverty alleviation, based on corporate social responsibility report information released by A-share listed companies in 2010–2017. The findings are as follows. (1) Companies in the western region, companies directly contacting consumers and large companies with good business performance are more willing to involve in poverty alleviation. (2) The participation of enterprises in poverty alleviation can increase the per capita income of rural residents, which is even better in underdeveloped areas. This revealed that social forces represented by companies have responded to China’s call of targeted poverty alleviation and fulfilled corporate social responsibilities, which will indeed help consolidate the achievements of poverty alleviation and lift underdeveloped areas out of poverty. From the perspective of corporate involvement in poverty alleviation, this paper extends related research on social forces and another participant in poverty alleviation, and enrich the literature on social benefits brought by companies performing social responsibilities.
1 Introduction and Literature Review
The government has been in the leading place throughout the history of poverty alleviation in China (Yan and Yu, 2008). In stages of relief-based poverty alleviation (1978–1985), development-driven poverty alleviation (1986–2000), and diversified support-driven poverty alleviation (2001–2012), the Chinese government implemented a series of policies targeted at liberating rural productivity and arousing the enthusiasm of farmers, such as transferring land management rights, raising agricultural produce prices and guiding rural labor into the secondary and tertiary industries, which has facilitated rural economic development and alleviated rural poverty fundamentally (Wang, 2008). As poverty alleviation went deeper, General Secretary Xi Jinping put forward the “targeted poverty alleviation” policy in 2013, and then targeted poverty alleviation kicked off nationwide. The Implementation Plan for Establishing a Mechanism of Action for Targeted Poverty Alleviation published that year further applied the idea of targeted poverty alleviation for fighting poverty. Following this guide, poverty alleviation has been going smoothly with exciting results. The proportion of the poor in China has continued to decline, and the incidence of poverty continues the decreasing trend.
Government-led poverty alleviation, however, has constraints and limitations. In the face of shocks, the government’s non-single objective function would lead to waste of resources and loss of efficiency because of policy failure (Meng, 2013; Zhu and Li, 2017). The direct relief to the poor crowded out private assistance resources and induced them to become dependent on relief, thereby weakening results of relief in reducing poverty (Lu and Lu, 2013). Also, it has caused the fact that the results in poorer areas were below the average effect, even though the government’s spending on poverty alleviation has largely grown the per capita income of rural residents (Liu, 2008).
To relax constraints on the government and make up for its weaknesses in poverty alleviation, the World Bank stressed the need to serve the poor by market strength towards the goal of poverty alleviation (World Bank, 2000). Some studies argued that poverty could be eradicated by innovating business models to serve consumers at the bottom of the pyramid. The core idea was that commercial organizations could “make profits by good deeds”, i.e., commercial organizations can gain benefits while engaging in poverty eradication innovatively (Prahalad, 2006; Kramer and Porter, 2011). For one thing, as commercial organizations, financial companies harvested substantial profits at the business level by convenient lending, such as microfinance, and helped local governments alleviate poverty at the social level (Banerjee et al., 2015). For another, non-profit organizations (NGOs) play a role in reducing poverty. Even in the United States, besides fiscal transfers for education, healthcare, employment, pensions and housing security which are State financially responsible for, there is no shortage of social organizations (e.g., NGOs, companies, etc.) offering relief to the poor.
In 2014, the Opinions on Steadily Pushing Forward Rural Poverty Alleviation and Development with Innovative Mechanisms and the Opinions on Mobilizing the Involvement of All Social Forces in Poverty Alleviation and Development were successively released by the Chinese government, and emphasized that the poverty alleviation mechanism must be innovated and extensive social forces, especially corporate power, should be mobilized into poverty alleviation when advancing poverty alleviation and development. As poverty alleviation has entered the deep end, it needs the strength of government and the involvement of social forces as the supplement. All forces must be used for a comprehensive setup for poverty alleviation with government-sponsored projects, sector-specific programs, and social assistance supplementing each other to fuel the economy, develop resources and upgrade industries in poverty-stricken areas.
According to the existing literature, it was found that economic growth and income distribution were the primary ways of anti-poverty, and hence it was generally believed the government should be dominant in poverty alleviation. A number of literatures were about the government’s role in anti-poverty: development-based poverty alleviation with government-sponsored public investment as the core could improve the efficiency and equity of backward areas (Ravallion and Jalan, 1999; Fan et al., 2002), while the ever-growing education and social expenditures in government poverty alleviation funds have enhanced the effectiveness of government public expenditures in narrowing the income gap between urban and rural areas (Li and Shen, 2007; Lyu and Liu, 2008). Regarding the role of social forces represented by companies in anti-poverty, only a few literatures noted the fulfillment of corporate social responsibility was of positive significance for alleviate poverty in poor countries, but with absence of specific empirical research (Amadi and Abdullah, 2012, Yan and Yu, 2008). In the aspect of corporate social responsibility, most existing research focused on corporate motives of performing social responsibilities and utility on themselves, while few literatures worked on the social results of companies performing social responsibilities and the research results have been controversial (Aakhus and Bzdak, 2012; Reyes et al., 2017; Beschorner and Hajduk, 2017).
In view of the above, with the corporate social responsibility report information released by A-share listed companies in 2010–2017, this paper empirically looks into the results of corporate involvement in poverty alleviation by application of DID (difference-in-differences) technique in the context of targeted poverty alleviation in China. Research findings are as follows. (1) Companies in the western region, companies directly contacting consumers and large companies with good business performance are more willing to involve in poverty alleviation. (2) Corporate involvement in poverty alleviation significantly increases the per capita income of local rural residents. (3) The results of corporate involvement in poverty alleviation are better in the western region, areas with low per capita GDP and areas under national poverty-stricken counties, i.e., there are significant differences among areas with respect to the results of corporate involvement in poverty alleviation, which is good for targeted poverty alleviation. (4) Companies improve the per capita income of rural residents mainly by developing distinctive agriculture locally. The findings reveal that social forces represented by companies have responded to China’s call of targeted poverty alleviation and fulfilled corporate social responsibilities, and actually pushed forward poverty alleviation in underdeveloped areas. For guarding against a return to poverty, corporate efforts for poverty alleviation must be combined with rural revitalization plans so market strength is utilized to consolidate the achievements of poverty alleviation.
Potential contributions of this paper are as follows. (1) In terms of the actors for poverty alleviation, while current research perspectives are focused on poverty reduction results of the government, this paper, from the perspective of corporate involvement in poverty alleviation, probes into the role of companies and extends the research on social forces, another contributors to poverty alleviation. This provides some empirical evidence for reviewing the theory and practice of socialism with Chinese characteristics that feature people who have got prosperous first helping others catch up and refining the theory of poverty alleviation in China. (2) Conventional research targets at the motives of companies bearing social responsibilities and the utility on themselves. This paper, from the perspective of corporate involvement in poverty alleviation, empirically tests the social benefits of companies fulfilling social responsibilities, and concludes that corporate involvement in poverty alleviation effectively helps social poverty reduction, by which the literature on social benefits brought by companies fulfilling social responsibilities is enriched.
2 Theoretical Analysis and Research Assumptions
2.1 Characteristics of Companies Involving in Poverty Alleviation
China has a vast territory, and the regional differences are great. Compared with border effects among different regions within a general country, the situation in China was more similar to the border effects between different countries around the EU (Huang and Wang, 2006). However, as Chinese companies have been influenced by clan culture to varying degrees (Pan et al., 2017), with strong local characteristics, they naturally focused on places of their location when involving in poverty alleviation. Besides, considering most of the poor in China were living in western provinces (Ravallion and Jalan, 1999), poverty alleviation and development were more frequently found in the performance appraisal of the western region (Yang et al., 2015; Zhu and Li, 2017), resulting in companies concerned with poverty alleviation in this political environment involving more in poverty alleviation. Based on this, from the perspective of local feelings and political demands, Assumption 1A is proposed.
Assumption 1A: Companies in the western region are more willing to involve in poverty alleviation.
Some studies have shown that companies involved in public welfare with an economic motive to improve corporate reputation, achieve advertising effects, and then increase their market share of products (Strahilevitz and Myers, 1998). Meanwhile, companies whose consumers directly purchased their products or services were more sensitive to advertising effects and more strongly motivated to raise corporate reputation through public welfare, and their donations exceeded those of other companies by 50% on average (Shan et al., 2008). Corporate involvement in poverty alleviation, as intensification of public welfare, also creates advertising effects. Compared with other public welfare undertakings, poverty alleviation projects could be better implemented locally, and the advertising effects are clearer and last longer. Therefore, from the view of advertising effects, Assumption 1B is proposed.
Assumption 1B: Companies directly contacting with consumers are more willing to involve in poverty alleviation.
The theory of constraints (TOC) for corporate social responsibility holds that a company often needs to balance its social value demands with operational conditions when performing corporate social responsibilities. First of all, the corporate scale was highly correlated with resource control capabilities (Yu et al., 2015), and large and performant companies were more willing to engage in public welfare (Adams and Hardwick, 1998; Godfrey, 2005). What’s more, poverty alleviation projects need long-term and continuous investment. Unlike general charity or philanthropy, poverty alleviation sets higher standards for corporate scale and operational capabilities, and only large companies with good business performance are capable and willing to respond to the governmental call for poverty alleviation. In addition, the motivation theory for corporate social responsibility believes corporate success is not separated from the support of the society. Large companies with good business performance were more likely to work for the vision of realizing social value, and then applied the resources at their disposal for poverty alleviation and development to enlarge overall social welfare (Doni and Ricchiuti, 2013), thereby “giving back” to society (Stewart, 2005). Hence from the point of constraints and motivation, Assumption 1C is proposed.
Assumption 1C: Large companies with good business performance are more willing to involve in poverty alleviation.
2.2 Results of Companies Involving in Poverty Alleviation
China’s poverty alleviation starting in 2013 and following the policy of targeted poverty alleviation is a pioneering work of the human society. Mobilizing social forces and properly handling the relationship between the government and the market are essential to government-led advancement of targeted poverty alleviation. In existing research, however, international organizations, NGOs and business associations often mentioned corporate contributions to anti-poverty (Kolk and Tulder, 2006), but poverty as a social issue was not covered by conventional corporate social responsibility (Barkemeyer, 2009; Hahn, 2012; Lobel, 2013), and there was still a debate over whether companies could help with anti-poverty (Banerjee, 2018; Medina-Muñoz and Medina-Muñoz, 2020). With the penetration of anti-poverty, the government needed companies to involve more in sustainable development, including poverty eradication and should bring into play the potential of multinational and domestic companies in anti-poverty (UNDP, 2004). Also, there has been research suggesting that corporate fulfillment of social responsibilities was a double-edged sword (Swift and Zadek, 2002). On the one hand, companies fulfilling corporate social responsibilities addressed material social and environmental issues, but on the other hand, perhaps companies deviated from the mission of creating profits to distort the market, reduce community welfare, and cause potential harm to society (Banerjee, 2018).
As domestic research has noted, companies could provide capital, human resources, knowledge and organizational supply for rural development by means of investment (Li, 2019). In China, targeted poverty alleviation has been characterized by government leadership, with all forces used for a comprehensive setup for poverty alleviation with government-sponsored projects, sector-specific programs, and social assistance supplementing each other, as a two-way poverty alleviation mechanism has been applied top-down and bottom-up for economic growth, resource development and industrial upgrading in poverty-stricken areas (Jia et al., 2017; Wang and Su, 2020). The coordination between the government, the society and the market helped to improve the quality and efficiency of poverty alleviation (Yan and Yu, 2008), and corporate potential and role in poverty alleviation should be enlarged by the policy synergy. This paper, therefore, proposes the following assumption.
Assumption 2: The per capita income of rural residents increase significantly in areas with corporate involvement in poverty alleviation.
Addressing the weakening of poverty alleviation results after entering the deep end is a major target of targeted poverty alleviation. To probe into whether corporate involvement could help with the policy of targeted poverty alleviation, the regional differences in the results of corporate involvement in poverty alleviation are further analyzed.
Some studies have shown the differences among different economic regions in China were large, with a convergence phenomenon in growth within the same economic region, i.e., economically underdeveloped regions and economically developed regions were conditionally convergent, respectively, revealed as different economic levels of the eastern, central and western regions (Liu, 2001; Pan, 2010). Others suggested regions with lower initial economic level registered higher growth rates of per capita income, i.e., the growth rates of per capita income of different regions were correlated negatively with the initial levels of per capita income; the difference in per capita income between different regions was narrowing over time (Lin and Liu, 2003; Lin et al., 2005). Impacted by external economic trends, it is inferred that the results of corporate involvement in poverty alleviation are better in the western region, and areas with low per capita GDP as well. Based on the above analysis, this paper proposes the following assumptions.
Assumption 3A: The results of corporate involvement in poverty alleviation are better in the western region.
Assumption 3B: The results of corporate involvement in poverty alleviation are better in areas with low per capita GDP.
As targeted poverty alleviation became a policy orientation, in March 2012, the State Council Leading Group Office for Poverty Alleviation and Development stepped up the policy support for deeply impoverished areas and announced the List of Prioritized Counties of National Poverty Alleviation and Development. The List of Prioritized Counties of National Poverty Alleviation and Development had a long-term positive impact on poverty alleviation in the corresponding areas, and the longer poverty-stricken areas were registered as prioritized counties, the larger the positive impact would be (Huang, 2018). Research suggested that the implementation of poverty alleviation policies could largely reduce the rural poor in poverty-stricken areas, as the poverty rate of poverty-stricken counties has dropped more than that of non-poor counties after these policies have taken effect (Xu et al., 2020). Companies involving in poverty alleviation projects of national poverty-stricken counties receive policy support in terms of taxation, credit, and land. It is inferred that the results of corporate involvement in poverty alleviation are better in areas under national poverty-stricken counties. Based on the above analysis, the following assumption is proposed.
Assumption 3C: The results of corporate involvement in poverty alleviation are better in areas under national poverty-stricken counties.
3 Research Design
3.1 Sample Selection and Data Sources
A corporate social responsibility (CSR) report is a corporate summary of achievements and weaknesses of direct or indirect operations for the society and people’s livelihood, environmental protection, etc., as well as the vision and measures for fulfilling social responsibilities. In the CSR report, a company discloses its efforts for poverty alleviation in the year, including dates, locations, projects and so on. This paper collects corporate social responsibility reports published by all listed companies in 2010–2017 on http://www.cninfo.com.cn, extracts the location information of poverty alleviation projects carried out by all listed companies each year by way of text collation, and matches how many listed companies in each county-level area nationwide help with local poverty alleviation each year, so corporate support for poverty alleviation in county-level areas is measured. The more poverty alleviation projects a county-level area receives, the larger the support it receives from companies. The rest of the data are derived from China County Statistical Yearbook, China Statistical Yearbook, provincial statistical yearbooks, China City Statistical Yearbook, Railway Passenger and Freight Transport Special Issue, Wind financial database, Qixinbao Database, etc.
Meanwhile, the data is processed according to the following standards. (1) To avoid the influence of outliers, the variables are performed with a 1% winsorization on the left and right; (2) For area-level data of Assumption 2 and Assumptions 3A , 3B and 3C, the areas without poverty alleviation projects in 2010–2013 are selected as the sample and those with poverty alleviation projects in 2010–2013 are excluded to construct the impact of corporate support on county-level areas, namely 2014 is taken as the first year of policy impact; areas with missing per capita income are excluded from the sample; only those with data of 2010–2013 and 2014–2017 stay to ensure the sample of areas exist before and after policy impact.
3.2 Model Design and Variable Description
For Assumptions 1A, 1B and 1C to be verified, the following equations are constructed:
where D_POVERTY is the dependent variable, which equals 1 if a listed company involved in poverty alleviation in that year, and 0 otherwise. WEST denotes the geographic location of the listed company, which is 1 if the company is located in the western region, and 0 otherwise; γ1 shall be significantly positive if Assumption 1A holds. INDU denotes the industrial nature of the listed company, which is 1 if the listed company’s products and services has direct contact with consumers, and 0 otherwise. γ2 shall be significantly positive if Assumption 1B holds. [1] SIZE denotes logarithm of the total assets of the listed company and is used to measure the scale of the listed company and ROE denotes the return on net assets of the listed company; γ3 and γ4 shall be significantly positive if Assumption 1C holds. SOE denotes the nature of the listed company, which is 1 if the listed company is a state-owned enterprise, and 0 otherwise; INCOME denotes logarithm of the total operating income of the listed company; LEV denotes the asset-liability ratio of the listed company; HOLD denotes the shareholding ratio of top ten shareholders of the listed company; IHP denotes the shareholding ratio of institutional investors; EPS denotes the earnings per share of the listed company. To alleviate endogeneity issues, all explanatory variables in equation (1) are lagged by one period.
For testing Assumption 2, the DID (difference-in-differences) technique is chosen to study the impact of corporate involvement in poverty alleviation on poverty reduction. General Secretary Xi Jinping proposed “targeted poverty alleviation” for the first time in November 2013, emphasizing that poverty alleviation must adapt to local reality. In January 2014, the CPC Central Committee and the General Office of the State Council of the People’s Republic of China jointly released the Opinions on Steadily Pushing Forward Rural Poverty Alleviation and Development with Innovative Mechanisms that called for establishing and improving a social involvement mechanism for poverty alleviation and development. In December 2014, the Opinions on Mobilizing the Involvement of All Social Forces in Poverty Alleviation and Development was published by the General Office of the State Council to vigorously advocate corporate involvement in poverty alleviation. Given that China’s call for social forces to involve in targeted poverty alleviation started in 2014, this paper regards 2014 as the first year of policy impact. If a county-level area gained poverty alleviation projects of listed companies in 2014 and later, it enters the treatment group. The equation is as follows:
where the subscript i denotes the county-level area and t the time. PINCOME represents the per capita income of rural residents. PT is a dummy variable, which is 1 if the county-level area obtained poverty alleviation projects of listed companies, and 0 otherwise. Since each county-level area is impacted by listed companies’ involvement in poverty alleviation at different times, the PT of all samples before 2014 should be 0, and it should be 0 if a county-level area has not obtained poverty alleviation projects of listed companies since 2014; if there are poverty alleviation projects from listed companies in that year, the PT in that year and subsequent years should be 1. β is the coefficient of PT and is used to measure the impact of poverty alleviation projects of listed companies on per capita income of rural residents. The larger β is, the better results of listed companies involving in poverty alleviation. X is a control variable, and γ denotes coefficient of the control variable. The per capita fiscal expenditure is used to measure governmental involvement in poverty alleviation, and per capita GDP, GDP growth rate, per capita industrial output value above designated size, per capita investment in fixed assets, financial development index and per capita value added of the primary industry and the opening of high-speed rail is applied to measure the impact of local economic features on per capita income of rural residents. C denotes the county-level area fixed effects that is constant, and year denotes the time fixed effects.
For Assumptions 3A, 3B and 3C to be verified, the following equation is constructed for heterogeneity analysis:
where DUMMY denotes dummy variables WEST, PGDP_DUMMY and POOR_COUNTY, respectively. If the county-level area is located in the western region, WEST is 1, otherwise it is 0; if the per capita GDP of the county-level area is higher than the median, PGDP_DUMMY is 1, otherwise it is 0; if the county-level area is a national-level poverty-stricken county, POOR_COUNTY is 1, otherwise it is 0. The coefficients of PT×WEST, PT×PGDP_DUMMY, and PT×POOR_COUNTY, respectively measure the impact of these local characteristics on listed companies’ involvement in poverty alleviation to raise the per capita income of rural residents. [1]
4 Empirical Results and Analysis
4.1 Assumption 1 Regression Results
Regression results of Assumption 1 are shown in Table 1. Column (1) shows regression results of the Probit model, and WEST is significantly positive, revealing that listed companies in the western region are more willing to involve in poverty alleviation. Assumption 1A holds. INDU is significantly positive, which means listed companies whose products and services are in direct contact with consumers are more willing to involve in poverty alleviation. Assumption 1B holds. SIZE is significantly positive, and it suggests large listed companies are more willing to involve in poverty alleviation; ROE is significantly positive, indicating listed companies with good business performance are more willing to involve in poverty alleviation. Assumption 1C holds. The above results also hold true in Logit regression and OLS regression, which means the results are robust.
Assumption 1 Regression Results
| (1) Probit D_POVERTY | (2) Logit D_POVERTY | (3) Ols D_POVERTY | |
|---|---|---|---|
| WEST | 0.3130*** (0.0693) | 0.5229*** (0.1131) | 0.1100*** (0.0250) |
| INDU | 0.1038* (0.0577) | 0.1760* (0.0954) | 0.0366* (0.0196) |
| SIZE | 0.1963*** (0.0297) | 0.3263*** (0.0496) | 0.0708*** (0.0101) |
| ROE | 0.5628* (0.2928) | 0.9481* (0.4837) | 0.1681* (0.0958) |
| Constant | −1.1880*** (0.1250) | −1.9479*** (0.2088) | 0.0735* (0.0429) |
| Control variable | YES | YES | YES |
| Time fixed effects | YES | YES | YES |
| N | 5334 | 5334 | 5334 |
| R2 | 0.0731 | 0.0733 | 0.0922 |
Note: ***, ** and * represent the significance levels of 1%, 5% and 10% respectively, and values in brackets are clustered standard errors at the corporate level.
While endogeneity issues could be partially alleviated by adopting one-period-lagged independent variables, this model has another potential endogeneity issue—sample selection bias. The percentage of listed companies releasing social responsibility reports in 2017 was approximately 30%, and it suggested the benchmark results could be subject to sample selection bias. The Heckman Model is also applied for testing sample selection bias, and the results are consistent.
4.2 Assumption 2 Regression Results
4.2.1 Benchmark Results
Regression results of Assumption 2 are shown in Table 2. For column (1), only individual fixed effects and time fixed effects are controlled; for column (2), control variables are added; for column (3), “province×time fixed effects” is controlled (“province×time fixed effects” captures policy differences among different provinces to further control the impact of governmental factors on per capita income). The coefficients of PT are all significantly positive, as is shown in the Table. It suggests that the impact of listed companies’ involvement in poverty alleviation is significantly positive on the per capita income of rural residents in county-level areas. Assumption 2 holds. The results reveal the significant role of social forces represented by companies in poverty alleviation. Encouraging corporate involvement in poverty alleviation helps with the goal of common prosperity for all. The theory of socialism with Chinese characteristics—people who have got prosperous first help others catch up—works, as is demonstrated.
4.2.2 Parallel Trends Assumption Testing
The DID (difference-in-differences) technique must have parallel trends assumption as the premise. The testing results of parallel trends assumption in this paper are shown in Figure 1. For county-level areas, before being impacted by listed companies’ involvement in poverty alleviation, the estimated coefficient is not significantly different from 0, i.e., there is no significant difference in the per capita income of rural residents between the treatment group and the control group; the estimated coefficient becomes significantly positive after the impact. The parallel trends assumption testing has passed.
Parallel Trends Assumption Testing
Note: The circles are estimated coefficients, and the upper and lower seals of the dotted lines are 95% confidence intervals.
Assumption 2 Regression Results
| (1) Complete samples PINCOME | (2) Complete samples PINCOME | (3) Complete samples PINCOME | (4) Samples of central and western regions PINCOME | (5) Samples of central and western regions PINCOME | (6) Samples of poverty- stricken counties PINCOME | (7) Samples of poverty-stricken counties PINCOME | |
|---|---|---|---|---|---|---|---|
| PT | 0.0372*** (0.0054) | 0.0265*** (0.0052) | 0.0175*** (0.0042) | 0.0255*** (0.0056) | 0.0125*** (0.0046) | 0.0159** (0.0073) | 0.0104* (0.0059) |
| Constant | 9.0658*** (0.0006) | 7.7752*** (0.1517) | 8.2750*** (0.1611) | 7.8145*** (0.1659) | 8.4280*** (0.1738) | 7.4077*** (0.2157) | 7.9381*** (0.2174) |
| Control variable | NO | YES | YES | YES | YES | YES | YES |
| Individual fixed effects | YES | YES | YES | YES | YES | YES | YES |
| Time effects fixed | YES | YES | NO | YES | NO | YES | NO |
| Province×time fixed effects | NO _ | NO | YES | NO | YES | NO | YES |
| N | 6812 | 6715 | 6689 | 4766 | 4749 | 2295 | 2286 |
| R2 | 0.9831 | 0.9841 | 0.9905 | 0.9829 | 0.9890 | 0.9642 | 0.9776 |
Note: ***, ** and * represent significance levels of 1%, 5% and 10% respectively, and values in brackets are clustered standard errors at the level of county-level areas. The same below.
4.2.3 Endogeneity
The DID (difference-in-differences) technique alleviates endogeneity issues well, but some of the issues are worth noting. First of all, China rolled out a series of poverty alleviation programs since 2013, including “poverty registration” and “targeted poverty alleviation” for poor households. Despite that in benchmark regression, per capita fiscal expenditure and per capita fiscal income as proxy indicators of poverty alleviation efforts at the government level are controlled and “province×time fixed effects” is used to capture the impact on per capita income as a result of policy differences between different provinces, there may still be variables left out to influence the results. Additionally, different regions vary largely in terms of economic development and local characteristics in China. The parallel trends assumption testing of DID (difference-in-differences) technique has passed, though, large regional differences may lead to inconsistent trends between the treatment group and the control group after the impact, which could result in bias in the estimated results. Finally, in the results of this paper, there may be a reverse causality between the progress in listed companies’ poverty alleviation projects and the increase of rural residents’ per capita income. That is to say, high initial per capita income of rural residents attracts corporate projects for poverty alleviation, while the increase of rural residents’ per capita income is more likely to benefit from economic foundations and time trends of high-income areas themselves instead of corporate involvement in poverty alleviation. This paper attempts to address these endogeneity issues in follows.
(1) Samples of Central and Western Regions and Samples of Poverty-Stricken Counties
As the eastern region is far ahead of the central and western regions in economic development, China’s poverty alleviation policies have focused on the central and western regions. In columns (4) and (5) of Table 2, the sample points of the central and western regions are regressed to partially eliminate potential impact of significant regional differences on results of this paper. In columns (6) and (7) of Table 2, the sample range is narrowed to national poverty-stricken counties to eliminate more impact of different regional characteristics. [1] Thanks to the good policy consistency, samples of national poverty-stricken counties would also help to further exclude the impact of government poverty alleviation policies on the per capita income of rural residents. The four columns of results are consistent with the benchmark results.
(2) Instrumental Variables
Companies’ selection of poverty alleviation projects seems to be random from the perspective of geographical distribution. That being said, to exclude the impacts of other potential unobservable factors in the same period on where to carry out poverty alleviation projects, the following two instrumental variables are applied for regression to further alleviate endogeneity issues.
One is the branch structure. With the Qixin Bao database, this paper constructs an instrumental variable BRANCH based on whether listed companies have established branches locally as they support poverty alleviation (Cai, 2016) to further solve the endogenous issues. For some employees must go to rural areas for the development of poverty alleviation projects and existing branches could make it easier for the work, companies are more likely to start poverty alleviation projects in areas where their branches are located. The branches selected in this paper are located in superior administrative areas of poverty alleviation projects, with no correlation (or weak correlation) with the per capita income of local residents, so BRANCH complies with the exogenous requirements for instrumental variables. Another is media coverage. We found the text version of Network News Broadcast (Xinwen Lianbo) in 2010–2017 on Tushare, and manually sorted out the number of times each prefecture-level city was covered by Network News Broadcast for poverty alleviation each year (MEDIA). A company is probably influenced by Network News Broadcast’s coverage on certain places when choosing where to carry out poverty alleviation projects (any place covered by Network News Broadcast for poverty alleviation-related news could be likely to attract poverty alleviation projects if the company conceives of political motives), and the coverage has no correlation (or weak correlation) with the per capita income of local residents, so MEDIA complies with the requirements for instrumental variables.
Regression results of instrumental variables are shown in Table 3. Columns (1) to (4) are regression results of BRANCH, where BRANCH_CITY and BRANCH_PROVINCE respectively denote whether the listed company has set up branches in corresponding prefecture-level cities and corresponding provinces at the involvement in poverty alleviation. Columns (5) and (6) are regression results of MEDIA. As is shown, regression results of two instrumental variables are still consistent with benchmark results.
Regression Results of Instrumental Variables
| (1) Phase I PT | (2) Phase II PINCOME | (3) Phase I PT | (4) Phase II PINCOME | (5) Phase I PT | (6) Phase II PINCOME | |
|---|---|---|---|---|---|---|
| PT | 0.0341*** | 0.0467*** | 0.6871** | |||
| (0.0103) | (0.0107) | (0.3083) | ||||
| BRANCH_CITY | 0.4952*** | |||||
| (0.0379) | ||||||
| BRANCH_PROVINCE | 0.3560*** | |||||
| (0.0921) | ||||||
| MEDIA | 0.0115** | |||||
| (0.0051) | ||||||
| −3.3076*** | 7.3573*** | −2.7756*** | 7.4031*** | −3.5274*** | 9.7234*** | |
| Constant | (0.4581) | (0.1432) | (0.4396) | (0.1419) | (0.4892) | (1.1468) |
| Control variable | YES | YES | YES | YES | YES | YES |
| Individual fixed effects | YES | YES | YES | YES | YES | YES |
| Time fixed effects | YES | YES | YES | YES | YES | YES |
| N | 6715 | 6715 | 6715 | 6715 | 6715 | 6715 |
| R2 | 0.3385 | 0.9432 | 0.3827 | 0.9429 | 0.2380 | 0.5849 |
(3) Reversal Causality
In view of the possible reverse causality between the development of listed companies’ poverty alleviation projects and the increase of rural residents’ per capita income, this paper verifies whether the higher initial rural residents’ per capita income attracts listed companies to involve in poverty alleviation. The verification equation is as follows:
where the subscript i denotes county-level areas and S_POVERTY the sum of poverty alleviation projects of all listed companies in each county-level area from 2014 to 2017. X denotes control variables of the basic economic characteristics and government behavior of each county-level area in 2013. PINCOME denotes the per capita income of rural residents in each county-level area in 2013. If the areas with high per capita income of rural residents attract more poverty alleviation projects of listed companies, the per capita income of rural residents in each county-level area in 2013 is given, and β shall be significantly positive. Regression results show that the coefficient of PINCOME is significantly negative, revealing the poverty alleviation projects of listed companies are often carried out in underdeveloped areas, and areas with high per capita income of rural residents do not attract more listed companies to involve in poverty alleviation. [1] It is inferred that the increase in the per capita income of rural residents amid corporate involvement in poverty alleviation is not because of the better economic foundation and time trends of the county-level areas themselves.
(4) Placebo Test
To exclude the impact of other policies and unobservable local characteristics, this paper conducts a Placebo Test by shuffling the impacted time and the impacted areas. By random sampling, 500 simulations are carried out, and the distribution map of estimated coefficients obtained from the 500 simulations is drawn. [2] The Placebo Test results suggest the increase in the per capita income of rural residents amid corporate involvement in poverty alleviation is unlikely to be driven by other policies and unobservable local characteristics.
Until now, endogeneity issues caused by national policy impacts, regional differences and reverse causality are dealt with, and the regression results remain stable. It proves that the conclusion that corporate involvement in poverty alleviation contributes significantly to poverty alleviation still holds in the case of further addressing endogenous issues.
4.2.4 Robustness Checks
To examine the robustness of the results of corporate involvement in poverty alleviation, the following robustness checks are conducted. First, the term PT of equation (2) is replaced by the number of poverty alleviation projects POVERTY for regression. As the POVERTY of all sample points in 2010–2013 is 0, it is essentially a difference-in-differences. Since POVERTY is a numerical variable, its coefficient can also measure the impact of the number of poverty alleviation projects on the per capita income of rural residents in county-level areas, i.e., the impact of listed companies’ poverty alleviation efforts on the per capita income of rural residents in county-level areas. Furthermore, since the locations of poverty alleviation projects carried out by some listed companies are disclosed to the prefecture-level city level and those of county-level areas are absent, this paper also conducts robustness checks with prefecture-level cities to avoid data leakage. Results of the two robustness checks are also consistent with benchmark results. [1]
4.3 Assumption 3 Regression Results
Regression results of Assumption 3 are shown in Table 4. The coefficient of the interaction term PT×WEST in column (1) is significantly positive, revealing that poverty alleviation results of listed companies in the western region are more effective. Assumption 3A holds. [2] The coefficients of the interaction term PT×PGDP_DUMMY in columns (2) and (3) are significantly negative, which suggests poverty alleviation results of listed companies in underdeveloped areas are more effective. Assumption 3B holds. The coefficients of the interaction term PT×POOR_COUNTY in columns (4) and (5) are significantly positive, indicating poverty alleviation results in national poverty-stricken counties are better. Assumption 3C holds. The above results also indicate that corporate involvement in poverty alleviation is more effective in poverty-stricken areas, facilitating the coordinated development of different economic regions and narrowing the development gap among regions. At the same time, this also shows that the involvement of social forces is indeed good for the progress of targeted poverty alleviation, which helps break old development bottlenecks in deeply impoverished areas to walk through the “last-mile” in poverty alleviation towards the common prosperity for all.
Assumption 3 Regression Results
| (1) PINCOME | (2) PINCOME | ( 3 ) PINCOME | ( 4 ) PINCOME | (5) PINCOME | |
|---|---|---|---|---|---|
| PT×WEST | 0.0636(0.0095*** ) | ||||
| PT×PGDP_DUMMY | −0.0706*** (0.0088) | −0.0333*** (0.0081) | |||
| PT×POOR_COUNTY | 0.0740(0.0081*** ) | 0.0466(0.0070*** ) | |||
| PT | −0.0007 (0.0056) | 0.0503*** (0.0061) | 0.0290*** (0.0052) | −0.0173*** (0.0057) | 0.0097** (0.0042) |
| Constant | 7.8747*** (0.1476) | 7.9088*** (0.1459) | 8.3208*** (0.1577) | 7.8570*** (0.1453) | 8.3216*** (0.1561) |
| Control variable | YES | YES | YES | YES | YES |
| Individual fixed effects | YES | YES | YES | YES | YES |
| Time fixed effects | YES | YES | NO | YES | NO |
| Province × time fixed effects | NO | NO | YES | NO | YES |
| N | 6715 | 6715 | 6689 | 6715 | 6689 |
| R2 | 0.9844 | 0.9844 | 0.9906 | 0.9845 | 0.9907 |
4.4 Analysis of Mechanism of Action
The above results suggest that listed companies’ involvement has been effective for poverty alleviation. The mechanism of action is analyzed as follows. Studies have found government-led poverty alleviation made it to the policy goal of fueling local economy by optimizing local industrial structure and increasing local investment in fixed assets (Huang, 2018). Furthermore, some scholars have found the primary channels provided by poverty alleviation policies to reduce the poor include guiding rural labors to work outside hometown, improving infrastructure construction, and increasing agricultural output (Xu et al., 2020). Besides human resources and land resources, other resource endowments are scarce in poverty-stricken areas. Listed companies, as social forces, generally pair up with areas in need and adopt the approach of developing local distinctive agriculture to raise the agricultural income of the poor based on local reality. For verifying whether this transmission mechanism holds, the regression is conducted as follows:
where PT×FIRST represents the interaction term of PT and the value added per capita of the primary industry. The testing logic is as follows. If companies increase the rural per capita income by influencing the development of the primary industry, then for the given impact of corporate involvement in poverty alleviation, the lower the value added per capita of the primary industry, the larger the rise in the per capita income of rural residents should be, i.e., φ is significantly negative in the equation. Empirical results are shown in Table 5. For column (1), only individual fixed effects and time fixed effects are controlled; for column (2) control variables are added; and for column (3), “province×time fixed effects” is further controlled. Each coefficient of the interaction term PT×FIRST is significantly negative. Regression results reveal that corporate involvement in poverty alleviation is more effective in areas with low value added per capita of the primary industry, and it suggests companies increase the per capita income of rural residents by influencing the development of the primary industry.
Regression Results of Mechanism of Action
| (1) PINCOME | (2) PINCOME | (3) PINCOME | |
|---|---|---|---|
| PT×FIRST | −0.0418*** (0.0092) | −0.0401*** (0.0088) | −0.0238*** (0.0077) |
| PT | 0.3790*** (0.0774) | 0.3611*** (0.0735) | 0.2161*** (0.0640) |
| FIRST | 0.0918*** (0.0092) | 0.0851*** (0.0095) | 0.0417*** (0.0121) |
| Constant | 8.3007*** (0.0767) | 7.7326*** (0.1535) | 8.2404*** (0.1632) |
| Control variable | NO | YES | YES |
| Individual fixed effects | YES | YES | YES |
| Time fixed effects | YES | YES | NO |
| Province × time fixed effects | NO | NO | YES |
| N | 6811 | 6715 | 6689 |
| R2 | 0.9837 | 0.9842 | 0.9906 |
5 Conclusions and Implications
As poverty alleviation has entered the deep end and government-led poverty alleviation is faced with multiple constraints and limitations, there urgently needs social forces, especially companies, to involve in poverty alleviation. Corporate involvement in poverty alleviation helps to make up for the weaknesses of government efforts and to facilitate the development of physical and human resources and the upgrading of industries in underdeveloped areas. With the help of corporate social responsibility reports released by Chinese listed companies, this paper has probed into whether corporate involvement is really helpful for poverty alleviation, and the principal conclusions are as follows. (1) Companies in the western region, companies directly contacting consumers and large companies with good business performance are more willing to involve in poverty alleviation. (2) Corporate involvement in poverty alleviation significantly increases the per capita income of local rural residents. (3) The results of corporate involvement in poverty alleviation are better in the western region, areas with low per capita GDP and areas under national poverty-stricken counties, i.e., there are significant differences among areas with respect to the results of corporate involvement in poverty alleviation, and it is good for targeted poverty alleviation. (4) Companies increase the per capita income of rural residents by developing distinctive agriculture locally. These reveal that social forces represented by companies have responded to China’s call of targeted poverty alleviation and fulfilled corporate social responsibilities, and have indeed pushed forward poverty alleviation in underdeveloped areas.
To enable the poor to help themselves and realize sustained increase of income, government departments need to absorb social forces represented by companies into poverty alleviation. After a moderately prosperous society in all respects was built in 2020, the government should focus on guarding against a return of out-of-poverty areas into poverty. Only by combining corporate involvement in poverty alleviation with rural revitalization plans organically will market forces be better used for poverty alleviation and consolidate existing achievements in China. Specifically, in the second half of poverty alleviation, corporate potential remains to be discovered, while breaking bottlenecks facing companies involving in poverty alleviation need systematic policy support schemes rolled out by government departments. First of all, the infrastructure construction in poverty-stricken areas must be improved and the business environment for companies to involve in poverty alleviation must be optimized. Mobilizing and guiding funds and resources at corporate disposal, based on basic conditions and resource endowments of poverty-stricken areas, so that poverty alleviation projects effectively satisfy local needs, and eradicate poverty from the mechanism. In addition, the orientation of respecting and encouraging common prosperity should be followed to stimulate the enthusiasm of all involved parties, intensify the recognition and improve social reputation incentives for companies involving in poverty alleviation to raise their expectations from poverty alleviation. In this way, an endogenous incentive mechanism featuring “people who have got prosperous first help others catch up” comes into being.
Funding statement: Fund project: “Research on Modern Central Banking System under Shock of New Financial Risks” project supported by the National Social Science Fund of China (20ZDA035). Thanks for the valuable comments of anonymous reviewers, and the content of this paper represents the views of the authors only and is their sole responsibility.
References
Aakhus, M., & Bzdak, M. (2012). Revisiting the Role of “Shared Value” in the Business-Society Relationship. Business and Professional Ethics Journal 31(2), 231–246.10.5840/bpej201231211Search in Google Scholar
Adams, M., & Hardwick, P. (1998). An Aanalysis of Corporate Donations: United Kingdom Evidence. Journal of management Studies 35(5), 641–654.10.1111/1467-6486.00113Search in Google Scholar
Amadi, B., & Abdullah, H. (2012). Poverty Alleviation through Corporate Social Responsibility in Niger Delta, Nigeria. Asian Social Science 8(4), 57.10.5539/ass.v8n4p57Search in Google Scholar
Banerjee, A., Karlan, D., & Zinman, J. (2015). Six Randomized Evaluations of Microcredit: Introduction and Further Steps. American Economic Journal: Applied Economics 7(1), 1–21.10.1257/app.20140287Search in Google Scholar
Banerjee, S. (2018). Transnational Power and Translocal Governance: The Politics of Corporate Responsibility. Human Relations 71(6), 796–821.10.1177/0018726717726586Search in Google Scholar
Barkemeyer, R. (2009). Beyond Compliance–Below Expectations? CSR in the Context of International Development. Business Ethics: A European Review 18(3), 273–289.10.1111/j.1467-8608.2009.01563.xSearch in Google Scholar
Beschorner, T., & Hajduk, T. (2017). Creating Shared Value. A fundamental Critique, Creating Shared Value. Springer 27–37.10.1007/978-3-319-48802-8_3Search in Google Scholar
Cai, W. (2016). Branch Market Access Relaxation, Cross-Regional Operation and Bank Performance. Journal of Financial Research (Jinrong Yanjiu) 6, 127–141.Search in Google Scholar
De Los Reyes Jr, G., Scholz, M., & Smith, N. C. (2017). Beyond the “Win-Win”: Creating Shared Value Requires Ethical Frameworks. California Management Review 59(2), 142–167.10.1177/0008125617695286Search in Google Scholar
Doni, N., & Ricchiuti, G. (2013). Market Equilibrium in the Presence of Green Consumers and Responsible Firms: A Comparative Statics Analysis. Resource and Energy Economics 35(3), 380–395.10.1016/j.reseneeco.2013.04.003Search in Google Scholar
Fan, S., Zhang, L., & Zhang, X. (2002). Growth, Inequality, and Poverty in Rural China: The Role of Public Investments. International Food Policy Research Institute.Search in Google Scholar
Godfrey, P. (2005). The Relationship between Corporate Philanthropy and Shareholder Wealth: A Risk Management Perspective. Academy of Management Review 30(4), 777–798.10.5465/amr.2005.18378878Search in Google Scholar
Hahn, R. (2012). Inclusive Business, Human Rights and the Dignity of the Poor: A Glance beyond Economic Impacts of Adapted Business Models. Business Ethics: A European Review 21(1), 47–63.10.1111/j.1467-8608.2011.01640.xSearch in Google Scholar
Huang, Z., & Wang, J. (2006). Local Protection and Market Segmentation: Empirical Data from China. China Industrial Economics (Zhongguo Gongye Jingji) 2, 60–67.Search in Google Scholar
Huang, Z. (2018). Does the Setup of National Poverty-Stricken Counties Drive Local Economic Development? An Empirical Study Based on PSM-DID Approach. Chinese Rural Economy (Zhongguo Nongcun Jingji) 5, 98–111.Search in Google Scholar
Jia, J., Qin, C., & Liu, Y. (2017). “Top-Down” and “Bottom-Up” Integrated Policy Design—An Empirical Analysis Based on Poverty Alleviation Projects for Rural Development. Social Sciences in China (Zhongguo Shehui Kexue) 9, 68–69.Search in Google Scholar
Kolk, A., & Van Tulder, R. (2006). Poverty Alleviation as Business Strategy? Evaluating Commitments of Frontrunner Multinational Corporations. World Development 34(5), 789–801.10.1016/j.worlddev.2005.10.005Search in Google Scholar
Kramer, M., & Porter, M. (2011). Creating Shared Value. FSG.Search in Google Scholar
Li, X. (2019). Corporate Involvement in Rural Revitalization from the Perspective of Relationship Investment. Business and Management Journal (Jingji Guanli) 41(11), 38–54.Search in Google Scholar
Li, Y., & Shen, K. (2007). Fiscal Expenditure Structure, Relative Poverty and Economic Growth. Management World (Guanli Shijie) 11, 14–26+171.Search in Google Scholar
Lin, G., Long, Z., & Wu, M. (2005). A Spatial Econometric Empirical Analysis of Regional Economic Convergence in China (1978–2002). China Economic Quarterly (Jingjixue (Jikan)) 4(B10), 67–82.Search in Google Scholar
Lin, Y., & Liu, P. (2003). Economic Development Strategies and Regional Income Gap in China. Economic Research Journal (Jingji Yanjiu) 3, 19–25+89.Search in Google Scholar
Liu, Q. (2001). China Economic Growth Convergence Analysis. Economic Research Journal (Jingji Yanjiu), 6, 70–77.Search in Google Scholar
Liu, Q. (2008). Growth, Inequality and Poverty: Incentive Paths to Balanced Government Expenditure. Finance & Trade Economics (Caimao Jingji) 12, 58–62.Search in Google Scholar
Lobel, S. (2013). Predicting Organizational Responsiveness to Poverty: Exploratory Model and Application to Brazil and the United States. European Management Journal 31(5), 522–535.10.1016/j.emj.2013.04.007Search in Google Scholar
Lu, S., & Lu, H. (2013). Can Government Aid Lift Low-Income Population out of Poverty? —An Empirical Study Based on CHNS Data from 1989 to 2009. Journal of Finance and Economics (Caijing Yanjiu) 39(1), 4–16.Search in Google Scholar
Lyu, W., & Liu, C. (2008). Research on Rural Public Investment, Social Expenditure and Poverty in China. Finance & Trade Economics (Caimao Jingji) 5, 61–69+128–129.Search in Google Scholar
Medina-Muñoz, R. D., & Medina-Muñoz, D. R. (2020). Corporate Social Responsibility for Poverty Alleviation: An Integrated Research Framework.Business Ethics: A European Review 29(1), 3–19.10.1111/beer.12248Search in Google Scholar
Meng, L. (2013). Evaluating China’s Poverty Alleviation Program: A Regression Discontinuity Approach. Journal of Public Economics 101, 1–11.10.1016/j.jpubeco.2013.02.004Search in Google Scholar
Pan, W. (2010). Differences and Convergence of Regional Economy in China. Social Sciences in China (Zhongguo Shehui Kexue) 1, 72–84+222–223.Search in Google Scholar
Pan, Y., Weng, R., & Liu, S. (2017). Ulterior Goodwill: New Evidence Based on Corporate Charitable Donations during Typhoons. China Industrial Economics (Zhongguo Gongye Jingji) 5, 133–151.Search in Google Scholar
Prahalad, C. (2006). Fortune at the Bottom of the Pyramid, the: Eradicating Poverty through Profits. Philadelphia: Wharton School Publishing.Search in Google Scholar
Ravallion, M., & Jalan, J. (1999). China’s Lagging Poor Areas. American Economic Review 89(2), 301–305.10.1257/aer.89.2.301Search in Google Scholar
Shan, L., Gan, L., & Zheng, T. (2008). Corporate Donations and Economic Motives: An Empirical Study of Chinese Listed Companies’ Donations after the Wenchuan Earthquake. Economic Research Journal (Jingji Yanjiu) 43(11), 51–61.Search in Google Scholar
Stewart, D. (2005). Social Status in an Open-Source Community. American Sociological Review 70(5), 823–842.10.1177/000312240507000505Search in Google Scholar
Strahilevitz, M., & Myers, J. (1998). Donations to Charity as Purchase Incentives: How Well They Work May Depend on What You Are Trying to Sell. Journal of Consumer Research 24(4), 434–446.10.1086/209519Search in Google Scholar
Swift, T., & Zadek, S. (2002). Corporate Responsibility and the Competitive Advantage of Nations. Copenhagen Centre.Search in Google Scholar
United Nations. (2004). Reducing Disaster Risk: A Challenge for Development. UNDP Report.Search in Google Scholar
Wang, S. (2008). Fight Poverty amid Development: A Summary and Evaluation of China’s 30 Years of Practices in Large-Scale Poverty Alleviation. Management World (Guanli Shijie) 11, 78–88.Search in Google Scholar
Wang, Y., & Su, Y. (2020). How Did Chinese Miracle of Poverty Alleviation Come About? The Targeted Administrative Mode of Poverty Alleviation and the Basis of National Governance System in China. Management World (Guanli Shijie) 36(4), 195–209.Search in Google Scholar
World Bank. (2000). World Development Report 2000/2001: Attacking Poverty.Search in Google Scholar
Xu, S., Wang, D., & Yang, R. (2020). Income Distribution Effects of the National Poverty-Stricken County Policy in China. Economic Research Journal (Jingji Yanjiu) 55(4), 134–149.Search in Google Scholar
Yan, K., & Yu, S. (2008). Theoretical Analysis and Policy Ideas of Public Finance—Based Poverty Reduction. Finance & Trade Economics (Caimao Jingji) 4, 61–67+129.Search in Google Scholar
Yang, L., Li, M., & Wang, S. (2015). Expression and Practice of Targeted Poverty Alleviation Policy in China. Rural Economy (Nongcun Jingji) 1, 8–12.Search in Google Scholar
Yu, Z., Lu, Y., & Wang, W. (2015). Monetary Policy Implementation Model, Financial Mismatch and Investment Constraints of Chinese Companies. Management World (Guanli Shijie) 9, 52–64.Search in Google Scholar
Zhu, M., & Li, S. (2017). The Priority of Targeted Poverty Alleviation Lies in Accurately Identifying the Poor: An Analysis of the Targeting Effect of the Rural Subsistence Security Policy in China. Social Sciences in China (Zhongguo Shehui Kexue) 9, 90–112.Search in Google Scholar
© 2022 Liya Liu, Shupeng Zhou, Yuanchen Pang, Xuehan Guo, published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
Articles in the same Issue
- Frontmatter
- China’s Economic Development in the New Era: Challenges and Paths
- Theoretical Connotation and Quantitative Measurement of Common Prosperity
- Corporate Power for Poverty Alleviation: Evidence from the Poverty Alleviation Results of Chinese Listed Companies
- Fiscal Transfer, Education Investment and Long-Term Educational Performance
- Research on the Innovative Development of China’s Comprehensive Transportation System
- Impact of Logistics Mode Innovation on Quality Consumption and Countermeasures
- Consumption Tax Reform in Accelerating the Establishment of a New Development Paradigm
Articles in the same Issue
- Frontmatter
- China’s Economic Development in the New Era: Challenges and Paths
- Theoretical Connotation and Quantitative Measurement of Common Prosperity
- Corporate Power for Poverty Alleviation: Evidence from the Poverty Alleviation Results of Chinese Listed Companies
- Fiscal Transfer, Education Investment and Long-Term Educational Performance
- Research on the Innovative Development of China’s Comprehensive Transportation System
- Impact of Logistics Mode Innovation on Quality Consumption and Countermeasures
- Consumption Tax Reform in Accelerating the Establishment of a New Development Paradigm
