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 The Impact of China’s Corruption on Its Economic Development

Introduction

The rise of China to a worldwide economic superpower during the last four decades has been nothing of unusual. Factors that have significantly role played in the development are her liberalization of its monetary system, immense comparative advantage in labour, and opening up its borders to foreign marketplaces (Bergsten et al., 2008). That notwithstanding, one of the most intriguing issues in China’s extraordinary economic development, and which forms the crux of this paper, is the role that has been played by corruption. Corruption has been an intriguing issue to the world’s economy in several fronts. The principal reason why corruption is an intriguing issue is because literature does not agree on the probable economic impacts of corruption. Cai and Qiao (2009) established that corruption is harmful to countries’ economic growth, and this insinuates the case in China. Others studies have shown that corruption is one of the ways used by companies to sidestep regulations set up by governments (Cole et al., 2009; Wang, 2009; Hsiung, 2012). Esfahani and Ramirez (2013) posit that businesses would not be able to thrive in China without corruption because of stringent regulations that are instituted by the government. It is against this backdrop that this paper will examine the impact of China’s corruption on its economic development.

Literature Review

Empirical Evidence of the Impact of Corruption on Economies

Certainly, corruption is one of the greatest challenges that policymakers and governments face. Literature regarding corruption is, however, inconclusive because of difficulties with regard to measuring it precisely. As such, it has been significantly difficult to point out its effects. While some studies have shown that corruption is a boon to countries’ economic growth, Egger and Winner (2005) reason that it is crucial to note that this is only specific to particular contexts. According to Jiang and Nie (2014), in an ideal situation and society, corruption has been shown to be detrimental to economies despite the fact that in economically limiting regimes or presence of poor organizational infrastructure allows companies to sidestep stringent policies and regulations.

Empirical studies have found robust proof that corruption functions to promote investment. Ramirez (2013) evaluated the situation in 74 underdeveloped and develop countries and found that corruption serves as a stimulus for the countries’ foreign direct investment (FDI). Ramirez (2013 further reasons that corruption is a way through which companies circumvent principles and regulations in fairly highly restricted economies (p. 77). Nonetheless, Ramirez (2013) makes it clear that his study does not support corrupt regimes, recommending that the best policy towards dealing with corruption in countries is eliminating strict regulatory measures and controls as opposed to circumventing them (p. 79-80). This finding backs the notion that corruption is only beneficial to economies within very specific contexts and not all.

Narrowing down to China, evidence points out that corruption has a distinct effects on FDI. According to a study that was done on FDI in China, it was found out that companies are extra likely to locate in regions and provinces that do whatever it takes to prevent corruption (Shera et al., 2014; Li, 2016).). Evidence further implies that regions and provinces with more effective and efficient regional governments have greater chances of attracting higher FDI levels (Stiglitz & Kennedy, 2013; Guo, 2014). This finding, nonetheless, seems to contradict previous studies that have shown that countries with high corruption levels tend to appeal to more investors and consequently more investments. Using regression analysis technique to determine the effect of corruption upon investment, Shambaugh (2013) found an inverse association between the two; one standard deviation fall in a country’s corruption level can result in about 4% growth in the country’s FDI.

Using company performance as a circumstantial framework for gauging the impacts of corruption in an economy, studies have found extra consistent outcomes. A study by Kimbro (2002) of company profitability within China established that higher profitability was associated with higher corruption levels within a region. Nevertheless, this was the case only for privately owned companies since state-owned firms were found to have less motivation to bribe officers. Similarly, this correlation was found to be more robust when regulations were more rigid compared to when they were relaxed, implying that regulatory/supervisory régimes may incentivize corruption (Kimbro, 2002, p. 328). The study went further to establish that when regulations are held constant, companies that have the ability to circumvent these regulations will be extra profitable. Whereas the study noted the positive impacts of corruption upon companies, it concluded that corruption is propelled and energized by high regulations. Thus, to eliminate corruption, administrations and governments must flex their principles and regulations regarding the free marketplace (Kimbro, 2002, p. 331).

Another study that explored more about corporate tax evasion found an alike role in the Chinese economy. In the study, corporate tax evasion was found to serve as a competitive advantage for companies within a marketplace wherein institutional infrastructure is comparatively poor (Siddique & Ghosh, 2015). This way, corruption can be of importance to companies, given the availability of poor organizational/institutional infrastructure. Nevertheless, the article’s authors reasoned that as China shifts to high-income economy, its greatest long term goal is to buttress its institutional infrastructure so that it can limit tax evasion (p. 24). Whereas Clifford (2017) asserts that tax evasion may give a company a short term competitive advantage, this missing tax income functions to lower a country’s expenditure upon crucial public goods and commodities such education and infrastructure. Similarly, the initial positive impacts of corruption may result in long term challenges for a country’s economic development and growth.

From the foregoing literature, it can be seen that there is lack of conclusive proof about the impact of corruption on a country’s economic growth, and this stems from the fact that it is difficult to measure corruption. Most corruption measures, like Transparency International (TI), are surveys which may be subjective. Yet, these surveys can as well be reliable since they report remarkably alike findings and judgments upon individual nations (Zhu & Zhang, 2017; Clifford, 2017). However, even if views regarding corruption may be alike across all surveys, precisely measuring what this implies and how it impacts upon an economic sphere of a nation is hard. Other people measure corruption using specific metrics, such as news reports about corruption activities (Bao & Lewellyn, 2017) or corruption-associated cases that are filed (Fungáčová et al., 2016; Siddique & Ghosh, 2015). In as much as these techniques give an extra quantitative measure of corruption, they do not capture the impact of other variables such as institutional bureaucracy and effectiveness (Zhang et al., 2017; Bao & Lewellyn, 2017), thus making isolation of the impacts of corruption hard.

Corruption in China

Chao-Hsiang and Chao-Jung (2012) state that the inherent secrecy about corruption makes it one of the nearly impossible to measure with certain correctness and accuracy. The Corruption Perception Index (CPI) that is published by TI, accumulates a variety of surveys that assess country’s perceived corruption levels (Wedeman, 2012). Out of 180 countries, China was ranked 87 by the 2018 CPI with a 39 score and this placed it in a tie with Serbia (Liu, 2018). This corruption level is far higher compared to that of most high-income countries and economies. According to the report, countries with comparatively low corruption levels are characteristically smaller and advanced ones. For instance, Denmark ranked first while New Zealand second in the 2018 index. However, larger and developed nations equally rated fairly well, with the US ranking twenty second and Japan eighteenth. Strong democratic institutions are also connected to lower corruption levels. According to the report, full democracies got an average score of 75, whereas fully autocratic ones only scored an average of 30 (Tang et al., 2018). The World Governance Indicators (WGI) project, in 2017, ranked China in the 47^th percentile universally for corruption control category (Nguyen et al., 2017) as shown in Figure 1 (in the appendix).

The nature of corruption changes from one country to another. TI posits that the most common kinds of corruption within China are diversion of government funds, bribery, and partiality by government officers (Yang, 2017). According to a survey that was done by Charney Research, it was found out that 35% of companies within China had paid some bribes to China’s government officials (Liu, 2018). Bribes, according to Nguyen et al. (2017), are pervasive in various regions or provinces as well. The 2017 Global Corruption Barometer (GCB) revealed that when obtaining government services, like those related to health care, education, and criminal justice system, 26% of participants in China had paid some bribes. According to the 2017-2018 Global Competitiveness Index, which functions to measure nation-level economic competitiveness, rated China 49^th position out of 137 nations in the frequency of countries with irregular bribes and payments and 20^th in partiality by public officials (Ionescu, 2018). Evidently, by both measures, China has significantly improved as from 2012’s 67^th with regard to bribes and irregular payments and 34^th in partiality by government officers (Tang et al., 2018). See figure 2 (in the appendix) for a summary of the report’s findings. For more insight on the effect of corruption of Chinese economy, see notes in the appendix section.

Data Description and Analysis

To determine the impact of corruption upon China’s economic growth, a regression model is employed. China’s provincial income is employed as the measure of the country’s economic growth. The model that is used in this paper as the study’s framework is the one that was developed by Guiheux (2007), which led to the finding that a country’s gross domestic product (GDP) is enhanced by higher life expectancy and schooling, lower government consumption, lower fertility, lower inflation, better maintenance of the country’s rule of law, and betterments of the country in terms of trade. The model in this paper will employ independent variables for purposes of controlling these determinants China’s economic growth so that the impacts of corruption can be isolated.

To gauge schooling, a province’s illiteracy rate (Illitrt) was used. Illiteracy rate is computed by dividing the total number of 15 and above years old individuals who are semi-literate or illiterate by the province’s total population of people aged 15 and above. We expect a negative sign on the rate’s coefficient, since a less literate population has high chances of being less schooled. According to Guo (2014), less education attainment contributes to a lower human capital level and technological growth rate.

As a determining factor of GDP, life expectancy (LifeExp) was used. Life expectancy, as Fungáčová et al. (2016) note, serves a proxy for the quality of human capital and health level within a population. For this reason, we anticipate a positive sign as the variable’s coefficient.

Another variable that was used is population growth rate (PopGrowthrt). Population growth rate is employed to gauge a population’s fertility rate (Rong, 2015). Studies have indicated that a lower fertility rate is connected with a higher rate of GDP growth since higher fertility rates compel economies to offer human capital to new employees as opposed to increasing human capital for inexistence employees (Zhu & Zhang, 2017; Chao-Hsiang & Chao-Jung, 2012). Thus, we expect this variable’s coefficient to have a negative sign since a rise in an economy’s fertility will possibly lower the economy’s GDP.

Government consumption, which was as well considered for this study, was used to help gauge China’s total government expenditure (GovExpen) within a China’s province. This study used China’s total government expenditure to better separate the impacts of corruption in China on its economic growth. The sign expected to be before this variable’s coefficient is negative since a rise in a country’s expenditure will most probably result in a fall in the country’s economic growth.

According to economic theory, high inflation levels within an economy signify the economy’s price instability, which can hamper the economy’s economic growth rates (Zhu & Zhang, 2017; Yang, 2017). Rong (2015) study established that inflation rates actually retard a country’s economic growth. For this reason, the sign before this variable’s coefficient is expected to have a negative sign. Another crucial variable that was used in this study is China’s terms of trade. Terms of trade measures a country’s import to export ratio for purposes of determining the country’s exporting’s import upon its actual GDP. The implication of this variable is that a country’s export prices must be fairly high to offset its imports as well as to have a positive impact upon its GDP (Wedeman, 2012). Since China has been using exports as its main economic growth drivers, we expect its coefficient to have a positive sign. Other variables that were used include FDI, population (pop), area (Area), population weighted express ways (PopExp), real wage growth (WageGrowth), unemployment rate (Unemrt), and special economic zone (SEZ). Using these variables, the fundamental; model that was used for this study is:

Income_pt = f(CurruptPrev_pt, Y_pt, X_pt,)

where income = total provincial earning (Exp) for a particular province p within year t,

CorruptPrev = provincial corruption prevention effort (CorruptPrev)

X = the impact of Barro’s framework

Y = the impact of other control variables

The above formula can be refined into:

Income_pt = ∝ + β₁(CorrupttPrev _pt) + β₂(Illitrt _pt) + β₃(LifeExp _pt) + β₄(PopGrowthrt _pt) + β₅(GoveExpen _pt) + β₆( _pt) + β₇(Exp _pt) + β₈(FDI _pt) + β₉(Pop _pt) + β₁₀(Area _pt) + β₁₁(PopExp _pt) + β₁₂(SEZ _pt) + β₁₃(WageGrowth _pt) + β₁₄(Unemprt _pt)                               

The researchers expect to find the following signs for each of the coefficients mentioned above.

Results

Table 1 (in the appendix) shows the t-statistic and coefficients for each and every independent variable that was used. The study’s null hypothesis for corruption deterrence effort (CorruptPrev) is not accepted. The proof study revealed that provinces that were more committed to minimizing or preventing corruption achieved higher total income levels. Table 2 (in the appendix) shows the relational coefficients of the study’s independent variables. The study’s adjusted r-squared for the study’s model was found to be 0.96 and statistically substantial at the 99% confidence level based upon the significance F.

Out of the study’s control variables’ coefficients, life expectancy, illiteracy rates, exports, government expenditure, population, SEZ, and FDI were found to be significant statistically at the 99% confidence level. As was anticipated, the life expectancy variable’s coefficient had a positive sign. This signified that higher life expectancy level contributes to an economy’s economic growth. The coefficient upon China’s government expenditure was found to fail to attain the sign that was anticipated. Nonetheless, using findings of Guo (2014), the positive coefficient upon government expenditure makes sense considering this model. According to the model that was employed for this study, total government expenditure that was employed included both non-productive and productive expenditures. According to Barro (1998), China gives concentrates its government expenditure upon providing support to its export marketplace, certainly in a productive disbursement.

It was found out that all FDI, SEZ, and exports had positive signs before their coefficients as was anticipated. The three variables reflect China’s main economic growth drivers. Population was equally found to have a positive sign before its coefficient, signifying that as the country’s population rises, its total provincial earning is as well expected to rise.

Of great concern is the positive sign upon illiteracy rate’s coefficient. According to the model that was employed, this positive sign suggests that higher levels of illiteracy contribute to a China’s higher provincial income. Whereas this can be justified by supposing that people in higher income provinces within China forgo education to work in production or manufacturing industries. This reflects the model’s weakness.

The area’s and inflation rate’s coefficients were found to be significant at 99% confidence level. Notwithstanding the fact that it was anticipated that coefficient of inflation rate was expected to have a negative sign based upon Barro’s model, Barro posits that the negative impacts of inflation upon an economy is more pronounced when the economy’s inflation is substantially higher above normal (about 68 and 69) (1998). The study indicated that China’s rate of inflation for most of its provinces varied between -2% and 2%, with some varying from -3% and 3%. According to Guo (2014), this reflects comparative price stability and ought not to negatively impact China’s economic growth. What is more, reverse causation was shown to play a role since as economies continue heating up, inflation is expected to increase. Thus, the rise in China’s provincial income with time may be contributing to the country’s inflation rate increase.

The justification for the positive coefficient of the area variable is alike to that of population: larger China’s provinces are anticipated to have higher incomes. Owing to the fact that a larger geographical area/province unnecessarily implies that the area/province has a higher population, the independent variable’s exploratory power is considerably weaker compared to that of its population. 

China’s population growth rate was found to be statistically significant at the 88% confidence level. Its coefficient’s negative sign may back Barro’s proof that lower rates of fertility within an economy contributes to the economy’s economic growth since human capital per individual involved is enhanced (p. 17) Unemployment rate, growth rate, and population-weighted expressways from the study were found to be significant statistically in this model.

To determine the strength of the correlation between income and corruption in China, different patterns and trends were considered from different provinces. Table 3 (in the appendix) shows the outcomes of this correlation for each province that were considered for the study. China’s corruption deterrence was found to be statistically significant at the 99% confidence level as a principal determinant of the provinces’ income.

Conclusion

This paper, through empirical research, has explored the impacts of corruption upon the Chinese economic growth at the country’s provincial level. Barro’s research regarding factors that determine a country’s economic growth was employed in constructing the study’s model. From the study, it has been revealed that corruption has a robust effect upon China’s economic growth, and its provinces can benefit from preventing corruption at all costs. From this study’s model, an increase by 1% in corruption deterrence effort within China will generate about .002% of the country’s national income increase. Owing to the economic benefits associated with corruption prevention, the Chinese government has taken stringent stand to fight against the high corruption level in China. With its potential to fight corruption, as made pronounced by the country’s history, China is well positioned to fight corruption.